Reviewerly/exHarmony huggingface dataset:
Effect of Tablet Fe on Improvement Hemoglobin in Adolescent Women Region Mining Morosi District

Introduction: Anemia is one of the nutritional problems in adolescents, especially at the Morosi Health Center with a prevalence of anemia in June 2022 of 27.95% who have an Hb of 9-10 g/dl, 9.67% have a Hb of 7-8 g/dl and 1.07% Hb&lt; 7 g/dl. The purpose of this study was to determine the effect of giving Fe tablets to increase hemoglobin in young women in the mining area of Morosi District.&#x0D; Methods: This type of research is pre-experimental, with a one-group pre-test-post-test design. The population is part of young women in WonuaMorini Village, Besu Village, and Mendikonu Village, amounting to 72 people taken using the Proporsionatestratifed random Sampling technique. Hb level data was obtained by easy touch meter. The data were analyzed using the Wilcoxon sign rank test.&#x0D; Results: Showed the average Hb level before intervention was 11,38 g/dl and after intervention was 11,38 g/dl. The second week 11,68 g/dl after the intervention. The third week after the intervention was 12.86 g/dl and the fourth week after the intervention was 12.86 g/dl.The results of the Wilcoxon sign rank test obtained a p-value (0.000) &lt; (0.05).&#x0D; Conclusion: There is no effect of giving Fe tablets in the first and there was an effect of giving Fe tablets in the Second to fourth weeks of increasing hemoglobin in young women in the mining area of ​​Morosi District. Research suggests that young women routinely consume blood-added tablets once a week.
A Comparative Analysis of Chinese-English Translation Quality Based on ChatGPT: A Case Study of Chinese Characteristic Words

The release of ChatGPT, a large-scale natural language interactive program, at the end of 2022 has once again garnered worldwide attention to artificial intelligence technology. As a deep learning-based natural language processing model, ChatGPT demonstrates unique advantages in the field of translation that are unparalleled by previous machine translation software. Chinese characteristic words, which lack direct equivalents in other cultures or countries, have always posed a challenge in Chinese-English translation, but they are also an essential part of effectively conveying the Chinese story. This article selected some Chinese characteristic words mentioned in the Chinese Government Work Report as translation materials and asked ChatGPT with completing tasks such as translating and polishing the translation according to instructions. By comparing the official Chinese-English translation version and traditional machine translation version, this article analyzed the translation quality of ChatGPT in C-E translation from the perspectives of syntactic structure, coherence, and domain adjustment, with the aim of summarizing machine-assisted translation methods and translation strategies for Chinese characteristic words.
A Plug-n-Play Framework for Scaling Private Set Intersection to Billion-Sized Sets

Motivated by the recent advances in practical secure computation, we design and implement a framework for scaling solutions for the problem of private set intersection (PSI) into the realm of big data. A protocol for PSI enables two parties each holding a set of elements to jointly compute the intersection of these sets without revealing the elements that are not in the intersection. Following a long line of research, recent protocols for PSI only have $${\approx }5\times $$ computation and communication overhead over an insecure set intersection. However, this performance is typically demonstrated for set sizes in the order of ten million. In this work, we aim to scale these protocols to efficiently handle set sizes of one billion elements or more. We achieve this via a careful application of a binning approach that enables parallelizing any arbitrary PSI protocol. Building on this idea, we designed and implemented a framework which takes a pair of PSI executables (i.e., for each of the two parties) that typically works for million-sized sets, and then scales it to billion-sized sets (and beyond). For example, our framework can perform a join of billion-sized sets in 83 min compared to 2000 min of Pinkas et al. (ACM TPS 2018), an improvement of $$25\times $$ . Furthermore, we present an end-to-end Spark application where two enterprises, each possessing private databases, can perform a restricted class of database join operations (specifically, join operations with only an on clause which is a conjunction of equality checks involving attributes from both parties, followed by a where clause which can be split into conjunctive clauses where each conjunction is a function of a single table) without revealing any data that is not part of the output.
Road Target Detection Algorithm Based on Improved YOLOv5

