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Search Results (28,212)

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21 pages, 3843 KiB  
Article
A Matrix Effect Calibration Method of Laser-Induced Breakdown Spectroscopy Based on Laser Ablation Morphology
by Hongliang Pei, Qingwen Fan, Yixiang Duan and Mingtao Zhang
Appl. Sci. 2025, 15(15), 8640; https://doi.org/10.3390/app15158640 (registering DOI) - 4 Aug 2025
Abstract
To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and [...] Read more.
To improve the accuracy of three-dimensional (3D) reconstruction under microscopic conditions for laser-induced breakdown spectroscopy (LIBS), this study developed a novel visual platform by integrating an industrial CCD camera with a microscope. A customized microscale calibration target was designed to calibrate intrinsic and extrinsic camera parameters accurately. Based on the pinhole imaging model, disparity maps were obtained via pixel matching to reconstruct high-precision 3D ablation morphology. A mathematical model was established to analyze how key imaging parameters—baseline distance, focal length, and depth of field—affect reconstruction accuracy in micro-imaging environments. Focusing on trace element detection in WC-Co alloy samples, the reconstructed ablation craters enabled the precise calculation of ablation volumes and revealed their correlations with laser parameters (energy, wavelength, pulse duration) and the physical-chemical properties of the samples. Multivariate regression analysis was employed to investigate how ablation morphology and plasma evolution jointly influence LIBS quantification. A nonlinear calibration model was proposed, significantly suppressing matrix effects, achieving R2 = 0.987, and reducing RMSE to 0.1. This approach enhances micro-scale LIBS accuracy and provides a methodological reference for high-precision spectral analysis in environmental and materials applications. Full article
(This article belongs to the Special Issue Novel Laser-Based Spectroscopic Techniques and Applications)
24 pages, 4519 KiB  
Article
Aerial Autonomy Under Adversity: Advances in Obstacle and Aircraft Detection Techniques for Unmanned Aerial Vehicles
by Cristian Randieri, Sai Venkata Ganesh, Rayappa David Amar Raj, Rama Muni Reddy Yanamala, Archana Pallakonda and Christian Napoli
Drones 2025, 9(8), 549; https://doi.org/10.3390/drones9080549 (registering DOI) - 4 Aug 2025
Abstract
Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This [...] Read more.
Unmanned Aerial Vehicles (UAVs) have rapidly grown into different essential applications, including surveillance, disaster response, agriculture, and urban monitoring. However, for UAVS to steer safely and autonomously, the ability to detect obstacles and nearby aircraft remains crucial, especially under hard environmental conditions. This study comprehensively analyzes the recent landscape of obstacle and aircraft detection techniques tailored for UAVs acting in difficult scenarios such as fog, rain, smoke, low light, motion blur, and disorderly environments. It starts with a detailed discussion of key detection challenges and continues with an evaluation of different sensor types, from RGB and infrared cameras to LiDAR, radar, sonar, and event-based vision sensors. Both classical computer vision methods and deep learning-based detection techniques are examined in particular, highlighting their performance strengths and limitations under degraded sensing conditions. The paper additionally offers an overview of suitable UAV-specific datasets and the evaluation metrics generally used to evaluate detection systems. Finally, the paper examines open problems and coming research directions, emphasising the demand for lightweight, adaptive, and weather-resilient detection systems appropriate for real-time onboard processing. This study aims to guide students and engineers towards developing stronger and intelligent detection systems for next-generation UAV operations. Full article
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24 pages, 6437 KiB  
Article
LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving
by Yunchuan Yang, Shubin Yang and Qiqing Chan
Sensors 2025, 25(15), 4800; https://doi.org/10.3390/s25154800 (registering DOI) - 4 Aug 2025
Abstract
The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO [...] Read more.
