Search Results (20,118)

Burn injury triggers a complex inflammatory cascade in which the interplay between pro- and anti-inflammatory mediators determines recovery or progression to sepsis, ventilator-associated pneumonia (VAP) or multi-organ dysfunction, and mortality. We systematically searched PubMed, Embase, Cochrane Library, Web of Science, and Scopus for studies published between 2006 and 2024, identifying 1883 records. We conducted a comprehensive systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After screening and eligibility assessment, 24 studies covering both pediatric and adult populations met the inclusion criteria. Data on cytokines, acute-phase proteins, complement fragments, and systemic inflammatory indices were synthesized narratively. The evidence indicates that the inflammatory response to burn injury is not a linear sequence of events but a dynamic and unstable equilibrium, where outcomes are determined less by the initial magnitude of cytokine release and more by the persistence of dysregulated inflammation or failure of compensatory mechanisms. Full article

Digital transformation and sustainable orientation have become key drivers of economic development within the European Union. This study investigates how progress in digitalization and sustainable orientation influences economic outcomes. To address this objective, we apply a combination of techniques, including factor analysis to reduce dimensionality and identify underlying structures, generalized linear models to estimate causal connections and cluster analysis to group countries with similar profiles. The findings highlight strong complementarities between digital transformation and sustainable development in nurturing higher levels of economic outcome, with digital readiness amplifying the effects of sustainable development practices. Moreover, cluster analysis methods reveal significant asymmetries among EU countries, underlining persistent regional disparities in the pace of digital and sustainable transitions. The study concludes that a systems-based approach to managing the twin transition is essential for promoting convergence, competitiveness, and resilience in the EU economic system. Full article

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock. Full article

Propiconazole (PRO), a triazole fungicide, controls fungal diseases by disrupting ergosterol production in fungal cells. It is used in crops such as cereals and fruits. However, there are concerns regarding its potential to disrupt the endocrine system and cause reproductive toxicity. This study examined the effects of PRO on mouse testes, germ cells, and GC-1 spermatogonia. After eight weeks, PRO reduced testicular diameter and downregulated key germ cell genes (Sall4, Piwil, Nanos2, and Dazl). A histological examination revealed smaller seminiferous tubules and fewer SALL4+ cells. PRO also impaired steroidogenesis by downregulating genes (StAR, Cyp11a1, 3β-HSD1) and reducing sperm motility, with a decline in Velocity Straight Line (VSL), Linearity (LIN), Straightness (STR), and motile sperm. PRO caused dose-dependent cytotoxicity in GC-1 spermatogonia, decreased proliferation, and increased apoptosis, marked by cleaved caspase-3 and BAX. PRO also induced autophagy, as presented by elevated levels of autophagy-related genes (LC3 and ATG12) and proteins (ATG5 and LC3A/B). 3-Methyladenine (3-MA), an autophagy inhibitor, downregulates levels of autophagy- and apoptosis-related proteins when 3-MA and PRO are simultaneously treated in vitro. This suggests that both apoptosis and autophagy contribute to PRO-induced testicular cytotoxicity. This study is the first to detail that PRO affects sperm motility in mice and induces autophagy-mediated apoptosis in GC-1 spg. Full article

This paper presents an efficient algorithm for Explicit Model-Following (EMF) control using an Output-derivative Feedback Control (OFC) scheme within the Reciprocal State Space (RSS) framework, aimed at overcoming the performance limitations associated with state-derivative dependence. For Lipschitz Nonlinear Systems (LNS), two approaches are proposed: a linear EMF (LEMF) strategy, which transforms the system into a Linear Parameter-Varying (LPV) representation via the Differential Mean Value Theorem (DMVT) to facilitate controller design, and a nonlinear EMF (NEMF) scheme, which enables the direct tracking of a nonlinear reference model. The stability of the closed-loop system is ensured by deriving control gains through Linear Quadratic Regulator (LQR) optimization. The proposed algorithms are validated through Real-Time Implementation (RTI) on an Arduino DUE platform, demonstrating their effectiveness and practical feasibility. Full article

