Miracle-dz/UniFAR-FTUs huggingface dataset:
Binary Mode Multinomial Deep Learning Model for more efﬁcient Automated Diabetic Retinopathy Detection

The ability to rapidly and accurately classify diabetic retinopathy from color fundus photographs is vital to maximize the ability to assess diabetic eye disease early. Our paper compares the performance of binary classiﬁcation (Refer/No Refer) to multinomial (Diabetic Retinopathy Severity) classiﬁcation using deep learning models. The binary mode multinomial experiment achieved very high performance for Refer/No Refer DR on clinical datasets with accuracy up to 97.69%. We show how annotating images using image processing improves Multinomial classiﬁcation in binary mode on a set of fundus images and yields equal if not better performance than a simple binary classiﬁcation on the same dataset.
Relation of Temperament and Problem Behaviors in Adolescents: A Mediating Effect of Dysfunctional Anger Expressions

Among many other factors associated with anger expressions and problem behaviors, the effect of temperament has not been much investigated in adolescents. Thus, the purpose of this study was to investigate the relationship between temperament and problem behaviors in adolescents, and whether dysfunctional anger expressions mediated this relation. A total of 360 students in either middle or high school participated but only data from 354 surveys were analyzed. We found that temperament was associated with dysfunctional anger expressions and problem behaviors, and the relationship between temperament and problem behaviors was mediated by dysfunctional anger expressions. Temperament adaptability was shown to be negatively related with dysfunctional anger expressions and problem behaviors, while temperament approach was shown to be positively related with anger-out expression and externalized problem behaviors. This study suggests that appropriate intervention of anger related issues in adolescents considering their temperament should is needed.
Gannot, Oran Elliptic boundary value problems for Bessel operators, with applications to anti-de Sitter spacetimes. (Problèmes aux limites elliptiques pour les opérateurs de Bessel, avec applica-

Summary: This paper considers boundary value problems for a class of singular elliptic operators that appear naturally in the study of asymptotically anti-de Sitter (aAdS) spacetimes. These problems involve a singular Bessel operator acting in the normal direction. After formulating a Lopatinskii condition, elliptic estimates are established for functions supported near the boundary. The Fredholm property follows from additional hypotheses in the interior. This paper provides a rigorous framework for mode analysis on aAdS spacetimes for a wide range of boundary conditions considered in the physics literature. Completeness of eigenfunctions for some Bessel operator pencils is shown.
Acceptance of Simulation; Insights and Guidelines from a DES Project

Companies are daily facing challenges to become more efficient and to gain market shares. To solve these challenges discrete event simulation (DES) can be used as a strategic and tactical decision-making tool. Using simulation will result in; more cost effective analyses, less resources spent on implementation and an increased understanding of why things happen in the system. However, even if simulation has many advantages, there remain some hesitance to use it. Together with a manufacturing company, this master thesis has been conducted as a simulation project to understand what influences acceptance of simulation. Given the stated situation, and the written literature, this master thesis aims to answer the following questions; How can a DES-project affect the acceptance of simulation? What factors can affect the acceptance of simulation, and how can these factors be changed? To answer the question, a literature study and two case studies were carried out, including observations, interviews and surveys from three in parallel simulation projects. Based on these, recommendations and guidelines have been formulated to aid future DES projects and to increase the usage of simulation as a business tool. Apart from the recommendations, three conclusions have been drawn; - Acceptance of simulation is dependent on many various things and it therefore requires understanding and involvement. - Factors with indirect impact on acceptance, so called moderators, are important to consider to obtain acceptance. - Many activities need some acceptance before they are performed, and therefore needs to be supported in the organization.
Progress on empirical mode decomposition-based techniques and its impacts on seismic attribute analysis

