Reviewerly/exHarmony huggingface dataset:
Deteksi Perubahan Berat Badan Akseptor KB Hormonal dengan Menggunakan Model Regresi Linear Berganda

Authors:
raw_author_name: Nurbaity Nurbaity
raw_affiliation_string: Program Studi D III Kebidanan STIKES Mitra Adiguna Palembang

Abstract: Hormonal contraception, which uses estrogen and progesterone preparations, is a contemporary method of contraception that is popular among active birth control participants. Apart from stopping population growth, it also has negative effects on its users. The side effect of increasing body weight can occur due to the nature of the acceptor and the use of hormonal contraceptives for a long period of time. The aim of the study was to detect changes in body weight of hormonal birth control acceptors based on age, parity and length of use of contraception using a multiple linear regression model. Non-experimental quantitative analytical research method with a cross sectional design. The research was conducted at several PMBs in Palembang City in July-September 2023 with samples consisting of hormonal birth control acceptors taken using a purposive sampling technique. Data were collected using questionnaires and body weight measurements and analyzed by multiple linear regression. The results of the research show that the prediction model for weight change = 5.789 - 0.098 Age + 1.4 Parity - 0.008 Length of birth control. Weight gain will definitely affect the health of the recipient and can increase the risk of obesity if it continues. To maintain a stable weight, birth control acceptors must pay attention to their daily diet and exercise regularly. It is very important to obtain information about the effects caused by the use of hormonal birth control with a high number of parities and ages at risk.
Elderly Fall Detection Method Using Threshold Based Method and Transfer Learning

Authors:
raw_author_name: Yuzhi Ma
raw_affiliation_string: School of Electronic Information Engineering, Harbin Institute of Information Engineering, Harbin, China
raw_author_name: Lei Tang
raw_affiliation_string: School of Electronic Information Engineering, Harbin Institute of Information Engineering, Harbin, China
raw_author_name: Jiahui Liu
raw_affiliation_string: School of Electronic Information Engineering, Harbin Institute of Information Engineering, Harbin, China

Abstract: The lack of elderly data is one of the most challenging issues in elderly fall detection. And the simulated young data is usually used in fall detection researches. In this paper, a wearable elderly fall detection method based on threshold based method (TBM) and transfer learning is proposed to shorten the distribution difference between young data and elderly data. In the process of training, labeled young data (source domain) and unlabeled elderly data (target domain) are utilized so as to weak the adverse effects of distribution difference between young data and elderly data. Finally, the better performance with sensitivity 98.63% and specificity 98.00% is illustrated with elderly data in Sisfall dataset.
A Deep Learning Capacity Estimation Method Based on Incremental Capacity Analysis and Differential Thermal Voltammetry

Authors:
raw_author_name: Yang Yu
raw_affiliation_string: Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan, China; Hubei Key Laboratory of Advanced Technology for Automotive Components, School of Automotive Engineering, Wuhan University of Technology, Wuhan, China

Abstract: Precise estimation of Lithium-ion battery capacity is critical for the battery management system (BMS). This paper proposed an innovative method that combinates convolutional neural network and feature curves which are incremental capacity analysis (ICA) and differential thermal voltammetry (DTV). Rather than extracting feature parameters of the IC curve as done in available research, the present method uses the whole IC and DTV curves in a certain range of voltage as the input to avoid complicated annual feature extraction and correlation analysis. The result shows that the max error of capacity estimation is less than 4.46 %, the mean absolute percentage error is less than 1.29 % for each battery, and overall MAPE is below 1.19 %.
New Tuning Rules of PI+CI Controllers for First-Order Systems

Authors:
raw_author_name: José Carlos Moreno
raw_affiliation_string: Department of Informatics, University of Almería, CIESOL, ceiA3, 04120 Almería, Spain