Background/Objectives: Gestational Diabetes Mellitus (GDM) is a global health issue with immediate and long-term maternal–fetal complications. Current diagnostic approaches, such as the Oral Glucose Tolerance Test (OGTT), have limitations in accessibility, sensitivity, and timing. This study aimed to identify key nodes and structural interactions associated with GDM using graph theory and network analysis to improve early predictive strategies. Methods: A literature review inspired by PRISMA guidelines (2004–2025) identified 44 clinically relevant factors. A directed graph was constructed using Python (version 3.10.12), and centrality metrics (closeness, betweenness, eigenvector), k-core decomposition, and a Minimum Dominating Set (MDS) were computed. The MDS, derived using an integer linear programming model, was used to determine the smallest subset of nodes with systemic dominance across the network. Results: The MDS included 20 nodes, with seven showing a high out-degree (≥4), notably Apo A1, vitamin D, vitamin D deficiency, and sedentary lifestyle. Vitamin D exhibited 15 outgoing edges, connecting directly to protective factors like HDL and inversely to risk factors such as smoking and obesity. Sedentary behavior also showed high structural influence. Closeness centrality highlighted triglycerides, insulin resistance, uric acid, fasting plasma glucose, and HDL as nodes with strong predictive potential, based on their high closeness and multiple incoming connections. Conclusions: Vitamin D and sedentary behavior emerged as structurally dominant nodes in the GDM network. Alongside metabolically relevant nodes with high closeness centrality, these findings support the utility of graph-based network analysis for early detection and targeted clinical interventions in maternal health. Full article

The rational placement of pollutant monitoring sensors has long been a prominent research focus in ocean environment science. Our method integrates an incremental Proper Orthogonal Decomposition with a mobility-constrained sensor selection strategy, enabling efficient monitoring and dynamic adaptation to spatio-temporal field changes. At each time step, the position of the sensors is updated based on the incoming measurements to minimize the reconstruction error while adhering to movement constraints. This online approach considers the need for mobility distance, making it suitable for long-term deployments in resource-limited scenarios. The proposed framework is validated in three scenarios: a linear advection–diffusion system with multiple moving pollution sources, the distribution of particulate matter with an aerodynamic diameter smaller than 2.5 $\mathsf{\mu }$m (PM2.5) across the United States, and scalar transport in flows past side-by-side cylinder arrays in the ocean. The results demonstrate that the method achieves high reconstruction accuracy with significantly fewer sensors. This study conducts a comparative analysis of three typical mobility constraints and their respective effects on reconstruction accuracy. In addition, the proposed localized sensor mobility strategy effectively tracks evolving plume structures and maintains a low approximation error, providing a generalizable solution for sparse monitoring of the marine environment. Full article

During the last two decades, relevant progress has been achieved in silicon photomultiplier (SiPM) technology, such that in an increasing number of radiation detection applications they are proposed as a viable alternative to traditional photomultiplier tubes (PMTs). Applications where the light from tiny scintillating crystals is detected by a single SiPM raise the question of the possible non-linearity of the response due to the saturation of the number of microcells involved. In other cases, where larger scintillators subtend arrays of SiPMs, the same question could hold. This work tries to disentangle such a question with a realistic numerical approach and a few tests showing that the possible saturation effects depend on the interplay between the features of the scintillator and of the SiPM (array). The quantitative results of this analysis can likely be used to better plan future radiation detection systems and to highlight their linearity boundaries. Full article

