Emerging Science Journal (ESJ)
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Methodology for Business Process Automation in SMEs: From Requirements Analysis to Practical Demonstration
Get PDFThis study aims to develop a methodology to assist Small and Medium Enterprises (SMEs) in effectively adopting Business Process Automation (BPA). Despite its growing importance in streamlining routine tasks and enabling employees to focus on more creative activities, numerous organizations face challenges in implementing BPA due to unclear procedures, insufficient knowledge of eligible processes, and uncertainty regarding the necessary technology. In response to these challenges, we introduce the Methodology for Business Process Automation (M4BPA), an artifact designed to guide SMEs through a structured BPA implementation process. The research follows the Design Science Research Methodology (DSRM). The requirements for the artifact came from the results of a previous Systematic Literature Review (SLR). M4BPA was demonstrated within real SME environments, providing solid evidence of its efficacy. The findings suggest that M4BPA significantly enhances SMEs' ability to implement BPA efficiently, offering a practical toolkit that facilitates the process. The novelty of this work lies in the development of a BPA methodology specifically tailored for SMEs, addressing existing gaps in current frameworks and providing a best-practice model for similar organizations. This research contributes to the intermediate results of a doctoral project, offering valuable insights for both practitioners and researchers in the field of BPA
Cognitive Insights from Emotional Intelligence: A Systematic Review of EI Models in Educational Achievement
Get PDFObjectives: This study aims to investigate the relationship between Emotional Intelligence (EI) and academic achievement within educational settings. It seeks to determine how different EI models”specifically, Ability EI and Trait EI”impact students' academic performance, behavior, engagement, and motivation. The goal is to provide insights that can guide the integration of EI into educational practices to foster a more supportive and effective learning environment. Methods/Analysis: The study is based on a systematic review of sixty-four (64) peer-reviewed studies published between 2016 and 2023. These studies include randomized controlled trials (RCTs), longitudinal studies, and meta-analyses. The selected studies were analyzed to explore the effects of EI on various academic outcomes, including performance, behavioral engagement, and motivation. Findings: The analysis reveals that both Ability EI and Trait EI are positively associated with academic success. These models of EI appear to contribute significantly to the development of key skills such as emotional control, empathy, and problem-solving, which in turn support effective classroom management and academic achievement. The findings suggest that incorporating EI into educational curricula can lead to improved communication, better problem-solving abilities, and enhanced relationships between students and faculty. These improvements can create a more conducive learning environment and enhance overall academic outcomes. Novelty/Improvement: This study adds to the existing literature by offering a comprehensive review of recent studies that examine the role of EI in academic settings. It highlights the importance of implementing EI-focused interventions and curricula in schools, emphasizing the practical implications for educators and policymakers. Doi: 10.28991/ESJ-2024-SIED1-016 Full Text: PD
Using Motion-Graphic Media to Educate Higher Education Students About Depression: A Randomized Controlled Trial
Get PDFObjective: This study aims to compare the effectiveness of motion graphics versus pamphlets for educating young adults about depression. Methods: A multicenter randomized controlled trial was conducted from April to June 2024; participants were randomly assigned to Group A (motion-graphic media) or Group B (pamphlets) in a 1:1 ratio. Pre- and post-intervention knowledge scores were collected, and satisfaction scores were collected after intervention from group A. Findings: A total of 78 participants with a median age of 19.0 years (IQR 2.0) and predominantly women (64.1%), completed pre- and post-intervention questionnaires. The median knowledge score for Group A increased from 15.0 (IQR 4.0) pre-intervention to 18.0 (IQR 3.0) post-intervention, while Group B's scores improved from 12.0 (IQR 4.0) to 16.0 (IQR 3.0). Post-intervention scores were significantly higher in Group A compared to Group B (p = 0.002). Participants in Group A also reported high satisfaction with the educational material. Novelty:This study highlights the potential of innovative media for patient education, particularly in addressing mental health issues. Long-term cohort studies are required to assess whether this approach can improve clinical outcomes and reduce the incidence of severe depression. Doi: 10.28991/ESJ-2024-SIED1-015 Full Text: PD
Organizational Climate Management in the Context of Initial Mathematics Teacher Education
