Search Results (36,481)

Medicinal plants play an essential role in the food, pharmaceutical, and cosmetic industries due to their ability to prevent and treat diseases. In this study, a three-factor, three-level Box–Behnken experimental design (BBD) with response surface methodology (RSM) was used to maximize the conditions of ultrasound-assisted extraction (UAE) of bioactive compounds from matico and chacruna leaves in terms of total extraction yield (TEY), total phenolic content (TPC) and antioxidant activity (AA) using ABTS and DPPH assays. The effect of methanol concentration (X1: 25%, 50%, and 75%), time (X2: 3, 6, and 9 min), and power (X3: 90, 270, and 450 W) was evaluated as independent variables. The experimental results were fitted to second-order polynomial models, and multiple regression analysis and analysis of variance were used to determine the suitability of the models, using which 3D response surface plots were generated. Considering the multivariable optimization, the best extraction conditions were 73.68% v/v methanol, 9 min, 269.32 W for matico, and 64.84% v/v methanol, 3 min, 344.44 W for chacruna. Under these conditions, the maximum value of 18.33 and 20.83% for TEY, 7.16 and 40.86 mg GAE/g dm for TPC, 56.88 and 526.38 µmol TE/g dm for DPPH were predicted for matico and chacruna, respectively. Practical Applications: This research focused on the modeling by response surface methodology (RSM) of Ultrasound-Assisted Extraction of bioactive compounds from matico and chacruna, Peruvian plants used in traditional medicine. The methodologies used allow the maximization of bioactive extraction, which presented a high recovery of phenolics with high antioxidant activity. These results highlight the use of Amazon plants in traditional medicine and their possible use in other industries such as cosmetic or food safety. Full article

The heading face is one of the zones most severely affected by dust pollution in underground coal mines, and dust control becomes even more challenging during roadway excavation with continuous miners. To improve dust mitigation in environments characterized by intense dust generation, high ventilation demand, and large cross-sectional areas, this study integrates numerical simulations, laboratory experiments, and field tests to investigate the physicochemical properties of dust, airflow distribution, dust migration behavior, and a comprehensive dust control strategy combining airflow regulation, foam suppression, and dust extraction fan systems. The results show that dust dispersion patterns differ markedly between the left-side advancement and right-side advancement of the roadway; however, the wind return side of the continuous miner consistently exhibits the highest dust concentrations. The most effective purification of dust-laden airflow is achieved when the dust extraction fan delivers an airflow rate of 500 m³/min and is positioned behind the continuous miner on the return side. After optimization of foam flow rate and coverage based on the cutting head structure of the continuous miner, the dust suppression efficiency reached 78%. With coordinated optimization and on-site implementation of wall-mounted ducted airflow control, foam suppression, and dust extraction fan systems, the total dust reduction rate at the heading face reached 95.2%. These measures substantially enhance dust control effectiveness, improving mine safety and protecting worker health. The resulting reduction in dust concentration also improves visibility for underground intelligent equipment and provides practical guidance for industrial application. Full article

Papaya (Carica papaya L.) processing generates by-products that can serve as sustainable sources of bioactive compounds. This study aimed to extract and characterize the bioactive compounds from the peel and seeds of different papaya varieties and evaluate their antioxidant and antimicrobial potential, as well as their behavior under simulated digestion. The results indicated that Maradol seeds possessed the highest total phenolic content and antioxidant values, demonstrating superior compositional and functional profiles, and that seed extracts overall had greater antibacterial efficacy than peel extracts, with Hawaiian seed extracts exhibiting the greatest overall inhibition. Furthermore, under simulated gastrointestinal conditions, the combined extracts from peel and seeds effectively preserved phenolics through the gastric and intestinal phases and notably enhanced the generation of acetate and propionate during colonic fermentation. These findings robustly substantiate the functional valorization of papaya by-products and suggest that selecting extracts based on their specific bioactive profiles can significantly enhance their applications as natural, functional ingredients in the food industry. Full article

