System and methods employing networked adaptive controlled release devices

Abstract

Disclosed herein is a networked system for vector-borne disease protection including: a plurality of adaptive controlled release devices (ACRDs), each including at least one reservoir storing an active ingredient and an actuator configured to release the active ingredient; and a server configured to receive and aggregate vector activity data, local environmental data, and / or user input data from the plurality of ACRDs and / or from external sources and to transmit control data to at least one of the plurality of ACRDs, wherein each ACRD is configured to transmit the local environmental and / or the user input data to the server, to receive control data from the server, and to adjust operation of the actuator to modulate release of the active ingredient in response to the control data.

Description

[0001] SYSTEM AND METHODS EMPLOYING NETWORKED ADAPTIVE CONTROLLED RELEASE DEVICES

[0002] CROSS REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority from US Provisional Patent Application No. 63 / 682,816 filed 08 / 14 / 2024, which is incorporated herein by reference in its entirety.

[0004] FIELD

[0005] Embodiments disclosed herein relate to devices for controlled release of active ingredients (Al) and methods of deployment thereof.

[0006] BACKGROUND

[0007] Vector-borne diseases pose a significant global health challenge, affecting millions of individuals each year. Transmitted by vectors such as mosquitoes, fleas and ticks, these diseases include malaria, Zika virus, and West Nile virus, among others. Current personal protection methods from vector-borne diseases rely on environmental treatments, such as deployment of larvicides and insecticides, and the use of barriers such as nets.

[0008] Other methods include the application of a topical repellent on the skin or in close proximity, such as DEET (N,N-diethyl-3 -methylbenzamide) . Spatial repellents based on synthetic pyrethroids, such as transfluthrin and metofluthrin, have also become more popular, and can be employed through different methods, such as through the use of a small fan, or through integration into clothing. However, concerns have been raised over the effects of transdermal absorption of the active ingredient.

[0009] Furthermore, methods to release other active ingredients or substances, such as fragrances or perfumes, rely on manual or pre-programmed activation.

[0010] SUMMARY

[0011] Consistent with disclosed examples, a networked system for vector-borne disease protection includes: a plurality of adaptive controlled release devices (ACRDs), each ACRD including at least one reservoir storing an active ingredient and an actuator configured to release the active ingredient; and a server configured to receive and aggregate vector activity data, local environmental data, and user input data from the plurality of ACRDs and / or from external sources, to generate control data based on the aggregated data, and to transmit the control data to at least one of the plurality of ACRDs, wherein each ACRD is configured to transmit the local environmental and / or the user input data to the server, to receive the control data from the server, and to adjust operation of the actuator to modulate release of the active ingredient in response to the control data.

[0012] In some examples, each ACRD or a mobile device paired with the ACRD includes environmental sensors for providing the local environmental data. In some examples, the external sources include at least one of: environmental sensors, traps, publicly available databases, or user reports. In some examples, each ACRD or a mobile device paired with the ACRD includes a communication module for communicating with the server or with another ACRD.

[0013] In some examples, the active ingredient is stored within a formulation. In some examples, the system further includes an application running on a paired mobile device, the application configured to receive the user input data from a user.

[0014] Consistent with disclosed examples, a networked system for controlled release of fragrances, includes: a plurality of adaptive controlled release devices (ACRDs), each includes at least one reservoir storing a fragrance and an actuator configured to release the fragrance; and a server configured to receive and aggregate environmental and / or physiological condition data from the plurality of ACRDs and / or from external sources, to generate control data based on the aggregated data, and to transmit the control data to the plurality of ACRDs, wherein each ACRD is configured to adjust operation of its actuator to modulate release rate of the fragrance in response to the control data.

[0015] In some examples, each ACRD or a mobile device paired with the ACRD includes sensors for providing environmental and / or physiological condition data. In some examples, the external sources include at least one of: environmental sensors, location services, or user reports. In some examples, the control data is based at least in part on condition data generated by another ACRD in the network. In some examples, each ACRD or a mobile device paired with the ACRD includes a communication module for communicating with the server or with another ACRD. In some examples, the fragrance is stored within a formulation. In some examples, the system further includes an application running on a paired mobile device, the application configured to receive the user input data from a user.

[0016] Consistent with disclosed examples, an ACRD includes: a cartridge array including one or more replaceable cartridges, each cartridge includes at least one reservoir configured to store an active ingredient (Al) or a formulation thereof; a plurality of micro electro mechanical system (MEMS) membranes positioned over respective reservoirs, each MEMS membrane configured to hermetically seal the respective reservoir until activation; and a control unit configured to rupture a selected MEMS membrane in response to an activation signal received from an application running on a mobile device, wherein rupture of the selected MEMS membrane exposes the Al or formulation to a fluid environment for volatilization and diffusion, wherein in a fully autonomous mode of operation the activation signal is generated automatically based on environmental parameters and aggregated network data from other ACRD devices, the determination being performed by a machine learning algorithm executed on a server, locally on the mobile device, or in a hybrid configuration, without any user selection or configuration of reservoir activation.

[0017] In some examples, the application or server accesses one or more databases containing Al efficacy, environmental, or vector activity data to support the determination of which reservoir to activate. In some examples, the machine learning algorithm processes historical efficacy data, environmental parameters, and aggregated network reports to refine Al selection and activation timing.

[0018] In some examples, the user may select a desired protection level from among low, medium, or high, and the machine learning algorithm determines the reservoir activation plan accordingly. In some examples, the user may input one or more parameters including target vector species, anticipated exposure time, or location, and the machine learning algorithm incorporates the user input into the determination of the reservoir activation plan.

[0019] As used herein, the term “activation plan” with respect to an ACRDs is defined as a plan in which the controlled release rate for each ACRD may be modulated dynamically. In an example in which a user is exposed to an area with high mosquito pressure, the activation plan will allow several actuators to be activated to increase the Al release rate in order to increase protection. In another example, a user may be in a high temperature and high humidity environment, and the activation plan will allow the increase in the release rate of fragrances. The change in the release rate may thus occur by, for example, by increasing the number of CRD reservoirs that release the Al, or by changing the number of activated actuators.

[0020] In some examples, the application or control unit is configured to activate two or more reservoirs in combination to release different active ingredients simultaneously or sequentially. In some examples, the cartridge array includes a pump, and the control unit is configured to activate the pump in response to an activation signal received from an application running on a mobile device.

[0021] Consistent with disclosed examples, a method for vector-borne disease protection, includes: deploying a plurality of ACRDs, each ACRD including at least one reservoir storing an active ingredient and an actuator configured to release the active ingredient; detecting local environmental data and / or receiving user input data via each ACRD or a mobile device paired with the ACRD; transmitting the local environmental data and / or the user input data from each ACRD to a server; receiving, at each ACRD, control data generated by the server based on aggregated vector activity data, local environmental data, and / or user input data from the plurality of ACRDs and / or external sources; and operating the actuator of each ACRD to modulate release of the active ingredient in response to the control data.

[0022] In some examples, detecting the local environmental data includes sensing environmental parameters using sensors integrated into the ACRD or into the paired mobile device. In some examples, the external sources include at least one of: environmental sensors, traps, publicly available databases, or user reports.

[0023] In some examples, transmitting the local environmental data and / or the user input data includes communicating via a communication module integrated into the ACRD or into the paired mobile device. In some examples, the active ingredient is stored within a formulation. In some examples, the method further includes receiving, via an application running on the paired mobile device, the user input data including at least one of: desired protection level, target vector species, or anticipated exposure time. In some examples, operating the actuator includes rupturing a micro electro mechanical system (MEMS) membrane that hermetically seals the reservoir until activation.

[0024] Consistent with disclosed examples, a method for controlled release of fragrances, includes: deploying a plurality of ACRDs, each ACRD including at least one reservoir storing a fragrance and an actuator configured to release the fragrance; detecting local environmental and / or physiological condition data via each ACRD or a mobile device paired with the ACRD; transmitting the local environmental and / or physiological condition data from each ACRD to a server; receiving, at each ACRD, control data generated by the server based on aggregated environmental and / or physiological condition data from the plurality of ACRDs and / or from external sources; and operating the actuator of each ACRD to modulate release of the fragrance in response to the control data.

[0025] As used herein, the term “adaptive controlled release device” (ACRD) refers to a controlled release device that may dynamically adjust its operation in response to changing inputs. The ACRD may include one or more actuators, such as MEMS membranes or other controllable openings, that selectively permit or restrict transfer of an active ingredient or fragrance from a reservoir to the external environment in response to a control or activation signal. The device is “adaptive” in that the actuator’s state and / or effective open area can be modulated in real time based on user input, sensor feedback, environmental conditions, or aggregated network data - including inputs from other ACRD users. This adaptivity extends the capabilities of a conventional controlled release device by enabling release rate or timing to be optimized dynamically, rather than being fixed to a pre-programmed schedule. A networked configuration may further extend the capabilities of the ACRDs.

