Search Results (241)

This study introduces a non-destructive computer vision method for estimating postharvest quality parameters of oranges, including maturity index, soluble solid content (expressed in degrees Brix), and firmness. A novel image-based descriptor, termed Citrus Color Index—Gray Level Co-occurrence Matrix Texture Features (CCI–GLCM-TF), was developed by integrating the Citrus Color Index (CCI) with texture features derived from the Gray Level Co-occurrence Matrix (GLCM). By combining contrast, correlation, energy, and homogeneity across multiscale regions of interest and applying geometric calibration to correct image acquisition distortions, the descriptor effectively captures both chromatic and structural information from RGB images. These features served as input to an Adaptive Neuro-Fuzzy Inference System (ANFIS), selected for its ability to model nonlinear relationships and gradual transitions in citrus ripening. The proposed ANFIS models achieved R-squared values greater than or equal to 0.81 and root mean square error values less than or equal to 1.1 across all quality parameters, confirming their predictive robustness. Notably, representative models (ANFIS 2, 4, 6, and 8) demonstrated superior performance, supporting the extension of this approach to full-surface exploration of citrus fruits. The results outperform methods relying solely on color features, underscoring the importance of combining spectral and textural descriptors. This work highlights the potential of the CCI–GLCM-TF descriptor, in conjunction with ANFIS, for accurate, real-time, and non-invasive assessment of citrus quality, with practical implications for automated classification, postharvest process optimization, and cost reduction in the citrus industry. Full article

Rural areas often lack convenient delivery logistics services, which has become a major obstacle to their economic development. Network design initiatives that synergize passenger and freight transport have been identified as effective solutions to address this challenge. Building upon this initiative, this study investigates a novel three-echelon location-routing problem that synergizes trucks and fixed-route buses (3E-LRP-TF). The model is designed with an innovative operational mode that enables fixed-route buses and trucks to travel in a parallel manner, representing a valuable extension to traditional integrated passenger–freight distribution network design. A mixed-integer nonlinear programming model with the objective of minimizing the total network cost is constructed to formulate the problem. Furthermore, a bottom-up three-phase adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem. A final empirical study was conducted, with Qingchuan County in China serving as a case study, with the aim of validating the effectiveness of the proposed model and algorithm. The results show that, compared with using trucks alone, the synergistic network system has the potential to reduce costs by more than 5% for parcel pickup and delivery services. The proposed algorithm can address larger-scale problems and exhibits better performance with regard to solution quality and efficiency. Sensitivity analysis indicates that the parcel transport capacity of bus routes exerts a nonlinear effect on total costs, and changes in service radius result in trade-offs between cost and accessibility. These findings provide actionable insights for policymakers and logistics operators. Full article

Nitrogen is an essential macronutrient for crop growth. Although sorghum can tolerate poor soils, its low-nitrogen (LN) tolerance mechanisms remain underexplored. We conducted a genome-wide association study (GWAS) and RNA sequencing (RNA-seq) to dissect LN tolerance mechanisms in a diverse panel of 232 sorghum accessions. Phenotypic analyses revealed extensive variation in nitrogen-use efficiency traits, with shoot dry weight and shoot nitrogen accumulation in (SNAcc) showing the highest diversity. GWAS identified 10 quantitative trait loci harboring pleiotropic single-nucleotide polymorphisms (SNPs), including q1 (Chr3: 8.59–8.68 Mb), which is associated with biomass and nitrogen accumulation. Transcriptome profiling under LN stress revealed 6208 differentially expressed genes, with nitrate transporters showing genotype-specific regulation. Integration prioritized SORBI_3004G286700, where Hap2 accessions (14.66%) showed superior agronomic performance under LN conditions. We also identified pivotal transcription factors (TFs) that govern LN tolerance in sorghum, notably bHLH35 (SORBI_3007G051800) and three WRKY TFs, demonstrating constitutive upregulation in tolerant genotypes, whereas three previously uncharacterized TFs (MYB, bZIP, and B3) exhibited > 5-fold genotype-specific induction under LN. The integration of GWAS and transcriptome analyses offers an effective strategy for exploring candidate genes and elucidating nitrogen adaptation mechanisms in sorghum, while providing actionable molecular targets for precise breeding of nitrogen-efficient cultivars. Full article

