Search Results (2,636)

Satellite-based land surface temperature (LST) products are frequently affected by cloud cover and atmospheric conditions, resulting in missing data that significantly limits the continuous monitoring of the thermal environment in complex terrains, such as the Tibetan Plateau. Existing spatiotemporal interpolation methods face clear accuracy limitations when addressing extensive data gaps, while physical models often struggle due to insufficient meteorological inputs in complex landscapes. Moreover, conventional data-driven approaches usually overlook local spatial variations, resulting in smoothed thermal patterns and systematic errors. To overcome these issues, we propose a Physically Constrained Spatial Residual Learning framework. In this framework, we use the Enhanced Annual Temperature Cycle (EATC) model to capture the temporal baseline of LST first. Then, we integrate multi-source auxiliary data into the Geographical-XGBoost (G-XGBoost) algorithm to model spatial nonlinear residuals. Using simulated cloud masks on the 2017 MODIS LST dataset from the Qinghai–Tibet Engineering Corridor, we show that the hybrid model outperforms both individual physical models and global machine learning models in accuracy and spatial detail recovery. Validation results yield an R² of 0.88, an RMSE of 1.92 K, and a mean bias of 0.07 K. Seasonal evaluations indicate best performance in winter (RMSE = 1.19 K) with robust performance in summer. Furthermore, the framework reduces boundary artifacts and accurately reproduces thermal spatial patterns in complex terrain through adaptive local bandwidth and weight adjustments. This approach provides a reliable method for high-precision LST reconstruction over heterogeneous alpine surfaces. Full article

Grasslands represent one of the most extensive terrestrial biomes globally, covering approximately one-third of the Earth’s land surface, yet they are increasingly threatened by land-use change and overgrazing, underscoring the need for reliable monitoring approaches. This study compares the performance of machine learning and deep learning algorithms for grassland mapping using multi-source remote sensing data derived from Sentinel-1, Sentinel-2, and terrain variables. The research was conducted in Mpumalanga Province, South Africa, a heterogeneous landscape comprising lowland savannas, high-altitude grasslands, escarpments, and riverine wetlands. Random Forest (RF) and Support Vector Machine (SVM) classifiers were implemented in Google Earth Engine using fused satellite and terrain datasets with field-collected samples for training and validation, while a One-Dimensional Convolutional Neural Network (1D-CNN) was developed in Python 3.13.5 using the same inputs. Results demonstrate that integrating multi-source data improves classification accuracy, with radar-based features contributing the most. RF achieved the highest performance, with an overall accuracy of 97.7% and grass-class precision, recall, and F1-score exceeding 0.97, closely followed by the 1D-CNN with 91% overall accuracy and complete grass detection. In contrast, SVM performed notably lower with an overall accuracy of 80,8%. These findings highlight the effectiveness of advanced learning approaches for grassland mapping and support their application in ecological restoration and environmental management. Full article

Anthrax is an endemic and undercharacterized zoonotic disease in the Middle East, including Jordan. Between 2018 and 2020, we conducted a comprehensive investigation of 13 confirmed anthrax outbreaks in Jordan, analyzing 822 samples from animal farm environments, including carcasses, asymptomatic livestock, and abiotic environmental surfaces. All samples were tested by qPCR targeting the chromosomal marker (ba177) and the plasmid marker pXO1 (pag). Among carcass samples, 75/195 (38.5%) were ba177-positive, of which 81% harbored the pXO1. Of specimens from live-animals and from environmental surfaces, 218/627 (35%) were positive by qPCR, likely reflecting environmental contamination during active outbreak periods. Serological analysis using anti-protective antigen (PA) ELISA revealed a high seroprevalence of 53% (75/141) among asymptomatic animals, indicating widespread sub-clinical exposure to B. anthracis antigens and previously undocumented endemicity. An integrated approach combining qPCR with ELISA demonstrated that 11% of seropositive animals with paired swab testing also yielded swab samples that were positive by qPCR, suggesting environment-to-host transition. Molecular strain typing using canonical SNP (canSNP) analysis identified the rare sublineage C.USA.A1055 within lineage C.Br.A1005 across two distinct outbreaks, suggesting the environmental persistence of this endemic lineage. Overall, our findings provide the first systematic molecular and serological surveillance baseline data for Jordan, demonstrating a complex genomic persistence and subclinical exposure landscape. This study suggests the need for enhanced surveillance strategies under the One Health framework to mitigate the risk of anthrax endemicity. Full article

