Search Results (83,525)

This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%. Full article

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations. Full article

In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry barrier. To address this, in this study, we propose and implement a visualization tool that supports multiple model selections and result visualization and eliminates the need for complex coding and mathematical derivations, helping users to efficiently conduct RUL prediction with lower technical requirements. This study introduces and summarizes various novel neural network models for DL-based RUL prediction. The models are validated using the NASA and HNEI datasets, and among the validated models, the LSTM model best met the requirements for remaining useful life (RUL) prediction. In order to achieve the low-code usage of deep learning for RUL prediction, the following tasks were performed: (1) multiple models were developed using the Python (3.9.18) language and were implemented on the PyTorch (1.12.1) framework, providing users with the freedom to choose their desired model; (2) a user-friendly and low-code RUL prediction interface was built using Streamlit, enabling users to easily make predictions; (3) the visualization of prediction results was implemented using Matplotlib (3.8.2), allowing users to better understand and analyze the results. In addition, the tool offers functionalities such as automatic hyperparameter tuning to optimize the performance of the prediction model and reduce the complexity of operations. Full article

The development of biometric techniques in domestic animals has greatly advanced scientific practices in wildlife research. The association between seminal characteristics and body and testicular biometry enables the selection of suitable breeders, though appropriate measurement techniques are required. The present study assessed differences among conventional methods and formulas for estimating testicular parameters. Testicular length, width, and thickness were measured using three methods in 13 adult male domestic cats. Testicular area, volume, and weight were estimated, from which the gonadosomatic index (GSI) was calculated. Sperm were collected using an alpha-2 adrenergic agonist and urethral catheterization, and characterized in terms of volume, vigor, total motility, progressive motility, concentration, plasma membrane integrity, and morphology. The three methods were consistent in terms of testicular area, volume, weight, and GSI. Moderate positive correlations were observed for testicular weight (r = 0.61, p < 0.05) and GSI (r = 0.58, p < 0.05). Testicular parameters showed strong positive correlations among each other (r > 0.80, p < 0.05). We observed a moderate positive correlation between head length and progressive motility (r = 0.65, p < 0.05). In conclusion, all testicular measurement and estimation techniques showed comparable performance. Therefore, testicular biometry is useful for selecting breeding males in feline conservation programs, wherein larger body biometrics are related to improved seminal and reproductive parameters. Full article

This research analyzes the innovative development of carnauba wax coatings enriched with essential oils (EOs: lemon, orange, grapefruit, clove, oregano, and cinnamon) or fruit by-products (FBPs: avocado, tomato, carrot, orange, lemon, and grapefruit) to improve postharvest preservation of organic oranges and lemons. Six EOs and six FBPs were evaluated for total phenolic content (TPC) and in vitro antifungal activity against Penicillium digitatum. Based on results, grapefruit, oregano, and clove EOs were selected for lemons, while avocado, orange, and grapefruit FBPs were selected for oranges. An in vivo test at 20 °C for 15 days with carnauba wax coatings assessed antifungal performance. Clove EO and avocado FBP showed strong in vitro inhibition and consistent hyphal suppression (~100 and ~82%, respectively). In vivo, coatings with grapefruit EO and avocado FBP significantly reduced fungal decay and sporulation (~75%) in lemons and oranges, respectively. Coated fruits also retained weight losses by ~25% compared to uncoated ones. These findings suggest that phenolic-rich natural extracts, especially from agro-industrial residues like avocado peels, offer a promising and sustainable strategy for postharvest citrus disease control. Further studies should test coating effectiveness in large-scale trials under refrigeration combined with other preservation strategies. Full article

Heart rate (HR) is strongly affected by the autonomic nervous system (ANS), while its spontaneous fluctuations, called heart rate variability (HRV), report about the dynamics of the complex, vegetative regulation of the heart rhythm. Hence, HRV is widely considered an important marker of the ANS effects on the cardiac system, and as such, a crucial diagnostic tool in cardiology. In order to obtain nontrivial results from HRV analysis, it would be desirable to establish exact, universal interrelations between the typical HRV parameters and HR itself. That, however, has not yet been fully accomplished. Hence, our aim was to perform a comparative statistical analysis of ECG recordings from a public database, with a focus on the HR dependence of typical HRV parameters. We revealed their fundamental connections, which were substantiated by basic mathematical considerations, and were experimentally demonstrated via the analysis of 24 h of ECG recordings of more than 200 healthy individuals. The large database allowed us to perform unique age-cohort analyses. We confirmed the HR dependence of typical time-domain parameters, such as RMSSD and SDNN, frequency-domain parameters such as the VLF, LF, and HF components, and nonlinear indices such as sample entropy and DFA exponents. In addition to shedding light on their relationship, we are the first, to our knowledge, to identify a new, diffuse structure in the VHF regime as an important indicator of SNS activity. In addition, the demonstrated age dependence of the HRV parameters gives important new insight into the long-term changes in the ANS regulation of the cardiac system. As a possible molecular physiological mechanism underlying our new findings, we suggest that they are associated with Piezo2 channel function and its age-related degradation. We expect our results to be utilized in HRV analysis related to both medical research and practice. Full article

