A preliminary site selection system for underground hydrogen storage in salt caverns and its application in Pingdingshan,China

Large‐scale underground hydrogen storage(UHS)provides a promising method for increasing the role of hydrogen in the process of carbon neutrality and energy transition.Of all the existing storage deposits,salt caverns are recognized as ideal sites for pure hydrogen storage.Evaluation and optimization of site selection for hydrogen storage facilities in salt caverns have become significant issues.In this article,the software CiteSpace is used to analyze and filter hot topics in published research.Based on a detailed classification and analysis,a“four‐factor”model for the site selection of salt cavern hydrogen storage is proposed,encompassing the dynamic demands of hydrogen energy,geological,hydrological,and ground factors of salt mines.Subsequently,20 basic indicators for comprehensive suitability grading of the target site were screened using the analytic hierarchy process and expert survey methods were adopted,which provided a preliminary site selection system for salt cavern hydrogen storage.Ultimately,the developed system was applied for the evaluation of salt cavern hydrogen storage sites in the salt mines of Pingdingshan City,Henan Province,thereby confirming its rationality and effectiveness.This research provides a feasible method and theoretical basis for the site selection of UHS in salt caverns in China.

Construction of Chimonanthus praecox(L.)Link Aquaporin CpTIP Plant Expression Vector with Mannose Selection System

[ Objective] The aim was to construct drought and saline-alkaline resistance plant expression vector with mannose as selective agent, and further breed unmarked resilient varieties. [ Method] The plant expression vector was constructed by using Chimonanthus praecox（ L. ）Link aquapor.in CpTIP cDNA and Escherichia coli pmi gene, combined stress resistance gene with mannose positive selection system. [ Result] The test successfully constructed the plant expression vector pPMI：：CpTIP. [ Conclusion] The constructed vector linked advantages of stress resistance gene and mannose positive selection system.

Optimization-based mode selection scheme for OAM millimeter wave system in the off-axis misalignment case

Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.

A dual-function selection system enables positive selection of multigene CRISPR mutants and negative selection of Cas9-free progeny in Arabidopsis

The CRISPR/Cas9 technology revolutionizes targeted gene knockout in diverse organisms including plants.However,screening edited alleles,particularly those with multiplex editing,from herbicide-or antibiotic-resistant transgenic plants and segregating out the Cas9 transgene represent two laborious processes.Current solutions to facilitate these processes rely on different selection markers.Here,by taking advantage of the opposite functions of a D-amino acid oxidase(DAO)in detoxifying D-serine and in metabolizing non-toxic D-valine to a cytotoxic product,we develop a DAO-based selection system that simultaneously enables the enrichment of multigene edited alleles and elimination of Cas9-containing progeny in Arabidopsis thaliana.Among five DAOs tested in Escherichia coli,the one encoded by Trigonopsis variabilis(TvDAO)could confer slightly stronger D-serine resistance than other homologs.Transgenic expression of TvDAO in Arabidopsis allowed a clear distinction between transgenic and nontransgenic plants in both D-serine-conditioned positive selection and D-valine-conditioned negative selection.As a proof of concept,we combined CRISPR-induced single-strand annealing repair of a dead TvDAO with D-serine-based positive selection to help identify transgenic plants with multiplex editing,where D-serine-resistant plants exhibited considerably higher co-editing frequencies at three endogenous target genes than those selected by hygromycin.Subsequently,D-valine-based negative selection successfully removed Cas9 and TvDAO transgenes from the survival offspring carrying inherited mutations.Collectively,this work provides a novel strategy to ease CRISPR mutant identification and Cas9 transgene elimination using a single selection marker,which promises more efficient and simplified multiplex CRISPR editing in plants.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Research on Partner Selection in Enterprise Innovation Ecosystem

In the face of fierce market competition,enterprises must ensure the competitiveness of their products or services through technological innovation.However,the complexity of technology often surpasses the capabilities of individual enterprises,leading them to deepen cooperation with other organizations.The entities within the enterprise innovation ecosystem depend on each other,collaborate closely,and rely on core enterprises to integrate resources,thereby creating system value and enhancing competitiveness.The purpose of this paper is to explore the process of selecting appropriate ecosystem partners.It begins by providing an overview of relevant concepts,characteristics,selection factors,and methods.Subsequently,it analyzes the roles,resources,and synergy evolution of the entities within the ecosystem.An evaluation system encompassing operation,core,synergy,and development capability is then established.This system comprises 16 indicators,including organization scale and reputation,and is accompanied by a hierarchical evaluation model.Finally,the validity of the evaluation system is confirmed through empirical analysis,utilizing the Analytic Hierarchy Process(AHP)and the fuzzy comprehensive evaluation method.

Feature Selection with Deep Reinforcement Learning for Intrusion Detection System

An intrusion detection system(IDS)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature Selection with DRL technique,called BBOFS-DRL for intrusion detection.The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFS-DRL model initially designs the BBOFS algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAS)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFS-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFS-DRL model over its recent state of art approaches.

A general evaluation system for optimal selection performance of radar clutter model

The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.

