bluuebunny/crossref_metadata_2025 huggingface dataset:
Sustainable Digital Transformation: Leveraging HR Analytics for Efficient Workforce Management
Authors: Tanvi Thakkar
Published: February 2025

The pursuit of sustainable digital transformation represents a strategic imperative in the contemporary business landscape. This research paper delves into the critical dimension of leveraging HR analytics for efficient workforce management for that both quantitative and qualitative analysis has done from the 100 respondents. As the field of HR analytics continues to evolve, this study investigates the ongoing challenges faced by organizations, even those with substantial HR analytics investments, in fully realizing its potential benefits (Wang et al., 2023). The transition to HRM 5.0 underscores a growing awareness among companies of the advantages conferred by technology adoption in human resources (Kiran et al., 2023).
In today's world, responsible digital transformation is not just a business necessity but a societal imperative. It demands a delicate balance between technological advancements, environmental considerations, ethical responsibilities, and social consequences (Kiran et al., 2023). By adopting an interdisciplinary approach, this paper seeks to address these multifaceted complexities and harness the transformative power of digital technologies to foster inclusivity, equity, and sustainability.
Furthermore, the digital transformation of HR practices, with a focus on technological innovation, has become paramount for organizations aiming to enhance their strategies and overall performance. This research contributes to the growing body of HR digitization literature by emphasizing the adoption of HR analytics, a potent tool for managing attrition, forecasting firm performance, and driving employee satisfaction (Pappas et al., 2023).
In today's fiercely competitive corporate environment, effective human resource management is central to achieving corporate success (Bonilla-Chaves & Palos-Sánchez, 2023). This research highlights the importance of embracing HR analytics in the quest for efficient workforce management and sustainable digital transformation. By investigating these key dynamics, this study provides insights that can help organizations navigate the complicated terrain of digital transformation and HR analytics, ultimately driving sustainable success.
Hierarchical Fuzzy Clustering and Sleep Scheduling for Load Balancing in 5G-IoT Networks
Authors: T. S. Rashad
Published: February 2025

Introduction: Small cells have been added to Fifth Generation (5G) networks for handling the increasing demands for ubiquitous service and mobile traffic. Furthermore, it makes it possible for mobile broadband technology to be used everywhere to locate Internet of Things (IoT) applications. Existing research suggests strategies to maintain network segment distribution to various applications effectively.
Objectives: The major objective of this work is to balance the loads among the cells in IoT-5G networks.
Methods: This paper proposes Hierarchical Fuzzy clustering and sleep scheduling (HFC-SS) technique for load balancing in 5G-IoT networks. This approach creates and groups sub-segments for every network segment for handling IoTs applications with various resource demands. The hierarchical Fuzzy clustering techniques is applied for sub-splitting based on a cumulative rank of Quality of service (QoS) metrics. Then, an adaptive sleep scheduling technique is applied on every small cell base station (SBS) depending on its load. The overloaded traffic may be sent to the Macro cell under the load balancing policy (LBP), when the mean load of any SBS surpasses the Macrocell's load.
Results: The validation of simulation results against analytical results demonstrates that the proposed techniques provide maximum delivery ratio and power efficiency with minimized energy usage and drop rate of SBSs.
Conclusions: The proposed HTC-SS technique thus provides efficient load balancing for each SBS.
Potential of Emerging Sensor Technologies like LiDar for Enhanced Spatial Computing: Unlocking the Future of Technology
Authors: Greeshma Arya
Published: February 2025

Integration of Light Detection and Ranging (LiDAR) technology with the developing area of spatial computing has driven a radical change across many fields in recent years. This mix is changing our interaction with the digital world and opening new possibilities in domains such augmented reality (AR), virtual reality (VR), autonomous navigation, and environmental mapping. This integration marks a significant transformation rather than just little technical enhancements that redefine ecosystems and open the path for a fresh wave of invention. The great possibilities of LiDAR as a new sensor technology and their major impact on spatial computing are investigated in this work. We investigate its many uses, underline its advantages, and assess the larger consequences of using it in contemporary technology environments. This work mostly focusses on the Apple Vision Pro, a novel AR/MR helmet that uses LiDAR to improve spatial awareness and provide remarkable user experiences. We want to show how LiDAR technology transforms spatial computing to hitherto unheard-of degrees, therefore facilitating smooth interaction between the digital and physical worlds by means of analysis of this case study. This work emphasises the transforming power of LiDAR in redefining spatial computing and provides fresh angles on its use and future possibilities.
Integrating Semantic Contextualization and Graph Neural Networks for Personalized Content Recommendations in OTT Platforms
Authors: Sanjib Kumar Swain
Published: February 2025

The exponential growth of Over-the-Top (OTT) platforms has significantly transformed the media consumption landscape, offering users a vast array of content options. However, the challenge of delivering personalized and contextually relevant recommendations persists, primarily due to the complexity and diversity of user preferences and content attributes. This paper presents an innovative framework, the Semantic-Enhanced Graph Model (SEGM), marking a notable advance in tackling these challenges. SEGM integrates semantic embeddings with Graph Neural Networks (GNNs) to capture intricate relationships among users, items, and contextual factors. Our experimental results, validated using the Anime dataset, reveal substantial improvements in recommendation accuracy, relevance, and user engagement. This research not only lays a strong foundation for advancing personalized recommendation systems within dynamic and data-intensive environments such as OTT platforms but also inspires optimism about the potential of this novel approach to shape the future of recommendation systems.
Transforming Customer Engagement in Digital Commerce: The Role of Conversational AI Frameworks
Authors: Neelam Phadnis
Published: February 2025

