Search Results (3,431)

Recently, researchers have been focused on the recycling as well as transforming of bio-waste streams into a valuable resource. Banana peels are promising for such application, due to their wide availability. In this context, the integration of banana peel-derived biochar with environmentally benign magnetite has significantly broadened its potential applications as a solar photocatalyst compared to the conventional photocatalysts. The materials are mixed in varied proportions of Ban-Char500-Mag@-(0:1), Ban-Char500@Mag-(1:1) and Ban-Char500@Mag-(2:1) and characterized using X-ray diffraction (XRD) and scanning electron microscopy (SEM) augmented with dispersive X-ray spectroscopy (EDX). Such modification is leading to an improvement in its application as a solar photocatalyst using the photochemical solar collector facility. The study discusses the factors controlling acetaminophen removal from aqueous effluent within 30 min of solar illumination time. Furthermore, the highlighted optimum parameters are pH 3.0, using 10 mg/L of the Ban-Char500@Mag-(1:1) catalyst and 100 mg/L of the hydrogen peroxide as a Fenton combination system for removing a complete acetaminophen from wastewater (100% oxidation). Also, the temperature influence in the oxidation system is studied and the high temperature is unfavorable, which verifies that the reaction is exothermic in nature. The catalyst is signified as a sustainable (recoverable, recyclable and reusable) substance, and showed a 72% removal even though it was in the six cyclic uses. Further, the kinetic study is assessed, and the experimental results revealed the oxidation process is following the first-order kinetic reaction. Also, the kinetic–thermodynamic parameters of activation are investigated and it is confirmed that the oxidation is exothermic and non-spontaneous in nature. Full article

Background/Objective: Sleep is a vital determinant of human health, where both its quantity and quality directly impact physical and mental well-being. Thermoregulation plays a pivotal role in sleep quality, as the body’s ability to regulate temperature varies across different sleep stages. This study examines the effects of a novel real-time temperature adjustment (RTA) mattress, which dynamically modulates temperature to align with sleep stage transitions, compared to constant temperature control (CTC). Through polysomnographic (PSG) assessments, this study evaluates how adaptive thermal regulation influences sleep architecture, aiming to identify its potential for optimizing restorative sleep. Methods: A prospective longitudinal cohort study involving 25 participants (13 males and 12 females; mean age: 39.7 years) evaluated sleep quality across three conditions: natural sleep (Control), CTC (33 °C constant mattress temperature), and RTA (temperature dynamically adjusted: 30 °C during REM sleep; 33 °C during non-REM sleep). Each participant completed three polysomnography (PSG) sessions. Sleep metrics, including total sleep time (TST), sleep efficiency, wake after sleep onset (WASO), and sleep stage percentages, were assessed. Repeated-measures ANOVA and post hoc analyses were performed. Results: RTA significantly improved sleep quality metrics compared to Control and CTC. TST increased from 356.2 min (Control) to 383.2 min (RTA, p = 0.030), with sleep efficiency rising from 82.8% to 87.3% (p = 0.030). WASO decreased from 58.2 min (Control) and 64.6 min (CTC) to 49.0 min (RTA, p = 0.067). REM latency was notably reduced under RTA (110.4 min) compared to Control (141.8 min, p = 0.002). The REM sleep percentage increased under RTA (20.8%, p = 0.006), with significant subgroup-specific enhancements in males (p = 0.010). Females showed significant increases in deep sleep percentage under RTA (12.3%, p = 0.011). Conclusions: Adaptive thermal regulation enhances sleep quality by aligning mattress temperature with physiological needs during different sleep stages. These findings highlight the potential of RTA as a non-invasive intervention to optimize restorative sleep and promote overall well-being. Further research could explore long-term health benefits and broader applications. Full article

Sugarcane juice (SJ) is a naturally sweet beverage rich in sucrose but prone to microbial contamination, raising concerns among health-conscious consumers. This study aimed to develop a functional SJ enriched with fructooligosaccharides (FOS) using enzymatic treatment, followed by high-pressure processing (HPP) to enhance its safety and quality. The enzymatic conversion of sucrose to FOS was achieved using Pectinex^® Ultra SP-L (commercial enzyme), with varying enzyme concentrations, temperatures and incubation times to identify the optimal conditions via response surface methodology (RSM). Under optimal conditions (1000 U/g enzyme concentration, 48 °C, 13 h), sucrose in raw SJ (124.33 g/L) decreased by 59.17 g/L, resulting in maximum reducing sugars (16.02 ± 0.58 g/L) and enhanced FOS yields, notably kestose (2.37 g/L) and nystose (9.35 g/L). After being treated with HPP at 600 MPa for 3 min, E. coli K12 and L. innocua were effectively inactivated by achieving > 5 log reduction, meeting USFDA standards. Furthermore, it was also observed that HPP could reduce yeast (6.56 × 10² CFU/mL). Meanwhile, mold, E. coli, and coliforms were not detected. Additionally, HPP maintained the juice’s physicochemical properties, outperforming thermal pasteurization (85 °C for 10 min) in quality preservation. This study highlights the potential of enzymatic treatment and HPP in improving SJ safety and functionality. Full article

