The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-laye...At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.展开更多
This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discom...This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discomfort,but also carry a significant emotional burden,often exacerbated by social stigma.Psycho-logical factors,including stress,anxiety,and depression,activate neuroendocrine pathways,such as the hypothalamic–pituitary–adrenal axis,disrupting the gut microbiota and leading to dysbiosis.This disruption can delay wound healing,prolong hospital stay,and intensify pain.Drawing on the findings of Hou et al,our article highlights the critical role of illness perception and negative emotions in shaping recovery outcomes.It advocates for a holistic approach that integrates psychological support and gut microbiota modulation,to enhance healing and improve overall patient outcomes.展开更多
Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers...Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.展开更多
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems...Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and ca...In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.展开更多
One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural ne...One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.展开更多
Tephritid fruit flies are considered one of the world’s most notorious pests of horticultural crops, including mango (Mangefera indica L.) in Sierra Leone, causing extensive direct and indirect damage. A survey was c...Tephritid fruit flies are considered one of the world’s most notorious pests of horticultural crops, including mango (Mangefera indica L.) in Sierra Leone, causing extensive direct and indirect damage. A survey was conducted among 60 mango farmers in 7 districts in Sierra Leone between June and August, 2022, to assess their perceptions regarding fruit fly pest status and the current management options adopted for the control of this pest. Semi-structured questions designed in an open and closed-ended fashion were used for the study. The majority (83%) of the farmers were already aware of the fruit fly problem in the country with 62% perceiving it to be very severe. The majority (60%) of farmers, however, demonstrated poor knowledge of identifying fruit fly species, especially Bactrocera dorsalis, Ceratitis capitata, and Ceratitis cosyra. Farmers were more conversant about the direct damage symptoms to host fruits and the economic impact of fruit flies. A total of 32% of growers took no action to control fruit flies on their farms. Sixty-nine percent (69%) of the farmers adopted cultural control measures, like practicing prompt harvesting, collection and disposal of infested fruits, and weeding to maintain better sanitary conditions on their farms. Recommended fruit fly management strategies such as the use of botanicals and resistant varieties were either unknown or inaccessible to growers. A total of 52% applied chemicals that were not recommended for the control of fruit flies without considering their environmental and health risks. It is important to train fruit growers to improve their capabilities for fruit fly management through extension agents that are appropriate for helping them acquire basic knowledge of fruit fly pests and their management.展开更多
To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross ...To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.展开更多
This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are...This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.展开更多
Objectives:This study aimed to explore the perceptions and recommendations of multiparas and health-related professionals regarding appropriate birth intervals(Bis)and key determinants.Methods:In-depth semi-structured...Objectives:This study aimed to explore the perceptions and recommendations of multiparas and health-related professionals regarding appropriate birth intervals(Bis)and key determinants.Methods:In-depth semi-structured interviews were conducted between April 1 and June 30,2022.Nine multiparas and thirteen health-related professionals were purposefully sampled until data saturation was reached.A thematic analysis approach was applied to the interview transcripts,utilizing dual independent coding and consensus validation in NVivo 12.0.Results:The data generated two overarching categories:1)balanced decision-making on the appropriate birth intervals and 2)internal and external determinants integrated with health and societal considerations.Four key themes emerged following the two categories:1)consistency and discrepancy between the actual and recommended birth intervals of multiparas;2)health-and developmentoriented professional recommendations;3)internal determinants related to individual-level factors;and 4)external determinants related to child-related factors,family support,and social security.Weighing women's reproductive health and career development,multiparas and health-related professionals perceived a length between 18 and 36 months as the appropriate Bl.Conclusion:Multiparas and health-related professionals shaped their balanced recommendations on a relatively appropriate birth interval ranging from 18 to 36 months,which was influenced by women's individual-level factors,child-related factors,family support,and social security.Targeted social and healthcare services should be offered to women and their families during the Bls.展开更多
