Based on the current situation of the operation and maintenance management of the pilot base in many chemical parks in China,this paper conducts an in-depth exploration of the operation and maintenance management and ...Based on the current situation of the operation and maintenance management of the pilot base in many chemical parks in China,this paper conducts an in-depth exploration of the operation and maintenance management and innovation mode of the pilot base from the aspects of the evaluation of the pilot project entering the park,safety and environmental protection supervision,pilot mode,and industrialization mode of the pilot test results,so as to provide solutions for accelerating the process and industrialization of the pilot project and realizing the innovation value of the pilot base.展开更多
The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method f...The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
Traumatic spinal cord injury often leads to the disintegration of nerve cells and axons,resulting in a substantial accumulation of myelin debris that can persist for years.The abnormal buildup of myelin debris at site...Traumatic spinal cord injury often leads to the disintegration of nerve cells and axons,resulting in a substantial accumulation of myelin debris that can persist for years.The abnormal buildup of myelin debris at sites of injury greatly impedes nerve regeneration,making the clearance of debris within these microenvironments crucial for effective post-spinal cord injury repair.In this review,we comprehensively outline the mechanisms that promote the clearance of myelin debris and myelin metabolism and summarize their roles in spinal cord injury.First,we describe the composition and characteristics of myelin debris and explain its effects on the injury site.Next,we introduce the phagocytic cells involved in myelin debris clearance,including professional phagocytes(macrophages and microglia)and non-professional phagocytes(astrocytes and microvascular endothelial cells),as well as other cells that are also proposed to participate in phagocytosis.Finally,we focus on the pathways and associated targets that enhance myelin debris clearance by phagocytes and promote lipid metabolism following spinal cord injury.Our analysis indicates that myelin debris phagocytosis is not limited to monocyte-derived macrophages,but also involves microglia,astrocytes,and microvascular endothelial cells.By modulating the expression of genes related to phagocytosis and lipid metabolism,it is possible to modulate lipid metabolism disorders and influence inflammatory phenotypes,ultimately affecting the recovery of motor function following spinal cord injury.Additionally,therapies such as targeted mitochondrial transplantation in phagocytic cells,exosome therapy,and repeated trans-spinal magnetic stimulation can effectively enhance the removal of myelin debris,presenting promising potential for future applications.展开更多
In view of the imperfect supply chain management of prefabricated building,inadequate information interaction among the participating subjects,and untimely information updates,the integration and development of BIM te...In view of the imperfect supply chain management of prefabricated building,inadequate information interaction among the participating subjects,and untimely information updates,the integration and development of BIM technology plus the supply chain of prefabricated building is analyzed,and the problems existing in the current supply chain and the application of BIM technology at various stages are elaborated.By analyzing the structural composition of the prefabricated building supply chain,an information sharing platform framework for prefabricated building supply chain based on BIM was established,which serves as a valuable reference for managing prefabricated building supply chains.BIM technology aligns well with assembly construction,laying a solid foundation for their synergistic development and offering novel research avenues for the prefabricated building supply chain.展开更多
Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbo...Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbon dioxide is one of the most important greenhouse gases contributing to climate change and is responsible for 60%of global warming.The facade of the building,as the main intermediary between the interior and exterior spaces,plays a significant role in adjusting the weather conditions and providing thermal comfort to the residents.In this research,715 different scenarios were defined with the combination of various types of construction materials,and the effect of each of these scenarios on the process of energy loss from the surface of the external walls of the building during the operation period was determined.In the end,these scenarios were compared during a one-year operation period,and the amount of energy consumption in each of these scenarios was calculated.Also,bymeasuring