Search Results (3,113)

Perovskite manganites (AMnO₃) and perovskite-like manganites (A’_1−xA_xMnO₃) are complex oxide materials that have attracted significant attention from the scientific community in recent years due to their structural flexibility, mixed-valence state, tunable electronic configuration, and multifunctional properties. This review systematically analyzes the synthesis methods, structural classification, and physicochemical characterization of perovskite manganites, as well as their magnetic, optical, electrical, dielectric, and catalytic properties. The influence of solid-state reactions, sol–gel, Pechini, hydrothermal, co-precipitation, microwave, and other mild chemical approaches on phase purity, morphology, particle size, and oxygen stoichiometry was examined. The structural diversity of perovskite and perovskite-like manganites, including simple ABO₃, double perovskites, multilayer, and low-dimensional systems, was characterized in relation to their functional properties. The review discussed the capabilities of methods for synthesizing and analyzing morphological properties, demonstrating the role of doping, cation substitution, oxygen vacancies, and Jahn–Teller distortions in controlling material properties. Prospects for the application of perovskite manganites in spintronics, magnetocaloric cooling, photocatalysis, gas-sensing devices, and energy conversion and storage systems were analyzed. This review highlights the structure–property–application relationship in perovskite manganites. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Accurate assessment of postural sway is essential for evaluating balance disorders, rehabilitation outcomes, and fall risk. Traditional laboratory-based motion capture systems provide precise center-of-pressure (CoP) measurements, but are expensive, non-portable, and impractical for widespread clinical use. This study describes the development and testing (reliability and validity) of a portable three-dimensional (3D) camera system (Intel RealSense D415) for quantifying sway and balance. Test–retest reliability was evaluated in healthy adults (n = 10; 6 males, 4 females; mean age 22.3 ± 1.6 years), yielding intraclass correlation coefficients ICC = 0.84–0.86 (95% CI: 0.61–0.95). Concurrent validity, established against a laboratory-based optical motion capture system (Optotrak), demonstrated strong correlations with a mean absolute percentage error of 10.5% relative to Optotrak-derived path length measurements and high levels of agreement. Operating at 30 Hz with end-to-end latency of <40 ms, the RealSense-based system provides a reliable, valid, and portable alternative to lab-based systems. Low-cost markerless motion capture systems based on standard RGB cameras have been validated for postural risk assessment, showing good consistency with gold-standard Vicon systems. These preliminary findings suggest that the system shows promise as a low-cost alternative; however, further validation in clinical populations is required before clinical deployment. Full article

Toll-like receptors (TLRs) are essential components of the innate immune system that enable host cells to sense microbial and endogenous danger signals and to initiate inflammatory and antimicrobial responses. Activation of TLRs triggers complex intracellular signaling networks that culminate in the induction of pro-inflammatory cytokines, type I interferons, and co-stimulatory molecules. In addition to the well-characterized nuclear factor κB (NF-κB) and interferon regulatory factor (IRF) pathways, mitogen-activated protein kinases (MAPKs) play a critical modulatory role in TLR signaling. MAPK/ERK kinase (MEK) inhibitors were originally developed for the treatment of cancer and are widely used in clinical oncology. Accumulating evidence indicates that pharmacological inhibition of MEK/extracellular signal regulated kinase (ERK) signaling profoundly affects immune cell function and TLR-driven responses. Depending on timing, dose, and disease context, MEK inhibition can attenuate excessive inflammation but may also interfere with protective host defense mechanisms. This duality highlights the context-dependent role of MEK/ERK signaling in infection and inflammation. In this review, I summarize current knowledge on the integration of MEK/ERK signaling into TLR-mediated innate immune responses and discuss the immunological consequences of MEK inhibition in infectious and inflammatory settings. By synthesizing mechanistic and translational studies, I aim to provide a framework for understanding MEK inhibitors as immune modulators rather than as broadly acting anti-inflammatory agents. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Ensuring the integrity of pavement structures necessitates a thorough evaluation of both surface-level damage and subsurface mechanical performance. This study proposes an integrated, non-destructive assessment framework tailored for semi-rigid base asphalt pavements subjected to repeated vehicular loading via MLS66 full-scale accelerated testing equipment. The proposed methodology integrates ground-penetrating radar (GPR) using the CO4080 system and dynamic response measurements from a falling weight deflectometer (FWD) to characterize structural conditions across multiple depths. Comparative analysis between pre-loading and post-loading data revealed significant deterioration trends in the surface layers, with stiffness loss closely associated with increasing load repetitions. In contrast, the underlying base layers exhibited stable deformation characteristics, with variations in deflection basin indices remaining within ±5%. Subgrade dielectric properties derived from GPR data confirmed consistent compaction quality throughout the test site. Statistical analysis further validated the synergy between GPR and FWD results, demonstrating that the combined application enhances diagnostic accuracy. The dual-method approach improved overall evaluation reliability by approximately 22–35% compared to using individual techniques alone under accelerated pavement testing scenarios. These findings support broader implementation of integrated sensing systems and highlight the potential for application across varied pavement types and loading conditions. Full article

