Search Results (42,648)

Waste lubricating oils (WLOs) represent a major stream of hazardous petroleum-based residues, with global generation exceeding 24 million tons annually. Improper disposal of WLOs poses risks to soil, water, and air quality, while their chemical composition makes them a potential secondary resource within circular economy frameworks. This review summarizes conventional, advanced, and emerging technologies reported for the recycling and valorization of WLOs into high-value petrochemicals and carbon-based materials. Established processes such as acid–clay treatment, solvent extraction, and vacuum distillation are discussed together with more recent approaches, including catalytic upgrading, hydrotreatment, membrane separation, and thermochemical conversion methods such as pyrolysis and catalytic cracking. Reported data on process performance, environmental considerations, techno-economic indicators, and life cycle assessment outcomes are comparatively analyzed to outline current trends, technical challenges, and future development directions in WLO recycling. Particular attention is given to thermochemical pathways capable of generating carbonaceous materials, including carbon black, porous carbons, and functional carbon nanostructures with potential applications in adsorption, catalysis, electrochemical systems, and tribological formulations. Hybrid and integrated process configurations described in the literature are highlighted for their potential to improve recovery efficiency, enhance product quality, and reduce environmental burdens. In addition, recent life cycle assessment (LCA) and techno-economic analysis (TEA) studies are reviewed to provide insight into the environmental and economic implications of advanced re-refining systems. Overall, the reviewed literature indicates that WLO recycling represents not only an important element of sustainable lubricant management but also a promising waste-to-carbon strategy for the production of value-added carbon-based materials and petrochemical products. Full article

Background: Apoptosis is a fundamental process for maintaining tissue homeostasis, and its dysregulation is closely linked to the development of numerous diseases, including colorectal cancer. In recent years, dietary polyphenols have gained interest due to their antioxidant, pro-apoptotic, and chemopreventive properties. Artichoke (Cynara scolymus L.) by-products are rich source of hydroxycinnamic acids and flavonoids, making them promising source of bioactive compounds. Methods: In this study we evaluated the cytotoxic and pro-apoptotic activity of four aqueous extracts obtained from artichoke bract by-products, including one commercial hybrid (CAPB) and three local Apulian varieties (BriB, VaMB, LMTB), in human colorectal adenocarcinoma cell lines (Caco-2 and HT29). The extracts were characterized according to their total polyphenol content and phenolic profile. Results: The selected artichoke by-product extracts exhibited significant cytotoxic effects both in a concentration- and time-dependent manner, with concentrations ≥ 2 mg/mL significantly reducing cell viability and nearly abolishing it at 4 mg/mL after 48 h. Moreover, treatment with the extracts modulated the expression of apoptosis-related proteins, characterized by an increase in pro-apoptotic markers (Bax, caspase-9, caspase-3) and a decrease in the anti-apoptotic protein Bcl-2, suggesting activation of the mitochondrial apoptotic pathway. In particular, the BriB extract was able to induce an apoptosis rate higher than 80% in Caco-2 cells and achieved comparable rates in HT29 cells at concentrations of 2–3 mg/mL. Conclusions: Overall, these findings demonstrate that artichoke by-product extracts exert significant pro-apoptotic effects in colorectal cancer cells and highlight their potential as sustainable sources of bioactive compounds for nutraceutical or adjuvant anticancer applications. Full article

Objective: To evaluate the clinical outcome, safety and efficacy of the APrevent Vocal Implant System (VOIS) in patients with unilateral vocal fold paralysis (UVFP), with particular emphasis on the timing and impact of postoperative saline adjustments. Methods: This retrospective−prospective longitudinal study included 11 patients with chronic UVFP who underwent VOIS medialization thyroplasty (MT) under local anesthesia (n = 2) and general anesthesia (n = 9). Multidimensional voice parameters were analyzed preoperatively and at 1, 3, 6, and 12 months postoperatively. Statistical analyses included the Friedman test for repeated measures and the comparison of outcomes between pre- and each postoperative timepoints was evaluated with the Wilcoxon signed-rank test. Results: Significant and sustained improvements were observed across all multidimensional voice parameters. Mean mVHI-10 decreased from 31.7 ± 4.5 preoperatively to 5.8 ± 5.1 at 12 months, while mean MPT increased from 7.1 ± 3.8 to 14.4 ± 4.5 s (p < 0.05, r > 0.7). Acoustic parameters, including jitter, shimmer, and NHR, demonstrated progressive improvement over 12 months. A high proportion of patients (72.73%) underwent postoperative saline adjustment at a mean interval of 6.23 ± 1.23 months, beyond the early postoperative edema phase, with each adjustment yielding further enhancement in voice outcomes. No major complications, including airway obstruction or hematoma, were observed. Conclusions: VOIS MT is safe and effective, providing sustained improvements in multidimensional voice outcomes. The ability to perform postoperative saline adjustments enables dynamic optimization of glottal closure, reducing the need for revision surgery and addressing evolving laryngeal biomechanics. These findings support VOIS as a flexible, adjustable alternative to static medialization techniques and provide dynamic voice optimization in patients with UVFP. Full article

