Miracle-dz/UniFAR-FTUs huggingface dataset:
E-WASTE AWARENESS AND PRACTICES OF ZIMBABWEAN UNIVERSITY STUDENTS A DESCRIPTIVE STUDY

Africa’s cyberspace is experiencing unprecedented growth as the continent joins the global village to spur socio-economic development. Information and communication technologies (ICT) have become part of everyday life, and when they reach their end of working life, they become electronic waste (e-waste) and should be appropriately discarded. However, the continent is streaming towards a major crisis as obsolete ICT equipment is indiscriminately disposed of to the detriment of the environment and public health. This study aimed to assess university students’ e-waste awareness and practices. This cross-sectional study was conducted amongst university students in Zimbabwe. After obtaining institutional ethical clearance, a predesigned pretested questionnaire was administered to university students with informed consent. The sample size calculated was 223 from four purposely selected state universities. Part two and four students were randomly selected. Chi-square Test was applied to get the Chi-square value and p-value. Multiple regression analysis was used to determine the significance of the independent variables in explaining the variability of the dependent variable. Our results show that the four independent variables (lack of knowledge, policies, poor practices and handling) positively influenced e-waste management by university students in Zimbabwe. Of the four independent variables, poor handling had the strongest effect on e-waste management with a regression coefficient of 0.420 and the lowest significance of 0.000. Although the knowledge of e-waste was high, students lacked knowledge of policies/laws that regulate environmental and health management. Advanced knowledge of e-waste did not translate into responsible management as e-waste was kept at home, transported and stored with municipal waste, and there were no designated bins for collecting it. Most respondents were unsure of what was happening regarding the generation, handling, storage, transportation and final disposal of e-waste. By analyzing students’ knowledge and practices, universities should intensify e-waste management advocacy by incorporating e-waste matters into their learning curriculum. The government should enact policies that govern the management of e-waste, and this will provide a framework for institutions to set up local-level policies that promote green initiatives.
EFFICIENT BIG DATA SECURITY: EVALUATING THE PERFORMANCE OF A PROPOSED HYBRID KEY MANAGEMENT ALGORITHM USING LIGHTWEIGHT CRYPTOGRAPHY

This research paper explores the integration of lightweight cryptography algorithms into the MapReduce framework for secure and efficient big data processing. Initially, lightweight cryptography was not a priority in the field of big data due to the focus on scalability, security trade-offs, potential performance impact, and compatibility with existing solutions. However, as the need for lightweight and efficient security measures emerged, researchers began to explore the integration of lightweight cryptography into MapReduce or develop specialized lightweight cryptographic solutions for big data processing. To address the resource-intensive nature of traditional cryptographic algorithms in big data applications, this paper proposes a new algorithm with a hybrid key management scheme that leverages the efficiency and effectiveness of popular lightweight cryptography algorithms. The proposed algorithm ensures secure data processing while maintaining low computational overhead. Experimental evaluations were conducted using different big data scenarios to measure processing time, memory utilization, and security of the algorithms. The results demonstrate that lightweight cryptography algorithms, such as Rabbit stream cipher and NOEKEON block cipher, offer practical and efficient solutions for securing large volumes of data. Depending on the specific requirements, AES and Chacha20 algorithms can be selected for confidentiality and integrity. Additionally, Rabbit stream cipher and NOEKEON block cipher are the preferred options when high speed is a crucial factor. These findings provide insights into the practical implementation of lightweight cryptography in MapReduce for secure and efficient big data processing.
subluxation on sagittal MR. recognition of isolated posterior element Lumbar spondylolysis without spondylolisthesis:

PURPOSE: To document the occurrence of isolated dorsal subluxation of posterior elements in cases of lumbar spondylolysis without spondylolisthesis both quantitatively (using spinal canal measurements) and qualitatively (by visual inspection) on sagittal MR images. METHODS: Retrospective analysis identified 63 patients with lumbar spondylolysis (confirmed by CT or conventional radiography) who had undergone MR imaging. From these we identified 12 patients with pars interarticularis defects but no evidence of spondylolisthesis. Measurements of anteroposterior spinal canal diameters were performed in these 12 patients to ascertain whether the sagittal canal diameter at the level of the spondylolysis exceeded the normal range as determined from 100 control subjects. RESULTS: In 9 of 12 patients the spinal canal was abnormally widened at the level of the spondylolysis because of dorsal subluxation of posterior elements. In 5 of these patients, the subluxation was readily visible on midline sagittal MR images. In 4 patients, spinal canal measurements were necessary to document this phenomenon. CONCLUSION: In the majority of patients with spondylolysis but without spondylolisthesis, sagittal MR images can show isolated dorsal subluxation of posterior spinal elements.
Malnutrition leads to the progression of coronary artery calci�cation in hemodialysis patients

Malnutrition is considered a risk factor for cardiovascular disease in patients on hemodialysis (HD). However, no in vivo studies using optical coherence tomography to evaluate the effect of nutritional status on coronary atherosclerosis in patients on HD exist. Therefore, we aimed to conduct a detailed analysis to address this gap. This study included 64 HD patients who underwent percutaneous coronary interventions. The patients were divided into two groups based on nutritional evaluation using the geriatric nutritional risk index (GNRI). Culprit and non-culprit lesions were evaluated at baseline and after 6 months. In the culprit lesions at baseline, the thickness and length of the calci�ed plaque were signi�cantly larger (p < 0.01). In the non-culprit lesions, the 6-month change in the angle of the calci�ed plaque was signi�cantly greater in the malnutrition group (p = 0.02). The signi�cant factors that affected the change in the angle of calci�cation were "malnutrition at GNRI" [odds ratio, 8.17; 95% con�dence interval (CI), 1.79 to 37.33; p < 0.01] and "serum phosphorus level" (odds ratio, 3.73; 95% CI, 1.42 to 9.81; p < 0.01). Appropriate management of nutritional status is crucial for suppressing the progression of coronary artery disease in HD patients.
Becker Digital

Total anomalous pulmonary venous return (TAPVR) is a rare congenital cardiac defect, accounting for 1.5-3% of cases of congenital heart disease. With prenatal ultrasonography, the majority of these patients are diagnosed in utero with definitive surgery performed during the neonatal period. However, as prenatal screening may not be available in developing countries, patients may present in later infancy. We pre-sent successful surgical repair of a 6-month-old infant with TAPVR who presented for medical care at 5 months of age in Lima, Peru. The late presentation of such infants and the limited resources available for the treatment of elevated pulmonary vascular resistance may impact successful surgical correction of such defects. The perioperative care of such infants in developing countries is discussed and strategies for managing postoperative pulmonary hypertension is reviewed.
FAKE NEWS DETECTION BASED ON WORD AND DOCUMENT EMBEDDING USING MACHINE LEARNING CLASSIFIERS

Fake news is a problem that has a major effect on our life. Detection of fake news considered an interesting research area that has some limitation of the available resources. In this research, we propose a classification model that is capable of detecting fake news based on both Doc2vec and Word2vec embedding as feature extraction methods. Firstly, we compare between the two approaches using different classification algorithms. According to the applied experiments, the classification based on Doc2vec model provided promising results with more than one classifier. The Support vector machine resulted the best accuracy with 95.5% followed by Logistic Regression 94.7% and the Long Short Term Memory produced the lowest accuracy. On the other hand, the classification based Word2vec embedding model results high accuracy only with Long Short Term Memory classifier with 94.3%. Secondly, the classification models based on proposed Doc2vec have shown to outperform a corresponding model that based on TF-IDF on the same dataset using Support Vector Machine and Logistic Regression classifiers.
Unlocking the Multifaceted Mechanisms of Bud Outgrowth: Advances in Understanding Shoot Branching