Conventional fixed-gain PID controllers face inherent limitations in maintaining optimal performance across the diverse and dynamic flight phases of cruise missiles. To overcome these challenges, we propose Time-Fusion Proximal Policy Optimization (TF-PPO), a novel adaptive reinforcement learning framework designed specifically for cruise missile control. TF-PPO synergistically integrates Long Short-Term Memory (LSTM) networks for enhanced temporal state perception and phase-specific reward engineering enabling self-evolution of PID parameters. Extensive hardware-in-the-loop experiments tailored to cruise missile dynamics demonstrate that TF-PPO achieves a 36.3% improvement in control accuracy over conventional PID methods. The proposed framework provides a robust, high-precision adaptive control solution capable of enhancing the performance of cruise missile systems under varying operational. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Terrorism remains a critical global challenge, and the proliferation of social media has created new avenues for monitoring extremist discourse. This study investigates stance detection as a method to identify Arabic tweets expressing support for or opposition to specific organizations associated with extremist activities, using Hezbollah as a case study. Thousands of relevant Arabic tweets were collected and manually annotated by expert annotators. After extensive preprocessing and feature extraction using term frequency–inverse document frequency (tf-idf), we implemented traditional machine learning (ML) classifiers—Support Vector Machines (SVMs) with multiple kernels, Multinomial Naïve Bayes, and Weighted K-Nearest Neighbors. ML models were selected over deep learning (DL) approaches due to (1) limited annotated Arabic data availability for effective DL training; (2) computational efficiency for resource-constrained environments; and (3) the critical need for interpretability in counterterrorism applications. While interpretability is not a core focus of this work, the use of traditional ML models (rather than DL) makes the system inherently more transparent and readily adaptable for future integration of interpretability techniques. Comparative experiments using FastText word embeddings and tf-idf with supervised classifiers revealed superior performance with the latter approach. Our best result achieved a macro F-score of 78.62% using SVMs with the RBF kernel, demonstrating that interpretable ML frameworks offer a viable and resource-efficient approach for monitoring extremist discourse in Arabic social media. These findings highlight the potential of such frameworks to support scalable and explainable counterterrorism tools in low-resource linguistic settings. Full article

In this study, the Binary Puma Optimizer (BPO) is introduced as a novel binary metaheuristic. The BPO employs eight Transfer Functions (TFs), consisting of four S-shaped and four V-shaped mappings, to convert the continuous search space of the original Puma Optimizer into binary form. To evaluate its effectiveness, BPO is applied to two well-known combinatorial optimization problems: the 0-1 Knapsack Problems (KPs) and the Uncapacitated Facility Location Problem (UFLP). The solver tailored for KPs is referred to as BPO1, while the solver for the UFLP is denoted as BPO2. In the UFLP experiments, only TFs are integrated into the solutions. Conversely, in the 0-1 KPs experiment, the additional mechanisms are (i) greedy-based population strategies; (ii) a crossover operator; (iii) a penalty algorithm; (iv) a repair algorithm; and (v) an improvement algorithm. Unlike KPs, the UFLP has no infeasible solutions, as facilities are assumed to be uncapacitated. Unlike KPs, the UFLP has no capacity constraints, as facilities are assumed to be uncapacitated. Thus, violations cannot occur, making improvement strategies unnecessary, and the BPO2 depends solely on TFs for binary adaptation. The proposed algorithms are compared with binary optimization algorithms from the literature. The experimental framework demonstrates the versatility and effectiveness of BPO1 and BPO2 in addressing different classes of binary optimization problems. Full article

YABBY transcription factors (TFs) are key regulators involved in diverse aspects of plant growth, organogenesis, and adaptation to environmental stresses. However, the functional characteristics of YABBY TFs in maize remain largely unexplored. In this study, we systematically identified 12 YABBY genes in the maize genome and characterized their gene structures, physicochemical properties, chromosome location, and genomic collinearity. Phylogenetic analysis classified these genes into five subfamilies, with members of each subfamily exhibiting highly conserved exon–intron structures and motif compositions, indicative of potential functional conservation within subfamilies. Cis-regulatory element analysis indicated that YABBY genes may be involved in developmental processes, abiotic stress responses, and light-mediated signaling pathways. Moreover, transcriptome sequencing combined with qRT-PCR validation demonstrated that several YABBY genes, including ZmYABBY2, ZmYABBY5, ZmYABBY8, and ZmYABBY9, are responsive to low-phosphorus and high-nitrogen conditions, implying their potential roles in nutrient stress adaptation. It is worth mentioning that this study redefined the composition of the maize YABBY gene family by excluding a previously annotated member and, for the first time, established a link between YABBY transcription factors and nutrient stress responses. Meanwhile, this is also the first time that protein structure analysis, cis-regulatory element analysis, interspecific collinearity analysis and subcellular localization have been performed on maize ZmYABBY gene family. In summary, our study provides valuable gene resources for maize molecular breeding and offers new insights into the functions of YABBY TFs. Full article

