Search Results (5,180)

Excavation adjacent to operating urban rail transit faces formidable deformation control challenges. To address this, a parametric collaborative optimization framework integrating micro steel pipe pile isolation and temporary intermediate partition wall reinforcement is proposed. Taking a foundation pit project at Shangdi Station of Beijing Metro Line 13 as a case study, a three-dimensional finite element model was established using the Hardening Soil constitutive model and calibrated with field monitoring data. Optimization analysis reveals that micro-pile spacing is the dominant factor controlling local rail settlement, while intermediate partition wall thickness primarily dictates global surface settlement. By balancing stringent safety limits with construction economy through a multi-objective evaluation, the preferred support configuration was calculated to be 273 mm diameter micro-piles at 500 mm spacing, combined with a 300 mm-thick partition wall. This collaborative configuration successfully truncates lateral soil displacement, reducing maximum rail settlement by over 55% and surface settlement by 53.6% compared to the baseline. Field monitoring results show high consistency with the numerical predictions (RMSE = 0.1438 mm), confirming the reliability of the proposed parametric collaborative optimization framework. Ultimately, this framework provides a validated, quantitative design methodology and a practical reference for support design in constrained excavations adjacent to existing sensitive infrastructure. Full article

High-availability (HA) systems—essential in many contemporary contexts—are designed to guarantee the availability of processes and data for more than 99% of their operational time. These systems are typically implemented as Cloud/Edge infrastructures that are properly maintained by human operators and intelligent agents in order to guarantee the required level of availability. Moreover, we are witnessing the widespread adoption of AI-based automation across many industries. AI-based software agents are increasingly being adopted to introduce more automation in highly available systems, particularly for monitoring and fault detection, fault prediction, recovery, and optimization processes. In this review paper, we discuss the state of the art of AI-based solutions for HA systems. In particular, we focus on the use of AI for the core operational mechanisms of monitoring, failure detection, and recovery. Our discussion begins by reviewing a few key background concepts of HA architectures, then we review recent work on AI-based solutions for monitoring, fault detection and recovery in HA systems. Full article

Highway landscape quality is important for visual comfort, environmental coordination, and infrastructure management. However, conventional assessment methods rely heavily on manual inspection and qualitative judgment, which are subjective and inefficient for large-scale applications. To address this issue, this study proposes an AI-based quantitative evaluation framework for highway landscape quality using an improved Panoptic-DeepLab model for panoramic image segmentation. The model identifies major landscape elements in highway scenes, including vegetation, sky, roads, buildings, and billboards. Based on the segmentation results, the proportions of natural elements, spatial openness, and artificial interference are integrated into a landscape quality score (LQS) model for quantitative assessment. Experimental results demonstrate that the proposed method achieves reliable segmentation performance and stable convergence in complex highway environments. Comparative analysis further shows that the method provides competitive accuracy with good computational efficiency. The proposed framework offers an effective tool for highway landscape evaluation and can support highway planning, landscape optimization, and visual environment management. Full article

Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

Globally, the transport sector accounts for almost a quarter of CO₂ emissions from fuel combustion and generates large amounts of pollutants, placing significant pressure on the environment and human health. By 2050, the European Green Deal requires a 90% reduction in transport-related emissions, making sustainability necessary across all modes of transport. Based on the relevant literature, this study examines the role and potential of railways in decarbonising the EU transport sector. Railway is highly efficient, consuming just 1.9% of transport sector energy while handling 16.9% of freight and 5.1% of passenger transport in the EU, yet is responsible for only 0.4% of total emissions. According to studies, greenhouse gas emissions can be reduced by improving energy efficiency, using low-carbon or renewable energy, and expanding train electrification. The greatest potential for decarbonisation lies in a modal shift to rail. However, this requires significant infrastructure investment: raising line speeds to at least 160 km/h, expanding networks, building terminals, digitalisation, and alignment with TEN-T standards. Although the EU supports the modal shift with funding programmes, the transition is not progressing as expected—the share of road freight transport increased from 74% in 2013 to 78% in 2023. Stronger investment is needed in Member States’ national policies for the development and modernisation of railways. The authors developed a Path Evaluation Matrix (PEM), a quantitative decision framework integrating the fields of energy, transport, politics, and economics. The PEM results indicate that BEMU (battery electric multiple units) is optimal for 68% of secondary lines in south-eastern Europe. Full article

