Search Results (133,153)

Background: Obesity has reached epidemic proportions, affecting over one billion adults worldwide. While incretin-based pharmacotherapies—GLP-1 receptor agonists (semaglutide) and dual GIP/GLP-1 agonists (tirzepatide)—have transformed obesity treatment, their use remains limited by high costs, adverse effects, restricted eligibility, and rapid weight regain following discontinuation. Plant-derived bioactive compounds represent a promising complementary approach to address these gaps. Objective: This narrative review synthesizes clinical trial evidence on plant-based interventions for obesity management and examines their potential role as adjunctive strategies before, during, and after incretin-based pharmacotherapy. Methods: A literature search was conducted in PubMed, Scopus, and Web of Science (2012–2026), prioritizing randomized controlled trials in adults with overweight or obesity reporting at least one obesity-related metabolic outcome. Sixty-one studies were selected and classified by efficacy tier based on the magnitude and breadth of observed clinical effects. Results: The strongest evidence supports polyphenol-rich dietary patterns, particularly the green-Mediterranean diet, producing significant reductions in body weight, visceral fat, and cardiometabolic risk markers. Specific extracts—including curcumin, bergamot polyphenols, and Lippia citriodora/Hibiscus sabdariffa combinations—demonstrated clinically meaningful metabolic improvements. Isolated high-dose resveratrol and several single-compound interventions showed limited benefit, largely attributable to poor bioavailability. The most effective compounds acted through multiple pathways, including AMPK activation, gut microbiota modulation, and appetite hormone regulation. Conclusions: Plant-derived bioactive compounds offer a safe, accessible adjunctive strategy for obesity management, particularly relevant for patients ineligible for or discontinuing pharmacotherapy. Future trials should directly evaluate plant-polyphenol combinations alongside GLP-1 receptor agonists. Full article

Carbon nanotubes (CNTs) serve as exceptional multifunctional supports for metal phthalocyanine (MPc)-based electrocatalysts, effectively addressing the inherent limitations of molecular catalysts such as poor conductivity and aggregation. This review systematically summarizes the recent advances in engineering the interface between MPcs and CNTs to optimize performance in sustainable energy conversion and environmental remediation. We categorize the engineering strategies into three synergistic dimensions: (1) dispersion and modification engineering, introducing the most direct physical anchoring dispersion strategy via non-covalent interactions and targeted modifications to yield highly active catalysts; (2) chemical bonding engineering, in which robust axial coordination or covalent grafting creates stable, well-defined active sites and prevents leaching; and (3) geometric and spatial engineering, which exploits CNTs’ unique curvature, atomic defects, inner cavities and one-dimensional architecture to induce strain, symmetry breaking, and nanoconfinement, thereby steering reaction pathways or to construct conductive nanocomposites. These strategies highlight that CNTs are not merely passive scaffolds but active regulators that geometrically and electronically modulate MPcs. By balancing molecular dispersion, charge transfer, and mass transport, CNT-supported MPcs exhibit superior activity, selectivity, and stability for critical electrochemical reactions, including the oxygen reduction reaction (ORR), CO₂ reduction reaction (CO₂RR), and nitrate reduction reaction (NO₃RR), demonstrating substantial potential for advancing sustainable energy technologies and environmental applications. Full article

Energy consumption in hot–arid and warm climates is driven by cooling demand during long, intense summers. In these contexts, the residential sector accounts for much of national electricity use, and cooling can exceed 70% of annual household consumption. Saudi Arabia exemplifies this pattern, with the residential sector consuming over half of the national electricity and cooling dominating demand in hot–arid cities like Riyadh. Against this background, this study explores the adaptation of the Passive House approach—originally developed in cold and temperate regions—for a cooling-dominated, hot–arid context. A detached villa in Riyadh was selected as a case study, and its energy performance was modeled in DesignBuilder using a baseline calibrated against 12 months of electricity bills. Passive House measures were then tested. The results showed that insulating walls and roofs provided the largest reductions in electricity use, at 21% and 15%, respectively, while high-performance glazing with external shading achieved an additional 3.4%. Improvements in airtightness and ventilation with heat recovery yielded only minor savings in a cooling-dominated climate. When all measures were implemented together, the villa’s annual electricity consumption was reduced by 48% compared with the baseline, and cooling demand was reduced by 72.3%. These findings demonstrate that Passive House measures can be effectively adapted to hot–arid conditions, with envelope insulation and solar-gain control delivering the most significant benefits. The Riyadh case underscores the potential of Passive House principles to reduce residential electricity use in cooling-dominated housing and to support energy-efficient design in hot–arid and warm-climate regions. Full article

