Reviewerly/exHarmony huggingface dataset:
Control of the BLDC Motor Using Ant Colony Optimization Algorithm for Tuning PID Parameters

A key component of industrial applications is the DC motor. Hence, due to their superior features and performance, Brushless DC (BLDC) motors are more suitable for fractional kilowatt motor’s applications. Albeit, for the purpose of controlling the speed of Brushless DC motor easily, it is quite difficult for obtaining the best controlling performance through the use of the conventional approaches of tuning. In order to search the Proportional – Integral - Derivative (PID) tuning parameters optimally for the different controllers taken into consideration, the use of modern bio-inspired metaheuristic technique called Ant Colony Optimization (ACO) algorithm is employed. This paper particularly discusses and presents on the tunning parameters of . The performance of traditional controller and novel approach is compared, analyzed and presented. Brushless DC motor is buildup in MATLAB and the usage, importance, efficiency and strength of the proposed approach is validated against traditional tunning methods. The obtained result shows better performance of the proposed system with the aid of the proposed controllers for different speed trajectories of the drive when compared with that of the classical Proportional – Integral - Derivative (PID) controllers. Ant Colony Optimization (ACO) seems to be the one of the most effective tuning techniques of Proportional – Integral - Derivative (PID) controllers. This research has significant impact on modern control applications.
Health Ware—A New Generation Smart Healthcare System

Information technology along with Artificial Intelligence playing very crucial roles in almost every sector to provide the services more efficiently and accurately. Technologies that are fully supported by electronic and digital technologies are more reliable and secure. Unfortunately, the lack of this type of electronic and digital assistance has caused issues for both patients and professionals in the healthcare industry. A few of the newer options include cloud data management, video conferencing, and virtual assistants. These technologies may be connected with current health sector services including online appointments, medical alarm systems, and prescription fulfillment. In the article, we combined the services already provided by the healthcare industry with newly developed facilities that are accessible in other service industries. We have designed and developed an application which facilitates virtual doctors, medicine alarm, online appointment, video calling features, also maintaining profile of patients and doctors. AWS cloud services are used for the maintenance of data with enhanced privacy and security. Our application provides end-to-end healthcare support and precise guidance and recommendations to the patient and doctors.
Development of Interactive Teaching Materials in Indonesian for Grade VI Elementary School Students

Interactive teaching materials are written materials in the form of sheets of paper that are bound and given covers and that present knowledge that is arranged systematically. This research aims to develop valid, effective, and practical teaching materials. This study uses a 4D research model with four stages: defining, designing, developing, and deploying. This teaching material has been validated by several experts, namely material experts, media design experts, and linguists. This study had three trials: individual trials, small group trials, and large group trials. Based on the combined percentage of validity of interactive teaching materials, according to experts, a percentage of 89% is categorized as "very valid." The practicality of interactive teaching materials is measured through RPP implementation sheets conducted in large group trials of meeting 1 of 92.72% and large group trials of meeting 2 of 92.98% categorized as "very practical." Effectiveness was obtained from large group trials with a percentage of 92.33% categorized as "very effective." Based on these results, it can be concluded that interactive teaching materials in grade VI SD Muhammadiyah 8 Dau Malang are suitable for use.
GRU-based chaotic sequence generation and its application in image encryption

To improve the security of chaotic image encryption algorithm, this paper proposes an image encryption algorithm based on GRU (Gated Recurrent Unit) prediction to generate chaotic sequences. First, chaotic sequences are generated as training and test sets by chaotic mapping, and the corresponding prediction sequences are generated by single-step prediction of chaotic sequences using GRU algorithm. Then, the prediction effect of the model is analyzed, and the properties of the predicted sequences are analyzed with the maximum Lyapunov exponent and NIST detection system. Finally, the images are encrypted and comparatively analyzed by using the prediction sequence as the core with two-way random dislocation and gray value masking. The experiments show that the algorithm meets all requirements of image encryption and can resist various common attacks.
Multi-TransPathoNet: a transfer learning-based neural network for pathology image classification with multi-task learning

