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Propolis is a complex natural compound that honeybees obtain from plants and contributes to hive safety. It is rich in phenolic and flavonoid compounds, which contain antioxidant, antimicrobial, and anticancer properties. In this study, the chemical composition and antioxidant activities of propolis were investigated; ABTS•+, DPPH• and DMPD•+ were prepared using radical scavenging antioxidant methods. The phenolic and flavonoid contents of propolis were 53 mg of gallic acid equivalent (GAE)/g and 170.164 mg of quercetin equivalent (QE)/g, respectively. The ferric ion (Fe3+) reduction, CUPRAC and FRAP reduction capacities were also studied. The antioxidant and reducing capacities of propolis were compared with those of butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), α-tocopherol and Trolox reference standards. The half maximal inhibition concentration (IC50) values of propolis for ABTS•+, DPPH• and DMPD•+ scavenging activities were found to be 8.15, 20.55 and 86.64 μg/mL, respectively. Propolis extract demonstrated IC50 values of 3.7, 3.4 and 19.6 μg/mL against α-glycosidase, acetylcholinesterase (AChE) and carbonic anhydrase II (hCA II) enzyme, respectively. These enzymes’ inhibition was associated with diabetes, Alzheimer’s disease (AD) and glaucoma. The reducing power, antioxidant activity and enzyme inhibition capacity of propolis extract were comparable to those demonstrated by the standards. Twenty-eight phenolic compounds, including acacetin, caffeic acid, p-coumaric acid, naringenin, chrysin, quinic acid, quercetin, and ferulic acid, were determined by LC-MS/MS to be major organic compounds in propolis. The polyphenolic antioxidant-rich content of the ethanol extract of propolis appears to be a natural product that can be used in the treatment of diabetes, AD, glaucoma, epilepsy, and cancerous diseases.

Pear pomace, a byproduct of juice production, represents a valuable reservoir of bioactive compounds with potential health benefits for humans. This study aimed to evaluate the influence of drying method and temperature on pear pomace, specifically focusing on the drying kinetics, grinding characteristics, color, phenolic profile (LC-MS/MS), and antioxidant activities of the powder. Drying using the contact method at 40 °C with microwave assistance demonstrated the shortest duration, whereas freeze-drying was briefer compared to contact-drying without microwave assistance. Freeze-drying resulted in brighter and more easily comminuted pomace. Lyophilized samples also exhibited higher total phenolic compound levels compared to contact-dried ones, correlating with enhanced antioxidant activity. Twenty-one phenolic compounds were identified, with dominant acids being quinic, chlorogenic, and protocatechuic. Flavonoids, primarily isoquercitrin, and rutin, were also presented. Pear pomace dried via contact at 60 °C contained more quinic and protocatechuic acids, while freeze-dried pomace at the same temperature exhibited higher levels of chlorogenic acid, epicatechin, and catechin. The content of certain phenolic components, such as gallic acid and epicatechin, also varied depending on the applied drying temperature.

As power systems around the world shift to incorporate more renewable energy sources, particularly wind power, maintaining grid stability becomes increasingly challenging due to the inherent variability of these sources. This paper introduces a novel bi-level robust optimization framework that enhances the capabilities of adaptive Under-Frequency Load Shedding (AUFLS) in managing the uncertainties brought by high penetration of wind energy and dynamic participation of electric vehicles (EVs). Central to this framework is an innovative adaptive non-parametric Kernel Density Estimation (AAKDE) technique, which sharpens the accuracy of wind power fluctuation predictions. This method enables more precise and efficient control of load-shedding events, which is crucial for preventing frequency drops that can lead to grid instability. This research proposes a strategic shedding queue mechanism that systematically prioritizes the discharge of EVs based on their real-time state-of-charge and charging behavior. This prioritization minimizes user discomfort and taps into the potential of EVs as flexible energy resources, thus providing substantial support to grid operations. To enhance the responsiveness of our AUFLS approach, we integrate a reinforcement learning model that adjusts in real time to grid conditions, optimizing decision-making for frequency stabilization. Our extensive MATLAB/SIMULINK simulations on an upgraded IEEE 39 bus test system demonstrate a significant reduction in load shedding requirements. Compared to traditional AUFLS methods, our approach cuts load shedding by over 50%, effectively maintains system frequency within safe operational limits, and shows superior performance in scenarios of high renewable variability and EV integration. This research highlights the potential of adaptive non-parametric methods in transforming AUFLS strategies, paving the way for smarter, more resilient power systems equipped to handle the complexities of modern energy landscapes.

