Search Results (172)

Minimizing carbon dioxide emissions is crucial due to the generation of energy from fossil fuels. The significance of carbon capture and storage (CCS) technology, which is highly successful in mitigating carbon emissions, has increased. On the other hand, hydrogen is an important energy carrier for storing and transporting energy, and technologies that rely on hydrogen have become increasingly promising as the world moves toward a more environmentally friendly approach. Nevertheless, the integration of CCS technologies into power production processes is a significant challenge, requiring the enhancement of the combined power generation–CCS process. In recent years, there has been a growing interest in process intensification (PI), which aims to create smaller, cleaner, and more energy efficient processes. The goal of this research is to demonstrate the process intensification potential and to model and simulate a hybrid integrated–intensified adsorptive-membrane reactor process for simultaneous carbon capture and hydrogen production. A comprehensive, multi-scale, multi-phase, dynamic, computational fluid dynamics (CFD)-based process model is constructed, which quantifies the various underlying complex physicochemical phenomena occurring at the pellet and reactor levels. Model simulations are then performed to investigate the impact of dimensionless variables on overall system performance and gain a better understanding of this cyclic reaction/separation process. The results indicate that the hybrid system shows a steady-state cyclic behavior to ensure flexible operating time. A sustainability evaluation was conducted to illustrate the sustainability improvement in the proposed process compared to the traditional design. The results indicate that the integrated–intensified adsorptive-membrane reactor technology enhances sustainability by 35% to 138% for the chosen 21 indicators. The average enhancement in sustainability is almost 57%, signifying that the sustainability evaluation reveals significant benefits of the integrated–intensified adsorptive-membrane reactor process compared to HTSR + LTSR. Full article

Cancer presents significant challenges owing to its complex molecular pathways and resistance to therapy. Natural metabolites have significant medicinal potential by regulating the sensing and signaling pathways associated with cancer development. Recognizing their interactions within the tumor microenvironment may unveil innovative techniques for inhibiting malignant activities and improve therapy success. This article highlights studies regarding ovarian cancer metabolism, signaling mechanisms, and therapeutic natural substances. This study summarizes clinical and experimental results to emphasise the synergistic effects of alkaloids, flavonoids, and terpenoids in improving therapeutic effectiveness and alleviating drug resistance. Bioactive compounds are essential in regulating ovarian cancer metabolism and signaling pathways, affecting glycolysis, lipid metabolism, and the survival of tumor cells. This review examines metabolic programming and essential pathways, including glycolysis, TCA cycle, lipid metabolism, PI3K/AKT/mTOR, AMPK, and MAPK, emphasizing their therapeutic significance. The integration of metabolic treatments with medicines based on natural compounds has significant potential for enhancing treatment effectiveness and mitigating therapeutic resistance. Ovarian cancer needs an integrated strategy that includes metabolic reprogramming, signaling modulation, and drugs derived from natural products. Natural chemicals provide intriguing approaches to address chemotherapy resistance and improve treatment efficacy. Further research is required to enhance these methodologies and evaluate their practical applicability for improved patient outcomes. Full article

Plant secondary metabolites possess significant antioxidant and anti-inflammatory properties, but their complex polypharmacological mechanisms remain poorly understood. Network pharmacology has emerged as a powerful systems-level approach for investigating multi-target interactions of natural products. This review systematically analyzes network pharmacology applications in elucidating the antioxidant and anti-inflammatory mechanisms of plant metabolites, evaluating concordance between computational predictions and experimental validation. A comprehensive literature search was conducted across major databases (2015–2025), focusing on network pharmacology studies with experimental validation. Analysis revealed remarkable convergence toward common molecular mechanisms, despite diverse chemical structures. For antioxidant activities, the Nrf2/KEAP1/ARE pathway emerged as the most frequently validated mechanism, along with PI3K/AKT, MAPK, and NF-κB signaling. Anti-inflammatory mechanisms consistently involved NF-κB, MAPK, and PI3K/AKT pathways. Key targets, including AKT1, TNF-α, COX-2, NFKB1, and RELA, were repeatedly identified. Flavonoids, phenolic acids, and terpenoids dominated as bioactive compounds. Molecular docking studies supported predicted interactions, with experimental validation showing good concordance for pathway modulation and cytokine regulation. Network pharmacology provides a valuable framework for investigating the complex bioactivities of plant metabolites. The convergence toward common regulatory hubs suggests that natural compounds achieve protective effects by modulating central nodes that integrate redox balance and inflammatory responses. Despite limitations, including database dependency, integrating network pharmacology with experimental validation accelerates mechanistic understanding in natural-product drug discovery. Full article

