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Search Results (3,539)

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Keywords = dynamic response measurement

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14 pages, 2702 KB  
Article
Albendazole Detection at a Nanomolar Level Through a Fabry–Pérot Interferometer Realized via Molecularly Imprinted Polymers
by Ines Tavoletta, Ricardo Oliveira, Filipa Sequeira, Catarina Cardoso Novo, Luigi Zeni, Giancarla Alberti, Nunzio Cennamo and Rogerio Nunes Nogueira
Sensors 2025, 25(20), 6456; https://doi.org/10.3390/s25206456 (registering DOI) - 18 Oct 2025
Viewed by 119
Abstract
Albendazole (ABZ) is a broad-spectrum anthelmintic drug whose residual presence in food and the environment raises public health concerns, requiring rapid and sensitive methods of detection. In this work, a sensitive Fabry–Pérot interferometer (FPI) probe was fabricated by realizing a cavity located at [...] Read more.
Albendazole (ABZ) is a broad-spectrum anthelmintic drug whose residual presence in food and the environment raises public health concerns, requiring rapid and sensitive methods of detection. In this work, a sensitive Fabry–Pérot interferometer (FPI) probe was fabricated by realizing a cavity located at the tip of a single-mode optical fiber core with a molecularly imprinted polymer (MIP) for ABZ detection. The fabrication process involved the development of a photoresist-based micro-hole filled by the specific MIP via thermal polymerization. Interferometric measurements obtained using the proposed sensor system have demonstrated a limit of detection (LOD) of 27 nM, a dynamic concentration range spanning from 27 nM (LOD) to 250 nM, and a linear response at the nanomolar level (27 nM–100 nM). The selectivity test demonstrated no signal when interfering molecules were present, and the application of the sensor for ABZ quantification in a commercial pharmaceutical sample provided good recovery, in accordance with bioanalytical validation standard methods. These results demonstrate the capability of a MIP layer-based FPI probe to provide low-cost and selective optical-sensing strategies, proposing a competitive approach to traditional analytical techniques for ABZ monitoring. Full article
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15 pages, 4896 KB  
Article
Typhoon-Driven Shifts in Dissolved Organic Carbon Across Mangrove Ecosystems of Varying Restoration Age
by Youwei Lin, Shengjie Han, Ruina Liu, Yunfeng Shi, Xiaoya Zhang, Zongbo Peng, Zhen Ni and Mingzhong Liu
Forests 2025, 16(10), 1599; https://doi.org/10.3390/f16101599 - 17 Oct 2025
Viewed by 86
Abstract
Mangrove ecosystems are vital to coastal carbon cycling, yet their response to extreme climatic events remains underexplored. This study assesses dissolved organic carbon (DOC) dynamics across four ecosystem types—primary mangrove, restored (5-year and 8-year), and bare land—during three typhoons (Maliksi, Yagi, and Trami) [...] Read more.
Mangrove ecosystems are vital to coastal carbon cycling, yet their response to extreme climatic events remains underexplored. This study assesses dissolved organic carbon (DOC) dynamics across four ecosystem types—primary mangrove, restored (5-year and 8-year), and bare land—during three typhoons (Maliksi, Yagi, and Trami) that occurred in 2024. DOC concentrations (mol m−2 s−1) were measured across pre-, during-, and post-event phases and analyzed using boxplots, heatmaps, and ANOVA. Results show that primary mangroves maintained stable DOC levels, indicating strong biogeochemical resilience. Restored plots exhibited phase-dependent DOC variability, with older restoration age linked to improved carbon retention. Bare land showed consistently high DOC release, especially post-event, reflecting vulnerability to hydrological stress. DOC peaks occurred after typhoons, suggesting delayed carbon mobilization via microbial turnover and detrital input. These findings highlight the role of restoration age and vegetation cover in stabilizing coastal carbon under intensifying climatic extremes. Full article
(This article belongs to the Section Forest Ecology and Management)
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26 pages, 2560 KB  
Review
A Review of Transmission Line Icing Disasters: Mechanisms, Detection, and Prevention
by Jie Hu, Longjiang Liu, Xiaolei Zhang and Yanzhong Ju
Buildings 2025, 15(20), 3757; https://doi.org/10.3390/buildings15203757 - 17 Oct 2025
Viewed by 232
Abstract
Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics [...] Read more.
