Search Results (16,878)

Plants must constantly adapt to biotic and abiotic stressors, which the global climate change crisis has intensified. To monitor plant health and predict their ability to face these challenges, various target molecules, such as hormones, glucose, and reactive oxygen species, are used as proxies for their physiological status. This review provides a systematic assessment of the current state of biosensor technology, an innovative analytical approach designed for in situ, minimally invasive, and real-time monitoring. Using the PICO (Problem, Intervention, Comparison, and Outcome) strategy, relevant research papers were identified. The review highlights how biosensors can detect physiological responses to stress before visual symptoms manifest, offering a significant advantage over traditional, often destructive, laboratory techniques, like gas chromatography–mass spectrometer (GC-MS) or high-performance liquid chromatography (HPLC). These advancements aim to improve precision agriculture and forestry management by providing sustainable methods to assess resilience in changing environments. Finally, the challenges of translating research from model organisms to complex woody species and choosing the correct target are discussed, and future perspectives, including the integration of biosensors with Artificial Intelligence-driven predictive models for large-scale environmental monitoring, are outlined. Full article

Reliable quantification of microplastics in water remains challenging because most Raman-based methods require filtration, drying, or complex flow systems, which can lead to particle loss and signal instability. Here, we propose a simple dual-laser Raman strategy for the direct, real-time quantification of microplastics in water without pretreatment. By simultaneously integrating backscattering and transmission geometries using two identical 532 nm lasers, spatial variations in Raman scattering cross-sections, arising from particle motion and focal depth fluctuations, are effectively mitigated. The dual-laser configuration enhances Raman intensity by approximately 1.5-fold compared with backscattering and threefold compared with transmission alone (p < 0.001), enabling robust real-time detection with a temporal resolution of 0.1 s. Accurate particle counting is demonstrated using polystyrene (PS) standard beads and further validated for polyamide 6 (PA6) and polyvinyl chloride (PVC) particles with irregular morphologies and broad size distributions, with no false-positive events observed. By prioritizing simplicity and quantitative reliability over ultimate size resolution, the proposed strategy provides a practical approach for routine monitoring of microplastics in drinking water and industrial aqueous systems. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

The Industrial Internet of Things (IIoT) has become a cornerstone of modern industrial automation, enabling real-time monitoring, intelligent decision-making, and large-scale connectivity across cyber–physical systems. However, the growing scale, heterogeneity, and dynamic behavior of IIoT environments significantly expand the attack surface and challenge the effectiveness of conventional security mechanisms. In this paper, we propose DeepSense, a hybrid and adaptive anomaly and intrusion detection framework specifically designed for resource-constrained and heterogeneous IIoT deployments. DeepSense integrates three complementary components: DataSense, a realistic data pipeline and experimental testbed supporting synchronized sensor and network data processing; RuleSense, a lightweight rule-based detection layer that provides fast, deterministic, and interpretable anomaly screening at the edge; and NeuroSense, a learning-driven detection module comprising an adaptive ensemble of 22 machine learning and deep learning models spanning classical, neural, hybrid, and Transformer-based architectures. NeuroSense operates as a second detection stage that validates suspicious events flagged by RuleSense and enables both coarse-grained and fine-grained attack classification. To support rigorous and practical assessment, this work further introduces a comprehensive performance evaluation framework that extends beyond accuracy-centric metrics by jointly considering detection quality, latency, resource efficiency, and detection coverage, alongside an optimization-based process for selecting Pareto-optimal model ensembles under realistic IIoT constraints. Extensive experiments across diverse detection scenarios demonstrate that DeepSense exhibits strong generalization, lower false positive rates, and robust performance under evolving attack behaviors. The proposed framework provides a scalable and efficient IIoT security solution that meets the operational requirements of Industry 4.0 and the resilience-oriented objectives of Industry 5.0. Full article

Liquid biopsy has emerged as a transformative tool in oncology, enabling minimally invasive and dynamic characterization of tumor biology. In prostate cancer, marked by high heterogeneity and frequent bone metastases, tissue biopsy is often challenging, highlighting the clinical value of circulating biomarkers. Circulating tumor DNA (ctDNA) is the most clinically advanced analyte, supporting detection of actionable alterations such as BRCA1/2 and ATM mutations, guiding targeted therapies, and enabling real-time monitoring of treatment response and resistance. Circulating tumor cells (CTCs) and extracellular vesicles (EVs) provide complementary insights into tumor biology and disease progression. However, challenges remain, including limited sensitivity in low tumor burden and biological confounders such as clonal hematopoiesis (CH), which can lead to false-positive findings. Emerging approaches, including fragmentomics and methylation profiling, offer improved tumor specificity and may help overcome these limitations. Together, these advances support the integration of liquid biopsy into clinical practice for personalized management and longitudinal monitoring in prostate cancer. Full article