Conversational Artificial Intelligence (AI) is revolutionizing customer interaction and service delivery in online commerce by addressing the limitations of traditional support systems, such as slow response times and lack of personalization. Leveraging Natural Language Processing (NLP) and machine learning, AI-driven chatbots provide 24/7 assistance, personalized engagement, and seamless user experiences, significantly enhancing customer satisfaction and operational efficiency. These advancements also highlight the growing need for AI systems to interpret and respond to complex emotional cues, fostering deeper consumer trust and brand loyalty. With advancements in AI technology, combining generative AI, sentiment analysis, and adaptive learning models will further enhance chatbot capabilities, ensuring more human-like and context-based interactions. Yet, ethical implementation is still of paramount importance, demanding transparency, data privacy measures, and bias prevention to enable responsible AI adoption. This paper discusses different chatbot frameworks, business uses, and industry-specific applications, discussing their advantages, challenges, and future prospects. In addition, it sets out a blueprint for optimizing AI-powered customer service, aligning automation with human judgment to build a wiser, more compassionate, and more effective digital commerce environment.
Numerical Modelling and Simulation-Based Optimization of NPID Controller for Robotic Manipulator System Using Genetic Algorithm
Authors: Aditi Saxena, Jitendra Kumar, Vinay Kumar Deolia
Published: February 2025

Robotic manipulation has always posed a challenge for researchers and scientists, especially when dealing with the nonlinearity of manipulators. To address this issue, this paper proposed a robust non-linear proportional integral derivative (NPID) control structure for regulating a non-linear, coupled, two-link stiff robotic manipulator system. The NPID controller employs an error-dependent non-linear factor to enhance its performance. The gains of the controllers were optimized using the meta-heuristic optimization technique Genetic Algorithm, with the objective function defined as the integral of the absolute error change in controller output. The paper compared the performance of PID and NPI controllers with the NPID Controller for reference trajectory tracking, noise suppression, disturbance rejection, and model uncertainty. The simulation results showed that the proposed NPID controller outperformed the other controllers. The NPID controller's improved performance is because of its ability to handle the nonlinearity of the manipulators more effectively than the PID and NPI controllers. This study is a significant contribution to robotic manipulation, as it provides a viable solution to improve the performance of robotic manipulators in various applications.
Cybersecurity Threats in Digital Payment Systems (DPS): A Data Science Perspective
Authors: Sheela Hundekari
Published: February 2025

The growing dependence on digital payment systems has resulted in a rise in cyber-attacks, therefore endangering user confidence and financial stability. Understanding these threats, evaluating their effect, and investigating data-driven solutions for reducing cybersecurity risks drives this research. The purpose of the study is to explore the cybersecutiy threats in digital payment systems (DPS) and the role of data science techniques in threat detection and prevention. The technique used in the current study is Exploratory Factor Analysis (EFA). It was found that the following are the three key factors reflecting cybersecurity threats i.e., Data Breach & Privacy Threats, Network & System Vulnerabilities, and Fraud & Identity Theft Threats. Data science finds threats, analyzes patterns, and supports proactive defensive mechanisms through Machine learning (ML) algorithms such as anomaly detection, neural networks, and decision trees.
Evaluating the Impact of Hybrid Deep Learning Model and Data Balancing Techniques on Classification Performance: A VEREMI Dataset Analysis
Authors: Nileema Pathak, Purushottam. R. Patil
Published: February 2025

The VEREMI dataset is used in this study to look into how well mixed deep learning models and data balancing methods work for binary and multiclass classification. The study uses many preprocessing steps, such as label encoding, data scaling, and handling missing values. It also uses the SMOTE method to fix data mismatch. Three models Autoencoder, Long Short-Term Memory (LSTM), and VEREMI_LA, a new hybrid model that combines Autoencoder and LSTM were learned and tested on datasets that were both balanced and skewed. In binary classification, the results show that the mixed VEREMI_LA model does better than individual models. It achieves an amazing 99.9% accuracy in both balanced and skewed situations. The F1-score also shows that the VEREMI_LA model has better performance; it consistently hits 99.9%, showing that it can handle different types of data. With an accuracy of only 50% and an F1-score of 34%, the Autoencoder model, on the other hand, did much worse, especially on the balanced dataset. In the multiclass classification task, the VEREMI_LA model once again proved to be the best, getting 86% of the correct answers on the uneven dataset and 97% of the correct answers on the balanced dataset. With only 52% accuracy, the Autoencoder, on the other hand, did the worst on the balanced sample. These results show that the mixed method works to make classification more accurate and reliable. The study stresses how important data balance methods and combining mixed models are for improving the performance of machine learning for difficult classification tasks. This study gives useful information to people who want to make classification models work better in the real world. It suggests that mixed methods like VEREMI_LA can greatly improve the accuracy of predictions in a wide range of datasets.
Validating a Blockchain Technology-Based System in Commodity Markets: A Measurement and Structural Model Assessment-Based Approach
Authors: Vivian Brian Lobo, Madhuri Rao
Published: February 2025

