Search Results (94)

The European cherry fruit fly (Rhagoletis cerasi L.) poses a significant pest threat to cherry production due to its rapid reproduction and host specificity, causing substantial economic damage. This study presents a novel, explainable, and biologically inspired data-driven classification model based on fruit characteristics to support targeted and sustainable pest control strategies. In research conducted at four different locations in Elazığ province, three population classes were determined based on the number of adult individuals caught in traps, and 10 different fruit characteristics were measured in fruit samples belonging to each class. The data used in this study are original data obtained by the authors. To examine the relationship between pomological characteristics of cherry fruit and cherry fruit fly density, the Chaotic Rule-based–Strength Pareto Evolutionary Algorithm2 (CRb-SPEA2) method, developed as a multi-objective and chaos-integrated evolutionary rule mining framework, was adapted. The developed algorithm aimed for high performance, interpretability, and transparency. Accuracy, Precision, and Recall metrics, which are conflicting objectives, were optimized with Pareto-optimal solutions, yielding selectable results for domain experts. To increase population diversity and reduce the risk of early convergence and getting stuck in a local optimum, the Tent chaotic mapping mechanism was also integrated into the system. Furthermore, the model was trained without the need for predefined automatic discretization of the continuous value ranges of the attributes. The proposed model achieved superior results across all classes, with the highest accuracy rate of 82.6% recorded in the High class, demonstrating excellent sensitivity and recall values. Full article

Enhancing the acid tolerance of Lactiplantibacillus plantarum is essential for improving its fermentation performance and metabolic activity under acidic conditions, thereby strengthening its probiotic functionality. In this study, the glutamate decarboxylase gene (gadA) and the arginine decarboxylase gene (speA) from Alicyclobacillus acidoterrestris DSM 3922^T were heterologously expressed in L. plantarum WCFS1 to enhance its acid resistance. Recombinant expression vectors pMG36e-gadA and pMG36e-speA were constructed and introduced into L. plantarum WCFS1 via electroporation. The acid tolerance, cell membrane integrity, intracellular pH, ATP content, gene expression profiles, and enzyme activities of the recombinant L. plantarum WCFS1-gadA and WCFS1-speA were systematically evaluated. The results demonstrate that both recombinant strains exhibited significantly higher acid tolerance than the control strains. Under acid stress, the expression of gadA and speA was up-regulated, accompanied by enhanced activities of glutamate and arginine decarboxylases. In addition, the recombinant strains maintained higher intracellular pH and ATP levels compared with the control strain. Furthermore, the fermentative activity results support their potential applicability in fruit juice fermentation. Collectively, the heterologous expression of gadA and speA effectively improved the acid tolerance of L. plantarum, providing both mechanistic insights into acid stress adaptation and a theoretical basis for developing industrially robust, acid-resistant probiotic strains. Full article

As intelligent construction technology advances, new projects have become more technology-intensive, collaborative, and multi-objective. Traditional team formation methods based on people’s experience can no longer meet their complex management needs. This study reframes team formation as a multi-objective optimization problem to maximize person–job fit and team collaboration. By introducing a hierarchical penalty mechanism for structured resumes and performing semantic feature extraction on unstructured text via the BERT-base-Chinese model, we develop a job competency model, quantify person–job fit with cosine similarity, and assess team collaboration through MBTI theory and a project-specific scoring framework. An improved algorithm, CSCD-NSGA-II, is proposed, which combines K-means clustering and a modified crowding distance, to maintain solution diversity under constraints. It improves HV by 1.55% and reduces SP by 10.81% compared to the standard NSGA-II. Validation using real projects, simulated data, and algorithm comparisons demonstrates that CSCD-NSGA-II generates teams more efficiently than manual methods. Survey results indicate improved role diversity and the feasibility of collaboration, along with similar task adaptability. The algorithm also outperforms NSGA-II, MOPSO, and SPEA2, supporting intelligent team formation in modern construction. Full article

Energy-aware distributed manufacturing has become a key focus in modern production systems due to the growing demand for sustainable and efficient operations. This study investigates the energy-aware distributed flexible job shop scheduling problem with job priority, where multiple factories cooperate to process prioritized jobs under energy consumption considerations. Considering job priorities is essential for reflecting the practical importance and urgency of different customer orders, which directly affects scheduling fairness and production responsiveness. The proposed bi-objective model aims to simultaneously minimize total weighted tardiness and total energy consumption, accounting for both processing and idle power. To effectively solve this complex NP-hard problem, a knowledge-guided deep reinforcement learning approach is developed. Domain knowledge is integrated into a double deep Q-network to guide the adaptive selection of local search operators, while a co-evolutionary mechanism maintains global exploration and accelerates convergence. Extensive computational experiments are conducted on 24 benchmark instances, which are categorized into five groups according to factory scale, with the maximum problem size reaching 160 jobs × 6 machines × 5 factories, together with a real-world case study. Compared with four state-of-the-art multi-objective baseline algorithms (NSGA-II, MOPSO, MOEA/D, and SPEA2), the proposed D2QN-COEA demonstrates substantial performance advantages. On average, it achieves an HV improvement of 23.1% compared with the best-performing baseline on each instance, while GD and IGD are reduced by 70.8% and 63.7%, respectively. When averaged across all four baseline algorithms, D2QN-COEA yields improvements of 203.4% in HV, 83.9% in GD, 79.9% in IGD, and 70.8% in Spacing, confirming its superior convergence accuracy and solution diversity. The results confirm that embedding domain knowledge into deep reinforcement learning enhances optimization robustness and provides an intelligent solution for energy-efficient distributed scheduling in modern manufacturing systems. Full article

