Search Results (554)

With the increasing penetration of distributed generation (DG), integrated main-distribution networks (IMDNs) face challenges in rapidly and effectively performing comprehensive operational risk assessments under multiple uncertainties. Thereby, using the traditional hierarchical economic scheduling method makes it difficult to accurately find the optimal scheduling operating point. To address this problem, this paper proposes a multi-objective dispatch decision-making optimization model for the IMDN with static security region (SSR) constraints. Firstly, the non-sequential Monte Carlo sampling is employed to generate diverse operational scenarios, and then the key risk characteristics are extracted to construct the risk assessment index system for the transmission and distribution grid, respectively. Secondly, a hyperplane model of the SSR is developed for the IMDN based on alternating current power flow equations and line current constraints. Thirdly, a risk assessment matrix is constructed through optimal power flow calculations across multiple load levels, with the index weights determined via principal component analysis (PCA). Subsequently, a scheduling optimization model is formulated to minimize both the system generation costs and the comprehensive risk, where the adaptive grid density-improved multi-objective particle swarm optimization (AG-MOPSO) algorithm is employed to efficiently generate Pareto-optimal operating point solutions. A membership matrix of the solution set is then established using fuzzy comprehensive evaluation to identify the optimal compromised operating point for dispatch decision support. Finally, the effectiveness and superiority of the proposed method are validated using an integrated IEEE 9-bus and IEEE 33-bus test system. Full article

To enhance the fire safety protection level of deeply buried metro stations, this study conducted full-scale fire experiments based on Wulichong Station of Guiyang Metro Line 3. It systematically investigated the laws of smoke movement and temperature distribution under the coupled effects of different fire source powers and smoke extraction system states. Through the set up of multiple sets of comparative test conditions, the study focused on analyzing the influence mechanism of the operation (on/off) of the smoke extraction system on smoke spread characteristics and temperature field distribution. The results indicate that under the condition where the smoke extraction system is turned off, the smoke exhibits typical stratified spread characteristics driven by thermal buoyancy, with the temperature rising significantly as the vertical height increases. When the smoke extraction system is activated, the horizontal airflow generated by mechanical smoke extraction significantly alters the flame morphology (with an inclination angle exceeding 45°), effectively extracting and discharging the hot smoke and leading to a more uniform temperature distribution within the space. Full article

Unconventional food plants (UFPs) are increasingly valued for their nutritional composition and bioactive potential. This study proposes a comprehensive characterization of the chemical and bioactive properties of Pereskia aculeata Miller (Cactaceae) (PA); Xanthosoma sagittifolium (L.) Schott (Araceae) (XS); Stachys byzantina K. Koch (Lamiaceae) (SB); and inflorescences from three cultivars of Musa acuminata (Musaceae) var. Dwarf Cavendish, var. BRS Platina, and var. BRS Conquista (MAD, MAP, and MAC), including the assessment of physical, nutritional, phytochemical, and biological parameters. Notably, detailed phenolic profiles were established for these species, many of which are poorly documented in the literature. XS was characterized by a unique abundance of C-glycosylated flavones, especially apigenin and luteolin derivatives, rarely described for this species. SB exhibited high levels of phenylethanoid glycosides, particularly verbascoside and its isomers (up to 21.32 mg/g extract), while PA was rich in O-glycosylated flavonols such as quercetin, kaempferol, and isorhamnetin derivatives. Nutritionally, XS had the highest protein content (16.3 g/100 g dw), while SB showed remarkable dietary fiber content (59.8 g/100 g). Banana inflorescences presented high fiber (up to 66.5 g/100 g) and lipid levels (up to 7.35 g/100 g). Regarding bioactivity, PA showed the highest DPPH radical scavenging activity (95.21%) and SB the highest reducing power in the FRAP assay (4085.90 µM TE/g). Cellular antioxidant activity exceeded 2000% in most samples, except for SB. Cytotoxic and anti-inflammatory activities were generally low, with only SB showing moderate effects against Caco-2 and AGS cell lines. SB and PA demonstrated the strongest antimicrobial activity, particularly against Yersinia enterocolitica, methicillin-resistant Staphylococcus aureus (MRSA), and Enterococcus faecalis, with minimum inhibitory concentrations ranging from 0.156 to 0.625 mg/mL. Linear discriminant analysis revealed distinctive chemical patterns among the species, with organic acids (e.g., oxalic up to 7.53 g/100 g) and fatty acids (e.g., linolenic acid up to 52.38%) as key discriminant variables. Overall, the study underscores the nutritional and functional relevance of these underutilized plants and contributes rare quantitative data to the scientific literature regarding their phenolic signatures. Full article