Protein–protein interactions (PPIs) represent a vast and largely underexplored landscape of therapeutic targets, yet their structural features—including large, flat, and dynamic interfaces—have historically limited their druggability. In this context, cyclic peptides have emerged as a powerful class of PPI modulators, sitting at the interface between biologics and small molecules, and thus garnering key advantages of both classes. Their conformational constraint enhances binding affinity, proteolytic stability and, in some instances, cell permeability, thus enabling access to intracellular targets. This review provides an updated overview of cyclic peptides as modulators of PPIs, focusing on both conceptual foundations and practical strategies for their discovery and optimization. The main discovery approaches include natural sources, de novo design based on secondary structure mimetics, high-throughput screening, and computational approaches. Integration of these complementary strategies is crucial to enhance success rates in the discovery of effective and developable cyclic peptides. Accordingly, the present review aims to provide a practical guide for researchers entering this rapidly growing field, outlining current opportunities, methodological advances, and remaining challenges in the development of cyclic peptide-based PPI modulators. Full article

Leptomeningeal metastasis (LM) is a severe and increasingly recognized complication of advanced non-small-cell lung cancer (NSCLC), particularly in patients with actionable genomic alterations. Although LM has historically been associated with poor outcomes, molecularly targeted systemic therapies with improved central nervous system (CNS) activity are reshaping its therapeutic landscape. This review summarizes current concepts in the pathogenesis, diagnosis, and risk stratification of LM, focusing on systemic treatment strategies for NSCLC harboring actionable driver alterations. We highlight the rationale and emerging evidence for next-generation tyrosine kinase inhibitors targeting EGFR, ALK, and other oncogenic drivers, and discuss their role as the cornerstone of LM management. Intrathecal chemotherapy, immunotherapy, and radiotherapy are also reviewed within a risk-adapted treatment framework. An individualized approach integrating molecular profiling, neurological status, and disease distribution is essential to optimize outcomes. Prospective studies are needed to refine systemic treatment strategies and establish evidence-based algorithms for this high-risk population. Full article

Reliable quantification of crop evapotranspiration (ET_c) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ET_c maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R² = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (K_c) derived from fused NDVI time series via the FAO-56 framework yielded ET_c ranging from 0.99 mm day⁻¹ (initial stage) to 6.40 mm day⁻¹ (peak crop development). Seasonal precipitation–ET_c deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ET_c monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ET_c validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions. Full article

Effective environmental sustainability decision-making requires the consideration of both positive and negative information, as well as the integration of weighting methods, to ensure decisions are accurate and representative of real-world conditions. This study presents a Systematic Literature Review (SLR) on the development of the Weighted Bipolar Fuzzy Soft Set (WBFSS) framework for environmental sustainability decision-making. Articles were retrieved from three databases: Scopus, ScienceDirect, and Dimensions. The screening process adhered to PRISMA 2020 guidelines and identified 27 relevant articles. VOSviewer was subsequently used to conduct a bibliometric analysis, mapping keyword co-occurrences and the structural landscape of research topics. The analysis examined the evolution of Bipolar Fuzzy Soft Set (BFSS) frameworks, their application domains, and the integration of weighting methods within BFSS and related Fuzzy Soft Set (FSS) frameworks. The review found that, although 11 studies addressed environmental sustainability applications, only three explicitly employed BFSS-based frameworks, indicating that the application of BFSS in this domain remains limited. Furthermore, the incorporation of explicit weighting techniques within BFSS remains scarce, particularly for objective, data-driven weighting approaches. These findings provide a comprehensive overview of current research trends, identify important methodological gaps, and support the conceptual development of the WBFSS framework as a direction for future research rather than an established decision-making framework. This study highlights opportunities to advance decision-support methods for environmental sustainability, which may support future climate-related and sustainability-oriented decision-making in the context of Sustainable Development Goal 13 (Climate Action). Full article