A Group A Streptococcus (GAS, Streptococcus pyogenes) is an exclusively human pathogen whose virulence is driven by a diverse array of surface structures, secreted toxins, and immune evasion mechanisms. Central to its pathogenicity is the M protein, a surface-anchored molecule that inhibits phagocytosis by interfering with complement deposition and binding host factors such as fibrinogen. GAS also secretes a wide range of toxins and enzymes that damage tissues and disrupt host defences. Streptolysin O and streptolysin S are potent cytolysins that lyse immune cells and contribute to tissue necrosis. Pyrogenic exotoxins (such as SpeA and SpeC) act as superantigens, triggering massive, dysregulated T cell activation and cytokine release, an underlying mechanism in streptococcal toxic shock syndrome. Additional factors like DNases and streptokinase facilitate bacterial spread by breaking down host tissue and counteracting neutrophil extracellular traps (NETs). Immune evasion is further supported by the production of enzymes that interfere with complement functions, like the cleavage of chemokines and the targeting of antibodies. Together, these virulence determinants allow GAS to cause a wide spectrum of diseases, ranging from uncomplicated pharyngitis and impetigo to invasive conditions like necrotising fasciitis and sepsis. This review provides a timely overview of the important GAS virulence factors and an update on the current vaccine landscape. Full article

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design. Full article

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia–Ukraine war, during the COVID-19 crisis and Russia–Ukraine war, and after the COVID-19 pandemic and during the Russia–Ukraine war. Metaheuristics, Non-dominated Sorting Genetic Algorithm (NSGAII), Strength Pareto Evolutionary Algorithm (SPEA2), and Particle Swarm Optimization (PSO) are applied to find the best allocation. The results reveal that there a significant preference for the S&P Green Bond during the four periods of study according to three algorithms, thanks to its portfolio diversification abilities. During the COVID-19 pandemic and the geopolitical crisis, the most optimal portfolio was Nikkei 225 because of its quick recovery from the pandemic and poor reliance on the Russia–Ukraine markets, while WTI crude oil and both dirty and clean cryptocurrencies were poor contributors to the investment portfolio because these assets are sensitive to geopolitical problems. After the end of the pandemic and during the ongoing Russia–Ukraine war, the three algorithms obtained remarkably different results: the NSGAII portfolio was invested in various assets, 32% of the SPEA2 portfolio was allocated to the S&P Green Bond, and half of the PSO portfolio was allocated to the S&P Green Bond too. This may be due to changes in investors’ preferences to protect their fortune and to diversify their portfolio during the war. From a risk-averse perspective, NSGAII does not underestimate the risk, while in terms of forecasting accuracy, PSO is an adequate algorithm. In terms of time, NSGAII is the fastest algorithm, while SPEA2 requires more time than the NSGAII and PSO algorithms. Our results have important implications for both investors and risk managers in terms of portfolio and risk management decisions, and they highlight the factors that influence investment choices during health and geopolitical crises. Full article

Laser engraving may be used in a variety of industries, from medicine to defense, and it has many uses that require high-quality precision production. However, in practice, operators have to adjust the laser settings manually, which can result in wasted material and poor color quality and even decrease productivity. Current optimization approaches mostly concentrate on single objectives, making it impossible to co-optimize engraving quality and production efficiency simultaneously. In this paper, an approach based on a multi-objective genetic algorithm, a combination of NSGA-II, SPEA2, and MOEA/D, is proposed to automatically establish the relationship between CMYK color attributes, which are extracted from images of engravings, and laser parameters (power, speed, and frequency). Anodized aluminum 6061 was laser-processed using an SPI 30W fiber laser. While the proposed framework is general, the experimental validation in this study was specifically constrained to this material. The results also indicate that MOEA/D converges in a short time and becomes relatively stable after 20 generations. NSGA-II results in solutions that are more diverse, and SPEA2 offers a good trade-off between the speed of convergence and solution size. This approach resulted in optimization in terms of both a decrease in material used and color matching between manual operations, with the average CMYK improvement being up to 28%. Our results indicate that multi-objective evolutionary optimization is feasible for the optimization of efficiency and quality in laser cutting. Full article

