Search Results (2,163)

Background: Proton pump inhibitors (PPIs) are widely used to prevent acid-related complications, yet concerns persist about infectious harm. Observational studies have linked PPIs to Clostridioides difficile infection (CDI) and pneumonia whereas randomized controlled trials (RCTs) consistently show reductions in upper gastrointestinal bleeding. We therefore conducted a systematic review and meta-analysis restricted to randomized controlled trials to evaluate whether PPIs increase the risk of CDI, and to assess pneumonia and gastrointestinal bleeding to contextualize net clinical benefit. Methods: A comprehensive search of randomized controlled trials (RCTs) was conducted using several databases including PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and SCOPUS until July 2025. All published English-language RCTs that met the inclusion criteria were included. Random-effects models were utilized to calculate pooled odds ratios (ORs) with 95% confidence intervals. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 Tool, and heterogeneity was quantified using I² statistics. Analysis was performed using STATA version 18 and RevMan 5.3. Results: Across eight RCTs (n = 30,019), PPIs did not increase C. difficile infection versus placebo (OR 1.29, 95% CI 0.82–2.02; p = 0.27; I² = 16%) with leave-one-out (LOO) analyses showing stable estimates. In six trials reporting pneumonia, there was no significant difference between groups (OR 1.00, 95% CI 0.92–1.09; p = 0.99; I² = 0%). For clinically important upper GI bleeding (seven trials), PPIs were associated with a statistically significant lower risk when compared to placebo (OR 0.51, 95% CI 0.27–0.94; p = 0.03; I² = 56%). Conclusions: Across randomized trials with follow-up ranging from 30 days to 3 years, PPI prophylaxis significantly reduced upper gastrointestinal bleeding without increasing the risk of CDI or pneumonia. These findings support the use of PPIs for prophylaxis when clinically indicated, while recognizing that larger trials are needed to better assess rare adverse events. Full article

The rapid digitalization of cities is reshaping urban planning practices; however, significant gaps persist between technological investments and institutional governance capacity, particularly in transition economies. This study investigates how digital tools can be systematically embedded within planning processes to improve decision-making quality, coordination, and administrative efficiency. Drawing on urban governance theory and an empirical implementation study conducted in Yerevan, Armenia (population 1.1 million) between 2019 and 2023, the paper develops and operationalizes a multi-layer governance framework that aligns digital instruments—including geospatial information systems, performance dashboards, and decision-support platforms—with strategic, tactical, and operational levels of city management. The framework is evaluated through institutional analysis of municipal policy documents, planning databases, and semi-structured interviews with planning officials. The results reveal substantial governance barriers, including data fragmentation, organizational silos, and limited digital capacity. Framework-based implementation produced measurable improvements: planning decision cycles shortened by 43%, GIS utilization increased from 18% to 68% of eligible projects, inter-agency data sharing rose sixfold, and annual cost savings of approximately $1.2 million were achieved through reduced duplication and faster approvals. By combining conceptual design with empirical validation, the study advances digital urban governance research and offers a transferable, evidence-based model for implementing resilient and efficient data-driven planning systems in resource-constrained contexts. Full article

Most of the dam structures around the world are approaching the end of their economic life of 50 to 70 years, especially due to sediment accumulation in reservoir areas. This situation necessitates the development of proactive infrastructure management strategies. This study presents an original framework for the process of renewal of aging dams that blends remote sensing techniques and meta-intuitive optimization methods. Within the scope of the study, the Hasanlar Dam located in Düzce was selected as a sample, and a new dam axis was determined in the upper part of the basin. A detailed volume–height curve was created using 12.5 m resolution ALOS PALSAR numerical height models (DEM) and GIS-based spatial data curation to calculate the reservoir storage capacity in precise increments of 2 m. To maximize the structural efficiency of the proposed “New Hasanlar Dam”, the cross-sectional area has been minimized through seven current algorithms such as Genetic Algorithm (GA), Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Crayfish Optimization Algorithm (CAO), and Cheetah Optimizer (CO). The findings obtained prove that the PSO and CAOs achieved a significant reduction in cross-sectional area by 29.36% and successfully approached the global optimum. The replacement of the 55.5 million m³ capacity of the existing Hasanlar Dam with a new structure with a height of 78 m will guarantee sustainability and structural safety in water management. As a result, this study reveals that the integration of high-resolution remote sensing data and advanced heuristic methods is a cost-effective and powerful tool in the strategic renovation of aging hydraulic infrastructures. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