Abstract: The reset control is a simple nonlinear control approach where the states of the controller are conducted to zero when a particular condition is satisfied. The PI+CI is a controller that mixes the simplicity of PI controllers with the benefits of a reset action to mitigate the fundamental limitations of linear control. However, the tuning of this kind of controller, with three parameters, two for the linear part and one for the nonlinear one, is not trivial. In this paper, simple tuning rules for PI+CI are proposed for both tracking and regulation problems, assuming first-order dynamics for the plant. The resulting control scheme, for which the reset coefficient is computed from exponential functions, is simulated and compared with an ideal PI+CI where the reset coefficient is obtained using rules available in the literature. Similar results are obtained for the tracking problem, and optimal performance based on the Integral Absolute Error (IAE) is also obtained for the regulation problem. These new rules, in contrast to those already existing in the literature, depend only on closed-loop specifications. Furthermore, the framework based on the minimization of IAE, used to obtain the proposed rules, makes it possible to consider for the first time the tracking and regulation problems simultaneously, i.e., cases where setpoint changes and disturbance arrivals can occur at the same time before reaching a new steady state. The results are validated using a set of study cases.
Calculation Model of Earthwork Volume in Open Pit Mined Area Based on Step Feature Line

Authors:
raw_author_name: Mingfeng Li
raw_affiliation_string: School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Xingke Cao
raw_affiliation_string: School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Dongsheng Xu
raw_affiliation_string: Second Geological Brigade, Jiangsu Bureau of Geology and Mineralogy, Nanjing, China
raw_author_name: Yu Huang
raw_affiliation_string: Second Geological Brigade, Jiangsu Bureau of Geology and Mineralogy, Nanjing, China
raw_author_name: Yuhui Jiang
raw_affiliation_string: School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, China
raw_author_name: Ding Tan
raw_affiliation_string: School of Geomatics Science and Technology, Nanjing Tech University, Nanjing, China

Abstract: To improve efficiency and accuracy of the calculation method of earthwork volume in the open pit mined area with complex terrain, a model based on the step feature line for the open pit mined area is constructed. With the Delaunay triangulation network constructed by the tilt photogrammetry photography of UAV, the step feature lines are extracted by the algorithm of RANSAC, and a detailed calculation model of earthwork volume is constructed by encrypting triangular network with the lines. With the experiments taken, it is shown that the accuracy of the new model is obviously improved as compared with both DTM model and unconstrained Delaunay triangulation model.
Enactment of Blockchain in NFT

Authors:
raw_author_name: Sudeep Varshney
raw_author_name: Amrit Suman

Abstract: Non-Fungible Tokens (NFTs) have arisen as a major term in the twenty-first century, signifying unique digital assets such as photographs, music, and movies. These tokens receive their value and legitimacy from blockchain technology, which is mostly used on the Ethereum platform. NFTs, which use smart contracts, enable the production, ownership, and trading of one-of-a-kind digital assets, revolutionizing sectors including as art, music, and gaming. While blockchain’s uses are broad, there are worries about the environmental effect of blockchain, notably Ethereum’s energy-intensive proof-of-work consensus. Despite their revolutionary potential, NFTs pose dangers connected to market instability, legal concerns, and technological weaknesses. NFT trading is enabled by a variety of third-party programmers and markets, with notable participants being OpenSea and MetaMask. Looking ahead, the future of NFTs in India is dependent on legislative changes, as the country sees an increase in interest in these digital assets among artists and producers, necessitating a balanced approach to encourage innovation while resolving possible difficulties. NFTs are a game changer in the digital economy, opening up new opportunities for producers and investors. However, environmental problems and regulatory uncertainties must be resolved in order for the NFT ecosystem to expand sustainably. Technological improvements, legislative reforms, and the continuous growth of blockchain technology will most likely influence the future of NFTs in India and throughout the world.
A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

Authors:
raw_author_name: Shrabani Sutradhar
raw_affiliation_string: Department of Computational Sciences, Brainware University, West Bengal, India; Dibrugarh University Institute of Engineering and Technology, Dibrugarh University, Dibrugarh, Assam 786004, India
raw_author_name: Sudipta Majumder
raw_affiliation_string: Dibrugarh University Institute of Engineering and Technology, Dibrugarh University, Dibrugarh, Assam 786004, India
raw_author_name: Rajesh Bose
raw_affiliation_string: Department of Computer Science & Engineering; JIS University, West Bengal, India
raw_author_name: Haraprasad Mondal
raw_affiliation_string: Dibrugarh University Institute of Engineering and Technology, Dibrugarh University, Dibrugarh, Assam 786004, India
raw_author_name: Debnath Bhattacharyya
raw_affiliation_string: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Abstract: The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.
A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion

Authors:
raw_author_name: Hongcai Xu
raw_affiliation_string: School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
raw_author_name: Junpeng Bao
raw_affiliation_string: School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
raw_author_name: Hui Li
raw_affiliation_string: School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
raw_author_name: Chao He
raw_affiliation_string: School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
raw_author_name: Feng Chen
raw_affiliation_string: School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing 400074, China

