Search Results (2,428)

Ride comfort is a critical issue in vehicle dynamics, as excessive vibrations adversely affect passenger comfort and human health. This paper presents a comparative performance analysis of a passive suspension system, fuzzy logic control (FLC), and a newly designed adaptive robust control (ARC) strategy applied to a nonlinear quarter-car suspension–seat–driver model. The primary objective is to improve ride comfort while maintaining vibration levels within accepted health criteria. First, the nonlinear dynamic model of the suspension–seat–driver system is established. The FLC structure and rule base are determined based on heuristic knowledge. Passive and FLC-based systems, while effective to some extent, suffer from limited adaptability to external disturbances and modeling uncertainties, slower convergence, and suboptimal vibration attenuation. The main contribution of this study is the design and implementation of a novel adaptive robust controller that effectively handles modeling uncertainties, external disturbances, and parameter variations. Different controller placement approaches within the system are also investigated. Numerical simulations are conducted under identical operating conditions for the uncontrolled system and all control strategies. The results demonstrate that although the FLC improves ride comfort compared to the passive system, the proposed ARC achieves the best overall performance, providing superior vibration attenuation, faster convergence, and enhanced robustness for nonlinear vehicle suspension systems. Quantitatively, the ARC reduces head acceleration RMS from 0.1693 m/s² (passive) and 0.1422 m/s² (FLC) to 0.0705 m/s², and upper torso RMS from 0.1689 m/s² (passive) and 0.1417 m/s² (FLC) to 0.0703 m/s², corresponding to approximately 58% reduction relative to passive and 50% improvement over FLC. Full article

With the increasing use of modern technologies in interior design, numerous recent studies have made the effects of kinetic-based design techniques on users’ perceptions a crucial topic, and sustainable performance has emerged as essential. From this standpoint, this study uses a space syntax approach to investigate how human behavioral performance in workspaces is affected by kinetic interiors. Three kinetic-based design strategies were employed to evaluate changes in spatial configuration characteristics, and the relevant terminology was adapted to account for the use of kinetic technology. The paper adopts a comparative analysis model to follow these changes using four syntactic measures: integration, choice, connectivity, and clustering coefficient. The proposed evaluation approach is applied to a traditional office building in Port Said, Egypt, showcasing various aspects of kinetic technology in workspaces. The study’s findings elucidate the correlations between design strategies and the resulting spatial characteristics, guiding designers in evaluating the features of each system and facilitating comparisons between them. Finally, the study’s main aim is to propose a three-step design process as a guideline for creating an integrated kinetic technology design, involving the evaluation of the proposed alternatives to achieve the desired spatial characteristics. Full article

Saccharomyces cerevisiae is a useful model to understand the biochemistry and biology of aging. Yeast speeds up the aging study due to its short lifespan, well-established genetics, and simple measurement for lifespan. The chronological lifespan in yeast specifically emphasizes the survival rate of the population, providing data that offer more direct feedback on experimental treatments than replicative lifespan. The advancement of the yeast chronological lifespan assay has enabled researchers to efficiently screen numerous potential antiaging compounds and delve into aging theories. Through the integration of robust genetic screening and high-throughput technologies, the yeast model has facilitated the identification of various antiaging factors with potential applications in humans, shedding light on the genetic mechanisms of aging. Many natural products, similar to calorie restriction, have been shown to effectively extend the lifespan of yeast, a benefit that is also conserved in mammals. In this review, we highlight the nutrient factors, natural compounds, and genes that contribute to extending the yeast lifespan, as well as the genetic regulations underlying the aging process in yeast. Full article

For safe and efficient human–robot interaction, lower-limb exoskeletons used for assistance and rehabilitation need to be precisely and energy-efficiently controlled. By creating an adaptive neuro-fuzzy fractional-order PID (ANFIS-FOPID) controller, this project seeks to improve tracking accuracy, robustness, and energy efficiency in a two-degree-of-freedom hip–knee exoskeleton. The Euler–Lagrange formulation is used to derive a nonlinear dynamic model, and a Lyapunov-based stability analysis is used to show that the closed-loop system remains uniformly ultimately bounded under disturbances and parameter uncertainties. The suggested controller performs noticeably better than traditional PID and fixed-parameter FOPID controllers, according to numerical simulations conducted under both normal and perturbed conditions. The ANFIS FOPID achieves root mean square errors below 0.028 rad and lowers the integral absolute errors at the hip and knee joints to 0.1454 and 0.1480, as opposed to 0.3496–0.3712 for PID controllers. Under ±10% parameter uncertainty, the total control-energy proxy drops from 2870.0 (PID) to 936.25, a 67.4% decrease, and stays at 1587.93. Statistically significant variations in energy consumption are confirmed by one-way ANOVA (p < 10⁻¹⁷⁶). Large effect sizes are found (η² = 0.237–0.314). These results demonstrate the superior tracking performance, robustness, and energy efficiency of the ANFIS-FOPID controller. The results set a quantitative standard for future experimental validation and hardware-in-the-loop implementation, despite being based on high-fidelity simulations. Full article

