Search Results (35)

Nitric oxide (NO) is a small, highly reactive gaseous signaling molecule that plays diverse and context-dependent roles in microbial physiology and host–microbe interactions. Over the past decade, increasing evidence has revealed the dual nature of NO as both an antimicrobial effector and a signaling mediator involved in microbial stress responses, metabolism, biofilm dynamics, quorum sensing, virulence regulation, and symbiotic interactions. In microbial systems, NO influences adaptation to environmental stress and contributes to mechanisms associated with persistence and antimicrobial resistance. In host organisms, NO functions as a key component of innate immunity while also participating in beneficial interactions involving rhizobia, mycorrhizal fungi, and probiotic microorganisms. Despite its biological significance, accurate detection and quantification of NO remain challenging because of its transient nature, high reactivity, low physiological concentrations, and interference from related reactive oxygen and nitrogen species. Recent advances in biosensing technologies have substantially improved NO detection capabilities through the development of electrochemical, optical, enzyme-based, microfluidic, wearable, and implantable sensing platforms. These innovations are complemented by analytical techniques including electron paramagnetic resonance spectroscopy, mass spectrometry, fluorescence-based imaging, and advanced microscopy, which enhance sensitivity, specificity, and spatiotemporal resolution in complex biological environments. Concurrently, statistical and computational approaches—including sensor calibration models, multivariate analyses, machine learning algorithms, and bioinformatics pipelines—have become increasingly important for extracting biologically meaningful information from NO-related datasets. Unlike previous reviews that primarily focus on either NO biology or sensing technologies, this review integrates current knowledge of NO-mediated microbial physiology and host–microbe interactions with recent developments in biosensor engineering, analytical methodologies, statistical frameworks, and emerging artificial intelligence (AI)-driven data interpretation. We further highlight applications of NO detection in infectious disease diagnostics, antimicrobial screening, probiotic and biofertilizer evaluation, environmental microbiome monitoring, and real-time studies of symbiosis and infection. Finally, future directions including miniaturized sensing platforms, multi-omics integration, AI-assisted analytics, and sensor standardization are discussed. By unifying molecular, analytical, and computational perspectives, this review provides a multidisciplinary framework and roadmap for advancing NO-based research and translational applications across microbial, environmental, and host-associated systems. Full article

Accurate prediction of temperature-induced degradation in concrete is essential for improving structural fire safety and supporting reliable post-fire engineering decisions. However, previous studies have generally focused on conventional machine learning applications or limited optimization strategies, while integrated frameworks combining systematic input screening, robust validation, large-scale metaheuristic optimization, and interpretable analysis remain limited. This study aims to develop a comprehensive predictive framework for estimating the temperature-induced weight loss and compressive strength of concrete using advanced machine learning techniques. First, a detailed collinearity analysis was performed to filter the input dataset, eliminate redundant correlations, and improve statistical reliability. For modeling consistency, all fiber-containing mixtures were treated as polymer-fiber systems, and fiber-related variables were interpreted as polymer-fiber descriptors. To reduce overfitting and ensure robust validation, 5-fold cross-validation was applied during training, while 23% of the dataset was reserved as a strictly independent test set. In addition, 25 metaheuristic algorithms were evaluated under a standardized computational budget of 5000 function evaluations to perform neural architecture search. The results showed that the Marine Predators Algorithm (MPA), Symbiotic Organisms Search (SOS), and Kepler Optimization Algorithm (KOA) achieved superior convergence behavior in optimizing hybrid Levenberg–Marquardt-trained networks. SHapley Additive exPlanations (SHAP)-based sensitivity analysis further revealed that matrix-related properties, particularly unit weight and water absorption capacity, were the dominant drivers of thermal degradation. Overall, the proposed framework provides not only a robust benchmarking platform for predictive modeling but also a practically relevant and interpretable tool for post-fire structural assessment and thermally resilient concrete design. Full article

