Search Results (1,541)

Precise control over nanostructure evolution is critical for optimizing the electrochemical performance of pseudocapacitive materials. In this work, Nb₂O₅ nanostructures were synthesized via a time-engineered hydrothermal route by systematically varying the reaction duration (6, 12, and 18 h) to elucidate its influence on structural development, charge storage kinetics, and supercapacitor performance. Structural and surface analyses confirm the formation of phase-pure monoclinic Nb₂O₅ with a stable Nb⁵⁺ oxidation state. Morphological investigations reveal that a 12 h reaction time produces hierarchically organized Nb₂O₅ architectures composed of nanograin-assembled spherical aggregates with interconnected porosity, providing optimized ion diffusion pathways and enhanced electroactive surface exposure. Electrochemical evaluation demonstrates that the NbO-12 electrode delivers superior pseudocapacitive behavior dominated by diffusion-controlled Nb⁵⁺/Nb⁴⁺ redox reactions, exhibiting high areal capacitance (5.504 F cm⁻² at 8 mA cm⁻²), fast ion diffusion kinetics, low internal resistance, and excellent cycling stability with 85.73% capacitance retention over 12,000 cycles. Furthermore, an asymmetric pouch-type supercapacitor assembled using NbO-12 as the positive electrode and activated carbon as the negative electrode operates stably over a wide voltage window of 1.5 V, delivering an energy density of 0.101 mWh cm⁻² with outstanding durability. This study establishes hydrothermal reaction-time engineering as an effective strategy for tailoring Nb₂O₅ nanostructures and provides valuable insights for the rational design of high-performance pseudocapacitive electrodes for advanced energy storage systems. Full article

This study examines the conceptualization of sustainability in heritage tourism in Saudi Arabia following the introduction of the Saudi Vision 2030 program and the country’s opening to tourism in 2019, both of which aim to diversify the economy and promote cultural heritage. A scoping review methodology based on the Arksey & O’Malley framework has been adopted; data were charted according to the Joanna Briggs Institute (JBI) charting method based on the PRISMA-ScR reporting protocol. Publications from 2019 to 2025 were systematically collected from the database and manual research, resulting in 25 fully accessible studies that met the inclusion criteria. Data were analyzed thematically, revealing six main areas of investigation, encompassing both sustainability outcomes and cross-cutting implementation enablers: heritage conservation and tourism development, architecture and urban planning, policy and governance, community engagement, marketing and technology, and geoheritage and environmental sustainability. The findings indicate that Saudi research in this field is primarily qualitative, focusing on ecological aspects. The studies reveal limited integration of social and technological dimensions, with significant gaps identified in standardized sustainability indicators, longitudinal monitoring, policy implementation, and digital heritage tools. The originality of this study lies in its comprehensive mapping of Saudi heritage tourism sustainability research, highlighting emerging gaps and future agendas. The results also provide a roadmap for policymakers, managers, and scholars to enhance governance policies, community participation, and technological integration, which can contribute to sustainable tourism development in line with Saudi Vision 2030 goals, thereby fostering international competitiveness while preserving cultural and natural heritage. Full article

