Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular characterization for some virulence and antibiotic resistance genes of Staphylococcus aureus isolated from dairy cattle's subclinical mastitis in EL- Sharkia Governorate

Staphylococcus aureus has emerged as a significant public health problem as it is often responsible for intramammary infection in bovine. The emergence of MRSA in animals was from an outbreak of mastitis in cattle which represent a great economic in milk industry. The present study was carried out to genotypically characterized S. aureus isolated from subclinical bovine mastitis in different farms in ELSharkia Governorate. A total of 15 S. aureus isolates were obtained from 100 subclinical mastitic milk samples and subjected to PCR for detection of some virulence and antimicrobial resistance genes using oligonucleotide primers that amplified genes encoding enterotoxin genes A to E (sea, seb, sec, sed, see), coagulase gene (coa), the IgG binding region of protein A (spa) and resistance gene as factor essential for expression of methicillin resistance (femA) which was used as an internal positive control and intrinsic methicillin resistance gene (mecA). PCR amplification revealed that all S. aureus isolates were enterotoxogenic and MRSA, harbored the genes encoding staphylococcal coagulase and the genes encoding the immunoglobulin G binding region of protein A. The data in the study provided an overview on the distribution of virulence determinants of MRSA strains which contributed to bovine mastitis problem in the Egypt farm.
The Timing of Repeat Purchases of Consumer Durable Goods: The Role of Functional Bases of Consumer Attitudes

In an attempt to bring consumer psychology theories into research on the timing of repurchase of consumer durables, the authors suggest that attitude functions (knowledge, value expressive, social adjustive, and utilitarian) can help explain and predict interpurchase intervals. Adopting an interactionist perspective, the authors propose that the effect of the attitude functions is contingent on contextual factors, which they theorize as the nature of the product (along public-private and luxury-necessity dimensions) and the nature of the decision (forced or unforced purchase decision). Hypothesis testing is facilitated by survey data on actual purchase decisions and hazard models that incorporate individual heterogeneity. The results support the suggested role of attitude functions in explaining and predicting interpurchase intervals and suggest means by which managers can position their products to shorten interpurchase intervals.
Extended optima and equilibria for continuous games

Abstract The new optima and equilibria discussed in the preceding two papers are compared with the results of bargaining experiments between two and three players performed by Fouraker and Siegel. Experiments where players have complete or incomplete information are considered. There is clear evidence that the new optima are operating, and that traditional optima–Cournot, Pareto and competitive (threat)–are less satisfactory in explaining the course of the whole bargaining process.
Deep convolutional network for tumor bud detection

DEEP CONVOLUTIONAL NETWORK FOR TUMOR BUD DETECTION Soner Koç M.S. in Computer Engineering Advisor: Çiğdem Gündüz Demir Co-Advisor: Selim Aksoy April 2019 The existence of tumor buds is accepted as a promising biomarker for staging colorectal carcinomas. In the current practice of medicine, these tumor buds are detected by the manual examination of a immunohistochemically (IHC) stained tissue sample under a microscope. This manual examination is time-consuming as well as it may lead to inter-observer variability. In order to obtain fast and reproducible examinations, developing computational solutions has been becoming more and more important. With this motivation, this thesis presents a fully convolutional network design for the purpose of automatic tumor bud detection, for the first time. This network design extends the U-net architecture by considering up-to-date learning mechanisms. These mechanisms include using residual connections in the encoder path, employing both ELU and ReLU activation functions in different layers of the network, training the network with a Tversky loss function, and combining outputs of different layers of the decoder path to reconstruct the final segmentation map. Our experiments on 3295 image tiles taken from 23 whole slide images of IHC stained colorectal carcinomatous samples show that this extended version helps alleviate the vanishing gradient problem and those related with having a high class-imbalance dataset. And as a result, this network design yields better segmentation results compared with those of the two state-of-the-art networks.
Graph Transformations for Model-based Testing

Model-based development uses modeling and simulation as essential means for specification, rapid prototyping, design, and realization of embedded systems. The classification-tree method complements model-based development with a formal approach for test case description and automation. This paper shows how “raw” classification trees are transformed into complete classification trees using an extensible tool, the classification-tree transformer (CTT). This tool and its domain specific extensions are generated using the graph rewriting system PROGRES.
EBOLA VIRUS DISEASE (EVD) OR EBOLA HEMORRHAGIC FEVER: A REVIEW

