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Search Results (2,893)

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25 pages, 3937 KB  
Review
Precision Forestry Revisited
by Can Vatandaslar, Kevin Boston, Zennure Ucar, Lana L. Narine, Marguerite Madden and Abdullah Emin Akay
Remote Sens. 2025, 17(20), 3465; https://doi.org/10.3390/rs17203465 - 17 Oct 2025
Abstract
This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance—rather than replace—established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web [...] Read more.
This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance—rather than replace—established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web of Science (up to 2025), the study identifies six main categories and eight components of precision forestry. The findings indicate that “forest management and planning” is the most common category, with nearly half of the studies focusing on this topic. “Remote sensing platforms and sensors” emerged as the most frequently used component, with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems being the most widely adopted tools. The analysis also reveals a notable increase in precision forestry research since the early 2010s, coinciding with rapid developments in small UAVs and mobile sensor technologies. Despite growing interest, robotics and real-time process control systems remain underutilized, mainly due to challenging forest conditions and high implementation costs. The research highlights geographical disparities, with Europe, Asia, and North America hosting the majority of studies. Italy, China, Finland, and the United States stand out as the most active countries in terms of research output. Notably, the review emphasizes the need to integrate precision forestry into academic curricula and support industry adoption through dedicated information and technology specialists. As the forestry workforce ages and technology advances rapidly, a growing skills gap exists between industry needs and traditional forestry education. Equipping the next generation with hands-on experience in big data analysis, geospatial technologies, automation, and Artificial Intelligence (AI) is critical for ensuring the effective adoption and application of precision forestry. Full article
(This article belongs to the Special Issue Digital Modeling for Sustainable Forest Management)
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27 pages, 7875 KB  
Article
Spatiotemporal Water Quality Assessment in Spatially Heterogeneous Horseshoe Lake, Madison County, Illinois Using Satellite Remote Sensing and Statistical Analysis (2020–2024)
by Anuj Tiwari, Ellen Hsuan and Sujata Goswami
Water 2025, 17(20), 2997; https://doi.org/10.3390/w17202997 - 17 Oct 2025
Abstract
Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their [...] Read more.
Inland lakes across the United States are increasingly impacted by nutrient pollution, sedimentation, and algal blooms, with significant ecological and economic consequences. While satellite-based monitoring has advanced our ability to assess water quality at scale, many lakes remain analytically underserved due to their spatial heterogeneity and the multivariate nature of pollution dynamics. This study presents an integrated framework for detecting spatiotemporal pollution patterns using satellite remote sensing, trend segmentation, hierarchical clustering and dimensionality reduction. Taking Horseshoe Lake (Illinois), a shallow eutrophic–turbid system, as a case study, we analyzed Sentinel-2 imagery from 2020–2024 to derive chlorophyll-a (NDCI), turbidity (NDTI), and total phosphorus (TP) across five hydrologically distinct zones. Breakpoint detection and modified Mann–Kendall tests revealed both abrupt and seasonal trend shifts, while correlation and hierarchical clustering uncovered inter-zone relationships. To identify lake-wide pollution windows, we applied Kernel PCA to generate a composite pollution index, aligned with the count of increasing trend segments. Two peak pollution periods, late 2022 and late 2023, were identified, with Regions 1 and 5 consistently showing high values across all indicators. Spatial maps linked these hotspots to urban runoff and legacy impacts. The framework captures both acute and chronic stress zones and enables targeted seasonal diagnostics. The approach demonstrates a scalable and transferable method for pollution monitoring in morphologically complex lakes and supports more targeted, region-specific water management strategies. Full article
(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)
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22 pages, 5340 KB  
Article
Circular Array Fiber-Optic Sub-Sensor for Large-Area Bubble Observation, Part I: Design and Experimental Validation of the Sensitive Unit of Array Elements
by Feng Liu, Lei Yang, Hao Li and Zhentao Chen
Sensors 2025, 25(20), 6378; https://doi.org/10.3390/s25206378 - 16 Oct 2025
Viewed by 262
Abstract
For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed [...] Read more.