Background: Kleefstra syndrome (KS) is a rare neurodevelopmental disorder caused by haploinsufficiency of EHMT1; it is characterized by global developmental delay, intellectual disability, hypotonia, distinctive facial features, and variable congenital anomalies. Autistic features, behavioral abnormalities and severe speech impairment are frequently reported. However, molecularly confirmed cases of KS from Africa remain extremely limited, largely due to restricted access to genomic diagnostic infrastructures. Methods: We describe a 15-month-old patient from Rwanda presenting with neonatal hypotonia, global developmental delay, short stature, and characteristic dysmorphic facial features. Comprehensive clinical evaluation was performed, followed by trio-based exome sequencing to identify the underlying genetic cause of this neurodevelopmental disorder. Results: Exome sequencing identified a de novo heterozygous frameshift variant in EHMT1 (NM_024757.5: c.2871dup; p. Phe958Leufs*219), confirming the diagnosis of KS. Conclusions: This report presents the first molecularly confirmed case of KS in Rwanda. It highlights additional clinical features like bilateral 5th toe clinodactyly, short stature and absence of obesity in KS. There is a need to further delineate the study of EHMT1 and investigate the natural history of KS across different populations for optimal patient management and to reduce diagnostic odyssey. The diagnostic utility of exome sequencing for neurodevelopmental disorders needs to be strengthened, with strong emphasis on expanding genomic medicine to help diagnose rare diseases in resource-limited settings. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

The problem of seepage beneath dams represents a major technical and economic challenge, particularly for countries such as Algeria, where agricultural and industrial development depends heavily on the management of water resources stored in reservoirs. Such seepage can not only cause significant water losses but also jeopardize the stability of the structure, particularly through the piping phenomenon, which poses a risk of sudden failure. Moreover, the evaluation of seepage becomes critical when it exceeds admissible thresholds, thereby requiring the search for solutions to ensure the waterproofing of foundations. Consequently, the design and optimization of devices such as cutoff walls or drainage systems aim to simultaneously reduce three key parameters: the leakage discharge, the uplift pressure, and the downstream hydraulic gradient, in order to guarantee the safety and durability of the infrastructure. The existing literature on cutoff walls beneath concrete dams does not allow for a comprehensive evaluation of the combined effects of geometric and operational parameters. This study aims to address this gap by systematically analyzing the interaction of these factors and their influence on the hydraulic response of the system. Numerical modeling was carried out using the Plaxis 2D software, considering various geometric and parametric configurations. The results indicate that the position, depth, and inclination of the cutoff wall significantly affect the hydraulic performance of the structure. Full article

Urbanization of cities demands efficient spatial management. The construction of utility lines significantly alters the spatial landscape. The subsurface space is often neglected, resulting in outdated or absent records of underground utility infrastructure. This clearly underscores the need and importance of maintaining accurate utility records. Modern non-destructive techniques for underground utility detection, such as ground penetrating radar (GPR), can enhance the documentation and mapping of subsurface infrastructure. The subject of this paper is the optimization of GPR survey and processing workflows to improve the accuracy of underground utility detection when using the Leica DS2000. The research comprises both theoretical and experimental analyses, including the application of various GPR data collection methods on test sites. The experimental component of the research was conducted using the Leica DS2000 GPR system. The geospatial data were processed using several software applications, including uNext Advanced, IQMaps, and Geolitix. Based on the multicriteria analysis of these results and an assessment of detection accuracy, an optimal workflow (decision diagram) was defined for the detection of underground utility infrastructure using Leica DS2000 under favorable soil conditions. This study explored the feasibility of efficiently updating the cadastral database of public utility infrastructure through non-invasive technologies, thereby contributing to the improvement of subsurface utility infrastructure management. Full article

Artificial intelligence presents a critical paradox for clean technology: while enabling unprecedented environmental optimization, AI deployment demands massive resource inputs that threaten to offset benefits. As global AI infrastructure investment approaches $500 billion annually, data center electricity consumption is projected to exceed 1000 TWh by 2030. We conducted a systematic literature review of 73 peer-reviewed empirical studies (2021–2025) to develop an Environmental Asset-Cost Framework categorizing AI’s impacts across five asset categories (energy optimization, production enhancement, green innovation, resource conservation, precision applications) and five cost categories (energy consumption, water use, e-waste, infrastructure, supply chain extraction). Our analysis reveals three critical insights: First, AI’s environmental impact follows a synthesized S-curve heuristic—a pattern derived from convergent but methodologically diverse evidence strands—characterized by initial emission reductions (0–2 years), mid-term rebound effects (2–5 years), and conditionally projected long-term optimization (5+ years). Second, geographical context creates 10–60× variation in outcomes; regions with high renewable electricity and water abundance achieve net benefits within 2–3 years, while fossil fuel-heavy, water-stressed regions may never reach net positive outcomes. Third, the rebound effect is predictable and manageable through strategic interventions. Our framework provides actionable deployment guidance, demonstrating that achieving AI’s net environmental benefits requires renewable energy infrastructure development before AI deployment, alternative cooling technologies, and policy frameworks incorporating temporal dynamics. Full article

