Reviewerly/exHarmony huggingface dataset:
Clinicopatological Description Of Prostate Disease at Sanglah Denpasar General Hospital in 2018-2020

Prostate disease is one of the diseases with a high incidence among elderly men. . In establishing the diagnosis of prostate disease, histopathological and laboratory examinations are vital. These histopathological results can be grouped into 3, namely benign lesions, PIN, and carcinoma. This study aims to determine the clinicopathological description of prostate diseases at Sanglah Hospital Denpasar from 2018-2020. The cross-sectional research was conducted using the descriptive and analytic method. Samples were selected based on several inclusion and exclusion criteria. Data were analyzed using SPSS version 26 to obtain a general clinicopathological picture based on age, histopathological diagnosis, the relationship between age and histopathological, diagnosis, PSA level, and the relationship between PSA level with histopathological diagnosis, and grade group carcinoma. The results showed that the prostate disease with the most patients at Sanglah Hospital Denpasar from 2018-2020 occurred between the age of 70-79 years by 39,0% with the dominant histopathological diagnosis being benign prostate 240 (74,3%), followed by PIN 27 (8,4%), and carcinoma 56 (17,3%). There was no relationship found between age, mean age and histopathological diagnosis with p=0,881 and p=0,119. The highest PSA level was &gt; 20ng/mL. It was found that the PSA level was significantly associated with histopathological diagnoses with a value of p=0,000. The most prevalent grade group characteristic was grade group 5 with 36 (64,3%).&#x0D; Keywords: Prostate Disease, Clinicopathologic, PSA Level
THE EFFECT OF USING VOCABULARY SELF-COLLECTION STRATEGY (VSS) ON STUDENT’S VOCABULARY MASTERY

Vocabulary is one of the important elements in learning English that make students easy to master English skills; speaking, reading, listening and writing. However, many students lack of vocabulary mastery, so it was difficult for them to determine the meaning or words in context. Therefore, this research was intended to find out the significant effect of using vocabulary self-collection Strategy on student vocabulary mastery. This research used quantitative approach and design was pre-experimental with one group pre-test and post-test. The researcher used a total sampling technique because it only had one class with a total of 23 students in eighth grade at SMPN 2 Sorkam. Data was collected through a vocabulary test with a pre-test and post-test design of 30 questions. Based on the data analysis, the result exposed that t-obtain was 17.52 while the t-table was 1.717. It means that the hypothesis (Ha) was accepted while the null hypothesis (Ho) was rejected. Therefore, the finding of the data present that there is a significant effect of using self-collection strategy on student vocabulary mastery at eighth grade of SMPN 2 Sorkam.
Solution of the Diophantine equation 22x+40y=z2

Since Diophantine equations play an important role in solving important real-world problems such as business investment problems, network flow problems, pole placement problems, and data privacy problems, researchers are increasingly interested in developing new techniques for analyzing the nature and solutions of the various Dio phantine equations. In this study we investigate the Diophantine problem 22x+40y = z2, where x, y, z are non-negative integers, and discover that it does not have a non-negative integer solution.
Lung Cancer Detection Refined: A Study on SVM Hyperparameter Tuning Using BayesOptimization

This research focuses on the application and enhancement of machine learning algorithms for the detection and differentiation of various types of cancers, with a primary emphasis on lung cancer. Central to this study is the integration of the Bayes Optimization algorithm for hyperparameter optimization and the XGBoost algorithm for predictive modelling. A significant aspect of this work involves the strategic reduction of hyper-features, aimed at refining the XGBoost model's performance. This process not only ensures a more efficient model but also contributes to a higher accuracy in cancer-type prediction. Additionally, a comparative analysis is conducted with other ensemble models to evaluate the relative performance improvements. The findings of this study are pivotal, as they demonstrate the optimized model's enhanced capability in accurately detecting different cancer types, particularly lung cancer, and show marked advancements over other contemporary models. The research highlights the potential of combining advanced machine learning techniques for significant improvements in oncological diagnostics and treatment planning.
Superresolution reconstruction based on complementary composite structural illumination

