Miracle-dz/UniFAR-FTUs huggingface dataset:
APPROXIMATE BAYESIAN INFERENCE FOR ANALYSIS OF SPATIO-TEMPORAL FLOOD FREQUENCY DATA BY ÁRNI

Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. Predictions of extreme floods within gauged and ungauged catchments is crucial to mitigate these disasters. In this paper, a Bayesian framework is proposed for predicting extreme floods using the generalized extreme-value (GEV) distribution. A major methodological challenge is to find a suitable parametrization for the GEV distribution when multiple covariates and/or latent spatial effects are involved and a time trend is present. Other challenges involve balancing model complexity and parsimony using an appropriate model selection procedure, and making inference based on a reliable and computationally efficient approach. We here propose a latent Gaussian modeling framework with a novel multivariate link function designed to separate the interpretation of the parameters at the latent level and to avoid unreasonable estimates of the shape and time trend parameters. Structured additive regression models, which include catchment descriptors as covariates and spatially correlated model components, are proposed for the four parameters at the latent level. To achieve computational efficiency with large datasets and richly parametrized models, we exploit a highly accurate and fast approximate Bayesian inference approach, which can also be used to efficiently select models separately for each of the four regression models at the latent level. We applied our proposed methodology to annual peak river flow data from 554 catchments across the United Kingdom. The framework performed well in terms of flood predictions for both ungauged catchments and future observations at gauged catchments. The results show that the spatial model components for the transformed location and scale parameters, as well as the time trend, are all important and none of these should be ignored. Posterior estimates of the time trend parameters correspond to an average increase of about 1.5% per decade with range 0.1% to 2.8% and reveal a spatial structure across the United Kingdom. When the interest lies in estimating return levels for spatial aggregates, we further develop a novel copula-based post-processing approach of posterior predictive samples, in order to mitigate the effect of the conditional independence assumption at the data level, and we demonstrate that our approach indeed provides accurate results.
ANTIMICROBIAL RESISTANCE AND BIOFILM FORMATION PATTERNS OF Escherichia coli ISOLATED FROM MARKET RAW MILK AT ZAGAZIG CITY

In the present study, one hundred samples of raw cow milk were collected randomlyfrom different dairy shops and markets in Zagazig city for isolation and identification of Escherichiacoli which is considered a reliable indicator for fecal contamination and an important cause of foodpoisoning. Identification was done microscopically, biochemically by different biochemical tests(IMVIC) and serologically. The incidence of E. coli in raw milk samples was 47%. Also, theserological identification of E. coli isolates revealed that O26 is the most predominant serogroup bypercentage of 21.3%. E. coli pose the greatest threat to human health because of its growing resistanceto antibiotics. Antimicrobial susceptibility testing (AST) was done by disc diffusion method against 10antimicrobials and the results revealed that E. coli isolates were highly resistant to amoxicillinclavulanate,ampicillin, cefotaxime and ceftazidime with percentages of 89.4%, 89.4%, 100.0% and100.0%, respectively. However, they were highly sensitive to chloramphenicol, ciprofloxacin andtetracycline with percentage of 100.0%, 100.0% and 93.6% respectively. In addition, 89.4% of E. coliisolates showed multi drug resistance (MDR). The ability of bacteria for adherence to food surfacesand biofilm formation is a source of food contamination that affect food safety and industry. Microtiter plate assay used for testing biofilm formation and represented that 78.7% of E. coli isolates werenon-biofilm producers, 6.4% were weak biofilm producers, 14.9% were moderate biofilm producersand none of isolates was strong biofilm producers
Embedding Augmented Reality Technology In The Educational Learning System

