Stability analysis of longwall top-coal caving face in extra-thick coal seams based on an innovative numerical hydraulic support model

The relationship between support and surrounding rock is of great significance to the control of surrounding rock in mining process.In view of the fact that most of the existing numerical simulation methods construct virtual elements and stress servo control to approximately replace the hydraulic support problem,this paper establishes a new numerical model of hydraulic support with the same working characteristics as the actual hydraulic support by integrating numerical simulation software Rhino,Griddle and FLAC3D,which can realize the simulation of different working conditions.Based on this model,the influence mechanism of the supporting strength of hydraulic support on surrounding rock stress regulation and coal stability in front of the top coal caving face in extra thick coal seam were researched.Firstly,under different support intensity,the abutment pressure of the bearing coal and the coal in front of it presents the “three-stage”evolution characteristics.The influence range of support intensity is 15%–30%.Secondly,1.5 MPa is the upper limit of impact that the support strength can have on the front coal failure area.Thirdly,within a displacement range of 2.76 m from the coal wall,a support strength of1.5 MPa provides optimal control of the horizontal displacement of the coal.

Aqueous-phase reforming of hydroxyacetone solution to bio-based H_(2)over supported Pt catalysts

Aqueous-phase reforming(APR)is an attractive process to produce bio-based hydrogen from waste biomass streams,during which the catalyst stability is often challenged due to the harsh reaction conditions.In this work,three Pt-based catalysts supported on C,AlO(OH),and ZrO_(2)were investigated for the APR of hydroxyacetone solution in afixed bed reactor at 225℃and 35 bar.Among them,the Pt/C catalyst showed the highest turnover frequency for H_(2)production(TOF of 8.9 molH_(2)molPt^(-1)min^(-1))and the longest catalyst stability.Over the AlO(OH)and ZrO_(2)supported Pt catalysts,the side reactions consuming H_(2),formation of coke,and Pt sintering result in a low H_(2)production and the fast catalyst deactivation.The proposed reaction pathways suggest that a promising APR catalyst should reform all oxygenates in the aqueous phase,minimize the hydrogenation of the oxygenates,maximize the WGS reaction,and inhibit the condensation and coking reactions for maximizing the hydrogen yield and a stable catalytic performance.

Active Fault Tolerant Nonsingular Terminal Sliding Mode Control for Electromechanical System Based on Support Vector Machine

Effective fault diagnosis and fault-tolerant control method for aeronautics electromechanical actuator is concerned in this paper.By borrowing the advantages of model-driven and data-driven methods,a fault tolerant nonsingular terminal sliding mode control method based on support vector machine(SVM)is proposed.A SVM is designed to estimate the fault by off-line learning from small sample data with solving convex quadratic programming method and is introduced into a high-gain observer,so as to improve the state estimation and fault detection accuracy when the fault occurs.The state estimation value of the observer is used for state reconfiguration.A novel nonsingular terminal sliding mode surface is designed,and Lyapunov theorem is used to derive a parameter adaptation law and a control law.It is guaranteed that the proposed controller can achieve asymptotical stability which is superior to many advanced fault-tolerant controllers.In addition,the parameter estimation also can help to diagnose the system faults because the faults can be reflected by the parameters variation.Extensive comparative simulation and experimental results illustrate the effectiveness and advancement of the proposed controller compared with several other main-stream controllers.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Confined cobalt single-atom catalysts with strong electronic metal-support interactions based on a biomimetic self-assembly strategy

Designing high-performance and low-cost electrocatalysts for oxygen evolu-tion reaction(OER)is critical for the conversion and storage of sustainable energy technologies.Inspired by the biomineralization process,we utilized the phosphorylation sites of collagen molecules to combine with cobalt-based mononuclear precursors at the molecular level and built a three-dimensional(3D)porous hierarchical material through a bottom-up biomimetic self-assembly strategy to obtain single-atom catalysts confined on carbonized biomimetic self-assembled carriers(Co SACs/cBSC)after subsequent high-temperature annealing.In this strategy,the biomolecule improved the anchoring efficiency of the metal precursor through precise functional groups;meanwhile,the binding-then-assembling strategy also effectively suppressed the nonspecific adsorption of metal ions,ultimately preventing atomic agglomeration and achieving strong electronic metal-support interactions(EMSIs).Experimental characterizations confirm that binding forms between cobalt metal and carbonized self-assembled substrate(Co–O_(4)–P).Theoretical calculations disclose that the local environment changes significantly tailored the Co d-band center,and optimized the binding energy of oxygenated intermediates and the energy barrier of oxygen release.As a result,the obtained Co SACs/cBSC catalyst can achieve remarkable OER activity and 24 h durability in 1 M KOH(η10 at 288 mV;Tafel slope of 44 mV dec-1),better than other transition metal-based catalysts and commercial IrO_(2).Overall,we presented a self-assembly strategy to prepare transition metal SACs with strong EMSIs,providing a new avenue for the preparation of efficient catalysts with fine atomic structures.

