Search Results (356)

Molecular mechanisms underlying the progression from compensated eccentric hypertrophy to heart failure in chronic aortic regurgitation (AR) remain poorly understood. We investigated associated transcriptional changes induced by chronic volume overload in an experimental model of severe AR. AR was induced in male Wistar rats (n = 10) by retrograde aortic valve perforation and compared with age-matched control rats (n = 8). Sixty days after surgery, cardiac remodeling was evaluated by echocardiography, invasive hemodynamics and myocardial gene-expression profiling using RT-qPCR. Chronic AR induced marked left ventricular dilatation and systolic dysfunction, consistent with decompensated eccentric hypertrophy. These functional alterations were accompanied by coordinated transcriptional changes involving pathways related to apoptosis, oxidative balance, metabolic regulation and calcium handling. AR hearts exhibited an increased BAX/BCL2 ratio, together with reduced SOD2 and increased GPX1 expression, suggesting a transcriptional profile consistent with activation of pro-apoptotic and antioxidant responses. Metabolic remodeling was characterized by decreased AMPKα1, PPARγ and GLUT4 expression, together with increased OLR1 and 15-LOX. KLK10 expression was increased. Reduced SERCA2A expression was observed, consistent with transcriptional alterations involving calcium-handling pathways. Chronic AR is associated with marked structural and functional cardiac remodeling accompanied by coordinated transcriptional alterations across multiple pathways implicated in myocardial dysfunction. This provides potential molecular pathways for further investigation. Full article

Road crashes undermine the sustainability of transportation systems by generating substantial human, social, and economic costs. Identifying the factors associated with severe crash outcomes is therefore essential for supporting safer and more sustainable mobility, in accordance with Vision Zero and Sustainable Development Goals 3.6 and 11.2. This study investigates the influence of behavioral, environmental, and infrastructural factors on road crash severity through a comparative evaluation of three modeling approaches: Ordinal Logistic Regression (OLR), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The analysis was based on 5194 police-reported crash records from Cyprus covering the period 2015–2024. The dataset included crash severity outcomes together with traffic violations and roadway and environmental conditions. The results identified speeding, substance use, and improper turning maneuvers as influential predictors of crash severity. Among the evaluated models, CatBoost achieved the strongest overall predictive performance, obtaining the highest accuracy (0.52), weighted area under the curve (AUC) (0.72), weighted precision (0.50), and weighted recall (0.52), while CatBoost and XGBoost tied for the highest weighted F1-score (0.48). CatBoost’s overall performance reflects its ability to efficiently process categorical variables and capture nonlinear relationships without extensive preprocessing. The findings highlight the dominant role of driver behavior in determining crash severity and demonstrate how the comparative modeling framework can serve as an analytical decision-support tool for identifying and monitoring crash-severity risk factors, prioritizing targeted interventions, and supporting progress towards safer and more sustainable transportation systems. Full article

This study evaluated the anaerobic valorization of real, non-detoxified sugarcane bagasse-derived pentose liquor. Direct fermentation without nutrients, co-substrate, or buffering was limited, reaching low-to-moderate (37–54%) carbohydrate conversion efficiency at low organic loading rate (OLR < 5.6 kg COD m⁻³ d⁻¹) levels. Hydrogen yield (HY) was also low (0.63–0.89 mol H₂ mol⁻¹ carbohydrate_converted) under these conditions. Increasing pentose liquor concentration up to 0.8 L L⁻¹ and OLR of 30.2 kg COD m⁻³ d⁻¹ suppressed hydrogen production, coinciding with lactate accumulation and the occurrence of homoacetogenesis. Enhanced fermentation with sucrose, nutrients, buffering, shorter hydraulic retention time (12 h) and 37 °C improved stability and carbohydrate conversion (50–61%) under higher OLR, although HY considerably decreased (0.14–0.35 mol H₂ mol⁻¹). Methanogenesis was effective in both single- and two-stage systems, with carbohydrate conversion usually above 95%, COD removal up to 83.5%, methane fractions above 60%, and methane yields reaching 299.8–301 NmL CH₄ g⁻¹ COD_removed. Furfural and 5-HMF showed phase-dependent transformation, indicating partial in situ detoxification during anaerobic conversion. Overall, pentose liquor is better valorized through integrated hydrogen–methane recovery than by fermentation alone. Future studies should focus on both applying more effective strategies to buffer the fermentative stage and adopting more conservative approaches (lower OLR) to start up the methanogenic systems. Full article

