Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesit...Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesity drug alone or combined with exercise training on body weight,body fat,fat-free mass and cardiorespiratory fitness in obese patients were retrieved from Pubmed and EMBASE up to May 2024.Risk of bias assessment was performed with RoB 2.0,and the GRADE approach assessed the certainty of evidence(CoE)of each main outcome.We included four publications summing up 202 patients.Two publications used orlistat as an anti-obesity drug treatment,while the other two adopted GLP-1 receptor agonist(liraglutide or tirzepatide)as a pharmacotherapy for weight management.Orlistat combined with exercise was superior to change body weight(mean difference(MD):−2.27 kg;95%CI:−2.86 to−1.69;CoE:very low),fat mass(MD:−2.89;95%CI:−3.87 to−1.91;CoE:very low),fat-free mass(MD:0.56;95%CI:0.40–0.72;CoE:very low),and VO_(2)Peak(MD:2.64;95%CI:2.52–2.76;CoE:very low).GLP-1 receptor agonist drugs combined with exercise had a great effect on body weight(MD:−3.96 kg;95%CI:−5.07 to−2.85;CoE:low),fat mass(MD:−1.76;95%CI:−2.24 to−1.27;CoE:low),fat-free mass(MD:0.50;95%CI:−0.98 to 1.98;CoE:very low)and VO_(2)Peak(MD:2.47;95%CI:1.31–3.63;CoE:very low).The results reported here suggest that exercise training remains an important approach in weight management when combined with pharmacological treatment.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,...In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.展开更多
Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evalua...Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.展开更多
In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heati...In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.展开更多
Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in...Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians,which has been a major challenge in the global acceptance and application of TCM.Therefore,a standardized TCM prescription system needs to be explored to promote its clinical application.In this study,we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation.We tested its efficacy against intracerebral hemorrhage(ICH).First,the top 100 ICH targets in the GeneCards database were screened according to their relevance scores.Then,SymMap and Traditional Chinese Medicine Systems Pharmacology(TCMSP)databases were applied to find out the target-related ingredients and ingredient-containing herbs,respectively.The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets.The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions.The absorbed components in the serum were detected.In a mouse model of ICH,the new prescription exerted multifaceted effects,including improved neurological function,as well as attenuated neuronal damage,cell apoptosis,vascular leakage,and neuroinflammation.These effects matched well with the core pathological changes in ICH.The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise,multipronged,unbiased,and standardized TCM prescriptions for complex diseases.This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.展开更多
Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/method...Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/methodology/approach–A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards,railway safety rules and regulations and existing safety data from railway transport enterprises is presented.The system comprises a guideline layer including safety committee formation,work safety responsibility,safety management organization and safety rules and regulations as its components,along with an index layer consisting of 12 quantifiable indexes.Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.Findings–The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.Originality/value–Construction of an evaluation index system that is quantifiable,generalizable and accessible,accurately reflects the main aspects of railway transportation enterprises’basic safety management capability and provides interoperability across various railway transportation enterprises.The application of the game theoretic combination weighting method to derive composite weights which combine experts’subjective evaluations with the objectivity of data.展开更多
As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for s...As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms,which are complex and time-consuming.In this paper,we propose a fast effective singlesolution based local search algorithm with an innovative adaptive weighting-based local search(AWLS)technique for solving FJSP.The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration.AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity.Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency,despite of its simplicity.Specifically,AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s.As a strongly non-deterministic polynomia(NP)-hard problem which has been extensively studied for nearly half a century,breaking the records on these classic instances is an arduous task.Nevertheless,AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.展开更多
The effect of prenatal exposure to ambient particulate matter(PM)on birth weight varies considerably across studies,and the findings remain inconclusive.In this study,we conducted a meta-analysis to assess the associa...The effect of prenatal exposure to ambient particulate matter(PM)on birth weight varies considerably across studies,and the findings remain inconclusive.In this study,we conducted a meta-analysis to assess the associations between exposure to PM_(2.5) and PM10 and birth weight.A total of 74 studies were identified through searches in Web of Science,PubMed,Embase,and Ovid Medline,as well as manual searches,up to October 2024.We found that for each 10μg/m^(3) increase in PM_(2.5),the risk of low birth weight(LBW)increased significantly during the entire pregnancy(odds ratio[OR]=2.41,95%confidence interval[CI]:1.99–2.91)and in all trimesters.Similarly,for every 10μg/m^(3) increase in PM10 concentration,the risk of LBW increased significantly during the entire pregnancy(OR=1.46,95%CI:1.16–1.84).Subgroup analysis by maternal age for PM_(2.5) showed that mothers aged 30 and above had a significantly higher risk of LBW(OR=3.69,95%CI:2.81–4.84),compared with those under 30.In conclusion,maternal exposure to PM_(2.5) and PM_(10) is associated with an increased risk of LBW across all trimesters.Additionally,mothers aged 30 and above are at a higher risk of LBW,compared with younger mothers.Further research is needed to clarify the biological mechanisms by which PM pollution may contribute to LBW.展开更多
