Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Ethanolic Extract of Hibiscus sabdariffa and 2, 4-Dinitrophenylhydrazine on Kidney Function Parameters of Rats

he aim of this study was to evaluate the effects of ethanolic extract of H. sabdariffa and 2, 4-dinitrophenylhydrazine on the kidney function parameters of rats. 20 rats were evenly distributed into 4 groups and treatment lasted for a week. For the first six (6) days, the animals in group one (1) and group four (4) were treated with water alone while groups two (2) and three (3) rats received ethanolic extract of H. sabdariffa alone. At the end of the 6 th day, the animals were fasted overnight and on the 7 th day, each animal in groups 3 and 4 received 28 mg per kg body weight of 2, 4-dinitrophenylhydrazine (2, 4-DNPH) alone and after 7 hours they were sacrificed under diethyl ether anesthesia and their serum and kidney samples taken and prepared for biochemical assays to determine their urea, sodium, potassium, bicarbonates, chloride, creatinine and malondialdehyde levels. The high levels of MDA and creatinine observed in the serum of the rats treated with 2, 4dinitrophenylhydrazine alone relative to their respective controls show the ability of 2, 4-dinitrophenylhydrazine to provoke cytotoxicity in the kidney. It can also be established from this study that the ethanolic extract of H. sabdariffa has anti-oxidant property which was shown in the significant reduction in the level of MDA and creatinine in the serum of rats treated with the ethanolic extract H. sabdariffa prior to the treatment with 2, 4-dinitrophenylhydrazine when compared to the rats treated with 2, 4-dinitrophenylhydrazine alone.
Comparisons In floristic Composition and Plant Species Diversity amongst Granite Outcrops of the Mamabolo Mountain Bushveld, South Africa

The geographical landscapes together with environmental conditions are well recognised drivers in determining a suitable habitat for plants and animals on rocky as well as granite outcrops. Granite outcrops of the Mamabolo Mountain Bushveld (Limpopo Province, South Africa) are ideal in investigating aspects of landscape geography and specific abiotic factors in order to understand floristic compositions amongst outcrops themselves and that of their surrounding vegetation matrix.
Heavy Metal Levels in Commonly used Cosmetic Products in Asia

Background: The heavy metals such as lead, arsenic, mercury, aluminum, zinc, chromium, and iron are found in a wide variety of personal care products including lipstick, whitening toothpaste, eyeliner, and nail color. Aims and Objectives: The main aim of our study was to find out the heavy metals in routinely used cosmetics such as surma, kajal, lipsticks, rose powder, sindoor, and turmeric powder. Materials and Methods: Samples of commonly used personal care products, i.e., cosmetics such as surma, kajal, lipsticks, rose powder, sindoor, and turmeric powder of different companies were purchased from local markets of Pune and Mumbai. Heavy metals such as lead, arsenic, cadmium, mercury, and strontium were estimated by X-ray fluorescence spectrometry from cosmetics of different companies. Results: The lead level was very high in surma and other cosmetics, i.e., kajal, lipsticks, sindoor powder, cheek rose powder, and turmeric powder are also contained the detectable amount of lead. Arsenic, cadmium, and mercury levels were also very high in surma samples. Strontium levels were above the level of detection in almost all the samples of cosmetics. Conclusions: All the cosmetics contained lead and strontium heavy metals. The absorption of these heavy metals might be more and affects all organs and systems. It is very essential to see the status of heavy metals in the blood, creating awareness and preventing the use of such cosmetics.
Pneumococo resistente à penicilina: implicações práticas Penicillin-resistant pneumococcus: Practical implications