Structured illumination moves high-frequency information with more details into the frequency band that the optical system can pass through, so as to achieve high-quality image super-resolution (SR) reconstruction. Although structured illumination microscopy (SIM) using this technique are already mature for optical SR imaging, continuous illumination through unidirectional multi-frame sinusoidal structured patterns greatly reduces temporal resolution and poses challenges for robust imaging in low-light and short-exposure environments. In order to introduce more high-frequency information into the original image with fewer frames, we propose an innovative illumination method using compound angle structured patterns. Only one frame of the structured pattern is loaded on a spatial light modulator (SLM), and then two images are captured by complementary modulation. The reconstruction algorithm uses end-to-end deep neural networks. In order to improve the applicability under real low-light conditions, we simulate the actual noise scenes for model training. Experimental results show that our method is robust to noise and can restore image details under low-light conditions to achieve image SR. Moreover, it effectively minimizes the exposure time of image acquisition and reduces the computing time of the network, leading to an enhancement in the efficiency of image reconstruction.
Learned Image Compression With Variable Neural Network Architecture

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of −14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of −5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity.
Literature Circle Method Practice with Preschool Teachers through Children's Books: An Action Research Example

The aim of this study is to increase the interaction between the educator and the book by discussing the picture storybooks used in the picture story reading activities carried out by the preschool educators through the literature circle methods. Since it aims to intervene in an educational situation and is carried out directly with preschool educators, this study was designed with action research, one of the qualitative research methods. The participants of the research are four preschool educators. As for the data collection, semi-structured interview form, participants’ diaries, and researcher's diary were used. Content analysis technique was used in the analysis of the data. At the end of the research, it was concluded that the literature circle method affected the reading process in three different ways. These are (a) professional development, (b) relativity for the student, and (c) planning the teaching process. It was concluded that the reading circle method had a positive effect on the professional development of the participants, enabling the proper reading process for the student and effective planning of in-class practices. Hands-on workshops can be carried out that enable preschool educators to make use of picture storybooks in their schools through different reading methods.
Discussion on improving the fine system of the Automobile Management Act for consumer protection

Fines under the Automobile Management Act are ultimately the government's revenue generated due to damage to consumers. Therefore, it is desirable that fines be used for the benefit of consumers who are victims. However, if the fine is transferred to the general account, it is transferred to the national treasury and cannot be spent for a specific purpose. Therefore, it is possible to consider a plan to transfer the fine to a special account or a specific fund and use it for a specific purpose, such as compensating for consumer damage. However, it is believed that transferring fines to a fund like the Fair Fund in the United States will be difficult as a consumer protection measure. The reason is that, first, in order to revert the fine to the fund, amendments to Schedule 1 or Schedule 2 of the National Finance Act must be made. Second, the possibility that the financial resources will be used in an opaque way cannot be ruled out if they are transferred to the fund. Third, since a certain level of funds must be maintained in order to operate the fund, excessive fines may be collected for this purpose.&#x0D; For this reason, restricting the use of fines in the Automobile Management Act by organizing a special account rather than transferring the fines to the fund could be a good way to utilize the fines in accordance with the fundamental purpose of the law. A more appropriate way to use fines would be to legislate to limit the use of fines to consumer protection rather than transferring fines to general accounting and using them for purposes unrelated to the victim.
Design and Implementation of a Real-Time Object Counting Detector: A Comprehensive Non-linear Approach

This study presents a thorough, nonlinear method for creating and putting into practice a real-time item counting detector. The process includes choosing suitable deep learning frameworks, building object classes, gathering various datasets, preparing data, and using pre-trained models, such as YOLO and SSD. Real-time processing is achieved by means of Non-Linear optimization strategies and transfer learning for fine-tuning. The study explores post-processing techniques, hardware issues, and the complexities of working with camera feeds in order to achieve accurate counting. The study highlights the mathematical models related to deep learning methods, including FPN, RetinaNet, LSTM, and the suggested DeepSORT. It does this by offering an informative comparison table that shows recall, accuracy, precision, and F1 Score. Finally, the study sheds insight on the dynamic history of the discipline while acknowledging the fundamental contributions of the early real-time object identification attempts. This paper provides a thorough grasp of the complexities involved in the process, making it an invaluable resource for anybody looking to create and implement efficient real-time item counting detectors.
Study on Web Services Architectural Operations and Performance

The International Scientific Research Organization for Science, Engineering and Technology (ISROSET) is a Non-Profit Organization; The ISROSET is dedicated to improvement in academic sectors of Science (Chemistry, Bio-chemistry, Zoology, Botany, Biotechnology, Pharmaceutical Science, Bioscience, Bioinformatics, Biometrics, Biostatistics, Microbiology, Environmental Management, Medical Science, Forensic Science, Home Science, Library Science, Material Science Military Science, Physical Science, Physical Education Science, Educational Science, Fisheries, seed technology, Agriculture, Forestry Science, Mathematics, Physics, Statistics and Geology/Earth Science), Computer Science, Engineering and Information Technology, Commerce, Management, Economics Sociology and Social Science.
Teaching Engineering Courses in the Digital Transformation Era