BACKGROUND Attention-deficit hyperactivity disorder(ADHD)and its pharmacological treatments may influence growth in children and adolescents.This meta-analysis aimed to clarify their effects on the physical developmen...BACKGROUND Attention-deficit hyperactivity disorder(ADHD)and its pharmacological treatments may influence growth in children and adolescents.This meta-analysis aimed to clarify their effects on the physical development,especially weight and height.AIM To investigate the effects of ADHD and its treatment on growth in children and adolescents.METHODS Researchers reviewed 18 studies published up to September 2023 from databases such as PubMed,EMBASE,Cochrane,and Web of Science.They analyzed changes in body weight,height,and body mass index(BMI)before and after ADHD treatment,along with the risks of overweight and obesity.RESULTS Children with ADHD undergoing long-term medication therapy showed decreased actual weight[weighted mean difference(WMD)=-9.50]and height(WMD=-0.15),along with a slight increase in weight standard deviation scores(WMD=0.23)and height z scores(WMD=0.10).BMI showed a non-significant downward trend(WMD=-1.72).Regarding overweight and obesity risks,the pooled odds ratios were 1.37 and 1.16,respectively,but these were not statistically significant.CONCLUSION Overall,the study suggests that long-term pharmacological treatment for ADHD may be associated with reduced growth in weight and height among young patients.However,no clear link was found between ADHD and increased risk of overweight or obesity.These findings highlight the importance of monitoring growth in children receiving medication for ADHD.展开更多
BACKGROUND Type 2 diabetes(T2D)is a major health concern globally and its prevalence is expected to continue to escalate.Lifestyle intervention is an integral part of T2D management.Meal replacements are often used as...BACKGROUND Type 2 diabetes(T2D)is a major health concern globally and its prevalence is expected to continue to escalate.Lifestyle intervention is an integral part of T2D management.Meal replacements are often used as part of lifestyle intervention programs in T2D and weight management programs.There are various trials being carried out to date;however,a thorough review regarding the usage of meal replacement on its types,dosage and associated outcomes and adverse events is still lacking.AIM To provide a comprehensive overview on existing studies regarding meal replacement usage among patients with T2D,and map out glycemic and weightrelated outcomes along with adverse effects incidences.METHODS This scoping review is conducted based on Arksey and O’Malley’s seminal framework for scoping reviews.A systematic search has been done for studies published between January 2020 and January 2024 across six online databases(Cochrane Library,PubMed,Science Direct,Scopus,Web of Science and Ebscohost Discovery)using specific keywords.Two researchers independently assessed the eligibility of the studies and extracted the data.The selected articles and extracted data were reviewed by all researchers.RESULTS The initial search resulted in an initial count of 53922 articles from which 133 articles were included in this review after eligibility screening.Included studies were categorized based on meal replacement type into low calorie/energy,low glycemic index,protein-rich,low-fat,diabetes-specific formulas,and combined lifestyle intervention programs.Fifty-nine studies reported improvements on hemoglobin A1c,and 70 studies reported positive changes in weight or BMI after the meal replacement intervention.The combination of meal replacements with education,counseling or structured lifestyle interventions has proved to be effective.Only 13 studies reported occurrence of adverse events related to the intervention.Most of the reported incidents were of mild occurrences with constipation being the most reported adverse event.CONCLUSION The results suggest that meal replacements,especially when combined with lifestyle intervention programs and counseling,are an effective and safe strategy in glycemic and weight management among patients with T2D.展开更多
Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets.Biometric authentication systems offer a strong solution.However,choosi...Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets.Biometric authentication systems offer a strong solution.However,choosing the best security system requires a structured decision-making framework,especially in complex scenarios involving multiple criteria.To address this problem,we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution(QSF-TOPSIS)methodology,integrating quantum mechanics principles and fuzzy theory.The proposed approach enhances decision-making accuracy,handles uncertainty,and incorporates criteria relationships.Criteria weights are determined using spherical fuzzy sets,and alternatives are ranked through the QSFTOPSIS framework.This comprehensive multi-criteria decision-making(MCDM)approach is applied to identify the optimal gate security system for an organization,considering critical factors such as accuracy,cost,and reliability.Additionally,the study compares the proposed approach with other established MCDM methods.The results confirm the alignment of rankings across these methods,demonstrating the robustness and reliability of the QSF-TOPSIS framework.The study identifies the infrared recognition and identification system(IRIS)as the most effective,with a score value of 0.5280 and optimal security system among the evaluated alternatives.This research contributes to the growing literature on quantum-enhanced decision-making models and offers a practical framework for solving complex,real-world problems involving uncertainty and ambiguity.展开更多