This comprehensive review of the synergistic use of Quality by Design (QbD) and the Pi–Buckingham theorem explores an innovative approach to enhancing product development and process optimization within the pharmaceutical industry. QbD is a systematic, proactive methodology that integrates quality considerations throughout the product lifecycle to ensure that pharmaceutical products meet regulatory standards for safety and efficacy from the outset of development. The Pi–Buckingham theorem serves as a foundational principle in dimensional analysis, facilitating the simplification of complex models by transforming physical variables into dimensionless parameters. This synergy enables researchers to better understand and control the factors affecting critical quality attributes (CQAs), thereby improving manufacturing outcomes and minimizing variability. Full article

Background/Objectives: Gypenosides (Gps) are the main active compounds of Gynostemma and show promise in managing diabetes; nevertheless, the mechanism by which Gps exert anti-diabetic effects is still not fully understood. The aim of this study is to clarify the molecular mechanisms of Gps in ameliorating glucose dysregulation. Methods: Qualitative and quantitative analyses on the chemical components of Gps were performed, respectively. Type 2 diabetes mellitus mouse models were established, and the mice were subsequently treated with Gps at doses of 200, 100, or 50 mg/kg for 4 weeks. Biochemical markers were measured. Histopathological assessments of hepatic and colonic tissues were conducted. The compositions of the intestinal microbiota, short-chain fatty acids (SCFAs), and bile acids (BAs) in fecal samples were analyzed. Western blotting was applied to examine the activation of relevant signaling pathways. Results: Gps have potent regulatory effects on metabolic homeostasis by improving glucose and lipid profiles and alleviating hepatic tissue damage. Treatment with Gps significantly reduced serum levels of lipopolysaccharides and key pro-inflammatory cytokines (interleukin-6 and tumor necrosis factor-α). Moreover, Gps enhanced the integrity of the gut barrier by upregulating the level of tight junction proteins (ZO-1 and occludin). Microbiota profiling revealed that Gps markedly increased microbial diversity and richness, decreased the ratio of Firmicutes/Bacteroidetes, and elevated Bacteroidia abundance from the phylum to the genus level. Targeted metabolomics further demonstrated that Gps modulated gut microbial metabolites by promoting SCFA production and reshaping BA profiles. Specifically, Gps elevated the primary-to-secondary BA ratio while reducing the 12α-hydroxylated to non-12α-hydroxylated BA ratio. Mechanistically, Western blotting demonstrated that Gps triggered the hepatic PI3K/AKT pathway and the intestinal BA/FXR/FGF15 axis, suggesting the coordinated regulation of metabolic and gut–liver axis signaling pathways. Conclusions: Gps significantly ameliorate hyperglycemia and hyperlipidemia through a multifaceted mechanism involving gut microbiota modulation, the restoration of intestinal barrier function, and the regulation of microbial metabolites such as SCFAs and BAs. These findings offer novel insights into their mechanism of action via the gut–liver axis. Full article

