Search Results (288)

To address the issues of uneven population initialization, insufficient individual information interaction, and passive boundary handling in the standard Golden Jackal Optimization (GJO) algorithm, while improving the accuracy and efficiency of multilevel thresholding in artistic image segmentation, this paper proposes an improved Golden Jackal Optimization algorithm (MGJO) and applies it to this task. MGJO introduces a high-quality point set for population initialization, ensuring a more uniform distribution of initial individuals in the search space and better adaptation to the complex grayscale characteristics of artistic images. A dual crossover strategy, integrating horizontal and vertical information exchange, is designed to enhance individual information sharing and fine-grained dimensional search, catering to the segmentation needs of artistic image textures and color layers. Furthermore, a global-optimum-based boundary handling mechanism is constructed to prevent information loss when boundaries are exceeded, thereby preserving the boundary details of artistic images. The performance of MGJO was evaluated on the CEC2017 (dim = 30, 100) and CEC2022 (dim = 10, 20) benchmark suites against seven algorithms, including GWO and IWOA. Population diversity analysis, exploration–exploitation balance assessment, Wilcoxon rank-sum tests, and Friedman mean-rank tests all demonstrate that MGJO significantly outperforms the comparison algorithms in optimization accuracy, stability, and statistical reliability. In multilevel thresholding for artistic image segmentation, using Otsu’s between-class variance as the objective function, MGJO achieves higher fitness values (approaching Otsu’s optimal values) across various artistic images with complex textures and colors, as well as benchmark images such as Baboon, Camera, and Lena, in 4-, 6-, 8-, and 10-level thresholding tasks. The resulting segmented images exhibit superior peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) compared to other algorithms, more precisely preserving brushstroke details and color layers. Friedman average rankings consistently place MGJO in the lead. These experimental results indicate that MGJO effectively overcomes the performance limitations of the standard GJO, demonstrating excellent performance in both numerical optimization and multilevel thresholding artistic image segmentation. It provides an efficient solution for high-dimensional complex optimization problems and practical demands in artistic image processing. Full article

In recent years, with the deepening of mining and tunnel excavation operations, the incidence of rock burst has also increased, prompting people to attracting increasing attention to microseismic monitoring technology. The location algorithm of microseismic events is the core of microseismic monitoring. In this study, a hybrid optimization algorithm, BP-GA-GN, which combines genetic algorithm (GA), BP neural network (BP) and Gauss-Newton method (GN), is introduced. The BP-GA-GN algorithm optimizes the initial weights and thresholds of the BP neural network through GA to avoid local optimum. The BP neural network is used to learn the nonlinear mapping between the sensor arrival time difference and the source position. Combined with the physical model constraints of GN, fine convergence is performed. We prove the robustness of the BP-GA-GN algorithm through a large number of numerical simulations. Compared with the traditional single algorithm, the algorithm shows excellent performance. Subsequently, the high precision and high efficiency of the method are further highlighted in the field data test of mine environment and tunnel environment. The average errors are 0.42 m and 2.54 m, respectively, rendering it a valuable tool for real-time microseismic monitoring. This study overcomes the limitations of traditional positioning methods. The algorithm can achieve high-speed training and high precision, thus significantly improving the early warning effect of rockburst risk. Full article

Freshwater scarcity severely limits sustainable cotton production in arid regions. This study aimed to establish the optimal salinity threshold for staged saline water supplemental irrigation (SWSI) and elucidate its canopy-level mechanisms in optimizing within-boll yield components and fiber quality. A two-year field trial (2023–2024) was conducted in Awati County, Xinjiang, using mulched drip irrigation at five SWSI levels (3.5–9.5 g L⁻¹) and a freshwater control (CK). Compared with CK, 3.5 g L⁻¹ treatment significantly increased lint yield by 31.4%, boll number per plant by 22.45%, and fibers per seed by 6.01–10.59%, while fiber length and strength rose by 6.98–10.38% and 2.69–6.00%, respectively. When salinity reached 8.0 g L⁻¹, yield declined by 8.5%, and a salinity of 9.5 g L⁻¹ reduced yield by 24.52%. Spatially, mid-fruiting branches (nodes 4–6) remained stable, maintaining high lint mass per seed even under high salinity, whereas upper branches (≥node 7) were most sensitive; at 9.5 g L⁻¹, the boll number (0.36) was 56.6% lower than at 3.5 g L⁻¹ (0.83), and the Q-score decreased by 6.7%. These results demonstrate that SWSI with ≤5.0 g L⁻¹ salinity (optimum 3.5 g L⁻¹) simultaneously enhances lint yield and fiber quality, providing a practical strategy for efficient saline water use in arid cotton regions. Full article