Hyperspectral image (HSI) and LiDAR data fusion is valuable for land-cover classification in complex surface scenes. Existing methods typically extract features from each modality independently and then consider how to fuse them, ignoring the semantic consistency between features of different modalities and across different hierarchical levels. Moreover, fully mining and exploiting the complementary information between multimodal remote sensing data remains a critical issue. To address these challenges, this paper proposes a hierarchical semantic consistency constraint (HSCC) framework for HSI and LiDAR data joint classification. The framework is co-constructed by a progressive interactive fusion network (PIFNet) and a semantic consistency constraint (SCC) strategy. Specifically, PIFNet progressively calibrates the semantic representations of multimodal features at different abstraction levels through Cross-Modal Shared Attention and Symmetric Cross-Attention mechanisms, promoting information parity in deep interactions. The SCC strategy establishes multi-level semantic associations and employs a semantic consistency constraint loss to guide the network to autonomously maintain the consistency of the same land-cover object across heterogeneous feature representations, thereby further enhancing the discriminative power of the fused features. Experiments on three public datasets, MUUFL, Houston2013, and Augsburg, demonstrate that HSCC outperforms current state-of-the-art methods, validating its effectiveness in multi-source remote sensing data fusion classification tasks. Full article

Lithium niobate on insulator (LNOI) has emerged as a promising platform for compact, low-loss phase modulators. The extant LNOI studies evaluate device performance almost exclusively through the Pockels effect, treating piezoelectric–photoelastic strain and thermo-optic drift as decoupled channels. Crucially, both mechanisms directly perturb the phase bias of a fiber-optic gyroscope (FOG), rendering them indispensable in sensing-oriented design. This work establishes a unified multiphysics model of an X-cut TFLN ridge phase modulator that self-consistently couples the electro-optic, piezoelectric–photoelastic, thermo-optic, and pyroelectric channels. The contributions of the four mechanisms are quantitatively decomposed under realistic FOG operating conditions, and the slab thickness, ridge-top width, and electrode gap are systematically optimized to balance modulation efficiency against environmental robustness. The co-optimization of the ridge geometry and electrode gap design maintains the EO overlap factor near 0.55, while reducing the half-wave voltage requirement. This results in a half-wave voltage length of V_πL = 1.65 V·cm at a 4.4 μm electrode gap. The optimized geometry and electrode gap (4.4 μm) are essentially temperature-independent: extracted from the Pockels modulation slope, V_πL remains stable at ≈1.65 V·cm (push–pull single-pass; within ~0.3%) across 25~85 °C. Furthermore, an externally imposed substrate temperature rise of 60 K (the upper end of the 25~85 °C FOG operating range) induces a mode-field-weighted thermal residual corresponding to approximately 27% of the Pockels modulation depth at an applied voltage of 5 V. The present study demonstrates that the DC-coupled operation of TFLN sensor-grade modulators is viable across the full FOG temperature range, without dedicated active temperature stabilization, and the residual thermal-bias offset is absorbed by the FOG’s standard closed-loop servo electronics. The results of the study provide quantitative design guidelines for high-performance, environmentally stable TFLN phase modulators in compact FOG systems. Full article

High-sensitivity detection of formaldehyde is critically important for environmental protection and public health. Zinc oxide (ZnO) is a widely used core material for chemiresistive gas sensors; however, its conventional low-index facets suffer from a limited number of active sites, creating a bottleneck for further sensitivity enhancement. To overcome this limitation, this study pioneers the application of the highly reactive ZnO $\left[20\overline{2}1\right]$ high-index polar surface for formaldehyde detection. By leveraging its unique stepped atomic configuration and unprecedented density of coordination-unsaturated active sites, we systematically investigate the formaldehyde adsorption behavior and the underlying sensing mechanism using first-principles calculations based on density functional theory (DFT). The pristine ZnO $\left[20\overline{2}1\right]$ surface exhibits intrinsic metallic character. At a coverage of 1 monolayer (ML), the most stable G1 configuration achieves an adsorption energy of −1.54 eV per CH₂O molecule. Within a 2 × 1 supercell, formaldehyde adopts both associative and dissociative adsorption modes. At a lower coverage, formaldehyde forms a stable bidentate structure through dual C–O and Zn–O bonding interactions. Electronic structure analysis reveals significant orbital hybridization and interfacial charge redistribution upon adsorption. Notably, associative adsorption opens a bandgap of 0.04 eV at the Fermi level, inducing a metal-to-semiconductor transition. In contrast, dissociative adsorption results in pronounced n-type doping, thereby elucidating the microscopic origin of the resistivity decrease observed in ZnO-based sensors. Overall, this work highlights the structural advantages of high-index facets and demonstrates for the first time the superior formaldehyde adsorption capability of the ZnO $\left[20\overline{2}1\right]$ facet, providing robust theoretical guidance for the rational design of next-generation, high-performance gas-sensing materials. Full article

