Empirical Assessment Research Articles

Empirical Assessment of Sustainability and Feasibility of Government Debt: The Philippines Case 

Empirical Assessment of Sustainability and Feasibility of Government Debt: The Philippines Case 

Empirical Assessment of Economic Assumptions, Domestic Regulations, and their Impacts on Cross-Border Investments into Nigeria.

Empirical Assessment of Economic Assumptions, Domestic Regulations, and their Impacts on Cross-Border Investments into Nigeria.

An empirical assessment of the factors causing delays in project completion in the construction sector of Lahore, Pakistan

An empirical assessment of the factors causing delays in project completion in the construction sector of Lahore, Pakistan

Empirical assessment of COVID-19 infections and information diffusion: a data science approach

Empirical assessment of COVID-19 infections and information diffusion: a data science approach

An Empirical Assessment of Factors Impacting Stock Prices of Nepalese Commercial Banks

This study examines the impact of various financial variables on the pricing of commercial banks in Nepal. Key financial metrics analyzed include earnings per share, dividend yield, price-toearnings (P/E) ratio, earnings yield, book value per share, money supply, market-to-book value ratio, and stock return. The research highlights the importance of market financial ratios in the investment decisions of Nepalese investors. Using data from 10 out of 20 commercial banks listed on the Nepal Stock Exchange, spanning 110 yearly observations from fiscal years 2012/13 to 2022/23, multiple regression models were employed to determine the relationships between these factors. The findings reveal that earnings per share, dividend yield, P/E ratio, book value per share, money supply, market-to-book value ratio, and stock return are significantly positively correlated with the prices of commercial banks. Conversely, earnings yield shows a negative correlation. Despite the strong preference for price and profitability metrics, the study's limited sample size and focus on commercial banks may affect the generalizability of the results. This research provides critical insights, particularly emphasizing the importance of earnings per share, dividend yield, P/E ratio, and book value per share in guiding investors' financial decisions in Nepal. The findings are anticipated to benefit investors, scholars, policymakers, and other stakeholders by enhancing their understanding of the factors influencing stock prices in the Nepalese banking sector.

Изменение имиджа профессии педагогического работника в условиях трансформации современного образовательного пространства

The article analyses the situation concerning the transformation of the teacher’s status under modern conditions. Changes associated with reconsidering the value of education as a whole place the status of the teacher in a contradictory position. On the one hand, the long-term conditions of the pandemic have demonstrated the need to create a new methodological system for all forms of education, thereby providing new requirements to the personality and professional level of the teacher. On the other hand, the personnel shortage of teaching staff limits the process of adopting the system of a new personality meeting modern educational challenges and demands. At the same time, scientific discussions remain valid about what exactly a modern teacher should be like so that his image, students and parents’ trust begin growing. Today, image is becoming the main attribute of social and professional relations. In pedagogical activity, the processes of creating a professional image, which consists of individual’s assimilation and reproduction of social experience, are becoming increasingly relevant. Connected with the rethinking of the processes of professional self-determination and formation, there is a need to analyze professional competencies in the context of training a future teacher. In many ways, the professional activity of a teacher involves constructive interaction with the subjects of the educational process: with colleagues, students and their parents. The way interactions are built and contacts are established let us make conclusions about the professional qualities of a teacher. Ushinsky: «The teacher is a key figure in the reform of education, in the matter of teaching and upbringing, nothing can be improved in the entire school business without bypassing the head of the teacher» [Egorychev, 1993. P.79]. At the same time, along with classical criteria, the image of a modern teacher is also determined by compliance with new requirements, namely, the presence of emotional intelligence and its correct projection onto students, taking into account their requests and social needs. It is advisable to lay the vector at the stage of mastering higher pedagogical education, systematically changing educational programs of training future teachers. The paper reveals issues related to the image characteristics of the status of a modern teacher, based on empirical assessments of the main subjects of the educational process. The promise of scientific understanding of the issue is noted, along with growing interest in the search for optimal mechanisms aimed at increasing the role and significance of the teaching profession under modern conditions.

