Search Results (56,462)

This study examines scientific and technical solutions designed to enhance thermodynamic processes in modern aircraft turbine engines by utilizing heat exchangers. A comprehensive literature review informed the development of a conceptual design for a turbofan engine incorporating both an intercooler and a recuperator. The research included an original parametric and constrained optimization analysis conducted for two engine configurations as follows: one intended for narrow-body and the other for wide-body aircraft. The study focused on achieving the required thrust while enhancing efficiency. Results indicate that integrating heat exchangers can significantly reduce specific fuel consumption (SFC) and/or increase engine power or thrust. Moreover, the recovery of residual heat from exhaust gases through recuperation contributes to improved overall energy efficiency. The study also explores a novel cryogenic design that utilizes liquid hydrogen for cooling the intercooler, recuperator, and turbine. Although not modeled directly, this concept demonstrates the potential to increase the bypass ratio, further reduce SFC, and lower NO_x emissions. These findings highlight the promise of combined intercooling and recuperation strategies for improving both economic and environmental performance, with optimal system parameters dependent on aircraft class. The research aligns with ongoing efforts in mechanical engineering and aviation to enhance turbine engine efficiency through innovative thermal management solutions. Full article

Aggregation-induced emission (AIE) luminogens are materials that exhibit enhanced light emission in the aggregated state, primarily due to the restriction of intramolecular motions, which reduces energy loss through non-radiative pathways. Tetraphenylethylene (TPE) and its derivatives are prominent examples of AIE-active materials, owing to their ease of synthesis, tuneable photophysical properties, and strong aggregation tendencies. This review provides an overview of the fundamental AIE mechanisms in TPE-based systems, with a focus on the role of restricted intramolecular rotation (RIR) and π-twisting in governing their emission behaviour. It explores the influence of molecular structure, electronic configuration, and intermolecular interactions on fluorescence properties. Furthermore, recent advances in practical applications of TPE-based AIE luminogens are highlighted across a spectrum of biological imaging domains, including cellular imaging, tissue and in vivo imaging, and organelle-targeted imaging. Additionally, their integration into multifunctional and theranostic platforms, along with the development of stimuli-responsive and self-assembled systems, underscores their versatility and expanding potential in biomedical research and diagnostics. This review aims to offer valuable insights into the design principles and functional potential of TPE-based AIE luminogens, guiding the development of next-generation materials for advanced bioimaging technologies. Full article

Background: Asthma is the most prevalent chronic illness in children worldwide, contributing to significant morbidity, health care utilization, and economic burden. In the United States, approximately five million children are affected by asthma. This review explores the environmental contexts and lifestyle determinants of pediatric asthma, with a focus on the Inland Empire (IE) region of Southern California. The IE’s unique geographic landscape and importance as a major transportation hub highlights its critical role for understanding how both environmental and structural factors exacerbate asthma burden within the pediatric population. Variables such as household income, parental education levels, and lack of community-based asthma programs were explored. Despite significant burdens, the IE remains under-represented in asthma research, contributing to persistent disparity. Methods: A narrative literature review and regional data analysis were conducted via PubMed, Scopus, and Google Scholar (2000–2025), alongside data from the CDC, CDPH, and American Lung Association. Key words used included “pediatric asthma, Inland Empire, air pollution, asthma disparity, emergency department utilization, socioeconomic status.” Inclusion criteria were: (1) studies or reports focusing on pediatric asthma (ages 0–17), (2) articles addressing environmental, socioeconomic, or health care-related risk factors, and (3) research with either national, state-level, or IE-specific data. Exclusion criteria were: (1) articles not in English, adult-only asthma studies, and (3) publications without original data or reference to pediatric asthma burden, management, or outcomes. Titles and abstracts were screened for relevance, and full texts were reviewed when abstracts met inclusion criteria. A total of 61 studies, reports, and data sources met this criterion and were included into this review. Results: The IE—comprised of San Bernardino (SB) and Riverside Counties— is home to four of the top five most polluted cities in North America. Vehicle emissions and industrial waste are concentrated in the region due to limited air circulation from surrounding mountains that entrap pollutants. Pediatric asthma ED visit rates in SB and Riverside were 60.5% and 59.3%, compared to California’s average of 56.7%. Hospitalization rates for children aged 0–4 were also higher in SB (24.4%) compared to the state average (17.3%). The elevated rates among school-aged children underscore the crucial need for interventions aimed at improving air quality, enhancing asthma management, and increasing access to preventive health care. Conclusions: Pediatric asthma in the IE reflects heightened environmental risks, socioeconomic barriers, and gaps in health care access. Addressing these disparities requires targeted interventions, policies, and region-specific research to enhance long-term management strategies and outcomes for vulnerable pediatric populations. Full article