In order to address the issues that the YOLOv5 network structure encounters in road target detection, such as low accuracy in identifying small targets at long distance, limited precision due to environmental factors, and insufficient understanding of complex road information, this article proposes a road object detection algorithm based on an improved Yolov5 and designed a new Yolov5 network model - FRM-Yolov5.The improved algorithm consists of three parts. Firstly, the Fasternet network is used to replace the original backbone network, and the RepGFPN is used to replace the neck network. Both have optimized network structures to enhance the extraction of feature information. Finally, the Mish activation function is used to replace the original SiLU activation function to improve the nonlinear expression ability of the network and enhance the generalization of the network model. By applying the improved network and the original network to the COCO2017 dataset for comparative experiments, the experimental results show that the mAP value of the improved algorithm reached 68%, a 2.5% improvement compared to the original network.
Decision Support System for PKH Assistance Recipients Promethee Method

The Family Hope Program (PKH) is a program providing conditional social assistance to Beneficiary Families (KPM) who are designated as PKH beneficiary families. With the current development of information technology, decision making can be assisted by using computers equipped with application programs to support the production of the required quality decisions. In selecting families who receive assistance from the Family Hope Program (PKH) using the Decision Support System using the Promethee method. Promethee is a method of determining the order (priority) in multi-criteria analysis, the result of this method is a ranking of alternatives based on the selected criteria. The decision support system application using the Promethee method can determine which disadvantaged families will receive PKH assistance with faster, more accurate and fair calculations and considerations. The results of research using this method using 5 alternatives, there are 3 alternatives with accepted status and 2 alternatives that are rejected. The accepted alternatives were Tiah, Mansyah and Mus Muliadi. Meanwhile, the alternatives that were rejected were Irmansyah and Ainun. By obtaining calculation results from a decision support system using the Promethee method, it can help and make it easier for sub-districts to determine recipients of the Family Hope Program (PKH) for underprivileged families.
45th Lithuanian National Physics Conference. October 25-27, 2023 Program and abstracts of presentations

The publication presents the program of the Lithuanian National Physics Conference, traditionally held every two years, as well as abstracts of oral and poster presentations.
Penerapan Aplikasi Kahoot dalam Meningkatkan Hasil Belajar Peserta Didik

The purpose of this study was to determine the increase in student learning outcomes using the Kahoot application in class XI IPS 1 SMA Negeri 3 Palembang. The research method used was classroom action research (CAR) with a sample size of 35. The data collection method used tests to obtain learning outcomes in the pre-cycle with an average grade of 65.4, then cycle I with an average grade of 73.5 , and cycle II with an average value of 85.6 which experienced an increase. It can be concluded that the application of the Kahoot application can improve the learning outcomes of students in class XI IPS 1 SMA Negeri 3 Palembang.
Geostatistical Modeling and Mapping of Arsenic Occurrence and Vulnerability—A Case Study on Bihar, India

Determination of the vulnerability of groundwater to arsenic contamination in general and mapping the vulnerable zones is essential for the preservation of groundwater quality. CGWB has explored and marked the occurrence of arsenic mainly along the Ganga–Brahmaputra-Meghna (GBM) river basin. The major aim of the current study is to conduct geostatistical modeling of arsenic occurrence in the groundwater employing the available secondary data based on the subsurface and hydrogeochemical parameters for the districts of Bihar. Further, the arsenic vulnerability index has been developed employing the Multi-Criteria Decision Making (MCDM) method after integrating various thematic layers in the GIS platform and generating a vulnerability map. This has helped to identify the arsenic-vulnerable zones. The north-western and the central parts, including Patna, Bhojpur, Munger, Bhagalpur, and Vaishali districts, exhibit high arsenic risk zones due to shallow depth of water and low elevation associated with high silicate and iron contents. It is found that high arsenic risk is associated with the districts lying along the Ganga River. The output was validated against the existing arsenic locations for 2015 (CGWB) and, 2019 (primary data of three districts named Saran, Samastipur, and Vaishali), and by the Receiver Operating Characteristics (ROC) metric.
Cluster Synchronization as a Mechanism of Free Recall in Working Memory Networks

This article studies free recall, i.e., the reactivation of stored memory items, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">patterns</i> , in any order, of a model of working memory. Our free recall model is based on a biologically plausible modular neural network composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> modules, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hypercolumns</i> , each of which is a bundle of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minicolumns</i> . The coupling weights and constant bias values of the network are determined by a Hebbian plasticity rule. Using techniques from nonlinear stability theory, we show that cluster synchronization is the central mechanism governing free recall of orthogonally encoded patterns. Particularly, we show that free recall's cluster synchronization is the combination of two main mechanisms: simultaneous activities of minicolumns representing an encoded pattern, i.e., within-pattern synchronization, together with time-divided activities of minicolumns representing different patterns. We characterize the coupling and bias value conditions under which cluster synchronization emerges. We also discuss the role of heterogeneous coupling weights and bias values of minicolumns' dynamics in free recall. Specifically, we compare the behaviour of two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H \times 2$</tex-math></inline-formula> networks with identical and non-identical coupling weights and bias values. For these two networks, we obtain bounds on couplings and bias values under which both encoded patterns are recalled. Our analysis shows that having non-identical couplings and bias values for different patterns increases the possibility of their free recall. Numerical simulations are given to validate the theoretical analysis.
Design Application of Solar Backpack for African Rural Area Students