Augmented Reality (AR) technology has effective potential that enhances and replaces the old way of learning into a new digitized smart learning environment by converting the school books into a virtual 3D environment, which helps students extracting and absorbing complex concepts in less amount of time. AR is a computer vision-based technology that works by tracking the surrounding using a smartphone camera and then superimposing virtual 3D objects into the real world. The capabilities of AR make classes more engaging, collaborative, and interactive. Studying may not be every student's cup of tea but with the help of a smart learning environment; school lessons can be much more interesting, interactive, and fun to learn. This article aims to brief insight that the interactive AR chemistry application may lead the students to grasp difficult concepts of chemistry more easily entertainingly and engagingly.
Personalized Pricing through User Profiling in Social Networks

User profiling allows product sellers to identify users’ willingness to pay and enable personalized pricing. However, users’ information exploited in profiling is usually private and hard to obtain accurately due to users’ privacy concerns. With the increasing popularity of social networks, where users reveal their private information through social interactions, more sellers today profile users through their social data. This paper is the first to study how a seller optimizes personalized pricing through user profiling on social networks, where users proactively react by controlling their social activities and information leakage. We formulate and analyze a dynamic Bayesian game played between users and the seller. First, users decide their social activities by trading off the social network benefit against the potential risk of revealing private information. Then, the seller exploits users’ profiles to determine the personalized prices for the profiled users and a uniform price for the non-profiled users. It is challenging to analyze the Perfect Bayesian Equilibrium (PBE) of this game due to i) the randomness in user profiling, and ii) the coupling among users’ activity levels and that between the seller’s pricing decisions and users’ social activities. Despite the difficulty, we propose to alternate backward induction and forward induction to successfully solve the PBE. We show the surprising result that users’ activity levels do not monotonically decrease as the profiling technology improves. Instead, when user profiling is of high accuracy, the seller strategically chooses a high uniform price to stimulate their increased social activities to profile more users.
Lesson study: Investigation of English teachers' planning behaviors and preparation activities for English tasks in social construction practice

The aim of the study is to identify and explain preparation activities of three English teachers in lesson study process. The English teachers dealt with activities such as setting objectives, building content, organizing classroom practices and identifying success criteria for learning objectives and reflection in three different lesson study cycles based on social constructivism for about 5 months. In this study, the single case study design was adopted. Criterion sampling method was used to determine the participants. The group-based activities of teachers were repeated in each research lesson at different subjects and different grades. Instead of preparing a new format of the existing lesson plan in each research lesson, the participants prepared new lesson plans for each research lesson. The data were obtained by class observation, camera recordings, interview, and document examination of the lesson plans. It was observed that the teachers made more qualified decisions in planning teaching tasks and there were positive changes in their planning skills and teaching practices in the process in lesson study cycle.
AKRUAL: Jurnal Akuntansi

This research aims to capture the accounting behavior phenomenon in accounting students at private universities in Manado. This research is developed from the study of Lin & Fawzi (2006), Zandi et al.(2013), and Odia & Ogiedu (2013). To find out the inconsistency of these researches, this research focus to study about the students’s intention in choosing an accounting program. Partial least square is used to analyze the research model.This study used questionnaires and deep interviewed to collect the data. It reveals that job opportunities deliver positive effects on accounting students’s attitudes, and these attitudes affect their intention to select the program. The reputation on higher education institutions on accounting do not determine the students’s attitudes to enroll in accounting. Subjective norm do not influence students intention. Perceived behavior control positivelyencouragesthe purpose of choosing the accounting program. These findings designate implications for accounting lecturers to explain the benefits, job opportunities, and temporary accounting issues to students. Management of the accounting department needs to re-design their curriculum; thus, it can drive college students to compete and have fun throughout the learning process. Lecturers are obligated to update their learning methods and include educational characters in the provision of material to increase students's motivation.
Comparison of two rapid immunoassays for screening of hcv infection in dialysis patients