The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the liv...The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
BACKGROUND Sex education was introduced early in foreign countries.For example,there is a more systematic sex education system abroad,which can better achieve the popularization of sex education.China's sex educat...BACKGROUND Sex education was introduced early in foreign countries.For example,there is a more systematic sex education system abroad,which can better achieve the popularization of sex education.China's sex education started relatively late,yet there are many problems in the development of sex education in China;for example,college students lack knowledge of sexuality.AIM To explore the perception of sex education among medical college students.METHODS Students majoring in medicine in a medical school were selected as the survey subjects.Anonymous online questionnaires were used to conduct the survey,and the results were analyzed using GraphPad Prism,SPSS,Microsoft Excel,and other software.The questionnaire was administered to understand the source of sexual knowledge,sexual responsibility,mastery of sexual knowledge,and distress caused by sexual problems.RESULTS Most students majoring in medicine had no formal sex education,lacked sexual knowledge,or had a biased understanding of sexual responsibility.This study analyzed future research trends in sex education based on relevant achievements in the Chinese context and abroad to further realize the practical significance and value of sex education popularization in China and provide recommendations for parents and schools at different levels.CONCLUSION Sex education should be conducted among college students,and medical colleges and universities should strengthen scientific sex education.展开更多
In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still...In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.展开更多
Objectives Nurses’clinical research activities have contributed to optimizing the care process and improving patient outcomes,and generative artificial intelligence(GAI)may help clinical nurses strengthen their resea...Objectives Nurses’clinical research activities have contributed to optimizing the care process and improving patient outcomes,and generative artificial intelligence(GAI)may help clinical nurses strengthen their research skills.To support research,this study aimed to explore the Chinese nurses’perceptions and experiences of GAI training.Methods This study used a descriptive qualitative design.The China Nurses Network conducted a three-day training session on“GAI for Nursing Research”theme,we selected 23 nurses by a convenience sampling method among participating in the training.The researchers conducted three focus group interviews at the end of each day.All focus groups were interviewed face-to-face to facilitate interaction,data collection,and observation.The data were analyzed using conventional content analysis and coded manually.Results The results showed that nurses’use of GAI to support scientific research was dynamic and characterized by evolving perceptions and practices.Four themes and 11 sub-themes emerged from the analysis:1)utilization efficacy:cope with research ability,affected by many factors;2)booster research:growth and challenges go hand in hand;3)role reversal:from GAI-dominated to nurse-dominated;4)beautiful dream:more features on research,more assistants on clinical care.Conclusions The effectiveness of GAI in supporting clinical nurses in conducting research is mainly limited by differences in personal research literacy,lack of ethical regulation,and information accuracy.In the future,it is necessary to improve nurses’relevant skills through specialized training and promote the standardization of technical regulations to ensure the appropriate application of GAI in nursing research.展开更多
Presents a novel approach of multi layer sensing for perception of high level environmental information related to many conventional physical quantities, such as temperature, humidity and brightness, which focuses on ...Presents a novel approach of multi layer sensing for perception of high level environmental information related to many conventional physical quantities, such as temperature, humidity and brightness, which focuses on the processing of multi functional variables in a multi layer framework, and consists of multi functional sensing and multi layer fusion. Concerning the first aspect, a CdS and Fe 3O 4 materials based multi function sensor has been developed to measure the three quantities, and provides a possible solution to the sensor multi functional measurement equations, especially when the sensor processes more than three quantities, and proposes ways to evaluate the concerned environment as degree of comfort, Quantity Creditability Tactics (QCT) of multi layer data fusion.展开更多
