Search Results (29,649)

Oxidative stress and sarcopenia are increasingly perceived as interdependent processes that significantly affect the course of cardiovascular diseases. Excessive production of reactive oxygen species leads to muscle cell damage, mitochondrial disorders, and chronic inflammation, which promote progressive loss of muscle mass and function. Methods: The aim of the study was to analyze the mechanisms linking oxidative stress and sarcopenia in the course of cardiovascular diseases. Our scoping review initially identified 854 articles, of which 3 were ultimately included in the review (after removing duplicates (n = 118), 736 articles remained; after re-screening the articles according to the inclusion and exclusion criteria (n = 302), 434 articles remained; 196 publications lacked full text and were excluded, leaving 238 articles). Results: An examination of the available literature indicates a potential association between increased oxidative stress and the possible development of sarcopenia in individuals with cardiovascular diseases. The studies identified in this review suggest that elevated levels of reactive oxygen species, together with reduced antioxidant capacity, may contribute to muscle fiber damage, mitochondrial disturbances, and the activation of chronic inflammatory processes, which could in turn be involved in the accelerated decline of muscle mass and strength. Conclusions: These results confirm that oxidative stress is a key pathophysiological element linking both disease entities and may be an important target of therapeutic interventions. Full article

This study presents the design, fabrication, and performance evaluation of a solar dryer capsule cabinet equipped with a parabola reflector, developed to enhance drying efficiency through the reflection of sunlight onto both the upper and lower surfaces of the product. Conventional solar drying exposes only the upper surface, resulting in uneven heating and the need for manual turning. The proposed system integrates a parabolic reflector and IoT-based monitoring sensors (BH1750 light sensor and DHT22 temperature-humidity sensor) to optimize heat distribution and record real-time environmental parameters. Dry experiments were conducted using Citrus hystrix DC. (Makrut lime) peels under natural sunlight from 9:00 a.m. to 5:00 p.m. The moisture loss achieved with the proposed dryer (P-DSD) was 45.66%, compared with 6.79% for direct solar drying (DSD). The drying rate increased from 3.05 g h⁻¹ (DSD) to 20.50 g h⁻¹ (P-DSD), while the specific energy consumption (SEC) decreased from 3519.75 kWh kg⁻¹ to 523.67 kWh kg⁻¹, representing an 85.13% energy reduction. Economic analysis showed a system cost of $1384 and a return on investment of 30.0%. These results demonstrate that the proposed solar dryer capsule cabinet with a parabola reflector offers a low-cost, eco-friendly, and high-efficiency solution for drying agricultural and herbal products, significantly shortening the drying time and improving product quality. Full article

Learning efficiency in a university context is predicated on a conducive learning environment. This in turn requires settings offering thermal comfort. In this study, we experimentally explored the relationship between the thermal environment of colleges and universities in hot-summer and cold-winter regions on the thermal comfort and learning efficiency of Chinese college students. Findings are intriguing in that temperatures delivering optimal thermal comfort and optimal learning efficiency differ. Specifically: (1) Students generally feel most comfortable when the room temperature is approximately 24 °C; (2) Combined studies comparing temperature on thermal comfort and learning efficiency found that college students learn better in slightly colder environments; (3) Based on the comprehensive value of satisfying the best thermal comfort and high learning efficiency, the optimal temperature range is 20.6 °C to 22.2 °C. Full article

In the complex urban path planning of unmanned ground vehicles (UGVs), the dung beetle optimization (DBO) algorithm is widely used due to its simple structure and fast convergence speed. However, it still has the disadvantages of poor convergence accuracy and is easy to fall into a local optimum. To solve these problems, this paper proposes a multi-strategy enhanced DBO algorithm (ESDBO). Firstly, sine mapping is introduced in the population initialization stage to enhance solution diversity. Secondly, an adaptive information volatilization mutation strategy is proposed, which dynamically balances the convergence and global search ability. Finally, a multi-mechanism co-evolution strategy is designed, which significantly improves the local search ability and stability. Through ablation experiments and CEC2017 benchmark tests, the optimization ability of the proposed strategy and the convergence accuracy and stability of ESDBO are verified. Further path planning experiments are carried out on the public Random MAPF benchmark map. The results show that ESDBO can generate global optimal paths with short path length, few turns, and high safety margin on different obstacle densities and map scales. The algorithm provides an efficient and reliable solution for autonomous navigation in complex urban environments. Full article

