Search Results (2,962)

The use of microwave irradiation energy for isolating bioactive compounds from plant materials has gained popularity due to its ability to penetrate cells and facilitate extraction of intracellular materials, with the added benefits of minimal or no use of organic solvents. This is particularly significant due to the possibility of using extracts in the food and pharmaceutical industries. The aim of this work is to examine the effect of microwave irradiation on the extraction of three of the most important isoflavones from soybean flour, glycitin, genistin, and daidzin, as well as their aglycones, glycitein, genistein, and daidzein. By varying the extraction time, temperature, and microwave power, we have established the optimal parameters (irradiation power of 75 W for 5 min) for the most efficient extraction of individual isoflavones. Compared to conventional maceration and ultrasound-assisted extraction, the total phenol content of the extracts increased from 3.66 to 9.16 mg GAE/g dw and from 4.67 to 9.16 mg GAE/g dw, respectively. The total flavonoid content increased from 0.38 to 0.83 mg CE/g dw and from 0.48 to 0.83 mg CE/g dw, and the antioxidant activity increased from 96.54 to 185.04 µmol TE/g dw and from 158.57 to 185.04 µmol TE/g dw, but also from 21.97 to 37.16 µmol Fe²⁺/g dw and from 30.13 to 37.16 µmol Fe²⁺/g dw. The positive correlation between microwave extraction and increased levels of total phenols, flavonoids, and antioxidant activity demonstrates the method’s effectiveness in producing bioactive compounds. Considering the growing recognition of glycitein’s potential role in medical and pharmaceutical applications, microwave-assisted extraction under optimized conditions has proven highly efficient. Full article

Perovskite can be used to prepare high-performance photodetectors due to its excellent optical properties. However, the detection range of perovskite photodetectors is mostly limited to the visible light range, restricting their further development and application. In recent years, combining perovskite with organic bulk heterojunctions to prepare photodetectors with broadband detection capability has proven to be an effective strategy. Through this approach, the response spectrum of the photodetector can be flexibly regulated, and organic compounds can improve the perovskite film quality by passivating defects and inhibit the penetration of water molecules in the air, thereby improving the device performance and stability. In this work, we propose and demonstrate the feasibility of combining MAPbI₃ perovskite with PTB7-Th:COTIC-4F to prepare high-performance photodetectors with wide spectral response characteristics. With the assistance of an organic bulk heterojunction, the defects of perovskite crystals are effectively passivated, and the detection spectrum of the device is successfully extended to about 1100 nm. As a result, the responsivity achieved is 0.58 A/W, 1.19 A/W, and 1.41 A/W under laser illumination of 532 nm, 808 nm, and 980 nm, with the power density of 5 μW/cm² at the bias voltage of −0.5 V, respectively, which is one of the best performances among vertical device structures of this type. Moreover, the stability of the final hybrid film has been greatly improved. This work provides a new approach to the preparation of high-performance and broadband perovskite photodetectors. Full article

This study aims to evaluate the effectiveness of microwave-assisted and conventional extraction using ethanol, hexane, and petroleum ether as solvents, and to optimize the process for extracting oil from Moringa oleifera Lam. seeds, with a focus on improving food-grade oil production. Response surface methodology (RSM) was applied to enhance the extraction process of the oil. Central composite rotational design (CCRD) was used to analyze the impact of solid–liquid ratio (x₁), power (x₂), and temperature (x₃) on oil yield. The optimization identified the optimal conditions as a solid/liquid ratio of 1:38, power of 175 W, and temperature of 50 °C, achieving a 42% oil yield. Notably, the microwave-assisted extraction reduced the processing time from 8 h (using conventional Soxhlet extraction) to just 1 h. Conventional extraction with hexane and petroleum ether was also performed for comparison, resulting in similar oil content and fatty acid profiles, predominantly, oleic acid. FTIR analysis confirmed that the microwave-extracted oil contained fatty acids and had similar characteristics to the conventionally extracted oil. Thus, the use of ethanol as a green solvent in the microwave has shown significant improvement in terms of time and energy savings compared to the Soxhlet method with toxic solvents. This study concludes that microwave-assisted extraction with ethanol provides a more energy efficient, environmentally friendly, and time-saving alternative for food-grade oil production, aligning with advancements in food engineering and production. Full article

