Search Results (4,306)

This study presents the first comprehensive spatial accessibility assessment of essential urban services in Belgrade, Republic of Serbia, conducted entirely with open-source tools and data. The analysis focused on six facility categories: primary healthcare centers, public pharmacies, primary and secondary schools, libraries, and green markets. Spatial accessibility was modelled using OpenRouteService (ORS) isochrones for walking travel times of 5, 10, and 15 min, combined with population data from the Global Human Settlement Layer (GHSL). Results indicate that 79% of residents live within a 15-min walk of a healthcare facility, 74% of a pharmacy, 89% of an elementary school, 52% of a high school, 60% of a library, and 62% of a green market. Central administrative units such as Vračar, Zvezdara, and Stari Grad demonstrated nearly complete service coverage, while peripheral areas, including Resnik, Jajinci, and Višnjica, exhibited substantial accessibility deficits, often coinciding with lower-income zones. The developed workflow provides a transparent, replicable approach for identifying underserved neighborhoods and prioritizing investments in public infrastructure. This research emphasizes the role of spatial accessibility analysis in advancing Sustainable Development Goals (SDGs), contributing to the creation of more inclusive, walkable, and sustainable urban environments, while on the other hand, it offers practical insights for improving urban equity, guiding policy formulation, and supporting necessary planning decisions. Subsequent research will focus on alternative facilities, other cities such as Novi Sad and Niš, and the disparity between urban and rural populations. Full article

The mathematical modeling and experimental validation of a non-conventional vertical-axis wind turbine (VAWT) with a variable-pitch angle are presented, employing the Double-Multiple Streamtube (DMST) method to simulate aerodynamic performance. The aerodynamic coefficients required by the model are obtained through a data-driven approach using a multi-input, two-output multilayer perceptron artificial neural network (MLP–ANN). The model is validated through numerical simulations under two distinct wind input profiles. An experimental evaluation with a prototype replicates the step input. It shows strong agreement with the simulations, particularly in the angular velocity response, which fluctuates between 35 and 55 RPM, with an average value in the range of 40–45 RPM. This hybrid methodology enhances the modeling fidelity of VAWTs and provides a scalable framework for real-time aerodynamic analysis and control. Full article

Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility of structuring PdM data from real hospital interventions in middle-income countries. This work presents a prototype data structuring pipeline applied to six public hospitals in the Casablanca–Settat region of Morocco. The pipeline consolidates 6816 validated maintenance interventions from 780 devices across 30 departments and integrates normalized reliability indicators (Failure Rate, MTBF, MTTR corrected with IQR, and Downtime Hours). It ensures semantic harmonization, auditability, and reproducibility, resulting in a structured and interoperable dataset that constitutes a regional first in the Moroccan hospital context. To illustrate predictive potential, a proof-of-concept Random Forest model was evaluated. It achieved AUROC = 0.65 on the full imbalanced dataset and AUROC = 0.82 on a balanced 2000-intervention subset, confirming the dataset’s discriminative value while reflecting real-world challenges. This work bridges the gap between conceptual PdM frameworks and operational hospital realities, and establishes a replicable foundation for AI-driven predictive maintenance in low-resource healthcare environments. Full article

In the contemporary urban context, safety in public space presents profound inequalities linked to gender, especially in the night period. This research explores how the subjective perception of security in the central district of Madrid affects women’s mobility patterns and use of public space. Through a mixed methodology, which combines spatial analysis with sensitive cartographies and collective mapping, it seeks to make visible the conditions of (in)security experienced in the city. The approach adopts a feminist and multi-scalar perspective, ranging from the object to the district scale. The analysis is structured around four layers: mobility, urban environment, green areas and night-time uses. Tools such as Geographic Information Systems were used for the treatment of objective data and qualitative techniques such as interviews and tours accompanied by a set of subjective perceptions. The results show the existence of multiple barriers that condition women’s access to and enjoyment of public space, revealing a discrepancy between what is planned and what is lived. The final considerations anticipate the possibility of replicating the methodology applied in urban planning, proposing future strategies to build safer, more inclusive and sensitive environments to the diversity of their inhabitants. Full article