[0026] Note that in some figures (e.g. 1A, IB, 4A, 4B, 5A, 6A and 7A), the acronym ACRD is replaced with “AB-CRD”. In this disclosure, the two terms are used interchangeably

[0027] The embodiment shown in FIGS. 1 - 4 represents one type of ACRD; in other examples, the ACRD may instead include one or more reservoirs, actuators, comms modules and associated control electronics that adaptively responds to user input, sensor feedback, environmental conditions, or aggregated network data as described herein, without being limited to the specific shape, arrangement, or ancillary components illustrated in these figures.

[0028] Further examples disclosed herein relate to a system including an ACRD including a wireless microchip based on MEMS, and methods for controlled release of an Al or formulation thereof from within the ACRD into a fluid environment. The ACRD can be programmed for activation wherein doses of the Al are released from the ACRD into a fluid environment. The disclosed system may include a number of ACRDs forming a network.

[0029] In some examples, the AIs serve for any one of spatial repellents, insecticides, herbicides, larvicides, or a combination of these. Optionally, the systems, devices and methods of the present disclosure serve for the release of AIs with other functions and general applications such as fragrances and perfumes. In some examples, devices disclosed herein can be implemented as wearable devices for protection against vectors such as mosquitoes and ticks, and can be deployed both indoors or outdoors.

[0030] In some examples, the ACRD can include several reservoirs, in which several Al or formulations thereof are stored. When activated, an ACRD is in an “active” state, which allows Al release to the fluid environment. When not activated, an ACRD is in an “inactive” state.

[0031] In some examples, the reservoirs include a matrix that includes the Al or the formulation thereof. In some examples, the matrix is a liquid absorbing material or structure in which the Al formulation is absorbed. The liquid absorbing material or structure may be a wick, a porous sponge material, a fiber material, or a non-woven fabric. In some examples, the matrix may be a gel. In some examples, the matrix may be a cavity, i.e. an empty container. In some examples, the Al formulation is selected from the group consisting of an Al combined with an organic solvent (OS), an Al combined with a gel, and a combination thereof. In some examples, the Al combined with an OS may be pumped out to an environment by means of an integrated actuator.

[0032] In some examples, each reservoir may be activated separately to provide versatile protection. The device may be activated through the use of a mobile application by the user, and may be operated using different methods. In one method, referred to as a manual mode of operation, each reservoir may be activated manually by the user at the required time of protection.

[0033] In other examples, each ACRD may be operated in a semi-autonomous mode of operation. In this mode, the user selects specific vectors and a protection intensity level. Additional weather parameters, which are acquired from the cellular device based on its GPS location, are also transmitted. An algorithm then selects specific reservoirs and an order of activation based on the user’s inputs.

[0034] In other examples, the device may be operated in a fully autonomous mode of operation. In this mode, the GPS, weather information and inputs from additional users which are connected to the application through a network, are transmitted to the algorithm. These parameters also the algorithm to identify relevant vectors, select the required intensity level, and select the order of activation, with no inputs from the user.

[0035] This Summary is provided to introduce a selection of concepts in a simplified form that may be further described in the Detailed Description below. It may be understood that this Summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure. The details of one or more embodiments disclosed herein may be set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings.

[0036] BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Aspects, embodiments and features disclosed herein will become apparent from the following detailed description when considered in conjunction with the accompanying drawings. Like elements may be numbered with like numerals in different drawings. In the drawings:

[0038] FIG. 1A shows an illustration of an ACRD system for controlled release of a vector control substance according to examples disclosed herein;

[0039] FIG. IB shows an illustration of a networked system in which multiple ACRD systems may be interconnected according to examples disclosed herein;

[0040] FIG. 2 shows an enlarged illustration of an ACRD according to examples disclosed herein;

[0041] FIG. 3 shows an illustration of a cross-section of a cartridge according to examples disclosed herein;

[0042] FIGS. 4 A and 4B show illustrations of the insertion and removal of a cartridge into the ACRD according to examples disclosed herein;

[0043] FIG. 5A shows a combined block and flow diagram illustrating a process for manual operation of an ACRD according to examples disclosed herein;

[0044] FIG. 5B shows a more detailed flow diagram of the process shown in FIG. 5A according to examples disclosed herein;

[0045] FIGS. 5C-5F show simplified flow diagrams illustrating four examples of different manual operation modes of the ACRD according to examples disclosed herein;

[0046] FIG. 6A shows a combined block and flow diagram illustrating a semi-autonomous process of operation of an ACRD according to examples disclosed herein;

[0047] FIG. 6B shows a more detailed flow diagram of the semi-autonomous operation process shown in FIG. 6A according to examples disclosed herein;

[0048] FIG. 6C shows a simplified flow diagram in which the semi-autonomous mode leverages environmental data according to examples disclosed herein.

[0049] FIG. 7A shows a combined block and flow diagram illustrating a process for an autonomous mode of operation of an ACRD according to examples disclosed herein;

[0050] FIG. 7B (divided for clarity into FIGS. 7B-1 and 7B-2) shows a more detailed flow diagram of the autonomous process shown in FIG. 7A according to examples disclosed herein;

[0051] FIG. 7C shows a simplified flow diagram of the autonomous process utilizing external weather conditions and a networked system according to examples disclosed herein;

[0052] FIG. 7D shows a simplified flow diagram of the autonomous process utilizing fully automated vector selection according to examples disclosed herein;

[0053] FIG. 8A shows an in vitro entomological experiment setup performed to evaluate the effects of an ACRD against vectors according to examples disclosed herein;

[0054] FIGS. 8B-8D show the results of the in vitro experiment described in FIG. 8 A according to examples disclosed herein.

[0055] DETAILED DESCRIPTION

[0056] Controlled release devices (CRD) for vector control in agricultural, military, or civilian applications have been introduced in order to address the problem of delivering AIs in a controlled release manner. Similar devices may be used in personal and commercial settings for release of fragrances and perfumes. The challenges facing development of effective CRDs include smart methods of deployment, to ensure release of the Al only when protection is needed. To address this issue, an Adaptive Controlled Release Device (ACRD) is disclosed herein including a microcontroller as well as additional electronics, to process user inputs, as well as external parameters such as weather conditions, to allow Al release in either a manual, semi-autonomous or autonomous modes of operation.

[0057] Each ACRD can be part of a network forming a system. Each ACRD can send and receive data that allows turning on, turning off, or modulating the release rate based on different inputs. The inputs may be from sensors integrated with the ACRD or from the network. For example, two different users may each wear an ACRD integrated with sensors. The first user may manually report to the network that there is a high-pressure zone of mosquitoes in a given region. The second user may use this information to increase the release rate of its ACRD. In a similar fashion, instead of the first user manually reporting to the network, the reporting may be performed autonomously. Similarly, the ACRD used by the second user may use this information (reported manually or autonomously) autonomously, as the activation plan may change dynamically. The methods, devices, and systems disclosed herein are thus designed to provide effective and adaptable protection against vector-borne threats. In various examples, the disclosed technology may enable:

[0058] • Improved adherence and compliance, through user-friendly operation and flexible deployment modes.

[0059] • Semi-autonomous or fully autonomous operation, allowing the device to function with minimal user intervention.

[0060] • Dynamic modulation of release kinetics based on environmental inputs such as temperature, humidity, and wind conditions, GPS location data, and information from public databases containing vector prevalence and activity patterns.

[0061] • Integration into a networked user ecosystem, enabling devices to both share actionable information and receive updates from other users in real time.

[0062] • Use of machine learning and artificial intelligence to optimize protection strategies, incorporating both self-reported and network-collected performance data. For example, if multiple users in the same vicinity report that a specific formulation - such as transfluthrin released from three reservoirs - provides effective mosquito repellence during a certain time period, that information can be used to “close the loop” and refine algorithmic recommendations for all connected devices.

[0063] FIG. 1A shows an illustration of a system 150 for controlled release of a vector control substance according to examples disclosed herein. System 150 may include an ACRD 100, which in some examples may integrate electronics, MEMS, and mobile communication in a single device. ACRD 100 may include a control unit 102 and a cartridge array 104. Control unit 102 may drive the operation of cartridge array 104, which may include multiple swappable cartridges containing respective active ingredients (Al) also referred to herein as Al formulations. System 150 may further include a mobile application (“app”) 106 running on a mobile device 105 that provides a user interface and communicates with control unit 102 via wireless communication, such as but not limited to Bluetooth. In some examples, ACRD 100 may be configured to be worn by a user.