Wheelchair users face significant mobility limitations related to both medical issues (e.g., musculoskeletal strain, pressure ulcers) and urban accessibility challenges. This pilot study introduces a sensor system integrating an inertial measurement unit (IMU), GPS (Global Positioning System), and a pressure-measuring seat to monitor distance travelled, speed, and posture in relation to real-world conditions. Seven participants navigated an approximately 800-metre outdoor course, divided into 13 sections, while real-time data were recorded. The results showed an average speed of 1.24 ± 0.41 m/s with peak speeds of up to 2.67 m/s. The centre of pressure on the seat fluctuated by an average of 25 mm in the x and y directions (left-right: COPx, back-forward: COPy). The data for average speed, COPx, and COPy showed significant differences between most of the 13 sections, with large, very large, and huge effect sizes. Comparing the speed, COPx, and COPy data with respect to distance travelled, and correlating them between the seven participants by applying the rank-sum method to the mean R² and calculating Kendall’s W, revealed that speed, COPx, and COPy were influenced by course conditions (R² medians between 0.013 and 0.499; W = 0.7857, strong agreement; χ²p = 0.0281). Small R² values indicate more individualised participant behaviour, while large R² values highlight the stronger influence of course conditions on the parameters. This non-invasive and cost-effective system provides objective motion data that can be used for future research in wheelchair design and rehabilitation strategies. Despite its advantages, this study was limited to able-bodied participants, so further clinical trials with individuals with mobility impairments are needed. Full article

Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R²) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively. Full article

This review comprehensively examines the incorporation of nanoparticles (NPs) into biopolymers for 3D printing in biomedical applications, integrating material design, processing strategies, and translational considerations within a unified framework. Different types of NPs are analyzed regarding their effects on mechanical reinforcement, rheological modulation, and structural organization of biopolymeric matrices. The discussion covers principal additive manufacturing technologies, including extrusion-based systems such as fused deposition modeling (FDM) and direct ink writing (DIW), vat photopolymerization, powder-bed fusion (SLS), and emerging in situ nanoparticle formation approaches, emphasizing how nanoparticle loading and surface functionalization govern yield stress, shear-thinning behavior, viscoelastic recovery, and dimensional fidelity while mitigating agglomeration and optimizing interfacial interactions. Comparative evaluation of compressive modulus, strength, toughness, crystallinity, and porosity establishes structure–property–processing relationships directly linked to printability and functional performance. Biomedical applications are addressed in tissue engineering, biosensing, controlled and targeted drug delivery, and bioimaging, highlighting the balance between bioactivity and manufacturability. Finally, critical challenges—including compatibility, reproducibility, biological safety, long-term stability, regulatory adaptation, and environmental impact—are discussed, alongside future perspectives focused on green nanomaterials, AI-driven predictive formulation design, and digital twins for real-time monitoring and quality control in nano-enabled additive manufacturing. Full article

The precise fabrication and controllable actuation of magnetic microspheres hold significant application value in biomedicine, microfluidic chips and other fields. Based on femtosecond laser two-photon polymerization technology (FLTPP), two methods are adopted to prepare magnetic microspheres in this study. Magnetic microspheres are fabricated via photoresist modification and post-treatment processes. Meanwhile, a 3D magnetic actuation system composed of a three-axis movable magnetic drive module and a real-time imaging system is constructed, enabling the flexible 3D actuation and real-time dynamic monitoring and visualized observation of magnetic microspheres. The results demonstrate that the magnetic microspheres exhibit sensitive magnetic response characteristics. The constructed magnetic actuation system features large travel range (XY: ±6.5 mm, Z: 10 mm), high precision (20 μm) and flexible manipulation, enabling stable locomotion of the microrobots in straight channels, L-shaped channels, and square channels. This study provides a technical reference for the fabrication and manipulation of magnetic micro/nano devices, and lays a foundation for their subsequent integrated applications in microfluidic systems. Full article

