Explore the vast range of titles available.

This study explores machine learning models to gain insights into dynamics of ionospheric irregularities over geodetic receivers in Mbarara (0.60° S, 30.74° E) and Kigali (1.94° S, 30.09° E). A seven-year rate of total electron content index (ROTI) database and two modeling approaches (multivariate and univariate) were employed. The motivation was to treat the database with time series techniques following a case study with and without the influence of solar wind parameters. The objective is to examine how each approach reconstructs the morphology of ROTI within 3-h time steps over a 24-h cycle. To achieve this, five machine learning models, including extreme gradient boosting (XGBoost), random forest (RF), bidirectional long-short term memory (BLSTM), unidirectional long-short term memory (LSTM) and nonlinear autoregressive with eXogenous input (NARX), were developed and evaluated. Test results demonstrate significant performance variations highlighting comparable ROTI reconstructions in the absence of the solar wind features. The RF model exhibited superior performance with the lowest mean absolute errors of 0.03 and 0.07 TECU/min and accuracies of 93% and 75% under multivariate and univariate modeling, respectively. Based on the RF model’s performance, we employed an extended database over the Ugandan (Mbar) station for further model development and validated its efficiency over a station in Rwanda (Nurk). The results provided promising insights, emphasizing the need for future research dedicated to robust and enhanced nowcasting models that leverage long-term ionospheric data, especially in regions with limited scintillation monitors.

  A new fractional non-Fourier (Cattaneo) photovoltaic (PV) model is presented to enhance the thermal performance of a PV system combined with a heat spreader (HS). The fractional Cattaneo model is shown to be effective in examining transient processes across the entirety of a PV system, in contrast to the conventional Fourier model’s inability to predict system performance. Consequently, a comparison is conducted between the classical Fourier model with the fractional Fourier and fractional Cattaneo models for the PV system. The impact of using an aluminum heat spreader, with rectangular and trapezoidal shapes, has been developed under hot and cold climate conditions. The findings show that adding a trapezoidal heat spreader reduced the cell temperature by 20 K in summer and 12 K in winter. The reduction in the PV temperature led to an enhancement in daily average power by approximately 28% and 37% in hot and cold weather, respectively. Moreover, economic, exergoeconomic, and enviroeconomic assessment is introduced. The outcomes revealed that the electrical production costs of the rectangular and trapezoidal HS systems are 0.272 and 0.214 $/kWh, respectively, while about 0.286 $/kWh for the conventional PV panel. Based on the environmental study, the estimated CO 2 reduction for PV, PV with rectangular HS, and PV with trapezoidal spreader is 0.5504, 0.7704, and 0.8012 tons, respectively. Finally, real experimental data are used to validate the fractional Cattaneo model. The results demonstrate that there is a great fitting with the measured data, with errors in PV power and exergy efficiency of just 0.628% and 3.84%, respectively, whereas their corresponding values for the classical model are 5.72 and 13.13%.

Introduction This research paper investigates the dynamics and control of a non-ideal magnetic levitation (Maglev) system, with its potential for energy harvesting. The system in view consists of a center body suspended by magnetic forces on the top and bottom with a shaker at the base. Purpose The study aims to explore the behavior of the Maglev system under varying conditions and its potential to enhance energy harvesting performance. Method Approximate solution of the nonlinear Maglev system oscillation is obtained by implementing a perturbation technique referred to as the method of multiple scales. A fourth-order numerical method is applied to obtain the time response and the outcomes are visually presented through Poincare maps, phase plane, bifurcation analysis and parametric variations. Result This research considered capacitance adjustment to induce internal resonance, yielding pronounced periodic oscillations with varying excitation voltage. Detailed analyses demonstrate periodic motion for the electric shaker's charge and for the middle block displacement, affirming analytical predictions. Conclusion The study emphasizes internal resonance potential for enhancing average power output and highlights a direct proportional relationship between the middle block and shaker’s velocity at low frequencies. Under internal resonance, average harvested power increases by approximately compared to no internal resonance case, showcasing its effectiveness in enhancing energy harvesting performance.

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

In a variety of applications, including signal processing, clock referencing, sensing, and others, microelectromechanical systems (MEMS) have been shown to be effective and broadly used. This study explores the dynamical response of a nonlinear MEMS resonator when subjected to a sudden mechanical shock under electrical excitation in the presence of quintic nonlinearity. The method of multiple scales (MMS) is utilized to construct the analytical formulas for analyzing the amplitude and phase response during primary resonance conditions. The analytical results are verified and compared with numerical simulations performed using the fourth-order Runge–Kutta method. Additionally, a parametric analysis is performed to examine the effect of different shock values on the resonator’s response and stability utilizing the Jacobian matrix. The agreement between analytical and numerical approaches proves MMS’s effectiveness in analyzing the shock impact on the MEMS resonator. The results provide valuable knowledge about the response and stability of MEMS resonators under mechanical shock, which is crucial for robust design in challenging conditions.

