Curve Classification Based onMean-Variance Feature Weighting and Its Application

The classification of functional data has drawn much attention in recent years.The main challenge is representing infinite-dimensional functional data by finite-dimensional features while utilizing those features to achieve better classification accuracy.In this paper,we propose a mean-variance-based(MV)feature weighting method for classifying functional data or functional curves.In the feature extraction stage,each sample curve is approximated by B-splines to transfer features to the coefficients of the spline basis.After that,a feature weighting approach based on statistical principles is introduced by comprehensively considering the between-class differences and within-class variations of the coefficients.We also introduce a scaling parameter to adjust the gap between the weights of features.The new feature weighting approach can adaptively enhance noteworthy local features while mitigating the impact of confusing features.The algorithms for feature weighted K-nearest neighbor and support vector machine classifiers are both provided.Moreover,the new approach can be well integrated into existing functional data classifiers,such as the generalized functional linear model and functional linear discriminant analysis,resulting in a more accurate classification.The performance of the mean-variance-based classifiers is evaluated by simulation studies and real data.The results show that the newfeatureweighting approach significantly improves the classification accuracy for complex functional data.

Comparative Efficacy of Lifestyle Modifications versus Pharmacotherapy on Weight Loss and Metabolic Health Outcomes: A Comprehensive Review

Background: Obesity has become a serious global public health challenge, given that it leads to various adverse health outcomes that include cardiovascular illnesses, diabetes, and certain types of cancer. The World Health Organization (WHO) has estimated that, at the end of 2022, 1 out of every 8 individuals were obese, and that the global adult obesity rates have over doubled since 1990, even as the adolescent obesity rates have quadrupled. Thus, as of 2022, nearly 2.5 billion adults, aged 18 years and above, were overweight, with 890 million being obese. Obesity and overweight incidence rate has been gradually increasing over the years, presenting significant challenges to the healthcare systems throughout the globe. In this regard, the objective of this systematic review was to evaluate the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. Methodology: To attain the above stated study objective, a systematic evaluation of previous studies was carried out, particularly studies that assessed the effectiveness and safety of lifestyle modifications (diet and physical activity) and pharmacotherapy in promoting weight loss and improving metabolic health in overweight adults. The authors have used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in the selection of eligible studies for inclusion in the study. Results: The findings indicate that lifestyle interventions resulted in 5% - 10% weight reduction and significant improvements in metabolic indicators, while pharmacotherapy (GLP-1 receptor agonists) achieved up to 15% weight reduction and considerable metabolic health benefits. Further, comparative studies show lifestyle modifications provide overall health benefits, while medication is necessary for non-responders. Conclusion: Individualized treatment strategies are crucial, and further research is needed on long-term consequences and combination therapies.

Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

Boosting Adaptive Weighted Broad Learning System for Multi-Label Learning

Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To address the above issues, we propose the multi-label weighted broad learning system(MLW-BLS) from the perspective of label imbalance weighting and label correlation mining. Further, we propose the multi-label adaptive weighted broad learning system(MLAW-BLS) to adaptively adjust the specific weights and values of labels of MLW-BLS and construct an efficient imbalanced classifier set. Extensive experiments are conducted on various datasets to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority over other advanced approaches.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Adjuvant chemotherapy for isolated resectable colorectal lung metastasis: A retrospective study using inverse probability treatment weighting propensity analysis

BACKGROUND The benefit of adjuvant chemotherapy(ACT)for patients with no evidence of disease after pulmonary metastasis resection(PM)from colorectal cancer(CRC)remains controversial.AIM To assess the efficacy of ACT in patients after PM resection for CRC.METHODS This study included 96 patients who underwent pulmonary metastasectomy for CRC at a single institution between April 2008 and July 2023.The primary end-point was overall survival(OS);secondary endpoints included cancer-specific survival(CSS)and disease-free survival(DFS).An inverse probability of treat-ment-weighting(IPTW)analysis was conducted to address indication bias.Sur-vival outcomes compared using Kaplan-Meier curves,log-rank test,Cox regre-ssion and confirmed by propensity score-matching(PSM).RESULTS With a median follow-up of 27.5 months(range,18.3-50.4 months),the 5-year OS,CSS and DFS were 72.0%,74.4%and 51.3%,respectively.ACT had no significant effect on OS after PM resection from CRC[original cohort:P=0.08;IPTW:P=0.15].No differences were observed for CSS(P=0.12)and DFS(P=0.68)between the ACT and non-ACT groups.Multivariate analysis showed no association of ACT with better survival,while sublobar resection(HR=0.45;95%CI:0.20-1.00,P=0.049)and longer disease-free interval(HR=0.45;95%CI:0.20-0.98,P=0.044)were associated with improved survival.CONCLUSION ACT does not improve survival after PM resection for CRC.Further well-designed randomized controlled trials are needed to determine the optimal ACT regimen and duration.