Hybrid energy storage systems combining LiFePO₄ batteries and supercapacitors can reduce high-rate battery loading in battery electric mining dump trucks operating under intensive regenerative braking conditions. This study proposes a constrained multi-objective sizing methodology for a semi-active battery–supercapacitor hybrid energy storage system applied to a 65 t payload-class mining electric dump truck. The model combines segment-level mining duty cycles, longitudinal vehicle dynamics, a first-order Thevenin battery representation, a usable supercapacitor energy window, bidirectional DC/DC converter limits, and constrained supervisory power splitting. Three mining duty cycles are considered: production haulage, reclamation/backfill operation, and mixed operation. The final sizing result is reported using a dimensionless usable energy buffer ratio rather than a direct comparison between supercapacitor capacitance and battery energy capacity. The results show that the required supercapacitor buffer is strongly duty cycle-dependent. For the regenerative-dominant backfill cycle, the hybrid configuration reduced peak battery charging current from approximately −950 A to −180 … −280 A and reduced battery root mean square (RMS) current by 52–64% relative to the pure battery configuration. The constrained stored fraction of regenerative energy also increased when the supercapacitor branch was included, while non-accepted braking power was assigned to the residual braking channel. The proposed approach provides a physically consistent basis for preliminary hybrid energy storage system (HESS) sizing and clarifies that battery current reduction should be interpreted as a degradation-relevant stress indicator rather than as a direct quantified lifetime prediction. Full article

Heavy metal pollution has become a critical concern in agricultural ecosystems driven by a complex matrix of industrial practices, high-input fertilizers, metal-based agrochemicals, and wastewater irrigation. While the previous literature typically highlights general physiological symptoms of heavy metal stress, this review provides a novel, comprehensive framework that bridges three independent pillars: specific industrial applications dictating elemental pathway, localizes active root-zone transport kinetics, and an engineering-based evaluation of emerging remediation strategies. We systematically synthesized literature from 2000 to 2026 across major databases (WoS, PubMed and Google Scholar), applying strict inclusion criteria based on data validation, experimental reproducibility, and mechanistic depth. We examine the geochemical behavior, cellular toxicity, and plant resilience mechanics of seven priority elements like cadmium, lead, arsenic, aluminum, mercury, chromium and molybdenum. Rather than merely reiterating superficial visual damage like chlorosis or stunted growth, we focus on physiological and molecular root causes of phytotoxicity, including the structural hijacking of essential nutrient networks, intracellular reduction cascades and organelle-specific oxidative disruption. This review also discussed the discovery of specialized, energy-dependent eukaryotic transport mechanisms like ABC transporters and a comparative operational blueprint evaluating physical–chemical conventional remediation techniques against advanced in situ and ex situ biotechnological approaches, including biochar assistance, microbial engineering, rhizosphere synergies, and engineered nanomaterials. By systematically linking industrial source dynamics with cellular toxicological mechanisms and field-scale engineering feasibility, this review establishes an actionable roadmap for future genetic, agronomic, and management interventions aimed at securing global food. Full article

This study proposes a novel hybrid mathematical framework that integrates the Time-Varying Parameter Vector Autoregression (TVP-VAR) connectedness approach with Long Short-Term Memory (LSTM) deep learning networks to analyze, forecast, and manage volatility spillovers in green financial markets. The framework is motivated by the increasing complexity of risk transmission across sustainable assets, including green bonds, renewable energy stocks, carbon markets, and conventional energy assets. The proposed methodology follows a two-stage structure. First, the TVP-VAR model is employed to quantify dynamic connectedness and time-varying spillover effects across markets. Second, the extracted connectedness measures are used as inputs to an LSTM network to forecast future systemic risk dynamics and generate forward-looking variance–covariance matrices for portfolio optimization and hedging purposes. Using daily data from 2015 to 2025, the empirical results reveal that renewable energy stocks are the dominant transmitters of volatility within the system, exerting substantial spillover effects on green bonds and other sustainable assets. The forecasting evaluation demonstrates that the proposed hybrid TVP-VAR-LSTM framework significantly outperforms traditional econometric models (ARIMA and GARCH) as well as conventional machine-learning benchmarks (SVR, Random Forest, and XGBoost), reducing the Root Mean Squared Error (RMSE) by more than 46% in out-of-sample forecasting. Moreover, the enhanced forecasting accuracy translates into economically meaningful benefits, leading to substantial reductions in realized portfolio risk and improved hedging effectiveness. The findings further highlight the importance of carbon pricing mechanisms and standardized green bond certification in mitigating volatility transmission across sustainable financial markets. Overall, this study contributes to the literature on financial mathematics, systemic risk modeling, and machine learning in green finance by providing a unified framework for volatility spillover analysis, forecasting, and dynamic portfolio optimization. Full article