Objective: To review epidemiological, laboratorial and clinical aspects of the penicillin-resistant pneumococcus, and to consider the impact of the rapidly increasing drug-resistance on the management of the main infections caused by these pathogens. Methods: Bibliographic review covering the last ten years, using both Medline system and direct research. Resultsandconclusions:TheStreptococcuspneumoniaeisan important agent in acute otitis media, pneumonia and meningitis in children. The clinical evaluation of the pneumococcal infections depends on the age and health condition of the patient, site and severity of the infection and the adequacy of the treatment. Penicillin was uniformly effective against pneumococcus until three decades ago, when the first reports of clinical resistance were published. Since then, there has been a rapidly increasing in the level and rates of resistance to penicillin, which parallels to others s lactams and antimicrobials. This paper includes a suscint review of the genetic and phenotypic mechanisms of the resistance to penicillin, the available bacteriologic tests for determination of in vitro susceptibility to antimicrobials and finally the importance of the pneumococcus in acute otitis media, pneumonia/bacteremia and meningitis. There has been new empirical therapeutic regimens recommended for meningitis, according to the community rates and levels of resistance to s lactams and to the presence of certain underlying conditions among the patients, such as immunedepressant diseases andfrequentantibioticconsumption.Therapidlyincreasingprevalence of drug-resistant strains emphasizes the importance of judicious antibiotic use and the development of a conjugate vaccine, effective to prevent invasive infections and to reduce the rate of carriage of the pneumococcus, even among infants.
Socio-Economic Projects Spillovers and Their Influence on Communities Development

The purpose of this paper is to gauge the different community socio-economic influences that comes as a result of the impacts and the potential of spillovers brought by these socio-economic projects. Based on the literature review, a theoretical framework is hypothesised. The framework proposes the relation between the socio-economic projects, four stages and their potential impact and spillovers. Selective cases relevant to projects of poverty elimination, healthcare development and women empowerment are carefully studied and dissected. Then, each case is evaluated in relevance to its value chain through a detailed summary. The impact and spillover of each of the sampled projects are illustrated and clarified. Despite its limitations, this study carries important implications as it brings in diverse tools for gauging the different efforts in journey related to creating a socio-economic outcome.
Factors contributing to medication administration errors and barriers to self-reporting among nurses: a review of literature

Background: Medication Administration Errors (MAEs) are common among nurses and can threaten all patients’ outcomes. Furthermore, MAEs are the leading cause of incidents in patient safety records worldwide. Objective: To review the types of medication administration errors, factors contributing to MAEs and barriers to self-reporting among nurses. Methods: A review of the literature was done and included original articles and grey literature from January 2011 until July 2017. An advanced search was done in Medline, HINARI, PubMed, CINAHL, Science direct, Google Scholar and gray literature using a Boolean combination of different keywords such as “medication administration error”, “adverse drugs reactions”, barriers and “self-reporting”. Results: 257 articles were eligible for review but only27 articles met the inclusion criteria. Types of MAEs mainly focused on seven rights (right patient, right drug, right dose, right time, right route, right reason and right documentation). With regards to the types of MAEs, the wrong time was most prevalent in this study. Being overworked was the main factor contributing to medication administration errors. The barriers to self-reporting during MAEs were dominated by fear of disciplinary action. Nurses’ characteristics such as age, experience, education, variations in how errors are defined and organizational factors such as power, distance and lack of reporting policies were found to be barriers to self-reporting. In addition to that, others were identified. Conclusion: Globally, MAEs remain a pervasive problem among nurses. Contributing factors range from individual to organizational factors, and barriers to self-report are due to fear of legal action and punishment. There is a need to devise measures that will prevent these errors and to promote positive clinical outcomes of patients in healthcare settings. Keywords: contributing factors, medication administration errors, barriers, self-reporting, nurse
Fixed-Time Adaptive Neural Tracking Control for a Class of Uncertain Nonlinear Pure-Feedback Systems