the amount of carbon emissions in buildings during the operation period and before that,let’s look at practical methods to reduce the effects of the construction industry on the environment.By comparing the research findings,it can be seen that the ranking of each scenario in terms of total energy consumption is not necessarily the same as the ranking of energy consumption for gas consumption or electricity consumption for the same scenario.That is,choosing the optimal scenario depends on the type of energy consumed in the building.Finally,we determined the scenarios with the lowest and highest amounts of embodied and operational carbon.In the end,we obtained the latent carbon compensation period for each scenario.This article can help designers and construction engineers optimize the energy consumption of buildings by deciding on the right materials.展开更多
Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in eac...Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.展开更多
The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelli...The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.展开更多
The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a tra...The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
Rainfall-induced landslides are often highly destructive.Reviewing and analyzing the causes,processes,impacts,and deficiencies in emergency response is critical for improving disaster prevention and management.From th...Rainfall-induced landslides are often highly destructive.Reviewing and analyzing the causes,processes,impacts,and deficiencies in emergency response is critical for improving disaster prevention and management.From the night of July 21 to the morning of July 22,2024,the Kencho Shacha Gozdi Village in Gezei Gofa,Southern Nations,Nationalities,and Peoples'Region,Ethiopia,suffered heavy rainfall that triggered two landslides.By July25,this event had claimed at least 257 lives.This study presents a detailed characterization of the landslides using multi-source data.By analyzing the landslide disaster process,this study summarizes key lessons and provides suggestions for preventing rainfall-induced geological hazards.The results indicate that rainfall has the greatest impact on the occurrence of landslides,while lithology and human activities have promoted and strengthened the landslide disaster.Despite the active disaster response in the local area,many problems were still exposed in the emergency response work.This analysis offers valuable insights for mitigating rainfall-induced geological hazards and enhancing emergency response capabilities.展开更多
The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,al...The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.展开更多
Compliance,as a very important aspect of corporate governance,has developed earlier in the world,while China's compliance management has developed relatively late and is currently lacking in development.However,ma...Compliance,as a very important aspect of corporate governance,has developed earlier in the world,while China's compliance management has developed relatively late and is currently lacking in development.However,many enterprises suffer serious losses without compliance management,especially import and export enterprises,which are forced to exit the market due to poor compliance management.This article is based on the urgent need of Chinese enterprises for compliance management,but the lack of research by scholars.It summarizes the literature on the factors affecting compliance,hoping to be helpful for the study of compliance management.展开更多
Urbanization in small-and medium-sized cities has often been overlooked in urban studies.Research on urbanization has predominantly focused on large metropolitan cities;however,urbanization in small-and medium-sized c...Urbanization in small-and medium-sized cities has often been overlooked in urban studies.Research on urbanization has predominantly focused on large metropolitan cities;however,urbanization in small-and medium-sized cities also contributes to the acceleration of urban sprawl.Urban growth boundary(UGB)is an ecological approach designed to limit urban development.This study aimedto analyze environmental protection priority zonesby combining ecological quality and sensitivity indices to identify the areas suitable for UGB implementation.Tegal City and its surrounding areas(including Tegal and Brebes regencies)of Indonesia were selected as the study area.The ecological quality index was calculated using the normalized difference vegetation index,humidity index,land surface temperature,and normalized difference bare soil index.These indices were subsequently subjected to principal component analysis(PCA)to extract orthogonal factors,which were summed to derive the final index value.In parallel,we mapped and evaluated ecological sensitivity based on spatial planning policies and regulations.The results revealed that ecological quality