Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning fo...Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.展开更多
Ecological security is a vital problem that people all over the world today have to face and solve, and the situation of ecological security is getting more and more severe and has begun to impede heavily the sustaina...Ecological security is a vital problem that people all over the world today have to face and solve, and the situation of ecological security is getting more and more severe and has begun to impede heavily the sustainable development of social economy. Ecological environment pre-warning has become a hotspot for the modern environment science. This paper introduces the theories of ecological security pre-warning and tries to constitute a pre-warning model of ecological security. In terms of pressure-state-response model, the pre-warning guide line of ecological security is constructed while the pre-warning degree judging model of ecological security is established based on fuzzy optimization. As a case, the model is used to assess the present condition pre-warning of the ecological security of Anhui Province. The result is in correspondence with the real condition: the ecological security situations of 8 cities are dangerous and 9 cities are secure. The result shows that this model is scientific and effective for regional ecological security pre-warning.展开更多
Pre-warning plays an important role in emergency handling, especially in urban areas with high population density like Beijing. Knowing the information dissemination mechanisms clearly could help us reduce losses and ...Pre-warning plays an important role in emergency handling, especially in urban areas with high population density like Beijing. Knowing the information dissemination mechanisms clearly could help us reduce losses and ensure the safety of human beings during emergencies. In this paper, we propose the models of pre-warning information dissemination via five classical media based on actual pre-warning issue processes, including television, radio, short message service (SMS), electronic screens, and online social networks. The population coverage ability and dissemination efficiency at different issue time of these five issue channels are analyzed by simulation methods, and their advantages and disadvantages are compared by radar graphs. Results show that SMS is the most appropriate way to issue long-term pre-warning for its large population coverage, but it is not suitable for issuing urgent warnings to large population because of the limitation of telecom company's issue ability. TV shows the best performance to combine the dissemination speed and range, and the performance of radio and electronic screens are not as satisfactory as the others. In addition, online social networks might become one of the most promising communication method for its potential in further diffusion. These models and results could help us make pre-warning issue plans and provide guidance for future construction of information diffusion systems, thus reducing injuries, deaths, and other losses under different emergencies.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework ba...To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of kn...Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.展开更多
Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in ...Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.展开更多
The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cereb...The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.展开更多
The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non...The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non-destructive detection and pre-warning analysis on the quality of bolt support in deep roadways of mining districts were performed in a number of mining areas. The measured data were obtained in the detection instances of abnormal in-situ stress and support invalidation etc. The corresponding relation between axial bolt load variation and roadway surrounding rock deformation and stability was summarized in different mining service stages. Pre-warning technology of roadway surrounding rock stability is proposed based on the detection of axial bolt load. Meanwhile, pre-warning indicators of axial bolt load in different mining service stages are offered and some successful pre-warning cases are also illustrated.The research results show that the change rules of axial bolt load in different mining service stages are quite similar in different mining areas. The change of axial bolt load is in accord with the adjustment of surrounding rock stress, which can consequently reflect the deformation and stability state of roadway surrounding rock. Through the detection of axial bolt load in different sections of roadways, the status of real-time bolt support quality can be reflected; meanwhile, the rationality of bolt support design can be evaluated which provides reference for bolting parameters optimization.展开更多
Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important ...Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important component of debris flows and is the most active factor when debris flows oc- cur. Rainfall also determines the temporal and spatial distribution characteristics of the hazards. A reasonable rainfall threshold target is essential to ensuring the accuracy of debris flow pre-warning. Such a threshold is important for the study of the mechanisms of debris flow formation, predicting the characteristics of future activities and the design of prevention and engineering control measures. Most mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming re- gions. Therefore, both the traditional demonstration method and frequency calculated method cannot satisfy the debris flow pre-warning requirements. This study presents the characteristics of pre-warning regions, included the rainfall, hydrologic and topographic conditions. An analogous area with abundant data and the same conditions as the pre-warning region was selected, and the rainfall threshold was calculated by proxy. This method resolved the problem of debris flow pre-warning in ar- eas lacking data and provided a new approach for debris flow pre-warning in mountainous areas.展开更多