[0064] Control unit 102, and mobile device 105 may be computing devices as defined herein. Control unit 102 and mobile device 105 may be implemented on a computer and may make use of cloud processing. Control unit 102 and mobile device 105 and the modules and components that are included in control unit 102 and mobile device 105 may include or may be in communication with a non-transitory computer readable medium (such as memory) containing instructions that when executed by at least one processor included in control unit 102 and mobile device 105 are configured to perform the functions and / or operations necessary to provide the functionality described herein. While control unit 102 and mobile device 105 are presented herein with specific components and modules, it should be understood by one skilled in the art, that the architectural configuration as shown may be simply one possible configuration and that other configurations with more or fewer components are possible. As referred to herein, the “components” of control unit 102 and mobile device 105 may include one or more of the modules or services shown in FIGS. 1-3 as being included within control unit 102 and mobile device 105.

[0065] Examples of an Al may include, but are not limited to, insecticides, insect repellents, or acaricides (such as transfluthrin, metofluthrin, or permethrin). Such an Al may be combined with organic solvents, such as isopropyl alcohol (IP A), as well as with other materials to form an Al formulation. The term “Al” 120 as used herein encompasses “Al formulations” and the terms are used interchangeably herein. Al 120 may also include additional pyrethroids such as allethrin, bifenthrin, cyhalothrin, lambda-cyhalothrin, cypermethrin, cyfluthrin, deltamethrin, etofenprox, fenvalerate, permethrin, phenothrin, prallethrin, resmethrin, tetramethrin, or tralomethrin. In other examples, the Al may include a topical repellent such as DEET (N,N-diethyl-3-methylbenzamide). Other AIs may include organic repellents, such as nootkatone. In further examples, fragrances or perfumes may be used to provide a personalized approach toward scent creation, or to implement a programmable modality for generating scents. In other examples, neutralizing agents may be used to mask, block, or neutralize undesired molecules in air. In some examples, the term Al as used herein may include a fragrance, perfume, or formulation containing these.

[0066] App 106 may be a mobile application that allows a user to wirelessly activate ACRD 100 via wireless communication with control unit 102. Mobile device 105, on which app 106 runs, may include functionality typically found in a smartphone or tablet, such as a touchscreen, wireless communication interfaces (e.g., Bluetooth, Wi-Fi, cellular), location sensing components, and input / output capabilities. These features may enable a user to interact with the app via graphical interfaces, provide control commands, view system feedback, and receive updates regarding device status or environmental parameters. App 106 may provide a user interface that allows specific activation of any one of the reservoirs of any one of the cartridges of cartridge array 104, which in turn may result in the release of the Al. App 106 may display information regarding each cartridge of cartridge array 104, such as activation status.

[0067] FIG. IB shows an illustration of a networked system 170 in which multiple ACRD systems 150-1 ... 150-n may be interconnected through a communications network 140 to a central ACRD server 160 according to examples disclosed herein. Each system 150 may include an ACRD 100 as described above, and may communicate bi-directionally with server 160 via network 140.

[0068] ACRD server 160 may be a computing device as defined herein. ACRD server 160 may be implemented on a computer, server, distributed server, virtual server, cloud-based server, and combinations thereof and may make use of cloud and software as a service (SaaS) processing. ACRD server 160 and the modules and components that are included in ACRD server 160 may include or may be in communication with a non-transitory computer readable medium (such as memory 162) containing instructions that when executed by at least one processor (such as processor 164) are configured to perform the functions and / or operations necessary to provide the functionality described herein. Processor 164 may manage the operation of the components of ACRD server 160 and may direct the flow of data between the components of ACRD server 160 and with system 150. Processor 164 may be implemented by various types of processor devices and / or processor architectures including, for example, embedded processors, communication processors, graphics processing unit (GPU), soft-core processors and / or embedded processors.

[0069] In some examples, server 160 may host one or more databases and machine-learning engines configured to process aggregated operational data from multiple systems 150. Such processing may include analyzing environmental conditions, Al 120 performance data, vector activity reports, and user-defined parameters to improve Al selection, release schedules, and protection profiles for individual systems 150. The databases resident on server 160 may include, for example, the Al efficacy database described with respect to FIGS. 5B and 6B, environmental condition datasets, and historical user-preference records. Server 160 may return optimized control parameters to each system 150 in near-real-time to update protection strategies dynamically.

[0070] In additional examples, network 140 may support user-to-user communication features, such as instant messaging or community-based alerts regarding local vector threats.

[0071] In alternative configurations, the ACRD 100 of each system 150 may operate without server 160, relying solely on the processing capabilities of control unit 102 and mobile device 105 for Al selection and scheduling. In such cases, the databases and algorithms described above may be stored locally in control unit 102 and / or mobile device 105, with environmental and vector- activity inputs obtained directly from local sensors, mobile device services, or manual user input. In some examples, networked system 170 may be configured for vector-borne disease protection by enabling each ACRD 100 in system 170 to function as a network node that both sends and receives operational data. Each ACRD 100 may transmit locally-detected environmental parameters (e.g., temperature, humidity, wind speed, GPS location), trap-based data, and / or user- provided reports to server 160. Server 160 may aggregate this information together with external sources such as publicly available vector surveillance databases and may generate control data (optionally using machine learning processes) for distribution back to the ACRDs 100. The control data may specify operational adjustments, such as increasing or decreasing the release rate of an active ingredient, switching to a different active ingredient, activating pump actuators, or selecting a particular reservoir for activation, based on aggregated data and determinations made by server 160. In some examples, environmental sensors 109 (FIG. 2) may be integrated into the ACRD 100 itself or into a paired mobile device 105 running app 106. In some examples, app 106 may receive user input, including user reports about vectors, and / or parameters such as desired protection level, target vector species, or anticipated exposure time, which may be factored into the control data. Communication between each ACRD 100 and server 160, or directly between ACRDs, may be via an integrated communication module or through a paired mobile device 105.

[0072] In some examples, networked system 170 may be configured for controlled release of fragrances by enabling each ACRD 100 (that stores fragrances instead of AIs) to function as a network node that sends and receives fragrance -related operational data. Each ACRD 100 may transmit locally-detected environmental or physiological condition data, such as ambient temperature, humidity, user activity level, perspiration, or location, to server 160. Server 160 may aggregate this information with external sources, such as weather services or location-based event data, and generate control data for distribution back to the ACRDs 100. The control data may specify operational adjustments, such as modulating the fragrance release rate, switching to a different fragrance, or combining multiple fragrances, in order to adapt to changing user conditions or environmental factors. In some examples, environmental or physiological sensors may be integrated into the ACRD 100 itself or into a paired mobile device 105 running app 106 that may allow the user to input preferences, such as desired fragrance intensity, fragrance type, or duration, which may be factored into the control data. Communication between each ACRD 100 and server 160, or directly between ACRDs, may be via an integrated communication module or through the paired mobile device 105. In some examples, the control data may initiate activation of ACRD 100 in response to a remote command, including activation of ACRD 100 associated with a different user, based on network- shared information or instructions provided by a user. For example, a user may remotely activate release of a specific fragrance.

[0073] In some examples, ACRD 100 may include one or more integrated sensors 109 configured to provide feedback for closed-loop operation. In some examples, sensors 109 are provided in device 105 and accessed by app 106 to retrieve sensor data. Such sensors may detect environmental parameters (e.g., temperature, humidity, wind speed), physiological conditions (e.g., perspiration level, body temperature), or the presence or concentration of a released active ingredient or fragrance. The sensed data may be transmitted to control logic in the ACRD 100, to app 106 on a paired mobile device 105, or to server 160 (or to server 160 via app 106), where it is processed to dynamically adjust actuator operation, reservoir selection, or release rate. In closed-loop operation, the control data is thus updated in real time or near real time based on sensor 109 feedback.

[0074] FIG. 2 shows an enlarged illustration of ACRD 100 with the different components of control unit 102 and cartridge array 104 according to examples disclosed herein. Control unit 102 may include microcontroller 110 (a processor in data communication with memory as described above) that may control ACRD 100 and wireless communication between control unit 102 and app 106. App 106 may send and receive data from mobile device 105, according to a user’s input to app 106. App 106 may generate an activation signal, sent wirelessly to control unit 102 indicating which cartridge of cartridge array 104 should be activated. After activation, ACRD 100 may relay back a status signal to app 106 to verify which cartridge was activated. Control unit 102 may transmit a status signal, including data from ACRD 100 to app 106 after verifying which reservoir was activated. Wireless connectivity, such as for communications from app 106, may be received via antennae (not shown). Control unit 102 may further include a battery 114, which may power ACRD 100. In some examples, battery 114 may be a USB-rechargeable lithium-ion battery with a battery life of up to four weeks.