in Tegal and Brebes regencies was predominantly categorized as good and very good ecological quality,whereas TegalCity exhibited moderate and poor ecological quality.Additionally,over 45.00%of the area in Tegal and Brebes regencies demonstrated very high ecological sensitivity.Consequently,more than 50.00%of the area in Tegal and Brebes regencies,along with 27.00%of Tegal City,were classified as ecological constraint zone,making them potential regionsfor UGB development.The UGB is expected to curtail urban expansion,promote compact city planning,and preserve ecosystem services to achieve urban sustainability.This study implies that planningsmall-and medium-sized cities is important to prevent urban sprawl and maintain environmental health.Designing UGB to limit urban expansion should be enhanced by better knowledge about its ecological functions in supporting urban sustainability.展开更多
BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ us...BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ using the competitive reaction time task(CRTT).Further investigation into the ERP correlates of aggression in SCZ would provide valuable insights into the neural processes involved.AIM To explore the neural mechanism of aggressive behavior in SCZ.METHODS Participants of this study included 40 SCZ patients and 42 healthy controls(HCs).The Reactive Proactive Aggression Questionnaire was used to assess trait of aggression.The Barratt Impulsiveness Scale 11 was used to measure impulsiveness.The Positive and Negative Symptom Scale(PANSS)was used to evaluate psychopathological features and disease severity.All participants were measured with ERP while performing the CRTT.Data of behavior,ERP components(P2,N2,and P3),and feedback-related negativity(FRN)were analyzed.RESULTS Analysis of the behavioral data revealed that compared with HCs,SCZ patients exhibited higher punishment choices.Analysis of ERP components showed that compared with HCs,SCZ patients exhibited higher N2 amplitudes and P2 amplitudes during the decision phase of the CRTT;however,SCZ patients exhibited lower FRN amplitudes and lower P3 amplitudes during the outcome phase of the CRTT.The N2 amplitudes evoked by highintensity provocation were positively related to PANSS-P scores.And the P3 amplitudes evoked in the winning trials were negatively correlated with the PANSS-G scores.CONCLUSION SCZ patients exhibit abnormal ERP characteristics evoked by the CRTT,which suggests the neural correlates of aggressive behavior in SCZ.展开更多
We presented a novel porous alumina ceramics(PACs)with superoleophilicity and superoleo-phobicity when immersed in different oil-water environments.The wettability,separation efficiency,permeation flux and reusability...We presented a novel porous alumina ceramics(PACs)with superoleophilicity and superoleo-phobicity when immersed in different oil-water environments.The wettability,separation efficiency,permeation flux and reusability of the PACs for oil/water separation were investigated and characterized via extensive ex-periments.The PACs material had favourable properties including mechanical strength and chemical durability compared with fabric-based materials and organic sponge-based materials previously reported in literature for oil/water separation.It is believed that the PACs material and methodology presented in this work may provide wastewater remediation industry with a promising alternative for dealing with the catastrophic ocean oil pollu-tion and other oil contamination.展开更多
Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backb...Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.展开更多
The social responsibility of small and medium-sized enterprises(SMEs)is very important for themselves and the whole society.The concept and connotation of social responsibility of SMEs have been defined and explained....The social responsibility of small and medium-sized enterprises(SMEs)is very important for themselves and the whole society.The concept and connotation of social responsibility of SMEs have been defined and explained.The research dimensions of previous scholars on corporate social responsibility(CSR)have been reviewed,and an evaluation index system for social responsibility of SMEs has been constructed based on their characteristics,including four key indicators:community,customers,employees,and environment.展开更多
文摘Based on the current situation of the operation and maintenance management of the pilot base in many chemical parks in China,this paper conducts an in-depth exploration of the operation and maintenance management and innovation mode of the pilot base from the aspects of the evaluation of the pilot project entering the park,safety and environmental protection supervision,pilot mode,and industrialization mode of the pilot test results,so as to provide solutions for accelerating the process and industrialization of the pilot project and realizing the innovation value of the pilot base.