BACKGROUND Beinaglutide,a short-acting glucagon-like polypeptide-1 receptor agonist,has shown variable efficacy in weight reduction and metabolic control in randomized controlled trials(RCTs).AIM To summarize the ther...BACKGROUND Beinaglutide,a short-acting glucagon-like polypeptide-1 receptor agonist,has shown variable efficacy in weight reduction and metabolic control in randomized controlled trials(RCTs).AIM To summarize the therapeutic effects of beinaglutide in patients with overweight/obesity with/without type 2 diabetes.METHODS RCTs involving patients receiving beinaglutide in the intervention arm and placebo or active comparator in the control arm were searched through multiple electronic databases.The change from baseline in body weight was the primary outcome;secondary outcomes included changes in body mass index(BMI),waist circumference(WC),blood pressure,glycemic parameters,lipids,and adverse events(AEs).RevMan web was used to conduct meta-analysis using random-effects models.Outcomes were presented as mean differences(MDs),odds ratios(ORs),or risk ratios(RRs)with 95%confidence intervals(95%CIs).RESULTS Six RCTs(n=800)with mostly some concerns about the risk of bias were included.Over 12-24 weeks,beinaglutide 0.1-0.2 mg thrice daily was superior to the control group in reducing total(MD=-3.25 kg,95%CI:-4.52 to-1.98,I^(2)=84%,P<0.00001)and percent(MD=-4.13%,95%CI:-4.87 to-3.39,I^(2)=54%,P<0.00001)body weight reduction.Beinaglutide also outperformed the control group in achieving weight loss by 5%(OR 4.61)and 10%(OR=5.34).The superiority of beinaglutide vs the control group was also found in reducing BMI(MD=-1.22 kg/m^(2),95%CI:-1.67 to-0.77)and WC(MD=-2.47 cm,95%CI:-3.74 to-1.19]).Beinaglutide and the control group had comparable impacts on blood pressure,glycemic parameters,insulin resistance,hepatic transaminases,and lipid profile.Beinaglutide posed higher risks of treatment discontinuation due to AEs(RR=3.15),nausea(RR=4.51),vomiting(RR=8.19),palpitation(RR=3.95),headache(RR=2.87),and dizziness(RR=6.07)than the control.However,the two groups had identical risks of total and serious AEs,diarrhea,fatigue,and hypoglycemia.CONCLUSION Short-term data from RCTs suggested that beinaglutide causes modest benefits in reducing body weight,BMI,and WC,with no significant difference in glycemic and other metabolic endpoints compared to the control arm.Safety data were consistent with those of the other drugs in the glucagon-like polypeptide-1 receptor agonist class.Larger RCTs are warranted to prove the longer-term metabolic benefits of beinaglutide.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
New energy-storage systems play a pivotal role in the development of the new power system for advancing the energy transition in China.In the“14th Five-Year Plan”for the New Energy-Storage Development,it is proposed...New energy-storage systems play a pivotal role in the development of the new power system for advancing the energy transition in China.In the“14th Five-Year Plan”for the New Energy-Storage Development,it is proposed to expand investment and construction models by promoting the deployment of energy-storage facilities through the ways of self-construction,leasing,and purchasing,and to encourage the development of the shared energy-storage.However,the current scarcity in the model of the shared energy-storage invest-ment and construction substantially restricts its development,particularly due to unclear mechanisms for cost and benefit allocation,which also discourages potential investors.To address the issue,this paper proposes investment and construction models for shared energy-storage that aligns with the present stage of energy storage development.In specific,three main models are introduced:(1)Cen-tralized Self-built Shared Energy-Storage model(CSSES),(2)Third-party Investment Shared Energy-Storage model(TISES),and(3)Distributed Self-built Shared Energy Storage(DSSES)model.The cost–benefit analysis is conducted for each model.The results indicate that the CSSES model achieves the highest internal rate of return(11.5%)and the shortest payback period,while the DSSES model per-forms acceptable with an IRR of 9.4%.In contrast,the TISES model shows the lowest IRR(6.7%)and requires higher electricity price for being feasible.Furthermore,the study employs the entropy weight method and the analytic hierarchy process(AHP)for indicator eval-uation,and integrates the technique for order preference by the similarity to an ideal solution(TOPSIS)for scheme optimization.The results show that both the CSSES model and the DSSES model achieve the highest proximity scores.Under environmental regulations,these models demonstrate superior economic benefits by optimizing energy storage utilization,reducing user costs,and enhancing overall profitability.展开更多
Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This stud...Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This study proposes an innovative solubility prediction approach.It addresses the limitations of traditional thermodynamic models.This is particularly important when experimental data from various sources contain inconsistencies.Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture.This methodology effectively removes local outliers while preserving data distribution integrity,and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases.Experimental validation demonstrates exceptional prediction performance across temperatures from−20℃to 150℃,achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization.This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models,offering theoretical guidance for industrial applications in salt lake resource utilization,separation process optimization,and environmental salt management systems.展开更多
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
BACKGROUND External factors in ulcerative colitis(UC)exacerbate colonic epithelial permea-bility and inflammatory responses.Keratin 1(KRT1)is crucial in regulating these alterations,but its specific role in the progre...BACKGROUND External factors in ulcerative colitis(UC)exacerbate colonic epithelial permea-bility and inflammatory responses.Keratin 1(KRT1)is crucial in regulating these alterations,but its specific role in the progression of UC remains to be fully eluci-dated.AIM To explore the role and mechanisms of KRT1 in the regulation of colonic epithelial permeability and inflammation in UC.METHODS A KRT1 antibody concentration gradient test,along with a dextran sulfate sodium(DSS)-induced animal model,was implemented to investigate the role of KRT1 in modulating the activation of the kallikrein kinin system(KKS)and the cleavage of bradykinin(BK)/high molecular weight kininogen(HK)in UC.RESULTS Treatment with KRT1 antibody in Caco-2 cells suppressed cell proliferation,induced apoptosis,reduced HK expression,and increased BK expression.It further downregulated intestinal barrier proteins,including occludin,zonula occludens-1,and claudin,and negatively impacted the coagulation factor XII.These changes led to enhanced activation of BK and HK cleavage,thereby intensifying KKS-mediated inflammation in UC.In the DSS-induced mouse model,administration of KRT1 antibody mitigated colonic injury,increased colon length,alleviated weight loss,and suppressed inflammatory cytokines such as interleukin(IL)-1,IL-6,tumor necrosis factor-α.It also facilitated repair of the intestinal barrier,reducing DSS-induced injury.CONCLUSION KRT1 inhibits BK expression,suppresses inflammatory cytokines,and enhances markers of intestinal barrier function,thus ameliorating colonic damage and maintaining barrier integrity.KRT1 is a viable therapeutic target for UC.展开更多