Objectives: The aim of this work was to determine the phenolic composition of sweet wormwood (Artemisia annua L., Asteraceae) from controlled cultivation in Serbia and to assess the potential antioxidant effects and cytotoxicity. Methods: High-performance liquid chromatography was used to determine the phenolic composition of Artemisia annua ethanolic extract. The antioxidant activity was studied using in vitro tests of inhibition of the neutralization of 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl (OH), and nitroso (NO) radicals, as well as the process of inhibiting lipid peroxidation and the ferric reducing antioxidant power (FRAP). The cytotoxicity was evaluated by the effect on three cell lines (the rat pancreatic insulinoma cell line (Rin-5F), the rat hepatoma cell line (H4IIE), and human hepatocellular carcinoma (Hep G2)) using the MTT test of viability. Results: Ethanol extract showed the highest potency in inhibiting the DPPH radical, and the half maximal inhibitory concentration (IC₅₀) was 5.17 μg/mL. Chlorogenic acid was the dominant phenolic compound with an amount of 651 μg/g of dry extract. The results of the MTT viability test showed that the extract has the potential to inhibit the growth of the Rin-5F and Hep G2 cell lines, while no growth inhibition was observed on the H4IIE cell line. Conclusions: Undoubtedly, Artemisia annua is a powerful plant and a rich source of phenolic compounds. Inhibitory activity on causes of oxidative stress shows that the plant has a good antioxidant effect. Also, the anticancer activity shown through the inhibition of cell growth is not negligible. Full article

Lippia multiflora Moldenke is widely used in Angola, on the African continent, and beyond for the treatment of many health conditions such as hypertension, enteritis, colds, gastrointestinal disturbances, stomachaches, jaundice, coughs, fevers, nausea, bronchial inflammation, conjunctivitis, malaria, and venereal diseases. However, there is limited literature about the active compounds linked with the reported biological activities. This study aims to assess the chemical profiles, antioxidant properties, and the cytotoxicity effects of the roots, stem bark, and leaves of L. multiflora. Chemical characterization of the crude extracts was assessed through quantification of total phenolic and flavonoid contents followed by Q exactive plus orbitrap™ ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS) screening. The correlation between the extracts and the correlation between the compounds were studied using the multivariate analysis. Principal component analysis (PCA) loading scores and principal component analysis (PCA) biplots and correlation plots were used to connect specific compounds with observed biological activities. The antioxidant activities of the crude extracts were carried out in vitro using DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging and reducing power assays, while the in vitro toxicology of the crude extracts was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. A total of twenty constituents were characterized and identified using the UHPLC–Q/Orbitrap/MS. The methanol leaf extract showed the highest antioxidant activity in the DPPH free radical scavenging activity (IC₅₀ = 0.559 ± 0.269 μg/mL); however, the stem bark extract had the highest reducing power (IC_0.5 = 0.029 ± 0.026 μg/mL). High phenolic and flavonoid content was found in the dichloromethane leaf extract (32.100 ± 1.780 mg GAE/g) and stem bark extract (624.153 ± 29.442 mg QE/g), respectively. The results show the stem bark, methanol leaf, and dichloromethane leaf extracts were well-tolerated by the Vero cell line at concentrations up to 50 µg/mL. However, at 100 µg/mL onward, some toxicity was observed in the root, methanol leaf, and dichloromethane leaf extracts. The UHPLC–Q/Orbitrap/MS profiles showed the presence of terpenoids (n = 5), flavonoids (n = 5), phenols (n = 4), alkaloids (n = 3), coumarins (n = 1), fatty acids (n = 1), and organic acids (n = 1). According to several studies, these secondary metabolites have been reported and proven to be the most abundant for antioxidant potential. The identified flavonoids (catechin, quercitrin, and (−)-epigallocatechin) and phenolic compound (6-gingerol) can significantly contribute to the antioxidant properties of different plant parts of L. multiflora. The research findings obtained in this study provide a complete phytochemical profile of various parts of L. multiflora that are responsible for the antioxidant activity using UHPLC–Q/Orbitrap/MS analysis. Furthermore, the results obtained in this study contribute to the scientific information or data on the therapeutic properties of Lippia multiflora and provide a basis for further assessment of its potential as a natural remedy. Full article

