Exploring the interplay of T cell receptor-V gene copy numbers and major histocompatibility complex selection pressure in avian species:Insights into immune system evolution and reproductive investment

Birds,a fascinating and diverse group occupying various habitats worldwide,exhibit a wide range of life-history traits,reproductive methods,and migratory behaviors,all of which influence their immune systems.The association between major histocompatibility complex(MHC)genes and certain ecological factors in response to pathogen selection has been extensively studied;however,the role of the co-working molecule T cell receptor(TCR)remains poorly understood.This study aimed to analyze the copy numbers of TCR-V genes,the selection pressure(ωvalue)on MHC genes using available genomic data,and their potential ecological correlates across 93 species from 13 orders.The study was conducted using the publicly available genome data of birds.Our findings suggested that phylogeny influences the variability in TCR-V gene copy numbers and MHC selection pressure.The phylogenetic generalized least squares regression model revealed that TCR-Vαδcopy number and MHC-I selection pressure were positively associated with body mass.Clutch size was correlated with MHC selection pressure,and Migration was correlated with TCR-Vβcopy number.Further analyses revealed that the TCR-Vβcopy number was positively correlated with MHC-IIB selection pressure,while the TCR-Vγcopy number was negatively correlated with MHC-I peptide-binding region selection pressure.Our findings suggest that TCR-V diversity is significant in adaptive evolution and is related to species’life-history strategies and immunological defenses and provide valuable insights into the mechanisms underlying TCR-V gene duplication and MHC selection in avian species.

Strategies of selective electroreduction of aqueous nitrate to N_(2) in chloride-free system:A critical review

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.

Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables.For a high dimensional nonparametric nonlinear system,however,identifying whether a variable contributes or not is not easy.Therefore,based on the Fourier spectrum of densityweighted derivative,one novel variable selection approach is developed,which does not suffer from the dimensionality curse and improves the identification accuracy.Furthermore,a necessary and sufficient condition for testing a variable whether it contributes or not is provided.The proposed approach does not require strong assumptions on the distribution,such as elliptical distribution.The simulation study verifies the effectiveness of the novel variable selection algorithm.

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.

Mechanism of selective laser trabeculoplasty:a systemic review

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.

Practical Privacy-Preserving ROI Encryption System for Surveillance Videos Supporting Selective Decryption

With the advancement of video recording devices and network infrastructure,we use surveillance cameras to protect our valuable assets.This paper proposes a novel system for encrypting personal information within recorded surveillance videos to enhance efficiency and security.The proposed method leverages Dlib’s CNN-based facial recognition technology to identify Regions of Interest(ROIs)within the video,linking these ROIs to generate unique IDs.These IDs are then combined with a master key to create entity-specific keys,which are used to encrypt the ROIs within the video.This system supports selective decryption,effectively protecting personal information using surveillance footage.Additionally,the system overcomes the limitations of existing ROI recognition technologies by predicting unrecognized frames through post-processing.This research validates the proposed technology through experimental evaluations of execution time and post-processing techniques,ensuring comprehensive personal information protection.Guidelines for setting the thresholds used in this process are also provided.Implementing the proposed method could serve as an effective solution to security vulnerabilities that traditional approaches fail to address.

Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

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.

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.

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

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

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.

Using multi-criteria decision-making and machine learning for football player selection and performance prediction:a systematic review

Evaluating and selecting players to suit football clubs and decision-makers (coaches, managers, technical, and medical staff) is a difficult process from a managerial-financial and sporting perspective. Football is a highly competitive sport where sponsors and fans are attracted by success. The most successful players, based on their characteristics (criteria and sub-criteria), can influence the outcome of a football game at any given time. Consequently, the D-day of selection should employ a more appropriate approach to human resource management. To effectively address this issue, a detailed study and analysis of the available literature are needed to assist practitioners and professionals in making decisions about football player selection and hiring. Peer-reviewed journals were selected for collecting published papers between 2018 and 2023. A total of 66 relevant articles (journal articles, conference articles, book sections, and review articles) were selected for evaluation and analysis. The purpose of the study is to present a systematic literature review (SLR) on how to solve this problem and organize the published research papers that answer our four research questions.

Analysis of handoff in distributed mobile communications system based on remote antenna unit selection

A remote antenna unit （RAU） selection model is presented, and two kinds of handoffs, intra-cell handoff （HO） and inter-cell HO, are defined in distributed mobile communications systems （DAS）. After that, an inter-cell HO model is proposed, in which the average power of the active set （AS） is used to predict the position of the mobile station （MS）. The total power of the AS and the handoff set （HOS） are utilized to determine whether an inter-cell HO is necessary. Furthermore, the relationship between HO parameters and performance metrics is studied in detail based on RAU selection. Simulation results show that both the intra-cell HO and the inter-cell HO can achieve oerfect performance by aoprooriate settings of HO parameters.