This paper presents an embedded gateway architecture that enables the seamless integration of Modbus-based industrial devices into Data Distribution Service (DDS) middleware for Industrial Internet of Things (IIoT) applications. The gateway, implemented on an STM32 microcontroller, acts as both a Modbus master and DDS-XRCE client, mapping Modbus registers directly to DDS topics with a configurable Quality of Service (QoS). Experimental validation demonstrates median latencies below 15 ms in four out of five scenarios, a throughput of up to 80 messages/s, and stable scalability to 160 subscribers with moderate resource usage. The results confirmed the feasibility and efficiency of Modbus–DDS integration on resource-constrained platforms. Full article

Traditional soaking plum wine production is time-consuming and often results in high levels of bitter amygdalin and toxic cyanide, posing health risks. In this study, response surface methodology (RSM) with a Box–Behnken design was employed to optimize pulsed electric field (PEF) parameters, developing a novel process integrating kernel detoxification and PEF pretreatment to mitigate these hazards, enhance the characteristic aroma (benzaldehyde), and shorten the maceration cycle. The experimental results showed that the contents of bitter amygdalin and cyanide in plum kernels after detoxification and PEF pretreatment were reduced by 62.34% and 59.62%, respectively, compared with the control group, and the contents of both were further reduced with the addition of plum flesh for further soaking in the new process. In addition, the PEF pretreatment also increased the amount of benzaldehyde extracted by 4.63% compared to the control group and resulted in a 10.53% reduction in equilibration time. Moreover, compared to the previous whole-fruit maceration process, the new process resulted in a 37.5% reduction in the final plum wine production cycle. This study provides a practical solution for improving the safety and efficiency of plum wine production and supports the industrial application of PEF technology. Full article

Over the past two decades, the meat industry has faced increasing pressure to prevent foodborne outbreaks and reduce economic losses associated with delayed detection of spoilage. This demand has accelerated the development of on-site, real-time sensing tools capable of identifying early signs of contamination. Electrospun nanofiber (NF) platforms have emerged as particularly promising due to their large surface area, tunable porosity, and versatile chemistry, which make them ideal scaffolds for immobilizing enzymes, antibodies, or aptamers while preserving bioactivity under field conditions. These NFs have been integrated into optical, electrochemical, and resistive devices, each enhancing response time and sensitivity for key targets ranging from volatile organic compounds indicating early decay to specific bacterial markers and antibiotic residues. In practical applications, NF matrices enhance signal generation (SERS hotspots), facilitate analyte diffusion through three-dimensional networks, and stabilize delicate biorecognition elements for repeated use. This review summarizes major NF fabrication strategies, representative sensor designs for meat quality monitoring, and performance considerations relevant to industrial deployment, including reproducibility, shelf life, and regulatory compliance. The integration of such platforms with data networks and Internet of Things (IoT) nodes offers a path toward continuous, automated surveillance throughout processing and cold-chain logistics. By addressing current technical and regulatory challenges, NF-based biosensors have the potential to significantly reduce waste and safeguard public health through early detection of contamination before it escalates into costly recalls. Full article

To address the issue of material spillage and equipment wear caused by conveyor belt deviation in complex industrial scenarios, this study proposes a detection method based on an improved U-Net. The approach adopts U-Net as the backbone network, with a ResNet34 encoder to enhance feature extraction capability. At the skip connections, a Multi-scale Adaptive Guidance Attention (MASAG) module is embedded to strengthen the fusion of semantic and detailed features. In the loss function design, a boundary loss is incorporated to improve edge segmentation accuracy. Furthermore, the segmentation results are refined via edge detection and RANSAC regression, and a reference line is constructed based on the physical stability of rollers in the image to enable quantitative measurement of deviation. Experiments on a self-constructed dataset demonstrate that the proposed method achieves higher accuracy (99.77%) compared with the baseline U-Net (99.65%) and also surpasses other categories of approaches, including detection-based (YOLOv5s), anchor-point-based (UFLD), and segmentation-based approaches represented by SEU-Net and DeepLabV3+, thereby exhibiting strong robustness and real-time performance across diverse complex operating conditions. The results validate the effectiveness of this method in practical applications and provide a reliable technical pathway for the development of intelligent monitoring systems for mining conveyor belts. Full article