In recent years, with the rapid advancement of science and technology, generative artificial intelligence has increasingly entered the public eye. Primarily through intelligent algorithms that simulate human logic and integrate vast amounts of network data, it provides designers with solutions that transcend traditional thinking, enhancing both design efficiency and quality. Compared to traditional design methods reliant on human experience, generative design possesses robust data processing capabilities and the ability to refine design proposals, significantly reducing preliminary design time. This study employs the CiteSpace visualization tool to systematically organize and conduct knowledge map analysis of research literature related to generative AI in architectural design within the Web of Science database from 2005 to 2025. Findings reveal the following: (1) International research exhibits a trend toward interdisciplinary convergence. In recent years, research in this field has grown rapidly across nations, with continuously increasing academic influence; (2) Research primarily focuses on technological applications within architectural design, aiming to drive innovation and development by providing superior, more efficient technical support; (3) Generative AI in architectural design has emerged as a prominent international research focus, reflecting a shift from isolated design to industry-wide integration; (4) Generative AI has become a core global architectural design topic, with future research advancing toward full-process intelligent collaboration. High-quality knowledge graphs tailored for the architecture industry should be constructed to overcome data silos. Concurrently, a multidimensional evaluation system for generative quality must be established to deepen the symbiotic design paradigm of human–machine collaboration. This significantly enhances efficiency while reducing the iterative nature of traditional methods. This study aims to provide empirical support for theoretical and practical advancements, offering crucial references for practitioners to identify business opportunities and policymakers to optimize relevant strategies. Full article

Effective engineering modeling requires simultaneously addressing feature selection and hyperparameter interdependence, a challenge exacerbated by high-dimensional data characteristics in complex engineering modeling. Traditional optimization methods typically address these two aspects separately, which limits overall model performance. This study introduces a hybrid framework to enhance the performance of extreme gradient boosting (XGBoost) in engineering applications. The framework comprises two main phases: first, preliminary feature selection guided by prior domain knowledge and statistical analysis to reduce data dimensionality while preserving interpretability; second, a discrete symbiotic organisms search algorithm with adaptive feature mutation (DMSOS) simultaneously optimizes feature subsets and XGBoost hyperparameters. The DMSOS employs a discretization strategy to separate feature selection from hyperparameter tuning, facilitating focused searches within distinct spaces. An adaptive mutation mechanism dynamically adjusts exploration intensity based on iteration progress and feature importance. Additionally, evaluations on 1414 field-measured blasting vibration data demonstrate that the proposed DMSOS-XGBoost model achieves superior prediction performance, with an r² of 0.96696 and RMSE of 0.02636, outperforming models optimized via traditional sequential approaches. Further interpretability analysis highlights spatial geometry and explosive load as critical features, offering actionable insights for environmental risk management. This research provides a valuable methodological reference for engineering modeling scenarios requiring simultaneous optimization of features and hyperparameters. Full article

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computational complexity in huge feature spaces. The UFS problem has been addressed in several research efforts. Recent studies have witnessed a surge in innovative techniques like nature-inspired algorithms for clustering and UFS problems. However, very few studies consider the UFS problem as a multi-objective problem to find the optimal trade-off between the number of selected features and model accuracy. This paper proposes a multi-objective symbiotic organism search algorithm for unsupervised feature selection (SOSUFS) and a symbiotic organism search-based clustering (SOSC) algorithm to generate the optimal feature subset for more accurate clustering. The efficiency and robustness of the proposed algorithm are investigated on benchmark datasets. The SOSUFS method, combined with SOSC, demonstrated the highest f-measure, whereas the KHCluster method resulted in the lowest f-measure. SOSFS effectively reduced the number of features by more than half. The proposed symbiotic organisms search-based optimal unsupervised feature-selection (SOSUFS) method, along with search-based optimal clustering (SOSC), was identified as the top-performing clustering approach. Following this, the SOSUFS method demonstrated strong performance. In summary, this empirical study indicates that the proposed algorithm significantly surpasses state-of-the-art algorithms in both efficiency and effectiveness. Unsupervised learning in artificial intelligence involves machine-learning techniques that learn from data without human supervision. Unlike supervised learning, unsupervised machine-learning models work with unlabeled data to uncover patterns and insights independently, without explicit guidance or instruction. Full article

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability. Full article

Supply chain agility has become a key success factor for businesses trying to handle upheavals and uncertainty in today’s quickly changing business environment. Proactive risk reduction is essential for achieving this agility. To facilitate real-time risk prevention and improve agility, this research study proposes an innovative strategy that makes use of machine learning as well as predictive analytics approaches. Traditional supply chain risk management frequently uses post-event analysis as well as historical data, which restricts its ability to address real-time interruptions. This research, on the other hand, promotes a futuristic methodology that uses predictive analytics to foresee possible disruptions. Based on contextual and historical data, machine learning models can be trained to find patterns and correlations as well as anomalies that point to imminent dangers. Organizations can identify risks as they arise and take preventative measures by incorporating these models into a real-time monitoring system. This study examines numerous predictive analytics methods, showing how they can be used to spot supply chain risks. These methods include time series analysis and anomaly detection as well as natural language processing. Additionally, risk assessment models are continuously improved and optimized using machine learning algorithms, assuring their accuracy and adaptability in changing contexts. This research clarifies the symbiotic relationship among predictive analytics and machine learning as well as supply chain agility using a synthesis of theoretical discourse and practical evidence. Case studies from various sectors highlight the usefulness and advantages of the suggested strategy. The advantages of this novel technique include improved risk visibility and quicker response times as well as the capacity to quickly modify operations. The development of a holistic framework that incorporates predictive analytics and machine learning into risk management procedures, setting the path for real-time risk identification as well as mitigation, is one of the theoretical contributions. On the practical side, the case studies offered in this paper show the actual benefits as well as the adaptability of the proposed approach across a wide range of businesses. Full article

