Reviewerly/exHarmony huggingface dataset:
Prediction of the breast cancer mortality rate and its effective factors using genetic algorithm and logistic regression

Introduction: Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model. The aim of present study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality.&#x0D; Methods: Data of 2836 people with breast cancer during the years 2014-2018 were examined. Information was registered in the cancer registration system of Kerman University of Medical Sciences. Death status was considered as the dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered as independent variables. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were used to compare the models.&#x0D; Results: The logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.60), specificity (0.80), area under the ROC curve (0.70), and accuracy (0.77)), and also genetic algorithm model (with sensitivity (0.21), specificity (0.96), area under the ROC curve (0.58) and accuracy (0.87)) did so.&#x0D; Conclusion: The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously.
Spectral Factor of Colored Solar Cells: A Case Study on the Main Urban Areas in Romania

PV modules for Building-Integrated Photovoltaic (BIPV) applications are made of different colors aimed at raising the visual aesthetic of the building. But a colored coating applied to the surface of a basic PV module is inherently associated to a decrease in conversion efficiency. From a different perspective, the efficiency of a PV module is evaluated under the industry standard test conditions (STC). Due to spectral mismatch, the efficiency of a PV module operating in outdoor conditions may substantially differ from the standard value evaluated at STC. In this study, the influence of spectral solar irradiance distribution on the colored PV module efficiency is evaluated in terms of spectral factor (SF). SF quantifies the relative power gain or loss caused by the spectral difference from STC. The theory is illustrated with a case study on the main four urban areas in Romania. The actual solar radiation spectrum is estimated with the simple Leckner spectral solar irradiance model, based on atmospheric parameters retrieved from the Aerosol Robotic Network (AERONET). The results emphasize that the aesthetic of BIPV comes at a high energy cost: depending on the color, a coating applied on the surface of a crystalline silicon PV module may reduce its conversion efficiency even by half.
RANCANG BANGUN TEKNOLOGI CLOUD COMPUTING VIRTUALISASI PROSES BELAJAR MENGAJAR

There is a lot of business competition in this case credit sales, where credit sales are made using the gradual payment method with requirements that must be agreed upon by the credit provider and credit recipient. In this XYZ company there are problems that often occur including when officers select and determine credit recipients (debtors) in accordance with the requirements given by the company, this company also still uses traditional methods in promotion and there is no decision-making system to be able to assist in determining the prospective debtor In addition, creditors (creditors) find it difficult to receive debtor data reports. In determining the debtor to get credit sales, in this study we will use the SPK K-Nearest Neighbor method where this method is to calculate the closeness between old cases and new cases in order to facilitate officers in determining prospective borrowers with a decision support system using the K-Nearest method Neighbor. In addition to facilitating and helping creditors to be able to find out the debtor data report in more detail and to increase sales. The results of this study, get results from calculating the closeness between new cases with old cases using the K-Nearest Neighbor method. And from some calculations with that method, the greatest value will be used to predict the next cases.
Emerging Technologies for Healthcare during the COVID-19 Pandemic

The COVID-19 pandemic has had significant changes in all aspects of life having economic, social and physiological consequences that the healthcare sector is in the priority of agile changes for the surveillance of humanity and saving the life of people. The global collaboration is a very prominent necessity at this fight and the new technologies and innovations have been developed based on current digital technologies for self-monitoring, contact tracking, remote care, hand-free measurements, increased security and consistency, vaccine developments and home working needs during the pandemic. The currents urgent developments will be part of our new normal life, improvingthe life people. In this Chapter, emergingwidely technologies in health will be explained with their practical usages and purposes. Later, some applications of these technologies are mentioned. The pandemic started in China but, it has shown great developments in the usage of smart technologies to decrease the effects of the pandemic. Technologies used by some other countries have mentioned that South Korea, Japan and China have shown exemplarymanagement againstthe COVID-19 with their smart developed capabilities and technologies. Every development has advantages and at the same time, they have disadvantages that challenges of these new technologies are explained with some solutions to overcome these challenges. Infodemi, bias and inconsistencies and wearable devices accuracy and validation are new challenges besides privacy concerns of current technologies. It is clear that the new bad (COVID-19) will bring new goods by improving self-healthcare management, increasing awareness, creating social distancing, increasing security and mostly bringing new more cost-effective treatment methods in healthcare.
Investigating the Washback Effects of TOEFL iBT on the Preparation Courses of the Training Institutions in Shanghai

