The task of selecting robotic mechanic assembly technologies (RMAT) is considered as a multi-criteria optimization task, which in this formulation is solved on the set of previously obtained solutions regarding the se...The task of selecting robotic mechanic assembly technologies (RMAT) is considered as a multi-criteria optimization task, which in this formulation is solved on the set of previously obtained solutions regarding the selection of RMAT. The purpose of the paper is to increase the efficiency of technological preparation of robotic mechanical assembly production of machine and instrument engineering due to a new approach to the selection of RMAT using Pareto optimization and the peculiarities of the selection task formulation. The novelty consists in the further development of a science-based approach to solving multi-criteria selection task, based on the first proposed formalisms of the specified process, which reflect the peculiarities of the selection task formulation, its meaningful essence and the content of the Pareto optimization method. The practical value of the research lies in the proposed engineering-acceptable approach to solving applied multi-criteria selection tasks on the example of RMAT selection, which is invariant to the statement of the selection task, the dimension of the task, and its meaningful essence. The methods of discrete optimization, fuzzy multi-criteria selection of alternatives, and the Pareto optimization method were used for the research. The main results of this work consist of the development of formalisms and the demonstration of the efficiency of the proposed approach for the applied task of RMAT selection. The peculiarity of the developed approach is the combination of Pareto optimization, performed on a discrete set of local criteria. Directions for further research are presented.展开更多
Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, and site selection evaluation. Sites must fulfill a numbe...Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, and site selection evaluation. Sites must fulfill a number of criteria that boil down to the following basics: they must be able to accept the desired volume of CO2 at the rate at which it is supplied from the CO2 source(s);they must as well be safe and reliable;and must comply with regulatory and other societal requirements. They also must have at least public acceptance and be based on sound financial analysis. Site geology;hydrogeological, pressure, and geothermal regimes;land features;location, climate, access, etc. can all be refined from these basic criteria. In addition to aiding in site selection, site characterization is essential for other purposes, such as foreseeing the fate and impacts of the injected CO2, and informing subsequent phases of site development, including design, permitting, operation, monitoring, and eventual abandonment. According to data from the IEA, in 2022, emissions from Africa and Asias emerging markets and developing economies, excluding Chinas, increased by 4.2%, which is equivalent to 206 million tonnes of CO2 and were higher than those from developed economies. Coal-fired power generation was responsible for more than half of the rise in emissions that were recorded in the region. The difficulty of achieving sustainable socio-economic progress in the developing countries is entwined with the work of reducing CO2 emissions, which is a demanding project for the economy. Organisations from developing countries, such as Bangladesh, Cameroon, India, and Nigeria, have formed partnerships with organisations in other countries for lessons learned and investment within the climate change arena. The basaltic rocks, coal seams, depleted oil and gas reservoirs, soils, deep saline aquifers, and sedimentary basins that developing countries (Bangladesh, Cameroon, India, and Nigeria etc.) possess all contribute to the individual countrys significant geological sequestration potential. There are limited or no carbon capture and storage or clean development mechanism projects running in these countries at this time. The site selection and characterization procedure are not complete without an estimate of the storage capacity of a storage location. Estimating storage capacity relies on volumetric estimates because a site must accept the planned volume of CO2 during the active injection period. As more and more applications make use of site characterization, so too does the body of written material on the topic. As the science of CO2 storage develops, regulatory requirements are implemented, field experience grows, and the economics of CO2 capture and storage improve, so too will site selection and characterisation change.展开更多
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...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.展开更多
Electroreduction of nitrate has been gaining wide attention in recent years owing to it's beneficial for converting nitrate into benign N_(2) from the perspective of electrocatalytic denitrification or into value-...Electroreduction of nitrate has been gaining wide attention in recent years owing to it's beneficial for converting nitrate into benign N_(2) from the perspective of electrocatalytic denitrification or into value-added ammonia from the perspective of electrocatalytic NH_(3) synthesis.By reason of the undesired formation of ammonia is dominant during electroreduction of nitrate-containing wastewater,chloride has been widely used to improve N_(2) selectivity.Nevertheless,selective electroreduction of nitrate to N2 gas in chloride-containing system poses several drawbacks.In this review,we focus on the key strategies for efficiently enhancing N_(2) selectivity of electroreduction of nitrate in chloride-free system,including optimal selection of elements,combining an active metal catalyst with another metal,manipulating the crystalline morphology and facet orientation,constructing core–shell structure catalysts,etc.Before summarizing the strategies,four possible reaction pathways of electro-reduction of nitrate to N_(2) are discussed.Overall,this review attempts to provide practical strategies for enhancing N2 selectivity without the aid of electrochlorination and highlight directions for future research for designing appropriate electrocatalyst for final electrocatalytic denitrifi-cation.展开更多
