Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

RBF-based cluster-head selection for wireless sensor networks

The radial basis function （RBF）, a kind of neural networks algorithm, is adopted to select clusterheads. It has many advantages such as simple parallel distributed computation, distributed storage, and fast learning. Four factors related to a node becoming a cluster-head are drawn by analysis, which are energy （ energy available in each node）, number （the number of neighboring nodes）, centrality （ a value to classify the nodes based on the proximity how central the node is to the cluster）, and location （the distance between the base station and the node）. The factors are as input variables of neural networks and the output variable is suitability that is the degree of a node becoming a cluster head. A group of cluster-heads are selected according to the size of network. Then the base station broadcasts a message containing the list of cluster-heads＇ IDs to all nodes. After that, each cluster-head announces its new status to all its neighbors and sets up a new cluster. If a node around it receives the message, it registers itself to be a member of the cluster. After identifying all the members, the cluster-head manages them and carries out data aggregation in each cluster. Thus data flowing in the network decreases and energy consumption of nodes decreases accordingly. Experimental results show that, compared with other algorithms, the proposed algorithm can significantly increase the lifetime of the sensor network.

基于SELECT理念腹腔镜下保留脾脏的胰体尾切除

腹腔镜下胰体尾切除术目前已经成为治疗胰腺体尾部肿瘤的标准术式。而腹腔镜下保留脾脏的胰体尾切除术由于可保留脾脏功能,减少脾切除术后并发症,越来越受到重视。然而,胰腺肿瘤发病隐匿、解剖位置复杂,这些对其诊断和保留脾脏的胰体尾切除术带来了挑战。近年来,我们团队积累了丰富的胰腺肿瘤诊疗经验,创新提出“SELECT”(S-Single-Operator Cholangiopancreatoscopy,E-ERCP,L-Laparoscopy,E-Endoscopic ultrasound,C-Choledochoscopy/Confocal laser endomicroscopy,T-Traditional Chinese medicine)多镜组合中西医微创诊治理念。根据胰腺肿瘤的类型及特点,应用多种内镜和腹腔镜技术,多种不同微创诊疗方法的优选组合,围手术期采用中医药,进行中西医结合加速康复。将SELECT理念充分应用于腹腔镜下保留脾脏的胰体尾切除术,有利于术前精准诊断、术中精准切除、术后并发症预判和治疗,一站式诊治胰腺肿瘤,使病人利益最大化。

Advances in selective conversion of carbohydrates into 5-hydroxymethylfurfural

Converting carbohydrates into 5-hydroxymethylfurfural(5-HMF) is an attractive and promising route for value-added utilization of agricultural and forestry biomass resource. As an important platform compound, 5-HMF possesses high active furan structure with hydroxymethyl and aldehyde group for production of various bio-chemicals and materials, meanwhile, which suffer from low stability and poor yield during the industrial biorefinery process. Hence, selective production of 5-HMF with high-yield and low-cost has attracted extensive attention from scientific and industrial researchers. This review sorted and described the latest advanced research on solvent and catalyst system, as well as energy field effect for production of 5-HMF with different feedstock in detail, emphatically discussing the solvent effect and its synergistic effect with other aspects. Besides, the future prospects and challenges for production of 5-HMF from carbohydrates were also presented, which provide a profound insight into industrial 5-HMF process with economic and environmental feature.

Dealuminated Hβ zeolite for selective conversion of fructose to furfural and formic acid

The fructose-to-furfural transformation is facing major challenges in the selectivity and high efficiency. Herein, we have developed a simple and effective approach for the selective conversion of fructose to furfural using Hβ zeolite modified by organic acids for dealuminization to regulate its textural and acidic properties. It was found that citric acid-dealuminized Hβ zeolite possessed high specific surface areas, wide channels and high Brønsted acid amount, which facilitated the selective conversion of fructose to furfural with a maximum yield of 76.2% at433 K for 1 h in the γ-butyrolactone(GBL)-H_(2)O system, as well as the concomitant formation of 83.0% formic acid. The^(13)C-isotope labelling experiments and the mechanism revealed that the selective cleavage of C1–C2 or C5–C6 bond on fructose was firstly occurred to form pentose or C5 intermediate by weak Brønsted acid, which was then dehydrated to furfural by strong Brønsted acid. Also this dealuminized Hβ catalyst showed the great recycling performance and was active for the conversion of glucose and mannose.