This research probes into the washback effects of TOEFL iBT (the Test of English as a Foreign Language Internet-Based Test) and how teachers in the training institutions instruct preparation course in Shanghai. The findings in this article show that teachers are supposed to transform the general language skills into teachable materials and need to be more aware of cross-cultural learning problems. The study points out that the TOEFL iBT has an influence both on course materials and teaching methodology. However, the effects can be intervened by using appropriate TOEFL iBT preparation materials.
Unsupervised probabilistic models for sequential Electronic Health Records

We develop an unsupervised probabilistic model for heterogeneous Electronic Health Record (EHR) data. Utilizing a mixture model formulation, our approach directly models sequences of arbitrary length, such as medications and laboratory results. This allows for subgrouping and incorporation of the dynamics underlying heterogeneous data types. The model consists of a layered set of latent variables that encode underlying structure in the data. These variables represent subject subgroups at the top layer, and unobserved states for sequences in the second layer. We train this model on episodic data from subjects receiving medical care in the Kaiser Permanente Northern California integrated healthcare delivery system. The resulting properties of the trained model generate novel insight from these complex and multifaceted data. In addition, we show how the model can be used to analyze sequences that contribute to assessment of mortality likelihood.
A simple path-aware optimization method for mobile robots*

We present an approach for a mobile robot to seek the global maximum of an initially unknown function defined over its operating space. The method exploits a Lipschitz assumption to defne an upper bound on the function from previously seen samples, and optimistically moves towards the largest upper-bound point. This point is iteratively changed whenever new samples make it clear that it is suboptimal. In simulations, the method finds the global maxima with much less computation than an existing, much more involved technique, while keeping performance acceptable. Real-robot experiments confirm the effectiveness of the approach.
Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network

Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.
Clinicopathological Characteristics and Curative Effect of Lymphoma Based on Sampling Theory

With the continuous development of society, people’s attention to the body is also increasing. This article explains and analyzes the clinicopathological characteristics of a more serious disease, lymphoma, and the effect of treatment. In the previous studies of this type of disease, the collected data was very limited, and the theory for data analysis was also lacking. The direct result was that the analysis of such disease types was not complete, and it was difficult to obtain statistical significance, which made the clinical guidance weak. In order to improve this problem, this paper applies the sampling theory in statistics to the clinicopathological characteristics of lymphoma and the effect of treatment. Through the scientific analysis of such samples, it is hoped to provide some ideas for clinical treatment. In the sampling theory, this paper selects two methods, Bayesian algorithm and adaptive sampling theory, and introduces the corresponding methods for the correlation analysis of the clinicopathological characteristics and curative effect of lymphoma according to the above two methods. These related processing methods start from a large number of samples and provide sufficient computational support for the final experiments. Experiments show that the score of clinical symptoms of lymphoma is not less than 96%, which means that the patient is in a state of recovery. When the evaluation score is between 63% and 96%, the corresponding treatment has a significant effect. These results show that, in the analysis of the clinicopathological characteristics of lymphoma and related treatment effects, by using the Bayesian algorithm in the sampling theory and the adaptive sampling theory, the data results can well correspond to the clinical effects.
Token ekonomi dengan media flashcard untuk meningkatkan kemampuan membaca permulaan pada anak disleksia

Dyslexia is a specific form of learning disorder related to difficulty reading and difficulty in writing expression. Most dyslexia children show low ability in overall phonological processing, leading to slower reading development than other children. Therefore, intervention methods are needed that are tailored to the characteristics of dyslexic children, one of which is a flashcard medium with the principle of economic tokens. The purpose of this study was to find out the effectiveness of flashcard media with the principle of economic token to the ability to read early in dyslexic children. The study uses a quantitative approach of single-case experimental design. The subjects in the study were a second grader aged 7 years and exhibited dyslexia characteristics as well as experiencing barriers in initial reading. Then the instrument used to measure the ability to early level reading ability EGRA (Early Grade Reading Assessment). The results of this study show that flashcard media with the principle of economic tokens is effective for improving the early level reading ability in dyslexia children. Keywords: Dyslexia, early level reading ability, flashcard, token economy
Pengaruh Model Pembelajaran Picture and Picture Berbantuan Quizizz terhadap Hasil Belajar Siswa Sekolah Dasar