Feature selection is an important task in medical applications, given that the dimensionality and numerosity of such datasets are very high. In these cases, the time parameter also becomes important, along with classification accuracy, in estimating the performance of a learning model. This approach proposes intelligent agent teams that are capable of automatically discovering the best time to build models while keeping the general accuracy at the highest levels. For computing attributes’ relevance for the classification process, several techniques were used: Wrapper Evaluation, Information Gain, gain ratio, correlation, and Relief Attribute Evaluator. One of our contributions is the Threshold Agent, which evaluates the attributes as class attributes and considers the relevance of the attributes returned by the Wrapper method. This agent selects the strongest attributes (above a threshold value) and returns a subset that is learnt by the next attribute evaluation method within the Feature Selection Agent. The proposed agents discovered that an optimum subset composed of 20 attributes (out of 133 attributes of the initial dataset) leads to accuracy rates equal to the ones registered on the entire dataset, meaning 98%, using the Naive Bayes learning model, while improving the time taken to build the model from 0.1 s to 0.03 s. For the proposed dataset, Naïve Bayes outperformed other classification techniques, such as J48, Random Forest, and Dl4MlpClassifier. The proposed agents also integrated the best discovered model into a chatbot that performs medical diagnoses based on the symptoms collected from users. Full article

Sarcophagidae are often the first sarcosaprophagous insects to colonize corpses in specialized cases such as indoor discoveries and burials, making them forensically crucial for estimating the minimum postmortem interval (PMI_min). Among these, Sarcophaga princeps Wiedemann (Diptera: Sarcophagidae) is a prominent species frequently associated with both buried and indoor bodies. In this study, the development time of S. princeps from larvae to adults at constant temperatures of 16, 19, 22, 25, 28, 31, and 34 °C was studied, and the times required were 1090.00 ± 57.65, 721.00 ± 8.72, 562.33 ± 27.21, 416.67 ± 27.70, 356.33 ± 16.01, 327.00 ± 7.94, and 313.67 ± 5.69 h, respectively, demonstrating a significant inverse relationship with temperature. Various developmental models were constructed using the basic developmental data, including the isomorphen diagram, isomegalen diagram, nonlinear thermodynamic Optim SSI model, and logistic regression model. These models enable the estimation of the developmental age of the specimens. In addition, the lower critical thermal threshold (T_L), intrinsic optimum temperature (T_Φ), and upper critical thermal threshold (T_H) estimated by the nonlinear thermodynamic Optim SSI model were 11.11 °C, 21.85 °C, and 35.88 °C. This study provides comprehensive developmental data of S. princeps for PMI_min estimation. Full article

The presence of 2-methylisoborneol (2-MIB) in water is a critical global concern due to its low threshold and resistance to conventional processes. In the present study, activated carbon/alginate (AC/alginate) composite beads were synthesized via ionic gelation method for the removal of 2-MIB from aqueous solution. The physicochemical characteristics of the adsorbent were determined using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) techniques. The effects of contact time, solution pH, initial 2-MIB concentration and adsorbent dose on adsorption were examined. Over 95% of 2-MIB removal was obtained under optimum conditions within 360 min. The adsorption equilibrium was well described by Langmuir (R² = 0.97) and Freundlich (R² = 0.96) models suggesting that 2-MIB adsorption involves both monolayer and multilayer adsorption. Kinetic modeling revealed that the pseudo-first order model showed strong fits to the experimental data, indicating the role of surface adsorption in controlling the rate of adsorption. The adsorbent demonstrated reasonable stability, retaining 59% removal efficiency after four adsorption–desorption cycles, highlighting its potential for repeated application in water treatment. Overall, the AC/alginate composite beads were found to be promising for the effective elimination of 2-MIB from water. Full article

