Search Results (70,973)

The integration of solar photovoltaic (PV) systems on offshore platforms has emerged as a promising solution to reduce reliance on diesel-based power generation and associated with greenhouse gas emissions. However, the combined influence of environmental and installation factors, particularly tilt angle and fouling, remains insufficiently explored in offshore conditions. This study aims to evaluate the interaction between tilt angle and fouling on PV system performance under practical offshore constraints. A series of simulations was conducted using PVsyst by varying tilt angles (10°, 26°, and 40°) and fouling factors (0%, 5%, and 10%). The results indicate that fouling has a significantly greater impact on system performance than tilt angle variation. Increasing fouling from 0% to 10% leads to energy yield reductions of approximately 9%, while variations in tilt angle within the tested range result in differences of less than 3%. The highest energy yield was achieved at a tilt angle of 26°, reaching approximately 179 MWh/year, whereas performance ratio shows a slight increase with higher tilt angles. These findings suggest that, under offshore environmental conditions, operational strategies such as fouling mitigation and maintenance play a more critical role than geometric optimization. This study provides practical insights for improving the reliability and energy efficiency of offshore PV systems. Full article

With an ever-growing recognition of Land Surface Temperature (LST) as a key Essential Climate Variable (ECV), it becomes utmost important to have such a variable at the fine spatial and temporal scales of urban spaces and dynamics. Sentinel-3 provides coarse LST (1 km, daily) based on thermal imagery acquired by its Sea and Land Surface Temperature Radiometer (SLSTR) as well as fine Spectral Directional Reflectances (SDRs, 300 m, every two days) synergically inferred from both SLSTR and the Ocean and Land Colour Instrument (OLCI), which gives the opportunity for using the latter as a predictor in the downscaling of the former. Herein, two scale-invariance-based architectures were developed: a single-timestamp (STS) model, trained with coarse data of the timestamp whose fine target it infers; and a multi-timestamp (MTS) one, trained with multiple timestamps. Note that while several Machine Learning (ML) models besides Linear Regression (LR) were considered for the MTS architecture, only LR was used for the STS one due to the limited amount of available data which the former require for hyperparameter tuning. The models were developed over four Danish Functional Urban Areas (FUAs) using SRD-derived indices and seasonal and geospatial predictors and validated against Landsat data. While Gradient Boosting (GB) achieved the best coarse-scale performance it corresponded to the worst fine-scale performer together with Random Forest (RF), indicating scale invariance breakdown. Tree-based models performed poorly due to extrapolation limitations, whereas Neural Net (NN) and LR proved more robust. After residual correction, single-timestamp LR achieved the best fine-scale performance, making it the most reliable and recommended architecture for operations. The overall results showed that, although ML models may better predict the target at their training scale, their performance may not significantly generalise at others, therefore revealing scale specificity. Furthermore, the results suggested that usage of the more general multi-timestamp architecture instead of the single one may deteriorate performance. Full article

Over 30,000 hip hemiarthroplasty (HA) operations are performed every year across England and Wales to treat fractured necks of the femur. HA reduces surgical and recovery time with lower complication rates; however, it may cause acetabular deterioration, which can lead to revision surgery and possible conversion to total hip arthroplasty (THA). This study assessed hip hemiarthroplasty under gait-representative loading in an anatomical hip simulator. Paired natural acetabula were tested against a CoCr femoral head with radial clearance (RC) of −0.75 mm (head larger than natural) and positive RCs of <0.6 mm (small), 2 mm–4 mm (large), and >4 mm (extra-large). Cartilage surface deterioration was quantified via photogrammetry. Cartilage surface changes were observed in all hemiarthroplasty groups, while no changes were observed in the control group. No statistically significant between-groups in the affected area were detected (Kruskal–Wallis, p > 0.29). The negative RC group showed statistically significant progressive worsening over time (Friedman: (χ²(2) = 8.00, p = 0.018). Groups differed in damage onset, location, intensity, and presence of delamination. Samples with negative RC (oversized head) produce earlier and progressive cartilage changes. The results highlight the importance of carefully measuring the native head diameter and choosing a femoral head size when performing HA. Full article