With the increasing importance of green design in the business landscape, designers are compelled to shift towards eco-design practices. However, existing methodologies face challenges related to resource requirements, abstract concepts, and industry specificity. To address these challenges and stimulate innovation, this study proposes a green design methodology that integrates TRIZ concepts and is anchored in TRIZ evolutionary trends. The methodology includes function and attribute analysis, the introduction of green features, the identification of TRIZ trends through a two-stage process, and the use of a developed system to improve calculation efficiency. Detailed design solutions are generated by combining green features, TRIZ trends, and inventive principles. A case study validates the methodology, showcasing its value in promoting sustainable development. By leveraging the evolutionary potential of products and incorporating TRIZ, the methodology offers a promising approach to address sustainability challenges and drive innovation. This research serves as a starting point for a practical and efficient design methodology that utilizes TRIZ concepts and a computer-aided application tool. Future steps involve stress-testing the methodology and exploring its application in different domains. Full article

Long-standing studies of historical cultural spaces have primarily focused on the preservation of heritage objects and landscapes, while insufficient attention has been paid to the structural relationships, land-use transformations, and cultural–ecological processes that sustain their long-term continuity. Taking the World Heritage site of Lijiang as a case, this study develops a three-dimensional structural decoding framework composed of spatial base, spatial network, and spatial entity, together with an analytical pathway of “Identification–Interpretation–Evaluation–Synthesis–Practice.” By integrating qualitative and quantitative approaches with multi-source data, the study establishes an evidence chain linking historical processes and contemporary conditions to examine the formation mechanisms, continuity, and contemporary deviations of Lijiang’s historical cultural space. The results show that terrain–habitat adaptability, water system coupling, and environmental risk avoidance shaped environmental adaptation; historical corridors, landscape perception, and core node associations organized spatial networks; and functional diversity, cultural capital agglomeration, and spatial-scale compatibility supported entity-based spatial practices. Although tourism development, urban expansion, and land-use transformation have not completely dismantled these historical relationships, they have caused localized deviations in ecological boundaries, path continuity, visual connections, functional vitality, and spatial scale. This study argues that the governance of historical cultural spaces should shift from preserving isolated heritage objects to sustaining cultural–ecological relationships that support memory, identity, spatial practice, and adaptive land governance. Full article

The rapid advancement of laboratory automation, robotics, and digital technologies has significantly transformed laboratory medicine worldwide, improving efficiency, diagnostic accuracy, and quality management. However, the adoption of these technologies in developing countries such as Indonesia remains uneven and is influenced by infrastructural, financial, regulatory, and workforce-related challenges. This structured narrative review aimed to critically examine the current landscape of laboratory automation and robotics in Indonesia, with particular emphasis on implementation challenges, workforce transformation among medical laboratory scientists (Ahli Teknologi Laboratorium Medik, ATLM), and pathways toward equitable integration. Studies published between 2015 and 2025 were identified through PubMed, Scopus, and Google Scholar, complemented by Indonesian regulatory documents, professional guidelines, and relevant grey literature. The review was informed by PRISMA principles and synthesized narratively to explore technological developments, operational impacts, policy contexts, and implementation barriers relevant to Indonesian laboratory systems. The findings indicate that automation and robotics offer substantial benefits, including improved turnaround time, enhanced quality assurance, reduced laboratory errors, and greater operational efficiency. Nevertheless, significant barriers persist, particularly disparities in digital infrastructure, financial constraints, limited workforce readiness, and the absence of comprehensive implementation frameworks. The review further highlights that automation is reshaping rather than replacing the role of ATLM, shifting professional responsibilities toward digital competency, automation oversight, data interpretation, and quality management. Achieving sustainable laboratory automation in Indonesia therefore requires an equity-centered and systems-oriented approach involving regulatory strengthening, workforce development, infrastructure investment, and multi-stakeholder collaboration. With strategic planning and policy alignment, laboratory automation and robotics hold considerable potential to modernize laboratory services and support Indonesia’s broader healthcare transformation agenda. Full article

