The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
In order to study the preferred skin color for printing images,two CMYK images from ISO 400 and one from iStock,including five skin color images of East Asian females was selected in this study.The images were adjuste...In order to study the preferred skin color for printing images,two CMYK images from ISO 400 and one from iStock,including five skin color images of East Asian females was selected in this study.The images were adjusted with the CMYK printing ink volume variation of the single,double and triple channels in the given 280%total ink limit conditions.A larger number of color vision normal observers were organized to carry out the color preference evaluation experiment,and the selected preferred skin colors were analyzed.The distribution range of the chromaticity values for skin color images were obtained and the results indicated that there are three regions for printing skin color preferences,and the observers have a memory preference for brighter,fairer skin colors in young female and a reddish skin colors in girl,which can provide the guidance for color adjustment of printed skin color images.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and m...By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and more likely to be dominated by structural trends,as in this market,the willingness to engage in passive trading exceeds that for active trading and investors’preference easy to reverse toward market volatility.The lack of incremental capital in the market often leads to sector-specific rallies rather than broad-based increases,which is one of the key reasons why the Chinese stock market has struggled to achieve overall growth over the long-term period.展开更多
Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivat...Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies.Methods A discrete choice experiment was conducted in nine provinces across China.Seven key attributes were identified to analyze the job preferences of CDC workers.Mixed logit models,latent class models,and policy simulation tools were used.Results A valid sample of 5,944 cases was included in the analysis.All seven attributes significantly influenced the job choices of CDC workers.Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility.Income-prioritizers were concerned with income and opportunities for career development,whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits.The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives.Conclusion Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers.Heterogeneity in job preferences was also identified.Based on the preference characteristics of different subgroups,policy content should be skewed to differentiate the importance of incentives.展开更多
A new wave of artificial intelligence(AI) development is sweeping the world. Considering its technology maturity and application scale, AI is in an infant stage of development. However, as the core technology and appl...A new wave of artificial intelligence(AI) development is sweeping the world. Considering its technology maturity and application scale, AI is in an infant stage of development. However, as the core technology and application driving the fourth scientific and technological revolution, AI has attracted worldwide attention for its potential transformative nature. The international community has expressed great concern over AI security, and the call for strengthening governance is growing. The international governance of AI has exhibited a strong preference for security. The underlying logic relates to AI's history,technological characteristics, and geopolitical changes. Such a security preference has determined the cognition, vision, and practical priorities of AI governance and will impact the future of AI and even the international balance of power.展开更多
Transient receptor potential(TRP)channels are a class of ion channel proteins that are closely related to thermosensation in insects.They are involved in detecting the ambient temperature and play vital roles in insec...Transient receptor potential(TRP)channels are a class of ion channel proteins that are closely related to thermosensation in insects.They are involved in detecting the ambient temperature and play vital roles in insect survival and reproduction.In this study,we identifed and cloned two variants of the TRPA subfamily gene in Myzus persicae,MperTRPA1(A)and MperTRPA1(B),and analyzed their tissue expression by real-time quantitative PCR.Subsequently,these two variants of MperTRPA1 were expressed in the Xenopus oocyte system,and their functions were investigated using the two-electrode voltage clamp technique.The role of the MperTRPA1 gene in temperature adaptation of M.persicae was further determined by RNA interference and a behavioral choice assay to evaluate responses to temperature gradients.The results showed that the MperTRPA1 gene is widely expressed in tissues of M.persicae,with MperTRPA1(A)highly expressed in the mouthparts and MperTRPA1(B)mainly expressed in the antennae.The functional characterization results showed that both variants of MperTRPA1 could be activated and were not desensitized when the temperature increased from 20 to 45℃.The current value and thermal sensitivity(coeffcient Q_(10)value)of MperTRPA1(B)were signifcantly higher than those of MperTRPA1(A).When the MperTRPA1 gene was knocked down,the behavioral preference of M.persicae for the optimal temperature was reduced and tended to be at a higher temperature,showing a shift in the temperature adaptation range compared to both the wild type and ds GFP-treated M.persicae.In summary,our results elucidated the molecular mechanism of adaptive temperature perception in M.persicae mediated by the thermal sensor MperTRPA1.展开更多