As the world is shifting toward cleaner energy sources, accurate forecasting of solar radiation is critical for optimizing the performance and integration of solar energy systems. In this study, we explore eight machine learning models, namely, Random Forest Regressor, Linear Regression Model, Artificial Neural Network, k-Nearest Neighbors, Support Vector Regression, Gradient Boosting Regressor, Gaussian Process Regression, and Deep Learning, as to their use in forecasting direct solar radiation across six climatically diverse regions in the Kingdom of Saudi Arabia. The models were evaluated using eight statistical metrics along with time-series and absolute error analyses. A key contribution of this work is the introduction of Trigonometric Cyclical Encoding, which has significantly improved temporal representation learning. Comparative SHAP-based feature-importance analysis revealed that Trigonometric Cyclical Encoding enhanced the explanatory power of temporal features by 49.26% for monthly cycles and 53.30% for daily cycles. The findings show that Deep Learning achieved the lowest root mean square error, as well as the highest coefficient of determination, while Artificial Neural Network demonstrated consistently high accuracy across the sites. Support Vector Regression performed optimally but was less reliable in some regions. Error and time-series analyses reveal that Artificial Neural Network and Deep Learning maintained stable prediction accuracy throughout high solar radiation seasons, whereas Linear Regression, Random Forest Regressor, and k-Nearest Neighbors showed greater fluctuations. The proposed Trigonometric Cyclical Encoding technique further enhanced model performance by maintaining the overall fitness of the models, which ranged between 81.79% and 94.36% in all scenarios. This paper supports the effective planning of solar energy and integration in challenging climatic conditions. Full article

The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios. Full article

High-precision implantable multi-channel neural recording systems are considered as having a crucial role in the diagnosis and treatment of neurological disorders. However, it is a significant design challenge to achieve an optimal trade-off among linear parameters, signal fidelity, power consumption, and circuit area. To address this challenge, a Simulink-based modeling approach has been proposed to incorporate adjustable non-linear parameters across the front-end circuits and analog-to-digital converter (ADC) stages. The model evaluates non-linearity impacts on system performance through both quantitative spike detection accuracy analysis and a neural decoding paradigm based on Chinese handwriting reconstruction. Simulated results show that total harmonic distortion (THD) can be set to −34.32 dB for the low-noise amplifier (LNA), −33.73 dB for the programmable gain amplifier (PGA), and −57.95 dB for the ADC in order to achieve reliable detection accuracy with minimal design cost. Moreover, ADC non-linearity has a greater influence on system performance than that of the LNA and PGA. The proposed approach offers quantitative and systematic hardware design guidance to balance signal fidelity and resource efficiency for future low-power, high-accuracy neural recording systems. Full article

Optimization algorithms play a crucial role in solving complex problems in reservoir geology and engineering, particularly those involving highly non-linear, multi-parameter, and high-dimensional systems. In the context of reservoir development, accurate optimization is essential for enhancing hydrocarbon recovery, improving production efficiency, and managing subsurface uncertainties. The Moss Growth Optimization (MGO) algorithm emulates the adaptive growth and reproductive strategies of moss. It provides a robust bio-inspired framework for global optimization. However, MGO often suffers from slow convergence and difficulty in escaping local optima in highly multimodal landscapes. To address these limitations, this paper proposes a novel algorithm called Strategic Moss Growth Optimization (SMGO). SMGO integrates two enhancements: an Outpost Mechanism (OM) and an Early Stopping Strategy (ESS). The OM improves exploitation by guiding individuals through multi-stage local search with Gaussian-distributed exploration around promising regions. This helps refine the search and prevents stagnation in sub-optimal areas. In parallel, the ESS periodically reinitializes the population using a run-and-reset procedure. This diversification allows the algorithm to escape local minima and maintain population diversity. Together, these strategies enable SMGO to accelerate convergence while ensuring solution quality. Its performance is rigorously evaluated on a suite of global optimization benchmarks and compared with state-of-the-art metaheuristics. The results show that SMGO achieves superior or highly competitive outcomes, with clear improvements in accuracy and stability. To demonstrate real-world applicability, SMGO is applied to production optimization in tight reservoirs. The algorithm identifies superior production strategies, leading to significant improvements in projected economic returns. This successful application highlights the robustness and practical value of SMGO. It offers a powerful and reliable optimization tool for complex engineering problems, particularly in strategic resource management for tight reservoir development. Full article