Atypical Weight Loss in Two Patients with Schizophrenia Treated with Clozapine: A Case Report

Clozapine is widely recognized as an effective antipsychotic medication for treatment-resistant schizophrenia, but it is typically associated with significant weight gain. This case report presents two unusual cases of patients with schizophrenia who experienced substantial weight loss while on long-term clozapine therapy. The first case involves a 35-year-old male who lost 21.3% of his initial body weight, and the second case describes a 54-year-old female who lost 30.2% of her initial weight, despite having comorbid hypothyroidism. Both patients showed improvement in psychiatric symptoms concurrent with the weight loss. Comprehensive investigations did not reveal other clear etiologies for the weight reduction. These cases challenge the conventional understanding of clozapine’s metabolic effects and highlight the potential for atypical responses in some individuals. The report discusses possible mechanisms for this unusual phenomenon, including genetic factors and altered pharmacokinetics. It also emphasizes the need for individualized monitoring and management strategies in clozapine therapy. These findings contribute to the growing body of evidence suggesting that metabolic responses to clozapine may be more complex and varied than previously thought, underscoring the importance of personalized approaches in schizophrenia treatment.

Sociodemographic determinants associated with breastfeeding in term infants with low birth weight in Latin American countries

BACKGROUND A progressive decrease in exclusive breastfeeding(BF)is observed in Latin America and the Caribbean compared with global results.The possibility of being breastfed and continuing BF for>6 months is lower in low birth weight than in healthy-weight infants.AIM To identify factors associated with BF maintenance and promotion,with particular attention to low-and middle-income countries,by studying geographic,socioeconomic,and individual or neonatal health factors.METHODS A scoping review was conducted in 2018 using the conceptual model of social determinants of health published by the Commission on Equity and Health Inequalities in the United States.The extracted data with common characteristics were synthesized and categorized into two main themes:(1)Sociodemographic factors and proximal determinants involved in the initiation and maintenance of BF in low-birth-weight term infants in Latin America;and(2)individual characteristics related to the self-efficacy capacity for BF maintenance and adherence in low-birth-weight term infants.RESULTS This study identified maternal age,educational level,maternal economic capacity,social stratum,exposure to BF substitutes,access to BF information,and quality of health services as mediators for maintaining BF.CONCLUSION Individual self-efficacy factors that enable BF adherence in at-risk populations should be analyzed for better health outcomes.

Several Classes of Two-weight or Three-weight Linear Codes and Their Applications

Recently,linear codes with a few weights have been extensively studied due to their applications in secret sharing schemes,constant composition codes,strongly regular graphs and so on.In this paper,based on the Weil sums,several classes of two-weight or three-weight linear codes are presented by choosing a proper defining set,and their weight enumerators and complete weight enumerators are determined.Furthermore,these codes are proven to be minimal.By puncturing these linear codes,two classes of two-weight projective codes are obtained,and the parameters of the corresponding strongly regular graph are given.This paper generalizes the results of[7].

Analysis of the COVID-19, Outbreak in Brazil Using Topological Weighted Centroid: An Intelligent Geographic Information System Approach

This study used Topological Weighted Centroid (TWC) to analyze the Coronavirus outbreak in Brazil. This analysis only uses latitude and longitude in formation of the capitals with the confirmed cases on May 24, 2020 to illustrate the usefulness of TWC though any date could have been used. There are three types of TWC analyses, each type having five associated algorithms that produce fifteen maps, TWC-Original, TWC-Frequency and TWC-Windowing. We focus on TWC-Original to illustrate our approach. The TWC method without using the transportation information predicts the network for COVID-19 outbreak that matches very well with the main radial transportation routes network in Brazil.