This study investigates near-infrared (NIR)-induced, Phyto-enhanced, second harmonic generation-mediated photodynamic therapy (Phyto-SHG-PDT) using barium titanate (BT)/rhein/polyethylene glycol 100 (PEG100) and BT/Yemenite “Etrog” leaf extract/PEG100 nanoconjugates. We compare continuous-wave (CW), multi-line Argon-ion laser illumination in the NIR range with high-peak-power femtosecond (fs) 800 nm pulses. Under CW NIR light, BT/rhein nanoconjugates reduced PC3 prostate cancer cell viability by 18% versus non-irradiated controls (p < 0.05), while BT/extract nanoconjugates exhibited 15% dark toxicity. The observed SHG signal matched theoretical predictions and previous CW laser studies. Reactive Oxygen Species (ROS) scavenger 1,3-diphenyl-isobenzofuran (DPBF) showed reduced absorbance at 410 nm upon NIR illumination, indirectly supporting SHG emission at 400 nm from nanoconjugates. Under fs-pulsed laser exposure, pronounced two-photon absorption (TPA) and SHG effects were observed in both nanoconjugate types. Our results demonstrate the effectiveness of BT/rhein nanoconjugates under both laser conditions, while the BT/extract nanoconjugates benefited from high-power pulsed excitation. These results highlight the potential of BT-based Phyto-SHG-PDT nanoconjugates for NIR and blue light applications, leveraging nonlinear optical effects for advanced photochemical cancer therapies. Full article

Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. Various EMG acquisition methods, including surface, intramuscular, and high-density surface EMG, are evaluated for their applicability in real-time control. The review addresses prevalent signal quality challenges, such as motion artifacts, power-line interference, and crosstalk. It also highlights both traditional filtering techniques and advanced methods, such as wavelet transforms, empirical mode decomposition, and adaptive filtering. Feature extraction techniques are explored to support pattern recognition and motion classification. Machine learning approaches are examined for their roles in pattern recognition-based and hybrid control architectures. This article emphasizes muscle synergy analysis and adaptive control algorithms to enhance personalization and fatigue compensation, followed by the benefits of multimodal sensing and edge computing in addressing the limitations of EMG-only systems. By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human–machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons. Full article

This paper addresses the critical need for detailed electricity and peak power demand maps for urban public transportation vehicles. Current approaches often rely on overly general assumptions, leading to considerable errors in specific applications or, conversely, overly specific measurements that limit generalisability. We aim to present a comprehensive data-driven methodology for analysing energy consumption within a large urban agglomeration. The method leverages a unique and extensive set of real-world performance data, collected over two years from onboard recorders on all public bus lines in the Capital City of Warsaw. This large dataset enables a robust probabilistic analysis, ensuring high accuracy of the results. For this study, three representative bus lines were selected. The approach involves isolating inter-stop trips, for which instantaneous power waveforms and energy consumption are determined using classical mathematical models of vehicle drive systems. The extracted data for these sections is then characterised using probability distributions. This methodology provides accurate calculation results for specific operating conditions and allows for generalisation with additional factors like air conditioning or heating. The direct result of this paper is a detailed urban map of energy demand and peak power for public transport vehicles. Such a map is invaluable for planning new traffic routes, verifying existing ones regarding energy consumption, and providing a reliable input source for strategic charger deployment analysis along the route. Full article