Bearing mechanism of roof and rib support structure in automatically formed roadway and its support design method

Non-pillar mining technology with automatically formed roadway is a new mining method without coal pillar reservation and roadway excavation.The stability control of automatically formed roadway is the key to the successful application of the new method.In order to realize the stability control of the roadway surrounding rock,the mechanical model of the roof and rib support structure is established,and the influence mechanism of the automatically formed roadway parameters on the compound force is revealed.On this basis,the roof and rib support structure technology of confined lightweight concrete is proposed,and its mechanical tests under different eccentricity are carried out.The results show that the bearing capacity of confined lightweight concrete specimens is basically the same as that of ordinary confined concrete specimens.The bearing capacity of confined lightweight concrete specimens under different eccentricities is 1.95 times higher than those of U-shaped steel specimens.By comparing the test results with the theoretical calculated results of the confined concrete,the calculation method of the bearing capacity for the confined lightweight concrete structure is selected.The design method of confined lightweight concrete support structure is established,and is successfully applied in the extra-large mine,Ningtiaota Coal Mine,China.

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

Prediction of Ground Vibration Induced by Rock Blasting Based on Optimized Support Vector Regression Models

Accurately estimating blasting vibration during rock blasting is the foundation of blasting vibration management.In this study,Tuna Swarm Optimization(TSO),Whale Optimization Algorithm(WOA),and Cuckoo Search(CS)were used to optimize two hyperparameters in support vector regression(SVR).Based on these methods,three hybrid models to predict peak particle velocity(PPV)for bench blasting were developed.Eighty-eight samples were collected to establish the PPV database,eight initial blasting parameters were chosen as input parameters for the predictionmodel,and the PPV was the output parameter.As predictive performance evaluation indicators,the coefficient of determination(R2),rootmean square error(RMSE),mean absolute error(MAE),and a10-index were selected.The normalizedmutual information value is then used to evaluate the impact of various input parameters on the PPV prediction outcomes.According to the research findings,TSO,WOA,and CS can all enhance the predictive performance of the SVR model.The TSO-SVR model provides the most accurate predictions.The performances of the optimized hybrid SVR models are superior to the unoptimized traditional prediction model.The maximum charge per delay impacts the PPV prediction value the most.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Bimetallic CoNi single atoms supported on three-dimensionally ordered mesoporous chromia:highly active catalysts for n-hexane combustion

Developing the alternative supported noble metal catalysts with low cost,high catalytic efficiency,and good resistance toward carbon dioxide and water vapor is critically demanded for the oxidative removal of volatile organic compounds(VOCs).In this work,we prepared the mesoporous chromia-supported bimetallic Co and Ni single-atom(Co_(1)Ni_(1)/meso-Cr_(2)O_(3))and bimetallic Co and Ni nanoparticle(Co_(NP)Ni_(NP)/mesoCr_(2)O_(3))catalysts adopting the one-pot polyvinyl pyrrolidone(PVP)-and polyvinyl alcohol(PVA)-protecting approaches,respectively.The results indicate that the Co_(1)Ni_(1)/meso-Cr_(2)O_(3)catalyst exhibited the best catalytic activity for n-hexane(C_(6)H_(14))combustion(T_(50%)and T_(90%)were 239 and 263℃ at a space velocity of 40,000 mL g^(-1)h^(-1);apparent activation energy and specific reaction rate at 260℃ were 54.7 kJ mol^(-1)and 4.3×10^(-7)mol g^(-1)_(cat)s^(-1),respectively),which was associated with its higher(Cr^(5+)+Cr^(6+))amount,large n-hexane adsorption capacity,and good lattice oxygen mobility that could enhance the deep oxidation of n-hexane,in which Ni_(1) was beneficial for the enhancements in surface lattice oxygen mobility and low-temperature reducibility,while Co_(1) preferred to generate higher contents of the high-valence states of chromium and surface oxygen species as well as adsorption and activation of n-hexane.n-Hexane combustion takes place via the Mars van Krevelen(MvK)mechanism,and its reaction pathways are as follows:n-hexane→olefins or 3-hexyl hydroperoxide→3-hexanone,2-hexanone or 2,5-dimethyltetrahydrofuran→2-methyloxirane or 2-ethyl-oxetane→acrylic acid→CO_x→CO_(2)and H_(2)O.