In the pursuit of achieving zero emissions, exploring the concept of recycling metal waste from industries and workshops (i.e., waste-free) is essential. This is because metal recycling not only helps conserve natural resources but also requires less energy as compared to the production of new products from virgin raw materials. The use of metal scrap in rapid tooling (RT) for injection molding is an interesting and viable approach. Recycling methods enable the recovery of valuable metal powders from various sources, such as electronic, industrial, and automobile scrap. Mechanical alloying is a potential opportunity for sustainable powder production as it has the capability to convert various starting materials with different initial sizes into powder particles through the ball milling process. Nevertheless, parameter factors, such as the type of ball milling, ball-to-powder ratio (BPR), rotation speed, grinding period, size and shape of the milling media, and process control agent (PCA), can influence the quality and characteristics of the metal powders produced. Despite potential drawbacks and environmental impacts, this process can still be a valuable method for recycling metals into powders. Further research is required to optimize the process. Furthermore, ball milling has been widely used in various industries, including recycling and metal mold production, to improve product properties in an environmentally friendly way. This review found that ball milling is the best tool for reducing the particle size of recycled metal chips and creating new metal powders to enhance mechanical properties and novelty for mold additive manufacturing (MAM) applications. Therefore, it is necessary to conduct further research on various parameters associated with ball milling to optimize the process of converting recycled copper chips into powder. This research will assist in attaining the highest level of efficiency and effectiveness in particle size reduction and powder quality. Lastly, this review also presents potential avenues for future research by exploring the application of RT in the ball milling technique.

Turkey is one of the most important centers of diversity for the genus Achillea L. in the world. Keeping in mind the immense medicinal importance of phenols, in this study, three species growing in Turkey, A. coarctata Poir. (AC), A. kotschyi Boiss. subsp. kotschyi (AK) and A. lycaonica Boiss. & Heldr. (AL) were evaluated for their phenolic compositions, total phenolic contents (TPC), antioxidant properties, wound healing potencies on NIH-3T3 fibroblasts and cytotoxic effects on MCF-7 human breast cancer cells. Comprehensive LC-MS/MS analysis revealed that AK was distinctively rich in chlorogenic acid, hyperoside, apigenin, hesperidin, rutin, kaempferol and luteolin (2890.6, 987.3, 797.0, 422.5, 188.1, 159.4 and 121.2 µg analyte/g extract, respectively). The findings exhibited a strong correlation between TPC and both free radical scavenging activity and total antioxidant capacity (TAC). Among studied species, the highest TPC (148.00 mg GAE/g extract) and TAC (2.080 UAE), the strongest radical scavenging (EC50 = 32.63 μg/mL), the most prominent wound healing and most abundant cytotoxic activities were observed with AK. The results suggested that AK is a valuable source of flavonoids and chlorogenic acid with important antioxidant, wound healing and cytotoxic activities. These findings warrant further studies to assess the potential of AK as a bioactive source that could be exploited in pharmaceutical, cosmetics and food industries.