The accurate detection of small objects remains a critical challenge in autonomous driving systems, where improving detection performance typically comes at the cost of increased model complexity, conflicting with the lightweight requirements of edge deployment. To address this dilemma, this paper proposes LEAD-YOLO (Lightweight Efficient Autonomous Driving YOLO), an enhanced network architecture based on YOLOv11n that achieves superior small object detection while maintaining computational efficiency. The proposed framework incorporates three innovative components: First, the Backbone integrates a lightweight Convolutional Gated Transformer (CGF) module, which employs normalized gating mechanisms with residual connections, and a Dilated Feature Fusion (DFF) structure that enables progressive multi-scale context modeling through dilated convolutions. These components synergistically enhance small object perception and environmental context understanding without compromising network efficiency. Second, the neck features a hierarchical feature fusion module (HFFM) that establishes guided feature aggregation paths through hierarchical structuring, facilitating collaborative modeling between local structural information and global semantics for robust multi-scale object detection in complex traffic scenarios. Third, the head implements a shared feature detection head (SFDH) structure, incorporating shared convolution modules for efficient cross-scale feature sharing and detail enhancement branches for improved texture and edge modeling. Extensive experiments validate the effectiveness of LEAD-YOLO: on the nuImages dataset, the method achieves 3.8% and 5.4% improvements in mAP@0.5 and mAP@[0.5:0.95], respectively, while reducing parameters by 24.1%. On the VisDrone2019 dataset, performance gains reach 7.9% and 6.4% for corresponding metrics. These findings demonstrate that LEAD-YOLO achieves an excellent balance between detection accuracy and model efficiency, thereby showcasing substantial potential for applications in autonomous driving. Full article
(This article belongs to the Section Vehicular Sensing)
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22 pages, 4189 KiB  
Article
A Hierarchical Path Planning Framework of Plant Protection UAV Based on the Improved D3QN Algorithm and Remote Sensing Image
by Haitao Fu, Zheng Li, Jian Lu, Weijian Zhang, Yuxuan Feng, Li Zhu, He Liu and Jian Li
Remote Sens. 2025, 17(15), 2704; https://doi.org/10.3390/rs17152704 (registering DOI) - 4 Aug 2025
Abstract
Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a [...] Read more.
Traditional path planning algorithms often fail to simultaneously ensure operational efficiency, energy constraint compliance, and environmental adaptability in agricultural scenarios, thereby hindering the advancement of precision agriculture. To address these challenges, this study proposes a deep reinforcement learning algorithm, MoE-D3QN, which integrates a Mixture-of-Experts mechanism with a Bi-directional Long Short-Term Memory model. This design enhances the efficiency and robustness of UAV path planning in agricultural environments. Building upon this algorithm, a hierarchical coverage path planning framework is developed. Multi-level task maps are constructed using crop information extracted from Sentinel-2 remote sensing imagery. Additionally, a dynamic energy consumption model and a progressive composite reward function are incorporated to further optimize UAV path planning in complex farmland conditions. Simulation experiments reveal that in the two-level scenario, the MoE-D3QN algorithm achieves a coverage efficiency of 0.8378, representing an improvement of 37.84–63.38% over traditional algorithms and 19.19–63.38% over conventional reinforcement learning methods. The redundancy rate is reduced to 3.23%, which is 38.71–41.94% lower than traditional methods and 4.46–42.77% lower than reinforcement learning counterparts. In the three-level scenario, MoE-D3QN achieves a coverage efficiency of 0.8261, exceeding traditional algorithms by 52.13–71.45% and reinforcement learning approaches by 10.15–50.2%. The redundancy rate is further reduced to 5.26%, which is significantly lower than the 57.89–92.11% observed with traditional methods and the 15.57–18.98% reported for reinforcement learning algorithms. These findings demonstrate that the MoE-D3QN algorithm exhibits high-quality planning performance in complex farmland environments, indicating its strong potential for widespread application in precision agriculture. Full article
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27 pages, 30231 KiB  
Article
Modelling and Simulation of a 3MW, Seventeen-Phase Permanent Magnet AC Motor with AI-Based Drive Control for Submarines Under Deep-Sea Conditions
by Arun Singh and Anita Khosla
Energies 2025, 18(15), 4137; https://doi.org/10.3390/en18154137 (registering DOI) - 4 Aug 2025
Abstract
The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, [...] Read more.
