Search Results (88,106)

Background: A functional balance between angiotensin II receptors (AT1R/AT2R) is associated with the regulation of cardiovascular, inflammatory, and tissue remodeling processes. Interventions that promote this balance have a significant impact on quality of life. Physical exercise stands out among these interventions, as the literature extensively identifies it as a therapeutic tool for managing conditions linked to the main axis of the renin–angiotensin system (RAS). Despite its beneficial role regarding the RAS, its effects on AT1R and AT2R receptor expression have not yet been systematically synthesized. Objective: To review the literature and systematically compile existing knowledge regarding the effect of physical exercise on the expression of angiotensin II receptors (AT1R and AT2R) within the RAS. Methods: This is a systematic review registered in PROSPERO (CRD420251267388). Searches were conducted in the PubMed/MEDLINE, PEDro, Cochrane Central Register of Controlled Trials, Scientific Electronic Library Online, Latin American and Caribbean Health Sciences Literature (LILACS), Web of Science, and Scopus databases. Risk of bias was assessed using the RoB 2 and SYRCLE tools. A total of 724 studies were identified, of which 11 met the eligibility criteria: 10 conducted using experimental animal models and 1 involving humans. Results: Eleven studies were included, predominantly animal experimental models. The findings indicate that chronic cyclic physical exercise is associated with reduced AT1R expression and increased AT2R expression in different tissues. In contrast, the only study that evaluated resistance exercise showed a distinct physiological response, with increased AT1R expression. Conclusions: The results suggest that chronic cyclical physical exercise promotes an increase in AT2R receptor expression and a reduction in AT1R expression in various body tissues, although the evidence is derived primarily from animal studies. Full article

Predicting wave slamming pressures on bulbous bows is essential for ship structural safety. This study employs an overset-grid RANS-VOF framework to investigate viscous scale effects on bulbous bow slamming loads. Multi-scale simulations were conducted across four geometric scale ratios of 1:50, 1:20, 1:15, and 1:10 (α = 50, 20, 15, 10) under critical pitch-heave resonant head waves (λ/L_WL = 1.2). While global motion responses follow Froude similitude, local dynamic slamming pressures show notable scale disparities. Smaller physical models develop a relatively thicker viscous boundary layer that acts as a hydrodynamic cushion, reducing peak pressures while broadening pulse durations. Consequently, direct Froude scaling from small-scale models tends to underestimate full-scale impact loads. To account for these viscous scale effects, an engineering extrapolation approach based on multi-scale regression is proposed, which demonstrates a reasonable linear correlation across the investigated range (R² = 0.88–0.99) in the primary impact region. This study provides physical insights into the scaling behavior of bulbous bow slamming and offers a practical reference for full-scale load estimation. Full article

Bio-asphalt–rubber (BAR) binders combine plant-based bio-oil, end-of-life tire crumb rubber, and petroleum asphalt binder, but their climate advantage remains uncertain because bio-oil supply chains, asphalt-binder inventories, and tire-rubber allocation choices can substantially change cradle-to-gate results. This study develops a probabilistic cradle-to-gate life-cycle assessment for one metric ton of binder at plant gate. Eight alternatives were evaluated: a neat petroleum binder, a rubberized binder, a bio-oil modified binder, and five BAR binders with crumb rubber contents of 20–30% and bio-oil contents of 5–15%, expressed relative to neat asphalt mass. The model includes A1 material production, A2 inbound transport, and A3 binder blending energy. Plant-based bio-oil was represented using a literature-derived inventory, while crumb rubber was evaluated under cut-off, avoided-burden, and clinker-fuel system-expansion scenarios. A 10,000-iteration Monte Carlo simulation propagated inventory, transport, energy, and allocation uncertainty. Under cut-off allocation, mean GWP decreased from 496 kg CO2e/t for the neat binder to 446–471 kg CO2e/t for BAR binders, with BAR_30CR_15BIO showing the lowest mean impact and a 98.8% probability of outperforming the control. Avoided-burden allocation strengthened the apparent benefit, whereas system expansion against clinker fuel reversed the conclusion for rubber-containing alternatives. Results show that BAR can reduce binder-level GWP, but the conclusion is not inherent to the material; it depends strongly on tire-rubber counterfactuals and asphalt-binder inventory assumptions. Full article