[0075] Control unit 102 may also include capacitor 116. Capacitor 116 may allow for the discharge of a high-power electrical pulse, through which control unit 102 may activate a specific reservoir 118 of a cartridge 108 of cartridge array 104. In some examples, capacitor 116 may apply a pulse of 10 mJ, released within 1 microsecond, although the power may be calibrated according to required specifications.

[0076] Cartridge array 104 may include an array of replaceable cartridges 108, having reservoirs 118 in which an Al or a formulation thereof 120 may be stored. In some examples, the Al formulation may be a mixture of the Al together with other materials. In some examples, cartridge array 104 may include three cartridges, which may be replaceable (i.e., disposable), although fewer or greater numbers of cartridges may be possible. In some examples, reservoir 118 may have a volume of 500 ml. Reservoirs 118 may include either a matrix or empty space for housing the Al formulation, as described in FIG. 3. In some examples, cartridge 108 may be manufactured by stereolithography (SLA) 3D printing using a thermoset polymer. In some examples, cartridges may be manufactured by injection molding,

[0077] Cartridge 108 may include reservoirs 118, gasket 122, and printed circuit board (PCB) 124. Gasket 122 may be positioned between PCB 124 and reservoirs 118 to hermetically seal the reservoirs and prevent Al release prior to activation. PCB 124 may sit atop gasket 122 and may include conductive tracks, for example made of copper, to enable electrical connections to each reservoir 118. As used herein, the term “actuator” 126 may include, in some examples, a micro electro mechanical system (MEMS) membrane 126 positioned to hermetically seal a reservoir until activation to thus act as sealing membranes for each reservoir 118. Rupture or displacement of MEMS membrane 126 functions as the actuation event, thereby releasing the active ingredient or formulation or fragrance from the reservoir. MEMS membranes (actuators) 126 may.

[0078] Upon activation of a specific reservoir 118, a signal may be sent from app 106 to control unit 102, where it may be processed by microcontroller 110. The microcontroller may trigger capacitor 116 to discharge a high-power electrical pulse to the selected MEMS membrane 126, causing sealing membrane 126 to burst. This may allow the Al or a formulation thereof to evaporate from the selected reservoir 118. In some examples, the user may initiate activation via app 106, which may wirelessly control one or more reservoirs 118 of device 102 in this manner. After depletion of all reservoirs 118 in a cartridge 108, the cartridge may be replaced.

[0079] In some examples, a single reservoir 118 may be sealed by multiple MEMS membranes 126, each of which may be independently ruptured to control the timing, duration, or rate of release. In some examples, multiple membranes 126 may be arranged in series or in parallel across an opening of reservoir 118, or at multiple outlet ports of the same reservoir 118. Selective actuation of different membranes 126 can modulate the effective open area and thereby adjust the mass transfer rate.

[0080] In some examples, the actuator 126 associated with each reservoir 118 may include or be combined with micro-scale devices to enhance mass transfer of the released Al 120. Such devices may include resistive heating elements to locally increase temperature; micro-pumps based on piezo-electric actuators to actively drive vapors or fluids out of the reservoir 118; or electrodes configured to nucleate bubbles or micro-bubbles within the reservoir 118 by heating or by electrolysis (using a pair of electrodes to generate gas bubbles). These mechanisms may be used alone or in combination to increase the rate of volatilization or dispersion of Al 120 or a fragrance. In some examples, each ACRD may contain different AIs or fragrances such that activation of a specific ACRD results in release of a specific fragrance.

[0081] PCB 124 may be secured and tightened against reservoirs 118, for example through the use of fasteners inserted through a cover 128. Cover 128 may hermetically seal the cartridge and may include exit holes 130. In a non-limiting example, there may be one membrane 126 and one respective exit hole 130 for each of reservoirs 118. In some examples, cartridge 108 may include more than one breakable membrane 126 for each reservoir, and more than one exit hole 130 for each reservoir 118. Furthermore, additional examples may include more exit holes 130 than breakable membranes 126 per reservoir 118. Exit holes 130 may allow for the evaporation and release of the Al from each reservoir 118 to diffuse into the fluid environment after rupture of the respective membrane 126. Membranes 126 and exit holes 130 may be sized to control the release kinetics of Al 120 or a formulation thereof.

[0082] FIG. 3 shows an illustration of a cross-section of a cartridge 108 according to examples disclosed herein. Reservoirs 118 may be filled with Al formulation 120, which in some examples may be absorbed in a matrix 132. Matrix 132 may include an absorbent medium such as a wick or porous sponge that includes Al formulation 120, for example, cellulose. In some examples, matrix 132 may be a wick with a density in the range of 0.1 g / cm3to 1 g / cm3. Matrix 132 may hold Al formulation 120 by absorption-adsorption mechanisms, and may optionally be provided with a high surface-to-volume ratio to increase evaporation area. In other examples, matrix 132 may include synthetic materials such as Polyurethane (ether & ester grades), Microcellular urethanes, reticulated polyurethane foam filters, crosslinked polyethylene, or other similar structures.

[0083] In some examples, reservoir 118 may include empty space, into which Al formulation 120 may be inserted directly as a liquid or as a gel. In some examples, Al 120 may be formulated as a gel instead of or in addition to a matrix. Injection holes 134 on the bottom of the cartridge may be used to insert Al 120, and may, for example, be sealed with UV-cured epoxy after filling.

[0084] In some examples, different reservoirs within the same cartridge may use different modes of delivery - e.g., one with a matrix, two without. This configuration may support varied protection strategies, such as combining spatial repellents (e.g., transfluthrin) and contact repellents (e.g., DEET). In some examples, cartridge 108 may include three reservoirs 118, wherein in one of the reservoirs, Al formulation 120 may be absorbed in a matrix 132, while the other two may include empty space in which Al formulation 120 may be inserted directly. In some examples, matrix 132 may be a cellulose wick, which may house the Al transfluthrin. In other examples, all reservoirs 118 may include a matrix 132 in which the Al or a formulation thereof may be stored. Each reservoir 118 may include a different Al 120, according to the required vector protection, or alternatively all reservoirs 118 may include the same Al. Examples of possible AIs are provided above. In further examples, all reservoirs 118 may include empty space in which an Al formulation 120 may be stored directly. In some examples, one reservoir 118 may deploy DEET or permethrin, which may be contact repellents, while other reservoirs 118 may deploy transfluthrin or metofluthrin, which may be spatial repellents. However, other variations of AIs and matrix configurations for each reservoir 118 may also be possible. This versatility may allow the system to offer personalized protection by allowing the user to deploy specific repellents, with the release rate further controlled by the number of reservoirs deployed.

[0085] FIG. 3 also shows activated and inactivated MEMS membranes 126, here numbered 136 and 138 respectively. Following activation of a certain reservoir 118 via the burst of MEMS membrane 126, Al 120 may be exposed to the fluid environment. In a non-limiting example, the fluid environment is air. Upon exposure to air, the Al may undergo volatilization and is able to diffuse in air outwards through exit hole 130.

[0086] In some examples, MEMS membrane 126 cannot be replaced onto PCB 124 to reseal reservoir 118 after being ruptured. In some examples, MEMS membranes 126 facilitate an electrothermal rupture release mechanism, such as the one described in Elman, N. M., et al. "Electro-thermally induced structural failure actuator (ETISFA) for implantable controlled drug delivery devices based on Micro-Electro-Mechanical-Systems." Lab on a Chip 10.20 (2010): 2796-2804. MEMS membranes 126 may include a base material, for example, silicon nitride, and one or more planar fuses including, for example, titanium, gold, and / or copper, that may be placed across the base material. In some examples, upon applying an electrical pulse with a given current, the fuses may act as an electro-thermal actuator that allows for rapid mechanical expansion of the base material, which may break open due to the thermo-electric reaction, resulting in reservoir exposure.

[0087] In some examples, MEMS membrane 126 may consist of a tantalum-copper-tantalum 100 pm wide fuse that runs across a silicon nitride 750 pm by 750 pm membrane, embedded in a substrate including a single crystal silicon chip. The fuse may be formed, for example, using chemical vapor deposition (CVD). In some examples, the fuse of MEMS membrane 126 may be deposited in three layers: a first 20 nm Ta layer, a second 200 nm Cu layer, and a final 20 nm protective Ta layer. The fuse may then be patterned on the membrane using photolithography. In some examples, each MEMS membrane 126 may be hermetically fixed to PCB 124 using a UV- cured epoxy. In some examples, MEMS membrane 126 may be connected to the conductive tracks of PCB 124 using two Au wires attached with an ultrasonic wire-bonder.

[0088] FIGS. 4A and 4B show illustrations of the insertion and removal of a cartridge 108 into ACRD 100 according to examples disclosed herein. In some examples, cartridge array 104 may include three cartridges 108, with the middle cartridge 108 being shown as inserted and removed in FIGS. 4A and 4B. One end of each cartridge 108 may include connectors that may fit into a spring-loaded base connector, allowing for direct electrical and mechanical connection of the cartridge 108 to control unit 102.