The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article
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29 pages, 4372 KiB  
Review
Hydrogen Cryomagnetic a Common Solution for Metallic and Oxide Superconductors
by Bartlomiej Andrzej Glowacki
Materials 2025, 18(15), 3665; https://doi.org/10.3390/ma18153665 (registering DOI) - 4 Aug 2025
Abstract
This article examines the physical properties, performance metrics, and cooling requirements of a range of superconducting materials, with a particular focus on their compatibility with hydrogen-based cryogenic systems. It analyses recent developments and challenges in this field, and considers how hydrogen cryomagnetic could [...] Read more.
This article examines the physical properties, performance metrics, and cooling requirements of a range of superconducting materials, with a particular focus on their compatibility with hydrogen-based cryogenic systems. It analyses recent developments and challenges in this field, and considers how hydrogen cryomagnetic could transform superconducting technologies, making them economically viable and environmentally sustainable for a variety of critical applications. The discussion aims to provide insights into the intersection of metallic and ceramic superconductors with the hydrogen economy and to chart a path towards scalable and impactful solutions in the energy sector. Full article
(This article belongs to the Special Issue Advanced Superconducting Materials and Technology)
21 pages, 2608 KiB  
Review
Recent Progress on the Research of 3D Printing in Aqueous Zinc-Ion Batteries
by Yating Liu, Haokai Ding, Honglin Chen, Haoxuan Gao, Jixin Yu, Funian Mo and Ning Wang
Polymers 2025, 17(15), 2136; https://doi.org/10.3390/polym17152136 - 4 Aug 2025
Abstract
The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and [...] Read more.
The global transition towards a low-carbon energy system urgently demands efficient and safe energy storage solutions. Aqueous zinc-ion batteries (AZIBs) are considered a promising alternative to lithium-ion batteries due to their inherent safety and environmental friendliness. However, conventional manufacturing methods are costly and labor-intensive, hindering their large-scale production. Recent advances in 3D printing technology offer innovative pathways to address these challenges. By combining design flexibility with material optimization, 3D printing holds the potential to enhance battery performance and enable customized structures. This review systematically examines the application of 3D printing technology in fabricating key AZIB components, including electrodes, electrolytes, and integrated battery designs. We critically compare the advantages and disadvantages of different 3D printing techniques for these components, discuss the potential and mechanisms by which 3D-printed structures enhance ion transport and electrochemical stability, highlight critical existing scientific questions and research gaps, and explore potential strategies for optimizing the manufacturing process. Full article
(This article belongs to the Special Issue Polymeric Materials for Next-Generation Energy Storage)
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17 pages, 3115 KiB  
Article
Functional Identification of Acetyl-CoA C-Acetyltransferase Gene from Fritillaria unibracteata
by Zichun Ma, Qiuju An, Xue Huang, Hongting Liu, Feiying Guo, Han Yan, Jiayu Zhou and Hai Liao
Horticulturae 2025, 11(8), 913; https://doi.org/10.3390/horticulturae11080913 (registering DOI) - 4 Aug 2025
Abstract
Fritillaria unibracteata is a rare and endangered medicinal plant in the Liliaceae family, whose bulbs have been used in traditional Chinese traditional medicine for over 2000 years. The mevalonate (MVA) pathway is involved in the growth, development, response to environmental stress, and active [...] Read more.