Abstract: A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities. Predicting time-aware entities is a challenging task due to significant differences in entity appearances over time, such as different timestamps and frequencies. Current embedding-based similarity-matching methods have been introduced for predicting temporal facts. However, they lack deterministic logical explainability and cannot model the dynamic evolution of entities over time. To address these challenges, we propose a novel framework for temporal knowledge graph reasoning based on multi-view feature fusion (MVFF). First, MVFF extracts logical rules and uses the Gumbel-Softmax trick to sample high-quality rules. Second, it integrates logical rules, temporal quadruples, and factual triples to capture semantic features, temporal information, and structural information to solve link prediction tasks. Through experiments on four benchmark datasets, we show that MVFF outperforms state-of-the-art methods, providing not only better performance but also interpretable results.
ISTVP: Independent single transaction verification protocol for light node using fraud proofs without collaborator

Authors:
raw_author_name: Binhao Ma
raw_affiliation_string: School of Computer Science, South-Central Minzu University, Wuhan, China
raw_author_name: Di Han
raw_affiliation_string: School of Computer Science, South-Central Minzu University, Wuhan, China
raw_author_name: Dejun Wang
raw_affiliation_string: School of Computer Science, South-Central Minzu University, Wuhan, China
raw_author_name: Bo Meng
raw_affiliation_string: School of Computer Science, South-Central Minzu University, Wuhan, China

Abstract: Abstract Most blockchain users run light nodes on mobile devices. Due to limited storage and computation, light nodes cannot perform transaction validation. This shortage makes opportunities for malicious nodes to produce blocks containing invalid transactions, which results in the loss of funds for light nodes. Fraud proofs play a significant role in ensuring transaction security for light nodes. However, existing fraud proof schemes require honest collaborators and the processing of entire blocks. To address these limitations, Independent Single Transaction Verification Protocol for Light node Using Fraud Proofs without Collaborator called ISTVP is proposed that enables light nodes to independently verify transactions and generate fraud proofs without relying on collaborators or processing the entire block. To support ISTVP, SVST is introduced, an efficient block structure for single‐transaction verification. SVST not only efficiently indexes historical transaction outputs to improve verification efficiency, but also significantly reduces the storage requirements for verifying transactions to . Furthermore, the authors analyze the security of ISTVP and demonstrate that it satisfies both persistence and liveness, while maintaining the level of security of full node.
IMPACT OF WOMEN'S POLITICAL PARTICIPATION ON GOOD GOVERNANCE IN NASARAWA STATE (2012-2023)

Authors:
raw_author_name: None Masud Ndagi Kabir
raw_affiliation_string: Isa Mustapha Agwai I Polytechnic, Lafia, Nigeria

Abstract: This study investigates the impact of womens political participation on good governance in Nasarawa State using a case study period between 2012 and 2023. The objectives of the study were; to examine the extent of womens representation in Nasarawa States political institutions, to evaluate the influence and impact of these women in policy making, budgetary allocations, and the implementation of programs that address gender-specific concerns within the state, and to analyze the socio-economic implications of womens political participation. The theoretical framework employed for this study is Kanters Glass-Ceiling Theory (1977). Citing specific literature and empirical studies on womens political participation and governance, and consulting scholarly articles, policy briefs, and reports published in reputable academic journals and accessible databases, the analysis was bolstered with evidence-based findings. Study revealed that in Nasarawa State as in other parts of Nigeria and the world, patriarchy has over the years dominated the political landscape, leading to significant challenges and barriers for women seeking political roles. However, findings suggest that womens political participation in Nasarawa State positively impacts good governance. Increased representation of women in decision-making bodies improved policy-making processes, prioritized gender-sensitive sectors, and enhanced accountability and transparency. The Study therefore advocated for increase in the number of women in political offices in Nasarawa state which could potentially contribute to better governance by reducing corrupt practices and promoting transparency in decision-making processes. KEYWORDS: Politics, Political participation, Governance, Gender Equality
Exploration of the influencing factors of independent learning in higher vocational students based on process ability analysis

Authors:
raw_author_name: Zun Jiao
raw_affiliation_string: Department of Literature, Qingdao Film Academy, Qing Dao, China.