[0089] After insertion of a cartridge 108, app 106 may display the status of the cartridge 108, including which of its reservoirs 118 have been activated. App 106 may also allow the user to activate specific reservoirs 118 of a specific cartridge 108, and may display general information regarding ACRD 100, including battery status.

[0090] FIG. 4B shows an example of the removal of a spent cartridge 108, which may need to be replaced after activation of all of its reservoirs 118. Cartridge 108 removal may be accomplished via the spring-loaded connectors, after which a new, unused cartridge 108 may be inserted. Upon removal of a cartridge 108, app 106 may indicate that no cartridge 108 is currently inserted.

[0091] FIG. 5A shows a combined block and flow diagram illustrating a process 500 for manual operation of ACRD 100 according to examples disclosed herein. A non-transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described at each of the steps in FIG. 5A. The non-transitory computer readable medium and at least one processor may correspond to one or more of control unit 102 and mobile device 105, and may include components such as microcontroller 110 and memory, or other computing hardware of system 150 configured to perform the described operations. Operations may begin in step 502, when a user inputs a command into app 106, running on mobile device 105. In some examples, a user command may include activation of a specific reservoir 118 of a specific cartridge 108. App 106 may also display information regarding ACRD 100 to the user, including battery life, reservoir 118 status, and the cartridge 108 insertion status, as previously described with respect to FIGS. 4A and 4B.

[0092] After receiving the user command, app 106 may transmit (in step 532) a signal via wireless communication to control unit 102 of ACRD 100. Control unit 102 may process the signal using microcontroller 110, which may trigger capacitor 116 to discharge a high-power pulse to activate a selected MEMS membrane 126, thereby rupturing the membrane 126 of the selected reservoir 118. This rupture may expose the Al formulation 120 within the selected reservoir 118 to ambient air, allowing for volatilization and diffusion of the Al 120, resulting in vector protection.

[0093] Following activation, in step 533, control unit 102 may transmit a signal back to app 106 to confirm the reservoir 118 status. App 106 may then display updated status information to the user regarding the selected cartridge 108 and reservoir 118.

[0094] As shown in FIG. 5A, in some examples, app 106 may further incorporate machine learning algorithms to optimize activation strategies based on input variables such as timing of release, number of reservoirs 118 to be activated, and combinations of Al 120. These parameters may be adjusted automatically or semi-automatically based on historical usage patterns, user preferences, and contextual data. Inputs to app 106 may include such timing data, the number and type of installed cartridges 108, and the selection of desired Al 120 combinations. Outputs may include updated information displayed to the user, such as battery life, reservoir 118 activation status, and the insertion or depletion status of each cartridge 108.

[0095] FIG. 5B shows a more detailed flow diagram of the process 500 shown in FIG. 5A according to examples disclosed herein. A non-transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described at each of the steps in FIG. 5B. The non-transitory computer readable medium and at least one processor may correspond to one or more of control unit 102 and mobile device 105, and may include components such as microcontroller 110 and memory, or other computing hardware of system 150 configured to perform the described operations. The process may begin in step 502, when the user inputs data into app 106, running on mobile device 105. User input may include information regarding target vectors (e.g., mosquitoes, ticks, sandflies) as well as environmental parameters such as temperature, humidity, wind speed, and the user's geographic location. In step 514, app 106 may process these inputs and return a set of available Al 120 present in the currently inserted reservoirs 118 across all installed cartridges 108, each appropriate for the identified vectors. In step 516, the user may select one or more of the recommended AIs or formulations thereof. In some examples, the selected formulations may include, but are not limited to: transfluthrin formulated with isopropyl alcohol (1-30% w / w), metofluthrin formulated with benzyl alcohol (1-30% w / w), or nootkatone formulated with isopropyl alcohol (1-30% w / w).

[0096] In step 518, app 106 may calculate and display the estimated duration of protection provided by each selected formulation in each relevant reservoir 118, based on Al-specific half-life data provided in step 520 from a database. In step 522, the user may define a set of reservoirs 118 to be activated in connection with each selected Al 120, corresponding to the required protection duration. In step 524, app 106 may calculate the projected period of protection for the selected combination of reservoirs 118.

[0097] The user may then proceed to configure additional sets of reservoirs 118 in steps 526, 528, and 530, allowing further control over the kinetic profile of release. This kinetic control may determine the overall protection curve - e.g., increasing, tapering, or steady release of Al 120 over time - by sequencing activations across multiple reservoirs 118.

[0098] In some examples, the user may adjust these sequences dynamically. For instance, if temperature drops below 18°C, app 106 may recommend or allow deactivation of certain reservoirs 118, given the reduced likelihood of vector activity at lower temperatures. This enables a context-sensitive modulation of the release profile as part of the activation plan.

[0099] Finally, in step 532, app 106 may process the user’s selections and transmit an activation signal 506 to control unit 102 via wireless communication. Upon receipt, control unit 102 may activate and thus rupture the corresponding membranes 126 to thereby expose the Al 120 in the selected reservoirs 118 to air. The volatilized Al 120 may then diffuse into the surrounding environment, thereby providing the desired vector protection in accordance with the defined release sequence.

[0100] FIGS. 5C-5F show simplified flow diagrams of process 500 illustrating four examples of different manual operation modes of ACRD 100 according to examples disclosed herein. Each example demonstrates various levels of user control and customization.

[0101] Example 1 (FIG. 5C): After initiating app 106 on mobile device 105, which establishes a wireless connection to control unit 102 (step 534), the user selects one or more reservoirs 118 containing the desired Al 120 in step 536. The user then inputs an activation command via app 106 in step 538, which results in transmission of an activation signal 506 to control unit 102. Upon receipt, control unit 102 initiates discharge of capacitor 116, triggering the selected MEMS membrane 126 to rupture the corresponding membrane 126, thereby releasing the Al 120 via volatilization.

[0102] Example 2 (FIG. 5D): Building on example 1, the user may additionally specify desired activation times in step 540. This allows for scheduled activation of each reservoir 118 at user- defined intervals, enabling timed protection.

[0103] Example 3 (FIG. 5E): In this example, the user selects a desired protection level - e.g., low, medium, or high - in step 542. App 106 may then determine the required number and sequence of reservoir 118 activations based on pre-defined mappings between protection levels, selected vectors, available Al 120 formulations, and installed cartridges 108.

[0104] Example 4 (FIG. 5F): The user inputs the targeted vectors in step 544 and selects the desired protection level in step 546. In step 548, a local algorithm running on app 106 or remotely via cloud service may calculate the optimal number of reservoirs 118, required Al 120 combinations, and an associated activation schedule. App 106 may then transmit corresponding activation signals 506 to control unit 102, which sequentially activates the selected reservoirs 118 as calculated.

[0105] These operational examples provide flexibility for manual configuration of ACRD 100, allowing users to tailor vector protection based on their situational needs and environmental context. For instance, in a non-limiting example where minimal protection is desired, the user may activate a single reservoir 118, thereby ensuring a low release rate. In contrast, when maximum protection is required, the user may choose to simultaneously activate multiple reservoirs 118. The selected reservoirs 118 may contain different Al 120 formulations - for example: one cartridge 108 may include a spatial repellent such as transfluthrin, another may include a contact repellent such as DEET, and a third may contain a natural repellent such as nootkatone.

[0106] In some examples, ACRD 100 may be programmed to follow a specific sequence of activations informed by machine learning models, which may be trained on historical user behavior, vector prevalence, or environmental data. This may provide a personalized and intelligent release strategy to support effective vector control. In some examples, where a micropump is integrated into ACRD 100, controlled release of AIs may be performed by modulating the pump flow manually, in a pre-programmed manner, or telemetrically (such as via system 170). FIG. 6A shows a combined block and flow diagram illustrating a semi-autonomous process 600 of operation of ACRD 100 according to examples disclosed herein. A non-transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described at each of the steps in FIG. 6A. The non-transitory computer readable medium and at least one processor may correspond to one or more of control unit 102 and mobile device 105, and may include components such as microcontroller 110 and memory, or other computing hardware of system 150 configured to perform the described operations. This mode may be similar in overall structure to the manual mode described in FIG. 5A, but differs in that the user does not provide a direct activation command. Instead, the method may begin in step 602, when the user inputs a set of environmental and contextual variables into app 106, running on mobile device 105. These variables may include, for example: Target vector species (e.g., mosquitoes, ticks, sandflies), Environmental conditions (e.g., temperature, rainfall, humidity), and / or Time of day and GPS location of the user.