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents an unsupervised framework with externally guided feature prioritization that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10 m spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information-based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. This study is designed as a pilot-site methodological demonstration using three representative 2 km × 2 km regions in Ontario, rather than a full provincial-scale land cover product. The resulting classification maps are validated against reference land cover data, demonstrating the effectiveness and potential scalability of the proposed external-label guided unsupervised mapping approach. Full article

Background: Missed nursing care related to infection prevention and control compromises patient safety and reflects clinical practice gaps and organizational constraints. The COVID-19 pandemic intensified awareness of infection-control practices; however, whether this translated into sustained reductions remains unclear. Aim: To compare missed infection-control nursing care between the pre-/early-pandemic period (2019–2020) and the post-pandemic period (2026) and identify persistent omissions with implications for healthcare policy and management. Methods: A descriptive study was conducted among 1570 nurses, including 774 participants in Group A (2019–2020) and 796 in Group B (2026). Data were collected online using the Missed Infection Control Nursing Care Questionnaire, tested for reliability and validity in Greek. Data were analyzed using SPSS 25.0, with statistical significance set at α = 0.05. Results: Item-level analysis showed lower mean omission scores in 34/37 infection-control nursing care practices, with 26 statistically significant reductions. The largest decreases were observed for glove use during antibiotic preparation/administration (1.493 to 1.070), hand hygiene before medication administration (1.340 to 0.962), multidrug-resistant organism (MDRO) admission screening (1.849 to 1.481), and intravenous access hub disinfection (1.978 to 1.668). In 2026, key residual omissions involved urinary catheter care (31.2%), hub disinfection (33.2%), oral hygiene (30.9%), and environmental hygiene before meals (29.1%). Conclusions: Missed infection-control nursing care declined in the post-pandemic period, but system-dependent omissions persisted, highlighting the need for staffing adequacy, balanced workload allocation, environmental support, and routine integration of infection-prevention practices. Full article

The response modification factor R is a fundamental parameter in seismic design, linking the elastic demand expected under strong ground motion to the reduced forces used in practice. It reflects the capacity of well-detailed structures to dissipate energy through stable inelastic behavior while maintaining sufficient strength, stiffness, and deformation capacity to prevent collapse. Accordingly, R directly influences design base shear, member forces, reinforcement demands and expected seismic performance. It is prescribed by seismic codes as a single typology dependent value, although analytical evidence indicates that its magnitude varies systematically with structural configuration. Therefore, this study decomposes R for twelve reinforced concrete moment-resisting frame archetypes that combine three heights (4, 8, and 14 stories) with four span configurations (1–4 spans) over a constant 12 m plan length. All frames are designed per ACI 318-19 and ASCE/SEI 7-22 for the Pedernales, Ecuador, subduction-zone seismic hazard. The response modification factor R is evaluated through a component-based decomposition that separates the effects of ductility, overstrength, and redundancy—namely the capacity ductility ${\mu }_c$, the demand ductility ${\mu }_d$, the overstrength $\Omega$, and a geometric redundancy index ${\rho }_g$, using bilinearized pushover analyses. Dynamic verification, used here as a consistency check, is explicitly restricted to the low-rise class (four-story frames) through nonlinear response-history analysis under eleven spectrum-matched ground-motion records; results for the 8- and 14-story frames are therefore pushover-based only. To bracket the inelastic reduction capacity, a demand-based companion factor $R^{*}$ is reported and defined as the demand-based counterpart of R, providing a capacity-oriented estimate R and a demand-oriented companion estimate $R^{*}$. R ranges from 3.80 to 14.56, whereas $R^{*}$ ranges from 1.82 to 7.63. The component ranges are the capacity ductility ${\mu }_c=6.04$–$11.50$, the demand ductility ${\mu }_d=3.04$–$5.98$, the overstrength $\Omega =1.19$–$1.38$, and the geometric redundancy index ${\rho }_g=0.485$–$1.000$. Capacity ductility saturates in taller frames (about $\sim 12\%$ variation). In addition, $\Omega$ and ${\rho }_g$ exhibit a mechanical trade-off that challenges the independence assumption implicit in the multiplicative decomposition. Dynamic results corroborate the pushover-implied demand only for the low-rise class; no extrapolation to taller frames is claimed. Overall, the findings motivate configuration-sensitive analytical calibration as a prerequisite for any future normative discussion on R. Full article

