Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the determinants of the perceptions of underemployment among young university graduates: A South African University case

Abstract There has been a wave of significant changes in labour markets worldwide, which has seen the pace of secure and stable employment decline significantly. South Africa has not been saved from this wave of change. Besides the country’s high unemployment rate, both at the national level and among the youth by global standards, non-standard employment such as casual and part-time work has been rising. Simultaneously, advanced education has additionally demonstrated no guarantee of employment. Rising labour market instability has therefore necessitated exploring alternative measures of labour underutilisation over and above the typical measures of unemployment. This is a critical study, particularly in South Africa, where youth’s extreme lack of employment opportunities is likely to push them towards non-standard forms of employment. Hence the primary objective of the study was to analyse underemployment among young university graduates in South Africa. The study followed a quantitative research approach that entailed primary data collection through a survey. Firstly, descriptive statistics of the frequency distribution for discrete data. Secondly, an ordinary least squares regression model was employed where three different types of regressions were used to analyse the determinants of the three types of underemployment. The main findings show race and marital status as significant factors determining underemployment status. The rest of the results are discussed, and conclusions and recommendations are drawn to show how dynamic youth labour markets are in developing countries. The paper looks at associations and makes no causality claims.
DRIVER DROWSINESS DETECTION USING MACHINE LEARNING

Drowsy driving is one of the major causes of road accidents and death. Drivers must keep a close eye on the road, so they can react to sudden events immediately. Driver fatigue often becomes a direct cause of many traffic accidents. Therefore, there is a need to develop the systems that will detect and notify the driver bad psychophysical condition, which could significantly reduce the number of fatigue related car accidents. However, the Development of such systems encounters many difficulties related to fast and proper recognition of a driver’s fatigue symptoms. Most of the conventional methods are either vehicle based, or behavioral based or physiological based. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low cost, real time driver’s drowsiness detection system is developed with acceptable accuracy. One of the technical possibilities to implement driver drowsiness detection systems is to use the vision-based approach. Here we are detecting the driver drowsiness by estimating vision system of him/her. We will be using Open CV for gathering the images from webcam and feed them into a Deep Learning model which will classify whether the person’s eyes are ‘Open’ or ‘Closed’. Webcam records the video and driver’s face is detected in each frame employing image processing techniques. Facial landmarks on the detected face are pointed and subsequently the eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their values, drowsiness is detected based on developed adaptive thresholding. Machine learning algorithms have been implemented as well in an offline manner.
Integrated Resource Allocation and Task Scheduling for Full-Duplex Mobile Edge Computing

Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system.
The paleoecology of the Late Miocene mammals 
from the Optima Local Fauna of Oklahoma, USA

The Optima Local Fauna represents an important glimpse into the ecological transition between savannah and grassland during the late Miocene (Hemphillian) of what is now the southcentral Great Plains of North America. Though dominated by horses, herbivores from the Optima are morphologically diverse, bearing adaptations for both browsing and grazing lifestyles. Likewise, the carnivorans show similar ranges of size and presumed dietary behavior. In this study, we used carbonate isotope, mesowear, and tooth breakage and wear analyses to investigate the dietary complexity of mammals from a single site collected by the Oklahoma Museum of Natural History. Seventeen taxa were analyzed, including five perissodactyls ( Teleoceras hicksi, Dinohippus interpolatus, Neohipparion eurystyle, Nannippus ingenuus, and Astrohippus ansae ), four artiodactyls ( Texoceros guymonensis, Pediomeryx hemphillensis, Megatylopus matthewi, and Platygonus sp . ), a single proboscidean ( Mammut sp.), two rodents ( Dipoides indet. and Umbogaulus monodon ), and five carnivorans ( Agriotherium schneideri, Amphimachairodus coloradensis, Borophagus secundus, Eucyon davisi, Pliotaxidea cf. nevadensis ). Both stable isotope analysis and dental mesowear indicate a broad dietary partitioning occurred among the Optima herbivores, where the artiodactyls were identified as mixed feeders and the perissodactyls were recovered as grazers. In the carnivorans, the large felid Amphimachairodus coloradensis was a hypercarnivore with limited tooth breakage and an enriched δ 13 C signature, indicating low carcass utilization and a prey preference for horses. The canids had a more generalized diet, with B. secundus showing a greater proportional consumption of carcasses through a higher tooth breakage rate. The large ursid Agriotherium schneideri is here interpreted as an omnivore based on depleted δ 13 C values. Overall, we found evidence for a diversity of dietary niches in both carnivores and herbivores during the late Hemphillian in Oklahoma, likely driven by the expansion of grasslands in the region.
Endothelial Dysfunction in the Pathogenesis of Abdominal Aortic Aneurysm