[0107] In some examples, these parameters may be used by app 106 to query an algorithm - either locally stored or cloud-based - that selects an optimal combination of Al 120 and corresponding reservoirs 118 from the available cartridges 108, based on predefined decision rules or learned behavior from previous uses, which may include data obtained from other users. This selection may account for vector prevalence patterns, climate sensitivity of the Al 120, and protection duration requirements.

[0108] Once the algorithm computes a release strategy, app 106 may transmit (in step 626) an activation signal to control unit 102, via wireless communication. Control unit 102, using microcontroller 110, may trigger capacitor 116 to discharge a high-power pulse to activate and thus rupture the selected MEMS membrane(s) 126. This rupture may expose the selected Al 120 to the environment, leading to volatilization and diffusion.

[0109] In step 627, the release of Al 120 occurs, resulting in vector protection tailored to the contextual variables. Following activation, in step 628, control unit 102 may transmit a status signal back to app 106, which may then present updated reservoir 118 and cartridge 108 status to the user, confirming that the scheduled release event occurred as expected.

[0110] FIG. 6B shows a more detailed flow diagram of the semi-autonomous operation process 600 shown in FIG. 6A according to examples disclosed herein. Process 600 is similar to the manual mode described in FIG. 5B but with added features enabling semi-autonomous functionality. A non-transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described at each of the steps in FIG. 6B. The non-transitory computer readable medium and at least one processor may correspond to one or more of control unit 102 and mobile device 105, and may include components such as microcontroller 110 and memory, or other computing hardware of system 150 configured to perform the described operations.

[0111] The process may begin with user inputs via app 106 in step 602. The user inputs may include target vectors (e.g., mosquitoes, ticks, sandflies) and additional parameters such as environmental conditions (e.g., temperature, humidity, wind speed), which may be manually input or automatically acquired via app 106 on mobile device 105 using GPS location data. In step 603, the user may also enter the required protection intensity level (e.g., scale 1-5, where 5 is maximum) and, in step 604, the required period of protection.

[0112] In step 606, the system may output information including a set of available Al formulations 120 from active reservoirs 118 in cartridge(s) 108 for the selected vectors. Step 608 may provide for incorporation of additional environmental parameters such as temperature, humidity, and wind speed / direction - either manually input or automatically acquired via app 106 based on the GPS location of mobile device 105. In step 610, a database may provide an optional selection of Al formulations according to the payload capacity of each reservoir 118, which may be transmitted in step 607 for each target vector based on empirical formulation efficacy data for the relevant genus / species under the prevailing conditions.

[0113] These semi-autonomous capabilities - such as automatic GPS location acquisition, weather data integration, and database correlation - may allow dynamic Al release adjustments based on real-time environmental data, which may not be present in the manual mode described in FIG. 5B.

[0114] In step 612, after determining of the desired Al formulation(s) in step 606, app 106 may output the period of protection for each formulation and reservoir 118 according to half-life calculations provided in step 614 such as from a database that may also incorporate public vectorsurveillance data (historical, current, and projected) to help select optimal AIs and reservoirs.

[0115] In step 616, app 106 may select a set of reservoirs 118 to activate during the desired protection period - whereas in the manual mode this step may be user-driven. Step 618 may calculate the protection period for each set of reservoirs. In steps 620, the user may add further reservoir sets for processing in step 622. In step 624, the user may confirm the final reservoir sets, which may then be activated in step 626. At this point, app 106 may send an activation signal 606 via wireless communication to control unit 102. Upon receiving the signal, microcontroller 110 may process it and trigger capacitor 116 to discharge a pulse to the selected MEMS membranes 126 thus rupturing membranes 126 to release Al 120. The Al 120 may then volatilize, providing vector protection per the program schedule.

[0116] This mode may allow ACRD 100 to operate with a higher degree of autonomy once the threat level is set in step 603. The activation rate may be dynamically adjusted according to algorithmic analysis of multiple variables, including environmental factors, GPS location, date, and time (which may be crucial for assessing diurnal or crepuscular peak vector activity). The aggregated data set may define which AIs 120 to deploy and at what rate for a given vector species.

[0117] FIG. 6C shows a simplified flow diagram of process 600 in which the semi-autonomous mode leverages environmental data according to examples disclosed herein. In step 632, app 106 may be started and communications may be established between ACRD 100 and mobile device 105. In step 634, the user may select the target vector. In step 636, the GPS location may be obtained from mobile device 105. In step 638, weather data, such as temperature, humidity, and wind speed, may be retrieved based on the GPS location. In step 640, the user may select a desired protection level. In step 642, the algorithm may determine the optimal Al formulation 120 and the number of reservoirs 118 required to provide the selected protection level. In step 644, microcontroller 110 may execute the release sequence at the required intensity level. In some examples, steps 636 and 638 may be periodically repeated to obtain real-time environmental condition updates (e.g., from mobile device 105 location services) to therewith adjust Al release schedules dynamically, which may further enhance protection efficacy.

[0118] FIG. 7 A shows a combined block and flow diagram illustrating a process 700 for an autonomous mode of operation of ACRD 100 according to examples disclosed herein. A non- transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described in FIG. 7 A. The non-transitory computer readable medium and at least one processor may correspond to one or more of server 160, control unit 102 and mobile device 105, and may include components such as processor 164, microcontroller 110 and memory 162, or other computing hardware of system 150 and / or networked system 170 configured to perform the described operations This mode may be similar to the semi-autonomous mode described in FIG. 6A above, except that it may fully rely on Artificial Intelligence (Al) through the implementation of machine learning (ML) algorithms. Operations may begin in step 702. In this step, instead of the user sending an explicit activation command into app 106, the system may obtain input data from networked system 170. As described above with reference to FIG. IB, networked system 170 may include multiple systems 150 connected via communications network 140 to ACRD server 160. ACRD server 160 may aggregate operational data from multiple users, including overall performance against specific target species as a function of multiple variables such as targeted vectors, environmental conditions (e.g., temperature, humidity, wind speed), GPS location, and time of day. ML algorithms executed on server 160 may process these inputs, optionally in combination with databases stored locally or on the server (e.g., Al efficacy databases, vector surveillance datasets), to determine a specific Al 120 and specific reservoirs 118 of ACRD 100 to control the release rate and degree of protection. In some examples, the algorithms and databases may be hosted entirely on control unit 102 and / or mobile device 105, allowing fully local operation without server 160. In other examples, hybrid configurations may be used, where local processing is supplemented by server-based optimization.

[0119] Once the Al 120 and reservoirs 118 are determined, an activation signal may be sent to ACRD 100 in step 732, processed as described above, and result in bursting of sealing membranes 126 and Al release in step 733. Reservoir 118 status may then be relayed back to the user in step 735.

[0120] FIG. 7B (divided for clarity into FIGS. 7B-1 and 7B-2) shows a more detailed flow diagram of the autonomous process 700 shown in FIG. 7 A according to examples disclosed herein. A non- transitory computer readable medium may contain instructions which, when executed by at least one processor, perform the method and operations described at each of the steps in FIG. 7B. The non-transitory computer readable medium and at least one processor may correspond to one or more of server 160, control unit 102 and mobile device 105, and may include components such as processor 164, microcontroller 110 and memory 162, or other computing hardware of system 150 and / or networked system 170 configured to perform the described operations.

[0121] The process may begin with inputs in step 702. These inputs may include the GPS location of the user, which may be obtained from mobile device 105. In step 703, the required intensity (protection) level may be selected, which may be scaled, for example, from 1-5 (5 being the highest intensity of protection). The intensity level may be manually input by the user or may be automatically determined by an algorithm based on aggregated data from step 714.

[0122] In step 714, data may be obtained from networked system 170 via communications network 140, including intensity levels selected by other users, historical reports, and external database records. These inputs may be processed by server 160 to produce a weighted average or other aggregated metric for determining the appropriate protection level.

[0123] The output may be provided to the user in step 716, which may include a set of available AIs 120 for the selected vectors from the different active reservoirs 118. This output may be based on additional parameters. For example, step 716a may provide for incorporation of additional environmental parameters such as temperature, humidity, and wind speed / direction acquired via app 106 based on the GPS location of mobile device 105. In step 716b, a database may provide an optional selection of Al formulations according to the payload capacity of each reservoir 118, which may be transmitted in step 716c for each target vector based on empirical formulation efficacy data for the relevant genus / species under the prevailing conditions.

[0124] After app 106 selects the required Al or formulations in step 718, app 106 may output the projected period of protection per Al formulation per reservoir 118 according to half-life calculations provided in step 720 by a database (which may be local or remote on server 160).

[0125] In step 722, app 106 may select a set of reservoirs 118 to activate for the desired protection period, and in step 724, app 106 may calculate the protection period for each selected reservoir set. Step 726 may allow app 106 to identify additional sets of reservoirs 118 and step 728 may allow app 106 to calculate the protection period for each additional selected reservoir set.