A common feature among neurodegenerative conditions is that ce rtain neuronal populations are selectively vulnerable to loss(Fu et al.,2018).By corollary,other neurons are selectively resilient,suggesting they may pos...A common feature among neurodegenerative conditions is that ce rtain neuronal populations are selectively vulnerable to loss(Fu et al.,2018).By corollary,other neurons are selectively resilient,suggesting they may possess unique features that support their survival.U nderstanding the basis of neuronal resilience or vulnerability would provide a logical strategy to identify factors that could be targeted therapeutically.展开更多
Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of si...Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.展开更多
Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We...Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.展开更多
Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is...Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.展开更多
Objective:A novel technique was explored using an airbag-selective portal vein blood arrester that circumvents the need for an intraoperative assessment of anatomical variations in patients with complex intrahepatic s...Objective:A novel technique was explored using an airbag-selective portal vein blood arrester that circumvents the need for an intraoperative assessment of anatomical variations in patients with complex intrahepatic space-occupying lesions.Methods:Rabbits undergoing hepatectomy were randomly assigned to 4 groups:intermittent portal triad clamping(PTC),intermittent portal vein clamping(PVC),intermittent portal vein blocker with an airbag-selective portal vein blood arrester(APC),and without portal blood occlusion(control).Hepatic ischemia and reperfusion injury were assessed by measuring the 7-day survival rate,blood loss,liver function,hepatic pathology,hepatic inflammatory cytokine infiltration,hepatic malondialdehyde levels,and proliferating cell nuclear antigen levels.Results:Liver damage was substantially reduced in the APC and PVC groups.The APC animals exhibited transaminase levels similar to or less oxidative stress damage and inflammatory hepatocellular injury compared to those exhibited by the PVC animals.Bleeding was significantly higher in the control group than in the other groups.The APC group had less bleeding than the PVC group because of the avoidance of portal vein skeletonization during hepatectomy.Thus,more operative time was saved in the APC group than in the PVC group.Moreover,the total 7-day survival rate in the APC group was higher than that in the PTC group.Conclusion:Airbag-selective portal vein blood arresters may help protect against hepatic ischemia and reperfusion injury in rabbits undergoing partial hepatectomy.This technique may also help prevent liver damage in patients requiring hepatectomy.展开更多
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...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.展开更多
Antimony-based anodes have attracted wide attention in potassium-ion batteries due to their high theoretical specific capacities(∼660 mA h g^(-1))and suitable voltage platforms.However,severe capacity fading caused b...Antimony-based anodes have attracted wide attention in potassium-ion batteries due to their high theoretical specific capacities(∼660 mA h g^(-1))and suitable voltage platforms.However,severe capacity fading caused by huge volume change and limited ion transportation hinders their practical applications.Recently,strategies for controlling the morphologies of Sb-based materials to improve the electrochemical performances have been proposed.Among these,the two-dimensional Sb(2D-Sb)materials present excellent properties due to shorted ion immigration paths and enhanced ion diffusion.Nevertheless,the synthetic methods are usually tedious,and even the mechanism of these strategies remains elusive,especially how to obtain large-scale 2D-Sb materials.Herein,a novel strategy to synthesize 2D-Sb material using a straightforward solvothermal method without the requirement of a complex nanostructure design is provided.This method leverages the selective adsorption of aldehyde groups in furfural to induce crystal growth,while concurrently reducing and coating a nitrogen-doped carbon layer.Compared to the reported methods,it is simpler,more efficient,and conducive to the production of composite nanosheets with uniform thickness(3–4 nm).The 2D-Sb@NC nanosheet anode delivers an extremely high capacity of 504.5 mA h g^(-1) at current densities of 100 mA g^(-1) and remains stable for more than 200 cycles.Through characterizations and molecular dynamic simulations,how potassium storage kinetics between 2D Sb-based materials and bulk Sb-based materials are explored,and detailed explanations are provided.These findings offer novel insights into the development of durable 2D alloy-based anodes for next-generation potassium-ion batteries.展开更多
Although selective laser trabeculoplasty(SLT)is a recognized method for the treatment of glaucoma,the exact changes in the target tissue and mechanism for its intraocular pressure lowing effect are still unclear.The p...Although selective laser trabeculoplasty(SLT)is a recognized method for the treatment of glaucoma,the exact changes in the target tissue and mechanism for its intraocular pressure lowing effect are still unclear.The purpose of this review is to summarize the potential mechanisms of SLT on trabecular meshwork both in vivo and in vitro,so as to reveal the potential mechanism of SLT.SLT may induce immune or inflammatory response in trabecular meshwork(TM)induced by possible oxidative damage etc,and remodel extracellular matrix.It may also induce monocytes to aggregate in TM tissue,increase Schlemm’s canal(SC)cell conductivity,disintegrate cell junction and promote permeability through autocrine and paracrine forms.This provides a theoretical basis for SLT treatment in glaucoma.展开更多
