Processing Tools Research Articles

Pengaruh Kompetensi Guru Dalam Penggunaan Teknologi Pembelajaran Untuk Meningkatkan Motivasi Belajar Siswa

This study aims to examine the influence of the relationship between teacher competence in using learning technology and student learning motivation. The study utilized a sample of 30 respondents, collecting data through a questionnaire to measure scores of teacher competence (X) and student learning motivation (Y). Correlation analysis yielded a correlation coefficient (r_xy) of 0.880, indicating a significant positive relationship between the two variables. The findings emphasize the importance of investing in training and developing teachers' abilities to effectively utilize technology as a supportive tool in the learning process. The practical implications of this research underscore the need for ongoing support from schools and governments to facilitate better and adequate integration of technology into the educational curriculum, with the aim of enhancing overall student learning motivation and supporting improved learning outcomes.

A method to enhance Apache Spark performance based on data segmentation and configuration parameters settings

When using modern big data processing tools, there is a problem of increasing the productivity of using modern frameworks in the context of effective setting of various configuration parameters. The object of the research is computational processes of processing big data with the use of technologies of high-performance frameworks. The subject is methods and approaches to the effective setting of configuration parameters of frameworks in the conditions of limitations of virtualization environments and local resources. The purpose of the study is to improve the performance of Apache Spark and Apache Hadoop deployment modes based on a combined approach that includes preprocess segmentation of input data and setting of basic and additional configuration parameters that take into account the limitations of the virtual environment and local resources. Achieving the set goal involves the following tasks: create a synthesized set of WordCount test data for using input data segmentation methods. Determine the composition of general and specific Apache Spark and Apache Hadoop configuration parameters that most affect the performance of frameworks in Spark Standalone and Hadoop Yarn (FIFO) deployment modes. Justify changes in the values of the configuration parameters (accepted by default) by setting the level of parallelism, the number of partitions of the input file according to the number of processor cores, the number of tasks assigned to each core and the system executor. Conduct experimental research to substantiate theoretical results and prove their use in practice. Methods. The research used the following methods: statistical analysis; a method of generating test data based on defined segmentation characteristics with arbitrary volumes of data; a systematic approach for comprehensive evaluation and analysis of performance of frameworks based on selected configuration parameters. The results. On the basis of the developed system of parameters for evaluating the performance of the studied frameworks, experiments were carried out, which include: the application of the method of segmentation of input data based on the division of the input file into paragraphs (lines) for different values of the ranges of the number of words and the number of letters in each word; setting the main parameters and specific ones, in particular, partitioning and parallelism, taking into account the characteristics of the virtual environment and the local resource. According to the obtained results, a detailed analysis of the use of the proposed methods to improve the performance of the studied frameworks with recommendations for choosing the optimal values of data segmentation parameters and configuration parameters was carried out. You are snowmen. The obtained results of the experiments allow us to conclude that the use of the proposed methods of setting the configuration parameters of Spark and Hadoop will increase the processing productivity: for small files (0.5–1 GB) on average up to 25–30%, for large ones (1.5–2.5 GB ) – up to 10–20% on average. At the same time, the average value of the execution time of one task decreased by 10-15% for files of different sizes and with different number of words in a line.

Adoption of ChatGPT and other Artificial Intelligence among Physicians around the world: Barriers and the way-forward

Artificial Intelligence (AI) has emerged as a transformative force in modern society, revolutionizing various industries, including healthcare. ChatGPT, a language processing tool, has garnered significant attention for its ability to simulate human-like conversations and assist in a myriad of tasks. Despite its potential, the adoption of ChatGPT among physicians worldwide remains limited, particularly in regions like Asia, Latin America and Sub-Saharan Africa. This reluctance poses barriers to leveraging AI for novel diagnosis and treatment approaches, hindering progress in global healthcare. In this study, we explore the medical importance of AI, particularly ChatGPT, highlighting its potential to reduce medical errors and provide accurate healthcare information. However, concerns regarding bias, dependency, and plagiarism have surfaced, necessitating cautious integration and human oversight in its application. Recommendations include increasing awareness among physicians, implementing ethical guidelines, and utilizing AI-detecting software to mitigate risks. Furthermore, collaboration between international medical associations, policymakers, and governing bodies is essential to promoting AI adoption and ensuring its ethical use. While AI holds promise for advancing healthcare, its full potential hinges on overcoming current barriers and fostering a symbiotic relationship between technology and human expertise. Future research must address lingering uncertainties and ethical considerations to realize the transformative impact of AI in medicine.