In recent years, commodity markets in India and in other parts of the world are receiving a significant amount of recognition because of blockchain. A commodity can be defined as an artifact or a thing that can be merchandized between two regions, states, or nations. In general, commodities can be broadly categorized as agricultural and non-agricultural. There exists several actors in a commodity trading ecosystem that usually get involved when commodity trading between nations needs to be performed, such as exporters, customs, importer, insurance, and importer bank, and each of them are assigned certain roles and responsibilities that need to be carried out such as L/C, BoL, factoring, exporting credit, and insurance. Blockchain is a DLT that can leverage commodity markets on the whole by supporting the actors and their set of associated activities. It has been observed that commodity markets face a lot of issues during the conduct of trade, which ultimately affects its business processes. The identification of pain points or issues w.r.t. commodity market-related business processes and easing them through blockchain, especially via systems approach, shall enable smoother functioning, thereby improving overall cost and efficiency along with enhanced digital security. This study aims to validate a blockchain-based system in commodity markets by means of a measurement and structural model assessment-based approach.
Security Key generation using RSA Algorithm
Authors: S.M.Jagdale
Published: February 2025

Prime numbers are fundamental parts of cryptography and communication systems in general. Prime numbers are being used more and more for encryption and decryption purposes. RSA keys use prime numbers. Many algorithms and techniques are available and implemented to generate prime numbers efficiently. The length of prime numbers extends up to 200 bits in cryptography for security concerns. There is a need for electronic hardware that can detect and tell if a number given to it as input is prime or not in minimum time and using minimum hardware for faster transfer of data in the communication system. A simple algorithm for detecting prime numbers is presented in this paper which takes 53.023ns and 9143 LUTs for detecting a prime number whereas papers considered for literature were consuming 5.5ms for generating 128-bit prime numbers using minimum process time algorithm.
Harnessing Machine Learning for Educational Insights: A Review and Comparative Analysis of Student Performance Prediction Models
Authors: Girish Chhimwal
Published: February 2025

The prediction of student academic performance has gained significant attention in educational research, driven by the rapid advancements in machine learning (ML) techniques. This study provides a comprehensive review and comparative analysis of various ML algorithms employed in forecasting student outcomes. The integration of ML models in education facilitates early identification of at-risk students, enabling timely interventions to improve learning outcomes. Traditional statistical methods often fall short in capturing complex patterns within student data, whereas ML techniques such as Decision Trees, Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Deep Learning models offer more robust and adaptive predictive capabilities. This paper systematically examines the strengths, limitations, and accuracy of these algorithms in diverse academic settings. Key performance metrics such as accuracy, precision, recall, and F1-score are analysed to evaluate the effectiveness of different ML models. Additionally, challenges such as data quality, feature selection, and ethical considerations in educational data mining are discussed. The review highlights the potential of ML-driven predictive models in transforming educational decision-making, enhancing personalized learning strategies, and fostering academic excellence. Future research directions are also proposed to optimize predictive frameworks for student performance assessment.
Thermal and Mechanical Performance of PCM-Modified Concrete: An Experimental Study
Authors: Sajjan Vaijinath Wagh, Vinaykumar C. H.
Published: February 2025

This research investigates the integration of phase change materials (PCMs) into concrete to enhance their thermal properties while maintaining mechanical integrity for sustainable building applications. The study specifically examines two commercially available PCMs: CT29P (CrodaTherm ME29P) and M24D (Micronal 24D). The investigation focuses on evaluating workability, heat of hydration, mechanical properties, and thermal heat storage capabilities of PCM-modified concrete compared to traditional concrete formulations. Samples containing varying PCM concentrations (0%, 5%, 10%, 15%, and 20% by mass of cement) were prepared and subjected to comprehensive testing. Results demonstrate that PCM incorporation significantly affects concrete workability, necessitating superplasticizer addition for optimal mix consistency. Literature analysis indicates that PCM-incorporated concrete exhibits reduced heat of hydration by 2-5°C and delays peak hydration temperature by 1-2 hours, suggesting potential applications in thermal crack mitigation in mass concrete structures. While compressive strength decreases at higher PCM concentrations, optimal performance was maintained with 10% PCM addition combined with 5% silica fume replacement by cement mass. The study reveals enhanced thermal mass in PCM concrete, resulting in temperature fluctuation reductions of 3-5°C and improved building energy efficiency. These findings establish PCM-enhanced concrete as a viable solution for creating energy-efficient and thermally comfortable structures, advancing sustainable construction practices.
Nexus Between Political Stability and Economic Stability: Case Study of Arab Countries
Authors: Abdel Hakeem Tariq Mutaib Al-Saeed, Mufeed Almula-Dhanoon
Published: February 2025

