Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomer...Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomers(p-phenylenediamine(Pa),benzidine(BD),and 4,4"-diamino-p-terphenyl(DATP))were used to synthesize a series of two-dimensional covalent-organic frameworks(COFs).The resulting COFs were named TpPa,TpBD,and TpDATP,respectively,and they showed uniform zincophilic sites,different pore sizes,and high Young's moduli on the Zn anode.Among them,TpPa and TpBD showed lower surface work functions and higher ion transfer numbers,which were conducive to uniform galvanizing/stripping zinc and inhibited dendrite growth.Theoretical calculations showed that TpPa and TpBD had wider negative potential region and greater adsorption capacity for Zn2+than TpDATP,providing more electron donor sites to coordinate with Zn^(2+).Symmetric cells protected by TpPa and TpBD stably cycled for more than 2300 h,whereas TpDATP@Zn and the bare zinc symmetric cells failed after around 150 and200 h.The full cells containing TpPa and TpBD modification layers also showed excellent cycling capacity at 1 A/g.This study provides comprehensive insights into the construction of highly reversible Zn anodes via COF modification layers for advanced rechargeable ZIBs.展开更多
The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.展开更多
During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolka...During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolkata has one of the highest levels of urban warming of any city around the world.In Kolkata,73%of the buildings are residential,and it is this type of building that contributes to a significant amount of this warming.With the city of Kolkata as the case study,this paper aims at understanding the multiple domains of urban heat islands and thermal comfort within the context of the city,from a macro perspective of an urban heat island down to a micro perspective of a building level,with the ultimate aim of mitigating global warming through this study.Various research works have been undertaken in India and abroad to understand the individual as well as composite effect of various building components on the indoor thermal comfort.Researches have also been undertaken to compare and comprehend the differential thermal comfort of old indigenous residences with that of the new residential buildings.Hence,this paper discusses methods that have been applied in past works to evaluate the thermal comfort of old and new residential buildings in a non-subjective manner,without having recourse to user feedback,in the final segment that views the process of learning from comparing old and new residential buildings.展开更多
Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first c...Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.展开更多
Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).I...Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.展开更多
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in mat...An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.展开更多
Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risk...Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risks probably due to negligence or by lack of knowledge. This work aimed to describe the working conditions and their impact on the workers’ health in a workshop of bodywork painting in Conakry. Material and Methods: This was a cross-sectional study over 06 months (from July 01, 2021, to December 31, 2021). Were included the bodybuilders-painters, the painters and the bodybuilders. The data was collected during an interview. We analysed the personal data of the workers, the physical environment factors (lighting, noise, etc.) and, the clinical manifestations felt by the workers. Results: The average age was 37 years extenting from 18 to 54 years and, they were all men. Over 80% of workers were exposed to more than 1000 lux and, 78.2% of workers were exposed to the vibratory intensity level of the cordless drill > 2.5 m/s2. The most frequent symptoms were back pain, headache, itchy eyes, and numbness of fingers and hands. The analysis of working conditions and clinical manifestations showed a significant relationship between the level of illumination and the tingling eyes (p = 0.0007), the vibratory intensity of the drill and the numbness of fingers and hands (p = 0.01). This study revealed that some of the complaints cited are related to the working conditions. Conclusion: Working conditions in a bodywork paint workshop are occupational risk factors that become dangerous if they are unknown. A longitudinal study on the assessment of working conditions could better enlighten us on this phenomenon.展开更多
Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working envir...Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
基金financially supported by the National Natural Science Foundation of China(62464010)Spring City Plan-Special Program for Young Talents(K202005007)+3 种基金Yunnan Talents Support Plan for Yong Talents(XDYC-QNRC-2022-0482)Yunnan Local Colleges Applied Basic Research Projects(202101BA070001-138)Key Laboratory of Artificial Microstructures in Yunnan Higher EducationFrontier Research Team of Kunming University 2023。
文摘Zinc-ion batteries(ZIBs)are inexpensive and safe,but side reactions on the Zn anode and Zn dendrite growth hinder their practical applications.In this study,1,3,5-triformylphloroglycerol(Tp)and various diamine monomers(p-phenylenediamine(Pa),benzidine(BD),and 4,4"-diamino-p-terphenyl(DATP))were used to synthesize a series of two-dimensional covalent-organic frameworks(COFs).The resulting COFs were named TpPa,TpBD,and TpDATP,respectively,and they showed uniform zincophilic sites,different pore sizes,and high Young's moduli on the Zn anode.Among them,TpPa and TpBD showed lower surface work functions and higher ion transfer numbers,which were conducive to uniform galvanizing/stripping zinc and inhibited dendrite growth.Theoretical calculations showed that TpPa and TpBD had wider negative potential region and greater adsorption capacity for Zn2+than TpDATP,providing more electron donor sites to coordinate with Zn^(2+).Symmetric cells protected by TpPa and TpBD stably cycled for more than 2300 h,whereas TpDATP@Zn and the bare zinc symmetric cells failed after around 150 and200 h.The full cells containing TpPa and TpBD modification layers also showed excellent cycling capacity at 1 A/g.This study provides comprehensive insights into the construction of highly reversible Zn anodes via COF modification layers for advanced rechargeable ZIBs.