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) model to identify factors associated with five-level drone delivery acceptance. The study then compares OLR, multinomial logistic regression (MNL), Random Forest (RF), XGBoost, and LightGBM under matched feature sets to evaluate whether nonlinear machine-learning models improve prediction beyond the interpretable statistical baseline. Open-ended responses are coded into LLM-derived sentiment labels and added as supplementary predictors to test whether unstructured feedback improves acceptance prediction. Results show that willingness to pay is the strongest predictor of acceptance, while equitable same-day delivery demand and post-pandemic attitude adjustment are also positively associated with higher acceptance. Household disability status and urban accessibility are not significant after adjustment. In the five-level analysis, OLR provides a strong ordinal baseline, while XGBoost and other tree-based models improve selected class-level prediction metrics. In the binary high-acceptance analysis, machine-learning models show stronger predictive performance, especially when structured predictors are combined with sentiment features. This study contributes to rural drone-delivery literature by linking service equity, perceived value, and LLM-derived sentiment within a comparable statistical and machine-learning framework for rural service design. Full article

The Tibetan Plateau (TP) plays a crucial role in the global climate system, yet long-term trends in its top-of-atmosphere (TOA) radiation budget remain poorly understood. Using 40-year (1981–2020) CLARA-A3 satellite data and reanalysis datasets, we quantify the spatiotemporal variations of TOA reflected shortwave radiation (RSF) and outgoing longwave radiation (OLR), and decompose the RSF into atmospheric and surface contributions. Additionally, we attribute observed trends to key drivers via multiple linear regression. The TP shows a significant decrease in RSF (−11.06 W/m²) and a significant increase in OLR (+4.88 W/m²). Spatially, OLR increases are widespread and statistically significant over the central and western plateau, while RSF trends are heterogeneous and mostly non-significant except for localized negative trends. The atmospheric contribution to RSF declines markedly (−0.329 W/(m²·year)), whereas the surface contribution increases (+0.061 W/(m²·year)). Attribution analysis identifies cloud fraction change (CFC) as the dominant drivers: it explains most of the RSF decline and the OLR increase. Cloud optical thickness and vertical structure play only minor roles. Our findings demonstrate that decreasing cloud cover is reshaping the TP’s TOA energy budget, providing essential geo-information for the spatial evaluation of climate models and the refinement of satellite-derived radiation products. Full article

Aerobic ceramic membrane bioreactors (AeCeMBR) have shown great potential in treating wastewater (WW) from the textile industry; however, their operation faces challenges such as process variability, membrane contamination, and the need for accurate prediction of treated water quality under varying conditions. In this study, chemical oxygen demand (COD) and turbidity were selected as key indicators, as they directly reflect organic load removal and solids separation efficiency in MBR systems. The effect of four operational parameters: hydraulic retention time (HRT), organic loading rate (OLR), mixed liquor suspended solids (MLSS), and transmembrane pressure (TMP), was investigated using a response surface methodology (RSM) based on a Box–Behnken design. A random forest (RF) model coupled with Monte Carlo simulation (MC) was also developed using 174 experimental data points to enhance predictive power and quantify uncertainty. The RSM model showed strong agreement with experimental results (coefficient of determination (R²) > 0.95), achieving approximately 96% removal for both COD and turbidity, with validation errors of less than 2%. MC simulation (10,000 iterations) was applied to assess the effect of ±10% variance under operating conditions, providing a probabilistic view of system performance. The RF-MC framework demonstrated high predictive accuracy, with strong correlations between predicted and observed values (R² = 0.92 for COD and 0.97 for turbidity) and low uncertainty. Overall, this study proposes an integrated RSM, RF–MC approach for AeCeMBR systems, providing a robust and uncertainty-aware framework for process optimization and performance prediction under changing operating conditions. Full article