Photoelectrochemical (PEC) small-molecule oxidation can selectively transform substrates into high-value-added fine chemicals and increase the rate of cathode hydrogen evolution. Nevertheless, achieving high-selectivi...Photoelectrochemical (PEC) small-molecule oxidation can selectively transform substrates into high-value-added fine chemicals and increase the rate of cathode hydrogen evolution. Nevertheless, achieving high-selectivity PEC oxidation of small molecules to produce specific products is a very challenging task. In general, selectivity can be improved by changing the surface catalyticsites of the photoanode and modulating the interfacial environments of the reactions. Herein, recent advances in approaches to improving selective PEC oxidation of small molecules are introduced. We first briefly discuss the basic concept and fundamentals of small-molecule PEC oxidation. The reported approaches to improving the performance of selective PEC oxidation of small molecules are highlighted from two aspects: (1) changing the surface properties of photoanodes by selecting suitable materials or modifying the photoanodes and (2) mediating the oxidation reactions using redox mediators. The PEC oxidation mechanism of these studies is emphasized. We also discuss the challenges in this research direction and offer a perspective on the further development of selective PEC-based small-molecule transformation.展开更多
Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it...Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.展开更多
Pacific oyster(Crassostrea gigas)is one of the most important mollusks cultured all around the world.Selective breeding programs of Pacific oysters in China is initiated since 2006 and developed the genetically improv...Pacific oyster(Crassostrea gigas)is one of the most important mollusks cultured all around the world.Selective breeding programs of Pacific oysters in China is initiated since 2006 and developed the genetically improved strain with fast-growing trait.However,little is known about the metabolic signatures of the fast-growing trait.In the present study,the non-targeted metabolomics was performed to analyze the metabolic signatures of adductor muscle tissue in one-year old Pacific oysters from fast-growing strain and the wild population.A total of 7767 and 10174 valid peaks were extracted and quantified in ESI^(+)and ESI^(−)modes,resulting in 399 and 381 annotated metabolites,respectively.PCA and OPLS-DA revealed that considerable separation among samples from fastgrowing strain and wild population,suggesting the differences in metabolic signatures.Meanwhile,81 significantly different metabolites(SDMs)were identified in the comparisons between fast-growing strain and wild population,based on the strict thresholds.It was found that there were highly correlation and conserved coordination among these SDMs.KEGG enrichment analysis indicated that the SDMs were tightly related to pantothenate and CoA biosynthesis,steroid hormone biosynthesis,riboflavin metabolism,and arginine and proline metabolism.Of them,the CoA biosynthesis and metabolism,affected by pantetheine and pantothenic acid,might be important for the growth of Pacific oysters under artificial selective breeding.The study provides the comprehensive views of metabolic signatures in response to artificially selective breeding,and is helpful to better understand the molecular mechanism of fastgrowing traits in Pacific oysters.展开更多
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 sel...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.展开更多
Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accurac...Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.展开更多
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 seri...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.展开更多
Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In...Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.展开更多
文摘The task of selecting robotic mechanic assembly technologies (RMAT) is considered as a multi-criteria optimization task, which in this formulation is solved on the set of previously obtained solutions regarding the selection of RMAT. The purpose of the paper is to increase the efficiency of technological preparation of robotic mechanical assembly production of machine and instrument engineering due to a new approach to the selection of RMAT using Pareto optimization and the peculiarities of the selection task formulation. The novelty consists in the further development of a science-based approach to solving multi-criteria selection task, based on the first proposed formalisms of the specified process, which reflect the peculiarities of the selection task formulation, its meaningful essence and the content of the Pareto optimization method. The practical value of the research lies in the proposed engineering-acceptable approach to solving applied multi-criteria selection tasks on the example of RMAT selection, which is invariant to the statement of the selection task, the dimension of the task, and its meaningful essence. The methods of discrete optimization, fuzzy multi-criteria selection of alternatives, and the Pareto optimization method were used for the research. The main results of this work consist of the development of formalisms and the demonstration of the efficiency of the proposed approach for the applied task of RMAT selection. The peculiarity of the developed approach is the combination of Pareto optimization, performed on a discrete set of local criteria. Directions for further research are presented.