Shoot branching is a complex and tightly regulated developmental process that is essential for determining plant architecture and crop yields. The outgrowth of tiller buds is a crucial step in shoot branching, and it is influenced by a variety of internal and external cues. This review provides an extensive overview of the genetic, plant hormonal, and environmental factors that regulate shoot branching in several plant species, including rice, Arabidopsis, tomato, and wheat. We especially highlight the central role of TEOSINTE BRANCHED 1 (TB1), a key gene in orchestrating bud outgrowth. In addition, we discuss how the phytohormones cytokinins, strigolactones, and auxin interact to regulate tillering/branching. We also shed light on the involvement of sugar, an integral component of plant development, which can impact bud outgrowth in both trophic and signaling ways. Finally, we emphasize the substantial influence of environmental factors, such as light, temperature, water availability, biotic stresses, and nutrients, on shoot branching. In summary, this review offers a comprehensive evaluation of the multifaced regulatory mechanisms that underpin shoot branching and highlights the adaptable nature of plants to survive and persist in fluctuating environmental conditions.
Can we probe the conductivity of the lithosphere and upper mantle using satellite tidal magnetic signals?

A few studies convincingly demonstrated that the magnetic fields induced by the lunar semidiurnal (M2) ocean flow can be identified in satellite observations. This result encourages using M2 satellite magnetic data to constrain subsurface electrical conductivity in oceanic regions. Traditional satellite‐based induction studies using signals of magnetospheric origin are mostly sensitive to conducting structures because of the inductive coupling between primary and induced sources. In contrast, galvanic coupling from the oceanic tidal signal allows for studying less conductive, shallower structures. We perform global 3‐D electromagnetic numerical simulations to investigate the sensitivity of M2 signals to conductivity distributions at different depths. The results of our sensitivity analysis suggest it will be promising to use M2 oceanic signals detected at satellite altitude for probing lithospheric and upper mantle conductivity. Our simulations also suggest that M2 seafloor electric and magnetic field data may provide complementary details to better constrain lithospheric conductivity.
A Comprehensive Overview of Parkinson's Disease and Homoeopathic Remedies: A Rubric-Based Analysis Using Homoeopathic Medical Repertory

: Parkinson's Disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms, resulting from the progressive loss of dopaminergic neurons. Parkinson's is the second-most common neurodegenerative disease after Alzheimer's disease. It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta. The typical indications and manifestations of Parkinson's disease generally do not appear until there has been a substantial loss of around 70-80% of dopaminergic neurons. Homoeopathy, a holistic system of medicine, treats the person through the individualization. This article explores the potential of Homeopathy, particularly using the Murphy Repertory, to manage PD comprehensively.
High Field Breakdown Characteristics of Carbon Nanotube Thin High Field Breakdown Characteristics of Carbon Nanotube Thin Film Transistors Film Transistors

The high-field properties of carbon nanotube (CNT) network thin film transistors (CN-TFTs) are important for their practical operation, and for understanding their reliability. Using a combination of experimental and computational techniques we show how the channel geometry (length L C and width W C ) and network morphology (average CNT length L t and alignment angle distribution θ ) affect heat dissipation and high-field breakdown in such devices. The results suggest that when W C ≥ L t , the breakdown voltage remains independent of W C but varies linearly with L C . The breakdown power varies almost linearly with both W C and L C when W C >> L t . We also find that the breakdown power is more susceptible to the variability in the network morphology compared to the breakdown voltage. The analysis offers new insight into the tunable heat dissipation and thermal reliability of CN-TFTs which can be significantly improved through optimization of the network morphology and device geometry
Reasoning on the response of logical signaling networks with Answer Set Programming