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO. Full article

In order to minimize makespan (C_max) without causing tool delay with the fewest copies of each tool type, this study investigates the concurrent scheduling of automated guided vehicles (AGVs), machines (MCs), tool transporter (TT), and tools in a multi-machine flexible manufacturing system (FMS). The tools are housed in a central tool magazine (CTM), accessible to and utilized by several machines. AGVs and the tool transporter (TT) move jobs and tools between machines. Since it involves allocating tool copies and AGVs to job operations, sequencing job operations on machines, and related trip operations, such as AGVs’ and TT’s empty trip and loaded trip times, this simultaneous scheduling problem is highly complicated. This issue is resolved using the symbiotic organisms search algorithm (SOSA), based on the symbiotic interaction strategies that organisms adapt to survive in the ecosystem. This study proposes a mixed nonlinear integer programming formulation to address this problem. Verification is performed using an industrial problem from a manufacturing organization. The results show that employing two copies for two tool types out of 22 tool kinds and one copy for the remaining tool types results in no tool delay, which causes a reduction in the C_max as well as cost. The industries that can benefit directly from this study are consumer electronics manufacturers, original equipment manufacturers, automobile manufacturers, and textile machine producers. The results demonstrate that the SOSA provides promising results compared to the flower pollination algorithm (FPA). Full article

Marine ecosystems are among the richest in terms of biodiversity, and at present, still remain largely unknown today. In the molecular biology era, several analyses have been conducted to unravel the biological processes in this ecosystem. These systems have provided biotechnological solutions to current problems, including the treatment of diseases, as well as for the development of new biotechnological tools with applications in biomedicine and/or agri-food. In addition, in the context of climate change and global warming, these studies become even more necessary for the development of molecular tools that allow a reliable follow-up of this situation to anticipate alterations and responses of bioindicator species and to create a database to prevent and predict the environmental and climatic changes before the damage is irreversible. Proteomics approaches have revealed their potential use to obtain the set of biological effectors that lead to the real biological station on a specific stage, the proteins. In addition, proteomics-based algorithms have allowed the discovery of proteins with new potential biotechnological applications from proteome data through “applied proteomics”. In this project, the first proteome analysis of the sea anemone, Anemonia sulcata, and its symbiont has been developed. These organisms present a wide distribution sea ecosystem. In Spain, it is accepted as a fishing and aquaculture species. Moreover, Anemonia sulcate has a symbiotic relation with autotroph Dinoflagellates, Symbiodinium spp., that allows the study of its relation at the molecular level. For the first characterization of A. sulcata proteome, three independent biological replicates were used, and proteins were extracted and analyzed by LC–MS/MS, allowing the quantification of 325 proteins, 81 from Symbiodinium spp. proteins and 244 from A. sulcata proteins. These proteins were subjected to gene ontology categorization by Cellular Component, Molecular Function and Biological Process. These analyzes have allowed the identification of biomarkers of gene expression as potential powerful emerging diagnostic tools to identify and characterize the molecular drivers of climate change stresses and improve monitoring techniques. In addition, through the application of novel algorithms for the detection of bioactive compounds based on the analysis of molecules of marine origin, the proteome has allowed the identification of proteins with potential applications in the fields of biomedicine and agri-food. Full article

To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm can easily fall into premature convergence, local optimum and destroy the optimal solution. Furthermore, the symbiotic organisms search (SOS) strategy can be introduced, which integrates the mutualism strategy and commensalism strategy to propose a new migration operator. To address the problem that the optimal solution is easily destroyed, a parasitic natural enemy insect mechanism is introduced, and predator mutation and parasitic mutation strategies with symmetry are defined, which can be guided according to the iterative characteristics of the population. By comparing with eight multi-objective benchmark test functions with four multi-objective algorithms, the results show that the algorithm outperforms other comparative algorithms in terms of the convergence of the solution set and the uniformity of distribution. Finally, the algorithm is applied to multi-objective, flexible job-shop scheduling (FJSP) to test its practical application value, and it is shown through experiments that the algorithm is effective in solving the multi-objective FJSP problem. Full article