The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeogr...The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeographic studies(Wagner and Liebherr 1992;Peeters and Ito 2001;Helms 2018).展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Two widespread bird species in Sri Lanka’s dry zone,Pycnonotus cafer(Red-vented Bulbul,RVBB)and Pycnonotus luteolus(White-browed Bulbul,WBBB),were studied to understand their foraging dynamics and ecology.The researc...Two widespread bird species in Sri Lanka’s dry zone,Pycnonotus cafer(Red-vented Bulbul,RVBB)and Pycnonotus luteolus(White-browed Bulbul,WBBB),were studied to understand their foraging dynamics and ecology.The research was conducted from October 2022 to February 2023 in Mihintale Sanctuary(80.30′11.24″E,8.21′04.63″N)and the Faculty of Applied Sciences,Rajarata University of Sri Lanka(80.502206″E,8.353090″N).Data were obtained through focal sampling,opportunistic observations,and mist netting.Both species predominantly foraged on twigs,using gleaning as the dominant food-handling technique.RVBB foraged mostly at the canopy level,while WBBB foraged primarily at the sub-canopy level.Fruits constituted the major food type for both species.RVBB and WBBB utilized 10 and 7 plant species,respectively,with Grewia helicterifolia being the primary foraging plant.Minimal foraging was observed on Croton sp.(RVBB)and Hugonia mistax(WBBB).The correlation between nutritional components and the consumption of both species revealed a preference for foods with lower protein,higher fat,and ash content.There was no linear correlation between gape width and fruit size(r=-0.21,P=0.69)for both species.The standardized dietary niche breadth indicated both species are specialists,with a high pairwise dietary niche overlap(0.9854).These findings highlight the niche-specific foraging adaptations of RVBB and WBBB within Mihintale,emphasizing their distinct strategies in utilizing plant species,fruit sizes,and foraging heights.Understanding such ecological dynamics is essential for habitat conservation efforts and ensuring the availability of key foraging resources for these species in the dry zone.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
Objective Although dietary preferences influence chronic diseases,few studies have linked dietary preferences to mortality risk,particularly in large cohorts.To investigate the relationship between dietary preferences...Objective Although dietary preferences influence chronic diseases,few studies have linked dietary preferences to mortality risk,particularly in large cohorts.To investigate the relationship between dietary preferences and mortality risk(all-cause,cancer,and cardiovascular disease[CVD])in a large adult cohort.Methods A cohort of 1,160,312 adults(mean age 62.48±9.55)from the Shenzhen Healthcare Big Data Cohort(SHBDC)was analyzed.Hazard ratios(HRs)for mortality were estimated using the Cox proportional hazards model.Results The study identified 12,308 all-cause deaths,of which 3,865(31.4%)were cancer-related and 3,576(29.1%)were attributed to CVD.Compared with a mixed diet of meat and vegetables,a mainly meat-based diet(hazard ratio[HR]=1.13;95%confidence interval[CI]:1.02,1.27)associated with a higher risk of all-cause mortality,while mainly vegetarian(HR=0.87;95%CI:0.78,0.97)was linked to a reduced risk.Furthermore,there was a stronger correlation between mortality risk and dietary preference in the>65 age range.Conclusion A meat-based diet was associated with an increased risk of all-cause mortality,whereas a mainly vegetarian diet was linked to a reduced risk.展开更多
Objectives:Positive family functioning(FF)is critical for adolescent development,yet only a few studies have examined this developmental trajectory pathway.This study aimed to identify different types of FF developmen...Objectives:Positive family functioning(FF)is critical for adolescent development,yet only a few studies have examined this developmental trajectory pathway.This study aimed to identify different types of FF development trajectories during junior high school students,investigate their influence on social avoidance(SA),and further examine the mediating role of preference for solitude(PS)between them.Methods:A three-wave longitudinal study was used with six-month intervals.Questionnaire data were collected from 436 junior high school students in Jiangxi Province,China.Participants ranged in age from 11 to 14 years old(Mean=12.89 years,SD=1.08;50.2%male).Results:Four heterogeneous types of FF trajectories were identified:(1)a high and increasing group(14.7%);(2)a consistently high group(36.24%);(3)a consistently moderate group(45.86%);and(4)a rapid growth group(3.2%).The developmental trajectories of FF among junior high students significantly varied in their levels of SA(F(3,432)=32.03,p<0.001).Compared to the high and increasing groups,the consistently high,consistently medium,and rapid growth groups exhibited higher levels of SA.PS mediated the association between the developmental trajectory of FF and SA.Conclusion:There was a close relationship between the developmental trajectory of FF and SA.Interventions focusing on family system optimization and solitary preference management could effectively mitigate SA behaviors.These findings are important for promoting healthy socialization in adolescents.展开更多