[0126] In step 730, the algorithm may confirm the final sets of reservoirs 118 to be activated, and in step 732, the activation may be executed. In step 731 a user may interact with app 106 to manually stop the activation process. Unlike the semi-autonomous mode, step 732 may be continuously updated according to GPS location data from mobile device 105 (step 702), resulting in an updated input intensity level in step 703 and enabling dynamic, real-time adjustment of protection parameters.

[0127] In some examples, process 700 may implement a fully autonomous operational mode for vector-borne disease protection in which each ACRD 100 operates without user selection or configuration of individual reservoir activation. In this mode, environmental data detected locally by the ACRD 100 or by a paired mobile device 105, along with aggregated network data from other ACRDs 100 and external sources, may be processed by a machine learning algorithm executed on server 160, on mobile device 105 using app 106, or in a hybrid configuration. The aggregated network data may include reports of mosquito pressure derived from trap data, user reports input using app 106, or publicly available databases, as well as environmental and location information. Server 160 may process these inputs to generate control data specifying which reservoirs to rupture, the type of active ingredient to release, and the corresponding release rate. In some examples, the control data is provided from server 160 to app 106 that in turn provides activation instructions to each ACRD 100 and acted upon by the actuator 126 to modulate active ingredient release in near real time. In some examples, the machine learning algorithm may dynamically update the control data as new aggregated network data is received, thereby adjusting operational parameters during ongoing protection. Although the mode is fully autonomous, in some cases the user may still optionally provide parameters such as desired protection level, anticipated exposure time, or target vector species, which may be incorporated into the control data determination.

[0128] In some examples, process 700 may implement a fully autonomous operational mode for controlled release of fragrances in which each ACRD 100 operates without user selection or configuration of individual reservoir activation. In this mode, environmental or physiological condition data detected locally by the ACRD 100 or by a paired mobile device 105, along with aggregated network data from other ACRDs 100 and external sources, may be processed by a machine learning algorithm executed on server 160, on mobile device 105, or in a hybrid configuration. The aggregated network data may include location information, environmental parameters such as temperature and humidity, and activity or condition indicators such as perspiration level, as well as user reports or data from external services. Server 160 may process these inputs to generate control data specifying which reservoirs to rupture, the type of fragrance to release, and the corresponding release rate. The control data may be transmitted to each ACRD 100 and acted upon by the actuator to modulate fragrance release in near real time. In some examples, the machine learning algorithm may dynamically update the control data as new aggregated network data is received, thereby adjusting operational parameters to maintain desired fragrance intensity or character. Although the mode is fully autonomous, in some cases the user may still optionally provide parameters such as desired fragrance intensity, fragrance type, or duration, which may be incorporated into the control data determination.

[0129] FIG. 7C shows a simplified flow diagram of autonomous process 700 utilizing external weather conditions and networked system 170 according to examples disclosed herein. The process may begin in step 734, where a connection may be established between ACRD 100 and mobile device 105 running app 106.

[0130] In step 736, the user may select a target vector, such as mosquitoes, ticks, or other pests. In step 738, the GPS location may be obtained by app 106 from mobile device 105, enabling the system to tailor the Al release strategy to the user’s current location. In step 740, weather data (e.g., temperature, humidity, wind speed) may be retrieved based on the GPS location, either from mobile device 105 sensors or from external weather services accessed via server 160.

[0131] In step 742, a protection level and duration of protection may be selected by the algorithm running on server 160 or app 106 - unlike in the semi-autonomous mode where these parameters may be defined manually. In step 744, the algorithm may determine the optimal Al formulation 120 and number of reservoirs 118 to activate based on the selections from prior steps.

[0132] In step 746, the algorithm may incorporate inputs from other systems 150-1 through 150-n connected via communications network 140. These inputs may be processed by server 160 and may include feedback on protection efficacy, observed weather changes, reports of additional vectors requiring protection, and other relevant field data. This information may be used to refine the selections made in steps 742 and 744, improving protection strategies.

[0133] In step 748, microcontroller 110 of ACRD 100 may activate the selected reservoirs 118 in a predefined sequence to release the Al formulations 120 at the required intensity level.

[0134] FIG. 7D shows a simplified flow diagram of autonomous process 700 utilizing fully automated vector selection according to examples disclosed herein. In step 736, the vector selection may be performed automatically by the algorithm based on aggregated user and device data received in step 746 from networked system 170.

[0135] In step 750, the algorithm may cross-reference the user’s GPS location with a vector population database, which may be hosted locally or on server 160, to identify geo-relevant target species. This step may ensure that only location-specific vector threats are addressed. Following this determination, the subsequent steps may proceed as in FIG. 7C, with the system dynamically adjusting Al selection, reservoir activation schedules, and protection levels without requiring any manual user inputs - thus enabling fully autonomous operation of ACRD 100.

[0136] In summary, processes 500, 600, and 700 differ primarily in the allocation of decisionmaking between the user and automated processing by the application and / or a remote server. In process 500 (manual mode), the user explicitly selects the reservoir to be activated via the application, and the application transmits the corresponding activation signal to the ACRD without further computational selection. In process 600 (semi-autonomous mode), the user provides information such as target vector species, anticipated exposure duration, location, and optional protection level, together with environmental parameters obtained from sensors, GPS, or other sources; the application or, in some embodiments, a remote server executes an algorithm using these inputs, together with data from one or more databases (e.g., Al efficacy data, environmental data, vector activity data), to select the reservoir(s) to activate without the user directly choosing the reservoir. In process 700 (fully autonomous mode), the application and / or a remote server determines the reservoir activation plan automatically based on environmental parameters and aggregated network data from other ACRD devices, and in certain embodiments executes a machine-learning algorithm trained on historical efficacy and environmental data to refine Al selection and activation timing; user input in this mode may be limited to selecting a desired protection level or providing optional parameters, with all other determinations performed automatically. This progression from process 500 to process 700 represents an increasing reliance on automated analysis and decreasing reliance on direct user selection of reservoirs.

[0137] FIG. 8A shows an in vitro entomological experiment setup that was performed to evaluate the effects of ACRD 100 against vectors 802. In this example, the vectors 802 were ticks. The experiment assessed behavioral changes relative to the ticks’ natural tendency to climb. In the experiment, ACRD 100 was fixed to the side of a test chamber and activated 20 minutes prior to tick introduction via a command from app 106, communicated using Bluetooth. ACRD 100 was placed in upper zone 804 of the chamber, defined as the top 5 cm of the chamber. Ticks 802 were able to climb on rods 806 positioned vertically inside the chamber; three 30 cm rods were used.

[0138] The movement of ticks 802 was tracked using video camera 808 with tracking software 810 for a pre-defined period of 10 minutes. Ticks 802 were initially placed in lower zone 812 of the chamber, with one tick per rod for each trial. The experiment was repeated five times. Three different groups were tested, with one, two, and three reservoirs 118 deployed to evaluate dose escalation (n = 15 ticks per group). The Al formulation 120 deployed was transfluthrin formulated with isopropyl alcohol at 30% w / w.

[0139] Ticks 802 were considered repelled if they did not reach upper zone 804 or if they detached from rods 806 by the end of the trial. The average climbing velocity of ticks 802 was also quantified.

[0140] FIGS. 8B-8D show the results of the in vitro experiment described in FIG. 8A: FIG. 8B shows the average movement velocity of ticks 802 for each group tested. The movement speed of control ticks was 2.8x greater than ticks exposed to one reservoir 118, 3.5x greater than those exposed to two reservoirs 118, and 8.4x greater than those exposed to three reservoirs 118.

[0141] FIG. 8C shows the percentage of repelled ticks, defined as the proportion of total ticks repelled divided by the total number of ticks tested. No control ticks were repelled, while one reservoir 118 repelled 80%, two reservoirs 118 repelled 93%, and three reservoirs 118 repelled 100% of ticks.

[0142] FIG. 8D shows the percentage of expelled ticks, defined as the number of detached ticks out of those that were repelled. Three-reservoir exposure was associated with the greatest detachment response, but the difference from one-reservoir exposure was not statistically significant, indicating that exposure levels beyond what is achieved with one reservoir may not greatly affect this specific behavior.

[0143] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The materials, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0144] Implementation of methods disclosed herein may involve performing or completing certain selected tasks or steps manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment, several selected steps may be implemented by hardware (HW) or by software (SW) on any operating system of any firmware, or by a combination thereof. For example, as hardware, selected steps could be implemented as a chip or a circuit. As software or algorithm, selected steps could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In any case, selected steps could be described as being performed by a data processor, such as a computing device for executing a plurality of instructions.