Histopathological images are one of the common diagnostic tools used in the medical field. However, the complex cellular and tissue structures inside colorectal cancer histopathological images make it difficult for physicians to perform efficient cancer diagnosis and quantitative analysis. Therefore, this study proposed a multi-task learning-based pathology image classification method Multi-TransPathoNet (A transfer learning-based neural network for pathology image classification with multi-task learning, Multi-TransPathoNet) for detection and visualization output of colorectal cancer histopathology images, thus helping to improve medical efficiency. To verify the practical application value of this method, this study used the paracancerous, highly differentiated, and high-moderately differentiated histopathological images which are more difficult to distinguish in colorectal cancer as the dataset for classification test, and our method obtained 92% accuracy. Besides, we compared with the single-task learning models Resnet50 and Vgg19 respectively, which also have good performance in image classification tasks, to comprehensively evaluate the proposed model’s performance.
Robot Path Planning Based on Interval Type-2 Fuzzy Controller Optimized by an Improved Aquila Optimization Algorithm

Uncertainty and complexity in the local path planning are hot topics. In this paper, a novel IAOFC algorithm is proposed for local path planning in the complex environment. Considering the uncertainty and complexity of local path planning, this paper uses interval type-2 fuzzy control to design path planning method, which can respond more quickly to the uncertainty of the environment and improve the computation speed and efficiency. To further improve the performance of the fuzzy controller, this paper uses an improved Aquila Optimizer (AO) algorithm to optimize the membership function of the interval type-2 fuzzy controller (renamed by IAOFC). By using the optimized fuzzy controller, the time cost and path cost can be reduced. In simulation experiments, the path planning in static environment is designed to verify the basic performance and efficiency of the algorithm, and the path planning in dynamic environment is validated to verify the robustness of the algorithm. Finally, the superiority of the IAOFC algorithm is proved by comparing it with some other algorithms. According to experiment results, IAOFC has an average cost reduction of 15% and 6% than other algorithms in static and dynamic environments, respectively.
Detection and Classification of Speech Disorder using FOA-SCNet

Speech disorders have turned out a great brunt on the ability of speech. The persons have speech disorders were struggling with communication and emotional expressions. The proposed work detects and classifies the speech disorders using combined CNN and SVM techniques. To improve the prediction accuracy of the proposed model, we use the Fruitfly optimization algorithm for improving the convergence rate and accuracy results. The study mainly focuses on young people in which the speech disorders are prevalent. The machine algorithm is utilized to predict a person has healthy or pathological voice accurately.
Secret Protections With Costs and Disruptiveness in Discrete-Event Systems Using Centralities

In this paper, we study a secret protection problem in discrete-event systems. The system is modeled by an automaton in which several states are assigned with different secret levels. Our aim is to protect some of the events in the system such that any sequence yielding a secret state contains a number of protected events no less than the required security level. We first study the secret protecting problem with minimum cost, i.e., to design a protecting policy whose cost is minimal. We prove that the decision version of such a secret protection problem is NP-hard, which implies that there unlikely exists a polynomial algorithm to solve it. As a result, we developed a heuristic method to obtain a locally optimal solution to protect the secrets using the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost-weighted centrality</i> . Then, we consider the disruptiveness, i.e., the degree of disruptiveness of protecting policies to legal users' normal operations. We formulate the disruptiveness to users incurred by the protection on events as a penalty function which describes the impact of events and transitions on the paths leading to marker states. A heuristic method based on the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cost-penalty-weighted centrality</i> is analogously developed to obtain a protecting policy which can well balance the cost and the disruptiveness to users.
Blockchain in the agrifood sector: From storytelling to traceability fact-checking up to new economic models

This paper aims to demystify a lot of misconceptions still widely circulating today about the alleged properties of blockchain and then illustrate the real opportunities that this technology offers for "food system" and how it must be correctly implemented for it to be truly useful, for producers and consumers, particularly in the agrifood sector. The concepts of blockchain opportunities and incompleteness of agri-food chain projects based on blockchain technology are then explained, setting out the minimum and necessary characteristics required to make the use of this technology useful and effective (Minimum Viable Ecosystem). The process governance levels for the development and maintenance of a blockchain traceability project are then illustrated, focusing on the role and responsibility of each player in the supply chain.Finally, the structure of a blockchain solution is described, focusing on a number of structural and technological solutions by outlining the concepts of consistency checking for the validation of input data with appropriate smart contracts, and of information frameworks for the subsequent scrutiny of data in audit operations and the assignment of levels of reliability.These are essential prerequisites for a collaborative blockchain data management to pursue the objective of actual reliability and transparency of information.
Enhanced YOLOv5: An Efficient Road Object Detection Method

Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.
Research on the functional game industry expandable based on VR realistic technology

In order to make the game industry chain be developed structurally, this paper designs a functional game industry expansion path based on VR realistic technology to enhance the interactivity of functional game industry expansion. The interactive 3D model is used to integrate the professional game engine and design the functional game expansion process. The motive of game expansion is guided by the ARCS model to support the construction of functional games. According to the VR realistic game experience, analyze the expandable paths from the modules of technological innovation and industrial integration in order to enhance the realistic experience of integrated industries in functional games. The simulation analysis of the expansion path of the functional game industry based on VR realistic technology shows that VR realistic technology improves the signal-to-noise ratio of functional games by 50% on average, height and cross-roll angle changes by 6.65f/ms and 6.79f/ms respectively, and the number of textures is up to 16.68MB. Therefore, VR realistic technology is beneficial to promote the transformation of the functional game industry it can ensure that the functional game industry steps into a sustainable and expandable track of transformation and upgrading.
A Case Study on Course Improvement for Enhancing Remote Teaching Competencies of Pre-service Teachers

The COVID-19 pandemic has underscored the need for emergency distance education in the classroom and heightened the demand for remote teaching competencies in pre-service teachers. However, systematic training for pre-service teachers is still lacking. This study aimed to explore and analyze comprehensive course improvements for enhancing remote teaching competencies of prospective teachers. We examined ten courses offered in 2022 at S University's College of Education in Seoul. Through interviews with professors, course improvement needs were identified and suggestions were derived based on the course syllabi. The study resulted in five directions for course improvement: 1) introduction of future-oriented teaching methods, 2) assignment of content creation for online learning, 3) provision of special lectures by in-service teachers, 4) establishment of distance education environments, and 5) implementation of edtech workshops. After applying these measures to the ten courses, all showed significant improvements in remote teaching competencies. Additionally, the author conducted in-depth interviews with course instructors to gather their perceptions of the course improvement process. The resulting qualitative analysis is presented here. This research presents suggestions for curricular and extracurricular interventions to enhance remote teaching competencies in teacher training colleges by proposing strategies for curriculum improvement. As a result, significant discussions and implications for course design and operation have been derived.
PELAKSANAAN PEMILIHAN PENGADAAN BAHAN PUSTAKA DI PERPUSTAKAAN MADRASAH ALIYAH NEGERI 3 PALEMBANG

Abstract: This study discusses "Implementation of Selection of Library Materials Procurement in the Library of Madrasah Aliyah Negeri 3 Palembang". The purpose of this study is to describe how the implementation of the selection of procurement of library materials in the library of Madrasah Aliyah Negeri 3 Palembang and to analyze the factors that influence the implementation of the selection of procurement of library materials in the library of Madrasah Aliyah Negeri 3 Palembang. The researcher used a qualitative approach with a descriptive type, and the main informants in this study were the head of the library and library staff. Data collection techniques used are observation, interviews, and documentation. The data analysis technique used is data reduction, data presentation and drawing conclusions. Test the validity of research data by source triangulation, technical triangulation and time triangulation. This research was conducted on May 17 – July 3, 2023. The research results obtained by researchers indicated that the implementation of the selection of procurement of library materials in the Palembang 3 Madrasah Aliyah Library is carried out once a year according to user needs. The supporting factors are human resources and infrastructure in the library, while the inhibiting factor is limited funds.Keywords: Implementation, Selection, Library Materials
A Study on the Issue of‘Relationship with Authentic Context’in Learning Experience: Interpretation o

This study had a goal to discuss the transfer, which is related with real life, based on situated learning theory. Situated learning theory is known to have provided a new theoretical basis for the design of teaching and learning based on learning through interaction and contextuality of learning, which had been overlooked so far. Since situated learning theory also has a discussion of situativity and contextuality, it is expected that the results derived from situated learning theory would broaden the horizon of understanding of 'learning linked to life'. And In this study, by examining situational learning theory suggested the transfer can happen better by practicing the gap between the context of the outside world and their own context than discovering similarity of problems had and while learners have practiced to negotiate the context between problem and themselves. Considering the research of situated learning theory, education for transfer that linked life goes beyond the meaning of real context to the need for educational design that leads inquiry’s context. It is hoped that this study contributed to interpretation of situational learning theory, and can provide implications for teaching and learning design in the educational field to translate “real life”
Topologically protected strong photon-exciton coupling