Reuse of wastewater through brackish water reverse osmosis presents a major challenge due to the generation of brine, which contains organic and inorganic compounds to be removed. This study focuses on analyzing and optimizing coagulation conditions for brackish reverse osmosis brine treatment by evaluating pollutant removal efficiencies under various scenarios and leveraging advanced modeling techniques. Jar tests were performed using polyaluminum chloride and ferric chloride, evaluating the removal of total organic carbon, turbidity, UV₅₂₄, and phosphorus. Models were developed using response surface methodology, support vector machines, and random forest. Although the same data sets were used, the characteristics of these models were found to be different: Response surface methodology delivered high-fidelity, smooth response surfaces (R² > 0.92), support vector machine pinpointed sharp threshold regions, and random forest defined robust operating plateaus. By overlaying model-specific optimum contours, the consensus regions were identified for reliable removal across total organic carbon, turbidity, and phosphate. This ensemble strategy enhanced predictive reliability and provided a comprehensive decision-support tool for multi-objective optimization. The findings underscore the potential of ensemble-based modeling to improve the design and control of brackish reverse osmosis brine treatment processes, offering a data-driven pathway for addressing one of the most critical bottlenecks in wastewater reuse systems. Full article

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article

Inorganic pyrophosphatase (PPase) is an enzyme that catalyzes the hydrolysis of pyrophosphate (PPi) into two phosphates. Ton1914, a thermophilic inorganic pyrophosphatase derived from Thermococcus onnurineus NA1, has good thermal stability and an extremely high optimum temperature and has been shown to reduce pyrophosphate inhibition. In this study, eight sites were selected based on sequence alignment and software calculations, and multiple single mutants were successfully constructed. After saturation and superposition mutations, six superior mutants were obtained. The enzyme activities of E97Y, D101K and L42F were increased 2.57-, 2.47- and 2.15-fold, respectively, while those of L42F/E97Y, L42F/D101K and E97Y/D101K were increased 2.60-, 2.63- and 1.88-fold, respectively, relative to the wild-type enzyme. Compared to Ton1914, all mutants more effectively increased PCR product quantity, reduced the number of qPCR cycles required to reach the threshold, and improved the efficiency of gene amplification. In the UDP-Galactose (UDP-Gal) synthesis reaction, the addition of mutants could further improve yield. When Ton1914 and mutants with the same activity were added, the yield of UDP-Gal was almost identical, effectively reducing the dosage of pyrophosphatase. Overall, the mutants showed greater prospects for industrial application. Full article

Attraction–Repulsion Optimisation Algorithm (AROA) is a newly proposed metaheuristic algorithm for solving global optimisation problems, which simulates the equilibrium relating to the attraction and repulsion phenomenon that occurs in the natural world, and aims to achieve a good balance between the development exploration phases. Although AROA has a more significant performance compared to other classical algorithms on complex realistic constrained issues, it still has drawbacks in terms of diversity of solutions, convergence precision, and susceptibility to local stagnation. To further improve the global optimisation search and application ability of the AROA algorithm, this work puts forward an Improved Attraction–Repulsion Optimisation Algorithm based on multiple strategies, denoted as IAROA. Firstly, the elite dynamic opposite (EDO) learning strategy is used in the initialisation phase to enrich the information of the initial solution and obtain high-quality candidate solutions. Secondly, the dimension learning-based hunting (DLH) exploration tactics is imported to increase the candidate solution diversity and enhance the trade-off between local and global exploration. Next, the pheromone adjustment strategy (PAS) is used for some of the solutions according to the threshold value, which extends the search range of the algorithm and also accelerates the convergence process of the algorithm. Finally, the introduction of the Cauchy distribution inverse cumulative perturbation strategy (CDICP) improves the local search ability of the algorithm, avoids falling into the local optimum, and improves the convergence and accuracy of the algorithm. To validate the performance of IAROA, algorithms are solved by optimisation with the original AROA and 13 classical highly cited algorithms on the CEC2017 test functions, among six engineering design problems of varying complexity. The experimental results indicate that the proposed IAROA algorithm is superior in terms of optimisation precision, solution stability, convergence, and applicability and effectiveness on different problems, and is highly competitive in solving complex engineering design problems with constraints. Full article