Stabilizing food prices is essential for ensuring food security in an aging society. Using panel data from 30 Chinese provinces spanning 2005 to 2022, this study employs a nonlinear panel model and a Spatial Durbin Model (SDM) to analyze the impact of rural population aging on food prices and its spatial spillover effects. This study derives the following findings based on empirical research: (1) Rural population aging exhibits a significant inverted U-shaped relationship with food prices, with an inflection point at approximately 19.03%. Before reaching this point, rural population aging helps facilitate food prices. When the inflection point is passed, rural population aging adversely impacts food prices. This effect is significant in western regions but not in eastern and central regions. (2) Farmland transfer and agricultural technological progress significantly influence this relationship, causing the curve to reverse into a U-shaped pattern, which implies a gradual future increase in food prices. (3) Local rural population aging has a significant U-shaped spillover effect on food prices in neighboring provinces. These findings indicate that China’s rural population aging presents a complex dynamic for food price fluctuations. To address the current changes in the population and capital structure and ensure food security, the government will need to formulate forward-looking policies, further improve socialized agricultural services, and systematically optimize food production models. Full article

Density measurement with vibrating tube density meters is a fundamental technique in biotechnology, for example, as the metrological base for spectroscopy calibrations. However, conventional multi-point adjustments frequently rely on hazardous halogenated solvents. In this work, green alternatives for density calibration are evaluated with a focus on replacing hazardous substances while maintaining analytical performance. Propylene carbonate is identified as an intrinsic candidate standard for densities up to 1.20 g/mL, eliminating reliance on conventional halogenated liquids. For routine verification and system suitability testing, binary aqueous solutions of sodium chloride and guanidine hydrochloride are proposed as secondary standards. Guanidine hydrochloride solutions provide particular advantages due to their moderate viscosity and high refractometric sensitivity, allowing independent verification of composition and extended usability. In addition, historical density data for NaCl and CsCl solutions were re-evaluated to showcase that in silico modeling can derive density–temperature–composition relations with reasonable overall accuracy. Overall, the proposed approach demonstrates that accurate density calibration in bioanalytical laboratories can be achieved using low-toxicity, non-halogenated substances thereby reducing environmental impact while supporting fit-for-purpose analytical performance. Full article

New loads, represented by electric vehicles, are emerging rapidly. Distribution networks therefore face increasingly complex spatiotemporal load fluctuations. Conventional planning methods inadequately consider traffic factors. They also overlook traffic-induced dynamic load variations. Accordingly, this study proposes a traffic load-coupled planning method for distribution networks. The primary novelty lies in identifying load features. It also uses classified scenarios as model inputs. This design reveals nonlinear impacts of dynamic traffic flow transfers on grid resilience across functional scenarios. First, load-feature clustering identifies scenario attributes for nodes, power lines, and traffic roads. Second, classified scenarios and historical data are used as model inputs. An improved model then generates high-confidence prediction intervals for power load and traffic flow. A dynamic mapping model is established between traffic flow and nodal power load. Then, adversarial reinforcement learning is used to obtain the planning scheme. An attack–defense game further enhances robustness under extreme disturbances. Finally, comparative simulations verify the superiority of the proposed scheme. Full article

Driven by the strategies of high-quality development and manufacturing transformation and upgrading, China’s economy is gradually shifting to an innovation-driven growth model. Empowering firms to lift innovation quality through new-generation artificial intelligence has become a core priority for industrial development and policy support. This paper adopts unbalanced panel data of A-share listed manufacturing firms from 2012 to 2023 and employs the Double Machine Learning model to systematically investigate the impact and transmission mechanisms of new-generation artificial intelligence on manufacturing firms’ innovation quality. The results reveal that new-generation artificial intelligence can significantly boost manufacturing firms’ innovation quality, and this core conclusion remains valid after a series of robustness tests. Mechanism verification confirms that new-generation artificial intelligence promotes the upgrading of firms’ human capital structure, deepens firms’ digital transformation, and raises firms’ R&D investment intensity. These three channels work synergistically to indirectly empower the upgrading of firms’ innovation quality. Heterogeneity analysis verifies that the innovation quality improvement effect of new-generation artificial intelligence exhibits prominent stratified differences across regions, industrial sectors, and environmental regulatory scenarios. Specifically, the empowerment effect of new-generation artificial intelligence (GAI) is significantly stronger for samples from environmental pilot cities, Southern Coastal, Eastern Coastal, and Northern Coastal regions, as well as high-tech manufacturing firms. In contrast, its positive driving effect is weak or insignificant for samples in the middle reaches of the Yellow River and Northwest China, along with non-high-tech industries. This paper improves the theoretical analytical framework for artificial intelligence empowering micro-firm innovation and enriches relevant research on digital technologies and corporate innovation. It provides a theoretical basis and practical implications for China’s manufacturing sector to break through innovation bottlenecks via intelligent technologies and achieve comprehensive high-quality innovative development across the manufacturing industry. Full article