This paper presents the development and evaluation of an autonomous solar dryer designed to enhance the drying efficiency of bee bread granules. In contrast to natural open-air drying, the proposed system utilizes solar energy in an oscillating operational mode to achieve a controlled and accelerated drying process. The dryer comprises a solar collector integrated into the base of the drying chamber, which facilitates convective heating of the drying agent (air). The system is further equipped with a photovoltaic panel to generate electricity for powering and controlling the operation of air extraction fans. The methodology combines numerical modeling with experimental studies, structured by an experimental design framework. The modeling component simulates variations in temperature (288–315 K) and relative humidity within a layer of bee bread granules subjected to a convective air flow. The numerical simulation enabled the determination of the following: the time required to achieve a stationary operating mode in the dryer chamber (20 min); and the rate of change in moisture content within the granule layer during conventional drying (18 h) and solar drying treatment (6 h). The experimental investigations focused on determining the effects of granule mass, air flow rate, and drying time on the moisture content and temperature of the granular layer of Bee Bread. A statistically grounded analysis, based on the design of experiments (DoE), demonstrated a reduction in moisture content from an initial 16.2–18.26% to a final 11.1–12.1% under optimized conditions. Linear regression models were developed to describe the dependencies for both natural and forced convection drying. A comparative evaluation using enthalpy–humidity (I-d) diagrams revealed a notable improvement in the drying efficiency of the proposed method compared to natural drying. This enhanced performance is attributed to the system’s intermittent operational mode and its ability to actively remove moist air. The results confirm the potential of the developed system for sustainable and energy-efficient drying of bee bread granules in remote areas with limited access to a conventional power grid. Full article

The article introduces the Guarantee-Based Bandwidth Traffic Engineering Queue (GB(Bw)-TEQ) and Guarantee-Based Utilization Traffic Engineering Queue (GB(U)-TEQ) models for queue management on router interfaces. These models implement the principles of Traffic Engineering Queues and support both DiffServ and IntServ. Their novelty lies in the ability to provide guarantees either for the bandwidth allocated to a class queue or for its utilization coefficient. Such guarantees stabilize and control the average queue length, positively affecting key Quality of Service (QoS) indicators, particularly average delay and packet loss probability. The unreserved portion of the interface bandwidth is allocated among queues in proportion to their classes. Therefore, the higher-priority queues have lower utilization, while lower-priority queues operate with higher utilization, which is consistent with DiffServ principles. The models are formulated as a mixed-integer linear programming problem with an optimality criterion and a system of constraints. Computational experiments confirmed the operability and efficiency of GB(Bw)-TEQ and GB(U)-TEQ compared to the known analogue CB-TEQ model, which does not provide service-level guarantees. The results demonstrate that the proposed models achieve the stated guarantees and enable differentiated service without blocking the lowest-class queues. These solutions can be applied to automate queue management in IP/MPLS switches and routers as well as in software-defined networks. Full article

Rubber bearings play a critical role as core components within the transmission systems of marine equipment. Investigating the evolution of their water-film stiffness coefficient under deep-sea conditions can provide deeper insights into the dynamic characteristics of water-lubricated transmission systems. Employing a viscoelastic mixed-lubrication framework designed for water lubricated rubber bearings, this paper examines the necessity of accounting for rubber hyperelasticity and extreme subsea conditions (high pressure and low temperature) when analyzing the water-film stiffness coefficient of such bearings (at a depth of 1000 m, the relative error in the k_xz component between the linear viscoelastic model and the visco-hyperelastic model reaches as high as 18.41%.). On this basis, the influence of subsea environments together with rotational velocity on the water-film stiffness coefficient is further investigated, and the dependence of the dimensionless critical mass on the eccentricity ratio for water-lubricated rubber bearings operating under deep-ocean conditions is explored. The results provide a theoretical analysis tool for evaluating the water-film stiffness coefficient of subsea rubber bearings, and offer guidance for the forward design of water-lubricated rubber bearings applied in deep-sea service. Full article