The health of cropland systems is directly related to the degree of food security guarantee,and the‘quantity-quality-ecology as a whole’protection is of great significance for maintaining the health of cropland syst...The health of cropland systems is directly related to the degree of food security guarantee,and the‘quantity-quality-ecology as a whole’protection is of great significance for maintaining the health of cropland systems.Taking the typical black soil region in Northeast China(TBSN)as an example,this paper combined the concept of‘quantity-quality-ecology as a whole’protection with crop-land systems health,constructed a health assessment model for cropland systems,and used Google Earth Engine to conduct a quantitat-ive analysis of the temporal and spatial evolution of cropland systems health in TBSN during 2003–2023.By coupling the geographical detector and the Multi-scale Geographically Weighted Regression(MGWR)model,the driving factors of cropland health changes were explored.The study finds that during the research period,the health status of cropland systems in TBSN showed a slight downward trend,and the distribution pattern of cropland systems health gradually shifted from‘better in the east’to‘high in the northeast and low in the southwest’.Changes in average annual sunshine duration,relative humidity,and precipitation had a significant impact on the spa-tial differentiation of cropland systems health in the early stages,and were considered as dominant factors.Meanwhile,the influence of dual dominant factors in the natural environment on cropland systems health is increasing.Furthermore,the MGWR model performed better in revealing the complex relationships between natural and social factors and changes in cropland systems health,demonstrating the significant spatial heterogeneity of the impacts of natural environment and human activities on cropland systems health.The re-search can provide scientific guidance for the sustainable development of TBSN and formulate more precise and effective cropland pro-tection policies.展开更多
6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,faul...6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.展开更多
基金supported by Brazilian agencies CAPES(Finance Code 001)CNPq through PQ productivity scholarship.
文摘Here we compare the efficacy of anti-obesity drugs alone or combined with exercise training on body weight and exercise capacity of obese patients.Randomized clinical trials that assessed the impact of any anti-obesity drug alone or combined with exercise training on body weight,body fat,fat-free mass and cardiorespiratory fitness in obese patients were retrieved from Pubmed and EMBASE up to May 2024.Risk of bias assessment was performed with RoB 2.0,and the GRADE approach assessed the certainty of evidence(CoE)of each main outcome.We included four publications summing up 202 patients.Two publications used orlistat as an anti-obesity drug treatment,while the other two adopted GLP-1 receptor agonist(liraglutide or tirzepatide)as a pharmacotherapy for weight management.Orlistat combined with exercise was superior to change body weight(mean difference(MD):−2.27 kg;95%CI:−2.86 to−1.69;CoE:very low),fat mass(MD:−2.89;95%CI:−3.87 to−1.91;CoE:very low),fat-free mass(MD:0.56;95%CI:0.40–0.72;CoE:very low),and VO_(2)Peak(MD:2.64;95%CI:2.52–2.76;CoE:very low).GLP-1 receptor agonist drugs combined with exercise had a great effect on body weight(MD:−3.96 kg;95%CI:−5.07 to−2.85;CoE:low),fat mass(MD:−1.76;95%CI:−2.24 to−1.27;CoE:low),fat-free mass(MD:0.50;95%CI:−0.98 to 1.98;CoE:very low)and VO_(2)Peak(MD:2.47;95%CI:1.31–3.63;CoE:very low).The results reported here suggest that exercise training remains an important approach in weight management when combined with pharmacological treatment.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金jointly funded by the National Natural Science Foundation of China(No.U2244220,No.42004125)the China Geological Survey Projects(No.DD20240119,No.DD20243245,No.DD20230114,No.DD20243244)the China Postdoctoral Science Foundation(No.2020M670601)。
文摘In the 3D inversion modeling of gravity and magnetic potential field data,the model weighting function is often applied to overcome the skin eff ect of inversion results.However,divergence occurs at the the deep area,and artificial weak negative anomalies form around the positive anomalies in the horizontal direction,resulting in a reduction in the overall resolution.To fully utilize the model weighting function,this study constructs a combined model weighting function.First,a new depth weighting function is constructed by adding a regulator into the conventional depth weighting function to overcome the skin eff ect and inhibit the divergence at the deep area of the inversion results.A horizontal weighting function is then constructed by extracting information from the observation data;this function can suppress the formation of artificial weak anomalies and improve the horizontal resolution of the inversion results.Finally,these two functions are coupled to obtain the combined model weighting function,which can replace the conventional depth weighting function in 3D inversion.It improves the vertical and horizontal resolution of the inversion results without increasing the algorithm complexity and calculation amount,is easy to operate,and adapts to any 3D inversion method.Two model experiments are designed to verify the effectiveness,practicability,and anti-noise of the combined model weighting function.Then the function is applied to the 3D inversion of the measured aeromagnetic data in the Jinchuan area in China.The obtained inversion results are in good agreement with the known geological data.