Convectively Coupled Kelvin Waves (CCKWs) play a key role in synoptic variability and can trigger extreme hydrometeorological events. This study characterizes the influence of CCKWs on seasonal precipitation patterns and extreme precipitation events (EPEs) over northern South America. Using a filtered OLR dataset, we found that precipitation anomalies associated with CCKWs are sensitive to the selected index region. A sensitivity analysis identified a region in the Colombian Pacific exhibiting the strongest precipitation anomalies linked to CCKWs. At seasonal scales, March–May (MAM) is the season with the highest CCKW activity, and its convective phase is associated with enhanced precipitation over the far eastern Pacific, western Amazonia, and northern Colombia, while suppressed convection dominates northwestern Brazil. In addition, three regions exhibit increases of up to 30% in EPE occurrence during convective-phase Kelvin waves: (i) the northwestern Amazon, (ii) northwestern Colombia, and (iii) the Peruvian coast. In contrast, EPE occurrence in the Colombian Pacific appears largely independent of CCKW passage, likely due to the strong background climatological rainfall in the region. We also analyze a flooding event in Turbo, Colombia, on 9 May 2007, that occurred during the passage of a convective-phase Kelvin wave and was preceded by days of enhanced low-level southwesterly flow convergence and persistent rainfall. Understanding the influence of these intraseasonal oscillations on precipitation and EPEs is essential for improving regional weather forecasts and supporting the development of early warning systems. Full article

Two-Stage Anaerobic Digesters (TSADs) have emerged as an effective strategy for improving the stability and efficiency of biogas production from high-strength substrates such as food waste. The separation of acidogenic and methanogenic phases enables better environmental control for distinct microbial communities, thereby enhancing methane yield and reducing process instability. This study investigates the dynamics of microbial populations of acidogens, acetogens, and methanogens in a TSAD using an extended Anaerobic Digester Model No. 1 framework incorporating stage-specific microbial growth kinetics. Simulation scenarios were performed across a range of operational parameters, including OLR (1–8 kg VS/m³ day), pH (5.0–8.0), temperature (35 °C and 45 °C), and HRT (10–30 days). The results demonstrate that balanced microbial population dynamics and syntrophic interactions strongly influence methane production and overall digester performance. Optimal methane yields were achieved within an OLR range of 3.5–4.5 kg VS/m³ day under mesophilic conditions. Elevated loading rates led to VFA accumulation and pH decline, resulting in the inhibition of methanogenic populations and reduced methane output. Preliminary parametric analysis suggests that the acetoclastic methanogen growth rate and ammonia inhibition constants are influential parameters affecting system performance. The findings highlight the importance of integrating microbial population dynamics into AD models to enhance predictive accuracy and support the development of intelligent control strategies for sustainable waste-to-energy systems. Full article