Plant roots interact with diverse fungi that are essential for maintaining the productivity and sustainability of pasture ecosystems,but how these root-associated fungi(RAF)differ between forage species and how they r...Plant roots interact with diverse fungi that are essential for maintaining the productivity and sustainability of pasture ecosystems,but how these root-associated fungi(RAF)differ between forage species and how they respond to nutrient enrichment and fungicide application are not well understood.Here,we constructed an 11-year experiment involving fungicide application(with or without)nested within four levels of experimental nitrogen(N)addition treatments in an alpine pasture,and the RAF communities,root traits,tissue nutrients,and shoot biomass of two dominant forage species(Carex capillifolia and Elymus nutans)were analyzed.The RAF community composition showed striking differences between the plant species and was strongly affected by both N addition level and fungicide applications.Fungicide,but not N application,dramatically reduced the RAF richness of all functional guilds in both plant species,and fungicide also simplified the co-occurrence network of the RAF for C.capillifolia.The RAF community correlated strongly with root traits,whereas their relationships became weakened or even vanished at the level of the individual plant species.The importance of RAF to plant nutrients and productivity varied between plant species,with significant contributions in C.capillifolia but not in E.nutans.This is the first report elucidating the long-term effect of fungicides on RAF in alpine pastures,and our findings emphasize the host-specific responses of RAF community structure and function to anthropogenic disturbances.展开更多
In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing perme...In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the fea...In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.展开更多
Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requi...Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.展开更多
Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.T...Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.This includes the analysis of BIM and AI technologies and their integration advantages,real-time monitoring and alarm strategies for construction site safety based on BIM and AI integration,as well as the development direction of BIM and AI integration in real-time monitoring and alarm for construction site safety.It is hoped that through this analysis,a scientific reference can be provided for the digital and intelligent management of construction site safety,promoting the digital and intelligent development of its safety management work.展开更多
Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facili...Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.展开更多
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
文摘In order to study the preferred skin color for printing images,two CMYK images from ISO 400 and one from iStock,including five skin color images of East Asian females was selected in this study.The images were adjusted with the CMYK printing ink volume variation of the single,double and triple channels in the given 280%total ink limit conditions.A larger number of color vision normal observers were organized to carry out the color preference evaluation experiment,and the selected preferred skin colors were analyzed.The distribution range of the chromaticity values for skin color images were obtained and the results indicated that there are three regions for printing skin color preferences,and the observers have a memory preference for brighter,fairer skin colors in young female and a reddish skin colors in girl,which can provide the guidance for color adjustment of printed skin color images.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
文摘By using the Chinese stock market data from 2018 to 2024,the weak association between structural trends stocks and market index under investors’preference effect in trading cause the market is lack of liquidity and more likely to be dominated by structural trends,as in this market,the willingness to engage in passive trading exceeds that for active trading and investors’preference easy to reverse toward market volatility.The lack of incremental capital in the market often leads to sector-specific rallies rather than broad-based increases,which is one of the key reasons why the Chinese stock market has struggled to achieve overall growth over the long-term period.
基金supported by the Major Program of the National Social Science Foundation of China(no.2022YFC3600801)the Operation of Public Health Emergency Response Mechanisms of the Chinese Center for Disease Control and Prevention(no.102393220020010000017)。
文摘Objective This study explored the job choice preferences of Center for Disease Prevention and Control(CDC)workers to provide CDC management information and recommendations for optimizing employee retention and motivation policies.Methods A discrete choice experiment was conducted in nine provinces across China.Seven key attributes were identified to analyze the job preferences of CDC workers.Mixed logit models,latent class models,and policy simulation tools were used.Results A valid sample of 5,944 cases was included in the analysis.All seven attributes significantly influenced the job choices of CDC workers.Heterogeneity analyses identified two main groups based on different levels of preference for attribute utility.Income-prioritizers were concerned with income and opportunities for career development,whereas bianzhi-prioritizers were concerned with bianzhi and welfare benefits.The policy simulation analysis revealed that income-prioritizers had a relatively higher sensitivity to multiple job preference incentives.Conclusion Income and bianzhi were the two key attributes influencing the job choices and retention preferences of CDC workers.Heterogeneity in job preferences was also identified.Based on the preference characteristics of different subgroups,policy content should be skewed to differentiate the importance of incentives.
