Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigation of Organizational Culture of Higher Education: The Case of Hawassa University

With an emphasis on Hawassa University, the study was set out to examine the organizational cultures of HEIs in Ethiopia in both existing and preferred situations. The study used a mixed methods approach and an explanatory research design. Respondents from the four colleges and institutes of Hawassa University selected using purposive sampling included academic personnel, administrative employees, and students. Out of the 436 surveys issued, 396 respondents, 60 were members of the academic staff, 120 were members of the administrative staff, and 216 were students, completed and returned the questionnaires correctly. The respondents were chosen using a simple random sampling procedure. Additionally, two student councils, three college deans, and two administrative staff directorates were purposely chosen to participate in the interview. Through surveys and interviews, both qualitative and quantitative data were gathered. Both descriptive statistics (percentage and mean) and inferential statistics (ANOVA) were applied to the data in order to analyze it. Even though Hawassa University’s staff and students prefer a clan culture, the study’s findings revealed that hierarchy culture is the institution’s predominant organizational culture. In addition, it is discovered that the hierarchy in the present and the clan in the favored contexts are a little stronger and more prominent than the other cultural types. However, out of the six cultural dimensions, the clan culture type is based on the dominant traits, organizational leadership, strategic emphasis, and success criteria, whereas in the preferred situation, management of employees and organizational glue are perceived as being adhocracy and market culture, respectively. In general, an organization’s culture influences not only the performance of its employees but also the effectiveness of the organization as a whole. Therefore, higher education officials must seek to change the current organizational culture of their institutions in order to serve the requirements and interests of stakeholders and to improve the overall performance of their organizations.
Towards explainable model extraction attacks

One key factor able to boost the applications of artificial intelligence (AI) in security‐sensitive domains is to leverage them responsibly, which is engaged in providing explanations for AI. To date, a plethora of explainable artificial intelligence (XAI) has been proposed to help users interpret model decisions. However, given its data‐driven nature, the explanation itself is potentially susceptible to a high risk of exposing privacy. In this paper, we first show that the existing XAI is vulnerable to model extraction attacks and then present an XAI‐aware dual‐task model extraction attack (DTMEA). DTMEA can attack a target model with explanation services, that is, it can extract both the classification and explanation tasks of the target model. More specifically, the substitution model extracted by DTMEA is a multitask learning architecture, consisting of a sharing layer and two task‐specific layers for classification and explanation. To reveal which explanation technologies are more vulnerable to expose privacy information, we conduct an empirical evaluation of four major explanation types in the benchmark data set. Experimental results show that the attack accuracy of DTMEA outperforms the predicted‐only method with up to 1.25%, 1.53%, 9.25%, and 7.45% in MNIST, Fashion‐MNIST, CIFAR‐10, and CIFAR‐100, respectively. By exposing the potential threats on explanation technologies, our research offers the insights to develop effective tools that are able to trade off security‐sensitive relationships.
Systematic Effects in Color Photometry Data

Abstract : Previous work with laboratory and field observations and computer simulations suggests that classes of spacecraft can be distinguished on the basis of their color or spectral properties. Empirical results also indicate that accounting for systematic effects reduces much of the observed scatter and thus increases separability of the classes. We seek to gain an understanding of the basic properties of satellite features that determine the spectral characteristics of individual satellites: surface materials and scattering properties, combination of materials, shape, orientation, illumination-viewing geometries, etc. Initial efforts have focused on systematic effects of solar phase angle on object brightness and color data.
Photometric H alpha and [O II] Luminosity Function of SDF and SXDF Galaxies: Implications for Future Baryon Oscillation Surveys