Underwater cultural heritage represents a fragile and largely unexplored component of historical landscapes, particularly in dynamic fluvial and coastal environments. Despite increasing international attention to its protection, the spatial identification of submerged heritage remains methodologically challenging. This study proposes a geo-historical approach that integrates historical cartography and Geographic Information Systems (GIS) to identify areas of high archaeological potential in underwater contexts. Focusing on the Douro River in Porto (Portugal), a UNESCO World Heritage city with a long maritime and fluvial history, the research analyses a set of key historical maps from the eighteenth and nineteenth centuries, complemented by documentary and archaeological sources. These cartographic materials were georeferenced and critically assessed in QGIS, enabling the digitisation of features associated with land–water interaction, navigation hazards, port infrastructures, and military defences. The resulting spatial dataset was used to generate an interpretative map and a kernel density model highlighting potential underwater heritage hotspots along the riverbed and riverbanks. The findings identify several priority zones, including the river mouth, historic quays, former shipbuilding areas, and sectors linked to nineteenth-century defensive structures. While the study does not include in situ verification, it demonstrates the value of historical maps as predictive tools for guiding targeted underwater surveys and proposes a transferable, cost-effective framework for heritage prospection and management in historically active fluvial–estuarine settings. Full article

University mobility in medium-sized cities faces increasing challenges arising from traffic congestion, urban sprawl, and the limited availability of sustainable transport options. In this context, the bicycle represents an efficient and environmentally low-impact alternative, provided that safe and connected infrastructure exists to facilitate its adoption. This study assesses the potential for bicycle use in the Andean city of Loja, Ecuador, taking as a case study the university community of the Universidad Técnica Particular de Loja (UTPL). Geographic Information Systems (GIS) tools, origin–destination (OD) matrices, and logistic models were integrated to analyze the relationship between three key variables: terrain slope, minimum travel time, and the percentage of protected cycling infrastructure. The results show that protected cycling infrastructure shows the strongest positive association with the modeled probability of use, while slopes greater than 15% and trips longer than twenty minutes are associated with lower modeled probabilities. The geospatial analysis identified priority corridors where improvements in cycling protection would yield higher modeled modal returns. It is concluded that strengthening cycling connectivity and the continuity of protected routes may inform scenario-based planning to support active university mobility, offering a replicable framework for medium-sized cities with similar topographic conditions. Full article

The development of Smart Tourism often overlooks the “Wilderness Last Mile”, leading to the spatial exclusion of people with disabilities in mountain areas. This problem exists because standard tourist maps and urban-centric accessibility models rely on averaged terrain data, failing to identify critical micro-scale barriers (e.g., short, sudden steep ascents) that pose severe safety and traction risks for off-road wheelchair users. To address this gap, this article presents a novel GIS methodology for planning accessible off-road tourism for electric Specialized Off-Road Wheelchairs. The proposed four-stage analytical model includes (1) graph-based trail network topologization to enable precise routing; (2) traction safety verification utilizing high-resolution (1 × 1 m) Digital Elevation Model (DEM) micro-segmentation to detect hidden slope barriers; (3) multi-criteria evaluation combining a user-calibrated Difficulty Index (EDI) and a Tourism Quality Index (TQI); and (4) a hub optimization algorithm that prioritizes locations maximizing the diversity of accessible routes. The method was empirically tested in a case study of the Bieszczady Mountains (Poland), calibrating the model with the technical limits (25% max slope) of a prototype wheelchair. The experimental results clearly validate the model’s superiority over traditional approaches: the micro-segmentation successfully identified hidden terrain traps, disqualifying 55% of the standard trail network that would have otherwise been deemed safe by average-slope assessments. Furthermore, the model identified a contiguous safe network of 153 km and pinpointed the optimal rental hub location, ensuring the highest inclusivity and route variety. Ultimately, this approach transforms raw spatial data into safe, ready-made tourism products, providing a precise tool with which to implement Universal Design in natural environments. Full article