The use of the lecture method causes students to be less interested in learning so that student learning outcomes are low. This study aims to find out the effect of applying the Picture and Picture Learning Model assisted by Quizizz on the acquisition of science learning outcomes for fourth grade elementary school students on animal life cycle material. This study applies a quantitative experimental method using a Pretest-Posttest Control Group research design with 20 multiple choice questions as research instruments. Data analysis techniques used descriptive statistics, normality test with Liliefors test, homogeneity test with Fisher's test, hypothesis testing with t-test, and test effect size. The results of hypothesis testing show that tcount &gt; ttable (4.5233 &gt; 2.0452), then H1 is accepted. It can be concluded that there is an influence from the use of the Picture and Picture Learning Model assisted by Quizizz on the science learning outcomes of fourth grade elementary school students.&#x0D; Penggunaan metode ceramah menyebabkan siswa kurang tertarik pada saat berlangsungnya pembelajaran sehingga hasil belajar siswa menjadi rendah. Penelitian ini memiliki tujuan untuk mencari tahu pengaruh penerapan model pembelajaran picture and picture berbantuan Quizizz terhadap perolehan hasil belajar IPA siswa kelas IV SD pada materi siklus hidup hewan. Penelitian ini menerapkan metode kuantitatif eksperimen menggunakan desain penelitian pretest-posttest control group dengan 20 butir soal pilihan ganda sebagai instrumen penelitian. Teknik analisis data menggunakan statistik deskriptif, uji normalitas dengan uji liliefors, uji homogenitas dengan uji fisher, uji hipotesis dengan uji-t, dan uji effect size. Hasil uji hipotesis menunjukkan bahwa thitung &gt; ttabel (4,5233 &gt; 2,0452), maka H1 diterima. Dapat disimpulkan bahwa terdapat pengaruh dari penggunaan model pembelajaran picture and picture berbantuan Quizizz pada hasil belajar IPA siswa kelas IV SD.
Using Enhanced Reordered Census Transform for Semiglobal Stereo Matching Algorithm

We present a semiglobal stereo matching technique that combines enhanced census transform with unidirectional dynamic programming optimization. This method not only improves matching accuracy but also substantially reduces noise interference by using the 8-domain pixel median instead of the centre pixel in the standard technique and optimizing the initial matching generation value with the 8-path aggregation optimization algorithm. The experimental findings show that this suggested strategy efficiently avoids interference produced by various noises and reduces the false matching rate significantly.
Time Series Visualization of Natural Disaster Prediction Using Transformer

The extension of the forecast time is an essential requirement for real-world applications, which includes early caution for severe climate conditions. In this paper, we come up with a new approach to time series forecasting. The time-series data is generic in lots of disciplines and engineering. Time series prediction is a vital assignment in time-series data modeling and is an important area of deep learning. We have developed a novel technique that makes use of a Transformer-based deep learning model for the prediction of time-series data. This technique works with the aid of self-attention mechanisms to study complicated patterns and dynamics from timeseries data. Moreover, it is a preferred framework and may be implemented in univariate and multivariate time series data, in addition to time series embedding. Using natural disasters such as flood forecasting as a case study, we show that the forecast outcomes produced using our technique are similar to the state-of-the-art.
Digital Evidence Collection

Computer forensics is a digital forensic division that basically deals with crimes that have been committed across various computing devices such as networks, computers, digital storage media etc. It mainly refers to a set of different methodological procedures and techniques to identify, gather, preserve, extract, interpret, document, and present the evidences from the computing equipment in such a manner that the discovered evidence/equipment is acceptable in the court of law. This chapter inspects the origin of science and scientific methods to shape the core premises for setting up measures to evaluate digital forensic as a science and hence justifying the use of digital forensic tools and their feasibility towards cybercrime. This chapter also describes the working framework of different sectors of digital forensics and their challenges while analyzing cybercrime cases. Described controlled frameworks have the potential to minimize the error and challenges in digital forensics and their various categories.
Teachers’ Gestures in EFL Classroom

