3981 results about "Virtual machine" patented technology

An AI orchestration system dynamically manages multiple artificial intelligence (AI) agents within a cloud computing environment to efficiently process user requests. A model orchestration subsystem determines whether a request is handled locally using a domain-specific database or by invoking one or more AI agents. The system maintains AI agents in active and inactive states, provisioning computing resources for inactive agents as needed. Real-time model metrics guide the selection of target AI agents, and if a degrading performance trend is detected, the system preemptively spins up additional AI instances. The system provisions processor cycles, memory, and network bandwidth through a cloud-based resource manager, instantiates containerized execution environments or virtual machines, and performs automated load balancing among AI instances.

The invention discloses a virtual machine scheduling method in a distributed environment based on deep reinforcement learning, and belongs to the technical field of cloud computing resource scheduling. According to the method, the defects of a traditional method in multi-objective optimization and mixed action space collaborative decision-making are overcome by constructing a mixed action space joint decision-making mechanism. The method specifically comprises the following steps: establishing a mixed action space containing discrete node selection and continuous resource allocation, filtering invalid nodes by adopting a dynamic mask mechanism, and ensuring resource ratio constraint through projection gradient descent; designing a hierarchical reward function to realize multi-target dynamic balancing, and dynamically adjusting the priorities of energy consumption, load balancing and SLA guarantee based on an adaptive weight strategy; a multi-agent collaborative framework is provided, cross-node topological dependence is captured by using a graph attention network, and dynamic fusion of spatio-temporal characteristics is realized through cross attention in combination with LSTM coding time sequence load characteristics; a course learning strategy and a priority experience playback mechanism are introduced to improve training efficiency and strategy robustness.

The invention relates to a feature defense method and system in a network transmission process, and the method comprises the steps: intercepting a network transmission data flow in a migration channel of a virtual machine in real time, and building a bidirectional flow mirror image pipeline; performing deep protocol analysis and recombination on the original transmission message in the mirror image pipeline to obtain a structured traffic log of complete load information and metadata labels; based on a preset traffic feature set, extracting a memory paging transmission time sequence feature and a disk data block check code distribution feature from the structured traffic log, and constructing a first detection sub-vector; according to the invention, a high-precision virtual machine migration security protection system is realized.

The invention relates to a competition management system and method based on cloud computing, and belongs to the technical field of resource allocation. The number of virtual machines, physical resources and competition levels are determined by dynamically acquiring competition information, resource priorities are automatically associated, stable allocation of high-level competition item resources is guaranteed, a scheduling model is constructed through a deep reinforcement learning algorithm, a virtual machine allocation probability matrix is generated, and long-term income is evaluated in combination with a reward function. Autonomous learning optimization of a scheduling strategy is realized; a multi-dimensional server state matrix is constructed, matching tasks and hardware characteristics are quantified, and the task interruption risk is reduced; a dynamic label system is generated in combination with static code analysis and runtime data, and key competition resource allocation is ensured; and staged competition items are staged, and resource demand vectors are defined, so that staged dynamic resource adjustment is realized. By remarkably improving the resource utilization rate and the load balancing capacity, the fairness of competition management, the system stability and the intelligent level are improved.

This invention pertains to a system optimized for reutilizing allocated underutilized or unused allocated DRAM, comprising a first host, a second host, and a resource composer, interconnected via CXL. Both hosts run packaged computing environments (PCEs), which may be containers or virtual machines, and are equipped to handle respective processes, P1 and P2. The resource composer is tasked with receiving data related to P1's memory usage from a kernel module on the first host, identifying underutilized DRAM mapped to P1, and subsequently remapping it to P2's address space on the second host. This process involves the use of CXL.mem commands, which are then translated into appropriate CXL.cache or CXL.io commands for DRAM access based on the mapping.

