Search Results (288)

The increasing demand for sustainable and high-performance energy storage underscores the limitations of lithium-ion batteries (LIBs), notably in terms of finite resources, safety issues, and rising costs. Multivalent metal-ion batteries (MMIBs)—employing Zn²⁺, Mg²⁺, Ca²⁺, and Al³⁺ ions—represent promising alternatives, as their multivalent charge carriers facilitate higher energy densities and greater electron transfer per ion. The widespread availability, lower cost, and favorable safety profiles of these metals further enhance MMIB suitability for large-scale deployment. However, MMIBs encounter significant obstacles, including slow ion diffusion, strong Coulombic interactions, electrolyte instability, and challenging interfacial compatibility. This review provides a systematic overview of recent advancements in MMIB research. Key developments are discussed for each system: electrode synthesis and flexible architectures for zinc-ion batteries; anode and cathode innovation alongside electrolyte optimization for magnesium-ion systems; improvements in anode engineering and solvation strategies for calcium-ion batteries; and progress in electrolyte formulation and cathode design for aluminum-ion batteries. The review concludes by identifying persistent challenges and future directions, with particular attention to material innovation, electrolyte chemistry, interfacial engineering, and the adoption of data-driven approaches, thereby informing the advancement of next-generation MMIB technologies. Full article

Background: Artificial Intelligence (AI) is increasingly applied in endurance sports to optimize performance, enhance recovery, and personalize nutrition and supplementation. This review synthesizes current knowledge on AI applications in endurance sports, emphasizing implications for metabolic health, nutritional strategies, and recovery optimization, while also addressing ethical considerations and future directions. Methods: A narrative review was conducted using targeted searches of PubMed, Scopus, and Web of Science with cross-referencing. Extracted items included sport/context, data sources, AI methods including machine learning (ML), validation type (internal vs. external/field), performance metrics, comparators, and key limitations to support a structured synthesis; no formal risk-of-bias assessment or meta-analysis was undertaken due to heterogeneity. Results: AI systems effectively integrate multimodal physiological, environmental, and behavioral data to enhance metabolic health monitoring, predict recovery states, and personalize nutrition. Continuous glucose monitoring combined with AI algorithms allows precise carbohydrate management during prolonged events, improving performance outcomes. AI-driven supplementation strategies, informed by genetic polymorphisms and individual metabolic responses, have demonstrated enhanced ergogenic effectiveness. However, significant challenges persist, including measurement validity and reliability of sensor-derived signals and overall dataset quality (e.g., noise, missingness, labeling error), model performance and generalizability, algorithmic transparency, and equitable access. Furthermore, limited generalizability due to homogenous training datasets restricts widespread applicability across diverse athletic populations. Conclusions: The integration of AI in endurance sports offers substantial promise for improving performance, recovery, and nutritional strategies through personalized approaches. Realizing this potential requires addressing existing limitations in model performance and generalizability, ethical transparency, and equitable accessibility. Future research should prioritize diverse, representative, multi-site data collection across sex/gender, age, and race/ethnicity. Coverage should include performance level (elite to recreational), sport discipline, environmental conditions (e.g., heat, altitude), and device platforms (multi-vendor/multi-sensor). Equally important are rigorous external and field validation, transparent and explainable deployment with appropriate governance, and equitable access to ensure scientifically robust, ethically sound, and practically relevant AI solutions. Full article

In recent years, there has been a global increase in environmental awareness, which has driven the application of natural materials or the synthesis of novel, environmentally compatible materials. Composite materials hold a prominent position among modern materials and are typically developed to achieve resistance to various damage mechanisms, thereby extending the service life of structures. This study presents the synthesis and characterisation of high-density metal–glass composite materials. The commercially available 316L stainless steel powder was used as the matrix material, while andesite basalt powder was used as the reinforcement phase. Andesite basalt aggregate, ground into powder, is a cost-effective, widely available, and environmentally friendly natural raw material. Powder metallurgy was employed to produce the composite materials. Sintering was performed at 1250 °C for 30 min in a vacuum. The density of the sintered composite samples was analysed as a function of andesite basalt content, with sintering conducted in the presence of a liquid phase. Composite materials were characterised using optical and scanning electron microscopy, X-ray structural analysis, and hardness testing. This study confirmed that the optimal combination of properties was achieved in the composite with 20 wt.% andesite basalt, present as a glass phase within the 316L steel matrix. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

