Search Results (66)

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Iraq has faced escalating water scarcity over the past two decades, driven by climate change, upstream water withdrawals, and prolonged economic instability. These factors have caused deterioration in irrigation systems, inefficient water distribution, and growing social unrest. As per capita water availability falls below critical levels, Iraq is entering a period of acute water stress. This escalating water scarcity directly impacts water and food security, public health, and economic stability. This study aims to develop a general framework combining decision support systems (DSSs) with Integrated Comprehensive Water Management Strategies (ICWMSs) to support water planning, allocation, and response to ongoing water scarcity and reductions in Iraq. Implementing such a system is essential for Iraq to alleviate its continuing severe situation and adequately tackle its worsening water scarcity that has intensified over the years. This integrated approach is fundamental for enhancing planning efficiency, improving operational performance and monitoring, optimizing water allocation, and guiding informed policy decisions under scarcity and uncertainty. The current study highlights various international case studies that show that DSSs integrate real-time data, artificial intelligence, and advanced modeling to provide actionable policies for water management. Implementing such a framework is crucial for Iraq to mitigate this critical situation and effectively address the escalating water scarcity. Furthermore, Iraq’s water management system requires modifications considering present and expected future challenges. This study analyzes the inflows of the Tigris and Euphrates rivers from 1933 to 2022, revealing significant reductions in water flow: a 31% decrease in the Tigris and a 49.5% decline in the Euphrates by 2021. This study highlights the future 7–20% water deficit between 2020 and 2035. Furthermore, this study introduces a flexible, tool-based framework supported by a DSS with the DPSIR model (Driving Forces, Pressures, State, Impacts, and Responses) designed to address and reduce the gap between water availability and increasing demand. This approach proposes a multi-hazard risk matrix to identify and prioritize strategic risks facing Iraq’s water sector. This matrix links each hazard with appropriate DSS-based response measures and supports scenario planning under the ICWMS framework. The proposed framework integrates hydro-meteorological data analysis with hydrological simulation models and long-term investment strategies. It also emphasizes the development of institutional frameworks, the promotion of water diplomacy, and the establishment of transboundary water allocation and operational policy agreements. Efforts to enhance national security and regional stability among riparian countries complement these actions to tackle water scarcity effectively. Simultaneously, this framework offers a practical guideline for water managers to adopt the best management policies without bias or discrimination between stakeholders. By addressing the combined impacts of anthropogenic and climate change, the proposed framework aims to ensure rational water allocation, enhance resilience, and secure Iraq’s water strategies, ensuring sustainability for future generations. Full article

Crack segmentation is crucial for health monitoring and preventive maintenance of concrete structures. However, the complex morphologies of cracks and the limited resources of mobile devices pose challenges for accurate and efficient segmentation. To address this, we propose an efficient dynamic-state-space-enhanced network termed DSS-MobileNetV3 for crack segmentation. The DSS-MobileNetV3 adopts a U-shaped encoder–decoder architecture, and a dynamic-state-space (DSS) block is designed into the encoder to improve the MobileNetV3 bottleneck module in modeling global dependencies. The DSS block improves the MobileNetV3 model in structural perception and global dependency modeling for complex crack morphologies by integrating dynamic snake convolution and a state space model. The decoder utilizes the upsampling and depthwise separable convolution to progressively decode and efficiently restore the spatial resolution. In addition, to suppress complex noise in the image background and highlight crack textures, the strip pooling module is introduced into the skip connection between the encoder and decoder for performance enhancement. Extensive experiments are conducted on three public crack datasets, and the proposed DSS-MobileNetV3 achieves SOTA performance in both accuracy and efficiency. Full article

This study examines the key obstacles that hinder the mass adoption of additive manufacturing (AM) processes for fabrication and processing of metal parts. To address these challenges, the necessity of integrating an intelligent decision support system (DSS) into the workflow of AM process engineers is demonstrated. The advantages of applying a two-level ontological approach to the creation of semantic information to develop an ontology-based DSS are pointed out. A key feature of this approach is that the ontological models are clearly separated from data and knowledge bases formed on this basis. An ensemble of ontological models is presented, which is the basis for the intelligent DSS being developed. The ensemble includes ontologies for equipment and materials reference databases, a library of laser processing technological operation protocols, knowledge base of settings used for laser processing and for mathematical model database. The ensemble of ontological models is implemented via the IACPaaS cloud platform. Ontologies, databases and knowledge base, as well as DSS, are part of the laser-based AM knowledge portal, which was created and is being developed on the platform. Knowledge and experience obtained by various technologists and accumulated within the portal will allow one to lessen a number of extensive trial-and-error experiments to find suitable processing settings. In the long term, the deployment of this portal is expected to reduce the qualification requirements for AM process engineers. Full article

