Search Results (29,991)

Landsat time series data, which have become freely available in recent years, are commonly used to detect changes in land cover and monitor ecosystem disturbances. Thyme habitats are areas under protection due to their high ecological value. However, human activity leading to land use competition, mainly from overgrazing, poses an increased threat to these habitats. The impact of these disturbances is underreported, and their detection remains essential for thyme conservation. The island of Lemnos was chosen as the study area, because of the significant areas of thyme habitats, which are currently under pressure due to rural abandonment, desertification, overgrazing, and systematic fires in recent decades. A long-term Landsat time series was generated, and the Normalized Difference Vegetation Index (NDVI) was calculated. The change detection algorithm (BFAST) was used to detect and characterize significant changes (breakpoints) within the time series and compare them to local fire events. The analysis showed that Lemnos thyme habitats have been significantly reduced in size due to fires and their conversion to new grazing areas for livestock production. Measures should be taken to conserve thyme habitats with the participation of local stakeholders, including livestock farmers and beekeepers. Satellite monitoring techniques are important tools that could facilitate this conservation process. Full article

Multimodal imaging techniques have the potential to enhance the interpretation of histology by offering additional molecular and structural information beyond that accessible through hematoxylin and eosin (H&E) staining alone. Here, we present a quantitative approach for comparing the information content of different image modalities, such as H&E and multimodal imaging. We used a combination of deep learning and radiomics-based feature extraction with different information markers, implemented in Python 3.12, to compare the information content of the H&E stain, multimodal imaging, and the combined dataset. We also compared the information content of individual channels in the multimodal image and of different Coherent Anti-Stokes Raman Scattering (CARS) microscopy spectral channels. The quantitative measurements of information that we utilized were Shannon entropy, inverse area under the curve (1-AUC), the number of principal components describing 95% of the variance (PC95), and inverse power law fitting. For example, the combined dataset achieved an entropy value of 0.5740, compared to 0.5310 for H&E and 0.5385 for the multimodal dataset using MobileNetV2 features. The number of principal components required to explain 95 percent of the variance was also highest for the combined dataset, with 62 components, compared to 33 for H&E and 47 for the multimodal dataset. These measurements consistently showed that the combined datasets provide more information. These observations highlight the potential of multimodal combinations to enhance image-based analyses and provide a reproducible framework for comparing imaging approaches in digital pathology and biomedical image analysis. Full article

Dynamic line rating (DLR) is an effective technique for real-time assessments on current-carrying capacities of overhead lines (OHLs), improving efficiencies and preventing overloads of transmission networks. Most research related to DLR forecasting mainly translates predictions of weather conditions into DLR forecasts or directly trains artificial intelligence models from DLR observations. Less attention has been given to the predictability of effective wind speeds (EWS) that describe overall convective cooling effects of varying weather conditions along OHLs, which could increase the reliability of DLR forecasts. To assess the effectiveness of EWS concepts in improving DLR predictions, this paper develops an EWS-based method for convective cooling predictions which are critical parameters dominating DLRs of overhead conductors. The EWS is first calculated from actual measurements of wind speeds and directions relative to OHL orientation based on the thermal model of overhead conductors. Then, an autoregressive model along with the Fourier series is employed to predict ultra-short-term EWS variations for up to three 10-min steps ahead, which are eventually converted into predictions of convective cooling effects along OHLs. The proposed EWS-based method is tested based on wind condition measurements in proximity to an OHL. Furthermore, to examine the impacts of angles between wind directions and line orientation on EWS estimation and thus EWS-based convective cooling predictions, the forecasting performance is assessed in the context of different line orientations. Results demonstrate that EWS-based ultra-short-term convective cooling predictions consistently outperform traditional forecasts from original wind conditions across all the tested line orientations. This highlights the significance of the EWS concept in reducing the complexity of DLR forecasting caused by the circular nature of wind directions, and in enhancing the accuracy of convective cooling predictions. Full article

