Search Results (5,693)

Background: Cardiovascular disease (CVD) is the leading global cause of death and is shaped by interacting biological, environmental, lifestyle, and social factors. Traditional models often treat risk factors in isolation and may miss dependencies among exposures and biomarkers. Objective: To map interdependencies among environmental, social, behavioral, and biological predictors of CVD risk using Bayesian network models. Methods: A cross-sectional analysis was conducted using NHANES 2017–2018 data. After complete-case procedures, the analytic sample included 601 adults and 22 variables: outcomes (systolic/diastolic blood pressure, total/LDL/HDL cholesterol, triglycerides) and predictors (BMI, C-reactive protein (CRP), allostatic load, Dietary Inflammatory Index, income, education, age, gender, race, smoking, alcohol, and serum lead, cadmium, mercury, and PFOA). Spearman’s correlations summarized pairwise associations. Bayesian networks were learned with two approaches: Grow–Shrink (constraint-based) and Hill-Climbing (score-based, Bayesian Gaussian equivalent score). Network size metrics included number of nodes, directed edges, average neighborhood size, and Markov blanket size. Results: Correlation screening reproduced expected patterns, including very high systolic–diastolic concordance (p ≈ 1.00), strong LDL–total cholesterol correlation (p = 0.90), inverse HDL–triglycerides association, and positive BMI–CRP association. The final Hill-Climbing network contained 22 nodes and 44 directed edges, with an average neighborhood size of ~4 and an average Markov blanket size of ~6.1, indicating multiple indirect dependencies. Across both learning algorithms, BMI, CRP, and allostatic load emerged as central nodes. Environmental toxicants (lead, cadmium, mercury, PFOS, PFOA) showed connections to sociodemographic variables (income, education, race) and to inflammatory and lipid markers, suggesting patterned exposure linked to socioeconomic position. Diet and stress measures were positioned upstream of blood pressure and triglycerides in the score-based model, consistent with stress-inflammation–metabolic pathways. Agreement across algorithms on key hubs (BMI, CRP, allostatic load) supported network robustness for central structures. Conclusions: Bayesian network modeling identified interconnected pathways linking obesity, systemic inflammation, chronic stress, and environmental toxicant burden with cardiovascular risk indicators. Findings are consistent with the view that biological dysregulation is linked with CVD and environmental or social stresses. Full article

Marine soft soils are characterized by high compressibility, low strength, and low permeability, which often result in excessive settlement and stability problems. Drainage consolidation methods are widely regarded as effective solutions for improving such soils. This review summarizes recent progress from four perspectives: optimization of traditional techniques, combined applications of multiple methods, development of emerging innovative approaches, and advances in drainage element materials and structures. Traditional methods such as surcharge and vacuum preloading have been refined through innovations in loading schemes, drainage improvements, and design approaches, while hybrid combinations with electroosmosis, thermal treatment, and dynamic loading have further enhanced their efficiency and applicability. In parallel, novel techniques such as siphon drainage, aerosol-assisted consolidation, and osmosis-based drainage show promise for sustainable applications. Furthermore, biodegradable and multifunctional drainage elements provide new directions for environmentally friendly and efficient soft soil improvement. Looking ahead, drainage consolidation technology is expected to move toward greener, low-carbon, and intelligent solutions. This review offers a comprehensive reference for engineering practice and a useful basis for guiding future research in marine soft soil improvement. Full article

Urban trees are subjected to stressful conditions caused by anthropogenic, biotic, and abiotic factors. These stressors can cause structural changes, increasing the risks of branch failure or even complete uprooting. To mitigate the risks to people’s safety, administrators must assess and evaluate the health and structural stability of trees. Risk analysis typically takes into account environmental vulnerability and tree characteristics, assessed at a specific point in time. However, although dynamic tests play a crucial role in risk assessment in urban environments, the high cost of the sensors significantly limits their widespread application across large tree populations. For this reason, the present study aims to evaluate the effectiveness of low-cost sensors in monitoring tree dynamics. A low-cost micro-electro-mechanical systems (MEMS) sensor is tested in the laboratory and the field using a pull-and-release test, and its performance is compared with that of seismic reference accelerometers. The collected data are analyzed and compared in terms of both the frequency and time domains. To obtain reliable measurements, the accelerations must be generated by substantial dynamic excitations, such as high wind events or abrupt changes in loading conditions. The results show that the MEMS sensor has lower accuracy and higher noise compared to the seismic sensor; however, the MEMS can still identify the main peaks in the frequency domain compared to the seismic sensor, provided that the input amplitude is sufficiently high. Full article

