Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are ofte...The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.展开更多
Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetland...Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.展开更多
Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe ...Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe structural damage or even collapse.Accurate measurement of ship dimensions is an effective way to monitor approaching over-height ships and avoid collisions.However,the performance of current techniques for estimating the size of moving objects can be undermined by large sensor-to-object distance,limiting their applicability.In this study,we propose a digital twin-assisted ship size measurement framework that can overcome such limitations through a predictive model and virtual-to-real-world transfer learning.Specifically,a 3D synthetic environment is first established to generate a synthetic dataset,which includes ship images,positions,and dimensions.Then the pixel information and spatial coordinates of ships are adopted as regressors,and ship dimensions are selected as the output variables to pre-train deep learning models using the generated dataset.Coordinate system transformations are applied to address dataset bias between the simulated world and real-world,as well as improve the model’s generalization.The pre-trained models are compared using supervised virtual-to-real-world transfer learning to select the version with optimal real-world performance.The mean absolute percentage error is only 3.74%across varying camera-to-ship distances,which demonstrates that the proposed method is effective for over-limit ship monitoring.展开更多
The status of woody plants in dry-land systems is a fundamental determinant of key ecosystem processes. Monitoring of this status plays an important role in understanding the dynamics of woody plants in arid and semi-...The status of woody plants in dry-land systems is a fundamental determinant of key ecosystem processes. Monitoring of this status plays an important role in understanding the dynamics of woody plants in arid and semi-arid ecosystems. The present study determined the dynamism of the Zagros forests in Iran using Remote Sensing and Geographic Information System techniques and statistical science. The results show that the density of trees varied from 10 to 53 % according to the physio- graphic and climatic conditions of semi-arid regions. The best and lowest correlation between vegetation indices and forest density were obtained for the global environmental monitoring index （GEMI; R2 = 0.94） and soil adjust vegetation index （R2 = 0.81）, respectively. GEMI is used to monitor land use changes over a 10-year period. Results show that 2720 ha2 of forest have been destroyed by human interference and tillage on steep slopes during this period which also resulted in the loss of the fertile soil layer. GEMI determined the areas with a biomass of trees and could normally separate border regions with low bio- mass density of trees from regions without canopy cover. The results revealed that assessment of forest and vegetation cover in arid and semi-arid arduous forest regions using satellite digital numbers and ordinary sampling is subject to uncertainty. A stratified grouping procedure should be established to increase the accuracy of assessment.展开更多
With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration...With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration system is important.The deterioration process of the discussed system is described by a Gamma process.The predictive maintenance is considered to be imperfect and formulated.The expected interval of two continuous preventive maintenances is derived.Then,the maintenance optimization model of the continuously monitored deterioration system is presented.In the model,the minimization of the expected operational cost per unit time and the maximization of the system availability are the optimization objectives.The improved ideal point method with the normalized objective functions is employed to solve the proposed model.The validity and sensitivity of the proposed multiobjective maintenance optimization model are analyzed by a numerical example.展开更多
Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reas...Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.展开更多
This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The ...This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.展开更多
In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this stu...In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.展开更多
A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to t...A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.展开更多
Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservat...Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservation.This study presents“Smart River Watch,”a low-cost,IoT-based ecological monitoring system designed for real-time assessment of key water quality parameters—temperature,pH,and turbidity—in tropical river environments.The system combines Arduino Mega microcontrollers and high-precision sensors with ESP32 WiFi for continuous data transmission to cloud and mobile platforms.Field deployment across five ecologically distinct sites along Indonesia’s Martapura River demonstrated strong performance,achieving exceptional accuracy(r>0.99;error<2%)compared to laboratory methods,a 98.7%transmission success rate,and 23.4-hour operational autonomy.The innovation of this research lies in bridging technological accessibility with ecological needs:enabling high-frequency,real-time monitoring that supports early pollution detection,enhances ecological insight,and empowers local communities through user-friendly mobile interfaces.The cost-effectiveness,rapid deployment(15 minutes per site),and community-based usability of the system make it a scalable solution for biodiversity protection and adaptive water resource management in developing regions.These findings highlight a paradigm shift in ecological monitoring—merging digital innovation with ecosystem stewardship to better protect freshwater biodiversity in the face of accelerating environmental change.展开更多