Logical networks provide a simple yet powerful qualitative modeling approach in systems biology. In what follows, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). In this context, the problem consisting of learning logical networks is crucial in order to achieve unbiased and robust discoveries. Furthermore, it has been shown that many networks can be compatible with a given set of experimental observations. Thus, ﬁrst we discuss how ASP can be used to exhaustively enumerate all these logical networks. In practice, this is a key step for the design of new experiments in order to reduce the uncertainty provided by such a family of models. Next, in order to gain control over the system, we look for intervention strategies that force a set of target species into a desired steady state. Altogether, this constitutes a pipeline for reasoning on logical signaling networks providing robust insights to systems biologists. In this chapter, we illustrate the usage of ASP for solving the aforementioned problems and discuss the novelty of our approach with respect to existing methods
MANAGEMENT OF SECURITIES PURCHASE-SALE WITHIN THE FRAMEWORK OF A DIFFERENTIAL GAME

The game model of a continuous process of trading operations with securities (SEC) is considered. It was shown that the controllability of this process can be described from the point of view of the game approach. It allows one to explore and recommend to players a constructive strategy for managing the purchase and sale of securities. The proposed solution is valid for situations of buying and selling securities in the area of both bullish and bearish trends. Moreover, in both situations, the possible worst actions of counterparty players are taken into account. The model proposed in the paper is distinguished by its use of the potential of a bilinear differential quality game with several terminal surfaces, unlike similar solutions in this subject area. In the course of research, an analytical solution has been obtained for a bilinear differential quality game with dependent motions. The article also presents the results of a computational experiment. Theoretical calculations were confirmed by the data of a computational experiment, during which different options for the ratio of parameters that describe the continuous process of trading operations with the securities have been taken into account.
Nonlinear Koopman Modes and Power System Stability Assessment Without Models

The loss of stability-an instability-can become a critical cause of emergent cascading outages leading to wide-spread blackouts. Penetration of renewable energy sources makes the problem of instability more urgent because of the highly fluctuating nature of such sources. Here we show a data-based approach to stability assessment of power systems without models. This approach is enabled by Koopman mode analysis for nonlinear dynamical systems, which detects an instability based on the properties of the point spectrum of the Koopman operator. We apply the technique to data on physical power flows sampled from the two major accidents, the 2011 Arizona-Southern California grid outage and the 2006 system disturbance of the European interconnected grid, and successfully detect unstable power flow patterns that govern the complex dynamics occurring during the accidents.
Percepción del uso de las TIC en estudiantes de nivel secundario de una institución educativa peruana Perception of ICT use among secondary school students in a Peruvian educational institution

This research was conducted with a non-experimental transectional quantitative approach at a descriptive level, with the objective of measuring the level of perception of the use of ICTs by students at this high school; for this purpose, a questionnaire previously validated by expert judgment was used as a data collection instrument, which was applied to
Hydrogen sulfide, a potential novel drug, attenuates concanavalin a-induced hepatitis

Background: Hydrogen sulfide (H 2 S) is known to exert anti-inflammatory properties. Apoptosis and autophagy play important roles in concanavalin A (Con A)-induced acute hepatitis. The purpose of this study was to explore both the effect and mechanism of H 2 S on Con A-induced acute hepatitis. Methods: BALB/c mice were randomized into sham group, Con A-injection group, and 14 µ mol/kg of sodium hydrosulfide (NaHS, an H 2 S donor) pretreatment group. Results: Aspartate aminotransferase, alanine aminotransferase, and pathological damage were significantly ameliorated by NaHS pretreatment. NaHS pretreatment significantly reduced the levels of interleukin-6 and tumor necrosis factor- α compared with those of the Con A group. The expression of Bcl-2, Bax, Beclin-1, and LC3-2, which play important roles in the apoptosis and autophagy pathways, were also clearly affected by NaHS. Furthermore, NaHS affected the p-mTOR and p-AKT. Conclusion: H 2 S attenuates Con A-induced acute hepatitis by inhibiting apoptosis and autophagy, in part, through activation of the PtdIns3K-AKT1 signaling pathway.
MACHINE LEARNING PREDICTION OF HEPATIC FIBROSIS IN HEPATITIS B EGYPTIAN PATIENTS BASED ON CLINICAL LABORATORY PARAMETERS

