Search Results (137)

In Internet of Things (IoT) systems, data collected by geographically distributed sensors is often incomplete due to device failures, harsh deployment conditions, energy constraints, and unreliable communication. Such data gaps can significantly degrade downstream data processing and decision-making, particularly when failures result in the loss of all locally redundant sensors. Conventional imputation approaches typically rely on historical trends or multi-sensor fusion within the same target environment; however, historical methods struggle to capture emerging patterns, while same-location fusion remains vulnerable to single-point failures when local redundancy is unavailable. This article proposes a correlation-aware, cross-location data fusion framework for data imputation in IoT networks that explicitly addresses single-point failure scenarios. Instead of relying on co-located sensors, the framework selectively fuses semantically similar features from independent and geographically distributed gateways using summary statistics-based and correlation screening to minimize communication overhead. The resulting fused dataset is then processed using a lightweight KNN with an Iterative PCA imputation method, which combines local neighborhood similarity with global covariance structure to generate synthetic data for missing values. The proposed framework is evaluated using real-world weather station data collected from eight geographically diverse locations across the United States. The experimental results show that the proposed approach achieves improved or comparable imputation accuracy relative to conventional same-location fusion methods when sufficient cross-location feature correlation exists and degrades gracefully when correlation is weak. By enabling data recovery without requiring redundant local sensors, the proposed approach provides a resource-efficient and failure-resilient solution for handling missing data in IoT systems. Full article

Accurate model aggregation is pivotal for the efficient dispatch and control of massive distributed photovoltaic (PV) and energy storage (ES) resources. However, the lack of unified standards across equipment manufacturers results in inconsistent data formats and resolutions. Furthermore, external disturbances like noise and packet loss exacerbate the problem. The resulting data are massive, multi-source, and heterogeneous, which poses severe challenges to building effective aggregation models. To address these issues, this paper proposes a hierarchical aggregation method based on multi-source multi-scale data fusion. First, a Multi-source Multi-scale Decision Table (Ms-MsDT) model is constructed to establish a unified framework for the flexible storage and representation of heterogeneous PV-ES data. Subsequently, a two-stage fusion framework is developed, combining Information Gain (IG) for global coarse screening and Scale-based Trees (SbT) for local fine-grained selection. This approach achieves adaptive scale optimization, effectively balancing data volume reduction with high-fidelity feature preservation. Finally, a hierarchical aggregation mechanism is introduced, employing the Analytic Hierarchy Process (AHP) and a weight-guided improved K-Means algorithm to perform targeted clustering tailored to the specific control requirements of different voltage levels. Validation on an IEEE-33 node system demonstrates that the proposed method significantly improves data approximation precision and clustering compactness compared to conventional approaches. Full article

Timely and accurate crop yield estimation is vital for food security and management decision-making. Integrating remote sensing with machine learning provides an effective solution. In this study, based on canopy hyperspectral data collected by an ASD FieldSpec 3 handheld spectrometer during the critical growth stages of winter wheat, 18 vegetation indices (VIs) were systematically calculated, and their correlation with yield was analyzed. At the same time, a continuous projection algorithm, Successive Projections Algorithm (SPA), was used to screen the characteristic bands. Recursive Feature Elimination (RFE) was employed to select optimal features from VIs and characteristic spectral bands, facilitating the construction of a multi-temporal fusion feature set. To identify the superior yield estimation approach, a comparative analysis was conducted among four machine learning models: Deep Forest (DF), Support Vector Regression (SVR), Random Forest (RF), and Gaussian Process Regression (GPR). Performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). Results indicate that the highest correlations between VIs and grain yield were observed during the flowering and grain-filling stages. Independent analysis showed that VIs reached absolute correlations of 0.713 and 0.730 with winter wheat yield during the flowering and grain-filling stages, respectively, while the SPA further identified key bands primarily in the near-infrared and short-wave infrared regions. On this basis, integrating multi-temporal features through RFE significantly improved the accuracy of yield estimation. Among them, the DF model with the fusion of flowering and filling stage features performed best (R² = 0.786, RMSE = 641.470 kg·hm⁻², rRMSE = 15.67%). This study demonstrates that combining hyperspectral data and VIs from different growth stages provides complementary information. These findings provide an effective method for crop yield estimation in precision agriculture. Full article

