Search Results (215)

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of $92.37\%$, a Precision of $89.41\%$, a Recall of $88.26\%$, an F1-score of $88.83\%$, an AUC of $93.06\%$, and a PR-AUC of $89.15\%$ in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of $87.95\%$, $86.42\%$, $86.88\%$, $84.57\%$, $86.73\%$, and $83.91\%$ under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and $20\%$ and $30\%$ malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

Fruit harvesting systems are undergoing a paradigm shift toward sustainable and energy-efficient mechanized platforms driven by robotics, artificial intelligence, and advanced sensing technologies. This review synthesizes recent engineering developments in fruit harvesting, focusing on system architecture, fruit detachment mechanics, and mechanized harvesting strategies. It examines harvesting classifications, mechanical principles governing detachment, and pre-harvest factors affecting performance, along with principal mechanisms including shaking, cutting, and alternative detachment techniques. Post-detachment handling and fruit recovery processes are also analyzed, together with economic and sustainability-related trade-offs between manual and mechanized harvesting systems. Recent progress in robotic harvesting systems, machine vision, and multi-sensor fusion is evaluated within the framework of smart orchard engineering, with increasing emphasis on energy-efficient design, resource optimization, reduced postharvest losses, and environmental sustainability as key performance drivers. Despite these advancements, current technologies remain constrained by fruit damage susceptibility, biological variability, limited cross-crop adaptability, and high implementation costs, limiting large-scale adoption in commercial orchards. The novelty of this review lies in establishing a unified engineering framework that links mechanical detachment principles with robotic systems and intelligent sensing technologies under an energy-efficient sustainability perspective, enabling a system-level understanding of harvesting performance and supporting the development of next-generation adaptive and sustainable fruit harvesting systems. Full article

Bionic sensors are emerging as powerful analytical platforms driving the development of next-generation detection technologies, particularly for small molecule sensing in complex environmental and biological systems. However, accurate and selective detection of small molecules remains fundamentally challenging due to their low molecular weight, limited structural specificity, and strong interference from complex matrices. This review provides a comprehensive overview of recent advances in bionic sensor technologies, focusing on how the integration of synthetic biology, nanomaterials, and artificial intelligence (AI) addresses these limitations. Key biorecognition elements, including enzymes, antibodies, aptamers, and molecularly imprinted polymers, are examined for their suitability in small molecule sensing applications. Advances in nanomaterials such as graphene, carbon nanotubes, quantum dots, and MXenes are discussed in relation to signal transduction enhancement, sensitivity improvement, and device miniaturization. In parallel, the roles of AI and machine learning in signal denoising, adaptive calibration, and molecular fingerprinting for complex datasets are highlighted. Applications in wearable and implantable biosensors, environmental monitoring, and food safety are analyzed, emphasizing real-time detection of metabolites, pollutants, and toxins. Key challenges associated with AI-driven systems, including scalability, cost, data reliability, and ethical concerns, are also discussed. Emerging trends such as hybrid sensing platforms, self-powered biosensors, and secure data integration frameworks are presented as future directions. This review aims to provide a problem-driven perspective on how next-generation bionic sensors can overcome current limitations and enable robust small molecule detection in real-world applications. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

Smart museums increasingly rely on digital media, interactive installations, artificial intelligence, augmented reality, and virtual reality to support cultural communication and visitor engagement. However, existing studies have mainly examined specific technologies, usability, or visitor satisfaction, while a systematic and quantitative framework for comparing interactive experience across different smart museums remains limited. To address this gap, this study proposes a hybrid multi-criteria decision-making framework for evaluating smart museum interactive experience. Based on the Strategic Experiential Modules, an evaluation system consisting of five dimensions—Sense, Feel, Think, Act, and Relate—and sixteen indicators was constructed. The Analytic Hierarchy Process was used to determine subjective weights from expert judgments, the entropy method was applied to capture the data-driven dispersion characteristics of expert evaluation data, and a game-theoretic combination weighting strategy was used to integrate the two weighting results. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to compare five representative smart museum cases. The results show that Zhejiang Provincial Museum achieved the highest relative closeness value (C_i = 0.9891), followed by Shanghai Museum (C_i = 0.8457) and Hunan Museum (C_i = 0.5326). Robustness analysis further showed that the ranking order remained consistent under entropy weights, AHP weights, average weights, and game-theoretic combined weights. The Friedman test indicated no significant difference in the relative closeness coefficients across weighting schemes (χ² = 1.200, p = 0.753). These findings indicate that the proposed framework can effectively identify relative strengths and weaknesses in smart museum interactive experience and provide a replicable decision-support tool for experience-oriented museum design and optimization. Full article