Background: Carotid atherosclerosis is a recognised manifestation of systemic vascular disease, and its association with coronary artery disease (CAD) has been well described. However, previous studies have largely been conducted under conventional diagnostic conditions and have focused on carotid plaque, intima–media thickness, or simple present-versus-absent stenosis classifications, rather than duplex-derived haemodynamic markers across the spectrum of angiographic CAD burden. The COVID-19 pandemic and post-pandemic period changed referral patterns and created more variable cardiovascular presentations, including symptoms that could resemble or mask obstructive CAD. Therefore, we investigated whether the established association between carotid stenosis severity and CAD burden remains detectable in a diagnostically heterogeneous real-world cohort, and whether routinely available carotid duplex haemodynamic parameters provide a clinically relevant signal in this setting. Methods: This single-centre, cross-sectional study was performed as a carotid-focused secondary analysis of the BG Study cohort. We included 902 consecutive patients who underwent invasive coronary angiography between 2021 and 2023 and carotid duplex ultrasonography during the same hospitalisation. CAD burden was defined according to the number of major coronary vessels with ≥70% diameter stenosis and classified as no CAD, one-vessel, two-vessel, or three-vessel disease. Carotid duplex parameters included peak systolic velocities of the common, internal, and external carotid arteries, as well as ICA stenosis severity graded according to NASCET criteria. Associations with CAD burden were assessed using a staged statistical approach combining χ² tests, Kruskal–Wallis tests with post hoc pairwise comparisons, Spearman correlation, inverse probability weighting, and ordered logistic regression. Results: The prevalence of measured ICA stenosis of any grade and severe ICA stenosis increased with greater CAD burden (both p < 0.001). Median PSV values of the bilateral ICAs and ECAs differed significantly across CAD groups on global intergroup testing. Post hoc pairwise analyses showed that significant corrected differences were concentrated between patients without CAD and those with multivessel or three-vessel CAD, particularly for ICA stenosis measures and bilateral ECA PSV. Spearman analysis demonstrated weak but statistically significant correlations between carotid parameters and CAD burden (ρ = 0.085–0.134). After inverse probability weighting, covariate balance was achieved, with all post-IPW standardised mean differences being <0.01. In ordered logistic regression (OLR) analysis, patient-reported history of carotid stenosis (OR 2.25, 95% CI 1.38–3.67; p < 0.001), right external carotid artery PSV per 10 cm/s (OR 1.31, 95% CI 1.09–1.57; p = 0.004), left ICA PSV per 10 cm/s (OR 1.17, 95% CI 1.01–1.36; p = 0.034), and left ICA stenosis per 10% (OR 1.24, 95% CI 1.11–1.39; p < 0.001) were independently associated with higher CAD burden. Exploratory ratio-based analyses showed that the ECA/CCA PSV ratio was associated with CAD presence and higher CAD burden, whereas the ICA/CCA ratio showed weaker associations; neither ratio-based index outperformed absolute ECA PSV. Conclusions: In this carotid-focused secondary analysis of a pandemic-era angiography cohort, carotid stenosis severity and duplex-derived haemodynamic parameters were independently but modestly associated with increasing angiographic CAD burden. These findings support carotid duplex markers as adjunctive indicators of systemic atherosclerotic burden rather than standalone tools for CAD detection or treatment decision-making. Future validation in vascular surgery populations is warranted to determine whether routinely available carotid duplex parameters can contribute to targeted cardiovascular risk recognition before major vascular procedures. Full article

Anaerobic digesters (ADs) are widely used for the recovery of energy from biomass waste, while performance deterioration of ADs sometimes occurs under high organic loads. Microbial electrolysis cells (MECs) have been examined for incorporation into ADs to improve methane production, while responses of MEC-assisted ADs (MEC-ADs) to changes in operational conditions have yet to be sufficiently examined. Here we operated laboratory ADs and MEC-ADs with food waste as a feed, and organic loading rates (OLRs) were varied by changing hydraulic residence times (HRTs). Analyses of AD performances, including methane production and chemical oxygen demand (COD) removal, show that MEC-ADs exhibit higher performances than ADs at OLRs of 7 g COD L⁻¹ D⁻¹ or higher (HRT of 10 days or less). In addition, pH was stably maintained in MEC-ADs at high OLRs. Metabarcoding of rRNA gene amplicons showed that Desulfuromonadaceae bacteria were enriched at the anodes in MEC-ADs, while Methanobacteriaceae archaea were increased at the cathodes. It is suggested that the MEC system is useful for stably operating ADs at high OLRs and/or short HRTs. Full article

