Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion of cosmic rays in MHD turbulence

We review some recent findings on diffusion of cosmic rays (CRs) in magnetohydrodynamic (MHD) turbulence obtained by adopting the numerically-tested model of MHD turbulence, including perpendicular superdiffusion of CRs, inefficient gyroresonant scattering by Alfvén and slow modes with scale-dependent turbulence anisotropy, resonance-broadened Transit Time Damping (TTD) interaction, and mirror diffusion. As the diffusion behavior of CRs strongly depends on the properties of MHD turbulence, theoretical modeling of CR diffusion, its numerical testing, and interpretation of CR-related observations require proper modeling of MHD turbulence.
Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks

The coronavirus is a family of viruses that cause different dangerous diseases that lead to death. Two types of this virus have been previously found: SARS-CoV, which causes a severe respiratory syndrome, and MERS-CoV, which causes a respiratory syndrome in the Middle East. The latest coronavirus, originated in the Chinese city of Wuhan, is known as the COVID-19 pandemic. It is a new kind of coronavirus that can harm people and was first discovered in Dec. 2019. According to the statistics of the World Health Organization (WHO), the number of people infected with this serious disease has reached more than seven million people from all over the world. In Iraq, the number of people infected has reached more than twenty-two thousand people until April 2020. In this article, we have applied convolutional neural networks (ConvNets) for the detection of the accuracy of computed tomography (CT) coronavirus images that assist medical staffs in hospitals on categorization chest CT-coronavirus images at an early stage. The ConvNets are able to automatically learn and extract features from the medical image dataset. The objective of this study is to train the GoogleNet ConvNet architecture, using the COVID-CT dataset, to classify 425 CT-coronavirus images. The experimental results show that the validation accuracy of GoogleNet in training the dataset is 82.14% with an elapsed time of 74 minutes and 37 seconds.
A Pen Is All You Need Online Handwriting Recognition using Transformers

In 2020, STABILO released the novel OnHW-chars dataset, which contains time series data recordings of characters written with a sensor-enhanced pen. This dataset was accompanied by an extensive exploratory work, presenting baseline accuracies for numerous types of Machine Learning and Deep Learning based classiﬁers. In 2021, STABILO released a new dataset, containing recordings of written mathematical equations. In this paper, we explore the possibility of applying the recently popularized Transformer architecture to this new dataset. We present a simple but eﬀective adaptation of the Trans-former model, where we use two convolutional layers for embedding the input sequence data. With this model, we achieve 92.69% accuracy per predicted individual to-ken. This accuracy successfully highlights the eﬀectiveness of the Transformer architecture in sequence to sequence problems, and encourages further experiments with this model on the OnHW datasets.
Assessment of water quality of Obueyinomo River, Ovia North East Local Government Area, Edo State, Southern Nigeria

This study was carried out to assess the water quality of Obueyinomo River using water quality index. Ambient and water temperatures were determined in-situ while total dissolved solids (TDS), total suspended solids (TSS), total solids (TS), turbidity, pH, conductivity, hardness, alkalinity, dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), chloride, nitrate, phosphate, calcium and magnesium levels were analyzed in the laboratory using standard techniques. With the exception of DO, the other physicochemical parameters showed no significant difference (p>0.05) across the stations. Distribution patterns of physicochemical parameters in stations 1, 2 and 3 were analyzed using PCA. There was a strong association for stations 1, 2 and 3 which was also highly associated with nitrates. Hardness, magnesium calcium and phosphate formed a cluster showing the contributions of these ions to water hardness. Alkalinity, suspended solids and chloride formed a cluster suggesting that the suspended solids are largely composed of chlorides. Turbidity, pH, BOD and DO at all the stations exceeded the Federal Ministry of Environment standards for surface water. The water quality index (WQI) values at stations 1, 2 and 3 were 138.45, 122.70 and 170.01 respectively. The parameters responsible for the high values of WQI as indicated by the water quality rating include turbidity, pH, dissolved oxygen and biochemical oxygen demand. The high WQI values in all the stations studied which exceeded the benchmark of 100 showed that the water from this river is unfit for drinking purposes and should be treated before consumption by inhabitants of the area. Keywords: Physicochemical parameters, River, Water quality index, Contamination
Diabetic Retinopathy Pathological Signs Detection using Image Enhancement Technique and Deep Learning

— The screening of diabetic retinopathy (DR) affects the visual inspection of retina images taken by ophthalmologists to detect the specific signs of pathology such as exudate, hemorrhage (HEM) and microaneurysm (MA). However, this process is currently conducted manually in many hospitals. Therefore, it is time-wasting and risky for humans to make mistake. In general, this paper introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. It also involves an image pre-processing enhancement technique to support accuracy on deep learning for DR classification. For the image
Innovative Flipped Classroom Approach for TESL Subjects: From Students’ Perspective