Abdominal aortic aneurysm (AAA), defined as a focal dilation of the abdominal aorta beyond 50% of its normal diameter, is a common and potentially life-threatening vascular disease. The molecular and cellular mechanisms underlying AAA pathogenesis remain unclear. Healthy endothelial cells (ECs) play a critical role in maintaining vascular homeostasis by regulating vascular tone and maintaining an anti-inflammatory, anti-thrombotic local environment. Increasing evidence indicates that endothelial dysfunction is an early pathologic event in AAA formation, contributing to both oxidative stress and inflammation in the degenerating arterial wall. Recent studies utilizing single-cell RNA sequencing revealed heterogeneous EC sub-populations, as determined by their transcriptional profiles, in aortic aneurysm tissue. This review summarizes recent findings, including clinical evidence of endothelial dysfunction in AAA, the impact of biomechanical stress on EC in AAA, the role of endothelial nitric oxide synthase (eNOS) uncoupling in AAA, and EC heterogeneity in AAA. These studies help to improve our understanding of AAA pathogenesis and ultimately may lead to the generation of EC-targeted therapeutics to treat or prevent this deadly disease.
Integrating Security And Software Engineering Advances And Future Visions

Thank you very much for downloading integrating security and software engineering advances and future visions. As you may know, people have look hundreds times for their favorite readings like this integrating security and software engineering advances and future visions, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some infectious virus inside their laptop.
Conflicting dynamics of public attitudes toward austerity: evidence from Europe (2010–2011)

ABSTRACT Recent research addresses two attitudes as key measures for public responses to austerity: the belief in the necessity of austerity and the willingness to tolerate its costs. These two measures, each representing preferential and behavioral attitudes toward austerity, are extensively discussed in existing literature. Little is known, however, about how they are related to each other. This paper raises the issue of compatibility between the two attitudes, investigating their individual/joint variations. Analysis of 22 European countries (2010–2011) gives evidence against the compatibility, calling special attention to behavioral reluctance to pay the costs of austerity and its implications for fiscal consolidation and redistributive politics in hard times.
Fast CBCT reconstruction using convolutional neural networks for arbitrary robotic C-arm orbits

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.
Impact of Increased Vertical Resolution in WACCM on the Climatology of Major Sudden Stratospheric Warmings

Sudden stratospheric warmings (SSWs) are a major mode of variability of the winter stratosphere. In recent years, climate models have improved their ability to simulate SSWs. However, the representation of the frequency and temporal distribution of SSWs in models depends on many factors and remains challenging. The vertical resolution of a model might be one such factor. Therefore, here we analyse the impact of increased vertical resolution on the simulation of major sudden stratospheric warmings (SSWs) in the Whole Atmosphere Community Climate Model (WACCM). We compare two versions of the model, WACCM3.5 and WACCM4. We find that the frequency of occurrence of SSWs is improved in the newer version and closer to that obtained using reanalysis. Furthermore, simulations with a coupled ocean best reproduce the behaviour of temperature during these events. Increasing vertical resolution increases the number of occurrences; however, it does not produce significantly different results than standard resolution. WACCM4 also does not reproduce vortex split events well, generating far fewer of these than observed. Finally, the ratio between polar vortex splits and displacement events in the model is slightly better for non-ocean-coupled simulations. We conclude that, at least for WACCM4, the use of the high vertical resolution configuration is not cost-effective for the study of SSWs.
CASH-RF: A Compiler-Assisted Hierarchical Register File in GPUs

