Search Results (1,617)

This review synthesizes recent regional applications of satellite, unmanned aerial vehicle (UAV), and hyperspectral/spectroradiometric remote sensing for vegetation cover discrimination, including crop and natural vegetation discrimination, plant disease and pest detection, and yield assessment. The reviewed evidence demonstrates complementary rather than universally superior capabilities among sensing platforms. Satellite observations provide repeated large-area monitoring but remain constrained by spatial resolution, cloud interference, and spectral mixing, whereas UAVs offer very-high-resolution and flexible field-scale observations at the expense of spatial coverage and greater acquisition and processing requirements. Hyperspectral and spectroradiometric approaches provide detailed spectral information for distinguishing subtle vegetation differences, but are limited by data complexity and operational scalability. The quantitative results reported in the reviewed studies illustrate this variability: satellite-based crop discrimination achieved approximately 90% overall accuracy with QuickBird and 81% overall accuracy (κ = 0.74) with Sentinel-2 at a 10 m resolution, while a UAV hyperspectral vegetation classification study achieved 94.5% accuracy. However, these values are not directly comparable because the vegetation targets, sensors, acquisition conditions, and analytical methods differed among studies. Recent evidence also indicates that the phenological timing, spectral band selection, spatial resolution, and representative training data strongly influence the discrimination performance, while the transfer of disease detection models from controlled experiments to operational field conditions remains a major challenge. By integrating evidence from satellite, UAV, and ground-based spectroradiometric approaches, this review provides a comprehensive framework for understanding the complementary capabilities of these technologies for vegetation cover discrimination and highlights their importance for improving vegetation monitoring, precision agriculture, and sustainable ecosystem management. Full article

Agricultural practices based on sustainable principles (e.g., conservation tillage, crop rotation, cover cropping, and the application of organic inputs) have been tested for their potential to improve soil structure, enhance soil organic matter, and support agrobiodiversity. These practices are directly linked to soil microbial diversity. Diverse soil microbial communities play multiple roles in promoting beneficial interactions between plants and their environment and in maintaining functional agroecosystems. Key functions enabled by soil microorganisms are carbon dynamics, nutrient cycling, soil structure improvement, pathogen suppression, plant growth promotion, and stress tolerance. Soil microorganisms are increasingly recognized as a promising but still underexploited source in tackling sustainability challenges in agricultural production. However, their potential varies depending on the interactions among abiotic and biotic factors, as well as the applied cultivation practices. In recent decades, advances in DNA extraction from soil and next-generation sequencing (NGS) technologies have enabled comprehensive characterization of microbial diversity, community composition, and functional potential for assessing soil health and agroecosystem functioning. Understanding, predicting, and exploring relevant plant–soil–microbiome interactions are essential for enhancing agroecosystem capacity for sustainable production. This review paper highlights how different factors and agricultural practices affect microbiome biodiversity. The focus is on microbiome approaches that integrate information on community composition with assessments of functional potential and measured microbial activity and ecosystem processes, combining state-of-the-art molecular monitoring, ecological indicators, and predictive modeling to support evidence-based management recommendations, distinguishing approaches that are currently applicable in agricultural practice from those that require further experimental validation. Full article

Understanding microbial community assembly mechanisms in aquatic habitats is fundamental to predicting ecosystem responses to environmental change, yet systematic comparisons of deterministic versus stochastic process contributions among different water types remain limited. We conducted monthly sampling over one year from four freshwater habitats, including two aquaculture ponds (WC01, WC02), an enclosed urban lake (TZ), and a flowing river (XJ), and applied a phylogenetic-bin-based null model framework to uncover how bacterial community assembly processes change among habitats and time/season. The results indicated that homogeneous selection (33.5%), dispersal limitation (31.0%), and drift (26.5%) jointly governed community assembly across all samples. Among the four investigated systems, water type, rather than season or their interactions, emerged as the primary factor regulating assembly process differentiation. Specifically, homogeneous selection was significantly stronger in the two aquaculture ponds (WC01, WC02) than in the natural water bodies (TZ and XJ), while the flowing river XJ exhibited the highest dispersal limitation and the lowest drift. At the phylogenetic bin level, over 98% of bins switched their dominant assembly strategies across the four water bodies, especially between the two aquaculture ponds and the two natural water bodies. Environmental factor analyses also revealed habitat-specific driving patterns: nitrogen and phosphorus nutrients dominated homogeneous selection in the aquaculture ponds, whereas dissolved oxygen, turbidity and oxidation reduction potential mainly regulated dispersal limitation in the natural waters. Collectively, these findings reveal a hierarchical pattern of freshwater bacterial assembly with multi-process coordination, habitat dominance, and lineage-level differentiation, and underscore that lineage-level analyses are essential for uncovering assembly patterns hidden at the community level, offering practical guidance for microbial management under diverse hydrological conditions. Full article