As Renewable Energy Communities (RECs) drive a shift toward decentralized power systems, innovative solutions to manage the inherent intermittency of distributed energy resources are essential. This paper investigates the potential of electric vehicles (EVs) as dynamic flexibility providers within the REC framework. By leveraging smart charging and Vehicle-to-Everything (V2X) technologies, EV fleets can act as key assets to facilitate the transition toward active distribution networks by providing upward and downward power reserves within local flexibility markets. This study presents a Mixed-Integer Linear Programming (MILP)-based Energy Management System (EMS) to optimally manage a case study REC in Northern Italy, characterized by renewable power plants and V2X-enabled EV charging stations for both electric cars and electric trucks. The proposed EMS model aims to simultaneously maximize the energy virtually shared within the REC and the provision of upward and downward reserves by the EV fleet over the considered time horizon. The EMS optimal results are analyzed for two distinct periods of the year, namely one week in spring and one in autumn, demonstrating that the flexibility guaranteed by the EVs can significantly impact the energy-sharing mechanism of the REC while at the same time providing additional revenues to the REC members. Full article

Thermoplastic composites offer substantial lightweighting potential for body-in-white (BIW). However, the application of long-fibre-reinforced thermoplastic direct processing (LFT-D) to deep-drawn rear-body parts remains largely unexplored. This work presents the first documented LFT-D glass-fibre/polypropylene spare wheel well for a production electric vehicle, validated under a full vehicle-level durability programme. Fibre orientation was characterised by X-ray computed tomography, and both isotropic and orthotropic finite element models were built. Prototypes passed six component-level validation tests: stiffness, constrained modal, thermal cycling, low-temperature impact, stone impact, and a 7000 km road simulation; the orthotropic model, validated against these tests, reduced the first natural frequency prediction error to 3.9%. No structural damage occurred in any test. The composite part achieved 35% mass saving at component level and 54% at system level versus the steel assembly. Adding up to 20 wt% regrind retained >89% of virgin tensile strength (95% confidence interval [CI] lower bound: 89.2%) and >92% of impact strength, and cradle-to-gate CO₂ emissions dropped by 42%. These results show that LFT-D can be applied to large, structurally critical BIW components, delivering both lightweighting and closed-loop recyclability for electric vehicles. Full article

Background/Objectives: Dietary nitrate, long framed through food-safety concerns about N-nitroso compound formation, is now also recognised as a substrate of the nitrate–nitrite–nitric oxide pathway. This review aims to define the mechanistic, dietary and host conditions under which nitrate bioactivation becomes functionally relevant, with particular attention to its microbial determinants and to the level of inference the evidence actually supports. Methods: We conducted a narrative review supported by a structured literature search (PubMed, Scopus and Web of Science; 1 January 1976 to 14 February 2026; full-text, peer-reviewed, English-language, human-relevant sources; 148 sources retained, of which 93 contributed to the evidence synthesis), with narrative synthesis of mechanistic, interventional, observational and regulatory sources addressing dietary source and food matrix, enterosalivary metabolism, oral and gut microbial function, and health-related outcomes. A PRISMA-style flow diagram summarises the documented screening and inclusion process, and the complete database-specific search strategies are provided in ^{Supplementary Table S1}; no meta-analysis was performed because of substantial heterogeneity in designs and outcomes. Results: Within the canonical enterosalivary pathway, nitrate-to-nitrite bioactivation is predominantly microbiota-dependent and downstream conversion is chemically conditional: within the enterosalivary cycle, nitrate-reducing bacteria on the tongue dorsum generate the nitrite required for downstream nitric oxide formation, and its conversion in the stomach depends on pH and on matrix constituents. Dietary source and food matrix therefore govern both the delivered dose and the chemistry that follows, so vegetables, beetroot products, inorganic salts, drinking water and processed meat are not interchangeable exposure models. The oral microbiota is the principal microbial determinant of the response, whereas the gut microbiota acts as a context-dependent modifier of intestinal redox tone, barrier function and microbial ecology, supported by markedly weaker human evidence. Nitrate-rich sources reproducibly raise nitrate and nitrite biomarkers, with variable effects on blood pressure, vascular function and exercise efficiency, limited or inconsistent effects on cognition, cerebral blood flow and metabolic endpoints, and a safety profile whose interpretation depends on food matrix, dose, exposure pattern and host context rather than concentration alone. Conclusions: We propose the Source–Matrix–Microbiota–Host (SMMH) framework, in which biological impact depends on the interaction between dietary source and dose, food matrix, microbial nitrate-reducing capacity and host susceptibility, rather than on nitrate dose alone, and in which pathway-level, physiological and clinical evidence are kept explicitly distinct. The evidence base is mechanistically robust for the oral microbiota, considerably less defined for the gut microbiota, and variable at the level of validated clinical endpoints; it does not yet support source-independent guidelines or population-level recommendations. Full article