[0145] Although the disclosure refers to a “computing device”, a “computer”, or “mobile device”, it should be noted that optionally any device featuring a data processor and the ability to execute one or more instructions may be described as a computing device, including but not limited to any type of personal computer (PC), a server, a distributed server, a virtual server, a cloud computing platform, a cellular telephone, an IP telephone, a smartphone, a smart watch or a PDA (personal digital assistant). Any two or more of such devices in communication with each other may form a “network” or a “computer network”.

[0146] As used herein the terms “machine learning” or “artificial intelligence” refer to use of algorithms on a computing device that parse data, learn from the data, and then make a determination or generate data, where the determination or generated data is not deterministically replicable (such as with deterministically oriented software as known in the art). In some embodiments, machine learning algorithms (also referred to herein as machine learning models or artificial intelligence) may be trained using training examples. Further, in some examples, training machine learning algorithms using the training examples may generate a trained machine learning algorithm, and the trained machine learning algorithm may be used to estimate outputs for inputs not included in the training examples. In some examples, a machine learning algorithm may have parameters and hyper parameters.

[0147] Disclosed embodiments include methods, systems, devices, and computer-readable media, for providing a technical solution to the challenging technical problem of vector control and fragrance release, and relate to a system for vector control and fragrance release with the system having at least one processor (e.g., processor, processing circuit or other processing structure described herein). For ease of discussion, example methods are described below with the understanding that aspects of the example methods apply equally to systems, devices, and computer-readable media. For example, some aspects of such methods may be implemented by a computing device or software running thereon. The computing device may include at least one processor (e.g., a CPU, GPU, DSP, FPGA, ASIC, or any circuitry for performing logical operations on input data) to perform the example methods. Other aspects of such methods may be implemented over a network (e.g., a wired network, a wireless network, or both).

[0148] As another example, some aspects of such methods may be implemented as operations or program codes in a non-transitory computer-readable medium. The operations or program codes may be executed by at least one processor. Non-transitory computer readable media, as described herein, may be implemented as any combination of hardware, firmware, software, or any medium capable of storing data that is readable by any computing device with a processor for performing methods or operations represented by the stored data. In a broadest sense, the example methods are not limited to particular physical or electronic instrumentalities, but rather may be accomplished using many differing instrumentalities. In the specification of the present application, unless otherwise stated, adjectives such as “substantially” and “about” modifying a condition or relationship characteristic of a feature or features of an embodiment of the invention, are understood to mean that the condition or characteristic is defined to within tolerances that are acceptable for operation of the embodiment for an application for which it is intended.

[0149] It should be understood that where the specification refers to “a” or “an” element, such reference is not to be construed as there being only one of that element.

[0150] In the description of the present application, each of the verbs, “comprise” “include” and “have”, and conjugates thereof, are used to indicate that the object or objects of the verb are not necessarily a complete listing of components, elements or parts of the subject or subjects of the verb.

Claims

CLAIMS1. A networked system for vector-borne disease protection comprising: a plurality of adaptive controlled release devices (ACRDs), each ACRD comprising at least one reservoir storing an active ingredient and at least one actuator configured to release the active ingredient; and a server configured to receive and aggregate vector activity data, local environmental data, and user input data from the plurality of ACRDs and / or from external sources, to generate control data based on the aggregated data, and to transmit the control data to at least one of the plurality of ACRDs, wherein each ACRD is configured to transmit the local environmental and / or the user input data to the server, to receive the control data from the server, and to adjust operation of the at least one actuator to modulate release of the active ingredient in response to the control data.

2. The system of claim 1, wherein each ACRD or a mobile device paired with the ACRD includes environmental sensors for providing the local environmental data.

3. The system of claim 1 , wherein the external sources comprise at least one of: environmental sensors, traps, publicly available databases, or user reports.

4. The system of claim 1, wherein each ACRD or a mobile device paired with the ACRD includes a communication module for communicating with the server or with another ACRD.

5. The system of claim 1, wherein the active ingredient is stored within a formulation.

6. The system of claim 1, further comprising an application running on a paired mobile device, the application configured to receive the user input data from a user.

7. A networked system for controlled release of fragrances, comprising: a plurality of adaptive controlled release devices (ACRDs), each ACRD comprising at least one reservoir storing a fragrance and at least one actuator configured to release the fragrance; and a server configured to receive and aggregate environmental and / or physiological condition data from the plurality of ACRDs and / or from external sources, to generate control data based onthe aggregated data, and to transmit the control data to at least one of the plurality of ACRDs, wherein each ACRD is configured to adjust operation of at least one of its actuators to modulate release rate of the fragrance in response to the control data.

8. The system of claim 7, wherein each ACRD or a mobile device paired with the ACRD includes sensors for providing environmental and / or physiological condition data.

9. The system of claim 7, wherein the external sources comprise at least one of: environmental sensors, location services, or user reports.

10. The system of claim 7, wherein the control data is based at least in part on condition data generated by another ACRD in the network.

11. The system of claim 7, wherein each ACRD or a mobile device paired with the ACRD includes a communication module for communicating with the server or with another ACRD.

12. The system of claim 7, wherein the fragrance is stored within a formulation.

13. The system of claim 7, further comprising an application running on a paired mobile device, the application configured to receive the user input data from a user.

14. An adaptive controlled release device (ACRD) comprising: a cartridge array including one or more replaceable cartridges, each cartridge comprising at least one reservoir configured to store an active ingredient (Al) or a formulation thereof; a plurality of micro electro mechanical system (MEMS) membranes positioned over respective reservoirs, each MEMS membrane configured to hermetically seal the respective reservoir until activation; and a control unit configured to rupture a selected MEMS membrane in response to an activation signal received from an application running on a mobile device, wherein rupture of the selected MEMS membrane exposes the Al or formulation to a fluid environment for volatilization and diffusion,wherein in a fully autonomous mode of operation the activation signal is generated automatically based on environmental parameters and aggregated network data from other ACRD devices, the determination being performed by a machine learning algorithm executed on a server, locally on the mobile device, or in a hybrid configuration, without any user selection or configuration of reservoir activation.

15. The device of claim 14, wherein the application or server accesses one or more databases containing Al efficacy, environmental, or vector activity data to support the determination of which reservoir to activate.

16. The device of claim 14, wherein the machine learning algorithm processes historical efficacy data, environmental parameters, and aggregated network reports to refine Al selection and activation timing.

17. The device of claim 14, wherein the user may select a desired protection level from among low, medium, or high, and the machine learning algorithm determines the reservoir activation plan accordingly.

18. The device of claim 14, wherein the user may input one or more parameters including target vector species, anticipated exposure time, or location, and the machine learning algorithm incorporates the user input into the determination of the reservoir activation plan.

19. The device of claim 14, wherein the application or control unit is configured to activate two or more reservoirs in combination to release different active ingredients simultaneously or sequentially.

20. The device of claim 14, wherein the cartridge array includes a pump, and wherein the control unit is configured to activate the pump in response to an activation signal received from an application running on a mobile device.

21. A method for vector-borne disease protection, comprising:deploying a plurality of adaptive controlled release devices (ACRDs), each comprising at least one reservoir storing an active ingredient and at least one actuator configured to release the active ingredient; detecting local environmental data and / or receiving user input data via each ACRD or a mobile device paired with the ACRD; transmitting the local environmental data and / or the user input data from each ACRD to a server; receiving, at each ACRD, control data generated by the server based on aggregated vector activity data, local environmental data, and / or user input data from the plurality of ACRDs and / or external sources; and operating the at least one actuator of each ACRD to modulate release of the active ingredient in response to the control data.

22. The method of claim 21, wherein detecting the local environmental data comprises sensing environmental parameters using sensors integrated into the ACRD or into the paired mobile device.

23. The method of claim 21, wherein the external sources comprise at least one of: environmental sensors, traps, publicly available databases, or user reports.

24. The method of claim 21 , wherein transmitting the local environmental data and / or the user input data comprises communicating via a communication module integrated into the ACRD or into the paired mobile device.

25. The method of claim 21, wherein the active ingredient is stored within a formulation.

26. The method of claim 21, further comprising receiving, via an application running on the paired mobile device, the user input data including at least one of: desired protection level, target vector species, or anticipated exposure time.

27. The method of claim 21, wherein operating the actuator comprises rupturing a micro electro mechanical system (MEMS) membrane that hermetically seals the reservoir until activation.

28. A method for controlled release of fragrances, comprising: deploying a plurality of adaptive controlled release devices (ACRDs), each comprising at least one reservoir storing a fragrance and an actuator configured to release the fragrance; detecting local environmental and / or physiological condition data via each ACRD or a mobile device paired with the ACRD; transmitting the local environmental and / or physiological condition data from each ACRD to a server; receiving, at each ACRD, control data generated by the server based on aggregated environmental and / or physiological condition data from the plurality of ACRDs and / or from external sources; and operating the actuator of each ACRD to modulate release of the fragrance in response to the control data.

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