文摘Growing regulatory demands for industrial safety and environmental protection in the chemical sector necessitate robust operational risk assessment to enhance management efficacy.Here,the HS Chemical Company is evaluated through a multidimensional framework encompassing market dynamics,macroeconomic factors,financial stability,governance,supply chains,and production safety.By integrating the Analytic Hierarchy Process(AHP)with entropy weighting,a hybrid weighting model that mitigates the limitations of singular methods is established.The analysis of this study identifies financial risk(weight:0.347)and production safety(weight:0.298)as dominant risk drivers.These quantitative insights offer a basis for resource prioritization and targeted risk mitigation strategies in chemical enterprises.
基金Supported by the National Natural Science Foundation of China(32072352)。
文摘In order to improve the quality of 3D printed raspberry preserves after post-processing,microwave ovens combining infrared and microwave methods were utilized.The effects of infrared heating temperature,infrared heating time,microwave power,microwave heating time on the center temperature,moisture content,the chroma(C*),the total color difference(ΔE*),shape fidelity,hardness,and the total anthocyanin content of 3D printed raspberry preserves were analyzed by response surface method(RSM).The results showed that under combining with the two methods,infrared heating improved the fidelity and quality degradation of printed products,while microwave heating enhanced the efficiency of infrared heating.Infrared-microwave combination cooking could maintain relatively stable color appearance and shape of 3D printed raspberry preserves.The AHP–CRITIC hybrid weighting method combined with the response surface test to determine the comprehensive weights of the evaluation indicators optimized the process parameters,and the optimal process parameters were obtained:infrared heating temperature of 190℃,infrared heating time of 10 min and 30 s,microwave power of 300 W,and microwave heating time of 2 min and 6 s.The 3D printed raspberry cooking methods obtained under the optimal conditions seldom had color variation,porous structure,uniform texture,and high shape fidelity,which retained the characteristics of personalized manufacturing by 3D printing.This study could provide a reference for the postprocessing and quality control of 3D cooking methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.:82174259 and 82304997)China Postdoctoral Followship Program of CPSF(Grant No.:GZC20233202)+4 种基金China Postdoctoral Science Foundation(Grant No.:2024M753698)the Key Research and Development Program of Hunan Province of China(Grant Nos.:2023SK2021 and 2022SK2015)the Natural Science Foundation of Hunan Province,China(Grant Nos.:2024JJ6632,2022JJ40853,and 2021JJ31117)the Hunan Traditional Chinese Medicine Scientific Research Program,China(Grant Nos.:B2024113,B2024114,and 2021032)the Fundamental Research Funds for the Central Universities of Central South University,China(Grant No.:1053320232786).
文摘Traditional Chinese medicine(TCM)exerts integrative effects on complex diseases owing to the characteristics of multiple components with multiple targets.However,the syndrome-based system of diagnosis and treatment in TCM can easily lead to bias because of varying medication preferences among physicians,which has been a major challenge in the global acceptance and application of TCM.Therefore,a standardized TCM prescription system needs to be explored to promote its clinical application.In this study,we first developed a gradient weighted disease-target-herbal ingredient-herb network to aid TCM formulation.We tested its efficacy against intracerebral hemorrhage(ICH).First,the top 100 ICH targets in the GeneCards database were screened according to their relevance scores.Then,SymMap and Traditional Chinese Medicine Systems Pharmacology(TCMSP)databases were applied to find out the target-related ingredients and ingredient-containing herbs,respectively.The relevance of the resulting ingredients and herbs to ICH was determined by adding the relevance scores of the corresponding targets.The top five ICH therapeutic herbs were combined to form a tailored TCM prescriptions.The absorbed components in the serum were detected.In a mouse model of ICH,the new prescription exerted multifaceted effects,including improved neurological function,as well as attenuated neuronal damage,cell apoptosis,vascular leakage,and neuroinflammation.These effects matched well with the core pathological changes in ICH.The multi-targets-directed gradient-weighting strategy presents a promising avenue for tailoring precise,multipronged,unbiased,and standardized TCM prescriptions for complex diseases.This study provides a paradigm for advanced achievements-driven modern innovation in TCM concepts.
基金supported by the China National Railway Group Co.,Ltd.Research and Development Project(P2023T002).
文摘Purpose–To systematically characterize and objectively evaluate basic railway safety management capability,creating a closed-loop management approach which allows continuous improvement and optimization.Design/methodology/approach–A basic railway safety management capability evaluation index system based on a comprehensive analysis of national safety management standards,railway safety rules and regulations and existing safety data from railway transport enterprises is presented.The system comprises a guideline layer including safety committee formation,work safety responsibility,safety management organization and safety rules and regulations as its components,along with an index layer consisting of 12 quantifiable indexes.Game theory combination weighting is utilized to integrate subjective and objective weight values derived using AHP and CRITIC methods and further combined using the TOPSIS method in order to construct a comprehensive basic railway safety management capability evaluation model.Findings–The case study presented demonstrates that this evaluation index system and comprehensive evaluation model are capable of effectively characterizing and evaluating basic railway safety management capability and providing directional guidance for its sustained improvement.Originality/value–Construction of an evaluation index system that is quantifiable,generalizable and accessible,accurately reflects the main aspects of railway transportation enterprises’basic safety management capability and provides interoperability across various railway transportation enterprises.The application of the game theoretic combination weighting method to derive composite weights which combine experts’subjective evaluations with the objectivity of data.
基金supported in part by the National Natural Science Foundation of China(NSFC)(62202192 and 72101094)the National Science Fund for Distinguished Young Scholars of China(51825502).