This study aimed to enhance the ultrasonic-assisted extraction (UAE) yield of seawater Arthrospira platensis polysaccharides (APPs) and investigate its structural characteristics and bioactivities. The optimization of UAE achieved a maximum crude polysaccharides yield of 14.78%. The optimal extraction conditions were a liquid–solid ratio of 30.00 mL/g, extraction temperature of 81 °C, ultrasonic power at 92 W and extraction time at 30 min. After purification through cellulose DEAE-52 and Sephadex G-100 columns, two polysaccharide elutions (APP-1 and APP-2) were obtained. APP-2 had stronger antioxidant and immunoregulatory activities than APP-1, thus the characterization of APP-2 was conducted. APP-2 was an acidic polysaccharide consisting of rhamnose, glucose, mannose and glucuronic acid at a ratio of 1.00:24.21:7.63:1.53. It possessed a molecular weight of 72.48 kDa. Additionally, APP-2 had linear and irregular spherical particles and amorphous structures, which contained pyranoid polysaccharides with alpha/beta glycosidic bonds. These findings offered the foundation for APP-2 as an antioxidant and immunomodulator applied in the food, pharmaceutical and cosmetic industries. Full article

We propose and demonstrate a photonic-assisted approach for generating arbitrary microwave waveforms based on a dual-polarization dual-parallel Mach–Zehnder modulator, offering significant advantages in terms of tunability of repetition rates and anti-dispersion capability. In order to generate diverse microwave waveforms, two sinusoidal radio frequency signals with distinct frequency relationships are applied to the dual-polarization dual-parallel Mach–Zehnder modulator. By adjusting the power of the applied sinusoidal radio frequency signal, the power ratio between these orthogonal polarized optical sidebands can be changed, and thereby desired radio frequency waveforms can be obtained after photoelectric conversion. In our proof-of-concept experiment, we systematically varied the repetition rate of triangular, rectangular and sawtooth waveforms. Meanwhile, we calculated the Root Mean Square Error (RMSE) to assess the approximation error in each waveform. The RMSEs are 0.1089, 0.2182 and 0.1185 for the triangular, rectangular and sawtooth microwave waveforms with repetition rate of 8 GHz, respectively. Furthermore, after passing through 25 km single mode fiber, the optical power decreased by approximately 5.6 dB, which verifies the anti-dispersion transmission capability of our signal generator. Full article

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism. Full article

Edge-assisted IoT technologies combined with conventional industrial processes help evolve diverse applications under the Industrial IoT (IIoT) and Industry 4.0 era by bringing cloud computing technologies near the hardware. The resulting innovations offer intelligent management of the industrial ecosystems, focusing on increasing productivity and reducing running costs by processing massive data locally. In this research, we design, develop, and implement an IIoT and edge-based system to monitor the energy consumption of a factory floor’s stationary and mobile assets using wireless and wired energy meters. Once the edge receives the meter’s data, it stores the information in the database server, followed by the data processing method to find nine additional analytical parameters. The edge also provides a master user interface (UI) for comparative analysis and individual UI for in-depth energy usage insights, followed by activity and inactivity alarms and daily reporting features via email. Moreover, the edge uses a data-filtering technique to send a single wireless meter’s data to the cloud for remote energy and alarm monitoring per project scope. Based on the evaluation, the edge server efficiently processes the data with an average CPU utilization of up to 5.58% while avoiding measurement errors due to random power failures throughout the day. Full article

The sustainable management of energy sources such as wind plays a crucial role in supplying electricity for both residential and industrial purposes. For this, accurate wind data are essential to bring sustainability in energy output estimations for wind stations. The choice of an appropriate distribution function significantly affects the actual wind data, directly influencing the estimated energy output. While the Weibull function is commonly used to describe wind speed at various locations worldwide, the variability of weather information across wind sites varies significantly. Probabilistic forecasting offers comprehensive probability information for renewable generation and load, assisting decision-making in power systems under uncertainty. Traditional probabilistic forecasting techniques based on machine learning (ML) rely on prediction uncertainty derived from previous distributional assumptions. This study utilized a Bayesian Recurrent Neural Network (BNN-RNN), incorporating prior distributions for weight variables in the RNN network layer and extending the Bayesian networks. Initially, a periodic RNN processes data for wind energy prediction, capturing trends and correlation characteristics in time-series data to enable more accurate and reliable energy production forecasts. Subsequently, the wind power meteorological dataset was analyzed using the reciprocal entropy approach to reduce dimensionality and eliminate variables with weak connections, thereby simplifying the structure of the prediction model. The BNN-RNN prediction model integrates inputs from RNN-transformed time-series data, dimensionality-reduced weather information, and time categorization feature data. The Winkler index is lower by 3.4%, 32.6%, and 7.2%, respectively, and the overall index of probability forecasting pinball loss is reduced by 51.2%, 22.3%, and 10.7%, respectively, compared with all three approaches. The implications of this study are significant, as they demonstrate the potential for more accurate wind energy forecasting through Bayesian optimization. These findings contribute to more precise decision-making and bring sustainability to the effective management of energy systems by proposing a Bayesian Recurrent Neural Network (BNN-RNN) to improve wind energy forecasts. The model further enhances future estimates of wind energy generation, considering the stochastic nature of meteorological data. The study is crucial in increasing the understanding and application of machine learning by establishing how Bayesian optimization significantly improves probabilistic forecasting models that would revolutionize sustainable energy management. Full article