Access to electricity is a significant challenge in Africa, the impact of which is even greater in schools in isolated rural areas. According to the World Bank, approximately 600 million people (nearly two-thirds of the population) in Sub-Saharan Africa lack access to electricity, leading to difficulties in people’s livelihood, including limited educational opportunities. It has been observed that students living in rural areas take long-distance walks (1h to 2hrs) to school early in the morning and return home after dark with no street lights. Moreover, many students study at night without access of light and electricity. Therefore, this study aims to explore the potential applications and benefits of solar-powered backpacks for African students living in rural areas. The study was conducted in Comoros, at the Mohammadia Community School in the village of Irohe Oichili and the village of Chomoni Oichili where students walk more than 6.5km to get to school. Three hundred and one students were interviewed. The questionnaire was employed to capture necessary data for the solar-powered backpack design and a vivid picture of the importance of this design in Comorian rural communities for students. The parameters within the questionnaire includes families’ financial status, whether electrical power shortage affects the students’ educational performance, whether the solar-powered backpack will benefit the students and so on. The results of the survey showed that 82% of the students came from families with low financial status. 80% of the students reported that electrical power shortage affects their educational performance. 90% of the students reported that they would benefit from using a solar-powered backpack. Therefore, a solar-powered backpack was designed with a solar panel attached to the front of the backpack, and a LED light was attached directly inside the backpack. For the charging facility, the backpack is occupied by a USB port, located on the bag’s exterior, connected to the charge center and the battery, while the battery inside the pack is stored neatly away. The battery provides power to the port through an extension system connected inside the backpack. The prototype was tested, and we found that the average time spent on the school way to charge is 1h48min. The capacity of the battery charged on the way is 36%. The period of usage for studying is 1h30min, and the battery life after usage is 16%. As a result, a solar-powered backpack could provide a convenient, portable and sustainable source of electricity for student living in remote areas who are unable to access the electrical grid. Future work is to update the prototype with an attached solar panel that can be disconnected for better charging when the student enters the classroom or at home.
Research and Design of the Safety Control Platform for the Railway Signaling

Based on the functions and safety requirements of railway signal, a railway signal safety control platform is designed and studied from three aspects: design principle, hardware structure and software structure. In terms of design principles, the safety control platform not only meets the reliability requirements of various signal safety control systems, but also has the versatility and scalability of the platform; In the view of hardware structure, the double two out of two redundant structure is adopted as the overall core architecture of the safety platform, which consists of power layer, logic layer, communication layer and IO execution layer. In terms of software architecture, it is divided into two parts: user application software and platform software. Finally, taking the computer interlocking system as an example, it is necessary to introduce the multi-node communication problem of the CAN bus of the signal safety control platform, and the related problems caused by it, such as real-time communication and data transmission reliability.
Development of Electronic Student Worksheets with a Scientific Approach to Static Fluids Material to Improve Critical Thinking Skills of SMAN 9 Merangin Students

Teaching materials in their role as providers of information are really needed by educators and student. The purpose of this research is to develop teaching materials in the form of e-LKPD with a scientific approach to static fluid material to improve critical thinking skills at SMAN 9 Merangin which can be used in the learning process. This development research uses a 4D development model. The test subjects consisted of a small group consisting of 10 people and a large group consisting of 30 people who were students of SMAN 9 Merangin. The instruments used in this research were interviews, questionnaires, pretest-posttest questions, and validation assessment sheets. The data analysis technique used in this research is descriptive research with a quantitative approach. The validation results from material and media experts show that the teaching materials developed are very valid for small group trials. Teacher assessments and student responses to the media are very good. The effectiveness of e-LKPD teaching materials in improving critical thinking skills was carried out by a Paired Sample T-Test with the decision that the hypothesis was accepted and a large group test was carried out to obtain a fairly effective level of effectiveness with an average value of 0.6187 or 61.8696%. It can be concluded that the development of e-LKPD teaching materials with a scientific approach to static fluid material to improve critical thinking skills is feasible and effective for use in the learning process. 6187 or 61.8696%. It can be concluded that the development of e-LKPD teaching materials with a scientific approach to static fluid material to improve critical thinking skills is feasible and effective in the learning process. 6187 or 61.8696%. It can be concluded that the development of e-LKPD teaching materials with a scientific approach to static fluid material to improve critical thinking skills is feasible and effective for use in the learning process.
Implementasi Metode Fuzzy AHP untuk Sistem Pendukung Keputusan Peminjaman pada Koperasi Kredit Sejahtera