Background: Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder characterized by excess body weight, hyperandrogenism, hyperglycemia, and insulin resistance often resulting in hirsutism and infertility. Dietary strategies have been shown to ameliorate metabolic disturbances, hormonal imbalances, and inflammation associated with PCOS. Recent evidence indicates that intermittent fasting (IF) could effectively enhance health outcomes and regulate circadian rhythm; however, its impact on PCOS remain unclear. Objective: Therefore, this systematic review and meta-analysis aims to examine the effect of IF on women diagnosed with PCOS. Methods: Comprehensive research was conducted across three major databases including PubMed, Scopus, and Web of Science without date restrictions. Meta-analysis was performed using Cochrane Review Manager Version 5.4 software. Results: Five studies fulfilled the inclusion criteria. IF significantly reduced body weight (MD = −4.25 kg, 95% CI: −7.71, −0.79; p = 0.02), BMI (MD = −2.05 kg/m², 95% CI: −3.26, −0.85; p = 0.0008), fasting blood glucose (FBG; MD = −2.86 mg/dL, 95% CI: −4.83, −0.89; p = 0.004), fasting blood insulin (FBI; MD = −3.17 μU/mL, 95% CI: −5.18, −1.16; p = 0.002), insulin resistance (HOMA-IR; MD = −0.94, 95% CI: −1.39, −0.50; p < 0.0001), triglycerides (TG; MD = −40.71 mg/dL, 95% CI: −61.53, −19.90; p = 0.0001), dehydroepiandrosterone sulfate (DHEA-S; MD = −33.21 μg/dL, 95% CI: −57.29, −9.13; p = 0.007), free androgen index (FAI; MD = −1.61%, 95% CI: −2.76, −0.45; p = 0.006), and C-reactive protein (CRP; MD = −2.00 mg/L, 95% CI: −3.15, −0.85; p = 0.006), while increasing sex hormone-binding globulin (SHBG; SMD = 0.50, 95% CI: 0.22, 0.77; p = 0.004). No significant changes were observed in waist-to-hip ratio (WHR), total cholesterol (TC), LDL, HDL, total testosterone (TT), or anti-Mullerian hormone (AMH). Conclusions: IF represents a promising strategy for improving weight and metabolic, hormonal, and inflammatory profiles in women with PCOS. However, the existing evidence remains preliminary, necessitating further robust studies to substantiate these findings. Full article

Huntington’s disease (HD) is a severe and progressive neurodegenerative disease for which therapeutic options have so far been confined to symptomatic treatment. Currently, the diagnosis relies on the signs and symptoms shown by patients; however, by that stage, the psychomotor issues have progressed to a point where reversal of the condition is unattainable. Although numerous clinical trials have been actively investigating therapeutic agents aimed at preventing the onset of disease or slowing down the disease progression, there has been a constant need for reliable biomarkers to assess neurodegeneration, monitor disease progression, and assess the efficacy of treatments accurately. Therefore, to discover the key biomarkers associated with the progression of HD, we employed bioinformatics and machine learning (ML) to create a robust pipeline that integrated differentially expressed gene (DEG) analysis with ML to select potential biomarkers. We performed a meta-analysis to identify DEGs using three Gene Expression Omnibus (GEO) microarray datasets from different platforms related to HD-affected brain tissue, applying both relaxed and strict criteria to identify differentially expressed genes. Subsequently, focusing only on genes identified through the inclusive threshold, we employed 19 diverse ML techniques to explore the common genes that contributed to the top three selected ML algorithms and the shared genes that had an impact on the ML algorithms and were observed in the meta-analysis using the stringent condition were selected. Additionally, a receiver operating characteristic (ROC) analysis was conducted on external datasets to validate the discriminatory power of the identified genes. Based on the results of an inverse variance weighted meta-analysis of the AUCs across both human and mouse cohorts, GABRD and PHACTR1 were identified as the most robust candidates and were selected as key biomarkers for HD. Our comprehensive methodology, which integrates DEG meta-analysis with ML techniques, enabled a systematic prioritization of these biomarkers, providing valuable insights into their biological significance and potential for further validation in clinical research. Full article

