Selection Mechanism Research Articles

Mechanistic insights into the orthogonal functionality of an AHL-mediated quorum-sensing circuit in Yersinia pseudotuberculosis

YpsR, a pivotal regulatory protein in the quorum-sensing (QS) of Yersinia pseudotuberculosis(Y. pstb), is essential for molecular signaling, yet its molecular mechanisms remain poorly understood. Herein, this study systematically investigates the interactions between YpsR and acyl-homoserine lactones (AHLs), shedding light on the selective mechanism of YpsR to various AHL molecules. Using molecular docking and surface plasmon resonance (SPR) analysis, we confirmed YpsR’s binding affinities, with the strongest observed for 3OC6-HSL, which notably inhibited Y. pstb growth. Additionally, we engineered a whole-cell biosensor based on YpsR-AHL interaction, which exhibited sensitivity to the signal molecule 3OC6-HSL produced by Y. pstb. Furthermore, key YpsR residues (S32, Y50, W54, D67) involved in AHL binding were identified and validated. Overall, this research elucidates the mechanisms of QS signal recognition in Y. pstb, providing valuable insights that support the development of diagnostic tools for detecting Y. pstb infections.

Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos.

Formulating learning systems for the detection of real-world anomalous events using only video-level labels is a challenging task mainly due to the presence of noisy labels as well as the rare occurrence of anomalous events in the training data. We propose a weakly supervised anomaly detection system that has multiple contributions including a random batch selection mechanism to reduce interbatch correlation and a normalcy suppression block (NSB) which learns to minimize anomaly scores over normal regions of a video by utilizing the overall information available in a training batch. In addition, a clustering loss block (CLB) is proposed to mitigate the label noise and to improve the representation learning for the anomalous and normal regions. This block encourages the backbone network to produce two distinct feature clusters representing normal and anomalous events. An extensive analysis of the proposed approach is provided using three popular anomaly detection datasets including UCF-Crime, ShanghaiTech, and UCSD Ped2. The experiments demonstrate the superior anomaly detection capability of our approach.

SR-Forest: A Genetic Programming-Based Heterogeneous Ensemble Learning Method

Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance. With the development of genetic programming-based symbolic regression methods, many papers begin to choose a popular ensemble learning method, random forests, as the baseline competitor. Instead of considering them as competitors, an alternative idea might be to consider symbolic regression as an enhancement technique for random forest. Genetic programming-based symbolic regression methods which fit a smooth function are complementary to the piecewise nature of decision trees, as the smooth variation is common in regression problems. In this article, we propose to form an ensemble model with symbolic regression-based decision trees to address this issue. Furthermore, we design a guided mutation operator to speed up the search on high-dimensional problems, a multi-fidelity evaluation strategy to reduce the computational cost and an ensemble selection mechanism to improve predictive performance. Finally, experimental results on a regression benchmark with 120 datasets show that the proposed ensemble model outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, the proposed method can provide notable insights on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods.

Interpretable few-shot learning with online attribute selection

Few-shot learning (FSL) presents a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot classification due to a greater chance of error compared to traditional classification. However, the majority of the previous FSL methods are black-box models. In this paper, we propose an inherently interpretable model for FSL based on human-friendly attributes. Previously, human-friendly attributes have been utilized to train models with the potential for human interaction and interpretability. However, such approaches are not directly extendible to the few-shot classification scenario. Moreover, we propose an online attribute selection mechanism to effectively filter out irrelevant attributes in each episode. The attribute selection mechanism improves accuracy and helps with interpretability by reducing the number of attributes that participate in each episode. We further propose a mechanism that automatically detects the episodes where the pool of available human-friendly attributes is insufficient, and subsequently augments it by engaging some learned unknown attributes. We demonstrate that the proposed method achieves results on par with black-box few-shot learning models on four widely used datasets. We also empirically evaluate the level of decision alignment between different models and human understanding and show that our model outperforms the comparison methods based on this criterion.

Research on Cooperative Tracking of Multiple Agents on Heterogeneous Ground

Abstract Pursuit-evasion scenarios are typical and significant area of study multi-agent behaviours. While previous research has primarily focused on training pursuit strategies on idealized flat, this paper explores pursuit strategies on heterogeneous ground that rely on ground features from the effects of friction and viscous forces. Since agent interacts with heterogeneous ground and changes its acceleration, we perform multi-agent modelling. Aiming at the multi-agent deep deterministic policy gradient (MADDPG) algorithm’s low training efficiency and slow convergence speed in pursuit learning, we use the prioritized experience selection mechanism of MADDPG algorithm (PES-MADDPG) which improve the experience extraction mechanism based on the policy evaluation function error and the experience extraction training frequency. With this approach, the design of rewards results in faster convergence of reward values. The experimental findings confirm that the suggested method delivers better performance and effectiveness for pursuers and evaders on heterogeneous ground, and both can acquire the relevant movement strategies through training.