The increasing production of fruit and vegetable by-products from the food processing industry presents both environmental challenges and opportunities for valorisation as sources of bioactive compounds. These by-products, including peels, seeds, pomace, and leaves, are rich in polyphenols, carotenoids, dietary fibres, glucosinolates, phytosterols, and essential oils, which exhibit antioxidant, anti-inflammatory, antimicrobial, and prebiotic activities. Recent advances in green extraction technologies, including ultrasound-, microwave-, supercritical fluid-, and cold plasma-assisted extraction, allow for an efficient and sustainable recovery of these compounds, while preserving their bioactivity. Incorporation of by-product-derived extracts into functional foods and nutraceuticals offers health-promoting benefits and supports circular bioeconomy strategies. However, challenges remain in standardisation, safety assessment, and regulatory approval, among others. This review summarises current progress and outlines future directions for the sustainable utilisation of fruit and vegetable by-products in health-oriented applications. Full article

Roselle (Hibiscus sabdariffa L.) is a plant rich in bioactive constituents, serving as a unique material for the food and beverage industry and therapeutic applications. Despite its significant utility, few studies have focused on the molecular breeding of the plant. Chloroplasts are organelles in plant cells with independent genetic information, making them ideal for investigating plant phylogeny and genetic evolution. In this study, the roselle breeding material ‘Zhe Xiao Luo 1’ was selected to assemble and analyze the entire chloroplast genome using the Illumina NovaSeq X Plus platform. The phylogenetic relationships between roselle and other species within Malvaceae family, particularly within the genus Hibiscus, were clarified. The results showed that the complete chloroplast genome of roselle was 162,428 bp in length, with nucleotide proportions of 31.14% (A), 18.73% (C), 18.01% (G), 32.12% (T), and 36.74% (GC). It exhibited a typical tetrad structure consisting of four segments: the large single copy (LSC) region (90,327 bp), the small single-copy (SSC) region (19,617 bp), and two inverted repeat sequences (IRa and IRb, each 26,242 bp). A total of 130 genes were identified, including 37 tRNA genes, 8 rRNA genes, and 85 mRNA genes, and no pseudogenes were detected. Phylogenetic analysis using 23 revealed a clear phylogenetic relationship between H. sabdariffa and H. esculentus (okra) among all tested species. Building on previous research, this study further explored the functional annotation of genes in the roselle chloroplast genome, as well as its codon preference, repetitive sequences, simple sequence repeats (SSR), Ka/Ks ratio, nucleotide diversity (pi) analysis, and boundary analysis. The complete gene sequences have been uploaded to the NCBI database (accession number PX363576). This study provides evidence for elucidating the phylogenetic relationships and taxonomic status of H. sabdariffa, laying a theoretical foundation for studies on molecular mechanism resolution and cultivar development. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

Multivariate time series anomaly detection is a critical task in modern engineering, with applications spanning environmental monitoring, network security, and industrial systems. While reconstruction-based methods have shown promise, they often suffer from overfitting and fail to adequately distinguish between normal and anomalous data, limiting their generalization capabilities. To address these challenges, we propose the AOST model, which integrates adversarial learning with an outlier suppression mechanism within a Transformer framework. The model introduces an outlier suppression attention mechanism to enhance the distinction between normal and anomalous data points, thereby improving sensitivity to deviations. Additionally, a dual-decoder generative adversarial architecture is employed to enforce consistent data distribution learning, enhancing robustness and generalization. A novel anomaly scoring strategy based on longitudinal differences further refines detection accuracy. Extensive experiments on three public datasets—SWaT, WADI, SMAP, and PSM—demonstrate the model’s superior performance, achieving an average F1 score of 88.74%, which surpasses existing state-of-the-art methods. These results underscore the effectiveness of AOST in advancing multivariate time series anomaly detection. Full article