Transmission line icing poses a significant natural disaster threat to power grid security. This paper systematically reviews recent advances in the understanding of icing mechanisms, intelligent detection, and prevention technologies, while providing perspectives on future development directions. In mechanistic research, although a multi-physics coupling framework has been established, characterization of dynamic evolution over complex terrain and coupled physical mechanisms remains inadequate. Detection technology is undergoing a paradigm shift from traditional contact measurements to non-contact intelligent perception. Visual systems based on UAVs and fixed platforms have achieved breakthroughs in ice recognition and thickness retrieval, yet their performance remains constrained by image quality, data scale, and edge computing capabilities. Anti-/de-icing technologies have evolved into an integrated system combining active intervention and passive defense: DC de-icing (particularly MMC-based topologies) has become the mainstream active solution for high-voltage lines due to its high efficiency and low energy consumption; superhydrophobic coatings, photothermal functional coatings, and expanded-diameter conductors show promising potential but face challenges in durability, environmental adaptability, and costs. Future development relies on the deep integration of mechanistic research, intelligent perception, and active prevention technologies. Through multidisciplinary innovation, key technologies such as digital twins, photo-electro-thermal collaborative response systems, and intelligent self-healing materials will be advanced, with the ultimate goal of comprehensively enhancing power grid resilience under extreme climate conditions. Full article
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20 pages, 4116 KB  
Article
Stability Matters: Revealing Causal Roles of G-Quadruplexes (G4s) in Regulation of Chromatin and Transcription
by Ke Xiao, Rongxin Zhang, Tiantong Tao, Huiling Shu, Hao Huang, Xiao Sun and Jing Tu
Genes 2025, 16(10), 1231; https://doi.org/10.3390/genes16101231 - 17 Oct 2025
Viewed by 208
Abstract
Background: G-quadruplexes (G4s) are non-canonical higher-order nucleic acid structures that form at guanine-rich motifs, with features spanning both secondary and tertiary structural levels. These dynamic structures play pivotal roles in diverse cellular processes. Endogenous G4s (eG4s) function through their dynamically formed structures, prompting [...] Read more.
Background: G-quadruplexes (G4s) are non-canonical higher-order nucleic acid structures that form at guanine-rich motifs, with features spanning both secondary and tertiary structural levels. These dynamic structures play pivotal roles in diverse cellular processes. Endogenous G4s (eG4s) function through their dynamically formed structures, prompting the hypothesis that their thermostability, as a key structural property, may critically influence their functionality. This study investigates the relationship between G4 stability and other functional genomic signals within eG4 regions and examines its broader impact on chromatin organization and transcriptional regulation. Methods: We developed a mapping strategy to associate in vitro-derived thermostability metrics and multi-omics functional signals with eG4 regions. A stability-centric analytical framework combining correlation analysis and causal inference using the Bayesian networks was applied to decipher causal relationships between G4 stability and the other related signals. We further analyzed the association between the stability of transcription start site (TSS)-proximal eG4s and the biological functions of their downstream genes. Results: Our analyses demonstrate that G4 thermostability exerts causal effects on epigenetic states and transcription factor binding, thereby influencing chromatin and transcription regulation. We further show distinct network architectures for G4-binding versus non-binding transcription factors. Additionally, we find that TSS-proximal eG4s are enriched in genes involved in core proliferation and stress-response pathways, suggesting that eG4s may serve as regulatory elements facilitating rapid stress responses through genome-wide coordination. Conclusions: These findings establish thermostability—though measured in vitro—as an intrinsic property that shapes eG4 functionality. Our study not only provides novel insights into the functional relevance of G4 thermostability but also introduces a generalizable framework for high-throughput G4 data interpretation, significantly advancing the functional decoding of eG4s across biological contexts. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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19 pages, 7898 KB  
Article
Drilling Monitoring While Drilling and Comprehensive Characterization of Lithology Parameters
by Huijie Zhai, Hui Chen, Bin Shi, Hongchao Zhao and Fei Gao
Appl. Sci. 2025, 15(20), 11134; https://doi.org/10.3390/app152011134 - 17 Oct 2025
Viewed by 87
Abstract
The monitoring technology used during drilling has become a crucial means of gathering information about the underground rock mass. However, the drilling response parameters are affected by the coupling of operating parameters and rock mass properties, which leads to the challenge of lithology [...] Read more.