Abstract In this paper we present a teaching model for Engineering couses, particularly courses of Applied Mechanics for the new generation of Engineering Programs. After the online terms due to the global pandemic, students and their professors returned to campuses and some lessons learned during that period were positive ones and students keep on demanding new models of teaching making use of the latest technologies. The use of digital media has become widely accepted by both, faculty members and students and most lectures are now recorded and students are used to have such material available on the same day of the class session. We present the students perceptions of what the new generation of Engineering courses should have in order to be attractive to students, help them develop some digital transformation competencies and, mainly, help them perform their best in courses that could be difficult, such as the Applied Mechanics courses. A total of 350 students were surveyed in the last four years, even prior to the pandemic to get their perceptions and their ideas on what an ideal Engineering course should contain, taking into account the era in which we are living, with particular focus on Digital Transformation Competencies and the use of new Artificial Intelligence tools such as ChatGPT®. We present a model that has been successfully implemented in our university and we think that Engineering educators worldwide can benefit from our experience and the recommendations our students provided.
Development and Implementation of an Augmented Reality Introductory Experience for Mechanical Engineering Freshman Students

Abstract Industry 4.0 is driving innovation at companies and in education. As more Industry 4.0 tools and technologies become accessible, engineering programs need to expose their students to these tools and technologies to better equip them to start their engineering careers. In support of this goal, the Mechanical Engineering (ME) program at Texas State University (TXST) included basic practical experiences involving selected Industry 4.0 tools in a freshman-level lab. One of those experiences uses Microsoft’s HoloLens 2 to introduce students to Augmented Reality (AR). Since AR devices are an emerging technology, the software applications currently available for devices like the HoloLens 2 are not cost-effective for implementing them in most academic courses or labs. To overcome this challenge, the ME program at TXST developed an AR application for the freshman-level lab using the Unity development platform. For that purpose, the CAD model of a product was created using SolidWorks, converted using Blender, and brought into the AR application that was developed. This paper presents the process and technical challenges of implementing the AR experience in the freshman-level lab and discusses student feedback and the plans for improving the application in the future. The authors hope that this information is useful for other mechanical engineering undergraduate programs that want to include AR experiences in the curriculum.
Empirical Evidence on the Impact of the Banking Sector on the Sierra Leone Economy

The study seeks to assess the impacts of the banking sector on the Sierra Leone economy. The Case study research design was adopted for this study. The Pooled OLS regression Model, Correlation Matrix, and Descriptive Statistics were employed in the study. E-Views was used to analyze the panel data using the Pooled OLS regression data, Descriptive statistics, and the Correlation Matrix. The Pooled OLS econometric result shows that Bank Liquid Reserves to Assets positively correlates with Gross Domestic Product with a coefficient value of 0.8425. It was further revealed that the relationship is highly significant (p-value of 0.0158 and a t-stat of 2.4560. Domestic credit to the Private Sector has a negative relationship with the Gross Domestic Product with a coefficient value of (-0.155367). This relationship was further revealed to be highly insignificant (p-value of 0.7533 and t-stats of -0.3152). The result also shows that Deposit Interest Rates have a positive coefficient value of (2.1040), which is also highly significant (p-value of 0.0013 and t-stats of 3.3050). The study further revealed that the Gross Domestic Product (% of GDP) has a negative relationship with the GDP, with a coefficient value of (-0.2055). It further revealed that the result is highly insignificant (p-value of 0.1383 and t-stats of -1.4944). On the other hand, Interest rate spread has a negative relationship with the Gross Domestic Product with a coefficient value of (- 3.0265). It was further revealed that the relationship is highly significant (pvalue of 0.0000 and t-stats of -4.8611)
ASYMMETRIC SENTIMENT ANALYSIS OF BUSINESS NEWS IN HISTORICAL DATA FORECASTING SYSTEMS