文摘As one of the most classical scheduling problems,flexible job shop scheduling problems(FJSP)find widespread applications in modern intelligent manufacturing systems.However,the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms,which are complex and time-consuming.In this paper,we propose a fast effective singlesolution based local search algorithm with an innovative adaptive weighting-based local search(AWLS)technique for solving FJSP.The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration.AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity.Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency,despite of its simplicity.Specifically,AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s.As a strongly non-deterministic polynomia(NP)-hard problem which has been extensively studied for nearly half a century,breaking the records on these classic instances is an arduous task.Nevertheless,AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.
文摘The effect of prenatal exposure to ambient particulate matter(PM)on birth weight varies considerably across studies,and the findings remain inconclusive.In this study,we conducted a meta-analysis to assess the associations between exposure to PM_(2.5) and PM10 and birth weight.A total of 74 studies were identified through searches in Web of Science,PubMed,Embase,and Ovid Medline,as well as manual searches,up to October 2024.We found that for each 10μg/m^(3) increase in PM_(2.5),the risk of low birth weight(LBW)increased significantly during the entire pregnancy(odds ratio[OR]=2.41,95%confidence interval[CI]:1.99–2.91)and in all trimesters.Similarly,for every 10μg/m^(3) increase in PM10 concentration,the risk of LBW increased significantly during the entire pregnancy(OR=1.46,95%CI:1.16–1.84).Subgroup analysis by maternal age for PM_(2.5) showed that mothers aged 30 and above had a significantly higher risk of LBW(OR=3.69,95%CI:2.81–4.84),compared with those under 30.In conclusion,maternal exposure to PM_(2.5) and PM_(10) is associated with an increased risk of LBW across all trimesters.Additionally,mothers aged 30 and above are at a higher risk of LBW,compared with younger mothers.Further research is needed to clarify the biological mechanisms by which PM pollution may contribute to LBW.
基金Supported by First-class Undergraduate Course Construction Project of Henan Province(Online and Offline Hybrid Course),No.[2021]21548and 2021 Pingdingshan Smart Nursing Key Laboratory.
文摘BACKGROUND Attention-deficit hyperactivity disorder(ADHD)and its pharmacological treatments may influence growth in children and adolescents.This meta-analysis aimed to clarify their effects on the physical development,especially weight and height.AIM To investigate the effects of ADHD and its treatment on growth in children and adolescents.METHODS Researchers reviewed 18 studies published up to September 2023 from databases such as PubMed,EMBASE,Cochrane,and Web of Science.They analyzed changes in body weight,height,and body mass index(BMI)before and after ADHD treatment,along with the risks of overweight and obesity.RESULTS Children with ADHD undergoing long-term medication therapy showed decreased actual weight[weighted mean difference(WMD)=-9.50]and height(WMD=-0.15),along with a slight increase in weight standard deviation scores(WMD=0.23)and height z scores(WMD=0.10).BMI showed a non-significant downward trend(WMD=-1.72).Regarding overweight and obesity risks,the pooled odds ratios were 1.37 and 1.16,respectively,but these were not statistically significant.CONCLUSION Overall,the study suggests that long-term pharmacological treatment for ADHD may be associated with reduced growth in weight and height among young patients.However,no clear link was found between ADHD and increased risk of overweight or obesity.These findings highlight the importance of monitoring growth in children receiving medication for ADHD.
文摘BACKGROUND Type 2 diabetes(T2D)is a major health concern globally and its prevalence is expected to continue to escalate.Lifestyle intervention is an integral part of T2D management.Meal replacements are often used as part of lifestyle intervention programs in T2D and weight management programs.There are various trials being carried out to date;however,a thorough review regarding the usage of meal replacement on its types,dosage and associated outcomes and adverse events is still lacking.AIM To provide a comprehensive overview on existing studies regarding meal replacement usage among patients with T2D,and map out glycemic and weightrelated outcomes along with adverse effects incidences.METHODS This scoping review is conducted based on Arksey and O’Malley’s seminal framework for scoping reviews.A systematic search has been done for studies published between January 2020 and January 2024 across six online databases(Cochrane Library,PubMed,Science Direct,Scopus,Web of Science and Ebscohost Discovery)using specific keywords.Two researchers independently assessed the eligibility of the studies and extracted the data.The selected articles and extracted data were reviewed by all researchers.RESULTS The initial search resulted in an initial count of 53922 articles from which 133 articles were included in this review after eligibility screening.Included studies were categorized based on meal replacement type into low calorie/energy,low glycemic index,protein-rich,low-fat,diabetes-specific formulas,and combined lifestyle intervention programs.Fifty-nine studies reported improvements on hemoglobin A1c,and 70 studies reported positive changes in weight or BMI after the meal replacement intervention.The combination of meal replacements with education,counseling or structured lifestyle interventions has proved to be effective.Only 13 studies reported occurrence of adverse events related to the intervention.Most of the reported incidents were of mild occurrences with constipation being the most reported adverse event.CONCLUSION The results suggest that meal replacements,especially when combined with lifestyle intervention programs and counseling,are an effective and safe strategy in glycemic and weight management among patients with T2D.