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed i...In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.展开更多
Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monit...Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on ...In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on permafrost,(2)to provide data for evaluation of climate-permafrost feedback,and(3)to provide input to a model-based permafrost data assimilation system.It is intended that the system will eventually consist of 30 active layer monitoring sites and 140 boreholes situated near existing weather stations.As of October 2024,the network comprised 38 sites spanning from the High Arctic islands to the Altai Mountains and across western and eastern Siberia.Among these sites,the lowest recorded temperature at the depth of zero annual amplitude is-11.3℃and the minimum active layer thickness is 0.3 m,as observed on the New Siberian Archipelago.In most boreholes,a positive vertical temperature gradient exists below the depth of zero annual amplitude,indicative of ongoing warming of the upper permafrost layer attributable to climate change.The annual maximum active layer thickness is observed in September with only two exceptions:at the High Arctic sites on Franz Josef Land and Wiese Island and in the low-latitude Sayan Mountain region,where maximum thawing is observed at the end of August.Talik was found in boreholes in Salekhard and Altai where the upper boundary of the permafrost is located at depth of 6-10 m.展开更多
The thickness and upper densification structure of an ice sheet are important parameters for dynamic ice sheet modeling and glacier mass balance studies.Seismic ambient noise methods,such as the horizontal-to-vertical...The thickness and upper densification structure of an ice sheet are important parameters for dynamic ice sheet modeling and glacier mass balance studies.Seismic ambient noise methods,such as the horizontal-to-vertical spectral ratio(H/V)method and ambient noise cross-correlation method,are becoming increasingly popular in glacier structure investigations.During China's 39th expedition to Antarctica,seismic ambient noise experiments were conducted to investigate the structure of the ice sheet at Kunlun Station,Dome A,using a seismic nodal system.We obtained a broad band(0.1–10 Hz)H/V curve with a 1-hour noise record from a seismic node.In addition,we extracted the Rayleigh wave dispersion curve with 5-day noise cross-correlation functions from a linear dense seismic array.Three clear peaks were observed in the H/V curve—a lower peak at~0.17 Hz and two higher frequency peaks at~3 Hz and~6 Hz.We inverted the ice sheet thickness using the lower frequency portion of the H/V curve and inverted the upper structure of the ice sheet using the higher frequency portion of the H/V curve jointly with the dispersion curve.Our estimations from ambient noise observations were consistent with those derived from the BedMachine ice sheet thickness dataset and the density profile determined by ground-penetrating radar investigations at the same site.展开更多
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
基金supported by the Scientific Research Projects of the Education Department of Zhejiang Province(Grant No.Y202454744)the Ningbo Public Welfare Science and Technology Project(Grant No.2024S077)+1 种基金International Sci-tech Cooperation Projects under the“Innovation Yongjiang 2035”Key R&D Programme(No.2024H019)the Ningbo Key R&D Program(Grant No.2024Z287).
文摘The existing 2D settlement monitoring systems for utility tunnels are heavily reliant on manual interpretation of deformation data and empirical predictionmodels.Consequently,early anomalies(e.g.,minor cracks)are often misjudged,and warnings lag by about 24 h without automated spatial localization.This study establishes a technical framework for requirements analysis,architectural design,and data-integration protocols.Revit parametric modelling is used to build a 3D tunnel model with structural elements,pipelines and 18 monitoring points(for displacement and joint width).Custom Revit API code integrated real-time sensor data into the BIM platform via an automated pipeline.The system achieved a spatial accuracy of±1 mm in locating deformation hotspots.Notifications are triggered within 10 s of anomaly detection,and the system renders 3D risk propagation paths in real-time.Realtime 3D visualization of risk propagation paths is also facilitated.The efficacy of the solution was validated in a Ningbo utility tunnel project,where it was demonstrated that it eliminates human-dependent judgment errors and reduces warning latency by 99.9%compared to conventional methods.The BIM-IoT integrated approach,which enables millimetre-level precision in risk identification and near-instantaneous response,establishes a new paradigm for intelligent infrastructure safety management.