Introduction: In case of diagnosis of infectious disease, dissonant results may have serious outcomes among the patients as it causes un-necessary mental stress and tension. For appropriate diagnosis of infection as well as disease management and prevention, detection of appropriate test kit is necessary. Method: ELISA was used as gold standard for comparative evaluation. Ninety two samples were collected and processed using Elisa and two rapid immune assay test kits (HCV Tridot, FLAVI check HCV). Results: From the results, it was revealed that, out of 92 serum samples collected, male preponderance (69.5%) was seen. Taking HCV Microlisa as a standard reference test, out of 92 samples, 35.8% sero-positivity was observed. In the present study, HCV Tridot sensitivity was 96.9% and FLAVI check sensitivity was 93.9%. Conclusion: Rapid assays must be used with care and it is also significant to validate these rapid assays by testing them in a given population to evaluate the effectiveness of these assays. Atleast two rapid assays should be carried out for detecting HCV antibodies in dialysis patients as preliminary tests and should further confirmed by gold standard assay method like ELISA for accuracy.
Audio-based Motion Detection

Motion detection is an essential technology and it has numerous use-cases, such as security tracking, automated door opening systems, and IP cameras. Commonly, passive infrared sensors or radio frequency sensors are used for motion detection. However, this thesis focuses on performing motion detection using existing hardware at audio frequencies. Specifically, it aims to implement a motion detection algorithm using existing speakers and microphones on each of two intercom devices designed by Axis Communications, AXIS I8016-LVE Network Video Intercom (Device 1) and AXIS A8207-VE Mk II Network Video Door Station (Device 2). The goal of implementing such an algorithm is to save power and cost by re-using existing hardware. In this thesis, data was collected and studied for both stationary and moving objects to understand the systems’ behaviour. The range detection capability for different signal parameters were compared and analysed using a stationary object, at different positions within the range of the systems. This was done to find the optimum signal parameters for the motion detection algorithm. Once these were determined, data was collected and analysed for a moving object. Based on this data, an optimisation and tracking algorithm was designed to obtain better results from the systems. The results show that for Device 1, the algorithm can detect any movement of a person within the range of the system. However, for Device 2, the algorithm can differentiate between different movements, allowing the detection of a person who is approaching the device, rather than just someone who is just passing by. The latter enables power saving for the device, since its camera will only turn on when someone approaches the device with the intention of using it.
SLIDE : Training Deep Neural Networks with Large Outputs on a CPU faster than a V100-GPU

Deep Learning (DL) algorithms are the central focus of modern machine learning systems. As data volumes keep growing, it has become customary to train large neural networks with hundreds of millions of parameters with enough capacity to memorize these volumes and obtain state-of-the-art accuracy. To get around the costly computations associated with large models and data, the community is increasingly investing in specialized hardware for model training. However, specialized hardware is expensive and hard to generalize to a multitude of tasks. The progress on the algorithmic front has failed to demonstrate a direct advantage over powerful hardware such as NVIDIA-V100 GPUs. This paper provides an exception. We propose SLIDE ( S ub-LI near D eep learning E ngine) that uniquely blends smart randomized algorithms, with multi-core parallelism and workload optimization. Using just a CPU, SLIDE drastically reduces the computations during both training and inference outperforming an optimized implementation of Tensorﬂow (TF) on the best available GPU. Our evaluations on industry-scale recommendation datasets, with large fully connected architectures, show that training with SLIDE on a 44 core CPU is more than 3.5 times (1 hour vs. 3.5 hours) faster than the same network trained using TF on Tesla V100 at any given accuracy level. On the same CPU hardware, SLIDE is over 10x faster than TF. We provide codes and scripts for reproducibility.
Comparison Between Virtual Reality and Physical Flight Simulators for Cockpit Familiarization