基金Supported by the Henan Province Key Research and Development Project(231111211300)the Central Government of Henan Province Guides Local Science and Technology Development Funds(Z20231811005)+2 种基金Henan Province Key Research and Development Project(231111110100)Henan Provincial Outstanding Foreign Scientist Studio(GZS2024006)Henan Provincial Joint Fund for Scientific and Technological Research and Development Plan(Application and Overcoming Technical Barriers)(242103810028)。
文摘The fusion of infrared and visible images should emphasize the salient targets in the infrared image while preserving the textural details of the visible images.To meet these requirements,an autoencoder-based method for infrared and visible image fusion is proposed.The encoder designed according to the optimization objective consists of a base encoder and a detail encoder,which is used to extract low-frequency and high-frequency information from the image.This extraction may lead to some information not being captured,so a compensation encoder is proposed to supplement the missing information.Multi-scale decomposition is also employed to extract image features more comprehensively.The decoder combines low-frequency,high-frequency and supplementary information to obtain multi-scale features.Subsequently,the attention strategy and fusion module are introduced to perform multi-scale fusion for image reconstruction.Experimental results on three datasets show that the fused images generated by this network effectively retain salient targets while being more consistent with human visual perception.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
基金supported by the National Natural Science Foundation of China,Nos.82271411(to RG),51803072(to WL)the International Cooperative Project of Talent Cultivation“Xinghai Project”at the China-Japan Union Hospital of Jilin University,No.XHLH202404(to WL)+1 种基金the Science and Technology Development Plan of Jilin Province,No.YDZJ202201ZYTS038(to WL)Jilin Provincial Finance Program,No.2022SCZ10(to WL)。
文摘Traumatic spinal cord injury often leads to the disintegration of nerve cells and axons,resulting in a substantial accumulation of myelin debris that can persist for years.The abnormal buildup of myelin debris at sites of injury greatly impedes nerve regeneration,making the clearance of debris within these microenvironments crucial for effective post-spinal cord injury repair.In this review,we comprehensively outline the mechanisms that promote the clearance of myelin debris and myelin metabolism and summarize their roles in spinal cord injury.First,we describe the composition and characteristics of myelin debris and explain its effects on the injury site.Next,we introduce the phagocytic cells involved in myelin debris clearance,including professional phagocytes(macrophages and microglia)and non-professional phagocytes(astrocytes and microvascular endothelial cells),as well as other cells that are also proposed to participate in phagocytosis.Finally,we focus on the pathways and associated targets that enhance myelin debris clearance by phagocytes and promote lipid metabolism following spinal cord injury.Our analysis indicates that myelin debris phagocytosis is not limited to monocyte-derived macrophages,but also involves microglia,astrocytes,and microvascular endothelial cells.By modulating the expression of genes related to phagocytosis and lipid metabolism,it is possible to modulate lipid metabolism disorders and influence inflammatory phenotypes,ultimately affecting the recovery of motor function following spinal cord injury.Additionally,therapies such as targeted mitochondrial transplantation in phagocytic cells,exosome therapy,and repeated trans-spinal magnetic stimulation can effectively enhance the removal of myelin debris,presenting promising potential for future applications.
基金“Education Department of Hebei Funding Project for Cultivating the Innovative Capabilities of Graduate Students”(Project No.:XJCX202510)。
文摘In view of the imperfect supply chain management of prefabricated building,inadequate information interaction among the participating subjects,and untimely information updates,the integration and development of BIM technology plus the supply chain of prefabricated building is analyzed,and the problems existing in the current supply chain and the application of BIM technology at various stages are elaborated.By analyzing the structural composition of the prefabricated building supply chain,an information sharing platform framework for prefabricated building supply chain based on BIM was established,which serves as a valuable reference for managing prefabricated building supply chains.BIM technology aligns well with assembly construction,laying a solid foundation for their synergistic development and offering novel research avenues for the prefabricated building supply chain.