Aging and deterioration of urban underground drainage pipelines frequently trigger road collapses, urban waterlogging and groundwater contamination, posing critical challenges to the operation, maintenance and disaster prevention of urban underground infrastructure. Conventional rehabilitation solutions, including cement-based linings and traditional polymer liners, suffer from inherent limitations such as reduced effective flow cross-sections caused by excessive lining thickness, unsatisfactory corrosion resistance and durability, and high construction disturbance. In this study, a modified polyurea (MPU) material was applied to the trenchless rehabilitation of drainage pipelines via spray-applied pipe lining technology. The mechanical properties and interfacial bonding performance of MPU were systematically characterized at the material scale; full-scale external pressure tests were conducted to investigate the effects of 3–8 mm thick MPU linings on the bearing capacity and failure characteristics of structurally damaged concrete pipes; and the anti-seepage repair performance for local perforation defects was evaluated through void-crossing testing. The results demonstrate that MPU lining can meet the engineering performance requirements for pipeline rehabilitation when applied with matched interfacial primer following standard construction procedures. Even the baseline bond strength tested without primer remains sufficient to ensure stable cooperative load bearing between the lining and the host concrete pipe. The 3–8 mm thick linings increase the cracking load of damaged pipes by 61.7–145.7% and the ultimate load by up to 162.2%, while transforming the failure mode from brittle fracture to ductile failure. For local perforation repair, the 3 mm thick MPU lining achieves a critical hydrostatic failure pressure of 1.23 MPa, maintaining favorable structural integrity and interfacial bonding stability under the test conditions. With a well-balanced combination of thin lining thickness, rapid curing and high structural strengthening efficiency, as well as favorable inherent corrosion resistance, the MPU lining provides novel material alternatives and fundamental experimental evidence for the green trenchless rehabilitation of aged underground pipelines and offers technical support for the safe operation and maintenance of urban underground infrastructure. Full article

Carbon-based materials, including graphene, carbon nanotubes, laser-induced graphene, and pyrolyzed glassy carbon, are widely used in sensing applications due to their high conductivity, large surface area, and tunable surface chemistry. Meanwhile, microfluidic systems enable precise fluid handling, reduced sample consumption, and enhanced analytical performance through improved mass transport and device miniaturization. The integration of carbon-based materials with microfluidic platforms has enabled the development of compact, portable, and highly sensitive devices for water monitoring. This review summarizes recent advances in carbon-based microfluidic sensors for water monitoring applications. Key carbon materials and their sensing mechanisms, particularly electrochemical transduction, are discussed. Various microfluidic integration strategies, including paper-based devices, polymer-based devices, MEMS-based systems, and flexible platforms, are highlighted, with emphasis on mass transport enhancement and overall system performance. Representative recent advances in carbon-based microfluidic sensors for water monitoring, including the detection of heavy metal ions, nutrients, and emerging contaminants, are reviewed. Finally, challenges related to scalable manufacturing, long-term operational stability, biofouling/surface fouling, and reproducible system integration are discussed, together with future perspectives on intelligent carbon-based microfluidic platforms featuring AI-assisted analytics, sense-response functionality, and self-healing and dynamic antifouling capabilities for water monitoring. These advances are expected to enable real-time, low-cost, and field-deployable water monitoring systems for environmental protection and public health management. Overall, this review highlights the critical role of integrating carbon-based sensing materials with microfluidic engineering in advancing next-generation water monitoring technologies. Full article