Global economic policy uncertainty has emerged as a significant challenge for developing regions, with Sub-Saharan Africa particularly vulnerable due to its fragile economies and social systems that rely on external support. This study examines the effect of global economic policy uncertainty on social sustainability and how this relationship is moderated by governance effectiveness and natural resource rents. These relationships were examined using 27 years of panel data from 45 Sub-Saharan African countries, spanning 1997 to 2023. The Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCMG), and the two-step difference Generalized Method of Moments (GMM) estimators are advanced methods for analyzing data and estimating relationships among variables. The study found that global economic policy uncertainty had a significant negative effect on social sustainability. Furthermore, the study revealed that governance effectiveness and natural resource rents positively and significantly moderate the relationship between global economic policy uncertainty and social sustainability. These findings have significant implications for policy and governance, highlighting the critical need for governments, especially in developing and resource-dependent regions, to strengthen institutional capacity and fiscal frameworks in order to manage the adverse effects of global economic policy uncertainty. They underscore the importance of developing responsive, transparent, and accountable governance structures that can effectively allocate resources toward social priorities even during periods of external economic volatility. Full article

Background/Objectives: Type 2 diabetes (T2D) remission can be defined as a return to a HbA1c < 6.5% (<48 mmol/mol) sustained without ongoing treatment for at least 3 months. Prevalence estimates and factors associated remain unknown for LMIC and resource-limited settings. Methods: We conducted a retrospective observational analysis of electronic medical records from 8463 adults who received multidisciplinary care at Mexico’s primary care specialized units (UNEMES-EC) between 2015 and 2019 and who were referred for inadequate metabolic control. Remission was defined per 2021 ADA criteria as HbA1c <6.5% sustained for ≥3 months without glucose-lowering medications. After estimating the prevalence of T2D remission, logistic regression models were used to evaluate its sociodemographic and clinical predictors, with particular attention to weight change and baseline adiposity interactions. Results: RT2D prevalence was 0.87% (95% CI: 0.68–1.10) over a median 393-day follow-up. Weight loss ≥10% (adjusted OR 2.75; 95% CI: 1.21-6.27) and systolic blood pressure (tertile 3 vs tertile 1: OR 2.49; 95% CI: 1.17–5.26) were positively associated with RT2D, while elevated baseline HbA1c (tertile 3 vs. tertile 1: OR 0.09; 95% CI: 0.02–0.33), triglyceride levels (tertile 3 vs. tertile 1: OR 0.49; 95% CI: 0.24–0.98) and intensive pharmacotherapy were inversely associated with RT2D. No associations with HDL and total cholesterol were found. Age, sex, educational attainment, and income demonstrated no independent associations with remission. Among lifestyle-treated patients achieving ≥5% weight loss, remission prevalence reached approximately 11%. No significant interaction between baseline BMI and weight change was detected (p = 0.60). Conclusions: This first large-scale Mexican study establishes RT2D as an achievable endpoint in patients with poor baseline metabolic control. The findings suggest that remission could be achieved with equity-focused, weight-centered interventions even in resource-constrained health systems and populations. Full article