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence. Full article

The COVID-19 pandemic exposed critical limitations in conventional screening protocols, particularly in high-traffic environments where rapid, accurate, and contactless health assessment became essential to mitigate transmission risks. In response, this study presents the development of an Artificial Intelligence-Enhanced Contactless Screening Kiosk (AICS-K) that integrates multimodal sensing, embedded systems engineering, and machine learning into a unified workflow. Utilizing a Raspberry Pi platform with computer vision, thermal sensing, QR-based contact tracing, and intelligent control logic, the system enables efficient real-time screening while minimizing human intervention. The proposed architecture demonstrates the potential of extensible, affordable AI-driven solutions for early signs detection and institutional health resilience. Full article

This review provides a comprehensive synthesis of robotic fruit harvesting systems, with a particular focus on the system-level integration of perception, manipulation, and fruit detachment within autonomous harvesting environments. Recent advances in machine vision, deep learning, sensor fusion, robotic end-effectors, grasping strategies, and motion planning are critically analyzed alongside cutting, pulling, and vibration-based detachment mechanisms under unstructured orchard conditions. Beyond component-level analysis, this review emphasizes the critical role of perception–action coupling and highlights key system integration challenges, including localization errors, perception-to-action latency, and environmental variability, which continue to limit reliable field deployment. In addition, orchard and pre-harvest-related factors such as canopy structure, fruit distribution, and detachment force variability are examined in relation to their direct impact on system performance, robustness, and harvesting efficiency. Furthermore, the review extends toward system-level considerations by incorporating performance evaluation metrics, economic feasibility, and scalability constraints, which are essential for transitioning robotic harvesting systems from experimental prototypes to commercially viable solutions, including practical field deployment in distributed and multi-robot harvesting systems. Emerging technologies, including artificial intelligence, advanced sensing, digital agriculture, and energy-aware system design, are discussed as key enablers for achieving adaptive, data-driven, and scalable autonomous harvesting. The novelty of this work lies in proposing an integrated framework that explicitly links perception, manipulation, and detachment with orchard-level constraints and deployment requirements, thereby bridging the gap between algorithmic advancements and real-world implementation of autonomous fruit harvesting systems. Full article

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection. Full article

Hyperscale data center construction has become one of the largest concentrated capital flows in the global built-environment sector, with global hyperscale capital expenditure exceeding USD 230 billion in 2024 and the leading hyperscalers committing more than USD 325 billion for 2025. Generative artificial intelligence workloads have driven a structural shift in mechanical and electrical design, increased delivery-pace demands, and concentrated supply-chain pressure on a small number of specialist general contractors. This paper develops and tests a microfoundational model of dynamic capabilities for hyperscale data center project delivery using fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of N = 18 primarily North American hyperscale-active general contractors. Capabilities are formalized as fuzzy sets X ⊆ Ω with membership functions μ_X: Ω → [0, 1] over the case space Ω; necessity and sufficiency consistency are computed using the canonical formulas Cons_N(X→Y) = Σ_i min(μ_X(i), μ_Y(i))/Σ_i μ_Y(i) and Cons_S(C→Y) = Σ_i min(μ_C(i), μ_Y(i))/Σ_i μ_C(i), and necessity is interpreted as the fuzzy-set containment PDP_high ⊆ X. Capability scores are coded from secondary public sources—ENR rankings, SEC filings, trade press, and company disclosures—rather than from primary survey or interview data. Three findings emerge: process microfoundations and seizing capability are necessary conditions for both project delivery performance and competitive advantage (Cons_N ≥ 0.97); the dominant causal recipe for high project delivery performance is the conjunction SEN ∩ SEI ∩ TRA ∩ ED with consistency 1.000 and coverage 0.771; the dominant recipe for high sensing capability is the conjunction IND ∩ PRO ∩ STR ∩ INT with consistency 1.000 and coverage 0.799. The findings are positioned against prior dynamic-capabilities studies and microfoundations theory, confirming convergence on conjunctive bundle structure while extending literature into a previously unstudied AEC segment with sharper containment relations. Full article