Efficient selection of emission line galaxies at z > 1 by photometric information in wide field surveys is one of the keys for future spectroscopic surveys to constrain dark energy using the baryon acoustic oscillation (BAO) signature. Here we estimate the H alpha and [O II] line luminosity functions of galaxies at z = 0.5-1.7 using a novel approach where multi-wavelength imaging data is used to jointly estimate both photometric redshifts and star-formation rates. These photometric estimates of line luminosities at high-redshift use the large data sets of the Subaru Deep Field and Subaru XMM-Newton Deep Field (covering \sim 1 deg^2) and are calibrated with the spectroscopic data of the local Sloan Digital Sky Survey galaxies. The derived luminosity functions (especially H alpha) are in reasonable agreement with the past estimates based on spectroscopic or narrow-band-filter surveys. This dataset is useful for examining the photometric selection of target galaxies for BAO surveys because of the large cosmological volume covered and the large number of galaxies with detailed photometric information. We use the sample to derive the photometric and physical properties of emission line galaxies to assist planning for future spectroscopic BAO surveys. We also show some examples of photometric selection procedures which can efficiently select these emission line galaxies.
Assessing a Video-Based Intervention to Promote Parent Communication Strategies with a Deaf Infant: A Feasibility and Acceptability Study

Infant–parent interaction forms the foundation for language learning. For the majority of deaf infants, hearing loss can impact access to, and the quality of communicative interactions, placing language development at risk. Support for families to meet the challenges faced during interaction is highly variable in the United Kingdom. In a step towards more standardized but tailorable family support, we co-produced an instructional, video-based intervention, testing for feasibility in terms of behavior change in seven communicative strategies and acceptability with 9 parents, forming study 1. Parents increased their use of the majority of behaviors and found content and delivery acceptable. However, further development was required to: (a) support use of semantically contingent talk and attention getting strategies to elicit infant attention, and (b) ensure the information was provided in a bite-size format that could be tailored to individual families. In study 2, the intervention was refined based on findings from study 1 and assessed for acceptability with 9 parents and 17 professionals, who reported similar high acceptability scores. Final refinements and modifications could be addressed in future interventions. The current studies provide a positive early step towards a standardized intervention to support communication that could be used in routine practice.
A Study on Prevalence and Clinico-Mycological Profile of Superficial Fungal Infections in a Tertiary Care Hospital

Fungal infections are worldwide in distribution of which superficial infections are the most common human infections (Brown et al., 2012). These are rarely life threatening but they have their own negative effects on the patient’s emotional, social and occupational status (Langan et al., 2010). In the tropical and subtropical countries like India superficial fungal infections are more prevalent due to the fact that heat and moist conditions are more suitable for the growth of fungi (Peerapur et al., 2004). Superficial mycoses refer to the diseases of the skin and its appendages caused by fungi caused by Dermatophytes, candida International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 01 (2019) Journal homepage: http://www.ijcmas. com
Mechanisms of Basement Membrane Micro-Perforation during Cancer Cell Invasion into a 3D Collagen Gel

Cancer invasion through basement membranes represents the initial step of tumor dissemination and metastasis. However, little is known about how human cancer cells breach basement membranes. Here, we used a three-dimensional in vitro invasion model consisting of cancer spheroids encapsulated by a basement membrane and embedded in 3D collagen gels to visualize the early events of cancer invasion by confocal microscopy and live-cell imaging. Human breast cancer cells generated large numbers of basement membrane perforations, or holes, of varying sizes that expanded over time during cell invasion. We used a wide variety of small molecule inhibitors to probe the mechanisms of basement membrane perforation and hole expansion. Protease inhibitor treatment (BB94), led to a 63% decrease in perforation size. After myosin II inhibition (blebbistatin), the basement membrane perforation area decreased by only 15%. These treatments produced correspondingly decreased cellular breaching events. Interestingly, inhibition of actin polymerization dramatically decreased basement membrane perforation by 80% and blocked invasion. Our findings suggest that human cancer cells can primarily use proteolysis and actin polymerization to perforate the BM and to expand perforations for basement membrane breaching with a relatively small contribution from myosin II contractility.
Determinants of Household Poverty in Ethiopia

