Search Results (642)

With the globalization of trade, maritime transport is playing an increasingly strategic role in sustaining international commerce. As a result, research into the tracking and fusion of multi-source vessel data in canal environments has become critical for enhancing maritime situational awareness. In the existing research and development, the heterogeneity of and variability in vessel flow data often lead to multiple issues in tracking algorithms, as well as in subsequent trajectory-matching processes. The existing tracking and matching frameworks typically suffer from three major limitations: insufficient capacity to extract fine-grained features from multi-source data; difficulty in balancing global context with local dynamics during multi-scale feature tracking; and an inadequate ability to model long-range temporal dependencies in trajectory matching. To address these challenges, this study proposes the Shape Similarity and Generalized Distance Adjustment (SSGDA) framework, a novel vessel trajectory-matching approach designed to track and associate multi-source heterogeneous vessel data in complex canal environments. The primary contributions of this work are summarized as follows: (1) an enhanced optimization strategy for trajectory fusion based on Enhanced Particle Swarm Optimization (E-PSO) designed for the proposed trajectory-matching framework; (2) the proposal of a trajectory similarity measurement method utilizing a distance-based reward–penalty mechanism, followed by empirical validation using the publicly available FVessel dataset. Comprehensive aggregation and analysis of the experimental results demonstrate that the proposed SSGDA method achieved a matching precision of 96.30%, outperforming all comparative approaches. Additionally, the proposed method reduced the mean-squared error between trajectory points by 97.82 pixel units. These findings further highlight the strong research potential and practical applicability of the proposed framework in real-world canal scenarios. Full article

Public–Private Partnership (PPP) procurement is a relatively new approach in Nigeria’s housing sector. This study introduces a Fuzzy Risk Allocation Decision Model (FRADM) designed to address the complex and subjective nature of risk allocation in PPP-procured Mass Housing Projects (MHPs). A structured quantitative approach involving 40 purposively selected PPP housing experts was employed. Using a fuzzy synthetic evaluation (FSE) technique, critical risk factors were assessed based on partners’ risk management capabilities and allocation criteria. Constants (Ci) normalized the risk-carrying capacity indices (RCCIs) of both public and private sectors. Results show that risk attitude ranks highest among nine allocation criteria (MIS = 6.21), with the private sector demonstrating higher overall risk management capability. For instance, the availability of finance risk is optimally shared 53.48% to the private and 46.52% to the public sector. The FRADM was validated as reliable, practical, and replicable. Implications point to enhanced transparency, equitable risk-sharing, and support for SDG 11. The model is a strategic tool for decision-makers in PPP housing delivery in Nigeria and can inform similar efforts in other emerging economies. Further research should examine applications across other infrastructure sectors. Full article

Accurate information on waste composition is essential for strategic planning in waste management and developing environmental technologies. However, detailed analyses of individual waste containers are both time- and cost-intensive, resulting in a limited number of available samples. Therefore, it is crucial to apply statistical methods that enable reliable estimation of average waste composition and its variability, while accounting for territorial differences. This study presents a statistical approach based on territorial stratification, aggregating data from individual waste container analyses to higher geographic units. The methodology was applied in a case study conducted in the Czech Republic, where 19.4 tons of mixed municipal waste (MMW) were manually analyzed in selected representative municipalities. The method considers regional heterogeneity, monitors the precision of partial estimates, and supports reliable aggregation across stratified regions. Three alternative approaches for constructing interval estimates of individual waste components are presented. Each interval estimate addresses variability from the random selection of waste containers and the selection of strata representatives at multiple levels. The proposed statistical framework is particularly suited to situations where the number of samples is small, a common scenario in waste composition analysis. The approach provides a practical tool for generating statistically sound insights under limited data conditions. The main fractions of MMW identified in the Czech Republic were as follows: paper 6.7%, plastic 7.3%, glass 3.6%, bio-waste 28.4%, metal 2.1%, and textile 3.0%. The methodology is transferable to other regions with similar waste management systems. Full article