文摘Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, and site selection evaluation. Sites must fulfill a number of criteria that boil down to the following basics: they must be able to accept the desired volume of CO2 at the rate at which it is supplied from the CO2 source(s);they must as well be safe and reliable;and must comply with regulatory and other societal requirements. They also must have at least public acceptance and be based on sound financial analysis. Site geology;hydrogeological, pressure, and geothermal regimes;land features;location, climate, access, etc. can all be refined from these basic criteria. In addition to aiding in site selection, site characterization is essential for other purposes, such as foreseeing the fate and impacts of the injected CO2, and informing subsequent phases of site development, including design, permitting, operation, monitoring, and eventual abandonment. According to data from the IEA, in 2022, emissions from Africa and Asias emerging markets and developing economies, excluding Chinas, increased by 4.2%, which is equivalent to 206 million tonnes of CO2 and were higher than those from developed economies. Coal-fired power generation was responsible for more than half of the rise in emissions that were recorded in the region. The difficulty of achieving sustainable socio-economic progress in the developing countries is entwined with the work of reducing CO2 emissions, which is a demanding project for the economy. Organisations from developing countries, such as Bangladesh, Cameroon, India, and Nigeria, have formed partnerships with organisations in other countries for lessons learned and investment within the climate change arena. The basaltic rocks, coal seams, depleted oil and gas reservoirs, soils, deep saline aquifers, and sedimentary basins that developing countries (Bangladesh, Cameroon, India, and Nigeria etc.) possess all contribute to the individual countrys significant geological sequestration potential. There are limited or no carbon capture and storage or clean development mechanism projects running in these countries at this time. The site selection and characterization procedure are not complete without an estimate of the storage capacity of a storage location. Estimating storage capacity relies on volumetric estimates because a site must accept the planned volume of CO2 during the active injection period. As more and more applications make use of site characterization, so too does the body of written material on the topic. As the science of CO2 storage develops, regulatory requirements are implemented, field experience grows, and the economics of CO2 capture and storage improve, so too will site selection and characterisation change.
基金supported by Program for National Natural Science Foundation of China(Nos.22178135,21978104 and 22278419)the National Key Research and Development Program of China(No.2021YFC2101601)。
文摘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.
基金supported by State Key Laboratory of Water Resource Protection and Utilization in Coal Mining(No.GJNY-18-73.17).
文摘Electroreduction of nitrate has been gaining wide attention in recent years owing to it's beneficial for converting nitrate into benign N_(2) from the perspective of electrocatalytic denitrification or into value-added ammonia from the perspective of electrocatalytic NH_(3) synthesis.By reason of the undesired formation of ammonia is dominant during electroreduction of nitrate-containing wastewater,chloride has been widely used to improve N_(2) selectivity.Nevertheless,selective electroreduction of nitrate to N2 gas in chloride-containing system poses several drawbacks.In this review,we focus on the key strategies for efficiently enhancing N_(2) selectivity of electroreduction of nitrate in chloride-free system,including optimal selection of elements,combining an active metal catalyst with another metal,manipulating the crystalline morphology and facet orientation,constructing core–shell structure catalysts,etc.Before summarizing the strategies,four possible reaction pathways of electro-reduction of nitrate to N_(2) are discussed.Overall,this review attempts to provide practical strategies for enhancing N2 selectivity without the aid of electrochlorination and highlight directions for future research for designing appropriate electrocatalyst for final electrocatalytic denitrifi-cation.
