Search Results (23,077)

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets. Full article

The present and appropriate concern regarding climate changes resulting from the combustion of fossil fuels in light passenger vehicles raises the necessity to develop appropriate instruments to investigate probable and feasible solutions for fleet decarbonization. Given the direct relationship between fossil fuel consumption and greenhouse gas emissions have historically been determined through experimental tests in the laboratory following standard cycles, to enhance the vehicle’s energy efficiency these should be complemented by numerical simulation tools, as they demonstrate fast response and adequate correlation to experimentation. In this aspect, one of the biggest challenges of numerical simulation is quantifying the impact of the various phenomena that affect the vehicle operation during the cold phase, in which the internal combustion engine loses efficiency. This study proposes a thermodynamic model for simulating the fuel consumption of light vehicles during the cold phase of operation. Measured lubricant temperature, ignition spark retardation, exhaust valve timing, and coolant temperature in the vehicles along the drive cycle are the required input data for the model. This thermodynamic procedure makes it possible to quantify the impact on fuel consumption while driving the vehicle in cold operation. The cold phase, with a 505 s duration, is responsible for approximately a 21% increase in fuel consumption in a standard urban drive cycle. It is considered that the shorter the route, the more pronounced and significant the cold phenomena are, and the impact of vehicles frequently driven on short urban routes can be accurately estimated for future analyses. Full article

This study looks at wall partition panels with hollow core wood elements and gypsum board as protection in fire conditions. In addition to our previous research, this study on wall partitions considers the effect of steel screws in the assembly of the elements, as well as the filling of the cavity with insulating material. The goal of this work is to calculate the fire resistance time and compare the results using different numerical models. The discussion of the results analyzes the effect of steel screws and the introduction of insulating material inside the cavities. The steel screws are verified with and without threads. The numerical models are based on the finite element method, using thermal and transient analysis with nonlinear materials. The thermal insulation criterion for measuring fire resistance is referenced by the EN 1363-1:2020 standard. The steel screws allow more heat to be concentrated and, therefore, distribute it throughout the wooden wall partition members. Based on the results obtained, the use of steel screws reduces fire resistance by 71.75 min, regardless of whether the wall partition is filled with or without insulating material. Full article

Serum uremic toxins markedly increase in cats with chronic kidney disease (CKD) and have deleterious consequences. Renaltec is an oral adsorbent that binds uremic toxin precursors in the gut. In this prospective cohort study utilizing 13 purpose-bred cats with remnant kidney model-induced CKD (12 IRIS Stage 2, 1 IRIS Stage 3) eating a standardized renal diet, we aimed to assess the effect of Renaltec administration on serum indoxyl sulfate (IDS) and p-cresol sulfate (pCS) concentrations. Cats were sequentially treated with standard of care for 56 days, 500 mg Renaltec orally once daily for 56 days, and then three months later, 500 mg Renaltec orally twice daily for 56 days. Serum IDS and pCS concentrations were measured 28 and 56 days after the administration of Renaltec. Blood pressure and kidney function were measured before and 56 days after the administration of Renaltec. Significant decreases in serum IDS and pCS concentrations were observed for both once- and twice-daily dosing, particularly during the first 28 days of administration. More cats with BID dosing had clinically significant reductions in serum IDS and pCS concentrations than with SID dosing. Renaltec can reduce the serum concentrations of deleterious gut-derived uremic toxins in cats with CKD. Full article

(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel⁻¹) and 50 m (35 mm pixel⁻¹) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R² values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps. Full article

Background: Coagulation abnormalities are common in sepsis patients and are associated with increased mortality. This study aimed to assess the hemostatic profile of sepsis patients using rotational thromboelastometry (ROTEM) and to find the ROTEM parameters best predicting short-term mortality. Methods: We conducted a prospective analysis of consecutive sepsis patients hospitalized in the intensive care unit. The inclusion criteria were diagnosis of sepsis or septic shock and pro-calcitonin concentration >0.5 ng mL⁻¹. Clinical, standard laboratory, and ROTEM analyses were performed. Results: The study group comprised 38 (49%) males and 40 (51%) females. Median Sequential Organ Failure Assessment (SOFA) score was 8 (interquartile range IQR 5–11) points. The most common primary sites of infection were pneumonia (n = 27/35%), intra-abdominal (n = 27/35%), urinary tract infection (n=20/26%), and others (n = 4/6%). The following parameters evaluating fibrinogen function were outside the reference range: clotting time (CT), clot amplitude (A) at 10 and 20 min, and maximal clot firmness (MCF). Out of 78 patients, 28 (36%) died in the intensive care unit. Significant differences between survivors and non-survivors of sepsis were present for the ROTEM parameters assessing fibrinolytic activity. Conclusions: ROTEM in the early phase of sepsis reveals increased coagulation mediated through the function of fibrinogen. Non-survivors showed slightly lower fibrinolytic activity than survivors; however, it was still within test reference values. The highest predicting value was obtained by a model incorporating, among others, extrinsic coagulation pathway fibrinolytic parameters. Full article