As the deployment of Internet of Things (IoT) ecosystems accelerates, safeguarding distributed networks against pervasive security and privacy threats has become a paramount concern. Integrating robust cryptographic protocols directly onto resource-limited edge devices offers a promising line of defense. However, severe hardware constraints historically complicate practical implementation. Because finite-field arithmetic fundamentally dictates the speed and efficiency of these cryptographic primitives, optimizing underlying multiplication techniques remains critical. To address these challenges, this paper presents an innovative, highly regular bit-serial systolic architecture tailored specifically for Dickson modular multiplication in binary extension fields. This is achieved via a streamlined systolic mapping over $GF\left(2^l\right)$ using dependency graph extraction, scheduling vectors, and projection directions. With localized pathways, the structure is highly optimized for VLSI integration. The performance and effectiveness of the proposed system are thoroughly evaluated and validated through comprehensive simulation results. Based on analytical and gate-level modeling, the design significantly enhances efficiency, lowering area by at least $162.8\%$, power by at least $214.3\%$, Area–Time Product by at least $5\%$, and Time–Power Product by at least $25.6\%$. These findings confirm that the proposed architecture substantially outperforms state-of-the-art bit-serial multipliers across these key evaluation metrics. Consequently, this solution serves as an ideal cryptographic engine for tightly constrained IoT hardware and embedded nodes, reinforcing secure and energy-aware data processing. By fostering resilient infrastructure and green digital practices, the work directly supports sustainable digital transformation and robust edge computing security. Full article

Nitrate contamination threatens surface water and groundwater quality in karst regions, posing risks to drinking water safety and aquatic ecosystems. Strong surface water–groundwater connectivity in karst recharge areas can accelerate contaminant transport through fractures and conduits. In this study, 166 samples, comprising 100 groundwater samples and 66 surface-water samples, were collected under wet-season, normal-flow, and dry-season conditions from a typical karst recharge area in Fengshan Township, Dafang County, Guizhou Province, China. Hydrochemical analyses, dual nitrate isotope analysis, and isotope-based mixing models were integrated to evaluate potential nitrate source contributions and examine the hydrochemical factors associated with nitrate variability. Groundwater was dominated by Ca–HCO₃ and mixed hydrochemical facies and exhibited relatively stable ionic compositions, whereas surface water showed more diverse facies and greater variability in total dissolved solids, SO₄²⁻, Na⁺, K⁺, and Cl⁻, reflecting a stronger response to external inputs and short-term hydrological processes. NO₃⁻ concentrations ranged from 0.02 to 16.24 mg/L in groundwater and from 0.00 to 41.20 mg/L in surface water, with mean concentrations of 3.07 and 4.38 mg/L, respectively. Mixing-model estimates identified manure and sewage (47%) and soil nitrogen (30%) as the leading potential contributors to groundwater nitrate, whereas manure and sewage had the largest estimated contribution to surface-water nitrate (68%). Given the overlap among the isotopic signatures of potential sources, these percentages represent probable source combinations rather than exact apportionments. The absence of consistent covariation between NO₃⁻ and Cl⁻ indicated that nitrate transport was not controlled solely by conservative mixing but was jointly regulated by source-input intensity, rapid surface-runoff responses, conduit transport, subsurface mixing, dilution, and water–rock interactions. Statistical modeling further showed that groundwater NO₃⁻ variability was associated with the major-ion composition, whereas surface-water NO₃⁻ variability was partly explained by a multiple regression model incorporating SO₄²⁻ and Cl⁻. Together, these findings support a conceptual source-to-transport framework involving external inputs, rapid surface-water responses, karst conduit transport, subsurface mixing, and water–rock interaction. This study provides insight into contrasting potential nitrate sources and transport processes in connected karst surface water-groundwater systems and supports pollution-source tracing, recharge-area management, and drinking-water source protection. Full article

