54 results about "Arctic" patented technology

1. Name of the designed product: ceiling lamp (North Pole Glacier series C185). 2. Use of the designed product: for lighting. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

The invention discloses a sea ice coverage on-satellite detection and updating method and device for a synthetic aperture radar (SAR) image, and belongs to the technical field of satellite remote sensing marine monitoring, and the method comprises the steps: carrying out the preprocessing of a dual-polarization SAR image on the ground, training a super-resolution model, and carrying out the knowledge distillation and lightweight processing, and deploying the super-resolution model to a spaceborne computer. After a satellite obtains a real-time SAR image in an on-orbit mode, preprocessing can be completed on the satellite, and a sea ice coverage binary image is directly output. In order to adapt to different satellite wave band data, the ground only needs to finely adjust part of parameters of the model and upload and update the parameters in a differential packet form, so that the whole packet is prevented from being retransmitted. According to the method, a closed loop of ground pre-training-on-satellite reasoning-differential updating is realized, the method has the characteristics of fast response, light weight, easy expansion and the like, and the sailing safety of the arctic navigation channel can be remarkably improved.

The invention discloses a bulb sound source release device applied to an arctic ice region, and relates to the technical field of underwater acoustic engineering. Aiming at low temperature, no power supply, deepwater and ice hole operation limitation in the arctic ice region, the device is driven by mechanical impact, and accurate breaking of the underwater vacuum bulb can be realized without a power supply. The device comprises a vacuum bulb cavity, a guide steel wire rope, a drop hammer, a firing pin assembly and an ice hole clamping frame. The vacuum bulb cavity comprises a pressure protection balance cavity, a pressure guide hole and a controllable knob; the drop hammer is released to fall freely along the steel wire rope, the firing pin is driven to break a vacuum bulb glass shell placed in an environment with the water depth larger than 50 m, implosion is triggered, and broadband sound waves are radiated. The device adopts a pressure protection balance cavity to counteract pre-compression of water pressure on the bulb, and the breaking reliability is ensured. The device is free of power supply, simple in structure, easy to arrange, large in action depth, good in generated sound source effect, and suitable for acoustic experiments and research in the Arctic ice region and the like.

The application discloses a kind of based on multi-scale sea level pressure field Arctic cyclone automatic detection and continuous tracking method, device and storage medium, belong to polar meteorology and climate data processing technical field.The method includes the following steps: (1) obtaining sea level pressure field data and pre-processing;(2) construct local pressure anomaly field;(3) identify candidate low pressure center;(4) calculate sea level pressure field curvature;(5) calculate local depth and background gradient parameter;(6) carry out multi-center cyclone identification and repeated center combination;(7) establish trajectory matching cost matrix;(8) cyclone continuous trajectory matching is realized using hungarian algorithm;(9) trajectory conflict optimization is carried out;(10) implement trajectory quality control.The application can improve the accuracy of arctic cyclone identification and trajectory continuity, reduce the false detection and false association problems caused by random noise, multi-center system and trajectory intersection.

The application discloses a collaborative scheduling method based on an arctic sea parrot optimization algorithm and peak-valley time-of-use electricity price, belongs to the technical field of micro-grid intelligent scheduling and renewable energy management, and comprises the following steps: step S1, a micro-grid system model is established; step S2, operation constraint conditions are constructed, including the power balance and the upper and lower limit constraints of the power of each component of the micro-grid and the state constraints of the storage battery which need to be satisfied by the wind turbine, the photovoltaic, the gas turbine, the fuel cell, the storage battery and the power exchange with the power grid in the micro-grid; step S3, an objective optimization function of operation cost and pollutant emission is constructed; and step S4, the optimal scheduling scheme is output by applying the objective optimization function. The collaborative scheduling method based on the arctic sea parrot optimization algorithm and the peak-valley time-of-use electricity price can significantly improve the optimization precision and convergence efficiency of the system, meanwhile, the high reliability and robustness of the scheduling process are ensured, and finally, the intelligent collaborative scheduling of the micro-grid energy is realized.

1. The name of the design product: balm round can (Arctic Ocean). 2. The use of the design product: for placing in the car, removing odor, for freshening the air, long-lasting fragrance. 3. The design points of the design product: in the combination of shape, pattern and color. 4. The picture or photo that best indicates the design points: perspective view. 5. The design claimed contains color.