The paper describes the conversion of carbon dioxide into methanol in a chemical reactor under standard operating conditions. Electro-analytical techniques, cyclic voltammetry, and chrono-amperometry characterize the process. The electrochemical redox reaction develops using various catalyzers to evaluate the performance of the carbon dioxide conversion into methanol process under variable chemical conditions. The results of the applied technique showed an incomplete redox reaction with an electronic change of z = 2.84 on average, below the ideal number, z = 6, that may be due to methanol decomposition (reverse reaction) because the system operates with a reaction constant above the equilibrium value. The methanol production may improve by draining the methanol/water solution from the chemical reactor to reduce the methanol concentration in the electrochemical cell, shifting the forward reaction towards the formation of methanol, increasing the electron change number, which approaches the ideal value, and improving the methanol production efficiency. The draining process shows a significant increase in methanol formation, which depends on the draining flow rate and the catalyzer type. A simulation process shows that if we operate in optimum conditions, with no methanol decomposition through a reverse reaction, the redox reaction fulfills the ideal condition of maximum electronic change. The experimental tests validate the simulation results, showing a relevant increase in the electron change number with values up to z = 4.2 for optimum draining flow rate conditions (0.2 L/s). The experimental results show a relative increase factor of 4.7 in methanol production, meaning we can produce more than four times more methanol compared with no draining techniques. The data analysis shows that the draining flow rate has a threshold of 0.2 L/s, beyond which the extent of the reaction reverses, reducing the methanol formation due to a chemical reaction disequilibrium. The paper concludes that using the draining method, the methanol production mass rate increases significantly from an average value of 20.9 kg/h for non-draining use, considering all catalyzer types, to a range between 91.9 kg/h and 104.3 kg/h, depending on the flow rate. Averaging all values for different flow rates and comparing with the non-draining case, we obtain an absolute methanol production mass rate of 77 kg/h, meaning an incremental percentage of 469.1%, more than four times the initial production. Although the proposed methodology looks promising, applying this procedure on an industrial scale may suffer from restrictions since the chemical reactions intervening in the methanol formation do not perform linearly. According to experimental tests, the best option among the six catalyzers used for methanol production is the plain copper, with copper oxides (Cu₂O, CuO) and copper Sulphur (CuS) as feasible alternatives. Full article

Classification prediction technology, which utilizes labeled data for training to enable autonomous decision, has emerged as a pivotal tool across numerous fields. The Elman neural network (ENN) exhibits potential in tackling nonlinear problems. However, its computational process faces inherent limitations in escaping local optimum and experiencing a slow convergence rate. To improve these shortcomings, an ENN classifier based on Hyperactivity Rat Swarm Optimizer (HRSO), named HRSO-ENNC, is proposed in this paper. Initially, HRSO is divided into two phases, search and mutation, by means of a nonlinear adaptive parameter. Subsequently, five search actions are introduced to enhance the global exploratory and local exploitative capabilities of HRSO. Furthermore, a stochastic roaming strategy is employed, which significantly improves the ability to jump out of local positions. Ultimately, the integration of HRSO and ENN enables the substitution of the original gradient descent method, thereby optimizing the neural connection weights and thresholds. The experiment results demonstrate that the accuracy and stability of HRSO-ENNC have been effectively verified through comparisons with other algorithm classifiers on benchmark functions, classification datasets and an AlSi10Mg process classification problem. Full article

