Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Epstein- Barr Virus (EBV) with the Development of Nasopharyngeal Carcinoma (NPC) in Western Region of Iraq: Unmatched Molecular Case-Control Study

Background: Nasopharyngeal Carcinoma (NPC) is uncommon in Iraq, but its incidence is raising due to increased exposure to diverse risk factors. Many of the NPC-related risk factors are becoming more and more apparent in Iraq. The exactly risk factors for nasopharyngeal carcinoma (NPC) in Anbar province, Iraq are not known. Objectives: To determine the association between Epstein- Barr virus (EBV) infections and other risk factors with the development NPC of Iraqi patients. Patients and Method: Sixty-seven paraffin-embedded tissues of NPC cases, 134 normal noncancerous nasopharyngeal biopsy samples, and tonsillectomy specimens from patients with chronic hypertrophic tonsillitis as controls were enrolled in the study that was conducted between 12 January 2012 and 21 January 2019. DNA of EBV was extracted from both controls and neoplastic tissues and analyzed by PCR technique using primers specific to EBV Latent Membrane Protein-1 Oncogene (LAMP-1) for the presence of EBV. A Questioners data form for all patients and controls were filled by the researchers regarding other risk factors of NPC including the patient’s age, sex, residence, radiation exposure, history of chronic rhinitis, family history of NPC, tobacco smoking, alcohol consumption, herbal medicines, tea consumption, exposure to formaldehyde and exposure to different inhalants. Results: The following risk factors were found to be independently associated with illness: EBV (adjusted odds ratio [OR] 7.852, 95% confidence interval [CI] 2.22–27.72), herbal medicine (OR 19.051, CI 7.56– 47.95) and Family history of NPC (OR 63.717, CI 6.67–607.96). Conclusion: Combination of family history of NPC , EBV exposure and herbal medicine was a strong risk factor for NPC.
Harnessing the therapeutic myco-potential for concrete-crack healing: Prospects and snags

There has been a continual upsurge on research pertaining to bio-based/ microbial healing of cracks in concrete (a pre-requisite component when it comes to construction design). Albeit, the application of bacteria in this realm has been documented widely over the years, howbeit, delving into fungus based self-healing under the deleterious ambience of concrete with oxygen and nutrient limitation, moisture deficit and high alkalinity has captured recent research impetus. In this context, we have tried to mine the current contextual information to gauge whether research on fungal-based self-healing concrete could be worthwhile. Recent systematic screening encompassing the application of genetically engineered strains, attests the profound untapped potential of specific fungal species in assisting sustainable self-healing to ensure resilient infrastructure. Known for their adaptability under a plethora of environmental stress-conditions and architecturally endowed with large surface-active biomass, fungi can display both biomineralization and organomineralization, leading to rapid and profuse precipitation of CaCO3 (a befitting concrete-filler) for prospective sealing of cracks, even of large width, plausibly without any negative trade-off with respect to concrete’s strength. This article is thus compiled to mirror the various prospects, practical hitches and future direction of research in using fungi for concrete crack healing.
Importance sampling on the coalescent with recombination

Performing inference on contemporary samples of homologous DNA sequence data is an important task. By assuming a stochastic model for ancestry, one can make full use of observed data by sampling from the distribution of genealogies conditional upon the sample configuration. A natural such model is Kingman's coalescent, with numerous extensions to account for additional biological phenomena. However, in this model the distribution of interest cannot be written down analytically, and so one solution is to utilize importance sampling.In this context, importance sampling (IS) simulates genealogies from an artificial proposal distribution, and corrects for this by weighting each resulting genealogy. In this thesis I investigate in detail approaches for developing efficient proposal distributions on coalescent histories, with a particular focus on a two-locus model mutating under the infinite-sites assumption and in which the loci are separated by a region of recombination. This model was originally studied by Griffiths (1981), and is a useful simplification for considering the correlated ancestries of two linked loci. I show that my proposal distribution generally outperforms an existing IS method which could be recruited to this model.Given today's sequencing technologies it is not difficult to find volumes of data for which even the most efficient proposal distributions might struggle. I therefore appropriate resampling mechanisms from the theory of sequential Monte Carlo in order to effect substantial improvements in IS applications. In particular, I propose a new resampling scheme and confirm that it ensures a significant gain in the accuracy of likelihood estimates. It outperforms an existing scheme which can actually diminish the quality of an IS simulation unless it is applied to coalescent models with care. Finally, I apply the methods developed here to an example dataset, and discuss a new measure for the way in which two gene trees are correlated.
Bengali text summarization using TextRank, Fuzzy C-means and aggregated scoring techniques