This work proposes an end-to-end solution for leaf segmentation, disease detection, and damage quantification, specifically focusing on citrus crops. The primary motivation behind this research is to enable the early detection of phytosanitary problems, which directly impact the productivity and profitability of Spanish and Portuguese agricultural developments, while ensuring environmentally safe management practices. It integrates an onboard computing module for Unmanned Aerial Vehicles (UAVs) using a Raspberry Pi 4 with Global Positioning System (GPS) and camera modules, allowing the real-time geolocation of images in citrus croplands. To address the lack of public data, a comprehensive database was created and manually labelled at the pixel level to provide accurate training data for a deep learning approach. To reduce annotation effort, we developed a custom automation algorithm for pixel-wise labelling in complex natural backgrounds. A SegNet architecture with a Visual Geometry Group 16 (VGG16) backbone was trained for the semantic, pixel-wise segmentation of citrus foliage. The model was successfully integrated as a modular component within a broader system architecture and was tested with UAV-acquired images, demonstrating accurate disease detection and quantification, even under varied conditions. The developed system provides a robust tool for the efficient monitoring of citrus crops in precision agriculture. Full article

Background: The relationship between medicinal efficacy and the geographical environment in Thesium chinense Turcz. (T. chinense Turcz.), a traditional Chinese herb, remains systematically unexplored. This study integrates metabolomics, machine learning, and ecological factor analysis to elucidate the geographical variation patterns and regulatory mechanisms of secondary metabolites in T. chinense Turcz. from Anhui, Henan, and Shanxi Provinces. Methods: Metabolomic profiling was conducted on T. chinense Turcz. samples collected from three geographical origins across Anhui, Henan, and Shanxi Provinces. Machine learning algorithms (Random Forest, LASSO regression) identified region-specific biomarkers through intersection analysis. Metabolic pathway enrichment employed MetaboAnalyst 5.0 with target prediction. Antioxidant activity (DPPH/hydroxyl radical scavenging) was quantified spectrophotometrically. Environmental correlation analysis incorporated 19 WorldClim variables using redundancy analysis, Mantel tests, and Pearson correlations. Results: We identified 43 geographical marker compounds (primarily flavonoids and alkaloids). Random forest and LASSO regression algorithms determined core markers for each production area: Anhui (4 markers), Henan (6 markers), and Shanxi (3 markers). Metabolic pathway enrichment analysis revealed these markers exert pharmacological effects through neuroactive ligand–receptor interaction and PI3K-Akt signaling pathways. Redundancy analysis demonstrated Anhui samples exhibited significantly higher antioxidant activity (DPPH and hydroxyl radical scavenging rates) than other regions, strongly correlating with stable low-temperature environments (annual mean temperature) and precipitation patterns. Conclusions: This study established the first geo-specific molecular marker system for T. chinense Turcz., demonstrating that the geographical environment critically influences metabolic profiles and bioactivity. Findings provide a scientific basis for quality control standards of geo-authentic herbs and offer insights into plant–environment interactions for sustainable cultivation practices. Full article

The gut serves as the main site for nutrient digestion and absorption. Simultaneously, it functions as the body’s largest immune organ, playing a dual role in sustaining physiological equilibrium and offering immunological defense against intestinal ailments. Maintaining the structural and functional integrity of the intestine is paramount for ensuring animal health and productivity. Puerarin, a naturally derived isoflavonoid from the Pueraria species, exhibits multifaceted bioactivities, such as antioxidant, anti-inflammatory, antimicrobial, and immunomodulatory properties. Emerging evidence highlights puerarin’s capacity to enhance gut health in farm animals through four pivotal mechanisms: (1) optimization of intestinal morphology via crypt-villus architecture remodeling, (2) augmentation of systemic and mucosal antioxidant defenses through Nrf2/ARE pathway activation, and (3) reinforcement of intestinal barrier function by regulating tight junction proteins (e.g., ZO-1, occludin), mucin secretion, intestinal mucosal immune barrier, the composition of microbiota, and the derived beneficial metabolites; (4) regulating the function of the intestinal nervous system via reshaping the distribution of intestinal neurons and neurotransmitter secretion function. This review synthesizes current knowledge on puerarin’s protective effects on intestinal physiology in farm animals, systematically elucidates its underlying molecular targets (including TLR4/NF-κB, MAPK, and PI3K/Akt signaling pathways), and critically evaluates its translational potential in mitigating enteric disorders such as post-weaning diarrhea and inflammatory bowel disease in agricultural practices. Full article

