Search Results (610)

Coagulation–flocculation, historically reliant on simple inorganic salts, has evolved into a technically sophisticated process that is central to the removal of turbidity, suspended solids, organic matter, and an expanding array of micropollutants from complex wastewaters. This review synthesizes six decades of research, charting the transition from classical aluminum and iron salts to high-performance polymeric, biosourced, and hybrid coagulants, and examines their comparative efficiency across multiple performance indicators—turbidity removal (>95%), COD/BOD reduction (up to 90%), and heavy metal abatement (>90%). Emphasis is placed on recent innovations, including magnetic composites, bio–mineral hybrids, and functionalized nanostructures, which integrate multiple mechanisms—charge neutralization, sweep flocculation, polymer bridging, and targeted adsorption—within a single formulation. Beyond performance, the review highlights persistent scientific gaps: incomplete understanding of molecular-scale interactions between coagulants and emerging contaminants such as microplastics, per- and polyfluoroalkyl substances (PFAS), and engineered nanoparticles; limited real-time analysis of flocculation kinetics and floc structural evolution; and the absence of predictive, mechanistically grounded models linking influent chemistry, coagulant properties, and operational parameters. Addressing these knowledge gaps is essential for transitioning from empirical dosing strategies to fully optimized, data-driven control. The integration of advanced coagulation into modular treatment trains, coupled with IoT-enabled sensors, zeta potential monitoring, and AI-based control algorithms, offers the potential to create “Coagulation 4.0” systems—adaptive, efficient, and embedded within circular economy frameworks. In this paradigm, treatment objectives extend beyond regulatory compliance to include resource recovery from coagulation sludge (nutrients, rare metals, construction materials) and substantial reductions in chemical and energy footprints. By uniting advances in material science, process engineering, and real-time control, coagulation–flocculation can retain its central role in water treatment while redefining its contribution to sustainability. In the systems envisioned here, every floc becomes both a vehicle for contaminant removal and a functional carrier in the broader water–energy–resource nexus. Full article

A taste sensor composed of a lipid/polymer membrane using tetradodecylammonium bromide (TDAB) as the lipid and modified with 2,6-dihydroxyterephthalic acid (2,6-DHTPA) has recently been reported to exhibit high sensitivity and selectivity toward the umami substance monosodium L-glutamate (MSG). In this study, we aimed to investigate whether this sensor can also detect another umami substance, inosine monophosphate (IMP), and whether it can evaluate the umami synergistic effect—an enhancement of umami intensity—observed when IMP is mixed with MSG. Furthermore, ¹H-NMR analysis was conducted to examine the nature of interactions between the membrane modifier and umami substances. The results demonstrated that IMP can be successfully detected using the sensor, and that, as previously reported for MSG, sensor sensitivity is influenced by the presence or absence of intramolecular hydrogen bonding within the modifier and intermolecular hydrogen bonding between the modifier and the umami substance. In addition, the response to mixed solutions of MSG and IMP was greater than the sum of individual responses, indicating that the umami synergistic effect can be evaluated using the taste sensor. NMR measurements also revealed that the presence of the membrane modifier enhances the interaction between IMP and MSG, supporting the observed synergistic effect. Full article

This study presents a novel ratiometric fluorescent sensor based on Förster resonance energy transfer (FRET) between glutathione (GSH)-coated CdTe quantum dots (CdTe/GSH QDs) and bovine serum albumin (BSA)-coated Au nanoclusters (AuNCs/BSA) for dopamine (DA) detection. The nanoparticles were characterized using transmission electron microscopy (TEM), zeta potential measurements, Fourier transform infrared (FTIR) spectroscopy, UV-Vis absorption and fluorescence spectroscopy. Key FRET parameters, including energy transfer efficiency (E), donor–acceptor distance (r), Förster distance (R₀), and the overlap integral (J), were determined. The interactions between the CdTe/GSH-AuNCs/BSA conjugate and DA were investigated, revealing a dual mechanism of QDs fluorescence quenching that involves both energy and electron transfer. The average lifetime values and spectral profiles of CdTe/GSH QDs, both in the absence and presence of DA, suggest a dynamic fluorescence quenching process. The variation in the ratiometric signal with increasing DA concentration demonstrated a linear response within the range of 0–250 µM, with a correlation coefficient of 0.9963 and a detection limit of 6.9 nM. This proposed nanosensor exhibited selectivity against potential interfering substances, including urea, glucose, BSA, GSH, citric acid, and metal ions such as Na⁺ and Ca²⁺. The conjugate also demonstrates excellent cytocompatibility and enhances cell proliferation in HeLa epithelial cells, making it suitable for biological applications. It was successfully employed for DA detection in urine samples, achieving recoveries ranging from 99.1% to 104.2%. The sensor is highly sensitive, selective, rapid, and cost-effective, representing a promising alternative for DA detection across various sample types. Full article