The global transition to a circular bioeconomy is accelerating the demand for sustainable, high-performance materials. Filamentous fungi represent a promising solution, as they function as living foundries that transform low-value biomass into advanced, self-assembling materials. While mycelium-based composites have proven potential, progress has been predominantly driven by empirical screening of fungal species and substrates. To unlock their full potential, a paradigm shift from empirical screening to rational design is required. This review introduces a conceptual framework centered on the biochemical programming of the fungal cell wall. Viewed through a materials science lens, the cell wall is a dynamic, hierarchical nanocomposite whose properties can be deliberately tuned. We analyze the contributions of its principal components—the chitin–glucan structural scaffold, the glycoprotein functional matrix, and surface-active hydrophobins—to the bulk characteristics of mycelium-derived materials. We then identify biochemical levers for controlling these properties. External factors such as substrate composition and environmental cues (e.g., pH) modulate cell wall architecture through conserved signaling pathways. Complementing these, an internal synthetic biology toolkit enables direct genetic and chemical intervention. Strategies include targeted engineering of biosynthetic and regulatory genes (e.g., CHS, AGS, GCN5), chemical genetics to dynamically adjust synthesis during growth, and modification of surface chemistry for specialized applications like tissue engineering. By integrating fungal cell wall biochemistry, materials science, and synthetic biology, this framework moves the field from incidental discovery toward the intentional creation of smart, functional, and sustainable mycelium-based materials—aligning material innovation with the imperatives of the circular bioeconomy. Full article

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

Bismuth vanadate (BiVO₄) has attracted significant attention as a photoanode material for photoelectrochemical (PEC) water splitting due to its suitable bandgap (~2.4 eV), strong visible light absorption, chemical stability, and cost-effectiveness. Despite these advantages, its practical application remains constrained by intrinsic limitations, including poor charge carrier mobility, short diffusion length, and sluggish oxygen evolution reaction (OER) kinetics. This review critically summarizes recent advancements aimed at enhancing BiVO₄ PEC performance, encompassing synthesis strategies, defect engineering, heterojunction formation, cocatalyst integration, light-harvesting optimization, and stability improvements. Key fabrication methods—such as solution-based, vapor-phase, and electrochemical approaches—along with targeted modifications, including metal/nonmetal doping, surface passivation, and incorporation of electron transport layers, are discussed. Emphasis is placed on strategies to improve light absorption, charge separation efficiency (η_sep), and charge transfer efficiency (η_trans) through bandgap engineering, optical structure design, and catalytic interface optimization. Approaches to enhance stability via protective overlayers and electrolyte tuning are also reviewed, alongside emerging applications of BiVO₄ in tandem PEC systems and selective solar-driven production of value-added chemicals, such as H₂O₂. Finally, critical challenges, including the scale-up of electrode fabrication and the elucidation of fundamental reaction mechanisms, are highlighted, providing perspectives for bridging the gap between laboratory performance and practical implementation. Full article

Farmland shelterbelts are vital ecological infrastructure for sustaining agriculture in arid regions, where high winds, soil erosion, and water scarcity severely constrain productivity. While their protective functions—reducing wind speed, controlling erosion, moderating microclimates, and enhancing yields—are well documented, previous studies have largely examined individual structural elements in isolation, leaving their interactive effects and trade-offs poorly understood. This review synthesizes current research on the structural optimization of shelterbelts, emphasizing the critical relationship between their physical and biological attributes and their protective functions. Key structural parameters—such as optical porosity, height, width, orientation, and species composition—are examined for their individual and interactive impacts on shelterbelt performance. Empirical and modeling studies indicate that moderate porosity maximizes wind reduction efficiency and extends the leeward protection zone, while multi-row, multi-species configurations effectively suppress soil erosion and improve microclimate conditions. Sheltered areas experience reduced evapotranspiration, increased humidity, and moderated temperatures, collectively enhancing crop water use efficiency and yielding significant improvements in crop production. Advanced methodologies, including field monitoring, wind tunnel testing, computational fluid dynamics, and remote sensing, are employed to quantify benefits and refine designs. A multi-objective optimization framework is essential to balance competing goals: maximizing wind reduction, minimizing water consumption, enhancing biodiversity, and ensuring economic viability. Future challenges involve adapting designs to climate change, integrating water-efficient and native species, leveraging artificial intelligence for predictive modeling, and addressing socio-economic barriers to implementation. Building on this evidence, we propose a multi-objective optimization framework to balance competing goals: maximizing wind protection, minimizing water use, enhancing biodiversity, and ensuring economic viability. We identify key research gaps including unresolved porosity thresholds, the climate resilience of alternative species compositions, and the limited application of optimization algorithms and outline future priorities such as region-specific design guidelines, AI-driven predictive models, and policy incentives. This review offers a novel, trade-off–aware synthesis to guide next-generation shelterbelt design in arid agriculture. Full article