Data on various air-quality metrics are collected repeatedly by numerous monitoring stations worldwide to closely assess the severity of pollution. Particle pollution from fine particulate matter PM_2.5 is one such measure used as an indicator of whether air quality is unhealthy. PM_2.5 is a specific form of air contamination that negatively impacts the environment and human health when levels are above a certain threshold. As data are collected repeatedly over time at multiple locations, there may be a temporal dependence among repeated outcomes and spatial dependence between neighboring stations. Thus, it is important to assess the impact of risk factors on trajectory risk prediction. However, due to the temporal and spatial dependencies, trajectory risk prediction for such data becomes complicated, as both types of dependences must be accounted for during model building. In this paper, we propose a modeling framework that accounts for both types of dependences by incorporating Markov chains and Markov regression, using autologistics for model fitting and trajectory risk prediction. The proposed model fitting and trajectory risk prediction are illustrated using PM_2.5 outdoor air pollution data from the United States from 2000 to 2020. Full article

Neuroinflammation mediated by reactive microglia, the immune cells of the brain, contributes to numerous neuropathologies. Damage-associated molecular patterns (DAMPs), released from stressed or damaged cells, are implicated in neuroinflammation. Succinate, a tricarboxylic acid cycle intermediate, can accumulate intracellularly and be released into the extracellular space where it may function as a DAMP-like molecule. However, its specific roles in central nervous system (CNS) neuroimmune responses, particularly when acting extracellularly, remain largely unexplored. This study utilizes cell membrane-impermeable disodium succinate to model extracellular action and cell-permeable diethyl succinate to assess the intracellular activity of this metabolite in cell culture models. We demonstrate that extracellular disodium succinate significantly reduces the secretion of pro-inflammatory cytokines tumor necrosis factor-α (TNF) and interleukin (IL)-6, and lowers neurotoxic and phagocytic activities of immune-stimulated BV-2 murine microglia. It also rescues lipopolysaccharide (LPS)-induced decreases in mitochondrial respiration in human peripheral blood mononuclear cells (PBMCs) used as microglia models, which correlates with its actions on phagocytosis. In contrast, while intracellular diethyl succinate reduces TNF and IL-6 secretion, it does not reduce BV-2 microglia toxicity towards murine NSC-34 neuronal cells, indicating location-dependent effects. These results support extracellular succinate as a novel CNS DAMP with a predominantly anti-inflammatory action on microglia. Full article

Large Language Models (LLMs) are emerging as a new class of intelligent systems capable of reasoning over heterogeneous knowledge and interacting with human operators, yet their role in renewable energy systems remains insufficiently synthesized. This review provides a dedicated, systematic examination of LLMs as knowledge-centric, human-oriented decision-support tools for renewable energy infrastructure. In contrast to existing surveys that primarily emphasize numerical optimization, forecasting, or conventional machine learning methods, this work focuses on how LLMs enable textual reasoning, regulatory interpretation, operational intelligence, and interactive support across energy system lifecycles. We present a structured overview of recent literature, categorizing LLM applications by their functional roles in analysis, control, operation, and policy support. Furthermore, we analyze the contributions of LLMs to key decision-support tasks, including information retrieval, incident analysis, operational coordination, and strategic planning in smart grids and microgrids. The review also critically examines current limitations and risks associated with deploying LLMs in energy systems, including hallucination, reliability, domain adaptation, explainability, and real-time operational constraints. Finally, we identify emerging research directions, including energy-efficient LLM deployment, sustainability-aware AI design, and the alignment of LLM-based solutions with the goals of resilient, low-carbon, and environmentally sustainable energy systems. Full article