This research considers an Artificial Intelligence (AI)-driven omnichannel logistics network for bioethanol supply from Russia to China. As a renewable, low-carbon transport fuel, bioethanol plays a critical role in energy diversification and decarbonization strategies for both Russia and China. However, its flammability and temperature sensitivity impose stringent requirements on transport infrastructure and supply chain management, making it a typical application scenario for exploring intelligent logistics models. The proposed model integrates information, transportation, and financial flows into a unified simulation framework designed to support flexible and sustainable cross-border (CB) logistics. Using a combination of machine learning, multi-objective evaluation, and reinforcement learning (RL), the system models and ranks alternative transportation routes under varying operational conditions. Results indicate that the mixed corridor through Kazakhstan and Kyrgyzstan achieves the best overall balance of cost, time, emissions, and customs reliability, outperforming single-country routes. The findings highlight the potential of AI-enhanced logistics systems in supporting low-carbon energy trade and CB infrastructure coordination. Full article

To address challenges posed by waste tires and greenhouse gas emissions associated with ordinary Portland cement, exploring eco-friendly construction materials is critical for sustainability. This study examines the workability and mechanical properties of straight steel fiber-reinforced rubberized geopolymer concrete (SFRRGC), where rubber powder is derived from recycled waste tires. The experimental variables included rubber powder (RP) content (0%, 6%, 12%, and 20% by volume of fine aggregate) and steel fiber (SF) content (0%, 0.5%, 1.0%, and 1.5% by volume). The results show that incorporating RP and SFs reduced the workability of SFRRGC but increased its peak strain. Specifically, RP addition decreased the elastic modulus, compressive strength, and toughness; increasing the SF content enhanced energy dissipation, while the effects of SF and RP contents on Poisson’s ratio were negligible. The specimens showed that a higher RP content would weaken the crack-bridging effect of SF. For example, specimens with 1.0% SF and 6% RP achieved 49.56 MPa compressive strength and 4.04 × 10⁻³ maximum peak strain; those with 0.5% SF and 20% RP had 118.40 J compressive toughness, which was 5.53% lower than that of the reference specimens (125.33 J). Furthermore, a constitutive model for SFRRGC was proposed, and its theoretical curves aligned well with the experimental results. This proposed model can reliably predict the stress–strain curves of geopolymer concrete with different SF and RP mixture proportions. Full article

This study explores how the integration of generative artificial intelligence, multi-objective evolutionary optimization, and reinforcement learning can enable sustainable and cost-effective decision-making in supply chain strategy. Using real-world retail demand data enriched with synthetic sustainability attributes, we trained a Variational Autoencoder (VAE) to generate plausible future demand scenarios. These were used to seed a Non-Dominated Sorting Genetic Algorithm (NSGA-II) aimed at identifying Pareto-optimal sourcing strategies that balance delivery cost and CO₂ emissions. The resulting Pareto frontier revealed favorable trade-offs, enabling up to 50% emission reductions for only a 10–15% cost increase. We further deployed a deep Q-learning (DQN) agent to dynamically manage weekly shipments under a selected balanced strategy. The reinforcement learning policy achieved an additional 10% emission reduction by adaptively switching between green and conventional transport modes in response to demand and carbon pricing. Importantly, the agent also demonstrated resilience during simulated supply disruptions by rerouting decisions in real time. This research contributes a novel AI-based decision architecture that combines generative modeling, evolutionary search, and adaptive control to support sustainability in complex and uncertain supply chains. Full article