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Background: Liquid biopsy using circulating tumor DNA (ctDNA) is rapidly reshaping gastrointestinal (GI) oncology. The highest-impact applications are molecular residual disease (mRD) detection after curative-intent therapy and early recognition of progression or resistance during systemic treatment. Methods: We performed a structured, clinically oriented narrative synthesis by using explicit search, eligibility, evidence prioritization, and clinical interpretation rules, integrating landmark prospective cohorts, randomized ctDNA-guided strategy trials where available, meta-analyses, key methodological research (e.g., pre-analytics, assay design, and clonal hematopoiesis (CH)/clonal hematopoiesis of indeterminate potential (CHIP)), and selected trial registries. Results: In resected colorectal cancer (CRC), postoperative ctDNA positivity is among the strongest known biomarkers of recurrence risk; large prospective studies demonstrate clear separation of disease-free survival (DFS)/overall survival (OS) between mRD+ and mRD− patients. In stage II colon cancer, randomized data (DYNAMIC) show that a ctDNA-guided strategy reduces adjuvant chemotherapy exposure without compromising long-term outcomes. In metastatic CRC, ctDNA supports early response monitoring and resistance tracking; ctDNA-selected anti-EGFR rechallenge provides a model of biomarker-driven actionability (CHRONOS). In gastroesophageal cancers, longitudinal ctDNA dynamics correlate with relapse risk and treatment efficacy, and in esophageal squamous cell carcinoma, ctDNA after neoadjuvant chemoradiotherapy informs residual disease risk and adjuvant stratification. In pancreatic ductal adenocarcinoma and hepatobiliary malignancies, sensitivity is constrained by low shedding and background cell-free DNA (cfDNA), yet ctDNA positivity remains clinically meaningful, and emerging data in resected extrahepatic cholangiocarcinoma (STAMP-linked analyses) show that ctDNA dynamics during adjuvant therapy predict recurrence. Conclusions: ctDNA is a clinically validated biomarker for mRD in CRC, whereas in other GI cancers, it remains a promising but methodologically heterogeneous tool whose clinical utility is tumor- and context-dependent. The next phase requires interventional trials demonstrating outcome improvement, harmonized sampling and reporting standards, and rigorous control of confounders (notably CH/CHIP). Full article

Industrial Control Systems (ICSs) are the backbone of modern critical infrastructure, such as electric power, water treatment, oil and gas distribution, and manufacturing operations. While the convergence of IT and OT has greatly increased efficiency and observability, it has also greatly expanded the attack surface of these once-isolated systems. High-profile cyber-physical attacks, including Stuxnet (2010), TRITON (2017), and the Colonial Pipeline ransomware attack (2021), have shown that ICS-targeted cyberattacks can cause physical damage, disrupt economic stability, and put public safety at risk. Despite the growing prevalence and intensity of such threats, ICS-based cybersecurity education remains largely under-resourced and underfunded. Traditional ICS training laboratories require highly specialized hardware, vendor-specific tools, and expensive licensing that significantly raise barriers to entry. Traditional labs typically require on-site participation and pose physical safety concerns when cyber-physical attack scenarios are performed. These barriers leave students unable to get necessary security training for ICSs. Therefore, this paper introduces ${\mathrm{AE}}^3$GIS: Agile Emulated Educational Environment for Guided Industrial Security—a fully virtual, lightweight, open-source platform designed to democratize ICS cybersecurity education. Based on the GNS3 network simulation tool, ${\mathrm{AE}}^3$GIS enables rapid deployment of comprehensive ICS environments containing IT and OT systems, industrial communication protocols, control logic, and diverse security tools. ${\mathrm{AE}}^3$GIS is designed to provide practical training for students using realistic ICS cybersecurity scenarios through a local or remote training platform without the cost, safety, or accessibility limitations of hardware-based labs. Full article