Abstract: To clarify the main factors affecting the independent learning ability of higher vocational students, corresponding data were collected via a questionnaire survey, and the data were analyzed to determine the relationships between the factors affecting the independent learning of higher vocational students and independent learning ability. A process model of the independent learning of higher vocational students is constructed, and the model is analyzed to determine the influence of different factors on independent learning to clarify the process-level relationship between them.
Integration of artificial intelligence and GPT API applied to education: challenges and potentialities of digital teaching

Authors:
raw_author_name: Jailson Domingos De Oliveira

Abstract: Research on the integration of digital technologies in education is key to understanding and optimizing the intersection between technological advancement and pedagogical development, ensuring the training of individuals capable of navigating and contributing effectively in the society of the 21st century. In this context, this article examines the incorporation of technology in the educational environment, highlighting the vital role of digital tools in reshaping teaching practices. The study analyzes how technology, especially the application of artificial intelligences such as Chat GPT, can transform the educational process, promoting more interactive and personalized learning. The challenges of preparing educators for this new reality are discussed, while underlining the importance of training critical and adaptable students in an increasingly digitized society.
Factors Affecting Teachers’ HOTS Implementation in Chinese College EFL Classrooms

Authors:
raw_author_name: Yue Yin

Abstract: Reviewing previous HOTS studies in China revealed that the implementation and instruction of higher order thinking skills had not been widely adopted in Chinese college EFL education. Despite the unanimous agreement on the crucial role of fostering students’ HOTS, the incorporation of HOTS is affected by many factors. With a mixed-method approach, this research aims to investigate the factors affecting Chinese college EFL teachers’ HOTS implementation from the teacher’s perspective. Data was collected from 100 Chinese college EFL teachers through quantitative questionnaires and structured interviews. The findings of this research suggest that implementing HOTS in the Chinese college EFL classroom is challenged by various factors, particularly those related to teacher, student, institutional, and environmental factors. The results also provide empirical evidence of the insufficient knowledge and proficiency in implementing HOTS among the surveyed teachers during their language teaching process. This implies the need to establish a supportive HOTS-promoting environment involving teachers, students, and administrators in successfully implementing HOTS instructions in Chinese college EFL classrooms.
Fusion of digital technologies and active methodologies in contemporary education

Authors:
raw_author_name: Claudia Kreuzberg Da Silva

Abstract: This study addressed the fusion of digital technologies and active methodologies in contemporary education, aiming to understand its impact on the teaching and learning process. The research problem focused on the need to investigate how the combination of these elements can influence current education. The overall objective was to analyze the benefits, challenges, and integration strategies of these approaches, as well as their impact on teaching and learning. The methodology involved a comprehensive literature review, data collection, and analysis of research and case studies. The results indicated that the integration of digital technologies and active methodologies promotes more engaged, personalized, and collaborative learning. However, challenges such as the need for teacher training and overcoming technological barriers were identified. Legislation and educational policies play a importante role in promoting these approaches. The final considerations highlight the ongoing importance of the fusion of digital technologies and active methodologies in contemporary education and the need to overcome the identified challenges.
A case study on digital training: benefits and unexplored opportunities

Authors:
raw_author_name: Marina Carulli
raw_affiliation_string: Via La Masa 1 Milano, MI 20154 Italy
raw_author_name: Marco Rossoni
raw_affiliation_string: via la Masa 1 Milano, Italy 20159 Italy
raw_author_name: Elena Spadoni
raw_affiliation_string: Via La Masa 1 Milano, 20156 Italy
raw_author_name: Nicolò Dozio
raw_affiliation_string: Via la Masa 1 Milano, 20156 Italy
raw_author_name: Francesco Ferrise
raw_affiliation_string: Via la Masa 1 Milano, Italy 20156 Italy
raw_author_name: Monica Bordegoni
raw_affiliation_string: Via La Masa 1 Milano, Italy 20156 Italy

Abstract: Abstract In an era characterized by the increasing complexity of products and the rapid turnover of the workforce across different companies, there is a growing need to invest significantly in quick and efficient training methods. Concurrently, the advancement of digitalization has rendered certain training practices anchored to paper-based materials obsolete. Numerous companies are directing their investments toward digital training, yet the optimal format to exploit the full advantages of digitalization remains unclear. This study undertakes a comparison of four distinct digital versions of the same training process with the aim of comprehending the tangible benefits. The findings indicate that to fully capitalize on the advantages of digital technology, a complete rethinking of training practices is necessary.