Synthetic aperture radar (SAR) is a type of imaging technology that can be used for a wide range of applications, including Earth observation, mapping, object tracking, change detection (e.g., Arctic ice), natural disaster monitoring, and many other uses. A high-altitude unmanned aerial vehicle for performing synthetic aperture radar (SAR) imaging comprises a fuselage, wings attached to the fuselage to provide lift to the aircraft, one or more electric propellers to provide thrust to the aircraft, a SAR payload for SAR imaging the Earth's surface as the aircraft flies, solar cells to generate power to drive one or more electric propellers and operate the SAR payload, and one or more batteries to store the electrical energy generated by the solar cells.

This invention provides a green and low-carbon intelligent planning method and system for Arctic shipping routes. The method includes: constructing a gridded environmental model of the Arctic sea area; establishing a mapping relationship between ship navigation resistance and propulsion power based on a semi-empirical ice resistance formula to construct an ice resistance-power-carbon emission coupled model, and determining the carbon emissions per unit distance; constructing a multi-objective reward function, including a carbon emission penalty term based on the estimated carbon emissions and a time penalty term based on navigation time; and performing path training and strategy optimization based on the gridded environmental model and the multi-objective reward function to output the optimal route that satisfies the multi-objective constraints. This invention, by constructing an ice resistance-power-carbon emission coupled model and introducing an equivalent turning time mechanism, achieves synergistic optimization of navigation efficiency and carbon emission control in complex ice-covered environments, improving the engineering feasibility and green and low-carbon level of planned routes, and is suitable for intelligent decision-making in Arctic mixed ice and water navigation scenarios.

The application provides an Arctic sea wave prediction method based on automatic fusion of sea ice density, and belongs to the technical field of weather prediction. The wind field data automatic acquisition system is established to ensure the continuity of the wind field driving data. The sea ice wave interaction optimization model is applied to dynamically adjust the IC4M2 sea ice wave parameterization scheme. The open boundary line is set in the high latitude sea area of the North Atlantic, and the radiation boundary condition algorithm is combined with the sponge layer absorption technology to process the boundary wave propagation. The WAVEWATCH III sea wave numerical model is started, the fourth-order precision finite difference discrete format is combined with the adaptive artificial viscosity control algorithm to calculate the Arctic sea wave field, and the sea wave prediction product business processing system is established, so that the technical problem of low accuracy of the Arctic sea wave prediction caused by the insufficient parameterization precision of the sea ice wave interaction is solved.

This application provides a method and apparatus for inverting the extent and type of Arctic sea ice. The method includes: acquiring a first identification model; the first identification model is a model established based on first observation data and the corresponding ice-water classification results; the first observation data is observation data collected by a satellite the previous day; acquiring second observation data; the second observation data is observation data collected by the satellite on the current day; and determining the ice-water classification results corresponding to the second observation data based on the first identification model and the second observation data. This method aims to improve the accuracy, timeliness, and efficiency of sea ice identification.

The present invention relates to a technology for liquefying natural gas, which is subsequently transported by rivers and oceans and ultimately regasified. Pretreated natural gas is compressed by a compressor 1, the heat of compression is removed in an air or water cooler 2, and multi-stage pre-cooling is subsequently performed by evaporating a heavy refrigerant in an evaporator 3. The light refrigerant vapor and its pre-evaporation cold are recovered in a shell-and-tube heat exchanger 7, where the pressure of the gas to be liquefied is reduced by a pressure reducer 10, unliquefied gas is separated, and liquefied natural gas is discharged. The heavy refrigerant obtained by evaporation is compressed by a compressor 4, condensed in an air or water cooler 5, and reused in the multi-stage pre-cooling of natural gas. A light refrigerant is compressed by at least two compressors 11, the heat of compression is removed in an air or water cooler 12, and then passes through continuous cooling by a low-pressure light refrigerant in a double-flow superheater 12 and an evaporating heavy refrigerant in an evaporator 3. The high-pressure light refrigerant is then split into two streams. The first stream is expanded through an isentropic process in an expander 14. The second stream passes through continuous cooling in a shell-and-tube heat exchanger 7, then expanded through an isenthalpic process in a throttle valve 15, and evaporated by subcooling natural gas in an evaporator 8. The streams are then mixed at equal pressure and used as a single stream of low-pressure light refrigerant to cool the second stream of light refrigerant before expansion and evaporation in the isenthalpic process with natural gas in the shell-and-tube heat exchanger 7, and to cool a single stream of high-pressure light refrigerant in a double-flow superheater 13. The present invention provides stronger cooling of the natural gas, reduces the amount of boil-off gas and the work required for its recompression and recycling, and provides a simpler hardware design for the heat exchanger.