Next-generation Intelligent Transportation Systems (ITSs) require ultra-reliable, low-latency Vehicle-to-Everything (V2X) communication frameworks that support safety-critical vehicular services. Conventional centralized, monolithic architectures suffer from excessive transmission latency, limited scalability, and authentication overheads that are ill-suited to the highly dynamic and dense vehicular environment. This paper presents a secure and low-latency edge–cloud architecture for intelligent V2X communications based on a lightweight microservice-driven design paradigm. A formal latency-constrained model is presented to ensure that the end-to-end delay satisfies tight real-time constraints. The proposed framework is lightweight and includes HMAC-based authentication, nonce-based replay protection, timestamp validation, and short-lived encrypted session tokens in a stateless architecture using the Laravel framework deployed at the edge layer. Security validation is performed at edge gateways, and asynchronous SQLite-backed job queues support non-blocking telemetry processing and scalable service orchestration. Experimental evaluation shows that the edge-based deployment achieves a mean response time of 2.58 ms with small variance under repeated request conditions, while centralized processing exhibits significantly higher latency. The results demonstrate that secure authentication and telemetry exchange can be achieved without breaching strict latency requirements. The proposed solution creates a deployable, scalable, and security-aware foundation for next-generation V2X ecosystems and Intelligent Transportation Systems (ITSs) in real time. Full article

Hyperspectral retrieval of nitrogen-related vegetation variables has undergone rapid methodological advances driven by the emergence of protein-sensitive radiative transfer models (RTMs), modern machine learning (ML), and operational imaging spectroscopy. This review synthesizes recent developments in hyperspectral retrieval of nitrogen-related vegetation variables across leaf and canopy scales, with particular emphasis on advances reported between 2020 and 2026. We examine the evolution from classical parametric regression and nonlinear ML approaches towards physically based RTM inversion and hybrid RTM–ML frameworks that integrate the complementary strengths of physical modeling and statistical learning. Particular attention is given to protein-sensitive RTMs, advanced ML approaches, and uncertainty-aware retrieval. Recent developments highlight the potential of hybrid RTM–ML frameworks to combine physical consistency with computationally efficient statistical learning, while probabilistic methods such as Gaussian Process Regression provide additional capabilities for uncertainty characterization. The review further discusses the transition from experimental studies to operational applications enabled by airborne and satellite imaging spectroscopy, including PRISMA, EnMAP, and forthcoming missions such as CHIME. Remaining challenges include the inherently ill-posed nature of nitrogen retrieval, limited and insufficiently representative calibration data, uncertainty characterization, and generalization across sensors, species, and ecosystems. Overall, the reviewed evidence points towards increasingly integrated retrieval frameworks, while emphasizing that robust transferability and operational implementation remain dependent on representative data, physical realism, and rigorous uncertainty assessment. Full article

The human gut microbiome is a complex adaptive ecosystem whose functions arise from interactions among microbial populations rather than from isolated taxa. Nevertheless, many microbiome-directed interventions still rely on administering individual strains, with limited consideration of the ecological processes governing community assembly, succession, and resilience. This review integrates evidence from microbial ecology, comparative genomics, systems biology, mechanistic physiology, and clinical microbiome research to propose a testable framework for ecologically engineering the human gut microbiome. Within this framework, selected spore-forming probiotics are hypothesized to function as transient pioneer organisms that modify intestinal physicochemical and metabolic conditions, thus facilitating the establishment and activity of functionally complementary microbial populations delivered through rationally designed synbiotic consortia. The proposed process comprises five stages: pioneer activity, niche remodeling, facilitated community assembly, functional-network stabilization, and the emergence of host-associated outcomes. Available genomic, physiological, and clinical observations support the biological plausibility of individual components of this model but do not yet demonstrate directed ecological succession as a complete causal process. Accordingly, the framework distinguishes established evidence from ecological inference and generates experimentally testable predictions of temporal niche modification, metabolic cross-feeding, functional redundancy, resilience after treatment withdrawal, and host metabolic responses. This ecological perspective shifts the objective of microbiome therapeutics from transient strain supplementation toward the predictable modulation of community trajectories, providing an experimental foundation for developing more resilient, mechanism-based interventions. Full article