Traditional grazing maintains essential ecosystem services, yet this activity is rapidly disappearing across Europe. Understanding the geobotanical features of traditionally grazed areas is critical for predicting biodiversity shifts driven by pastoral decline. This study provides a geobotanical characterisation of traditional sheep farms in north-western Spain. We integrated bioclimatic, phytosociological, and biogeographical approaches with spatial autocorrelation analyses, including global Moran’s I, Local Indicators of Spatial Association (LISA), and join-count tests, to assess spatial patterns in vegetation richness and plant community organisation. The results indicate that 28.22% of the studied farms were located in the Castilian Duero sector, 93.45% within the supramediterranean thermotype, and 75.46% within the subhumid ombrotype. A high diversity of vegetation was recorded, with 111 plant communities identified. These include several priority habitats of community interest within the European Union, notably belonging to the phytosociological classes Molinio-Arrhenatheretea, Festuco-Brometea, and Poetea bulbosae. This spatial approach characterises the vegetation mosaics within a fixed buffer around the holdings, although it does not directly measure actual forage use. As a key scientific novelty, this work provides, for the first time, a macro-regional and quantitatively validated integration that explicitly links broad environmental filters with localized pastoral vegetation mosaics. By providing a statistically robust diagnosis of landscape aggregation and segregation, this geobotanical characterisation serves as a fundamental tool for land managers and shepherds, contributing directly to the conservation and sustainable management of endangered traditional pastoral landscapes under changing environmental conditions. Full article

Cannabis sativa L. is an economically significant multi-use crop valued for fiber, seed, and phytochemical production. Compared with other crops, advancement in Cannabis sativa has been slow due to regulatory constraints and genetic resource limitations. Recent advances in technology have transformed the research landscape, supporting a deeper understanding of the genetic architecture underlying key agronomic traits. This review summarizes current progress in Cannabis sativa genetics and genomics, mainly focusing on structural genome organization, including chromosome-level assemblies and emerging pangenomic resources that capture species-wide diversity. We explore the molecular basis of key agronomic traits, including sex determination, cannabinoid biosynthesis, fiber quality, seed composition, disease resistance, and abiotic stress tolerance, highlighting their complex regulatory networks. Functional genomics tools including virus-induced gene silencing, transient expression systems, and CRISPR/Cas9 genome editing are reviewed as approaches enabling direct gene functional validation. We further review integration of these resources with molecular breeding strategies, including marker-assisted and genomic selection, to accelerate elite genotype development. Finally, we address persistent challenges such as genomic complexity, reference bias, and phenotyping limitations while outlining future research directions. Together, these advances position C. sativa as a compelling system for both fundamental plant biology and applied crop improvement. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

Urbanization is a major driver of land-use change and ecological shifts, especially in semi-arid regions with high environmental sensitivity. This study examined urban land growth and its ecological impacts in Astana, Kazakhstan, from 2000 to 2020 and forecasted trends for 2030. Landsat imagery was classified using a Support Vector Machine (SVM) approach, and ecological conditions were assessed through spectral indices, including Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), a Tasseled Cap Wetness index (Wet), and a Normalized Difference Bare-Soil and Built-up Index (NDBSI). The Future Land Use Simulation (CA–Markov) model simulated land use under Business-as-Usual (BAU) and Ecological Priority (EP) scenarios. The results showed a significant increase in built-up land, mainly at the expense of cropland and grassland, with increased landscape fragmentation and rising LST, indicating intensifying urban heat. Ecological indices showed spatially varied responses, with localized greening in protected areas and overall environmental pressure in expanding zones. Scenario simulations suggest that policy interventions under the EP scenario can mitigate cropland loss, limit fragmentation, and enhance ecological connectivity compared with BAU. Overall, the findings show that integrating remote sensing, machine learning, and scenario modeling offers an effective framework for assessing urban–ecological dynamics and supports evidence-based planning for sustainable urban development in semi-arid cities. Full article

Structural inefficiencies and labor shortages within the global tomato agrifood chain pose significant threats to its economic sustainability. While vision-dominated systems, encompassing structural and spectral dimensions, have pioneered intelligent management, their limitations in environmental robustness and computational overhead necessitate a new approach. Furthermore, dimensional incompleteness remains a challenge in decoding internal states. Multisensory perception, integrating physical (tactile and auditory) and chemical (olfactory and gustatory) modalities, enables the quantitative characterization of tomato physiological states. Based on technological advancements at the leading edge of knowledge, a critical perspective on the mushrooming field of multisensory perception is highlighted. Grounded in the capabilities and bottlenecks of visual perception, the discussion outlines significant progress in multisensory perception, along with its challenges and prospects. Crucially, it delineates the construction pathway of digital fingerprints that couple instrumental sensing signals with human sensory experiences and envisions the landscape of multimodal fusion to address practical challenges. This perspective provides a roadmap for sensorially transparent evaluation systems in the tomato agrifood chain. Full article