Analysis on flutter performance of flexible photovoltaic support based on full-order method

Taking a three-cable flexible photovoltaic(PV)support structure as the research subject,a finite element model was established.Utilizing a full-order flutter analysis method,the flutter critical wind speed and flutter frequency of the flexible PV support structure at a tilt angle of 0°were calculated.The results showed good agreement with wind tunnel test data.Further analysis examined the pretension effects in the load-bearing and stabilizing cables on the natural frequency and flutter critical wind speed of the flexible PV support structure.The research findings indicate increasing the pretension in the load-bearing cables significantly raises the natural frequencies of the first four modes.Specifically,as the pretension in the load-bearing cables increases from 22 to 102 kN,the flutter critical wind speed rises from 17.1 to 21.6 m/s.By contrast,the pretension in the stabilizing cable has a smaller effect on the natural frequency and flutter critical wind speed of the flexible PV support structure.When the pretension in the stabilizing cable increased from 22 to 102 kN,the flutter critical wind speed increased from 17.1 to 17.7 m/s.For wind-resistant design of flexible PV support structures,it is recommended to prioritize increasing the pretension in the load-bearing cables to enhance the structural flutter performance.

Experimental and Numerical Investigation on the Aerodynamic Characteristics of High-Speed Pantographs with Supporting Beam Wind Deflectors

Aiming to mitigate the aerodynamic lift force imbalance between pantograph strips,which exacerbates wear and affects the current collection performance of the pantograph-catenary system,a study has been conducted to support the beam deflector optimization using a combination of experimental measurements and computational fluid dynamics(CFD)simulations.The results demonstrate that the size,position,and installation orientation of the wind deflectors significantly influence the amount of force compensation.They also indicate that the front strip deflectors should be installed downwards and the rear strip deflectors upwards,thereby forming a“π”shape.Moreover,the lift force compensation provided by the wind deflectors increases with the size of the deflector.Alternative wind compensation strategies,such as control circuits,are also discussed,putting emphasis on the pros and cons of various pantograph types and wind compensation approaches.

Predicting Turbidite Channel in Deep-Water Canyon Based on Grey Relational Analysis-Support Vector Machine Model:A Case Study of the Lingshui Depression in Qiongdongnan Basin,South China Sea

The turbidite channel of South China Sea has been highly concerned.Influenced by the complex fault and the rapid phase change of lithofacies,predicting the channel through conventional seismic attributes is not accurate enough.In response to this disadvantage,this study used a method combining grey relational analysis(GRA)and support vectormachine(SVM)and established a set of prediction technical procedures suitable for reservoirs with complex geological conditions.In the case study of the Huangliu Formation in Qiongdongnan Basin,South China Sea,this study first dimensionalized the conventional seismic attributes of Gas Layer Group I and then used the GRA method to obtain the main relational factors.A higher relational degree indicates a higher probability of responding to the attributes of the turbidite channel.This study then accumulated the optimized attributes with the highest relational factors to obtain a first-order accumulated sequence,which was used as the input training sample of the SVM model,thus successfully constructing the SVM turbidite channel model.Drilling results prove that the GRA-SVMmethod has a high drilling coincidence rate.Utilizing the core and logging data and taking full use of the advantages of seismic inversion in predicting the sand boundary of water channels,this study divides the sedimentary microfacies of the Huangliu Formation in the Lingshui 17-2 Gas Field.This comprehensive study has shown that the GRA-SVM method has high accuracy for predicting turbidite channels and can be used as a superior turbidite channel prediction method under complex geological conditions.