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.

A Collaborative Approach to Detecting DDoS Attacks in SDN Using Entropy and Deep Learning

Software-defined networking (SDN) is an approach to network management allowing to enhance the performance of the network and making it more flexible. The centralized architecture of SDN makes it vulnerable to cyberattacks, especially distributed denial of service (DDoS) attacks. Existing research investigates the detection of DDoS attacks separately on the control plane and data plane. However, there is a need for efficient and accurate detection of these attacks using features obtained from both control and data planes. Therefore, we present a mechanism for identifying DDoS attacks using entropy, multiple feature selection mechanisms, and deep learning. Initially, we use entropy on the control plane to detect anomalous activity and identify suspicious switches. Next, we capture traffic on the suspicious switches to detect DDoS attacks. To detect these attacks, we utilize multi-layer perceptron (MLP) deep learning models, convolutional neural network (CNN), and the long short-term memory (LSTM) approach. An InSDN dataset is used to train the model and test data are generated using Mininet emulation and the Ryu controller. The results reveal that LSTM outperforms MLP and CNN, achieving an accuracy of 99.83%.

Insight into the nonradical selective oxidation mechanism for versatile organic pollutants removal by perovskite activated peroxymonosulfate system

The heterogeneous catalysis of peroxymonosulfate (PMS) through nonradical pathway has shown many advantages in environmental remediation. It is necessary to unveil the factors affecting oxidation routes and selectivity toward different organic pollutants. Herein, a series of ABO3-type BiFexCo1-xO3 perovskite catalysts were fabricated via simple hydrothermal process to realize the changeover of catalytic activity and mechanism in PMS activation and pollutants oxidation. The as-prepared BiFe0.5Co0.5O3 (BFCO-3) achieved a 98 % removal efficiency of SMZ with a high kinetic constant (kobs = 0.334 min−1) in 10 min. The superior catalytic performance was ascribed to the introduction of bimetals at B-site, which enhanced electron donating capacity to PMS, confirming by d-band center calculations. Several parameters such as inorganic anions and humic acids affected the SMZ degradation slightly, owing that singlet oxygen was verified to be the primary reactive species in the BFCO-3/PMS system. Moreover, twelve organic pollutants including amoxicillin crystalline (AMX), cephalexin hydrate (CFX), ciprofloxacin (CIP), levofloxacin (LFX), Sulfadiazine (SDZ), sulfamethoxazole (SMZ), 4-acetamidophenol (ACP), benzoic acid (BA), bisphenol A (BPA), carbamazepine (CBZ), metronidazole (MNZ) and naproxen (NPX) were utilized to investigate the selective oxidation mechanism. A linear correlation between lnkobs and their half-wave potential (φ1/2) values (except for BA and MNZ) suggesting the dominant role of nonradical catalysis where phenolic compounds with lower anodic potential tend to lose electrons are more easily degraded. Furthermore, the reactive sites of representative SMZ for electrophilic species attack were precisely identified based on the Fukui index. And the degradation intermediates of SMZ were further determined by LC-MS/MS technology for proposing their degradation pathway and predicting ecotoxicity. This study not only provides an efficient advanced oxidation system for organic pollutants removal but also advances our understanding of the selective oxidation mechanism in nonradical-dominated catalytic process.

Differences Between the Chinese and American Higher Education Systems and Implications

Despite receiving considerable global attention, the higher education systems of China and the United States exhibit disparities and complementarities that highlight ongoing deficiencies in educational reform and development. This paper holds a comparative analysis of the strengths, weaknesses, and developmental trends within the higher education frameworks of both nations. It reveals that China's system is defined by its uniform policies and stringent teaching methodologies, characterized by fixed curricula and rigorous mechanisms for teacher training and student selection. In contrast, the US system is celebrated for its diversity, flexible policies, innovative teaching approaches, and diverse curricula. It places a strong emphasis on teacher autonomy and the individualization of student assessments. Based on these findings, this study offers a series of recommendations designed to advance the development of higher education in both countries. These include optimizing the admissions and examination systems, reforming educational philosophies, broadening international perspectives, and enhancing the allocation and fairness of educational resources.

Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation

This paper presents an improved genetic algorithm focused on multi-threshold optimization for image segmentation in digital pathology. By innovatively enhancing the selection mechanism and crossover operation, the limitations of traditional genetic algorithms are effectively addressed, significantly improving both segmentation accuracy and computational efficiency. Experimental results demonstrate that the improved genetic algorithm achieves the best balance between precision and recall within the threshold range of 0.02 to 0.05, and it significantly outperforms traditional methods in terms of segmentation performance. Segmentation quality is quantified using metrics such as precision, recall, and F1 score, and statistical tests confirm the superior performance of the algorithm, especially in its global search capabilities for complex optimization problems. Although the algorithm’s computation time is relatively long, its notable advantages in segmentation quality, particularly in handling high-precision segmentation tasks for complex images, are highly pronounced. The experiments also show that the algorithm exhibits strong robustness and stability, maintaining reliable performance under different initial conditions. Compared to general segmentation models, this algorithm demonstrates significant advantages in specialized tasks, such as pathology image segmentation, especially in resource-constrained environments. Therefore, this improved genetic algorithm offers an efficient and precise multi-threshold optimization solution for image segmentation, providing valuable reference for practical applications.

Objective Sliding Mode Control for the Flotation Motor Replacement Process Based on a Stepwise Stochastic Configuration Network

Flotation motors face constant vibration and friction in the working process, resulting in wear and often need to be replaced, reducing production efficiency. In order to ensure the efficient operation of flotation equipment, we designed a manipulator to assist the work. The manipulator model is difficult to be accurate, and the diversity of external information brings great challenges to the precise control of the manipulator. Therefore, an Objective Sliding Mode Control (OSMC) strategy combined with Stepwise Stochastic Configuration Networks (SSCNs) is developed to solve the above problems while reducing the oscillation of the system. The SSCN is distinguished by its innovative node selection mechanism for the hidden layer, which operates in two distinct phases: initially, ‘single time’, where weights and biases are randomly assigned through a uniform distribution, and subsequently, ‘double time’, where these parameters are adjusted based on a normal distribution centered around the values selected in the first phase. This approach enhances the system’s robustness by incorporating a disturbance observer based on SSCN, designed to accurately estimate and compensate for system uncertainties. The simulation model realistically integrates these uncertainties and disturbances, demonstrating the effectiveness of the proposed OSMC in satisfying the stringent control requirements of mechanical arms under uncertain conditions. This advancement offers a promising new method for the precise control of robotic systems in challenging environments.

Attentional templates for target features versus locations

Visual search is guided by visual working memory representations (i.e., attentional templates) that are activated prior to search and contain target-defining features (e.g., color). In the present study, we tested whether attentional templates can also contain spatial target properties (knowing where to look for) and whether attentional selection guided by such feature-specific templates is equally efficient than selection that is based on feature-specific templates (knowing what to look for). In every trial, search displays were either preceded by semantic color or location cues, indicating the upcoming target color or location, respectively. Qualitative differences between feature- and location-based template guidance were substantiated in terms of selection efficiency in low-load (one target color/location) versus high-load trials (two target colors/locations). Behavioral and electrophysiological (N2pc) measures of target selection speed and accuracy were combined for converging evidence. In line with previous studies, we found that color search was highly efficient, even under high-low conditions, when multiple attentional templates were activated to guide attentional selection in a spatially global fashion. Importantly, results in the location task almost perfectly mirrored the findings of the color task, suggesting that multiple templates for different target locations were activated concurrently when two possible target locations were task relevant. Our findings align with accounts that assume a common neuronal network during preparation for location and color search, but regard spatial and feature-based selection mechanisms as independent.

Cytogenetic analysis of 2554 samples of the products of conception from early reproductive losses

Aim. Early pregnancy losses (EPL), to some extent, can be considered a mechanism of natural selection that prevents the development of defective fetuses, since it is believed that even up to 80 % of the causes have genetic disorders. Among the genetic factors of EPL karyotype disorders should be included first of all. Overall, up to 50 % of early miscarriages are caused by karyotype abnormalities. The purpose of this study was studied the contribution of chromosomal abnormalities to the genesis of EPL. Methods. Banding cytogenetic and interphase mFISH with the centromeric probe panel for chromosomes 13, 14, 15, 16, 17, 18, 20, 21, 22, X and Y were used. Results. Were examined 2554 cases of material of early reproductive loss. A normal karyotype was established in 1638 cases (64.1 %), and a pathological karyotype in 916 (35.9 %). Karyotype abnormalities are as follows: autosomal aneuploidy – 478 (52.2 %), polyploidy – 256 (27.9 %), gonosomal aneuploidy – 182 (19.9 %). Conclusions. Among autosomal aneuploidies prevailed trisomy 16 (26.9 %), among polyploidy – triploidy (24.6 %) and among gonosomal aneuploidy – monosomy X (18.4 %).