In the 21st century, the adoption of solar energy has witnessed significant growth, driven by the increased use of ground-mounted photovoltaic (GPV) systems, recognized as solar farms, which have emerged as major players in this sector. Nevertheless, their extensive land utilization may impact local ecosystem services (ESSs), especially those related to water resources. In the context of the water–energy–food–ecosystem (WEFE) nexus, it becomes vital to investigate how solar park construction will impact water-related ESSs. This paper developed a framework that assesses the effect of constructing a solar park on water-related ecosystem services. We focused on solar farm construction and its interactions with various hydrological cycle components; then, we evaluated the implications for water-related ESSs. This approach encompasses a systematic literature review that identifies the hydrological factors most affected by the construction of solar farms. As a result, thirteen ESSs were selected to be included in an evaluation framework, and a definition of a scoring system of each ESS was defined based on the economic value, a predetermined indicator, or land use and land cover (LULC) properties. The allocation of weighting factors for these scores can be determined based on individual experience and stakeholders. This study presents a DSS-integrated framework to assess the impact of solar park constructions on water-related ecosystem services (ESSs) within the WEFE nexus. The framework was applied to a case study in Darstadt, Bavaria, revealing that, among the water-related ESSs in favor of ground-mounted PV systems (GPVs) compared to traditional agricultural practices, there could be soil erosion and nitrate leaching reduction. The DSS tool enables stakeholders to efficiently evaluate trade-offs between energy production and ecosystem impacts. The findings underscore the potential of integrating renewable energy projects with ecosystem management strategies to promote sustainable land-use practices. Full article

Decision support systems (DSSs) can improve decision making in integrated pest management (IPM), but are still underutilised despite proven environmental and economic benefits. To overcome the barriers to DSS adoption, this study analyses survey data from 31 farmers and 94 farm advisors, researchers and developers across 11 European countries. Using machine learning techniques, respondents were first categorised into clusters based on their responses to the questionnaire. The clusters were then explained using classification trees. For each cluster, customised approaches were proposed to overcome the barriers to DSS adoption. For farmers, these include building trust through co-development, offering free trials, organising practical workshops and providing clear instructions for use. For farm advisors and researchers, involvement in the development of DSS and giving them access to information about the characteristics of the DSS is crucial. IPM DSS developers should focus on 14 key recommendations to improve trust and the ease of use, increase the transparency of DSS descriptions and validation, and extend development to underserved sectors such as viticulture and vegetable farming. These recommendations aim to increase the uptake of DSSs to ultimately improve the implementation of IPM practises and help reduce the risk and use of pesticides across Europe despite the ever-growing challenges in agriculture. Full article

Vineyard is one of the most complex and vulnerable agroecosystems, and ongoing climate change makes it necessary to identify effective management and adaptation practices. For this reason, a water balance model tailored for viticulture was developed to be implemented within a Decision Support System (DSS) aimed at supporting winemakers both in the vineyard’s planning and management phase. Starting from a simple monthly water balance, based on the Thornthwaite–Mather method, the model returns the water stress risk class through the connection to a soil and climate database; the user can however customize the response by inserting information related to a specific vineyard (e.g., planting, soil, and management layout). The model was tested using data from a three-year field experiment carried out in a vineyard under permanent grass cover (PG) or continuous tillage (CT), allowing for the evaluation of its performance in terms of water balance estimation. The model provided results consistent with the measured soil moisture values, and the annual risk of water stress corresponds to what was measured in the field, differing at most by only one class. The model can guide the user in finding the best solutions for designing new vineyards or managing the inter-row by simulating the adoption of different strategies (trellis system, planting density, type of cover crop or soil tillage) or suggesting alternative solutions (needs of irrigation supply, more suitable cultivars, or rootstocks). Full article