Machine condition monitoring increasingly depends on distributed sensing, edge intelligence, and cloud analytics, yet timely and trustworthy health assessment remains constrained by latency, bandwidth, privacy, and reliability requirements. Cloud-only architectures provide scalable computation and historical data integration but often fail to satisfy real-time industrial needs, whereas edge-only deployments are limited by restricted computing resources and fragmented local knowledge. Edge–cloud collaboration has, therefore, emerged as a practical architecture for distributing perception, inference, learning, and coordination across hierarchical industrial systems. This review examines 147 publications on edge–cloud collaboration for machine condition monitoring published between 2019 and February 2026. A four-dimensional taxonomy is developed to organize the literature into model-centric, data-centric, resource and task-centric, and architecture and trust-centric mechanisms, while 13 survey and review papers are considered separately for contextual comparison. On this basis, the review analyzes representative collaboration mechanisms and enabling technologies, with particular attention to federated learning, transfer learning, knowledge distillation, digital twins, and deep reinforcement learning, and surveys their deployment in manufacturing, energy, transportation, and infrastructure monitoring scenarios. The literature remains dominated by model-centric collaboration, while architecture and trust-centric studies increasingly provide the system foundations required for practical deployment. The review further identifies major open challenges, including robust generalization under changing operating conditions, efficient data transmission, real-time resource coordination, interoperability, and trustworthy large-scale deployment, and outlines future directions in foundation-model-based edge–cloud collaboration, continual learning, dual digital twins, trustworthy collaboration, and privacy-preserving industrial ecosystems. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

Plant extracts are widely used as natural pesticides, cosmetic ingredients, and in pharmaceutical applications. However, their poor water solubility and stability limit their usability. Lipid nanoparticles (LNPs) offer an effective encapsulation strategy to overcome these challenges. This study demonstrates the encapsulation of three representative substances from these industries: quercetin as a pesticide, irones as a cosmetic ingredient, and nucleic acids for pharmaceutical use. Ultrasonic treatment was used for the encapsulation of quercetin and irones, and a concept for continuous encapsulation in a plug flow reactor was proposed for process intensification. Inline multi-angle light scattering and dynamic light scattering measurements proved effective for real-time monitoring and enabled the replacement of traditional batch measurements. In the pharmaceutical area, mRNA-based therapies require LNP encapsulation to prevent nucleic acid degradation. Plant-based β-sitosterol was used as an alternative helper lipid to cholesterol, resulting in an average particle diameter of 72 nm and an encapsulation efficiency of 91%, comparable to commercial formulations such as the Comirnaty vaccine. Furthermore, a novel process model based on population balances was developed to simulate the entire manufacturing process, from rapid mixing in a T-mixer to particle stabilization via buffer exchange during diafiltration. By applying a quantitative and distinctive model validation workflow, the model was shown to be as accurate and precise as the experimental data, enabling its use as a digital twin for autonomous continuous operation. In summary, this study contributes to reducing the facility footprint and cost of goods through the implementation of continuous processing and model-based control. This approach improves productivity by 20% and reduces process time by a factor of two. Full article

As complex agricultural machinery, traditional combine harvesters face numerous challenges during operation due to their reliance on manual observation. To meet the demands of modern agriculture, intelligent combine harvesters have emerged. Intelligent sensing uses multi-sensor fusion and deep learning to monitor crop lodging, feed rate, loss rate, and impurity content. Under suboptimal conditions, multi-source fusion strategies improve perception reliability. Information processing and decision-making enable dynamic optimization of operational parameters and reduce harvest losses. Multi-machine coordination transforms single-machine operations into fleet control, while remote monitoring leverages a cloud edge collaboration architecture to enable status visualization, remote control, and predictive maintenance for faults. Unmanned operations utilize high-precision positioning and intelligent path planning to improve fleet efficiency and field coverage. However, the field still faces common challenges, including insufficient real-time processing capabilities for multi-source heterogeneous data, poor adaptability to complex agronomic scenarios, and limited economic feasibility. In this review, we examine the complete technology chain, which includes intelligent perception, intelligent decision-making and coordination, remote monitoring, and unmanned operations. We conduct a comparative analysis of the current state of these systems and the challenges they face, providing a systematic reference for future research and industrial applications. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article