In this world, it is very difficult and time consuming for humans to summarize large documents, reports, news and research articles. Multiple text summarization techniques play vital roles in picking the important points and sentences thus reducing the time and effort required to read a whole article. Numerous summarization techniques have been applied to the English language but comparatively work on Bengali text summarization is still limited. Furthermore, in our country, Bangladesh, all summarization is mainly done by humans. Keeping that in mind we aim to find a simple way of summarizing Bengali texts with the technology at hand. Text summarization can be of two types, either abstractive or extractive. In this paper we will use extractive text summarization to summarize Bengali passages, using Fuzzy C-Means, TextRank and Aggregate Sentence Scoring methodologies. We have also done a comparative study, among the 3 methodologies we have used and aim to find the most precise methodology for Bengali text summarization.
Scale and mealybug insects invade citrus orchards at Menoufia Governorate, Egypt.

00201066519100 / nehal.swelam@agr.menofia. edu.eg ABSTRACT A survey of insects infesting citrus orchards was conducted in Menoufia governorate at three localities during 2019. The most common insects were, cottony cushion scale, Icerya purchase, seychelles scale, Icerya seychellarum, comma scale, Lepidosaphes beckii, citrus black scale, Chrysomphalus aonidum, and red scale, Aonidiella aurantii. The obtained results revealed no infestation was recorded along with July and August of Icerya (Mealybugs) insects. However, there were two peaks recorded in May and November, as well as the highest averages were recorded in Tala center while the lowest were recorded in Alshohadaa center, moreover Shebin Elkom center occupied intermediate status between the two centers. There were no mealybugs insects during July and August, but the highest average numbers were recorded in May. Tala center orchard has the highest population of tested insects on Naval Orange along with the experiment. There were no Chrysomphalus aonidum insects at Shebin Elkom and Alshohadaa of Balady mandarin orchards during year months. Tala and Shebin Elkom orchards have similar numbers but Alshohadaa has smaller averages. Shebin Elkom orchard has the highest infection with Lepidosaphes beckii, and Aonidiella aurantia, while Alshohadaa has the least numbers. It could be recommended that control of these pests must be done along with June, July, and August, where the least population of insects.
Improving Technology Integration for the NESDI Program

The mission of the Navy Environmental Sustainability Development to Integration (NESDI) program is to provide innovative solutions “to minimize operational environmental risk, constraints, and costs while ensuring fleet readiness. ” The NESDI program currently faces difficulty in integrating developed technology, techniques, and tools (TTT) at the enterprise level. Through case study analysis, this research utilizes a combined method of quantitative and qualitative analyses aimed to improve technology integration. Quantitatively, measures of effectiveness (MOEs) are developed to determine the level of technology integration for completed NESDI projects and to identify significant technology characteristics to assist in predicting the likelihood of integration for future projects. Qualitative, comparative analyses between the NESDI process and the systems engineering (SE) process result in developing frameworks to guide the NESDI program’s integration of future technologies. Combined with a predictive model this effort improves the NESDI integration process.
Clustering and 5G-Enabled Smart Cities

This chapter highlights the importance of vehicular ad-hoc networks (VANETs) in the context of the 5G-enabled smarter cities and roads, a topic that attracts significant interest. In order for VANETs and its associated applications to become a reality, a very promising avenue is to bring together multiple wireless technologies in the architectural design. 5G is envisioned to have a heterogeneous network architecture. Clustering is employed in designing optimal VANET architectures that successfully use different technologies. Therefore, clustering has the potential to play an important role in the 5G-VANET-enabled solutions. This chapter presents a survey of clustering approaches in the VANET research area. The survey provides a general classification of the clustering algorithms, presents some of the most advanced and latest algorithms in VANETs, and it is among the fewest works in the literature that reviews the performance assessment of clustering algorithms.
Automatic Recognition of Handwritten Score Digits

Despite printed text being widely used since the introduction of computers and printers, several areas such as office automation, e-government, banking and education field still rely on manual data entry. Undeniably, manual data entry is very time consuming and human are prone to make mistakes during this task especially when the amount of the data to be entered is huge. Thus, recognition of handwritten digits plays an important role in life nowadays as it speeds up the data entry process. However, handwritten numerals recognition is a challenging problem as the handwriting styles are varying from person to person. In this project, a handwritten numerals recognition system is developed using Histogram of Oriented Gradients (HOG) as the feature extraction method. Several classifiers were also examined to determine the classifying method with the highest accuracy. The handwriting samples are scanned using an optical scanner and converted into digital images. After that, pre-processing steps such as segmentation, size normalization, and noise removal are applied to the scanned image to facilitate the feature extraction process.
Cross-linguistic variation in space-based distance for size depiction in the lexicons of six sign
 languages