1. Name of the designed product: down jacket (arctic region). 2. Use of the designed product: for wearing. 3. Design points of the designed product: combination of shape and pattern. 4. Picture or photo best indicating the design points: front view. 5. Omitted views: the bottom surface of the designed product is a part that is not easily seen or cannot be seen when in use, so the top view is omitted; the content of the view has been expressed in other submitted views, so the plan view is omitted.

The invention provides a ship target fusion detection method in a polar region channel based on SAR and optical images, and belongs to the field of remote sensing image processing and target detection. In order to solve the problems of difficulty in moving target association and high sea ice false alarm rate in different-time-phase images of the arctic ice region, the technical scheme comprises the following steps: respectively carrying out target initial detection on SAR and optical images, and extracting features to remove sea ice false alarms; secondly, saliency detection is carried out on the image to extract a channel area, and target parameters such as the target main axis angle, the position and the intersection-to-union ratio are calculated; meanwhile, the motion state is judged according to the target parameter difference, and the maximum correlation area is determined by combining the maximum speed constraint; and finally, target matching is completed in the region, and decision-level fusion is carried out by adopting a D-S evidence theory. According to the method, accurate correlation and fusion detection of the SAR and the time-shifting ship target in the optical image are realized, background interference is effectively overcome by comprehensively utilizing bimodal information, and the method has important application value in the fields of polar region navigation safety monitoring and the like.

The application discloses a micro-grid scheduling optimization method and system based on a multi-target arctic sea parrot optimization algorithm, and the method comprises the following steps: establishing a multi-target optimization scheduling model of a micro-grid, wherein at least an operation and maintenance cost target function and an environmental protection cost target function are included in the multi-target optimization scheduling model; solving the multi-target optimization scheduling model by using a multi-strategy fusion multi-target arctic sea parrot optimization algorithm to obtain a Pareto optimal solution set; and determining an optimal output strategy of each distributed power generation unit in the micro-grid according to the Pareto optimal solution set. The algorithm has space exploration ability in the early stage and optimization precision in the later stage, and the optimization effect of micro-grid scheduling is improved.

The invention provides a synthetic aperture radar-based one-dimensional sea wave spectrum inversion method for an arctic sea area. The method comprises the following steps: acquiring a Sentinel-1 dual-polarized SAR image, sea ice concentration data and a WAVEWATCH-III simulated one-dimensional sea wave spectrum of the arctic sea area; estimating sea ice concentrations from the SAR image by using a K-means clustering algorithm, grouping the sea ice concentrations, and adopting different hydrodynamic modulation transfer function parameters for different groups of sea ice concentrations and polarization modes; an XGBoost machine learning model is adopted, and an SAR one-dimensional sea wave spectrum inversion algorithm in the north pole sea area is established; and the accuracy of the SAR one-dimensional sea wave spectrum inversion algorithm in the arctic sea area is verified. According to the method, the problems that traditional one-dimensional sea wave spectrum inversion depends on an initial guess spectrum, and sea ice influences modeling are complex are solved, and high-precision one-dimensional sea wave spectrum inversion of the arctic sea ice coverage area is achieved.

The application discloses an Arctic shipping route navigation safety evaluation method and system based on multi-source data fusion and ship adaptability, and relates to the cross technical field of marine navigation safety guarantee, meteorological and hydrological data analysis and intelligent navigation. The method comprises the following steps: collecting multi-source data of the Arctic shipping route, dynamically fusing weights in combination with real-time reliability calculation of the data source after preprocessing and space-time alignment, completing confidence dynamic weighted fusion to obtain comprehensive environmental data; constructing a coupled physical stress model based on the data and inherent parameters of the target ship, introducing ice type and sea current factors to quantify real-time navigation risk, and generating a voyage full-cycle dynamic risk corridor; planning a route based on the risk corridor by using a hybrid heuristic algorithm, and combining a multi-index judgment mechanism with a self-defined weight to realize navigation safety grade judgment and global optimal route output. The application improves evaluation accuracy and ship adaptability, prolongs the dynamic prediction period, realizes global optimal route planning, and has strong operability in decision support.