文摘Global security threats have motivated organizations to adopt robust and reliable security systems to ensure the safety of individuals and assets.Biometric authentication systems offer a strong solution.However,choosing the best security system requires a structured decision-making framework,especially in complex scenarios involving multiple criteria.To address this problem,we develop a novel quantum spherical fuzzy technique for order preference by similarity to ideal solution(QSF-TOPSIS)methodology,integrating quantum mechanics principles and fuzzy theory.The proposed approach enhances decision-making accuracy,handles uncertainty,and incorporates criteria relationships.Criteria weights are determined using spherical fuzzy sets,and alternatives are ranked through the QSFTOPSIS framework.This comprehensive multi-criteria decision-making(MCDM)approach is applied to identify the optimal gate security system for an organization,considering critical factors such as accuracy,cost,and reliability.Additionally,the study compares the proposed approach with other established MCDM methods.The results confirm the alignment of rankings across these methods,demonstrating the robustness and reliability of the QSF-TOPSIS framework.The study identifies the infrared recognition and identification system(IRIS)as the most effective,with a score value of 0.5280 and optimal security system among the evaluated alternatives.This research contributes to the growing literature on quantum-enhanced decision-making models and offers a practical framework for solving complex,real-world problems involving uncertainty and ambiguity.
文摘BACKGROUND Beinaglutide,a short-acting glucagon-like polypeptide-1 receptor agonist,has shown variable efficacy in weight reduction and metabolic control in randomized controlled trials(RCTs).AIM To summarize the therapeutic effects of beinaglutide in patients with overweight/obesity with/without type 2 diabetes.METHODS RCTs involving patients receiving beinaglutide in the intervention arm and placebo or active comparator in the control arm were searched through multiple electronic databases.The change from baseline in body weight was the primary outcome;secondary outcomes included changes in body mass index(BMI),waist circumference(WC),blood pressure,glycemic parameters,lipids,and adverse events(AEs).RevMan web was used to conduct meta-analysis using random-effects models.Outcomes were presented as mean differences(MDs),odds ratios(ORs),or risk ratios(RRs)with 95%confidence intervals(95%CIs).RESULTS Six RCTs(n=800)with mostly some concerns about the risk of bias were included.Over 12-24 weeks,beinaglutide 0.1-0.2 mg thrice daily was superior to the control group in reducing total(MD=-3.25 kg,95%CI:-4.52 to-1.98,I^(2)=84%,P<0.00001)and percent(MD=-4.13%,95%CI:-4.87 to-3.39,I^(2)=54%,P<0.00001)body weight reduction.Beinaglutide also outperformed the control group in achieving weight loss by 5%(OR 4.61)and 10%(OR=5.34).The superiority of beinaglutide vs the control group was also found in reducing BMI(MD=-1.22 kg/m^(2),95%CI:-1.67 to-0.77)and WC(MD=-2.47 cm,95%CI:-3.74 to-1.19]).Beinaglutide and the control group had comparable impacts on blood pressure,glycemic parameters,insulin resistance,hepatic transaminases,and lipid profile.Beinaglutide posed higher risks of treatment discontinuation due to AEs(RR=3.15),nausea(RR=4.51),vomiting(RR=8.19),palpitation(RR=3.95),headache(RR=2.87),and dizziness(RR=6.07)than the control.However,the two groups had identical risks of total and serious AEs,diarrhea,fatigue,and hypoglycemia.CONCLUSION Short-term data from RCTs suggested that beinaglutide causes modest benefits in reducing body weight,BMI,and WC,with no significant difference in glycemic and other metabolic endpoints compared to the control arm.Safety data were consistent with those of the other drugs in the glucagon-like polypeptide-1 receptor agonist class.Larger RCTs are warranted to prove the longer-term metabolic benefits of beinaglutide.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
基金supported by the Humanities and Social Sciences of Ministry of Education Planning Fund of China(Grant No.21YJA790009)the National Natural Science Foundation of China(Grant No.72140001).
文摘New energy-storage systems play a pivotal role in the development of the new power system for advancing the energy transition in China.In the“14th Five-Year Plan”for the New Energy-Storage Development,it is proposed to expand investment and construction models by promoting the deployment of energy-storage facilities through the ways of self-construction,leasing,and purchasing,and to encourage the development of the shared energy-storage.However,the current scarcity in the model of the shared energy-storage invest-ment and construction substantially restricts its development,particularly due to unclear mechanisms for cost and benefit allocation,which also discourages potential investors.To address the issue,this paper proposes investment and construction models for shared energy-storage that aligns with the present stage of energy storage development.In specific,three main models are introduced:(1)Cen-tralized Self-built Shared Energy-Storage model(CSSES),(2)Third-party Investment Shared Energy-Storage model(TISES),and(3)Distributed Self-built Shared Energy Storage(DSSES)model.The cost–benefit analysis is conducted for each model.The results indicate that the CSSES model achieves the highest internal rate of return(11.5%)and the shortest payback period,while the DSSES model per-forms acceptable with an IRR of 9.4%.In contrast,the TISES model shows the lowest IRR(6.7%)and requires higher electricity price for being feasible.Furthermore,the study employs the entropy weight method and the analytic hierarchy process(AHP)for indicator eval-uation,and integrates the technique for order preference by the similarity to an ideal solution(TOPSIS)for scheme optimization.The results show that both the CSSES model and the DSSES model achieve the highest proximity scores.Under environmental regulations,these models demonstrate superior economic benefits by optimizing energy storage utilization,reducing user costs,and enhancing overall profitability.
基金support of the Natural Science Foundation of Qinghai Province of China(2024-ZJ-940)Qinghai University Research Ability Enhancement Project(2025KTST02)are greatly appreciated.