This research proposes a green belt as a strategic response to urban expansion in Lomas del Paraíso, Villa María del Triunfo, Lima. Uncontrolled urban growth threatens the local ecosystem, exacerbates the lack of public spaces, and limits employment opportunities. The study employs an integrated methodology that includes urban, community, and especially environmental analysis. This involved the collection of climatic data, and the identification of local flora and fauna, supported by digital tools such as Google Earth, AutoCAD 2023, Revit, and 3D Sun-Path. The proposal incorporates urban, environmental, technological, and community-based design strategies grounded in permaculture principles, circular economy frameworks, and the Sustainable Development Goals (SDGs). These approaches emphasize the symbiotic relationship between the community and the Lomas ecosystem. The feasibility and potential impact of the proposed green belt were compared with similar case studies, such as Medellín’s metropolitan green belt (Jardín Circunvalar) and the Arví Ecotourism Park. These benchmarks highlight the relevance of community involvement and user awareness in ecological preservation. In conclusion, implementing a green belt in Lomas del Paraíso would not only curb unregulated urban sprawl but also enhance community–nature connectivity and promote sustainable development through integrated environmental, social, and urban strategies. Full article

The escalating threat of climate-driven wildfires, land abandonment, wildland–urban interface expansion, and inadequate forest management poses an existential challenge to Mediterranean oak ecosystems, for which traditional fire suppression has proven insufficient. This paper presents a combination of integrated fire management (IFM) and closer-to-nature forest management (CTNFM) in a representative mixed Pyrenean oak (Quercus pyrenaica) forest at Quinta da França (QF), in Portugal. It is structured around three main objectives designed to evaluate this pioneer integrated approach: (1) to describe the integration of IFM and CTNFM within an agro-silvo-pastoral landscape; (2) to qualitatively assess its ecological, operational, and socio-economic outcomes; and (3) to quantitatively evaluate the effectiveness of two key nature-based solutions (NbSs), that is, prescribed burning and planned grazing, in reducing wildfire risk and enhancing forest resilience and biodiversity. By strategically combining proactive fuel reduction with biodiversity-oriented silviculture, the QF case provides a replicable model for managing analogous Mediterranean forested areas facing similar risks. This integrated approach supports forest multifunctionality, advancing both prevention and adaptation goals, and directly contributes to the ambitious targets set by the European Union’s New Forest and Biodiversity Strategies for 2030, marking a significant step towards a more sustainable and fire-resilient future for such Mediterranean landscapes. Full article

Surgical education and training have seen significant advancements with the integration of innovative technologies. This paper presents a novel approach to surgical education using a multi-view capturing system and bullet-time generation techniques to enhance the learning experience for aspiring surgeons. The proposed system leverages an array of synchronized cameras strategically positioned around a surgical simulation environment, enabling the capture of surgical procedures from multiple angles simultaneously. The captured multi-view data is then processed using advanced computer vision and image processing algorithms to create a “bullet-time” effect, similar to the iconic scenes from The Matrix movie, allowing educators and trainees to manipulate time and view the surgical procedure from any desired perspective. In this paper, we propose the technical aspects of the multi-view capturing system, the bullet-time generation process, and the integration of these technologies into surgical education programs. We also discuss the potential applications in various surgical specialties and the benefits of utilizing this system for both novice and experienced surgeons. Finally, we present preliminary results from pilot studies and user feedback, highlighting the promising potential of this innovative approach to revolutionize surgical education and training. Full article