Leprosy is characterized by complex biological and cellular interactions, driven primarily by the interplay between Th${}_1$ and Th${}_2$ cells. In this study, we develop a deterministic mathematical model to investigate the interactions among healthy and infected Schwann cells, M. leprae bacteria, and Th${}_1$–Th${}_2$ immune responses. The positivity and boundedness of the proposed five-dimensional system are established, and the disease persistence condition is characterized in terms of the basic reproduction number (${\mathcal{R}}_0$). The existence conditions for the endemic equilibrium are derived, while the global asymptotic stability of the endemic state is established through the construction of an appropriate Lyapunov function. To evaluate the robustness of the proposed model, parameter sensitivity with respect to ${\mathcal{R}}_0$ is investigated using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient sensitivity analysis. Furthermore, Monte Carlo uncertainty analysis (UA) is incorporated to account for the inherent uncertainty in the system, whereas a Sobol-based global sensitivity analysis quantifies the contribution of individual model parameters to the overall uncertainty. To further validate the dynamical behavior of the proposed system numerically, Lyapunov exponents are computed using the Benettin (renormalization) algorithm. The impact of combined multidrug therapy (MDT) and IL-10 inhibitor therapy is subsequently investigated within an optimal control framework, and the corresponding optimal treatment strategies are derived using Pontryagin’s maximum principle. Numerical simulations demonstrate that suppressing the Th${}_2$-mediated weakening of the host immune response through IL-10 inhibitor therapy provides superior long-term control of leprosy. The proposed treatment strategy therefore identifies IL-10 inhibitor therapy as a promising adjunct immunomodulatory intervention alongside MDT for enhancing protective cellular immunity against M. leprae in a cost-effective manner. Full article

Low-impact development (LID) and nonpoint-source pollution reduction facilities are increasingly expected to function as long-term components of urban infrastructure rather than as isolated environmental features. Their sustainability depends not only on technical design but also on institutional review, construction assurance, operation and maintenance, performance verification, information continuity, and adaptive policy learning. This study comparatively examines the content and structure of three major South Korean guidance documents issued in 2013, 2016, and 2020: the national LID Technology-Element Guideline, Seoul’s Guidance for the LID Prior-Consultation System, and the national Manual for the Installation and Management/Operation of Nonpoint-Source Pollution Reduction Facilities. A directed qualitative content analysis was conducted using seven literature-informed lifecycle-governance dimensions covering problem framing, planning and approval, facility selection and sizing, installation and construction control, operation and maintenance, monitoring and performance verification, and adaptive feedback. Documentary provisions were assessed using a four-level ordinal scale ranging from absent to strong coverage. The analysis indicates a functional expansion of governance provisions across the three documents, from LID principles and technology selection to pre-permit review, quantitative runoff-management requirements, installation guidance, operation and maintenance, monitoring, and performance testing. However, the documents collectively show discontinuities between approval and construction verification, construction and long-term operation, maintenance and compliance, monitoring and policy learning, and lifecycle information management. In response, this study proposes a six-stage lifecycle-governance framework linking planning, pre-permit verification, construction commissioning, asset registration, risk-based maintenance and monitoring, and adaptive policy learning. The framework is presented as an analytical and normative synthesis rather than an empirically validated governance model. Because the study is limited to documentary analysis, the findings do not demonstrate actual implementation effectiveness, regulatory compliance, maintenance quality, or environmental performance and should be validated through future empirical and comparative research. Full article