Enhancing indoor environmental quality while reducing building energy consumption represents a critical challenge for sustainable building design, particularly in hot arid climates where cooling loads dominate energy use. Despite extensive research on green wall systems (GWSs), robust quantitative data on their combined impact on air quality and thermal performance in real-world office environments remains limited. This research quantified the synergistic effects of an active indoor green wall system on key indoor air quality indicators and cooling energy consumption in a contemporary office environment. A comparative field study was conducted over 12 months in two identical office rooms in Dhahran, Saudi Arabia, with one room serving as a control while the other was retrofitted with a modular hydroponic green wall system. High-resolution sensors continuously monitored indoor CO₂, volatile organic compounds via photoionization detection (VOC_PID; isobutylene-equivalent), and PM2.5 concentrations, alongside dedicated sub-metering of cooling energy consumption. The green wall system achieved statistically significant improvements across all parameters: 14.1% reduction in CO₂ concentrations during occupied hours, 28.1% reduction in volatile organic compounds, 20.9% reduction in PM2.5, and 13.5% reduction in cooling energy consumption (574.5 kWh annually). Economic analysis indicated financial viability (2.0-year payback; benefit–cost ratio 3.0; 15-year net present value SAR 31,865). Productivity-related benefits were valued from published relationships rather than measured in this study; base-case viability remained strictly positive in energy-only and conservative sensitivity scenarios. Strong correlations were established between evapotranspiration rates and cooling benefits (r = 0.734), with peak performance during summer months reaching 17.1% energy savings. Active indoor GWSs effectively function as multifunctional strategies, delivering simultaneous air quality improvements and measurable cooling energy reductions through evapotranspiration-mediated mechanisms, supporting their integration into sustainable building design practices. Full article

Rail transport is widely regarded as a sustainable and environmentally friendly option for long-distance freight and passenger movement during its operation phase. However, its construction and maintenance phases often result in substantial environmental impacts, which must be addressed to improve the overall sustainability of railways. This study aims to identify solutions that improve the performance of railway tracks, reduce maintenance requirements, and minimize environmental impact. With this objective, the potential of artificial inclusions and innovative composite materials in enhancing the sustainability of railway tracks is investigated through a comprehensive methodology, combining experimental, analytical and numerical approaches. A novel composite material, comprising soil, scrap tire aggregates and an adhesive, demonstrated strong potential as a sustainable base layer for ballastless railway tracks, exhibiting minimal strain accumulation (0.29–0.98%) under 50,000 load cycles and adequate damping. Incorporation of cellular artificial inclusions in the substructure layers of ballasted tracks reduced cumulative settlement by up to 33% and slowed track geometry deterioration. Use of planar artificial inclusions beneath a pile-supported railway embankment enhanced the load transfer efficiency and curtailed settlement, while also lowering environmental impact by reducing concrete usage. The findings of this study highlight strong potential of these approaches in improving track performance and the overall sustainability of railways. Full article

Steel structures that support machines and industrial process installations should ideally be flexible, adaptable, and easily reconfigurable. However, in current practice, new profiles are frequently used and discarded whenever layout modifications are required, leading to considerable material waste, increased costs, and environmental burdens. Such practices conflict with the principles of the circular economy, in which reusability is preferable to recycling. This paper presents a life cycle sustainability assessment (life cycle cost, LCC, and life cycle assessment, LCA) applied to six structural typologies: (a) welded IPE profiles, (b) bolted IPE profiles, (c) welded tubular profiles, (d) bolted tubular profiles, (e) clamped IPE profiles with demountable joints, and (f) flanged tubular profiles with demountable joints. The assessment integrates structural calculations with an updatable database of costs, operation times, and service lives, providing a systematic framework for evaluating both economic and environmental performance in medium-load industrial structures (0.5–9.8 kN/m²). Application to nine representative case studies demonstrated that demountable clamped and flanged joints become economically competitive after three life cycles, and after only two life cycles under high-load conditions (9.8 kN/m²). The findings indicate relative cost savings of up to 75% in optimized configurations and carbon-footprint reductions of approximately 50% after three cycles. These results provide quantitative evidence of the long-term advantages of demountable and reconfigurable steel structures. Their capacity for repeated reuse without loss of performance supports sustainable design strategies, reduces environmental impacts, and advances circular economy principles, making them an attractive option for modern industrial facilities subject to frequent modifications. Full article