This paper presents a semiotic study of the distribution of a type of size depiction in lexical signs in six sign languages. Recently, a growing number of studies are focusing on the distribution of two representation techniques, i.e. the use of entity handshapes and handling handshapes for the depiction of hand-held tools (e.g. Ortega et al. 2014). Padden et al. (2013) find that there is cross-linguistic variation in the use of this pair of representation techniques. This study looks at variation in a representation technique that has not been systematically studied before, i.e. the delimitation of a stretch of space to depict the size of a referent, or space-based distance for size depiction. It considers the question whether the cross-linguistic variation in the use of this representation technique is governed by language-specific patterning as well (cf. Padden et al. 2013). This study quantifies and compares the occurrence of space-based distance for size depiction in the lexicons of six sign languages, three of Western European origin, and three of West African origin. It finds that sign languages differ significantly from each other in their frequency of use of this depiction type. This result thus corroborates that the selection and distribution of representation techniques does not solely depend on features of the depicted image, but also on language-specific patterning in the distribution of representation techniques, and it adds another dimension of iconic depiction in which sign languages may vary from each other (in addition to the entity/handling handshape distinction). Moreover, the results appear to be areally defined, with the three European languages using this representation technique significantly more often than the three African languages.
Stochastic Distributed Frequency and Load Sharing Control for Microgrids With Communication Delays

This paper addresses the frequency restoration and accurate load power sharing problem of ac microgrids with massive penetration of distributed generators (DG) under time-delay communication in noisy environments. Existing distributed control methods commonly assume ideal communication among DGs. However, the channels are prone to additive noises and time delays, while each DG obtains noisy and delayed measurements of the states of its neighbors due to environmental noises and communication delays. The proposed distributed cooperative control strategy will achieve frequency restoration and accurate load power sharing among DGs through a sparse communication network subject to time delays and noise disturbances. The theoretical concepts and necessary conditions for stability and robust performance of the proposed distributed cooperative control strategy are outlined by the Lyapunov-Krasovskii functional method and stochastic differential equation theory. Furthermore, the proposed control strategy is implemented through sparse communication networks and thus, satisfies the plug-and-play feature of the future smart grid. Simulation results on an islanded microgrid test system are provided to reveal the effectiveness of the proposed control method to provide accurate proportional power sharing in the MATLAB/SimPowerSystems Toolbox.
Computation Enhancement using Reconfigurable Computing

In light of the industry’s constant need for better computer performance, this project aims to choose and evaluate an approach for facing this issue. The targeted category of computers is single board computers (e.g. Raspberry Pi). The approach utilized for enhancing performance is the use of reconfigurable computing as to execute computationally expensive calculations on a runtime custom-tailored hardware. The objective of this project is the test of the potential this approach has for increasing computers performance through comparing a software implementation of an algorithm with an FPGA assisted implementation of the same algorithm. The platforms chosen for this project are the Rapsberry Pi and the Parallella P1602 board with its Zynq SoC for the software implementation and the FPGA assisted implementation in that order. The chosen algorithm is Fourier Fast Transform due to its part in many DSP applications and its suitability for the project objective. While the software solution worked successfully resulting in an asymptotic cost of O(N log N); the reconfigurable computing solution couldn’t be completed due to time constraints and lack of experience of the student. Future work should complete the experiment and add a multicore implementation of the same algorithm to add yet another class to the comparison.
Resource Allocation in Uplink OFDMA for IEEE 802.11ax Networks via Lyapunov Optimization

We consider the scheduling and resource allocation problem in AP-initiated uplink OFDMA transmissions of IEEE 802.11ax networks. Due to the peculiar subcarrier allocation model of IEEE 802.11ax, the OFDMA resource allocation problem is non-convex and difficult to solve. However, because users have independent uplink power budgets, the problem of maximizing the utility of instantaneous user rates can be formulated as an assignment problem and, hence, can be efficiently solved by the Hungarian algorithm. In this paper, we aim to develop resource allocation polices that optimize the long-term system performance under certain QoS requirements and power expenditure limits of users. To this end, we maximize the utility of long-term average user rates subject to average rate and power constraints using Lyapunov optimization theory (LOT). By employing LOT, the original stochastic utility maximization problem is converted into a network stability problem of virtual queues and is solved by an online algorithm, dubbed drift-plus-penalty, with provable performance guarantees. The resulting scheduling policies have polynomial time complexity and perform arbitrarily close to optimal. Extensive simulations are finally conducted demonstrating the advantages of our approach over existing methods. Index Terms IEEE 802.11ax, OFDMA, Scheduling and Resource Allocation, Lyapunov Optimization
Passive multi-sensor target tracking method based on strength filter