ABSTRACT The purpose of this study is to investigate the determinants of household poverty in Ethiopia. The study used the national survey data collected by the World Bank during the second phase in 2014. The study employed a binary logistic regression model to identify the determinants of household poverty. Results show that educational level of the household head, family size, dependency ratio, residence of the household, the region in which the household resides, ownership of non-farm enterprises (construction), economic status of the household (high), source of start-up enterprises (credit and saving institutions), and lack of supporting institutions were the factors affecting the probability that a household is poor.
Combined Use of Spent Mushroom Substrate Biochar and PGPR Improves Growth, Yield, and Biochemical Response of Cauliflower (Brassica oleracea var. botrytis): A Preliminary Study on Greenhouse Cultivation

This paper investigated the impact of the combined use of spent mushroom substrate (SMS) biochar and plant-growth-promoting rhizobia (PGPR) on the growth, yield, and biochemical response of cauliflower (Brassica oleracea var. botrytis). A preliminary study was conducted under greenhouse condition using six treatments (sextuplicate) as control (no addition), T1 (PGPR), T2 (5 g/Kg biochar), T3 (5 g/Kg biochar + PGPR), T4 (10 g/Kg biochar), and T5 (10 g/Kg biochar + PGPR) under greenhouse conditions. The Scanning Electron Microscopy (SEM-Zeiss), Energy Dispersive Spectroscopy (EDS), and Fourier’s transform infrared spectroscopy (FTIR) analyses showed that biochar produced from slow pyrolysis of SMS had advantageous structural, functional, and morphological properties for agricultural use. Results showed that SMS biochar addition aids the acceleration of soil nutrient properties. SMS biochar and PGPR application also significantly (p < 0.05) improved the selected growth, yield, and biochemical parameters of cauliflower. In particular, the highest cauliflower yield (550.11 ± 10.05 g), fresh plant biomass (1.66 ± 0.04 Kg), dry plant biomass (149.40 ± 4.18 g), plant height (22.09 ± 0.14 cm), root length (11.20 ± 0.05 cm), plant spread (28.35 ± 0.18 cm), and the number of leaves (12.50 ± 0.50) were observed in T5 treatment. Similarly, the best values for biochemical parameters and enzyme activities such as total chlorophyll (TC: 3.13 ± 0.07 mg/g), superoxide dismutase (SOD: 79.12 ± 1.29 µg/g), catalase (CAT: 55.70 ± 2.52 µg/g), peroxidase (POD 30.18 ± 0.37 µg/g), total phenolics (TP: 19.50 ± 0.31 mg/g), ascorbic acid (AA: 14.18 ± 0.55 mg/g), and total carotenoids (TCT: 150.17 ± 8.20 µg/100 g) were also recorded in the T5 treatment. The application of SMS biochar and PGPR showed a positive correlation with growth, yield, and biochemical response of cauliflower, as indicated by the Pearson correlation analysis. The findings of this study suggest efficient recycling of mushroom industry waste for biochar production and the use of PGPR to improve nutrient utilization in sustainable agriculture.
Solvable Models Of Infrared Gupta-Bleuler Quantum Electrodynamics

Hamiltonian models based on two different infrared approximations are studied in order to obtain an explicit comparison with the standard analysis of the infrared contributions, occurring in the relativistically covariant perturbative formulation of Quantum Electrodynamics. Moller operators, preserving respectively the Hilbert scalar product, for the Coulomb-gauge models, and an indefinite metric, for the models formulated in Feynman's gauge, are obtained in the presence of an infrared cutoff, after the removal of an adiabatic switching and with the aid of a suitable mass renormalization. In the presence of a dipole approximation, spurious contributions to the infrared factors are shown to necessarily arise in Feynman's gauge, with respect both to the Coulomb-gauge model and to the amplitudes of Quantum Electrodynamics, and the connection of this result with a recent work on the Gupta-Bleuler formulation of non-relativistic models is discussed. It is finally proven that by dropping the dipole approximation and adopting an expansion around a fixed charged particle four-momentum, first introduced and employed in the study of the infrared problem by Bloch and Nordsieck, the infrared diagrammatic is fully reproduced and spurious low-energy effects are avoided.
Triglyceride glucose index and its combination with the Get with the Guidelines-Heart Failure score in predicting the prognosis in patients with heart failure