文摘Although currently,a large part of the existing buildings is considered inefficient in terms of energy,the ability to save energy consumption up to 80%has been proven in residential and commercial buildings.Also,carbon dioxide is one of the most important greenhouse gases contributing to climate change and is responsible for 60%of global warming.The facade of the building,as the main intermediary between the interior and exterior spaces,plays a significant role in adjusting the weather conditions and providing thermal comfort to the residents.In this research,715 different scenarios were defined with the combination of various types of construction materials,and the effect of each of these scenarios on the process of energy loss from the surface of the external walls of the building during the operation period was determined.In the end,these scenarios were compared during a one-year operation period,and the amount of energy consumption in each of these scenarios was calculated.Also,bymeasuring the amount of carbon emissions in buildings during the operation period and before that,let’s look at practical methods to reduce the effects of the construction industry on the environment.By comparing the research findings,it can be seen that the ranking of each scenario in terms of total energy consumption is not necessarily the same as the ranking of energy consumption for gas consumption or electricity consumption for the same scenario.That is,choosing the optimal scenario depends on the type of energy consumed in the building.Finally,we determined the scenarios with the lowest and highest amounts of embodied and operational carbon.In the end,we obtained the latent carbon compensation period for each scenario.This article can help designers and construction engineers optimize the energy consumption of buildings by deciding on the right materials.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 61763009 and 72172025)。
文摘Research on the self-similarity of multilayer networks is scarce, when compared to the extensive research conducted on the dynamics of these networks. In this paper, we use entropy to determine the edge weights in each sub-network,and apply the degree–degree distance to unify the weight values of connecting edges between different sub-networks, and unify the edges with different meanings in the multilayer network numerically. At this time, the multilayer network is compressed into a single-layer network, also known as the aggregated network. Furthermore, the self-similarity of the multilayer network is represented by analyzing the self-similarity of the aggregate network. The study of self-similarity was conducted on two classical fractal networks and a real-world multilayer network. The results show that multilayer networks exhibit more pronounced self-similarity, and the intensity of self-similarity in multilayer networks can vary with the connection mode of sub-networks.
基金supported by the National Natural Science Foundation of China(No.52275104)the Science and Technology Innovation Program of Hunan Province(No.2023RC3097).
文摘The monitoring signals of bearings from single-source sensor often contain limited information for characterizing various working condition,which may lead to instability and uncertainty of the class-imbalanced intelligent fault diagnosis.On the other hand,the vectorization of multi-source sensor signals may not only generate high-dimensional vectors,leading to increasing computational complexity and overfitting problems,but also lose the structural information and the coupling information.This paper proposes a new method for class-imbalanced fault diagnosis of bearing using support tensor machine(STM)driven by heterogeneous data fusion.The collected sound and vibration signals of bearings are successively decomposed into multiple frequency band components to extract various time-domain and frequency-domain statistical parameters.A third-order hetero-geneous feature tensor is designed based on multisensors,frequency band components,and statistical parameters.STM-based intelligent model is constructed to preserve the structural information of the third-order heterogeneous feature tensor for bearing fault diagnosis.A series of comparative experiments verify the advantages of the proposed method.
文摘The intertwined challenges of climate change, resource scarcity, and conflict require innovative integrated solutions that address both environmental and societal vulnerabilities. Technological innovation offers a transformative pathway for climate change adaptation and peacebuilding, with emphasis on a holistic approach to managing resource conflicts and environmental challenges. This paper explores the synergies between emerging technologies and strategic framework to mitigate climate-induced tensions and foster resilience. It focuses on the application of renewable energy systems to reduce dependence on contested resources, blockchain technology to ensure transparency in climate finance, equitable resource allocation and Artificial Intelligence (AI) to enhance early warning systems for climate-related disaster and conflicts. Additionally, technologies such as precision agriculture and remote sensing empower communities to optimize resource use, adapt to shifting environmental conditions, and reduce competition over scares resources. These innovations with inclusive governance and local capacity-building are very primordial. Ultimately, the convergence of technology, policy, and local participation offers a scalable and replicable model for addressing the dual challenges of environmental degradation and instability, thereby paving the way for a more sustainable and peaceful future.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
基金supported by the National Institute of Natural Hazards,Ministry of Emergency Management of China(2023-JBKY-57)the National Natural Science Foundation of China(42077259)。
文摘Rainfall-induced landslides are often highly destructive.Reviewing and analyzing the causes,processes,impacts,and deficiencies in emergency response is critical for improving disaster prevention and management.From the night of July 21 to the morning of July 22,2024,the Kencho Shacha Gozdi Village in Gezei Gofa,Southern Nations,Nationalities,and Peoples'Region,Ethiopia,suffered heavy rainfall that triggered two landslides.By July25,this event had claimed at least 257 lives.This study presents a detailed characterization of the landslides using multi-source data.By analyzing the landslide disaster process,this study summarizes key lessons and provides suggestions for preventing rainfall-induced geological hazards.The results indicate that rainfall has the greatest impact on the occurrence of landslides,while lithology and human activities have promoted and strengthened the landslide disaster.Despite the active disaster response in the local area,many problems were still exposed in the emergency response work.This analysis offers valuable insights for mitigating rainfall-induced geological hazards and enhancing emergency response capabilities.
