Miracle-dz/UniFAR-FTUs huggingface dataset:
The School Principal and Teachers’ Roles in Improving Learners’ Academic Performance at a Selected School in the Karas Region (Namibia)

Objective. The purpose of the study was to investigate the principal and teachers’ roles in improving learner’s academic performance at a selected school in the Karas region (Namibia). Materials & Methods. The researcher applied a qualitative approach to obtain the qualitative information and deep understanding of the role of the principal and teachers in improving learner’s academic performance at a selected school. The teachers and the director of the Karas region took part in the study. The sample consisted of one principal and fourteen teachers, which gave a total sample size of 15 participants. For the study, the researcher used one of the best schools in the region. A purposive sampling was used to choose the participants. Results. The findings of the research revealed that the principal’s and teachers’ roles in improving learners’ academic performance are supervision and monitoring, discipline and decision making, assessment as well as involvement of the learners in extramural activities. Upon executing those roles, they faced some challenges such as lack of teaching and learning resources, misconducting by teachers and learners, failure to meet the set targets, late coming and overcrowded classrooms. These challenges were overcome through motivation, improvising resources, continuous professional development and team building activities. Conclusions. In this scenario, principals are required to work with other stakeholders. Collaborative teaching and discussion should be improved as it will help teachers to improve on teaching and learning. The school principal and teachers should continue to inspire their learners by talking. Teachers are encouraged to continue to play their role in classroom management, guide learners in their learning activities and let the learners use their study time judiciously. Strategic planning should be developed as it will help the stakeholders to focus on key targets for improvement.
The Bayes rule of the parameter in (0,1) under the power-log loss function with an application to the beta-binomial model

ABSTRACT We propose the power-log loss function plotted in Figure 1 for the restricted parameter space , which satisfies all the six properties listed in Table 1 for a good loss function on . In particular, the power-log loss function penalizes gross overestimation and gross underestimation equally, is convex in its argument, and attains its global minimum at the true unknown parameter. The power-log loss function on is an analog of the power-log loss function on , which is the popular Stein's loss function. We then calculate the Bayes rule (estimator) of the parameter in under the power-log loss function, the posterior expected power-log loss (PEPLL) at the Bayes estimator, and the integrated risk under the power-log loss (IRPLL) at the Bayes estimator, which is also the Bayes risk under the power-log loss (BRPLL). We also calculate the usual Bayes estimator under the squared error loss, which has been proved to be larger than that under the power-log loss. Next, we analytically calculate the Bayes estimators and the PEPLL at the Bayes estimators under a beta-binomial model. Finally, the numerical simulations and a real data example of some monthly magazine exposure data exemplify our theoretical studies of two size relationships about the Bayes estimators and the PEPLLs.
Optically trapped atom interferometry using the clock transition of large rubidium-87 bose-einstein condensates

We present a Ramsey-type atom interferometer operating with an optically trapped sample of 106 Bose-condensed 87Rb atoms. We investigate this interferometer experimentally and theoretically with an eye to the construction of future high precision atomic sensors. Our results indicate that, with further experimental refinements, it will be possible to produce and measure the output of a sub-shot-noise limited, large atom number BEC-based interferometer. The optical trap (shown in Figure 1 (c)) allows us to couple the |F = 1, mF = 0〉 → |F = 2, mF = 0〉 clock states using a single photon 6.8 GHz microwave transition, while state selective readout is achieved with absorption imaging. We analyse the process of absorption imaging and show that it is possible to observe atom number variance directly, with a signal-to-noise ratio ten times better than the atomic projection noise limit on 106 condensate atoms. We discuss the technical and fundamental noise sources that limit our current system, and present theoretical and experimental results on interferometer contrast, de-phasing and miscibility.
DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffective. Although prior writers have proposed numerous high-quality approaches, static and dynamic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tailored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves applying a newly proposed detection model to classify android apps; this model uses a neural network optimized with an improved version of HHO. Application of binary and multi-class classification is used, with binary classification for benign and malware apps and multi-class classification for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classification. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.
Metabolic Dysregulation Explains the Diverse Impacts of Obesity in Males and Females with Gastrointestinal Cancers