This computational study examined how variations in the seatback angle of two generic child restraint systems (CRSs) affect spinal loading in young occupants (1.5 YO and 3 YO) during frontal impacts, performed according to the specifications included in UNECE R129. CRS seatback angle dictates torso recline, which in turn influences head, chest, and spine kinematics and loading. While manufacturers typically recommend 30–45° for rear-facing CRSs and an upright position for forward-facing CRSs, little is known about the biomechanical implications of deviating from these guidelines. Using PIPER human body models representing a 1.5-year-old in a rear-facing CRS and a 3-year-old in a forward-facing CRS, simulations were performed under UN-R129 frontal impact conditions. The seatbacks were rotated 5° and 10° more upright or reclined relative to the nominal angle, with occupants restrained by a five-point harness and CRSs secured with ISOFIX, top tether, or three-point belt. The results showed that reclined configurations generally increased the predictions of spinal loading (forces and/or moments) given by the PIPER model, while nominal or more upright angles reduced loads, particularly in the lumbar spine of the 3-year-old model. Overall, the study highlights how computational tools can guide CRS design improvements to optimize spinal protection and enhance child safety beyond current regulatory requirements. Full article

The current real estate market is in disarray, implying that reforms and incorporation of sustainable and green elements are crucial, especially for younger generations. This becomes more vivid for the case of developing countries and the Middle East as there is a growing interest in green living concepts. This study focuses on Turkish young adults and their purchasing intentions of green real estate options in line with the sustainability agenda for Turkish development goals. In this sense, the indirect impacts of greenwashing and perceived value are examined to address the underlying determinants of purchasing intentions. The theoretical setting of the research combines the stimulus–organism–response model and the theory of planned behavior. Through combined purposive and convenience sampling methods and using partial least squares structural equation modeling (PLS-SEM), a total of 203 surveys were analyzed. The results highlight that a well-established green marketing campaign can uplift perceived value, which in turn enhances purchasing intentions during the evaluation process among potential buyers. Greenwashing is a major diminisher for consumers’ intentions as it creates doubt, distrust, and negative emotions, thus creating a mental barrier for forming intentions towards purchasing green housing options. The findings of this research provide both theoretical and practical implications for improving housing options for young adults through empirical analysis of marketing and consumer behavior mechanisms. Full article

For a long time, glycolysis and mitochondrial oxidative phosphorylation were opposed to each other. Glycolysis works when there is a lack of oxygen; the mitochondria supply ATP in an oxygen environment. In recent decades, it has been discovered that glycolysis in vivo always works and the final product is lactate. Lactate can accumulate and is the transport form for pyruvate. In this review, we look at how obligate lactate formation during glycolysis affects the tricarboxylic acid (TCA) cycle and mitochondrial respiration. We conclude that fatty acid β-oxidation is a prerequisite for obligate lactate formation during glycolysis, which in turn promotes and enhances the anaplerotic functions of the TCA cycle. In this way, a supply of two types of substrates for mitochondria is formed: fatty acids as the basic energy substrates, and lactate as an emergency substrate for the heart, skeletal muscles, and brain. High steady-state levels of lactate and ATP, supported by β-oxidation, stimulate gluconeogenesis and thus support the lactate cycle. It is concluded that mitochondrial fatty acids β-oxidation and glycolysis constitute a single interdependent system of energy metabolism of the human body. Full article

Recently, unmanned aerial vehicles (UAVs) based on electric propulsion systems are being increasingly adopted in various fields, including industrial and military applications. Outer-rotor surface-mounted permanent magnet synchronous motors (SPMSMs) are predominantly applied in UAV propulsion systems. However, these motors are vulnerable to the price fluctuations of rare-earth materials and supply chain instability. In addition, the magnets in these motors are prone to detachment at high rotational speeds, and demagnetization under high-temperature conditions may reduce output performance. To address these limitations, research is being actively conducted on non-permanent magnet motors, among which, wound-rotor synchronous motors (WRSMs) offer the advantage of controllable field excitation at high speeds. Furthermore, WRSMs can use both magnetic and reluctance torques, thereby increasing power density relative to other non-permanent magnet motors. However, the adoption of an additional field winding increases copper loss, thus reducing motor efficiency. This study investigates the application of the toroidal winding structure, which is already widely applied in permanent magnet and brushless direct current machines, to WRSMs. The performance of these motors is compared with that of motors using conventional tooth-coil windings. The toroidal windings are circumferentially distributed along both the inner and outer stator yoke paths, effectively reducing the end-turn length relative to that of conventional tooth-coil windings. Two WRSMs, one with tooth-coil and another with toroidal windings, are designed using identical specifications to compare performances via finite element analysis. The armature copper loss in the proposed model decreased by approximately 28% because the toroidal winding structure reduced the end-turn length. As a result, the efficiency increased by about 1.9% due to the reductions in copper, core, and eddy current losses. Full article