Fritillaria unibracteata is a rare and endangered medicinal plant in the Liliaceae family, whose bulbs have been used in traditional Chinese traditional medicine for over 2000 years. The mevalonate (MVA) pathway is involved in the growth, development, response to environmental stress, and active ingredient production of plants; however, the functional characterization of MVA-pathway genes in the Liliaceae family remains poorly documented. In this study, an Acetyl-CoA C-acetyltransferase gene (FuAACT) was first cloned from F. unibracteata. It exhibited structural features of the thiolase family and showed the highest sequence identity with the Dioscorea cayenensis homolog. The Km, Vmax, and Kcat of the recombinant FuAACT were determined to be 3.035 ± 0.215 μM, 0.128 ± 0.0058 μmol/(min·mg), and 1.275 ± 0.0575 min−1, respectively. The optimal catalytic conditions for FuAACT were ascertained to be 30 °C and pH 8.9. It was stable below 50 °C. His361 was confirmed to be a key amino acid residue to enzymatic catalysis by site-directed mutagenesis. Subsequent subcellular localization experiments demonstrated that FuAACT was localized in chloroplasts and cytoplasm. FuAACT-overexpressing transgenic Arabidopsis thaliana plants showed higher drought tolerance than wild-type plants. This phenotypic difference was corroborated by significant differences in seed germination rate, lateral root number, plant height, and leaf number (p < 0.05). Furthermore, the FuAACT transgenic plants resulted in the formation of a more developed fibrous root system. These results indicated that the FuAACT gene revealed substantial biological activity in vitro and in vivo, hopefully providing the basis for its further research and application in liliaceous ornamental and medicinal plants. Full article
(This article belongs to the Special Issue Tolerance of Horticultural Plants to Abiotic Stresses)
18 pages, 1388 KiB  
Review
Simulation in the Built Environment: A Bibliometric Analysis
by Saman Jamshidi
Metrics 2025, 2(3), 13; https://doi.org/10.3390/metrics2030013 - 4 Aug 2025
Abstract
Simulation has become a pivotal tool in the design, analysis, and optimization of the built environment, and has been widely adopted by professionals in architecture, engineering, and urban planning. These techniques enable stakeholders to test hypotheses, evaluate design alternatives, and predict performance outcomes [...] Read more.
Simulation has become a pivotal tool in the design, analysis, and optimization of the built environment, and has been widely adopted by professionals in architecture, engineering, and urban planning. These techniques enable stakeholders to test hypotheses, evaluate design alternatives, and predict performance outcomes prior to construction. Applications span energy consumption, airflow, thermal comfort, lighting, structural behavior, and human interactions within buildings and urban contexts. This study maps the scientific landscape of simulation research in the built environment through a bibliometric analysis of 12,220 publications indexed in Scopus. Using VOSviewer 1.6.20, it conducted citation and keyword co-occurrence analyses to identify key research themes, leading countries and journals, and central publications in the field. The analysis revealed seven primary thematic clusters: (1) human-focused simulation, (2) building-scale energy performance simulation, (3) urban-scale energy performance simulation, (4) sustainable design and simulation, (5) indoor environmental quality simulation, (6) building aerodynamics simulation, and (7) computing in building simulation. By synthesizing these trends and domains, this study provides an overview of the field, facilitating greater accessibility to the simulation literature and informing future interdisciplinary research and practice in the built environment. Full article
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30 pages, 966 KiB  
Review
A Review on Anaerobic Digestate as a Biofertilizer: Characteristics, Production, and Environmental Impacts from a Life Cycle Assessment Perspective
by Carmen Martín-Sanz-Garrido, Marta Revuelta-Aramburu, Ana María Santos-Montes and Carlos Morales-Polo
Appl. Sci. 2025, 15(15), 8635; https://doi.org/10.3390/app15158635 (registering DOI) - 4 Aug 2025
Abstract
Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits [...] Read more.
Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article
(This article belongs to the Special Issue Novel Research on By-Products and Treatment of Waste)
14 pages, 1379 KiB  
Article
Biodegradable Polyalphaolefins for Gear Lubrication in Electrical Drives: Aging and Wetting
by Kevin Holderied, Joachim Albrecht, Elisabeth Distler, Katharina Weber and Nahed El-Mahallawy
Lubricants 2025, 13(8), 347; https://doi.org/10.3390/lubricants13080347 (registering DOI) - 4 Aug 2025
Abstract
Electric propulsion requires engines and transmission systems that run at higher speeds compared to combustion engines. For improving sustainability and environmental protection, biodegradable oils are suggested for the lubrication of high-speed gears that require particularly quick wetting of the steel surfaces. Newly developed [...] Read more.