Spin-transfer torque magnetic random-access memory (STT-MRAM) is an emerging nonvolatile memory technology that has been received significant attention due to its higher density and lower leakage current over SRAM. One compelling use case is to employ STT-MRAM as a graphics processing unit (GPU) register file (RF) to reduce its massive energy consumption. One critical challenge is that STT-MRAM has longer access latency and higher dynamic power consumption than SRAM, which motivates the hierarchical RF that places a small SRAM-based register cache (RC) between functional units and STT-MRAM RF. The RC acts as the write buffer, so all the writes on the RF are first performed on the RC. In the presence of a conflict miss, the RC writes back the corresponding cache line into the RF. In this work, we observe that a large amount of such write-back operations are unnecessary because they include register values that are never used again. Leveraging this observation, we propose a compiler-assisted hierarchical RF in GPUs (CASH-RF) that optimizes STT-MRAM accesses by removing dead register values. In CASH-RF, unnecessary write-back operations are detected by the compiler via the last consumer analysis. At runtime, the corresponding RC lines are discarded after the last references without being updated to the RF. Compared to the baseline GPUs, CASH-RF removes 59.5% of write-back operations, which leads to 54.7% lower RF energy consumption with only 2.6% of performance degradation.
Lipid-Based Nanoparticle Formulation of Diallyl Trisulfide Chemosensitizes the Growth Inhibitory Activity of Doxorubicin in Colorectal Cancer Model: A Novel In Vitro, In Vivo and In Silico Analysis

Garlic’s main bioactive organosulfur component, diallyl trisulfide (DATS), has been widely investigated in cancer models. However, DATS is not suitable for clinical use due to its low solubility. The current study seeks to improve DATS bioavailability and assess its chemopreventive and chemosensitizing properties in an AOM-induced colorectal cancer model. The polyethylene glycol coated Distearoylphosphatidylcholine/Cholesterol (DSPC/Chol) comprising DATS-loaded DATSL and doxorubicin (DOXO)-encapsulated DOXL liposomes was prepared and characterized. The changes in the sensitivity of DATS and DOXO by DATSL and DOXL were evaluated in RKO and HT-29 colon cancer cells. The synergistic effect of DATSL and DOXL was studied by cell proliferation assay in the combinations of IC10, IC25, and IC35 of DATSL with the IC10 of DOXL. AOM, DATSL, and DOXL were administered to different groups of mice for a period of 21 weeks. The data exhibited ~93% and ~46% entrapment efficiency of DATSL and DOXL, respectively. The size of sham liposomes was 110.5 nm, whereas DATSL and DOXL were 135.5 nm and 169 nm, respectively. DATSL and DOXL exhibited significant sensitivity in the cell proliferation experiment, lowering their IC50 doses by more than 8- and 14-fold, respectively. However, the DATSL IC10, IC25, and IC35 showed escalating chemosensitivity, and treated the cells in combination with DOXL IC10. Analysis of histopathological, cancer marker enzymes, and antioxidant enzymes revealed that the high dose of DATSL pretreatment and DOXL chemotherapy is highly effective in inhibiting AOM-induced colon cancer promotion. The combination of DATSL and DOXL indicated promise as a colorectal cancer treatment in this study. Intermolecular interactions of DATS and DOXO against numerous cancer targets by molecular docking indicated MMP-9 as the most favourable target for DATS exhibiting binding energy of −4.6 kcal/mol. So far, this is the first research to demonstrate the chemopreventive as well as chemosensitizing potential of DATSL in an animal model of colorectal cancer.
Equiosmolar hypertonic saline and mannitol for brain relaxation in patients undergoing supratentorial tumor surgery: A systematic review and meta-analysis