A novel interference suppression scheme applying complex weighted matrix interleaving mapping for UFMC systems

In response to the problem of inter-carrier interference(ICI)and inter-subband interference(IBI)in the received signals of universal filtered multi-carrier(UFMC)systems,a novel interfer-ence suppression design scheme applying the method of complex weighted matrix inter-leaving map-ping(CWMIM)is proposed on the basis of the existing suppression scheme of conjugate weighted butterfly interleaving mapping(CWBIM).The proposed scheme performs matrix interleaving map-ping on the transmitted signal,which not only improves the carrier interference ratio(CIR)of the received signal by combining the original IBI and ICI terms,but also further inhibits the probability of burst error in the received signal.Meanwhile,the scheme can further decrease the impact of phase rotation errors in the received signal by increasing the number of rotation factors.Theoretical analysis and simulation results demonstrate that compared with CWBIM-UFMC,the proposed CWMIM-UFMC can obtain more effective ICI and IBI suppression and better system bit error rate(BER)performance with only a little bit increase in computational complexity.

Relationship of sociodemographic factors and low birth weight in toddlers

Objective:This study analyzes the relationship between sociodemographic factors and low birth weight(LBW)in toddlers.Methods:The research design uses a correlational method.The population is 303 mothers with toddlers aged 12-60 months in Lojejer Wuluhan Jember Village,East Java,Indonesia.The cluster sampling took 172 samples in total.The sociodemographic variables measured included the father’s and the mother’s age,the father’s and mother’s education,family income,the father’s occupation,the mother’s occupation,and the child’s gender.Data collection techniques used questionnaires and document studies in the Maternal Child Health(MCH)handbook.Data were analyzed using logistic regression.Results:The results showed that the variable age of the father and mother≥20 years was a protective factor for the incidence of LBW.Family income<IDR 3,000,000 per month,fathers with farm workers and fishermen as occupation,male sex,and low father’s education were predictor factors for LBW.Conclusions:This study concluded that the variable sociodemographic factors related to LBW in toddlers in Lojejer Wuluhan Village,Jember district,East Java Province,Indonesia.Therefore,the government needs to establish stricter policies in terms of maturing the age of marriage to reduce the incidence of LBW.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Application of Fuzzy Comprehensive Evaluation Method Based on Entropy Weight Theory in Evaluation of Salt Tolerance of Cotton

[Objective] The aim was to propose a new entropy weight fuzzy compre- hensive evaluation method for assessing cotton salt tolerance, realizing the objective, accurate and comprehensive evaluation of salt tolerance of cotton. [Method] A sand culture experiment under salt stress of 150 mmol/L of NaCI was designed. The in- dicator weight was determined with the entropy weight fuzzy comprehensive evalu- ation method, based on the salt injury index of indicators. The salt tolerance of cotton was evaluated comprehensively. [Result] At the germination stage, the entropy and weight of salt injury index of germination energy, vigor index, hypocotyl length and fresh weight were highest, followed by germination rate and germination index, and of root length were lowest. At the seedling stage, the entropy and weight of salt injury index of plasma membrane permeability, root vigor and leaf expansion rate were highest, followed by plant height and net photosynthetic rate, and of shoot dry weight and root dry weight were lowest. The salt tolerance of cotton differed a- mong growth stages and cultivars. Among the 11 cultivars, CCRI-44 and CCRI-75 were steadily salt-tolerant at both germination and seedling stages; CCRI-17, Sumi- an 22, Sumian 15 and Dexiamianl had a stable moderate salt tolerance; while Sumian 12 and Simian 3 were steadily salt-sensitive. [Conclusion] The evaluated result was objective and exact, which indicated that this method could be used in comprehensive evaluation of salt tolerance of cotton.