The monitoring technology used during drilling has become a crucial means of gathering information about the underground rock mass. However, the drilling response parameters are affected by the coupling of operating parameters and rock mass properties, which leads to the challenge of lithology inversion based on drilling parameters in complex strata. At present, the precise quantitative response mechanism between operating parameters and drilling parameters is still not clear in the common lithology of mining, which restricts the further improvement of the accuracy of lithology identification while drilling and the optimization of drilling technology. In order to improve the measurement of drilling technology, the relationship between rock parameters and drilling parameters in the process of mining drilling is explored. This paper carried out physical and mechanical experiments; built a small drilling platform (including magnetic suction drilling, a data monitoring system, and a rock confining pressure system); carried out three different specifications, 330 r/min, 360 r/min, and 390 r/min, of the initial speed of the drilling experiment; and added 330 r/min initial-speed-drilling different-strength rock-drilling experiments. The experimental results show that rock drilling is divided into three stages: the initial stage of drilling, the crack propagation stage, and the bit retreating stage. The rotation speed has a great influence on the drilling speed, torque, weight on bit, and drilling time. According to the Pearson fitting relationship of drilling parameters, the correlation between F and PR is −0.783, indicating a strong positive correlation, and the correlation between RPM and PR is 0.827, indicating a strong negative correlation. The power function y = axb is used to fit the drilling parameters and rock parameters. The fitting effect is good, and the torque and uniaxial tensile strength R2 is as high as 0.9966. The experimental conclusion provides a theoretical basis for lithology identification in intelligent mining drilling and discusses the feasibility of a dynamic monitoring scheme for the drilling rig. Full article
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18 pages, 4255 KB  
Article
Enhanced Velocity Extraction of Moving Subject Using Through-Wall-Imaging Radar
by Yea-Jun Jung, Hak-Hoon Lee and Hyun-Chool Shin
Appl. Sci. 2025, 15(20), 11120; https://doi.org/10.3390/app152011120 - 16 Oct 2025
Viewed by 114
Abstract
Detecting human movement through walls is vital for disaster response and security, where obstacles obscure visibility and endanger rescue operations, necessitating advanced through-wall radar (TWR) solutions. This study introduces a novel method to enhance velocity estimation accuracy in TWR systems despite signal attenuation [...] Read more.
Detecting human movement through walls is vital for disaster response and security, where obstacles obscure visibility and endanger rescue operations, necessitating advanced through-wall radar (TWR) solutions. This study introduces a novel method to enhance velocity estimation accuracy in TWR systems despite signal attenuation caused by walls. Our proposed method dynamically adjusts the beamforming range based on radar-target distance, improving point cloud reconstruction and enabling precise velocity measurements. We conducted experiments in 1D and 2D indoor environments, with and without a brick wall, to validate the proposed method’s effectiveness. In both 1D and 2D experiments, the proposed method successfully restored the velocity information of five subjects located behind a brick wall, achieving root mean square error (RMSE) values of approximately 0.1–0.2 m/s in most cases. Furthermore, statistical comparisons before and after applying the proposed method in the brick wall environment revealed significant reductions in RMSE (p < 0.05) and significant increases in the number of detected point clouds (p < 0.05), confirming the method’s effectiveness in enhancing both velocity extraction accuracy and detection capability. Full article
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17 pages, 2475 KB  
Article
YOLO-LMTB: A Lightweight Detection Model for Multi-Scale Tea Buds in Agriculture
by Guofeng Xia, Yanchuan Guo, Qihang Wei, Yiwen Cen, Loujing Feng and Yang Yu
Sensors 2025, 25(20), 6400; https://doi.org/10.3390/s25206400 - 16 Oct 2025
Viewed by 257
Abstract
Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection [...] Read more.
Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection model based on the YOLOv11n architecture. First, a Multi-scale Edge-Refinement Context Aggregator (MERCA) module is proposed to replace the original C3k2 block in the backbone. MERCA captures multi-scale contextual features through hierarchical receptive field collaboration and refines edge details, thereby significantly improving the perception of fine structures in tea buds. Furthermore, a Dynamic Hyperbolic Token Statistics Transformer (DHTST) module is developed to replace the original PSA block. This module dynamically adjusts feature responses and statistical measures through attention weighting using learnable threshold parameters, effectively enhancing discriminative features while suppressing background interference. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the original network structure, enabling the adaptive fusion of semantically rich and spatially precise features via bidirectional cross-scale connections while reducing computational complexity. In the self-built tea bud dataset, experimental results demonstrate that compared to the original model, the YO-LO-LMTB model achieves a 2.9% improvement in precision (P), along with increases of 1.6% and 2.0% in mAP50 and mAP50-95, respectively. Simultaneously, the number of parameters decreased by 28.3%, and the model size reduced by 22.6%. To further validate the effectiveness of the improvement scheme, experiments were also conducted using public datasets. The results demonstrate that each enhancement module can boost the model’s detection performance and exhibits strong generalization capabilities. The model not only excels in multi-scale tea bud detection but also offers a valuable reference for reducing computational complexity, thereby providing a technical foundation for the practical application of intelligent tea-picking systems. Full article
(This article belongs to the Section Smart Agriculture)
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24 pages, 502 KB  
Article
Exception-Driven Security: A Risk-Aware Permission Adjustment for High-Availability Embedded Systems
by Mina Soltani Siapoush and Jim Alves-Foss
Mathematics 2025, 13(20), 3304; https://doi.org/10.3390/math13203304 - 16 Oct 2025
Viewed by 202
Abstract
Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the [...] Read more.
Real-time operating systems (RTOSs) are widely used in embedded systems to ensure deterministic task execution, predictable responses, and concurrent operations, which are crucial for time-sensitive applications. However, the growing complexity of embedded systems, increased network connectivity, and dynamic software updates significantly expand the attack surface, exposing RTOSs to a variety of security threats, including memory corruption, privilege escalation, and side-channel attacks. Traditional security mechanisms often impose additional overhead that can compromise real-time guarantees. In this work, we present a Risk-aware Permission Adjustment (RPA) framework, implemented on CHERIoT RTOS, which is a CHERI-based operating system. RPA aims to detect anomalous behavior in real time, quantify security risks, and dynamically adjust permissions to mitigate potential threats. RPA maintains system continuity, enforces fine-grained access control, and progressively contains the impact of violations without interrupting critical operations. The framework was evaluated through targeted fault injection experiments, including 20 real-world CVEs and 15 abstract vulnerability classes, demonstrating its ability to mitigate both known and generalized attacks. Performance measurements indicate minimal runtime overhead while significantly reducing system downtime compared to conventional CHERIoT and FreeRTOS implementations. Full article
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17 pages, 4241 KB  
Article
Spatiotemporal Dynamics of Forest Fire Risk in Southeastern China Under Climate Change: Hydrothermal Drivers and Future Projections
by Dapeng Gong and Min Jing
Atmosphere 2025, 16(10), 1189; https://doi.org/10.3390/atmos16101189 - 15 Oct 2025
Viewed by 121
Abstract
Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density [...] Read more.