This paper presents fixed-time adaptive neural tracking control for a class of uncertain nonlinear pure-feedback systems. To overcome the design difficulty arising from the nonaffine structure of nonlinear pure-feedback systems, the mean value theorem is introduced to separate the nonaffine appearance of nonlinear pure-feedback systems. Radial basis function (RBF) neural networks are employed to approximate designed unknown functions <inline-formula> <tex-math notation="LaTeX">${{\hat {f}}_{i}}({Z_{i}})$ </tex-math></inline-formula>. By combining RBFs and Lyapunov functions, a novel fixed-time controller is designed, and semiglobal uniform ultimate boundedness of all signals in the closed-loop control system is guaranteed in a fixed time. Sufficient conditions are given to ensure that the system has semiglobal fixed-time stability. The main purpose of this paper is to design a controller for an unknown nonlinear pure-feedback system so that the system output <inline-formula> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> can track the reference signal <inline-formula> <tex-math notation="LaTeX">${y_{d}}$ </tex-math></inline-formula>. The simulation experiments indicate that the selection of sufficient design parameters makes the tracking error converge on a domain of the origin. Compared with the existing finite-time control and fixed-time control, the proposed fixed-time control scheme reduces the size of the tracking error.
An Unsupervised Remote Sensing Single-Image Super-Resolution Method Based on Generative Adversarial Network

Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications. Many remote sensing image SR algorithms based on deep learning have been proposed recently, which can effectively improve the spatial resolution under the constraints of HR images. However, images acquired by remote sensing imaging devices typically have lower resolution. Hence, an insufficient number of HR remote sensing images are available for training deep neural networks. In view of this problem, we propose an unsupervised SR method that does not require HR remote sensing images. The proposed method introduces a generative adversarial network (GAN) that obtains SR images through the generator; then, the SR images are downsampled to train the discriminator with low resolution (LR) images. Our method outperformed several methods in terms of the quality of the obtained SR images as measured by 6 evaluation metrics, which proves the satisfactory performance of the proposed unsupervised method for improving the spatial resolution of remote sensing images.
Error Performance of NOMA-Based Cognitive Radio Networks With Partial Relay Selection and Interference Power Constraints

Non-orthogonal multiple access (NOMA)-based cognitive radio (CR) networks have recently emerged as a promising solution to enhance the spectral efficiency and massive connectivity problems. In this paper, we investigate the error rate performance of relay-assisted NOMA with partial relay selection in an underlay cognitive radio network. In this setup, $K$ relays are used to assist in transmission between secondary NOMA users and a secondary base station (SBS), where the relay (R) with the strongest link with the SBS is selected to amplify-and-forward (AF) its received signals to the secondary receivers. We derive an accurate approximation for the pairwise error probability (PEP) of the secondary users with imperfect successive interference cancellation (SIC). Subsequently, the derived PEP expression is utilized to deduce a union bound, which is considered as an upper bound on the bit error rate (BER). We further formulate an optimization problem to calculate the optimum power coefficients that minimize the derived union bound. Numerical and Monte Carlo simulation results are presented to corroborate the derived analytical expressions and give some useful insights into the error rate performance of each user.
Integrated Motion and Powertrain Predictive Control of Intelligent Fuel Cell/Battery Hybrid Vehicles

This article considers intelligent fuel cell/battery hybrid vehicles (FCHVs) that can make autonomous decisions at both the vehicle and powertrain levels. Since the vehicle and powertrain level dynamics are inherently integrated, we propose an integrated motion and powertrain model predictive control approach for intelligent FCHVs by jointly optimizing the vehicle acceleration and fuel cell current. The control goals are to achieve vehicle mobility, minimal hydrogen consumption, and battery state-of-charge maintenance within system constraints. The main challenge in an integrated control is that the electric motor can operate in both propelling and generating modes coupling with vehicle and powertrain states. This hybrid operation is handled by the mixed logical dynamical modeling resulting in a mixed integer nonlinear control problem. To relieve the possible heavy computational burden, two simplification approaches are proposed: hierarchical control and successive linearizations. Two standard driving cycles and a typical vehicle cruising scenario are employed to test the effectiveness of the proposed modeling and control algorithms. Simulation results show that the hierarchical linear control is more suitable for real-time applications with comparable control performance with that of the integrated control. However, additional constraints must be carefully designed to compensate for the ignored coupling dynamics and constraints.
Robust Positioning for Road Information Services in Challenging Environments

