Browse Articles

Rectal glands and tergal glands as sources of volatile pheromones in cucumber fruit fly, Zeugodacus Cucumis

A preliminary exploration of establishing a mice model of hypoxic training

Puerarin pretreatment provides protection against myocardial ischemia/reperfusion injury via inhibiting excessive autophagy and apoptosis by modulation of HES1

Electrical impedance-based tissue classification for bladder tumor differentiation

AbstractIncluding sensor information in medical interventions aims to support surgeons to decide on subsequent action steps by characterizing tissue intraoperatively. With bladder cancer, an important issue is tumor recurrence because of failure to remove the entire tumor. Impedance measurements can help to classify bladder tissue and give the surgeons an indication on how much tissue to remove. Over the years of research, it became obvious that electrical impedance spectroscopy is a very promising tool for tissue differentiation, but also a very sensitive one. While differentiation in preliminary studies shows great potential, challenges arise when transferring this concept to real, intraoperative conditions, mainly due to the influence of preoperative radiotherapy, possibly different tumor types, and mechanical tissue deformations due to peristalsis or unsteady contact force of the sensor. This work proposes a patient-based classification approach that evaluates the distance of an unknown measurement to a healthy reference of the same patient, essentially a relative classification of the difference in impedance that is robust against inter-individual differences and systematic errors. A diversified dataset covering multiple disturbance scenarios is recorded. Two alternatives to define features from the impedance data are investigated, namely using measurement points and model-based parameters. Based on the distance of the feature vector of a unknown measurement to a healthy reference, a Gaussian process classifier is trained. The approach achieves a high classification accuracy of up to 100% on noise-free impedance data recorded under controlled conditions. Even when the differentiation is more ambiguous due to external disturbances, the presented approach still achieves a classification accuracy of 80%. These results are a starting point to tackle intraoperative bladder tissue characterization and decrease the recurrence rate.

Using transformer-based models and social media posts for heat stroke detection

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study

An integrated DEA-fuzzy AHP method for prioritization of renewable energy sources in India

AbstractAs India’s population grows and urbanization accelerates, energy demand is increasing sharply while conventional sources fall behind. To tackle energy shortages and climate change, India must prioritize renewable energy sources (RES), which offer sustainable solutions. The country is rich in RES, which can enhance fuel mix for electricity generation. This study analyzes various RES in India-solar, geothermal, hydro, biomass, wave, onshore, and offshore wind energy -using an integrated data envelopment analysis (DEA) and fuzzy analytic hierarchy process (Fuzzy AHP) methodology. Four main parameters-technical, economic, environmental, and socio-political -are identified and supported by 19 criteria, with environmental parameters including both desirable and undesirable criteria. In first phase, undesirable criteria are transformed into desirable criteria using Modified Ratio model. DEA is then applied to calculate initial efficiency score of RES under each parameter category. Fuzzy AHP determines weights for each parameter. The weights and initial efficiency scores are then combined to calculate overall efficiency score and ranking of RES. Sensitivity analysis shows that results obtained from proposed methodology are significant, and robust. Offshore wind ranks highest in efficiency, followed by hydro and onshore wind, while geothermal scores lowest. This methodology could benefit developing nations and guide policymakers in adopting RES.

The tight balance state and mechanism of disaster-resilient resources in karst small towns: a Chinese karst landform case study

Surgical frequency analysis of patients clustered according to postoperative pain trajectory: a retrospective study

Cognitive load detection through EEG lead wise feature optimization and ensemble classification

Differential regulatory effects of the N-terminal region in SYK-fusion kinases reveal unique activation-inducible nuclear translocation of ITK-SYK

Novel numerical and artificial neural computing with experimental validation towards unsteady micropolar nanofluid flow across a Riga plate

Enhancing electric vehicle battery lifespan: integrating active balancing and machine learning for precise RUL estimation

AbstractElectric vehicles (EVs) rely heavily on lithium-ion battery packs as essential energy storage components. However, inconsistencies in cell characteristics and operating conditions can lead to imbalanced state of charge (SOC) levels, resulting in reduced capacity and accelerated degradation. This study presents an active cell balancing method optimized for both charging and discharging scenarios, aiming to equalize SOC across cells and improve overall pack performance. The proposed system includes two balancing strategies: a charging balance that redistributes excess charge from high-SOC cells to maximize capacity, and a discharging balance that addresses low-SOC cells to extend discharge duration. Experimental results confirm that this method effectively reduces SOC disparities, enhancing both charging and discharging capacities. Additionally, to accurately predict battery lifespan and remaining useful life (RUL), seven machine learning models are evaluated using R-squared (R2) and Mean Absolute Error (MAE) metrics. Among these, k-nearest Neighbors and Random Forest models deliver the highest accuracy, achieving R2 values of 0.996 and above with low MAE, demonstrating strong predictive capability. The integration of active balancing and RUL prediction enables a feedback loop where balanced SOC levels promote battery health, and RUL predictions inform optimal balancing strategies. This comprehensive approach advances EV battery management, enhancing lifespan and reliability through proactive balancing and predictive insights.