The global increase in energy consumption, driven by population growth and improved living standards, has led to a heavy reliance on fossil fuels, causing significant environmental concerns. This has prompted a shift toward sustainable energy sources, with biomass, especially lignocellulosic forest biomass, emerging as a key alternative due to its abundance and carbon-neutral potential. Microwave-assisted pyrolysis (MAP) is an efficient method for converting forest biomass into valuable bioproducts and bioenergy with reduced energy use. This review introduces biomass types, focusing on forest biomass and its role in global energy production. It compares MAP to conventional pyrolysis, highlighting the benefits of rapid, uniform heating and improved product yields. Key operational conditions, such as temperature, microwave power, biomass size, and catalyst ratios, are discussed in relation to their impact on product quality and yield. Despite its advantages, MAP faces challenges, particularly in temperature control, which can affect bio-oil yield and quality. High temperatures may cause unwanted secondary reactions, while low temperatures can lead to incomplete decomposition. Research into biomass dielectric properties and process modeling is essential in order to optimize MAP and scale it up for industrial use. Addressing bio-oil quality issues through catalytic upgrading is also critical for broader adoption. Full article

In this study, the hot-air drying of caraway seeds was enhanced using two nonthermal physical field technologies: cold plasma (CP) and ultrasonic waves (US). Air drying temperatures of 35, 45, and 55 °C with CP pretreatment exposure times (CP_t) of 25 and 50 s were used. When convective drying was accompanied by US, power levels (US_p) of 60, 120, and 180 W were applied. Experimentally, the most effective contribution was found by using both CP pretreatment (25 s) and US (180 W), in which the maximum decreases of 31% and 39% were estimated for the drying period and specific energy consumption, respectively. The total color change, the rupture force, TPC, TFC, and antioxidant capacity were also estimated for evaluating the quality of dried products. In a CP-US-assisted drying program (25 s, 180 W), the minimum change in color and the rupture force were found to be 6.40 N and 20.21 N, respectively. Compared to the pure air drying, the combined application of CP and US resulted in a mean increase of 53.2, 43.6, and 24.01% in TPC, TFC, and antioxidant capacity of extracts at the temperature of 35 °C. Based on the response surface methodology (RSM) approach and obtained experimental data, accurate mathematical predictive models were developed for finding the optimal drying condition. The optimization process revealed that 39 °C, 180 W, and 23 s resulted in a desirability of 0.78 for drying caraway seeds. Full article

With the increasing global concern regarding climate change and the need to reduce greenhouse gas emissions, carbon capture and utilization (CCU) technologies are seen as one of the primary steps toward large-scale decarbonization prospects. In this context, a thorough assessment of each CCU pathway is required from both the techno-economic and environmental perspectives. In this work, the potential of carbon biofixation through microalgae cultivation is evaluated through the preliminary technical design and calculation of plant economics in the case of the Turakurgan natural gas-fired combined cycle power plant located in the eastern part of Uzbekistan. The primary data used in this study are obtained from the open access project report of the targeted power station, along with recently published literature sources. According to the results, although the purchase and installation costs of photobioreactors require significant investments in the capital costs, the technology would still be cost competitive as long as there is a carbon tax imposition of around USD 50 per ton of CO₂ emissions. However, CO₂ biofixation can be relatively more suitable compared to benchmark absorption, particularly in low-CO₂-concentration conditions. Future research will involve a more comprehensive examination of CO₂-based microalgae cultivation and its comparison with chemical absorption and membrane-assisted separation techniques. Full article