Accurate power load forecasting is critical for the efficient operation of industrial microgrids. However, raw meteorological and consumption data typically exhibit non-stationary characteristics, complicating the hyperparameter tuning of deep learning models, and subsequently degrading the prediction accuracy of these frameworks. To address the aforementioned challenges, a new hierarchical forecasting structure denoted as INRBO-SSA-LSTM is proposed in this paper. First, Pearson correlation analysis is employed for feature reduction, identifying the four main factors to mitigate the dimensionality curse. Building upon this foundation, a refined Newton-Raphson-Based Optimizer (INRBO) is introduced, integrating a cosine adaptive t-distribution perturbation, a boundary-aware non-uniform steering scheme, and a fitness-aware hybrid perturbation mechanism. Evaluated against the CEC2022 benchmark suite, comprehensive evaluations reveal that the INRBO demonstrates superior global exploration and local refinement capabilities compared to baseline algorithms when assessed on the CEC2022 benchmark suite for foundational optimization performance. Furthermore, rigorous testing on the CEC2017 suite across 10, 30, and 50 dimensions successfully validates its exceptional robustness and search capabilities in high-dimensional spaces. INRBO functions as a dual-stage optimizer within the proposed framework; in the initial phase, it dynamically calibrates the parameters of Singular Spectrum Analysis (SSA) to extract deterministic load patterns, achieving a maximum signal-to-noise ratio of 15.87 dB; in the second phase, it optimizes the global hyperparameters of the Long Short-Term Memory (LSTM) network. Validated using actual industrial microgrid data in Jiangsu Province, China, the proposed method significantly outperforms traditional baseline models across all indicators; specifically, the prediction error (RMSE = 10.9764, MAPE = 3.7866%) is substantially minimized, and the coefficient of determination ($R^2$ = 0.9741) is highly optimal. This adaptable framework effectively accommodates temporal demand variations, offering a robust foundation for the advancement of intelligent power management technology. Full article

4-aminoquinazoline derivatives exhibit unique physiological activities, including antitumor, anti-inflammatory, and antibacterial biological activities. Afatinib (BIBW-2992), the representative tyrosine kinase inhibitor, has been developed for the treatment of non-small cell lung cancer. Following our expanded medical chemistry research program, we report a novel 4-aminoquinazoline derivative named JSLN-P (1), (5S)-5-[(2-(5-bromo-2-methoxyphenyl) quinazolin-4-ylamino)methyl]-3-(3-fluoro-4-morpholino phenyl) oxazolidin-2-one, aimed for developing new drugs with antiglioma properties. The title compound JSLN-P (1) was successfully synthesized by amination approaches following benzylamination and oxazolone cyclization, further condensation with 4-(4-bromo-2-fluorophenyl) morpholine, reduction in debenzylation and halogenated amination of quinazolin. The structure of JSLN-P (1) was confirmed by ¹H and ¹³C nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS). Full article