Nuclear Factor Y C (NF-YC) transcription factors (TFs) are central regulators of plant development and stress adaptation. However, there remains a gap in identifying NF-YC gene family members in blueberry (Vaccinium corymbosum), a globally significant fruit crop renowned for its nutritional value and good adaptability. In this study, a total of 31 NF-YC genes (designated VcNF-YC1–31) were identified in the blueberry genome, and their basic physicochemical properties, gene structures, motif patterns, and conserved domains were investigated using bioinformatic methods. The cis-acting elements in the promoters of VcNF-YCs were mainly enriched in phytohormone signaling, metabolism, and stress response. qRT-PCR analysis showed that VcNF-YCs were expressed at higher levels in leaves than in roots and stems. Transcriptional profiling revealed rapid upregulation of 24, 25, and 16 VcNF-YC genes upon ABA, salt, and cold treatments, respectively, indicating stress-specific induction patterns. The results of the yeast transformation assay revealed that VcNF-YC10 and VcNF-YC15 lacked transcription-activating activity. The results of tobacco leaf injection revealed that these two TFs were localized in the nucleus. These findings indicate the potentially important roles in abiotic stress responses of blueberry, offering potential targets for molecular breeding to enhance plant resilience. Full article

Drought stress is a major abiotic constraint that severely restricts the growth of Medicago falcata L. by inducing the accumulation of reactive oxygen species (ROS) in plants. WRKY transcription factors (TFs) play a key role in regulating plant responses to drought stress. In this study, we investigated the role of the MfWRKY40 gene in drought tolerance. Under mannitol and ABA stress treatments, MfWRKY40-overexpressing lines (OEs) showed significantly longer primary roots, increased lateral roots, and higher fresh weight compared to wild-type (Col) lines, indicating significantly enhanced growth and drought tolerance. Similarly, under soil drought conditions, transgenic Arabidopsis thaliana exhibited enhanced drought tolerance. NBT staining demonstrated decreased ROS accumulation in transgenic lines after stress treatment. Correspondingly, the MfWRKY40-overexpressing lines displayed significantly lower levels of hydrogen peroxide (H₂O₂), superoxide anion (O₂⁻), and malondialdehyde (MDA) compared to Col, along with elevated activities of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), as well as increased proline (Pro) content. Furthermore, MfWRKY40 upregulated the expression of antioxidant enzyme genes (AtPOD3, AtSOD4, and AtCAT1) and modulated the expression of other drought-related genes. In summary, our results demonstrate that MfWRKY40 enhances drought tolerance in A. thaliana by improving ROS scavenging capacity. This study provides a theoretical foundation for further exploration of MfWRKY40’s functional mechanisms in drought stress adaptation. Full article

The pods from Neltuma ruscifolia (vinal), an underutilized species, are rich in bioactive functional compounds. However, the extraction procedures to obtain the highest proportion of these compounds, considering sustainability aspects, have not been optimized. This study aimed to optimize and compare three affordable extraction methods—dynamic maceration (DME), ultrasound-assisted extraction (UE), and microwave-assisted extraction (ME)—to obtain enriched extracts. The effects of temperature, ethanol-to-water ratio in the solvent, extraction time, and frequency (for ME) were evaluated using a Box–Behnken design and response surface methodology to optimize total polyphenolic content (TPC), total flavonoids (TF), and antioxidant capacity (DPPH). Energy consumption and carbon footprints were also assessed, and phenolic compounds in the optimized extracts were identified by HPLC. The ethanol-to-water ratio emerged as the most influential factor, showing synergistic effects with both time and temperature, enabling optimal yields at intermediate ethanol concentrations. Gallic acid, rutin, and theobromine were found to be the most abundant components, followed by cinnamic, caffeic, and chlorogenic acids. Although UE exhibited the lowest energy consumption (0.64 ± 0.03 Wh/mg of TPC), the simple and easily implementable DME—optimized at 40 min, 50 °C, and 42% ethanol—proved to be the most efficient method, combining high extractive performance (TPC 1432 mg GAE/100 g Dw), reduced solvent use, and intermediate energy efficiency (1.84 Wh/mg of TPC). These findings highlight the potential of vinal as a natural source of bioactive ingredients obtained through simple and cost-effective techniques adaptable to small producers while underscoring the value of experimental design in optimizing sustainable extraction technologies and elucidating the interactions between key processing factors. Full article