The invention provides a security verification method for a large model service operation environment, a medium, equipment and a product, and relates to the technical field of computers, large model services are deployed in a container group created in a virtual machine, and the virtual machine is deployed in a hardware-based trusted execution environment, so that the security verification of the large model service operation environment is realized. The method comprises the following steps: acquiring environment measurement corresponding to a running environment of a large model service through a remote attestation service deployed in a hardware-based trusted execution environment, the environment measurement at least comprising a first measurement corresponding to the trusted execution environment in which a virtual machine is located and a container measurement under a container stack dimension corresponding to a container group; the remote attestation service generates a remote attestation report according to the environmental metrics to enable a user to validate the security of the operating environment through the remote attestation report. According to the invention, not only can the attack from the IaaS layer be shielded, but also the credibility verification under the dimension of the virtual machine and the credibility verification under the dimension of the container stack can be provided, so that the credibility of the user to the service provided by the service provider is improved.

The invention discloses a full-scene task autonomous execution method and system based on a large model, belongs to the technical field of computer application and artificial intelligence, and aims to solve the technical problems that an existing RPA technology is lack of autonomous thinking ability, and a browser use technology is limited in application range, is greatly influenced by factors such as webpage change and the like, and is poor in reliability. The Computer use technology is inaccurate in operation, high in development difficulty and has security risks, and the technical scheme comprises the following steps: establishing an MCP service, and compiling a task autonomous execution tool set containing various basic operation instructions including software opening, browser starting, precise clicking and information filling; the MCP service is deployed in the Windows virtual machine environment, and stable operation of the service is ensured; constructing a task autonomous execution workflow agent with task understanding, planning and scheduling capabilities; the user inputs a task target through a natural language, and the agent receives and records the specific demand of the user as a target guide for subsequent task generation and execution.

The invention relates to the technical field of virtualization management, in particular to a virtual machine operation management system based on RISC-V. The system comprises an instruction set mapping module, a resource scheduling optimization module, a virtual machine monitoring integration module, an operation tuning update module and an operation configuration distribution module. According to the method, by analyzing the operation instruction of the virtual machine and combining the RISC-V underlying characteristics, accurate instruction mapping is achieved, the processing efficiency and accuracy are remarkably improved, the performance loss and conversion delay caused by too high abstraction level of a traditional virtual machine on the instruction level are overcome, the optimal allocation scheme is screened, the resource utilization rate and the system response speed are effectively improved, and the system performance is improved. The operation state and performance indexes of the virtual machine are integrated, anomaly detection and resource recovery are embedded, system stability and effective resource reutilization are ensured, basic configuration is continuously and incrementally updated, operation configuration refinement and automatic management and updating are achieved, the overall operation efficiency and management flexibility are improved, and the manual intervention cost is reduced.

The invention relates to the technical field of wind power generation. The invention provides a wind field unit comparison type fault diagnosis method, system and device and a storage medium. The method comprises the following steps: synchronously acquiring operation and corresponding environment data of units of the same type in the same wind field, and establishing a multi-dimensional data set; building a normal working condition multi-dimensional parameter reference model by using clustering analysis and updating in real time; performing multi-scale comparison on a target and a reference unit, and extracting feature fusion to generate a comprehensive health index; establishing a double-layer diagnosis model, constructing a virtual unit based on digital twinning in the first layer, comparing residual errors and positioning a potential fault source, detecting outliers by using an improved isolated forest algorithm in the second layer, determining a fault propagation path in combination with an association rule, and establishing a fault mode knowledge base; and triggering an early warning mechanism according to the comprehensive health index and the fault mode classification. The problems that a traditional operation and maintenance mode is difficult in fault prediction, high in cost and lack of a reliable evaluation system, state monitoring is limited to a single parameter, multi-unit comparative analysis means are insufficient, and fault diagnosis is lagged are solved.