From time to time, a series of unpredictable and conflicting contingencies can lead to angular instability of the power system and even blackouts if not adequately handled by an out-of-step (OOS) protection system. The key contribution of this research work, to the theory of out-of-step protection, is the identification and isolation after a given disruption of many unstable swings. This paper presents a proposed method to avoid false operation for distance function by out-of-step blocking to improve the system stability by using optimally placed PMUs for the fast detection of system analogue quantities. The studies were performed on a modified Eskom transmission network in the Western Cape with 765 kV and 400 kV voltage levels. The aim is to investigate the IEC 61850-90-5 standard for predictive dynamic stability maintaining systems using PMUs for out-of-step conditions of synchronous generators. The power system modelling and simulation are performed in the RSCAD-FX for the proposed multi-area power system network. An experimental lab-scale implementation is built to test the proposed out-of-step algorithm in a real-time digital simulator. Software-based PMU is incorporated to test and validate the IEC 61850-90-5 standard sampled values. Simulation and experimental results are presented. Full article

In this work, the authors provide a case study of time series regression techniques for water quality forecasting. With the constant striving to achieve the Sustainable Development Goals (SDG), the need for sensitive and reliable water management tools has become critical. Continuous online surface water quality monitoring systems that record time series data about surface water parameters are essential for the supervision of water conditions and proper water management practices. The time series data obtained from these systems can be used to develop mathematical models for the prediction of the temporal evolution of water quality parameters. Using these mathematical models, predictions can be made about future trends in water quality to pinpoint irregular behaviours in measured data and identify the presence of anomalous events. We compared the performance of regression models with different structures for the forecasting of water parameters by utilizing a data set collected from the Kis-Balaton Water Protection System (KBWPS) wetland region of Hungary over an observation period of eleven months as a case study. In our study, autoregressive integrated moving average (ARIMA) regression models with different structures have been compared based on forecasting performance. Using the resulting models, trends of the oxygen saturation, pH level, electrical conductivity, and redox potential of the water could be accurately forecast (validation data residual standard deviation between 0.09 and 20.8) while in the case of turbidity, only averages of future values could be predicted (validation data residual standard deviation of 56.3). Full article

Children with obstructive sleep apnea (OSA) frequently experience chronic low-grade systemic inflammation, with the inflammasome playing a central role in OSA. This cross-sectional study evaluated the relationship between weight status, autonomic function, and systemic inflammation in a cohort of 55 children with OSA, predominantly boys (78%) with an average age of 7.4 ± 2.2 years and an apnea-hypopnea index of 14.12 ± 17.05 events/hour. Measurements were taken of body mass index (BMI), sleep heart-rate variability, morning circulatory levels of interleukin-1β, interleukin-1 receptor antagonist, and interleukin-6, and tumor necrosis factor-α, anthropometry, and polysomnography. Multiple linear regression modeling showed that an apnea-hypopnea index was significantly associated with BMI, the standard deviation of successive differences between normal-to-normal intervals during N3 sleep, and the proportion of normal-to-normal interval pairs differing by more than 50 ms during rapid-eye-movement sleep. A moderated mediation model revealed that interleukin-1 receptor antagonist levels mediated the association between BMI and interleukin-6 levels, with sympathovagal balance during N3 sleep and minimum blood oxygen saturation further moderating these relationships. This study highlights the complex relationships between BMI, polysomnographic parameters, sleep heart-rate-variability metrics, and inflammatory markers in children with OSA, underlining the importance of weight management in this context. Full article

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data. Full article

The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R² values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models’ robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials. Full article