This invention discloses an ultra-low temperature zinc-free hydraulic oil for engineering machinery in extremely cold regions and its preparation method. The hydraulic oil comprises the following raw materials by weight percentage: 65-75% base oil, 24-35% viscosity index improver, 0.3-1.0% anti-wear agent, 0.1-1.5% antioxidant, 0.01-0.1% rust inhibitor, 0.005-0.02% demulsifier, and 0.005-0.02% defoamer. The ultra-low temperature hydraulic oil for engineering machinery in extremely cold regions prepared by this invention uses polyalphaolefin and light white oil as the main raw materials, resulting in excellent low-temperature fluidity and viscosity-temperature properties. The oil has a viscosity index greater than 250 and a kinematic viscosity less than 1500 mmHg at -50°C. 2 With a pour point below -60°C, this ultra-low temperature hydraulic oil for engineering machinery in extremely cold regions ensures that the hydraulic system can start at -50°C and achieve rapid start-up at -40°C, making it highly suitable for mobile hydraulic systems in extremely cold regions, especially in Antarctica and the Arctic.

The invention discloses an improved ant colony algorithm-based arctic channel path planning method, which belongs to the technical field of navigation, and comprises the following steps of: (1) calculating a navigable area of an icebreaker in an arctic region by combining ice condition data, meteorological data and the level of the icebreaker; (2) initializing pheromone concentration for effective nodes in the navigable area; (3) finding out a next-step transferable node set naavi of the kth ant starting from the current node i at the moment t, and calculating the probability of the ant starting from the node i to the node j in the naavi according to the pheromone concentration; completing one iteration; and (4) updating the pheromone concentration on the path from the node i to the node j, and entering the next iteration until a termination condition is met. According to the north pole channel path planning method based on the improved ant colony algorithm, the initial pheromone concentration is optimized while the characteristics of the ant colony algorithm are reserved, the early blind search is remarkably reduced, the calculation amount is reduced, and the search efficiency is improved.

The present disclosure relates to methods, apparatuses, and systems for unmanned underwater vehicles capable of operation in the Arctic region. An example unmanned underwater vehicle includes: a hull; one or more guide rails extending from the hull and attached to the hull by one or more legs; a vertically-deployed mast, wherein the vertically-deployed mast is configured to penetrate through an ice sheet; and a communications antenna, wherein the communications antenna is deployed with the vertically-deployed mast and enables communication above the ice sheet. The dorsally-located guide rails of an example define an extended position and a retracted position, wherein the guide rails disposed in the extended position are disposed further from the hull than the guide rails disposed in the retracted position. Embodiments described herein include systems employing air-deployable buoys, water-deployed buoys and small UUVs, and ice-penetrating buoys to support communications in the Arctic region.

The invention provides an Arctic sea wave forecasting method based on sea ice concentration automatic fusion, and belongs to the technical field of weather forecast, the continuity of wind field driving data is ensured by establishing a wind field data automatic acquisition system, and dynamic parameter adjustment is performed on an IC4M2 sea ice wave parameterization scheme by applying a sea ice wave interaction optimization model, so that the sea ice concentration automatic fusion is realized. An open boundary line is arranged in a high-latitude sea area of the North Atlantic Ocean, a radiation boundary condition algorithm is combined with a spongy layer absorption technology to process boundary wave propagation, a WAVEWATCH III sea wave numerical mode is started, and a fourth-order precision finite difference discrete format is combined with a self-adaptive artificial viscosity control algorithm to carry out North polar sea wave field calculation. A sea wave forecasting product business post-processing system is established, and the technical problem that the accuracy of north pole sea wave forecasting is low due to the fact that the parameterization precision of the sea ice wave interaction is insufficient is solved.