Nowadays, with the accumulation of large amounts of stress-response transcriptomic data in plants, it is possible to clarify the key genes and transcription factors (TFs) involved in these processes. Here, we present the comprehensive transcriptomic meta-analysis of the high light (HL) response in photosynthetic tissues of Arabidopsis thaliana (L.) Heynh., offering new insights into adaptation mechanisms of plants to excessive light and involved gene regulatory networks. We analyzed 21 experiments covering 58 HL conditions in total, yielding 218,000 instances of differentially expressed genes (DEGs) corresponding to 19,000 unique genes. Based on these data, we developed the publicly accessible AraLightDEGs resource, which offers multiple search filters for experimental conditions and gene characteristics, and we conducted a detailed meta-analysis using our R pipeline, AraLightMeta. Our meta-analysis highlighted distinct transcriptional programs between short- and long-term HL responses in leaves, revealing novel regulatory interactions and refining the understanding of key DEGs. In particular, long-term HL adaptation involves key TFs such as CRF3 and PTF1 regulating antioxidant and jasmonate signaling; ATWHY2, WHY3, and emb2746 coordinating chloroplast and mitochondrial gene expression; AT2G28450 governing ribosome biogenesis; and AT4G12750 controlling methyltransferase activity. We integrated these findings into a conceptual scheme illustrating transcriptional regulation and signaling processes in leaf cells responding to long-term HL stress. Full article

Drought stress significantly affects the yield and quality of agricultural crops. Plants have developed various adaptations to cope with drought stress. These adaptations involve the regulation of physiological and biochemical mechanisms regulated by many genes. Therefore, identification of cultivars with strong responses to drought stress will provide important contributions to breeding programs. In this study, Hayward and Matua kiwifruit cultivars were used and the plants were subjected to drought in vitro in nutrient media containing PEG 6000 (Polyethyleneglycol) at concentrations of 0, 1, 2, and 3%. The morphological parameters of the plants were examined during the culture period and WRKY TF was utilized to determine the molecular regulations induced by drought stress in plants. For this purpose, the expression levels of WRKY3, WRKY9, WRKY21, WRKY28, WRKY41, WRKY47, WRKY65 and WRKY71 genes were analyzed in leaf and root tissues of the cultivars. The findings showed that the plants in the 2% and 3% PEG media were significantly affected by drought stress, with a notably low root formation performance. The gene expression analysis revealed that the expression levels of genes in the leaf and root tissues of plants under drought conditions were higher compared to the control group. The data obtained from the analyses indicated that the Hayward and Matua cultivars exhibited strong responses to drought both morphologically and genetically. Full article

In this study, the pied kingfisher optimizer (PKO) algorithm is adapted to the uncapacitated facility location problem (UFLP), and its performance is evaluated. The PKO algorithm is binarized with fourteen different transfer functions (TF), and each variant is tested on a total of fifteen different Cap problems. In addition, performance improvement was realized by adding the Levy flight strategy to BinPKO, and this improved method was named BinIPKO. The experimental results show that the TF1 transfer function for BinIPKO performs very well on all problems in terms of both best and mean solution values. The TF2 transfer function performed efficiently on most Cap problems, ranking second only to TF1. Although the other transfer functions provided competitive solutions in some Cap problems, they lagged behind TF1 and TF2 in terms of overall performance. In addition, the performance of BinIPKO was also compared with the well-known PSO and GWO algorithms in the literature, as well as the recently proposed APO and EEFO algorithms, and it was found that BinIPKO performs well overall. In line with this information, it is seen that the IPKO algorithm, especially when used with the TF1 transfer function, provides an effective alternative for UFLP. Full article

Anthocyanins, a subclass of flavonoid pigments, impart vivid red, purple, and blue coloration to horticultural plants, playing essential roles in ornamental enhancement, stress resistance, and pollinator attraction. Recent studies have identified B-box (BBX) proteins as a critical class of transcription factors (TFs) involved in anthocyanin biosynthesis. Despite these advances, comprehensive reviews systematically addressing BBX proteins are urgently needed, especially given the complexity and diversity of their roles in regulating anthocyanin production. In this paper, we provide an in-depth overview of the fundamental structures, biological functions, and classification of BBX TFs, along with a detailed description of anthocyanin biosynthetic pathways and bioactivities. Furthermore, we emphasize the diverse molecular mechanisms through which BBX TFs regulate anthocyanin accumulation, including direct activation or repression of target genes, indirect modulation via interacting protein complexes, and co-regulation with other transcriptional regulators. Additionally, we summarize the known upstream regulatory signals and downstream target genes of BBX TFs, highlighting their significance in shaping anthocyanin biosynthesis pathways. Understanding these regulatory networks mediated by BBX proteins will not only advance fundamental horticultural science but also provide valuable insights for enhancing the aesthetic quality, nutritional benefits, and stress adaptability of horticultural crops. Full article