The research aims to Explain and measure measure the impact of political stability on economic stability and identify the direction of the reciprocal relationship between the two, focusing on 15 Arab countries during a period marked by significant political and economic transformations from 1996 to 2022. The researcher employed a model that considers the influence of political stability on financial stability, using the Political Stability Index from the World Bank (2022) WGI and the GDP growth rate as a representative indicator of economic stability, sourced from the World Bank (2022) WDI. The study also accounted for other variables that influence political and economic stability, incorporating them as control variables in the model. Population growth (annual %) Average years of schooling for the population is 25+, Control of Corruption, Gross capital formation (% of GDP)The study utilized the Autoregressive Distributed Lag (ARDL) model with the Pooled Mean Group (PMG) approach to analyze both short- and long-term relationships and the "Toda-Yamamoto" method was employed to test causality between political and economic stability. The results indicate a positive long-term relationship between political and economic stability, with causality tests confirming a bidirectional relationship. Finally, the findings suggest that political stability is a crucial factor in achieving economic stability in Arab countries and vice versa, indicating that improvements in either can strengthen the other.
Effectiveness of Random Forest Model for Flash Flood Susceptibility in the Himalayan Region
Authors: Hemwati Nandan Bahuguna
Published: February 2025

Machine learning (ML) approach is being increasingly recognized as vital tool for disaster risk reduction owing to its ability to address both the scale and the impact of a disaster. Risk of flash floods is currently a major problem across the Himalayan region. In present study we evaluated effectiveness of Random Forest (RF) model for flash flood susceptibility, based on real-world data in the Indian Central Himalaya Region (ICHR), where recurrent flash floods are being experienced every year. A geospatial dataset including 200 flash flood locations and eight conditioning factors namely elevation, slope, aspect, profile curvature, distance from river, annual rainfall, land use land cover (LULC) and lithology was used for performance evaluation of Random Forest model for flash flood susceptibility assessment. The effectiveness of the model is evaluated using area under the ROC curve (0.922), accuracy (0.925), precision (0.903), recall (0.921) and F-score (0.911) metrics. The results show that random forest could be an effective tool for flash flood susceptibility assessment in the Indian Central Himalayan Region. Furthermore, by considering optimum conditioning factors based on topographical, geological and hydrological conditions, the model can be used by managers and planners for flash flood management and sustainable conservation of the human society in the other Himalayan regions.
Navigating the Future: An In-Depth Exploration of Quantum Computing in Swarm Intelligence based Multi Robot Systems
Authors: Vandana Dabass, Suman
Published: February 2025

In recent times, Multi-Robot Systems (MRS) have garnered extensive attention for their versatility and potential to tackle diverse real-world challenges. Among the myriad problems these systems aim to resolve the Multi-Robot Task Allocation (MRTA) stands out due to its pivotal role in optimizing collective robot performance. MRTA focuses on the efficient distribution of tasks among a group of robots, with objectives often centered around minimizing operational time or maximizing efficiency. Delving into optimization-based approaches, we critically review various studies to highlight their strengths and limitations. This examination reveals the innovative strategies that have emerged in the field, underscoring both the achievements and the persisting challenges within MRTA research. By identifying these gaps, we aim to outline potential directions for future inquiry, suggesting pathways for advancements in MRS efficiency and application breadth using quantum computing. The integration of quantum computing into swarm-based multi-robot systems is an emerging interdisciplinary field that promises to enhance the capabilities of robotic collectives. By leveraging principles of quantum mechanics, such as superposition and entanglement, these systems can achieve more efficient coordination, decision-making, and problem-solving.Furthermore, this paper presents evolution of MRTA strategies over recent years, identifying prevalent methods and noting shifts in research focus. Through this analysis, we aim to expose a extensive overview of the state-of-the-art in MRTA, encouraging further exploration and interdisciplinary collaboration. The integration of quantum computing into Multi-Robot Task Allocation (MRTA) represents a significant advancement in the field, promising to enhance the efficiency and capabilities.
The Effect of Intellectual Capital, Organizational Capability, and Governance, Risk, and Compliance (GRC) on Sustainability Competitive Advantage (SCA) with Bank Performance as an Intervening Variable at Regional Development Banks (BPD) in Sumatra Region
Authors: Roy Prakoso, Sri Widyastuti, Irvandi Gustari
Published: February 2025

This research was conducted with the aim of analyzing what factors influence the competitiveness of Regional Development Banks (BPD) in the Sumatra region, such as intellectual capital, organizational capabilities, and Governance Risk Management Compliance (GRC). This research uses eight banks in the Sumatra region, namely BPD Riau Kepri (Riau and Riau Islands), BPD North Sumatra, BPS Sumbel Babel, BPD Nagari, BPD Lampung, BPD Jambi, BPD Bengkulu, and BPD Aceh as the population and sampling through questionnaires to 60 respondents who are at the level of directors to top-level managers at the eight banks. Model estimation is done using SEM PLS to see the impact of independent variables on Sustainable Competitive Advantage (SCA) with bank performance variables as intervening variables, see the impact of independent variables on bank performance, and see the impact of independent variables on Sustainable Competitive Advantage. The results of this study indicate that each independent variable of intellectual capital, organizational capability and Governance Risk Compliance shows a positive influence on Sustainabale Competitive Advantage through bank performance as an intervention variable and the influence of independent variables on bank performance and Sustainabale Competitive Advantage shows a positive influence.
Evaluation of Personnel Management for Expert Staff and Administrative Staff of DPR RI Members using CIPP Analysis
Authors: Novita Wijayanti
Published: February 2025