Forest fire regimes are undergoing systematic reorganization under climate change, particularly in monsoon–human coupled ecosystems such as Southeastern China, where risk dynamics remain poorly quantified. This study proposes a meteorology-driven machine learning model designed to assess long-term forest fire risk. Using kernel density estimation and standard deviational ellipse analysis, we assessed the spatiotemporal patterns of fire risk during the observational period and their future shifts across the SSP1-2.6 and SSP5-8.5 scenarios. The results indicate a significant overall decline in fire frequency from 2008 to 2024 (−467.3 fires/year, representing an annual average reduction of 10.8%, p < 0.001), which is attributed primarily to enhanced regional fire prevention and control measures, yet with a notable reversal after 2016 in Guangdong and Fujian. Fires are highly seasonal, with 74% occurring in the dry season (December–March). The meteorologically driven random forest model exhibited excellent performance (R2 = 0.889), validating meteorological conditions as key drivers of regional fire dynamics. It is projected that intensified warming (+5.5 °C under SSP5-8.5) and increased precipitation variability (+23%) are likely to drive pronounced northward and inland migration in high-risk zones. Our projections indicate that by the end of the century, high-risk area coverage could expand to 19.2%, with a shift from diffuse to clustered patterns, particularly in Jiangsu and Zhejiang. These findings underscore the critical role of hydrothermal reconfiguration in reshaping fire risk geography and highlight the need for dynamic, region-specific fire management strategies in response to compound climate risks. Full article
(This article belongs to the Section Climatology)
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22 pages, 2440 KB  
Article
Behaviors of Sediment Particles During Erosion Driven by Turbulent Wave Action
by Fei Wang, Jun Xu and Bryce Vaughan
GeoHazards 2025, 6(4), 66; https://doi.org/10.3390/geohazards6040066 - 15 Oct 2025
Viewed by 167
Abstract
Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization [...] Read more.
Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization and the factors governing displacement patterns. This study employed a custom-built wave flume and a 3D-printed sampler to examine sediment behavior under controlled wave conditions. Rounded glass beads, chosen to eliminate the influence of particle shape, were used as sediment analogs with a similar specific gravity to natural sand. Ten experiments were conducted to systematically assess the effects of particle size, particle number, input voltage (wave power), and water depth on sediment response. The results revealed that (1) only a fraction of particles were mobilized, with the remainder forming stable interlocking structures; (2) the number of displaced particles increased with particle size, particle count, and water depth; (3) a threshold wave power is required to initiate erosion, though buoyancy under shallow conditions reduces this threshold; and (4) wave steepness, rather than voltage or wave height alone, provided the strongest predictor of sediment displacement. These findings highlight the central role of wave steepness in erosion modeling and call for its integration into predictive frameworks. The study concludes with methodological limitations and proposes future research directions, including expanded soil types, large-scale flume testing, and advanced flow field measurements. Full article
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24 pages, 6915 KB  
Article
A Framework for Sustainable Power Demand Response: Optimization Scheduling with Dynamic Carbon Emission Factors and Dual DPMM-LSTM
by Qian Zhang, Xunting Wang, Jinjin Ding, Haiwei Wang, Fulin Zhao, Xingxing Ju and Meijie Zhang
Sustainability 2025, 17(20), 9123; https://doi.org/10.3390/su17209123 - 15 Oct 2025
Viewed by 159
Abstract
In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response [...] Read more.