The invention discloses a passive multi-sensor target tracking method based on a strength filter, so as to solve the problem that multiple targets cannot be precisely tracked under conditions that the clutter intensity is unknown and new targets are unknown in the prior art. The technical scheme for achieving the passive multi-sensor target tracking method based on the strength filter comprises the following steps: firstly, acquiring coordinates corresponding to passive multi-sensor pure angle measurements by a least-square cross bearing method based on the Newton iteration, associating the coordinates of a previous moment and a subsequent moment, thereby acquiring a new target set; secondly, generating a new target sample set based on a status distribution of the new targets; finally, utilizing the strength filter to perform the multi-target tracking. By the passive multi-sensor target tracking method based on the strength filter, not only the new target sample set can be quickly acquired, the target sample number can be reduced, the efficiency of tracking the targets can be increased, but also the clutter intensity can be updated in real time upon estimating the status of the targets. Therefore, the passive multi-sensor target tracking method based on the strength filter can be used for tracking multiple targets.
Life Satisfaction and Extra-curricular Activities among Medical Students at Ain Shams University

Background: A full understanding of life satisfaction (LS) and extracurricular activities (EAs) participation prevalence and reported participation barriers is imperative to successfully enhance and support the mental well-being and academic achievement of medical students. Objectives: To measure the level of life satisfaction using The Multidimensional Students‟ Life Satisfaction Scale (MSLSS), to measure the percentage of students participating in EAs among medical students at Ain Shams University, and to identify the relationship between LS, EAs, and academic achievement among the studied group. Method: a cross-sectional study was applied on a sample of 240 students using a self-administered questionnaire including socio-demographic data, faculty related data including average grade, data related to EAs participation and the MSLSS. Results: Mean life satisfaction score among the studied sample was 4.0±0.73. Participation in EAs was reported by 42.9% of students, the most frequently reported EAs (73.8%) was volunteering in health awareness campaigns. Multiple linear regression analysis revealed that female gender, living alone, being physically active, spending less free time on the internet not for studying purpose, spending more time with family and in studying explain about 15% of the variation in life satisfaction (R =0.149, F=2.43, P<0.01) Conclusion and Recommendations: Designing interventions to encourage students to increase their physical activity and to spend less free time surfing the internet to promote life satisfaction. Furthermore, integrating EAs in the medical curriculum to boost the students‟ quality of life, personal development, future career, and mental health.
Synthetic Datasets for Neural Program Synthesis

The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches achieve impressive results after training on randomly generated I/O examples in limited domain-specific languages (DSLs), as with string transformations in RobustFill. However, we empirically discover that applying test input generation techniques for languages with control flow and rich input space causes deep networks to generalize poorly to certain data distributions; to correct this, we propose a new methodology for controlling and evaluating the bias of synthetic data distributions over both programs and specifications. We demonstrate, using the Karel DSL and a small Calculator DSL, that training deep networks on these distributions leads to improved cross-distribution generalization performance.
Reduction and Introducer Concepts in d-Dimensional Contexts

Concept lattices are well-known conceptual structures that organise interesting patterns -the concepts- extracted from data. In some applications, the size of the lattice can be a problem, as it is often too large to be efficiently computed and too complex to be browsed. In others, redundant information produces noise that makes understanding the data difficult. In classical FCA, those two problems can be attenuated by, respectively, computing a substructure of the lattice -such as the AOC-poset- and reducing the context. These solutions have not been studied in d-dimensional contexts for d > 3. In this paper, we generalise the notions of AOC-poset and reduction to $d$-lattices, the structures that are obtained from multidimensional data in the same way that concept lattices are obtained from binary relations.
Comparative study on Apriori and FP growth algorithms in big data

A dataset is a collection of data. Today the huge amount of the data is being captured by information sensing devices such as mobiles, computers, sensors etc. These huge amounts of the data are now called as big data. Frequent Itemset mining is a tool for identifying the frequently occurring items together. There are many frequent Itemset mining algorithms like apriori, Eclat and FP growth. In the proposed work we use FP growth algorithm and compare it with Apriori algorithm and we show that FP growth algorithm is better when compared to the apriori algorithm.
Implementation of an Evolutionary Facial Recognition Algorithm on JETSON TK 1

This paper aims to contribute in visual computing over the implementation of face recognition algorithms on Jetson TK1. This powerful tool is a NVIDIA’s embedded Linux development platform featuring a Tegra K1 SOC which contains a CPU, a GPU and an ISP in a single chip. The Tegra K1 ‘class one’ is very faster than the former Tegra 2 ‘class two’ and is the most efficient GPU in the ARM world. This particularity added to some optimizations we have made on implementation strategy and on recognition face algorithms conduct us to reach good results in real time systems RTS.