The present work describes Ebola virus disease (EVD), formerly known as Ebola hemorrhagic fever, is a severe, often fatal illness in humans. The virus is transmitted to people from wild animals and spreads in the human population through human-to-human transmission. The average EVD case fatality rate is around 50%. Case fatality rates have varied from 25% to 90% in past outbreaks. The first EVD outbreaks occurred in remote villages in Central Africa, near tropical rainforests, but the most recent outbreak in west Africa has involved major urban as well as rural areas. Community engagement is key to successfully controlling outbreaks. Good outbreak control relies on applying a package of interventions, namely case management, surveillance and contact tracing, a good laboratory service, safe burials and social mobilization. Early supportive care with rehydration, symptomatic treatment improves survival. There is as yet no licensed treatment proven to neutralize the virus but a range of blood, immunological and drug therapies are under development.
Determining the Hospital Information System (HIS) Success Rate: Development of a New Instrument and Case Study

BACKGROUND: Hospital Information System (HIS) is a type of health information system which is widely used in clinical settings. Determining the success rate of HISs is an ongoing area of research since its implications are of interest for researchers, physicians and managers. AIM: In the present study, we develop a novel instrument to measure HIS success rate based on users’ viewpoints in a teaching hospital. METHODS: The study was conducted in Ebnesina and Dr Hejazi Psychiatric Hospital and education centre in Mashhad, Iran. The instrument for data collection was a self-administered structured questionnaire based on ISSM, covering seven dimensions, which includes system quality, information quality, service quality, system use, usefulness, satisfaction, and net benefits. The verification of content validity was carried out by an expert panel. The internal consistency of dimensions was measured by Cronbach’s alpha. Pearson’s correlation coefficient was calculated to evaluate the significance of associations between dimensions. The HIS success rate on users’ viewpoints was determined. RESULTS: A total of 125 users participated in the study. The instrument was validated by an expert panel with the Content Validity Index (CVI): 0.85 and Content Validity Ratio (CVR): 0.86. The overall Cronbach’s alpha value of the instrument was 0.93. The Pearson correlation coefficient showed significant positive relationships among the investigated dimensions. On average, HIS success rate in the hospital under study was 65% (CI: 64%, 67%). The dimensions of “usefulness”, “system quality”, and “net benefits” showed the highest rates of success, respectively. CONCLUSION: The instrument used in this study can be adopted for HIS evaluation in future studies. In the current study, a method was developed to determine the HIS success rate based on users’ viewpoints. This method allows for the comparison of HIS success rates in various hospitals. As well, our findings underscore the viewpoints of HIS users in a developing country. ’
Numerical Analysis of Metal-Foam Application for Trailing Edge Noise Reduction

This manuscript presents a numerical investigation of the turbulent boundary layer-trailing edge (TBL-TE) noise reduction with an open-cell porous material. The implementation of the porous media is verified by emulating a facility for characterizing the flow resistivity of the porous material. Subsequently, the porous media is applied on the trailing edge of a NACA 0018 airfoil to examine its capability to mitigate TBL-TE noise. The airfoil is set at zero angle of attack and the chord-based Reynolds number is 2.8 × 10 5. Boundary layer profiles and integral boundary layer quantities have been compared with reference experimental data. The noise reduction obtained with the porous trailing edge at low to mid frequency ranges has been found to be in good agreement with the experiment. However, the simulation is unable to predict the noise increase at high frequency, which is considered due to the neglected surface roughness effects in the adopted porous media model. Conventional beamforming is also used to locate the dominant sound sources. In contrast with the solid trailing edge case, it has been found that the solid-porous interface is the location of the dominant sound source for the porous trailing edge case.
Food for Thought: How Skipping Lunch and Psychiatric Illness Affect Cognition