The present study provides a comprehensive characterization of soil elemental composition in the Nile Delta, Egypt. The soil samples were analyzed using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), highly appropriative for the major element determination and Inductively Coupled Plasma Mass Spectrometry (ICP–MS), outstanding for the trace element analysis. A total of 55 elements were measured across 53 soil samples. A variety of statistical and analytical techniques, including both descriptive and inferential methods, were employed to assess the elemental composition of the soil. Bivariate and multivariate statistical analyses, discriminative ternary diagrams, ratio biplots, and unsupervised machine learning algorithms—such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Hierarchical Agglomerative Clustering (HAC)—were utilized to explore the geochemical similarities between elements in the soil. The application of t-SNE for soil geochemistry is still emerging and is characterized by the fact that it preserves the local distribution of elements and reveals non-linear relationships in geochemical research compared to PCA. Geochemical background levels were estimated using Bayesian inference, and the impact of outliers was analyzed. Pollution indices were subsequently calculated to assess potential contamination. The findings suggest that the studied areas do not exhibit significant pollution. Variations in background levels were primarily attributed to the presence of outliers. The clustering results from PCA and t-SNE were consistent in terms of accuracy and the number of identified groups. Four distinct groups were identified, with soil samples in each group sharing similar geochemical properties. While PCA is effective for linear data, t-SNE proved more suitable for nonlinear dimensionality reduction. These results provide valuable baseline data for future research on the studied areas and for evaluating their environmental situation. Full article

Extreme ultraviolet (EUV) ptychography is a promising actinic mask metrology technique capable of providing aberration-free images with subwavelength resolution. However, its performance is fundamentally constrained by the strong absorption of EUV light and the limited detection of high-frequency diffraction signals, which are critical for resolving fine structural details. In this study, we demonstrate significant improvements in EUV ptychographic imaging by implementing an upgraded EUV source system with reduced source etendue and applying an illumination aperture to spatially refine the probe. This approach effectively enhances the photon flux and spatial coherence, resulting in an increased signal-to-noise ratio of the high-frequency diffraction components and an extended maximum detected spatial frequency. Simulations and experimental measurements using a Siemens star pattern confirmed that the refined probe enabled more robust phase retrieval and higher-resolution image reconstruction. Consequently, we achieved a half-pitch resolution of 46 nm, corresponding to a critical dimension of 11.5 nm at the wafer plane. These findings demonstrate the enhanced capability of EUV ptychography as a high-fidelity actinic metrology tool for next-generation EUV mask characterization. Full article

This study examines the impact of environmental, social, and governance (ESG) practices on green innovation and financial performance among 174 publicly listed firms across ASEAN countries over the period from 2019 to 2023. Utilizing an unbalanced panel dataset of firms from key ASEAN economies, the analysis employs panel regression techniques. Green innovation performance is measured through innovation disclosures related to environmental technologies, while financial success is assessed via return on assets (ROA) and Tobin’s Q. The findings reveal that environmental and governance disclosure scores positively influence green innovation, whereas social scores exert a more immediate impact on financial performance. Moreover, green innovation is found to partially mediate the relationship between overall ESG practices and long-term market valuation. These results highlight the strategic role of ESG transparency in enhancing innovation-driven competitiveness, responsible business conduct, and sustainable employment across Southeast Asian markets. Implications are discussed for corporate managers, policymakers, and socially responsible investors. The study reinforces the case for ESG-aligned strategy as a pathway to both innovation, inclusive economic growth, and long-term competitiveness in ASEAN markets. Full article

Amyotrophic lateral sclerosis (ALS) is still a heterogeneous neurodegenerative disorder that can be identified clinically and biologically, without a strong set of biomarkers that can adequately measure its fast rate of progression and molecular heterogeneity. In this review, we intend to consolidate the most relevant and timely advances in ALS biomarker discovery, in order to begin to bring molecular, imaging, genetic, and digital areas together for potential integration into a precision medicine approach to ALS. Our goal is to begin to display how several biomarkers in development (e.g., neurofilament light chain (NfL), phosphorylated neurofilament heavy chain (pNfH), TDP-43 aggregates, mitochondrial stress markers, inflammatory markers, etc.) are changing our understanding of ALS and ALS dynamics. We will attempt to provide a framework for thinking about biomarkers in a systematic way where our candidates are not signals alone but part of a tethered pathophysiological cascade. We are particularly interested in the fast progressor phenotype, a devastating and under-characterized subset of ALS due to a rapid axonal degeneration, early respiratory failure, and very short life span. We will try to highlight the salient molecular features of this ALS subtype, including SOD1 A5V toxicity, C9orf72 repeats, FUS variants, mitochondrial collapse, and impaired autophagy mechanisms, and relate these features to measurable blood and CSF (biomarkers) and imaging platforms. We will elaborate on several interesting tools, for example, single-cell transcriptomics, CSF exosomal cargo analysis, MRI techniques, and wearable sensor outputs that are developing into high-resolution windows of disease progression and onset. Instead of providing a static catalog, we plan on providing a conceptual roadmap to integrate biomarker panels that will allow for earlier diagnosis, real-time disease monitoring, and adaptive therapeutic trial design. We hope this synthesis will make a meaningful contribution to the shift from observational neurology to proactive biologically informed clinical care in ALS. Although there are still considerable obstacles to overcome, the intersection of a precise molecular or genetic association approach, digital phenotyping, and systems-level understandings may ultimately redefine how we monitor, care for, and treat this challenging neurodegenerative disease. Full article