Osteoarthritis (OA) is a debilitating joint disorder characterized by cartilage degradation, inflammation, and loss of joint function. While mesenchymal stem cells (MSCs) hold promise for OA therapy due to their regenerative and immunomodulatory properties, challenges such as poor survival, suboptimal differentiation, and an inflammatory microenvironment limit their clinical efficacy. Natural products, including curcumin, resveratrol, quercetin, and epigallocatechin gallate (EGCG), have emerged as a complementary strategy to enhance MSC-based therapies for OA. These bioactive compounds modulate key inflammatory pathways (NF-κB, MAPK, PI3K/AKT), reduce oxidative stress, and promote chondrogenic differentiation of MSCs. Preclinical studies demonstrate the synergistic effects of MSCs and natural products in attenuating inflammation, enhancing cartilage repair, and improving joint function in OA models. However, clinical translation is hindered by challenges in bioavailability, standardization of MSC protocols, and regulatory hurdles. Future research should focus on optimizing delivery systems, conducting large-scale randomized controlled trials, and establishing personalized treatment strategies based on patient biomarkers. By addressing these challenges, the integration of natural products into MSC-based therapies could revolutionize OA treatment, offering a disease-modifying approach for millions of patients worldwide. Full article

The inherent randomness and intermittency of offshore renewable energy sources, such as wind and solar power, pose significant challenges to the stable and secure operation of the power grid. These fluctuations directly affect the performance of grid-connected systems, particularly in terms of harmonic distortion and load response. This paper addresses these challenges by proposing a novel harmonic control strategy and load response optimization approach. An integrated three-winding transformer filter is designed to mitigate high-frequency harmonics, and a control strategy based on converter-side current feedback is implemented to enhance system stability. Furthermore, a hybrid PI-VPI control scheme, combined with feedback filtering, is employed to improve the system’s transient recovery capability under fluctuating load and generation conditions. Experimental results demonstrate that the proposed control algorithm, based on a transformer-oriented model, effectively suppresses low-order harmonic currents. In addition, the system exhibits strong anti-interference performance during sudden voltage and power variations, providing a reliable foundation for the modulation and optimization of offshore wind–solar coupled hydrogen production power supply systems. Full article

Antioxidant peptides derived from woody oil resource by-products exhibit strong free radical scavenging abilities and offer potential applications in functional foods, nutraceuticals, and cosmetics. This review summarizes the latest advances in preparation technologies, including enzymatic hydrolysis, microbial fermentation, chemical synthesis, recombinant expression, and molecular imprinting, each with distinct advantages in yield, selectivity, and scalability. The structure–activity relationships of antioxidant peptides are explored with respect to amino acid composition, molecular weight, and 3D conformation, which collectively determine their bioactivity and stability. Additionally, emerging delivery systems—such as nanoliposomes, microencapsulation, and cell-penetrating peptides—are discussed for their role in enhancing peptide stability, absorption, and targeted release. Mechanistic studies reveal that antioxidant peptides from woody oil resources act through network pharmacology, engaging core signaling pathways, including Nrf2/ARE, PI3K/Akt, AMPK, and JAK/STAT, to regulate oxidative stress, mitochondrial health, and inflammation. Preliminary safety data from in vitro, animal, and early clinical studies suggest low toxicity and favorable tolerability. The integration of omics technologies, molecular docking, and bioinformatics is accelerating the mechanism-driven design and functional validation of peptides. In conclusion, antioxidant peptides derived from woody oil resources represent a sustainable, multifunctional, and scalable solution for improving human health and promoting a circular bioeconomy. Future research should focus on structural optimization, delivery enhancement, and clinical validation to facilitate their industrial translation. Full article