Background Heart failure (HF) is associated with generalized insulin resistance (IR). Recent studies demonstrated that triglyceride glucose (TyG) is an effective alternative index of IR. However, the relationship between the TyG index and in-hospital mortality in patients with HF is unclear. In the present study, we aimed to clarify the association between the TyG index and in-hospital mortality in patients with HF. Methods A retrospective study consisting of 4,411 patients diagnosed with HF from 2015 to 2018 was conducted. All-cause mortality during hospitalization was the primary endpoint. The association between the TyG index and in-hospital mortality was assessed using the logistic regression analysis. Results The risk of in-hospital mortality was significantly associated with increased TyG index (OR: 1.886, 95% CI: 1.421–2.501, p < 0.001) under logistic regression with multivariable adjustment. When divided into three groups based on the TyG index, Tertile 3 demonstrated significantly higher in-hospital mortality than the other two Tertiles (OR: 2.076, 95% CI: 1.284–3.354, p = 0.001). Moreover, the TyG index improved the prediction efficiency of the Get with the Guidelines-Heart Failure (GWTG-HF) score (absolute integrated discrimination improvement = 0.006, p < 0.001; category-free net reclassification improvement = 0.075, p = 0.005). In subgroup analysis, the TyG index exhibited similar predictive performance of in-hospital mortality when groups were stratified based on type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD). Conclusion TyG is a potential index for predicting in-hospital mortality in patients with HF, independent of T2DM or CAD status. The TyG index may be combined with the GWTG-HF score to further improve its predictive efficacy.
The Formation, Structural Characteristics, Absorption Pathways and Bioavailability of Calcium–Peptide Chelates

Calcium is one of the most important mineral elements in the human body and is closely related to the maintenance of human health. To prevent calcium deficiency, various calcium supplements have been developed, but their application tends to be limited by low calcium content and highly irritating effects on the stomach, among other side effects. Recently, calcium–peptide chelates, which have excellent stability and are easily absorbed, have received attention as an alternative emerging calcium supplement. Calcium-binding peptides (CaBP) are usually obtained via the hydrolysis of animal or plant proteins, and calcium-binding capacity (CaBC) can be further improved through chromatographic purification techniques. In calcium ions, the phosphate group, carboxylic group and nitrogen atom in the peptide are the main binding sites, and the four modes of combination are the unidentate mode, bidentate mode, bridging mode and α mode. The stability and safety of calcium–peptide chelates are discussed in this paper, the intestinal absorption pathways of calcium elements and peptides are described, and the bioavailability of calcium–peptide chelates, both in vitro and in vivo, is also introduced. This review of the research status of calcium–peptide chelates aims to provide a reasonable theoretical basis for their application as calcium supplementation products.
Deep Residual Learning Image Recognition Model for Skin Cancer Disease Detection and Classification

Skin cancer is undoubtedly one of the deadliest diseases, and early detection of this disease can save lives. The usefulness and capabilities of deep learning in detecting and categorizing skin cancer based on images have been investigated in many studies. However, due to the variety of skin cancer tumour shapes and colours, deep learning algorithms misclassify whether a tumour is cancerous or benign. In this paper, we employed three different pre-trained state-of-the-art deep learning models: DenseNet121, VGG19 and an improved ResNet152, in classifying a skin image dataset. The dataset has a total of 3297 dermatoscopy images and two diagnostic categories: benign and malignant. The three models are supported by transfer learning and have been tested and evaluated based on the criteria of accuracy, loss, precision, recall, f1 score and ROC. Subsequently, the results show that the improved ResNet152 model significantly outperformed the other models and achieved an accuracy score of 92% and an ROC score of 91%. The DenseNet121 and VGG19 models achieve accuracy scores of 90% and 79% and ROC scores of 88% and 75%, respectively. Subsequently, a deep residual learning skin cancer recognition (ResNetScr) system has been implemented based on the ResNet152 model, and it has the capacity to help dermatologists in diagnosing skin cancer.
Recent Advances in Encapsulation Techniques of Plant Growth-Promoting Microorganisms and Their Prospects in the Sustainable Agriculture