Climate change is driving the need for forest management strategies that simultaneously enhance ecosystem resilience and contribute to climate change mitigation. Voluntary carbon markets (VCMs), regulated in the European Union by the Carbon Removal Certification Framework (CRCF), offer potential financial incentives for such management, but eligibility criteria—particularly biodiversity requirements—limit the applicability of certain species. This study assessed the ecological and economic outcomes of six alternative management scenarios for a 4.7 ha, 99-year-old Scots pine (Pinus sylvestris) stand in western Hungary, comparing them against a business-as-usual (BAU) regeneration baseline. Using field inventory data, species-specific yield tables, and the Forest Industry Carbon Model, we modelled living and dead biomass carbon stocks for 2025–2050 and calculated potential CO₂ credit generation. Economic evaluation employed total discounted contribution margin (TDCM) analyses under varying carbon credit prices (€0–150/tCO₂). Results showed that an extended rotation yielded the highest carbon sequestration (958 tCO₂ above BAU) and TDCM but was deemed operationally unfeasible due to declining stand health. Black locust (Robinia pseudoacacia) regeneration provided high mitigation potential (690 tCO₂) but was ineligible under CRCF rules. Grey poplar (Populus × canescens) regeneration emerged as the most viable option, balancing biodiversity compliance, climate adaptability, and economic return (TDCM = EUR 22,900 at €50/tCO₂). The findings underscore the importance of integrating ecological suitability, market regulations, and economic performance in planning carbon farming projects, and highlight that regulatory biodiversity safeguards can significantly shape feasible mitigation pathways. Full article

Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidity of hard labels and the simplicity of label smoothing under-represent inter-class relations and compositional structures, degrading cross-domain robustness. While current domain generalization methods can alleviate these issues, they typically rely on multi-source domain data. However, considering the limitations of equipment operational conditions and data acquisition costs in industrial applications, only one or two independently distributed source datasets are typically available. In this work, an adaptive label refinement network (ALRN) was designed for learning with imperfect labels under source-scarce conditions. Compared to hard labels and label smoothing, ALRN learns richer, more robust soft labels that encode the semantic similarities between fault classes. The model first trains a convolutional neural network (CNN) to obtain initial class probabilities. It then iteratively refines the training labels by computing a weighted average of predictions within each class, using the sample-wise cross-entropy loss as an adaptive weighting factor. Furthermore, a label refinement stability coefficient based on the max-min Kullback–Leibler (KL) divergence ratio across classes is proposed to evaluate label quality and determine when to terminate the refinement iterations. With only one or two source domains for training, ALRN achieves accuracy gains exceeding 22% under unseen operating conditions compared with a conventional CNN baseline. These results validate that the proposed label refinement algorithm can effectively enhance the cross-domain diagnostic performance, providing a novel and practical solution for learning with imperfect supervision in cross-domain compound fault diagnosis. Full article

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

In the modern business environment, where uncertainty and complexity make decision-making difficult, the need for robust, transparent and adaptable support tools is highlighted. The proposed method, named Flexible Decision Navigator for Evaluating Trends and Strategies (F-DeNETS), offers a complementary perspective to classic Artificial Intelligence (AI), Big Data and Multi-Criteria Decision-Making (MCDM) tools. Despite their broad use, these methods frequently suffer from critical sensitivities In the weighting of criteria and the handling of uncertainty, leading to compromised reliability and limited practical utility in environments with limited data availability. To bridge this gap, F-DeNETS integrates intuition and uncertainty into a transparent and statistically grounded process. It introduces a balanced approach that combines statistical evidence with human judgment, extending the boundaries of classic AI, Big Data and MCDM methods. Classic MCDM methods, although useful, are sometimes limited by subjectivity, staticity and dependence on large volumes of data. To fill this gap, F-DeNETS, a hybrid framework combining Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Non-Asymptotic Fuzzy Estimators (NAFEs) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), transforms expert judgments into statistically sound fuzzy quantifications, incorporates dynamic adaptation to new data, reduces bias and enhances reliability. A numerical application from the shipping industry demonstrates that F-DeNETS offers a flexible and interpretable methodology for optimal decisions in environments of high uncertainty. Full article