Airlines and flying schools use high-end physical flight simulators (PFS) to reduce costs and risks of pilot training. However, such PFS with full-scale cockpits have very high acquisition and operation costs. In contrast, recent consumer-grade and off-the-shelf soft- and hardware can be used to create increasingly realistic virtual reality flight simulators (VRFS) that could potentially serve as cost-efficient and flexible alternatives. We present a user study with 11 participants to determine whether consumer-grade VRFS can supplement or even replace a PFS during cockpit familiarization training (CFT). We compared a full-scale Boeing 737-800NG PFS with a VRFS based on off-the-shelf flight simulator software combined with a consumer-grade head-mounted display and either finger tracking or a handheld controller as input device. Participants performed instrument reading tasks and check procedures from the aircraft’s operating manual. We did not observe statistically significant differences in successful instrument reading tasks, error rates and task completion between PFS and VRFS during CFT. However, we found that VRFS’ Mental Demand, Physical Demand, Effort, task completion times, and levels of simulator sickness were significantly higher and exceeded acceptable levels. We conclude that future consumer-grade VRFS will need to improve soft- and hardware for interacting with simulated switches and reduce simulator sickness before they can serve as PFS alternatives for CFT.
Selection of coagulant using jar test and analytic hierarchy process: A case study of Mazandaran textile wastewater

Textile factories are one of the industries which its wastewater treatment is a challenging issue, especially in developing countries and a conventional treatment cannot treat all its pollutants properly. Using chemical coagulants is a technique for physical and chemical primary treatment of the wastewater. We applied jar test for selection of suitable coagulant among the five coagulants including alum, calcium hydroxide, ferrous sulfate, ferrous chloride and barium chloride for the effluent of wastewater in Mazandran textile factory located in Mazandran Province, Iran. In addition, jar test, we also used analytic hierarchy process (AHP) method considering criteria which included coagulation cost, sensitivity to pH change, the amount of sludge generation and side effects for coagulation. The results of the jar test indicated that calcium hydroxide was proper among the coagulants which it removed 92.9% total suspended solid (TSS), 70%dye and30% chemical oxygen demand. The AHP analysis presented that calcium hydroxide is more suitable than other coagulants considering five criteria. wastewater treatment using the jar test. Barredo-Damas et al. applied jar test before and after using ozone for treatment of wastewater of textile factories. Their result indicated removal of 57% COD and 95% turbidity. al. used electrical coagulation, including aluminum and iron electrode factory of TSS and Selection of coagulant using jar test and analytic hierarchy process. technique for Enrichment Evaluation (PROMETHEE) and hierarchy Grey Relation Analysis (GRA) techniques for appropriate selection of wastewater treatment. They suggested that the fuzzy analytical hierarchy process (FAHP) is integrated with a preference ranking organization method for enrichment evaluation (PROMETHEE) technique and the FAHP is integrated with hierarchy Grey Relation Analysis (GRA) method.
P&S Requirements for ePartner-supported Astronaut-Rover Teams during Planetary Surface Operations

In the Mission Execution Crew Assistant (MECA) project, an industrial team has been compiling the Requirements Baseline document for a future manned planetary surface exploration mission to Mars. The project assumes that, by the time such a mission launches, astronauts will routinely work together as a team with autonomous rovers. The MECA system will form a network in which each MECA unit is an ePartner, capable of sensing and effecting the physical, cognitive, and affective status of its user. This paper presents the current MECA requirements for planning and scheduling for astronaut-rover teams supported by ePartners during planetary surface operations.
Efficacy of Silvernano Bacterial Cellulose Insole on Micrococcus sedentarius Growth

Silver nanoparticles (Ag0) impregnated into bacterial cellulose (BC) by immersion in an antimicrobial concentration of 1,700 μg/ml (10 mM) AgNO3 solution were used to synthesize silver nanoparticle-impregnated bacterial cellulose (Ag0BC) composites. Ag0BC incorporated into insertable footpad was developed for reduction of foot odor associated with pitted keratolysis, primarily caused by Micrococcus sedentarius. A scanning electron microscope (SEM) revealed the porous structure of Ag0BC fibers impregnated with Ag0 particles. The antibacterial activity of Ag0BC for Micrococcus sedentarius revealed a growth inhibition zone of 30 mm in diameter by disc diffusion method. The minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC) of AgNO3 solution against Micrococcus sedentarius were 0.42 μg/ml and 0.83 μg/ml, respectively. The effficiency of an Ag0BC footpad applied to shoe samples was measured via total bacterial counts over a time period of 14 days. As expected, the percentage of surviving bacteria in three different types of shoe samples was gradually decreased in the period beginning on the 2nd day. Moreover, no bacterial growth was found in any shoe sample within 8–14 days. However, the cytotoxicity of 5.31–1,700 μg/ml AgNO3 concentrations revealed 50% growth inhibition (IC50) on human dermal fibroblast cells by resazurin microplate assay (REMA). Thus, it can be concluded that Ag0BC, synthesized with lower doses of AgNO3 concentration (< 1,700 μg/ml), may be able to apply in Ag0BC footpad production for inhibition of Micrococcus sedentarius growth and prevention of foot odour in different types of footwears.
Study of Ambient Light Impact on RSS-Based Indoor Positioning System Using Visible Light Communication