The present study introduces a comprehensive machine-learning framework for modeling, interpretation and optimization of the CNC turning procedure employing coated cutting inserts. The primary novelty of this work lies in the integrated pipeline that leverages a multimodal experimental dataset in order to simultaneously model surface roughness and residual stresses, as well as to interpret these predictions within a unified optimization scheme. Particularly, a deep learning model was developed incorporating a convolutional encoder for analyzing time-series signals and a static encoder for the investigated machining parameters. This fused representation enabled accurate multi-task predictions, capturing the thermo-mechanical interactions that govern surface integrity. Additionally, to ensure interpretability, a surrogate meta-model based on the deep model’s predictions was established and evaluated via Shapley Additive Explanations. This analysis quantified the relative influence of each cutting parameter, linking data-driven insights to contact-mechanical principles. Furthermore, a multi-objective optimization scheme was implemented to derive Pareto optimal trade-offs among the examined parameters that could enhance the machining efficiency. Overall, the integration of deep learning, interpretable modeling and optimization established a coherent framework for data-driven decision making in turning, highlighting the importance of model transparency in advancing intelligent manufacturing systems. Full article

As social media influencers increasingly shape sustainable consumption, understanding the psychological mechanisms underlying consumer responses is essential. Drawing on social influence theory and reactance theory, this study examines how influencer characteristics affect sustainable behavioral intentions through perceived pressure and consumer reactance, while considering the moderating role of green self-identity. Using survey data from 382 respondents, the proposed model was tested using partial least squares structural equation modeling (PLS-SEM). Given the cross-sectional research design and the reliance on self-reported data, the findings should be interpreted as associational rather than strictly causal. The results show that influencer expertise, homophily, and social influence significantly increase perceived pressure. Perceived pressure, in turn, positively influences consumer reactance, which negatively affects sustainable purchase intention and positively affects avoidance intention. In addition, green self-identity significantly moderates the relationship between perceived pressure and reactance, such that consumers with a stronger green self-identity exhibit heightened sensitivity to perceived pressure and experience stronger reactance responses, indicating heightened sensitivity among environmentally self-identified consumers. These findings extend existing sustainability and influencer marketing research by revealing the dual and potentially counterproductive effects of persuasive communication. The study highlights the importance of autonomy-supportive and identity-consistent messaging for promoting sustainable consumption and provides practical guidance for designing effective influencer-based sustainability strategies. Full article

This paper examines the long-run relationship between economic growth and CO₂ emissions in Croatia over the period 1990–2023 using the ARDL bounds testing approach. The analysis aims to assess the presence of an Environmental Kuznets Curve (EKC) and to shed light on Croatia’s position along the growth–emissions trajectory, an issue that has remained inconclusive in earlier studies. The results provide evidence of an inverted U-shaped relationship between the GDP per capita and CO₂ emissions, consistent with the EKC hypothesis. The estimates of marginal effects suggest that the impact of income on emissions weakens and may eventually turn negative at higher income levels, although the precise income level at which this transition occurs is sensitive to model specification and sample composition. Energy consumption emerges as the strongest long-run driver of emissions, while a higher share of renewable energy contributes significantly to their reduction. Institutional quality is found to be positively associated with emissions in the long run, reflecting growth-enhancing effects during the post-transition period rather than immediate environmental improvements. The contribution of this study lies in the use of a longer time span and a dynamic empirical framework that allows for a more nuanced assessment of the growth–emissions relationship in Croatia. Overall, the findings point to a gradual decoupling of economic growth from carbon emissions while highlighting that the sustainability of this trajectory depends critically on continued progress in the energy transition and on the alignment of institutional development with climate and energy objectives. Full article

Grounded in Self-Determination Theory (SDT), this study tested the hypothesis that body image perception delineates distinct motivational pathways, linking the perceived interpersonal style of exercise professionals to basic psychological needs, motivation quality, and long-term exercise persistence intentions. A sample of 821 regular exercisers was divided into two groups based on body image: “Satisfied” (n = 276) and “Dissatisfied due to Overweight” (n = 545). Participants completed validated measures of perceived interpersonal behaviors (supportive/thwarting), basic psychological need satisfaction/frustration, motivational regulation, and exercise persistence intention. A clear divergent pattern emerged, strongly supporting the main hypothesis. The “Satisfied” group reported a positive pathway: perceiving more need-supportive behaviors from instructors was associated with greater satisfaction of autonomy, competence, and relatedness, which in turn correlated with more self-determined motivation and stronger persistence intentions. Conversely, the “Dissatisfied” group reported a negative pathway: perceiving more need-thwarting behaviors was associated with greater need frustration, which correlated with more non-self-determined motivation and weaker persistence intentions. Measurement invariance confirmed these pathways are comparable across groups. The findings highlight that body image perception is a key correlate of distinct motivational experiences in exercise settings. Crucially, they underscore the significant association between the professional’s perceived interpersonal style and these pathways. Fostering need-supportive environments that enhance autonomy, competence, and relatedness is associated with more adaptive motivation and adherence, offering a valuable framework for practitioners aiming to support clients, particularly those with body image concerns. Full article