In order to accurately test gas concentration and effectively pre-waming when the gas concentration over-limited on work face, used the high-performance and low prices SCM and the low-cost and high transfer efficiency...In order to accurately test gas concentration and effectively pre-waming when the gas concentration over-limited on work face, used the high-performance and low prices SCM and the low-cost and high transfer efficiency bluetooth technology to forecast the gas concentration in real time. The data tested by SCM, then got the corresponding mathematical model of the data. Put forward the idea of using fuzzy structured element theory to dynamic forecast the gas concentration, analyzed the features in abnormal-effusing on work face and judge whether there was the possibility of abnormal gas-effusion. Simulation results show that mathematical model of this system about gas concentration is correct. This system changes coal mine monitoring system＇s traditional way of after-alarming into early-warning, and thus enhances its feasibility.展开更多
This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus ...This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus on deepening application of farmland security prewarning models,revealing mechanism of changes in different farmland resources,establishing pre-warning models suitable for research areas,accurate evaluation and prediction of farmland security,and exploring establishing and improving farmland security monitoring system and operating mechanism of all levels.展开更多
In this study, we analyze whether there is a change in driver actions because of voice navigation information provided by utilizing ITS （intelligent transport systems）. Specifically, when a vehicle approaches a sign...In this study, we analyze whether there is a change in driver actions because of voice navigation information provided by utilizing ITS （intelligent transport systems）. Specifically, when a vehicle approaches a signalized intersection, the driver is provided with the voice navigation information that the signal light will shortly change red. We focus on what is effective when the voice navigation information is provided. Even if the timing of the provision of voice information is delayed, we have evaluated whether the vehicle is able to stop safely. From the results of the analysis, by providing voice navigation information, we know that a vehicle will stop safely at a signalized intersection. Even if the information provided by voice navigation has been delayed, it could be shown to be safe compared with the case where no information was provided.展开更多
基金Supported by the National Natural Science Foundation of China(11072035)
文摘Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.
基金Undertheauspicesof China Postdoctoral Science Foundation (No.2004035175), and the Natural Science Founda-tionof Anhui Provincial Bureau of Education (No.2003KJ043ZD)
文摘Ecological security is a vital problem that people all over the world today have to face and solve, and the situation of ecological security is getting more and more severe and has begun to impede heavily the sustainable development of social economy. Ecological environment pre-warning has become a hotspot for the modern environment science. This paper introduces the theories of ecological security pre-warning and tries to constitute a pre-warning model of ecological security. In terms of pressure-state-response model, the pre-warning guide line of ecological security is constructed while the pre-warning degree judging model of ecological security is established based on fuzzy optimization. As a case, the model is used to assess the present condition pre-warning of the ecological security of Anhui Province. The result is in correspondence with the real condition: the ecological security situations of 8 cities are dangerous and 9 cities are secure. The result shows that this model is scientific and effective for regional ecological security pre-warning.
基金Project supported by the Science Fund from the Ministry of Science and Technology of China(Grant No.2018YFC0807000).
文摘Pre-warning plays an important role in emergency handling, especially in urban areas with high population density like Beijing. Knowing the information dissemination mechanisms clearly could help us reduce losses and ensure the safety of human beings during emergencies. In this paper, we propose the models of pre-warning information dissemination via five classical media based on actual pre-warning issue processes, including television, radio, short message service (SMS), electronic screens, and online social networks. The population coverage ability and dissemination efficiency at different issue time of these five issue channels are analyzed by simulation methods, and their advantages and disadvantages are compared by radar graphs. Results show that SMS is the most appropriate way to issue long-term pre-warning for its large population coverage, but it is not suitable for issuing urgent warnings to large population because of the limitation of telecom company's issue ability. TV shows the best performance to combine the dissemination speed and range, and the performance of radio and electronic screens are not as satisfactory as the others. In addition, online social networks might become one of the most promising communication method for its potential in further diffusion. These models and results could help us make pre-warning issue plans and provide guidance for future construction of information diffusion systems, thus reducing injuries, deaths, and other losses under different emergencies.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the confidential research grant No.a8317。