Reliable short-term electric load forecasting (STLF) is essential for enhancing grid stability, optimizing energy distribution, and minimizing operational costs in modern power systems. However, existing forecasting models, including statistical approaches and deep learning architectures such as multi-layer perceptron (MLP), struggle to capture complex nonlinear load variations while maintaining computational efficiency. To overcome these limitations, a self-adaptive Kolmogorov–Arnold network (SADE-KAN), an optimized forecasting framework that combines the power of Kolmogorov–Arnold networks (KAN) with self-adaptive differential evolution (SADE) is introduced to enhance both predictive accuracy and computational efficiency. Unlike conventional MLP models, KAN replaces fixed activation functions with spline-based learnable functions that offers greater flexibility in capturing temporal dependencies. However, these learnable activation functions introduce a new set of hyperparameters that require careful optimization to ensure efficient training and manage network complexity. To address this, SADE dynamically tunes these hyperparameters, ensuring an optimal balance between accuracy, complexity, and training efficiency. The proposed SADE-KAN model is validated on ISO-NE hourly load data (2019–2023, ~ 1 million observations) across multiple forecasting horizons (24, 48, 96, and 168 h). Experimental results demonstrate that SADE-KAN reduces mean absolute percentage error (MAPE) by up to 35% and root mean squared error (RMSE) by 38% compared to MLP models, while requiring 35% fewer learnable parameters. Despite a slightly higher training time, SADE-KAN significantly enhances generalization and robustness, capturing rapid load fluctuations more effectively than MLP, conventional KAN and other recently published advanced models. These findings establish SADE-KAN as a computationally efficient and highly accurate forecasting framework, offering a robust solution for real-time power system applications, demand response strategies, and energy market operations.

Regional clusters of energy producers and consumers can be realized by integrating household Battery Energy Storage (BES) systems with Renewable Energy Sources (RES) and linking them to the main utility grid. These clusters, functioning as grid-connected microgrids (MGs), act as controllable units within the broader energy distribution network. As distribution systems evolve to include higher MG penetration, the need for efficient and scalable energy management becomes critical to ensure technical compatibility with grid objectives and operational constraints. Additionally, understanding the impact of battery usage patterns on degradation is essential for developing long-term, cost-effective energy management strategies. This paper presents a novel Grid-Connected Microgrid Energy Management (GCM-EM) model that incorporates both economic and technical constraints, with Battery Energy Storage (BES) as the central flexible resource. The proposed model supports both uncoordinated (microgrid-autonomous) and coordinated (DSO-integrated) scheduling schemes. The novelty lies in its ability to capture real-world BES degradation dynamics—including cycle aging and depth-of-discharge (DoD) effects—within an optimization-based energy scheduling framework. The scheduling model leverages mixed-integer programming, AC optimal power flow, and rolling-horizon control to achieve both local and system-level operational goals. The model’s performance was validated using simulations on two representative test systems: a university campus distribution grid and a standardized 33-bus power network. Results demonstrate that localized MG optimization can reduce energy costs by up to 2%. At the same time, coordination with the Distribution System Operator (DSO) further enhances grid-level cost efficiency—though sometimes at the expense of local MG economic optimality. Importantly, the model preserves data privacy during coordination and maintains compliance with distribution grid constraints. Furthermore, the model was implemented in a real building-level microgrid (BMG), where it effectively minimized BES operational and degradation costs. Compared to conventional EMS frameworks that ignore battery wear, the proposed model achieved a 3% reduction in combined annual energy and degradation costs. Integration into actual EMS platforms also enabled optimized BES dispatch, reduced municipal grid dependence, enhanced MG operational flexibility, and lowered overall network operating expenses. This research provides a comprehensive and practically validated energy management architecture for BES-integrated microgrids. By combining advanced scheduling strategies with accurate degradation modeling and multi-agent coordination, the proposed system represents a significant advancement toward economically sustainable and technically robust distributed energy networks.

Individuals with obstructive sleep apnea (OSA) face increased accident risks due to excessive daytime sleepiness. PERCLOS, a recognized drowsiness detection method, encounters challenges from image quality, eyewear interference, and lighting variations, impacting its performance, and requiring validation through physiological signals. We propose visual-based scoring using adaptive thresholding for eye aspect ratio with OpenCV for face detection and Dlib for eye detection from video recordings. This technique identified 453 drowsiness (PERCLOS ≥ 0.3 || CLOSDUR ≥ 2 s) and 474 wakefulness episodes (PERCLOS < 0.3 and CLOSDUR < 2 s) among fifty OSA drivers in a 50 min driving simulation while wearing six-channel EEG electrodes. Applying discrete wavelet transform, we derived ten EEG features, correlated them with visual-based episodes using various criteria, and assessed the sensitivity of brain regions and individual EEG channels. Among these features, theta–alpha-ratio exhibited robust mapping (94.7%) with visual-based scoring, followed by delta–alpha-ratio (87.2%) and delta–theta-ratio (86.7%). Frontal area (86.4%) and channel F4 (75.4%) aligned most episodes with theta–alpha-ratio, while frontal, and occipital regions, particularly channels F4 and O2, displayed superior alignment across multiple features. Adding frontal or occipital channels could correlate all episodes with EEG patterns, reducing hardware needs. Our work could potentially enhance real-time drowsiness detection reliability and assess fitness to drive in OSA drivers.