The importance of active learning and higher order thinking skills among students at higher learning institutions have steered lecturers to employ the flipped classroom approach in their classes. The move is hoped to assist students to become active recipients of knowledge and break away from the traditional learning method where knowledge is only imparted by lecturers. A study was conducted to investigate: (1) the TESL students’ ability to research and present their findings related to a teaching method via group posters when the flipped classroom approach is utilized in their Methodology in TESL 1 subject, and (2) their views on the feasibility of using flipped classroom approach for other TESL subjects. This study had adopted the mixed method approach in which an interview guide was used to address the first research question and a questionnaire was used to address the second research question. From the interview, it is found that majority of the TESL students had managed to research the teaching method assigned to them, had understood what they were reading, and were able to present the teaching method in the form of a poster. From the questionnaire data, it was found that majority of the TESL students agreed the flipped classroom approach can be implemented for other TESL subjects although a few of them felt that it might only be suitable for certain topics or subtopics as they were afraid of inaccurate knowledge being transferred if there is no proper guidance from the lecturer. Nevertheless, for this study, it can be concluded that the flipped classroom approach had enhanced the students’ understanding of the given topic, and it is feasible for other TESL subjects.
The Role and Value of Data in Realising Circular Business Models – a Systematic Literature Review

Purpose: A systematic review of the literature on circular business models was performed, for synthesis of what it reveals about the role and value of data in those models. The increasing quantity of supply-chain and life-cycle data available has potential to be a significant driver of circular business models. The paper describes the current state of knowledge and identifies avenues for further research related to use of various forms of data in the models. Design: A systematic review of literature on the use of data in circular business models was carried out, to inform understanding of the state of knowledge and provide a firm foundation for further research. Findings: The literature reviewed points to fragmented understanding of the role and value of data in circular business models. Nonetheless, scholars and practitioners commonly see data as a driver and enabler of circular economy. The article identifies two distinct approaches to value for data as presented in the corpus and discusses what types of data seem to be valuable in a circular business-model context. Among the further research opportunities are work on data as a source of business-model innovation and on collaboration in capturing the value of data in circular business models. Value: The study provides new insight on the nexus of circular business models and data, and it represents one of the first comprehensive reviews addressing data’s value in a networked circular-economy context. Please cite this paper as: Luoma, P., Toppinen, A. , and Penttinen, E. (2021), The Role and Value of Data in Realising Circular Business Models – a Systematic Literature Review, Journal of Business Models, Vol. 9, No. 2, pp. 44-71
Vegetation management of the western sierras and plains municipality of Catorce , San Luis Potosí

Plant community management can use multivariate methods to find response patterns and generate hypotheses about possible relationships in species composition with respect to environmental gradients. The objective was to record patterns of distribution and uses of vegetation in the municipality of Catorce, San Luis Potosi. In 2019, six transects were shown, from the foot of the undergrowth to the west of the Sierra de Catorce, an endorheic drainage valley or plain and a low mountain range folded with a hill. Floristic composition, vertical and horizontal structure, and soil characteristics were analyzed at each site. Desert thickets of rosetophile, microphile and thorny forest were found. The indirect environmental factors that best explain the distribution are the porosity of the soil, pH, K, Ca, Fe and altitude, factors that influence the availability of water. The main use of plants is traditional medicinal.
HeLayers: A Tile Tensors Framework for Large Neural Networks on Encrypted Data

Privacy-preserving solutions enable companies to offload confidential data to third-party services while fulfilling their government regulations. To accomplish this, they leverage various cryptographic techniques such as Homomorphic Encryption (HE), which allows performing computation on encrypted data. Most HE schemes work in a SIMD fashion, and the data packing method can dramatically affect the running time and memory costs. Finding a packing method that leads to an optimal performant implementation is a hard task. We present a simple and intuitive framework that abstracts the packing decision for the user. We explain its underlying data structures and optimizer, and propose a novel algorithm for performing 2D convolution operations. We used this framework to implement an inference operation over an encrypted HE-friendly AlexNet neural network with large inputs, which runs in around five minutes, several orders of magnitude faster than other state-of-the-art non-interactive HE solutions.
Reproducing Reality with a High-Dynamic-Range Multi-Focal Stereo Graphics