In addition to changing global demography and global warming, agricultural production systems around the world are threatened by intensive agricultural practices (overuse of land and excessive use of chemical fertilizers and pesticides) that deplete soils by affecting their dynamics and their fertility, pollute the environment, lower production, and alter biodiversity on a large scale. The use of bioformulations based on PGPMs (plant growth-promoting microorganisms) seems to be a promising and sustainable strategy to overcome these threats, thanks to their tolerance to various biotic and abiotic stresses and via their beneficial effects in promising plant growth, pest protection, bioremediation, and restoration of degraded lands. In recent years, particular attention has been paid to encapsulated formulations because they offer several advantages over conventional bioformulation (liquid and solid) related to shelf life, problems of survival and viability in the environment, and the efficiency of rhizospheric colonization. This review focuses on the types of encapsulations and the different technologies used in this process as well as the most commonly used substrates and additives. It also provides an overview on the application of encapsulated bioformulations as biofertilizers, biopesticides, or other biostimulators and summarizes the knowledge of the scientific literature on the development of nanoencapsulation in this sector.
microRNAs Control Antiviral Immune Response, Cell Death and Chemotaxis Pathways in Human Neuronal Precursor Cells (NPCs) during Zika Virus Infection

Viral infections have always been a serious burden to public health, increasing morbidity and mortality rates worldwide. Zika virus (ZIKV) is a flavivirus transmitted by the Aedes aegypti vector and the causative agent of severe fetal neuropathogenesis and microcephaly. The virus crosses the placenta and reaches the fetal brain, mainly causing the death of neuronal precursor cells (NPCs), glial inflammation, and subsequent tissue damage. Genetic differences, mainly related to the antiviral immune response and cell death pathways greatly influence the susceptibility to infection. These components are modulated by many factors, including microRNAs (miRNAs). MiRNAs are small noncoding RNAs that regulate post-transcriptionally the overall gene expression, including genes for the neurodevelopment and the formation of neural circuits. In this context, we investigated the pathways and target genes of miRNAs modulated in NPCs infected with ZIKV. We observed downregulation of miR-302b, miR-302c and miR-194, whereas miR-30c was upregulated in ZIKV infected human NPCs in vitro. The analysis of a public dataset of ZIKV-infected human NPCs evidenced 262 upregulated and 3 downregulated genes, of which 142 were the target of the aforementioned miRNAs. Further, we confirmed a correlation between miRNA and target genes affecting pathways related to antiviral immune response, cell death and immune cells chemotaxis, all of which could contribute to the establishment of microcephaly and brain lesions. Here, we suggest that miRNAs target gene expression in infected NPCs, directly contributing to the pathogenesis of fetal microcephaly.
Regge calculus models of closed lattice universes

This paper examines the behaviour of closed `lattice universes' wherein masses are distributed in a regular lattice on the Cauchy surfaces of closed vacuum universes. Such universes are approximated using a form of Regge calculus originally developed by Collins and Williams to model closed FLRW universes. We consider two types of lattice universes, one where all masses are identical to each other and another where one mass gets perturbed in magnitude. In the unperturbed universe, we consider the possible arrangements of the masses in the Regge Cauchy surfaces and demonstrate that the model will only be stable if each mass lies within some spherical region of convergence. We also briefly discuss the existence of Regge models that are dual to the ones we have considered. We then model a perturbed lattice universe and demonstrate that the model's evolution is well-behaved, with the expansion increasing in magnitude as the perturbation is increased.
The Interplay Among Savings Accounts and Network-Based Financial Arrangements: Evidence from a Field Experiment

This paper studies how formal financial access affects network-based financial arrangements. We use a field experiment that granted access to a savings account to a random subset of households in 19 Nepalese villages. Exploiting a unique panel dataset that follows all bilateral informal financial transactions before and after the intervention, we show that households that were offered access to an account increased their loans and total transfers to others, independent of the treatment status of the receiver. The increase seemed to be driven by treatment households with more assets and greater financial inclusion at baseline.