Adaptable electro-optic detection of THz radiation using a laser-written bull’s-eye antenna

Cs2AgBiBr6 and related Halide double perovskite porous single crystals

Expression profiles of miR-101-3p and miR-431-5p as potential diagnostic biomarkers for rheumatoid arthritis

AbstractRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation of the synovial joints, leading to cartilage and bone destruction. This study aimed to evaluate the diagnostic utility of specific microRNAs (miRNAs) as potential biomarkers for RA. The study was conducted on 60 patients with RA disease along with 20 control participants. Comprehensive analysis of patient data, encompassing serological, hematological, and biochemical markers, revealed significantly elevated levels of miR-99b-5p, miR-101-3p, and miR-431-5p in RA patients compared to healthy controls. Among these, miR-101-3p demonstrated the highest diagnostic accuracy, with an area under the curve (AUC) of 0.873. These findings contribute to a deeper understanding of RA pathogenesis and suggest that miR-101-3p may serve as a valuable biomarker for early disease detection and potentially improved patient management. Further research is warranted to elucidate the precise mechanisms underlying miRNA involvement in RA and to explore their potential as therapeutic targets.

Microstructure and properties of joint of Al/Cu welded by resistance element welding with an auxiliary gasket of Ni

Preliminary study on the explosive performance of coal dust and prospects for engineering applications

AbstractThis study aims to evaluate the efficiency and energy release characteristics of different types of coal in pulse detonation engines (PDE) to advance the development of deep coal fluidization detonation technology, achieving more efficient and cleaner coal utilization. Using a custom PDE setup, experiments were conducted with four coal types at mass flow rates from 30 to 120 g/s. High-frequency pressure sensors assessed pressure dynamics and detonation wave propagation, complemented by numerical simulations for accuracy. Results show that the maximum detonation pressure (Pmax) increases linearly with coal mass flow, with anthracite reaching 1.52 MPa at 120 g/s. A consistent linear relationship between Pmax and maximum temperature (Tmax) was observed. Detonation combustion efficiency (ηDCE) improved with mass flow, with peat rising from 52.3% to 83.7%. These findings provide a foundation for advancing deep coal fluidized detonation technology, contributing to more efficient and cleaner coal utilization.

Advancements in microalgal biomass conversion for rubber composite applications

AbstractCarbon black (CB) as rubber reinforcement has raised environmental concerns regarding this traditional petroleum-based filler, which is less susceptible to biodegradability. Although it has great reinforcing properties, the production technique is no longer sustainable, and its cost increases regularly. For these reasons, it is wise to look for sustainable replacement materials. Microalgal biomass (MB) has demonstrated great potential for use as biodegradable nano fillers in rubber composites. Microalgal has a high biomass productivity compared to traditional crops. They can produce a large amount of biomass per unit of land area, making them highly efficient in terms of resource utilization. In the present research, microalgal biomass was blended with CB at different concentrations for preparing two different kinds of rubber composites: Nitrile rubber Acrylonitrile-butadiene rubber (NBR) and styrene-butadiene rubber (SBR) are two common synthetic rubbers. In this study, the researchers investigated using microalgal biomass as filler in rubber composites. They assessed the filler-matrix interaction by evaluating the processability, mechanical characteristics, Payne effect, and swelling properties of the MB/CB-filled composites and compared them to CB-filled composites. The results show that rubber composites incorporating dual fillers (microalgal biomass and carbon black) had faster cure times, increased torque, and improved mechanical properties. The results prove biomass helps to minimize bulk quantities of CB and may be used as a partial replacement while still improving the mechanical properties. According to the study, microalgal biomass can successfully replace up to 50% of the CB filler. This will reduce petroleum dependence and possibly costs, depending on current petroleum prices.

Machine learning techniques for non-destructive estimation of plum fruit weight

AbstractPlum fruit fresh weight (FW) estimation is crucial for various agricultural practices, including yield prediction, quality control, and market pricing. Traditional methods for estimating fruit weight are often destructive, time-consuming, and labor-intensive. In this study, we addressed the problem of predicting plum FW using artificial intelligence (AI) methods based on fruit dimensions. We aimed to evaluate various machine learning (ML) techniques for this purpose. Images of fruit samples were captured using a smartphone camera, processed to extract binary images, and used to calculate dimensions. We tested several ML methods, including Support Vector Regression (SVR), Multivariate Linear Regression (MLR), Multi-Layer Perceptron (MLP), and Decision Tree (DT). The SVR model with a Pearson-VII kernel (PUK) function and penalty value (c) of 0.1 was the most accurate, achieving an R2 of 0.9369 and root mean squared error (RMSE) of 0.4850 (gr) during training, and 0.9267 and 0.4863 (gr) during testing. This method is important for researchers and practitioners seeking efficient, quick, and non-destructive ways to estimate fruit weight. Future research can build on these findings by applying the model to other fruit types and conditions.