X-ray crystallography remains the gold standard for high-resolution structural biology, yet obtaining diffraction-quality crystals continues to pose a major bottleneck due to inherently low success rates. This review advocates a paradigm shift from probabilistic screening to rational engineering, reframing crystallization as a controllable self-assembly process. We provide a comprehensive overview of strategies that connect fundamental physicochemical principles to practical applications, beginning with contact design, which involves the active engineering of crystal contacts through surface entropy reduction (SER), introduction of electrostatic patches. Complementing these molecular approaches, we discuss physicochemical strategies that exploit heterogeneous nucleation on functionalized surfaces and gold nanoparticles (AuNPs) to lower the energy barrier for crystal formation. We also address scaffold design, utilizing rigid fusion partners and polymer-forming chaperones to promote crystallization even from low-concentration solutions. Furthermore, we highlight principles for controlling the behavior of multi-component complexes, based on our experimental experience. Finally, we examine de novo lattice design, which leverages AI tools such as AlphaFold and RFdiffusion to program crystal lattices from first principles. Together, these strategies establish an integrated workflow that links thermodynamic stability with crystallizability. Full article

Staggered synthetic aperture radar (SAR) is an innovative concept capable of achieving an ultrawide continuous swath with fine azimuth resolution by variable pulse repetition interval. However, the inherent data gaps and nonuniform sampling introduce severe azimuth artifacts, degrading image quality. Existing methods can mitigate these artifacts but struggle to effectively balance imaging quality and computational cost, especially under low oversampling conditions. To address this challenge, this paper proposes a low-artifact preview image generation method for staggered SAR. First, the artifact characteristics are analyzed through the derivation of a staggered SAR signal model. Then, a three-stage processing framework is introduced, consisting of constant-gradient phase extrapolation, artifact-based inverse filtering, and result fusion. Additionally, data nonuniformity is addressed using a weighted nonuniform fast Fourier transform. Simulation results demonstrate that the proposed method significantly improves processing speed compared to existing techniques while maintaining good imaging quality, making it suitable for rapid scene screening in wide-area SAR applications. Full article

Titanium alloys are widely employed for biomedical implants due to their high strength, biocompatibility, and corrosion resistance, yet their lack of intrinsic antibacterial activity remains a major limitation. Incorporating copper, an antibacterial and β-stabilising element, offers a promising strategy to enhance implant performance. This study investigates Ti-6Al-7Nb modified with 1–9 wt.% Cu via in situ alloying during metal-based laser powder bed fusion (PBF-LB/M), with the aim of assessing processability, microstructural evolution, and mechanical properties. Highly dense samples (>99.9%) were produced across all Cu levels, though chemical homogeneity strongly depended on processing parameters. Increasing Cu content promoted β-phase stabilisation, Ti₂Cu precipitation, and pronounced grain refinement. Hardness and yield strength increased nearly linearly with Cu addition, while ductility decreased sharply at ≥5 wt.% Cu due to intermetallic formation, hot cracking, and brittle fracture. These results illustrate both the opportunities and constraints of rapid alloy screening via PBF-LB/M. Overall, moderate Cu additions of 1–3 wt.% provide the most favourable balance between mechanical performance, manufacturability, and potential antibacterial functionality. These findings provide a clear guideline for the design of Cu-functionalised titanium implants and demonstrate the efficiency of in situ alloy screening for accelerated materials development. Full article

This study is dedicated to summarizing and performing an in-depth analysis of the antioxidant properties of ascorbic acid fatty acid esters. The esterification reaction mechanism of ascorbic acid with palmitic acid, lauric acid, and oleic acid in food systems was elaborated in detail, and its antioxidant mechanism was discussed in depth. The free radical scavenging mechanism and oxidative inhibition effect of two mainstream determination methods, DPPH and ABTS, were analyzed. Esterification, as a core organic synthesis reaction, is widely used in the production of food antioxidants, pharmaceutical ingredients, chemical polymers, and cosmetic oil-based matrices. At the same time, in view of the wide application of machine learning as a multidisciplinary core technology, this paper selects free radical scavenging rate and esterification yield as characteristic parameters and normalizes the offspring into random forest model training to achieve accurate prediction of antioxidant performance. Finally, in the future, it is necessary to expand the data set, optimize the model structure, explore multi-model fusion to improve the prediction effect, and promote the application of machine learning in the screening design of new antioxidants and the optimization of green synthesis processes to promote the intelligent and sustainable development of food antioxidant research. Full article

