In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Inf...In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.展开更多
This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business v...This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.展开更多
The neutral surface of a concave thin mirror is too close to the mirror surface,which makes it difficult to effectively mount the flexible structure and increases the mirror surface shape error.To address this problem...The neutral surface of a concave thin mirror is too close to the mirror surface,which makes it difficult to effectively mount the flexible structure and increases the mirror surface shape error.To address this problem,we design a flexible support structure including connectors,a support plate,and flexible structures,and construct an equivalent mirror by installing connectors and a support plate on the back of the mirror.While ensuring that the neutral surface of the equivalent mirror is moved away from the mirror surface,we optimize the support structure so that the rotary center of the flexible structure is located on the neutral surface of the equivalent mirror,avoiding the tilting moment.Following design and modeling of the structure,we analyze the static and dynamic characteristics using a finite element simulation,finding a root-mean-square(RMS)value for the surface shape error of 9.28 nm under the coupled effects of 1g gravity load,4℃ temperature rise,and 0.005 mm unevenness assembly error,with a fundamental frequency of 170.75 Hz,which all meet the design requirements.Finally,we carry out a surface shape error test of the mirror assembly,confirming it to meet the design index requirement of the mirror assembly.Simulation and test results verify the reliability and effectiveness of our proposed support structure.展开更多
Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,B...Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.展开更多
BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burde...BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burden are limited.Electroacupuncture has been proposed as a potential adjunctive therapy,but its psychological benefits remain inadequately studied.AIM To investigate the therapeutic effect of electroacupuncture on emotional recovery and gastrointestinal function in patients with moderate to severe LARS,and to explore its potential advantages in psychologically vulnerable subgroups.METHODS We conducted a retrospective,controlled study involving 100 patients with moderate to severe LARS(LARS score≥21)treated at two tertiary hospitals in China between January 2022 and December 2024.Patients received either standard postoperative care alone(n=50)or in combination with a standardized 4-week electroacupuncture protocol(n=50).Psychological and functional outcomes were assessed using validated instruments including Hospital Anxiety and Depression Scale(HADS),Body Image Scale(BIS),General Self-Efficacy Scale,Perceived Social Support Scale(PSSS),LARS score,and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 at four time points.The primary endpoint was emotional remission,defined as a≥3-point reduction in HADS-Anxiety subscale(HADS-A).Analyses included repeated-measures comparisons,Kaplan-Meier survival curves,Cox regression models,and subgroup-interaction testing.RESULTS At baseline,demographic,surgical,and psychosocial characteristics were comparable among groups.By week 4,patients receiving electroacupuncture demonstrated significantly greater reductions in anxiety(HADS-A:4.8±2.6 vs 7.3±3.0;P<0.001),depression,and body-image disturbance(BIS:8.7±3.6 vs 11.9±4.2;P<0.001),alongside enhanced coping capacity(Brief Coping Orientation to Problems Experienced),perceived social support(PSSS),and bowel function(LARS score).Emotional remission-defined as a≥3-point HADS-A reduction-was achieved more rapidly in the electroacupuncture group,as confirmed by Kaplan-Meier analysis(log-rank P<0.001;odds ratio=4.7).Multivariate Cox regression identified higher baseline LARS and BIS scores as independent predictors of delayed emotional recovery.Subgroup analyses revealed significantly amplified treatment benefits in patients with high baseline anxiety(HADS-A≥8),elevated body-image disturbance(BIS≥12),or low perceived social support(PSSS<60),with consistent interaction effects(P for interaction<0.05 across subgroups).CONCLUSION Electroacupuncture may accelerate emotional recovery and improve functional and psychosocial outcomes in patients with LARS.Its integration into postoperative care may offer particular benefits for psychologically vulnerable subgroups.展开更多
An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supporte...The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.展开更多
