Search Results (266,582)

Diabetic kidney disease (DKD) is a prevalent complication associated with diabetes in which podocyte dysfunction significantly contributes to the development and progression of the condition. Ring finger protein 183 (RNF183) is an ER-localized, transmembrane ring finger protein with classical E3 ligase activity. However, whether RNF183 is involved in glomerular podocyte dysfunction, which is the mechanism of action of DKD, is still poorly understood. In this study, we first demonstrated that RNF183 expression in glomerular podocytes of patients with DKD decreased as the disease progressed. Additionally, our transcriptome sequencing analysis of kidney tissues from diabetic mice revealed a significant reduction in RNF183 expression within the kidney cortex. Similarly, the expression of RNF183 was significantly reduced both in the kidneys of diabetic mice and in human podocytes exposed to high glucose conditions. The downregulation of RNF183 resulted in a suppression of autophagic activity, an increase in apoptotic cell death, and reduced expression of cellular markers in HPC cells. We found that RNF183 was modified via N6-methyladenosine (m6A) RNA methylation. Meanwhile, treatment with meclofenamic acid 2 (MA2), an m6A demethylase inhibitor, resulted in the upregulation of RNF183 expression in HPC cells cultured in high glucose conditions. Furthermore, high glucose treatment decreased the transcription and protein levels in both the m6A writer methyltransferaselike3 (METTL3) and the m6A reader insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2). IGF2BP2 assisted with METTL3, which is jointly involved in the transcription of RNF183. Furthermore, we confirmed that RNF183 directly ubiquitinates M2 pyruvate kinase (PKM2) through co-immunoprecipitation (Co-IP) and liquid chromatography–mass spectrometry (LC-MS) experiments. The level of PKM2 ubiquitination was increased following RNF183 overexpression, leading to enhanced PKM2 protein degradation and subsequently alleviating high glucose-induced podocyte damage. The results of this study indicated that RNF183 was regulated via m6A methylation modification and that RNF183 expression was reduced in HPC cells treated with high glucose, which resulted in decreased PKM2 ubiquitination levels and subsequently aggravated podocyte injury. The findings suggest that RNF183 may serve as a potential therapeutic target for diabetic kidney injury, offering new insights into its role in the progression of DKD. Full article

Additive manufacturing (AM) techniques have transformed the production of parts and components with intricate geometries and customized designs, driving innovation in sustainable manufacturing practices. The additive manufacturing technology used in this work was selective laser melting (SLM), a process that uses laser energy to sinter powdered metals into solid structures. Among the various materials utilized in AM, Ti6Al4V titanium alloys are of particular interest due to their favorable mechanical properties, corrosion resistance, biocompatibility, and potential for reducing material waste However, the machining of additively manufactured titanium parts presents challenges due to the material’s low conductivity, elastic modulus, and chemical affinity with cutting tools, which impact tool wear and surface finish quality. Milling, a commonly employed process for finishing titanium parts, often involves significant energy use and tool wear, highlighting the need for optimized and eco-conscious machining strategies. This study aims to establish correlations among four key aspects: (1) surface finish of machined Ti6Al4V AM parts, (2) cutting tool damage, (3) dry milling parameters including different cutting tools, and (4) variation of temperature at the contact surface of AM parts and tools using infrared thermography. By examining parameters such as feed per tooth (Fz), axial depth of cut (Ap), spindle trajectories (trochoidal, helicoidal, and linear), and cutting tool diameters, this work identifies conditions that enhance process efficiency while reducing environmental impact. Infrared thermography provides insights into temperature variations during milling, correlating these changes to surface roughness and critical machining parameters, thus contributing to the development of sustainable and high-performance manufacturing practices. Full article

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions. Full article

Short-course antibiotic therapies for common infections treated in hospital are supported by national guidelines. Hospital clinicians’ knowledge of the course length recommendations for the management of common infections has not been fully explored. This study aims to assess doctors’ knowledge of and explores their opportunity and motivation for prescribing short-course therapy. A survey was emailed to all prescribers working in adult medical specialties in two hospitals in England. The survey responses from both hospitals were pooled before analysis. One hundred and sixty-five responses were provided. Knowledge of the recommended short course lengths was high overall, except for severe community-acquired/hospital-acquired pneumonia (CAP/HAP), with only 44% of respondents opting for shorter-course therapy. The majority did not believe longer courses were more effective than shorter courses. We identified a gap in prescriber knowledge for appropriate antibiotic course lengths for severe CAP/HAP. Addressing this gap may contribute to antimicrobial stewardship efforts to reduce course lengths in line with national guidelines. Full article