Natural hazards significantly influence landscape patterns and ecological processes, yet their integration into land-use simulation remains limited. This study incorporates landslide susceptibility into the Patch-generating Land Use Simulation (PLUS) model to evaluate the effect of construction restrictions on future landslide exposure in Nanbu County, Sichuan Province, China. Landslide susceptibility was assessed using the certainty factor–logistic regression (CF-LR) model based on multi-period historical data, while future land-use changes (2020–2050) were projected with the PLUS model under two scenarios: a baseline scenario (without landslide constraints) and a susceptibility-constrained scenario (where high-susceptibility zones are restricted for construction). The results indicate that landslide-prone areas are spatially heterogeneous, with non-high-susceptibility zone concentrated in the west and northwest, and high-susceptibility zones clustered in the central area near the county seat. Under the baseline scenario, construction land within high-susceptibility zones increases by 107.47% over the 2020–2050 period. By contrast, the susceptibility-constrained scenario reduces construction land in these zones by 56.6% relative to the baseline 2050 projection, and by 10.01% relative to its own 2020 level. Ecosystem service value (ESV) assessment reveals that the two scenarios achieve nearly identical total ESV (with only a 0.09% difference), but the susceptibility-constrained scenario preserves 10.36 km² of high-value ecosystems within high-susceptibility zones compared to the baseline scenario. These findings demonstrate that incorporating landslide susceptibility as a spatial constraint reduces future landslide exposure without compromising ecosystem service value, providing a practical, science-based framework for decision makers to inform land-use planning in mountainous regions prone to landslides. Full article

The growth of big data ecosystems has shifted the classical paradigm of selective storage toward an approach that preserves large volumes of heterogeneous data for subsequent exploitation, thereby strengthening the adoption of repositories such as data warehouses and, particularly, data lakes. In this context, data ingestion from multiple sources, formats, and structures is a critical activity in implementing and operating these environments. However, it is often carried out in a highly ad hoc manner, with low levels of standardization and with variability managed informally. Beyond the operational complexity of ingestion itself, the variability in features such as source types, ingestion frequencies, transformation needs, and loading strategies constitutes an additional engineering problem that must be addressed systematically. This work tackles both issues in the context of a consulting firm involved in data migration projects to data lakes under governance constraints defined by clients in the BFSI sector. The goal is to formalize the data ingestion process through an architecture that provides technical, documentation, and training support for engineering teams, while also incorporating a variability management tool to model, analyze, and guide the configuration of ingestion solutions according to project-specific needs. In this way, the proposal seeks to reduce uncertainty, improve development quality, optimize resource utilization, and provide a more systematic treatment of variability in data ingestion projects. The proposal was evaluated through a structured survey answered by two cohorts totaling 29 respondents: an enterprise cohort of 15 practitioners (60% of the firm’s staff) and a prospective cohort of 14 engineering interns. For the enterprise cohort, the survey obtained average scores of 76 (individual) and 83.1 (role-averaged) for usability and 85.71 (individual) and 91.56 (role-averaged) for perceived quality; for the intern cohort, the corresponding individual averages were 70.18 for usability and 74.74 for perceived quality. In addition, a before/after comparison against the previous ad hoc workflow, covering objective engineering indicators, was conducted with both cohorts, providing task-based evidence that complements the perception-based results. Full article

The functions and application scenarios of AI glasses continue to expand; however, research on design optimization informed by user needs and usage experiences remains limited. This study develops a natural language processing framework for design optimization that integrates BERTopic, large language model–based EIP-CABSA, and explainable machine learning. A total of 17,116 user reviews of AI glasses were collected from JD. A large language model was then used to construct a structured analytical matrix, while platform ratings were employed as an observed measure of overall user satisfaction. The results indicate that CatBoost achieved the best predictive performance among the candidate models. AI Interaction and Intelligence, together with Imaging and Audio Perception, exhibited high global importance, whereas After-sales and Support were characterized by low coverage but high importance. Connection and Ecosystem, together with Workmanship and Durability, showed high conditional importance, while privacy-related issues were mentioned infrequently but were associated with a concentration of negative evaluations. Based on these predictive associations, this study outlines a tiered set of design priorities for subsequent evaluation. It provides an interpretable quantitative approach for identifying nonlinear associations between design aspects and overall user satisfaction, thereby supporting the design optimization of consumer-grade AI glasses. Full article