The dye-sensitized solar cell (DSSC) is a promising candidate, offering an attractive substitute for conventional silicon-based photovoltaic technologies. The performance advantages of the DSSC have led to a surge in research activity reflected in the number of publications over the years. To deliver data-driven analysis of DSSC performance, machine learning models have been applied. As a first step, a literature-based database has been developed and after the data preprocesses, Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), xgboost (XGB), and Artificial Neural Network (ANN) algorithms were applied with stratified train-test splits. The performance of the models has been assessed via metrics, and the model interpretability relied on SHAP analysis. Based on the employed metrics and the confusion matrix, DT, RF, and KNN are the most accurate models for predicting DSSC efficiency on the developed dataset. Furthermore, it was revealed that synthesis temperature and the thickness of thin film were identified as the dominant drivers, followed by precursor and dye. Mid-tier contributors were morphological structure, electrolyte concentrations, and the absorption maximum. The results suggest that in optimizing the manufacturing process, targeted tuning of the synthesis temperature, the thickness of thin film, the precursor, and the dye are likely to improve the performance of the device. Therefore, experimental effort should concentrate on these factors. Full article

Background: Diabetes mellitus (DM), periodontal disease (PD), and cardiovascular disease (CVD) are highly prevalent global health conditions with overlapping pathophysiological mechanisms. Emerging evidence suggests a bidirectional and synergistic relationship among them, driven by chronic inflammation, immune dysregulation, oxidative stress, and microbial dysbiosis. Objective: This review synthesizes current literature on the interconnectedness of DM, PD, and CVD, emphasizing shared molecular pathways, clinical implications, and opportunities for integrated management. Methods: A systematic review and narrative synthesis of recent clinical trials, observational studies, and multi-omics investigations was conducted to explore the mechanisms linking these three conditions. A structured literature search was performed across PubMed, Scopus, and Web of Science from database inception until 30 June 2025. Key findings were contextualized within systems biology, precision medicine, and real-world clinical strategies. Results: DM exacerbates periodontal inflammation and accelerates tissue destruction via hyperglycemia-induced inflammatory mediators, while periodontitis worsens glycemic control and insulin resistance. Both conditions independently elevate cardiovascular risk, and their co-occurrence significantly amplifies the incidence of adverse cardiovascular events. Shared biomarkers such as Interleukin (IL)-6, Tumor Necrosis Factor (TNF)-α, and CRP, as well as overlapping genetic and epigenetic signatures, underscore a common inflammatory axis. Periodontal therapy has demonstrated modest but meaningful benefits on glycemic control and endothelial function, while cardiometabolic therapies (e.g., statins, Glucagon-Like Peptide (GLP-1) receptor agonists, SGLT2 inhibitors) show potential to improve periodontal outcomes. Probiotics, microbiome-targeted therapies, and AI-based risk models are emerging as future tools. Conclusions: DM, PD, and CVD form a mutually reinforcing triad mediated by systemic inflammation and metabolic dysregulation. Integrated, multidisciplinary care models and precision health strategies are essential to address this inflammatory burden and improve long-term outcomes. Further large-scale interventional trials and mechanistic human studies are needed to establish causal links and optimize combined therapeutic approaches. Full article

Digital transformation has re-engineered tourism marketing and how destination branding competes for tourist attention, yet scholarship offers little systematic quantification of these changes. Drawing on 160 peer-reviewed studies published between 1990 and 2025, we combine grounded-theory thematic synthesis with a random-effect meta-analysis of 60 datasets to trace branding performance across five technological eras (pre-Internet and brochure era: to mid-1990s; Web 1.0: 1995–2004; Web 2.0: 2004–2013; mobile first: 2013–2020; AI-XR: 2020–2025). Results reveal three structural shifts: (i) dialogic engagement replaces one-way promotion, (ii) credibility migrates to user-generated content, and (iii) artificial intelligence–driven personalisation reconfigures relevance, while mobile and virtual reality marketing extend immersion. Meta-analytic estimates show the strongest gains for engagement intentions (g = 0.57), followed by brand awareness (g = 0.46) and image (g = 0.41). Other equity dimensions (attitudes, loyalty, perceived quality) also improved on average, but to a lesser degree. Visual, UGC-rich, and influencer posts on highly interactive platforms consistently outperform brochure-style content, while robustness checks (fail-safe N, funnel symmetry, leave-one-out) confirm stability. We conclude that digital tools amplify, rather than replace, co-creation, credibility, and context. By fusing historical narrative with statistical certainty, the study delivers a data-anchored roadmap for destination marketers, researchers, and policymakers preparing for the AI-mediated decade ahead. Full article

In endurance sports, athletes and coaches shift increasingly from intuition-based decision-making to data-driven approaches powered by modern technology and analytics. Since 2018, the field has experienced significant advances, influencing endurance sports disciplines. This systematic literature review identified 75 peer-reviewed studies on intelligent data analysis in endurance sports training. Each study was categorized by its intelligent method (e.g., machine learning, deep learning, computational intelligence), the types of sensors and wearables used, and the specific training application and approach. Our synthesis reveals that machine learning and deep learning are among the most used approaches, with running and cycling identified as the most extensively studied sports. Physiological and environmental data, such as heart rate, biomechanical signals, and GPS, are often used to aid in generating personalized training plans, predicting injuries, and increasing athletes’ long-term performance. Despite these advancements, challenges remain, related to data quality and the small participant sample sizes. Full article