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems. Full article

In the domain of course control, traditional methods such as proportional–integral–derivative (PID) control often exhibit limitations when addressing complex nonlinear systems and uncertain disturbances. To mitigate these challenges, the adaptive neuro-fuzzy inference system (ANFIS) has been integrated into course control strategies. The primary objective of this study is to investigate the course control characteristics of vessels governed by the ANFIS controller under both normal and severe sea conditions. A three-degree-of-freedom (3-DOF) maneuvering model set (MMG) was employed and validated through sea turning tests. The design of the ANFIS controller involved a combination of the backpropagation algorithm with the least square method. Training data for the ANFIS control system were derived from a linear control framework, followed by simulation tests conducted under normal and severe sea conditions to assess control performance. The simulation results indicate that in normal sea conditions, ANFIS has more stable heading control (smaller A_ψ), but at the cost of more energy consumption (larger I_δ). Notably, response time is reduced by approximately 36.7% compared to that of the linear controller. Conversely, during severe sea conditions, ANFIS exhibits an increase in response time by about 33.3% relative to the linear controller while maintaining a smaller I_δ. In the whole course control stage, the stability is better than the linear controller, and it has better energy-saving characteristics. Under scenarios involving small and large course alterations, A_ψ values for ANFIS are approximately 11.28% and 13.97% higher than those observed with the best-performing linear controller (λ_ψ = 60), respectively. As the propeller speed increases, the A_ψ value of the ANFIS controller decreases significantly, to about 62.71%, indicating that the energy efficiency is improved and the course stability is also enhanced. In conclusion, it can be asserted that the implementation of an ANFIS controller yields commendable performance in terms of controlling vessel courses effectively. Full article

Background/Objectives: To systematically evaluate scientific evidence related to the influence of psychological stress on the response to periodontal treatment. Methods: PubMed/NCBI (National Center for Biotechnology Information, US National Library of Medicine), Web of Science (ClarivateTM), EBSCOHost, SCOPUS, and ProQuest databases were searched for published clinical studies in English up to May 2024. The quality of each study was assessed using the Ottawa–Newcastle scale. Results: Of 803 relevant articles identified, 8 were included in the qualitative synthesis qualitative synthesis. These studies involved 445 patients who completed the follow-up period, ranging from 6 weeks to 6 months. Stressed patients were more likely to experience higher levels of PPD and BOP compared to non-stressed patients. In total, 75% of the included studies showed a positive relationship between stress and response to NSPT, 12.5% observed a negative relationship, and the remaining 12.5% found some degree of relationship in the results of clinical periodontal parameters. The level of evidence is categorized according to the quality of the synthesis presented. Conclusions: There is a positive correlation between psychological stress and periodontal treatment response, indicating that stress may negatively influence the clinical outcomes of NSPT. Stress may reduce the inflammatory response, which is crucial for eliminating periodontal micropathogens after periodontal treatment. Full article

Insecticide resistance surveillance systems for vector-borne diseases are crucial for early detection of resistance and the implementation of evidence-based resistance management strategies. While insecticide susceptibility bioassays are typically conducted under controlled laboratory conditions, mosquitoes in the field experience varying environmental conditions, with temperature being a key determinant. Understanding the relationship between temperature and insecticide toxicity is essential for interpreting and extrapolating assay results across different climate zones or more locally across days with different weather conditions. In this study, we examined Aedes aegypti mosquitoes with different genetic backgrounds of insecticide resistance. Mosquitoes were homozygous for the knockdown resistance (kdr) F1534C mutation, plus either (1) homozygous for the kdr 1016V wildtype allele, (2) homozygous for the kdr V1016I mutant allele, or (3) heterozygous genetic crosses. These three genotypes were exposed to deltamethrin using WHO tube tests at three temperatures (22 °C, 27 °C, and 32 °C) and varying dosages. LC50 values were determined for each genotype and temperature combination. A negative temperature coefficient was observed exclusively in female mosquitoes homozygous for the 1016V wildtype allele, indicating reduced pyrethroid toxicity at higher temperatures. No temperature–toxicity relationship was found in males of this genotype or in other genotypes of either sex. These findings suggest that temperature may interact with kdr mutations and possibly even sex, highlighting the complex interactions between genetic mutations and environmental factors, such as temperature, in determining the insecticide resistance phenotype. Given the wide distribution of Ae. aegypti, understanding how local climate conditions influence insecticide performance will help improve control strategies and slow resistance evolution, protecting public health efforts against mosquito-borne diseases Full article