The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly f...The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly formed neurons to the damaged area.However,only a small percentage of these neurons survive,and many do not reach the damaged area,possibly because the corpus callosum impedes the migration of subventricular zone-derived stem cells into the lesioned cortex.A second major obstacle to stem cell therapy is the strong inflammatory reaction induced by cerebral ischemia,whereby the associated phagocytic activity of brain macrophages removes both therapeutic cells and/or cell-based drug carriers.To address these issues,neurogenesis was electrically stimulated in the subventricular zone,followed by isolation of proliferating cells,including newly formed neurons,which were subsequently mixed with a nutritional hydrogel.This mixture was then transferred to the stroke cavity of day 14 post-stroke mice.We found that the performance of the treated animals improved in behavioral tests,including novel object,open field,hole board,grooming,and“time-to-feel”adhesive tape tests.Furthermore,immunostaining revealed that the stem cell marker nestin,the neuroepithelial marker Mash1,and the immature neuronal marker doublecortin-positive cells survived in the transplanted area for 2 weeks,possibly due to reduced phagocytic activity and supportive angiogenesis.These results clearly indicate that the transplantation of committed subventricular zone stem cells combined with a protective nutritional gel directly into the infarct cavity after the peak of stroke-induced neuroinflammation represents a feasible approach to improve neurorestoration after cerebral ischemia.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex int...Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.展开更多
With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Ni...With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Nickel-based catalysts are renowned for their outstanding activity and selectivity in this process.The impact of metal-support interaction(MSI),on Ni-based catalyst performance has been extensively researched and debated recently.This paper reviews the recent research progress of MSI on Ni-based catalysts and their characterization and modulation strategies in catalytic reactions.From the perspective of MSI,the effects of different carriers(metal oxides,carbon materials and molecular sieves,etc.)are introduced on the dispersion and surface structure of Ni active metal particles,and the effect of MSI on the activity and stability of DRM reactions on Ni-based catalysts is discussed in detail.Future research should focus on better understanding and controlling MSI to improve the performance and durability of nickel-based catalysts in CH_(4)-CO_(2)reforming,advancing cleaner energy technologies.展开更多
Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductiv...Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.展开更多
Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effe...Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃...To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.展开更多
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially as...BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.展开更多
In this study,supported Pd catalysts were prepared and used as heterogeneous catalysts for the activation of peroxymonosulfate(PMS)which successfully degrade bisphenol F(BPF).Among the supported catalysts(i.e.,Pd/SiO_...In this study,supported Pd catalysts were prepared and used as heterogeneous catalysts for the activation of peroxymonosulfate(PMS)which successfully degrade bisphenol F(BPF).Among the supported catalysts(i.e.,Pd/SiO_(2),Pd/CeO_(2),Pd/TiO_(2)and Pd/Al2O3),Pd/TiO_(2)exhibited the highest catalytic activity due to the high isoelectric point and high Pd0 content.Pd/TiO_(2)prepared by the deposition method leads to high Pd dispersion,which are the key factors for efficient BPF degradation.The influencing factors were investigated during the reaction process and two possible degradation pathways were proposed.Density functional theory(DFT)calculations demonstrate that stronger BPF adsorption and BPF degradation with lower reaction barrier occurs on smaller Pd particles.The catalytic activities are strongly dependent on the structural features of the catalysts.Both experiments and theoretical calculations prove that the reaction is actuated by electron transfer rather than radicals.展开更多
文摘In order to facilitate spare parts management,an integrated approach of BP neural network and supportability analysis(SA)was proposed to evaluate the criticality of spare parts as well as to prioritize spare parts.Influential factors of prioritizing spare parts were detailedly analyzed.Framework of the integrated method was established.The modelling process based on BP neural network was presented.As the input of the neural network,the values of influential factors were determined by supportability analysis data.Based on the presented method,spare parts could be automatically prioritized after supportability analysis for a new system.A case study results showed that the new method was applicable and effective.
文摘This paper proposes a practical and framework-based approach to design an architecture transformation strategy and roadmap aiming to transform or modernize critical legacy enterprise systems.The approach is business value driven with IT supportability in terms of lower application operational and support costs,higher business value and shorter time to market of application delivery.The approach introduces a robust enterprise application architecture assessment framework with an emphasis on technical(internal)and strategic(external)perspectives to guide the application assessment and also a finance selfsupport transformation strategy to aid its transformation roadmap design.The approach was applied in multiple large enterprises successfully and received endorsements and positive feedback from the sponsors.The paper also presents a case study detailing the successful application of the approach to modernize an enterprise logistics transportation management system.