Industrial heritage—particularly large modern cement plants—serves as a crucial witness to the architectural and technological evolution of modern urbanization. In Europe, North America, and East Asia, many decommissioned cement factories have been transformed into cultural venues, creative districts, or urban landmarks, while a greater number of sites still face the risks of functional decline and spatial disappearance. In China, early large-scale cement plants have received limited attention in international industrial heritage research, and their conservation and adaptive reuse practices remain underdeveloped. This study takes the Huaxin Cement Plant, founded in 1907, as the research object. As the birthplace of China’s modern cement industry, it preserves the world’s only complete wet-process rotary kiln production line, representing exceptional rarity and typological significance. Combining social media perception analysis with the Hidalgo-Giralt four-quadrant model, the study aims to clarify the plant’s value positioning and propose a design-oriented pathway for spatial activation. Based on 378 short videos and 75,001 words of textual data collected from five major platforms, the study conducts a value-tag analysis of public perceptions across five dimensions—historical, technological, social, aesthetic, and economic. Two composite indicators, Cultural Representativeness (CR) and Utilization Intensity (UI), are further established to evaluate the relationship between heritage value and spatial performance. The findings indicate that (1) historical and aesthetic values dominate public perception, whereas social and economic values are significantly underrepresented; (2) the Huaxin Cement Plant falls within the “high cultural representativeness/low utilization intensity” quadrant, revealing concentrated heritage value but insufficient spatial activation; (3) the gap between value cognition and spatial transformation primarily arises from limited public accessibility, weak interpretive narratives, and a lack of immersive experience. In response, the study proposes five optimization strategies: expanding public access, building a multi-layered interpretive system, introducing immersive and interactive design, integrating into the Yangtze River Industrial Heritage Corridor, and encouraging community co-participation. As a representative case of modern Chinese industrial heritage distinguished by its integrity and scarcity, the Huaxin Cement Plant not only enriches the understanding of industrial heritage typology in China but also provides a methodological paradigm for the “value positioning–spatial utilization–heritage activation” framework, bearing both international comparability and disciplinary methodological significance. Full article

Blockchain is increasingly applied in the agri-food sector to enhance traceability, data integrity, and accountability. However, its broader role in food system sustainability remains insufficiently characterized, particularly when examined against global megatrends shaping future agri-food transitions. This paper investigates how blockchain technology can reinforce sustainable, inclusive, and resilient food systems under the effect of major global megatrends. A structured literature review of peer-reviewed and industry sources was conducted to identify evidence on blockchain-enabled improvements in transparency, certification, and supply chain coordination. Complementary analysis of a curated dataset of European and international pilot implementations evaluated technological architectures, governance models, and demonstrated performance outcomes. Additionally, stakeholder-based foresight activities and scenarios representing alternative blockchain adoption pathways, developed within the TRUSTyFOOD project (GA: 101060534), were used to examine the interconnection between blockchain adoption and megatrends. Evidence from the literature and pilot cases indicates that blockchain can strengthen product-level traceability and improve verification of sustainability and safety claims. Cross-case analysis also reveals persistent constraints, including heterogeneous technical standards, limited interoperability, high deployment costs for smallholders, and governance risks arising from consortium-led platforms. Blockchain can function as an enabling digital layer for sustainable and resilient food systems and should be embedded in wider, participatory strategies that align digital innovation with long-term sustainability and equity goals in the agri-food sector. Full article

Uric acid is closely related to diseases such as gout, kidney failure, and metabolic disorders. A conventional method for measuring uric acid over 24 h is time intensive and cumbersome for patients who have to take samples to the hospital. At present, hospitals use only laboratory instruments to determine 24-h uric acid concentrations in the urine. This study presents the proof-of-concept of a portable point-of-care tool called Uricia, designed to improve the quality of life of patients monitoring uric acid. Spectrophotometry was performed at a fixed wavelength of 295 nm. The urine sample contained within the cuvette absorbs ultraviolet light, with uric acid specifically responsible for this absorption, thereby allowing the device to measure its concentration. An internal calibration algorithm was used to accommodate the nonlinear optical response of Uricia and was calibrated to a benchtop GENESYS 10S UV–Vis spectrophotometer. The experiments further evaluated potential urinary interferences, revealing that while most constituents had minimal impact, ascorbic acid demonstrated the highest interference, contributing up to 15% of the total signal at high physiological concentrations. This device and the corresponding spectrophotometry method revealed that high concentrations of uric acid precipitated insoluble crystals. A dilution set to an alkali solution vial to be premixed and dissolve the uric acid crystals was added, increasing the detection window to 10 mg/dL, with an LOD of 0.0232 mg/dL and LOQ of 0.0702 mg/dL. Cloud-based data measurement enables spot analysis, which is meant to provide insight into patient status development. These results validated the technical architecture of a controlled matrix for measuring uric acid. Full article

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF’s internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems. Full article