(c). With well-established methods for producing photo-realistic results, the next big challenge of graphics and display technologies is to achieve perceptual realism — producing imagery indistinguishable from real-world 3D scenes. To deliver all necessary visual cues for perceptual realism, we built a High-Dynamic-Range Multi-Focal Stereo Display that achieves high resolution, accurate color, a wide dynamic range, and most depth cues, including binocular presentation and a range of focal depth. The display and associated imaging system have been designed to capture and reproduce a small near-eye three-dimensional object and to allow for a direct comparison between virtual and real scenes. To assess our reproduction of realism and demonstrate the capability of the display and imaging system, we conducted an experiment in which the participants were asked to discriminate between a virtual object and its physical counterpart. Our results indicate that the participants can only detect the discrepancy with a probability of 0.44. With such a level of perceptual realism, our display apparatus can facilitate a range of visual experiments that require the highest fidelity of reproduction while allowing for the full control of the displayed
Assessment of small holder farmers garlic (Allium sativum L.) production practices under irrigated farming system in the Highlands of Ethiopia

Garlic is a fundamental component of many dishes in Ethiopia. However, there is a low production of this crop in the Yilmana Densa district of Amhara Region (Ethiopia). Thus, a study was initiated in 2014/2015 to assess farmer’s garlic production practices under an irrigated farming system in the Yilmana Densa district (Adet Zuria, Goshaye and Ambesit kebels) of Ethiopia. Production data were collected using semi-structured questionnaires from 30 garlic producing households where the whole value chain of garlic production system were recorded. Results showed that the agronomic and cultural practices employed by farmers contributed to the low productivity of garlic. Major constraints of garlic production include lack of suitable irrigated land, lack of quality planting material, shortage of irrigation water, fertilizers and pesticides, lack of money for input purchase, and disease incidences (white rot). For enhancement of production and productivity of garlic, it is necessary to solve problems associated with the production of garlic through extension activities.
Channel Islands CALIFORNIA STATE UNIVERSITY Feasibility of Real-Time Weed Detection in Turfgrass on an Edge Device

Weed control is a challenging issue in turfgrass management. While Precision Agriculture is robust in the literature and production, it is lagging in the Groundskeeping industry. One area that can benefit from Artificial Intelligence is weed management in turfgrass. Object Detection is one of the critical tasks in state-of-the-art autonomous systems. Recent developments in Deep Learning technology and soft­ ware have allowed significant increases in detection accuracy and speed. A combination of lightweight Convolutional Neural Network mod­ els, platform-specific optimizations, and model quantization have con­ tributed to the performance increase seen in object detectors. This thesis explores multi-dimensional trade-offs in Object Detection to as­ sess the feasibility of real-time weed detection on an edge device. Two viable models were discovered that could run in real-time, at least 30FPS, on the Jetson Nano 4GB. These results imply weed detection within turfgrass is feasible using a low-cost energy-constrained edge device and serves as a precursor to an autonomous robotic implemen­
Deprescribing of Inappropriately Consumed Proton Pump Inhibitors and its Clinical Consequences

The study aimed to deprescribe inappropriate consumption among Proton Pump Inhibitor (PPI) users and assess the clinical consequences of deprescribing. This is a prospective interventional study carried out among patients aged >18 years and consuming PPI. The “potentially inappropriate” patients were counselled on harms of inappropriate PPI usage, impact of healthy dietary and lifestyle habits employing a Patient Information Leaflet (PIL). The patients deprescribed were followed up through telephonic communication. Global Overall Symptom scale (GOS) was utilized to assess gastric discomfort before and after deprescribing. From 745 patients recruited, 226 (30.33%) were classified under “potentially inappropriate”, out of which 113 (15.16%) were deprescribed. When paired t-test was applied, GOS scale indicated significant difference in symptoms before and after deprescribing (Pvalue<0.01). Deprescribing of PPI decreased the incidence of long-term ADR, cost burden and promotes healthy lifestyle therefore, may help improving Quality of Life (Q0L).
The Political Economy of Decentralization: Evidence from Bank Bailouts∗

We exploit a unique set-up in Germany to explore how the organizational design of bailout institutions affects the efficacy of the distress resolution. Financial distress of savings bank is resolved either by a county-level politician (decentralized) or by a state-level association (centralized). While the occurrence of distress is not related to the electoral cycle, the probability of local politicians injecting taxpayers’ money into a bank in distress is 30 percent lower in the year directly preceding an election. Using the electoral cycle as an instrument for who rescues the distressed bank, we find that banks that are rescued by local politicians perform more poorly, provision credit less efficiently, and grant more preferential loans when compared with association led bailouts. We also observe lower growth in income and employment in areas exposed to decentralized bailouts. Overall, our results highlight the political economy of decentralization, with local politicians deriving private benefits from controlling the bank.
Evolutionary algorithms and nuclear magnetic resonance of oriented molecules

Chemistry Department, University of British Columbia, Vancouver, BC, Canada Laser Centre, Vrije Universiteit, Amsterdam, The Netherlands Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada Felix Laboratory, Molecularand Biophysics Group, Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands Department of Chemistry, The University of Winnipeg, Winnipeg, MB, Canada
Professional self-concept and job satisfaction among nurses in psychiatric clinics

