Reviewerly/exHarmony huggingface dataset:
Green Accounting and Corporate Performance of Selected Quoted Oil &amp; Gas Firms in Nigeria

The objective of this study is to ascertain the relationship between green accounting and the corporate performance of quoted oil and gas firms in Nigeria. The study specifically evaluates the effect of oil spillage, gas flaring, water pollution and energy consumption disclosures on the corporate performance of quoted oil and gas firms in Nigeria. The study adopted the ex- post facto research design. The population was drawn from the ten quoted oil and gas companies listed on the Nigerian Exchange Group (NGX) as of 1st June 2022. The final sample was delimited to eight firms with annual financial information for the study period. The study relied on secondary sources of data, i.e., from annual financial statements of the oil and gas firms. The data were analysed using descriptive and inferential statistical techniques. The hypotheses were tested using the pooled OLS technique consistent with prior authors on disclosure measurements. The results showed that oil spillage disclosure has a positive non- significant effect on Tobin’s Q; there is a positive and significant effect of gas flaring disclosure on Tobin’s Q; water pollution disclosure has a negative and significant effect on Tobin’s Q; and, there is a positive non-significant effect of energy consumption disclosure on Tobin’s Q. Based on the above the study recommended that shareholders should monitor compliance of managers with the numerous laws and strategies used to reduce GHG flaring and the resulting carbon emissions in the company. Managers develop a water pollution system for managing their negative environmental impact on water. Regulators should constantly monitor firms with excessive energy consumption to pay relevant fees and fines which can encourage the move to sustainable energy consumption.
Human Resources Management Impact on College Education Quality

The role of human resource management (HRM) in forming the educational environment and ensuring excellence at higher education institutions is great. In German universities and organizations, HRM practices are adopted to measure performance of teaching with respect to student outcomes. The research questions impact of HRM on quality education and roadblocks found while implementing for improvements. HRM’s pivotal role in raising the standards of education and its singularity as people-centeredness aspects in educational institutions when presented by a literature review. The methodology used is a descriptive qualitative study which describes the understanding by College administrators in regard to Human Resource Management and related programs. The study includes a literature review of HRM practices in higher institutions. The findings uncover difficulties in teacher recruitment and retention but highlight the role of HRM practices, including faculty development programs, mentorship initiatives, or thorough support to academic staff members when it comes to ensuring education quality. The limitations are the poor teacher training and motivations of teachers to deliver good quality education, as well as poverty statements or socioeconomic statuses on which students come from. Among the suggestions, there are filling talent gaps, increasing work process effectiveness and its smoother continuity on a long-term basis by enhancing efficiency in staff knowledge issues as well as closer cooperation between faculties and HR departments. With all this information, it is possible to conclude that HR planning for analyzing tasks, costs and workforce requirements are necessary in promoting the functions of educational institutions towards achieving academic goals.
Impact of Knowledge Application and Knowledge Conversion on Organizational Performance: An Empirical Analysis

This work examines the effect of knowledge application and knowledge conversion on organizational performance among manufacturing firms in Ibadan Oyo State. The research design adopted is the survey research design. Stratified random sampling technique was used. The sample size of two hundred respondents was selected from six manufacturing firms in Ibadan that served as the population of this study. Of the two hundred (200) questionnaire administered to the respondents, one hundred and ninety-two (192) copies were successfully completed and retrieved for the analysis. Both descriptive and inferential statistics was used for the analysis. Results of the finding show that knowledge application has a significant positively relationship with four sub- variables of organizational performance namely, revenue (r = .492**, p = .000), financial strength (r = .538**, p = .000), public image (r = .543**, p = .000) and goodwill (r = .463**, p = .000). Knowledge conversion is also positively related to the four sub-variables of organizational performance namely, revenue (r = .442**, p = .000), financial strength (r = .537**, p = .000), public image (r = .478**, p = .000) and goodwill (r = .443**, p = .000). The study concluded that the knowledge management has a significantly effect on organizational performance. It was recommended that organizations should redefine ways to promote knowledge conversion in order to maximize human potential in the workplace. Also, organization needs to combine the technology infrastructure, processes, incentives and culture in order to motivate employees to integrate and use knowledge management, in order to keep up with ever-changing business environment, and to ensure organizational success in addressing unforeseen challenges.
A literature survey on public blockchain technology for Cryptocurrency

Blockchain and cryptocurrencies have emerged as revolutionary technologies that have garnered substantial interest in the past few years. A blockchain functions as a distributed and transparent ledger, facilitating secure transactions without the requirement of intermediaries. Digital assets like Bitcoin and Ethereum are examples of cryptocurrencies, leveraging blockchain technology to enable secure and unrestricted transactions. These innovations have the potential to revolutionize industries such as finance, supply chain management, and healthcare. However, challenges such as regulatory concerns and scalability issues must be addressed for their widespread adoption. Overall, blockchain and cryptocurrencies offer new opportunities for innovation and financial inclusion
Elderly Fall Detection Method Using Threshold Based Method and Transfer Learning

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

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

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

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

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

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

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

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)

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
Roadmap for Unconventional Computing with Nanotechnology

Abstract In the “Beyond Moore’s Law” era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50×faster than Moore’s law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges.
Exploration of the influencing factors of independent learning in higher vocational students based on process ability analysis

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

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

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

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.