Background: The limited availability of cardiac MRI data significantly constrains deep learning applications in cardiovascular imaging, necessitating innovative approaches to address data scarcity while preserving critical cardiac anatomical features. Methods: We developed a specialized denoising diffusion probabilistic model incorporating an attention-enhanced UNet architecture with strategically placed attention blocks across five hierarchical levels. The model was trained and evaluated on the OCMR dataset and compared against state-of-the-art generative approaches including StyleGAN2-ADA, WGAN-GP, and VAE baselines. Results: Our approach achieved superior image quality with a Fréchet Inception Distance of 77.78, significantly outperforming StyleGAN2-ADA (117.70), WGAN-GP (227.98), and VAE (325.26). Structural similarity metrics demonstrated excellent performance (SSIM: 0.720 ± 0.143; MS-SSIM: 0.925 ± 0.069). Clinical validation by cardiac radiologists yielded discrimination accuracy of only 60.0%, indicating near-realistic image quality that is challenging for experts to distinguish from real images. Comprehensive anatomical analysis revealed that 13 of 20 cardiac metrics showed no significant differences between real and synthetic images, with particularly strong preservation of left ventricular features. Discussion: The generated synthetic images demonstrate high anatomical fidelity with expert-level quality, as evidenced by the difficulty radiologists experienced in distinguishing synthetic from real images. The strong preservation of cardiac anatomical features, particularly left ventricular characteristics, indicates the model’s potential for medical image analysis applications. Conclusions: This work establishes diffusion models as a robust solution for cardiac MRI data augmentation, successfully generating anatomically accurate synthetic images that enhance downstream clinical applications while maintaining diagnostic fidelity. Full article

Phage therapy, long overshadowed by antibiotics in Western medicine, has a well-established history in some Eastern European countries and is now being revitalized as a promising strategy against antimicrobial resistance (AMR). This resurgence of phage therapy is driven by the urgent need for innovative countermeasures to AMR, which will cause an estimated 10 million deaths annually by 2050. However, the emergence of phage-resistant variants presents challenges similar to AMR, thus necessitating a deeper understanding of phage resistance mechanisms and control strategies. The highest priority must be to prevent the emergence of phage resistance. Although phage cocktails targeting multiple receptors have demonstrated a certain level of phage resistance suppression, they cannot completely suppress resistance in clinical settings. This highlights the need for strategies beyond simple resistance suppression. Notably, recent studies examining fitness trade-offs associated with phage resistance have opened new avenues in phage therapy that offer the potential of restoring antibiotic susceptibility and attenuating pathogen virulence despite phage resistance. Thus, controlling phage resistance may rely on both its suppression and strategic redirection. This review summarizes key concepts in the control of phage resistance and explores evolutionary engineering as a means of optimizing phage therapy, with a particular focus on Pseudomonas infections. Harnessing evolutionary dynamics by intentionally breaking the spontaneous evolutionary trajectories of target bacterial pathogens could potentially reshape bacterial adaptation by acquisition of phage resistance, unlocking potential in the application of phage therapy. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Higher education and science (HES) is one of the key drivers of a country’s economic growth. In this study, we examine national projects and research capacity in HES in Kazakhstan from 2014 to 2024. We conducted a content review and scientometric analysis with network and temporal visualizations. Our data sources included policy documents, statistical reports, and the Scopus database. Our findings suggest that, while Kazakhstan aligns with global trends in the field (e.g., digitalization, scientometrics monitoring, and internationalization), these are achieved through a state-led, policy-driven approach shaped by its post-Soviet context. Additionally, we note a dual structure in Kazakhstan’s HES sector, characterized by a strong top-down direction and increasing institutional engagement. In terms of the thematic trends from the temporal analysis, the country experienced a three-staged evolution: foundational reforms and system modernization (2014–2017), capacity building and evaluation (2018–2021), and, most recently, strategic expansion, inclusivity, and globalization (2022–2024). Throughout the analyzed period, low R&D intensity, disciplinary imbalances, and structural barriers still undermine desired development efforts in HES. The analyzed case of Kazakhstan can serve as “lessons learned” for policymakers and researchers working in the science evaluation and scholarly communication area in similar emerging or transition countries. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