An optical wireless positioning system that uses white light-emitting diodes (LEDs) based on Visible Light Communication (VLC) is presented in this paper. In the proposed system, four LEDs are employed for illumination and communication. The trilateration technique is used for determining the receiver’s location and the practical Received Signal Strength (RSS) measurements are used for distance estimation. The impact of ambient light on Signal-to-Noise-Ratio (SNR) is studied by taking into consideration the ﬁrst-order light reﬂection oﬀ of the ceiling, ﬂoor, and walls for channel modeling with both direct and indirect sunlight exposure. A modeling equation from the RSS measurements is proposed. The results show that the illuminance in all of the places in the room can be satisﬁed. Furthermore, the average positioning error by adopting the derived equation and trilateration technique was an error of less than 3 cm.
PGPM_A_268986 381..393

1Department of Clinical Pathology, National Cancer Institute, Cairo University, Cairo, Egypt; 2Department of Clinical Pathology, Faculty of Medicine, Zagazig University, Zagazig, Egypt; 3Biochemistry Department, Faculty of Pharmacy, Damanhour University, Damanhour, Egypt; 4Clinical Pharmacy Department, Faculty of Pharmacy, Ahram Canadian University, Giza, Egypt; 5Clinical Pharmacy and Pharmacy Practice Department, Faculty of Pharmacy, Damanhour University, Damanhour, Egypt Introduction: Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults. HLA-DR and CD117 (c-Kit) are important diagnostic markers of AML. Our objective is to determine the prognostic significance of HLA-DR and CD117 expressions in newly diagnosed AML patients and determine the correlation between HLA-DR and CD117 expressions and other prognostic markers such as cytogenetic abnormalities, FLT3ITD, response to treatment, and patient’s survival. Methods: This study included 100 newly diagnosed AML patients. All patients were subjected to clinical, morphological, cytochemical, cytogenetic analysis, molecular genetic analysis to detect FLT3-ITD, and Flowcytometric detection of HLA-DR, CD117, and CD 34. Results: The results showed that HLA-DR expression was found in 75 patients (77.3%), while CD117 expression was found in 63 patients (64.9%). Patients with HLA-DR expression showed significantly higher mean Hb concentration, significantly higher platelet count, associated with AML-FAB subtypes (M0, M1, and M2), CD34 expression, and favorable cytogenetic group. M3 subtype was significantly associated with HLA-DR-ve. While patients with CD117 expression showed significantly lower platelets count. Double positive patients (HLA-DR+ve/CD117+ve) showed significant association with the intermediate cytogenetic group, while double-negative patients (HLA-DR-ve/CD117-ve) were associated with the favorable and intermediate cytogenetic group and either positive (HLA-DR+ve /CD117-ve or HLA-DR-ve/CD117+ve) associated with poor cytogenetic groups. FLT3-ITD expression had significantly worse overall survival. Conclusion: The current study suggested that the expression of CD117 and HLA-DR may be a prognostic marker in AML, as they are associated with M0, M1, and M2 FAB subtypes; moreover, patients with combined HLA-DR and CD117 positive expression are associated with CD34 expression and intermediate cytogenetic group.