This study explores the environmental sensitivity of first-year teacher education stu dents,focusing on the relationship between their Earth Science performance,demographic factors,and their cognitive and emotional re...This study explores the environmental sensitivity of first-year teacher education stu dents,focusing on the relationship between their Earth Science performance,demographic factors,and their cognitive and emotional responses to environmental challenges.Using a descriptive correlational design within a mixed-methods framework,the research incorporates tools such as the Environmental Sensitivity Test(EST),focus group discussions(FGDs),and eco-mapping to comprehensively collect and analyze data.The findings reveal that while students exhibit a general awareness of environmental issues,this awareness does not consistently translate into sustainable practices,particularly in areas such as water conservation and waste management.A weak and statistically insignificant correlation was identified between Earth Science performance and environmental sensitivity,indicating that academic achievement in the subject does not necessarily lead to environmentally responsible behaviors.The results underscore the importance of teacher education programs integrating principles of behavioral psychology,experiential learning,and focused environmental education.Specifically,secondary science teachers should be equipped with practical strategies,such as implementing project-based learning,organizing community-centered environmental initiatives,and fostering interdisciplinary approaches to sustainability.These interventions address the gap in preparing future educators to effectively advocate for and implement sustainable practices.Strengthening teacher preparation programs with these components ensures that science educators are better equipped to cultivate a new generation of environmentally responsible citizens.展开更多
Objective:This article employs a scoping review methodology,integrates knowledge and information about current pediatric nurses'practices and perceptions regarding family-centered care in pediatric settings.Method...Objective:This article employs a scoping review methodology,integrates knowledge and information about current pediatric nurses'practices and perceptions regarding family-centered care in pediatric settings.Methods:Published articles were retrieved from databases including EBSCO host,PubMed,Springer,Science Direct,Ovid,and CINAHL between 2013 and 2023.Results:The finding shows a better understanding of pediatric nurses'perceptions of family-centered care in association with their clinical settings.However,the evidence indicates that integrating family-centered care components into health care services is difficult and confusing to nurses and is often not implemented in a clinical setting.As evidenced by this review,studies have consistently reported similar results;family-centered care was a good perception and understood by pediatric nurses as a concept but inconsistently used in a daily practice setting.Conclusions:This scoping review is the first phase in promoting a strategic plan to provide educational interventions for pediatric nurses to implement family-centered care in their daily practice settings.It's necessary to recognize pediatric nurses'perceptions and practices concerning family-centered care to provide optimal healthcare services in pediatric settings.展开更多
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
基金supported by the National Key Research and Development Program of China(No.2022YFB3404700)the National Natural Science Foundation of China(Nos.52105313 and 52275299)+2 种基金the Research and Development Program of Beijing Municipal Education Commission,China(No.KM202210005036)the Natural Science Foundation of Chongqing,China(No.CSTB2023NSCQ-MSX0701)the National Defense Basic Research Projects of China(No.JCKY2022405C002).
文摘At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.
文摘This article examines the complex relationship between disease perception,negative emotions,and their impact on postoperative recovery in patients with perianal diseases.These conditions not only cause physical discomfort,but also carry a significant emotional burden,often exacerbated by social stigma.Psycho-logical factors,including stress,anxiety,and depression,activate neuroendocrine pathways,such as the hypothalamic–pituitary–adrenal axis,disrupting the gut microbiota and leading to dysbiosis.This disruption can delay wound healing,prolong hospital stay,and intensify pain.Drawing on the findings of Hou et al,our article highlights the critical role of illness perception and negative emotions in shaping recovery outcomes.It advocates for a holistic approach that integrates psychological support and gut microbiota modulation,to enhance healing and improve overall patient outcomes.