基金supported by Research Supporting Project Number(RSPD2025R585),King Saud University,Riyadh,Saudi Arabia.
文摘The liver is a crucial gland and the second-largest organ in the human body and also essential in digestion,metabolism,detoxification,and immunity.Liver diseases result from factors such as viral infections,obesity,alcohol consumption,injuries,or genetic predispositions.Pose significant health risks and demand timely diagnosis and treatment to enhance survival rates.Traditionally,diagnosing liver diseases relied heavily on clinical expertise,often leading to subjective,challenging,and time-intensive processes.However,early detection is essential for effective intervention,and advancements in machine learning(ML)have demonstrated remarkable success in predicting various conditions,including Chronic Obstructive Pulmonary Disease(COPD),hypertension,and diabetes.This study proposed a novel XGBoost-liver predictor by integrating distinct feature methodologies,including Ranking and Statistical Projection-based strategies to detect early signs of liver disease.The Fisher score method is applied to perform global interpretation analysis,helping to select optimal features by assessing their contributions to the overall model.The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests.Firstly,the performance of the proposed model is evaluated using individual and hybrid features.Secondly,the XGBoost-Liver model performance is compared to that of commonly used classifier algorithms.Thirdly,its performance is compared with the existing state-of-the-art computational models.The experimental results show that the proposed model performed better than the existing predictors,reaching an average accuracy rate of 92.07%.This paper demonstrates the potential of machine learning to improve liver disease prediction,enhance diagnostic accuracy,and enable timely medical interventions for better patient outcomes.
文摘Compliance,as a very important aspect of corporate governance,has developed earlier in the world,while China's compliance management has developed relatively late and is currently lacking in development.However,many enterprises suffer serious losses without compliance management,especially import and export enterprises,which are forced to exit the market due to poor compliance management.This article is based on the urgent need of Chinese enterprises for compliance management,but the lack of research by scholars.It summarizes the literature on the factors affecting compliance,hoping to be helpful for the study of compliance management.
基金funded by the Directorate of Research and Community Service, Ministry of Education, Culture, Research, and Technology, Indonesia (027/E5/PG.02.00.PL/2024)Bogor Agricultural University for their invaluable support and resources that made this research possibleDirectorate of Research and Community Service, Ministry of Education, Culture, Research, and Technology, Indonesia, for their financial and administrative support
文摘Urbanization in small-and medium-sized cities has often been overlooked in urban studies.Research on urbanization has predominantly focused on large metropolitan cities;however,urbanization in small-and medium-sized cities also contributes to the acceleration of urban sprawl.Urban growth boundary(UGB)is an ecological approach designed to limit urban development.This study aimedto analyze environmental protection priority zonesby combining ecological quality and sensitivity indices to identify the areas suitable for UGB implementation.Tegal City and its surrounding areas(including Tegal and Brebes regencies)of Indonesia were selected as the study area.The ecological quality index was calculated using the normalized difference vegetation index,humidity index,land surface temperature,and normalized difference bare soil index.These indices were subsequently subjected to principal component analysis(PCA)to extract orthogonal factors,which were summed to derive the final index value.In parallel,we mapped and evaluated ecological sensitivity based on spatial planning policies and regulations.The results revealed that ecological quality in Tegal and Brebes regencies was predominantly categorized as good and very good ecological quality,whereas TegalCity exhibited moderate and poor ecological quality.Additionally,over 45.00%of the area in Tegal and Brebes regencies demonstrated very high ecological sensitivity.Consequently,more than 50.00%of the area in Tegal and Brebes regencies,along with 27.00%of Tegal City,were classified as ecological constraint zone,making them potential regionsfor UGB development.The UGB is expected to curtail urban expansion,promote compact city planning,and preserve ecosystem services to achieve urban sustainability.This study implies that planningsmall-and medium-sized cities is important to prevent urban sprawl and maintain environmental health.Designing UGB to limit urban expansion should be enhanced by better knowledge about its ecological functions in supporting urban sustainability.