CoMP-transmission-based energy-efficient scheme selection algorithm for LTE-A systems

In order to achieve higher system energy efficiency （EE）,a new coordinated multipoint （CoMP）-transmission-based scheme selection energy saving （CTSES）algorithm is proposed for downlink homogeneous cellular networks.The problem is formulated as an optimization of maximizing system EE,under the constraints of the data rate requirement and the maximum transmit power.The problem is decomposed into power allocation and alternative scheme selection problems.Optimal power allocation is calculated for CoMP-JT （joint transmission）and CoMP-CS （coordinated scheduling） transmissions,and the scheme with higher EE is chosen. Since the optimal problem is a nonlinear fractional optimization problem for both CoMP transmission schemes, the problem is transformed into an equivalent problem using the parametric method. The optimal transmit power and optimal EE are obtained by an iteration algorithm in CoMP-JT and CoMP-CS schemes.Simulation results show that the proposed algorithm offers obvious energy-saving potential and outperforms the fixed CoMP transmission scheme.Under the condition of the same maximum transmit power limit,the empirical regularity of user distribution for scheme choice is presented, and using this regularity, the computational complexity can be reduced.

Employing temporal channel correlation in user selection for MIMO broadcast systems

Taking the time varying nature of wireless channels into account, two user selection schemes with lower complexity are developed for multiple-input multiple-output broadcast （MIMO BC）systems. According to the relationship between coherence time and Doppler frequency, an information frame is divided into several segments. At the beginning of each segment, the user selection is carded out with the greedy selection algorithm. In the simplified user selection algorithms employing the temporal correlation （SUSTC）, the selection results are applied for all the remaining slots in each segment. But in the improved simplified user selection algorithms employing the temporal correlation（ISUSTC）, at the remaining slots, users are kept with favorable channel conditions selected at the previous slot, and other users are updated from the candidate pool to communicate simultaneously. Simulations show that compared with the greedy user selection method, the proposed algorithms can reduce the selection complexity with a little sum capacity loss.

EXPERT SYSTEM AND ITS APPLICATION IN THE SELECTION OF PIT RETAINING STRUCTURES

This paper describes the development of an expert system(ES) on earth retaining structures for the selection and design.The ES retaining is an interactive menudriven system and consists of two main parts—the selection part,selectwall and the design part.Selectwall is developed using the knowledge base and it makes a choice of the most appropriate retaining structure.The design part is developed by three independent subprograms which perform detailed design including strength,deformation,stability of the retaining structure.The calculation results are illustrated by plotting the diagram.Using this program,the design procedure of the retaining structure can be performed automatically.

基于SELECT理念腹腔镜下保留脾脏的胰体尾切除

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

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.

SOFTWARE SYSTEM FOR SELECTIVE LASER SINTERING

The advent of rapid prototyping & manufacturing techniques represents a major breakthrough in production engineering. This paper is concerned with the software system aspects of the selective laser sintering (SLS),i.e.the issues that deal with an external geometric CAD model to automatically control the physical layering fabrication process as directly as possible ,regardless of the source of the model. The general issues are described and some key methods are given in this paper.

Improving the phenotypic expression of rice genotypes:Rethinking “intensification” for production systems and selection practices for rice breeding

Intensification in rice crop production is generally understood as requiring increased use of material inputs: water, inorganic fertilizers, and agrochemicals. However, this is not the only kind of intensification available. More productive crop phenotypes, with traits such as more resistance to biotic and abiotic stresses and shorter crop cycles, are possible through modifications in the management of rice plants, soil, water, and nutrients, reducing rather than increasing material inputs. Greater factor productivity can be achieved through the application of new knowledge and more skill, and(initially) more labor, as seen from the System of Rice Intensification(SRI), whose practices are used in various combinations by as many as 10 million farmers on about 4 million hectares in over 50 countries. The highest yields achieved with these management methods have come from hybrids and improved rice varieties, confirming the importance of making genetic improvements. However,unimproved varieties are also responsive to these changes, which induce better growth and functioning of rice root systems and more abundance, diversity, and activity of beneficial soil organisms. Some of these organisms as symbiotic endophytes can affect and enhance the expression of rice plants' genetic potential as well as their phenotypic resilience to multiple stresses, including those of climate change. SRI experience and data suggest that decades of plant breeding have been selecting for the best crop genetic endowments under suboptimal growing conditions, with crowding of plants that impedes their photosynthesis and growth, flooding of rice paddies that causes roots to degenerate and forgoes benefits derived from aerobic soil organisms, and overuse of agrochemicals that adversely affect these organisms as well as soil and human health. This review paper reports evidence from research in India and Indonesia that changes in crop and water management can improve the expression of rice plants' genetic potential, thereby creating more productive and robustphenotypes from given rice genotypes. Data indicate that increased plant density does not necessarily enhance crop yield potential, as classical breeding methods suggest. Developing cultivars that can achieve their higher productivity under a wide range of plant densities—breeding for density-neutral cultivars using alternative selection strategies—will enable more effective exploitation of available crop growth resources. Density-neutral cultivars that achieve high productivity under ample environmental growth resources can also achieve optimal productivity under limited resources, where lower densities can avert crop failure due to overcrowding. This will become more important to the extent that climatic and other factors become more adverse to crop production. Focusing more on which management practices can evoke the most productive and robust phenotypes from given genotypes is important for rice breeding and improvement programs since it is phenotypes that feed our human populations.