Meal skipping is a common disordered eating behavior in college-aged individuals. This behavior is associated with a variety of health risks, including nutritional deficits and an increased risk for eating pathology. Research has indicated that meal skipping is also associated with impairments in various domains of cognitive functioning, including in tasks involving working memory, sustained attention, and set-shifting ability. However, a "post-lunch dip" in cognitive performance has been shown in individuals who consume lunch for approximately two hours after consumption. Possible moderating factors within the relationship between meal skipping and cognitive functioning have yet to be examined, particularly in regards to the presence of psychopathology. Both depression and anxiety symptoms have been associated with impairments in tasks involving working memory, sustained attention, set-shifting ability, and motor speed, indicating that individuals with these disorders may be particularly vulnerable to cognitive impairments seen with meal skipping behavior. This study investigated how skipping lunch affects various domains of cognitive functioning (working memory, sustained attention, set-shifting ability, and motor speed) after the post-lunch dip period in a sample of college students (aged 18-25; N = 99), primarily focusing on whether depression and/or anxiety symptoms moderate this relationship. Understanding the mechanisms by which meal skipping behavior affects cognition by examining potential moderating effects of common eating disorder comorbidities, such as depression and anxiety, has implications for encouraging healthier eating habits and preventing eating disorder onset in a vulnerable population. SKIPPING LUNCH, PSYCHIATRIC ILLNESS, & COGNITION 3 Food for Thought: How Skipping Lunch and Psychiatric Illness Affect Cognition Meal skipping is a common disordered eating behavior in undergraduate students, with over 8 in 10 college females reporting skipping meals for weight loss (Kelly-Weeder, 2011; Huang, Song, Schemel, & Hoerr, 1994). In fact, meal skipping has been shown to be most prevalent in young adults (aged 18-30) compared to other age groups, with prevalence estimates reaching up to 87% in this population (Pendergast, Livingstone, Worsley, & McNaughton, 2016). Meal skipping is associated with depression, nutritional deficits, and an increased risk for eating pathology (Utter, Denny, Robinson, Ameratunga, & Crengle, 2012; T.S. Rao, Asha, Ramesh, & K.S. Rao, 2008). Given the associations of skipping meals with negative physical and mental health symptoms, it is critical to better understand (1) whether skipping meals may contribute to or exacerbate mental health symptoms, (2) whether these consequences are more severe for certain vulnerable individuals, and if so, (3) to explore the mechanisms whereby this behavior confers these consequences.
Detection and Isolation of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer

The detection and isolation problem of false data injection (FDI) attacks in large-scale smart grid systems, is investigated in this paper. The FDI attacks can bypass the traditional bad data detection techniques, by falsifying the process of state estimation. For this reason, the emergency of FDI attacks brings great risk to the security of smart grids. To address this crucial problem, a novel detection and isolation scheme against the FDI attacks for the large-scale smart grid system is proposed. We first design an interval observer to estimate the interval state of internally physical system accurately, based on the constructed physical dynamics of grid systems. Taking the bounds of internal state and external disturbance into account, the detection criterion that an alarm is generated when the interval residuals does not include the zero value is proposed. To address the limitation of precomputed threshold, we use the interval residuals regarded as a nature detection threshold to replace the evaluation function and detection threshold used in traditional attack detection methods. Furthermore, an attack signature logical judgment matrix-based isolation algorithm is further proposed to isolate the sensors, in which the FDI attacks may be injected into the attacked subarea. Finally, the effectiveness of the developed detection and isolation scheme is demonstrated by using detailed case studies on the IEEE 128-bus smart grid system.
Low Bit-Width Convolutional Neural Network on RRAM

The emerging resistive random-access memory (RRAM) has been widely applied in accelerating the computing of deep neural networks. However, it is challenging to achieve high-precision computations based on RRAM due to the limits of the resistance level and the interfaces. Low bit-width convolutional neural networks (CNNs) provide promising solutions to introduce low bit-width RRAM devices and low bit-width interfaces in RRAM-based computing system (RCS). While open questions still remain regarding: 1) how to make matrix splitting when a single crossbar is not large enough to hold all parameters of one weight matrix; 2) how to design a pipeline to accelerate the inference based on line buffer structure; and 3) how to reduce the accuracy drop due to the parameter splitting and data quantization. In this paper, we propose an RRAM crossbar-based low bit-width CNN (LB-CNN) accelerator. We make detailed discussion on the system design, including the matrix splitting strategies to enhance the scalability, and the pipelined implementation based on line buffers to accelerate the inference. In addition, we propose a splitting and quantizing while training method to incorporate the actual hardware constraints with the training. In our experiments, low bit-width LeNet-5 on RRAM show much better robustness than multibit models with device variation. The pipeline strategy achieves approximately $6.0\times $ speedup to process each image on ResNet-18. For low-bit VGG-8 on CIFAR-10, the proposed accelerator saves 54.9% of the energy consumption and 48.3% of the area compared with the multibit VGG-8 structure.
Archaeology, museums and virtual reality