The on-machine measurement (OMM) of aero-engine blades is a critical technology for enabling closed-loop manufacturing. However, when using line laser sensors with a fixed scanning pose to measure free-form surfaces, the variation in surface geometry leads to changing incident angles, which in turn induce non-stationary noise. To address this issue, this paper proposes a multi-sensor fusion method utilizing Adaptive Heteroscedastic Gaussian Process Regression (AHGPR) based on a Spatial-Angle-Balanced Sampling (S-ABS) strategy. The AHGPR explicitly integrates the physical mapping of incident angle errors into its covariance structure, thereby automatically adjusting observation weights according to the local geometric posture. Concurrently, the S-ABS strategy captures the high-error characteristic points with large incident angles while maintaining a globally uniform spatial distribution. The experimental data indicate that this approach addresses the sampling deficiency encountered at the leading and trailing edges and in areas with large incident angles. The proposed approach reduced the impact of optical deviations on measurement accuracy and improved the precision of the process. Full article

The rising volume of sludge production poses significant environmental threats. Sludge has a high moisture content (MC), which increases its disposal and transport expenses. On the other hand, sludge has low dewaterability due to its high concentration of soluble organic compounds. To reduce sludge production, understanding and improving preconditioning and mechanical dewatering are crucial for breakthroughs in advanced sludge dewatering. The sludge samples used in this analysis were obtained from the Sarabium municipal wastewater treatment plant, with a moisture content of 97% and a specific filtration resistance (SRF) of 9.15463 × 10¹⁵ m/kg. Sludge dewatering was enhanced by treating the samples chemically with ferric chloride, aluminum sulfate, Moringa olifera, and cationic polyacrylamide CPAM and physically with wood chips, slag, rice husk, and wheat straw. The experiments examined the sludge’s initial characterization (specific resistance to filtration (SRF) and time to filtrate (TTF)). To verify the structural characteristics (density), elemental composition, and the presence of various functional groups, a characterization investigation was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and energy-dispersive X-ray spectroscopy (EDS). The results showed that chemical conditioning with ferric chloride is better than aluminum sulfate and Moringa. Wood chips also provide better results for physical conditioning than rice husk, wheat straw, and slag. The reaction occurred at the carbonyl group, where FTIR showed more activated sites during SEM analysis, as evidenced by the FTIR results. Still, when CPAM was added to conditioned sludge, there was no difference in sludge dewatering performance, and the activated sites remained unchanged. Hence, this research found that mechanical sludge dewatering was improved by conditioning with ferric chloride (pH of 6 and dose of 0.12 g/g of dry solid) and wood chips (dose of 1.5 g/g of dry solid), which reduced sludge volume after dewatering by 82.5% under low pressure, which in turn minimizes transportation, energy, and handling costs. This study supports SDG 3 and SDG 6 by improving sludge dewatering efficiency and promoting sustainable wastewater management using natural wood chips. Full article

In this study, a three-dimensional (3D) model is developed based on an actual small forestry crawler tractor, to analyze its overturning and rollover behaviors, and a corresponding simulation model is constructed. The accuracy of the 3D model is validated by comparing its dimensions and center of gravity with those of the physical tractor, and the fidelity of the simulation model is verified using static sidelong falling angle, minimum turning radius, and driving tests. The developed simulation framework was employed to investigate the dynamic behavior of the small forestry crawler tractor, focusing on roll and pitch angular velocities across different obstacle heights, slope angles, and driving speeds. Backward rollover was not observed within the tractor’s realistic operating speed range, indicating that backward rollover is not the dominant risk mode. In contrast, lateral overturning occurs under all driving scenarios, and increases in driving speed and obstacle height lead to higher roll angular velocities, increasing the risk of lateral overturning. Across all conditions, the likelihood of lateral overturning surges when the roll angular velocity enters the 80–100°/s range, with obstacle height exerting the greatest influence. In conclusion, the small forestry crawler tractor is more prone to lateral overturning than backward rollover when driving on inclined surfaces. A distinct threshold roll angular velocity is identified as the onset point of lateral overturning, which will vary according to the tractor’s specifications. This study is a quantitative study of a small forestry crawler tractor and does not correlate with a full-scale tractor. While angular velocity values vary during lateral overturning and backward rollover, this study was conducted to identify trends under various driving conditions. Further work is required to apply the proposed analysis methodology to full-scale agricultural and forestry machinery and validate it with real-world operational data. Full article