基金funded by the National Eye Institute(NIH) EY029360the Whitehall Foundation+1 种基金the TIRR Foundationthe Levy-Longenbaugh Research Award to NMT National Institute on Aging (NIH) F31AG067676 to CAW。
文摘A common feature among neurodegenerative conditions is that ce rtain neuronal populations are selectively vulnerable to loss(Fu et al.,2018).By corollary,other neurons are selectively resilient,suggesting they may possess unique features that support their survival.U nderstanding the basis of neuronal resilience or vulnerability would provide a logical strategy to identify factors that could be targeted therapeutically.
文摘Pattern matching method is one of the classic classifications of existing online portfolio selection strategies. This article aims to study the key aspects of this method—measurement of similarity and selection of similarity sets, and proposes a Portfolio Selection Method based on Pattern Matching with Dual Information of Direction and Distance (PMDI). By studying different combination methods of indicators such as Euclidean distance, Chebyshev distance, and correlation coefficient, important information such as direction and distance in stock historical price information is extracted, thereby filtering out the similarity set required for pattern matching based investment portfolio selection algorithms. A large number of experiments conducted on two datasets of real stock markets have shown that PMDI outperforms other algorithms in balancing income and risk. Therefore, it is suitable for the financial environment in the real world.
基金Major Project for New Generation of AI Grant No.2018AAA0100400)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.21A0350,21C0439,22A0408,22A0414,2022JJ30231,22B0559)the National Natural Science Foundation of Hunan Province,China(Grant No.2022JJ50051).
文摘Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis.It has received great attention due to its huge application prospects in recent years.We can know that the number of features selected by the existing radiomics feature selectionmethods is basically about ten.In this paper,a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed.Based on the combination between features,it decomposes all features layer by layer to select the optimal features for each layer,then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally.Compared with the currentmethod with the best prediction performance in the three data sets,thismethod proposed in this paper can reduce the number of features fromabout ten to about three without losing classification accuracy and even significantly improving classification accuracy.The proposed method has better interpretability and generalization ability,which gives it great potential in the feature selection of radiomics.
基金Supported by the Guangdong Province Basic and Applied Basic Research Fund Project(No.2020A1515110826)the National Natural Science Foundation of China(No.42006115)the Major Scientific and Technological Projects of Hainan Province(No.ZDKJ2021036)。
文摘Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.
基金supported by the Hainan Provincial Natural Science Foundation of China(No.821QN0982 and No.2019RC373)。
文摘Objective:A novel technique was explored using an airbag-selective portal vein blood arrester that circumvents the need for an intraoperative assessment of anatomical variations in patients with complex intrahepatic space-occupying lesions.Methods:Rabbits undergoing hepatectomy were randomly assigned to 4 groups:intermittent portal triad clamping(PTC),intermittent portal vein clamping(PVC),intermittent portal vein blocker with an airbag-selective portal vein blood arrester(APC),and without portal blood occlusion(control).Hepatic ischemia and reperfusion injury were assessed by measuring the 7-day survival rate,blood loss,liver function,hepatic pathology,hepatic inflammatory cytokine infiltration,hepatic malondialdehyde levels,and proliferating cell nuclear antigen levels.Results:Liver damage was substantially reduced in the APC and PVC groups.The APC animals exhibited transaminase levels similar to or less oxidative stress damage and inflammatory hepatocellular injury compared to those exhibited by the PVC animals.Bleeding was significantly higher in the control group than in the other groups.The APC group had less bleeding than the PVC group because of the avoidance of portal vein skeletonization during hepatectomy.Thus,more operative time was saved in the APC group than in the PVC group.Moreover,the total 7-day survival rate in the APC group was higher than that in the PTC group.Conclusion:Airbag-selective portal vein blood arresters may help protect against hepatic ischemia and reperfusion injury in rabbits undergoing partial hepatectomy.This technique may also help prevent liver damage in patients requiring hepatectomy.