Herbicides are widely used in modern agriculture to control weeds and maintain crop productivity, but their persistence in soil raises concerns about unintended effects on non-target organisms. Pendimethalin, a dinitroaniline herbicide extensively applied to cereal and vegetable crops, is designed to target plant microtubules and is generally considered unlikely to pose genotoxic risks to animals. However, information on its sublethal effects on beneficial soil arthropods remains limited. In this study, we investigated the cytotoxic and histopathological effects of a commercial pendimethalin-based formulation on the ground beetle Pterostichus melas italicus, an ecologically relevant predatory species in agroecosystems. Adult males collected from an organic farm were exposed under laboratory conditions to soil treated at the recommended field dose and maintained for up to 7 days, corresponding to subchronic exposure. Individuals were sampled after 2 and 7 days, and the midgut and Malpighian tubules were analysed using histological and transmission electron microscopy. Exposure induced marked but non-lethal ultrastructural alterations, particularly in the Malpighian tubules, including reduction in the basal labyrinth, cytoplasmic vacuolisation, mitochondrial swelling, increased phagolysosome abundance, and nuclear karyorrhexis. These effects were transient under laboratory conditions and occurred without detectable impacts on survival, highlighting the Malpighian tubules as sensitive targets for the early detection of herbicide-induced physiological disturbances. However, the observed recovery may reflect compensatory physiological processes that could entail energetic costs and, under field conditions characterized by multiple concurrent stressors, potentially compromise physiological performance and predatory efficiency. Consequently, this study underscores the necessity of integrating sublethal ultrastructural biomarkers into environmental risk assessment frameworks for non-target beneficial insects. Full article

Against the backdrop of global warming, China has set forth its ‘dual carbon’ goals, striving to achieve carbon neutrality by 2060. As a vital engine of economic development, the Yangtze River Delta region has formulated implementation plans, prioritizing carbon emission reduction. The transport industry, a major source of carbon emissions, plays a crucial role through its transition to clean energy, making it pivotal for advancing regional carbon neutrality. This study categorizes carbon emission drivers based on an assessment of current emissions and dynamic evolution analysis, integrating policy evolution and technological innovation trajectories. These drivers are classified into: transport structure, transport intensity, energy intensity, year-end resident population, per capita GDP, and industrial structure. Using the extended STIRPAT-Ridge model, quantitative analysis of carbon emission drivers is conducted. Employing the Tapio decoupling model, the decoupling state between carbon emissions and economic growth is deconstructed. Empirical findings reveal that carbon emissions from the transport industry in the Yangtze River Delta are influenced by multiple factors, with year-end resident population and industrial structure emerging as primary drivers. The decoupling between carbon emissions and economic growth exhibits fluctuating characteristics, but has been progressively strengthened in recent years by government policy initiatives and market mechanisms. Full article

In landslide treatment, h-shaped combined circular piles are widely used, and the bending deformation of the piles under the action of sliding force has been deeply studied. However, most previous studies have not considered the difference in the force distribution on the front and rear sides of the pile body. This study established a modified analysis model that takes this difference into account, and through model tests, we calculated and verified the force distribution characteristics of single-row circular piles and h-shaped combined circular piles under various sliding forces. The results show that the proposed modified model can accurately predict the force distribution characteristics on the pile bodies of single-row circular piles and h-shaped combined circular piles, and the calculated values are more consistent with the model test data compared to the results of existing theoretical models. The overall structure of the h-shaped combined circular piles leads to more reasonable force distribution on the pile body than that of the single-row circular piles, resulting in reductions of approximately 20% in soil pressure, 10% in the bending moment of the pile body, and 20% in the pile top displacement. Therefore, the h-shaped combined circular piles are more effective in resisting sliding forces in the model tunnels and provide better protection. Under the same load, the bending moment, soil pressure, and final displacement of the model tunnels of h-shaped combined piles are significantly smaller than those of single-row circular piles. In summary, the proposed modified model provides a reference basis for the internal force verification in the design of h-shaped combined circular piles in actual engineering projects. Full article

This scoping review aimed to investigate (i) the methods used to measure tibial load during human gait, and (ii) the effect of gait mode and velocity on tibial load in healthy adults. This review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Two databases (EMBASE, MEDLINE) were searched, revealing twelve studies that met the inclusion criteria from the previous 24 years. Tibia load was measured using indirect (in vivo strain gauges) and direct (accelerometers, inertial measurement units [IMUs]) sensor methods, and three-dimensional motion analysis systems. A range of surfaces were used, including treadmill (motorized, curved non-motorized) and overground conditions. Velocity was a key determinant of tibia load, with surface type and gait modifications further influencing the loads. The non-standardized measurement methods resulted in varied tibial load results, particularly from varied anatomical positions used in indirect sensor methods. The findings suggest that, based on the current literature, prescribing load management recommendations for healthy adults is not currently possible, given the variability in results. Future research should aim to develop standardized measurement protocols to improve injury risk reduction strategies and to inform rehabilitation programs to support individuals in resuming participation in gait-related activities. Full article