The prevalence of obesity, defined as the body mass index (BMI) ≥ 30 kg/m2, has reached epidemic levels. Obesity is associated with an increased risk of various cancers, including gastrointestinal ones. Recent evidence has suggested that obesity disproportionately impacts males and females with cancer, resulting in varied transcriptional and metabolic dysregulation. This study aimed to elucidate the differences in the metabolic milieu of adenocarcinomas of the gastrointestinal (GI) tract both related and unrelated to sex in obesity. To demonstrate these obesity and sex-related effects, we utilized three primary data sources: serum metabolomics from obese and non-obese patients assessed via the Biocrates MxP Quant 500 mass spectrometry-based kit, the ORIEN tumor RNA-sequencing data for all adenocarcinoma cases to assess the impacts of obesity, and publicly available TCGA transcriptional analysis to assess GI cancers and sex-related differences in GI cancers specifically. We applied and integrated our unique transcriptional metabolic pipeline in combination with our metabolomics data to reveal how obesity and sex can dictate differential metabolism in patients. Differentially expressed genes (DEG) analysis of ORIEN obese adenocarcinoma as compared to normal-weight adenocarcinoma patients resulted in large-scale transcriptional reprogramming (4029 DEGs, adj. p < 0.05 and |logFC| > 0.58). Gene Set Enrichment and metabolic pipeline analysis showed genes enriched for pathways relating to immunity (inflammation, and CD40 signaling, among others) and metabolism. Specifically, we found alterations to steroid metabolism and tryptophan/kynurenine metabolism in obese patients, both of which are highly associated with disease severity and immune cell dysfunction. These findings were further confirmed using the TCGA colorectal adenocarcinoma (CRC) and esophageal adenocarcinoma (ESCA) data, which showed similar patterns of increased tryptophan catabolism for kynurenine production in obese patients. These patients further showed disparate alterations between males and females when comparing obese to non-obese patient populations. Alterations to immune and metabolic pathways were validated in six patients (two obese and four normal weight) via CD8+/CD4+ peripheral blood mononuclear cell RNA-sequencing and paired serum metabolomics, which showed differential kynurenine and lipid metabolism, which corresponded with altered T-cell transcriptome in obese populations. Overall, obesity is associated with differential transcriptional and metabolic programs in various disease sites. Further, these alterations, such as kynurenine and tryptophan metabolism, which impact both metabolism and immune phenotype, vary with sex and obesity together. This study warrants further in-depth investigation into obesity and sex-related alterations in cancers that may better define biomarkers of response to immunotherapy.
Detection of EASs at high altitude with ARGO-YBJ

The ARGO-YBJ experiment has been in stable data taking for about 5 years at the YangBaJing Cosmic Ray Observatory (Tibet, P.R. China, 4300 m a.s.l., 606 g/cm2). With a duty-cycle greater than 86% the detector collected about 5×1011 events in a wide energy range, from few hundreds GeV up to about 10 PeV. Exploiting the full coverage approach with a high segmentation of the readout at high altitude, ARGO-YBJ imaged the front of Extensive Air Showers (EAS) with unprecedented resolution and detail. A number of important problems in galactic cosmic ray physics has been faced through different analyses. In this contribution we summarize the latest results in gamma-ray astronomy and in cosmic ray physics.
Bali Smart Island: Smart City Implementation in Bali Province

This study aims to explore and examine SPBE Towards Bali Smart Island in the Provincial Government of Bali. This study used a qualitative descriptive research method with an inductive approach. Data collection was carried out through interviews, observation, and documentation techniques. The results showed that SPBE towards Bali Smart Island has been going well and has implemented smart city development. However, ASN’s digital literacy and competence are still weak, post-pandemic regional budgets are not stable, cyberattacks, and the large number of application systems built by the center and each region are obstacles to implementing SPBE Towards Bali Smart Island. Strategies that can be implemented to overcome the obstacles encountered in implementing SPBE towards Bali Smart Island include: collaborating with pentahelix elements to hold local, national and global scale activities and events based on digital technology scale such as the Bali Digital Festival which is held every year; encouraging international investors to develop the digital technology industry in Bali so as to have a domino effect in developing smart city dimensions in realizing Bali Smart Island; forming an internal audit/assessor team to carry out periodic assessments related to SPBE development so as to create strong control and evaluation; optimizing the Cyber Lantern program using social media platforms in the form of providing training and sharing knowledge related to the use of digital technology, data security, technology ethics and so on as a medium for increasing digital literacy; forming the SPBE Forum throughout the Province of Bali to form integrated development of the Bali Smart Island program and hold training on the use of certified digital technology.
Solving the robust shortest path problem with interval data using a probabilistic metaheuristic approach