Achieving high-precision anomaly detection with incomplete sensor data is a critical challenge in industrial automation and intelligent manufacturing. This incompleteness often results from sensor failures, environmental interference, occlusions, or acquisition cost constraints. This study explicitly targets both types of incompleteness commonly encountered in industrial multimodal inspection: (i) incomplete sensor data within a given modality, such as partial point cloud loss or image degradation, and (ii) incomplete modalities, where one sensing channel (RGB or 3D) is entirely unavailable. By jointly addressing intra-modal incompleteness and cross-modal absence within a unified cross-distillation framework, our approach enhances anomaly detection robustness under both conditions. First, a teacher–student cross-modal distillation mechanism enables robust feature learning from both RGB and 3D modalities, allowing the student network to accurately detect anomalies even when a modality is missing during inference. Second, a dynamic voxel resolution adjustment with edge-retention strategy alleviates the computational burden of 3D point cloud processing while preserving crucial geometric features. By jointly enhancing robustness to missing modalities and improving computational efficiency, our method offers a resilient and practical solution for anomaly detection in real-world manufacturing scenarios. Extensive experiments demonstrate that the proposed method achieves both high robustness and efficiency across multiple industrial scenarios, establishing new state-of-the-art performance that surpasses existing approaches in both accuracy and speed. This method provides a robust solution for high-precision perception under complex detection conditions, significantly enhancing the feasibility of deploying anomaly detection systems in real industrial environments. Full article

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation. Full article

The spatial quantification of multiple sources within the urban environment is crucial for understanding urban air quality and implementing measures to mitigate air pollution levels. At the same time, emissions from road traffic contribute significantly to these concentrations. However, uncertainties arise when assessing the contribution of multiple sources affecting a single receptor. This study aims to evaluate an inverse dispersion modelling methodology that combines Computational Fluid Dynamics (CFD) simulations with the Metropolis–Hastings Markov Chain Monte Carlo (MCMC) algorithm to quantify multiple traffic emissions at the street scale. This approach relies solely on observational data and prior information on each source’s emission rate range and is tested within the Augsburg city centre. To address the absence of extensive measurement data of a real pollutant correlated with traffic emissions, a synthetic observational dataset of a theoretical pollutant, treated as a passive scalar, was generated from the forward dispersion model, with added Gaussian noise. Furthermore, a sensitivity analysis also explores the influence of sensor configuration and prior information on the accuracy of the emission estimates. The results indicate that, when the potential emission rate range is narrow, high-quality predictions can be achieved (ratio between true and estimated release rates, $\mathsf{\Delta }q\le 2$) even with networks using data from only 10 sensors. In contrast, expanding the allowable emission range leads to reduced accuracy ($2\le \mathsf{\Delta }q\le 6$), particularly in networks with fewer than 50 sensors. Further research is recommended to assess the methodology’s performance using real-world measurements. Full article

This paper presents a novel gateway-centric architecture for context-aware trust evaluation in Internet of Battle Things (IoBT) environments. The system is structured across multiple layers, from embedded sensing devices equipped with internal modules for signal filtering, anomaly detection, and encryption, to high-level data processing in a secure cloud infrastructure. At its core, the gateway evaluates the trustworthiness of sensor nodes by computing reputation scores based on behavioral and contextual metrics. This design offers operational advantages, including reduced latency, autonomous decision-making in the absence of central command, and real-time responses in mission-critical scenarios. Our system integrates supervised learning, specifically Decision Tree Regression (DTR), to estimate reputation scores using features such as transmission success rate, packet loss, latency, battery level, and peer feedback. The results demonstrate that the proposed approach ensures secure, resilient, and scalable trust management in distributed battlefield networks, enabling informed and reliable decision-making under harsh and dynamic conditions. Full article