Biotope mapping provides an essential spatial framework for biodiversity conservation, ecosystem management, and sustainable landscape planning. However, a standardized and transferable GIS-based methodological framework for biotope identification and mapping is still lacking in Türkiye, limiting the systematic integration of biodiversity considerations into spatial planning processes. This study develops an integrated GIS-based biotope mapping framework for Çınarcık District, Yalova Province, Türkiye, integrating Digital Elevation Model (DEM), CORINE Land Cover 2018, Forest Management Plans, stand characteristics, vegetation, floristic, and hydrological data through spatial analyses. Additionally, 30 national and 15 international studies were systematically reviewed to identify the common indicators, data sources, and methodological components used in biotope mapping and to establish the proposed GIS-based framework. The proposed approach identified four main biotope groups (forest, aquatic, agricultural, and urban). Based on ecological similarity and growing environment characteristics, 12 sub-biotope types were identified within the forest biotopes. Forest biotopes were the dominant ecological units, mainly characterized by broadleaved communities dominated by Fagus orientalis, Castanea sativa, Tilia tomentosa, and Quercus petraea. A total of 72 forest stand types were identified, with Fagus orientalis-dominated forests in plateau environments representing the largest sub-biotope type (36.40%). The proposed framework provides a repeatable and transferable, inventory-based methodology that can serve as a preliminary decision-support tool for biodiversity assessment, conservation planning, and sustainable landscape management in forested landscapes with similar ecological characteristics, pending future field-based validation. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Nature-Based Solutions (NBSs) are increasingly recognized as strategic tools for climate adaptation and urban regeneration, yet their implementation requires balancing environmental performance, territorial identity, and governance feasibility. This article investigates the application of the euPOLIS H2020 methodology in Palermo, Italy, focusing on the proposed transformation of Villa Turrisi—a residual peri-urban agricultural area representing one of the last remnants of the historical Conca d’Oro landscape—into a new public green infrastructure within the framework of the new Municipal Development Plan (PRG/PUG). This study examines how a structured co-design process involving municipal actors, citizens, and local associations can be coupled with ecosystem service assessments. Rather than selecting a single optimal design, the analysis evaluates two alternative vision scenarios—a multifunctional framework aligned with euPOLIS principles and an intensive urban forest model—to generate quantitative and qualitative baseline data. Environmental indicators (carbon sequestration, microclimatic regulation, and hydrological resilience) are used not as deterministic selection criteria but as theoretical decision-support evidence within a predominantly qualitative, value-driven local planning process. The core contribution of this work lies in demonstrating the operational transferability of the euPOLIS framework from front-runner to follower cities within a Mediterranean planning context. The Palermo case shows how quantitative environmental simulations can effectively inform—rather than dictate—participatory governance, heritage preservation, and regulatory feasibility. Ultimately, this article offers a transferable planning and policy framework that bridges European NBS research with municipal decision-making, providing actionable insights for integrating climate-resilient green infrastructure into local urban plans. Full article

Changing disturbance regimes and uncertain recovery trajectories under global change have created a need for conceptual models to guide forest management within a sustainability framework. This conceptual article develops on operational framework for ecosystem memory in forest ecosystems. Conventional views of resilience may foster the misleading assumption that ecosystem development follows a fixed trajectory within the boundaries of the natural range of variation. However, climate change may transform resilience mechanisms, causing forest ecosystems to shift beyond their historical range of variation. Under such conditions, the concept of ecosystem memory offers a framework for analyzing and quantifying temporal system properties. The constraints inherited through memory patterns are themselves likely to be modified under changing environmental conditions. A valid interpretation of ecosystem memory requires a clear ontological understanding of memory components as material entities organized under temporal and spatial constraints. The human memory metaphor is an appealing entry point for understanding ecosystem memory but risks erroneously attributing semantic encoding and representational processes to ecological systems, where no such mechanisms exist. Based on a narrative synthesis of 39 publications, supplemented by an updated Web of Science search that identified 396 records, we develop an operational ecosystem-memory framework that distinguishes persistent ecological legacies from functionally active memory by linking ecological imprints, ecosystem engrams, and measurable ecosystem responses. The framework provides a basis for identifying and monitoring measurable legacy variables, including deadwood, retained trees, regeneration, soil properties, and refugial structures, in post-disturbance forest management. Full article