The widespread adoption of cloud computing has amplified the demand for electric power. It is strategically important to address the limitations of reliable sources and sustainability of power. Research and investment in data centres and power infrastructure are therefore critically important for our digital economy. A novel heuristic for the minimisation of energy consumption in cloud computing is presented. It draws similarities to the concept of “network slices”, in which an orchestrator enables multiplexing to reduce the network “churn” often associated with significant losses of energy consumption. The novel network slicing–ring fencing ratio is a heuristic calculated through an iterative procedure for the reduction in cloud energy consumption. Simulation results show how the non-convex equation optimises power by reducing energy from 10,680 kJ to 912 kJ, which is a 91.46% efficiency gain. In comparison, the Heuristic AUGMENT Non-Convex algorithm (HA-NC, by Hossain and Ansari) reported a 312.74% increase in energy consumption from 2464 kJ to 10,168 kJ, while the Priority Selection Offloading algorithm (PSO, by Anajemba et al.) also reported a 150% increase in energy consumption, from 10,738 kJ to 26,845 kJ. The proposed network slicing–ring fencing ratio is seen to successfully balance energy consumption and computing performance. We therefore think the novel approach could be of interest to network architects and cloud operators. Full article

This study provides a comprehensive assessment and synthesis of the literature on short-selling. It performs a lexicometric analysis, providing a quantitative review of 1093 peer-reviewed journal articles to identify and illustrate the main themes in short-selling research. Almost half the published literature on short-selling is thematically clustered around portfolio management techniques. Other key themes involve short-selling as it relates to risk management, strategic management, and market irregularities. Descending hierarchical classification examines the overall structure of the textual corpus of the short-selling literature and the relationships between its key terms. Similarity analysis reveals that the short-selling literature is highly concentrated, with most conceptual groups closely aligned and fitting into overlapping or conceptually similar areas. Some notable groups highlight prior short-selling studies of market dynamics, behavioral factors, technological advancements, and regulatory frameworks, which can serve as a foundation for market regulators to make more informed decisions that enhance overall market stability. Additionally, this study proposes a conceptual framework in which short-selling can be either a driver or an outcome by integrating the literature on its antecedents, consequences, explanatory variables, and boundary conditions. Finally, it suggests directions for future research. Full article

The increasing demand for critical raw materials, such as antimony—a semimetal with strategic relevance in fire-retardant applications, electronic components, and national security—has made the identification of European sources essential for the European Union’s strategic autonomy. Remote sensing offers a valuable tool for detecting alteration minerals associated with subsurface gold and antimony deposits that reach the surface. However, the coarse spatial resolution of the most freely available satellite data remains a limiting factor. The PlanetScope satellite constellation presents a promising low-cost alternative for the academic community, providing 3 m spatial resolution and eight spectral bands. In this study, we evaluated PlanetScope’s capacity to detect Fe³⁺-bearing iron oxides—key indicators of hydrothermal alteration—by applying targeted band ratios (BRs) in northern Portugal. A comparative analysis was conducted to validate its performance using established BRs from Sentinel-2, ASTER, and Landsat 9. The results were assessed through relative comparison methods, enabling both quantitative and qualitative evaluation of the spectral similarity among sensors. Spatial patterns were analyzed, and points of interest were identified and subsequently validated through fieldwork. Our findings demonstrate that PlanetScope is a viable option for mineral exploration applications, capable of detecting iron oxide anomalies associated with alteration zones while offering finer spatial detail than most freely accessible satellites. Full article

As one of the important lithium resource sources, lepidolite has become a new energy strategic resource research hot spot. The efficient flotation of lepidolite directly affects the recovery and economic value of lithium resources. This paper systematically reviews the flotation research progress of lepidolite, focusing on the influence of the type of capture agent and process parameters (pH, activator, and depressant) on flotation. In view of the separation problems caused by the similarity of the surface properties of lepidolite and its associated gangue minerals (albite, feldspar, and quartz), the strategies for regulating the crystal structure of the minerals and their surface properties are analyzed. In addition, the lepidolite flotation process and its challenges are summarized, including poor selectivity of chemicals, fine mineral embedded size, easy to form sludge, and insufficient environmental friendliness, etc. The future development direction of lepidolite flotation technology is also prospected, which provides theoretical support and reference for the efficient recovery of lepidolite. Full article