Accurate estimation of urban land surface temperature (ULST) is critical for studying urban heat islands, but complex three-dimensional (3D) structures and materials in urban areas introduce significant adjacency effects into remote sensing retrievals. To investigate the influence of different factors on the adjacency effects, this study employed the DART model to quantify brightness temperature differences (ΔTb) of urban pixels by comparing their simulated radiance in two scenarios: (1) an isolated state (no adjacent buildings) and (2) an adjacent state (with surrounding buildings). ΔTb, representing the adjacency effect, was systematically analyzed across spatial resolutions (1–120 m), building geometry (building height BH, roof area index ${\lambda }_p$, adjacent obstruction degree SVF_Obs.), and material reflectance (reflectance R = 0.05, 0.1, 0.15) to determine key influencing factors. The results demonstrate that (1) adjacency effects intensify significantly with higher spatial resolution (mean ΔTb ≈ 5 K at 1 m vs. ≈2 K at 30 m), with 60–90 m identified as the critical resolution range where the adjacency-induced error is attenuated to a level (ΔTb < 1 K) that is commensurate with the intrinsic uncertainty of current mainstream ULST algorithms; (2) increased building height, reduced density (${\lambda }_p$), and greater adjacent obstruction (SVF_Obs.) exacerbate adjacency effects; (3) material emissivity (ε = 1 − R) is the dominant factor, where low-ε materials (high R) exhibit markedly stronger adjacency effects than geometric influences (e.g., ΔTb at R = 0.15 is approximately three times higher than at R = 0.05); and (4) temperature differences among surface components exert minimal influence on adjacency effects (ΔTb < 0.5 K). This study clarifies key factors driving adjacency effects in high-resolution ULST retrieval and defines the critical spatial resolution for simplifying inversions, providing essential insights for accurate urban temperature estimation. Full article

Background: Since the 1990s, numerous investigations have assessed the diagnostic effectiveness—specifically sensitivity, specificity, and accuracy—of exercise stress testing (EST), stress echocardiography (SE), stress myocardial single-photon emission computed tomography (SPECT), and stress cardiac magnetic resonance imaging (CMR). However, the outcomes of these studies have often been inconsistent and inconclusive. To provide a clearer comparison, we conducted systematic reviews and meta-analyses aimed at quantitatively evaluating and comparing the aggregated diagnostic performance of these four commonly used techniques for detecting coronary artery disease (CAD). Methods: A comprehensive search of PubMed, Scopus, Embase, Cochrane Library, and Web of Science was conducted to identify cohort studies evaluating the diagnostic accuracy of EST, SE, stress myocardial SPECT, and stress CMR in symptomatic patients with suspected or confirmed CAD. The main goal was to compare their diagnostic value by pooling sensitivity and specificity results. Each study’s data were extracted in terms of true positives, false positives, true negatives, and false negatives. Results: A total of 104 studies, comprising 16,824 symptomatic individuals with either suspected or known CAD, met the inclusion criteria. The pooled sensitivities for CAD detection were 0.66 (95% CI: 0.59–0.72, p < 0.001) for EST, 0.81 (95% CI: 0.79–0.83, p < 0.001) for SE, 0.82 (95% CI: 0.78–0.85, p < 0.001) for stress myocardial SPECT, and 0.83 (95% CI: 0.81–0.85, p < 0.001) for stress CMR. Corresponding specificities were 0.61 (95% CI: 0.55–0.67, p < 0.001), 0.85 (95% CI: 0.82–0.87, p < 0.001), 0.74 (95% CI: 0.70–0.78, p < 0.001), and 0.89 (95% CI: 0.86–0.92, p < 0.001), respectively. Considerable heterogeneity was observed across the studies, as reflected by I² values ranging from 82.5% to 92.5%. Egger’s generalized test revealed statistically significant publication bias (p < 0.05 for all methods), likely due to the influence of smaller studies reporting more favorable results. Despite this, sensitivity analyses supported the overall robustness and reliability of the pooled findings. Conclusions: Among the diagnostic tools assessed, EST demonstrated the lowest accuracy for detecting obstructive CAD, whereas stress CMR exhibited the highest. Although stress myocardial SPECT showed strong sensitivity, its specificity was comparatively limited. SE emerged as the most balanced option, offering good diagnostic accuracy combined with advantages such as broad availability, cost-effectiveness, and the absence of ionizing radiation. Full article