The Effect of Debt To Equity Ratio (DER) and Cash Ratio on Profitability in Pharmaceutical Companies Listed on the Indonesian Stock Exchange for the 2018-2022 Period

The aim of this research is to examine the relationship between cash ratio, debt to equity ratio, and profitability. This research combines quantitative techniques with an associative descriptive approach. Over a period of five years, samples of financial report data from eleven companies were collected using a census approach. The Chow and Hausman tests are used in selecting the panel data regression model. The data processing tool used is called E-Views 12. The selected model is used to confirm conventional wisdom. This research involves model selection, panel data regression analysis, coefficient of determination, and testing its significance using t and F tests. The results of the research show that the Debt to Equity Ratio (DER) and Cash Ratio have a significant influence on profitability. It was determined that a random effects model was appropriate. The debt to equity ratio (DER) and cash ratio both have a positive effect on profitability, based on the findings of the significance test. However, the impact on profitability is slightly higher

Creativity and artificial intelligence: A study with prospective teachers

Artificial Intelligence (AI) brings enormous opportunities into learning, teaching, and assessment processes. Among them, it is convenient to explore its ability to channel students’ creativity, which is described as a basic competence in the training of people with both the OECD and the recent Spanish LOMLOE law pointing to the need to foster it in educational settings. In this context, the objective of this research is to explore the creative potential of prospective elementary school teachers related to storytelling, via a project including the rational use of AI generative tools. A combination of qualitative and quantitative instruments was used to get insight on the implications of those AI tools in the creative process and to gain understanding on the concerns of prospective teachers about AI at both their training and future teaching practice. The results show the potential of AI from an educational point of view, specially in self-assessment and co-evaluation processes, since it allows confronting not only the result of the creative task, but also the process itself by reflecting on the asked questions. Finally, the importance of continuing research on the ability to ask questions (a creative skill in itself) in the new context of AI is discussed.

Scheduling twin-cluster tools for complete concurrent processing with wafer residence time constraints

Scheduling twin-cluster tools for complete concurrent processing with wafer residence time constraints

Pedagogical games in the educational process as a tool for developing students’ meta-subject skills

Introduction. In the modern educational space, innovative methods and approaches are being actively introduced to improve the quality and accessibility of education. Particular attention is paid to the use of gaming technologies in the educational process to develop students' meta-subject skills, which is important for preparing for the rapidly changing conditions of our time. The purpose of the study is to study the effectiveness of using pedagogical games during extracurricular time for the formation of meta-subject skills in students studying in various pedagogical profiles. Materials and methods. A total of 145 students of a pedagogical university took part in the study, of which 66 students were included in the focus group. All students were examined using certain psychodiagnostic techniques aimed at identifying parameters for the development of creative and critical thinking, as well as communication and organizational abilities. Over the course of 2 months, students selected for the focus group participated in a formative experiment, within the framework of which 7 pedagogical games were developed and conducted, aimed at developing certain meta-subject skills among the experiment participants – communication and creative abilities, willingness to cooperate and critical thinking. The entire series of 7 games was developed by specialists from the A. Dinaev Pedagogical Workshop, operating at the Chechen State Pedagogical University. Results. Based on the results of the study, empirical data was obtained indicating the effectiveness of pedagogical games in the development of certain meta-subject skills in students, such as creativity and some communication skills. The results of the study indicate that, as a result of the formative experiment, students who took part in specially organized game-format events demonstrated significantly higher rates in the development of creative abilities (t=2.879; p=0.005) and certain communication skills (t=2.058; p =0.041), which indicates the effectiveness of using pedagogical game technologies for the purpose of developing meta-subject skills in students. Conclusion. Pedagogical games are an effective tool in the educational process for the development of students' meta-subject skills. Their use not only helps to increase motivation and interest in the educational process, but also develops in students important competencies necessary for successful adaptation to modern challenges. The results of the study can be used for the further development of pedagogical methods and the introduction of gaming technologies into the educational process.