基金the National Natural Science Foundation of China(22368014)the Guizhou Provincial S&T Project(ZK[2022]011,GCC[2023]011)+2 种基金the Natural Science Foundation of Guangxi Zhuang Autonomous Region(2023JJA120098)the Guangxi Key Laboratory of Green Chemical Materials and Safety Technology,the Beibu Gulf University(2022SYSZZ02,2022ZZKT04)the Guizhou Provincial Higher Education Institution Program(Qianjiaoji[2023]082)。
文摘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.
基金financially supported by the Science and Technology Development Program of Jilin Province(YDZJ202101ZYTS185)the National Natural Science Foundation of China(21975250)。
文摘Antimony-based anodes have attracted wide attention in potassium-ion batteries due to their high theoretical specific capacities(∼660 mA h g^(-1))and suitable voltage platforms.However,severe capacity fading caused by huge volume change and limited ion transportation hinders their practical applications.Recently,strategies for controlling the morphologies of Sb-based materials to improve the electrochemical performances have been proposed.Among these,the two-dimensional Sb(2D-Sb)materials present excellent properties due to shorted ion immigration paths and enhanced ion diffusion.Nevertheless,the synthetic methods are usually tedious,and even the mechanism of these strategies remains elusive,especially how to obtain large-scale 2D-Sb materials.Herein,a novel strategy to synthesize 2D-Sb material using a straightforward solvothermal method without the requirement of a complex nanostructure design is provided.This method leverages the selective adsorption of aldehyde groups in furfural to induce crystal growth,while concurrently reducing and coating a nitrogen-doped carbon layer.Compared to the reported methods,it is simpler,more efficient,and conducive to the production of composite nanosheets with uniform thickness(3–4 nm).The 2D-Sb@NC nanosheet anode delivers an extremely high capacity of 504.5 mA h g^(-1) at current densities of 100 mA g^(-1) and remains stable for more than 200 cycles.Through characterizations and molecular dynamic simulations,how potassium storage kinetics between 2D Sb-based materials and bulk Sb-based materials are explored,and detailed explanations are provided.These findings offer novel insights into the development of durable 2D alloy-based anodes for next-generation potassium-ion batteries.
文摘Although selective laser trabeculoplasty(SLT)is a recognized method for the treatment of glaucoma,the exact changes in the target tissue and mechanism for its intraocular pressure lowing effect are still unclear.The purpose of this review is to summarize the potential mechanisms of SLT on trabecular meshwork both in vivo and in vitro,so as to reveal the potential mechanism of SLT.SLT may induce immune or inflammatory response in trabecular meshwork(TM)induced by possible oxidative damage etc,and remodel extracellular matrix.It may also induce monocytes to aggregate in TM tissue,increase Schlemm’s canal(SC)cell conductivity,disintegrate cell junction and promote permeability through autocrine and paracrine forms.This provides a theoretical basis for SLT treatment in glaucoma.
基金the National Natural Science Foundation of China (No. 22136005)the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDB36000000).
文摘Photoelectrochemical (PEC) small-molecule oxidation can selectively transform substrates into high-value-added fine chemicals and increase the rate of cathode hydrogen evolution. Nevertheless, achieving high-selectivity PEC oxidation of small molecules to produce specific products is a very challenging task. In general, selectivity can be improved by changing the surface catalyticsites of the photoanode and modulating the interfacial environments of the reactions. Herein, recent advances in approaches to improving selective PEC oxidation of small molecules are introduced. We first briefly discuss the basic concept and fundamentals of small-molecule PEC oxidation. The reported approaches to improving the performance of selective PEC oxidation of small molecules are highlighted from two aspects: (1) changing the surface properties of photoanodes by selecting suitable materials or modifying the photoanodes and (2) mediating the oxidation reactions using redox mediators. The PEC oxidation mechanism of these studies is emphasized. We also discuss the challenges in this research direction and offer a perspective on the further development of selective PEC-based small-molecule transformation.
基金supported by the earmarked fund for China Agriculture Research System(CARS-35)the National Natural Science Foundation of China(32022078)supported by the National Supercomputer Centre in Guangzhou。
文摘Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.
基金supported by grants from the Earmarked Fund for Agriculture Seed Improvement Project of Shandong Province(Nos.2021ZLGX03 and 2022LZGCQY010)the China Agriculture Research System Project(No.CARS-49).