This review explores the potential of magistral formulas (MFs) as a viable option to meet the needs of neonates, given the lack of adequate therapies for this vulnerable group. The scientific literature on medicines available for neonates is limited. The physiological differences between neonates and adults make it difficult to formulate these medicines. In addition, there are a variety of difficulties in conducting research on neonates: few clinical trials are performed, and there is frequent use of unauthorized medicines. Pharmacokinetics in neonates was investigated in comparison to adults, and different aspects of the absorption, distribution, metabolism, and excretion were observed. One of the main problems is the different pharmacokinetics between the two populations. It is necessary to promote and allow research related to pediatric drug design, approve a specific authorization for use in age-appropriate dosage forms, and improve the quality and availability of information on drugs. This study focused on the MFs typically used for pediatrics, specifically for neonates, analyzing the pharmaceutical forms currently available and the presence of indications and dosage recommendations of the European Medicines Agency. Medications were classified according to therapeutic group, as antihypertensives, corticosteroids, and antiepileptics. The use of off-label medicines remains high in neonatal intensive care units and in primary healthcare, besides in the preparation of MFs by pharmacists. The shortage of medicines specifically designed and approved for neonates is a serious problem for society. Neonates continue to be treated, on numerous occasions, with off-label medicines. Studies and research should be expanded in this vulnerable population group. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article

Eccentric muscle actions are integral to human movement, rehabilitation, and performance training due to their characteristic high force output (overload) and low energy cost and perceived exertion. Despite the growing use of eccentric devices, a gap in the research exists exploring multi-muscle activation profiles during multi-joint eccentric-only, isokinetic exercise. This study aimed to quantify and compare surface electromyographic (EMG) activity of four leg muscles—vastus lateralis (VL), tibialis anterior (TA), biceps femoris (BF), and medial gastrocnemius (GM)—during a standardized (isokinetic) submaximal eccentric multi-joint exercise using the Eccentron dynamometer. Eighteen healthy adults performed eccentric exercise at 40% of their maximal eccentric strength. Surface EMG data were analyzed using root mean square (RMS) and integrated EMG (iEMG) variables. Repeated-measures ANOVAs and effect sizes (ES) were used to evaluate within-subject differences across muscles. Results showed significantly greater activation in the VL compared to all other muscles (p < 0.05; and ES of 1.28–3.17 versus all other muscles), with the TA also demonstrating higher activation than the BF (p < 0.05). The BF exhibited the lowest activation, suggesting limited hamstring engagement. These findings highlight the effectiveness of the multi-joint isokinetic eccentric leg press movement (via an Eccentron machine) in targeting the quadriceps and dorsiflexors, while indicating the possible need for supplementary hamstring and plantar flexor exercises when aiming for a comprehensive lower body training routine. This study provides important insights for optimizing eccentric training protocols and rehabilitation strategies. Full article

Background/Objectives: Fibromyalgia syndrome (FMS) is a chronic pain condition in which executive function (EF) alterations have been reported, though strikingly, relationships between simple executive functions (EFs) (updating, inhibition, and mental flexibility) and high-order ones, such as planning and problem-solving, have not been addressed yet in this population. This research aimed to firstly explore how low-level EFs play a role in planning and problem-solving performances. Methods: Thirty FMS patients and thirty healthy participants completed a series of neuropsychological tests evaluating low- and high-order EFs. Clinical and emotional symptoms were assessed with self-report questionnaires, while pain and fatigue levels were measured with numerical scales. Importantly, specific drug restrictions were accounted for. Results: Patients scored lower in most neurocognitive tests, with statistical significance noted only for visuospatial working memory (WM) and two planning and problem-solving tests. Pain, fatigue, and sleep disturbances showed important effects on most of the cognitive outcomes. Multiple regression analyses reflected that planning and problem-solving were successfully and partially predicted by updating, inhibition, and mental flexibility (though differences emerged between tasks). Conclusions: Our study confirms the presence of cognitive impairments in FMS, especially in high-order EFs, supporting patients’ complaints. Clinical symptoms play a role in FMS dyscognition but do not explain it completely. For the first time, as far as the authors know, simple EF influences on planning and problem-solving tests have been described for FMS patients. These results might help in unraveling the dysexecutive profile in FMS to design more adjusted treatment options. Full article