(Purpose) The evaluation of the personnel management system for Expert Staff (TA) and Administrative Staff of Members (SAA) at the Indonesian House of Representatives (DPR RI) is crucial in supporting the constitutional functions of its members. This study aims to analyze the effectiveness of the TA and SAA management system using the CIPP (Context, Input, Process, Product) evaluation model to identify areas for improvement. (Methods) The CIPP evaluation model is used to assess the context, input, process, and product aspects of personnel management. Data were collected through interviews and document studies related to human resource management policies at DPR RI. (Results) The findings show that the "Context" component had the highest score, indicating that the organization's vision, mission, and philosophical foundation are well-established. However, the "Product" component received the lowest score, with key findings highlighting the lack of clear performance standards, disproportionate division of work units, and inefficient discipline and resource management. This research underscores the need for the application of New Public Management (NPM) principles in TA and SAA management, particularly in the development of performance standards, enhancement of professional competition, and resource efficiency. (Implications) Furthermore, recommendations are made to categorize TA and SAA as political staff, while the Secretariat General focuses on administrative and technical support, to enhance the effectiveness and efficiency of DPR RI's legislative performance.
Tourist Engagement to Develop Ecotourism With Nature Relatedness as a Moderating Variable on Behavioral Intention at Kaligua Tea Plantation, Indonesia
Authors: Andi Yulianto, Roby Setiadi, Melly Fera
Published: February 2025

This study explores the interplay between tourist engagement, perception, perceived value, and nature relatedness on tourists' behavioral intentions towards pro-environmental actions at Kaligua Tea Plantation, Indonesia. Emphasizing the role of nature relatedness as a moderating variable, this research seeks to identify the complex relationships these factors share in promoting sustainable tourism practices. Tourist engagement involves active and emotional participation, which has been found to enhance the tourist's connection to the destination, thereby influencing their pro-environmental intentions. Tourist perception, which encompasses views on authenticity, environmental quality, and alignment with ecotourism principles, directly impacts the sustainability and reputation of the destination. Meanwhile, perceived value—which tourists ascribe based on the economic, social, and environmental benefits received—further influences their commitment to sustainable practices. The study utilizes a quantitative approach with a Likert scale to measure responses from 350 purposively sampled visitors, revealing that nature relatedness not only strengthens the positive effects of tourist engagement and perceived value but also serves as a critical factor in fostering a deeper environmental commitment among tourists. This research contributes to ecotourism literature by providing nuanced insights into how ecotourism operators can enhance tourist experiences and satisfaction, ultimately leading to more sustained and effective conservation efforts.
Importance Of Legal Regulations In The Use Of Social Networks For Sustainable Business Growth In Ecuador And Latin America
Authors: Humberto Pedro Segarra-Jaime
Published: February 2025

The use of social networks has transformed the business ecosystem in Ecuador and Latin America, providing new opportunities for market expansion and strengthening competitiveness. However, the lack of clear regulations in the digital realm represents a significant challenge to the sustainability of companies, exposing them to legal, privacy and reputational risks. This study conducts a systematic review of the scientific literature registered in Web of Science (WoS) and Scopus, following the PRISMA methodology, with the aim of analyzing the importance of legal regulations in the use of social networks for sustainable business growth in the region.
The results show that digital legislation is heterogeneous and still incipient in several Latin American countries, which generates uncertainty for companies operating on digital platforms. Three key areas were identified in the regulation of the use of social networks: protection of personal data, intellectual property and digital advertising, essential aspects to avoid unfair practices and fraud in the digital environment. Likewise, there is evidence of a direct relationship between the implementation of regulations and the strengthening of corporate reputation, transparency in business and compliance with international standards.
The study highlights the need for companies to adapt their digital strategies to the evolution of the regulatory framework and promote regulatory compliance policies to minimize legal risks. At the same time, it highlights the fundamental role of the State in the formulation of public policies that balance innovation with the protection of the rights of users and consumers in the digital environment. Finally, it is concluded that the harmonization of regulations in the region is crucial to promote a sustainable, safe and competitive business environment in the use of social networks.
MSME Sector Contributing on B2B Electronic Market Places Successes: A Systematic Literature Review & Future Research Agenda
Authors: Ajay Kumar, Balgopal Singh
Published: February 2025