This article looks at the idea that the virtual archaeological reconstructions seen in museums cannot be considered Virtual Reality (VR) as they are based on an artistic conception of the discipline. The cause is to be found in the origins of Archaeology, which began in the 18 th century and was closely linked to the History of Art. In the era of New Technologies, this concept has become both the cause and the consequence: determining the characteristics of VR from within the discipline, whilst simultaneously reinforcing the virtual reconstructions. To assess the relationship between VR and Archaeology, we must first establish a definition of Virtual Reality. Subsequently, we can take a brief look at the history so as to be able to understand the evolution of Archaeology and museums. This leads us to the analysis of some examples of VR in museums, from which we can gain conclusions on the current use of VR. Finally, we look at the possibilities for VR in terms of publicising Archaeology.
Integrating disaster risk reduction and climate change adaptation: a systematic literature review

ABSTRACT Climate Change is widely acknowledged to be increasing the scale and intensity of disasters. Literatures stress the need to link Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) to make more efficient use of resources and avoid overlapping. Despite growing recognition of the necessity for such linkages, little study has yet investigated the challenges confronting integration and how best to overcome them. We conducted a systematic literature review to better articulate the rationale for integrating DRR and CCA, to identify acknowledged challenges to integration, and to provide directions for future research. The review followed PRISMA guidelines and examined 37 peer-reviewed studies on linking DRR and CCA. Key challenges in integration are found to be: lack of capacity among actors and institutions, policy gaps, mismatches, governance failure, lack of coordination, fund-shortages, and obstructive influence from the influential decision makers. These challenges are underpinned by the political economy— the nexus of influencing actors, and their interests in funding mechanisms surrounding DRR and CCA. Given the underrepresentation of political economy analysis in the literature, this study recommends the needs for an in-depth political economy analysis to determine efficient governance frameworks, improved policies, and mechanisms for effective integration of DRR and CCA in different contexts.
Motivational Interviewing and Self–Determination Theory

Motivational interviewing has become widely adopted as a counseling style for promoting behavior change; however, as yet it lacks a coherent theoretical framework for understanding its processes and efficacy. This article proposes that self–determination theory (SDT) can offer such a framework. The principles of motivational interviewing and SDT are outlined and the parallels between them are drawn out. We show how both motivational interviewing and SDT are based on the assumption that humans have an innate tendency for personal growth toward psychological integration, and that motivational interviewing provides the social–environmental facilitating factors suggested by SDT to promote this tendency. We propose that adopting an SDT perspective could help in furthering our understanding of the psychological processes involved in motivational interviewing.
Unintended consequences of regulation FD: a natural experiment from China

ABSTRACT The aim of Regulation Fair Disclosure (Reg FD) is to prohibit selected disclosure and improve the information environment of capital markets. Using a natural experiment from China and applying a difference-in-differences design, we find that Reg FD constrains private communication between firms and analysts but has the unintended consequence of increasing earnings management, which deteriorates accounting information quality. Furthermore, the effect of Reg FD on earnings management is concentrated in firms without public management earnings forecasts (MFs), firms with strong incentives to meet or beat analysts’ targets, and firms with poor corporate governance. Overall, this study sheds light on the effect of Reg FD on the methods that managers use to meet analysts’ expectations and on how this effect varies with underlying firm characteristics.
Youth homelessness and self identity: a view from Canada

ABSTRACT This paper is based on the analysis of data collected from the National Canadian Homeless Youth Survey and addresses the question: why do some youth who received support from social service agencies that cater to the needs of homeless youth self-identify as being homeless while others do not? According to self-report survey data obtained from 1103 young people from over 50 youth homeless service centres from across Canada, findings show that youth who self-identified as being homeless shared several backgrounds and current situational characteristics not common among youth in the sample who did not consider themselves to be homeless. The findings illustrate the importance of past trauma and the intensity of day to day hardship in self-identifying as a homeless young person.
Evaluation of cortical and hippocampal cholinergic systems in rats treated with sparteine.

Sparteine was administered subcutaneously to male neonate rats, and the effects on muscarinic subunit receptor types m-m 4 from different brain regions were studied. A semi-quantitative reverse transcription-polymerase chain reaction and western blot was used to measure expression levels in the cerebral cortex and hippocampus. Sparteine (25 mg/kg body weight, on postnatal days 1 and 3) produced an important reduction of m 1 , m 2 and m 3 of mRNA and protein during the first three weeks of age in those animals under sparteine treatment compared to control group in cerebral cortex and hippocampus. Changes in the expression of the subunit m 4 were increased during the first two weeks of age in those animals under sparteine treatment compared to control group in cerebral cortex and hippocampus. We propose that an early excessive activation of muscarinic receptors could modify subunit expression and its structural composition on postnatal development. This, as part of a compensatory response by an altered neuronal circuit, suggests that these subunits will have an important role in the mechanisms of neuronal death.