Background: Hypertonic saline (HS) and mannitol are hyperosmolar agents that are usually used to reduce intracranial pressure (ICP) and provide a satisfactory brain relaxation. The aim of the study was to perform a systematic review and meta-analysis to compare the efficacy of HS and mannitol on brain relaxation intraoperatively in patient undergoing craniotomies for supra-tentorial brain tumors. Methods: A systematic review and meta-analysis of randomized control trials. We included randomized control trials that compared equiosmolar HS and mannitol in supratentorial tumors craniotomies and reported at least one of the following outcomes: degree of brain relaxation, ICP, central venous pressure, mean arterial pressure, perioperative fluid input, urine output, Na+ levels, and K+ levels. We searched Medline, Cochrane Central Register of Controlled Trials, and Embase using MESH terms and keywords. The bibliographic references of included studies and trial registries were also searched. Results: Seven articles were included. The degree brain of relaxation was comparable across the two groups with slight tendency toward HS (RR = 1.13, 95% CI 0.99–1.29; P = 0.08). Mannitol was associated with significantly higher urine output (standardized mean difference [SMD] = −1.33, 95% CI −1.56–−1.10; P < 0.001). Na+ levels were higher in HS group (SMD = 1.47, 95% CI 0.86–2.09; P < 0.001). Mannitol was associated with non-significant decrease in CVP and increase fluid input (SMD = 0.42, 95% CI 0.00–0.85 and SMD = −0.18, 95% CI −0.37–0.02, respectively). Conclusion: Both HS and mannitol are associated with satisfactory brain relaxation with a non-statistically significant tendency for HS to achieve better relaxation scores with mannitol resulting in higher urine output while HS with higher Na+ levels.
Comprehensive Landscape of ARID Family Members and Their Association with Prognosis and Tumor Microenvironment in Hepatocellular Carcinoma

As one of the most lethal forms of cancers, hepatocellular carcinoma (HCC) claims many lives around the world, and it is especially common in China. The ARID family plays key roles in the pathogenesis and development of human cancers. The potential of several functional genes used as novel biomarkers has attracted more and more attention. However, the prognostic values of the ARID family in HCC patients are rarely known by people. In this study, we performed comprehensive analysis using TCGA datasets, finding that the expressions of ARID4B, ARID2, ARID3B, JARID2, ARID1A, ARID1B, and ARID3A were increased in HCC specimens compared to nontumor specimens, while the expressions of ARID4A and ARID3C were decreased in HCC specimens. According to the Pearson correlation data, the methylation levels of the majority of ARID members were negatively correlated. Upregulation of ARID3A, ARID5B, and ARID1A was related to a poor HCC outcome according to the data of multivariate assays. Then, we built a LASSO Cox regression model based on ARID3A, ARID5B, and ARID1A in HCC. Overall survival rates were considerably lower for those with high risk scores compared to those with low risk scores. Finally, we studied the associations between risk scores and several types of infiltrating immune cells. The data revealed that the risk score was positively related to the infiltration of CD8+ T cells, B cell, T cell CD8+, neutrophil, macrophage, and myeloid dendritic cell. This study conducted a thorough analysis of the ARID members, resulting in new insights for further examination of the ARID family members as prospective targets in the treatment of HCC.
Estimation of Short-Term Bus Travel Time by Using Low-Resolution Automated Vehicle Location Data

Estimation of short-term bus travel time is an essential component of effective intelligent transportation systems (ITS), including traveler information systems and transit signal priority (TSP) strategies. Several technologies, such as automated vehicle location (AVL) systems, can provide real-time information for estimation of bus travel time. However, low resolution of data from such technologies presents a challenge to accurate estimation of travel time. Several models for estimation of bus travel time at signalized urban arterials were developed and tested. These models used low-frequency AVL data and required only knowledge of network specifications such as locations of bus stops and intersections. First, a linear regression model was developed; it decomposed total travel time into its components, including running travel time, dwell time at bus stops, and delay at signalized intersections. Second, various machine learning models, including support vector regression (SVR) with nonlinear kernel, ridge, Lasso, decision tree, and Bayesian ridge were trained by using Python libraries such as scikit-learn and evaluated. A segment of Washington Street in Boston, Massachusetts, was selected as the study site. The results indicate that the SVR model outperformed other regression models in generalized error measures, in particular those of mean absolute error and root mean square error. The findings of this study can lead to improved traveler information systems and more-efficient TSP strategies and, overall, can contribute to better transit quality of service.