Effect of Different Nitrogen Application Amount on the Net Increment of Fresh Dry Weight in Hybrid Rape

[ Objective] The aim of the research was to provide reference for reasonable application of nitrogen fertilizer for high yield.cultivation of hybrid rape cuhivar Youyan 9 and Youyan 10. [ Method] The net increment changes of individual plant fresh weight and dry matter weight of Youyan 9 and Youyan 10 with different nitrogen application treatments were studied. [ Result] The differences among average fresh weight increments of individual plant and average dry matter weight increment of individual plant with different treatments reached 0. 01 extremely significant level. Fresh weight increment and dry matter weight net increment of individual plant declined gradually with the increase of nitrogen application. In growtheourse ,fresh weight net increment of individual plant increased firstly then decreased and the maximum was in beginning flowering stage, besides that dry matter net increment increased gradually and the maximum was in mature period. The correlations among fresh net increment, dry matter weight net increment and yield net increment were positive or extremely positive. [ Conclusion] Under experimental condition, when nitrogen application was 225 kg/hm^2, hybrid rape Yanyou 9 and Yanyou 10 with low erucic,low glucosinolate could obtain high yield.

A min-max optimization approach for weight determination in analytic hierarchy process

A min-max optimization method is proposed as a new approach to deal with the weight determination problem in the context of the analytic hierarchy process. The priority is obtained through minimizing the maximal absolute difference between the weight vector obtained from each column and the ideal weight vector. By transformation, the. constrained min- max optimization problem is converted to a linear programming problem, which can be solved using either the simplex method or the interior method. The Karush-Kuhn- Tucker condition is also analytically provided. These control thresholds provide a straightforward indication of inconsistency of the pairwise comparison matrix. Numerical computations for several case studies are conducted to compare the performance of the proposed method with three existing methods. This observation illustrates that the min-max method controls maximum deviation and gives more weight to non- dominate factors.

Application of combination weighting method in contract risk's evaluation of third party logistics

First,the analytical hierarchy process(AHP),which stands for the subjective weighting method,and the entropy method,which stands for the objective weighting method,are chosen to calculate the index weights of the contract risks of third party logistics(TPL),respectively.Then,they can determine the combination weights using the combination weighting method.Second,using the combination weights,the contract risks of TPL are evaluated through the fuzzy comprehensive evaluation method.According to the combination weights,the most important risk factor of the contract risks of TPL is choosing sub-contractors.The results are basically consistent with the facts and show that the weights determined by the combination weighting method can avoid the man-made deviations of the subjective weighting method on the one hand,and prevent results opposite to the reality brought about by the objective weighting method on the other hand.Meanwhile,the results of the fuzzy comprehensive evaluation are that the contract risks of TPL are at a high risk level.Roughly this matches real situations,and it indicates that the combination weighting method can generate the comprehensive assessment more scientifically and more reasonably as well.

Application of Fuzzy Comprehensive Evaluation Method Based on Entropy Weight to Evaluate Pond Water Quality

[Objective] This study was to provide references for the evaluation of water quality in aquaculture ponds by evaluating the pond water quality using fuzzy comprehensive evaluation method based on entropy weight. [Method] The fuzzy compre- hensive evaluation method based on entropy weight was used to evaluate the water quality in the ponds with Ukraine scale carp （Cyprinus carpio） as the main cultivated fish. The average size of the fish was 71.4 g/ind, and totally three groups of pond were set with the population density of 6 000, 9 000, 12 000 ind/hm2. [Result] According to the GB3838-2002 Environmental Quality Standards for Surface Water of China, the water quality of 6 000 ind/hm2 group was Grade I, and the water quality of 9 000 and 12 000 ind/hm2 were Grade V. [Conclusion] With the increasing of feeding density, the pond water quality would worsen, however, there is no difference on water quality between 9 000 and 12 000 ind/hm2 groups.

The Application of Weighted Association Rules in Host-Based Intrusion Detection System

Association rules are useful for determining correlations between items. Applying association rules to intrusion detection system (IDS) can improve the detection rate, but false positive rate is also increased. Weighted association rules are used in this paper to mine intrustion models, which can increase the detection rate and decrease the false positive rate by some extent. Based on this, the structure of host-based IDS using weighted association rules is proposed.