Synergy of heterogeneous Co/Ni dual atoms enabling selective C-O bond scission of lignin coupling with in-situ N-functionalization

Selective cleavage of Csp^(2)-OCH_(3)bond in lignin without breaking other types of C-O bonds followed by N-functionalization is fascinating for on-purpose valorization of biomass.Here,a Co/Ni-based dual-atom catalyst CoNiDA@NC prepared by in-situ evaporation and acid-etching of metal species from tailor-made metal–organic frameworks was efficient for reductive upgrading of various lignin-derived phenols to cyclohexanols(88.5%–99.9%yields),which had ca.4 times higher reaction rate than the single-atom catalyst and was superior to state-of-the-art heterogeneous catalysts.The synergistic catalysis of Co/Ni dual atoms facilitated both hydrogen dissociation and hydrogenolysis steps,and could optimize adsorption configuration of lignin-derived methoxylated phenols to further favor the Csp^(2)-OCH_(3)cleavage,as elaborated by theoretical calculations.Notably,the CoNi_(DA)@NC catalyst was highly recyclable,and exhibited excellent demethoxylation performance(77.1%yield)in real lignin monomer mixtures.Via in-situ cascade conversion processes assisted by dual-atom catalysis,various high-value N-containing chemicals,including caprolactams and cyclohexylamines,could be produced from lignin.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Selective leaching of lithium from spent lithium-ion batteries using sulfuric acid and oxalic acid

Traditional hydrometallurgical methods for recovering spent lithium-ion batteries(LIBs)involve acid leaching to simultaneously extract all valuable metals into the leachate.These methods usually are followed by a series of separation steps such as precipitation,extraction,and stripping to separate the individual valuable metals.In this study,we present a process for selectively leaching lithium through the synergistic effect of sulfuric and oxalic acids.Under optimal leaching conditions(leaching time of 1.5 h,leaching temperature of 70°C,liquid-solid ratio of 4 mL/g,oxalic acid ratio of 1.3,and sulfuric acid ratio of 1.3),the lithium leaching efficiency reached89.6%,and the leaching efficiencies of Ni,Co,and Mn were 12.8%,6.5%,and 21.7%.X-ray diffraction(XRD)and inductively coupled plasma optical emission spectrometer(ICP-OES)analyses showed that most of the Ni,Co,and Mn in the raw material remained as solid residue oxides and oxalates.This study offers a new approach to enriching the relevant theory for selectively recovering lithium from spent LIBs.

Recent developments in selective laser processes for wearable devices

Recently,the increasing interest in wearable technology for personal healthcare and smart virtual/augmented reality applications has led to the development of facile fabrication methods.Lasers have long been used to develop original solutions to such challenging technological problems due to their remote,sterile,rapid,and site-selective processing of materials.In this review,recent developments in relevant laser processes are summarized under two separate categories.First,transformative approaches,such as for laser-induced graphene,are introduced.In addition to design optimization and the alteration of a native substrate,the latest advances under a transformative approach now enable more complex material compositions and multilayer device configurations through the simultaneous transformation of heterogeneous precursors,or the sequential addition of functional layers coupled with other electronic elements.In addition,the more conventional laser techniques,such as ablation,sintering,and synthesis,can still be used to enhance the functionality of an entire system through the expansion of applicable materials and the adoption of new mechanisms.Later,various wearable device components developed through the corresponding laser processes are discussed,with an emphasis on chemical/physical sensors and energy devices.In addition,special attention is given to applications that use multiple laser sources or processes,which lay the foundation for the all-laser fabrication of wearable devices.

Selective internal radiation therapy segmentectomy:A new minimally invasive curative option for primary liver malignancies?