Renewable energy sources (RESs) are increasingly combined into the power system due to market liberalization and environmental and economic benefits, but their weather-dependent variability causes power production and demand mismatches, leading to issues like frequency and regional power transmission fluctuations. To maintain synchronization in power systems, frequency must remain constant; disruptions in the proper balance of production and load might produce frequency variations, risking serious issues. Therefore, a mechanism known as load frequency control (LFC) or automated generation control (AGC) is needed to keep the frequency and tie-line power within predefined stable limits. In this study, advanced proportional–integral–derivative PID controllers such as fractional-order PID (FOPID), cascaded PI(PDN), and PI(1+DD) for LFC in a two-area power system integrated with RES are optimized using the catch fish optimization algorithm (CFA). The controllers’ optimal gains are attained through using the integral absolute error (IAE) and ITAE objective functions. The performance of LFC with CFA-tuned PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers is compared to other optimization techniques, including sine cosine algorithm (SCA), particle swarm optimization (PSO), brown bear algorithm (BBA), and grey wolf optimization (GWO), in a two-area power system combined with RESs under various conditions. Additionally, by contrasting the performance of the PID, PI, cascaded PI(PDN), and FOPID, PI(1+DD) controllers, the efficiency of the CFA is confirmed. Additionally, a sensitivity analysis that considers simultaneous modifications of the frequency bias coefficient (B) and speed regulation (R) within a range of ±25% validates the efficacy and dependability of the suggested CFA-tuned PI(1+DD). In the complex dynamics of a two-area interconnected power system, the results show how robust the suggested CFA-tuned PI(1+DD) control strategy is and how well it can stabilize variations in load frequency and tie-line power with a noticeably shorter settling time. Finally, the results of the simulation show that CFA performs better than the GWO, BBA, SCA, and PSO strategies. Full article

Excessive phosphorus (P) fertilization has led to high soil P accumulation in pear orchards across China, increasing the risk of P loss while limiting economic returns. Orchard grassing has been proposed as a strategy to optimize soil P content and reduce P loss; however, its limited economic benefits have hindered widespread adoption. To address this, we developed a novel planting and mowing ryegrass (MF) system, integrating P loss mitigation with improved economic returns. A two-year field experiment was conducted in the Yangtze River Basin to assess the effects of this system on soil P fractions, P loss risk, and pear production. The results showed that soil available nitrogen (N), available potassium (K), and total P content were significantly lower in the MF treatment compared to natural grassing (NG) at different growth stages. Moreover, the MF treatment increased pear yield by 14.7–16.7% and reduced titratable acidity by 23.5–47.1%, with these improvements primarily driven by changes in phosphorus-related indicators (NaOH-Pi, NaHCO₃-Pi, and intermediate P) across different years. Additionally, the reduction in NaHCO₃-Pi in the MF treatment contributed to a decline in P leaching risk indicators, including Olsen-P and CaCl₂-P. These findings highlight the potential of the MF system as a sustainable orchard management strategy, effectively optimizing soil P dynamics, mitigating P leaching risks, and enhancing pear yield and quality under high P conditions. Full article

This study presents an advanced hybrid energy management system (EMS) designed for isolated microgrids, aiming to optimize the integration of renewable energy sources with backup systems to enhance energy efficiency and ensure a stable power supply. The proposed EMS incorporates solar photovoltaic (PV) and wind turbine (WT) generation systems, coupled with a battery energy storage system (BESS) for energy storage and management and a microturbine (MT) as a backup solution during low generation or peak demand periods. Maximum power point tracking (MPPT) is implemented for the PV and WT systems, with additional control mechanisms such as pitch angle, tip speed ratio (TSR) for wind power, and a proportional-integral (PI) controller for battery and microturbine management. To optimize EMS operations, a novel hybrid optimization algorithm, the JSO-GJO (Jellyfish Search and Golden Jackal hybrid Optimization), is applied and benchmarked against Particle Swarm Optimization (PSO), Bacterial Foraging Optimization (BFO), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA). Comparative analysis indicates that the JSO-GJO algorithm achieves the highest energy efficiency of 99.20%, minimizes power losses to 0.116 kW, maximizes annual energy production at 421,847.82 kWh, and reduces total annual costs to USD 50,617,477.51. These findings demonstrate the superiority of the JSO-GJO algorithm, establishing it as a highly effective solution for optimizing hybrid isolated EMS in renewable energy applications. Full article

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence. Full article