In the highly volatile realm of global security, the necessity for leading-edge and effectual border resilience tactics has never been more imperative. This PRISMA 2020 guided systematic literature review (SLR) examines the intersection of artificial intelligence (AI), open-source intelligence (OSINT), and social media intelligence (SOCMINT) for enhancing border protection. Our systematic investigation across major databases (IEEE Xplore, Scopus, SpringerLink, MDPI, ACM) and grey literature sources yielded 3932 initial records and, after screening and eligibility assessment, 73 studies and reports from acknowledged organizations, contributing to the evidence synthesis. Three research questions (RQ1–RQ3) were addressed concerning the following: (a) the effectiveness and application of AI in OSINT/SOCMINT for border protection, its (b) data, technical, and operational limitations, and its (c) ethical, legal, and societal implications (GELSI). Evidence matrices summarize the findings, while narrative syntheses underline and thematically group the extracted insights. Results indicate that AI techniques—fluctuating from machine learning (ML) and natural language processing (NLP) to computer vision and emerging large language models (LLMs)—produce quantifiable improvements in forecasting irregular migration, detecting human trafficking, and supporting multimodal intelligence fusion. However, limitations include misinformation, data bias, adversarial vulnerabilities, governance deficits, and sandbox-to-production gaps. Ethical and societal concerns highlight risks of surveillance overreach, discrimination, and insufficient oversight, among others. To our knowledge, this is the first SLR at this intersection. We conclude that, AI-assisted OSINT/SOCMINT presents transformative potential for border protection requiring, nonetheless, balanced governance, robust validation, and future research on LLM/agentic AI, human–AI teaming, and oversight mechanisms. Full article

Limited functional solubility of peroxidases in Escherichia coli (E. coli) remains a pervasive bottleneck for their application in biocatalytic processes such as tryptophan hydroxylation. Here, a peroxidase (Kerl) with poor solubility derived from Candidatus Entotheonella factor was selected as the model enzyme to address this bottleneck. Fusion tag screening identified NusA as the optimal solubility enhancer, enabling soluble expression with preserved activity. Structure-guided mutagenesis was performed to identify residues involved in catalytic enhancement and substrate preference. Variant I284Q exhibited a 2.67-fold increase in catalytic efficiency, and residue R275 was identified as a key determinant of substrate discrimination. Molecular dynamics (MD) simulations were further employed to elucidate the structural basis the improved catalytic performance. This study presents an integrated framework for solubility enhancement and functional optimization of peroxidases. Full article

This work reports the design, fabrication, and validation of a modular ergonomic saddle for rehabilitation cycling, developed through a combined additive manufacturing approach. The saddle consists of a metallic support produced by Laser Powder Bed Fusion (LPBF) in AISI 316L stainless steel and a polymeric ergonomic covering fabricated via Selective Laser Sintering (SLS) using thermoplastic polyurethane (TPU). A preliminary material screening between TPU and polypropylene (PP) was conducted, with TPU selected for its superior elastic response, energy dissipation, and more favourable SLS processability, as confirmed by thermal analyses. A series of gyroid lattice configurations with varying cell sizes and wall thicknesses were designed and mechanically tested. Cyclic testing under both stress- and displacement-controlled conditions demonstrated that the configuration with 8 mm cell size and 0.3 mm wall thickness provided the best balance between compliance and stability, showing minimal permanent deformation after 10,000 cycles and stable force response under repeated displacements. Finite Element Method (FEM) simulations, parameterized using experimentally derived elastic and density data, correlated well with the mechanical results, correlated with the mechanical results, supporting comparative stiffness evaluation. Moreover, a cost model focused on the customizable TPU component confirmed the economic viability of the modular approach, where the metallic base remains a reusable standard. Finally, the modular saddle was fabricated and successfully mounted on a cycle ergometer, demonstrating functional feasibility. Full article