Acidogenic fermentation of organic wastes represents a strategic platform for the co-production of H₂, CO₂, and volatile fatty acids (VFAs), which are potential key intermediates for cost-effective polyhydroxyalkanoate (PHAs) biosynthesis. This typically relies on carbon sources that are too expensive and hinder the commercialization of PHAs. This study provides metagenomic insights into the microbial dynamics underpinning the acidogenic conversion of waste melon under increasing organic loading rates (OLRs). Metabarcoding revealed that Megasphaera dominated the community, with its abundance rising markedly from 5 to 20 gCOD/L, accompanied by relevant contributions from Solobacterium, Prevotella, and Clostridium. These taxa were associated with the formation of acetic, propionic, and butyric acids and with enhanced hydrogenogenesis. Higher OLRs, up to 20 gCOD/L, promoted hydrogen-producing species while suppressing lactic acid bacteria, thereby improving H₂ and VFAs yields up to 26.7% v/v and 13 gCOD/L, respectively. By linking microbial shifts to metabolic outputs, this work advances the understanding of acidogenic pathways essential for integrating dark fermentation-derived H₂, CO₂, and VFAs into sustainable PHAs production systems. Full article

In 2024, China generated approximately 130 million tons of food waste. This study focuses on food wastewater characterized by exceptionally high organic strength (chemical oxygen demand (COD) > 80 g·L⁻¹, total suspended solids (TSS) > 20 g·L⁻¹) content. Conventional continuous stirred tank reactors (CSTRs) inherently couple hydraulic retention time (HRT) and sludge retention time (SRT), making them prone to microbial washout under high organic loading. To overcome this limitation, this study employed two anaerobic sequencing batch reactors (ASBRs) for treating such high-strength food wastewater. This study systematically evaluated the impacts of temperature (mesophilic: 37 °C and thermophilic: 55 °C) and organic loading rate (OLR) on fermentation performance. Under stable operation (OLR = 5.6 kg_COD·m⁻³·d⁻¹; HRT = 16 days), the mesophilic ASBR achieved a specific methane yield of 307 mL CH₄·g_CODremoved⁻¹, an average COD removal efficiency of 81%, and a volatile fatty acids-to-total alkalinity (VFA/TA) ratio of 0.2, indicating robust process stability. In contrast, the thermophilic ASBR exhibited a VFA/TA ratio of 0.5, signaling incipient acidification. Microbial community analysis revealed significantly higher bacterial and archaeal alpha diversity in the mesophilic system. Notably, Methanothrix—a versatile acetoclastic methanogen—dominated the mesophilic archaeal community (66.65%), conferring functional redundancy and resilience against organic shock loads. By contrast, the thermophilic system was overwhelmingly dominated by the hydrogenotrophic Methanothermobacter (99.28%), resulting in low functional diversity and structural fragility. Compared with a benchmark mesophilic CSTR (specific methane yield: 276 mL CH₄·g_CODremoved⁻¹; COD removal efficiency: 70.6%), the mesophilic ASBR improved methane yield by 11%, COD removal efficiency by 15%, and operational stability (VFA/TA = 0.2 vs. 0.6). This work addresses a gap in ASBR applications for high-strength food wastewater treatment and provides experimental validation of the performance, stability, and scalability of mesophilic ASBRs. The proposed process represents a technically feasible, resource-efficient, and operationally robust solution for the valorization of organic wastewater with COD > 80 g·L⁻¹ and TSS > 20 g·L⁻¹. Full article