This paper addresses the robust shortest path problem with interval data, i.e. the case of classical shortest path problem with given source and sink when arc weights are not fixed but take their values from some intervals associated with arcs. The problem consists in finding a shortest path that minimizes so called robust deviation, i.e. deviation from an optimal solution under the worst case realization of interval weights. As it was proven in [9], the problem is NP-hard, therefore it is of great interest to tackle it with some metaheuristic approach, namely simulated annealing, in order to calculate an approximate solution for the large scale instances efficiently. We describe theoretical aspects and present the results of computational experiments. To the best of our knowledge, this is the first attempt to develop metaheuristic approach for solving the robust shortest path problem.
Propagation and evolution of electric fields associated with solar wind pressure pulses based on spacecraft and ground‐based observations

We investigate spatial and temporal evolution of large‐scale electric fields in the magnetosphere and ionosphere associated with sudden commencements (SCs) using multipoint equatorial magnetospheric (THEMIS, RBSP, and GOES) and ionospheric (C/NOFS) satellites with radars (SuperDARN). A distinct SC event on 17 March 2013 shows that the magnetospheric electric field in the equatorial plane propagates from dayside toward nightside as a fast‐mode wave. The ionospheric electric field responds ~41 s after the onset of dayside magnetospheric electric field, which can be explained by the propagation of the Alfvén wave along magnetic field lines. The wavelet analysis shows that the Alfvén wave is dominant in the plasmasphere. Poynting fluxes toward the ionosphere support these propagations. From a statistical analysis of response time, tailward propagation speed is estimated at about 1000–1100 km/s. We also statistically derive a spatial distribution and time evolution of the magnetospheric electric field in the dawn‐dusk direction (Ey). Our result shows that negative Ey (dawnward) propagates from noon toward the magnetotail, followed by positive Ey (duskward). The propagation characteristics of electric fields in the equatorial plane depend on magnetic local time. At noon, negative Ey lasts for about 1 min, and positive Ey becomes dominant about 2 min after the SC onset. Negative Ey soon attenuates in the nightside region, while the positive Ey propagates fairly well to the premidnight or postmidnight regions while maintaining a certain amplitude. The enhancement of positive Ey is due to the enhancement of magnetospheric convection associated with the main impulse of SCs.
One-pass selection of a sample with probability proportional to size

This note shows how the method of McLeod and Belihouse for simple random sampling without replacement can be adapted to yield a sample with selection probabilities proportional to a size measure, in a single pass through a list of sampling units without knowing the total size of the list. A variation given by Pinkham can also be adapted: this calls for the generation of a much lower number of random deviates.
Thymoquinone Ameliorates Carfilzomib-Induced Renal Impairment by Modulating Oxidative Stress Markers, Inflammatory/Apoptotic Mediators, and Augmenting Nrf2 in Rats

Chemotherapy-induced kidney damage is an emerging problem that restricts cancer treatment effectiveness. The proteasome inhibitor carfilzomib (CFZ) is primarily used to treat multiple myeloma and has been associated with severe renal injury in humans. CFZ-induced nephrotoxicity remains an unmet medical need, and there is an urgent need to find and develop a nephroprotective and antioxidant therapy for this condition. Thymoquinone (TQ) is a bioactive compound that has been isolated from Nigella sativa seeds. It has a wide range of pharmacological properties. Therefore, this experimental design aimed to study the effectiveness of TQ against CFZ-induced renal toxicity in rats. The first group of rats was a normal control (CNT); the second group received CFZ (4 mg/kg b.w.); the third and fourth groups received TQ (10 and 20 mg/kg b.w.) 2 h before receiving CFZ; the fifth group received only TQ (20 mg/kg b.w.). This experiment was conducted for 16 days, and at the end of the experiment, blood samples and kidney tissue were collected for biochemical assays. The results indicated that administration of CFZ significantly enhanced serum marker levels such as BUN, creatinine, and uric acid in the CFZ group. Similarly, it was also noticed that CFZ administration induced oxidative stress by reducing antioxidants (GSH) and antioxidant enzymes (CAT and SOD) and increasing lipid peroxidation. CFZ treatment also enhanced the expression of IL-1β, IL-6, and TNF-α production. Moreover, CFZ increased caspase-3 concentrations and reduced Nrf2 expression in the CFZ-administered group. However, treatment with 10 and 20 mg/kg TQ significantly decreased serum markers and increased antioxidant enzymes. TQ treatment considerably reduced IL-1β, IL-6, TNF-α, and caspase-3 concentrations. Overall, this biochemical estimation was also supported by histopathological outcomes. This study revealed that TQ administration significantly mitigated the negative effects of CFZ treatment on Nrf2 expression. Thus, it indicates that TQ may have utility as a potential drug to prevent CFZ-induced nephrotoxicity in the future.
P‐5.3: A super resolution reconstruction algorithm based on spatial autoregression regularization