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
文摘During the past few decades,we have witnessed the phenomenon of constant warming occurring everywhere on the globe.Cities have suffered from urban warming to a greater extent than any other part of the world,and Kolkata has one of the highest levels of urban warming of any city around the world.In Kolkata,73%of the buildings are residential,and it is this type of building that contributes to a significant amount of this warming.With the city of Kolkata as the case study,this paper aims at understanding the multiple domains of urban heat islands and thermal comfort within the context of the city,from a macro perspective of an urban heat island down to a micro perspective of a building level,with the ultimate aim of mitigating global warming through this study.Various research works have been undertaken in India and abroad to understand the individual as well as composite effect of various building components on the indoor thermal comfort.Researches have also been undertaken to compare and comprehend the differential thermal comfort of old indigenous residences with that of the new residential buildings.Hence,this paper discusses methods that have been applied in past works to evaluate the thermal comfort of old and new residential buildings in a non-subjective manner,without having recourse to user feedback,in the final segment that views the process of learning from comparing old and new residential buildings.
基金supported by the 2023 Youth Fund for Humanities and Social Sciences Research by the Ministry of Education of the People’s Republic of China(Grant No.23YJC740004).
文摘Based on BERTopic Model,the paper combines qualitative and quantitative methods to explore the reception of Can Xue’s translated works by analyzing readers’book reviews posted on Goodreads and Lovereading.We first collected book reviews from these two well-known websites by Python.Through topic analysis of these reviews,we identified recurring topics,including details of her translated works and appreciation of their translation quality.Then,employing sentiment and content analysis methods,the paper explored the emotional attitudes and the specific thoughts of readers toward Can Xue and her translated works.The fingdings revealed that,among the 408 reviews,though the reception of Can Xue’s translated works was relatively positive,the current level of attention and recognition remains insufficient.However,based on the research results,the paper can derive valuable insights into the translation and dissemination processes such as adjusting translation and dissemination strategies,so that the global reach of Chinese literature and culture can be better facilitated.
基金supported in part by the National Nature Science Foundation of China under Grant 62001168in part by the Foundation and Application Research Grant of Guangzhou under Grant 202102020515.
文摘Wireless Sensor Network(WSN)is a cornerstone of Internet of Things(IoT)and has rich application scenarios.In this work,we consider a heterogeneous WSN whose sensor nodes have a diversity in their Residual Energy(RE).In this work,to protect the sensor nodes with low RE,we investigate dynamic working modes for sensor nodes which are determined by their RE and an introduced energy threshold.Besides,we employ an Unmanned Aerial Vehicle(UAV)to collect the stored data from the heterogeneous WSN.We aim to jointly optimize the cluster head selection,energy threshold and sensor nodes’working mode to minimize the weighted sum of energy con-sumption from the WSN and UAV,subject to the data collection rate constraint.To this end,we propose an efficient search method to search for an optimal energy threshold,and develop a penalty-based successive convex approximation algorithm to select the cluster heads.Then we present a low-complexity iterative approach to solve the joint optimization problem and discuss the implementation procedure.Numerical results justify that our proposed approach is able to reduce the energy consumption of the sensor nodes with low RE significantly and also saves energy for the whole WSN.
文摘An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples, the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
文摘Context: Working conditions in the car repair sector are difficult in general. This leads to health risk factors for inexperienced staff. In the bodywork painting workshop, the staff seemed less interested in the risks probably due to negligence or by lack of knowledge. This work aimed to describe the working conditions and their impact on the workers’ health in a workshop of bodywork painting in Conakry. Material and Methods: This was a cross-sectional study over 06 months (from July 01, 2021, to December 31, 2021). Were included the bodybuilders-painters, the painters and the bodybuilders. The data was collected during an interview. We analysed the personal data of the workers, the physical environment factors (lighting, noise, etc.) and, the clinical manifestations felt by the workers. Results: The average age was 37 years extenting from 18 to 54 years and, they were all men. Over 80% of workers were exposed to more than 1000 lux and, 78.2% of workers were exposed to the vibratory intensity level of the cordless drill > 2.5 m/s2. The most frequent symptoms were back pain, headache, itchy eyes, and numbness of fingers and hands. The analysis of working conditions and clinical manifestations showed a significant relationship between the level of illumination and the tingling eyes (p = 0.0007), the vibratory intensity of the drill and the numbness of fingers and hands (p = 0.01). This study revealed that some of the complaints cited are related to the working conditions. Conclusion: Working conditions in a bodywork paint workshop are occupational risk factors that become dangerous if they are unknown. A longitudinal study on the assessment of working conditions could better enlighten us on this phenomenon.
基金supported byan ENGAGE Grant from the Natural Sciences and Engineering Research Council of Canada(NSERC),[funding reference number 11R01296].
文摘Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