基金supported by the Jiangsu Provincial Department of Science and Technology Social Development Project(No.BE2020787)。
文摘Objectives Diabetes remains a major global health challenge in China.Artificial intelligence(AI)has demonstrated considerable potential in improving diabetes management.This study aimed to assess healthcare providers’perceptions regarding AI in diabetes care across China.Methods A cross-sectional survey was conducted using snowball sampling from November 12 to November 24,2024.We selected 514 physicians and nurses by a snowball sampling method from healthcare providers across 30 cities or provinces in China.The self-developed questionnaire comprised five sections with 19 questions assessing medical workers’demographic characteristics,AI-related experience and interest,awareness,attitudes,and concerns regarding AI in diabetes care.Statistical analysis was performed using t-test,analysis of variance(ANOVA),and linear regression.Results Among them,20.0%and 48.1%of respondents had participated in AI-related research and training,while 85.4%expressed moderate to high interest in AI training for diabetes care.Most respondents reported partial awareness of AI in diabetes care,and only 12.6%exhibited a comprehensive or substantial understanding.Attitudes toward AI in diabetes care were generally positive,with a mean score of 24.50±3.38.Nurses demonstrated significantly higher scores than physicians(P<0.05).Greater awareness,prior AI training experience,and higher interest in AI training in diabetes care were strongly associated with more positive attitudes(P<0.05).Key concerns regarding AI included trust issues from AI-clinician inconsistencies(77.2%),increased workload and clinical workflow disruptions(63.4%),and incomplete legal and regulatory frameworks(60.3%).Only 34.2%of respondents expressed concerns about job displacement,indicating general confidence in their professional roles.Conclusions While Chinese healthcare providers show moderate awareness of AI in diabetes care,their attitudes are generally positive,and they are considerably interested in future training.Tailored,role-specific AI training is essential for equitable and effective integration into clinical practice.Additionally,transparent,reliable,ethical AI models must be prioritized to alleviate practitioners’concerns.
基金the National Natural Science Foundation of China(No.U1764264/61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1733/1807)。
文摘Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
基金funded by the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)
文摘In the early exploration of many oilfields,low-resistivity-low-contrast(LRLC)pay zones are easily overlooked due to the resistivity similarity to the water zones.Existing identification methods are model-driven and cannot yield satisfactory results when the causes of LRLC pay zones are complicated.In this study,after analyzing a large number of core samples,main causes of LRLC pay zones in the study area are discerned,which include complex distribution of formation water salinity,high irreducible water saturation due to micropores,and high shale volume.Moreover,different oil testing layers may have different causes of LRLC pay zones.As a result,in addition to the well log data of oil testing layers,well log data of adjacent shale layers are also added to the original dataset as reference data.The densitybased spatial clustering algorithm with noise(DBSCAN)is used to cluster the original dataset into 49 clusters.A new dataset is ultimately projected into a feature space with 49 dimensions.The new dataset and oil testing results are respectively treated as input and output to train the multi-layer perceptron(MLP).A total of 3192 samples are used for stratified 8-fold cross-validation,and the accuracy of the MLP is found to be 85.53%.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.12072217).
文摘One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.
文摘Tephritid fruit flies are considered one of the world’s most notorious pests of horticultural crops, including mango (Mangefera indica L.) in Sierra Leone, causing extensive direct and indirect damage. A survey was conducted among 60 mango farmers in 7 districts in Sierra Leone between June and August, 2022, to assess their perceptions regarding fruit fly pest status and the current management options adopted for the control of this pest. Semi-structured questions designed in an open and closed-ended fashion were used for the study. The majority (83%) of the farmers were already aware of the fruit fly problem in the country with 62% perceiving it to be very severe. The majority (60%) of farmers, however, demonstrated poor knowledge of identifying fruit fly species, especially Bactrocera dorsalis, Ceratitis capitata, and Ceratitis cosyra. Farmers were more conversant about the direct damage symptoms to host fruits and the economic impact of fruit flies. A total of 32% of growers took no action to control fruit flies on their farms. Sixty-nine percent (69%) of the farmers adopted cultural control measures, like practicing prompt harvesting, collection and disposal of infested fruits, and weeding to maintain better sanitary conditions on their farms. Recommended fruit fly management strategies such as the use of botanicals and resistant varieties were either unknown or inaccessible to growers. A total of 52% applied chemicals that were not recommended for the control of fruit flies without considering their environmental and health risks. It is important to train fruit growers to improve their capabilities for fruit fly management through extension agents that are appropriate for helping them acquire basic knowledge of fruit fly pests and their management.