Postpartum depression and partner support during the period of lactation:Correlation research and its influencing factors

BACKGROUND Postpartum depression(PPD)not only affects the psychological and physiological aspects of maternal health but can also affect neonatal growth and development.Partners who are in close contact with parturient women play a key role in communication and emotional support.This study explores the PPD support relationship with partners and its influencing factors,which is believed to establish psychological well-being and improve maternal partner support.AIM To explore the correlation between PPD and partner support during breastfeeding and its influencing factors.METHODS Convenience sampling was used to select lactating women(200 women)who underwent postpartum examinations at the Huzhou Maternity and Child Health Care Hospital from July 2022 to December 2022.A cross-sectional survey was conducted on the basic information(general information questionnaire),depression level[edinburgh postnatal depression scale(EPDS)],and partner support score[dyadic coping inventory(DCI)]of the selected subjects.Pearson’s correlation analysis was used to analyze the correlation between PPD and DCI in lactating women.Factors affecting PPD levels during lactation were analyzed using multiple linear regression.RESULTS The total average score of EPDS in 200 lactating women was(9.52±1.53),and the total average score of DCI was(115.78±14.90).Dividing the EPDS,the dimension scores were:emotional loss(1.91±0.52),anxiety(3.84±1.05),and depression(3.76±0.96).Each dimension of the DCI was subdivided into:Pressure communication(26.79±6.71),mutual support(39.76±9.63),negative support(24.97±6.68),agent support(6.87±1.92),and joint support(17.39±4.19).Pearson’s correlation analysis demonstrated that the total mean score and individual dimension scores of EPDS during breastfeeding were inversely correlated with the total score of partner support,stress communication,mutual support,and cosupport(P<0.05).The total mean score of the EPDS and its dimensions were positively correlated with negative support(P<0.05).Multiple linear regression analysis showed that the main factors affecting PPD during breastfeeding were marital harmony,newborn health,stress communication,mutual support,negative support,cosupport,and the total score of partner support(P<0.05).CONCLUSION PPD during breastfeeding was associated with marital harmony,newborn health,stress communication,mutual support,negative support,joint support,and the total DCI score.

Science and Technology Commissioners Supporting High-Quality Development Project for Hundreds of Counties, Thousands of Towns, and Myriads of Villages in the Context of Rural Revitalization

This paper discusses the important role of science and technology commissioners in the high-quality development of hundreds of counties,thousands of towns,and myriads of villages in the context of rural revitalization,including building bridges,accelerating the transformation of achievements,promoting the value-added of the whole agricultural industry chain,and promoting the rapid development of rural industrial economy.It also discusses the working achievements of science and technology commissioners,in order to promote further development of rural revitalization in Guangdong Province.

Supportive care in transplantation: A patient-centered care model to better support kidney transplant candidates and recipients

Kidney transplantation(KT),although the best treatment option for eligible patients,entails maintaining and adhering to a life-long treatment regimen of medications,lifestyle changes,self-care,and appointments.Many patients experience uncertain outcome trajectories increasing their vulnerability and symptom burden and generating complex care needs.Even when transplants are successful,for some patients the adjustment to life post-transplant can be challenging and psychological difficulties,economic challenges and social isola-tion have been reported.About 50%of patients lose their transplant within 10 years and must return to dialysis or pursue another transplant or conservative care.This paper documents the complicated journey patients undertake before and after KT and outlines some initiatives aimed at improving patient-centered care in transplantation.A more cohesive approach to care that borrows its philosophical approach from the established field of supportive oncology may improve patient experiences and outcomes.We propose the"supportive care in transplantation"care model to operationalize a patient-centered approach in transplantation.This model can build on other ongoing initiatives of other scholars and researchers and can help advance patient-centered care through the entire care continuum of kidney transplant recipients and candidates.Multi-dimensionality,multi-disciplinarity and evidence-based approaches are proposed as other key tenets of this care model.We conclude by proposing the potential advantages of this approach to patients and healthcare systems.Core Tip:Kidney transplant recipients and candidates face several uncertainties in their care journey and have several expressed unmet healthcare needs.We recommend a structured and comprehensive approach to transplant care across the entire continuum of a transplant patient’s journey similar to what has been developed in the field of oncology.The supportive care in transplantation model can operationalize patient-centered care and build on the efforts of other researchers in the field.We postulate that such a model would significantly improve care delivery and patients’experiences and outcomes and potentially decrease healthcare utilization and cost.INTRODUCTION Patients with kidney failure benefit from(KT)[1,2],and experience improved survival rates when compared with dialysis[3-6].KT studies,using validated instruments,have also consistently demonstrated that kidney transplant recipients(KTRs)experience better health-related quality of life and several improvements in other disease-specific domains when compared with dialysis[7].In countries where dialysis is out of reach for many,the diagnosis of kidney failure would be futile without KT[8].Thus,increasing KT has been a priority for the nephrology and transplant communities.This priority has been reflected in recent global trends:Of the 79 countries where data were available,the International Society of Nephrology’s Global Kidney Atlas reported that the prevalence of KTRs in 2023 was 279 per million population which represented an increase of 9.4%from the data published four years prior[8].Despite this growth,KT can be a challenging journey for many patients and it is sometimes regarded as a‘cure’,which does not conform with the reality that many patients experience[9-13].KTRs must maintain a life-long treatment regimen of medications,lifestyle changes,self-care and medical appointments[14-17].As poignantly stated by a young female transplant recipient,“I thought everything would change once I got my kidney.I thought I would be healthy again”but after experiencing multiple side effects of immunosuppressive medications and graft loss,she stated,“I am just a different kind of patient now”[18].Indeed,a significant proportion of patients experience graft failure and return to dialysis;it is estimated that over 50%return to dialysis within 10 years of KT[19-23].Patients are often not prepared for this outcome and report several psychosocial and physical ramifications of graft failure[24,25].Overall,high symptom burden,adverse effects of immunosuppressants,risk of graft rejection or failure and mortality,contribute to complex needs,vulnerability and uncertainties for patients,increasing their care needs and treatment burden[26-30].In this paper,we highlight the complex journey that KTRs and candidates undertake that can generate varied outcome trajectories and complex healthcare needs.We highlight the need for a comprehensive patient-centered approach to care and conclude with a proposal for a“supportive care in transplantation”care model.