Forecasting data in financial markets is a pertinent task in the modern world. The ability to predict market movements helps investors avoid obvious risks and spare themselves additional expenses. Numerous trading platforms have been developed to quickly access extensive historical data, enabling real-time analysis of the financial market from any corner of the planet using only a laptop or personal computer. Such platforms allow the development of unique strategies and approaches based on fundamental or technical analysis, taking into account news about a particular company, its earnings, capitalization, and the amount of dividends it is expected to pay on time. Business news is a crucial source of information about the state of the economy and markets. They can be used for forecasting future events. One method of forecasting based on business news is sentiment analysis. Sentiment analysis allows assessing the positivity or negativity of business news.Traditional sentiment analysis methods employ a symmetric approach. This means that positive and negative news are equally considered in forecasting. However, in the real world, positive news may have a greater impact on markets than negative news. This is because positive news can stimulate economic activity, while negative news may hinder it. The article explores the application of asymmetric sentiment analysis of business news in financial data forecasting systems. Various methods of sentiment analysis of business news, their advantages, and disadvantages are analyzed. A new approach to sentiment analysis of business news is proposed, which comprehensively utilizes artificial neural networks and principal component analysis.
DEVELOPMENT OF THE ONTOLOGICAL MODEL OF THE KNOWLEDGE BASE "LIBRARY" BASED ON THE PROTÉGÉ ENVIRONMENT

Modern development of society, increasing the degree and pace of integration of information technology achievements into the sphere of human life, traditional approaches to the construction of information systems cease to be effective or become too cumbersome. The development and use of knowledge-based systems is one way to solve this problem. The material of the work is devoted to the ontological modeling of the new subject area «Library». Ontology is considered in the context of knowledge sharing. The created ontological model of the «Library» knowledge base is quite modern and relevant today. The developed ontological model of the knowledge base in this field can be implemented on thematic web resources and will significantly facilitate the semantic search of information within the subject area in comparison with the existing ones. The terminological dictionary of this subject area is defined using the term system. An analysis of the capabilities of the Protégé ontology development environment for modeling the specific subject area «Library» was carried out. The basic principle of ontology modeling in the form of a semantic network was chosen. The proposed network has the opportunity to expand and deepen knowledge about the subject area «Library». Using the Protégé environment for the implementation of the ontological model of the knowledge base made it possible to use the advantages and features of the created «Library» model, such as: structuredness, completeness, reflexivity, functionality, transitivity, reliability and consistency of information. The criterion for assessing the correctness of the ontological model of the knowledge base was selected and used. Testing of the developed ontological knowledge base was conducted and a fairly high level of its correctness in the process of information search was confirmed. The average indicator (Average) according to the SUM metric, determined for all users, is equal to 83.25%, which is an acceptable indicator of the ontological knowledge base. When using the classic relational model of database organization to implement the «Library» database. the average SUM metric for 10 users is 75.26%. An example of the developed ontology in Protégé is considered, a graphical representation of the basic graph of the ontological model of the «Library» knowledge base is given, the model includes 10 classes and subclasses, 2 properties-relations and up to 10 properties-data are defined for each class and subclass, an example of the classes of the ontological model is given «Library», an example of the "property-relations" of the ontological model «Library» is given, an example of the "property-data" of the ontological model «Library» is given. Possible directions of further development of the ontological model «Library» have been determined.
ANALYSIS OF THE LOSSY IMAGE COMPRESSION ALGORITHMS

The article discusses and conducts an analytical review of lossy image compression algorithms. Substantiated the relevance of the research with the help of statistical data. Considered and analyzed the color subsampling method. Reviewed, described, and analyzed the color quantization method, in particular, existing studies on the application of color quantization in combination with the discrete cosine transform. Highlighted the shortcomings of the existing research and formulated the possibility of further research using an expanded sample of images. Considered and analyzed in detail the compression based on the discrete cosine transform. Singled out the search for optimal quantization matrices as a promising direction of further research on improving the efficiency of the application of discrete cosine transformation. Highlighted the adaptive allocation of larger, multiples of the standard data blocks as a promising direction of research. Considered and analyzed the image compression method based on the wavelet transform. Formulated the direction of further research on the use of wavelets other than Cohen-Dobechy-Feuvo and LeGall-Tabatabay wavelet for image compression. Considered and analyzed the method of fractal compression. Formulated directions for further research, such as limiting the search depth and applying fractal compression in combination with discrete cosine transformation. Summarized directions for further research to improve the functional characteristics of the considered algorithms. The main scientific result of the conducted research is the selection of a list of promising research topics that will allow increasing the amount of data on methods, models and means of image compression. The practical value of the research is that it contains a list of research topics that can be used by researchers as material for further research.