基金Educational Research Project of Social Science for Young and Middle Aged Teachers in Fujian Province,China(No.JAS19371)Social Science Research Project of Education Department of Fujian Province,China(No.JAS160571)Key Project of Education and Teaching Reform of Undergraduate Universities in Fujian Province,China(No.FBJG20190130)。
文摘To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.
文摘This main contribution of this work is to propose a new approach based on a structure of MLPs （multi-layer perceptrons） for identifying current harmonics in low power distribution systems. In this approach, MLPs are proposed and trained with signal sets that arc generated from real harmonic waveforms. After training, each trained MLP is able to identify the two coefficients of each harmonic term of the input signal. The effectiveness of the new approach is evaluated by two experiments and is also compared to another recent MLP method. Experimental results show that the proposed MLPs approach enables to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. The new approach can be applied in harmonic compensation strategies with an active power filter to ensure power quality issues in electrical power systems.
基金supported by the Key Discipline Program of the Fifth Round of the Three-Year Public Health Action Plan(2020-2022 Year)of Shanghai,China(GWV-10.1-XK08).
文摘Objectives:This study aimed to explore the perceptions and recommendations of multiparas and health-related professionals regarding appropriate birth intervals(Bis)and key determinants.Methods:In-depth semi-structured interviews were conducted between April 1 and June 30,2022.Nine multiparas and thirteen health-related professionals were purposefully sampled until data saturation was reached.A thematic analysis approach was applied to the interview transcripts,utilizing dual independent coding and consensus validation in NVivo 12.0.Results:The data generated two overarching categories:1)balanced decision-making on the appropriate birth intervals and 2)internal and external determinants integrated with health and societal considerations.Four key themes emerged following the two categories:1)consistency and discrepancy between the actual and recommended birth intervals of multiparas;2)health-and developmentoriented professional recommendations;3)internal determinants related to individual-level factors;and 4)external determinants related to child-related factors,family support,and social security.Weighing women's reproductive health and career development,multiparas and health-related professionals perceived a length between 18 and 36 months as the appropriate Bl.Conclusion:Multiparas and health-related professionals shaped their balanced recommendations on a relatively appropriate birth interval ranging from 18 to 36 months,which was influenced by women's individual-level factors,child-related factors,family support,and social security.Targeted social and healthcare services should be offered to women and their families during the Bls.
基金Supported by the National Natural Science Foundation of China(Grant No.52172409)Postdoctoral Innovation Talents Support Program(Grant No.BX20240298)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.2682024GF023)Heilongjiang Province Postdoctoral Foundation Project(Grant No.LBH-Z23041).
文摘The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金Supported by Scientific Research Project of College Students in Liaoning Province,No.S202410164004.
文摘BACKGROUND Sex education was introduced early in foreign countries.For example,there is a more systematic sex education system abroad,which can better achieve the popularization of sex education.China's sex education started relatively late,yet there are many problems in the development of sex education in China;for example,college students lack knowledge of sexuality.AIM To explore the perception of sex education among medical college students.METHODS Students majoring in medicine in a medical school were selected as the survey subjects.Anonymous online questionnaires were used to conduct the survey,and the results were analyzed using GraphPad Prism,SPSS,Microsoft Excel,and other software.The questionnaire was administered to understand the source of sexual knowledge,sexual responsibility,mastery of sexual knowledge,and distress caused by sexual problems.RESULTS Most students majoring in medicine had no formal sex education,lacked sexual knowledge,or had a biased understanding of sexual responsibility.This study analyzed future research trends in sex education based on relevant achievements in the Chinese context and abroad to further realize the practical significance and value of sex education popularization in China and provide recommendations for parents and schools at different levels.CONCLUSION Sex education should be conducted among college students,and medical colleges and universities should strengthen scientific sex education.
基金Project(2023YFC3707800)supported by the National Key Research and Development Program of China。
文摘In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.