The growing global demand for rapid, sensitive, and cost-effective food safety monitoring has driven the development of nanozyme-based biosensors as alternatives to natural enzyme-based methods. Among various nanozymes, bimetallic gold–platinum (AuPt) nanozymes show superior catalytic performance compared to monometallic and other Au-based bimetallic hybrids. This is due to their synergistic colorimetric, catalytic, geometric, and ensemble properties. This review systematically evaluates AuPt nanozymes in food safety applications, focusing on their synthesis, structural design, and practical uses. Various structural types are highlighted, including plain, magnetic, porous nanomaterial-labeled, and flexible nanomaterial-loaded AuPt hybrids. Key synthesis methods such as seed-mediated growth and one-pot procedures with different reducing agents are summarized. Detection modes covered include colorimetric, electrochemical, and multimodal sensing, demonstrating efficient detection of important food contaminants. Key innovations include core–shell designs for enhanced catalytic activity, new synthesis strategies for improved structural control, and combined detection modes to increase reliability and reduce false positives. Challenges and future opportunities are discussed, such as standardizing synthesis protocols, scaling up production, and integration with advanced sensing platforms. This review aims to accelerate the translation of AuPt nanozyme technology into practical food safety monitoring solutions that improve food security and public health. Full article

This study addresses the need for sustainable, high-performance photocatalytic materials by developing novel polylactide (PLA)/MXene composites. A one-step plasma-liquid synthesis method was employed, utilizing a direct current discharge between metal electrodes (Ti, Mo) in a carbon tetrachloride and PLA solution. This single-step process simultaneously exfoliates MXene nanosheets (Ti₂CCl_x, Mo₂CCl_x, Mo₂TiC₂Cl_x) and incorporates them into the polymer matrix. The resulting composite films exhibit a highly porous morphology and significantly enhanced optical absorption, with band gaps reduced to 0.62–1.15 eV, enabling efficient visible-light harvesting. The composites demonstrate excellent photocatalytic activity for degrading a mixture of organic dyes (Methylene Blue > Rhodamine B > Reactive Red 6C) under visible light. The developed plasma-liquid technique presents a streamlined, efficient route for fabricating visible-light-driven PLA/MXene photocatalysts, offering a sustainable solution for advanced water purification applications. Full article

This work presents the results of a research study focused on the development and evaluation of an algorithmic optimal control framework for energy-efficient operation of screw compressors in smart power systems. The proposed approach is based on the Pontryagin maximum principle (PMP), which enables the synthesis of a mathematically grounded regulator that minimizes the total energy consumption of a nonlinear electromechanical system composed of a screw compressor and a variable-frequency induction motor. Unlike conventional PID controllers, the developed algorithm explicitly incorporates system constraints, nonlinear dynamics, and performance trade-offs into the control law, allowing for improved adaptability and energy-aware operation. Simulation results obtained using MATLAB/Simulink confirm that the PMP-based regulator outperforms classical PID solutions in both transient and steady-state regimes. Experimental tests conducted in accordance with standard energy consumption evaluation methods showed that the proposed PMP-based controller provides a reduction in specific energy consumption of up to 18% under dynamic load conditions compared to a well-tuned basic PID controller, while maintaining high control accuracy, faster settling, and complete suppression of overshoot under external disturbances. The control system demonstrates robustness to parametric uncertainty and load variability, maintaining a statistical pressure error below 0.2%. The regulator’s structure is compatible with real-time execution on industrial programmable logic controllers (PLCs), supporting integration into intelligent automation systems and smart grid infrastructures. The discrete-time PLC implementation of the regulator requires only 10³ arithmetic operations per cycle and less than 10² kB of RAM for state, buffers, and logging, making it suitable for mid-range industrial controllers under 2–10 ms task cycles. Fault-tolerance is ensured via range and rate-of-change checks, residual-based plausibility tests, and safe fallbacks (baseline PID or torque-limited speed hold) in case of sensor faults. Furthermore, the proposed approach lays the groundwork for hybrid extensions combining model-based control with AI-driven optimization and learning mechanisms, including reinforcement learning, surrogate modeling, and digital twins. These enhancements open pathways toward predictive, self-adaptive compressor control with embedded energy optimization. The research outcomes contribute to the broader field of algorithmic control in power electronics, offering a scalable and analytically justified alternative to heuristic and empirical tuning approaches commonly used in industry. The results highlight the potential of advanced control algorithms to enhance the efficiency, stability, and intelligence of energy-intensive components within the context of Industry 4.0 and sustainable energy systems. Full article