Decarbonising the building sector requires addressing both embodied carbon in materials and operational energy for indoor conditioning. This article critically reviews the potential of mycelium, hygromorphic wood and hemp as emerging natural materials for low-carbon construction, comparing them with conventional materials only within function-specific applications such as envelopes, insulation, non-load-bearing components and passive systems. Recent comparative literature shows that mycelium composites can reach thermal conductivities of 0.026–0.12 W/m·K and densities of 51–280 kg/m³; in wood–mycelium formulations, an average density of 167.5 kg/m³ and climate impact of 2.13 kg CO₂-eq/kg with a conventional electricity mix, reduced to 0.66 kg CO₂-eq/kg with renewable energy, have been reported. Hemp shows typical densities of 140–540 kg/m³, conductivities of 0.061–0.12 W/m·K, compressive strengths of 0.3–3.5 MPa and potentially negative climate performance of about −40 to −80 kg CO₂-eq/m³, compared with +300 to +400 kg CO₂-eq/m³ for conventional concrete. Hygromorphic wood is relevant not as insulation or a primary structural replacement, but as a passive actuation material for adaptive envelopes. Hemp is currently the most mature option, mycelium is promising for circular non-structural panels and insulation, and hygromorphic wood is an operational-modification strategy whose building-level energy benefits still require direct quantification. Cross-study comparisons should be interpreted as bounded ranges because composition, fabrication, testing and LCA (Life Cycle Assessment) system boundaries vary substantially across the literature. Full article

Landfills represent a significant source of methane emissions, with important environmental, climatic and safety impacts due to the widespread and variable nature of these emissions. Traditional monitoring methods, such as flow chambers coupled with flame ionisation detectors (FIDs), provide high accuracy but are limited in terms of spatial representativeness, operational flexibility and cost, especially during large-scale or continuous monitoring campaigns. Within this context, the European ESCAPE project aims to develop a low-cost, portable and modular platform for the detection and quantification of low methane concentrations in ambient air at complex environmental sites. The system is based on commercial MOX and NDIR sensors integrated into portable toolboxes equipped with dedicated chambers, regulated suction systems and autonomous data acquisition units with real-time transmission. This work describes the development and testing of two identical toolboxes to assess system reproducibility and the transferability of predictive models between devices. Laboratory and field tests were carried out under controlled and real landfill conditions, with comparisons against portable FID measurements. Results showed good agreement between predicted methane concentrations and reference data, with correlation indexes up to 0.77. Moreover, transferring the machine learning model between toolboxes did not produce statistically significant performance reductions, demonstrating promising robustness and generalizability of the proposed calibration strategy. Full article

The use of non-conventional systems for domestic wastewater management has gained attention in rural areas of the Global South, where centralised infrastructure is often limited. This study presents the design, construction, and pilot-scale evaluation of a solar still unit operated under passive and photovoltaic-assisted active modes as a separation and polishing component for domestic wastewater from a rural site in Barrancabermeja, Colombia. Performance was assessed through physicochemical and microbiological characterisation of influent wastewater and treated condensate, together with hourly monitoring of distillate production, water temperature, glass-cover temperature, and ambient conditions. Under passive operation, a theoretical distillation model was applied, empirically adjusted, and evaluated using MAE, RMSE, MAPE, and R². Under the tested conditions, indicative within-mode reductions reached 80.9% and 89.3% for chemical oxygen demand (COD), 95.6% and 93.8% for biochemical oxygen demand (BOD₅), and 94.0% and 94.4% for total suspended solids (TSS) under passive and active modes, respectively. Microbial indicators showed minimum estimated reductions above 99.9%, with faecal coliforms reduced to very low levels in passive mode and not detected in the analysed active-mode condensate sample. Maximum daily condensate production reached 1.445 L m⁻² day⁻¹ in passive mode and 2.262 L m⁻² day⁻¹ in active mode, confirming the low-flow nature of the unit. Approximately 50% of daily production occurred between 12:00 and 15:00 h. The model reproduced the main diurnal production pattern, although empirical correction was required. Overall, the unit may improve condensate quality under pilot-scale conditions and shows potential as a polishing component within decentralised, low-flow treatment trains. Full article