基金supported by a grant from the National Natural Science Foundation of China(No.72174130)。
文摘Objectives Nurses’clinical research activities have contributed to optimizing the care process and improving patient outcomes,and generative artificial intelligence(GAI)may help clinical nurses strengthen their research skills.To support research,this study aimed to explore the Chinese nurses’perceptions and experiences of GAI training.Methods This study used a descriptive qualitative design.The China Nurses Network conducted a three-day training session on“GAI for Nursing Research”theme,we selected 23 nurses by a convenience sampling method among participating in the training.The researchers conducted three focus group interviews at the end of each day.All focus groups were interviewed face-to-face to facilitate interaction,data collection,and observation.The data were analyzed using conventional content analysis and coded manually.Results The results showed that nurses’use of GAI to support scientific research was dynamic and characterized by evolving perceptions and practices.Four themes and 11 sub-themes emerged from the analysis:1)utilization efficacy:cope with research ability,affected by many factors;2)booster research:growth and challenges go hand in hand;3)role reversal:from GAI-dominated to nurse-dominated;4)beautiful dream:more features on research,more assistants on clinical care.Conclusions The effectiveness of GAI in supporting clinical nurses in conducting research is mainly limited by differences in personal research literacy,lack of ethical regulation,and information accuracy.In the future,it is necessary to improve nurses’relevant skills through specialized training and promote the standardization of technical regulations to ensure the appropriate application of GAI in nursing research.
文摘Presents a novel approach of multi layer sensing for perception of high level environmental information related to many conventional physical quantities, such as temperature, humidity and brightness, which focuses on the processing of multi functional variables in a multi layer framework, and consists of multi functional sensing and multi layer fusion. Concerning the first aspect, a CdS and Fe 3O 4 materials based multi function sensor has been developed to measure the three quantities, and provides a possible solution to the sensor multi functional measurement equations, especially when the sensor processes more than three quantities, and proposes ways to evaluate the concerned environment as degree of comfort, Quantity Creditability Tactics (QCT) of multi layer data fusion.
文摘This study explores the environmental sensitivity of first-year teacher education stu dents,focusing on the relationship between their Earth Science performance,demographic factors,and their cognitive and emotional responses to environmental challenges.Using a descriptive correlational design within a mixed-methods framework,the research incorporates tools such as the Environmental Sensitivity Test(EST),focus group discussions(FGDs),and eco-mapping to comprehensively collect and analyze data.The findings reveal that while students exhibit a general awareness of environmental issues,this awareness does not consistently translate into sustainable practices,particularly in areas such as water conservation and waste management.A weak and statistically insignificant correlation was identified between Earth Science performance and environmental sensitivity,indicating that academic achievement in the subject does not necessarily lead to environmentally responsible behaviors.The results underscore the importance of teacher education programs integrating principles of behavioral psychology,experiential learning,and focused environmental education.Specifically,secondary science teachers should be equipped with practical strategies,such as implementing project-based learning,organizing community-centered environmental initiatives,and fostering interdisciplinary approaches to sustainability.These interventions address the gap in preparing future educators to effectively advocate for and implement sustainable practices.Strengthening teacher preparation programs with these components ensures that science educators are better equipped to cultivate a new generation of environmentally responsible citizens.
文摘Objective:This article employs a scoping review methodology,integrates knowledge and information about current pediatric nurses'practices and perceptions regarding family-centered care in pediatric settings.Methods:Published articles were retrieved from databases including EBSCO host,PubMed,Springer,Science Direct,Ovid,and CINAHL between 2013 and 2023.Results:The finding shows a better understanding of pediatric nurses'perceptions of family-centered care in association with their clinical settings.However,the evidence indicates that integrating family-centered care components into health care services is difficult and confusing to nurses and is often not implemented in a clinical setting.As evidenced by this review,studies have consistently reported similar results;family-centered care was a good perception and understood by pediatric nurses as a concept but inconsistently used in a daily practice setting.Conclusions:This scoping review is the first phase in promoting a strategic plan to provide educational interventions for pediatric nurses to implement family-centered care in their daily practice settings.It's necessary to recognize pediatric nurses'perceptions and practices concerning family-centered care to provide optimal healthcare services in pediatric settings.