Purpose – The purpose of this study is to examine basic principles of technology adoption in the MSME sector, with a focus on how they contribute to the improvement of B2B electronic markets. To do this, we perform an extensive literature evaluation, which advances knowledge on the subject, of the material that has previously been published.
Design/methodology/approach – This study uses a systematic literature review methodology that goes through the following steps in order: choosing relevant databases, identifying keywords and search strings, establishing inclusion and exclusion criteria, and conducting searches across selected databases within a set amount of time. After a thorough procedure, 140 peer-reviewed publications were examined, and the results were then qualitatively analysed.
Findings – The study reveals the identification of eight influential categories of concepts related to technology adoption through an examination of recent works of literature. The present piece underscores the fragmentation in the existing literature, which primarily focuses on a limited number of concept categories. Additionally, the study underscores the scattered nature of theoretical and conceptual frameworks and the limited application of these theories, underscoring the necessity for the creation of a more dynamic framework to research the uptake of new technologies, taking into account how quickly current technologies are changing.
Research limitations/implications – Building upon the insights gained from the aforementioned discoveries, future research directions should centre on examining technology adoption through a process-oriented lens. This includes delving into relatively underexplored factors like Resources, strategy, infrastructure, and rules and how these affect SMEs' use of technology. In addition, a thorough framework for technology adoption must be created that takes these important factors into account.
Practical implications – Both professionals and decision-makers stand to gain valuable insights by obtaining a comprehensive overview of the obstacles and facilitators that shape their attempts to incorporate technology. The understanding acquired from this study will enable them to create the instruments required for technology adoption in their organizations and provide a more profound comprehension of the factors that may impact their path.
Originality/value – The understanding acquired from this study will enable them to create the instruments required for technology adoption in their organizations and provide a more profound comprehension of the factors that may impact their path. Moreover, it advances our understanding of how to adapt technology in the dynamic, quickly evolving digital world of today.
The Adoption of Plasma Fine Bubble Innovation in a Seaweed Processing Factory to Implement Blue Economy Concept (A Case Study of PT. Hakiki Donarta)
Authors: Nimmi Zulbainarni
Published: February 2025

Introduction: Seaweed is one of Indonesia's leading commodities in the mariculture sector, and its production produces wastewater. PT Hakiki Donarta is a leading seaweed processing company that currently handles its IPAL still using Polyaluminium chloride chemicals and aeration ponds with the help of water wheels.
Objectives: This condition receives special attention in the waste handling process so that the business can be environmentally friendly and sustainable as the blue economy concept is applied. Thus, Plasma Fine Bubble (PFB) will be tested to compare chemical handling and PFB handling to manage wastewater and support the blue economy concept.
Methods: Two of the principles of the blue economy are innovation and adaptation. IPB has innovative Plasma Fine Bubble (PFB) technology and has the expertise to develop commercialization strategies for PFB to support the implementation of the Blue Economy. To be in line with the roadmap, there are five main steps (key phases) and milestones in program development (innovation solutions), as follows: Prepare the Plasma Fine Bubble (PFB) System to be implemented; PFB System manufacturing and assembly process; Implementation and performance testing of the PFB generator at the Factory; Adopt and Scale the PFB System using Benefit-Cost Analysis; Create a business plan using PFB technology.
Results: The 5 key phases are completed step-by-step. Key phases 1 and 2 were completed from July to September 2023. The results of completing key phases 1 and 2 are explained.  Key Phase 1: Preparing the Fine Bubble (PFB) plasma system to be implemented. Functional requirements of the PFB system implementation. Condition of the wastewater of the factory. The current situation surrounding the factory. Key Phase 2: PFB System Manufacturing and Assembly Process
Conclusions: The study confirms that Plasma Fine Bubble (PFB) technology is highly effective and sustainable for managing seaweed processing waste. By reducing waste management costs by 2%, reducing chemical costs by 40%, and addressing environmental concerns, PFB represents a significant step forward in advancing the blue economy.
Exploring Smart Contracts and Artificial Intelligence in FinTech
Authors: Sachin Dixit, Jagdish Jangid
Published: February 2025

Financial technology (fintech) faces growing demands for faster data processing, reduced delays, and better security as the sector rapidly advances. Current centralized systems are vulnerable to various threats including data manipulation, service outages, and security breaches that can compromise financial transactions. This research examines how combining blockchain technology, smart contracts, and machine learning could solve key challenges in fintech-related to security, transparency, and operational performance. The study also considers how these technologies affect regulatory compliance, legal frameworks, and ethical oversight. The research methodology involves analyzing ten years of literature on blockchain in fintech, specifically focusing on decentralized ledgers, automated smart contracts, and machine learning for data analysis. The findings are presented visually through diagrams and data visualizations that demonstrate improvements in operations, security, and cost efficiency. The study shows that blockchain provides transparent, secure financial record-keeping through its decentralized structure. Smart contracts help reduce costs and make financial services more accessible to underserved groups by automating processes. Machine learning enhances these blockchain applications by enabling predictive analysis and data-driven choices. While progress has been significant, there are still obstacles to overcome, particularly in developing governance frameworks that ensure ethical use and regulatory compliance. This analysis contributes to new understanding by examining how blockchain and machine learning work together in fintech, with special attention to previously understudied areas like operational efficiency, security, and regulatory compliance. The research outlines how these technologies can transform finance while providing practical solutions to current challenges, working toward a more secure, inclusive, and efficient financial system.
Contributions to the Application of Non-Participant Observation as a Research Technique.
Authors: Hermes Emilio Martinez Barrios
Published: February 2025