Background: Blockchain has emerged as a promising enabler for improving transparency, trust, and operational efficiency in logistics systems. In PI-oriented logistics environments, where openness, interoperability, and streamlined information exchange are emphasized, blockchain offers a decentralized alternative to conventional coordination methods. However, its economic feasibility remains uncertain due to substantial system development and operational costs. Existing literature largely isolates qualitative benefits from quantitative cost structures. Methods: This study proposes a quantitative–qualitative evaluation framework to assess blockchain adoption in PI-oriented logistics systems. Two Mixed-Integer Linear Programming (MILP) cost-minimization models were constructed to represent alternative coordination approaches: PI–BC (blockchain-enabled coordination) and PI–Human (traditional human-centered coordination). The results of the optimization analysis were integrated into an Analytic Hierarchy Process (AHP) evaluation alongside qualitative criteria such as interoperability, reliability, and transparency. Results: Numerical findings show that although PI–BC incurs higher operational costs, it performs considerably better in qualitative dimensions related to information visibility and robustness. Conclusions: These results suggest that blockchain provides particular value in PI-oriented contexts at the adoption stage. However, the framework does not provide a universal recommendation, as the relative advantage of PI–BC is highly contingent on decision-makers’ subjective criterion weight assignments, as revealed by the sensitivity analysis. Full article

Hepatitis B virus (HBV) and human T-lymphotropic virus type 1 (HTLV-1) are blood-borne pathogens with overlapping transmission routes, resulting in an increased prevalence of HBV among individuals infected with HTLV-1. Notwithstanding the widespread application of mathematical modeling to the study of each virus in isolation, the within-host dynamics of HBV–HTLV-1 co-infection remain insufficiently characterized. This study introduces a novel within-host co-infection model that characterizes the interactions between HBV and HTLV-1, where HTLV-1 infects CD4⁺ T cells and HBV targets hepatocytes. A comprehensive qualitative analysis yields four threshold parameters ($R_i,i=1,2,3,4$) governing the existence and stability of equilibrium points, with global stability established using Lyapunov functions. Numerical simulations validate the analytical results, and sensitivity analysis identifies parameters that most strongly influence the basic reproduction numbers for HBV ($R_1$) and HTLV-1 ($R_2$) mono-infections. Our results corroborate that, in patients with HBV, the presence of HTLV-1 contributes to an elevated HBV viral load and CD4⁺ T cells play a crucial role in controlling HBV infection. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS^® platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article

The control systems for models described by non-commensurate fractional-order differential equations are based on their transformation into large commensurate-order systems, which impose difficulties in determining control laws. In this context, in this paper, a new control method for this class of systems is proposed. The results obtained are based on Lyapunov methods for differential equations with fractional exponents and on the application of the Yakubovich–Kalman–Popov lemma adapted for this class of systems. The stability criterion is presented as a frequency criterion and represented graphically by familiar frequency plots similar to those of the Nyquist or Popov type. If the parameters that define the model can be defined in a closed domain, the frequency criterion can be interpreted as “Popov’s circle criterion”. The two numerical applications present two important cases. The first studies the stability criterion in the case where the viscosity coefficients determine non-commensurate fractional-order exponents in the dynamic model of the system. The second example studies the complex problem of the human–machine system in which the human model imposes dynamics determined by non-commensurate fractional-order systems. The proposed investigation methods allow for a reduction in computational effort by several orders of magnitude for non-commensurate fractional-order systems, eliminate stability conditions that use matrix-based criteria for large-scale systems, and introduce standard frequency-domain criteria. Full article

Human civilization embodies a rich cultural heritage shaped over long historical periods by numerous ethnic groups, each employing distinctive motifs and patterns in religious spaces, architecture, clothing, utensils, and other artifacts. Such motifs commonly originate from elementary geometric primitives that are organized through symmetric or asymmetric compositions to convey symbolic and esthetic meaning. This study focuses on Mongolian patterns derived from the nomadic heritage of Mongolia and still prevalent in contemporary design. These patterns draw inspiration from nature, geometry, animals, plants, and symbolic forms. This article proposes a mathematical modeling-driven digitization framework for the systematic analysis and digitization of Mongolian patterns, with the objective of generating accurate digital representations in the form of computer-aided design (CAD) models. A concise review of related work is first presented, followed by a structured digitization framework and a taxonomy of representative Mongolian motifs. A case study demonstrates that, when combined through distance-preserving and shape-preserving geometric operations such as translation, rotation, and reflection, four fundamental geometric entities, namely the circle, circular arc, spiral, and astroid, are sufficient to retain the intrinsic symmetry and compositional coherence of complex patterns observed in selected artifacts. Furthermore, the proposed analytical modeling approach enables the generation of vector-based line drawings that support precise CAD model construction. Accordingly, this study establishes a computational design workflow that integrates cultural heritage patterns into CAD-based modeling environments, thereby supporting digital preservation and fabrication with high geometric fidelity. Full article