Basketball is a high-intensity, intermittent sport in which physical demands fluctuate depending on different contextual variables. Most studies addressed these demands in isolation without integrative approaches. Therefore, the present study aimed to identify key variables explaining players’ physical workload across game quarters and playing positions through principal component analysis (PCA). Ninety-four elite U18 male basketball players were registered during the EuroLeague Basketball ANGT Finals using WIMU PRO™ multi-sensor wearable devices that integrate local positioning systems (LPS) and microelectromechanical systems (MEMS). From over 250 recorded variables, 31 were selected and analyzed by PCA for dimensionality reduction, analyzing the effects of game quarter and playing position. Five to eight principal components explained 61–73% of the variance per game quarter, while between four and seven components explained 64–69% per playing position. High-intensity variables showed strong component loadings in early quarters, with explosive distance (loading = 0.898 in total game, 0.645 in Q1) progressively declining to complete absence in Q4. Position-based analysis revealed specific workload profiles: guards required seven components to explain 69.25% of the variance, with complex movement patterns, forwards showed the highest explosive distance loading (0.810) among all positions, and centers demonstrated concentrated power demands, with PC1 explaining 34.12% of the variance, dominated by acceleration distance (loading = 0.887). These findings support situational and individualized training approaches, allowing coaches to design individual training programs, adjust rotation strategies during games, and replicate demanding scenarios in training while minimizing injury risk. Full article

This study reports on the direct electron transfer (DET) ability of the enzyme spermidine dehydrogenase (SpDH) and its use in a DET-type enzymatic sensor for detecting spermine. SpDH was found to exhibit internal electron transfer from its cofactor, flavin adenine dinucleotide (FAD), to heme b. This was confirmed by observing the heme b-derived reduction peak at 560 nm in the presence of spermine, the substrate. SpDH was immobilized on a gold electrode via a dithiobis (succinimidyl hexanoate) self-assembled monolayer. The cyclic voltammetry analysis of the SpDH-immobilized gold electrode revealed an increased oxidation current in the presence of 0.1 mM spermine with an onset potential of −0.14 V vs. Ag/AgCl in the absence of an additional external electron acceptor. This result confirmed that SpDH is capable of DET. Chronoamperometric analyses were conducted using an SpDH-immobilized gold electrode with spermine as the substrate under a 0 V oxidation potential vs. Ag/AgCl using an artificial saliva matrix containing 10 µM ascorbic acid and 100 µM uric acid. The sensor exhibited good linear correlation between the current increase and spermine concentration from 0.2 to 2.0 µM, with a limit of detection of 0.084 µM, which encompasses the physiologically relevant spermine concentration found in the saliva. Primary structure alignments and 3D structure predictions revealed that all SpDH homologs possess two conserved histidine residues in the same location on the surface as the heme b ligand of SpDH. This indicates their potential for DET-ability with an electrode. Full article

The integration of unmanned aerial vehicles (UAVs) and deep learning (DL) has significantly advanced crop disease detection by enabling scalable, high-resolution, and near real-time monitoring within precision agriculture. This systematic review analyzes peer-reviewed literature indexed in the Web of Science Core Collection as articles or proceeding papers through 2024. The main selection criterion was combining “unmanned aerial vehicle*” OR “UAV” OR “drone” with “deep learning”, “agriculture” and “leaf disease” OR “crop disease”. Results show a marked surge in publications after 2019, with China, the United States, and India leading research contributions. Multirotor UAVs equipped with RGB sensors are predominantly used due to their affordability and spatial resolution, while hyperspectral imaging is gaining traction for its enhanced spectral diagnostic capability. Convolutional neural networks (CNNs), along with emerging transformer-based and hybrid models, demonstrate high detection performance, often achieving F1-scores above 95%. However, critical challenges persist, including limited annotated datasets for rare diseases, high computational costs of hyperspectral data processing, and the absence of standardized evaluation frameworks. Addressing these issues will require the development of lightweight DL architectures optimized for edge computing, improved multimodal data fusion techniques, and the creation of publicly available, annotated benchmark datasets. Advancements in these areas are vital for translating current research into practical, scalable solutions that support sustainable and data-driven agricultural practices worldwide. Full article

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms. Full article

The diverse usage of Unmanned Aerial Vehicles (UAVs) across commercial, military, and civil domains has significantly heightened the need for robust cybersecurity mechanisms. Given their reliance on wireless communications, real-time control systems, and sensor integration, UAVs are highly susceptible to cyber intrusions that can disrupt missions, compromise data integrity, or cause physical harm. This paper presents a comprehensive literature review of Intrusion Detection Systems (IDSs) that leverage artificial intelligence (AI) to enhance the security of UAV and UAV swarm environments. Through rigorous analysis of recent peer-reviewed publications, we have examined the studies in terms of AI model algorithm, dataset origin, deployment mode: centralized, distributed or federated. The classification also includes the detection strategy: online versus offline. Results show a dominant preference for centralized, supervised learning using standard datasets such as CICIDS2017, NSL-KDD, and KDDCup99, limiting applicability to real UAV operations. Deep learning (DL) methods, particularly Convolutional Neural Networks (CNNs), Long Short-term Memory (LSTM), and Autoencoders (AEs), demonstrate strong detection accuracy, but often under ideal conditions, lacking resilience to zero-day attacks and real-time constraints. Notably, emerging trends point to lightweight IDS models and federated learning frameworks for scalable, privacy-preserving solutions in UAV swarms. This review underscores key research gaps, including the scarcity of real UAV datasets, the absence of standardized benchmarks, and minimal exploration of lightweight detection schemes, offering a foundation for advancing secure UAV systems. Full article