Background/Objectives: Agave amica (Medik.) Thiede & Govaerts (Asparagaceae family) is an ornamental bulbous species, widely used for its fragrance but less studied as a medicinal species. This study is aimed at assessing the phenolic profile and selected biological properties of ethanolic extracts obtained from the aerial parts and bulbs of A. amica cultivated in Romania. Methods: The phenolic composition was characterized by spectrophotometric methods and LC/MS analysis. The antioxidant activity was evaluated by DPPH (2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity) and FRAP (Ferric reducing antioxidant power) tests, while the in vitro antimicrobial capacity was investigated by the agar-well diffusion, the broth microdilution, and the antibiofilm assays. Cytotoxicity was tested on a colorectal adenocarcinoma cell line (DLD-1) by a CCK-8 assay. Results: Both ethanolic extracts showed important polyphenol content and caffeic acid as their main compound. Significantly higher amounts of total polyphenols (44.25 ± 1.08 mg/g), tannins (12.55 ± 0.34 mg/g), flavonoids (9.20 ± 0.19 mg/g), caffeic acid derivatives (19.95 ± 0.05 mg/g), and also antioxidant activity (IC₅₀ = 0.82 ± 0.02 mg/mL, and 79.75 ± 1.80 µM TE/g, respectively) were found for the aerial parts extract compared to the bulbs one (p < 0.001). Notable anti-Candida albicans activity and moderate efficacy against Listeria monocytogenes and Staphylococcus aureus were displayed by both extracts against planktonic cells and biofilm. A dose-dependent cytotoxicity towards the colorectal adenocarcinoma cell line was recorded as well. Conclusions: This study brings novelty to the scientific literature by characterizing the phenolic profile and in vitro antimicrobial, antibiofilm, antioxidant, and antiproliferative activities of the ethanolic extracts obtained from A. amica, thus highlighting this herbal species’s medicinal potential. Full article

Background: Colon cancer is one of the leading causes of cancer-related deaths today. Crucial research continues for the ideal chemotherapy. In this context, natural compounds of plant origin play an important role in the development of new anticancer drugs. Methods: In this study, the effects of Consolida orientalis ethanol extract (flower parts), Smyrnium rotundifolium ethanol extract (aerial parts), and Euphorbia virgata ethanol extract (aerial parts) samples on HT-29 (human colorectal adenocarcinoma cell line) and healthy CCD-18Co (human normal colon fibroblast cell line) were investigated for the first time in the literature by applying 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) test within the scope of in vitro cytotoxicity analysis. Results: As a result of the study, it was observed that all plant extracts were most effective at 72 h. S. rotundifolium ethanol extract (aerial parts) was found to be the most effective on the HT-29 cell line. Both the higher cell viability of C. orientalis in healthy cells applied to it compared to S. rotundifolium and its effectiveness on colon cancer cell lines make C. orientalis more advantageous. Conclusions: When evaluating the efficacy of extracts on cancer cells, the load on healthy cells should be taken into account. Therefore, C. orientalis ethanol extract (flower parts) was found to have the potential to be a chemotherapeutic agent against colon cancer. Chemical reactivities of the dominant components of bioactive components were analyzed via Conceptual Density Functional Theory-based calculations. The power of the interactions with EGFR kinase of these compounds is checked via Molecular Docking Calculations. It was noted that Chlorogenic acid, which is the most reactive bioactive component, has a stronger binding to the mentioned enzyme. Full article

Wildfire detection in power transmission corridors is essential for providing timely warnings and ensuring the safe and stable operation of power lines. However, this task faces significant challenges due to the large number of smoke-like samples in the background, the complex and diverse target morphologies, and the difficulty of detecting small-scale smoke and flame objects. To address these issues, this paper proposed an improved Oriented R-CNN model enhanced with metric learning for wildfire detection in power transmission corridors. Specifically, a multi-center metric loss (MCM-Loss) module based on metric learning was introduced to enhance the model’s ability to differentiate features of similar targets, thereby improving the recognition accuracy in the presence of interference. Experimental results showed that the introduction of the MCM-Loss module increased the average precision (AP) for smoke targets by 2.7%. In addition, the group convolution-based network ResNeXt was adopted to replace the original backbone network ResNet, broadening the channel dimensions of the feature extraction network and enhancing the model’s capability to detect flame and smoke targets with diverse morphologies. This substitution led to a 0.6% improvement in mean average precision (mAP). Furthermore, an FPN-CARAFE module was designed by incorporating the content-aware up-sampling operator CARAFE, which improved multi-scale feature representation and significantly boosted performance in detecting small targets. In particular, the proposed FPN-CARAFE module improved the AP for fire targets by 8.1%. Experimental results demonstrated that the proposed model achieved superior performance in wildfire detection within power transmission corridors, achieving a mAP of 90.4% on the test dataset—an improvement of 6.4% over the baseline model. Compared with other commonly used object detection algorithms, the model developed in this study exhibited improved detection performance on the test dataset, offering research support for wildfire monitoring in power transmission corridors. Full article