Background: Nurses’ professional self and job satisfaction directly affect their professional practices. For this reason, it is important to find out about their perceptions of their professional self, the factors that affect and are affected by the professional self, and nurses’ job satisfaction. Purpose: The aim of this study was to determine the relationship between the professional self-concept and job satisfaction of nurses working in psychiatry clinics. Material and Methods: The universe of this descriptive and correlational study was the psychiatric nurses in Turkey and its sample comprised of 103 nurses employed in the psychiatry clinics and polyclinics of university hospitals randomly selected from provinces representing the seven geographical regions in Turkey. The data collection tools used in the study were Information Form, Professional Self Concept Scale and Nursing Job Satisfaction Scale in Nurses. Results: Psychiatric nurses have a high professional self-concept and moderate job satisfaction. There is a statistically significant positive correlation between the professional self-concept and job satisfaction of nurses. Conclusion: Professional self-concepts and job satisfaction of psychiatric nurses are affected by demographic characteristics and these two variables are related to each other.
Performance Evaluation of Different Machine Learning Frameworks for Facial Expression Recognition

: Image processing is one of the hot topics nowadays. Machine learning frameworks like Pytorch, Keras, Tensor Flow etc. all provides support for solving image processing related problems. But each framework has its own philosophy and system requirements. It is difficult for the user to choose the framework perfect for his system. The goal of this work is to provide a comparative analysis of different machine learning algorithms in terms of memory and system requirements. The whole experiment was performed on google collaboration platform, used convolutional neural network algorithm and coded in Pytorch, Keras and Tensor Flow. From the experiments, most of the cases we found out that increasing the batch training size, the training time decreases in all frameworks.
CMAR_A_241620 4889..4898

Xiaoting Zhao 1,2 Mei Jiang Ziyu Wang Xiaohong Chen 3 Hongzhen Wang 4 Wentao Yue 1 Chao Cai 1Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Cellular and Molecular Biology, Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Tongzhou, Beijing, People’s Republic of China; 3Department of Otolaryngology, Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Department of Oncology, Rizhao City Hospital of Traditional Chinese Medicine, Rizhao, Shandong, People’s Republic of China; 5Beijing YouAn Hospital, Capital Medical University, Beijing, People’s Republic of China; 6Beijing Chest Hospital, Capital Medical University/Beijing Tuberculosis and Thoracic Tumor Research Institute, Tongzhou, Beijing, People’s Republic of China Background: Laryngeal carcinoma is a common cancer among head and neck tumors, accounting for 0.5–1% new cancer cases or deaths of all tumors throughout the body. Despite improvements in diagnostic and therapy, the prognosis of laryngeal carcinoma patients still remains poor. Thus, it is very important to identify the biomarkers involved in the molecular pathogenesis of laryngeal carcinoma. Cyclin Y (CCNY) is a conserved cell cycle regulator that acts as a growth factor in many cancers. The clinical significance of CCNY in laryngeal carcinoma remains unknown. The function of CCNY in laryngocarcinoma was studied in this paper. Materials and Methods: CCNY knock-out cells were constructed by CRISPR/CAS9 technique. CCNY overexpression cells were also constructed based on CCNY knock-out cells. Cell growth ability was detected by MTS assay, high-content cell analysis, colony formation assays, and anchorage-independent growth assays. The protein levels in laryngocarcinoma cells were determined by Western blot. The role of CCNY in cell cycle progression was evaluated by flow cytometry. Results: CCNY knock-out cells and CCNY up-regulation cell models were obtained successfully. Suppression of CCNY expression inhibited Hep2 cell growth. Cell growth was enhanced by the up-regulation of CCNY. The percentage of cells in G1 phase was altered when CCNY expression was down-regulated or up-regulated. The phosphorylation level of MEK and ERK as well as cyclin E protein level was also regulated by the expression level of CCNY. Conclusion: In laryngocarcinoma cell line Hep2 cells, cell proliferation was controlled by CCNY. The expression of CCNY was involved in the cell cycle progress of Hep2 cells. It indicated that CCNY could promote cell growth by activating MEK/ERK/cyclin E signaling pathway.
Edinburgh Research Explorer Beam-Target Helicity Asymmetry for np in the NResonance Region

We report the first beam-target double-polarization asymmetries in the γ þ n ð p Þ → π − þ p ð p Þ reaction spanning the nucleon resonance region from invariant mass W ¼ 1500 to 2300 MeV. Circularly polarized photons and longitudinally polarized deuterons in solid hydrogen deuteride (HD) have been used with the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab. The exclusive final state has been extracted using three very different analyses that show excellent agreement, and these have been used to deduce the E polarization observable for an effective neutron target. These results have been incorporated into new partial wave analyses and have led to significant revisions for several γ nN (cid:1) resonance photocouplings.