Evaluation of compliance of emergency severity index triage levels and demographic characteristics with the outcome of patients referred to the emergency department: A retrospective study

This research sought to examine the relationship between emergency severity index (ESI) triage levels, demographic factors, and patient outcomes. A total of 350 patient cases were analyzed from the emergency department (ED) of Khoy University of Medical Sciences and Health Services Hospital, where ESI triage was systematically implemented. A simple random sampling method was employed for selection. The study identified key variables including age, gender, outcomes, and triage levels, categorizing levels 1 and 2 as high-priority triage and levels 3, 4, and 5 as low priority. Data were retrospectively gathered from the medical records of all patients involved. The outcomes assessed included hospitalization, discharge from the hospital, patient satisfaction, and mortality. Statistical analysis was conducted using SPSS software version 20, employing non-parametric chi-square tests, analysis of variance (ANOVA), and multiple regression, with a significance threshold set at 0.05. The findings revealed a statistically significant correlation between ESI triage levels, demographic characteristics, and the outcomes of patients presenting to the ED (P<0.0001). The results indicate that the ESI triage system demonstrates high accuracy in prioritizing patients and effectively predicting outcomes, thereby serving as a valuable tool in hospital triage processes.

Phân tích ảnh hưởng của trí tuệ nhân tạo đối với quy trình tuyển dụng nhân sự trong doanh nghiệp tại Thành phố Hồ Chí Minh

Artificial Intelligence (AI) has become an indispensable tool in the human resource recruitment process of businesses in Ho Chi Minh City (HCMC). With its automation and data analysis capabilities, AI helps optimize the recruitment process, from resume screening to predicting candidate performance. This not only saves time and costs for businesses but also enhances the quality of selected candidates. Moreover, AI plays a crucial role in eliminating biases and hidden prejudices in the recruitment process, ensuring fairness and diversity. By creating engaging and convenient interactive experiences for candidates through technologies such as chatbots and human-machine interfaces, AI also helps enhance the company’s image and attract high-quality talent. For businesses in HCMC, using artificial intelligence in the recruitment process is not only a trend but also a key factor for success in today’s competitive labor market. Based on practical research, the article has proposed four implicit solutions to enhance the effectiveness of artificial intelligence in the recruitment process of businesses in HCMC, Vietnam.

Assessing Laterality Errors in Radiology: Comparing Generative Artificial Intelligence and Natural Language Processing

PurposeWe compared the performance of generative artificial intelligence (AI) (Augmented Transformer Assisted Radiology Intelligence [ATARI, Microsoft Nuance, Microsoft Corporation, Redmond, Washington]) and natural language processing (NLP) tools for identifying laterality errors in radiology reports and images. MethodsWe used an NLP-based (mPower, Microsoft Nuance) tool to identify radiology reports flagged for laterality errors in its Quality Assurance Dashboard. The NLP model detects and highlights laterality mismatches in radiology reports. From an initial pool of 1,124 radiology reports flagged by the NLP for laterality errors, we selected and evaluated 898 reports that encompassed radiography, CT, MRI, and ultrasound modalities to ensure comprehensive coverage. A radiologist reviewed each radiology report to assess if the flagged laterality errors were present (reporting error—true-positive) or absent (NLP error—false-positive). Next, we applied ATARI to 237 radiology reports and images with consecutive NLP true-positive (118 reports) and false-positive (119 reports) laterality errors. We estimated accuracy of NLP and generative AI tools to identify overall and modality-wise laterality errors. ResultsAmong the 898 NLP-flagged laterality errors, 64% (574 of 898) had NLP errors and 36% (324 of 898) were reporting errors. The text query ATARI feature correctly identified the absence of laterality mismatch (NLP false-positives) with a 97.4% accuracy (115 of 118 reports; 95% confidence interval [CI] = 96.5%-98.3%). Combined vision and text query resulted in 98.3% accuracy (116 of 118 reports or images; 95% CI = 97.6%-99.0%), and query alone had a 98.3% accuracy (116 of 118 images; 95% CI = 97.6%-99.0%). ConclusionThe generative AI-empowered ATARI prototype outperformed the assessed NLP tool for determining true and false laterality errors in radiology reports while enabling an image-based laterality determination. Underlying errors in ATARI text query in complex radiology reports emphasize the need for further improvement in the technology.

Automated, image-based quantification of peroxisome characteristics with perox-per-cell.

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. Supplementary data are available at Bioinformatics online.

