Search Results (425)

This study evaluates the 2022 rollout of the Clean School Bus Rebate Program (CSBRP) to understand how eligibility rules and data practices shape funding distribution across communities with varying needs. We ask whether more accurate maps can improve environmental funding outcomes or whether challenges stem from how agencies define and apply eligibility criteria. Using logistic regression and dasymetric mapping, we find that prioritization criteria helped direct funds to underserved areas, but reliance on school district boundaries introduced inconsistencies that affected program reach. Including charter schools as independent applicants increased competition and sometimes diverted funds from larger public systems serving more. Our geospatial analysis shows that while refined mapping approaches improve resource targeting and reduce goal-outcome mismatches, agency discretion and administrative rules remain key factors in ensuring equitable outcomes. Full article

Authorities responsible for critical infrastructure, particularly bridges, face significant challenges. Many bridges, constructed in the 1960s and 1970s, are now approaching or have surpassed their intended service life. A report from the German Federal Ministry for Digital and Transport (BMVI) indicates that about 12% of the 40,000 federal trunk road bridges in Germany are in “inadequate or unsatisfactory” condition. Similar issues are observed in other countries worldwide. Economic constraints prevent ad hoc replacements, necessitating continued operation with frequent and costly inspections. This situation creates an urgent need for cost-effective, permanent monitoring solutions. This study explores the potential use of low-cost acceleration sensors for monitoring infrastructure structures. Inclination is calculated from the acceleration data of the sensor, using gravitational acceleration as a reference point. Long-term changes in inclination may indicate a change in the geometry of the structure, thereby triggering alarm thresholds. It is particularly important to consider specific challenges associated with low measurement accuracy and the susceptibility of sensors to environmental influences in a low-cost setting. The results of laboratory tests allow for an estimation of measurement accuracy and an analysis of the various error characteristics of the sensors. The article outlines the methodology for developing low-cost inclination sensor systems, the laboratory tests conducted, and the evaluation of different measures to enhance sensor accuracy. Full article

In Brazil, approximately 93 trillion litres of water are withdrawn annually from surface and groundwater sources, with urban human use being the second-largest water consumer. Therefore, reducing water consumption in buildings is crucial. This study performed a technical and economic analysis of isolated and combined water-saving strategies at the Central Library of the Federal University of Santa Catarina (UFSC). The strategies assessed included water-saving appliances, rainwater harvesting, and greywater and blackwater reuse, individually and in four combined scenarios. User surveys provided data on the frequency and duration of water appliance use and cleaning activities, while on-site water flow measurements enabled the estimation of water end uses. The potential for potable water savings was then determined for each strategy and scenario. The highest savings (77.96%) were achieved by combining water-saving appliances with blackwater reuse, followed by a combination of water-saving appliances, greywater reuse, and rainwater harvesting (65.73%). All strategies were economically viable, except the combination of water-saving appliances with greywater reuse, which showed a negative net present value. The scenario combining water-saving appliances and blackwater reuse generated the most significant financial savings (R$7782.48 per month), with a payback period of 50 months. Given its environmental and economic benefits, these scenarios were recommended for implementation. The study may be replicated worldwide, and one key conclusion is that libraries consume a significant amount of potable water for non-potable purposes, which should be supplemented with alternative sources. It is essential to consider whether the building is already built or under design, as some implementation processes require design modifications. Full article

Real-time, high-resolution monitoring of chemically diverse water pollutants remains a critical challenge for smart water management. Here, we report a fully integrated, multi-modal nano-sensor array, combining graphene field-effect transistors, Ag/Au-nanostar surface-enhanced Raman spectroscopy substrates, and CdSe/ZnS quantum dot fluorescence, coupled to an edge-deployable CNN-LSTM architecture that fuses raw electrochemical, vibrational, and photoluminescent signals without manual feature engineering. The 45 mm × 20 mm microfluidic manifold enables continuous flow-through sampling, while 8-bit-quantised inference executes in 31 ms at <12 W. Laboratory calibration over 28,000 samples achieved limits of detection of 12 ppt (Pb²⁺), 17 pM (atrazine) and 87 ng L⁻¹ (nanoplastics), with R² ≥ 0.93 and a mean absolute percentage error <6%. A 24 h deployment in the Cherwell River reproduced natural concentration fluctuations with field R² ≥ 0.92. SHAP and Grad-CAM analyses reveal that the network bases its predictions on Dirac-point shifts, characteristic Raman bands, and early-time fluorescence-quenching kinetics, providing mechanistic interpretability. The platform therefore offers a scalable route to smart water grids, point-of-use drinking water sentinels, and rapid environmental incident response. Future work will address sensor drift through antifouling coatings, enhance cross-site generalisation via federated learning, and create physics-informed digital twins for self-calibrating global monitoring networks. Full article