Transarterial radioembolization or selective internal radiation therapy(SIRT)has emerged as a minimally invasive approach for the treatment of tumors.This percutaneous technique involves the local,intra-arterial delivery of radioactive microspheres directly into the tumor.Historically employed as a palliative measure for liver malignancies,SIRT has gained traction over the past decade as a potential curative option,mirroring the increasing role of radiation segmentectomy.The latest update of the BCLC hepatocellular carcinoma guidelines recognizes SIRT as an effective treatment modality comparable to other local ablative methods,particularly well-suited for patients where surgical resection or ablation is not feasible.Radiation segmentectomy is a more selective approach,aiming to deliver high-dose radiation to one to three specific hepatic segments,while minimizing damage to surrounding healthy tissue.Future research efforts in radiation segmentectomy should prioritize optimizing radiation dosimetry and refining the technique for super-selective administration of radiospheres within the designated hepatic segments.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Selection of dialysis methods for end-stage kidney disease patients with diabetes

The increasing prevalence of diabetes has led to a growing population of endstage kidney disease(ESKD)patients with diabetes.Currently,kidney transplantation is the best treatment option for ESKD patients;however,it is limited by the lack of donors.Therefore,dialysis has become the standard treatment for ESKD patients.However,the optimal dialysis method for diabetic ESKD patients remains controversial.ESKD patients with diabetes often present with complex conditions and numerous complications.Furthermore,these patients face a high risk of infection and technical failure,are more susceptible to malnutrition,have difficulty establishing vascular access,and experience more frequent blood sugar fluctuations than the general population.Therefore,this article reviews nine critical aspects:Survival rate,glucose metabolism disorder,infectious complications,cardiovascular events,residual renal function,quality of life,economic benefits,malnutrition,and volume load.This study aims to assist clinicians in selecting individualized treatment methods by comparing the advantages and disadvantages of hemodialysis and peritoneal dialysis,thereby improving patients’quality of life and survival rates.

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.

High adsorption selectivity of activated carbon and carbon molecular sieve boosting CO_(2)/N_(2) and CH_(4)/N_(2) separation

Flue gas and coal bed methane are two important sources of greenhouse gases.Pressure swing adsorption process has a wide range of application in the field of gas separation,and the selection of adsorbent is crucial.In this regard,in order to assess the better adsorbent for separating CO_(2) from flue gas and CH_(4) from coal bed methane,adsorption isotherms of CO_(2),CH_(4) and N_(2) on activated carbon and carbon molecular sieve are measured at 303.15,318.15 and 333.15 K,and up to 250 kPa.The experimental data fit better with Langmuir 2 compared to Langmuir 3 and Langmuir-Freundlich models,and Clausius-Clapeyron equation was used to calculate the isosteric heat.Both the order of the adsorbed amount and the adsorption heat on the two adsorbents are CO_(2)>CH_(4)>N_(2).The adsorption kinetics are calculated by the pseudo-first kinetic model,and the order of adsorption rates on activated carbon is N_(2)-CH_(4)>CO_(2),while on carbon molecular sieve,it is CO_(2)-N_(2)>CH_(4).It is shown that relative molecular mass and adsorption heat are the primary effect on kinetics for activated carbon,while kinetic diameter is the main resistance factor for carbon molecular sieve.Moreover,the adsorption selectivity of CH_(4)/N_(2) and CO_(2)/N_(2) were estimated with the ideal adsorption solution theory,and carbon molecular sieve performed best at 318.15 K for both CO_(2) and CH_(4) separation.The study suggested that activated carbon is a better choice for separating flue gas and carbon molecular sieve can be a strong candidate for separating coal bed methane.

Selective his bundle pacing eliminates crochetage sign:A case report

BACKGROUND Crochetage sign is a specific electrocardiographic manifestation of ostium secundum atrial septal defects(ASDs),which is associated with the severity of the left-to-right shunt.Herein,we reported a case of selective his bundle pacing(SHBP)that eliminated crochetage sign in a patient with ostium secundum ASD.CASE SUMMARY A 77-year-old man was admitted with a 2-year history of chest tightness and shortness of breath.Transthoracic echocardiography revealed an ostium secundum ASD.Twelve-lead electrocardiogram revealed atrial fibrillation with a prolonged relative risk interval,incomplete right bundle branch block,and crochetage sign.The patient was diagnosed with an ostium secundum ASD,atrial fibrillation with a second-degree atrioventricular block,and heart failure.The patient was treated with selective his bundle pacemaker implantation.After the procedure,crochetage sign disappeared during his bundle pacing on the electrocardiogram.CONCLUSION S-HBP eliminated crochetage sign on electrocardiogram.Crochetage sign may be a manifestation of a conduction system disorder.