The stabilization of two-stage bioenergy systems and optimization of the energy recovery efficiency have been found to be closely related to the management of VFA-rich effluent obtained after the dark fermentation of food waste. In this study, the performance of a mesophilic UASB reactor was investigated at different OLR levels. The focus of this study was to assess methane yield and substrate degradation during anaerobic digestion. The results revealed that the performance of the UASB reactor was stable within the range of 2.5–7.0 kg COD m⁻³ d⁻¹. At this range, it was possible to convert VFAs into methane gas through the synergistic interaction of fermentative bacteria and methanogenic archaea. The methane content was 67.9%, TCOD removal efficiency was 89.7% at the optimal OLR of 7.0 kg COD m⁻³ d⁻¹, and volumetric methane production rate was 2.41 L_CH4 L⁻¹ d⁻¹. The increase in OLR to 10.0 kg COD m⁻³ d⁻¹ resulted in instability of the anaerobic digestion process. The instability of the anaerobic digestion process was characterized by propionate accumulation and a high VFA/alkalinity ratio of 0.89. The Grau second-order and Modified Stover–Kincannon models were found to describe COD removal efficiency. The Monod model was found to show limited preliminary agreement under the conditions tested for high-rate granular sludge. The highest methane yield and efficiency of energy recovery were obtained at 7.0 kg COD m⁻³ d⁻¹. The recoverable energy obtained at this OLR was 2.04 MJ d⁻¹. The results of this study revealed that it is possible to integrate dark fermentation and anaerobic digestion through the use of UASB reactors. Full article

Growing municipal solid wastes, environmental deterioration, and the world’s increasing energy demand highlight the urgent need for effective, sustainable energy recovery solutions. Uncontrolled municipal solid wastes contribute explicitly to the global crises of climate change, pollution, and biodiversity loss. Food and organic waste are converted into value-added products using biochemical and thermochemical techniques. Anaerobic digestion (AD) is a versatile, multi-phase waste-to-energy technology that transforms organic waste into renewable energy in an oxygen-free environment. AD uses microorganisms to break down waste, yielding biogas (mostly methane and carbon dioxide) and digestate, a nutrient-fortified by-product. Compared with traditional Single-Stage Anaerobic Digesters (SSAD), Two-Stage Anaerobic Digesters (TSAD) offer notable benefits by separating hydrolysis–acidogenesis from acetogenesis–methanogenesis. These include increased methane yield, improved process control, increased microbial stability, and resistance to inhibitory substances. According to the literature, TSAD systems have been shown to increase methane yield by about 10–30% compared to SSAD. This article covers the dynamics of the microbial population at various stages, the impact of operational factors (HRT, OLR, pH, and temperature), and novel reactor designs with modular and multi-state functions. In line with Oman’s Vision 2040, this study discusses the continuous operation of a two-phase AD co-digestion process and the in-depth techno-economic feasibility of decentralized waste management through optimized biogas production. Optimizing the carbon-to-nitrogen (C/N) ratio within the range of 20–30 in co-digestion systems significantly enhances microbial activity and methane production. The potential of recent developments, such as microbial immobilization, biogas generation techniques, and hybrid integration with photobioreactors or electrochemical systems, to enhance the scalability and efficiency of bioconversion is addressed in a TSAD system. In addition to encouraging circular economy principles through efficient organic waste valorization, this review identifies TSAD as a promising approach to achieving the SDGs related to sustainable cities, clean energy, and responsible consumption. Full article

The energy and environmental advantages of anaerobic digestion have led to a gradual growth in interest in biogas technology in recent years. Opportunities are presented by anaerobic digestion technology to produce renewable energy, minimize greenhouse gas discharge into the atmosphere, and minimize the release of waste in landfills. The study aims to consider the state of the art of biogas development while exploring emerging trends in optimization parameters and tools. Optimizing process parameters such as temperature, HRT, pH, and OLR were considered, which are essential to maximize digesting efficiency and biogas yield. Some optimizing tools were also discussed, such as Aspen Plus, MATLAB/Simulink, RSM, and ANN. The effective use of the optimization process parameters and tools will help to promote the optimum use of biomass for biogas generation, thereby promoting clean and affordable energy, as well as policies that minimize the emission of GHG, and contribute to the UN SDGs 7 and 13. More research needs to be carried out on the development and utilization of advanced optimization tools and techniques, which will enhance the use of biomass for biogas generation. Full article