The research analyzed the teachers’ gestures in EFL classrooms. It covered the teachers’ gestures, the functions of the teachers’ gestures, and the effect of the teachers’ gestures on the students. The research applied a qualitative research design. The subjects of the research were 2 English teachers and 14 students of a senior high school in Majene. The research instruments were classroom observation, teachers’ and students interviews. The results of this research revealed that the teachers performed hand gestures and head gestures in the classroom. The hand gestures were pointing, beckoning/inviting, giving examples, clapping hands, knocking on the table, hitting the whiteboard, illustrating, and numbering. In contrast, for head gestures, they used nodding and shaking head. The teachers used the gestures mainly regarding management, regulation, input, and instruction. But in some meetings, one of the teachers used head gestures combined with a smile when rewarding students and showing affection. These teachers’ gestures were primarily used in conjunction with words or verbal messages (speech-related gestures) to complement, accentuate, and repeat the words. On the other hand, a small number of gestures are also used to substitute words. These gestures are stand-alone or are called speech-independent gestures. Teachers' use of gestures in the classroom consciously and unconsciously affects students and the learning process. Students agreed that using gestures by the teacher could improve their understanding of the material. However, using improper or too many gestures can distract their focus, making it difficult for them, to be nervous and difficult to engage in the learning process. So, it is urgent for the teachers to understand that some gestures should be increased or decreased to create a better learning atmosphere.
Estimation of the Global Solar Radiation in Anyigba Kogi State Using Hargreaver And Samani

ABSTRACT: This work estimates the global solar radiation in Anyigba with latitude 7.498 using Hargreaves and Samani model. The average monthly global radiation for Anyigba was found to be 32 MJ/m2 /day with a MBE of 15.6333 MJ/m2 /day and RMSE OF 5.985 MJ/m2 /day.
DL-Based Physical Tamper Attack Detection in OFDM Systems with Multiple Receiver Antennas: A Performance–Complexity Trade-Off

This paper proposes two deep-learning (DL)-based approaches to a physical tamper attack detection problem in orthogonal frequency division multiplexing (OFDM) systems with multiple receiver antennas based on channel state information (CSI) estimates. The physical tamper attack is considered as the unwanted change of antenna orientation at the transmitter or receiver. Approaching the tamper attack scenario as a semi-supervised anomaly detection problem, the algorithms are trained solely based on tamper-attack-free measurements, while operating in general scenarios that may include physical tamper attacks. Two major challenges in the algorithm design are environmental changes, e.g., moving persons, that are not due to an attack and evaluating the trade-off between detection performance and complexity. Our experimental results from two different environments, comprising an office and a hall, show the proper detection performances of the proposed methods with different complexity levels. The optimal proposed method achieves a 93.32% true positive rate and a 10% false positive rate with a suitable level of complexity.
TINGKAT KECERDASAN EMOSIONAL MAHASISWA DALAM PROSES PEMBELAJARAN DI MASA PANDEMIC COVID-19 DI KAMPUS STIKES GETSEMPENA LHOKSUKON

Intellectual intelligence (IQ) only contributes 20% to success, while 80% is contributed by other factors, including emotional intelligence (EQ), namely the ability to motivate oneself, overcome frustration, control impulses, regulate mood (mood), empathy and the ability to work together. Getsempena STIKes students must have good emotional intelligence in order to be able to pass the educational process during the COVID-19 pandemic running smoothly and have qualified skills. The research design uses the Corelation Analysis approach. The research was conducted at the Getsempena Lhoksukon STIKes Campus which is located in the village of Ulhee Tanoh, North Aceh Regency. In this study, the researchers took a sample of 85 samples that had been selected based on the criteria determined by the researcher. Researchers can conclude that the level of emotional intelligence of STIKes Getsempena Lhoksukon students is in the bad category with a percentage of 75.3% which is caused by changes in misconceptions in the learning process, network constraints and motivation in learning. However, for the online learning process itself, students are in the good category, namely 94.1%, the researcher assumes that the lack of supervision and active interaction during the teaching and learning process causes students to only attend online lectures and feel unsupervised so they are less concerned. P Value 0.326, which means that there is no relationship between emotional intelligence and online learning during the covid-19 pandemic.