文摘To address the issues of frequent identity switches(IDs)and degraded identification accuracy in multi object tracking(MOT)under complex occlusion scenarios,this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling.By constructing a joint tracking model centered on“intra-class independent tracking+cross-category dynamic binding”,designing a multi-modal matching metric with spatio-temporal and appearance constraints,and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy,this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion,cross-camera tracking,and crowded environments.Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes,the proposed method improves Face-Pedestrian Matching F1 area under the curve(F1 AUC)by approximately 4 to 43 percentage points compared to several traditional methods.The joint tracking model achieves overall performance metrics of IDF1:85.1825%and MOTA:86.5956%,representing improvements of 0.91 and 0.06 percentage points,respectively,over the baseline model.Ablation studies confirm the effectiveness of key modules such as the Intersection over Area(IoA)/Intersection over Union(IoU)joint metric and dynamic threshold adjustment,validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability.Our_model shows a 16.7%frame per second(FPS)drop vs.fairness of detection and re-identification in multiple object tracking(FairMOT),with its cross-category binding module adding aboute 10%overhead,yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金funded by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation):project ID 431549029-SFB 1451the Marga-und-Walter-Boll-Stiftung(#210-10-15)(to MAR)a stipend from the'Gerok Program'(Faculty of Medicine,University of Cologne,Germany)。
文摘Noninvasive brain stimulation techniques offer promising therapeutic and regenerative prospects in neurological diseases by modulating brain activity and improving cognitive and motor functions.Given the paucity of knowledge about the underlying modes of action and optimal treatment modalities,a thorough translational investigation of noninvasive brain stimulation in preclinical animal models is urgently needed.Thus,we reviewed the current literature on the mechanistic underpinnings of noninvasive brain stimulation in models of central nervous system impairment,with a particular emphasis on traumatic brain injury and stroke.Due to the lack of translational models in most noninvasive brain stimulation techniques proposed,we found this review to the most relevant techniques used in humans,i.e.,transcranial magnetic stimulation and transcranial direct current stimulation.We searched the literature in Pub Med,encompassing the MEDLINE and PMC databases,for studies published between January 1,2020 and September 30,2024.Thirty-five studies were eligible.Transcranial magnetic stimulation and transcranial direct current stimulation demonstrated distinct strengths in augmenting rehabilitation post-stroke and traumatic brain injury,with emerging mechanistic evidence.Overall,we identified neuronal,inflammatory,microvascular,and apoptotic pathways highlighted in the literature.This review also highlights a lack of translational surrogate parameters to bridge the gap between preclinical findings and their clinical translation.
文摘Myasthenia gravis is a chronic autoimmune disorder that affects the neuromuscular junction leading to fluctuating skeletal muscle fatigability. The majority of myasthenia gravis patients have detectable antibodies in their serum, targeting acetylcholine receptor, muscle-specific kinase, or related proteins. Current treatment for myasthenia gravis involves symptomatic therapy, immunosuppressive drugs such as corticosteroids, azathioprine, and mycophenolate mofetil, and thymectomy, which is primarily indicated in patients with thymoma or thymic hyperplasia. However, this condition continues to pose significant challenges including an unpredictable and variable disease progression, differing response to individual therapies, and substantial longterm side effects associated with standard treatments(including an increased risk of infections, osteoporosis, and diabetes), underscoring the necessity for a more personalized approach to treatment. Furthermore, about fifteen percent of patients, called “refractory myasthenia gravis patients”, do not respond adequately to standard therapies. In this context, the introduction of molecular therapies has marked a significant advance in myasthenia gravis management. Advances in understanding myasthenia gravis pathogenesis, especially the role of pathogenic antibodies, have driven the development of these biological drugs, which offer more selective, rapid, and safer alternatives to traditional immunosuppressants. This review aims to provide a comprehensive overview of emerging therapeutic strategies targeting specific immune pathways in myasthenia gravis, with a particular focus on preclinical evidence, therapeutic rationale, and clinical translation of B-cell depletion therapies, neonatal Fc receptor inhibitors, and complement inhibitors.
基金supported by the Grant PID2021-126715OB-IOO financed by MCIN/AEI/10.13039/501100011033 and"ERDFA way of making Europe"by the Grant PI22CⅢ/00055 funded by Instituto de Salud CarlosⅢ(ISCⅢ)+6 种基金the UFIECPY 398/19(PEJ2018-004965) grant to RGS funded by AEI(Spain)the UFIECPY-396/19(PEJ2018-004961)grant financed by MCIN (Spain)FI23CⅢ/00003 grant funded by ISCⅢ-PFIS Spain) to PMMthe UFIECPY 328/22 (PEJ-2021-TL/BMD-21001) grant to LM financed by CAM (Spain)the grant by CAPES (Coordination for the Improvement of Higher Education Personnel)through the PDSE program (Programa de Doutorado Sanduiche no Exterior)to VSCG financed by MEC (Brazil)
文摘The brain is the most complex human organ,and commonly used models,such as two-dimensional-cell cultures and animal brains,often lack the sophistication needed to accurately use in research.In this context,human cerebral organoids have emerged as valuable tools offering a more complex,versatile,and human-relevant system than traditional animal models,which are often unable to replicate the intricate architecture and functionality of the human brain.Since human cerebral organoids are a state-of-the-art model for the study of neurodevelopment and different pathologies affecting the brain,this field is currently under constant development,and work in this area is abundant.In this review,we give a complete overview of human cerebral organoids technology,starting from the different types of protocols that exist to generate different human cerebral organoids.We continue with the use of brain organoids for the study of brain pathologies,highlighting neurodevelopmental,psychiatric,neurodegenerative,brain tumor,and infectious diseases.Because of the potential value of human cerebral organoids,we describe their use in transplantation,drug screening,and toxicology assays.We also discuss the technologies available to study cell diversity and physiological characteristics of organoids.Finally,we summarize the limitations that currently exist in the field,such as the development of vasculature and microglia,and highlight some of the novel approaches being pursued through bioengineering.
