Miracle-dz/UniFAR-FTUs huggingface dataset:
STUDY TO KNOW KNOWLEDGE AND PRACTICES FOR HEALTHY LIFESTYLE OF DIABETICS

Objective: To assess the knowledge and Practices for healthy lifestyle among diabetics in Lahore, Pakistan. Study Design: Observational / Cross sectional study/ DescriptivePlace and Duration of Study: This study was conducted at the Outpatient Departments (OPD),S e r v i c e s H o s p i t a l L a h o r e f r o m M a r c h 2 0 1 6 t o M a r c h 2 0 1 7 Materials and Methods:SPSS 21 was used for data interpretation, percentages, frequencies were calculated and table was formed. Sample of 100 diabetic patients was taken. Sample was selected by convenient non-probability method of sampling. People of age group 20 to 75 years having history of diagnosed diabetes for at least 2 years were selected.Results: The results of present study revealed that diabetes is most prevalent in age group of 40-60 years (54%). Health practices were found to be good in those who attended university (88.88%) as compared to those who never went to school. Disease was distributed in respondents of upper economic class 0 of respondents). Males had better knowledge (63%)than females( 37%). 58% people who had disease were those with Positive family history. 57% of people believed that diabetics should take multiple small meals instead of few large ones . 67% of respondents believed that all underground vegetables are prohibited for diabetics and 50% believed that fish is best type of meat for their health. 83% of the sample believed that vegetable oil as best kind of fat. 93% believed that regular talk is important for disease control and 70% believed that foot care is necessary in diabetics. Only 35% people believed had adequate knowledge about HbA1c. The percentage of people who believed that diet drinks be consumed as much as wanted was 45%. Conclusion: The study sample concludes that overall community awareness about disease duration, Complications and preventive measures of diabetes mellitus is satisfactory. The diabetic patients w h o a r e w e l l e d u ca t ed have good knowledge about disease and regular checkup. Complications and preventive measures than less or uneducated patients. Males have good knowledge than females, but overall knowledge about diet schedule, medication and timings of test was not satisfactory. Care about feet was not practiced. Males were more affected; especially who belonged to better socioeconomic status between ages of 40-60 years. Health practices were not found to be up to the mark. So, appropriate education about dietary control, life style, regular checkups and proper use of medicines should be given to control the disease. Corresponding author:
A survey on Power Allocation in PD-NOMA for 5G Wireless Communication Systems

Non-Orthogonal Multiple Access (NOMA) is a proposed technology for 5G NR (New Radio) networks in the future to increase capacity, user fairness, spectrum efficiency, and mass connectivity. NOMA permits several users to share identical radio resources like time slots, subcarriers, or spreading code simultaneously. Therefore, in designing NOMA the main challenge is to select resource allocation algorithms since user pairing and power allocation go hand in hand. NOMA is a general framework that can be easily blended with several recently proposed 5G multiple access schemes. In this survey paper, the NOMA scheme is discussed and thereby provides an overview of the algorithms designed for optimizing power allocation in the power domain NOMA, considering three different parameters: Max-Min Fairness(MMF), maximizing Sum Rate, and maximizing Energy Efficiency in different radio networks.
Efficient CNN Architecture on FPGA Using High Level Module for Healthcare Devices

Modern wearable healthcare devices require new technologies with resource efficiency in terms of high performance, low energy consumption and diagnostic accuracy. In the field of artificial intelligence, the convolutional neural network (CNN) has performed as an effective algorithm. Field-programmable gate arrays (FPGAs) have been extensively utilised to construct hardware accelerators for CNNs. This paper suggests using an accelerator to create a specific 1-D CNN to classify the electrogram (ExG). ExGs used here include electrocardiogram, electroencephalogram and electromyography. The pipelined structure is designed with a register in the middle to facilitate easy data transfer. A 1-D CNN using an accelerator to categorise ExG signals implemented on Xilinx Zynq xc7z045 platform outperforms FPGA peer applications on the same platform by 1.14× in terms of speed. In addition, the 1-D CNN proposed accelerator operates very efficiently due to the use of a tristate buffer in the multiplexer and the substitution of the shift for the multiplier, resulting in a resource-efficient accelerator with 161 GOP/s/W energy efficiency and 28 GOP/s/KLUT, an improvement of 1.67 over the previous model. Finally, the performance of the accelerator applied to a Xilinx Zynq xc7z045 FPGA operating at 442.948MHz was calculated, achieving 1.145 TFLOP/s.
SciDeBERTa: Learning DeBERTa for Science and Technology Documents and Fine-tuning Information Extraction Tasks