For large-scale measurement of microbubble parameters on the ocean surface beneath breaking waves, a buoy-type bubble sensor (BBS) is proposed. This sensor integrates a panoramic bubble imaging sub-sensor with a circular array fiber-optic sub-sensor. The sensitive unit of the latter sub-sensor is designed via theoretical modeling and experimental validation. Theoretical calculations indicate that the optimal cone angle for a quartz fiber-optic-based sensitive unit ranges from 45.2° to 92°. A prototype array element with a cone angle of 90° was fabricated and used as the core component for feasibility experiments in static and dynamic two-phase (gas and liquid) identification. During static identification, the reflected optical power differs by an order of magnitude between the two phases. For dynamic sensing of multiple microbubble positions, the reflected optical power varies from 13.4 nW to 29.3 nW, which is within the operating range of the array element’s photodetector. In theory, assembling conical quartz fiber-based sensitive units into fiber-optic probes and configuring them as arrays could overcome the resolution limitations of the panoramic bubble imaging sub-sensor. Further discussion of this approach will be presented in a subsequent paper. Full article
(This article belongs to the Section Optical Sensors)
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39 pages, 2094 KB  
Article
Exploring Success Factors for Underserved Graduate Students in STEM
by Karen M. Collier and Wayne A. Hickman
Trends High. Educ. 2025, 4(4), 63; https://doi.org/10.3390/higheredu4040063 - 15 Oct 2025
Viewed by 50
Abstract
Inequalities in enrollment in STEM persist for those entering higher education as first-generation college students, underserved racial and ethnic groups, female and nonbinary individuals, and those from lower socioeconomic backgrounds. The current study aims to better understand the relationship students have with graduate [...] Read more.
Inequalities in enrollment in STEM persist for those entering higher education as first-generation college students, underserved racial and ethnic groups, female and nonbinary individuals, and those from lower socioeconomic backgrounds. The current study aims to better understand the relationship students have with graduate school success factors by redistributing the Graduate Student Success Survey+ (GSSS+) at an R2 institution in the southeastern United States. Exploratory factor analysis was used to test the survey’s validity, with 242 participants. A 7-factor, 40-item model was developed, comprising the following subscales: mentor support, peer support, imposter phenomenon, financial support, microaggressions (related to race and gender), access and opportunity (for research, writing, and presentations), and resilience. Item analysis identified perceived barriers (e.g., microaggressions, imposter phenomenon, and financial stress) for underserved students (i.e., females, underserved racial and ethnic groups, and part-time students). Regression analysis on resilience revealed a positive relationship with mentor support, peer support, and financial support. A negative relationship with resilience was associated with a greater perception of imposter phenomenon. Findings from this study underscore the need for additional support from mentors and other university entities to foster a stronger sense of resilience in students, along with increased opportunities for participation in research, academic writing, and publication. Full article
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33 pages, 12260 KB  
Article
Open-Source Smart Wireless IoT Solar Sensor
by Victor-Valentin Stoica, Alexandru-Viorel Pălăcean, Dumitru-Cristian Trancă and Florin-Alexandru Stancu
Appl. Sci. 2025, 15(20), 11059; https://doi.org/10.3390/app152011059 - 15 Oct 2025
Viewed by 105
Abstract
IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart [...] Read more.