There is a need to ensure that the application of artificial intelligence (AI) in increasingly automated operations is safe, human-centric, and trustworthy, and respects ethical principles. To this end, this paper presents an innovative holistic assessment framework to support certification-aware design of AI-based sociotechnical systems with a range of levels of automation along multiple design stages from low to high technology and human readiness levels (TRLs/HRLs). The holistic scope considers a range of relevant key performance areas (KPAs): safety, resilience, security, Human Factors, accountability, responsibility, liability, efficiency, societal sustainability, and environmental sustainability. The core of the framework is a seven-step cycle that assesses the KPAs for critical scenarios and evaluates the combined performance, including uncertainty and trade-offs. This provides feedback to either adapt the design at the same TRL/HRL or refine it at higher TRLs/HRLs. The framework enacted by a toolbox of assessment methods for the KPAs. The framework has been developed in the aviation domain, but it is formulated in a generic manner, enabling application to various AI techniques and operational domains. Its application is illustrated in detail for an air traffic management use case that employs an AI-based system to support air traffic controllers in sequencing aircraft. It is concluded that the framework provides a viable approach for holistic assessment of AI-based sociotechnical systems. Full article

In reliability engineering research, obtaining accurate information about the life expectancy of products or materials is essential. However, collecting such data under normal operating conditions is often challenging, particularly for highly reliable items. This paper addresses the problem of statistical inference for lifetime data following the power Rayleigh distribution. To reduce experimental cost and time, a partially step-stress-accelerated life test is employed under a Type-I generalized hybrid censoring scheme (GHCS). Point estimators of the model parameters, as well as the acceleration factor, are derived using both maximum likelihood and Bayesian approaches. Furthermore, interval estimation is developed based on the asymptotic normality of maximum likelihood estimators, in addition to a bootstrap method and Markov-chain Monte Carlo techniques. A real-life dataset is analyzed to demonstrate the applicability of the proposed model. Finally, a Monte Carlo simulation study is conducted to evaluate and compare the performance of the suggested model and estimation procedures. Full article

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives criteria weights, the Analytic Hierarchy Process (AHP) ratings evaluate alternatives, and a risk assessment stage consolidates the final ranking. The primary insights of this research include: (i) the development of a structured supplier evaluation model that encompasses dimensions like closed-loop integration, end-of-life management, material efficiency, and waste management into a multi-criteria perspective; (ii) applying BWM to derive consistent criteria weights, clarifying how circular performance attributes shape supplier prioritization; (iii) applying AHP ratings and risk assessment to consolidate the evaluation into a final ranking of alternatives; and (iv) demonstrating the operational feasibility and applicability of the framework through a real-world case analysis, providing empirical evidence for assessing circular supplier performance in industrial environments. Full article

Background/Objectives: Postoperative pulmonary complications (PPCs) are a major source of morbidity following cardiac surgery, particularly in older adults. While malnutrition is linked to adverse outcomes, the optimal screening tool for identifying patients at risk of PPCs remains uncertain. This study compared the predictive performance of the Geriatric Nutritional Risk Index (GNRI), Mini Nutritional Assessment–Short Form (MNA-SF), Prognostic Nutritional Index (PNI), and Nutrition Alert Form (NAF) for PPCs in older adults undergoing elective cardiac surgery. Methods: This prospective cohort study enrolled 217 patients aged ≥ 60 years at a tertiary university hospital. Preoperative nutritional status was assessed using the GNRI, MNA-SF, PNI, and NAF. The primary outcome was PPC development during hospitalization. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and multivariable logistic regression identified independent predictors. Results: PPCs occurred in 86 patients (39.6%). Patients who developed PPCs had significantly higher NAF scores than those who did not (median [IQR]: 7.5 [3–12] vs. 5 [2–8], p < 0.001), whereas GNRI, MNA-SF, and PNI scores did not differ significantly. NAF demonstrated the highest predictive performance (AUC: 0.643, 95% CI: 0.567–0.719), followed by PNI, MNA-SF, and GNRI. However, after adjusting for clinical covariates, none of the nutritional assessment tools remained independently associated with PPCs. Conclusions: Among the four tools evaluated, NAF showed the highest predictive performance among the evaluated nutritional assessment tools; however, its discriminative ability was modest, and none of the nutritional assessment tools remained independently associated with PPCs after multivariable adjustment. Nutritional assessment should complement, rather than replace, established clinical risk factors in perioperative risk stratification. Full article