We propose that strong photon-exciton coupling performed by a plasmonic nanoantenna and a single emitter can be protected in the well-designed topological photonic structure as an environment. Topological robustness leads to a reduction of metal loss, which makes Rabi splitting much larger. The strong coupling regime gets more stable in the topological systems because the sensitivity characterized by Rabi splitting can always remain small compared to the sensitive nontopological systems. Moreover, we show that the Rabi splitting under topological protection is immune to defects or impurities that can destroy strong coupling in nontopological systems. This topologically protected strong coupling mechanism offers more improvement in the applications of on-chip quantum integration, single quantum sources, and quantum information processing.
Learning Situation Analysis For Hotel Accommodation Students’ At Vocational High School

English is a language that is needed by many people, one of which is vocational high school, especially hotel accommodation students. Based on pre-observation, it was found that there are still many students who have difficulty speaking English. For this reason, this research was conducted with the aim of analyzing the desire of hotel accommodation students in learning English by using qualitative descriptive methods and random sampling to determine the respondents. The respondents of this study were 22 students of class XI hospitality accommodation (Ph2). Researchers collected data by distributing questionnaires to students. The stages that researchers use to analyze data are Reading, Describing, Classifying, and Interpreting. The researcher used six indicators from Learning Situation Analysis with the following results; 1) Hotel accommodation students learn English to be able to fluently communicate with local guests and foreigners. 2) Hotel accommodation students need diverse learning activities that can fulfill their purpose of learning English. 3) Hotel Accommodation Students' want teachers who understand their needs and can fulfill them. 4) The majority of Hotel Accommodation Students' use Minang as their mother tongue, making it difficult to speak English. 5) Hotel accommodation students' want a comfortable and quiet place with complete facilities to study. 6) Hotel accommodation students' choose to learn English in the morning with a duration of no more than 2 hours/day and more than once a week. From the above results it can be concluded that Target Situation Analysis can help teachers know the desire of hospitality accommodation students in learning English. So teachers should do a needs analysis before teaching.Kata kunci: Target Situation Analysis, Hotel Accommodation Students’
Klasifikasi Penyakit Jantung Menggunakan Algoritma Analisis Diskriminan Linier

The number of documented deaf people continues to increase. To communicate with each other, the deaf use sign language. The problem arises when Muslims with hearing impairment or deafness need to recite the Al-Quran. Muslims recite Al-Quran using their voice, but for the deaf, there are no available means to do the reciting. Thus, learning hijaiyah letters using finger gestures is considered important to develop. In this study, we use the recognition of hijaiyah letters based on pictures as the learning model. The real-time-based recognition then uses the learning model. This study uses 4 CNN pre-trained models, namely MnetV2, VGG16, ResNet50, and Xception. The learning process shows that MnetV2, VGG16, and Xception reach the accuracy limit of 99.85% in 2, 3, and 11 s, respectively. Meanwhile, ResNet50 cannot reach the accuracy limit after processing 100 s. ResNet50 achieves 82.12% accuracy. The testing process shows that MnetV2, VGG16, and ResNet50 achieve 100% precision, recall, f1-score, and accuracy. ResNet50 shows figures 81.55%, 86.04%, 82.04%, and 82.58%. The implementing process of the learning outcomes from MnetV2 shows good performance for recognizing finger shapes in real-time.
Quantifying systemic risk in the cryptocurrency market: A sectoral analysis

Lack of regulations and malicious activities exacerbate systemic risk exposure in the cryptocurrency market. Considering this, we explore Bitcoin, Ethereum, and thirteen cryptocurrency sectors to analyze the systemic risk level of the cryptocurrency market. We also examine the causality network among the sectors under extreme downside market conditions. Results show that smart contracts, along with Bitcoin and Ethereum, are sources of systemic risk. The extent of connectedness is 40% during extreme loss periods. Our findings are useful for investors and risk managers who wish to take appropriate measures against the sectors that destabilize the market.