Driven by the rapid advancement of artificial intelligence (AI) workloads, the demand for high-bandwidth and energy-efficient die-to-die (D2D) wireline communication has significantly increased. The article presents an original lumped-parameter model for the analysis of ultra-short-reach (USR) channels in D2D links. The model leads to a length-dependent crosstalk theory showing that the far-end crosstalk (FEXT) and near-end crosstalk (NEXT) exhibit approximately identical waveform amplitudes and pulse widths, differing only in polarity. Based on the property, a 32 Gb/s single-ended transmitter (TX) incorporating a TX-based-only adaptive crosstalk cancellation (XTC) scheme is presented. The proposed scheme eliminates receiver-side FEXT sensing, thereby simplifying calibration logic and reducing power consumption. Implemented in 28 nm CMOS, post-layout simulations demonstrate an energy efficiency of 0.41 pJ/bit at 32 Gb/s and more than 60% reduction in crosstalk-induced jitter (CIJ) across channel lengths of 0.5–2 mm and pitches of 5–20 µm. Full article

Ultraviolet (UV) light is emerging as an important tool for biosensing, biomedical signal readout, and dose monitoring because of its strong and selective interactions with nucleic acids, proteins, and other biological components. This review summarizes recent progress in UV sensing-guided biomedical systems, with emphasis on three interconnected directions: label-free and surface-weighted imaging, wearable and embedded UV dosimetry, and sensor-assisted therapeutic guidance. Representative examples include ultraviolet photoacoustic microscopy (UV-PAM) for label-free nuclear imaging, microscopy with ultraviolet surface excitation (MUSE) for rapid slide-free histology-like readout, epidermal and flexible UV dosimeters for skin-level exposure quantification, and UV therapeutic platforms that are increasingly supported by sensing, dosimetry, and feedback for safer dose delivery. Across these applications, we emphasize the shared biosensing principles of signal generation, optical or acoustic transduction, quantitative readout, calibration, and feedback-informed decision support. We also discuss the role of artificial intelligence in virtual staining, image enhancement, domain correction, dose prediction, and decision support. The review concludes with key translational challenges in standardization, uncertainty quantification, multimodal integration, and feedback-driven system design. Overall, this sensing-centered perspective helps define the role of UV technologies more clearly within biosensors-oriented biomedical engineering. Full article

Modern distributed computing systems face increasingly complex architectural evolution and potentially costly failures, calling for efficient and robust automated diagnosis to ensure the stability of large-scale data processing. Existing data-driven approaches are constrained by scarce labeled data and black-box behaviors, while expert-based knowledge-driven solutions suffer from high construction costs and insufficient coverage of dynamic scenarios, especially when domain expertise is limited. This work proposes a fault diagnosis framework that integrates a unified causal graph (UCG) with large language models (LLMs), leveraging a dual knowledge-driven and data-driven mechanism to construct causal graph representations and dynamically generate structured diagnostic reasoning chains-of-thought based on system state awareness. Here, “causal” is used in a restricted sense, combining knowledge-driven dependencies with data-driven statistical regularities. Experimental results indicate that, using GPT-4o as an example, this study achieves accurate fault identification across the eight evaluated fault scenarios within the controlled evaluation scope of this study. Labeled instances are partitioned using stratified sampling into 80% for training and 20% for held-out evaluation; the procedure is repeated five times with independent train–test partitions, and reported matching rates are averaged across these runs. Compared with baselines that rely solely on fault information or on symptom information, the fault matching rate improves by 41.4% and 33.5%, respectively. By tightly coupling structured causal logic with generative artificial intelligence, the approach significantly enhances the interpretability and reliability of the diagnostic process and provides high-value, expert-level support for intelligent operations and maintenance (O&M) in distributed computing systems. Full article

Rice wine fermentation involves complex biochemical dynamics that challenge traditional empirical control, highlighting the need for precise analytical characterization. This narrative review synthesizes the technological evolution of metabolomics from a descriptive tool to a driver of intelligent biomanufacturing. The progression from first-generation compositional profiling to third-generation strategies integrating high-resolution mass spectrometry, real-time sensing, multi-omics approaches, and artificial intelligence is delineated. This evolution has shifted research focus from static component cataloging to dynamic pathway elucidation, enabling deeper interpretation of flavor biosynthesis, functional metabolite formation, and accumulation of safety-related metabolites. Furthermore, this review critically analyzes how multi-omics integration reveals microbiome-metabolite interactions and provides mechanistic targets for quality regulation. Despite these advances, a gap remains between laboratory-scale analytical capabilities and industrial implementation. Key translational bottlenecks are identified, and a future roadmap toward AI-driven digital twin systems and real-time adaptive control is proposed. This framework positions metabolomics not merely as an analytical technique, but as a key foundation of next-generation smart fermentation strategies. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article