Using a balanced panel of ten major fishing and trading nations (China, Chile, Indonesia, Peru, Thailand, Vietnam, Norway, India, Denmark, and Canada) over the years 2000–2020, this study investigated the relationship between international fishery trade and the fishing footprint, a consumption-based ecological indicator measuring the bioproductive marine area required to sustain seafood consumption. Cross-sectional dependence tests, second-generation panel unit root tests (PANICCA), LM bootstrap cointegration analysis, and long-run coefficient estimation using fully modified OLS (FMOLS), dynamic OLS (DOLS), fixed effects, and method of moments quantile regression (MMQR) are all part of the sequential econometric framework used in this analysis. Findings consistently show that the domestic fishing footprint is positively correlated with imports, domestic production, real GDP, and per capita food consumption, but adversely correlated with fishery exports. Additionally, MMQR estimates show that the negative export link becomes stronger at higher quantiles of the distribution of fishing footprint, indicating that the moderating influence of exports is strongest in nations that are already under a lot of ecological strain. Although the panel data do not allow for direct dissection of these channels, these findings are interpreted considering three potential mechanisms: certification-linked catch limits, aquaculture substitution in export volumes, and distant-water fleet displacement. It is recommended that policymakers include sustainability criteria into import laws, broaden the scope of eco-certification, and make investments in aquaculture to supplement the management of wild-capture fisheries. The findings of this study contribute significantly to the monitoring of global sustainability agendas, particularly aligning with United Nations Sustainable Development Goal (SDG) 12 (Responsible Consumption and Production) and SDG 14 (Life Below Water) by providing empirical evidence on how trade dynamics influence the fishing footprint. Full article

Air pollution negatively impacts human health and environmental sustainability, particularly in areas with high industrial activity. This study comparatively evaluated deep learning-based models for estimating PM₁₀ and SO₂ pollutants in Dilovası and Ereğli (Turkey), industrial areas with high pollutant loads. The study utilized Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), an RNN–GRU stacked hybrid model, an attention-based hybrid model, and the proposed GRU–LSTM dual-path hybrid model. The proposed method consists of four main stages: data conversion into a time-series format, data preprocessing and feature generation, model architecture development, and model training and performance evaluation. The dataset consisted of 365 daily PM₁₀ and SO₂ observations obtained from the Air Monitoring Center for the Dilovası and Ereğli monitoring stations. Model performance was evaluated using the coefficient of determination (R²), training time, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) metrics. The findings showed that the hybrid models provided higher accuracy compared to the single-track models. Specifically, the proposed GRU–LSTM dual-path hybrid model produced the highest R² and lowest error values for both pollutant parameters in both the Dilovası and Ereğli regions. In Dilovası, this model achieved R² = 0.97 for SO₂ and R² = 0.96 for PM₁₀; in Ereğli, it reached R² = 0.92 for SO₂ and R² = 0.98 for PM₁₀. Thus, it has been shown that the GRU–LSTM dual-path hybrid model, which models short-term and long-term temporal dependencies in parallel, is an effective and reliable method for air pollutant forecasting in industrial areas. These findings demonstrate the potential of the proposed model to support air quality monitoring, early warning systems, and environmental decision-making in industrial regions. Full article

Background: The transition toward sustainable development models has increased the relevance of the circular economy (CE) as a strategy for improving resource efficiency and reducing environmental impacts. In this context, higher education may contribute to strengthening sustainability-oriented competencies and environmental awareness among university students. Methods: This study aimed to assess differences in knowledge of the circular economy, perceptions regarding higher education in circular economy education, and sustainability dimensions among university students in Peru, Colombia, and Mexico. A quantitative, non-experimental, cross-sectional design was adopted using a structured questionnaire administered to 702 university students. The analysis included descriptive statistics, the Kruskal–Wallis test, and Dunn’s post hoc comparisons. Results: The results showed significant differences among countries regarding knowledge of CE principles, sustainability initiatives, and perceptions associated with higher education in circular economy education. Peruvian students generally reported higher levels of knowledge and more positive perceptions across several indicators, whereas Mexican students presented comparatively lower scores. Differences were also identified across the environmental, social, and economic dimensions of sustainability, particularly in the economic dimension. Conclusions: Overall, the findings suggest that higher education may support the development of CE-related competencies and sustainability-oriented educational strategies within diverse Latin American contexts. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