IoT (Internet of Things)-enabled solar irradiance sensors are evolving toward energy harvesting, interoperability, and open-source availability, yet current solutions remain either costly, closed, or limited in robustness. Based on a thorough literature review and identification of future trends, we propose an open-source smart wireless sensor that employs a small photovoltaic module simultaneously as sensing element and energy harvester. The device integrates an ESP32 microcontroller, precision ADC (Analog-to-Digital converter), and programmable load to sweep the PV (photovoltaic) I–V (Current–Voltage) curve and compute irradiance from electrical power and solar-cell temperature via a calibrated third-order polynomial. Supporting Modbus RTU (Remote Terminal Unit)/TCP (Transmission Control Protocol), MQTT (Message Queuing Telemetry Transport), and ZigBee, the sensor operates from batteries or supercapacitors through sleep–wake cycles. Validation against industrial irradiance meters across 0–1200 W/m2 showed average errors below 5%, with deviations correlated to irradiance volatility and sampling cadence. All hardware, firmware, and data-processing tools are released as open source to enable reproducibility and distributed PV monitoring applications. Full article
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41 pages, 12462 KB  
Article
Real-Time Efficient Approximation of Nonlinear Fractional-Order PDE Systems via Selective Heterogeneous Ensemble Learning
by Biao Ma and Shimin Dong
Fractal Fract. 2025, 9(10), 660; https://doi.org/10.3390/fractalfract9100660 - 13 Oct 2025
Viewed by 139
Abstract
Rod-pumping systems represent complex nonlinear systems. Traditional soft-sensing methods used for efficiency prediction in such systems typically rely on complicated fractional-order partial differential equations, severely limiting the real-time capability of efficiency estimation. To address this limitation, we propose an approximate efficiency prediction model [...] Read more.
Rod-pumping systems represent complex nonlinear systems. Traditional soft-sensing methods used for efficiency prediction in such systems typically rely on complicated fractional-order partial differential equations, severely limiting the real-time capability of efficiency estimation. To address this limitation, we propose an approximate efficiency prediction model for nonlinear fractional-order differential systems based on selective heterogeneous ensemble learning. This method integrates electrical power time-series data with fundamental operational parameters to enhance real-time predictive capability. Initially, we extract critical parameters influencing system efficiency using statistical principles. These primary influencing factors are identified through Pearson correlation coefficients and validated using p-value significance analysis. Subsequently, we introduce three foundational approximate system efficiency models: Convolutional Neural Network-Echo State Network-Bidirectional Long Short-Term Memory (CNN-ESN-BiLSTM), Bidirectional Long Short-Term Memory-Bidirectional Gated Recurrent Unit-Transformer (BiLSTM-BiGRU-Transformer), and Convolutional Neural Network-Echo State Network-Bidirectional Gated Recurrent Unit (CNN-ESN-BiGRU). Finally, to balance diversity among basic approximation models and predictive accuracy, we develop a selective heterogeneous ensemble-based approximate efficiency model for nonlinear fractional-order differential systems. Experimental validation utilizing actual oil-well parameters demonstrates that the proposed approach effectively and accurately predicts the efficiency of rod-pumping systems. Full article
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20 pages, 3922 KB  
Article
Both Benzannulation and Heteroatom-Controlled Photophysical Properties in Donor–π–Acceptor Ionic Dyes: A Combined Experimental and Theoretical Study
by Przemysław Krawczyk and Beata Jędrzejewska
Materials 2025, 18(20), 4676; https://doi.org/10.3390/ma18204676 - 12 Oct 2025
Viewed by 297
Abstract
Donor–π–acceptor (D–π–A) dyes have garnered significant attention due to their unique optical properties and potential applications in various fields, including optoelectronics, chemical sensing and bioimaging. This study presents the design, synthesis, and comprehensive photophysical investigation of a series of ionic dyes incorporating five- [...] Read more.