Alchemilla species (Rosaceae) are popularly known as ‘Lady’s Mantle, Lion’s claw’ and are used for medicinal purposes as diuretic, laxative, tonic, and wound healing agents. Bioactivities and phenolic content of Alchemilla pseudocartalinica Juz. species have yet to be investigated. Our research focused on assessing the antioxidant characteristics of A. pseudocartalinica methanol (MEAP) and water extracts (WEAP), as well as their inhibitory effects on acetylcholinesterase (AChE), α-glycosidase (α-gly), and human carbonic anhydrase II (hCA II) enzymes. Additionally, we conducted chemical characterization using UPLC-MS/MS and investigated the correlation between major phenolic compounds and enzymes through molecular docking analysis. To assess the antioxidant activities of the MEAP and WEAP, six test systems were employed, including DPPH, ABTS, DMPD, FRAP, CUPRAC, and Fe3+ reducing assays. The outcome showed that the methanol extract of the plant generally has stronger antioxidant activity. In addition, UPLC-MS/MS analysis indicated, miquelianin (44.095 mg/g), quinic acid (17.054 mg/g), and ellagic acid (6.492 mg/g) were significant in the methanol extract. A molecular docking study revealed a significant affinity for binding between the hCAII enzyme and quinic acid, miquelianin, and AChE/α-gly enzymes. A. pseudocatalinica methanol and water extracts have high antioxidant activity and good inhibition effect against AChE, α-glycosidase, and hCA II enzymes.

Graphitic carbon is a valuable material that can be utilized in many fields, such as electronics, energy storage and wastewater filtration. Due to the high demand for commercial graphite, an alternative raw material with lower costs that is environmentally friendly has been explored. Amongst these, an agricultural bio-waste material has become an option due to its highly bioactive properties, such as bioavailability, antioxidant, antimicrobial, in vitro and anti-inflammatory properties. In addition, biomass wastes usually have high organic carbon content, which has been discovered by many researchers as an alternative carbon material to produce graphite. However, there are several challenges associated with the graphite production process from biomass waste materials, such as impurities, the processing conditions and production costs. Agricultural bio-waste materials typically contain many volatiles and impurities, which can interfere with the synthesis process and reduce the quality of the graphitic carbon produced. Moreover, the processing conditions required for the synthesis of graphitic carbon from agricultural biomass waste materials are quite challenging to optimize. The temperature, pressure, catalyst used and other parameters must be carefully controlled to ensure that the desired product is obtained. Nevertheless, the use of agricultural biomass waste materials as a raw material for graphitic carbon synthesis can reduce the production costs. Improving the overall cost-effectiveness of this approach depends on many factors, including the availability and cost of the feedstock, the processing costs and the market demand for the final product. Therefore, in this review, the importance of biomass waste utilization is discussed. Various methods of synthesizing graphitic carbon are also reviewed. The discussion ranges from the conversion of biomass waste into carbon-rich feedstocks with different recent advances to the method of synthesis of graphitic carbon. The importance of utilizing agricultural biomass waste and the types of potential biomass waste carbon precursors and their pre-treatment methods are also reviewed. Finally, the gaps found in the previous research are proposed as a future research suggestion. Overall, the synthesis of graphite from agricultural bio-waste materials is a promising area of research, but more work is needed to address the challenges associated with this process and to demonstrate its viability at scale.