Antarctic research stations require reliable low-carbon power under extreme conditions. This study compiles a synchronized PV-meteorological time-series data set on Horseshoe Island (Antarctica) at 30 s, 1 min, and 5 min resolutions and compares four PV module types (monocrystalline, polycrystalline, flexible mono, and semitransparent) under controlled field operation. Model development adopts an interpretable, multi-objective framework: a modified SPEA-2 searches rule sets on the Pareto front that jointly optimize precision and recall, yielding transparent, physically plausible decision rules for operational use. For context, benchmark machine-learning models (e.g., kNN, SVM) are evaluated on the same splits. Performance is reported with precision, recall, and complementary metrics (F1, balanced accuracy, and MCC), emphasizing class-wise behavior and robustness. Results show that the proposed rule-based approach attains competitive predictive performance while retaining interpretability and stability across panel types and sampling intervals. Contributions are threefold: (i) a high-resolution field data set coupling PV output with solar radiation, temperature, wind, and humidity in polar conditions; (ii) a Pareto-front, explainable rule-extraction methodology tailored to small-power PV; and (iii) a comparative assessment against standard ML baselines using multiple, class-aware metrics. The resulting XAI models achieved 92.3% precision and 89.7% recall. The findings inform the design and operation of PV systems for harsh, high-latitude environments. Full article

The rapid development of telematics technology has greatly supported high-computing applications like autonomous driving and real-time road condition prediction. However, the limited computational resources and dynamic topology of in-vehicle terminals pose challenges such as delay, load imbalance, and bandwidth consumption. To address these, a three-layer vehicular network architecture based on cloud–edge–end collaboration was proposed, with V2X technology used for multi-hop transmission. Models for delay, energy consumption, and edge caching were designed to meet the requirements for low delay, energy efficiency, and effective caching. Additionally, a dynamic pricing model for edge resources, based on load-awareness, was proposed to balance service quality and cost-effectiveness. The enhanced NSGA-III algorithm (ADP-NSGA-III) was applied to optimize system delay, energy consumption, and system resource pricing. The experimental results (mean of 30 independent runs) indicate that, compared with the NSGA-II, NSGA-III, MOEA-D, and SPEA2 optimization schemes, the proposed scheme reduced system delay by 21.63%, 5.96%, 17.84%, and 8.30%, respectively, in a system with 55 tasks. The energy consumption was reduced by 11.87%, 7.58%, 15.59%, and 9.94%, respectively. Full article

Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments. Full article

This paper proposes a hybrid methodology for portfolio optimization by integrating the data envelopment analysis (DEA) model with the mean semivariance (MSV) framework. The goal is to construct portfolios that achieve targeted returns while minimizing downside risk. The methodology comprises two stages: (1) identifying efficient stocks through DEA, where semivariance and beta ($\beta$) are employed as input risk metrics and the expected return serves as the output, and (2) determining optimal portfolio weights through the MSV model, solved using artificial neural networks (ANNs) and evolutionary algorithms. The empirical results demonstrate that portfolios optimized with ANNs exhibit significantly lower risk compared to those derived from evolutionary algorithms, highlighting the superiority of ANN-based approaches in balancing risk and return under the proposed framework. This study underscores the potential of hybrid DEA-MSV models enhanced by machine learning techniques for advanced portfolio management. Full article

Dueto uncertainties in real-world production, dynamic factors have become increasingly critical in the research of distributed flexible job shop scheduling problems. Effectively responding to dynamic events can significantly enhance the adaptability and quality of scheduling solutions, thereby improving the resilience of manufacturing systems. This study addresses the dynamic distributed flexible job shop scheduling problem, which involves random job arrivals and machine breakdowns, and proposes an effective discrete improved gray wolf optimization (DIGWO) algorithm-based predictive–reactive method. The first contribution of our work lies in its dynamic scheduling strategy: a periodic- and event-driven approach is used to capture the dynamic nature of the problem, and a static scheduling window is constructed based on updated factory and workshop statuses to convert dynamic scheduling into static scheduling at each rescheduling point. Second, a mathematical model of multi-objective distributed flexible job shop scheduling (MODDFJSP) is established, optimizing makespan, tardiness, maximal factory load, and stability. The novelty of the model is that it is capable of optimizing both production efficiency and operational stability in the workshop. Third, by designing an efficacious initialization mechanism, prey search, and an external archive, the DIGWO algorithm is developed to solve conflicting objectives and search for a set of trade-off solutions. Experimental results in a simulated dynamic distributed flexible job shop demonstrate that DIGWO outperforms three well-known algorithms (NSGA-II, SPEA2, and MOEA/D). The proposed method also surpasses completely reactive scheduling approaches based on rule combinations. This study provides a reference for distributed manufacturing systems facing random job arrivals and machine breakdowns. Full article

As early detection of voice disorders can significantly improve patients’ situation, the automated detection using Artificial Intelligence techniques can be crucial in various applications in this scope. This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. The optimization problem has been formulated as a wrapper-based algorithm for feature selection and multi-objective optimization relying on four machine learning algorithms: K-Nearest Neighbour algorithm (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Three publicly available voice disorder datasets have been utilized, and results have been compared based on Inverted-Generational Distance, Hypervolume, spacing, and spread. The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions. Full article