For the first time, the effect of low-temperature vacuum drying (LTVD) on wild murta (Ugni molinae Turcz) was evaluated, in comparison with freeze-drying (FD) and vacuum drying (VD), to assess their capacity to preserve bioactive compounds and associated bioactivities. Murta was dried using LTVD at 20, 30, and 40 °C under a constant vacuum of 10 mbar, where FD and VD at 60 °C (VD 60) were included as comparative methods. The content of fatty acids and tocols, along with the retention of bioactive compounds and their antioxidant, anti-inflammatory, cytotoxic, and α-glucosidase inhibitory activities, were systematically analyzed. LTVD- and VD-dried murta exhibited higher polyunsaturated-to-saturated fatty acid ratios (>9.0) and markedly greater tocol contents, whereas FD maintained a more balanced ratio (<5.0) but with lower tocol levels. While FD was most effective in preserving catechin, higher levels of other phenolic compounds were observed in samples dried by LTVD at 20 and 40 °C, as well as VD 60, possibly due to the release of bound forms during processing. The drying method significantly influenced murta bioactivity. LTVD 30 preserved the highest antioxidant capacity, while topical anti-inflammatory effects on skin lesions varied by pathway, with LTVD 40 being the most effective in the TPA model and FD in the AA model. These effects were evaluated only using a topical inflammation model in BALB/c mice of both sexes; dietary effects were not assessed in this study. Regarding other bioactivities, VD 60 extracts excelled in both cytotoxic and α-glucosidase inhibitory effects, whereas FD extracts were the most effective against AGS cells and LTVD 20 against α-glucosidase. In conclusion, LTVD emerges as a promising alternative to FD and VD, showing potential to preserve bioactive compounds and key bioactivities of wild murta, although further studies are needed to elucidate the underlying mechanisms. Full article

In this study, the behavior of the spherical bearing component of the L-100 bridge part (AlfaTech LLC, Perm, Russia) is considered within the framework of a finite element model. The influence of the pattern of the coupling of the antifriction interlayer with the lower steel plate on the operation of the part is examined in terms of ideal contact, full adhesion, and frictional contact. The thickness of the antifriction interlayer varied from 4 to 12 mm. The dependencies of the contact parameters and the stress–strain state on the thickness were determined. Structurally modified polytetrafluoroethylene (PTFE) without AR-200 fillers was considered the material of the antifriction interlayer. The gradual refinement of the behavioral model of the antifriction material to account for structural and relaxation transitions was carried based on a wide range of experimental studies. The elastic–plastic and primary viscoelastic models of material behavior were constructed based on a series of homogeneous deformed-state experiments. The viscoelastic model of material behavior was refined using data from dynamic mechanical analysis over a wide temperature range [−40; +80] °C. In the first approximation, a model of the deformation theory of plasticity with linear elastic volumetric compressibility was identified. As a second approximation, a viscoelasticity model for the Maxwell body was constructed using Prony series. It was established that the viscoelastic model of the material allows for obtaining data on the behavior of the part with an error of no more than 15%. The numerical analog of the construction in an axisymmetric formulation can be used for the predictive analysis of the behavior of the bearing, including when changing the geometric configuration. Recommendations for the numerical modeling of the behavior of antifriction layer materials and the coupling pattern of the bearing elements are given in this work. A spherical bearing with an antifriction interlayer made of Arflon series material is considered for the first time. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