n-Pentanol, a promising biofuel, can reduce greenhouse gas emissions while remaining compatible with internal combustion engines. We present a reduced kinetic mechanism comprising 66 species and 292 reactions that captures both high- and low-temperature ignition and flame propagation dynamics for this fuel. The mechanism, developed by integrating a detailed n-pentanol sub-mechanism with the San Diego mechanism and applying sensitivity and steady-state approximations criteria as reduction strategies, accurately reproduces key phenomena, including the negative temperature coefficient behavior (NTC). Validation against experimental data for ignition delay times, laminar flame speeds, and speciation measurements in a jet-stirred reactor confirms its predictive capability across a wide range of conditions. Full article

This review comprehensively presents the development of energy-efficient pneumatic propulsion systems (PPSs) in road vehicle applications, which are classified as green vehicles. The advantages and disadvantages of PPSs were presented, and PPSs were compared with combustion propulsion systems (CPSs) and electric propulsion systems (EPSs), as well as their power-to-weight ratios (PWRs), energy densities, and CO₂ emissions. The review of compressed air vehicles (CAVs) focuses on their historical development and future prospects. This review discusses the use of PPSs with compressed air engines (CAEs) as an alternative propulsion system in green vehicles, providing a simple, energy-saving, and environmentally friendly solution. This review also discusses hybrid air propulsion, which, when combined with internal combustion engines (ICEs) or electric motors (EMs), offers innovative energy-efficient propulsion systems that are more economical than conventional hybrid propulsion systems. This review focuses on recent advances in lightweight air vehicles that improve vehicle handling, increase efficiency, and reduce propulsion energy consumption. Discussion of the study results concerns the use of PPSs in a three-wheeled rehabilitation tricycle (RTB). A comprehensive computation model of the RTB was presented, and the key performance parameters crucial to its operation were analyzed. The results of the RTB simulation were verified through field tests. Full article

Rising household carbon emissions (HCEs) substantially increase residential energy consumption. This review evaluates the four principal quantification methods: Emission Coefficient Method (ECM), Input–Output Analysis (IOA), Consumer Lifestyle Approach (CLA), and Life Cycle Assessment (LCA). The methods are compared according to data requirements, uncertainty levels, and scale suitability. The study synthesizes multidimensional determinants—including household income, household size, urbanization, energy intensity and composition, population aging, and household location—and translates these insights into behavior-informed mitigation pathways grounded in behavioral economics principles. Combining compact-city planning, targeted energy-efficiency incentives, and behavior-nudging measures can reduce HCEs without compromising living standards, providing local governments with an actionable roadmap to carbon neutrality. Full article