基金support through the“Trans-Disciplinary Research”Grant(No.R/Dev/IoE/TDRProjects/2023-24/61658),which played a crucial role in enabling this research endeavor.
文摘Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.
基金supported by the National Natural Science Foundation of China(Nos.52338011 and 52108274)the Start-up Research Fund of Southeast University(No.RF1028624058),Chinasupport from the SEU Innovation Capability Enhancement Plan for Doctoral Students(No.CXJH_SEU 26112),China.
文摘Long-span bridges are usually constructed over waterways that involve substantial ship traffic,resulting in a risk of collisions between the bridge girders and over-height ships.The consequences of this can be severe structural damage or even collapse.Accurate measurement of ship dimensions is an effective way to monitor approaching over-height ships and avoid collisions.However,the performance of current techniques for estimating the size of moving objects can be undermined by large sensor-to-object distance,limiting their applicability.In this study,we propose a digital twin-assisted ship size measurement framework that can overcome such limitations through a predictive model and virtual-to-real-world transfer learning.Specifically,a 3D synthetic environment is first established to generate a synthetic dataset,which includes ship images,positions,and dimensions.Then the pixel information and spatial coordinates of ships are adopted as regressors,and ship dimensions are selected as the output variables to pre-train deep learning models using the generated dataset.Coordinate system transformations are applied to address dataset bias between the simulated world and real-world,as well as improve the model’s generalization.The pre-trained models are compared using supervised virtual-to-real-world transfer learning to select the version with optimal real-world performance.The mean absolute percentage error is only 3.74%across varying camera-to-ship distances,which demonstrates that the proposed method is effective for over-limit ship monitoring.
文摘The status of woody plants in dry-land systems is a fundamental determinant of key ecosystem processes. Monitoring of this status plays an important role in understanding the dynamics of woody plants in arid and semi-arid ecosystems. The present study determined the dynamism of the Zagros forests in Iran using Remote Sensing and Geographic Information System techniques and statistical science. The results show that the density of trees varied from 10 to 53 % according to the physio- graphic and climatic conditions of semi-arid regions. The best and lowest correlation between vegetation indices and forest density were obtained for the global environmental monitoring index （GEMI; R2 = 0.94） and soil adjust vegetation index （R2 = 0.81）, respectively. GEMI is used to monitor land use changes over a 10-year period. Results show that 2720 ha2 of forest have been destroyed by human interference and tillage on steep slopes during this period which also resulted in the loss of the fertile soil layer. GEMI determined the areas with a biomass of trees and could normally separate border regions with low bio- mass density of trees from regions without canopy cover. The results revealed that assessment of forest and vegetation cover in arid and semi-arid arduous forest regions using satellite digital numbers and ordinary sampling is subject to uncertainty. A stratified grouping procedure should be established to increase the accuracy of assessment.
基金supported by the Fundamental Research Funds for the Central Universities (N090303005)Key National Science and Technology Special Project (2010ZX04014-014)
文摘With the development of the monitoring technology,it is more and more common that the system is continuously monitored.Therefore,the research on the maintenance optimization of the continuously monitored deterioration system is important.The deterioration process of the discussed system is described by a Gamma process.The predictive maintenance is considered to be imperfect and formulated.The expected interval of two continuous preventive maintenances is derived.Then,the maintenance optimization model of the continuously monitored deterioration system is presented.In the model,the minimization of the expected operational cost per unit time and the maximization of the system availability are the optimization objectives.The improved ideal point method with the normalized objective functions is employed to solve the proposed model.The validity and sensitivity of the proposed multiobjective maintenance optimization model are analyzed by a numerical example.
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
基金supported by the National Natural Science Foundation of China(Nos.12172248,12302022,12021002,and 12132010)the Tianjin Research Program of Application Foundation and Advanced Technology of China(No.23JCZDJC00950)。
文摘Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.