In this work we consider a cognitive radio network with a energy harvesting relay that facilitates coexistence between primary users and multiple secondary users (SU) and a secondary destination. We analyze the outage performance of this cognitive radio network that employs a decode-and-forward (DF) relay and harvests energy from the secondary user’s transmitted signal. Cumulative distribution function-based user scheduling is performed for equitable allocation of channels to each SU. We consider block Rayleigh fading channels and derive novel closed-form expressions for the outage probability and system throughput. Monte Carlo simulations are conducted to verify the accuracy of the outage expression. We also develop the expression for the outage probability at a high interference threshold and utilize it to examine the impact of the power splitting factor ($\alpha$) on overall performance. Our results demonstrate that through using an optimum power splitting factor the outage performance can be enhanced by nearly 2 dB. Full article

Crop health assessment is essential for the early detection of nutrient deficiencies, diseases, and pests, allowing for timely interventions that optimize yield, reduce losses, and support sustainable agricultural practices. While traditional methods and satellite-based remote sensing offer broad scale monitoring, they often suffer from coarse spatial resolution, and insufficient precision at the plant level. These limitations hinder accurate and dynamic assessment of crop health, particularly for high-resolution applications such as nutrient diagnosis during different crop growth stages. This study addresses these gaps by leveraging high-resolution UAV (Unmanned Aerial Vehicle) imagery to monitor the health of paddy crops across multiple temporal stages. A novel methodology was implemented to assess the crop health condition from the predicted Above-Ground Biomass (AGB) and essential macro-nutrients (N, P, K) using vegetation indices derived from UAV imagery. Four machine learning models were used to predict these parameters based on field observed data, with Random Forest (RF) and XGBoost outperforming other algorithms, achieving high regression scores (AGB > 0.92, N > 0.96, P > 0.92, K > 0.97) and low prediction errors (AGB < 80 gm/m², N < 0.11%, P < 0.007%, K < 0.08%). A significant contribution of this study lies in the development of decision-making rules based on threshold values of AGB and specific nutrient critical, optimum, and toxic levels for the paddy crop. These rules were used to derive crop health maps from the predicted AGB and NPK values. The resulting spatial health maps, generated using RF and XGBoost models with high classification accuracy (Kappa coefficient > 0.64), visualize intra-field variability, allowing for site-specific interventions. This research contributes significantly to precision agriculture by offering a robust, plant-level monitoring approach that supports timely, site-specific nutrient management and enhances sustainable crop production practices. Full article

Background/Objectives: Multiparametric MRI (mpMRI) is widely established as the standard imaging modality for detecting clinically significant prostate cancer (csPCa), yet it can be limited by cost, accessibility, and the need for specialized radiologist interpretation. Micro-ultrasound (micro-US) has recently emerged as a more accessible alternative imaging modality. This review evaluates whether the evidence base for micro-US meets thresholds comparable to those that led to MRI’s guideline adoption, synthesizes diagnostic performance data compared to mpMRI, and outlines future research priorities to define its clinical role. Methods: A targeted literature review of PubMed, Embase, and the Cochrane Library was conducted for studies published between 2014 and May 2025 evaluating micro-US in csPCa detection. Search terms included “micro-ultrasound,” “ExactVu,” “PRI-MUS,” and related terminology. Study relevance was assessed independently by the authors. Extracted data included csPCa detection rates, modality concordance, and diagnostic accuracy, and were synthesized and, rarely, restructured to facilitate study comparisons. Results: Micro-US consistently demonstrated non-inferiority to mpMRI for csPCa detection across retrospective studies, prospective cohorts, and meta-analyses. Several studies reported discordant csPCa lesions detected by only one modality, highlighting potential complementarity. The recently published OPTIMUM randomized controlled trial offers the strongest individual-trial evidence to date in support of micro-US non-inferiority. Conclusions: Micro-US shows potential as an alternative or adjunct to mpMRI for csPCa detection. However, additional robust multicenter studies are needed to achieve the evidentiary strength that led mpMRI to distinguish itself in clinical guidelines. Full article