The pursuit of sustained economic growth remains a fundamental objective for all nations, as it directly contributes to improving living standards and the overall quality of life for citizens. This research examines how human development, technological innovation and environmental taxation influence long-term economic performance across twenty-two European Union (EU) countries over the 1990 to 2022 period. Method of Moments Quantile Regression (MMQR) is employed for data analysis and the robustness check is achieved by employing the Pooled Mean Group (PMG) and Panel Corrected Standard Errors (PCSE) methods. Key findings reveal the importance of human development, research and development and investment by sector in raising the Gross Domestic Product (GDP) per capita. Moreover, the MMQR findings shows that environmental taxes exhibit positive relationships with GDP per capita in the lower and middle quantiles, while insignificant relationships prevail in the upper quantiles. Therefore, environmental taxes are subject to some upper limits on their influence on GDP per capita. Once the threshold is achieved, environmental taxes tend to harm production. The PCSE findings show that the relationship of environmental taxes and GDP per capita is a weak positive one, while the PMG results shows that these factors are negatively related. Renewable energy is observed to be negatively related with GDP per capita as supported by the MMQR, PMG and PCSE results. These findings offer valuable policy implications, reinforcing the importance of aligning economic strategies with the Sustainable Development Goals (SDGs) to foster inclusive and environmentally sustainable growth within the European context. Full article

Microplastics (MPs) are widely recognized as emerging contaminants in freshwater environments. Their identification often relies on extensive sample preparation and chemical treatments, which increase analysis time, reagent use, and overall resource consumption. Consequently, there is a growing need for sustainable analytical approaches enabling reliable MP detection while minimizing sample handling. This study proposes an analytical workflow based on hyperspectral imaging (HSI) as a proof-of-concept approach for direct identification of MPs in freshwater samples. Water samples collected from three different rivers, containing heterogeneous natural materials, were spiked with MPs (250–1000 μm) of three common polymers, namely high-density polyethylene (HDPE), polystyrene (PS), and polypropylene (PP), to simulate realistic contamination scenarios. HSI acquisitions were performed in the short-wave infrared range (SWIR: 1000–2500 nm). Spectral preprocessing and principal component analysis (PCA) were applied for data exploration, while a hierarchical partial least squares-discriminant analysis (Hi-PLS-DA) model was developed to classify five target classes: natural materials, water, HDPE, PS, and PP. Despite sample complexity, the proposed workflow achieved satisfactory classification results, as demonstrated by the predicted class map and the corresponding statistical metrics (sensitivity, specificity, precision, and F1-score: 0.900–0.999). These results highlight the potential of the SWIR-HSI-based approach as a rapid and sustainable method for direct MP identification in freshwater samples and provide methodological insights for rapid MP screening strategies requiring minimal sample preparation. Full article

This research investigates how green entrepreneurship, green innovation, and domestic credit to the private sector, contribute to environmental sustainability in Turkey from 1990 to 2022. Environmental sustainability is proxied by ecological footprint. The methods used include Wavelet Quantile Regression (WQR) and Wavelet Quantile Correlation (WQC). This research also ascertained if interacting domestic credit and green entrepreneurship and domestic credit and green innovation could contribute to environmental sustainability using the Multivariate Wavelet Quantile Regression (MWQR) approach. The WQR and WQC outcomes confirm the following: (1) green entrepreneurship reduces ecological footprint across all quantiles and periods; (2) green innovation reduces ecological footprint in the short and medium term, while in the long term, it increases ecological footprint; (3) domestic credit increases ecological footprint across all quantiles and periods. The MWQR results confirm that domestic credit and green entrepreneurship interaction reduces ecological footprint, while domestic credit and green innovation interaction reduces ecological footprint in the short term. Policies are recommended for implementation. Full article