AbstractSpectral decomposition plays a significant role in seismic data processing and is commonly used to generate seismic attributes that are useful for interpretation and reservoir characterization. Among several techniques that are applied to this finality, complete ensemble empirical mode decomposition (CEEMD) is an alternative procedure that has proven higher spectral-spatial resolution than the short-time Fourier transform or wavelet transform, thus offering potential in highlighting subtle geologic structures that might otherwise be overlooked. We have analyzed a recent development in CEEMD, which we call improved CEEMD (ICEEMD), and its impacts on seismic attribute analysis commonly used in the empirical mode decomposition framework. By replacing the estimation of modes by the estimation of local means, the mode mixing and the presence of noise in the modes are reduced. Application on a synthetic and real data reveals that ICEEMD improves the signal decomposition and the energy concentration in t.
Comparison of Neural Cell Differentiation of Human Adipose Mesenchymal Stem Cells Derived from Young and Old Ages

: Recently, adipose mesenchymal stem cells (AdMSC) that are similar to bone marrow MSC and blood derived MSC are thought to be another source for stem cell therapy. However, the diseases that can be applied for stem cells therapy are age-dependent degenerative diseases. Accordingly, the present study investigated the growth and differentiation potential to neural cells of human AdMSC (hAdMSC) obtained from aged thirty, forty and fifty. The growth of cells and cell viability were measured by passage and neural differentiation of hAdMSC was induced in neural differentiation condition for 10 days. Our results demonstrated that cell number, viability and morphology were not different from hAdMSC by age and passage. Immunofluorescence analysis of neural cell marker (TuJ1, NSE, Sox2, GFAP or MAP2) demonstrated no significant differences in neural cell differentiation by age and passage. As the number of passage was increased, the mRNA level of MAP2 and Sox2 was decreased in hAdMSC from age of 50 compared to hAdMSC from age of 30. In conclusion, the present study demonstrated that ability of neural cell differentiation of hAdMSC was maintained with ages, suggesting that autologous stem cells from aged people can be applied for stem cell therapy with age-dependent neural disease with the same stem cell quality and ability as stem cell derived from young age.
Study on the Financial Performance of Yunnan Baiyao in the Context of Mixed Ownership Reform

: Since the 18th Party Congress, the reform of state-owned enterprises has entered a pilot phase and accelerated. To improve corporate performance, a large number of outstanding enterprises have carried out mixed ownership reform. As a major restructured enterprise in Yunnan Province, Yunnan Baiyao's performance capability has been declining year by year under the influence of medical industry consolidation, but its brand value is high, so Yunnan's state-owned assets department has carried out mixed ownership reform of Yunnan Baiyao in order to be able to improve the financial performance of the enterprise. Using Yunnan Baiyao as a case study, this paper describes the process and motivation of the mixed ownership reform of Yunnan Baiyao, and analyzes the financial performance of Yunnan Baiyao before and after the mixed ownership reform in four dimensions, including profitability, solvency, development, and operational capacity, and the results show that the financial performance of the enterprise has been enhanced after the mixed ownership.
System Identification of Postural Tremor in Wrist Flexion-Extension and Radial-Ulnar Deviation