The invention discloses a flood storage and detention area start decision-making method, system, equipment and medium based on a swarm intelligent optimization algorithm, and the method comprises the steps: collecting hydrological engineering, spatial geography and historical case data through a multi-source data integration architecture, and forming structured data with a unified time scale after cutting, abnormal value processing and standardization; and the uncertainty scene generation architecture is used for flood scene prejudgment. Outputting a flood routing trend and an uncertainty interval based on an LSTM deep learning model, and automatically generating various disturbance scenes of flood peak flow, peak present time and downstream risk levels; and the swarm intelligence optimization decision-making architecture is used for solving an optimal scheme. All scenes are traversed through an Arctic Halorot optimization algorithm, the total flood diversion cost is minimized under the conditions of hard constraint and soft constraint, and a flood diversion critical point and an optimal starting scheme are output through dynamic iteration. According to the method, the dynamic adaptability and the decision robustness are remarkably improved, the multi-target balance and the business landing are considered, and the flood control risk and the disaster loss are effectively reduced.

The present invention discloses a method and system for short-term Arctic sea ice forecasting that combines physical constraints with a twin network. The method comprises the following steps: acquiring and preprocessing Arctic sea ice data to obtain a training set and a test set; performing modal decomposition on the sea ice density data in the training set based on empirical orthogonal decomposition to obtain time series information and spatial modal information; obtaining a physical constraint dataset based on the time series information and spatial modal information; constructing an empirical orthogonal decomposition loss function; constructing a twin network; constructing and training a multi-factor forecast model that combines physical constraints based on the empirical orthogonal decomposition loss function, the twin network, and the physical constraint dataset; testing the trained multi-factor forecast model that combines physical constraints based on the test set; and completing a short-term forecast of Arctic sea ice density. The present invention can effectively improve the accuracy of SIC forecasts and the interpretability of forecast results, providing forecast data support for Arctic resource development and navigation.

The invention relates to a north pole cloud detection method, in particular to a north pole cloud detection method for a remote sensing image of a FY-3D / MERS-II satellite, which comprises the following steps of: S1, acquiring all-year and all-day FY-3D / MERS-II data of a north pole region, and preprocessing the all-year and all-day FY-3D / MERS-II data; s2, selecting characteristic factors of a north pole cloud detection method, and synthesizing the characteristic factors into an FY-3D / MERS-II multispectral remote sensing image; s3, dividing the FY-3D / MERSI-II multispectral remote sensing image of the arctic region all year round into data of different seasons and different time periods; s4, constructing a Dense-Net cloud detection model based on a deep learning method, and obtaining cloud detection results of the arctic region in different seasons and different time periods; according to the invention, the accuracy and the automation degree of north pole cloud detection are improved; according to the method, FY-3D / MERS-II remote sensing image cloud detection of the arctic region in different seasons and different time periods is realized through a Dense-Net cloud detection model based on deep learning, and another research thought is provided for arctic cloud detection; the overall cloud detection precision is high.

The application provides an automatic Arctic atmosphere coupling prediction method of fusing sea ice density and thickness, and belongs to the technical field of meteorology. First, GFS background field data are acquired and corrected by applying an ice-sea heat exchange correction equation, then sea ice density and thickness information are extracted and physical consistency verification is carried out based on a thermodynamic equilibrium equation. Then, multi-element fusion is carried out through an Arctic ice-sea assimilation model, sea ice surface temperature is calculated by applying a polar sea ice heat flux equation, and a sea ice density thickness boundary layer interaction equation set is established to describe the modulation effect of sea ice on the boundary layer. The core step is to apply a feature fusion model to deeply fuse various sea ice features, to adaptively adjust the weight based on the ice-air interface balance index, to generate an optimized model initial field and boundary condition, and finally to set a parameter scheme to execute an integral program to obtain a prediction result, so that the technical problem of low prediction accuracy caused by the physical inconsistency of sea ice information is solved.

The utility model provides a cold energy utilization and carbon capture system for a large-scale LNG (Liquefied Natural Gas) factory in an extremely cold region, which comprises a natural gas liquefaction unit, a carbon capture unit and a carbon capture unit, the gas turbine power generation unit is connected with the natural gas liquefaction unit and is used for combusting raw material natural gas to generate power and transmitting electric power to the natural gas liquefaction unit; one end of the flue gas capturing and recycling unit is connected with the gas turbine power generation unit, and the flue gas capturing and recycling unit is used for receiving flue gas discharged by the gas turbine and carrying out deep purification and CO2 enrichment and separation on the flue gas; the other end of the cold energy recovery unit is introduced into the natural gas liquefaction unit, penetrates out and returns, and is used for realizing low-temperature liquefaction and recovery of CO2 by utilizing cold energy of the natural gas liquefaction unit; according to the system, cold energy of the extremely cold environment in the arctic region and process pressure difference energy of a large LNG factory are integrated and utilized, meanwhile, CO2 in exhausted smoke of the system is efficiently purified, enriched, captured, liquefied and recycled, the overall energy efficiency of the large LNG factory is remarkably improved, carbon reduction and emission reduction in the polar region are promoted, and the greenhouse effect is relieved.