基金supported by the National Natural Science Foundation of China(12473085).
文摘The neutral surface of a concave thin mirror is too close to the mirror surface,which makes it difficult to effectively mount the flexible structure and increases the mirror surface shape error.To address this problem,we design a flexible support structure including connectors,a support plate,and flexible structures,and construct an equivalent mirror by installing connectors and a support plate on the back of the mirror.While ensuring that the neutral surface of the equivalent mirror is moved away from the mirror surface,we optimize the support structure so that the rotary center of the flexible structure is located on the neutral surface of the equivalent mirror,avoiding the tilting moment.Following design and modeling of the structure,we analyze the static and dynamic characteristics using a finite element simulation,finding a root-mean-square(RMS)value for the surface shape error of 9.28 nm under the coupled effects of 1g gravity load,4℃ temperature rise,and 0.005 mm unevenness assembly error,with a fundamental frequency of 170.75 Hz,which all meet the design requirements.Finally,we carry out a surface shape error test of the mirror assembly,confirming it to meet the design index requirement of the mirror assembly.Simulation and test results verify the reliability and effectiveness of our proposed support structure.
文摘Andrew Wangota,a 48-year-old Ugandan farmer,has been using agrivoltaics technology,a solar technology that uses agricultural land for both food production and solar power generation,on his farm in Bunashimolo Parish,Bukyiende Subcounty in Uganda where he has been cultivating plantain,coffee and Irish potatoes for the past 16 years.
文摘BACKGROUND Low anterior resection syndrome(LARS)is a prevalent and debilitating complication following sphincter-preserving surgery for rectal cancer.Evidence-based interventions for the concurrent psychological burden are limited.Electroacupuncture has been proposed as a potential adjunctive therapy,but its psychological benefits remain inadequately studied.AIM To investigate the therapeutic effect of electroacupuncture on emotional recovery and gastrointestinal function in patients with moderate to severe LARS,and to explore its potential advantages in psychologically vulnerable subgroups.METHODS We conducted a retrospective,controlled study involving 100 patients with moderate to severe LARS(LARS score≥21)treated at two tertiary hospitals in China between January 2022 and December 2024.Patients received either standard postoperative care alone(n=50)or in combination with a standardized 4-week electroacupuncture protocol(n=50).Psychological and functional outcomes were assessed using validated instruments including Hospital Anxiety and Depression Scale(HADS),Body Image Scale(BIS),General Self-Efficacy Scale,Perceived Social Support Scale(PSSS),LARS score,and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 at four time points.The primary endpoint was emotional remission,defined as a≥3-point reduction in HADS-Anxiety subscale(HADS-A).Analyses included repeated-measures comparisons,Kaplan-Meier survival curves,Cox regression models,and subgroup-interaction testing.RESULTS At baseline,demographic,surgical,and psychosocial characteristics were comparable among groups.By week 4,patients receiving electroacupuncture demonstrated significantly greater reductions in anxiety(HADS-A:4.8±2.6 vs 7.3±3.0;P<0.001),depression,and body-image disturbance(BIS:8.7±3.6 vs 11.9±4.2;P<0.001),alongside enhanced coping capacity(Brief Coping Orientation to Problems Experienced),perceived social support(PSSS),and bowel function(LARS score).Emotional remission-defined as a≥3-point HADS-A reduction-was achieved more rapidly in the electroacupuncture group,as confirmed by Kaplan-Meier analysis(log-rank P<0.001;odds ratio=4.7).Multivariate Cox regression identified higher baseline LARS and BIS scores as independent predictors of delayed emotional recovery.Subgroup analyses revealed significantly amplified treatment benefits in patients with high baseline anxiety(HADS-A≥8),elevated body-image disturbance(BIS≥12),or low perceived social support(PSSS<60),with consistent interaction effects(P for interaction<0.05 across subgroups).CONCLUSION Electroacupuncture may accelerate emotional recovery and improve functional and psychosocial outcomes in patients with LARS.Its integration into postoperative care may offer particular benefits for psychologically vulnerable subgroups.