Next-generation Intelligent Transportation Systems (ITS) of future road traffic monitoring will be required to provide reports on traffic status, road conditions, and driver behaviour. Road surface anomalies contribute to increasing the risk of traffic accidents, reduced driver comfort and increased vehicles’ damage. The conventional integrated Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) positioning solutions can suffer from errors because of inertial sensor noises and biases, especially when low-cost and commercial grade inertial sensors are used. In this work, we use a reduced inertial sensor system utilizing Micro-Electro-Mechanical-System (MEMS) based inertial sensors, to integrate with the GNSS receiver and provide robust positioning in urban canyons. To provide acceptable performance in challenging urban environments, our method de-noises the MEMS-based inertial sensor measurements using a technique based on a Bi-orthonormal search, which separates the monitored motion dynamics from both the inertial sensor bias errors and high-frequency noises. As a result, the performance of the positioning system is improved, providing reliable positioning accuracy during extended GNSS outages that occur in various areas. To show the significant enhancement achieved by the proposed approach, we examined the system performance over three road test trajectories involving MEMS-based inertial sensors and GNSS receivers mounted on our test vehicle. The superior performance of our proposed INS/GNSS integrated positioning system is demonstrated in this paper during various GNSS outages, in different areas, and under multiple driving scenarios.
Enhancing latency performance through intelligent bandwidth allocation decisions: a survey and comparative study of machine learning techniques

Converged access networks consolidating 5G and beyond and fixed optical fiber access are expected to support future latency-sensitive human-to-machine applications over the Tactile Internet. Making intelligent bandwidth allocation decisions among end users/machines/robots of the converged network is thus crucial to meeting stringent latency requirements. The recent renewed interest in machine learning (ML) has contributed towards a plethora of undeniable performance improvements in communication networks. Current insights into how ML can be exploited to provide intelligent bandwidth allocation decisions to enhance latency performance, along with guidance on the most suitable ML technique in that regard, remain elusive. This paper provides the first insights, to the best of our knowledge, into the suitability of commonly adopted ML techniques for this purpose by first presenting an in-depth survey focusing on the technical details of these techniques and how each technique is used in existing studies. The benefits, drawbacks, resultant time and space complexity incurred, and prediction accuracy are then evaluated for each ML technique reviewed. Next, a comprehensive comparative study of the ML techniques is presented for the first time, to our best knowledge, to provide guidance on the selection of ML technique that provides intelligent bandwidth allocation decisions towards supporting emerging latency-sensitive applications. The uplink latency performance of a converged network adopting an artificial neural network (ANN) supervised bandwidth allocation scheme is then compared with those arising from using existing bandwidth allocation schemes. Results highlight the ability of the ANN to learn the association among bandwidth demand, network parameters, and the resulting uplink latency such that, in operation, the allocated bandwidth will always be optimized to enhance latency performance.
Towards the Integration of a Post-Hoc Interpretation Step into the Machine Learning Workflow for IoT Botnet Detection