China has abundant biomass and renewable energy resources suitable for producing green methanol via biomass thermochemical conversion. Given China’s increasing demand for sustainable fuel alternatives and the urgency to reduce carbon emissions, optimizing biomass utilization through gasification is critical. Research has highlighted the potential of integrating biomass gasification with water electrolysis to enhance efficiency in green methanol production, leveraging China’s vast biomass reserves to establish a cleaner energy pathway. Four main biomass gasification technologies—fixed-bed, fluidized-bed, pressurized fluidized-bed, and entrained-flow—have been investigated. Fixed-bed and bubbling fluidized-bed gasification face low gas yield and scaling issues; whereas, circulating fluidized-bed gasification (CFB) offers better gas yield, carbon efficiency, and scalability, though it exhibits high tar and methane in syngas. Pressurized fluidized-bed gasification improves gasification intensity, reaction rate, and equipment footprint, yet stable feedstock delivery under pressure remains challenging. Entrained-flow gasification achieves high carbon conversion and low tar but requires finely crushed biomass, restricted by biomass’ low combustion temperature and fibrous nature. Current industrially promising routes include oxygen-enriched and steam-based CFB gasification with tar cracking, which reduces tar but requires significant energy and investment; oxygen-enriched combustion to produce CO₂ for methanol synthesis, though oxygen in flue gas can poison catalysts; and a new high oxygen equivalence ratio CFB gasification technology proposed here, which lowers tar formation and effectively removes oxygen from syngas, thereby enabling efficient green methanol production. Overcoming feedstock challenges, optimizing operating conditions, and controlling tar and catalyst poisoning remain key hurdles for large-scale commercialization. Full article

The decarbonization of the building sector is essential to mitigate climate change, aligning with the EU’s Energy Performance of Buildings Directive (EPBD) and the transition from near-Zero-Energy Buildings (nZEBs) to Zero-Emission Buildings (ZEBs). This study introduces a novel and streamlined Life Cycle Assessment (LCA) methodology, in accordance with EN 15978, to holistically evaluate the Global Warming Potential (GWP) of buildings. Our approach integrates a calibrated dynamic simulation of operational energy use, performed with DesignBuilder, to determine precise operational CO₂ emissions. This is combined with a comprehensive assessment of embodied emissions, encompassing construction materials and transportation phases, using detailed Environmental Product Declarations (EPDs). Applied to the IndUVa nZEB case study, the findings reveal that embodied emissions dominate the life cycle GWP, accounting for 69%, while operational emissions contribute just 31% over 50 years. The building’s use of 63.8% recycled materials highlights the transformative role of circular economy strategies in reducing embodied impacts. A comparative analysis of three energy-efficiency scenarios demonstrates the IndUVa building’s exceptional performance, achieving energy demand reductions of 78.4% and 85.6% compared to the ASHRAE and CTE benchmarks, respectively. This study underscores the growing significance of embodied emissions as operational energy demand declines. Achieving ZEBs requires prioritizing embodied carbon reduction through sustainable material selection, recycling, and reuse, targeting a minimum of 70% recycled content. By advancing the LCA framework, this study presents a pathway for achieving ZEBs, driving a substantial reduction in global energy consumption and carbon emissions, and contributing to climate change mitigation. Full article

Traction motors in electric transport are most often synchronous permanent magnet motors (PMSMs). Induction motors (IMs) have large dimensions and stator current amplitudes under comparable loads. Traditional IM control methods do not solve these problems. Recent studies have shown that by changing the main magnetic flux in the IM in accordance with the load, these characteristics of the asynchronous electric drive can be significantly improved. Standard frequency converters do not allow for the implementation of these algorithms. But it makes sense to conduct a potential assessment of the capabilities of this algorithm to reduce the total stator currents of traction IMs. This article analyzes the results of real tests of a special vehicle for transporting rock inside mines, conducted several years ago at a mining equipment plant and in several mines in Russia. The prototype of the special transport vehicle has a load capacity of 15 tons, and its traction electric drive is based on four motor wheels with a total power of 100 kW and a frequency converter from the company “Vacon” (Vaasa, Finland). The tests were conducted at the plant’s testing ground and in real mine conditions. These tests allowed us to obtain information about the operation of the asynchronous electric drive under dynamically changing loads in a wide range, which is very difficult to obtain on laboratory benches or in industrial enterprise conditions. The experiments confirmed the efficiency of the optimization algorithm for asynchronous electric drives with frequency control. At the same time, the weight, size, and electrical parameters of the drive are as close as possible to those of direct current drives. Full article