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork. Full article

This paper presents the design and implementation of a modular cloud-based architecture that enables generative music capabilities in Digital Audio Workstations through a MIDI microservices backend and a user-friendly VST plugin frontend. The system comprises a generative harmony engine deployed as a standalone service, a microservice layer that orchestrates communication and exposes an API, and a VST plugin that interacts with the backend to retrieve harmonic sequences and MIDI data. Among the microservices is a dedicated component that converts textual chord sequences into MIDI files. The VST plugin allows the user to drag and drop the generated chord progressions directly into a DAW’s MIDI track timeline. This architecture prioritizes modularity, cloud scalability, and seamless integration into existing music production workflows, while abstracting away technical complexity from end users. The proposed system demonstrates how microservice-based design and cross-platform plugin development can be effectively combined to support generative music workflows, offering both researchers and practitioners a replicable and extensible framework. Full article

The mitochondrial DNA of trypanosomatid parasites consists of thousands of catenated minicircles and dozens of maxicircles that form a complex network structure, the kinetoplast (kDNA). Although kDNA replication and segregation during mitotic division are well studied, its inheritance during genetic exchange events remains unclear. In Trypanosoma brucei, hybrids inherit minicircles biparentally but retain maxicircles from a single parent. Although biparental inheritance of minicircles has been described in natural Trypanosoma cruzi hybrids, this process has not been explored in laboratory-generated hybrids of this parasite. In the present study, we analyzed kDNA inheritance in T. cruzi experimental hybrids using a comprehensive minicircle hypervariable region (mHVR) database and genome sequencing data. Our findings revealed biparental inheritance of minicircles, with hybrid lines retaining mHVRs from both parents for over 800 generations. In contrast, maxicircles were exclusively inherited from one parent. Unexpectedly, we observed an increase in kDNA content in hybrids, affecting both minicircles and maxicircles, and exhibiting instability over time. To explain these findings, we propose a Replicative Mixing (REMIX) model, where the hybrid inherits one kinetoplast from each parent and they are replicated allowing minicircle mixing. Instead maxicircle networks remain physically separated, leading to uniparental fixation after segregation in the first cell division of the hybrid. This model challenges previous assumptions regarding kDNA inheritance and provides a new framework for understanding kinetoplast dynamics in hybrid trypanosomes. Full article

The agro-industry is among the largest methane emitters, posing a critical challenge for sustainability. In rural areas, producers lack effective technologies to manage daily organic waste. Anaerobic digestion (AD) offers a circular pathway by converting waste into biogas and biofertilizers; however, its adoption is limited by inappropriate designs and insufficient operational control. Theoretical-applied research addresses these barriers by improving the design and operation of small-scale biodigesters, elevating pH and Electrical Conductivity (EC) from passive indicators to first-order control variables. Based on the design of a compact biodigester previously validated in the Chillón Valley and replicated in Huaycán under a utility model patent process (INDECOPI, Exp. 001087-2025/DIN), a stoichiometric NaHCO${}_3$ strategy with joint pH–EC monitoring was formalized, defining operational windows (pH 6.92–6.97; EC 6200–6300 μS/cm and dose–response curves (0.3–0.4 kg/day for 3–4 day) to buffer VFA shocks and preserve methanogenic ionic strength. The system achieved stable productions of 370–462 L/day, surpassing the theoretical potential of 352.88 L/day calculated by Buswell’s equation. A multivariable predictive model (linear, quadratic, interaction terms pH × EC, temperature, and loading rate) was developed and validated with field data: R² = 0.78; MAPE = 2.7%; MAE = 11.2 L/day; RMSE = 13.8 L/day; r = 0.89; residuals normally distributed (Shapiro–Wilk p = 0.79). The proposed approach enables daily decision-making in low-instrumentation environments and provides a replicable and scalable pathway for the safe valorization of organic waste in rural areas. The design consolidates the shift from reactive to proactive and co-optimized pH–EC control, laying the foundation not only for standardized protocols and training in rural systems but also for improved environmental sustainability. Full article

