Search Results (12,062)

This study explored the optimization of green hydrogen production via seawater electrolysis powered by a hybrid photovoltaic (PV)-wind system in KwaZulu-Natal, South Africa. A Box–Behnken Design (BBD), adapted from Response Surface Methodology (RSM), was utilized to address the synergistic effect of key operational factors on the integration of renewable energy for green hydrogen production and its economic viability. Addressing critical gaps in renewable energy integration, the research evaluated the feasibility of direct seawater electrolysis and hybrid renewable systems, alongside their techno-economic viability, to support South Africa’s transition from a coal-dependent energy system. Key variables, including electrolyzer efficiency, wind and PV capacity, and financial parameters, were analyzed to optimize performance metrics such as the Levelized Cost of Hydrogen (LCOH), Net Present Cost (NPC), and annual hydrogen production. At 95% confidence level with regression coefficient (R² > 0.99) and statistical significance (p < 0.05), optimal conditions of electricity efficiency of 95%, a wind-turbine capacity of 4960 kW, a capital investment of $40,001, operational costs of $40,000 per year, a project lifetime of 29 years, a nominal discount rate of 8.9%, and a generic PV capacity of 29 kW resulted in a predictive LCOH of 0.124$/kg H₂ with a yearly production of 355,071 kg. Within the scope of this study, with the goal of minimizing the cost of production, the lowest LCOH observed can be attributed to the architecture of the power ratios (Wind/PV cells) at high energy efficiency (95%) without the cost of desalination of the seawater, energy storage and transportation. Electrolyzer efficiency emerged as the most influential factor, while financial parameters significantly affected the cost-related responses. The findings underscore the technical and economic viability of hybrid renewable-powered seawater electrolysis as a sustainable pathway for South Africa’s transition away from coal-based energy systems. Full article

Background: Intracranial aneurysm (IA) rupture is a life-threatening cerebrovascular event with a mortality rate of up to 40%, affecting approximately 500,000 people globally each year. Although environmental pollutants such as 2,2′,4,4′-tetrabromodiphenyl ether (PBDE-47) have been implicated in the pathogenesis of IA, the causal relationship and underlying mechanisms remain unclear. This study aims to systematically explore the potential causal role of PBDE-47 in the development of IA by integrating multi-omics approaches. Methods: We utilized the UK Biobank Drug Proteomics Project (UKB-PPP) genome-wide association study (GWAS) data, including 2940 plasma proteins and 1400 metabolites, along with IA genetic data from 456,348 individuals, to perform a two-sample Mendelian randomization (MR) analysis. Instrumental variables were selected based on genome-wide significance (p < 5 × 10⁻⁸) or suggestive thresholds (p < 5 × 10⁻⁵). Analytical methods included inverse variance weighting (IVW), MR-Egger, weighted median, MR-PRESSO, and Steiger filtering for sensitivity analysis. Molecular docking and 100-nanosecond molecular dynamics simulations were used to evaluate interactions between PBDE-47 and proteins. Mediation analysis assessed the roles of plasma metabolites and miRNAs, and SMR-HEIDI tests were used to verify causal relationships. Results: MR analysis identified 93 plasma proteins potentially causally associated with IA, including 53 protective factors and 40 risk factors. By integrating PBDE-47 targets, IA-related genes, and metabolite-related genes, we identified 15 hub genes. Molecular docking revealed potential binding between PBDE-47 and F2R (binding energy: −5.516 kcal/mol), and SMR-HEIDI testing supported F2R as a potential causal risk factor for IA. Molecular dynamics simulations indicated the stability of the complex structure. Mediation analysis suggested that F2R may influence IA risk through eight plasma metabolites, and miR-130b-3p may indirectly promote IA development by upregulating F2R. Conclusions: Our findings suggest that exposure to PBDE-47 may have a potential causal relationship with IA risk, potentially mediated through the “PBDE–47–F2R–metabolite–miRNA” regulatory axis. These results provide preliminary evidence for early diagnostic biomarkers and targeted interventions for IA. The multi-omics analytical framework established in this study offers new insights into environmental determinants of neurovascular diseases, although further validation is needed to address potential limitations. Full article

Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity, while local-descriptor approaches can lose context and suffer from inter-class local-pattern overlap. To address these limitations, we propose a Global–Local Feature Fusion network that combines a frozen, pretrained global feature branch with a self-attention based multi-local feature fusion branch. Multiple random crops are encoded by a shared backbone (ResNet-12), projected to Query/Key/Value embeddings, and fused via scaled dot-product self-attention to suppress background noise and highlight discriminative local cues. The fused local representation is concatenated with the global feature to form robust class prototypes used in a prototypical-network style classifier. On four benchmarks, our method achieves strong improvements: Mini-ImageNet 70.31% ± 0.20 (1-shot)/85.91% ± 0.13 (5-shot), Tiered-ImageNet 73.37% ± 0.22/87.62% ± 0.14, FC-100 47.01% ± 0.20/64.13% ± 0.19, and CUB-200-2011 82.80% ± 0.18/93.19% ± 0.09, demonstrating consistent gains over competitive baselines. Ablation studies show that (1) naive local averaging improves over global-only baselines, (2) self-attention fusion yields a large additional gain (e.g., +4.50% in 1-shot on Mini-ImageNet), and (3) concatenating global and fused local features gives the best overall performance. These results indicate that explicitly modeling inter-patch relations and fusing multi-granularity cues produces markedly more discriminative prototypes in few-shot regimes. Full article