The development of the digital economy has become a significant driving force for the innovation of green technology in the manufacturing sectors. Green technology innovation in the manufacturing sectors is not only a key engine for realizing economic green transformation and achieving the goal of achieving peak carbon emissions by 2030 and carbon neutrality by 2060, but also an important path for cultivating new quality productivity. Based on Schumpeter’s endogenous growth theory, in this study, we constructed an analytical model with a unified framework of digital economic development and environmental regulation, systematically explored the mechanism of digital economic development with respect to green technological innovation in the manufacturing sectors and the moderating effect of environmental regulation, and carried out empirical research based on panel data at the provincial level and the level of the subdivided manufacturing sectors in China. We found that the development of the digital economy promotes green technology innovation in the manufacturing industry. However, according to the theory of increasing marginal information costs, it shows a significant nonlinear relationship. Absorptive capacity is the key means of support that manufacturing enterprises can leverage to improve their level of green technological innovation. Environmental regulation plays a crucial role in guiding green technological innovation in the manufacturing sectors. A further heterogeneity analysis showed that the development of the digital economy exerts a stronger positive impact on green technological innovation in cleaner-production-oriented manufacturing sectors and those located in regions with more advanced financial regions and in technology-intensive industries. This study provides theoretical support for understanding the driving mechanisms of green technological innovation in the manufacturing sector against the backdrop of the digital economy, offering practical implications for optimizing environmental regulation policies and enhancing the level of green development in manufacturing. Full article

Background/Objectives: Cardiac sarcoidosis (CS) is a granulomatous inflammatory cardiomyopathy with heterogeneous presentations, from palpitations to heart failure and sudden cardiac arrest. Despite advances in imaging and immunosuppressive (IS) therapy, relapse patterns and long-term outcomes remain poorly defined. This study aimed to characterize relapse and identify predictors of relapse and major adverse cardiac events (MACE) in a real-world CS cohort. Methods: This retrospective single-center study included 25 adults diagnosed with CS at Geneva University Hospitals between 2016 and 2024, classified per the 2024 American Heart Association diagnostic criteria. Relapse was defined as clinical, arrhythmic, or imaging deterioration requiring treatment escalation. MACE included cardiovascular hospitalization, device therapy, left ventricular assist device, heart transplant, or death. Statistical methods included Kaplan–Meier analysis with log-rank tests and multivariable Cox regression adjusted for age and sex. Results: Relapse occurred in 13 patients (56%), frequently subclinical (61.5%) and detected incidentally on routine PET-CT during IS tapering. In the multivariate model, predictors of relapse included right ventricular FDG uptake (aHR 13.1; 95% CI 1.3–133.7; p = 0.03) and second-line immunosuppression duration ≤24 months (aHR 20.1; 95% CI 1.1–363.8; p = 0.04). Relapse-free patients were more often maintained on dual or triple IS therapy (71.4% vs. 15.4%; p = 0.02) and low-dose prednisone (<10 mg/day) (57.1% vs. 7.7%; p = 0.03). Conclusions: Relapse is common in CS, often subclinical, and associated with PET-CT findings and premature IS tapering. Maintenance therapy may reduce risk. Multimodal imaging remains critical for disease monitoring, though tracers with higher specificity are needed. Further research should refine relapse definitions and support personalized treatment strategies. Full article

Sulfur dioxide not only affects the ecological environment and endangers health but also restricts economic development. The reasonable prediction of sulfur dioxide emissions is beneficial for formulating more comprehensive energy use strategies and guiding social policies. To this end, this article uses a multiparameter combination optimization gray prediction model (PGM(1, N)), which not only defines the difference between the sequences represented by variables but also optimizes the order of all variables. To this end, this article proposes an improved algorithm for the Dung Beetle Optimization (DBO) algorithm, namely, CSLDDBO, to optimize two important parameters in the model, namely, the smoothing generation coefficient and the order of the gray generation operators. In order to overcome the shortcomings of DBO, four improvement strategies have been introduced. Firstly, the use of a chain foraging strategy is introduced to guide the ball-rolling beetle to update its position. Secondly, the rolling foraging strategy is adopted to fully conduct adaptive searches in the search space. Then, learning strategies are adopted to improve the global search capabilities. Finally, based on the idea of differential evolution, the convergence speed of the algorithm was improved, and the ability to escape from local optima was enhanced. The superiority of CSLDDBO was verified on the CEC2022 test set. Finally, the optimized PGM(1, N) model was used to predict China’s sulfur dioxide emissions. From the results, it can be seen that the error of the PGM(1, N) model is the smallest at 0.1117%, and the prediction accuracy is significantly higher than that of other prediction models. Full article