Electric propulsion requires engines and transmission systems that run at higher speeds compared to combustion engines. For improving sustainability and environmental protection, biodegradable oils are suggested for the lubrication of high-speed gears that require particularly quick wetting of the steel surfaces. Newly developed promising candidates include short-chained polyalphaolefins. In the present work, a study on the applicability of such oil is presented and discussed with respect to different aging levels based on biodegradable properties. It focuses on the wettability of metallic surfaces investigated through time-resolved contact angle measurements. Carbon steels with different carbon contents and microstructures are selected as the most commonly used materials for gears. Effects of steel composition, surface roughness and oil oxidation are studied. The results show that in most cases, the application of biodegradable polyalphaolefins is not critical; however, a combination of steels with inhomogeneous microstructure, high surface roughness and aged oil can be critical because of limited wetting. Full article
(This article belongs to the Special Issue Tribology of Electric Vehicles)
14 pages, 1567 KiB  
Article
Determining the Benzo[a]pyrene Degradation, Tolerance, and Adsorption Mechanisms of Kefir-Derived Bacterium Bacillus mojavensis TC-5
by Zhixian Duo, Haohao Li, Zeyu Wang, Zhiwei Zhang, Zhuonan Yang, Aofei Jin, Minwei Zhang, Rui Zhang and Yanan Qin
Foods 2025, 14(15), 2727; https://doi.org/10.3390/foods14152727 - 4 Aug 2025
Abstract
Microbial detoxification, as an environmentally friendly strategy, has been widely applied for benzo[a]pyrene (BaP) degradation. Within this approach, food-derived microbial strains offer unique advantages in safety, specificity, and sustainability for detoxifying food-borne BaP. In this study, we aimed to explore the potential of [...] Read more.
Microbial detoxification, as an environmentally friendly strategy, has been widely applied for benzo[a]pyrene (BaP) degradation. Within this approach, food-derived microbial strains offer unique advantages in safety, specificity, and sustainability for detoxifying food-borne BaP. In this study, we aimed to explore the potential of such strains in BaP degradation. Bacillus mojavensis TC-5, a strain that degrades BaP, was isolated from kefir grains. Surprisingly, 12 genes encoding dehydrogenases, synthases, and oxygenases, including betB, fabHB, qdoI, cdoA, and bioI, which are related to BaP degradation, were up-regulated by 2.01-fold to 4.52-fold in TC-5. Two potential degradation pathways were deduced. In pathway I, dioxygenase, betaine aldehyde dehydrogenase, and beta-ketoacyl-ACP synthase III FabHB act sequentially on BaP to form 4H-pyran-4-one,2,3-dihydro-3,5-dihydroxy-6-methyl via the phthalic acid pathway. In the presence of the cytochrome P450 enzyme, BaP progressively mediates ring cleavage via the anthracene pathway, eventually forming 3-methyl-5-propylnonane in pathway II. Notably, TC-5 achieved an impressive BaP removal efficiency of up to 63.94%, with a degradation efficiency of 32.89%. These results suggest that TC-5 has significant potential for application in addressing food-borne BaP contamination. Moreover, our findings expand the application possibilities of Xinjiang fermented milk products and add to the available green strategies for BaP degradation in food systems. Full article
(This article belongs to the Section Nutraceuticals, Functional Foods, and Novel Foods)
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23 pages, 2353 KiB  
Article
Seaweeds of the Israeli Mediterranean Sea: Nutritional and Biotechnological Potential Through Seasonal and Species Variation
by Doron Yehoshua Ashkenazi, Félix L. Figueroa, Julia Vega, Shoshana Ben-Valid, Guy Paz, Eitan Salomon, Avigdor Abelson and Álvaro Israel
Mar. Drugs 2025, 23(8), 320; https://doi.org/10.3390/md23080320 - 4 Aug 2025
Abstract
Macroalgae (seaweeds) produce unique bioactive metabolites that have enabled their survival for millions of years, offering significant potential for human benefits. In the Israeli Mediterranean Sea, no comprehensive systematic surveys of seaweeds have been published since the 1990s, and their chemical composition remains [...] Read more.