The analysis of the interplay between the feature selection and the post-hoc local interpretation steps in a machine learning workflow followed for IoT botnet detection constitutes the research scope of the present paper. While the application of machine learning-based techniques has become a trend in cyber security, the main focus has been almost on detection accuracy. However, providing the relevant explanation for a detection decision is a vital requirement in a tiered incident handling processes of the contemporary security operations centers. Moreover, the design of intrusion detection systems in IoT networks has to take the limitations of the computational resources into consideration. Therefore, resource limitations in addition to human element of incident handling necessitate considering feature selection and interpretability at the same time in machine learning workflows. In this paper, first, we analyzed the selection of features and its implication on the data accuracy. Second, we investigated the impact of feature selection on the explanations generated at the post-hoc interpretation phase. We utilized a filter method, Fisher's Score and Local Interpretable Model-Agnostic Explanation (LIME) at feature selection and post-hoc interpretation phases, respectively. To evaluate the quality of explanations, we proposed a metric that reflects the need of the security analysts. It is demonstrated that the application of both steps for the particular case of IoT botnet detection may result in highly accurate and interpretable learning models induced by fewer features. Our metric enables us to evaluate the detection accuracy and interpretability in an integrated way.
Corporate Social Responsibility in Higher Education: A PLS-SEM Neural Network Approach

Corporate social responsibility (CSR) has become vital to the competitiveness of organizations. However, there is very limited research on CSR in the context of higher education. Therefore, this study investigates the impact of CSR factors on the university competitiveness with other universities. The contribution of this study includes applying a multi-analytical methodology by applying Partial Least Squares- Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). First, PLS-SEM analysis was applied to measure which CSR factor has the most significant influence on the increased competitiveness of Saudi Arabian universities. Then, an ANN model analysis was applied to rank the relative influence of the significant CSR factors attained from the PLS-SEM analysis. The findings show that the CSR factors such as market-oriented CSR activities, society-oriented CSR activities and workforce-oriented CSR activities have a significant positive impact on increased competitiveness of Saudi Arabian universities. However, based on the ANN findings, society-oriented CSR is the most significant predictor of a university’s increased competitiveness, followed by workforce-oriented CSR and finally by market-oriented CSR. The practical implications of this study provide Saudi universities to focus on society-oriented CSR to maintain and sustain their competitive advantage over other universities.
Overcurrent for Aluminum Bonding Wires in WBG Power Semiconductors

When using Wide Band-gap Power Semiconductor Devices in power control and protection apparatus, the packaging of these devices faces unique challenges. These challenges include expected high overcurrent (for example, eight times nominal current, result from the IEC standard 60947) and high short circuit current through a relatively small chip area. This paper uses FEA simulation and laboratory testing to study bonding wire performance against the overcurrent in AC power control and protection applications and verify if bonding wires are a constraining factor in these applications.
Experimental Evaluation of Time-Dependent Dielectric Breakdown for GaN MIS HEMTs under Various Substrate Biases

Time-dependent dielectric breakdown (TDDB) under constant voltage stress (CVS) of GaN power device subjected to positive and negative substrate biases are evaluated experimentally. Under different substrate biases, the breakdown mechanisms determine the shape β of Weibull distributions and buffer traps affect the scale factor η of mean time to failure. The progressive breakdown, relating to the percolation path establishment, has been examined also. The relationship between electron trapping and de-trapping processes in the dielectric layer and GaN buffer traps revealed by the substrate bias effect are investigated.
Fastest Mixing Reversible Markov Chain: Clique Lifted Graphs and Subgraphs

Markov chains are one of the well-known tools for modeling and analyzing stochastic systems. At the same time, they are used for constructing random walks that can achieve a given stationary distribution. This paper is concerned with determining the transition probabilities that optimize the mixing time of the reversible Markov chains towards a given equilibrium distribution. This problem is referred to as the Fastest Mixing Reversible Markov Chain (FMRMC) problem. It is shown that for a given base graph and its clique lifted graph, the FMRMC problem over the clique lifted graph is reducible to the FMRMC problem over the base graph, while the optimal mixing times on both graphs are identical. Based on this result and the solution of the semidefinite programming formulation of the FMRMC problem, the problem has been addressed over a wide variety of topologies with the same base graph. Second, the general form of the FMRMC problem is addressed on stand-alone topologies as well as subgraphs of an arbitrary graph. For subgraphs, it is shown that the optimal transition probabilities over edges of the subgraph can be determined independent of rest of the topology.