The integration of computer vision (CV) and generative artificial intelligence (GenAI) into smart agriculture has revolutionised traditional farming practices by enabling real-time monitoring, automation, and data-driven decision-making. This review systematically examines the applications of CV in key agricultural domains, such as crop health monitoring, precision farming, harvesting automation, and livestock management, while highlighting the transformative role of GenAI in addressing data scarcity and enhancing model robustness. Advanced techniques, including convolutional neural networks (CNNs), YOLO variants, and transformer-based architectures, are analysed for their effectiveness in tasks like pest detection, fruit maturity classification, and field management. The survey reveals that generative models, such as generative adversarial networks (GANs) and diffusion models, significantly improve dataset diversity and model generalisation, particularly in low-resource scenarios. However, challenges persist, including environmental variability, edge deployment limitations, and the need for interpretable systems. Emerging trends, such as vision–language models and federated learning, offer promising avenues for future research. The study concludes that the synergy of CV and GenAI holds immense potential for advancing smart agriculture, though scalable, adaptive, and trustworthy solutions remain critical for widespread adoption. This comprehensive analysis provides valuable insights for researchers and practitioners aiming to harness AI-driven innovations in agricultural ecosystems. Full article

Root-knot nematodes, Meloidogyne spp., are widespread phytoparasites that cause a significant reduction in the yield of tomato Solanum lycopersicum. In the Russian Federation, where the use of chemical nematicides is limited due to environmental and toxicological risks, the cultivation of resistant varieties and hybrids remains the most effective and environmentally safe method to control Meloidogyne. In the course of this study, the resistance screening of 20 tomato varieties/hybrids and 21 mutant lines from the collection of the FSBSI FRCBPP to M. hapla was carried out using a comprehensive approach that included morphological and biochemical analysis methods. Resistance was assessed by calculating the gall formation index, the degree of root system damage, and biochemical parameters of fruits—vitamin C content and titratable acidity. In addition, molecular screening was carried out using the SCAR marker Mi23 to identify the Mi-1.2 gene, known as a key factor in resistance to a number of Meloidogyne spp. Although Mi-1.2 is not typically associated with resistance to M. hapla, all genotypes carrying this gene showed phenotypic resistance. This unexpected correlation suggests the possible involvement of Mi-associated or parallel mechanisms and highlights the need for further investigation into noncanonical resistance pathways. It was found that when susceptible genotypes were infected with M. hapla, there was a tendency for the vitamin C content to decrease, while resistant lines retained values close to the control. The presence of the Mi-1.2 gene was confirmed in 9.5% of samples. However, the phenotypic resistance of some lines, such as Volgogradets, which do not contain a marker for the Mi-1.2 gene, indicates a polygenic nature of resistance, alternative genetic mechanisms, or the possible influence of epigenetic mechanisms. The obtained data highlight the potential of using the identified resistant genotypes in breeding programs and the need for further studies of the molecular mechanisms of resistance, including the search for new markers specific to M. hapla, to develop effective strategies for tomato protection in sustainable agriculture. Full article

Wildlife-vehicle collisions (WVCs) threaten biodiversity, particularly in the Gulf of Mexico, where road expansion increases habitat fragmentation. This research analyzes WVC patterns in southeastern Mexico, estimating collision rates across road types and assessing environmental factors influencing roadkill frequency. Field monitoring in 2016 and 2023 recorded vertebrate roadkills along roads in Campeche, Chiapas, and Tabasco. Principal Component Analysis (PCA) and Generalized Additive Models (GAM) evaluated landscape influences on WVC occurrences. A total of 354 roadkill incidents involving 73 species of vertebrates were recorded, with mammals accounting for the highest mortality rate. Hotspots were identified along Federal Highway 259 and State Highways Balancán, Frontera-Jonuta, and Salto de Agua. Road type showed no significant effect. Land cover influenced WVCs, with cultivated forests, grasslands, and savannas showing the highest incidences. PCA identified temperature and elevation as key environmental drivers, while GAM suggested elevation had a weak but notable effect. These findings highlight the risks of road expansion in biodiversity-rich areas, where habitat fragmentation and increasing traffic intensify WVCs. Without targeted mitigation strategies, such as wildlife corridors, underpasses, and road signs, expanding infrastructure could further threaten wildlife populations by increasing roadkill rates and fragmenting habitats, particularly in ecologically sensitive landscapes like wetlands, forests, and coastal areas. Full article