Hydrogen-assisted fatigue cracking presents a critical challenge to the structural integrity of legacy carbon steel natural gas pipelines being repurposed for hydrogen transport, posing a major barrier to the deployment of hydrogen infrastructure. This study systematically evaluates the fatigue crack growth (FCG) behavior of API 5L X60 pipeline steel under varying hydrogen–natural gas (H₂–NG) blending conditions to assess its suitability for long-term hydrogen service. Experiments are conducted using a custom-designed autoclave to replicate field-relevant environmental conditions. Gas mixtures range from 0% to 100% hydrogen by volume, with tests performed at a constant pressure of 6.9 MPa and a temperature of 25 °C. A fixed loading frequency of 8.8 Hz and load ratio (R) of 0.60 ± 0.1 are applied to simulate operational fatigue loading. The test matrix is designed to capture FCG behavior across a broad range of stress intensity factor values (ΔK), spanning from near-threshold to moderate levels consistent with real-world pipeline pressure fluctuations. The results demonstrate a clear correlation between increasing hydrogen concentration and elevated FCG rates. Notably, at 100% hydrogen, API X60 specimens exhibit crack propagation rates up to two orders of magnitude higher than those in 0% hydrogen (natural gas) conditions, particularly within the Paris regime. In the lower threshold region (ΔK ≈ 10 MPa·√m), the FCG rate (da/dN) increased nonlinearly with hydrogen concentration, indicating early crack activation and reduced crack initiation resistance. In the upper Paris regime (ΔK ≈ 20 MPa·√m), da/dNs remained significantly elevated but exhibited signs of saturation, suggesting a potential limiting effect of hydrogen concentration on crack propagation kinetics. Fatigue life declined substantially with hydrogen addition, decreasing by ~33% at 50% H₂ and more than 55% in pure hydrogen. To complement the experimental investigation and enable predictive capability, a modular machine learning (ML) framework was developed and validated. The framework integrates sequential models for predicting hydrogen-induced reduction of area (RA), fracture toughness (FT), and FCG rate (da/dN), using CatBoost regression algorithms. This approach allows upstream degradation effects to be propagated through nested model layers, enhancing predictive accuracy. The ML models accurately captured nonlinear trends in fatigue behavior across varying hydrogen concentrations and environmental conditions, offering a transferable tool for integrity assessment of hydrogen-compatible pipeline steels. These findings confirm that even low-to-moderate hydrogen blends significantly reduce fatigue resistance, underscoring the importance of data-driven approaches in guiding material selection and infrastructure retrofitting for future hydrogen energy systems. Full article

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

Deep-learning generative AI promises to transform architectural design, yet its potential employment and ready-to-use capacity for professional workflows are unclear. This study presents a systematic review conducted in accordance with PRISMA 2020 guidelines, synthesizing peer-reviewed work from 2015 to 2025 to assess how GenAI methods align with architectural practice. A total of 1566 records were initially retrieved across databases, of which 42 studies met eligibility criteria after structured screening and selection. Each was evaluated using five indicators with a three-tier rubric: Output Representation Type, Pipeline Integration, Workflow Standardization, Tool Readiness, and Technical Skillset. Results show that most outputs are raster images or non-editable objects, with only a minority producing CAD/BIM-ready geometry. Workflow pipelines are often fragmented with manual hand-offs and most GenAI methods map only onto the early conceptual design stage. Prototypes frequently require bespoke coding and advanced expertise. These findings indicate a persistent gap between experimentation with ideation-oriented GenAI and the pragmatism of CAD/BIM-centered delivery. By framing the proposed rubric as a workflow maturity model, this review contributes a replicable benchmark for assessing practice readiness and identifying pathways toward mainstream adoption. For GenAI to move from prototypes to mainstream architectural design practice, it is essential to address not only technical barriers, but also cultural issues such as professional skepticism and reliability concerns, as well as ecosystem challenges of data sharing, authorship, and liability. Full article