This paper presents a novel approach to designing a CMOS inverter using the Mayfly Optimization Algorithm (MA). The MA is utilized in this paper to obtain symmetrical switching of the inverter, which is crucial in many digital electronic circuits. The MA method is found to have a fast convergence rate compared to other optimization methods, such as the Symbiotic Organisms Search (SOS), Particle Swarm Optimization (PSO), and Differential Evolution (DE). A total of eight different sets of design parameters and criteria were analyzed in Case I, and the results confirmed compatibility between the MA and Spice techniques. The maximum discrepancy in fall time across all design sets was found to be 2.075711 ns. In Case II, the objective was to create a symmetrical inverter with identical fall and rise times. The difference in fall and rise times was minimized based on Spice simulations, with the maximum difference measuring 0.9784731 ns. In Case III, the CMOS inverter was designed to achieve symmetrical fall and rise times as well as propagation delays. The Spice simulation results demonstrated that symmetry had been successfully achieved, with the minimum difference measuring 0.312893 ns and the maximum difference measuring 1.076540 ns. These Spice simulation results are consistent with the MA results. The results conclude that the MA is a reliable and simple optimization technique and can be used in similar electronic topologies. Full article

In this research, the effectiveness of a novel optimizer dubbed as decomposition-based multi-objective symbiotic organism search (MOSOS/D) for multi-objective problems was explored. The proposed optimizer was based on the symbiotic organisms’ search (SOS), which is a star-rising metaheuristic inspired by the natural phenomenon of symbioses among living organisms. A decomposition framework was incorporated in SOS for stagnation prevention and its deep performance analysis in real-world applications. The investigation included both qualitative and quantitative analyses of the MOSOS/D metaheuristic. For quantitative analysis, the MOSOS/D was statistically examined by using it to solve the unconstrained DTLZ test suite for real-parameter continuous optimizations. Next, two constrained structural benchmarks for real-world optimization scenario were also tackled. The qualitative analysis was performed based on the characteristics of the Pareto fronts, boxplots, and dimension curves. To check the robustness of the proposed optimizer, comparative analysis was carried out with four state-of-the-art optimizers, viz., MOEA/D, NSGA-II, MOMPA and MOEO, grounded on six widely accepted performance measures. The feasibility test and Friedman’s rank test demonstrates the dominance of MOSOS/D over other compared techniques and exhibited its effectiveness in solving large complex multi-objective problems. Full article

This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology is developed for estimating heating load (H_L) in residential buildings. Moreover, the SOS is comparatively assessed with several identical optimizers, namely political optimizer, heap-based optimizer, Henry gas solubility optimization, atom search optimization, stochastic fractal search, and cuttlefish optimization algorithm. The dataset used for this study lists the H_L versus the corresponding building conditions and the model tries to disclose the nonlinear relationship between them. For each mode, an extensive trial and error effort revealed the most suitable configuration. Examining the accuracy of prediction showed that the SOS–ANN hybrid is a strong predictor as its results are in great harmony with expectations. Moreover, to verify the results of the SOS–ANN, it was compared with several benchmark models employed in this study, as well as in the earlier literature. This comparison revealed the superior accuracy of the suggested model. Hence, utilizing the SOS–ANN is highly recommended to energy-building experts for attaining an early estimation of the H_L from a designed building’s characteristics. Full article

As the volume of data generated by information systems continues to increase, machine learning (ML) techniques have become essential for the extraction of meaningful insights. However, the sheer volume of data often causes these techniques to become sluggish. To overcome this, feature selection is a vital step in the pre-processing of data. In this paper, we introduce a novel K-nearest neighborhood (KNN)-based wrapper system for feature selection that leverages the iterative improvement ability of the weighted superposition attraction (WSA). We evaluate the performance of WSA against seven well-known metaheuristic algorithms, i.e., differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), flower pollination algorithm (FPA), symbiotic organisms search (SOS), marine predators’ algorithm (MPA) and manta ray foraging optimization (MRFO). Our extensive numerical experiments demonstrate that WSA is highly effective for feature selection, achieving a decrease of up to 99% in the number of features for large datasets without sacrificing classification accuracy. In fact, WSA-KNN outperforms traditional ML methods by about 18% and ensemble ML algorithms by 9%. Moreover, WSA-KNN achieves comparable or slightly better solutions when compared with neural networks hybridized with metaheuristics. These findings highlight the importance and potential of WSA for feature selection in modern-day data processing systems. Full article