Search Results (5,807)

Aiming at the issues of low recognition accuracy and high model computational complexity for power quality disturbances (PQDs) in strong-noise environments, this paper proposes a novel lightweight PQD-recognition method that integrates a hybrid architecture of variational mode decomposition (VMD), convolutional neural network (CNN), and transformer. Firstly, a hybrid optimization algorithm named the monkey–genetic hybrid optimization algorithm (MGHOA) is proposed to optimize VMD parameters for denoising disturbance signals, thereby enhancing recognition accuracy in noisy environments. Secondly, to fully extract disturbance signal features and reduce the computational complexity of the model, a lightweight CNN–transformer model is designed. Depthwise separable convolution (DSC) is employed to extract local features and the multi-head attention mechanism of transformer is utilized to mine the long-distance dependence and global features, thereby enhancing the feature representation. Thirdly, a multitask joint-learning method is proposed to collaboratively optimize classification accuracy and temporal localization tasks, enhancing the discrimination of similar disturbances. Additionally, a dual-pooling global feature fusion strategy is designed to further enhance the model’s ability to discriminate complex disturbances. Comparative experiments on 16 typical PQD types demonstrate that the proposed method achieves excellent performance in recognition accuracy, model robustness, and computational efficiency. The integration of the MGHOA–VMD module improves recognition accuracy by 1.08%, while the multitask joint-learning method contributes an additional 0.55% improvement. When achieving recognition accuracy comparable to complex models, the training time of the proposed method is 36.51% of that required by DeepCNN and merely 5.90% of that required by bidirectional long short-term memory (BiLSTM), with a 31.22% reduction in parameter scale. This work provides a novel solution for intelligent power quality disturbance recognition. Full article

The global demand for Salvia miltiorrhiza Bunge in the botanical medicine market is steadily increasing. However, its production has long relied on asexual root propagation, making it highly susceptible to germplasm degradation. Transitioning to seed reproduction offers the advantage of genetic renewal, yet it is constrained by unstable seed vigor and slow seedling growth. In the present study, comprehensive physiological and microbiome analyses of S. miltiorrhiza seeds from 14 regions across 7 provinces in China were conducted to elucidate the association between the seed microbiome and vigor, and to identify plant growth-promoting (PGP) strains. The results demonstrated: (1) Seed physical traits and germination characteristics varied significantly across geographic origins. Seed vigor, exhibiting the highest coefficient of variation, served as a key parameter reflecting germination quality. (2) High-vigor seeds harbored distinct microbial communities characterized by higher diversity indices, greater network complexity, and the significant enrichment of potentially beneficial bacteria (e.g., Pseudomonas). (3) Through correlation-directed screening of isolated pure cultures, Pseudomonas mendocina P-6 and Enterobacter ludwigii BM-12 were identified as exhibiting robust, multi-trait PGP capacity. In planta validation showed that these two strains significantly promoted the growth of 1-month-old S. miltiorrhiza seedlings, increasing total fresh weight by 33.9–71.3%. This study reveals the microecological drivers of seed vigor and provides candidate strains for inoculant development, thereby supporting the sustainable, seed-based propagation of S. miltiorrhiza. Full article