System Identification of Postural Tremor in Wrist Flexion-Extension and Radial-Ulnar Deviation Sydney Bryanna Ward Department of Mechanical Engineering, BYU Master of Science Generic simulations of tremor propagation through the upper limb have been achieved using a previously developed postural tremor model, but this model had not yet been compared with experimental data or utilized for subject-specific studies. This work addressed these two issues, which are important for optimizing peripheral tremor suppression techniques. For tractability, we focused on a subsystem of the upper limb: the isolated wrist, including the four prime wrist muscles (extensor carpi ulnaris, flexor carpi ulnaris, extensor carpi radialis, and flexor carpi radialis) and the two degrees of freedom of the wrist (flexion-extension and radial-ulnar deviation). Muscle excitation and joint displacement signals were collected while subjects with Essential Tremor resisted gravity. System identification was implemented for three subjects who experienced significant tremor using two approaches: 1. Generic linear timeinvariant (LTI) models, including autoregressive-exogenous (ARX) and state-space forms, were identified from the experimental data, and characteristics including model order and modal parameters were compared with the previously developed postural tremor model; 2. Subjectspecific parameters for the previously developed postural tremor model were directly estimated from experimental data using nonlinear least-squares optimization combined with regularization. The identified LTI models fit the experimental data well, with coefficients of determination of 0.74 ± 0.18 and 0.83 ± 0.13 for ARX and state-space forms, respectively. The optimal model orders identified from the experimental data (4.8 ± 1.9 and 6.4 ± 1.9) were slightly lower than the orders of the ARX and state-space forms of the previously developed model (6 and 8). For each subject, at least one pair of identified complex poles aligned with the complex poles of the previously developed model, whereas the identified real poles were assumed to represent drift in the data rather than characteristics of the system. Subject-specific parameter estimates reduced the sum of squared-error (SSE) between the measured and predicted joint displacement signals to be between 10% and 50% of the SSE using generic literature parameters. The predicted joint displacements maintained high coherence at the tremor frequency for flexion-extension (0.90 ± 0.10), which experienced the most tremor. We successfully applied multiple system identification techniques to identify tremor propagation models using only tremorogenic muscle activity as the input. These techniques identified model order, poles, and subject-specific model parameters, and indicate that tremor propagation at the wrist is well approximated by an LTI model.
A Review Study on Big Data Analysis Using R Studio on COVID 19

:- This is focuses on scalable big-data systems, which include a set of tools and mechanisms to load, extract, and improve disparate data while leveraging the massively parallel processing power to perform complex transformations and analysis. In the near future, however, big data access at high transmission rates will be. This is a review based on COVID 19 patients data on accessible big-data systems that include a set of tools and technique to load, extract, and improve dissimilar data while leveraging the immensely parallel processing power to perform complex transformations and analysis. “Big-Data” system faces a series of technical challenges. worldwide. WHO (under International Health Regulations) has declared this outbreak as a “Public Health Emergency of International Concern” (PHEIC) on 30thJanuary 2020. WHO subsequently declared COVID-19 a pandemic on 11th March, 2020. Members of the family Corona virus cause a broad spectrum of animal and human diseases. Uniquely, replication of the RNA genome proceeds through the generation of a nested set of viral mRNA molecules. Human coronavirus (HCoV) infection causes respiratory diseases with mild to severe outcomes. In the last 15 years, we have witnessed the emergence of two zoonotic, highly pathogenic HCoVs: severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). Replication of HCoV is regulated by a diversity of host factors and induces drastic alterations in cellular structure and physiology. In this review all (as we possible) information about Corona viruses are given.
QaN We Pay More Attention?

Question answering (QA) is one of the hardest challenges in NLP. Unlike information retrieval tasks like named-entity recognition, QA requires a model to develop deep syntactic and semantic understanding of text as well as efficiently represent relationships between context and query. In our project, we are given the SQuAD 2.0 dataset (Stanford Question Answering Dataset) [5] and a baseline that implements BiDAF model (Bidirectional Attention Flow) with word-level embeddings. Our goal is experiment with new deep learning techniques that can improve over the baseline. For this project, we first improved the Baseline BiDAF model by adding Character-Level Embeddings. Then we implemented the QANet architecture and performed analysis on how the complexity of various modules of the system affect its performance. We also proposed a new architecture for the QANet Output Layer which helped to improve the model performance. Our best model QANet-Ensemble achieves F1 score of 71.995 and EM score of 68.678 on the Dev leaderboard and F1 score of 69.943 and EM score of 66.712 on the test leaderboard, placing us at the 6th position on the test leaderboard. Our submissions can be found under the name squad_trainers .
Examination of the Distracter Efficiency and Reliability Coefficient of Teacher Made Achievement Test of Senior High Schools in Ghana