Current wound healing strategies must confront numerous challenges. Helminth-induced immunomodulation offers a promising therapeutic avenue for inflammatory diseases and injury repair. However, research on the role of helminths in damage recovery remains limited. We utilized glycogen—a naturally occurring biomaterial—to encapsulate SJMHE1, a bioactive peptide derived from Schistosoma japonicum, and successfully developed a facilely prepared hydrogel formulation denoted as SJMHE1-gel. The properties of SJMHE1-gel, its effect on cell activity, and its performance in a murine full-thickness skin defect model were evaluated. The glycogen-based hydrogel exhibited a uniform pore size, excellent biocompatibility, and sustained release of SJMHE1. Topical application of SJMHE1-gel enhanced collagen deposition, promoted angiogenesis, facilitated the regeneration of hair follicles and sebaceous glands, and accelerated full-thickness wound healing. SJMHE1-gel also promoted M2 macrophage polarisation and suppressed inflammatory cytokine expression both in vivo and in vitro. Mechanistically, SJMHE1-treated macrophages upregulate TGF-β, which in turn promotes the migration of L929 fibroblasts and human umbilical vein endothelial cells (HUVECs) via the Smad3 pathway. Neutralization of TGF-β attenuates phosphorylated Smad3 (p-Smad3) levels and impairs the migratory capacity of both fibroblasts and HUVECs. Additionally, SJMHE1-treated macrophages upregulate VEGFA, thereby enhancing angiogenic tube formation in HUVECs. This easy-to-prepare hydrogel can regulate macrophage polarization, inhibit inflammation, promote angiogenesis, and accelerate collagen deposition, acting across wound healing stages to provide a novel therapeutic strategy. Full article

Stress can cause many disorders, including mental and physical ones, if it persists. To take timely and effective early intervention measures, mental stress levels must be carefully monitored. This study investigates the rapidly growing topic of mental stress detection, focusing on the primary goals and mechanisms of existing detection frameworks. The main objectives and mechanisms will be highlighted. This study examines physiological sensors, stressors, algorithms, monitoring methods, and validation tools used to assess and classify mental stress. The study targets physiological sensors. Wearable sensors are becoming more popular because they can continuously monitor physiological responses in human-like environments. This allows them to reveal relevant stress patterns across various work environments. Numerous physiological sensors are used regularly. Galvanic skin response (GSR), electrocardiogram (ECG), photoplethysmography (PPG), electroencephalography (EEG), and pupil diameter camera systems are examples of these sensors. The combination of these sensors provides a wealth of cognitive and autonomic response data for stress detection. This review examines AI-based methods for interpreting complex physiological data. Machine learning and ensemble models are emphasized for improving stress classification accuracy and reducing incorrect classifications. In addition, this article discusses stressors used to induce reliable physiological responses. Validated self-report instruments are being reviewed as benchmarking tools for objective sensor-based measurements. STAI and PSS-10 are examples. These instruments demonstrate a strong correlation between stress and anxiety and physiological health outcomes. In conclusion, this review discusses future research avenues, focusing on advanced artificial intelligence-driven approaches and sophisticated sensors. These developments aim to better define stress levels and physiological factors that have not been thoroughly studied. Full article

The use of artificial intelligence (AI) to recognize postures is a promising approach for the prevention of work-related musculoskeletal disorders (WMSDs). The aim was to conduct a systematic review with meta-analysis to assess the performance of work posture recognition systems during occupational activity. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were screened without date restrictions. Two authors independently selected articles and extracted data. Studies were included if they presented a performance analysis of an AI deep learning (DL) or machine learning (ML) method that assessed the WMSD risk associated with working postures. Only peer-reviewed studies written in English including accuracy, precision, specificity, sensitivity, or F1-score values were included. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Of the 157 unique records, 58 studies were selected. The five performance parameters were investigated and averaged for seven occupational activities, eight posture categories, and the AI methods (ML vs. DL). Statistical analyses showed that DL methods produced better results. The reported systems detected sitting and standing postures with high accuracy. The solutions proposed in Manufacturing and Construction were the most numerous and the most effective on average. The major limitation lies in the wide variety of methods used. This analysis is a valuable source of information for designing new detection systems that are effective, ergonomic, easy to use, and acceptable so that humans remain at the center of the production process as defined by Industry 5.0. Full article