The intensive use of agricultural inputs and the increasing incorporation of nano-materials into crop management practices raise concerns about their ecotoxicological interactions in plant systems. This study evaluated phytotoxicity, cytotoxicity, and genotoxicity in Allium cepa L. under experimental nano-agrochemical exposure scenarios combining two conventional nitrogen fertilizers—ammonium sulfate (AS) and urea—with silver nanoparticles (AgNPs). Biological responses were assessed across fertilizer concentrations (0.03–0.5 g/L), applied individually, simultaneously, and sequentially, to identify modulatory effects of AgNPs on plant proliferative activity and genomic stability. Results showed the relative stability of morphophysiological indicators associated with root growth, whereas cytogenetic biomarkers exhibited selective alterations under specific conditions. Significant increases in genetic damage markers were detected at intermediate ammonium sulfate concentrations, suggesting sublethal phytotoxicity windows not reflected by macroscopic growth parameters. In addition, modulation of the mitotic index and absence of generalized genotoxic effects in most combined or sequential treatments indicate that AgNPs primarily acted as modulators of proliferative responses rather than direct cytotoxic agents. Overall, these findings highlight the dynamic and non-linear nature of nano-agrochemical interactions in plant systems and underscore the importance of multibiomarker approaches for the early detection of genomic instability. The results provide experimental evidence relevant to the environmental risk assessment of nano-enabled fertilization strategies under realistic mixed-exposure scenarios. This study contributes to advancing the ecotoxicological understanding of emerging agricultural technologies and supports the need for further mechanistic research and field-based evaluations to guide the safe and sustainable use of nanomaterials in crop production. Full article

Mosquito-borne infectious diseases remain a major challenge to public health, highlighting the need for efficient and accessible gene editing approaches. Receptor-mediated ovary transduction of cargo (ReMOT) offers an alternative to embryonic microinjection, in which P2C, an ovary-targeting peptide, enables ovarian delivery of the editing components. However, key design parameters and operational boundaries of the P2C-based ReMOT system have not been clearly defined. Here, we performed a methodological evaluation of the P2C-mediated ReMOT CRISPR/Cas9 system in Aedes aegypti. Cas9-P2C fusion proteins with different configurations were constructed and assessed through ovarian targeting assays, in vitro cleavage analyses, and in vivo gene editing experiments. Our results show that full-length Cas9-P2C fusion proteins exhibit nuclease activity and enable effective ovarian delivery. In contrast, linear truncation of the P2C peptide markedly reduced ovarian targeting, indicating a dependence on structural integrity. Using this delivery strategy, we generated kynurenine monooxygenase (KMO) edited mosquitoes, demonstrating feasibility under the conditions tested. In addition, protein injection was also associated with reduced reproductive performance, providing physiological reference for ReMOT applications. Overall, this study defines the key design parameters and operational boundaries of the P2C-based ReMOT system, providing methodological guidance for its application and optimization in future mosquito genetic studies. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

The optimal design of long-span structures is hindered by the combination of prohibitively high computational costs and the limited physical consistency of purely data-driven surrogates. To address this challenge, this study proposes a multi-stage automated design framework that shifts the workflow from repeated per-task solving to reusable digital asset creation. First, a large-scale surrogate-optimized dataset containing 100,000 design samples is generated by embedding a high-speed MLP emulator into a Genetic Algorithm (GA). The core innovation lies in training a physics-regularized neural design generator. By incorporating a reduced-order total potential energy term derived from the principle of minimum potential energy as a regularization constraint, the network learns the mapping from external design conditions to validated near-optimal internal parameter combinations while suppressing mechanically unfavorable configurations associated with low stiffness. This mechanism improves mechanical admissibility, particularly in data-sparse regions. Validation results show that the generator achieves millisecond-level candidate generation and reduces the prediction error to 31% of that of conventional models under sparse-data conditions. In a like-for-like case study with identical external input parameters, the generated candidate design achieves a 21.1% reduction in total steel consumption. The proposed framework is therefore best understood as a rapid preliminary design tool for producing weight-efficient and mechanically admissible candidate schemes, which can then be subjected to subsequent high-fidelity analysis and code-based verification. Full article