Pengaruh Media Sosial, Aksebilitas dan Fasilitas Islami terhadap Minat Berkunjung Ke Wisata Pulau Larea-Rea Kabupaten Sinjai dengan Kepercayaan sebagai Variabel Moderating

The purpose of this study is to evaluate the impact of social media, accessibility, and interest in visiting Islamic facilities. Larea-Rea Island Tourism, Sinjai Regency with Having faith is a moderating factor. In this research, the sample is the people of Sinjai district. A quantitative technique with a descriptive methodology is the research method employed. In this study, questionnaires, interviews, and documentation were utilized as primary and secondary data sources gather information. Analysis of research was conducted using the data processing tool SmartPLS 4.0. Regression analysis shows that interest in visiting (Y) Larea-rea island tourism is positively and significantly influenced by social media (X1), accessibility (X2), and Islamic facilities (X3). Moreover, the impact of social media on visiting interest might be strengthened or moderated by the trust variable (Z). Have faith can moderate (strengthen) the accessibility variable on visiting interest. However, it cannot moderate (strengthen) Islamic facilities on interest in visiting Larea-rea island tourism with confidence.

Utilization of the Google Earth Engine for the evaluation of daily soil temperature derived from Global Land Data Assimilation System in two different depths over a semiarid region

AbstractThe Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers.

Early evaluation of a natural language processing tool to improve access to educational resources for surgical patients.

Accessible patient information sources are vital in educating patients about the benefits and risks of spinal surgery, which is crucial for obtaining informed consent. We aim to assess the effectiveness of a natural language processing (NLP) pipeline in recognizing surgical procedures from clinic letters and linking this with educational resources. Retrospective examination of letters from patients seeking surgery for degenerative spinal disease at a single neurosurgical center. We utilized MedCAT, a named entity recognition and linking NLP, integrated into the electronic health record (EHR), which extracts concepts and links them to systematized nomenclature of medicine-clinical terms (SNOMED-CT). Investigators reviewed clinic letters, identifying words or phrases that described or identified operations and recording the SNOMED-CT terms as ground truth. This was compared to SNOMED-CT terms identified by the model, untrained on our dataset. A pipeline linking clinic letters to patient-specific educational resources was established, and precision, recall, and F1 scores were calculated. Across 199 letters the model identified 582 surgical procedures, and the overall pipeline after adding rules a total of 784 procedures (precision = 0.94, recall = 0.86, F1 = 0.91). Across 187 letters with identified SNOMED-CT terms the integrated pipeline linking education resources directly to the EHR was successful in 157 (78%) patients (precision = 0.99, recall = 0.87, F1 = 0.92). NLP accurately identifies surgical procedures in pre-operative clinic letters within an untrained subspecialty. Performance varies among letter authors and depends on the language used by clinicians. The identified procedures can be linked to patient education resources, potentially improving patients' understanding of surgical procedures.

The rise of and demand for identity‐oriented media coverage

AbstractWhile some assert that social identities have become more salient in American media coverage, existing evidence is largely anecdotal. An increased emphasis on social identities has important political implications, including for polarization and representation. We first document the rising salience of different social identities using natural language processing tools to analyze all tweets from 19 media outlets (2008–2021) alongside 553,078 URLs shared on Facebook. We then examine one potential mechanism: Outlets may highlight meaningful social identities—race/ethnicity, gender, religion, or partisanship—to attract readers through various social and psychological pathways. We find that identity cues are associated with increases in some forms of engagement on social media. To probe causality, we analyze 3,828 randomized headline experiments conducted via Upworthy. Headlines mentioning racial/ethnic identities generated more engagement than headlines that did not, with suggestive evidence for other identities. Identity‐oriented media coverage is growing and rooted partly in audience demand.

Optimization of rural environment through drainage system modernization: analysis of investment feasibility indicator in the modernization of drainage ditches on former agricultural lands

The benefits of installing pipes and drainage manholes, as well as covering open drainage ditches in built-up areas, have been listed in the report. A cost-effectiveness indicator for the investment, taking into account proprietary weighting coefficients, has been presented, which can be used as a formal argument determining the need for modernization of existing solutions in built-up areas. The indicator serves as an objective tool in the decision-making process at the municipal level. Modernization involving the installation of pipes and covering ditches, which previously served agricultural purposes, leads to an improvement in spatial order in a given area and also contributes to the protection of the natural environment through proper water management in former agricultural areas