Liver fibrosis stage prediction in chronic hepatitis B virus (HBV) infected patients is vital. Liver biopsy is the reference style and gold standard to evaluate fibrosis stage but with many drawbacks. Therefore, using noninvasive methods are better alternatives. In this study, seven clinical laboratory parameters of 235 chronic HBV Egyptian patients with Hepatitis B virus were collected from HBV clinic at National Liver Institute that belongs to Menoufia University in Egypt. The aim of this study is to experiment multiple machine-learning methods based on clinical parameters to build efficient classification models that predict two liver related issues: the fibrosis stage and cirrhosis of liver in chronic HBV Egyptian patients. Also, attribute selection methods were applied to reduce the dimensionality and find the most relevant parameters. For fibrosis stage prediction, a classification model based on Logistic Regression achieved AUROC of 0.991 and accuracy of 93.61%. Besides, using only four parameters selected as the most relevant, AUROC of 0.971 and accuracy of 95.74% were achieved. For cirrhosis of liver prediction, a classification model based on Logistic Regression and cost sensitive with penalty of 2 achieved AUROC of 0.936 and accuracy of 91.49%. Besides, using only three parameters selected as the most relevant, AUROC of 0.92 and accuracy of 85.11% were achieved. The classification models outperformed noninvasive index-based method, FIB-4 that depends on four clinical parameters, in both fibrosis stage and liver cirrhosis prediction in chronic HBV Egyptian patients.
Validity of the Polar H10 for RR intervals acquisition during different conditions for heart rate variability analysis in ski mountaineers

Wearable devices enable RR interval (RRi) measurements during various conditions. We aimed to assess the validity of the Polar H10 for RRi acquisition during pre-rest stabilization, rest, sympathetic nervous system activity stimulation and recovery conditions for heart rate variability (HRV) analysis in ski mountaineers. RRi were simultaneously obtained via electrocardiogram and the Polar H10 with V800 wrist-watch among eleven elite athletes in the supine position. Short-term (5-min) and ultra-short-term (1-min) heart rate (HR) and HRV parameters were analyzed. Mean absolute percentage difference between parameters from different devices ranged from 0% to 5.4%. Intraclass correlation and concordance correlations ranged between 0.76 and 1.00. Limits of agreement (LoA) for short-term measures did not exceed the defined maximum acceptable difference (SWC) through all conditions for mean RRi, HR, time-domain and nonlinear indices, and for frequency-domain (fast-Fourier-related) during the rest condition. LoA exceeded the SWC for mean, minimal and maximal HR, time-domain and nonlinear parameters from select 1-minute samples in all conditions. The Polar H10 provides RRi that could be used for short-and ultra-short-term HRV analysis from stable resting conditions in elite ski mountaineers. Ultra-short-term parameters from exercise should be analyzed with caution especially from later minutes of activity
On the resolution sensitivity in a GFDL global atmospheric model

14 We performed a series of aquaplanet simulations at the horizontal resolution from 50km 15 to 6km with identical parameterization settings using the Geophysical Fluid Dynamics 16 Laboratory’s Atmosphere Model version 4 implemented with the two-moment Morrison-17 Gettelman cloud microphysics with prognostic precipitation (GFDL AM4-MG2). At the 18 finer resolution, the global mean resolved-scale precipitation increases and that from cu-19 mulus parameterization decreases. The model also simulates less/thinner clouds over the 20 low latitudes and more/thicker clouds over the high latitudes as the resolution increases. 21 The precipitation over the deep tropics is investigated in detail. We find little resolution 22 sensitivity in the daily mean precipitation extremes. Changes of the equatorial resolved 23 precipitation with resolution cannot be fully explained by the resolution dependence in 24 the vertical velocity amplitude. We report a robust sensitivity in the convective organ-25 ization over the deep tropics to the model resolution. In simulations of finer resolution, 26 the localized convection is suppressed, and the organized convective system associated 27 with large-scale circulations becomes more prominent. 28