Irrigation plays a pivotal role in Southern Europe, and its importance is expected to further increase due to rising climatic variability. Concurrently, the European Water Framework Directive (WFD) mandates the safeguarding of water bodies and the implementation of incentive pricing strategies to promote efficient water management. Within this context, Irrigation Scheduling Decision Support Systems (IS-DSS) could contribute to the achievement of these objectives, although there are still obstacles to their adoption due to uncertainties regarding their potential benefits. This paper aims to derive a pricing model that reflects actual water use through the adoption of an IS-DSS. The innovation of this study lies in showing that adopting an IS-DSS to reduce irrigation volumes can potentially lower concession fees in collective irrigation systems. Thus, it contributes to the fulfilment of the WFD’s objectives regarding incentive water pricing. Notably, the tool is evaluated using the case study of a farm located in the Mediterranean region. The results demonstrate the dual benefits of IS-DSS adoption: on the one hand, it helps preserve water resources with a 24% reduction in irrigation volumes; on the other, it decreases irrigation costs by 20% at the farm level and by 9.4% at the irrigation district level. Therefore, the presented study provides insights into the potential of IS-DSS to enhance water pricing policies to promote efficient water management in Southern European agriculture in alignment with the WFD requirements. Full article

This study seeks to understand the key success factors that underpin efficiency, transparency, and user trust in automated decision support systems (DSS) that leverage AI technologies across industries. The aim of this study is to facilitate more accurate decision-making with such AI-based DSS, as well as build trust through the need for visibility and explainability by increasing user acceptance. This study primarily examines the nature of AI-based DSS adoption and the challenges of maintaining system transparency and improving accuracy. The results provide practical guidance for professionals and decision-makers to develop AI-driven decision support systems that are not only effective but also trusted by users. The results are also important to gain insight into how artificial intelligence fits into and combines with decision-making, which can be derived from research when thinking about embedding systems in ethical standards. Full article

Transitioning to a circular economy is crucial for sustainable energy development; yet, current energy supply chains lack comprehensive assessment tools. This study introduces the Holistic Multi-Indicator Decision Support System (HMI_DSS), an innovative tool grounded in life cycle thinking and advanced multi-criteria decision-making methodologies, including Entropy and PROMETHEE II. The HMI_DSS quantifies and assesses sustainability and circularity in energy systems by employing 49 indicators, with a focus on energy efficiency and greenhouse gas emissions. A case study on the rice straw energy supply chain for biogas production illustrates the tool’s effectiveness, comparing a baseline scenario to an alternative. The results show that the global warming potential (GWP) of the baseline is 122 gCO₂eq/kWh, while the alternative is 116 gCO₂eq/kWh. However, the baseline scenario has lower energy consumption (1.72 × 10⁷ MJ annually) than the alternative (1.98 × 10⁷ MJ). Overall, the alternative outperforms the baseline in terms of sustainability and circularity. The HMI_DSS offers a flexible and robust framework for evaluating trade-offs in energy systems, providing valuable insights for energy companies and researchers in adopting circular economy principles to achieve sustainable development. Full article

Background/Objectives: Covered self-expandable metallic stents (CSEMS) are commonly used to treat malignant distal biliary obstructions. A 10-mm CSEMS carries the risk of obstructing the pancreatic and cystic duct orifices by adhering to the bile duct; therefore, postoperative pancreatitis and cholecystitis are reported to occur at a certain frequency. We have adopted a new drainage technique for malignant distal biliary obstruction called ‘‘double-slim SEMS stenting” (DSS), where two small-diameter CSEMS are placed side-by-side. We aimed to compare the efficacy and safety of biliary drainage using DSS with those of conventional CSEMS. Methods: In total, 50 patients who underwent endoscopic biliary drainage for malignant distal biliary obstructions between April 2019 and March 2022 at Hyogo Medical University Hospital were enrolled. Patients were divided into DSS and Conventional groups, and the technical success rate, clinical success rate, adverse events, success rate for reintervention, recurrent biliary obstruction (RBO) rate, and time to RBO (TRBO) were evaluated. Results: There were no significant differences in patient characteristics between the DSS (n = 20) and Conventional groups (n = 30). The technical and clinical success rates were 100% in the DSS group. The incidence of adverse events was not significantly different between the two groups (DSS/Conventional: 10.0% [2/20]/20.0% [6/30]) (p = 0.34). No acute cholecystitis was observed in the DSS group. The incidence rates of RBO were 30% (6/20) and 43% (13/30) in the DSS and Conventional groups, respectively (p = 0.92). The median TRBO in the DSS group was 378 days, while the TRBO in the Conventional group was 195 days (p = 0.03), resulting in significantly longer TRBO in the DSS group. Conclusions: DSS emerges as a viable and safe approach for biliary drainage in malignant distal biliary obstruction, demonstrating a lower incidence of adverse events and longer TRBO compared to conventional CSEMS. Full article