Measurement and Simulation of the Propagation of Impulsive Acoustic Emission Sources in Pipes

Abstract: Acoustic Emission (AE) testing is a non-destructive evaluation technique that has gained significant attention in pipeline monitoring. Pencil-lead breaks (PLBs) are commonly used in reproducing and characterising sensors used in AE applications and have emerged as a valuable tool for calibration processes. This technique involves breaking a pencil lead by pressing it on the surface of the test structure and applying a bending moment at a given angle on a surface. The applied force produces a local deformation on the test surface, which is released when the lead breaks. The fracture in these PLBs is assumed to be a step unload; however, this is not the case. In this work, a series of PLB source experiments complemented with parallel numerical simulations were carried out to investigate the actual unload rate by correlating the relationship between AE speed, frequency, and power from PLBs. This was achieved by varying the simulation unload rates recorded over a duration of 2 s on a steel pipe and comparing to the experiment. Analysis of the investigated results from the experimental and numerical models suggests that although the AE line structure of a PLB can be reproduced by simulation for short times only (1 µs), the actual unload rate for PLBs is in the region of 10–8 s. It is concluded that FEA has the potential to help in the recovery of the temporal structure from real AE structures. The establishment of this model will provide a theoretical basis for future studies on the monitoring of non-impulsive AE sources such as impact on pipelines using finite element analysis.

Economic Growth and Development of the Country through EVoting, Free and Fair Elections

Elections are an important means to elect the democratic governments for aspecific period to rule the country and work for the growth and development of thepeople. Voting, in this regard, is used as a tool in this democratic process. Thetraditional voting systems witnessed low turnout of masses, result delays, riggingand formation of handicap governments, litigation, protests and several otherproblems including high cost of this futile exercise. In this scenario, e-Voting(Electronic Voting) seems the ultimate solution to pull the county out of jeopardyand helps in the creation of popular governments duly elected by the majorityvotes of the masses. This study is an attempt to develop a solution model for allsuch countries, including Pakistan, where Elections are of paramount importance,but the outcomes of elections are compromised thereby resulting in weakdemocratic system which remains vulnerable and lacks popular support of massesin general.

Utilizing Python for Scalable Data Processing in Cloud Environments

In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The study also examines Python's interoperability with cloud services like AWS, Google Cloud Platform, and Microsoft Azure in data input, transformation, and analysis. Many case studies and real-world applications demonstrate how Python has been used in banking, healthcare, and e-commerce. Python is useful for managing massive amounts of data, streamlining processing processes, and scaling cloud applications, as shown in the case studies. The report also analyzes Python-based cloud systems' performance indicators and cost consequences, revealing best practices and possible issues. The article explores Python's involvement in cloud computing trends and technology. Serverless architectures, Docker and Kubernetes, and Python interaction with cloud-native tools and services are examples. These patterns show how data processing is changing and how Python is improving to meet current data needs. This study concludes that Python is a reliable and scalable cloud data processing option. The language's strengths, alignment with cloud technologies, and practical applications in many areas are covered in detail. The results indicate that Python's versatility and cloud scalability provide a robust foundation for handling and analyzing massive datasets, enabling better decision-making and innovation across fields.

STUDY OF THE EFFECTIVENESS OF THE DEMULSIFIER COMPOSITION ON THE DESTRUCTION OF LOCAL OIL-WATER EMULSION

The effect of the demulsifier composition on the destruction of local oil-water emulsions was studied in this work. One of the urgent problems in the development of deposits is to increase the efficiency of the preparation of hydrocarbons in the fields. The solution of this problem makes it possible to significantly increase the degree of oil preparation, reduce the loss of hydrocarbons with drainage waters, thereby improve the environment and bring additional profit to the enterprise. A complex of theoretical and experimental studies was used, consisting in generalization and analysis of literary data, as well as by analogy, modeling, quantitative and qualitative observation, laboratory tests, conducting a multifactorial experiment, data processing using methods of mathematical statistics. Information processing tools based on computer software products were used. The results of laboratory tests of oil-water emulsion, physico-chemical analysis of water composition, new chemical reagents-demulsifiers recommended for field testing are presented. It can be concluded, based on the research conducted, that the demulsifier EASY-DE03-15 is the most effective reagent for dehydration of oil-water emulsions and desalination of MIX samples.