The purpose of this review article is to address non-participant observation as a research technique, considering its characteristics, advantages and disadvantages. In addition, it seeks to offer a description of two variants of this methodology, as well as to provide recommendations for its design. The methodology employed in this text is framed within the parameters of the interpretive paradigm and uses the hermeneutic method to understand and interpret the arguments proposed by authors such as Weber (1997), Mejía (2002), Fernández (2009), Díaz (2011), Campos and Lule (2012), Rekalde et al (2014), Martínez-Cansola (2020), Martínez et al (2022), Rodríguez (2024), among others. These researchers contribute to a greater understanding of the use of non-participant observation as a valuable tool in the field of scientific research, highlighting its applicability and the contexts in which it can be most effective. Through this analysis, it is hoped that readers will gain a more complete picture of the use of this technique in their own research.
DOI: https://doi.org/10.52783/pst.988
Peran Ekonomi Kreatif terhadap Perekonomian Indonesia Selama Pandemi dengan Analisis Tabel Input-Output
Authors: Bernika Levina , Monika Anugerah Karta
Published: February 2025

Ekonomi kreatif (ekraf) merupakan sektor yang berpotensi menjadi sumber pertumbuhan di tengah perlambatan ekonomi akibat mewabahnya Covid-19 di Indonesia sehingga dijadikan sebagai sasaran dalam program prioritas nasional dalam rangka percepatan pemulihan ekonomi. Namun, sumber data ekraf yang terbatas mengakibatkan kurang beragamnya informasi untuk menunjang pengembangan sektor ini. Penelitian ini bertujuan untuk memperkaya ketersediaan data ekraf dan menganalisis peran ekraf terhadap perekonomian Indonesia dengan menggunakan analisis deskriptif, analisis input-output, serta analisis uji peringkat bertanda Wilcoxon. Hasil analisis menunjukkan kontribusi sektor ekraf terhadap perekonomian Indonesia selama Covid-19 terus meningkat signifikan dilihat dari sisi permintaan. Subsektor kuliner, aplikasi dan game developer, serta televisi dan radio merupakan subsektor unggulan sehingga dapat menjadi fokus pembangunan pemerintah untuk mencapai target yang telah ditetapkan.
ESPAÇOS NÃO FORMAIS NA PRÁTICA PEDAGÓGICA DE PROFESSORES DE CIÊNCIAS NOS MUNICÍPIOS DE PRIMAVERA E QUATIPURU - NORDESTE PARAENSE
Authors: Hugo Edilberto Ramos de Oliveira  , Lilliane Miranda Freitas
Published: February 2025

O emprego dos espaços não formais vem sendo utilizado pelos professores da Educação Básica devido a esses ambientes possibilitarem para os estudantes um aprendizado prático e experimental, o desenvolvimento de habilidades sociais, o incentivo à criatividade e a inovação. Esta pesquisa faz uma análise do uso dos espaços não formais a partir da concepção e experiências de professores do Estado do Pará, Brasil. A investigação foi desenvolvida através da aplicação de questionário com seis professores de Ciências e Biologia atuantes nos municípios de Primavera e Quatipuru, situados no nordeste do Estado do Pará. Como resultados, verificamos que apesar do reconhecimento de que a utilização dos espaços não formais contribui para o fortalecimento do ensino e a aprendizagem dos alunos, essa prática de ensino ainda é um desafio para os professores em exercício nesses municípios devido algumas dificuldades enfrentadas, como: liberação dos alunos para realização das aulas nos espaços não formais, o incentivo para realizar essa prática de ensino, lacunas na formação inicial e continuada; e ainda dificuldades com transporte. Ainda que esse método de ensino apresente alguns empecilhos, os professores consideram como essencial a utilização dos espaços não formais para a contribuição do ensino e aprendizado dos alunos e também consideram que eles proporcionam aspectos que facilitam práticas pedagógicas contextualizadas na disciplina de Ciências e Biologia.
PRÁTICAS COMO COMPONENTES CURRICULARES NA FORMAÇÃO INICIAL DE PROFESSRES DE CIÊNCIAS E BIOLOGIA
Authors: Rafael Alberto Moretto  , Andréa Cristina Tomazelli  , Cibele Randi Barbosa  , Glaucya de Figueiredo Mecca  , Janaina Fernanda de Souza Gonçalves Neto  , Lucila Costa Zini Angelotti
Published: February 2025

Este trabalho tem como tema central a formação de professores de Biologia, realizada no âmbito de um curso de licenciatura em Ciências Biológicas de uma Instituição de Ensino Superior particular do estado de São Paulo. De maneira específica, analisa a importância das atividades de Prática como Componente Curricular (PCC) em diferentes disciplinas do curso. O objetivo deste trabalho, portanto, é discutir como a PCC pode contribuir para o desenvolvimento profissional e para o fortalecimento da identidade dos futuros professores de Biologia, articulando teoria e prática. De cunho qualitativo, o estudo tem como corpus as atividades de PCC articuladas às disciplinas de Geologia e Paleontologia, Anatomia e Morfologia Vegetal, Parasitologia e Zoologia, o qual foi discutido na perspectiva do estudo de casos múltiplos, onde cada atividade de PCC realizada é considerada como um caso. Dessa forma, entendemos tratar-se de uma pesquisa exploratório-descritiva. Como resultado deste trabalho, são apresentadas algumas das atividades de PCC desenvolvidas pelos licenciandos de diferentes anos do curso de Ciências Biológicas em diferentes disciplinas, articuladas com as competências e habilidades da BNCC e em consonância com as competências do Projeto Pedagógico do Curso de Graduação e o perfil do egresso que pretendemos formar, contribuindo, de maneira direta, para a formação dos futuros professores, além de incentivando-os a refletir sobre sua práxis pedagógica.
Analisis Tata Kelola Bumdes Tanjung Intan Di Desa Sungai Kupah Kabupaten Kubu Raya
Authors: Lutfiatunnisa Lutfiatunnisa , Yurisinthae Erlinda , Aritonang Marisi
Published: February 2025