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

Driven by advances in multifunctional materials design, silver halides—both natural (AgCl, AgBr, AgI, and mixed phases such as embolite) and synthetic—have emerged as versatile functional materials characterized by tunable crystallography, phase stability, and compositional variability. This study investigates global research trends, interdisciplinary development, and emerging application areas of silver halides through a bibliometric analysis of 23,841 publications indexed in the Web of Science (1975–2025). CDPI, TELM, VOSviewer, and Excel were employed to evaluate publication growth, disciplinary integration, and thematic evolution. Research output increased markedly after 2005, reaching approximately 700–1000 publications annually during 2020–2025. China (18.3%) and the United States (17.5%) were the leading contributors, while the Chinese Academy of Sciences, Russian Academy of Sciences, and CNRS showed the highest scientific impact. Materials Science Multidisciplinary (CDPI = 0.72), Chemistry Multidisciplinary (0.70), and Physical Chemistry (0.67) exhibited the strongest interdisciplinary integration, whereas Nanoscience and Nanotechnology demonstrated the fastest growth. Keyword co-occurrence analysis identified six major research domains focused on functional materials engineering, including environmental remediation, catalysis, crystal growth, antibacterial materials, interfacial processes, and electroanalytical systems. Recent studies increasingly emphasize structure–property relationships and synthetic control of crystal size, morphology, and surface characteristics to enhance performance in photocatalysis, sensing, antimicrobial coatings, and advanced optical applications. Overall, the results highlight the growing importance of silver halides as strategic functional materials and provide a quantitative framework for future research and technological development. A limitation of this study is its exclusive reliance on the Web of Science database, which may underrepresent relevant publications indexed elsewhere. Full article

The hybrid golden pompano “Chenhai No. 1” (CH), generated through distant hybridization [(♀ Trachinotus ovatus × ♂ T. blochii) × ♂ T. ovatus], exhibits significantly enhanced growth performance compared to its parental T. ovatus (TO). To elucidate the molecular mechanisms underlying this rapid growth, we performed integrated transcriptomic and untargeted metabolomic profiling of muscle tissue. Transcriptomic analysis identified 3172 differentially expressed genes (DEGs), with weighted gene co-expression network analysis (WGCNA) highlighting the ‘darkorange2’ module as strongly associated with rapid growth. Key DEGs, including mapk8a, acacb, and pkmb, were upregulated and implicated in energy metabolism, glycolysis, and signal transduction. Metabolomic profiling detected 576 significantly altered metabolites, predominantly enriched in glycolysis, the tricarboxylic acid (TCA) cycle, amino acid metabolism, lipid biosynthesis, and mTOR signaling. Integrated analysis revealed coordinated alterations between core module genes and differential metabolites in interrelated pathways, including correlations between pfkpa/pfkma and glyceraldehyde-3-phosphate, acacb and phosphatidylcholine/phosphatidylethanolamine, and sesn2 and leucine. These findings suggest that the growth advantage of CH arises from the coordinated enhancement of energy metabolism, amino acid sensing, and lipid metabolic remodeling, establishing a synergistic transcription–metabolism regulatory network. This study provides multi-omics insights into the molecular basis of rapid growth in an economically important teleost fish. Full article

A nitrogen/sulfur co-doped reduced graphene oxide (N,S-RGO) material was rationally prepared via a modified Hummers method followed by microwave-assisted reduction. The resulting material was uniformly deposited onto a glassy carbon electrode (GCE) to fabricate an electrochemical sensor for nitrobenzene (NB) detection. The prepared N,S-RGO material was characterized in detail using Fourier-transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy, confirming the successful incorporation of heteroatoms. Furthermore, electrochemical studies, including cyclic voltammetry (CV) and linear sweep voltammetry (LSV), revealed the enhanced electrical conductivity of the material. The fabricated N,S-RGO/GCE sensor exhibited remarkable electroanalytical performance, achieving a low detection limit (LOD) of 7 nM within a linear concentration range of 0.05 to 147 µM. The enhanced sensing performance is attributed to the synergistic effect of nitrogen and sulfur doping, which improves electron transfer kinetics and abundant active sites for NB reduction. Furthermore, the sensor demonstrated outstanding selectivity toward NB in the presence of common interfering substances. Its practical applicability was confirmed through the successful detection of NB in environmental water samples, yielding convincing recovery rates. These results highlight the potential of the N,S-RGO/GCE platform as an efficient and reliable electrochemical sensor for environmental monitoring of NB contamination. Full article