Inbreeding and its effect on the gene fund in dog breeding

Aim. Identifying problems and approaches to dog breeding that emphasize the importance of the quality of parents and hereditary factors in determining the offspring's quality. Methods. Theoretical review of inbreeding and its impact on the gene pool in dog breeding, comparing it with outbreeding, and exploring natural selection mechanisms and their influence on animal reproduction. Results. The paper discusses the principles of selecting breeders in dog breeding and provides recommendations for avoiding mating dogs with deficiencies in a particular trait. It also highlights the study of genetic mechanisms and their impact on the inheritance of traits in dogs. Two main breeding methods are considered in animal husbandry – inbreeding and outbreeding. The study findings indicate types of inbreeding and their influence on the genetic homogeneity of the offspring. Conclusions. The breeder must clearly define the type of dog he wants to obtain, specifying desired characteristics such as appearance, health, and temperament. In the initial stages, the breeder must achieve stability in breeding offspring with desired characteristics, creating animals with a typical temperament. After achieving stability, one can begin fine-tuning specific traits by selecting for improvements in breed aspects. A dog should be viewed as a holistic system, striving for overall correctness and balance, paying attention to the interrelation between different aspects. The breeder must ensure the preservation of unique breed characteristics during selection. Effective selection involves careful planning, observation, evaluation, and collaboration with experienced veterinarians and breed specialists.

Research on the Path and Mechanism of Enhancing Student Subjectivity in the Process of University Textbook Selection: A Case Study of S College

This paper discusses how to promote students' subjectivity in the process of selecting teaching materials in colleges and universities and constructs an effective teaching material selection mechanism. By analyzing the actual situation of S College, this paper reveals the marginalization of students in the decision-making of textbook selection and probes into the significance of enhancing students' subjectivity from both theoretical and practical dimensions. A number of strategies are put forward, including establishing a textbook selection committee, developing an online textbook evaluation system, encouraging individualized textbook selection, and implementing a textbook recycling strategy in order to build an open, active, and student-centered textbook selection mechanism. These strategies not only help to improve teaching quality and students' learning experience but also promote students' all-round development. The purpose of this study is to provide a reference for the reform of higher education and promote the promotion of students' subjectivity and the continuous improvement of education quality.

Spatial attention for human-centric visual understanding: An Information Bottleneck method

The selective visual attention mechanism in the Human Visual System (HVS) restricts the amount of information that reaches human visual awareness, allowing the brain to perceive high-fidelity natural scenes in real-time with limited computational cost. This selectivity acts as an “Information Bottleneck (IB)” that balances information compression and predictive accuracy. However, such information constraints are rarely explored in the attention mechanism for deep neural networks (DNNs). This paper introduces an IB-inspired spatial attention module for DNNs, which generates an attention map by minimizing the mutual information (MI) between the attentive content and the input while maximizing that between the attentive content and the output. We develop this IB-inspired attention mechanism based on a novel graphical model and explore various implementations of the framework. We show that our approach can yield attention maps that neatly highlight the regions of interest while suppressing the backgrounds, and are interpretable for the decision-making of the DNNs. To validate the effectiveness of the proposed IB-inspired attention mechanism, we apply it to various computer vision tasks including image classification, fine-grained recognition, cross-domain classification, semantic segmentation, and object detection. Extensive experiments demonstrate that it bootstraps standard DNN structures quantitatively and qualitatively for these tasks.

Study of selective collecting capacity and mechanism of novel collector PPB1 in the separation of cerussite and smithsonite

The study investigated a novel hydrophobic, selective, low-toxicity, and low-cost sulfhydryl collector, PPB1 for the flotation separation of cerussite and smithsonite, analyzing the underlying separation mechanism. Micro-flotation experiments indicated that efficient flotation separation of cerussite and smithsonite could be achieved at pH 8, with a PPB1 dosage of 200 mg·L−1, and without the addition of depressants or pre-sulfurization. PPB1, as a collector, exhibits excellent selectivity for cerussite and can obtain cerussite concentrate with a recovery of over 90 % from artificial mixed minerals and raw ores. Zeta potential and contact angle measurements on pure minerals revealed that PPB1 exhibited stronger adsorption on the cerussite surface, thus significantly altering the potential and increasing the hydrophobicity of the cerussite surface. FT-IR, XPS analyses and DFT calculations indicated that the sulfhydryl group of PPB1 formed a thiol-metal bond with Pb sites on the cerussite surface by stable chemical adsorption, but without interaction with the smithsonite surface, showcasing its high-selectivity.