文摘A new wave of artificial intelligence(AI) development is sweeping the world. Considering its technology maturity and application scale, AI is in an infant stage of development. However, as the core technology and application driving the fourth scientific and technological revolution, AI has attracted worldwide attention for its potential transformative nature. The international community has expressed great concern over AI security, and the call for strengthening governance is growing. The international governance of AI has exhibited a strong preference for security. The underlying logic relates to AI's history,technological characteristics, and geopolitical changes. Such a security preference has determined the cognition, vision, and practical priorities of AI governance and will impact the future of AI and even the international balance of power.
基金funded by the National Natural Science Foundation of China(32472553 and 31872039)the Major Special Projects for Green Pest Control,China(110202201017(LS-01))+1 种基金the Shenzhen Science and Technology Program,China(KQTD20180411143628272)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences。
文摘Transient receptor potential(TRP)channels are a class of ion channel proteins that are closely related to thermosensation in insects.They are involved in detecting the ambient temperature and play vital roles in insect survival and reproduction.In this study,we identifed and cloned two variants of the TRPA subfamily gene in Myzus persicae,MperTRPA1(A)and MperTRPA1(B),and analyzed their tissue expression by real-time quantitative PCR.Subsequently,these two variants of MperTRPA1 were expressed in the Xenopus oocyte system,and their functions were investigated using the two-electrode voltage clamp technique.The role of the MperTRPA1 gene in temperature adaptation of M.persicae was further determined by RNA interference and a behavioral choice assay to evaluate responses to temperature gradients.The results showed that the MperTRPA1 gene is widely expressed in tissues of M.persicae,with MperTRPA1(A)highly expressed in the mouthparts and MperTRPA1(B)mainly expressed in the antennae.The functional characterization results showed that both variants of MperTRPA1 could be activated and were not desensitized when the temperature increased from 20 to 45℃.The current value and thermal sensitivity(coeffcient Q_(10)value)of MperTRPA1(B)were signifcantly higher than those of MperTRPA1(A).When the MperTRPA1 gene was knocked down,the behavioral preference of M.persicae for the optimal temperature was reduced and tended to be at a higher temperature,showing a shift in the temperature adaptation range compared to both the wild type and ds GFP-treated M.persicae.In summary,our results elucidated the molecular mechanism of adaptive temperature perception in M.persicae mediated by the thermal sensor MperTRPA1.
基金funded by the“Departments of Excellence”program of the Italian Ministry for University and Research(MIUR,2018-2022 and MUR,2023-2027).
文摘The ability of queens and males of most ant species to disperse by flight has fundamentally contributed to the group’s evolutionary and ecological success and is a determining factor to take into account for biogeographic studies(Wagner and Liebherr 1992;Peeters and Ito 2001;Helms 2018).
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
文摘Two widespread bird species in Sri Lanka’s dry zone,Pycnonotus cafer(Red-vented Bulbul,RVBB)and Pycnonotus luteolus(White-browed Bulbul,WBBB),were studied to understand their foraging dynamics and ecology.The research was conducted from October 2022 to February 2023 in Mihintale Sanctuary(80.30′11.24″E,8.21′04.63″N)and the Faculty of Applied Sciences,Rajarata University of Sri Lanka(80.502206″E,8.353090″N).Data were obtained through focal sampling,opportunistic observations,and mist netting.Both species predominantly foraged on twigs,using gleaning as the dominant food-handling technique.RVBB foraged mostly at the canopy level,while WBBB foraged primarily at the sub-canopy level.Fruits constituted the major food type for both species.RVBB and WBBB utilized 10 and 7 plant species,respectively,with Grewia helicterifolia being the primary foraging plant.Minimal foraging was observed on Croton sp.(RVBB)and Hugonia mistax(WBBB).The correlation between nutritional components and the consumption of both species revealed a preference for foods with lower protein,higher fat,and ash content.There was no linear correlation between gape width and fruit size(r=-0.21,P=0.69)for both species.The standardized dietary niche breadth indicated both species are specialists,with a high pairwise dietary niche overlap(0.9854).These findings highlight the niche-specific foraging adaptations of RVBB and WBBB within Mihintale,emphasizing their distinct strategies in utilizing plant species,fruit sizes,and foraging heights.Understanding such ecological dynamics is essential for habitat conservation efforts and ensuring the availability of key foraging resources for these species in the dry zone.