Deep learning-based language models (LMs) have transcended the gold standard (human baseline) of SQuAD 1.1 and GLUE benchmarks in April and July 2019, respectively. As of 2022, the top five LMs on the SuperGLUE benchmark leaderboard have exceeded the gold standard. Even people with good general knowledge will struggle to solve problems in specialized fields such as medicine and artificial intelligence. Just as humans learn specialized knowledge through bachelor’s, master’s, and doctoral courses, LMs also require a process to develop the ability to understand domain-specific knowledge. Thus, this study proposes SciDeBERTa and SciDeBERTa (CS) as pretrained LMs (PLMs) specialized in the science and technology domain. We further pretrained DeBERTa, which was trained with a general corpus, with the science and technology domain corpus. Experiments verified that SciDeBERTa (CS) continually pretrained in the computer science domain achieved 3.53% and 2.17% higher accuracies than SciBERT and S2ORC-SciBERT, respectively, which are science and technology domain specialized PLMs, in the task of recognizing entity names in the SciERC dataset. In the JRE task of the SciERC dataset, SciDeBERTa (CS) achieved a 6.7% higher performance than the baseline SCIIE. In the GENIA dataset, SciDeBERTa achieved the best performance compared to S2ORC-SciBERT, SciBERT, BERT, DeBERTa and SciDeBERTa (CS). Furthermore, re-initialization technology and optimizers after Adam were explored during fine-tuning to verify the language understanding of PLMs.
The relationship between confidence and mathematics learning achievement of junior high school students

Self-confidence is very important for students to actualize their potential and information on the level of student confidence is very helpful for teachers as a basis for decision making, as well as providing feedback to improve the learning process. This study aims to investigate whether there is a positive and significant relationship between self-confidence and student mathematics achievement. This research uses quantitative research methods, namely ex post facto type with a population of VII grade students of SMP Puspita Tangerang which consists of 5 classes total 188 students. Determination of the number of samples looked at the table of Issac and Michael with α = 0.05 and obtained a sample of 123 students. The data analysis of this study used a high confidence level of 95%. Based on the results of the simple linear regression test, it is found that the equation Ŷ = 21,714 + 0,374x means that there is a positive and significant relationship between self-confidence and student mathematics achievement. Then, the simple correlation coefficient test result count.
Incorporating Full Elastodynamic Effects and Dipping Fault

24 Numerical modeling of earthquake dynamics and derived insight for seismic hazard relies on credi25 ble, reproducible model results. The SEAS (Sequences of Earthquakes and Aseismic Slip) initiative 26 has set out to facilitate community code comparisons, and verify and advance the next gener27 ation of physics-based earthquake models that reproduce all phases of the seismic cycle. With 28 the goal of advancing SEAS models to robustly incorporate physical and geometrical complexities, 29 here we present code comparison results from two new benchmark problems: BP1-FD considers 30 full elastodynamic effects and BP3-QD considers dipping fault geometries. Eight modeling groups 31 participated in each benchmark, allowing us to explore these physical ingredients across multiple 32 codes and better understand associated numerical considerations. We find that numerical resolution 33 and computational domain size are critical parameters to obtain matching results, with increasing 34 domain-size requirements posing challenges for volume-based codes even in 2D settings. Codes 35 for BP1-FD implemented different criteria for switching between quasi-static and dynamic solvers, 36 which require tuning to obtain matching results. In BP3-QD, proper remote boundaries conditions 37 consistent with specified rigid body translation are required to obtain matching surface displace38 ments. With these numerical and mathematical issues resolved, we obtain good agreement among 39 codes in long-term fault behavior, earthquake recurrence intervals, and rupture features of peak 40 slip rates and stress drops for both benchmarks. Including full inertial effects generates events 41 with larger slip rates and rupture speeds compared to the quasi-dynamic counterpart. For BP342 QD, both dip angle and sense of motion (thrust versus normal faulting) alter ground motion on 43 the hanging and foot walls, and influence event patterns, with some sequences exhibiting similar44 sized characteristic earthquakes, and others exhibiting several earthquakes of differing magnitudes. 45 These findings underscore the importance of considering full dynamics and non-vertical dip angles 46 in SEAS models, as both influence short and long-term earthquake behavior, and associated hazards. 47 48
An Empirical Study on Consumption Intention of Virtual Tour An Empirical Study on Consumption Intention of Virtual Tour Streaming Streaming

This study employs the social interaction motivation of the audience to explore the social capital dual-model relationship generated by the audience of “Virtual Tour Streaming,” a term that describes virtual tour streaming’s nascent digital economy. This is situated in a virtual tour streaming platform to ascertain how it influences the intention of the audience and to use “Swift Guanxi” as the interaction variable to actual intention behavior. This is done to understand the contributions of virtual tour streaming adoption in a direct dial platform of different audience levels and their consumption behavior. The remaining sections discuss the theoretical and practical implications of the study
How the Application of Machine Learning Systems Changes Business Processes: A Multiple Case Study