There have been many criticisms about the low performance of students in mathematics by the examinations conducted by the West African Secondary School Certificate Examination (WASSCE). Comparing the performance of students in mathematics in the district to their performance in the WASSCE leaves some discrepancies. This study, therefore, examined the distracter efficiency and reliability coefficient of teacher made achievement test of senior high schools in Ghana . The research design adopted for the study was a descriptive survey. Purposive sampling was used to select the school for the study. The data was gathered from an adopted teacher-made test developed from the mathematics syllabus of the Ghana Education Service by the teachers. The fifty items were randomly sampled from the previous (2018-2021) end-of-term mathematics test of Brakwa Senior High and Technical School teachers. Kuder Richardson version 20 (KR20) statistics was used to check for the reliability of the study instrument and percentages for the functional distractors and non-functional distractors. The finding revealed that out of fifty (50) items, 38 were good, and 12 were poor as indicated by the distracter efficiency. It could be concluded from the findings that generally the distractor and reliability analyses result of the teacher-made test of Brakwa Senior High and Technical in mathematics was better. It was recommended that heads of the mathematics department of Brakwa Senior High and Technical School should organise more in-service training on item analysis for mathematics teachers to upgrade and improve their test construction skills.
Analyzing Input Patterns of Smartphone Applications in Touch Interfaces

Touch sensor interface has become the most useful input device in a smartphone. Unlike keypad/keyboard interfaces used in electronic dictionaries and feature phones, smartphone’s touch interfaces allow for the recognition of various gestures that represent distinct features of each application’s input. In this paper, we analyze application-specific input patterns that appear in smartphone’s touch interfaces. Specifically, we capture touch input patterns from various Android applications, and analyze them. Based on this analysis, we observe a certain unique characteristics of application’s touch input patterns. This can be utilized in various useful areas like user authentications, prevention of executing application by illegal users, or digital forensic based on logged touch patterns.
Investigating Possible Government Misreporting from 2009-2020 of Data on Human Trafficking in India

: Human trafficking is defined as the movement of people from one place to another through force or coercion into situations of exploitation, usually for the economic benefit of traffickers. Rates of human trafficking in India are high, with poverty and a lack of job opportunities being among the main causes. Data reported by the National Crime Records Bureau has been called into question by many prominent news organisations, activists and NGOs. This study will evaluate the accuracy of government reported data using literature on human trafficking in India from 2009-2020.
DIAGNOSIS AND TREATMENT OF PAINFUL DIABETIC PERIPHERAL NEUROPATHY

| Diabetic neuropathies are the most common chronic complications of diabetes, with an estimated lifetime prevalence exceeding 50% in people with diabetes. Among various forms of neuropathy, diabetic peripheral neuropathy (DPN) is the most common and has the strongest evidence base regarding therapeutic approaches. This American Diabetes Association clinical compendium summarizes the latest information about screening for, diagnosing, and treating painful DPN in routine clinical practice. It opens with an overview of the epidemiology of DPN, followed by a description of the pathophysiology of the disease and its often severely painful symptoms. The authors recommend a stepwise approach to effectively diagnose DPN and offer a novel perspective on the impact of social determinants of health on the development and management of DPN. They summarizes the latest guidance on effective therapies, including pharmacological oral and topical agents, nutraceutical products, and nonpharmacological therapies, including physical activity and dietary interventions, passive modalities, and energy or nerve stimulation techniques. Throughout the publication, the authors identify knowledge gaps that need to be addressed and advocate a personalized care approach to reduce the burden of painful DPN and optimize quality of life for individuals affected by it. be taken with any prescribed medications. Starting at the lowest doses with slow up-titration is recommended in such cases.
A triple oxygen isotope analysis of altered oceanic crust and its buffering effect on the steady state oxygen isotope composition of seawater