An Extended Fuzzy-DEMATEL System for Factor Analyses on Social Capital Selection in the Renovation of Old Residential Communities

China has been promoting the renovation of old residential communities vigorously.Due to the financial pressure of the government and the sustainability of the renovation of old residential communities,public-private partnerships(PPP)have already gained attention.The selection of social capital is key to improving the efficiency of the PPP model in renovating old residential communities.In order to determine the influencing factors of social capital selection in the renovation of old residential communities,this paper aims to find an effective approach and analyze these factors.In this paper,a fuzzy decision-making and trial evaluation laboratory(fuzzy-DEMATEL)technique is extended and amore suitable systemis developed for the selection of social capital using the existing group decisionmaking theory.In the first stage,grounded theory is used to extract the unabridged key influencing factors for social capital selection in the renovation of old residential communities.Secondly,by considering the impact of expert weights,the key influencing factors are identified.The interactions within these influencing factors are discussed and the credibility of the results is verified by sensitivity analysis.Finally,these key influencing factors are sorted by importance.Based on the results,the government should focus on a technical level,organizationalmanagement abilities,corporate reputation,credit status,etc.This study provides the government with a theoretical basis and a methodology for evaluating social capital selection.

Selection of Wind Turbine Systems for the Sultanate of Oman

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a solution.In addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind turbines.Furthermore,the use of small-scale energy from wind devices has been on the rise in recent years.This upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from wind.To calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal solution.As a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of Oman.This research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of Oman.Further,six distinct miniature wind turbines were investigated for four different locations.The outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small businesses.The government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

The increasing number of security holes in the Internet of Things(IoT)networks creates a question about the reliability of existing network intrusion detection systems.This problem has led to the developing of a research area focused on improving network-based intrusion detection system(NIDS)technologies.According to the analysis of different businesses,most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques.However,these techniques are not suitable for every type of network.In light of this,whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains.The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets.Six ML models were used in this study,namely,logistic regression(LR),decision trees(DT),Naive Bayes(NB),random forest(RF),K-nearest neighbors(KNN),and linear SVM.The primary detection algorithms used in this study,Principal Component(PCA)and Gini Impurity-Based Weighted Forest(GIWRF)evaluated against three global ToN-IoT datasets,UNSW-NB15,and Bot-IoT datasets.The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm.It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used.Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

Selection of Surgical Approach and Clinical Significance of Lower Cervical Spine Injuries Guided by SLIC Scoring System

Objective: To explore the feasibility and clinical significance of surgical approach selection for cervical spine injury guided by SLIC scoring system. Methods: The clinical data of 75 patients with lower cervical injury surgery from January 2020 to November 2022 were retrospectively analyzed, including 48 males and 27 females. Age: 28 - 65 years old. Causes of injury: 39 cases of traffic accidents, 15 cases of ice and snow sports, 12 cases of falling from high places, 9 cases of heavy objects. There were 12 cases of C3-4, 33 cases of C4-5, 21 cases of C5-6, and 9 cases of C6-7. Time from injury to medical treatment: 4 h - 2 d. Cervical spine X-ray, MRI, MDCT examination and preoperative SLIC score were performed on admission. Anterior approach was performed by subtotal cervical vertebrae resection or discectomy, titanium Cage or cage supported bone grafting and anterior titanium plate fixation. Posterior approach was performed with cervical laminoplasty, lateral mass or pedicle screw fixation and fusion. The combined anterior-posterior operation was performed by the anterior methods+ posterior methods. The time from injury to surgery is 12 h to 3 d. The function before and after operation was evaluated by JOA efficacy evaluation criteria. The correlation between the three surgical approaches and postoperative efficacy and SLIC score was compared. SPSS 22.0 software was used for statistical analysis of the data. Results: In this group of 75 patients, 32 cases of anterior operation, 22 cases of posterior operation and 21 cases of combined operation were followed up for no less than 12 months. There was no significant difference in age, gender, injury cause, injury segment, time from injury to treatment, and time from injury to operation among the three surgical approaches, which were comparable. The SLIC scores of mild, moderate and severe injuries of anterior surgery, posterior surgery and combined anterior and posterior surgery, They were (5.26 ± 1.24, 5.86 ± 1.67, 8.25 ± 0.21), (5.57 ± 1.43, 5.99 ± 1.85, 9.00 ± 0.25), (0, 5.98 ± 0.33, 9.44 ± 0.34), respectively. By comparing the SLIC scores and JOA scores of anterior surgery and posterior surgery, there was no difference in SLIC scores and JOA scores between the two groups for mild and moderate injuries (P > 0.05). However, the JOA scores at 3 months, 6 months and 12 months after surgery were different from those before surgery, and the postoperative efficacy and JOA scores were significantly improved (P & lt;0.05), indicating that the two surgical methods had the same therapeutic effect, that is, anterior or posterior surgery could be used to treat mild or moderate injuries (P > 0.05). There were differences in SLIC scores among the three surgical approaches for severe injury (P 0.05). The postoperative efficacy and JOA score of combined anterior-posterior approach were significantly improved compared with those before operation (P Conclusion: SLIC score not only provides accurate judgment for conservative treatment or surgical treatment of cervical spine injury, but also provides evidence-based medical basis and reference value for the selection of surgical approach and surgical method. According to the SLIC score, the surgical approach is safe and feasible. When the SLIC score is 4 - 7, anterior surgery is selected for type A injury, and posterior surgery is selected for type B injury. When the SLIC score is ≥8, combined anterior-posterior surgery should be selected. It is of great significance for clinical formulation of precision treatment strategy.