Machine Learning (ML) systems are applied in organizations to substitute or complement human knowledge work. Although organizations invest heavily in ML, the resulting business benefits often remain unclear. To explain the impact of ML systems, it is necessary to understand how their application changes business processes and affects process performance. In our exploratory multiple case study, we analyze the application of multiple productive ML systems in one organization to (1.) describe how activity composition, allocation, and sequence change in ML-supported processes; (2.) distinguish how the applied ML system type and task characteristics influence process changes; and (3.) explain how process efficiency and quality are affected. As a result, we develop three preliminary change patterns: Lift & Shift, Divide & Conquer, and Expand & Intensify. Our research aims to contribute to the future of work and IS value literature by connecting the emerging knowledge on ML systems to their process-level implications.
Survey of insect pests infected Navelorange trees , population dynamic of some dominant insects and effect of cultivation and intercropping on population density

This research was carried out on navel orange (Washington variety) located at Sharkia Governorate in Abo-Hammad and Belbis centeries during 2015 & 2016 seasons, to study survey the insect pests infested navel orange by using plant sampling and sweeping net, population dynamics of such dominant insech and relation to some meterolgical climate factors and effect of some Agricultural operations such as cultivation, non-cultivation and intercropping and non-intercropping on dominant insect i.e purple scale, cotton aphid and CLM, insect. The surveyed insects, revealed 20 species belonging to 17 genera, 14 species from Homoptera, 1 species from Lepidoptera, 2 species from Diptera and 3 species from Cleoptera. The dominant and high density of the surveyed insect species (purple scale insect, cotton aphid and CLM) were studied in details. Purple scale Lepidosaphes beckii have three peaks of generations were recorded per year. Three peaks of infestation were recorded in early May or late April, early or mid September and mid November in both seasons of studied. The relationships between the population density of insect stages and weather factors temperature and relative humidity were studied and found positive correlations between mean of temperature and insect population. While, A. gossypii on navel orange trees showed three peaks of population density. The first one occurred at the four week of November. The second peak was recorded at the four week of March for both seasons. The third peak was recorded at the four week of June (2014) and the two week of July (2015) on navel orange trees. The obtained results appeared that negative significant correlation between population density of A. gossypii and mean temperature in both seasons. The effect of relative humidity was positive and insignificant in the two seasons. CLM (Phyllocnistis citrella) infested navel orange and navel orange was the most preferable host, where it was infested throughout the year, with the highest population density of the pest during the period from March to December, coinciding with the appearance of different flushes. The abundance of pest population showed 7 peaks in the season. A positive high significant correlation was obtained between mean temperature, and pest population on navel orange in both seasons. Concerning the effect of cultivation and (purple scale insect, cotton aphid and CLM). It obviously the cultivation operation significant decrease the population density of dominant insects while, noncultivation significant increase the dominant insect in both seasons. As for, the effect of intercropping mandarin interfiller trees between navel orange trees significant increase density number of purple scale insect, cotton aphid and CLM as compared with navel orange trees nonintercropping decrease such dominant insects in both seasons.
Analysis of Speed Differences in Data from Remote Traffic Microwave Sensors and Floating Car Data Systems on Expressways

The remote traffic microwave sensor (RTMS) and the floating car data (FCD) system are two important sources of traffic data; both provide key speed information. However, these two data processes gather data differently. The RTMS detects spot speeds at specific cross sections. The FCD system collects travel speed along a segment of a road link. Although the difference between time mean speed (TMS) and space mean speed (SMS) has been discussed for decades, the speed differences between RTMS and FCD have been underestimated in many engineering applications. This study examined the speed differences between the RTMS and FCD data on expressways in Beijing. First, the differences between the two data collections over 5 days were analyzed. The correlation between the difference and the value of the speeds was investigated. The relationships between TMS and SMS in existing studies were then compared with the relationship derived from the field data. It was found that the existing relationships between TMS and SMS were not valid for representing the relationship between the RTMS and FCD speeds. The flow–speed relationship from each data group was then investigated by using the Van Aerde traffic flow model; it was found that free-flow speed and speed at capacity determined on the basis of the RTMS data were significantly overestimated. It was inaccurate to apply the RTMS speed to the analysis of fundamental traffic flow diagrams. Finally, the repeatability and stability of the relationship between these two data groups were validated.
A Tuning Method for Diatom Segmentation Techniques