The aim of this work was to determine the concentrations, distribution, and fate of potentially toxic elements [lead (Pb), zinc (Zn), nickel (Ni), copper (Cu), mercury (Hg), arsenic (As), and cadmium (Cd)] in sediments of a hydropower plant water reservoir located in the Brazilian Cerrado biome (used as system model). The purpose of this study was achieved with an analysis of the level of contamination based on the geoaccumulation index (Igeo) and factor contamination (FC) and comparisons with values established by environmental legislation. The physical–chemical–biological properties of sediment samples, the distribution, and the fate of potentially toxic elements (PTEs) in the basin of the stream studied were also investigated using Pearson’s correlation coefficient (r) and principal component analysis (PCA). Cu, Hg, and Cd concentrations in the sediment samples from most of the points analyzed were above level II of the categorization stipulated in environmental legislation, characterizing sediments of poor quality. Moreover, Igeo and FC values indicated potential pollution of the water reservoir sediment by Cd. Concentrations of Cd exceeding 0.34 mg kg⁻¹ surpassed the reference values for water quality established by Conama Resolution No. 454/2012, highlighting the urgent need for ongoing sediment quality monitoring strategies. Hence, the study water reservoir was classified as being moderately to extremely polluted due to the fate of potentially toxic metals in the sediment samples. Frequent monitoring of the sediment quality in watersheds with hydropower plants is indispensable for the assessment of water resources, considering the importance of the water supply and power generation for the population. Moreover, water contaminated by PTEs poses potential risks to river basins, as well as to human and animal health. The results of this work can assist in the investigation of other water reservoirs around the world. Full article

Future 6G networks will inherit and develop Network Function Virtualization (NFV) architecture. With the NFV-enabled network architecture, it becomes possible to establish different virtual networks within the same infrastructure, create different Virtual Network Functions (VNFs) in different virtual networks, and form Service Function Chains (SFCs) that meet different service requirements through the orderly combination of VNFs. These SFCs can be deployed to physical entities as needed to provide network functions that support different services. To meet the highly dynamic service requirements in the future 6G Internet of Things (IoT) scenario, the highly flexible and efficient SFC reconfiguration algorithm is the key research direction. Deep-learning-based algorithms have shown their advantages in solving this type of dynamic optimization problem. Considering that the efficiency of the traditional Actor Critic (AC) algorithm is limited, the policy does not directly participate in the value function update. In this paper, we use the Proximal Policy Optimization (PPO) clip function to restrict the difference between the new policy and the old policy, to ensure the stability of the updating process. We combine PPO with AC, and further bring the historical decision information as the network knowledge to offer better initial policies, to accelerate the training speed. We also propose the Knowledge = Assisted Actor Critic Proximal Policy Optimization (KA-ACPPO)-based SFC reconfiguration algorithm to ensure the Quality of Service (QoS) of end-to-end services. Simulation results show that the proposed KA-ACPPO algorithm can effectively reduce computing cost and power consumption. Full article

Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid’s state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided. Full article

This study evaluated the antioxidant and anti-glycemic properties of black lemon Chenpi (BLC) (Citrus limon (L.) Burm. f. cv. Eureka), processed using three thermal browning models—hot-air drying (HAL), high temperature and humidity, and steam-drying cycle (SCL)—and compared them to fresh lemon peel and commercial Chenpi. The moisture-assisted aging technology (MAAT) is an environmentally friendly process for inducing browning reactions in the lemon peel, enhancing its functional properties. Our results demonstrated significant increases in sucrose, total flavonoid content, and antioxidant capacities (2,2-diphenylpicrylhydrazyl: 12.86 Trolox/g dry weight; ferric reducing antioxidant power: 14.92 mg Trolox/g dry weight) with the MAAT-HAL model. The MAAT-SCL model significantly improved the browning degree, fructose, total polyphenol content, narirutin, and 5-hydroxymethylfurfural synthesis (p < 0.05). Additionally, aged lemon peel exhibited potential α-glucosidase inhibitory activity (28.28%), suggesting its role in blood sugar regulation after meals. The multivariate analysis (principal component and heatmap analyses) indicated that BLC processed using the MAAT-SCL model exhibited similarities to commercial Chenpi, indicating its potential for functional food development. Our results indicate that MAAT-SCL can enhance the economic value of lemon by-products, offering a sustainable and functional alternative to traditional Chenpi. Full article