Duchenne muscular dystrophy (DMD) is a fatal neuromuscular disorder that occurs due to inactivating mutations in DMD gene, leading to muscular dystrophy. Prediction of pathological complications of DMD and the identification of female carriers are important research points that aim to reduce disease burden. Herein, we describe a case of a late DMD patient and his immediate female family members, who all carry same DMD mutation and exhibited varied degrees of symptoms. In our study, we sequenced the whole miRNome in leukocytes and plasma of the family members and results were validated using real-time PCR. Our results highlighted the role of miR-409-3p, miR-424-5p, miR-144-3p as microRNAs that show correlation with the extent of severity of muscular weakness and can be used for detection of asymptomatic carriers. Cellular and circulating levels of miR-494-3p had shown significant increase in symptomatic carriers, which may indicate significant roles played by this miRNA in the onset of muscular weakness. Interestingly, circulating levels of miR-206 and miR-410-3p were significantly increased only in the severely symptomatic carrier. In conclusion, our study highlighted several miRNA species, which could be used in predicting the onset of muscle and/or neurological complications in DMD carriers.

In today’s globalized world, 21st century skills, such as communication and collaboration, are essential for success. For EFL (English as a foreign language) learners in Pakistan, acquiring these skills can be challenging due to the unique linguistic and cultural barriers they face. Web 2.0 tools, such as Padlet, can provide a platform for EFL learners to improve their communication and collaboration skills in a collaborative and engaging manner. The present study explores the potential of using Padlet to improve the 21st century skills of EFL learners in Pakistan. A quasi-experiment is conducted to compare the effectiveness of using Padlet versus traditional language-learning methods to improve EFL learners’ communication and collaboration skills. Learners’ perceptions of using Padlet in a collaborative learning context are also investigated. The findings indicated that the use of Padlet has a significant and positive effect on learners’ collaboration and communication skills, and that learners have a positive perception of using this tool in a collaborative learning context. The study provides preliminary and context-specific novel insights for language educators and learners on the potential of using Padlet to enhance the 21st century skills of EFL learners in Pakistan.

Obesity has long been linked to adverse health effects over time. As the prevalence of obesity continues to rise, it is important to anticipate and minimize the complications that obesity brings in the anesthesia setting during surgery. Anesthetic departments must recognize the innumerable risks when managing patients with obesity undergoing surgery, including anatomical and physiological changes as well as comorbidities such as diabetes, cardiovascular diseases, and malignancies. Therefore, the purpose of this review is to analyze the current literature and evaluate the current and recent advances in anesthetic care of obese patients undergoing surgery, to better understand the specific challenges this patient population faces. A greater understanding of the differences between anesthetic care for obese patients can help to improve patient care and the specificity of treatment. The examination of the literature will focus on differing patient outcomes and safety precautions in obese patients as compared to the general population. Specifically highlighting the differences in pre-operative, intra-operative, and post-operative care, with the aim to identify issues and present possible solutions.

Fractional differential equations are widely applied in physics, chemistry as well as engineering fields. Therefore, approximating the solution of differential equations of fractional order is necessary. We consider the quadratic polynomial spline function based method to find approximate solution for a class of boundary value problems of fractional order. We derive a consistency relation which can be used for computing approximation to the solution for this class of boundary value problems. Convergence analysis of the method is discussed. Four numerical examples are included to illustrate the practical usefulness of the proposed method.

The widespread dissemination of misinformation on online platforms has become a significant societal challenge, influencing public opinion, political landscapes, and social stability. Traditional rule-based and statistical methods for fake news classification often struggle to generalize across different datasets due to the evolving nature of misinformation. To address this, deep learning and natural language processing (NLP) techniques have emerged as effective solutions for detecting deceptive content. In this study, a novel fake news classification framework is proposed, integrating Transformer-based feature extraction with an XGBoost classifier. The methodology leverages RoBERTa embeddings, Term Frequency-Inverse Document Frequency (TF-IDF)-based tokenization, and metadata processing to capture both linguistic and contextual cues essential for accurate classification. The model is trained and evaluated on the PolitiFact and GossipCop datasets, achieving state-of-the-art performance with an accuracy of 0.9930 and 0.9764, respectively. Comparative analysis with existing methods demonstrates the effectiveness of our approach in improving precision, recall, and F1-score. The findings underscore the importance of combining deep learning-based feature extraction with ensemble learning techniques for robust and scalable fake news detection.