This study investigates the axial performance of continuous helix screw piles compared to helical piles through full-scale compression and tension load testing in layered soils. Twenty-three piles were installed and tested. The results demonstrate that screw piles can achieve considerable axial capacity with lower installation torque than helical piles, particularly under tensile loading. The capacity-torque relationship for screw piles was more consistent across both compression and tension, likely due to reduced soil disturbance from the smaller helix projection. Strain gauge measurements indicated that screw piles act primarily as friction piles with the threaded shaft carrying most of the load, especially in stiff clay. On the other hand, the smooth portion of the pile shaft contributed only marginally to resistance in compression and none in tension. The calculated capacity based on theoretical equations aligned well with field results in compression, with screw piles best represented by cylindrical shear failure in sand and a combination of cylindrical shear and individual bearing failure in clay. However, there is greater variability between calculated and measured uplift capacity, possibly due to soil disturbance effects. Additionally, the commonly used helix spacing ratio (S/D) was found to be less applicable to screw piles in predicting failure mode due to their smaller shaft-to-helix diameter difference. Full article

With the development of offshore wind power progressing towards larger-scale and deeper-water projects, the float-over installation of offshore converter stations has become a mainstream solution due to its high carrying capacity, efficiency and cost-effectiveness. This study addresses the dynamic positioning (DP) challenges during this operation, where traditional PID controllers often struggle with performance under complex environmental loads. An Observer-Based Robust Controller (OBRC) is proposed and integrated with a constant parameter time-domain model (CPTDM) to simulate the DP process of a novel T-U barge. Time-domain simulations for both standby and entry phases were conducted under various wave directions and periods. The results demonstrate that the OBRC significantly outperforms the conventional PID controller in maintaining positioning accuracy. The findings provide critical insights into motion responses and control strategies, offering valuable guidance for the design and safe operation of future float-over installations. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

This study investigates the utilisation of AI tools, including Grammarly Free, QuillBot Free, Canva Free Individual, and others, to enhance learning outcomes for 180 s-year telecommunications engineering students at Universidad Politécnica de Madrid. This research incorporates teaching methods like problem-based learning, experiential learning, task-based learning, and content–language integrated learning, with English as the medium of instruction. These tools were strategically used to enhance language skills, foster computational thinking, and promote critical problem-solving. A control group comprising 120 students who did not receive AI support was included in the study for comparative analysis. The control group’s role was essential in evaluating the impact of AI tools on learning outcomes by providing a baseline for comparison. The results indicated that the pilot group, utilising AI tools, demonstrated superior performance compared to the control group in listening comprehension (98.79% vs. 90.22%) and conceptual understanding (95.82% vs. 84.23%). These findings underscore the significance of these skills in enhancing communication and problem-solving abilities within the field of engineering. The assessment of the pilot course’s forum revealed a progression from initially error-prone and brief responses to refined, evidence-based reflections in participants. This evolution in responses significantly contributed to the high success rate of 87% in conducting complex contextual analyses by pilot course participants. Subsequent to these results, a project for educational innovation aims to implement the AI-PBL-CLIL model at Universidad Politécnica de Madrid from 2025 to 2026. Future research should look into adaptive AI systems for personalised learning and study the long-term effects of AI integration in higher education. Furthermore, collaborating with industry partners can significantly enhance the practical application of AI-based methods in engineering education. These strategies facilitate benchmarking against international standards, provide structured support for skill development, and ensure the sustained retention of professional competencies, ultimately elevating the international recognition of Spain’s engineering education. Full article