A system and method for real-time or near real-time multilingual language transformation in telecommunications environments using distributed artificial intelligence (Al). The system includes at least one processor configured to instantiate a plurality of Al agents, each corresponding to a user in a communication session, and to manage their operation via an Al-agent runtime adapter comprising a microkernel agent manager and a virtual machine layer. A language model abstraction layer enables access to language models and Al algorithm libraries through publish-subscribe interfaces. The system supports contextual transformation of speech based on user profile data such as sentiment, emotional tone, and cultural background. Execution can occur on telecommunication-class switches, edge devices, or general-purpose computing hardware. Al agents operate asynchronously, access shared or private memory, and produce personalized, bidirectional linguistic output. The architecture supports secure, scalable deployment across heterogeneous devices and networks.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

The invention discloses a network intercommunication method and system, electronic equipment, a storage medium and a program product, and relates to the field of cloud computing, and the method comprises the following steps: for first virtual machine traffic output by a virtual machine in a computing node, performing route matching on the first virtual machine flow through a routing table corresponding to a first virtual route and a forwarding instance in the computing node; and instructing a target switch to send the obtained second virtual machine traffic to the physical server node, so that network intercommunication between the virtual machine and the physical server node is realized, and the target switch is connected to the physical server node, the routing reflector and the computing node. The problem that the network intercommunication effect between the virtual machine and the bare metal node is poor due to the defects of bandwidth forwarding bottleneck and high scheme cost of a soft gateway used between the virtual machine and the bare metal node in the computing node in the cloud computing environment is solved, and the network intercommunication effect between the virtual machine and the bare metal node is improved.

A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.

The invention provides a credential and credential environment adaptation method and system in combination with a cloud computing platform, and relates to the technical field of credential and credential environment adaptation, and the method comprises the steps: generating adaptation analysis basic features based on static structure information; performing interface adaptation analysis by using the API support database and the adaptation analysis basic features; after a virtual credential environment is established by utilizing the virtual machine, executing the operation record of the target software, and establishing an operation data set; behavior capture of the operation data set is carried out under the guidance of the risk hot area of the compatibility matching graph by utilizing a behavior abnormity identification channel, and clustering analysis is carried out on behavior capture results; uI presentation of the target software is recorded, presentation consistency verification is carried out based on the UI presentation, and a presentation consistency score is established; and establishing an adaptation result according to the adaptation risk score table, the behavior stability score and the presentation consistency score. According to the invention, the technical problem of poor adaptation stability in the prior art can be solved, and the technical effect of improving the adaptation stability is achieved.

The invention discloses an operation management method and device for a virtual machine, equipment, a storage medium and a program, and relates to the technical field of virtual machines, and the operation management method comprises the steps of receiving a first control instruction issued by a target object and used for operating the virtual machine, and analyzing the first control instruction to obtain a first operation command, the first control instruction is a natural language instruction for controlling the life cycle of the virtual machine based on a natural language, and the first operation command is an executable virtual machine operation command converted from the natural language instruction; under the condition that the first operation command enters execution, monitoring the execution process of the first operation command to obtain multiple pieces of process data; and identifying abnormal data in the multiple pieces of process data, rolling back the process data from an abnormal data occurrence time point to a time state node before the occurrence of the abnormality according to a predetermined rule, and starting to perform operation management on the virtual machine again based on target process data corresponding to the time state node.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant regulation and control method and system for realizing new energy consumption, and the system comprises a multi-source heterogeneous data collection module, an intelligent prediction analysis module, a resource aggregation modeling module, an optimization decision module, a block chain cooperation module, and a digital twinborn evaluation module. Multi-time-scale coupling prediction is carried out on new energy output and load demand through the deep space-time convolutional neural network, fluctuation and intermittency characteristics of new energy can be described, short-term and ultra-short-term prediction precision is improved, wind curtailment and light curtailment rate and load reduction risk are reduced, and the prediction efficiency is improved. According to the method, high matching between a virtual power plant scheduling plan and an actual operation condition is guaranteed, a flexible resource feature matrix is constructed, and distributed energy storage, interruptible load and electric vehicle multi-element resources are subjected to refined modeling and aggregation, so that a virtual unit capable of being efficiently scheduled can be formed, the resource utilization efficiency is improved, and the overall scheduling cost is reduced.