Biomass torrefaction must be self-sustaining and continuous to be commercially viable, eliminating dependence on additional fuels while achieving industrial-scale production. This study presents a predictive model of a full-scale continuous biomass torrefaction process that explicitly incorporates the radiation absorption properties of torrefaction gas, with a focus on water vapor. Previous research, primarily based on lab-scale batch processes, has not adequately addressed scale-up challenges or the dynamic evolution of torrefaction gas. Industrial insights from Perpetual Next confirm that water vapor significantly impacts reactor performance by absorbing heat and reducing radiative flux to the biomass. Simulations show that neglecting water vapor absorption in reactor design can lead to throughput deviations of 10–20%, affecting process stability and efficiency. Industrial-scale validation demonstrates that the model accurately predicts this effect, ensuring realistic energy demand and throughput expectations. By explicitly incorporating water vapor absorption into the radiation balance, the model provides a validated framework for optimizing reactor design and process scale-up. It demonstrates that failing to consider this effect can lead to operational instability and deviations from the intended torrefaction severity, ultimately affecting industrial-scale performance and self-sustaining operation. Full article

This study focuses on the optimization of valve assemblies in downhole rod pumping units (DRPUs), which remain the predominant artificial lift technology in oil production worldwide. The research addresses the critical issue of premature failures in DRPUs caused by leakage in valve pairs, i.e., a problem that accounts for approximately 15% of all failures, as identified in a statistical analysis of the 2022 operational data from the Uzen oilfield in Kazakhstan. The leakage is primarily attributed to the accumulation of mechanical impurities and paraffin deposits between the valve ball and seat, leading to concentrated surface wear and compromised sealing. To mitigate this issue, a novel valve assembly design was developed featuring a flow turbulizer positioned beneath the valve seat. The turbulizer generates controlled vortex motion in the fluid flow, which increases the rotational frequency of the valve ball during operation. This motion promotes more uniform wear across the contact surfaces and reduces the risk of localized degradation. The turbulizers were manufactured using additive FDM technology, and several design variants were tested in a full-scale laboratory setup simulating downhole conditions. Experimental results revealed that the most effective configuration was a spiral plate turbulizer with a 7.5 mm width, installed without axis deviation from the vertical, which achieved the highest ball rotation frequency and enhanced lapping effect between the ball and the seat. Subsequent field trials using valves with duralumin-based turbulizers demonstrated increased operational lifespans compared to standard valves, confirming the viability of the proposed solution. However, cases of abrasive wear were observed under conditions of high mechanical impurity concentration, indicating the need for more durable materials. To address this, the study recommends transitioning to 316 L stainless steel for turbulizer fabrication due to its superior tensile strength, corrosion resistance, and wear resistance. Implementing this design improvement can significantly reduce maintenance intervals, improve pump reliability, and lower operating costs in mature oilfields with high water cut and solid content. The findings of this research contribute to the broader efforts in petroleum engineering to enhance the longevity and performance of artificial lift systems through targeted mechanical design improvements and material innovation. Full article

Objective: Uncovering the renoprotective and anti-inflammatory effects of atorvastatin treatment in diabetic-and-obese rats by employing traditional renal function indicators (urea and creatinine) and four prototypical cytokines (IL-1β, il-6, IL-17α, TNFα). Method: Twenty-eight male Wistar rats, aged 6 months, 350–400 g, were randomized into four groups. The first group, G-I, the denominated control, were fed standard chow over the whole course of the experiments. The rodents in G-II were exposed to a High-Fat Diet. The last two groups were exposed to Streptozotocin peritoneal injection (35 mg/kg of body weight). A short biochemical assessment was performed before diabetes model induction to ensure appropriate glucose metabolism before experiments. Following model induction, only rodents in group G-IV were gradually introduced to the same High-Fat Diet as received by G-II. Model confirmation 10 days after injections marked the start of statin treatment in group G-IV, by daily gavage of atorvastatin 20 mg/kg of body weight/day for 21 days. At the end of the experiments, the biochemical profile of interest comprised typical renal retention byproducts (urea and creatinine) and the inflammatory profile described using plasma levels of TNFα, IL-17α, IL-6, and IL-1β. Results: Treatment with Atorvastatin was associated with a statistically significant improvement in renal function in G-IV compared to untreated diabetic rodents in G-III. Changes in inflammatory activity showed partial association with statin therapy, TNFα and IL-17α mirroring the trend in urea and creatinine values. Conclusions: Our results indicate that atorvastatin treatment yields a myriad of pleiotropic activities, among which renal protection was clearly demonstrated in this model of diabetic-and-obese rodents. The statin impact on inflammation regulation may not be as clear-cut, but the potential synergy of renal function preservation and partial tapering of inflammatory activity requires further research in severely metabolically challenged models. Full article