Dendritic tip selection during solidification of alloys:Insights from phase-field simulations

The effect of undercooling DT and the interface energy anisotropy parameter e4 on the shape of the equiaxed dendritic tip has been investigated by using a quantitative phase-field model for solidification of binary alloys.It was found that the tip radius r increases and the tip shape amplitude coefficient A4 decreases with the increase of the fitting range for all cases.The dendrite tip shape selection parameter sdecreases and then stabilizes with the increase of the fitting range,and sincreases with the increase of e4.The relationship between sand e4 follows a power-law function sµea 4,and a is independent of DT but dependent on the fitting range.Numerical results demonstrate that the predicted sis consistent with the curve of microscopic solvability theory(MST)for e4<0.02,and sobtained from our phase-field simulations is sensitive to the undercooling when e4 is fixed.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

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.

Process design and intensification of multicomponent azeotropes special distillation separation via molecular simulation and system optimization

This work provides an overview of distillation processes,including process design for different distillation processes,selection of entrainers for special distillation processes,system integration and intensification of distillation processes,optimization of process parameters for distillation processes and recent research progress in dynamic control strategies.Firstly,the feasibility of using thermodynamic topological theories such as residual curve,phase equilibrium line and distillation boundary line to analyze different separation regions is discussed,and the rationality of distillation process design is discussed by using its feasibility.Secondly,the application of molecular simulation methods such as molecular dynamics simulation and quantum chemical calculation in the screening of entrainer is discussed for the extractive distillation process.The thermal coupling mechanism of different distillation processes is used to explore the process of different process intensifications.Next,a mixed integer nonlinear optimization strategy for the distillation process based on different algorithms is introduced.Finally,the improvement of dynamic control strategies for different distillation processes in recent years is summarized.This work focuses on the application of process intensification and system optimization in the design of distillation process,and analyzes the challenges,prospects,and development trends of distillation technology in the separation of multicomponent azeotropes.

A Federated Learning Framework with Blockchain-Based Auditable Participant Selection

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.

The suitability assessment on site selection for bottom seeding scallop culture based on analytic hierarchy process

Scallop culture is an important way of bottom-seeding marine ranching,which is of great significance to improve the current situation of fishery resources.However,there are some problems in site-selection evaluation of marine ranching,such as imperfect criteria system,complex structure,untargeted criteria quantification,etc.In addition,no site-selection evaluation method of bottom-seeding culture areas for scallops is available.Therefore,we established a hierarchy structure model according to the analytic hierarchy process(AHP)theory,in which social,physical,chemical,and biological environments are used as main criteria,and marine functional zonation,water depth,current,water temperature,salinity,substrate type,water quality,sediment quality,red tide,phytoplankton,and zooplankton are used as sub-criteria,on which a multi-parameter evaluation system is set up.Meanwhile,the dualism method,assignment method,and membership function method were used to quantify sub-criteria,and a quantitative evaluation for the entire criteria was added,including the evaluation and analysis of two types of unsuitable environmental situations.By overall consideration in scallop yield,quality,and marine ranching construction objectives,the weight of the main criteria could be determined.Five grades in the suitability corresponding to the evaluation result were divided,and the Python language was used to create an evaluation system for efficient calculation and intuitive presentation of the evaluation outcome.Eight marine cases were simulated based on existing survey data,and the results prove that the method is feasible for evaluating and analyzing the site selection of bottom-seeding culture areas for scallops under various environmental situations.The proposed evaluation method can be promoted for the site selection of bottom-seeding marine ranching.This study provided theoretical and methodological references for the site selection evaluation of other types of marine ranching.

Analysis of environmental selection pressure of superoxide dismutase in deep-sea sea cucumber

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.

A Support Data-Based Core-Set Selection Method for Signal Recognition

In recent years,deep learning-based signal recognition technology has gained attention and emerged as an important approach for safeguarding the electromagnetic environment.However,training deep learning-based classifiers on large signal datasets with redundant samples requires significant memory and high costs.This paper proposes a support databased core-set selection method(SD)for signal recognition,aiming to screen a representative subset that approximates the large signal dataset.Specifically,this subset can be identified by employing the labeled information during the early stages of model training,as some training samples are labeled as supporting data frequently.This support data is crucial for model training and can be found using a border sample selector.Simulation results demonstrate that the SD method minimizes the impact on model recognition performance while reducing the dataset size,and outperforms five other state-of-the-art core-set selection methods when the fraction of training sample kept is less than or equal to 0.3 on the RML2016.04C dataset or 0.5 on the RML22 dataset.The SD method is particularly helpful for signal recognition tasks with limited memory and computing resources.

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.