The production of Portland Cement Concrete (PCC) generates significant environmental impacts that increase climate change and decrease people’s quality of life. Recent studies highlight the potential to reduce these environmental burdens by partially replacing Portland cement with Supplementary Cementitious Materials (SCMs) and coarse aggregates with Recycled Concrete Aggregate (RCA). However, designing PCCs with simultaneous contents of SCMs and RCA is not easily manageable because current design procedures fail to adjust all of the variables involved. In order to overcome these limitations, this research introduces a novel mathematical model designed to develop operationally efficient PCC mixes that are both environmentally sustainable and cost-effective. The proposed model, denominated OptiCon, employs the Life-Cycle Assessment and Life-Cycle Costs Analysis methodologies to evaluate the incorporation of three different SCMs (i.e., fly ash, silica fume, and steel slag) and RCA into PCC mixes. OptiCon is also integrated within a graphical user interface in order to make its implementation straightforward for potential users. Thus, OptiCon is operationalized through an algorithm, offering a replicable approach that can be adapted to various contexts, providing both a theoretical framework and a practical tool for state agencies, engineers, suppliers, and other stakeholders to adopt more environmentally friendly practices in concrete production. Furthermore, a case study from northern Colombia analyzed thirty mix design scenarios with varying supplier conditions (foreign, local, or mixed), calculating costs and CO₂ emissions for a fixed concrete volume of 1 m³. The findings demonstrated that utilizing OptiCon can achieve substantial reductions in both CO₂ emissions and production costs, underscoring the model’s efficiency and practical impact. Full article

Deep reinforcement learning (DRL) trains agents to make decisions by learning from rewards and penalties, using trial and error. It combines reinforcement learning (RL) with deep neural networks (DNNs), enabling agents to process large datasets and learn from complex environments. DRL has achieved notable success in gaming, robotics, decision-making, etc. However, real-world applications, such as self-driving cars, face challenges due to complex state and action spaces, requiring precise control. Researchers continue to develop new algorithms to improve performance in dynamic settings. A key algorithm, Deep Q-Network (DQN), uses neural networks to approximate the Q-value function but suffers from overestimation bias, leading to suboptimal outcomes. To address this, Double Deep Q-Network (DDQN) was introduced, which decouples action selection from evaluation, thereby reducing bias and promoting more stable learning. This study evaluates the effectiveness of DQN and DDQN in autonomous driving using the CARLA simulator. The key findings emphasize DDQN’s advantages in significantly reducing overestimation bias and enhancing policy performance, making it a more robust and reliable approach for complex real-world applications like self-driving cars. The results underscore DDQN’s potential to improve decision-making accuracy and stability in dynamic environments. Full article

Reducing losses during pig transport is essential for breeders and transporters, particularly in semi-arid regions, where high temperatures exacerbate transport-related stress and risk of losses. This study aimed to evaluate the effect of transport duration (short vs. long trips) on animal welfare and production losses during the commercial transport of weaner pigs in a semi-arid region. A total of 20 commercial journeys were monitored, with transport times of 30 min (15 km) and 150 min (170 km). Upon arrival, physiological and behavioral stress indicators were assessed in 960 weaner pigs (26.4 ± 2.8 kg body weight, 48 per journey). Production losses were determined by calculating the percentage of injured pigs (NAI), fatigued pigs (NANI), and those that were dead on arrival (DOA), whereas the total loss was expressed by the sum of NAI + NANI + DOA. Weaner pigs transported for 30 min exhibited significantly higher (p < 0.05) rectal temperature, respiratory rate, and stress biomarkers (cortisol and creatine kinase levels). Additionally, this group showed a higher percentage (p < 0.05) of “sitting” pigs and a lower percentage of “lying” pigs in transit, as well as a higher frequency of agonistic behavior after transport compared to those transported for 150 min. Furthermore, higher production losses were recorded in the 30 min transport group, primarily due to the increased percentage of fatigued pigs and DOA pigs. Therefore, shorter transport operations in the Brazilian semi-arid region increased the risk to animal welfare and productive losses, likely due to handling-induced stress during loading. Full article

Honey, a natural product with a rich history of medicinal use, has gained increasing recognition for its potent antimicrobial properties, particularly against antibiotic-resistant pathogens. This review focuses on the antimicrobial mechanisms of honey, including its efficacy against resistant bacteria, such as Methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa. The antimicrobial action of honey is multifactorial, involving hydrogen peroxide production, phenolic compounds, high sugar concentrations, and the presence of bee defensin-1. The composition of honey varies based on its floral source, which can influence its antimicrobial strength. Certain types, such as Manuka honey, are particularly effective in clinical applications due to their higher levels of bioactive compounds. Honey has also been shown to disrupt bacterial biofilms, a major factor in antibiotic resistance, enhancing its therapeutic potential in treating chronic wounds and infections, especially in patients with compromised immune systems. Moreover, honey’s ability to improve wound healing, reduce inflammation, and promote tissue regeneration highlights its broad therapeutic profile. As antibiotic resistance continues to challenge modern healthcare, honey offers a promising complementary treatment in antimicrobial therapy. Research into its specific bioactive components and potential synergistic effects with other natural agents, like ginger and propolis, could expand its applications. Standardizing honey products for medical use and establishing clinical guidelines are essential for optimizing its therapeutic benefits. As scientific understanding of honey’s antimicrobial mechanisms deepens, its integration into healthcare systems as an adjunct therapy is expected to increase, offering a natural and effective alternative in the fight against infectious diseases. Full article