文摘Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential.It provides theoretical support for salt lake resource development and wastewater treatment technologies.This study proposes an innovative solubility prediction approach.It addresses the limitations of traditional thermodynamic models.This is particularly important when experimental data from various sources contain inconsistencies.Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture.This methodology effectively removes local outliers while preserving data distribution integrity,and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases.Experimental validation demonstrates exceptional prediction performance across temperatures from−20℃to 150℃,achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization.This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models,offering theoretical guidance for industrial applications in salt lake resource utilization,separation process optimization,and environmental salt management systems.
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
基金Supported by the National Natural Science Foundation of China,No.82160113the“Xingdian Talents”Support Project of Yunnan Province,No.RLMY20220007+1 种基金the Yunnan Province Clinical Research Center for Digestive Diseases,No.202102AA100062the Applied Basic Research Projects of Yunnan Province,No.2019FE001-039.
文摘BACKGROUND External factors in ulcerative colitis(UC)exacerbate colonic epithelial permea-bility and inflammatory responses.Keratin 1(KRT1)is crucial in regulating these alterations,but its specific role in the progression of UC remains to be fully eluci-dated.AIM To explore the role and mechanisms of KRT1 in the regulation of colonic epithelial permeability and inflammation in UC.METHODS A KRT1 antibody concentration gradient test,along with a dextran sulfate sodium(DSS)-induced animal model,was implemented to investigate the role of KRT1 in modulating the activation of the kallikrein kinin system(KKS)and the cleavage of bradykinin(BK)/high molecular weight kininogen(HK)in UC.RESULTS Treatment with KRT1 antibody in Caco-2 cells suppressed cell proliferation,induced apoptosis,reduced HK expression,and increased BK expression.It further downregulated intestinal barrier proteins,including occludin,zonula occludens-1,and claudin,and negatively impacted the coagulation factor XII.These changes led to enhanced activation of BK and HK cleavage,thereby intensifying KKS-mediated inflammation in UC.In the DSS-induced mouse model,administration of KRT1 antibody mitigated colonic injury,increased colon length,alleviated weight loss,and suppressed inflammatory cytokines such as interleukin(IL)-1,IL-6,tumor necrosis factor-α.It also facilitated repair of the intestinal barrier,reducing DSS-induced injury.CONCLUSION KRT1 inhibits BK expression,suppresses inflammatory cytokines,and enhances markers of intestinal barrier function,thus ameliorating colonic damage and maintaining barrier integrity.KRT1 is a viable therapeutic target for UC.
基金Under the auspices of National Natural Science Foundation Youth Fund Project(No.41701424)Open Research Fund of State Key Laboratory of Remote Sensing Science(No.OFSLRSS201716)+1 种基金Jilin Province Science and Technology Development Plan Project(No.20240701167FG)Science and Technology Research Project of Education Department of Jilin Province(No.JJKH20230502KJ)。
文摘The health of cropland systems is directly related to the degree of food security guarantee,and the‘quantity-quality-ecology as a whole’protection is of great significance for maintaining the health of cropland systems.Taking the typical black soil region in Northeast China(TBSN)as an example,this paper combined the concept of‘quantity-quality-ecology as a whole’protection with crop-land systems health,constructed a health assessment model for cropland systems,and used Google Earth Engine to conduct a quantitat-ive analysis of the temporal and spatial evolution of cropland systems health in TBSN during 2003–2023.By coupling the geographical detector and the Multi-scale Geographically Weighted Regression(MGWR)model,the driving factors of cropland health changes were explored.The study finds that during the research period,the health status of cropland systems in TBSN showed a slight downward trend,and the distribution pattern of cropland systems health gradually shifted from‘better in the east’to‘high in the northeast and low in the southwest’.Changes in average annual sunshine duration,relative humidity,and precipitation had a significant impact on the spa-tial differentiation of cropland systems health in the early stages,and were considered as dominant factors.Meanwhile,the influence of dual dominant factors in the natural environment on cropland systems health is increasing.Furthermore,the MGWR model performed better in revealing the complex relationships between natural and social factors and changes in cropland systems health,demonstrating the significant spatial heterogeneity of the impacts of natural environment and human activities on cropland systems health.The re-search can provide scientific guidance for the sustainable development of TBSN and formulate more precise and effective cropland pro-tection policies.
基金supported in part by the National Key Research and Development Project under Grant 2020YFB1806805partially funded through a grant from Qualcomm。
文摘6G is desired to support more intelligence networks and this trend attaches importance to the self-healing capability if degradation emerges in the cellular networks.As a primary component of selfhealing networks,fault detection is investigated in this paper.Considering the fast response and low timeand-computational consumption,it is the first time that the Online Broad Learning System(OBLS)is applied to identify outages in cellular networks.In addition,the Automatic-constructed Online Broad Learning System(AOBLS)is put forward to rationalize its structure and consequently avoid over-fitting and under-fitting.Furthermore,a multi-layer classification structure is proposed to further improve the classification performance.To face the challenges caused by imbalanced data in fault detection problems,a novel weighting strategy is derived to achieve the Multilayer Automatic-constructed Weighted Online Broad Learning System(MAWOBLS)and ensemble learning with retrained Support Vector Machine(SVM),denoted as EMAWOBLS,for superior treatment with this imbalance issue.Simulation results show that the proposed algorithm has excellent performance in detecting faults with satisfactory time usage.