The primary objective of this study is to bridge the gap between descriptive geometry and mechanistic design by establishing a dual-domain fractal framework to analyze the internal architecture of asphalt mixtures. This research quantitatively assesses the sensitivity of volumetric indicators—namely air voids (VV), voids in mineral aggregate (VMA), and voids filled with asphalt (VFA)—by employing the coarse aggregate fractal dimension (D_c), the fine aggregate fractal dimension (D_f), and the coarse-to-fine ratio (k) through Grey Relational Analysis (GRA). The findings demonstrate that whereas D_f and k substantially influence macro-volumetric parameters, the mesoscopic void fractal dimension (D_V) remains structurally unchanged, indicating that gradation predominantly dictates void volume rather than geometric intricacy. Sensitivity rankings create a prevailing hierarchy: Process Control (Compaction) > Skeleton Regulation (D_c) > Phase Filling (Pb) > Gradation Adjustment (k, D_f). D_c is recognized as the principal regulator of VMA, while binder content (Pb) governs VFA. A “Robust Design” methodology is suggested, emphasizing D_c to stabilize the mineral framework and reduce sensitivity to construction variations. A comparative investigation reveals that the optimized gradation (OG) achieves a more stable volumetric condition and enhanced mechanical performance relative to conventional empirical gradations. Specifically, the OG group demonstrated a substantial 112% enhancement in dynamic stability (2617 times/mm compared to 1230 times/mm) and a 75% increase in average film thickness (AFT), while ensuring consistent moisture and low-temperature resistance. In conclusion, this study transforms asphalt mixture design from empirical trial-and-error to a precision-engineered methodology, providing a robust instrument for optimizing the long-term durability of pavements in extreme cold and arid environments. Full article

This paper presents a systematic review conducted under the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, analyzing 286 scientific articles focused on vibration-based predictive maintenance strategies for wind turbines within the context of advanced Prognostics and Health Management (PHM). The review combines international standards (ISO 10816, ISO 13373, and IEC 61400) with recent developments in sensing technologies, including piezoelectric accelerometers, microelectromechanical systems (MEMS), and fiber Bragg grating (FBG) sensors. Classical signal processing techniques, such as the Fast Fourier Transform (FFT) and wavelet-based methods, are identified as key preprocessing tools for feature extraction prior to the application of machine-learning-based diagnostic algorithms. Special emphasis is placed on machine learning and deep learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and autoencoders, as well as on hybrid digital twin architectures that enable accurate Remaining Useful Life (RUL) estimation and support autonomous decision-making processes. The bibliometric and case study analysis covering the period 2020–2025 reveals a strong shift toward multisource data fusion—integrating vibration, acoustic, temperature, and Supervisory Control and Data Acquisition (SCADA) data—and the adoption of cloud-based platforms for real-time monitoring, particularly in offshore wind farms where physical accessibility is constrained. The results indicate that vibration-based predictive maintenance strategies can reduce operation and maintenance costs by more than 20%, extend component service life by up to threefold, and achieve turbine availability levels between 95% and 98%. These outcomes confirm that vibration-driven PHM frameworks represent a fundamental pillar for the development of smart, sustainable, and resilient next-generation wind energy systems. Full article

Sleep plays a central role in shaping emotional development during childhood and adolescence, yet increasing evidence indicates that these processes unfold differently in boys and girls. This narrative review synthesizes current findings on sex-specific associations between sleep patterns, neurodevelopmental trajectories, and emotional regulation across pediatric populations. It examines how biological factors, including pubertal timing, sex hormones, circadian physiology, and maturation of fronto-limbic circuits, interact with environmental influences to generate distinct vulnerabilities to anxiety, depression, and behavioral dysregulation. Growing data suggest that girls exhibit greater sensitivity to sleep disturbances, particularly during the pubertal transition, with stronger links to internalizing symptoms such as anxiety and mood disorders. In contrast, boys appear more prone to externalizing behaviors and show differential responses to circadian misalignment and short sleep duration. Emerging evidence on sex-specific sleep architecture, REM-related emotional processing, and the bidirectional pathways through which sleep quality affects affective functioning are explored. Finally, clinical implications for early detection, personalized prevention, and targeted interventions tailored by sex and developmental stage are discussed. Understanding sex-based differences in sleep–emotion interactions offers a critical opportunity to refine pediatric mental health strategies and improve outcomes across developmental trajectories. Full article