Cell towers play a key role in providing telecommunications infrastructure, especially in remote mountainous regions. This paper presents an approach to the efficient design of 42-metre-high cell towers intended to install high-power equipment in remote mountainous regions of the Carpathians (750 m above sea level). The region requires rapid deployment of many standardized towers adapted to geographical features. The main design challenges were the limited space available for the base, the impact of extreme weather conditions, and the need for a fast project implementation due to the critical importance of ensuring stable communication. Special methodological attention is given to how the transition between pyramidal and prismatic segments in cell tower shafts influences overall structural performance. The effect of this geometric boundary on structural efficiency and material usage has not been addressed in previous studies. A dedicated investigation shows that positioning the transition at a height of 33 m yields the best compromise between stiffness and weight, minimizing a generalized penalty function that accounts for both the horizontal displacement of the tower top and its total mass. Modal analysis confirms that the chosen configuration maintains a natural frequency of 1.68 Hz, ensuring a safe margin from resonance. For the final analysis of the behavior of towers with elements of different cross-sectional shapes, finite element modeling was used for a detailed numerical study of their structural and performance characteristics. This allowed us to assess the impact of geometric constraints of structures and take into account the most unfavorable combinations of static and dynamic loads. The study yields a concise rule of thumb for towers with compact foundations, namely that the pyramidal-to-prismatic transition should be placed at roughly 78–80% of the total tower height. Full article

Over 35% of bridges in the United States are currently rated in fair or poor condition, highlighting ongoing challenges in maintaining safety and performance amid aging infrastructure, limited budgets, and extended repair timelines. While Accelerated Bridge Construction (ABC) offers a faster solution, its adoption requires comprehensive decision frameworks. This paper presents a multi-criteria decision support tool (DST) that builds on the Connecticut Department of Transportation (CTDOT) ABC decision matrix. This DST quantifies the benefits of ABC for road and work zone safety, social equity, and environmental justice (SEEJ) and integrates them with structural, traffic, and construction factors to provide a comprehensive approach for determining the suitability of ABC techniques in bridge construction projects. Crash costs and corresponding safety benefits are quantified based on crash severity and frequency. While the tool incorporates both safety and SEEJ criteria, it also allows decision makers to consider either criterion individually based on their preferences. To demonstrate the applicability and benefits of the tool, it was applied to case studies in Connecticut. The results demonstrated how the considerations of safety and SEEJ can affect ABC decision-making. The presented DST is simple (Excel-based) and offers a practical and flexible tool that utilizes readily available data from national databases, making it applicable to all state DOTs across the United States. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

Sustainability is gaining relevance across organizations, yet significant challenges remain in how it is implemented and translated into daily operations. This paper examines how Lean Six Sigma can be used to address operational challenges while also supporting the integration of sustainability objectives in industrial contexts. The study is based on a project conducted in a fish processing plant, aiming to increase production capacity and reduce delays. Using the DMAIC framework, the team addressed key bottlenecks through demand-based workload leveling, earlier production planning, and targeted maintenance to improve equipment performance. These actions led to measurable gains in throughput, resource use, and schedule reliability. In parallel, they contributed to sustainability outcomes, including reduced rework, lower waste, and improved working conditions. The results suggest that Lean Six Sigma, typically focused on performance, can also act as a platform for embedding sustainability into existing routines. The findings offer insight into how performance-driven approaches can support sustainability transitions in process-intensive industries. Full article

We investigate the applicability of visual foundation models, a recent advancement in artificial intelligence, for archaeological remote sensing. In contrast to earlier approaches, we employ a strictly zero-shot methodology, testing the hypothesis that such models can perform archaeological feature detection without any fine-tuning or other adaptation for the remote sensing domain. Across five experiments using satellite imagery, aerial LiDAR, and drone video data, we assess the models’ ability to detect archaeological features. Our results demonstrate that such foundation models can achieve detection performance comparable to that of human experts and established automated methods. A key advantage lies in the substantial reduction of required human effort and the elimination of the need for training data. To support reproducibility and future experimentation, we provide open-source scripts and datasets and suggest a novel workflow for remote sensing projects. If current trends persist, foundation models may offer a scalable and accessible alternative to conventional archaeological prospection. Full article

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article

Second-, third-, and fourth-generation biofuels represent an important response to the challenges of clean energy supply and climate change. In this context, the Horizon 2020 “TO-SYN-FUEL” project aimed to produce advanced biofuels together with phosphorus from municipal wastewater sludge through a combination of technologies including a Thermo-Catalytic Reforming system, Pressure Swing Adsorption for hydrogen separation, Hydrodeoxygenation, and biochar gasification for phosphorous recovery. This article presents the environmental performance results of the demonstrator installed in Hohenberg (Germany), with a capacity of 500 kg per hour of dried sewage sludge. In addition, four alternative scenarios are assessed, differing in the source of additional thermal energy used for sludge drying: natural gas, biogas, heat pump, and a hybrid solar greenhouse. The environmental performance of these scenarios is then compared with that of conventional fuel. The comparative study of these scenarios demonstrates that the biofuel obtained through wood gasification complies with the Renewable Energy Directive, while natural gas remains the least sustainable option. Heat pumps, biogas, and greenhouse drying emerge as promising alternatives to align biofuel production with EU sustainability targets. Phosphorus recovery from sewage sludge ash proves essential for compliance, offering clear environmental benefits. Although sewage sludge is challenging due to its high water content, it represents a valuable feedstock whose sustainable management can enhance both energy recovery and nutrient recycling. Full article