The grassland caterpillar Gynaephora qinghaiensis (Lepidoptera: Lymantriidae) is a dominant pest species in the alpine meadows of the Tibetan Plateau. Elucidating changes in key gene expression patterns will provide molecular insights into the adaptive evolutionary mechanisms of insects. Quantitative real-time PCR (qRT-PCR) is currently the predominant analytical methodology for assessing gene expression levels. However, variability among samples can compromise result reliability. Thus, selecting stably expressed reference genes for target gene normalization under diverse scenarios is critical. To date, suitable reference genes for G. qinghaiensis under varying experimental conditions have remained unidentified. In this study, the transcriptome data of G. qinghaiensis were obtained using the RNA-seq technique, and 13 candidate reference genes were selected. Four independent algorithms—ΔCt, geNorm, NormFinder, and BestKeeper—as well as a comprehensive online platform, RefFinder, were employed to evaluate the stability under six experimental conditions (tissues, developmental stages, sexes, temperatures, starvation, and insecticide treatments). Our findings identified the following optimal reference gene combinations for each experimental condition: RPS18, RPS15, and RPL19 for tissue samples; RPL19, RPS15, and RPL17 across developmental stages; RPS18 and RPS15 for different sexes; RPS8 and EF1-α under varying temperature conditions; RPL17 and RPL15 during starvation; and RPL19 and RPL17 following insecticide treatments. To validate the feasibility of the reference genes, we examined the expression of the target gene HSP60 in different tissues and under different temperatures. Our results established essential reference standards for qRT-PCR with G. qinghaiensis samples, laying the foundation for precise gene expression quantification in the future. Full article

Pipeline systems are essential across various industries for transporting fluids over various ranges of distances. A notable application is natural circulation through thermo-syphoning, driven by temperature-induced density variations that generate fluid flow in closed loops. This passive mechanism is widely employed in sectors such as process engineering, oil and gas, geothermal energy, solar water heaters, fertilizers, etc. Natural Circulation Loops eliminate the need for mechanical pumps. While this passive mechanism reduces energy consumption and maintenance costs, maintaining stability and efficiency under varying operating conditions remains a challenge. This study investigates thermo-syphoning in a rectangular closed-loop system and develops optimal control strategies like using a Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC) to ensure stable and efficient heat removal while explicitly addressing physical constraints. The results demonstrate that MPC improves system stability and reduces energy usage through optimized control actions by nearly one-third in the initial energy requirement. Compared to the LQR and unconstrained MPC, MPC with active constraints effectively manages input limitations, ensuring safer and more practical operation. With its predictive capability and adaptability, the proposed MPC framework offers a robust, scalable solution for real-time industrial applications, supporting the development of sustainable and adaptive natural circulation pipeline systems. Full article

The impregnation and curing process of dry bushing requires the epoxy material for bushing to have a good process performance. In addition, the actual operating conditions of dry bushing put forward high requirements on the dielectric properties of the epoxy material. Amine accelerators can not only improve the technological properties of epoxy materials such as gel time and curing exothermic temperature rise by regulating the reaction rate of epoxy resin and anhydride curing agent, but also optimize the dielectric properties of epoxy materials by regulating the crosslinking density of epoxy materials. However, there are many types of amine accelerators, and the effects of amine accelerators with different nucleophilicity on epoxy materials vary greatly. In this paper, four kinds of amine accelerators with different nucleophilic ability were selected to study the influence of the nucleophilic ability of amine accelerators on the process and dielectric properties of epoxy materials. The results show that the stronger the nucleophilicity of the amine accelerator, the shorter the gel time of the epoxy mixture and the higher the exothermic temperature rise during curing, indicating a poorer processing performance. However, stronger nucleophilicity also endows the epoxy material with superior dielectric properties. Among them, the strong nucleophilic ability of TEA shortens the gel time of the material by 50% and increases the curing exothermic temperature rise by 55.3% compared with the weak nucleophilic ability of the DET epoxy system; the dielectric constant and dielectric loss of the material are reduced by 8.3% and 39.5%, respectively, and the breakdown strength is improved by 11.4%. This paper reveals the contradictory relationship between the process and dielectric performance of epoxy materials triggered by the difference in the nucleophilic ability of amine accelerators, and it also provides a new research idea for the improvement of the process and in the dielectric performance of epoxy materials for dry bushing. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

Lithium–sulfur (Li-S) batteries are promising energy storage solutions due to their high density and cost-effectiveness. However, the risk of thermal failure limits their widespread use. Understanding thermal failure characteristics and developing accurate prediction methods are crucial for ensuring battery safety and reliability. This study aims to analyze the thermal failure characteristics of Li-S batteries and offer machine learning-based prediction methods for the early detection of potential thermal failures. The research begins with collecting temperature data from sensors deployed over numerous planes of a Li-S battery module under varied operating conditions. The data are created using proven numerical models that simulate various failure conditions. To improve model stability and learning efficiency, temperature data are preprocessed using min–max normalization to scale them to a consistent range. We suggest using a machine learning algorithm, such as the Energy Valley Optimizer Muted Multilayer Perceptrons with Mutual Information (EneVO-MPMI) algorithm. These models are trained on temperature data which are combined with Multilayer Perceptrons (MPs) to capture complicated, nonlinear correlations in thermal failure predictions, whereas the Energy Valley Optimizer (EneVO) optimizes the model’s structure and hyperparameters to avoid overfitting. Mutual Information (MI) assists in the selection of relevant features, resulting in accurate prediction from sensor data. To assess the models’ generalizability, five-fold cross-validation is used and achieves an average F1-score of 97.2%, a recall of 97.6%, an accuracy of 97.3%, and a precision of 96.9%. The EneVO-MPMI method emerges as the most effective, delivering a higher accuracy in forecasting thermal failure while requiring less training and prediction time. It shows that the EneVO-MPMI method is the most accurate and efficient at forecasting thermal breakdown in Li-S batteries. The technique can be used to identify Li-S battery defects early on, reducing the possibility of thermal instability and improving battery safety in a variety of applications. Full article