Surgical wards constitute a significant contributor to global medical waste (MW), accounting for over one-third of total healthcare sector trash. Medical interventions produce hazardous, infectious, and potentially toxic byproducts, making effective MW management crucial, especially where current mechanisms are insufficient. Substantial disparities persist between high-income and low- and middle-income countries regarding MW infrastructure, enforcement, and adoption of safe, sustainable treatment technologies. Proper segregation, recycling, treatment, and disposal are key to protecting public health, environmental integrity, and promoting healthcare sustainability. Waste treatment technologies divide into thermal and physico-chemical processes, requiring thorough evaluation of advantages, disadvantages, and suitability for each waste type. This narrative review updates MW knowledge by synthesizing data from scientific literature, institutional documents, and regulatory sources. Key quantitative data indicate operating rooms generate up to 30% of total hospital waste, with recyclable materials representing over 40% of that volume. Improper segregation rates remain high, and incineration remains dominant despite sustainability concerns. The Romanian case study highlights progressive EU alignment, enforcing standardized MW classification, color-coded segregation, and specialized disposal protocols in surgical wards. Despite legal compliance, Romania is advancing incrementally, with systematic audits, digital tracking, and national outcome-based evaluations yet to be fully established. The Plastic Surgery Unit at Oradea County Emergency Clinical Hospital demonstrates good protocol adherence; however, strengthening data feedback mechanisms would enhance hospital-wide performance optimization and strategic waste reduction. Training and monitoring represent important areas for continued development. Coordinated professional engagement, modernized infrastructure, and enforceable audits are identified as critical priorities for improving MW handling in surgical environments. Future research should emphasize management innovation, evidence-based policy formulation, and a systematic strategy to achieve sustainable MW. Full article

Latin America faces growing challenges in the management of municipal solid waste (MSW). This is particularly evident in medium-sized and metropolitan cities where rapid urbanization, limited infrastructure, and high proportions of organic waste (40–70%) converge. This review synthesizes the most recent advances in organic waste management, valorization strategies, environmental performance, and policy frameworks in Mexico and Latin America. To provide a comprehensive overview, evidence from studies on informal recycling systems, route optimization, sustainable landfill siting, food waste valorization, life cycle assessments (LCAs), and biogas production is integrated. Techno-economic analyses of energy recovery from organic fractions are specifically reviewed. This review highlights that valorization of organic waste through composting, anaerobic digestion, food supplementation, and bioproduct generation can reduce greenhouse gas emissions by 40–70% compared to landfilling, with AD–composting hybrids achieving the highest reductions of 60–70%. Community composting achieved moderate reductions, 30–50%, but at significantly lower cost and with greater social co-benefits. These alternatives for valorizing the organic fraction extend the lifespan of both confined and open landfills. It also contributes to mitigating the public health impacts related to open dumping, disease vectors, and contaminated leachate. In short, this review also highlights shortcomings in policy coherence, financial mechanisms, source separation, and technology adoption. A strategic framework is proposed that prioritizes decentralized treatment systems, the integration of informal recyclers, tax incentives, community-based waste separation, and planning based on Life Cycle Assessment (LCA). The findings point to a viable strategy for transitioning from landfill dependency to circular waste management systems that improve the quality of life for the population of Latin America and the Caribbean. Full article

Accelerated urbanization and continuous infrastructure renewal have led to a rapid increase in construction and demolition waste (CDW), which accounts for approximately 20–50% of municipal solid waste in many developed countries. Consequently, effective management and resource utilization of CDW have become critical challenges for sustainable urban development. To address these challenges, this study develops an integrated analytical framework for CDW recycling systems. Specifically, it constructs a “cloud-edge-terminal” collaborative recycling system and clarifies the interactions among material, information, and value flows. A three-dimensional coupling framework is further established to reconceptualize CDW management as a multivariate decision-making problem, alongside a multidimensional evaluation structure to support practical implementation and system optimization. Methodologically, the study adopts an integrative review approach supported by knowledge mapping analysis. A structured literature search and screening process was conducted using the Web of Science Core Collection (2015–2026) to ensure transparency and reproducibility in the literature identification and sample construction. The results propose a multidimensional coupling framework integrating resource coordination, information communication, and market trading into a unified decision system. The framework contributes an engineering-oriented analytical paradigm that promotes hierarchical decision coordination, dynamic multi-objective regulation, and integrated management of CDW recycling systems. Full article