This invention discloses an Arctic submersible transport system adaptable to both nuclear and conventional diesel power, belonging to the field of Arctic transport equipment technology. The system includes a dual-power mode compatible submersible transport platform module, a piston-type self-cleaning storage and transport module, a multimodal three-dimensional collaborative operation module, an under-ice communication and navigation module, an intelligent ballast control module, and a safety assurance module. The core platform adopts a modular power compartment design, adaptable to either nuclear or conventional diesel power, and can stably submerge at a depth of 5 meters under 2 meters of Arctic ice, with a rated crude oil carrying capacity of 460,000 tons. It is equipped with multiple unmanned surface vessels, ice-surface hovercraft, and aerial resupply and protection mechanisms to form a three-dimensional collaborative system. The storage and transport module uses a partitioned piston-type oil tank structure, enabling crude oil loading and unloading, self-cleaning, and dynamic control of ballast water. This system enables year-round, all-weather navigation along Arctic routes, mitigating geopolitical risks, balancing long-range economics with construction approval flexibility, and significantly improving the efficiency, safety, and adaptability of polar energy and bulk commodity transportation.

The present invention provides an Arctic waterway fleet scheduling optimization method based on BeiDou III, comprising: designing an Arctic waterway multi-base station fleet information service framework based on BeiDou III, reshaping the information interaction process between ships and between ships and shores under BeiDou service; constructing an Arctic waterway fleet scheduling optimization model based on BeiDou III based on the designed Arctic waterway multi-base station fleet information service framework; and improving the differential evolution algorithm to solve the non-deterministic polynomial problem in the fleet scheduling process. The present invention designs a BeiDou-based multi-base station fleet information service framework to provide support for the dynamic scheduling of Arctic waterway fleets, solving the problem of difficulty in real-time information interaction on Arctic routes. With the support of the BeiDou multi-base station fleet information service framework, the information obtained by the framework is used to solve the dynamic scheduling optimization model of the Arctic waterway fleet. The model enables the fleet to quickly modify the freight scheduling plan after the navigation environment information is distorted, thereby reducing economic losses.

This invention belongs to the field of computer vision technology and provides a weakly supervised pixel-level annotation method for shipborne sea ice images: The method involves acquiring the original image of Arctic shipborne sea ice, cropping the sky and visually blurred areas, removing ship pixels using a ship region mask, and obtaining an effective observation image containing only sea ice and seawater. The image is then partitioned from far to near, converted to the LAB color space, and pixel feature vectors are extracted. These feature vectors are input into an SPGMM model, and pixel categories are determined based on the posterior probability of sea ice and seawater. The partitioned annotation results are then stitched together and post-processed to obtain a pixel-level binary mask for sea ice. This invention addresses the problems of high manual annotation costs for shipborne sea ice images and the poor adaptation of traditional global segmentation models to image spatial heterogeneity, leading to low segmentation accuracy, blurred boundaries, and misclassification.

This invention discloses a method and system for predicting Arctic ship navigation risks based on a GA-BP neural network model, belonging to the field of risk prediction technology. Step 1: Identify key risk factors affecting Arctic ship navigation, determine the input and output of the GA-BP neural network model, and construct a dataset; Step 2: Normalize the dataset obtained in Step 1; Step 3: Establish a BP neural network model and determine its structure; Step 4: Optimize the weights and thresholds of the BP neural network using a genetic algorithm (GA) to obtain a GA-BP prediction model; Step 5: Train and test the GA-BP prediction model obtained in Step 4 on the normalized dataset from Step 2 to predict the risks of ice entrapment and ship-ice collision. This invention addresses the problem of cumbersome and inefficient steps in existing technologies for assessing and analyzing navigation risks in the Arctic.