The external cavity frequency doubling technique serves as a potent method for generating short-wavelength lasers, yet achieving high-power outputs remains challenging due to the thermal lens effect. This study systematically investigates the generation mechanism of the thermal lens effect and its impact on laser performance. By optimizing the bow-tie cavity design and leveraging a large beam waist of 106 µm to suppress thermal-induced distortions, we demonstrate a tunable 420 nm laser with up to 800 mW of output power and a peak conversion efficiency of 77%. The fundamental light source, a Ti:Sa laser locked to an ultra-stable cavity, ensures a narrow linewidth, flexible tunability, and long-term frequency stability. This high-performance blue laser enables the efficient Rydberg excitation of rubidium atoms, presenting critical applications in quantum computing, quantum simulation, and quantum precision measurement. Full article

Introduction: Recurrent ankle sprains can lead to chronic ankle instability. The flossing technique aims to modify the function and characteristics of fascial tissue. The objective was to evaluate the effectiveness of flossing and sliding techniques in improving subjects with previous ankle sprains. Methods: Randomized, double-blind clinical study with a follow-up period. Twenty-six subjects were assigned to two study groups: experimental (flossing technique and passive manual therapy techniques) and placebo control group (flossing technique without compression and manual therapy techniques without sliding). The intervention lasted three weeks, with two sessions per week. The study variables were dorsiflexion under load (Leg Motion^®), ankle mobility under unloaded conditions (goniometer), pressure pain threshold (algometer), and stability (Rs Scan^® pressure platform). Three measurements were taken: pre-treatment (T0), post-treatment (T1), and after 3 weeks of follow-up (T2). Results: There were significant intergroup differences in dorsiflexion under load (F = 4.90; p = 0.02). Range of motion in plantar flexion without load (F = 3.78; p = 0.04), in the ellipse area (F = 4.72; p = 0.01), left stability (F = 3.74; p = 0.03), and right stability (F = 3.73; p = 0.03) without visual support. Conclusions: A physiotherapy protocol using flossing and manual sliding therapy can increase loaded dorsal flexion in young adults with previous ankle sprains. This intervention can also improve ankle plantar flexion under unloaded conditions. The area of the ellipse without visual support can improve in young adults with a history of ankle sprains following a program of flossing and manual therapy. Full article

With the continuous increase in mining activities, effective tailings management has become a critical concern in geotechnical and environmental engineering. This study systematically investigates the microstructural characteristics and 3D reconstruction behavior of copper tailings with different particle sizes using X-ray computed tomography (micro-CT), digital image processing, and 3D modeling techniques. Two particle size groups (fine: 0.075–0.15 mm; coarse: 0.15–0.3 mm) were analyzed to quantify differences in particle morphology, pore structure, and orientation anisotropy. Binary images and reconstructed models revealed that coarse particles tend to have more irregular and angular shapes, while fine particles exhibit more complex pore networks with higher fractal dimensions. The apparent porosity derived from CT data was consistently lower than laboratory measurements, likely due to internal agglomeration effects. Orientation analysis indicated that particle alignment and anisotropy vary systematically with section angle relative to the principal stress direction. These findings offer new insights into the particle-scale mechanisms affecting the packing, porosity, and anisotropy of tailings, providing a scientific basis for enhancing the structural evaluation and sustainable management of tailings storage facilities. Full article

Despite their crucial biological role as metabolites, reactive oxygen and reactive nitrogen species (ROS and RNS) can have a negative effect on organisms when their cellular contents overwhelm the normal equilibrium provided by antioxidant defenses. Important biomolecules, such as lipids, proteins, and nucleic acids (i.e., DNA), can be damaged by their oxidative effects, resulting in malfunction or a shorter lifespan of cells and, eventually, of the whole organism. Oxidative stress can be defined as the consequence of an imbalance of pro-oxidants and antioxidants due to external stress sources (e.g., exposure to xenobiotics, UV radiation, or thermic stress). It can be evaluated by monitoring specific biomarkers to determine the state of health of breathing organisms. Assessments of ROS, RNS, specific degenerative oxidative reaction products, and antioxidant system efficiency (antioxidant enzyme activities and antioxidant compound contents) have been extensively performed for this purpose. A wide variety of analytical methods for measuring these biomarkers exist in the literature; most of these methods involve indirect determination via spectrophotometric and spectrofluorometric techniques. This review reports a collection of studies from the last decade regarding contaminant-induced oxidative stress in insects, with a brief description of the analytical methods utilized. Full article