This study analyzes the impact of research and development (R&D) investment on economic growth in Panama, an emerging economy with structural challenges in its innovation system. Using a multivariate econometric approach that included elastic net regularization and fixed-effect panel data estimation, the analysis incorporated key explanatory variables such as public education expenditure, inflation, infrastructure investment, population growth, and exports. The results indicated that both R&D and education spending have a positive and statistically significant effect on GDP growth, while inflation has a negative impact and exports show no significant effect. To ensure robustness, the study applied the augmented Dickey–Fuller test for stationarity, nonparametric bootstrapping (1000 replications), and multiple diagnostic tests, including RMSE, adjusted R², Durbin–Watson statistic, and White’s test. Scenario-based projections suggest that gradual and sustained increases in R&D investment, supported by stronger institutional coordination and absorptive capacity, could enhance Panama’s long-term productivity and innovation outcomes. The findings underscore that improving R&D funding alone is not sufficient; effective governance and coherent science, technology, and innovation (STI) policies are essential. This research contributes empirical evidence to a relatively underexplored area in the development literature and offers strategic insights for policymakers seeking to build more integrated and sustainable STI ecosystems in emerging economies. Full article

Salinity is one of the major problems limiting plant growth, development, survival, yield, and quality. Climate change and increasing salinity levels force a concentration on sustainable production systems. Therefore, this study aimed to determine the effects of different doses of gibberellic acid (GA₃) (0, 150, and 300 mg/L) and salicylic acid (SA) (0, 0.25, and 0.50 mM) priming on some morphological and antioxidant enzyme activities of silage maize (Zea mays L.) seedlings exposed to salinity stress. Four different NaCl (0, 75, 150, and 225 mM) concentrations as salt stress and three different doses of both SA and GA₃ were investigated. The data obtained were subjected to analysis of variance according to a randomized complete block design using a factorial experimental design with four replications per treatment in 3 L pots. The results showed that GA₃ and SA priming had statistically significant effects on all investigated traits under different salt concentrations (except water content). Findings revealed that shoot, root, and leaf development, as well as antioxidant enzymes, were suppressed by salinity stress. The silage maize plant was statistically significantly affected starting from the lowest dose of 75 mM, depending on salt concentrations. Increasing salt concentrations negatively affected above-ground and below-ground parameters. However, SA and GA₃ treatments had positive impacts on all examined traits. SA and GA₃ priming treatments emerged as important strategies supporting root and shoot growth under saline conditions, thereby strengthening plant adaptation. The best results were obtained in groups exposed to 75 mM salt stress, where 300 mg/L GA3 was applied, and in groups without salt stress, where the same GA3 dose was applied. It was concluded that GA₃ priming treatments, in particular, were more effective than SA treatments, alleviating salt stress and positively contributing to plant development. Full article

Optimising spraying operations in coffee cultivation can enhance both application efficiency and effectiveness. However, no studies have specifically assessed droplet deposition on leaves adjacent to the spray application band—fraction of droplet deposition referred to as ‘transfer’ in this study. Therefore, this study aimed to quantify droplet deposition and transfer resulting from different application rates and nozzle types in coffee trees. The experiment was conducted in a factorial design including three application rates (200, 400, and 600 L ha⁻¹) and two nozzle types (hollow cone and flat fan), with four replicates. Deposition was quantified at multiple positions: two application sides (left and right), three sections of the plant (upper, middle, and lower), and two branch positions (inner and outer). Thus, all measurements across sides, plant sections, and branch positions were nested, resulting in correlated data that were analysed using linear mixed-effects models (lme4 package), with parameters estimated using the restricted maximum likelihood method. The flat fan nozzle achieved the highest reference deposition, particularly on outer canopy thirds, while spray transfer (~29% of total deposition) was mainly driven by operational factors. Hollow cone nozzles at 200 L ha⁻¹ minimized transfer while maintaining adequate deposition. Optimizing applications requires maximizing reference deposition and minimizing transfer, which can be achieved through operational adjustments, airflow management, and complementary strategies such as adjuvants, electrostatic spraying, or tunnel sprayers. Full article

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning. Full article