Macroalgae (seaweeds) produce unique bioactive metabolites that have enabled their survival for millions of years, offering significant potential for human benefits. In the Israeli Mediterranean Sea, no comprehensive systematic surveys of seaweeds have been published since the 1990s, and their chemical composition remains largely unexplored. This study presents an extensive survey of intertidal seaweed communities along the shallow Israeli coastline, documenting their spatial, temporal, and biochemical diversity. Of the 320 specimens collected, 55 seaweed species were identified: 29 red (Rhodophyta), 14 brown (Phaeophyceae), and 12 green (Chlorophyta). A significant shift in species abundance was documented, with a single dominant annual bloom occurring during spring, unlike previously reported biannual blooms. Chemical analysis of the dominant species revealed significant seasonal variations in compound levels, with higher protein content in winter and increased antioxidant capacity during spring. Phenolic and natural sunscreen compounds (mycosporine-like amino acids, MAAs) showed no general seasonal trend. These findings highlight the optimal environmental conditions for seaweed growth and underscore their potential for aquaculture and biotechnology. We hypothesize that the ecologically unique conditions of the Israeli Mediterranean Sea may foster resilient seaweed species enriched with distinctive chemical properties, suitable for nutritional, health, pharmaceutical, and nutraceutical applications, particularly as climate-adaptive bioresources. Full article
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25 pages, 2418 KiB  
Review
Contactless Vital Sign Monitoring: A Review Towards Multi-Modal Multi-Task Approaches
by Ahmad Hassanpour and Bian Yang
Sensors 2025, 25(15), 4792; https://doi.org/10.3390/s25154792 (registering DOI) - 4 Aug 2025
Abstract
Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and [...] Read more.
Contactless vital sign monitoring has emerged as a transformative healthcare technology, enabling the assessment of vital signs without physical contact with the human body. This review comprehensively reviews the rapidly evolving landscape of this field, with particular emphasis on multi-modal sensing approaches and multi-task learning paradigms. We systematically categorize and analyze existing technologies based on sensing modalities (vision-based, radar-based, thermal imaging, and ambient sensing), integration strategies, and application domains. The paper examines how artificial intelligence has revolutionized this domain, transitioning from early single-modality, single-parameter approaches to sophisticated systems that combine complementary sensing technologies and simultaneously extract multiple vital sign parameters. We discuss the theoretical foundations and practical implementations of multi-modal fusion, analyzing signal-level, feature-level, decision-level, and deep learning approaches to sensor integration. Similarly, we explore multi-task learning frameworks that leverage the inherent relationships between vital sign parameters to enhance measurement accuracy and efficiency. The review also critically addresses persisting technical challenges, clinical limitations, and ethical considerations, including environmental robustness, cross-subject variability, sensor fusion complexities, and privacy concerns. Finally, we outline promising future directions, from emerging sensing technologies and advanced fusion architectures to novel application domains and privacy-preserving methodologies. This review provides a holistic perspective on contactless vital sign monitoring, serving as a reference for researchers and practitioners in this rapidly advancing field. Full article
(This article belongs to the Section Biomedical Sensors)
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17 pages, 3360 KiB  
Article
Efficient and Selective Multiple Ion Chemosensor by Novel Near-Infrared Sensitive Symmetrical Squaraine Dye Probe
by Sushma Thapa, Kshitij RB Singh and Shyam S. Pandey
Chemosensors 2025, 13(8), 288; https://doi.org/10.3390/chemosensors13080288 - 4 Aug 2025
Abstract
A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu2+ and Ag+ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the [...] Read more.
A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu2+ and Ag+ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu2+ in acetonitrile and Ag+ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu2+ and Ag+, accompanied by hypochromism. The detection limits are 0.09 μM for Cu2+ and 0.38 μM for Ag+, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu2+ to 59–99% for Ag+. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article
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