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
文摘The year 2025 marks both the 25th anniversary of the Forum on China-Africa Cooperation(FOCAC)and the first year of implementation of the outcomes of the 2024 FOCAC Beijing Summit.Throughout the year,China has supported Africa in addressing historical injustices at diplomatic and political levels and firmly backed South Africa in hosting the G20 Leaders’Summit,further deepening China-Africa strategic mutual trust.
基金supported by European Union Funding Programme,PNRR,No. 760058(to DMH)the UEFISCDI Project,No. PN-III-P4-IDPCE-2020-059(to APW)
文摘The major aim of stroke therapy is to stimulate brain repair and improve behavioral recovery after cerebral ischemia.One option is to stimulate endogenous neurogenesis in the subventricular zone and direct the newly formed neurons to the damaged area.However,only a small percentage of these neurons survive,and many do not reach the damaged area,possibly because the corpus callosum impedes the migration of subventricular zone-derived stem cells into the lesioned cortex.A second major obstacle to stem cell therapy is the strong inflammatory reaction induced by cerebral ischemia,whereby the associated phagocytic activity of brain macrophages removes both therapeutic cells and/or cell-based drug carriers.To address these issues,neurogenesis was electrically stimulated in the subventricular zone,followed by isolation of proliferating cells,including newly formed neurons,which were subsequently mixed with a nutritional hydrogel.This mixture was then transferred to the stroke cavity of day 14 post-stroke mice.We found that the performance of the treated animals improved in behavioral tests,including novel object,open field,hole board,grooming,and“time-to-feel”adhesive tape tests.Furthermore,immunostaining revealed that the stem cell marker nestin,the neuroepithelial marker Mash1,and the immature neuronal marker doublecortin-positive cells survived in the transplanted area for 2 weeks,possibly due to reduced phagocytic activity and supportive angiogenesis.These results clearly indicate that the transplantation of committed subventricular zone stem cells combined with a protective nutritional gel directly into the infarct cavity after the peak of stroke-induced neuroinflammation represents a feasible approach to improve neurorestoration after cerebral ischemia.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金funded by the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture under Grant GJZJ20220802。
文摘Accurately estimating the State of Health(SOH)and Remaining Useful Life(RUL)of lithium-ion batteries(LIBs)is crucial for the continuous and stable operation of battery management systems.However,due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance,direct measurement of SOH and RUL is challenging.To address these issues,the Twin Support Vector Machine(TWSVM)method is proposed to predict SOH and RUL.Initially,the constant current charging time of the lithium battery is extracted as a health indicator(HI),decomposed using Variational Modal Decomposition(VMD),and feature correlations are computed using Importance of Random Forest Features(RF)to maximize the extraction of critical factors influencing battery performance degradation.Furthermore,to enhance the global search capability of the Convolution Optimization Algorithm(COA),improvements are made using Good Point Set theory and the Differential Evolution method.The Improved Convolution Optimization Algorithm(ICOA)is employed to optimize TWSVM parameters for constructing SOH and RUL prediction models.Finally,the proposed models are validated using NASA and CALCE lithium-ion battery datasets.Experimental results demonstrate that the proposed models achieve an RMSE not exceeding 0.007 and an MAPE not exceeding 0.0082 for SOH and RUL prediction,with a relative error in RUL prediction within the range of[-1.8%,2%].Compared to other models,the proposed model not only exhibits superior fitting capability but also demonstrates robust performance.
基金supported by the Natural Science Foundation of Shanxi Province(202203021221155)the Foundation of National Key Laboratory of High Efficiency and Low Carbon Utilization of Coal(J23-24-902)。
文摘With ongoing global warming and increasing energy demands,the CH_(4)-CO_(2)reforming reaction(dry reforming of methane,DRM)has garnered significant attention as a promising carbon capture and utilization technology.Nickel-based catalysts are renowned for their outstanding activity and selectivity in this process.The impact of metal-support interaction(MSI),on Ni-based catalyst performance has been extensively researched and debated recently.This paper reviews the recent research progress of MSI on Ni-based catalysts and their characterization and modulation strategies in catalytic reactions.From the perspective of MSI,the effects of different carriers(metal oxides,carbon materials and molecular sieves,etc.)are introduced on the dispersion and surface structure of Ni active metal particles,and the effect of MSI on the activity and stability of DRM reactions on Ni-based catalysts is discussed in detail.Future research should focus on better understanding and controlling MSI to improve the performance and durability of nickel-based catalysts in CH_(4)-CO_(2)reforming,advancing cleaner energy technologies.