Partition walls play a crucial role in buildings, influencing their aesthetics, functionality, and integration with other architectural elements. However, the selection process for partition wall types is often challenging due to the multitude of options available, varying decision criteria, and inadequate decision-making practices in the Architecture, Engineering, and Construction (AEC) industry. To address these challenges and improve decision-making, a hybrid Decision Support System (DSS) named PartitionWall Pro is proposed. This tool combines both document-driven and model-driven approaches to assist in the selection and design of partition walls. The document-driven aspect utilizes a choosing-by-advantages (CBA) model to compare the advantages of different partition wall options, while the model-driven component employs computational design models to analyze the structural integrity of unreinforced masonry partition walls. Validation procedures ensure the reliability and accuracy of the DSS in practical applications. Through case studies involving a warehouse and a school, the study demonstrates how the DSS simplifies decision-making processes and encourages the adoption of cost-effective partition wall solutions. The results underline the potential of the DSS to enhance efficiency, foster stakeholder discussions, and improve communication in building design projects, thereby offering valuable insights for researchers and industry professionals alike, ultimately transforming partition wall design practice. Full article

As global concerns about climate change intensify, the need for effective strategies to reduce carbon emissions, has never been more urgent. This review paper explores the crucial role of digital technologies (i.e., data automation (DA) and decision support systems (DSSs)) in enhancing decision making and achieving a ZERONET initiative (decarbonation efforts) within the realms of solid waste management (SWM), wastewater treatment (WWT), and contaminated soil remediation (CSR). Specifically, the paper provides (a) an overview of the carbon footprint (CFP) in relation to environmental management (EM) and the role of DA and DSS in decarbonization; (b) case studies in areas of SWM, WWT, and CSR in relation to the use of (i) digital technology; ((ii) life cycle assessment (LCA)-based DSS; and (iii) multi-criteria decision analysis (MCDA)-based DSS; and (c) optimal contractual delivery method-based DSS case studies in EM practices. This review concludes that the adoption of DA and DSSs in SWM, WWT, and CSR holds significant potential for enhancing decision making and decarbonizing EM processes. By optimizing operations, enhancing resource efficiency, and integrating renewable energy sources, smart EM technologies can contribute to a reduction in GHG emissions and the promotion of sustainable EM practices. As the demand for more effective and eco-friendly solutions grows, the role of DA and DSSs will become increasingly pivotal in achieving global decarbonization goals. Full article

Nonpoint source pollutants primarily originate from agricultural areas, settlements, and contaminated lands. Soil erosion and deposition are the means of transportation of pollutants since soil particles not only absorb but also transport contaminants through the stream network. Nature-based solutions (NbSs) are quite popular around the world to mitigate soil erosion and deposition, which has accelerated due to climate change and other anthropogenic activities. To promote their adoption, we developed an online decision support system (DSS) to provide land and water managers and particularly stakeholders with the optimal NbSs and ecosystem-based approaches (EbAs) that could help protect watersheds, streams, and consequently seas from pollutants. This DSS incorporates a descriptive data management system to handle datasets (questions, answers/criteria, outputs/solutions) from various stakeholders (e.g., policymakers, urban planners, environmentalists) and other non-experts. The questions of the DSS are related to different characteristics (criteria) of the areas of interest for the NbS or EbA. The questions provide various answers (which serve as descriptive data) in order to weigh the criteria/characteristics and, ultimately, the proposed NbS. The NbSs of the DSS were recorded based on a bibliographic review and from stakeholders’ responses via forums, meetings, workshops, etc. The primary testing results by stakeholders showed that the online DSS has the potential to be used as a complementary service in the near future. Specifically, it can provide the optimal NbS based on the participants’ answers about the study area. This communication paper may act as an invitation to reach a greater audience of stakeholders for the improvement of the online DSS. Full article