基金supported by the National Natural Science Foundation of China(12064027,12464010)2022 Jiangxi Province High-level and Highskilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang"Xuncheng Talents"(No.JJXC2023032)Jiujiang Natural Science Foundation Project(Key Technologies Research on Autonomous Cruise Solar-Powered UAV-2025-1).
文摘This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.
文摘In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.
基金The Scientific Research Projects Coordination Unit of Akdeniz University(Türkiye)under contract No.FBA-2022-5542.
文摘A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.
文摘Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservation.This study presents“Smart River Watch,”a low-cost,IoT-based ecological monitoring system designed for real-time assessment of key water quality parameters—temperature,pH,and turbidity—in tropical river environments.The system combines Arduino Mega microcontrollers and high-precision sensors with ESP32 WiFi for continuous data transmission to cloud and mobile platforms.Field deployment across five ecologically distinct sites along Indonesia’s Martapura River demonstrated strong performance,achieving exceptional accuracy(r>0.99;error<2%)compared to laboratory methods,a 98.7%transmission success rate,and 23.4-hour operational autonomy.The innovation of this research lies in bridging technological accessibility with ecological needs:enabling high-frequency,real-time monitoring that supports early pollution detection,enhances ecological insight,and empowers local communities through user-friendly mobile interfaces.The cost-effectiveness,rapid deployment(15 minutes per site),and community-based usability of the system make it a scalable solution for biodiversity protection and adaptive water resource management in developing regions.These findings highlight a paradigm shift in ecological monitoring—merging digital innovation with ecosystem stewardship to better protect freshwater biodiversity in the face of accelerating environmental change.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
基金Research Committee,National Technical University of Athens。
文摘In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.
文摘Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by the Key Innovative Project of National Importance“Unified National System for Monitoring Climate-active Substances”。
文摘In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on permafrost,(2)to provide data for evaluation of climate-permafrost feedback,and(3)to provide input to a model-based permafrost data assimilation system.It is intended that the system will eventually consist of 30 active layer monitoring sites and 140 boreholes situated near existing weather stations.As of October 2024,the network comprised 38 sites spanning from the High Arctic islands to the Altai Mountains and across western and eastern Siberia.Among these sites,the lowest recorded temperature at the depth of zero annual amplitude is-11.3℃and the minimum active layer thickness is 0.3 m,as observed on the New Siberian Archipelago.In most boreholes,a positive vertical temperature gradient exists below the depth of zero annual amplitude,indicative of ongoing warming of the upper permafrost layer attributable to climate change.The annual maximum active layer thickness is observed in September with only two exceptions:at the High Arctic sites on Franz Josef Land and Wiese Island and in the low-latitude Sayan Mountain region,where maximum thawing is observed at the end of August.Talik was found in boreholes in Salekhard and Altai where the upper boundary of the permafrost is located at depth of 6-10 m.
基金supported by the National Natural Science Foundation of China(NSFC),grant number 42076234the National Key Research and Development Program of China,grant number 2021YFC2801404。
文摘The thickness and upper densification structure of an ice sheet are important parameters for dynamic ice sheet modeling and glacier mass balance studies.Seismic ambient noise methods,such as the horizontal-to-vertical spectral ratio(H/V)method and ambient noise cross-correlation method,are becoming increasingly popular in glacier structure investigations.During China's 39th expedition to Antarctica,seismic ambient noise experiments were conducted to investigate the structure of the ice sheet at Kunlun Station,Dome A,using a seismic nodal system.We obtained a broad band(0.1–10 Hz)H/V curve with a 1-hour noise record from a seismic node.In addition,we extracted the Rayleigh wave dispersion curve with 5-day noise cross-correlation functions from a linear dense seismic array.Three clear peaks were observed in the H/V curve—a lower peak at~0.17 Hz and two higher frequency peaks at~3 Hz and~6 Hz.We inverted the ice sheet thickness using the lower frequency portion of the H/V curve and inverted the upper structure of the ice sheet using the higher frequency portion of the H/V curve jointly with the dispersion curve.Our estimations from ambient noise observations were consistent with those derived from the BedMachine ice sheet thickness dataset and the density profile determined by ground-penetrating radar investigations at the same site.
基金supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