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

There is increasing interest in the role of parks as potential cool refuges in the age of climate change. Such potential refuges result from the Park Cool Island (PCI) effect, reflecting the temperature differential between the park and surrounding urban areas. However, this study of different park typologies in Perth, Australia, illustrates that while surface temperatures are 10–15 °C lower in parks during summer afternoons (much less than at other times), air temperatures are generally no different from the adjacent streetscape for the smaller parks. Only the largest park in the study had 1–2 °C lower morning and mid-afternoon air temperature differentials. The study illustrates that while the PCI is a real phenomenon, the magnitude in terms of air temperature is small, and it is of less relevance to the conditions felt by humans in average summer daytime conditions than the direct effects of solar radiation. Many studies have assessed the PCI effect, an indicator that has shown a wide range across different studies and measurement techniques. However, this novel paper utilises satellite remote-sensed land surface temperatures, on-ground measurements of surface temperatures, air temperatures, and humidity, as well as modelling using the microclimatic simulation software ENVI-met version 5.0. A reliance on land surface temperature, which in isolation has a marginal correlation with human experience of thermal comfort, has led some researchers to overstate the PCI effect and its influence on adjoining urban areas. The research reported in this paper illustrates that it is the shade provided by the canopy in parks, rather than parks themselves, that provides meaningful thermal comfort benefits. Accordingly, adaptation to increasing temperatures requires the creation of a continuous canopy, ideally over parks, streetscapes, and private lots in an interconnected network. Full article

Suicide is a leading cause of death among construction workers, particularly younger and lower-skilled employees. Barriers such as stigma, low mental health literacy, and traditional masculine norms hinder help-seeking in this male-dominated sector. Few mental health interventions are tailored to this context. This study developed a co-designed, theory-informed training to improve mental health literacy, reduce stigma, and increase help-seeking among construction workers in Ireland. Using the Medical Research Council’s framework, the training was developed with the Theory of Planned Behavior (TPB), Behavior Change Techniques, and extensive stakeholder co-design. Two systematic reviews, a broad literature review, and focus groups with industry managers informed the content and structure. The training will be pilot-tested using validated measures: the Literacy of Suicide Scale (LOSS), the Stigma of Suicide Scale (SOSS), and the General Help-Seeking Questionnaire (GHSQ), the results of which will be the subject of a separate study. CAIRDE is a promising, evidence-based training that addresses key mental health barriers in Irish construction. Embedding the TPB within a co-design methodology has resulted in the development of a training program that is underpinned by theoretical fidelity and cultural relevance and provides a framework for other male-dominated industries to draw upon. Future work should address remaining challenges related to stigma and help-seeking, and explore broader implementation through integration into mandatory safety training. Full article

The precise, non-destructive monitoring of fish length and weight is a core technology for advancing intelligent aquaculture. However, this field faces dual challenges: traditional contact-based measurements induce stress and yield loss. In addition, existing computer vision methods are hindered by prediction biases from imbalanced data and the deployment bottleneck of balancing high accuracy with model lightweighting. This study aims to overcome these challenges by developing an efficient and robust deep learning framework. We propose ECR-MobileNet, a lightweight framework built on MobileNetV3-Small. It features three key innovations: an efficient channel attention (ECA) module to enhance feature discriminability, an original adaptive multi-scale contrastive regression (AMCR) loss function that extends contrastive learning to multi-dimensional regression for length and weight simultaneously to mitigate data imbalance, and a dependency-graph-based (DepGraph) structured pruning technique that synergistically optimizes model size and performance. On our multi-scene largemouth bass dataset, the pruned ECR-MobileNet-P model comprehensively outperformed 14 mainstream benchmarks. It achieved an R² of 0.9784 and a root mean square error (RMSE) of 0.4296 cm for length prediction, as well as an R² of 0.9740 and an RMSE of 0.0202 kg for weight prediction. The model’s parameter count is only 0.52 M, with a computational load of 0.07 giga floating-point operations per second (GFLOPs) and a CPU latency of 10.19 ms, achieving Pareto optimality. This study provides an edge-deployable solution for stress-free biometric monitoring in aquaculture and establishes an innovative methodological paradigm for imbalanced regression and task-oriented model compression. Full article