基金the State Key Research Development Program of China(No.2016YFC0600705)the Fundamental Research Funds for the Central Universities(No.2015XKZD06)+1 种基金the National Natural Science Foundation of China(Nos.51227003,51404250,51504243,51474215,51404262 and 51323004)the Natural Science Foundation of Jiangsu Province,China(Nos.BK20150191 and BK20140213)
文摘The bolt support quality of coal roadways is one of the important factors for the efficiency and security of coal production. By means of a self-developed technique and equipment of random non-destructive testing, non-destructive detection and pre-warning analysis on the quality of bolt support in deep roadways of mining districts were performed in a number of mining areas. The measured data were obtained in the detection instances of abnormal in-situ stress and support invalidation etc. The corresponding relation between axial bolt load variation and roadway surrounding rock deformation and stability was summarized in different mining service stages. Pre-warning technology of roadway surrounding rock stability is proposed based on the detection of axial bolt load. Meanwhile, pre-warning indicators of axial bolt load in different mining service stages are offered and some successful pre-warning cases are also illustrated.The research results show that the change rules of axial bolt load in different mining service stages are quite similar in different mining areas. The change of axial bolt load is in accord with the adjustment of surrounding rock stress, which can consequently reflect the deformation and stability state of roadway surrounding rock. Through the detection of axial bolt load in different sections of roadways, the status of real-time bolt support quality can be reflected; meanwhile, the rationality of bolt support design can be evaluated which provides reference for bolting parameters optimization.
基金supported by the National Natural Science Foundation of China(Nos.40830742 and 40901007)
文摘Debris flows are the one type of natural disaster that is most closely associated with hu- man activities. Debris flows are characterized as being widely distributed and frequently activated. Rainfall is an important component of debris flows and is the most active factor when debris flows oc- cur. Rainfall also determines the temporal and spatial distribution characteristics of the hazards. A reasonable rainfall threshold target is essential to ensuring the accuracy of debris flow pre-warning. Such a threshold is important for the study of the mechanisms of debris flow formation, predicting the characteristics of future activities and the design of prevention and engineering control measures. Most mountainous areas have little data regarding rainfall and hazards, especially in debris flow forming re- gions. Therefore, both the traditional demonstration method and frequency calculated method cannot satisfy the debris flow pre-warning requirements. This study presents the characteristics of pre-warning regions, included the rainfall, hydrologic and topographic conditions. An analogous area with abundant data and the same conditions as the pre-warning region was selected, and the rainfall threshold was calculated by proxy. This method resolved the problem of debris flow pre-warning in ar- eas lacking data and provided a new approach for debris flow pre-warning in mountainous areas.
文摘In order to accurately test gas concentration and effectively pre-waming when the gas concentration over-limited on work face, used the high-performance and low prices SCM and the low-cost and high transfer efficiency bluetooth technology to forecast the gas concentration in real time. The data tested by SCM, then got the corresponding mathematical model of the data. Put forward the idea of using fuzzy structured element theory to dynamic forecast the gas concentration, analyzed the features in abnormal-effusing on work face and judge whether there was the possibility of abnormal gas-effusion. Simulation results show that mathematical model of this system about gas concentration is correct. This system changes coal mine monitoring system＇s traditional way of after-alarming into early-warning, and thus enhances its feasibility.
文摘This paper summarized theory discussion,main research methods,contents,empirical and engineering researches of farmland prewarning in China.It stated that future researches of farmland pre-warning in China will focus on deepening application of farmland security prewarning models,revealing mechanism of changes in different farmland resources,establishing pre-warning models suitable for research areas,accurate evaluation and prediction of farmland security,and exploring establishing and improving farmland security monitoring system and operating mechanism of all levels.
文摘In this study, we analyze whether there is a change in driver actions because of voice navigation information provided by utilizing ITS （intelligent transport systems）. Specifically, when a vehicle approaches a signalized intersection, the driver is provided with the voice navigation information that the signal light will shortly change red. We focus on what is effective when the voice navigation information is provided. Even if the timing of the provision of voice information is delayed, we have evaluated whether the vehicle is able to stop safely. From the results of the analysis, by providing voice navigation information, we know that a vehicle will stop safely at a signalized intersection. Even if the information provided by voice navigation has been delayed, it could be shown to be safe compared with the case where no information was provided.