Advanced membrane contactor coupled with electrodialysis metathesis for efficient carbon dioxide capture and waste salt remediation

Rapid development of society has led to significant increases in carbon dioxide (CO2) emissions, primarily driven by intensified industrial activities and urbanization. Capture and utilization of CO2 not only mitigate greenhouse gas emissions but also support the transition towards a circular economy by recycling CO2 into high-value products. This study proposed a membrane contactor and electrodialysis metathesis (MC-EDM) for CO2 capture, NH4HCO3 generation, and NaHCO3 conversion. The MC facilitated the selective capture of CO2 from gas streams. Concurrently, EDM enhanced the NaHCO3/NH4Cl conversion from NH4HCO3/NaCl through selective ion transport mechanisms. The key influencing factors (e.g., gas flow rate, liquid flow rate, and ammonia concentration) for the MC process were optimized to achieve 1.014 mol/L NH4HCO3 concentration with 88.73 % yield and 78.94 % CO2 removal efficiency. Moreover, the parameters (e.g., solid-liquid ratio, membrane type, operating voltage, and salt component ratio) for the EDM process were optimized to achieve NaHCO3 with a 90.9 % yield and 97.80 % purity. Economic analysis indicated the overall process for NaHCO3 recovery was approximately 0.3267 $/kg. The cyclic experiments demonstrated the stability of MC-EDM for high-value NaHCO3 recovery. Thus, the MC-EDM pathways contribute significantly to reducing greenhouse gas emissions from industrial processes while promoting resource efficiency and economic sustainability.

Sperm Storage and Use Following Multiple Insemination in Aedes albopictus: Encouraging Insights for the Sterile Insect Technique.

The key to success in the application of the sterile insect technique (SIT) relies on the ability of released, sterile males to outcompete their fertile wild male counterparts to mate with wild females. However, many insect species exhibit multiple-mating behavior, which can be a way for females to select paternity for their progeny. This study aims to recognize the consequences of potential double-matings during an SIT program and to detect any evidence of sperm selection favoring sperm from fertile mates. This report provides a descriptive analysis of the storage and use of sperm by female Aedes albopictus. Stable isotopes were used to mark the sperm of fertile and sterile males. Mated females were allowed to oviposit before dissecting the spermathecae to link the presence of each type of sperm to the sterility of the eggs laid. It was found that sperm in females inseminated by both males was distributed in the three spermathecae with no obvious pattern, mostly mixed but also separately, and no evidence of any mechanism for sperm selection, sperm precedence, or sperm competition in Ae. albopictus females could be found. The fact that only a few double-mated females were double-inseminated and could also produce semi-sterile eggs, together with the finding that the sperm of sterile males appeared to be no less viable than that of fertile males, is an encouraging outcome for SIT approaches.

Research on the technology path selection of China's manufacturing industry under the high-quality development strategy

As the foundation and leading force of national economic development, manufacturing determines a country's comprehensive strength and international competitiveness, and is the key to achieving transformation, upgrading and high-quality development. Based on the perspective of global value chain embedding, this paper explores the issue of technology path selection and high-end development in my country's manufacturing industry. Against the background of Internet development, this paper analyzes the basis for technology path selection in my country's manufacturing industry to achieve intelligent transformation and high-quality development. By constructing an analytical framework for technology path selection from two aspects of internal and external factors, the theoretical basis for technology path selection in my country is proposed. Guided by high-quality development, this paper studies the internal mechanism of technology path selection that can promote the high-end, intelligent, service-oriented and green development of manufacturing industry, and discusses the technology path selection for manufacturing industry to achieve green and sustainable development. Based on an in-depth analysis of the impact and mechanism of global value chain embedding, Internet development, productive services and environmental constraints on technology path selection, this paper proposes the optimal decision equation for R&D resource allocation in my country based on factor endowment conditions, thereby promoting the transformation of manufacturing industry from factor-driven to innovation-driven, accelerating the process of achieving high-end, intelligent, service-oriented and green development, which is of great significance to promoting the high-quality development of manufacturing industry.