Pendirian BUMDes oleh pemerintah merupakan salah satu upaya untuk mewujudkan desa mandiri dan mengelola perekonomian desanya. Hal ini tertuang dalam Pasal 117 Undang-Undang Nomor 11 Tahun 2020 tentang Cipta Kerja yang menyatakan bahwa pengelolaan desa sebagai suatu badan hukum yang didirikan oleh dan/atau bersama-sama dengan desa untuk keperluan pemanfaatan aset dan pengembangan penanaman modal perusahaan (BUMDes).Memberikan produktivitas dan jasa serta jenis usaha lainnya untuk kepentingan masyarakat desa. Aspek penting yang harus diperhatikan oleh pemerintah desa dan BUMDes dalam menciptakan layanan BUMDes yang berkualitas adalah penerapan prinsip tata kelola yang baik. Tujuan penelitian ini adalah untuk mengetahui bagaimana pengaruh prinsip legitimasi (rule of law), partisipasi, akuntabilitas, dan transparansi terhadap pengelolaan Badan Usaha Milik Desa (BUMDes) Tanjung Intan. Hasil penelitian ini menunjukkan bahwa BUMDes Tanjung Intan belum maksimal dalam mencapai tata kelola yang baik. Berdasarkan tingkat perkembangannya, BUMDes Tanjung Intan termasuk dalam kategori BUMDes berkembang. Hal ini ditandai dengan karena sifat operasional yang dilakukan oleh BUMDes, potensi pertanian di desa masih dieksploitasi secara terbatas
Analisis Faktor Penyebab Resiliensi Petani Padi Menghadapi Perubahan Iklim Di Desa Krendetan Kecamatan Bagelen Kabupaten Purworejo
Authors: Prakoso Ove Anom , Mulyani Altri , Situmorang Sawitania Christiany D.U Boru
Published: February 2025

Pertanian padi di Indonesia sudah berjalan dengan cukup baik namun akhir-akhir ini mulai muncul perubahan iklim yang dapat mengganggu dan membahayakan pertanian padi. Penurunan intensitas hari hujan pada musim penghujan, suhu global yang semakin panas, dan adanya indikasi mundurnya musim penghujan merupakan dampak dari perubahan iklim yang mulai dirasakan dan mengancam petani. Upaya mengatasi perubahan iklim yang dihadapi petani tersebut, maka dibutuhkan resiliensi yang baik untuk menghadapi ancaman tersebut. Penelitian ini bertujuan untuk mengetahui tingkat resiliensi petani padi dan faktor yang mempengaruhi tingkat resiliensi petani padi di Desa Krendetan Kecamatan Bagelen Kabupaten Purworejo. Penelitian ini menggunakan metode survey dengan teknik sampling yang digunakan ialah simple random sampling. Subjek yang diteliti yaitu petani padi sebanyak 61 responden. Penelitian dilaksanakan pada bulan Maret – April 2024. Analisis data yang digunakan yaitu analisis regresi liniear berganda dan analisis tingkat resiliensi menggunakan CD-RISC. Hasil penelitian menunjukkan bahwa petani padi di Desa Krendetan resilien terhadap perubahan iklim dengan Tingkat resiliensi sebesar 84,68. Nilai tersebut mengindikasikan adanya resiliensi yang tinggi pada petani padi di Desa Krendetan Kecamatan Bagelen Kabupaten Purworejo. Faktor yang mempengaruhi resiliensi petani padi di Desa Krendetan Kecamatan Bagelen Kabupaten Purworejo yaitu tingkat optimisme, kompetensi petani, orang lain yang dianggap penting, kapasitas sumber daya pertanian, dan teknologi pertanian.
Vi khuẩn gây nhiễm trùng huyết cộng đồng ở trẻ em và tính nhạy cảm với kháng sinh
Authors: Hải Đỗ Thiện , Lâm Nguyễn Văn , Hoằng Nguyễn Văn
Published: February 2025

Nghiên cứu mô tả, phân tích 79 bệnh nhi được chẩn đoán nhiễm khuẩn huyết mắc phải tại cộng đồng có xác định được vi khuẩn gây bệnh bằng kỹ thuật cấy máu, điều trị tại Trung tâm bệnh nhiệt đới thu được kết quả như sau: Vi khuẩn gram dương chiếm tỷ lệ 83,54%. Trong đó các căn nguyên chính bao gồm S. aureus với tỷ lệ 69,6%, tiếp đó là E. coli (12,7%) và S. pneumoniae (12,7%). Vi khuẩn gram dương còn nhạy với Linezolide 100%; Vancomycin 98%; Ciprofloxacin 92%, Tobramycin 87,5%. Vi khuẩn Ecoli còn nhạy với Meropenem 62,5%; Levofloxacin 44,4%; Amikacin 85,7%.