The invention relates to the technical field of GPUs, in particular to a GPU virtualization video memory management method and device, a storage medium and a program product. The method comprises the steps that a virtual machine transmits a page table root address corresponding to a target application program running in the virtual machine to a host machine; the virtual machine calls the non-memory operation type API in response to the target application program, intercepts calling of the non-memory operation type API and forwards the calling to the host machine; and in the host machine, accessing a video memory area corresponding to a target application program based on the page table root address through the drive of the non-memory operation type API, executing an instruction of the non-memory operation type API, and returning a processing result of the instruction of the non-memory operation type API to the virtual machine. According to the method and the device, the rear-end driver in the host machine can directly access the video memory resources allocated by the virtual machine, so that the resource utilization rate and the task execution efficiency are improved.

The invention discloses a method for communication between a user program and a virtual machine on a microkernel Hypervisor, the microkernel Hypervisor is provided with two shared memory areas, the shared memory area 1 is accessed by the user program and a root service Rootserver, the shared memory area 2 is accessed by the Rootserver and the virtual machine, the user program writes communication request data with the virtual machine into the shared memory area 1, and the user program writes communication request data with the virtual machine into the shared memory area 2. The method comprises the following steps that a VMM sub-thread is used as a shared memory area 1, a Rootserver is notified through inter-process communication, the Rootserver reads communication request data from the shared memory area 1, the communication request data is written into a shared memory area 2 after being analyzed by the VMM sub-thread, a system calls a syscale to transmit a communication request to a kernel, the kernel injects virtual interrupt into a virtual machine, and the virtual machine sends the communication request to the Rootserver. And an interrupt processing program of the virtual machine processes the communication request and writes a processing result into the shared memory area 2, then the processing result is returned to the Hypervisor through the Hypercall, and a Rootserver of the Hypervisor feeds back the processing result to a user program through the IPC. The method is designed for the microkernel Hypervisor environment, and the overall performance and efficiency of the embedded virtualization system are improved.

A method for dynamically optimizing and modifying allocation of virtual resources to processes includes allocating, by a first hypervisor, a first amount of a first virtual resource to a process executing in a virtual machine on a first machine. An agent, in communication with a resource allocation process executing in the virtual machine, determines a second amount of the first virtual resource to be utilized by the process. A scheduler receives an identification of the determined second amount and directs migration of the process to a second machine. A second hypervisor executing on the second machine allocates a third amount of the first virtual resource, the third amount substantially similar to the second amount. The method includes communicating, by the resource allocation process, to the process, that the first amount of the first virtual resource is allocated to the at least one process.

The invention provides a service capability calling method and system in application software. The method comprises the following steps of: monitoring real-time temperature data and load state data of each available service node in a server cluster in real time, and combining a preset load balancing strategy and a virtual machine dynamic migration technology to ensure the stable operation of the server cluster in a high-concurrency scene. And when it is detected that the real-time temperature of the available server node exceeds the preset temperature threshold value or the capacity of the server node reaches the capacity upper limit, the service capability of the available server node is migrated to the standby node, and service interruption is avoided. Meanwhile, the real-time temperature data, the load state data and the execution frequency of the migration operation are subjected to correlation analysis, and a load balancing strategy and a migration trigger threshold value are dynamically optimized, so that service continuity is maintained, and the temperature fluctuation range of hardware resources is controlled. According to the technical scheme provided by the invention, continuous response of service requests and temperature stability of hardware resources in a high-concurrency scene can be realized.

The invention provides a real-time monitoring system and method for sorting of a digital twin-driven Delta mechanical arm. According to the system, firstly, a physical experiment platform is built, and a virtual model is built through SolidWorks, 3DMAX and Unity 3D. A communication framework is designed, and wireless communication of the PLC, the sensor, the mechanical arm, the conveying belt and the digital twin system is achieved. Positive and inverse kinematics analysis and Cartesian space trajectory planning are carried out on a Delta robot, and a related algorithm is packaged into an MATLAB function and compiled into a DLL library for C # calling. The system collects joint angle data in real time based on a rotary encoder, transmits the joint angle data to a digital twin system through an OPC UA protocol, and drives a virtual mechanical arm model after resolving, so that high-precision and low-delay virtual-real synchronous pose reconstruction is realized. In addition, the system also develops a collision detection function based on a bounding box algorithm, and performs visual detection by using camera calibration and a YOLO model.