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.
基金supported by the National Natural Science Foundation of China(No.82425052).
文摘Objective Although dietary preferences influence chronic diseases,few studies have linked dietary preferences to mortality risk,particularly in large cohorts.To investigate the relationship between dietary preferences and mortality risk(all-cause,cancer,and cardiovascular disease[CVD])in a large adult cohort.Methods A cohort of 1,160,312 adults(mean age 62.48±9.55)from the Shenzhen Healthcare Big Data Cohort(SHBDC)was analyzed.Hazard ratios(HRs)for mortality were estimated using the Cox proportional hazards model.Results The study identified 12,308 all-cause deaths,of which 3,865(31.4%)were cancer-related and 3,576(29.1%)were attributed to CVD.Compared with a mixed diet of meat and vegetables,a mainly meat-based diet(hazard ratio[HR]=1.13;95%confidence interval[CI]:1.02,1.27)associated with a higher risk of all-cause mortality,while mainly vegetarian(HR=0.87;95%CI:0.78,0.97)was linked to a reduced risk.Furthermore,there was a stronger correlation between mortality risk and dietary preference in the>65 age range.Conclusion A meat-based diet was associated with an increased risk of all-cause mortality,whereas a mainly vegetarian diet was linked to a reduced risk.
基金supported by the National Natural Science Foundation of China(72164018)National Social Science Fund Project(BFA200065)Jiangxi Social Science Foundation Project(21JY13).
文摘Objectives:Positive family functioning(FF)is critical for adolescent development,yet only a few studies have examined this developmental trajectory pathway.This study aimed to identify different types of FF development trajectories during junior high school students,investigate their influence on social avoidance(SA),and further examine the mediating role of preference for solitude(PS)between them.Methods:A three-wave longitudinal study was used with six-month intervals.Questionnaire data were collected from 436 junior high school students in Jiangxi Province,China.Participants ranged in age from 11 to 14 years old(Mean=12.89 years,SD=1.08;50.2%male).Results:Four heterogeneous types of FF trajectories were identified:(1)a high and increasing group(14.7%);(2)a consistently high group(36.24%);(3)a consistently moderate group(45.86%);and(4)a rapid growth group(3.2%).The developmental trajectories of FF among junior high students significantly varied in their levels of SA(F(3,432)=32.03,p<0.001).Compared to the high and increasing groups,the consistently high,consistently medium,and rapid growth groups exhibited higher levels of SA.PS mediated the association between the developmental trajectory of FF and SA.Conclusion:There was a close relationship between the developmental trajectory of FF and SA.Interventions focusing on family system optimization and solitary preference management could effectively mitigate SA behaviors.These findings are important for promoting healthy socialization in adolescents.
基金supported by the National Key Research and Development Program of China(2023YFF0805602)the National Natural Science Foundation of China(U21A20186,32171579,32371592 and 32471674)the Natural Science Foundation of Gansu Province,China(23JRRA1029 and 23JRRA1034)。
文摘Plant roots interact with diverse fungi that are essential for maintaining the productivity and sustainability of pasture ecosystems,but how these root-associated fungi(RAF)differ between forage species and how they respond to nutrient enrichment and fungicide application are not well understood.Here,we constructed an 11-year experiment involving fungicide application(with or without)nested within four levels of experimental nitrogen(N)addition treatments in an alpine pasture,and the RAF communities,root traits,tissue nutrients,and shoot biomass of two dominant forage species(Carex capillifolia and Elymus nutans)were analyzed.The RAF community composition showed striking differences between the plant species and was strongly affected by both N addition level and fungicide applications.Fungicide,but not N application,dramatically reduced the RAF richness of all functional guilds in both plant species,and fungicide also simplified the co-occurrence network of the RAF for C.capillifolia.The RAF community correlated strongly with root traits,whereas their relationships became weakened or even vanished at the level of the individual plant species.The importance of RAF to plant nutrients and productivity varied between plant species,with significant contributions in C.capillifolia but not in E.nutans.This is the first report elucidating the long-term effect of fungicides on RAF in alpine pastures,and our findings emphasize the host-specific responses of RAF community structure and function to anthropogenic disturbances.