An accurate classification of material stability often requires fusing multiple features under uncertainty. Dempster–Shafer (D–S) theory is a powerful framework for multi-source information fusion under uncertainty. However, its effectiveness critically depends on the quality of basic probability assignments (BPAs), which are typically assigned heuristically. To overcome this limitation, we proposed a classification model that integrates genetic algorithm (GA)-optimized kernel density estimation (KDE) with a weighted D–S fusion strategy. The GA automatically selects the optimal kernel function and bandwidth for KDE, enabling data-driven and accurate BPA construction without manual parameter tuning. The proposed method is first validated on benchmark datasets (Iris, Wine, Ionosphere, and Hepatitis), achieving competitive or superior performance compared to the existing methods. More importantly, when applied to predict the thermodynamic stability of double perovskite halide materials, our method achieves 93.7% accuracy and 85.3% precision under 10 × five-fold cross-validation, substantially outperforming CatBC (76.9% precision) and XGBC (75.0% precision). Notably, the model maintains robust performance even for compositions containing chemical elements absent from the training set, demonstrating strong transferability. These results highlight the potential of our GA-KDE-DS framework as a practical tool for accelerating the discovery of novel functional materials under limited data and uncertain conditions. Full article

The biochemical diversity among tea plant (Camellia sinensis) cultivars serves as the core material basis associated with tea quality and is of great significance for the innovation of tea germplasm resources and the genetic improvement of tea varieties. Here, we systematically analyzed 16 biochemical components, 7 mineral elements, and water-soluble fluoride (WSF) in 65 tea cultivars using multivariate analysis. These cultivars were grouped into high-component, high-epigallocatechin (EGC), low-component, and balanced-quality clusters. Significant variation was observed in quality-related parameters, including tea polyphenols, catechins, and amino acids and related quality indices. Mineral elements were significantly correlated with quality components, with potassium and boron showing significant correlation with the accumulation of these components. WSF content exhibited a pronounced cultivar-dependent variation, with more than 72% of cultivars containing less than 100 mg·kg⁻¹. The balanced-quality cluster exhibited broad processing adaptability, making it suitable for producing various tea types. The high-EGC cluster is ideal for developing specialty functional teas. The high-component cluster offers core parental material for breeding cultivars high in tea polyphenols and epigallocatechin gallate. This study provides a scientific basis for the screening and utilization of tea germplasm resources and the development of new, high-quality, and safe tea varieties. Full article

Blood is a key component of organisms, serving numerous functions, including metabolism, innate and humoral responses, and hemostasis. Variations in hematological parameters can indicate the presence of infectious and non-infectious diseases, chronic stress, and other pathological or physiological conditions. Complete blood count testing is common in human and veterinary medicine and, when combined with clinical examination, contributes to disease diagnosis and prognosis and the monitoring of therapeutic progression. Nevertheless, hematological analysis is not routinely performed in sheep due to the lack of case-specific reference intervals, complicating the interpretation of the results. Indeed, hematological parameters may be affected by various non-pathological (environmental, genetic, physiological) and pathological factors, and they require further understanding and relevant adjustments to be universally applicable. Therefore, the objective of this paper is to summarize the existing literature and describe how various pathological and non-pathological factors affect hematological parameters in sheep, thereby supporting their incorporation into health management practices. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs. Full article

This work proposes solving the Traveling Salesman Problem by applying combined heuristic global and local search methods. The proposed method is divided into three phases: first, it evaluates an initial route and chooses the minimum value of rows in a distance matrix. The next phase seeks to improve the route’s cost globally and with a 2-opt local search method, remove the crossings, and further minimize the cost of departure. Finally, the last phase evaluates and conserves each cost using tabu search, proposing a parameter $\beta$ that describes the algorithm convergence factor. This paper assessed 29 TSPLIB instances and compared them with other algorithms: the ant colony optimization algorithm (ACO), artificial neural network (ANN), particle swarm optimization (PSO), and genetic algorithm (GA). With the proposed algorithm, results close to the optimal ones are obtained, and the proposed algorithm is assessed on 29 TSPLIB instances. Based on 30 independent runs per instance, the method achieves a mean absolute percentage error (MAPE) of 1.4484% relative to the known optima, demonstrating its accuracy. Furthermore, statistical comparisons using the coefficient of variation (CV) for runtime and the Wilcoxon signed-rank test confirm that the proposed hybrid algorithm is significantly faster than traditional ant colony optimization (T-ACO) and a new ant colony optimization algorithm (N-ACO) while maintaining competitive solution quality. Full article