As emerging media technologies, virtual reality (VR) applications are used to elicit emotional engagement across various contexts—entertainment, education, therapy, training, marketing, and fitness, to name a few—often with the hope of effecting behavioral change. Within the healthcare domain, VR technologies have been successfully leveraged to manage acute pain, but understanding of their efficacy in managing chronic pain remains limited. Current models emphasize the necessity of examining discrete effects of VR across multiple dimensions of pain, particularly its emotional impact. An emotion receiving increased attention by media scholars over the past few years is awe, the complex response to perceived vastness. Awe is a highly relevant, yet largely unexplored, emotion in chronic pain research, as it involves cognitive processes—vastness appraisals, feelings of connectedness, accommodation of new ideas—which may influence psychological processes shaping pain perception. To date, no study has examined the feasibility of inducing awe through VR among individuals with chronic pain. Accordingly, this preliminary study deployed a descriptive mixed-methods approach to examine the feasibility of utilizing a head-mounted display for VR-induced awe. Participants (n = 10) with CLBP viewed 360-degree virtual nature scenes depicting a mountain ascension, combined with music previously validated to elicit awe. Participants completed self-report measures before and after the VR session documenting pain intensity and emotional experiences. Semi-structured qualitative interviews were conducted following the post-session surveys. Results from both methods support the feasibility of inducing awe among individuals with chronic pain via virtual immersion, showing overall positive affective changes and reduction in frequency of self- and pain-related thoughts. Full article

To better understand the actual flow fields in blood-contacting mechanical medical devices, such as ventricular assist devices (VADs), accurate in vitro and in silico studies are essential. Traditionally, these studies have treated blood as a single-phase fluid. In reality, however, blood is a multi-phase fluid consisting of plasma and suspended blood cells. Cell migration leads to heterogeneous cell distribution, which significantly impacts flow dynamics, particularly in the narrow gaps of these devices. This migration is not usually considered in in vitro analyses using blood analog fluids or in in silico simulations of VADs. This study presents an advanced viscosity modeling approach that accounts for cell migration effects under gap-relevant conditions. The model is based on local particle distribution data obtained from experiments with blood and particle-laden blood analog fluids in microchannels. It is applicable to both blood and particle-laden blood analog fluids, covering gap heights of 150 µm, Reynolds numbers in the range of 50 < Re < 150 and particle volume fractions of up to 30%. Previous works have investigated blood flows with significantly lower volume fractions of up to $5\%$. The model’s accuracy was validated by comparing results with experimental data on pressure losses of blood flowing through a microchannel, demonstrating good agreement. By incorporating variations in local viscosity, this enhanced viscosity distribution model improves the accuracy of flow simulations, offering a more realistic representation of blood flow in narrow gaps compared to the single-phase assumption. Future work will extend the model to accommodate physiological particle volume fractions of up to 45%. Full article

Accurate prediction of the mechanical performance of polymer components fabricated by fused deposition modelling (FDM) remains challenging owing to the complex nonlinear relationships between process parameters and material properties, limiting reliable process planning and broader industrial adoption of polymer additive manufacturing. This study develops and experimentally validates a heterogeneous multi-output stacked ensemble learning framework for the simultaneous prediction of tensile strength, flexural strength, compressive strength, Rockwell hardness, and Charpy impact strength of acrylonitrile styrene acrylate (ASA), a high-performance engineering thermoplastic with excellent weatherability and ultraviolet resistance that remains comparatively underexplored in data-driven FDM research. A Definitive Screening Design (DSD) was employed to investigate eight critical process parameters: extrusion temperature (ET), bed temperature (BT), infill density (ID), layer height (LH), print speed (PS), raster angle (RA), build orientation (BO), and cooling fan speed (CFS). Multiple supervised learning algorithms were systematically benchmarked, and the highest-performing complementary models were integrated into a heterogeneous stacked ensemble for simultaneous multi-output prediction. The proposed framework achieved an overall R² of 0.9943 with an overall RMSE of 0.9758, while the individual prediction models attained R² values ranging from 0.9898 to 0.9967. Beyond improving predictive accuracy, the proposed AI-assisted framework provides a data-driven basis for mechanical-property prediction and establishes a surrogate modelling framework that may subsequently be coupled with dedicated optimization or decision-making methods. Full article