This study investigates the early-age compressive strength development of concrete incorporating ground granulated blast-furnace slag (GGBFS) under varying water-to-binder (W/B) ratios (35%, 45%, and 55%) and curing temperatures (5 °C, 20 °C, and 35 °C). Concrete mixtures were prepared with 0%, 20%, and 40% GGBFS replacement levels, maintaining a constant slump of 180 mm. The influence of GGBFS on fresh properties was evident, as higher GGBFS content reduced the demand for high-performance air-entraining water-reducing admixture (AEWR) by up to 72% at 40% GGBFS and W/B of 35%. All mixtures maintained target air content within 4.5 ± 1.5%. The Nurse–Saul maturity method was applied to determine the datum temperature $T_0$ (The minimum temperature required for the degree of maturity to increase) for early-age strength prediction. The optimal $T_0$ was found to be −3 °C for both OPC and GGBFS-blended concretes, replacing the conventional −10 °C value. Compressive strength predictions were conducted using Plowman, Logistic, and Gompertz models within the 5–10 MPa range. The Plowman and Gompertz models predicted early-age compressive strength with an error of approximately 10% in the 5–10 MPa range. In the lower strength range of 3–5 MPa, the Gompertz model exhibited superior predictive performance, with prediction errors 0.5–1 MPa lower than those obtained using the Plowman model. These findings will help in enhancing the maturity method’s reliability for low-temperature or time-constrained construction and support the use of GGBFS as a sustainable cement replacement. The study offers practical insights into optimizing early-age performance in blended cementitious systems. Full article

Nuclear fuel cladding serves as the primary barrier to the release of radioactive fission products and is subjected to high-temperature and high-pressure environments during both normal reactor operation and accident scenarios such as loss of coolant accidents (LOCAs). Predicting the burst behavior of cladding is essential for ensuring structural integrity, especially under varying heating rates—an aspect inadequately addressed in existing empirical models. In this study, a finite element-based damage model is developed to simulate the ballooning and burst behavior of Zircaloy-4 cladding. The model incorporates creep deformation, stress triaxiality, and time-dependent damage accumulation. Material behavior is characterized using experimentally determined creep constants and the model is calibrated against burst test data from the literature. A new heating-rate-dependent burst correlation is proposed based on model outputs. The results indicate that increasing the heating rate raises the burst temperature due to reduced exposure time in the temperature regime where creep damage accumulates significantly. The model accurately reproduces burst behavior across a wide range of internal pressures (1–10 MPa) and heating rates (5–100 °C/s). The newly developed correlation improves predictive capability in accident analysis tools and can be directly implemented into safety analysis codes for Indian pressurized heavy water reactors (PHWRs), contributing to enhanced reactor safety evaluations. Full article

Metal Injection Molding (MIM) is based on the processing of highly filled polymers via the well established polymer injection molding process. It offers a highly efficient processing route for the indirect manufacturing of especially small and complex metal parts. In this regard, polyoxymethylene (POM) is often used as a primary binder component in MIM feedstocks due to its high debinding rate through a time-saving catalytic debinding process, utilizing the acid-catalyzed degradation of POM for polymer removal. However, thermally induced degradation of POM under processing conditions can also lead to changes in processing behavior, which is particularly important in highly filled polymers due to their already challenging processability. In this context, the present work demonstrates the impact of POM homopolymers (POM-H) and copolymers (POM-C) with varying viscosities on feedstock characteristics, their influence on the thermal processing stability, and their significance for the properties of the green parts. Within the study, the thermal degradation of both material types was assessed by viscosity measurements and thermogravimetry, with POM-H exhibiting more significant degradation compared to the thermally more stable POM-C, especially at higher temperatures. Catalytic debinding performance was found to be adequate for all materials. However, lower viscosity POM-C grades are preferred to optimize processability in MIM. Full article