基金supported by the National Natural Science Foundation of China(No.52127816),the National Key Research and Development Program of China(No.2020YFA0715000)the National Natural Science and Hong Kong Research Grant Council Joint Research Funding Project of China(No.5181101182)the NSFC/RGC Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and the National Natural Science Foundation of China(No.N_PolyU513/18).
文摘Latent heat thermal energy storage(TES)effectively reduces the mismatch between energy supply and demand of renewable energy sources by the utilization of phase change materials(PCMs).However,the low thermal conductivity and poor shape stability are the main drawbacks in realizing the large-scale application of PCMs.Promisingly,developing composite PCM(CPCM)based on porous supporting mate-rial provides a desirable solution to obtain performance-enhanced PCMs with improved effective thermal conductivity and shape stability.Among all the porous matrixes as supports for PCM,three-dimensional carbon-based porous supporting material has attracted considerable attention ascribing to its high ther-mal conductivity,desirable loading capacity of PCMs,and excellent chemical compatibility with various PCMs.Therefore,this work systemically reviews the CPCMs with three-dimensional carbon-based porous supporting materials.First,a concise rule for the fabrication of CPCMs is illustrated in detail.Next,the experimental and computational research of carbon nanotube-based support,graphene-based support,graphite-based support and amorphous carbon-based support are reviewed.Then,the applications of the shape-stabilized CPCMs including thermal management and thermal conversion are illustrated.Last but not least,the challenges and prospects of the CPCMs are discussed.To conclude,introducing carbon-based porous materials can solve the liquid leakage issue and essentially improve the thermal conductivity of PCMs.However,there is still a long way to further develop a desirable CPCM with higher latent heat capacity,higher thermal conductivity,and more excellent shape stability.
基金funded by the National Natural Science Foundation of China (No. 52304133)the National Key R&D Program of China (No. 2022YFC3004605)the Department of Science and Technology of Liaoning Province (No. 2023-BS-083)。
文摘Rockbursts, which mainly affect mining roadways, are dynamic disasters arising from the surrounding rock under high stress. Understanding the interaction between supports and the surrounding rock is necessary for effective rockburst control. In this study, the squeezing behavior of the surrounding rock is analyzed in rockburst roadways, and a mechanical model of rockbursts is established considering the dynamic support stress, thus deriving formulas and providing characteristic curves for describing the interaction between the support and surrounding rock. Design principles and parameters of supports for rockburst control are proposed. The results show that only when the geostress magnitude exceeds a critical value can it drive the formation of rockburst conditions. The main factors influencing the convergence response and rockburst occurrence around roadways are geostress, rock brittleness, uniaxial compressive strength, and roadway excavation size. Roadway support devices can play a role in controlling rockburst by suppressing the squeezing evolution of the surrounding rock towards instability points of rockburst. Further, the higher the strength and the longer the impact stroke of support devices with constant resistance, the more easily multiple balance points can be formed with the surrounding rock to control rockburst occurrence. Supports with long impact stroke allow adaptation to varying geostress levels around the roadway, aiding in rockburst control. The results offer a quantitative method for designing support systems for rockburst-prone roadways. The design criterion of supports is determined by the intersection between the convergence curve of the surrounding rock and the squeezing deformation curve of the support devices.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
基金The National Natural Science Foundation of China(No.52338011).