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

Examining the impact of early enteral nutritional support on postoperative recovery in patients undergoing surgical treatment for gastrointestinal neoplasms

BACKGROUND Patients with gastrointestinal tumors often suffer from poor nutritional status during treatment.Surgery is the main treatment for these patients,but the long postoperative recovery period is often accompanied by digestive and absorption dysfunction,leading to further deterioration of the nutritional status.Early enteral nutrition support is hypothesized to be helpful in improving this situation,but the exact effects have yet to be studied in depth.AIM To observe the effect of early enteral nutritional support on postoperative recovery in patients with surgically treated gastrointestinal tract tumors,with the expectation that by improving the nutritional status of patients,the recovery process would be accelerated and the incidence of complications would be reduced,thus improving the quality of life.METHODS A retrospective analysis of 121 patients with gastrointestinal tract tumors treated in our hospital from January 2020 to January 2023 was performed.Fifty-three of these patients received complete parenteral nutrition support as the control group for this study.The other 68 patients received early enteral nutritional support as the observation group of this study.The clinical indicators comparing the two groups included time to fever,time to recovery of postoperative bowel function,time to postoperative exhaustion,and length of hospital stay.The changes in immune function and nutritional indexes in the two groups were compared.Furthermore,we utilized the SF-36 scale to compare the changes in the quality of life between the two groups of patients.Finally,the occurrence of postoperative complications between the two patient groups was also compared.RESULTS The postoperative fever time,postoperative bowel function recovery time,postoperative exhaustion time,and hospitalization time were all higher in the control group than in the observation group(P<0.05).The levels of CD3+,CD4+,immunoglobulin(Ig)A,IgM,and IgG in the observation group were significantly higher than those in the control group at 1 d and 7 d postoperatively,while CD8+was lower than in the control group(P<0.05).Total protein,albumin,prealbumin,and transferrin levels were significantly higher in the observation group than in the control group at 7 d postoperatively(P<0.05).The SF-36 scores of patients in the observation group were significantly higher than those in the control group(P<0.0001).The overall incidence of adverse reactions after the intervention was significantly lower in the control group than in the observation group(P=0.021).CONCLUSION We found that patients with gastrointestinal tumors are nutritionally vulnerable,and early enteral nutrition support programs can improve the nutritional status of patients and speed up postoperative recovery.This program can not only improve the immune function of the patient and protect the intestinal function,but it can also help to improve the quality of life of the patient.However,this program will increase the incidence of complications in patients.Caution should be taken when adopting early enteral nutrition support measures for patients with gastric cancer.The patient's condition and physical condition should be comprehensively evaluated and closely monitored to prevent possible complications.