The invention discloses an ebpf-based soft lock detection analysis method and system, a medium and a product, and the method comprises the steps: carrying out the soft lock detection based on the guest mode quitting information of a vCPU in the execution state of the virtual machine vCPU monitored by an ebpf code on a host system during the soft lock of the virtual machine, and if the guest mode quitting information of the vCPU is not found in a plurality of continuous soft lock detection periods, carrying out the soft lock detection. If yes, judging that the result is caused by a host system; if it is found that the quit frequency statistical information of the vCPU quitting the guest mode in multiple continuous soft lock detection periods exceeds a preset threshold value and the quit reason is matched with a preset rule, it is judged that the result is caused by the internal reason of the virtual machine. The method aims at accurately judging the reason for the soft lock problem of the virtual machine under the condition that the kernel of the virtual machine is not changed, and determining whether the soft lock problem of the virtual machine is caused by the resource scheduling of a host system or the internal part of the virtual machine.

The invention provides a hot upgrade method for a virtual machine monitor (VMM), which comprises the following steps of: pausing the operation of a virtual machine, storing state information of an original VMM, and reserving a kernel-based virtual machine KVM extension page table and a memory management structure of the original VMM; reserving a page global directory and a virtual memory area VMA corresponding to a virtual machine RAM area according to the memory management structure, and refreshing a process address space described by the memory management structure into a new VMM execution program so as to complete switching of the original VMM into the new VMM; and executing the new VMM, recovering the state information to the new VMM, and inheriting the KVM extension page table by the new VMM to recover the operation of the virtual machine. The invention further provides computer equipment, a computer readable medium and a computer program product.

The invention discloses a hybrid heterogeneous cloud workflow scheduling method based on reinforcement learning, and belongs to the technical field of cloud computing. The method comprises the following steps: aiming at a cloud workflow scheduling problem, by taking minimization of completion time as a target and taking cost and resources as constraints, a three-dimensional collaborative constraint model is constructed, and the cost constraints comprise server-free function budget and virtual machine budget; integrating the hyper-heuristic framework into a reinforcement learning algorithm; an improved reinforcement learning algorithm is adopted to solve the cloud workflow scheduling problem, optimal execution resources are selected, and an optimal scheduling scheme is obtained; and performing real-time scheduling according to the optimal scheduling scheme, and introducing a deviation feedback mechanism to monitor an execution error in real time. According to the method, the workflow scheduling problem is decomposed into a closed-loop optimization process of state perception and action decision, dynamic environment perception, multi-target tradeoff and online strategy optimization are deeply fused, the global search function is achieved, local optimization can be achieved, the algorithm complexity is low, and robustness is high.

Techniques for host fleet management in a cloud provider network are described. Forecast data including a forecasted demand for virtual machines in each capacity pool of a set of capacity pools is obtained. A mathematical optimizer application is executed to generate a first optimal fleet plan, the mathematical optimizer application having an objective function to minimize a number of new host computer systems to add to the set of host computer systems to satisfy the forecasted demand for each capacity pool, the first optimal fleet plan includes an identification of a set of hardware types and, for each hardware type in the set, a quantity of new host computer systems of the hardware type. A plurality of new host computer systems is deployed, based on the first optimal fleet plan, for a hardware type in the set of hardware types into the set of host computer systems.