Monitoring the dynamic response of structures subjected to operational loads is a key component of structural health assessment, providing valuable information for safety evaluation and maintenance planning. In the last decade, video-based measurements have received growing attention for modal identification and damage detection applications, offering a promising alternative to traditional sensor-based approaches. Unlike conventional monitoring systems, which provide discrete measurements and often require extensive instrumentation, computer vision techniques enable dense, non-contact measurements while reducing installation costs and accessibility constraints. Moreover, Motion Magnification algorithms can be combined with computer vision-based identification techniques to amplify displacements within selected frequency ranges, facilitating the detection of low-amplitude structural vibrations. In this work, a semi-automated methodology for structural identification is presented and validated through two experimental applications involving vibrating systems monitored with commercial cameras. The proposed framework combines computer vision algorithms, Motion Magnification (MM), correlation analysis, and Principal Component Analysis (PCA), the latter being adopted as a noise-reduction and dimensionality-reduction tool to extract the most informative features from large sets of time-histories. In contrast to previous studies primarily focused on damage detection and frequency evolution tracking, the present work specifically investigates the influence of key user-defined parameters on the reliability of the identified frequencies and provides practical calibration guidelines for future applications. The methodology was validated against reference measurements obtained from an optical monitoring system and it successfully identified the natural frequencies of the analysed structures with errors ranging from 0.84% to 1.75%. Sensitivity analyses performed on the region of interest size and position, as well as on the correlation threshold, demonstrated the robustness of the proposed workflow. The results confirm that the proposed approach represents a reliable, low-cost, and minimally invasive alternative to conventional dynamic monitoring techniques, while providing practical recommendations for its implementation in real-world structural health monitoring applications. Full article

Water level prediction for the backwater reaches of large reservoirs is a critical step for many tasks of reservoir operation and flood control, directly affecting the sustainability of water–energy–ecosystem balance. The problem is very challenging due to arbitrarily complicated hydrodynamic mechanisms and various types of influencing factors. This paper proposes a method based on time series decomposition for feature extraction from data samples by a novel neural architecture. To accurately quantify the complex hydraulic conditions of large reservoirs, we investigate a type of neural basis expansion to incorporate exogenous variables (e.g., reservoir regulation and storage, upstream confluence, and flow travel time). Unlike the traditional LSTM-based methods, our method is free from recurrent architecture. It can exploit backward and forward residual links as a backbone to ensure the validity and structural distribution of the information during the model training. Extensive experiments on real data of the Three Gorges Reservoir are implemented to evaluate the performance of the proposed method. The results show that the proposed method shows state-of-the-art performance on all evaluation metrics and can provide reliable technical support for the refined and sustainable operation of large reservoirs. Full article

This study examines the applicability of Plasma Transferred Arc (PTA) surface treatment as an advanced technique for the refurbishment of railway wheel treads. Conventional wheel reprofiling, typically performed on semi-automatic lathes, requires the removal of a minimum of 6 mm of material from the running surface, which accelerates rim thinning and ultimately necessitates wheel replacement. Moreover, the reprofiled surfaces are not subjected to any subsequent treatment aimed at enhancing their durability. To overcome these limitations, PTA cladding was selected due to its ability to generate surface layers with superior mechanical and tribological properties. In contrast to widely used diode laser technologies, PTA enables the deposition of alloying materials in powder form, ensuring a stable, controllable, and efficient cladding process. The resulting microstructure consists of a heat-affected zone, a transition zone, and a re-melted zone, each exhibiting significantly increased hardness relative to the untreated base material. The process facilitates the incorporation of metallic particles into the surface layer, promoting the formation of a dense, wear-resistant coating. These materials possess huge potential utility regarding the wear resistance reaching even ca 10% of the base material wear in the case of 505 PTA and over 20% in the case of the 15 E material. The findings indicate that PTA surface treatment has substantial potential to extend the operational lifespan of railway wheels by providing a highly durable and mechanically robust surface, thereby reducing maintenance frequency and the associated costs. Full article