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS–AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%–100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS–AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems. Full article

This study examines the governance of digitalization in municipal administration, with a focus on city planning services, specifically spatial planning, building permits, and geodata management, in a large Swedish municipality. Digitalization is understood here not as the adoption of isolated technologies, but as organizational and process-oriented transformation enabled by digital systems such as GIS platforms, case management systems, and digital planning information. While national policy frameworks set ambitious digitalization goals, previous research shows that local authorities often face significant obstacles, including fragmented processes, technical limitations, and complex governance structures. These challenges create a persistent gap between strategic ambitions and daily work practices. This study employs a qualitative case study approach drawing on semi-structured interviews with employees in technical, operational, and strategic roles, as well as an analysis of policy documents and internal process descriptions. Using a socio-technical perspective, the analysis applies the Technology–Organization–Environment (TOE) framework to examine how digital systems, organizational structures, and external institutional demands interact in practice. The findings highlight substantial challenges related to system integration, data quality, uneven digital competencies, and the ongoing disconnect between strategic goals and operational realities. The study emphasizes the need for clearer governance structures, stronger cross-functional collaboration, and work practices that bridge technical and organizational dimensions. Building on the empirical analysis, the study proposes a conceptual framework that extends the TOE framework by identifying three interrelated structural mechanisms: technological lock-in, organizational inertia, and institutional uncertainty. This framework contributes theoretically by deepening the understanding of socio-technical digitalization dynamics in local government. Practically, it provides municipalities with an analytical tool to assess and reflect on their digitalization conditions. Full article

Rapid urbanization and increasing traffic volumes necessitate effective road safety measures, particularly in metropolitan areas. Enhancing road safety is a fundamental pillar of social sustainability as it directly reduces the socio-economic burden of traffic accidents and promotes resilient urban environments. This article analyzes the impact of infrastructural traffic-calming devices on road safety parameters using a GIS-based method. This study provides a quantitative tool for monitoring and measuring the effectiveness of sustainable transport infrastructure. The study examines six different types of devices across 44 locations within the GZM Metropolis, Poland, utilizing official police data (Accident and Collision Records System—SEWIK) from a period of two years before and two years after implementation. The primary parameters analyzed include the frequency of incidents, the severity of injuries, and the structure of accident types. The results demonstrate a substantial positive association following the interventions, with an average 41.33% reduction in road incidents across all tested devices. Specifically, speed bumps proved most effective, reducing incidents by over 66%. However, the analysis revealed a critical anomaly: While pedestrian refuge islands decreased the overall number of minor injuries, they correlated with an increase in the number of severe injuries, suggesting a need for careful consideration. Furthermore, the study confirms a positive shift in the structure of incidents, notably a substantial decrease in rear-end and side-impact collisions. The findings offer practical evidence for evidence-based urban policies, contributing to the development of safe, inclusive, and sustainable transport systems in line with global sustainability goals. Full article

This study presents a comprehensive GIS-based multicriteria decision analysis (MCDA) framework for identifying prospective groundwater abstraction sites in a 9 municipality region of South-East Poland (Podkarpackie Voivodeship), covering approximately 830 km². The analysis integrated hydrogeological parameters (aquifer thickness, quality, productivity, water table depth, protection degree, recharge zones) with spatial risk factors (contamination sources, exclusion zones) and population density patterns. The MCDA approach provides a decision support tool for municipal authorities tasked with water infrastructure planning under conditions of limited baseline data. The framework demonstrates the utility of a carefully specified GIS-MCDA framework to provide such support, while highlighting the need for improved data sharing to enable full statistical validation. Full article