基金supported by the National Natural Science Foundation of China (Grant No.52122405)Science and Technology Major Project of Shanxi Province,China (Grant No.202101060301024)Science and Technology Major Project of Xizang Autonomous Region,China (Grant No.XZ202201ZD0004G0204).
文摘In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金the financial support of the Natural Science Foundation of Hubei Province,China (Grant No.2022CFB770)。
文摘In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
基金the research result of the 2024 Guangxi Higher Education Undergraduate Teaching Reform Project“OBE-Guided,Digitally Empowered‘Hadoop Big Data Development Technology’Course Ideological and Political Construction Innovation Exploration and Practice”(Project No.:2024JGA396).
文摘Aiming at the problem that the traditional SRP-PHAT sound source localization method performs intensive search in a 360-degree space,resulting in high computational complexity and difficulty in meeting real-time requirements,an innovative high-precision sound source localization method is proposed.This method combines the selective SRP-PHAT algorithm with real-time visual analysis.Its core innovations include using face detection to dynamically determine the scanning angle range to achieve visually guided selective scanning,distinguishing face sound sources from background noise through a sound source classification mechanism,and implementing intelligent background orientation selection to ensure comprehensive monitoring of environmental noise.Experimental results show that the method achieves a positioning accuracy of±5 degrees and a processing speed of more than 10FPS in complex real environments,and its performance is significantly better than the traditional full-angle scanning method.
基金“Research on AI-Intelligent Management Technology for Construction Safety Based on BIM Technology and Smart Construction Site Scenarios”(Project No.:KJQN202401904)“Research on Intelligent Monitoring System for Construction Quality and Safety Based on BIM and AI Technologies”(Project No.:202412608006)。
文摘Combining the background of modern construction engineering site safety management,this article analyzes the real-time monitoring and alarm strategies for site construction safety under the integration of BIM and AI.This includes the analysis of BIM and AI technologies and their integration advantages,real-time monitoring and alarm strategies for construction site safety based on BIM and AI integration,as well as the development direction of BIM and AI integration in real-time monitoring and alarm for construction site safety.It is hoped that through this analysis,a scientific reference can be provided for the digital and intelligent management of construction site safety,promoting the digital and intelligent development of its safety management work.
基金supported in part by the National Natural Science Foundation of China(No.31470714 and 61701105).
文摘Tree trunk instance segmentation is crucial for under-canopy unmanned aerial vehicles(UAVs)to autonomously extract standing tree stem attributes.Using cameras as sensors makes these UAVs compact and lightweight,facilitating safe and flexible navigation in dense forests.However,their limited onboard computational power makes real-time,image-based tree trunk segmentation challenging,emphasizing the urgent need for lightweight and efficient segmentation models.In this study,we present RT-Trunk,a model specifically designed for real-time tree trunk instance segmentation in complex forest environments.To ensure real-time performance,we selected SparseInst as the base framework.We incorporated ConvNeXt-T as the backbone to enhance feature extraction for tree trunks,thereby improving segmentation accuracy.We further integrate the lightweight convolutional block attention module(CBAM),enabling the model to focus on tree trunk features while suppressing irrelevant information,which leads to additional gains in segmentation accuracy.To enable RT-Trunk to operate effectively under diverse complex forest environments,we constructed a comprehensive dataset for training and testing by combining self-collected data with multiple public datasets covering different locations,seasons,weather conditions,tree species,and levels of forest clutter.Com-pared with the other tree trunk segmentation methods,the RT-Trunk method achieved an average precision of 91.4%and the fastest inference speed of 32.9 frames per second.Overall,the proposed RT-Trunk provides superior trunk segmentation performance that balances speed and accu-racy,making it a promising solution for supporting under-canopy UAVs in the autonomous extraction of standing tree stem attributes.The code for this work is available at https://github.com/NEFU CVRG/RT Trunk.