基金Supported by Wuxi Municipal Health Commission Major Project,No.Z202107Wuxi Taihu Talent Project,No.WXTTP 2021.
文摘BACKGROUND The neural mechanisms underlying aggressive behavior in schizophrenia(SCZ)remain poorly understood.To date,no studies have reported on the event-related potential(ERP)characteristics of aggression in SCZ using the competitive reaction time task(CRTT).Further investigation into the ERP correlates of aggression in SCZ would provide valuable insights into the neural processes involved.AIM To explore the neural mechanism of aggressive behavior in SCZ.METHODS Participants of this study included 40 SCZ patients and 42 healthy controls(HCs).The Reactive Proactive Aggression Questionnaire was used to assess trait of aggression.The Barratt Impulsiveness Scale 11 was used to measure impulsiveness.The Positive and Negative Symptom Scale(PANSS)was used to evaluate psychopathological features and disease severity.All participants were measured with ERP while performing the CRTT.Data of behavior,ERP components(P2,N2,and P3),and feedback-related negativity(FRN)were analyzed.RESULTS Analysis of the behavioral data revealed that compared with HCs,SCZ patients exhibited higher punishment choices.Analysis of ERP components showed that compared with HCs,SCZ patients exhibited higher N2 amplitudes and P2 amplitudes during the decision phase of the CRTT;however,SCZ patients exhibited lower FRN amplitudes and lower P3 amplitudes during the outcome phase of the CRTT.The N2 amplitudes evoked by highintensity provocation were positively related to PANSS-P scores.And the P3 amplitudes evoked in the winning trials were negatively correlated with the PANSS-G scores.CONCLUSION SCZ patients exhibit abnormal ERP characteristics evoked by the CRTT,which suggests the neural correlates of aggressive behavior in SCZ.
基金Fund by the Science and Technology Programme Project of Bengbu City(No.2023gx01)the Key Technologies R&D Program of CNBM(No.2021HX0809)。
文摘We presented a novel porous alumina ceramics(PACs)with superoleophilicity and superoleo-phobicity when immersed in different oil-water environments.The wettability,separation efficiency,permeation flux and reusability of the PACs for oil/water separation were investigated and characterized via extensive ex-periments.The PACs material had favourable properties including mechanical strength and chemical durability compared with fabric-based materials and organic sponge-based materials previously reported in literature for oil/water separation.It is believed that the PACs material and methodology presented in this work may provide wastewater remediation industry with a promising alternative for dealing with the catastrophic ocean oil pollu-tion and other oil contamination.
文摘Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.
基金2022 Anhui Province Key Humanities and Social Science Projects(2022AH052219)Provincial Quality Engineering Projects in 2022(2022tsgsp065)2024 Anhui Province Key Humanities and Social Science Projects(2024AH053142).
文摘The social responsibility of small and medium-sized enterprises(SMEs)is very important for themselves and the whole society.The concept and connotation of social responsibility of SMEs have been defined and explained.The research dimensions of previous scholars on corporate social responsibility(CSR)have been reviewed,and an evaluation index system for social responsibility of SMEs has been constructed based on their characteristics,including four key indicators:community,customers,employees,and environment.