In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response methods often overlook the dynamic characteristics of power system carbon emissions and fail to accurately characterize the complex relationship between power consumption and carbon emissions, which results in suboptimal emission reduction results. To address this challenge, this paper proposes and validates an innovative low-carbon demand response optimization scheduling method as a sustainable tool. The core of this method is the development of a dynamic carbon emission factor (DCEF) assessment model. By innovatively integrating marginal and average carbon emission factors, it becomes a dynamic sustainability indicator that can measure the environmental performance of the power grid in real time. To characterize the relationship between power consumption behavior and carbon emissions, we employ an adaptive Dirichlet process mixture model (DPMM). This model does not require a preset number of clusters and can automatically discover patterns in the data, such as grouping holidays and working days with similar power consumption characteristics. Based on the clustering results and historical data, a dual long short-term memory (LSTM) deep learning network architecture is designed to achieve a coordinated prediction of power consumption and DCEFs for the next 24 h. On this basis, a method is established with the goal of maximizing carbon emission reduction while considering constraints such as fixed daily power consumption, user comfort, and equipment safety. Simulation results demonstrate that this approach can effectively reduce regional carbon emissions through accurate prediction and optimized scheduling. This provides not only a quantifiable technical path for improving the environmental sustainability of the power system but also decision-making support for the formulation of energy policies and incentive mechanisms that align with sustainable development goals. Full article
(This article belongs to the Special Issue Smart Electricity Grid and Sustainable Power Systems)
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16 pages, 1948 KB  
Review
Process-Based Modeling of Forest Soil Carbon Dynamics
by Mingyi Zhou, Shuai Wang, Qianlai Zhuang, Zijiao Yang, Chongwei Gan and Xinxin Jin
Forests 2025, 16(10), 1579; https://doi.org/10.3390/f16101579 - 14 Oct 2025
Viewed by 161
Abstract
Forests play a pivotal role in the global carbon cycle, yet accurately simulating forest soil carbon dynamics remains a significant challenge for process-based models. This review systematically compares the mechanistic foundations of traditional models (e.g., Century, CLM5) with emerging microbial-explicit models (e.g., MEND), [...] Read more.
Forests play a pivotal role in the global carbon cycle, yet accurately simulating forest soil carbon dynamics remains a significant challenge for process-based models. This review systematically compares the mechanistic foundations of traditional models (e.g., Century, CLM5) with emerging microbial-explicit models (e.g., MEND), highlighting key differences in mathematical formulation (first-order kinetics vs. Michaelis–Menten kinetics), carbon pools partitioning (measurable vs. non-measurable experimentally), and the representation of soil carbon stabilization mechanisms (inherent recalcitrance, physical protection, and chemical protection). Despite advances in process-based models in predicting forest soil organic carbon (SOC), improving prediction accuracy, and assessing SOC response to climate change, current research still faces several challenges. These include difficulties in capturing depth-dependent variations in critical microbial parameters such as microbial carbon use efficiency (CUE), limited capacity to distinguish the relative contributions of aboveground and belowground litter inputs to SOC formation, and a general lack of long-term observational data across soil profiles. To address these limitations, this study emphasizes the importance of integrating remote sensing data and refining cross-scale simulation approaches. Such improvements are essential for enhancing model predictive accuracy and establishing a more robust theoretical basis for forest carbon management and climate change mitigation. Full article
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22 pages, 2258 KB  
Article
Designing Light for Emotion: A Neurophysiological Approach to Modeling Affective Responses to the Interplay of Color and Illuminance
by Xuejiao Li, Ruili Wang and Mincheol Whang
Biomimetics 2025, 10(10), 696; https://doi.org/10.3390/biomimetics10100696 - 14 Oct 2025
Viewed by 433
Abstract
As the influence of indoor environments on human emotional regulation and cognitive function becomes increasingly critical in modern society, there is a growing need for intelligent lighting systems that dynamically respond to users’ emotional states. While previous studies have investigated either illuminance or [...] Read more.