文摘To investigate the wind⁃induced vibration re⁃sponse characteristics of multispan double⁃layer cable photo⁃voltaic(PV)support structures,wind tunnel tests using an aeroelastic model were carried out to obtain the wind⁃induced vibration response data of a three⁃span four⁃row double⁃layer cable PV support system.The wind⁃induced vibration characteristics with different PV module tilt angles,wind speeds,and wind direction angles were analyzed.The results showed that the double⁃layer cable large⁃span flexible PV support can effectively control the wind⁃induced vibration response and prevent the occur⁃rence of flutter under strong wind conditions.The maxi⁃mum value of the wind⁃induced vibration displacement of the flexible PV support system occurs in the windward first row.The upstream module has a significant shading effect on the downstream module,with a maximum effect of 23%.The most unfavorable wind direction angles of the structure are 0°and 180°.The change of the wind direction angle in the range of 0°to 30°has little effect on the wind vi⁃bration response.The change in the tilt angle of the PV modules has a greater impact on the wind vibration in the downwind direction and a smaller impact in the upwind di⁃rection.Special attention should be paid to the structural wind⁃resistant design of such systems in the upwind side span.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
文摘BACKGROUND Kidney and liver transplantation are two sub-specialized medical disciplines,with transplant professionals spending decades in training.While artificial intelligencebased(AI-based)tools could potentially assist in everyday clinical practice,comparative assessment of their effectiveness in clinical decision-making remains limited.AIM To compare the use of ChatGPT and GPT-4 as potential tools in AI-assisted clinical practice in these challenging disciplines.METHODS In total,400 different questions tested ChatGPT’s/GPT-4 knowledge and decision-making capacity in various renal and liver transplantation concepts.Specifically,294 multiple-choice questions were derived from open-access sources,63 questions were derived from published open-access case reports,and 43 from unpublished cases of patients treated at our department.The evaluation covered a plethora of topics,including clinical predictors,treatment options,and diagnostic criteria,among others.RESULTS ChatGPT correctly answered 50.3%of the 294 multiple-choice questions,while GPT-4 demonstrated a higher performance,answering 70.7%of questions(P<0.001).Regarding the 63 questions from published cases,ChatGPT achieved an agreement rate of 50.79%and partial agreement of 17.46%,while GPT-4 demonstrated an agreement rate of 80.95%and partial agreement of 9.52%(P=0.01).Regarding the 43 questions from unpublished cases,ChatGPT demonstrated an agreement rate of 53.49%and partial agreement of 23.26%,while GPT-4 demonstrated an agreement rate of 72.09%and partial agreement of 6.98%(P=0.004).When factoring by the nature of the task for all cases,notably,GPT-4 demonstrated outstanding performance,providing a differential diagnosis that included the final diagnosis in 90%of the cases(P=0.008),and successfully predicting the prognosis of the patient in 100%of related questions(P<0.001).CONCLUSION GPT-4 consistently provided more accurate and reliable clinical recommendations with higher percentages of full agreements both in renal and liver transplantation compared with ChatGPT.Our findings support the potential utility of AI models like ChatGPT and GPT-4 in AI-assisted clinical practice as sources of accurate,individualized medical information and facilitating decision-making.The progression and refinement of such AI-based tools could reshape the future of clinical practice,making their early adoption and adaptation by physicians a necessity.
基金supported by the National Natural Science Foundation of China(NSFC)(No.21978137).
文摘In this study,supported Pd catalysts were prepared and used as heterogeneous catalysts for the activation of peroxymonosulfate(PMS)which successfully degrade bisphenol F(BPF).Among the supported catalysts(i.e.,Pd/SiO_(2),Pd/CeO_(2),Pd/TiO_(2)and Pd/Al2O3),Pd/TiO_(2)exhibited the highest catalytic activity due to the high isoelectric point and high Pd0 content.Pd/TiO_(2)prepared by the deposition method leads to high Pd dispersion,which are the key factors for efficient BPF degradation.The influencing factors were investigated during the reaction process and two possible degradation pathways were proposed.Density functional theory(DFT)calculations demonstrate that stronger BPF adsorption and BPF degradation with lower reaction barrier occurs on smaller Pd particles.The catalytic activities are strongly dependent on the structural features of the catalysts.Both experiments and theoretical calculations prove that the reaction is actuated by electron transfer rather than radicals.