Donor–π–acceptor (D–π–A) dyes have garnered significant attention due to their unique optical properties and potential applications in various fields, including optoelectronics, chemical sensing and bioimaging. This study presents the design, synthesis, and comprehensive photophysical investigation of a series of ionic dyes incorporating five- and six-membered heterocyclic rings as electron-donating and electron-withdrawing units, respectively. The influence of the dye structure, i.e., (a) the systematically varied heteroatom (NMe, S and O) in donor moiety, (b) benzannulation of the acceptor part and (c) position of the donor vs. acceptor, on the photophysical properties was evaluated by steady-state and time-resolved spectroscopy across solvents of varying polarity. To probe solvatochromic behavior, the Reichardt parameters and the Catalán four-parameter scale, including polarizability (SP), dipolarity (SdP), acidity (SA) and basicity (SB) parameters, were applied. Emission dynamics were further analyzed through time-resolved fluorescence spectroscopy employing multi-exponential decay models to accurately describe fluorescence lifetimes. Time-dependent density functional theory (TDDFT) calculations supported the experimental findings by elucidating electronic structures, charge-transfer character, and dipole moments in the ground and excited states. The experimental results show the introduction of O or S instead of NMe causes substantial hypsochromic shifts in the absorption and emission bands. Benzannulation enhances the photoinduced charge transfer and causes red-shifted absorption spectra to be obtained without deteriorating the emission properties. Hence, by introducing an appropriate modification, it is possible to design materials with tunable photophysical properties for practical applications, e.g., in opto-electronics or sensing. Full article
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11 pages, 1351 KB  
Article
Construction of High-Resolution Goos–Hänchen Shift Measurement System
by Xinmin Fan, Hui Liu, Zhonglin Lv, Shande Li, Yan Wang, Fuyong Qin, Chunyan Wang and Xiaodong Huang
Photonics 2025, 12(10), 1002; https://doi.org/10.3390/photonics12101002 - 11 Oct 2025
Viewed by 158
Abstract
Accurate measurement of the Goos–Hänchen (GH) shift serves as a crucial foundation for the deepening of its theories and the expansion of its applications. To meet the requirements for GH shift measurement, this study constructed a complete experimental system. Composed of a stable [...] Read more.
Accurate measurement of the Goos–Hänchen (GH) shift serves as a crucial foundation for the deepening of its theories and the expansion of its applications. To meet the requirements for GH shift measurement, this study constructed a complete experimental system. Composed of a stable laser light source, a high-precision optical path control unit with adjustable incident angles, a high sensitivity detection scheme, and an integrated control and data processing module, this system possesses the capability of full-process measurement covering optical signal generation, adjustment, detection, and data analysis. To effectively obtain the GH shift, this research adopted the TE/TM polarization differential method for measurement experiments and discussed the performance indicators of the system. Experimental verification shows that the system can accomplish the GH shift measurement task accurately and reliably. The experimental platform established in this study provides a practical tool for in-depth theoretical research and application exploration of the GH shift. Furthermore, its high-precision measurement capability not only lays a foundation for the research and development of optical sensing technologies based on the GH shift phenomenon but also offers important support for further revealing the physical essence of the beam shift effect and exploring its potential technical application value. Full article
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25 pages, 13143 KB  
Article
Downscaling Method for Crop Yield Statistical Data Based on the Standardized Deviation from the Mean of the Comprehensive Crop Condition Index
by Ke Luo, Jianqiang Ren, Xiangxin Bu and Hongwei Zhao
Remote Sens. 2025, 17(20), 3408; https://doi.org/10.3390/rs17203408 - 11 Oct 2025
Viewed by 166
Abstract
Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting [...] Read more.
Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decision-making. However, owing to an insufficient understanding of the factors affecting yield, accurately depicting its spatial differences remains challenging. Taking Hailun city, Heilongjiang Province, as an example, this study proposes a yield downscaling method based on the standardized deviation from the mean of the comprehensive crop condition index (CCCI) during key phenological periods of the growing season. First, Sentinel-2 remote sensing data were used to retrieve crop condition parameters during key phenological periods, and the CCCI was constructed using the correlation between crop condition parameters in key phenological periods and statistical yield as the weight. Subsequently, regression analysis and the entropy weight method were applied to determine the spatiotemporal dynamic weights of the CCCI during key phenological stages and to calculate the standardized deviation from the mean. By combining these two components, the comprehensive spatial difference index of the crop growth condition (CSDICGC) was derived, which offered a new way to characterize the discrepancies between the pixel-level yield and statistical yield, thereby downscaling the yield statistical data from the administrative unit to the pixel scale. The results indicated that this method achieved a regional accuracy close to 100%, with a strong fit at the pixel scale. Pixel-level accuracy validation against ground-truth maize yield data resulted in an R2 of 0.82 and a mean relative error (MRE) of 4.75%. The novelty of this study was characterized by the integration of multistage crop condition parameters with dynamic spatiotemporal weighting to overcome the limitations of single-index methods. The crop yield statistical data downscaling spatialization method proposed in this paper is simple and efficient and has the potential to be popularized and applied over relatively large regions. Full article
(This article belongs to the Special Issue Near Real-Time (NRT) Agriculture Monitoring)
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13 pages, 1564 KB  
Article
Impact of Light-Activated Nanocomposite with Erythrosine B on agr Quorum Sensing System in Staphylococcus aureus
by Larysa Bugyna, Ľubomír Švantner, Katarína Bilská, Marek Pribus and Helena Bujdáková
Antibiotics 2025, 14(10), 1010; https://doi.org/10.3390/antibiotics14101010 - 11 Oct 2025
Viewed by 306
Abstract
Backround: The agr (accessory gene regulator) quorum sensing (QS) system of Staphylococcus aureus participates significantly in its virulence and biofilm formation—either through its activation or suppression. The aim of this study was to investigate the impact of photoactive nanomaterials that have been functionalized [...] Read more.