International Board Reforms and Earnings Opacity: An Empirical Assessment of Economic Channels

International Board Reforms and Earnings Opacity: An Empirical Assessment of Economic Channels

Impact of the 13 Years of Guaranteed Education Programme (13YGEP) on Employment Potential and Income: A Survey in Kandy District of Sri Lanka

Teenagers who drop out of mainstream education face risks of unemployment and social exclusion. In Sri Lanka, many students who fail to qualify for further studies at the GCE O/L examination consequently dropout from school education. The 13 Years of Guaranteed Education Programme (13YGEP) is a vocational subject stream introduced to the school curriculum in 2017 providing a new pathway for the students who fail GCE O/L to learn vocational subjects at school. The purpose of this study was to evaluate the short-term impact of the new vocational programme on employment potential and income of the youngsters. Primary data were collected from a sample of 200 participants living in Kandy district. Data analysis was performed using Chi-Square tests of independence and graphical methods. The results suggest that the vocational programme does not have a statistically significant impact on early employment potential. However, 15 percent of the teenagers who acquired vocational education have perused self-employment, an outstanding progress which none of the school dropouts in the sample could achieve. The students who left school and perused employment early earn more compared to those who studied vocational subjects, indicating that work experience contributes to higher early-career income. While the students who completed the programme appreciate the benefits, those who left school do not regret the forgone opportunity. The results highlight the importance of facilitating and supporting those who complete the programme to start new businesses. This is the first ever empirical assessment of the impact of the 13YGEP which is specifically targeted at GCE O/L dropouts in Sri Lanka. The long-term outcomes of the programme should be further studied in larger scale to evaluate its effectiveness.

Empirical Assessment of Deepfake Detection: Advancing Judicial Evidence Verification Through Artificial Intelligence

Empirical Assessment of Deepfake Detection: Advancing Judicial Evidence Verification Through Artificial Intelligence

Empirical Assessment of AI-Powered Tools for Vocabulary Acquisition in EFL Instruction

Empirical Assessment of AI-Powered Tools for Vocabulary Acquisition in EFL Instruction

Empirical assessment of antecedents for m-payment adoption using TACT and valence model - age as the moderator

Empirical assessment of antecedents for m-payment adoption using TACT and valence model - age as the moderator

Empirical assessment of antecedents for m-payment adoption using TACT and valence model - age as the moderator

Empirical assessment of antecedents for m-payment adoption using TACT and valence model - age as the moderator

Building bridges with the Pennsylvania State Police: A pilot assessment of a Law Enforcement Community Outreach Program

Abstract In 2021, the Pennsylvania State Police (PSP) created and implemented the Building Bridges Community Outreach Program (BBCOP), which is a positive law enforcement-initiated non-enforcement contact intervention designed to educate the public and improve community relations. To date, there have been no empirical assessments of the influence that this program has had on public perceptions of police. The current study attempts to fill this gap in the literature by offering an assessment of BBCOP participants’ perceptions of the program, and their perceptions of police before and after participating in the program. Specifically, the current study uses a mixed-method analysis of matched (i.e. pre- and post-intervention) survey data (n = 106) collected from student participants of initial iterations of the PSP BBCOP intervention occurring between the end of January 2023 and the end of January 2024. Findings reveal that BBCOP participants overwhelmingly reported positive sentiments about their experiences with the program. Further, statistical analyses revealed significant improvements in perceptions of law enforcement effectiveness, procedural justice, and legitimacy following participation in the program. Implications based on these findings are discussed within.

Retraction: An empirical assessment of financial literacy and behavioral biases on investment decision: Fresh evidence from small investor perception.

[This retracts the article DOI: 10.3389/fpsyg.2022.977444.].

SecureSIoTChain: A relationship enhanced Blockchain Operational Security Framework for the Social Internet of Things

The Social Internet of Things (SIoT) is a fast growing technology that autonomously connects things, allowing for exchange of information depending on relationships. However, the open nature of SIoT exposes it to various security challenges, such as default passwords, brute-forcing attacks, data privacy breaches, and an increasing number of IoT botnets. To address these vulnerabilities, a novel operational security framework for SIoT, leveraging the robust properties of blockchain technology. The primary objectives of the operational security model are to establish a secure and trusted environment in SIoT, protect sensitive data, ensure reliable device authentication, and enable secure and encrypted communication in SIoT operations. The proposed model combines key features of blockchain, including decentralization, immutability, transparency, and cryptographic mechanisms, with SIoT security operations. Techniques like encryption, anonymization and differential privacy are employed to preserve privacy in SIoT devices and safeguard sensitive data. An empirical assessment of this operational security framework yields promising outcomes in the pursuit of its defined objectives. It furnishes a decentralized and tamper-proof foundation for device authentication, thereby cementing the trustworthiness of SIoT devices. Moreover, the proposed Graph Neural Network (GNN) model eclipses the performance of traditional models, boasting a 95% accuracy, a 96% precision, a 95% recall, and a 95% F1-score. This achievement can be attributed to the incorporation of computations that encapsulate both transitivity and composability among devices. In tandem, the suggested framework emerges triumphant when compared to other methodologies, spanning diverse metrics such as throughput, latency, security score, privacy score, and trust score. Notably, this framework not only secures communication but also upholds data integrity and confidentiality, ensuring exclusive access to SIoT devices and data by authorized entities. The proposed SIoT framework, with 280 Tx/s throughput, 2.2 seconds latency, 9.5 security score, 9.0 privacy score, and 9.5 trust score, outperforms other authors’ models across all the parameters.

Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images.

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary diagnosis. In response, we introduce the novel Deep Augmented Metric Learning (DAML) network, specifically tailored for ultrasound-based prostate cancer classification. The DAML network represents a significant innovation in the metric learning space, introducing the Semantic Differences Mining Strategy (SDMS) to effectively discern and represent subtle differences in prostate ultrasound images, thereby enhancing tumor classification accuracy. Additionally, the DAML network strategically addresses class variability and limited sample sizes by combining the Linear Interpolation Augmentation Strategy (LIAS) and Permutation-Aided Reconstruction Loss (PARL). This approach enriches feature representation and introduces variability with straightforward structures, mirroring the efficacy of advanced sample generation techniques. We carried out comprehensive empirical assessments of the DAML model by testing its key components against a range of models, ensuring its effectiveness. Our results demonstrate the enhanced performance of the DAML model, achieving classification accuracies of 0.857 and 0.888 for benign and malignant cancers, respectively, underscoring its effectiveness in prostate cancer classification via medical imaging.

An empirical assessment of the influence of digital transformation on sports corporate sustainability.

The trend of digital transformation fosters enterprise change, helps cultivate enterprises' own competitive advantages and is crucial to the advancement of sports enterprises' sustainable development in the framework of the emerging digital economy as a national strategy. However, there have been few empirical studies on the microlevel of digital transformation and its impact on the sustainability of sports organizations. Therefore, the sustainable growth dynamic model is used to construct indicators of corporate sustainability by referencing 48 sports corporations listed on Shanghai and Shenzhen A-shares markets and the New Third Board in China from 2012 to 2021. The intrinsic relationship between digital transformation and the sustainable development of sports enterprises and the underlying mechanism of action are explored by constructing a panel fixed effects model, a chain mediating effects model, and a panel threshold model. The most important contribution is as follows: To provide a useful reference for analyzing enterprise digital transformation, a more complete indicator indicating the extent of corporate digital transformation is built. The micro viewpoint broadens our awareness of sustainable development in sports organizations and deepens our understanding of the interaction model between sustainable development and enterprise digital transformation. This study provides methodical evidence and insights for an accurate understanding of digital transformation for sustainable enterprise development, looking into the "black box" of the mechanism between digital transformation and sustainable business development. The results show that digital transformation significantly aids sports enterprises in their pursuit of long-term sustainability. Heterogeneity tests demonstrate the pivotal role of digital transformation in advancing the sustained growth of sports firms and high-tech sports enterprises situated in the eastern region of China. Regarding transmission mechanisms, the chain mediating effect of enterprises' digital transformation on improved technological innovation and TFP, which in turn promote long-term business growth, has yet to be validated. Further examination exposes that within the context of the correlation between digital transformation and the sustainability of corporations, there is a single threshold effect based on financing restrictions and operational costs and a double threshold effect based on operational efficiency.

Determinants of Investors' Awareness of Online Trading in the Nepal Stock Market: An Empirical Assessment Using Binary Logit Model