Phytoplankton such as diatoms or desmids are useful for monitoring water quality. Manual image analysis is impractical due to the huge diversity of this group of microalgae and its great morphological plasticity, hence the importance of automating the analysis procedure. High-resolution images of phytoplankton cells can now be acquired by digital microscopes, which facilitate automating the analysis and identification process of specimens. Therefore, new systems of image analysis are potentially advantageous compared to manual methods of counting for solution identification. Segmentation is an important step in the analysis of phytoplankton images. Many standard techniques like thresholding and edge detection are employed in the segmentation of diatoms and other phytoplankton, which are crucial organisms in microscopy images. However, in general, they require several parameters to be fixed beforehand by the user in order to get the best results. This process is usually done by comparing results and looking for the best parameters. To automatize this process, we propose an automatic tuning method to find the optimal parameters in an iterative procedure, called Parametric Segmentation Tuning (PST). This technique compares successive segmentation results, choosing the ones that gets the maximal similarity. In this paper, tuning is formulated as an optimization problem using a similarity function within the solution space. This space consists of the set of binary images that are generated by the segmentation technique to be tuned, where these binary images are seen as a function of the original images and the segmentation parameters. The PST technique was tested with two of the most popular techniques employed to segment phytoplankton images: the Canny edge detection and a binarisation method. The results of the thresholding technique were validated by comparing them to those of the Otsu method and the Canny method with a ground truth. They show that PST is effective to find the best parameters.
A NON-INTRUSIVE STRATIFIED RESAMPLER FOR REGRESSION

Our goal is to solve certain dynamic programming equations associated to a given 5 Markov chain X, using a regression-based Monte Carlo algorithm. More specifically, we assume that 6 the model for X is not known in full detail and only a root sample X1, . , XM of such process 7 is available. By a stratification of the space and a suitable choice of a probability measure ν, we 8 design a new resampling scheme that allows to compute local regressions (on basis functions) in each 9 stratum. The combination of the stratification and the resampling allows to compute the solution to 10 the dynamic programming equation (possibly in large dimensions) using only a relatively small set 11 of root paths. To assess the accuracy of the algorithm, we establish non-asymptotic error estimates 12 in L2(ν). Our numerical experiments illustrate the good performance, even with M = 20 − 40 root 13 paths. 14
EMERGING MARKET ECONOMIES: A LITERATURE REVIEW

In the era of globalization, the financial systems of all countries have faced severe challenges and their negative impacts such as overheated economy, high inflation, stock market crash, financial crisis, and other financial collapses. It has raised significant concerns influencing the effects of macroeconomic policies and responses of the financial system of each country all over the world even in normal times and turbulent times in various respects. And stock markets in emerging market economies are no exceptions. Indeed, it is proven by more and more studies that have been implemented to assess stock market liquidity in emerging market economies in different periods and the impacts of certain macroeconomic drivers on monetary policy. This study aimed to systematically review the literature on monetary policy and stock market liquidity measures and how monetary policy affects stock market liquidity. The study summarized the essential findings and approaches in the extant literature. Numerous reputable academic databases were used via a systematic methodology of literature review. Generally, this study sheds light on the crucial macroeconomic role of monetary policy as a potential determinant of stock market liquidity in different timelines. Recommendations and theoretical discussions given by researchers provide an overall review of the relationship between monetary policy and stock market liquidity in emerging market economies.
World Journal on Educational Technology: Current Issues

Gamification is a learning technique that transfers the mechanics of games to the educational environment in order to achieve better pedagogical results mediated by technological tools, this technique is used as an alternative to the traditional teaching methodology that is increasingly used in higher education, which is why the objective of the study is to know how gamification influences the teaching-learning process in higher education. In order to answer this question, a systematic review of the literature is carried out, based on the analytical method, using the scientific databases Web of Science and SCOPUS for the search of information. The impact of gamification within the formative context is significant since its evolution as a didactic strategy manages to meet the educational objectives improving learning outcomes, its application is methodical that considers the principles and appropriate elements of the game to solve problems and promote learning, which causes the change of student behavior, because it takes advantage of the main motivations of human behavior in two ways connected to each other: reinforcements and emotions, thus obtaining effective and motivated learning, reaching several types of knowledge, such as: declarative, conceptual, regulated, procedural, soft skills, affective and psychomotor mastery, which demonstrates its versatility and usefulness in learning environments.
CMAR_A_245928 2961..2977

Paulina Stefaniuk Agnieszka Szymczyk 2 Monika Podhorecka 1Department of Haematooncology and Bone Marrow Transplantation, Medical University of Lublin, Lublin, Poland; 2Department of Clinical Transplantology, Medical University of Lublin, Lublin, Poland Abstract: Despite the presence of many hematological prognostic indexes, clinical course and overall survival are often highly variable even within the same patient subgroup. Recent studies suggest that simple, cost-effective, low-risk tests such as neutrophil to lymphocyte ratio (NLR) and lymphocyte to monocyte ratio (LMR) may be used to evaluate the prognosis. Their role has been well confirmed in diffuse large B-cell lymphoma (DLBCL), Hodgkin lymphoma (HL) and multiple myeloma (MM), but until now the prognostic significance of NLR and LMR in leukemias has not been widely reported. In this article, we analyze the literature data on prognostic value of NLR and LMR in haematological malignancies in the context of classic prognostic factors and clinical course.