As the influence of indoor environments on human emotional regulation and cognitive function becomes increasingly critical in modern society, there is a growing need for intelligent lighting systems that dynamically respond to users’ emotional states. While previous studies have investigated either illuminance or color in isolation, this study concentrates on quantitatively analyzing the interaction of these two key elements on human emotion and cognitive control capabilities. Utilizing electroencephalography (EEG) and electrocardiography (ECG) signals, we measured participants’ physiological responses and subjective emotional assessments in 18 unique lighting conditions, combining six colors and three levels of illuminance. The results confirmed that the interaction between light color and illuminance significantly affects physiological indicators related to emotion regulation. Notably, low-illuminance purple lighting was found to promote positive emotions and inhibit negative ones by increasing frontal alpha asymmetry (FAA) and gamma wave activity. Conversely, low-illuminance environments generally diminished cognitive reappraisal and negative emotion inhibition capabilities. Furthermore, a random forest model integrating time-series data from EEG and ECG predicted emotional valence and arousal with accuracies of 87% and 79%, respectively, demonstrating the validity of multi-modal physiological signal-based emotion prediction. This study provides empirical data and a theoretical foundation for the development of human-centered, emotion-adaptive lighting systems by presenting a quantitative causal model linking lighting, physiological responses, and emotion. These findings also provide a biomimetic perspective by linking lighting-induced physiological responses with emotion regulation, offering a foundation for the development of adaptive lighting systems that emulate natural light–human interactions. Full article
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24 pages, 4471 KB  
Article
Analysis of the Effect of Machining Parameters on the Cutting Tool Deflection in Curved Surface Machining
by Michał Leleń, Magdalena Zawada-Michałowska, Paweł Pieśko, Katarzyna Biruk-Urban, Jerzy Józwik, Jarosław Korpysa, Kamil Anasiewicz, Witold Habrat and Joanna Lisowicz
Appl. Sci. 2025, 15(20), 11013; https://doi.org/10.3390/app152011013 - 14 Oct 2025
Viewed by 121
Abstract
The aim of this study is to investigate the impact of machining parameters on the deflection of a cutting tool (i.e., end mill) in the milling of a surface with a curvilinear profile. Test samples were made of aluminium alloy EN AW-7075 T651. [...] Read more.
The aim of this study is to investigate the impact of machining parameters on the deflection of a cutting tool (i.e., end mill) in the milling of a surface with a curvilinear profile. Test samples were made of aluminium alloy EN AW-7075 T651. Experiments were conducted using the Gocator 2530 laser line profile sensor for real-time measurement of dynamic tool displacement with an inspection speed up to 10 kHz at resolution ranging from 0.028 to 0.054 mm. Response surface methodology was used. Five main technological factors were analysed: cutting speed, feed per tooth (cutting parameters), amplitude, term (curvilinear profile parameters), and the number of flutes (end mill parameter). Obtained data were filtered and visualised as 3D plots. The results showed that cutting speed and amplitude had the greatest impact on tool deflection, while feed per tooth also played a significant role in process stability. In particular, the use of tools with a higher number of flutes led to a considerable reduction in tool deflection, confirming their positive effect on the stability of the machining process. These findings may serve as a basis for the optimisation of machining parameters by taking into account the dynamic deformation of cutting tools. Full article
(This article belongs to the Special Issue Advances in Precision Machining Technology)
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36 pages, 2906 KB  
Review
Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures
by Paraskevas Koukaras and Christos Tjortjis
Big Data Cogn. Comput. 2025, 9(10), 258; https://doi.org/10.3390/bdcc9100258 - 13 Oct 2025
Viewed by 223
Abstract
The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval [...] Read more.
The increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge. This review addresses the organisation, indexing, and access strategies, which enable scalable and responsive retrieval of structured patterns. We examine the underlying types of data and pattern outputs, common retrieval operations, and the variety of query types encountered in practice. Key indexing structures are surveyed, including prefix trees, inverted indices, hash-based approaches, and bitmap-based methods, each suited to different pattern representations and workloads. Storage designs are discussed with attention to metadata annotation, format choices, and redundancy mitigation. Query optimisation strategies are reviewed, emphasising index-aware traversal, caching, and ranking mechanisms. This paper also explores scalability through parallel, distributed, and streaming architectures, and surveys current systems and tools, which integrate mining and retrieval capabilities. Finally, we outline pressing challenges and emerging directions, such as supporting real-time and uncertainty-aware retrieval, and enabling semantic, cross-domain pattern access. Additional frontiers include privacy-preserving indexing and secure query execution, along with integration of repositories into machine learning pipelines for hybrid symbolic–statistical workflows. We further highlight the need for dynamic repositories, probabilistic semantics, and community benchmarks to ensure that progress is measurable and reproducible across domains. This review provides a comprehensive foundation for designing next-generation pattern retrieval systems, which are scalable, flexible, and tightly integrated into analytic workflows. The analysis and roadmap offered are relevant across application areas including finance, healthcare, cybersecurity, and retail, where robust and interpretable retrieval is essential. Full article
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