Background: Online trading is becoming a prominent method of conducting stock exchanges through web platforms. While many regions worldwide have embraced online trading, its introduction in Nepal is relatively recent. The Nepalese stock market is considerably smaller than its neighboring nations, highlighting the significance of capital in a country's economic progress.
 Objective: The main objective of this paper is to investigate the factors that influence investor awareness of online trading within the Nepal Stock Exchange. The study focuses on investors who engage in stock market activities online, examining the variables of better online trading (dependent variable), facility in online trading, and investors' intention to invest (independent variables) in the context of online trading in Nepal.
 Methods: To achieve this objective, the study employed a non-probability convenience sampling technique, selecting 403 samples from the population of online investors. The research process incorporated three key instruments: expert opinions, structured questionnaires for surveys, and observational data collection.
 Results: The study's findings reveal that the majority of investors (83.4%) in Nepal possess a comprehensive awareness of online trading. Notably, the primary source of this awareness is brokers, accounting for 58% of investor knowledge dissemination.
 Conclusion: This research underscores the growing awareness of online trading within the Nepal stock exchange. Although Nepal's stock market is relatively diminutive compared to neighbouring nations, brokers play a vital role in educating investors about online trading. As investor interest in online trading rises, stakeholders and regulators must adapt to this evolving financial landscape.
 Implications: Understanding the factors that drive awareness and participation in online trading is critical for the development and growth of the financial sector in Nepal. Stakeholders must enhance educational efforts and facilitate investor-friendly policies further to bolster the online trading ecosystem in the country.
 Keywords: Online trading, Stock market, NEPSE, SEBON, Nepal
 JEL Classification: G24; G32

Procedure for Natural Gas Compressibility Factor Determination

The article is devoted to the development of an experimental stand and a methodology for experimental determination of the gas compressibility factors, to ensure the possibility of obtaining reliable information about the values of natural gas compressibility factor within the temperature range 223 - 250 K. An overview of empirical assessment methods of the compressibility factor values of gas media and approaches used to organize the hardware design of experimental studies is given. The method of compressibility factor determination is proposed and the description of the developed experimental stand is given. The error of the proposed method was assessed by testing in media with known compressibility factors in a wide range of temperatures and pressures: air, nitrogen, argon, methane. The test results are presented in the form of experimentally obtained data deviations from the compressibility factor values published in literature. The air compressibility factor uncertain values obtained as a result of the study are in the range (-0.02 -0.07%), for nitrogen - (-0.06 - 0.02%), for argon - (- 0.04 - 0.04%), for methane - (-0.08 - 0.06%). The obtained empirical values are taken satisfactory with the information given in literary sources. The good agreement of the experimental and design data obtained for pure gases and air shows that the developed method of compressibility factor experimental determination is reliable, has the required accuracy and can be used to determine the natural gas compressibility factor of various component composition.

Hybrid Features-Based Intrusion Detection for The Internet of Vehicles using Dynamic Adaptation

Abstract: The evolving landscape of the Internet of Vehicles (IoV) has brought to the forefront a discernible array of challenges about network security. In response, this study delves into applying deep learning-based intrusion detection techniques to fortify the IoV against potential network threats. Notably, prevailing approaches often rely on a singular deep learning model for either temporal or spatial feature extraction, with a serial sequence of spatial feature extraction followed by temporal feature extraction. Such methodologies tend to exhibit shortcomings in adequately capturing the Spatiotemporal intricacies inherent in IoV dynamics, thereby adversely impacting intrusion detection efficacy, and contributing to an elevated false-positive rate. To address these challenges, this research proposes an innovative intrusion detection method tailored for the IoV, premised on the parallel analysis of Hybrid features. The methodology commences with the construction of an optimal feature subset based on inter-feature correlations within IoV traffic. Subsequently, a parallelized application of Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) architectures is employed to extract spatio-temporal features from IoV traffic. In acknowledgment of the dynamic nature of IoV environments, a novel Dynamic Adaptation Mechanism is introduced. This mechanism continuously monitors the real-time IoV traffic, detecting feature drifts and triggering adaptations in the spatiotemporal feature extraction process. The adaptation requirements are then seamlessly communicated to the intrusion detection model through a Feedback Loop for Model Updating, ensuring that the model remains adept at discerning emerging network threats. The culmination of this process involves the fusion of parallelly extracted spatiotemporal features, facilitated by the self-attention mechanism. Subsequently, intrusion detection is executed utilizing a Multilayer Perceptron, providing a comprehensive framework that dynamically adapts to the evolving IoV environment. Empirical assessments utilizing the NSLKDD dataset demonstrate the efficacy of the proposed method, manifesting in a notable 2.05% reduction in the false-positive rate. Additionally, the proposed method surpasses baseline performance metrics, including accuracy and F1 score, thereby affirming its proficiency in enhancing intrusion detection capabilities within the Internet of Vehicles paradigm, especially in the context of the introduced Dynamic Adaptation Mechanism.