In a lot of micro display applications, we need to enlarge and recognize a region of interest (ROI), which often get a low‐resolution image because of limited resolution source image. This paper proposed a novel image reconstruction algorithm based on spatial autoregression regularization and sparse representation. This reconstruction algorithm trained an image dictionary with the same sparse coefficient by sparse K‐SVD, and then import the autoregressive regularization item to construct the objective function which can realize local adaptive control of the image. In order to obtain further clear image, the degradation model was used which can realize the global constraints. The experimental results show that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.
Definitions of parasitism, considering its potentially opposing effects at different levels of hierarchical organization

Abstract Abstract An annotated synthesis of textbook definitions of parasitism is presented. Most definitions declare parasitism is a long-lasting relationship between individuals of different species harming the hosts. The infection-induced costs are interpreted as diseases in the medical-veterinary literature. Alternatively, evolutionary ecologists interpret it as a reduction of host's fitness (longevity, fertility or both). Authors often assume that such effects decrease host population growth and select for antiparasitic defences, which is not necessarily true because infections may simultaneously express opposite effects at different levels of biological organization. (i) At the cellular level, infection-induced cell growth, longevity and multiplication may yield tumours maladaptive at higher levels. (ii) At the individual level, reduced host longevity, fertility or both are interpreted as disease symptoms or reduced fitness. (iii) Contrary to common sense, the growth rate of infected host lineages may increase in parallel with the individuals' reduced survival and fertility. This is because selection favours not only the production of more offspring but also their faster production. (iv) Finally, infections that reduce host individuals' or lineages' fitness may still increase infected host populations' growth rate in the context of ecological competition. Therefore, differences between parasitism and mutualism may depend on which level of organization one focuses on.
Efficient Feature Relation Learning Network for Cross-Spectral Image Patch Matching

Recently, cross-spectral image patch matching methods based on feature difference aggregation have achieved excellent performance, but they introduce a large number of parameters, limit matching speed, and have poor scalability. At the same time, only using feature difference learning (FD) to extract differential features will lead to the loss of consistent features between cross-spectral image patches. Therefore, we construct a novel four-branch efficient feature relation learning network (EFR-Net) without feature difference aggregation. Specifically, a new four-branch feature relation learning strategy is proposed, which reasonably combines multiple feature relation learning to comprehensively and effectively extract the differential features and consistent features between image patches. At the same time, we construct an efficient local attention (ELA) module with negligible parameters, which can learn some global context information, enhance the interaction of local information, and promote the extraction of discriminative features. In addition, a combined metric network is introduced to facilitate network optimization and improve network generalization. Furthermore, a public optical and synthetic aperture radar (SAR) image patch matching dataset with a patch size of $64\times64$ pixels is constructed based on the OS dataset, which is called the OS patch dataset. We also establish an experimental benchmark on this new dataset. Extensive experimental results show that the proposed EFR-Net achieves excellent performance on cross-spectral image patch matching (OS patch dataset, VIS–NIR patch dataset) and single spectral image patch matching (Brown dataset).
Unsupervised Contrastive Representation Learning for 3D Mesh Segmentation (Student Abstract)

3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uniform 3D objects. However, meshes are often challenging to annotate due to their high computational complexity. Therefore, it is desirable to train segmentation networks with limited-labeled data. Self-supervised learning (SSL), a form of unsupervised representation learning, is a growing alternative to fully-supervised learning which can decrease the burden of supervision for training. Specifically, contrastive learning (CL), a form of SSL, has recently been explored to solve limited-labeled data tasks. We propose SSL-MeshCNN, a CL method for pre-training CNNs for mesh segmentation. We take inspiration from prior CL frameworks to design a novel CL algorithm specialized for meshes. Our preliminary experiments show promising results in reducing the heavy labeled data requirement needed for mesh segmentation by at least 33%.