Coastal and maritime regions and their entities face accelerated degradation due to the combined effects of environmental stressors and anthropogenic activities. Coastal degradation can be identified, visualized and estimated through periodic monitoring over a region of interest using earth observation, climate, meteorological, seasonal, waves, sea level rising, and other ocean- and maritime-related datasets. Usually, these datasets are provided through different sources, in different structures or data types; in many cases, a complete dataset can be large in size and needs some kind of preprocessing (information filtering) before use in the intended application. Recently, the term data cube introduced in the scientific community and frameworks like Google Earth Engine and Open Data Cubes have emerged as a solution to earth observation data harmonization, federation, and exchange framework; however, these sources either completely lack the ability to process climate, meteorological, waves, sea lever rising, etc., data from open sources, like CORDEX and WCRP, or preprocessing is required. This study describes and utilizes the Ocean-DC framework for modular earth observation and other data types to resolve major big data challenges. Compared to the already existing approaches, the Ocean-DC framework harmonizes several types of data and generates ready-to-use data cubes products, which can be merged together to produce high-dimensionality visualization products. To prove the efficiency of the Ocean-DC framework, a case study at Crete Island, emphasizing the Port of Heraklion, demonstrates the practical utility by revealing degradation trends via time-series analysis of several related remote sensing indices calculated using the Ocean-DC framework. The results show a significant reduction in processing time (up to 89%) compared to traditional remote sensing approaches and optimized data storage management, proving its value as a scalable solution for environmental resilience, highlighting its potential use in early warning systems and decision support systems for sustainable coastal infrastructure management. Full article

Increasing concern over climate change has made Carbon Capture and Storage (CCS) an important tool. Operators use deep geologic reservoirs as a form of favorable geological storage for long-term CO₂ sequestration. However, the success of CCS hinges on the integrity of wells penetrating these formations, particularly legacy wells, which often exhibit significant uncertainties regarding cement tops in the annular space between the casing and formation, especially around or below the primary seal. Misalignment of cement plugs with the primary seal increases the risk of CO₂ migrating beyond the seal, potentially creating pathways for fluid flow into upper formations, including underground sources of drinking water (USDW). These wells may not be leaking but might fail to meet the legal requirements of some federal and state agencies such as the Environmental Protection Agency (EPA), Railroad Commission of Texas (RRC), California CalGEM, and Pennsylvania DEP. This review evaluates the impact of CO₂ exposure on cement and casing integrity including the fluid transport mechanisms, fracture behaviors, and operational stresses such as cyclic loading. Findings revealed that slow fluid circulation and confining pressure, primarily from overburden stress, promote self-sealing through mineral precipitation and elastic crack closure, enhancing well integrity. Sustained casing pressure can be a good indicator of well integrity status. While full-physics models provide accurate leakage prediction, surrogate models offer faster results as risk assessment tools. Comprehensive data collection on wellbore conditions, cement and casing properties, and environmental factors is essential to enhance predictive models, refine risk assessments, and develop effective remediation strategies for the long-term success of CCS projects. Full article

Artificial-intelligence-based robotics has recently garnered considerable attention, with the prediction of sample attributes becoming essential for artificial-intelligence-based environmental data analysis and decision-making processes in smart equipment and IoT devices. Based on a masked autoencoder (MAE), this study introduces the FedMAE regressor, a federated learning regression framework designed to precisely predict critical nutrients such as nitrogen, phosphorus, and potassium in agricultural and environmental monitoring devices while ensuring data privacy. The proposed adaptive regressor integrates deep learning methodologies within a federated learning architecture. Layer normalization is employed to enhance the model’s stability in distributed environments, and its structure is optimized with residual connections and GELU activation functions. An adaptive normalization method, a multi-layer feature transformation system, and a balanced data allocation technique are introduced to mitigate data distribution biases in edge devices. Furthermore, the AdaBelief optimizer and a dynamic learning rate scheduling approach are implemented to improve the model’s resilience. Experimental results show that the proposed method outperforms baseline and state-of-the-art models in terms of nitrogen prediction and demonstrates notable adaptability in phosphorus and potassium prediction tasks. This research paves the way for the application of federated-learning-based approaches in various ecological and industrial contexts, providing a robust solution for time-series prediction challenges in diverse domains. Full article

Achieving organizational efficiency requires the selection of an appropriate operating model. To date, no objective indicators, methods of measuring, or criteria for evaluating the effectiveness and efficiency of forest management organizations have been developed. In the heterogeneous forest management of the European Union (EU), multiple objectives and functions—from production to social and ecological services—coexist at regional and national levels. This study provides an overview of the organizational and legal forms of EU forestry, taking into account environmental conditions, ownership structures, and the role of the forestry sector in national economies. The legal information of EU countries on forest management was verified. We examine the impact of the entity’s organizational and legal form on the implementation of sustainable forest management and the objectives of the New EU Forest Strategy 2030, particularly in terms of absorbing external capital for forest protection and climate-related activities. Joint stock companies, public institutions, and enterprises are the most relevant. The private sector is dominated by individual farms, associations, chambers of commerce, and federations. A clear trend toward transforming state-owned enterprises into joint-stock companies and expanding their operational scope has been confirmed. Multifunctional forest management is practiced in both state and private forests. Economic efficiency, legal and property liability, and organizational goals depend on the chosen organizational and legal form. Full article