The invention relates to a multi-channel parallel virtual machine online migration method and system based on memory layering, and belongs to the technical field of cloud computing. According to the method, a to-be-migrated virtual machine memory is divided into a static data layer and a dynamic data layer, and the static data layer is directly transmitted to a target end through shared storage; the dynamic data layer is divided into a plurality of areas according to memory addresses, independent transmission channels are distributed for parallel migration, and the sizes of the partitions are dynamically adjusted according to the dirty page rate; calculating an iteration convergence factor in real time, and switching to a post-copy mode and backing up dirty pages to a shared storage when continuous iteration is not converged for multiple times; and recovering data from the shared storage when the network fails, and finally finishing the migration of the remaining dirty pages. Through memory hierarchical optimization, parallel transmission acceleration and a network fault-tolerant mechanism, the problems of low efficiency, iteration divergence and service interruption risks of large-memory and high-write-dirty-rate virtual machine migration in an AI scene are solved, the migration speed and success rate are remarkably improved, and continuous execution of an AI task is guaranteed.

The invention relates to the field of cloud platforms, and particularly provides a Terraform-based multi-cloud virtual machine elastic scaling method and system, a terminal and a medium, and the method comprises the steps: synchronizing basic parameters of each cloud platform; calling each cloud platform monitoring interface in real time to collect resource performance indexes of the cloud platform to generate a time sequence monitoring data set; based on the real-time load state reflected by the data set, generating a scaling instruction in combination with a preset cost priority, performance priority or hybrid strategy; according to a target instance specification in the scaling instruction, selecting a resource configuration template corresponding to the cloud platform from a preset template library, and injecting a target instance number and available region parameters to generate a variable file; and calling Terraform to load the resource configuration template and the variable file on the connector, generating an infrastructure change scheme through a resource change pre-verification command, and executing a resource change execution command to complete resource capacity expansion and contraction. According to the invention, minute-level, low-cost, high-performance and cross-cloud escape closed-loop elastic telescoping is realized.

The invention discloses a computing power comprehensive scheduling method and system for cloud computing, and relates to the technical field of computing power scheduling, and the method comprises the steps: determining the task priority of a cloud computing task, preliminarily generating a mapping matrix, and respectively defining task preference and virtual machine preference by using a bilateral preference game mechanism, a stable matching algorithm is used for preliminarily matching the tasks with the virtual machines, idle fragment resources of the virtual machines are determined, a rhododendron behavior scheduling algorithm is used, the scheduling probability of unmatched tasks is calculated, task matching is updated, and an egg replacement mechanism is used for replacement again to match the tasks for local optimization. According to the method, through matching, parasitism and replacement chained control, full-process control from static gaming to dynamic fault tolerance is completed, the task scheduling success rate, the task entering density and the unit utilization efficiency of resources are improved, and the overall scheduling failure rate and the resource vacancy rate of the system are reduced.

The dual-mode self-healing method of the automatic test system based on virtualization hierarchical monitoring comprises the following steps: initializing a platform dual-mode monitoring module, and establishing communication between the platform dual-mode monitoring module and a test machine; constructing a three-layer three-dimensional monitoring system and a cross-level correlation analysis engine to monitor the state of the test machine; performing automatic testing on a testing machine; monitoring and recording hardware monitoring data of the test machine, and recording possible fault data in the monitoring process; predicting the task success rate of the test; and the platform dual-mode monitoring module monitors the abnormal state of the test machine through a three-layer stereoscopic monitoring system and a cross-hierarchy association analysis engine, obtains an abnormal fault branch and determines a corresponding self-healing measure. According to the method, automatic identification and correlation analysis of double-layer faults of the virtual machine and the host machine can be carried out, hardware faults, virtual machine faults and cross faults can be accurately judged, fault self-healing is realized through a self-healing decision, the test efficiency is improved, and the stability of the test process is ensured.

The invention relates to a video memory management method and device, electronic equipment and a storage medium, the method is applied to a GPU drive in a virtual machine, and the method comprises the steps that when it is detected that the remaining amount of a GPU video memory is lower than a set remaining amount threshold value, cold pages in a GPU page table are recognized; wherein the cold page is a page table item which is not accessed in a recent preset time interval; and under the condition that the cold page is a video memory, replacing a physical address corresponding to the virtual address in the cold page to a physical address in a system memory so as to replace video memory data corresponding to the cold page to the system memory. The embodiment of the invention can improve the overall utilization rate of the video memory.