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

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

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

Offshore platforms need to be made, from the start of their construction, to withstand the extreme environmental conditions they will be facing. This study investigates the welding-induced residual stress and distortion in a Y-shaped tubular joint extracted from an offshore wind turbine jacket substructure. While similar joints are commonly used in offshore platforms, their welding behavior remains underexplored in the existing literature. The joint configuration is representative of critical load-bearing connections commonly used in offshore platforms exposed to harsh marine environments. A finite element model has been developed to simulate the welding process in a typical offshore tubular joint through thermal and mechanical simulation. Validation of the model has been achieved with results against reference experimental data, with temperature and distortion errors of 3.9 and 5.3%, respectively. Residual stress and distortions were analyzed along predefined paths in vertical, transverse, and longitudinal directions. A mesh sensitivity study was conducted to balance computational efficiency and result accuracy. Furthermore, clamped and free displacement boundary conditions are analyzed, demonstrating reduced deformation and stress for the second case. Full article

Airborne fungal exposure in confined indoor environments is a growing public health concern, however the microbial composition of air inside private vehicles remains underexplored. This study aimed to characterize culturable airborne fungi in vehicle cabins and evaluate their association with environmental and behavioral variables. Air samples (100 L) were collected from 69 vehicles using a standardized culture-based method. Simultaneously, a detailed survey was administered to vehicle owners to document usage patterns, maintenance habits, and odor perception. Results revealed a total culturable fungal load of 31,901 CFU/m³, with Cladosporium, Aspergillus, and Penicillium as the most frequently isolated genera. Statistical analysis showed that fungal abundance and community composition were significantly associated with vehicle usage factors such as air disturbance, parking environment, air filter maintenance, and perception of musty odors. Vehicles parked outdoors had significantly higher Bipolaris levels, while lack of regular filter replacement was strongly associated with elevated Alternaria abundance. The presence of musty or moldy odors correlated with a 2.5-fold increase in Aspergillus levels. Redundancy analysis confirmed that odor perception and parking behavior were the strongest predictors of fungal community structure, with specific genera displaying distinct ecological preferences across usage conditions. Usage patterns and maintenance habits significantly influence in-cabin fungal communities, with implications for respiratory health, particularly due to the presence of allergenic and opportunistic genera like Aspergillus, Alternaria, and Bipolaris. Regular air filter maintenance and attention to odor cues may help reduce fungal load and associated health risks. Full article

Food processing wastewater (FPW) poses significant environmental risks due to its high nutrient load yet offers untapped potential as a low-cost feedstock for high-value compound production. This review critically evaluates the valorization of FPW for astaxanthin production through the mixotrophic fermentation of microalgae. Key microalgal species (e.g., Haematococcus pluvialis and Chromochloris zofingiensis) effectively remediate nutrients (nutrients removal of up to 100%) while synthesizing astaxanthin under stress-inducing conditions, such as nutrient starvation, salinity, and oxidative stress. Advanced strategies, such as two-stage cultivation, nutrient profile adjustment, and microbial co-cultivation, which could enhance astaxanthin yields and wastewater treatment efficiency were reviewed comprehensively. The resulting astaxanthin-rich biomass demonstrates multifunctional benefits in animal feed, improving meat quality, immunity, growth, and shelf life. However, this review identifies some challenges, including wastewater management risks, low digestibility of microalgae biomass, and astaxanthin instability during feed processing, which should be addressed properly in real-world applications. This integrated approach aligns with circular bio-economy principles, transforming FPW from an environmental liability into a resource for sustainable biotechnology. Full article

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO₂, NO_x, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems. Full article

The growing urgency for transportation network development in seasonally frozen regions brings attention to two critical symmetrical factors: cyclic loading and freeze–thaw cycles. In saline soil areas, these symmetrical mechanical and environmental processes, along with varying salt content, significantly affect soil mechanical properties, posing considerable challenges for engineering design. In this study, the dynamic triaxial tests were conducted on a type of carbonate saline soil considering four factors, including moisture content, salt content, freeze–thaw cycle and confining pressure, and the variations in dynamic parameters, including dynamic strength and dynamic elastic modulus, with the above four factors were studied, and the influential mechanisms of four factors were fully discussed. The results demonstrated that the variations in dynamic strength (τ_d) versus vibration cycles (N_F) were better fitted by logarithmic functions than by a linear one. An increase in moisture content, salt content, and freeze–thaw cycle all reduced the τ_d and dynamic elastic modulus (E_d); in addition, the E_d decreased significantly when the dynamic axial strain was less than 0.2%, and then stabilized with further increases in dynamic axial strain. The dynamic parameters of saline soil became nearly constant after undergoing five freeze–thaw cycles, and increased significantly with increasing confining pressure. Moreover, the relationship between the maximum dynamic elastic modulus (E_dmax) and the four factors could be described by power functions. These findings could provide certain references for addressing the combined effects of symmetrical cyclic loading and freeze–thaw cycles in subgrade design for saline soil regions. Full article