Understanding the spatiotemporal evolution of runoff and its driving mechanisms is of great significance for water resources development, utilization, and sustainable management in mid- to high-latitude river basins under climate change. However, runoff variability is jointly influenced by multiple meteorological factors, and a comprehensive understanding of its multi-scale response characteristics and the relative contributions of different drivers remains limited. In this study, runoff data from three hydrological stations in the Songhua River Basin during 1980–2022 were analyzed. A set of statistical and time-series methods, including the Mann–Kendall test, Pettitt change-point test, Hurst exponent, wavelet analysis, and wavelet coherence, was applied, and a random forest model was used to quantify the influence of key climatic factors such as precipitation, air temperature, and evapotranspiration. The results show that air temperature exhibits significant increasing trends in all four seasons, with the strongest warming occurring in spring (Sen’s slope ≈ 0.06 °C a⁻¹). Precipitation displays pronounced spatial heterogeneity and interannual variability, while evapotranspiration shows an overall increasing trend. Both runoff and major meteorological variables exhibit significant spatial heterogeneity across the basin. Hydro-meteorological variables also show distinct periodic variations among seasons, with temperature, precipitation, and evapotranspiration exhibiting stronger seasonal fluctuations during summer. Wavelet coherence analysis indicates that short-term runoff variability is mainly driven by temperature and precipitation. Temperature exhibits significant coherence with runoff across multiple time scales ranging from approximately 2 to 20 years, whereas precipitation shows stronger coherence at medium- to long-term scales (approximately 10–35 years), with evident seasonal differences. Random forest results indicate that evapotranspiration is the most important contributor to runoff variability at all three stations, accounting for 33.5%, 28.6%, and 26.2% of the total importance at Jiamusi, Fuyu, and Jiangqiao stations, respectively. Temperature and sunshine duration rank second, while precipitation and relative humidity contribute comparatively less. These findings indicate that evapotranspiration plays a key regulatory role in long-term water balance. In addition, runoff exhibits multi-scale variability and a transition from gradual changes to stage-like abrupt shifts. The findings provide a scientific basis for water resources management, flood mitigation, and climate change adaptation in the Songhua River Basin. Full article

The high dropout rate in massive open online courses (MOOCs) continues to limit their potential in promoting inclusive and sustainable learning. Although many prediction models have been used to identify potential dropouts, most studies view dropout as a static classification problem and fail to clearly reveal the dynamic trajectory of learner participation over time. Therefore, this study introduces a phased analysis perspective, treating MOOC dropout as a process that continuously evolves at different stages. On the basis of the KDDCUP2015 dataset, we constructed behavioral characteristics at three time points: the first week, the third week, and the fifth week. By combining robust feature analysis and interpretable models, we systematically examined the changing patterns of dropout modes. The results revealed significant differences across the different stages. In the early stage of the course, dropout was related mainly to the unstable interaction behaviors of learners, such as restricted access to resources and irregular participation rhythms. In the middle and late stages, task-oriented behaviors, especially those related to video-based learning activities, gradually became key factors. Notably, high-frequency video participation does not always reduce the risk of dropout; when video activity is high but the overall interaction rate is low, it is more likely to indicate an increase in the risk of dropout. These results indicate that the combination of behaviors is more crucial than mere activity levels. By revealing the changing characteristics of behaviors at different stages, this study helps support the design of more practical early warning methods. Full article

Background: Conventional transdermal drug delivery systems are often limited by poor skin permeability and low drug loading efficiency, necessitating the development of advanced delivery platforms. Objectives: This study aimed to develop and optimize photopolymerizable bioinks (PBs) for liquid crystal display (LCD)-based 3D printing of crosslinked hydrogel microneedles (cHMNs) to enhance transdermal delivery of estradiol valerate (E2V). Methods: A Box–Behnken design (BBD) was used to optimize the effects of Gantrez™ S-97, Jurymer™, and polyvinyl alcohol (PVA) on viscosity, exposure time, hardness, and elasticity, with strong predictive performance (R² = 0.9702–0.9907). Results: Estradiol valerate-loaded nanoparticles (E2V-NPs) were prepared via ionotropic gelation, exhibiting a particle size of 698.33 (0.78) nm, PDI of 0.50 (0.06), zeta potential of −39.09 (7.32) mV, and high encapsulation efficiency (86.87 (0.78)%). The optimized PBs enabled fabrication of uniform cHMNs (~800 µm height) with adequate mechanical strength (hardness 20.45 (1.23) N; elasticity 2.97 (0.49) MPa) and effective insertion capability. The E2V-NPs-loaded cHMNs exhibited sustained drug release over 12 days (~56.92 (4.27)%). Skin permeation studies showed a significantly enhanced flux (10.81 (4.55) µg/cm²/h) and cumulative permeation (12.94 (2.06) µg/cm²) compared to topical E2V-NPs and suspension, along with increased skin accumulation (38.55 (0.10) µg). Cytotoxicity studies confirmed that E2V and E2V-NPs were biocompatible (>80% viability), while PBs showed concentration-dependent cytotoxicity. Conclusions: Overall, this integrated platform combining design of experiment, nanoparticles, microneedles, and LCD 3D printing offered a promising strategy for enhancing transdermal drug delivery efficiency and reproducibility. Full article