Optical sensing technologies are revolutionizing global food safety surveillance through exceptional sensitivity, rapid response, and high portability. This review systematically evaluates five major platforms, revealing unprecedented detection capabilities from sub-picomolar to single-cell resolution. Surface plasmon resonance achieves $0.021 \mathrm{ng}/\mathrm{mL}$ detection limits for veterinary drugs with superior molecular recognition. Quantum dot fluorescence sensors reach $0.17 \mathrm{nM}$ sensitivity for pesticides, enabling rapid on-site screening. Surface-enhanced Raman scattering attains $0.2 \mathrm{pM}$ sensitivity for heavy metals, ideal for trace contaminants. Laser-induced breakdown spectroscopy delivers multi-elemental analysis within seconds at $0.0011 \mathrm{mg}/\mathrm{L}$ detection limits. Colorimetric assays provide cost-effective preliminary screening in resource-limited settings. We propose a stratified detection framework that strategically allocates differentiated sensing technologies across food supply chain nodes, addressing heterogeneous demands while eliminating resource inefficiencies from deploying high-precision instruments for routine screening. Integration of microfluidics, artificial intelligence, and mobile platforms accelerates evolution toward multimodal fusion and decentralized deployment. Despite advances, critical challenges persist: matrix interference, environmental robustness, and standardized protocols. Future breakthroughs require interdisciplinary innovation in materials science, intelligent data processing, and system integration, transforming laboratory prototypes into intelligent early warning networks spanning the entire food supply chain. Full article

Ammonia is a key factor in the water, impacting the physiological functions of aquatic organisms. To explore the effect of ammonia stress on mature eggs, female A. baerii at the end of the fourth stage of ovarian development were subjected to varying ammonia concentrations (0 mg/L (control, C), 10 mg/L (low concentration, T1), and 50 mg/L (high concentration, T2)) for 96 h. After 96 h of stress, histological analysis revealed that the follicular membranes of group T1 remained intact and clear compared to group C, although the vacuole fusion had begun. In contrast, the T2 group exhibited ruptured follicular membranes and adhered yolk granules compared to the C group, indicating structural damage. Transcriptome analysis generated 97.89 Gb of clean data, with each sample yielding over 6.09 Gb. A total of 5576, 3719, and 9446 differentially expressed genes (DEG_S) were screened from T1 vs. C, T2 vs. C, and T2 vs. T1 comparisons, respectively. Gene Ontology analysis (GO) functional enrichment analysis showed that DEG_S were significantly enriched in multicellular organism processes (T1 vs. C), cell surface receptor signaling pathways (T2 vs. C), and immune system processes (T2 vs. T1) during biological processes. It indicates that ammonia exposure may enrich cellular components in the extracellular space, potentially disrupting the function of the extracellular matrix. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment indicated significant impacts on amino acid metabolism, particularly glutamate and arginine pathways, as well as key pathways involved in steroid biosynthesis and antioxidation. Weighted gene co-expression network analysis (WGCNA) revealed that a total of 26,369 DEGs were divided into 29 distinct modules, displaying obvious associations with their traits. In the T2 vs. C group, antioxidation-related genes such as GST and GCLM were significantly downregulated, and the expressions of key enzymes for steroid synthesis, such as CYP11A1, CYP17, and CYP19A1 were suppressed, indicating that high ammonia nitrogen concentrations impair oocyte function by inducing oxidative stress and disrupting hormone synthesis. This study provides a comprehensive repertoire of candidate genes associated with ammonia stress in the mature egg of A. baerii, which will be useful for development of sturgeon breeding and reproduction. Full article