The secular chemical marine sediment trend showing an increase in δ18O over geologic time has traditionally been investigated using δ18O modelling of ancient ocean compositions, ocean surface temperatures, and sample diagenetic alteration. This study presents a triple oxygen isotope mass balance for the oceans using high precision δ18O and Δ'17O measurements of altered oceanic crust. The mass balance model predicts an ice-free seawater with δ18O = -0.29‰ and Δ’17O = -0.002‰ with steady state reached at approximately (0.5 to 1)×109 years. Modifications to the hydrothermal alteration and continental weathering fluxes to examine Archean ocean compositions predict seawater evolution slopes of λ = 0.524 and λ = 0.529, respectively. A model generated seawater of δ18O = -4.1‰ and Δ'17O = 0.014‰ predicts previously published Archean chert data precipitated in an ocean with 50 60°C surface temperatures and lost primary δ18O and Δ'17O signals from diagenetic alteration with low δ18O fluids.
IMAGE ANALYSIS IN DIGITAL PATHOLOGY BASED ON MACHINE LEARNING TECHNIQUES & DEEP NEURAL NETWORKS

Detection of regions of interest (e.g. mitosis or histologic primitives) in Whole Slide Images in a clinical setting is a highly subjective and labor-intensive task. In this thesis we explore recent developments in Machine Learning and Computer Vision algorithms to assess their possible usage and performance in tasks such as the above, in order to enhance and accelerate healthcare procedures. A state-of-the-art Deep Learning framework (Detectron2) is trained on the TUPAC16 dataset for object detection, and on the JPATHOL dataset for instance segmentation. We evaluate its predictions against competing models and discuss further possible improvements.
Therapeutic of four antiviral drugs in treatment of COVID-19: A protocol for systematic review and Network Meta-analysis

Introduction The COVID-19 pandemic has resulted in a global crisis in public health, however, there are still no safe and effective drugs to resist COVID-19 until now. We will use network meta-analysis to analysis available evidence from RCTs to compare the safety and efficacy of four antiviral drugs (including Ribavirin, Arbidol, Chloroquine Phosphate and Interferon) alone or in combination, in patients with COVID-19 on the basis of standard treatment, to reveal the robustness and strength of evidence for relative efficacy against COVID-19, which will provide better evidence for future clinical decision-making. Methods Using English and Chinese search strategies to search 8 databases including PubMed, Web of Science, Embase, the Cochrane Library, CNKI, CBM, WANFANG Database and VIP. In addition, manual search for references in publications has been supplemented by electronic search. To enhance the effectiveness of this study, only randomized controlled trials of four antiviral drugs (Ribavirin, Arbidol, Chloroquine Phosphate, Interferon) used alone or in combination with the primary therapy shall be included. Analysis The nucleic acid turning negative, complete absorption of lung inflammation, adverse reactions, aggravation and death shall be the primary outcome measures; whereas temperature return to normal, hospitalization, and positive rate after discharge will be the secondary outcomes. To ensure the quality of the systematic evaluation of this study, study screening, data extraction and quality evaluation will be carried out independently by two reviewers, and any differences will be resolved through consultation between them or by a third reviewer. Ethics and dissemination This systematic review will evaluate the efficacy of four antiviral drugs (Ribavirin, Arbidol, Chloroquine Phosphate and Interferon) for Covid-19 in adults. Since all included data will be obtained from published articles, it does not require ethical approval and will be published in a peer-reviewed journal. PROSPERO registration number: CRD42022300104.
eGEN: an energy-saving modeling language and code generator for location-sensing of mobile apps

Given the limited tool support for energy-saving strategies during the design phase of android applications, developing battery-aware, location-based android applications is a non-trivial task for developers. To this end, we propose eGEN, consisting of (1) a Domain-Specific Modeling Language (DSML) and (2) a code generator to specify and create native battery-aware, location-based mobile applications. We evaluated eGEN by instrumenting the generated battery-aware code in five location-based, open-source android applications and compared the energy consumption with non-eGEN versions. The experimental results show 188 mA (8.34% of battery per hour) of average reduction in battery consumption while showing only 97 meters of degradation in location accuracy over three kilometers of a cycling path. Hence, we see this tool as a first step in helping developers write battery-aware code in location-based android applications. The GitHub repository with source code and all artifacts is available at https://github.com/Kowndinya2000/egen, and the tool demo video at https://youtu.be/Iadfh4cCw8I.