Resource-constrained devices, including low-power Internet of Things (IoT) nodes, microcontrollers, and edge computing platforms, have increasingly become the focal point for deploying on-device intelligence. By integrating artificial intelligence (AI) closer to data sources, these systems aim to achieve faster responses, reduce bandwidth usage, and preserve privacy. Nevertheless, implementing AI in limited hardware environments poses substantial challenges in terms of computation, energy efficiency, model complexity, and reliability. This paper provides a comprehensive review of state-of-the-art methodologies, examining how recent advances in model compression, TinyML frameworks, and federated learning paradigms are enabling AI in tightly constrained devices. We highlight both established and emergent techniques for optimizing resource usage while addressing security, privacy, and ethical concerns. We then illustrate opportunities in key application domains—such as healthcare, smart cities, agriculture, and environmental monitoring—where localized intelligence on resource-limited devices can have broad societal impact. By exploring architectural co-design strategies, algorithmic innovations, and pressing research gaps, this paper offers a roadmap for future investigations and industrial applications of AI in resource-constrained devices. Full article

The demand for interoperable, lifecycle-oriented data exchange in the port and waterway sector is intensifying amid global digital transformation and infrastructure modernisation. Traditional Building Information Modelling (BIM) practices often fail to capture the domain-specific complexity and multidisciplinary collaboration required in maritime infrastructure. This paper critically evaluates the IFC 4.3 schema as a foundational standard for openBIM-based integration in this sector, offering a semantic alignment framework designed for the planning, design, and operational phases of port projects. Rather than proposing schema extensions, the framework interprets existing IFC constructs to model port-specific assets while supporting environmental and geospatial integration. Two case studies, a master planning project for a shipyard and a design coordination project for a ship lock complex, demonstrate the schema’s capability to facilitate federated modelling, reduce semantic discrepancies, and enable seamless data exchange across disciplines and software platforms. The research delivers actionable implementation strategies for practitioners, identifies technical limitations in current toolchains, and outlines pathways for advancing standardisation efforts. It further contributes to the evolving discourse on digital twins, GIS-BIM convergence, and semantic enrichment in infrastructure modelling. This work provides a scalable, standards-based roadmap to improve interoperability and enhance the digital maturity of port and waterway infrastructure. Full article

Background: Monitoring genetic parameters is important for setting effective conservation and management strategies, particularly for small, fragmented, and isolated populations. Small, isolated populations face increased rates of genetic drift and inbreeding, which increase extinction risk especially when gene flow is limited. Methods: Here, we applied a Genotyping-in-Thousands by sequencing (GT-seq) panel to inform recovery action for the federally threatened northern Idaho ground squirrel (Urocitellus brunneus). We evaluated genetic diversity, structure, connectivity, and effective population size to address species recovery goals. Results: We delineated three types of conservation units: (1) three evolutionarily significant units that represent long-term population structure and variation, (2) nine management units that reflect current demographic connectivity and restrictions to gene flow, and (3) three adaptive units that capture adaptive differentiation across the species range. Effective population sizes per management unit were small overall (mean 38.16, range 2.3–220.9), indicating that recovery goals of 10 subpopulations with N_e > 500 have not been reached. Conclusions: Our results support the maintenance of connectivity within evolutionarily significant units through the restoration of dispersal corridors. Next steps could include further sampling of some subpopulations with low sample sizes, unsampled subpopulations, and subpopulations that are geographically isolated. Genotyping future samples with the same GT-seq panel would help to detect dispersal, assess effective population size, monitor the effects of inbreeding, and evaluate adaptive differentiation to monitor the effects of management action and environmental change. Full article

Metropolitan Planning Organizations (MPOs) are required to prepare a Transportation Improvement Plan (TIP) that outlines a fiscal strategy over a four-year period in order to qualify for federal funding. However, the growing population and limited financial resources available often pose significant challenges for transportation agencies in aligning their needs with available budgets. This article examines the project selection criteria used by 20 MPOs in the Southeastern United States to identify the best practices for prioritizing projects in TIPs. Using document analysis, this study categorizes the most commonly used criteria into nine broad groups: safety and security; environmental impacts; mobility, accessibility, and connectivity; preservation; environmental justice; equity; economic factors; alignment with other plans; and local support. Many of these categories are further divided into subcategories and metrics. Despite variations in criteria, weighting, scoring, and methodologies across these MPOs, the study identifies several shared factors that support effective decision-making in regional transportation planning. These findings can help transportation planners and policymakers refine their project prioritization strategies, promote consistency, and lead to improved decision-making frameworks for future TIP development. Full article