文摘Pacific oyster(Crassostrea gigas)is one of the most important mollusks cultured all around the world.Selective breeding programs of Pacific oysters in China is initiated since 2006 and developed the genetically improved strain with fast-growing trait.However,little is known about the metabolic signatures of the fast-growing trait.In the present study,the non-targeted metabolomics was performed to analyze the metabolic signatures of adductor muscle tissue in one-year old Pacific oysters from fast-growing strain and the wild population.A total of 7767 and 10174 valid peaks were extracted and quantified in ESI^(+)and ESI^(−)modes,resulting in 399 and 381 annotated metabolites,respectively.PCA and OPLS-DA revealed that considerable separation among samples from fastgrowing strain and wild population,suggesting the differences in metabolic signatures.Meanwhile,81 significantly different metabolites(SDMs)were identified in the comparisons between fast-growing strain and wild population,based on the strict thresholds.It was found that there were highly correlation and conserved coordination among these SDMs.KEGG enrichment analysis indicated that the SDMs were tightly related to pantothenate and CoA biosynthesis,steroid hormone biosynthesis,riboflavin metabolism,and arginine and proline metabolism.Of them,the CoA biosynthesis and metabolism,affected by pantetheine and pantothenic acid,might be important for the growth of Pacific oysters under artificial selective breeding.The study provides the comprehensive views of metabolic signatures in response to artificially selective breeding,and is helpful to better understand the molecular mechanism of fastgrowing traits in Pacific oysters.
基金supported in part by the Natural Science Youth Foundation of Hebei Province under Grant F2019403207in part by the PhD Research Startup Foundation of Hebei GEO University under Grant BQ2019055+3 种基金in part by the Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP-2021A06in part by the Fundamental Research Funds for the Universities in Hebei Province under Grant QN202220in part by the Science and Technology Research Project for Universities of Hebei under Grant ZD2020344in part by the Guangxi Natural Science Fund General Project under Grant 2021GXNSFAA075029.
文摘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.
基金supported by the Key-Area Research and Development Program of Guangdong Province under Grant No.2020B0101090004the National Natural Science Foundation of China under Grant No.62072215,the Guangzhou Basic Research Plan City-School Joint Funding Project under Grant No.2024A03J0405+1 种基金the Guangzhou Basic and Applied Basic Research Foundation under Grant No.2024A04J3458the State Archives Administration Science and Technology Program Plan of China under Grant 2023-X-028.
文摘Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.
基金financially supported by the Young Scientists Fund of the National Natural Science Foundation of China(Nos.52104395 and 52304365)the Science and Technology Planning Project of Guangzhou,China(Nos.202102021080 and 2024A04J10006)+1 种基金the National Key R&D Program of China(No.2021YFC2902605)the Natural Science Foundation of Guangdong Province,China(Nos.2023A1515030145 and 2023A1515011847)。
文摘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.
基金This research was funded by the Short-Term Electrical Load Forecasting Based on Feature Selection and optimized LSTM with DBO which is the Fundamental Scientific Research Project of Liaoning Provincial Department of Education(JYTMS20230189)the Application of Hybrid Grey Wolf Algorithm in Job Shop Scheduling Problem of the Research Support Plan for Introducing High-Level Talents to Shenyang Ligong University(No.1010147001131).
文摘Feature Selection(FS)is a key pre-processing step in pattern recognition and data mining tasks,which can effectively avoid the impact of irrelevant and redundant features on the performance of classification models.In recent years,meta-heuristic algorithms have been widely used in FS problems,so a Hybrid Binary Chaotic Salp Swarm Dung Beetle Optimization(HBCSSDBO)algorithm is proposed in this paper to improve the effect of FS.In this hybrid algorithm,the original continuous optimization algorithm is converted into binary form by the S-type transfer function and applied to the FS problem.By combining the K nearest neighbor(KNN)classifier,the comparative experiments for FS are carried out between the proposed method and four advanced meta-heuristic algorithms on 16 UCI(University of California,Irvine)datasets.Seven evaluation metrics such as average adaptation,average prediction accuracy,and average running time are chosen to judge and compare the algorithms.The selected dataset is also discussed by categorizing it into three dimensions:high,medium,and low dimensions.Experimental results show that the HBCSSDBO feature selection method has the ability to obtain a good subset of features while maintaining high classification accuracy,shows better optimization performance.In addition,the results of statistical tests confirm the significant validity of the method.