Insulator defect detection is important in ensuring power systems’ safety and stable operation. To solve the problems of its low accuracy, high delay, and large model size in complex environments, following the principle of progressive extraction from high-entropy details to low-entropy semantics, an improved YOLOv8 target detection network for insulator defects based on bidirectional weighted feature fusion was proposed. A C2f_DSC feature extraction module was designed to identify more insulator tube features, an EMA (encoder–modulator–attention) mechanism and a BiFPN (bidirectional weighted feature pyramid network) fusion layer in the backbone network were introduced to extract different features in complex environments, and EIOU (efficient intersection over union) as the model’s loss function was used to accelerate model convergence. The CPLID (China Power Line Insulator Dataset) was tested to verify the effectiveness of the proposed algorithm. The results show its model size is only 6.40 M, and the mean accuracy on the CPLID dataset reaches 98.6%, 0.8% higher than that of the YOLOv8n. Compared with other lightweight models, such as YOLOv8s, YOLOv6, YOLOv5s, and YOLOv3Tiny, not only is the model size reduced, but also the accuracy is effectively improved with the proposed algorithm, demonstrating excellent practicality and feasibility for edge devices. Full article

In live-line power distribution operations, 3D pose and action recognition of workers holds critical significance for safety assurance and intelligent monitoring. We propose a novel neural network architecture for fast fitting-based parametric 3D human reconstruction (FFMN) from monocular images in live-line work scenarios. FFMN employs convolutional neural networks to extract feature information from input images and adopts an optimization strategy for inverse problems by reprojecting keypoints from the human model onto feature maps to acquire feedback. A transformer-based updater module then adjusts the model to better align with the person in the image. Unlike conventional regression or recurrent models, FFMN trains faster, utilizes fewer parameters, and achieves shorter inference times. Moreover, our FFMN demonstrates a significant inference speed advantage (latency = 15 ms) on the 3DPW and Human3.6M datasets while maintaining competitive accuracy (MPJPE < 50 mm), highlighting its high practicability in real-world applications. Full article

Power line segmentation plays a critical role in ensuring the safety of transmission line UAV inspection flights. To address the challenges of small target scale, complex backgrounds, and excessive model parameters in existing deep learning-based power line segmentation algorithms, this paper introduces RGS-UNet, a lightweight segmentation model integrating a residual-like cross-layer feature fusion module. First, ResNet18 is adopted to reconstruct a UNet backbone network as an encoder module to enhance the network’s feature extraction capability for small targets. Second, ordinary convolution in the residual block of ResNet18 is optimized by introducing the Ghost Module, which significantly reduces the computational load of the model’s backbone network. Third, a residual-like addition method is designed to embed the SIMAM attention mechanism module into both encoder and decoder stages, which improves the model’s ability to extract power lines from complex backgrounds. Finally, the Mish activation function is applied in deep convolutional layers to maintain feature extraction accuracy and mitigate overfitting. Experimental results demonstrate that compared with classical UNet, the optimized network achieves 2.05% and 2.58% improvements in F1-Score and IoU, respectively, while reducing the parameter count to 57.25% of the original model. The algorithm achieves better performance improvements in both accuracy and lightweighting, making it suitable for edge-side deployment. Full article

The safety of power transmission lines is crucial to public well-being, with insulators being prone to failures such as self-detonation. However, images captured by unmanned aerial vehicles (UAVs) carrying optical sensors often face challenges, including uneven object scales, complex backgrounds, and difficulties in feature extraction due to distance, angles, and terrain. Additionally, conventional models are too large for UAV deployment. To address these issues, this paper proposes RML-YOLO, an improved insulator defect detection method based on YOLOv8. The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. Additionally, the multi-scale feature extraction network (MSFENet) is designed to prioritize large-scale features while adding an extra detection layer for small objects, improving multi-scale object detection precision. A lightweight multi-scale shared detection head (LMSHead) reduces model size and parameters by sharing features across layers, addressing scale distribution imbalances. Lastly, the receptive field attention channel attention convolution (RFCAConv) module aggregates features from various receptive fields to overcome the limitations of standard convolution. Experiments on the UID, SFID, and VISDrone 2019 datasets show that RML-YOLO outperforms YOLOv8n, reducing model size by 0.8 MB and parameters by 500,000, while improving AP by 7.8%, 2.74%, and 3.9%, respectively. These results demonstrate the method’s lightweight design, high detection performance, and strong generalization capability, making it suitable for deployment on UAVs with limited resources. Full article