Studies on dental implant abutments’ geometric design and material selection offer significant innovations and results. These studies aim to improve the abutments’ functionality and aesthetic performance, minimize microcavities’ formation, and ensure implant-supported prostheses’ longevity. For example, CAD-CAM fabricated custom abutments have been found to produce a better marginal fit and fewer microgaps than standard abutments. In an in vitro study, transepithelial abutments offered lower microgap values than titanium-based abutments and provided a better fit at the implant–abutment interface. It is known that studies to improve mechanical and biological performance with Polyether Ether Ketone (PEEK) material have been addressed. New materials such as PEEK and zirconia have offered significant advantages in biocompatibility and aesthetics. Along with those studies, different abutment designs are also important. Abutment geometry is optimized to improve stress distribution and minimize peri-implant bone loss. In implant and abutment connections with different angles, mechanical life performances may vary depending on static and dynamic load. These studies emphasize the importance of material research on different types of connections to improve dental implants’ durability, homogeneous load distribution, and reliability. The abutment parts used in implant treatment are insufficient to distribute the load homogeneously against chewing pressure due to their materials and geometry. Non-uniform load distribution damages the abutment and the prosthetic crown, accelerating the wear process. This study aimed to create different abutment designs to improve dental implants’ biomechanical performance and longevity. This study aimed to increase the mechanical durability of the implant–abutment connection by reducing stress concentrations in response to masticatory compression on the abutment in different directions and forces and to guarantee the long-term success of the implant system by providing a more homogeneous stress distribution. It aimed to apply different forces in the axial direction to these models in a simulation environment and to calculate and compare the deformation and stress load distribution. As a method, three-dimensional models of the parts used in implant treatments and forming the implant system were designed. Different abutment designs were created with these models. Taking the current material values used in implant treatments as a reference, finite element analysis (FEA) was performed by applying different axial loads to each implant system model in the ANSYS software (version 24.1). Comparative analysis graphs were prepared and interpreted for the stress values obtained after the applied load. This study evaluated the mechanical performance of different abutment models (A, B, C, D, and E) under a 100 N load using the Kruskal–Wallis test. The Kruskal–Wallis test showed significant differences between the groups (p < 0.001). The greatest difference was observed between models E and A (q′ = 6.215), with a significant difference also found between models C and A (q′ = 3.219, p < 0.005). Regarding stress values, the highest stress on the abutment was observed in Model B (97.4 MPa), while the lowest stress was observed in Model E (9.6 MPa). The crown exhibited the highest stress in Model B (22.7 MPa) and the lowest in Model E (17.3 MPa). The implant stress was highest in Model C (14.8 MPa) and lowest in Model B (11.3 MPa). The stress values for the cortical bone and cancellous bone were quite similar across the models, showing no significant differences. These findings indicate that the abutment design and material selection significantly impact mechanical performance. Among the implant systems created with five different abutment models, in which the existing abutment geometry was also compared, homogeneous and axial distribution of the load on the abutment was achieved, especially with viscoelastic and surface area increased abutment designs. Clinically, the inadequacy and limited mounting surface or geometry of the abutments used in today’s implant treatment applications have led to different design searches. It was concluded that the designs in this study, which are considered alternatives to existing abutment models, contribute positively to the mechanical life of the abutment material, considering the von Mises stresses and directions. This study brings a new perspective to today’s practices and offers an alternative to treatment practices. Full article

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. Full article

Agricultural machinery services are an important guaranteed way to promote Conservation Agriculture. It is of great significance to study how to encourage farmers to choose agricultural machinery services to promote the standard implementation of Conservation Agriculture technology. In order to promote the implementation of Conservation Agriculture and improve the supply of agricultural machinery services, this paper identifies the stakeholders of normative Conservation Agriculture technology adoption behavior and the relationship between agricultural machinery service organizations, farmers and agriculture-related governments. An evolutionary game model was established to evaluate the decision-making characteristics of tripartite behavior and simulate the evolution trend of stakeholder behavior. The results show that agriculture-related governments, agricultural machinery service organizations and farmers can achieve evolutionarily stable strategies. The punishments and subsidies of agriculture-related governments and the supervision cost of all links of agricultural machinery social service organizations can significantly affect the behavior strategies of the three parties. The government set up reasonable subsidy and punishment mechanisms, and the agricultural machinery service organization controls the supervision cost of all links to ensure the stability of the three-party behavior strategy. This study provides theoretical guidance for scientific decision making and active cooperative development of the government, farmers and agricultural machinery service organizations and lays a foundation for countermeasures and suggestions to further promote farmers’ implementation of Conservation Agriculture technology. Full article

The urban soundscape contributes significantly to defining human perception and experience. Several standard assessment methods for data collection refer to in situ evaluations to determine how people perceive urban acoustic qualities. These methods, which generally involve soundwalks accompanied by questionnaires, are valuable but need to be validated in different cultural contexts. To address this need, international efforts such as the Soundscape Attribute Translation Project (SATP) are underway to ensure the effectiveness of a data collection standard in non-English-speaking regions. As a part of the SATP project, this study explores potential variations in how people experience urban soundscapes in North Africa. A standardized listening experiment was used to compare how Arabic speakers and French speakers rate the perceived affective qualities (PAQ) of urban soundscapes. Using data collected in public urban spaces in London, participants from both language groups rated 27 recorded urban soundscapes using a PAQ questionnaire. Findings from the Kruskal–Wallis H-test suggest that the perception of pleasant, chaotic, and vibrant are significant, while the dimensions of eventful, monotonous, and quiet show no significant distinctions between the two PAQ groups. Furthermore, opposing Pearson correlations were observed for the attributes of pleasantness and eventfulness, along with contradictions for vibrant, monotonous, and calm. The two-dimensional circumplex models visually map the differences in perceptual responses between the two PAQ groups, displaying distinct circular distortions along the monotone-vibrant axis for Arabic PAQs and the chaotic-calm axis for the French PAQs. The findings of this study suggest that further investigations are needed to understand whether the differences in the urban soundscape perception between these two PAQs are due to linguistic factors or other factors. Full article