Sustainable agriculture increasingly relies on biostimulants like protein hydrolysates (PHs) to enhance crop resilience. This study characterized and compared three plant-derived PHs (PH1, PH2, and PH3) from the Malvaceae, Brassicaceae, and Fabaceae families, respectively, under optimal (40 µM Fe³⁺-EDTA) and iron (Fe)-deficient (4 µM Fe³⁺-EDTA) conditions. Initial assays demonstrated that the PHs possessed significant antioxidant capacity and influenced biological activity: PH2 and PH3 promoted pollen germination, while PH1 exhibited a weaker stimulatory effect. In vivo experiments on tomato plants revealed that PH application effectively modulated root architecture and biomass accumulation. Moreover, PH2 and PH3 significantly mitigated Fe deficiency’s impact, by maintaining biomass and preventing chlorosis. Interestingly, while Fe deficiency typically triggers massive root Fe³⁺-chelate reductase activity, PH treatments, particularly PH2, significantly down-regulated this response. This suggests that PHs may improve internal Fe use efficiency or facilitate alternative uptake pathways. Overall, these findings establish a link between the intrinsic bioactive properties of PHs and their biostimulant action, highlighting their potential as innovative tools for improving nutrient use efficiency and crop resilience in sustainable farming systems. Full article

The increasing complexity of contemporary urban systems necessitates decision-making frameworks capable of systematically integrating multidimensional sustainability considerations into policy evaluation processes. While existing urban sustainability assessment approaches predominantly focus on isolated environmental or socio-economic indicators, they often lack methodological coherence and direct applicability to operational decision-making. This study proposes a multi-layer sustainability indicator framework explicitly designed to support evidence-based urban decision-making under conditions of uncertainty, institutional constraints, and competing policy objectives. The framework integrates environmental, economic, social, and institutional dimensions of sustainability into a structured decision-support architecture. Methodologically, the study employs a two-stage approach combining expert-based weighting techniques (Analytic Hierarchy Process and Best–Worst Method) with multi-criteria decision-making methods (TOPSIS and VIKOR) to evaluate and rank alternative urban policy scenarios. The proposed framework is empirically validated through an urban case study, demonstrating its capacity to translate abstract sustainability indicators into comparable decision outcomes and policy priorities. The results indicate that the integration of multi-layer indicator systems with formal decision-analysis tools enhances transparency, internal consistency, and strategic coherence in urban governance processes. By bridging the gap between sustainability measurement and decision implementation, the study contributes to the advancement of urban governance scholarship and provides a replicable analytical model applicable to cities facing complex sustainability trade-offs. Full article

Background/Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in children presents with a heterogeneous clinical spectrum, whereas multisystem inflammatory syndrome in children (MIS-C) is a distinct immunological entity characterized by a hyperinflammatory phenotype and a distinct biological architecture. Identifying routine biomarkers with early discriminatory utility is essential for rapid differentiation between MIS-C and coronavirus disease 2019 (COVID-19). Methods: We conducted a retrospective comparative study of 144 pediatric patients with COVID-19 or MIS-C admitted to a single specialized medical center. The analyses integrated classical statistical methods, Benjamini–Hochberg false discovery rate correction (FDR), penalized regression models, and machine learning algorithms to identify biomarkers with discriminative value, using only routine laboratory tests. Results: MIS-C was associated with an intense inflammatory profile, characterized by increases in C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), lymphopenia, and selective electrolyte disturbances, highlighting a coherent biological architecture. In contrast, COVID-19 showed limited associations with traditional inflammatory markers. Predictive models identified a stable core of biomarkers with excellent performance in Random Forest analysis (area under the curve, AUC = 0.95), and reproducible thresholds (CRP ~3.7 mg/dL, NLR ~3.3, PLR ~376; potassium ~4.2 mmol/L). These findings were independently confirmed using penalized Ridge regression, where the reduced model achieved superior discrimination compared to the full 13-variable model (AUC = 0.93 vs. 0.89) and maintained stable performance under internal cross-validation, reinforcing the clinical relevance of this compact biomarker panel. Conclusions: MIS-C is clearly distinguished from COVID-19 by a specific and reproducible immunological signature. The identified biomarkers may represent a potential foundation for the development of simple clinical algorithms for pediatric triage and risk stratification, opening the prospect of a simplified scoring tool applicable in emergency settings. Full article

Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves ($C_b=0.90$) than for monocot leaves ($C_b=0.79$), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article

Background: Pectus excavatum (PE) is the most common congenital chest wall deformity, affecting approximately 1 in 400 live births. Although familial clustering supports a genetic contribution, the molecular basis of PE remains poorly defined. This systematic review synthesizes existing evidence on genetic variants associated with PE to guide future genome-wide association studies (GWAS) and Mendelian randomization (MR) analyses. Methods: A comprehensive systematic search was conducted across all electronic databases, including Google Scholar, PubMed/MEDLINE, Web of Science, and arXiv, from inception to November 2025. Nine studies met the inclusion criteria. The search strategy utilized the terms “pectus excavatum”, “genetic variants”, “SNPs”, and “GWAS”, combined with Boolean operators. Eligible studies reported genetic associations, family-based analyses, or mechanistic investigations. The Newcastle–Ottawa Scale was used to assess study quality. Results: No population-level GWAS of isolated PE was identified. Fourteen genetic loci were reported across diverse study designs, including family-based exome sequencing (REST, SMAD4, COL5A1, COL5A2), case reports (COL1A1, COL27A1, NF1, BICD2, PTPN11), candidate gene analyses (ACAN), mouse models (GPR126, GAL3ST4), and linkage analysis implicating chromosome 18q. These genes converge on four key biological pathways: extracellular matrix and collagen metabolism, TGF-β/BMP signaling, cartilage development, and transcriptional regulation. Importantly, none of the included studies reported SNP-level effect sizes, allele frequencies, or odds ratios, precluding construction of valid MR instruments. Conclusions: Current genetic evidence for PE is largely derived from rare variants and family-based studies, with no population-level GWAS available. This critical gap limits causal inference, underscoring the urgent need for large-scale international GWAS to identify common variants and clarify the genetic architecture of PE. Full article

Multi-branch deep neural networks like AASIST3 achieve state-of-the-art comparable performance in audio anti-spoofing, yet their internal decision dynamics remain opaque compared to traditional input-level saliency methods. While existing interpretability efforts largely focus on visualizing input artifacts, the way individual architectural branches cooperate or compete under different spoofing attacks is not well characterized. This paper develops a framework for interpreting AASIST3 at the component level. Intermediate activations from fourteen branches and global attention modules are modeled with covariance operators whose leading eigenvalues form low-dimensional spectral signatures. These signatures train a CatBoost meta-classifier to generate TreeSHAP-based branch attributions, which we convert into normalized contribution shares and confidence scores ($C_b$) to quantify the model’s operational strategy. By analyzing 13 spoofing attacks from the ASVspoof 2019 benchmark, we identify four operational archetypes—ranging from “Effective Specialization” (e.g., A09, Equal Error Rate (EER) 0.04%, $C=1.56$) to “Ineffective Consensus” (e.g., A08, EER 3.14%, $C=0.33$). Crucially, our analysis exposes a “Flawed Specialization” mode where the model places high confidence in an incorrect branch, leading to severe performance degradation for attacks A17 and A18 (EER 14.26% and 28.63%, respectively). These quantitative findings link internal architectural strategy directly to empirical reliability, highlighting specific structural dependencies that standard performance metrics overlook. Full article