Interplay of laser power and pore characteristics in selective laser melting of ZK60 magnesium alloys:A study based on in-situ monitoring and image analysis

This study offers significant insights into the multi-physics phenomena of the SLM process and the subsequent porosity characteristics of ZK60 Magnesium(Mg)alloys.High-speed in-situ monitoring was employed to visualise process signals in real-time,elucidating the dynamics of melt pools and vapour plumes under varying laser power conditions specifically between 40 W and 60 W.Detailed morphological analysis was performed using Scanning-Electron Microscopy(SEM),demonstrating a critical correlation between laser power and pore formation.Lower laser power led to increased pore coverage,whereas a denser structure was observed at higher laser power.This laser power influence on porosity was further confirmed via Optical Microscopy(OM)conducted on both top and cross-sectional surfaces of the samples.An increase in laser power resulted in a decrease in pore coverage and pore size,potentially leading to a denser printed part of Mg alloy.X-ray Computed Tomography(XCT)augmented these findings by providing a 3D volumetric representation of the sample internal structure,revealing an inverse relationship between laser power and overall pore volume.Lower laser power appeared to favour the formation of interconnected pores,while a reduction in interconnected pores and an increase in isolated pores were observed at higher power.The interplay between melt pool size,vapour plume effects,and laser power was found to significantly influence the resulting porosity,indicating a need for effective management of these factors to optimise the SLM process of Mg alloys.

Covalency competition induced selective bond breakage and surface reconstruction in manganese cobaltite towards enhanced electrochemical charge storage

Manganese cobaltite(MnCo_(2)_(4))is a promising electrode material because of its attractive redox chemistry and excellent charge storage capability.Our previous work demonstrated that the octahedrally-coordinated Mn are prone to react with the hydroxyl ions in alkaline electrolyte upon electrochemical cycling and separates on the surface of spinel to reconstruct into d-MnO_(2) nanosheets irreversibly,thus results in a change of the reaction mechanism with Kþion intercalation.However,the low capacity has greatly limited its practical application.Herein,we found that the tetrahedrally-coordinated Co_(2) þions were leached when MnCo_(2)_(4) was equilibrated in 1 mol L^(-1) HCl solution,leading to the formation of layered CoOOH on MnCo_(2)_(4) surface which is originated from the covalency competition induced selective breakage of the CoT–O bond in CoT–O–CoO and subsequent rearrangement of free Co_(6) octahedra.The as-formed CoOOH is stable upon cycling in alkaline electrolyte,exhibits conversion reaction mechanism with facile proton diffusion and is free of massive structural evolution,thus enables utilization of the bulk electrode material and realizes enhanced specific capacity as well as facilitated charge transfer and ion diffusion.In general,our work not only offers a feasible approach to deliberate modification of MnCo_(2)_(4)'s surface structure,but also provides an in-depth understanding of its charge storage mechanism,which enables rational design of the spinel oxides with promising charge storage properties.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

A critical review towards the causes of the iron-based catalysts deactivation mechanisms in the selective oxidation of hydrogen sulfide to elemental sulfur from biogas

Hydrogen sulfide(H_(2)S) not only presents significant environmental concerns but also induces severe corrosion in industrial equipment,even at low concentrations.Among various technologies,the selective oxidation of hydrogen sulfide(SOH_(2)S) to elemental sulfur(S) has emerged as a sustainable and environmentally friendly solution.Due to its unique properties,iron oxide has been extensively investigated as a catalyst for SOH_(2)S;however,rapid deactivation has remained a significant drawback.The causes of iron oxide-based catalysts deactivation mechanisms in SOH_(2)S,including sulfur or sulfate deposition,the transformation of iron species,sintering and excessive oxygen vacancy formation,and active site loss,are thoroughly examined in this review.By focusing on the deactivation mechanisms,this review aims to provide valuable insights into enhancing the stability and efficiency of iron-based catalysts for SOH_(2)S.