This paper makes two distinct contributions to the security and federated learning communities. First, we identify and empirically demonstrate a critical vulnerability in Krum, a widely deployed Byzantine-resilient aggregation algorithm, showing catastrophic failure (44.7% accuracy degradation) when applied to high-dimensional neural networks. We provide comprehensive analysis of five alternative algorithms and validate FLTrust as a more resilient solution, though requiring trusted infrastructure. This finding has immediate implications for production federated learning systems. Second, we present a rigorous feasibility analysis of quantum-enhanced security operations through simulation-based exploration. We document fundamental deployment barriers including (1) environmental electromagnetic interference exceeding sensor capabilities by 6-9 orders of magnitude, (2) infrastructure costs of USD 3–5M with unproven benefits, (3) an absence of validated correlation mechanisms between quantum measurements and cyber threats, and (4) O($n^2$) scalability constraints limiting deployments to 20 nodes. This is purely theoretical research using simulated data without physical quantum sensors. Physical validation through empirical noise characterization and sensor deployment in operational environments represents the critical next step, though faces significant challenges from EMI shielding requirements and calibration procedures. Together, these contributions provide actionable insights for current federated learning deployments while preventing premature investment in quantum sensing for cybersecurity. Full article

The object of the research is the process of intelligent monitoring and diagnosis of animal health using IoT technology in the context of a digital farm. The problem lies in the absence of an integrated approach that can provide near-real-time assessment of an animal’s physiological and behavioral state, predict potential health risks, and adapt decision-making algorithms to specific species and environmental conditions. Traditional monitoring methods rely heavily on periodic manual inspection and limited sensor data, which reduces the timeliness and accuracy of diagnostics, especially for large-scale farms. To address this issue, a comprehensive model is proposed that integrates an IoT-based tag device for livestock, a data collection and transmission system, and an intelligent analysis module. The system utilizes statistical profiling to create baseline health parameters for each animal, applies anomaly detection methods to identify deviations, and leverages machine learning algorithms to predict health deterioration. The novelty of the approach lies in the combination of individualized baseline modeling, continuous sensor-based monitoring, and adaptive decision-making for early intervention. The approach scales across farm sizes and multi-sensor setups, making it practical for precision livestock farming. From a sustainability perspective, the approach enables earlier and more targeted interventions that can reduce unnecessary treatments, avoid preventable productivity losses, and support animal welfare. The design uses energy-aware IoT practices (on-device 60 s aggregation with one-minute uplinks) and lightweight analytics to limit device power use and network load, aligning the system with resource-efficient livestock operations. Full article

Medium- and long-term international road safety goals require continued advancement of scientific research, especially with regard to the human component. Recent technological advances in sensor technology offer new opportunities to more accurately characterize driving behavior, helping to reduce the uncertainty associated with driver reactions. This study evaluated the effectiveness of specific physiological variables, detected by low-cost wearable sensors, to obtain reliable indicators of the driver’s workload. Heart rate and skin conductivity were analyzed in a real driving environment, in the absence of evident emotional stresses, to test their sensitivity to an ordinary level of physical and mental engagement. An experiment was conducted on a sample of users (10 drivers) along a rural road in Sicily, Italy. Data analysis, carried out through ANOVA and generalized linear models on three distinct curves, produced preliminary results indicating that subtle road geometry changes can be detected by physiological sensors, validating their potential for integration into driver monitoring systems. Statistically significant mean differences were found for speed (for all curves, p < 0.001), heart rate (R1 vs. R2, p = 0.009), and tonic GSR (R1 vs. R2, p = 0.006; R2 vs. R3, p = 0.013; A vs. B, p = 0.013; A vs. C, p = 0.006) as a function of different radius (R1, R2, R3) and deviation angle values (A, B, C). Future developments will require a significant increase in the sample size and the number of scenarios to achieve results of general utility. Full article