Detecting surface cracks on sugarcane is a critical step in ensuring product quality control, with detection precision directly impacting raw material screening efficiency and economic benefits in the sugar industry. Traditional methods face three core challenges: (1) complex background interference complicates texture feature extraction; (2) variable crack scales limit models’ cross-scale feature generalization capabilities; and (3) high computational complexity hinders deployment on edge devices. To address these issues, this study proposes a lightweight sugarcane surface crack detection model, SCS-YOLO (Surface Cracks on Sugarcane-YOLO), based on the YOLOv10 architecture. This model incorporates three key technical innovations. First, the designed RFAC2f module (Receptive-Field Attentive CSP Bottleneck with Dual Convolution) significantly enhances feature representation capabilities in complex backgrounds through dynamic receptive field modeling and multi-branch feature processing/fusion mechanisms. Second, the proposed DSA module (Dynamic SimAM Attention) achieves adaptive spatial optimization of cross-layer crack features by integrating dynamic weight allocation strategies with parameter-free spatial attention mechanisms. Finally, the DyHead detection head employs a dynamic feature optimization mechanism to reduce parameter count and computational complexity. Experiments demonstrate that on the Sugarcane Crack Dataset v3.1, compared to the baseline model YOLOv10, our model achieves mAP50:95 to 71.8% (up 2.1%). Simultaneously, it achieves significant reductions in parameter count (down 19.67%) and computational load (down 11.76%), while boosting FPS to 122 to meet real-time detection requirements. Considering the multiple dimensions of precision indicators, complexity indicators, and FPS comprehensively, the SCS—YOLO detection framework proposed in this study provides a feasible technical reference for the intelligent detection of sugarcane quality in the raw materials of the sugar industry. Full article

Amid intensifying global competition, industrial product quality has become a critical determinant of competitive advantage. However, persistent quality-related faults in production environments threaten product integrity. To address this challenge, a Fusion Maximal Information Coefficient-based Quality-Related Kernel Component Analysis (FMIC-QRKCA) methodology is proposed in this paper by capitalizing on information fusion principles and statistical metric theory. Based on information fusion principles, a Fusion Maximal Information Coefficient (FMIC) strategy is first studied to quantify correlations between process variables and multivariate quality indicators. Subsequently, by integrating the proposed FMIC method with Kernel Principal Component Analysis (KPCA), a Quality-Related Kernel Component Analysis (QRKCA) method is proposed. In the proposed QRKCA strategy, the complete latent variable space is first obtained; on this basis, FMIC is further applied to quantify the correlation between each latent variable and quality variables, thereby completing the screening of quality-related latent variables. Additionally, the $T^2$ and squared prediction error monitoring statistics are used as the key indices to determine the occurrence of faults. This integration overcomes the limitation of conventional KPCA, which does not explicitly consider quality indicators during the principal component extraction, thereby enabling precise isolation of quality-related fault features. Validation through the numerical case and the industrial process case demonstrates that FMIC-QRKCA significantly outperforms established methods in detection accuracy for quality-related faults. Full article

This systematic literature review addresses the critical challenge of ensuring robustness and adaptability in AI-based machine health monitoring (MHM) systems. While the field has seen a surge in research, a significant gap exists in understanding how to effectively manage data scarcity, unknown fault types, and the integration of diverse data streams for real-world industrial applications. The problem is magnified by the rarity of failure events, which leads to imbalanced datasets and hampers the generalizability of predictive models. To synthesize the current state of research and identify key solutions, we followed a rigorous, modified PRISMA methodology. A comprehensive search across Scopus, IEEE Xplore, Web of Science, and Litmaps initially yielded 3235 records. After a multi-stage screening process, a final corpus of 85 peer-reviewed studies was selected. Data were extracted and synthesized based on a thematic framework of 13 core research questions. A bibliometric analysis was also conducted to quantify publication trends and research focus areas. The analysis reveals a rapid increase in research, with publications growing from 1 in 2018 to 35 in 2025. Key findings highlight the adoption of transfer learning and generative AI to combat data scarcity, with multimodal data fusion emerging as a crucial strategy for enhancing diagnostic accuracy. The most active research themes were found to be Predictive Maintenance and Edge Computing, with 12 and 10 references, respectively, while critical areas like standardization remain under-explored. Overall, this review shows that AI benefits machine health monitoring but still faces challenges in reproducibility, benchmarking, and large-scale validation. Its main limitation is the focus on English peer-reviewed studies, excluding industry reports and non-English work. Future research should develop standardized datasets, energy-efficient edge AI, and socio-technical frameworks for trust and transparency. The study offers a structured overview, a roadmap for future work, and underscores the importance of AI in Industry 4.0. Full article