Koperasi Kredit Sejahtera is a cooperative engaged in savings and loan services in the Bogor area. One form of KKS service to employees is by providing loan funds to help solve financial problems for employees. The problem faced by KKS so far is that decision-making activities in lending are still inadequate which results in non-performing loans or bad debts because the cooperative does not have a special system for decision making. Based on the problems that occur, researchers will build a decision support system that can handle credit problems using the Fuzzy AHP method using 9 criteria, namely salary, term, other loans, membership status, expenses, accuracy of previous payments, work, loan purpose and loan amount so that it can help the cooperative in determining the provision of loans to prospective creditors quickly, precise and accurate according to the ranking of the fuzzy AHP calculation results. Blackbox testing is carried out to test the achievement of the functional requirements of the system which shows that the functional requirements are met 100% of the total 8 functional consisting of 5 functional on admin actor and 3 functional on officer actor can be done/valid. UAT testing is used to test the achievement of non-functional requirements. By conducting a questionnaire to 16 respondents, the result is 100% valid and reliable. So that 100% of non-functional needs are successfully met.
Efficient social media sentiment analysis using confidence interval-based classification of online product brands

This paper presents an efficient method for categorizing the sentiments of Internet users, with a focus on social media users, using a confidence interval to estimate the reliability of sentiment predictions. The classification is based on the sentiments expressed in their posts, which are divided into positive, negative, and neutral categories. The paper presents an analysis table that analyzes sentiments and opinions about online product brands. The process includes two steps: 1) analyzing sentiments from text data using machine learning techniques, and 2) describing a five-step sentiment and opinion classification process that includes data collection, preprocessing, algorithm application, validation, and visualization. The proposed solution is implemented using Python, along with the scikit-learn, NumPy, pandas, and Dash libraries, and leverages the use of confidence intervals to assess the accuracy and reliability of the sentiment analysis model.
Prompts of Large Language Model for Commanding Power Grid Operation

Large Language Models (LLMs) like ChatGPT can assist people’s general workflows, where the prompt is necessary to inspire the potential of LLMs to solve problems from specified or professional domains like robotics. In the electrical engineering subject or the electric power utility industry, experienced operators and professional experts monitor power grid operation statuses and interact with the grid via human commands on the screen, and components in the grid execute the commands to keep the complex grid safe and economical operation. In this process, human experts edit commands to operate the corresponding software. Human commands are the natural language that the LLM can process. The power grid is composed of generation, transmission, distribution, and other components. Therefore, we redesign the human-computer interaction frame between practitioners and the grid via recurrent prompts to apply the LLM to generate computer programming instructions from the multi-step natural language commands. The programming instruction is executed on system components after being confirmed or revised by human experts, and the quality of generated programs will be gradually improved through human feedback. The idea of this study is originally inspired by studies on controlling individual robotic components by ChatGPT. In the future, we will apply the designed prompt templates to drive the general LLM to generate desired samples which could be used to train an LLM professional in the domain knowledge of electrical engineering to operate multiple types of software for power grid operators.
The distribution of defective multivariate polynomial systems over a finite field

This paper deals with properties of the algebraic variety defined as the set of zeros of a “defective” sequence of multivariate polynomials. We consider two types of varieties: ideal-theoretic complete intersections and absolutely irreducible varieties. F
The Fast Super Block Partitioning Algorithm Based on Depth Residual Network

The superblock partitioning process based on rate distortion optimization in VP9 is analyzed in depth, and a fast algorithm for superblock partitioning based on deep residual network is proposed. Firstly, we perform statistical analysis on the division depth distribution of superblocks, and conclude that superblock partitioning has a high correlation with image content. Then, the superblock partitioning process is modeled as a classification problem, and a hierarchical deep residual network is designed to predict superblock partitioning. An effective neural network model is obtained through offline training. Experimental results show that under the premise of quality loss within an acceptable range, the method in this chapter can effectively reduce the complexity of VP9 coding.