Background/Objectives: The Black Caribbean population of Honduras, also referred to locally as Negro Inglés, constitutes one of the country’s nine recognized indigenous and Afro-descendant peoples. Predominantly settled in the Bay Islands and sections of the Caribbean coast, this community traces its ancestry predominantly to West Africa and has remained culturally and linguistically distinct for more than three centuries. Despite its demographic and historical relevance, no population-specific short tandem repeat (STR) database has been established for this group. Methods: Allele frequencies for 23 autosomal STR loci were characterized in 100 unrelated Black Caribbean individuals from the department of Islas de la Bahía. DNA was extracted from blood on FTA cards and amplified with the PowerPlex Fusion 6C System (Promega Corporation). Statistical parameters were computed using Genepop v4.2, Arlequin v3.5 and GDA v1.0. Results: A total of 241 distinct alleles were detected across all 23 loci (mean 10.48 ± 3.85 alleles/locus). Expected heterozygosity ranged from 0.6541 (D13S317) to 0.9350 (SE33), with a mean of 0.8150 ± 0.0664—values consistent with a population of predominantly West African origin. No locus exhibited a significant departure from Hardy–Weinberg equilibrium after Bonferroni correction (α = 0.0022). The combined power of discrimination exceeded 99.9999% and the combined chance of exclusion surpassed 99.9999%. Conclusions: This first genetic characterization of the Honduran Black Caribbean population delivers an essential, population-specific reference dataset for forensic casework, paternity testing, and population genetics research. The data also deepen the understanding of Afro-descendant genetic diversity in Central America and constitute a critical step towards equitable forensic genetic services for all Honduran ethnic communities. Full article

This paper studies the reliability and availability of a k-out-of-(M+S) retrial machine repair system with two-way communication, consisting of M primary components and S warm standby components. The system incorporates the retrial behavior of failed components. When the repairman becomes idle, he initiates outgoing calls after a random period either to failed components in the orbit for repair or to components outside the orbit for preventive maintenance. The main contribution of this study is the incorporation of proactive repairman behavior, which more realistically captures operational practices in certain engineering systems. By employing the matrix analytic method together with a recursive approach, the steady-state probabilities of the system are obtained, and several important performance measures are derived. Furthermore, the Runge–Kutta method is used to evaluate the system reliability and the mean time to failure. A sensitivity analysis is conducted to investigate the effects of key system parameters, supported by numerical experiments and graphical illustrations. Finally, a cost–benefit model is formulated, and a genetic algorithm is implemented to determine the optimal values of the decision variables that minimize the cost–benefit ratio. Full article

Background/Objectives: The widespread adoption of short tandem repeat (STR) marker technology in genetic analysis has led to the collection of substantial STR data from diverse populations. Allele-frequency data provide robust forensic utility and support accurate likelihood ratio calculations, highlighting the importance of regional databases. Methods: The presented study aimed to determine the allelic frequencies and statistical parameters for 16 autosomal genetic STR markers included in the NGM Detect^TM PCR Amplification Kit in a population sample of 220 unrelated individuals from the South-West region of the Republic of Bulgaria. Results: We found that the most polymorphic and informative marker for the Bulgarian population in the southwestern region is SE33, with the next most informative markers being D1S1656, D12S391, D18S51, and FGA. In contrast, D22S1045, D16S539, and D2S441 showed comparatively lower genetic variability and informativeness. At the same time, no deviations from the Hardy–Weinberg equilibrium were observed for the 16 loci studied. Conclusions: This work not only enriches knowledge of the genetic diversity of the Bulgarian population but also provides the Bulgarian and international justice systems with an objective, scientifically sound basis for expert decision-making. Full article