Backround: The agr (accessory gene regulator) quorum sensing (QS) system of Staphylococcus aureus participates significantly in its virulence and biofilm formation—either through its activation or suppression. The aim of this study was to investigate the impact of photoactive nanomaterials that have been functionalized with erythrosine B (EryB) on the modulation of this agr QS system on three methicillin-resistant S. aureus (MRSA). Methods: The functionality of the agr system was determined by the CAMP test and by quantitative PCR (qPCR) to analyze the expression of the hld gene, which is located within the RNAIII and encodes δ-hemolysin. The biofilm was evaluated by crystal violet assay and fluorescence microscopy. The anti-biofilm activity was determined by calculating the colony-forming units. The relative expression of the hld gene, determined by qPCR. Results: Using the CAMP test, S66 and S68 strains were found to be agr-positive, and strain S73 was agr-negative. The relative expression of the hld gene increased only in the agr-positive strains (600- and 1000-fold). In these strains, the biofilm was less compact compared to the dense biofilm formed by the agr-negative strain. The anti-biofilm effectiveness on the nanocomposite with EryB after irradiation reduced the growth of biofilm cells by 100- to 1000-fold compared to the biofilm on polyurethane alone. The qPCR results showed a significant decrease in the relative expression of the hld gene in the agr-positive strains after irradiation compared to the non-irradiated samples. Conclusions: These results suggest that photoactive nanocomposites with EryB can significantly reduce biofilm formed by MRSA strains, regardless of the functionality of the agr QS system. Full article
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8 pages, 2675 KB  
Proceeding Paper
Enhancing Tetracorder Mineral Classification with Random Forest Modeling
by Hideki Tsubomatsu and Hideyuki Tonooka
Eng. Proc. 2025, 94(1), 25; https://doi.org/10.3390/engproc2025094025 - 10 Oct 2025
Viewed by 139
Abstract
Hyperspectral (HS) remote sensing is a valuable tool for geological surveys and mineral classification. However, mineral maps derived from HS data can exhibit inconsistencies across different imaging times or sensors due to complex factors. In this study, we propose a novel method to [...] Read more.
Hyperspectral (HS) remote sensing is a valuable tool for geological surveys and mineral classification. However, mineral maps derived from HS data can exhibit inconsistencies across different imaging times or sensors due to complex factors. In this study, we propose a novel method to enhance the robustness and temporal consistency of mineral mapping. The method combines the spectral identification capabilities of the Tetracorder expert system, developed by United States Geological Survey (USGS), with a data-driven classification model, involving the application of Tetracorder to high-purity pixels identified through the pixel purity index (PPI) analysis to generate reliable training labels. These labels, along with hyperspectral bands transformed by the minimum noise fraction (MNF), are used to train a random forest classifier. The methodology was evaluated using multi-temporal images of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), acquired over Cuprite, Nevada, between 2011 and 2013. The results demonstrate that the proposed method achieves accuracy comparable to Tetracorder while improving map consistency and reducing inter-annual mapping errors by approximately 30%. Full article
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25 pages, 15963 KB  
Article
Real-Time Lossless Compression System for Bayer Pattern Images with a Modified JPEG-LS
by Xufeng Li, Li Zhou and Yan Zhu
Mathematics 2025, 13(20), 3245; https://doi.org/10.3390/math13203245 - 10 Oct 2025
Viewed by 270
Abstract
Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the [...] Read more.
Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the JPEG-LS algorithm to make it more suitable for high-speed hardware implementation and application to Bayer pattern data. This paper addresses the current limitations by proposing a real-time lossless compression system specifically tailored for Bayer pattern images from spaceborne cameras. The system integrates a hybrid encoding strategy modified from JPEG-LS, combining run-length encoding, predictive encoding, and a non-encoding mode to facilitate high-speed hardware implementation. Images are processed in tiles, with each tile’s color channels processed independently to preserve individual channel characteristics. Moreover, potential error propagation is confined within a single tile. To enhance throughput, the compression algorithm operates within a 20-stage pipeline architecture. Duplication of computation units and the introduction of key-value registers and a bypass mechanism resolve structural and data dependency hazards within the pipeline. A reorder architecture prevents pipeline blocking, further optimizing system throughput. The proposed architecture is implemented on a XILINX XC7Z045-2FFG900C SoC (Xilinx, Inc., San Jose, CA, USA) and achieves a maximum throughput of up to 346.41 MPixel/s, making it the fastest architecture reported in the literature. Full article
(This article belongs to the Special Issue Complex System Dynamics and Image Processing)
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18 pages, 4994 KB  
Article
Enhanced Design and Characterization of a Wearable IMU for High-Frequency Motion Capture
by Diego Valdés-Tirado, Gonzalo García Carro, Juan C. Alvarez, Diego Álvarez and Antonio López
Sensors 2025, 25(19), 6224; https://doi.org/10.3390/s25196224 - 8 Oct 2025
Viewed by 436
Abstract
This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the [...] Read more.
This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation—including noise, bias, scale factor, power consumption, and drift—demonstrates the device’s reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions. Full article
(This article belongs to the Special Issue Advanced Sensors for Human Health Management)
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23 pages, 6928 KB  
Article
Sustainable Floating PV–Storage Hybrid System for Coastal Energy Resilience
by Yong-Dong Chang, Gwo-Ruey Yu, Ching-Chih Chang and Jun-Hao Chen
Electronics 2025, 14(19), 3949; https://doi.org/10.3390/electronics14193949 - 7 Oct 2025
Viewed by 355
Abstract
Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar [...] Read more.
Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article
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21 pages, 2239 KB  
Review
Unequal Horizons: Global North–South Disparities in Archaeological Earth Observation (2000–2025)
by Athos Agapiou
Remote Sens. 2025, 17(19), 3371; https://doi.org/10.3390/rs17193371 - 6 Oct 2025
Viewed by 397
Abstract
This systematic review analyzes 4359 archaeologically relevant publications spanning 25 years to examine global disparities in archaeological remote sensing research between Global North and Global South participation. This study reveals deep inequalities among these regions, with 72.1% of research output originating from Global [...] Read more.
This systematic review analyzes 4359 archaeologically relevant publications spanning 25 years to examine global disparities in archaeological remote sensing research between Global North and Global South participation. This study reveals deep inequalities among these regions, with 72.1% of research output originating from Global North-only institutions, despite these regions hosting less than half of UNESCO World Heritage Sites. The temporal analysis demonstrates exponential growth, with 47.2% of all research published in the last five years, indicating rapid technological advancement concentrated in well-resourced institutions. Sub-Saharan Africa produces only 0.6% of research output while hosting 9.4% of World Heritage Sites, highlighting a technology gap in heritage protection. The findings suggest an urgent need for coordinated interventions to address structural inequalities and promote technological fairness in global heritage preservation. The research employed bibliometric analysis of Scopus database records from four complementary search strategies, revealing that just three countries—Italy (20.3%), the United States (16.7%), and the United Kingdom (10.0%)—account for nearly half of all archaeological remote sensing research and applications worldwide. This study documents patterns that have profound implications for cultural heritage preservation and sustainable development in an increasingly digital world where advanced Earth observation technologies have become essential for effective heritage protection and archaeological research. Full article
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