Get PDFObjective: This study aims to analyze the impact of organizational management on motivation, satisfaction and commitment in teacher training in the context of Chilean quality assurance policies that emphasize a positive school climate and good coexistence. Methods: Using a Likert scale with a Cronbach's alpha of 0.957, we surveyed 62 students in a mathematics teacher education program. A multiple linear regression analysis was conducted to examine how motivation, satisfaction, and engagement were related to organizational management and climate. Findings: The results show that motivation and satisfaction are significant predictors, explaining 61.5% of the variance in organizational management, while commitment also influences climate, but to a lesser extent. These results underscore the importance of motivation and satisfaction for effective organizational management and suggest that these factors may be more important than commitment in shaping a positive organizational climate. Novelty/Improvement:This study contributes to the literature by highlighting the need for management models that are tailored to specific educational contexts and calls for future research to examine additional variables that influence organizational climate in higher education to improve our understanding of the factors that influence educational environments. Doi: 10.28991/ESJ-2024-SIED1-018 Full Text: PD
The Assessment of the Green Development of the Tobacco Industry Using a Multicriteria Method
Get PDFThe tobacco industry is heavily regulated due to the significant health implications associated with tobacco use. The industry also involves numerous stakeholders, including farmers, manufacturers, distributors, retailers, regulators, and consumers. The aim of this research is to select the most relevant environmental criteria for the green development of the tobacco industry. This article uses Analytical Hierarchy Process (AHP) methods to create a hierarchical structure of the criteria and subcriteria necessary for green business development, establishing the relative weights of these subcriteria to find the areas in which attention and resources are most urgently required. The assessment of the concordance of expert opinions shows a satisfactory level of agreement. The article advances a more comprehensive view towards the evaluation of green criteria that are significant for the whole industry, seeking to highlight the need to think holistically. According to the views of experts, the most significant sub-criteria for the green development of the tobacco industry are increasing energy efficiency; safeguarding against hazardous wastewater in the environment; reducing the content of hazardous materials used in products; improving air, land, and water quality where economic activity takes place; sustainable forest management; eco-design, especially for efficient material use, biodegradability, and recyclability; and collaboration with suppliers. The entire industry should collaborate in seeking global green development by gradually investing in the improvement of green criteria. Doi: 10.28991/ESJ-2025-09-01-018 Full Text: PD
DC Motor Angular Speed Controller Using an Embedded Microcontroller-Based PID Controller
No full textThis research presents the implementation of a Proportional Integral Derivative (PID) controller to control the angular speed of a Direct Current (DC) motor using an embedded system (microcontroller). The system’s hardware consists of an Arduino microcontroller, a DC motor with an encoder sensor, a driver motor, and a power supply. Proportional control regulates the response proportionally to the calculated error, while integral control manages the cumulative error over time, and derivative control responds to the rate of change of the error, preventing overshoot. With a proper combination, PID control achieves stability, speeds up response, and reduces overshoot, improving overall system performance. Based on experimental data, the DC motor angular speed control system using PID control achieves the best results, in which the parameter values are Kp=1; Ki=0.3; and Kd=0.6. The augmented system responded with 0.0890 seconds of the rise time, 11.772 seconds of settling time, and 0.12 seconds of the peak time, with an overshoot of less than 10% (7%)
Agarose-Based Antibacterial Films from Gracilaria sp.: Isolation, Characterization, and Metal Nanoparticle Incorporation
Get PDFThe incorporated metal nanoparticles in a polysaccharide-based film exhibit efficient antibacterial activity against harmful germs. However, previous studies have used a commercial polysaccharide for their film production. Therefore, this study aimed to develop a natural polysaccharide-based film extracted from the local algae Gracilaria sp. originating from Sinjai Regency, South Sulawesi, Indonesia. Firstly, the polysaccharide agarose was isolated and its properties compared with those of commercial agarose. A present low-cost isolation process produces agarose with 1.8% (w/w) of yield. Results also showed physicochemical properties similar to those of the commercial agarose. Secondly, the agarose-based antibacterial film was synthesized at 0, 0.5, and 1% glycerol concentrations. The synthesized film was incorporated with silver (Ag) and copper (Cu) nanoparticles (NPs). Morphological, mechanical, and physicochemical properties of the incorporated Ag-agarose and Cu-agarose films were characterized using Field Emission Scanning Electron Microscope (FESEM), Universal Testing Machine (UTM), and Fourier Transform Infrared Spectroscopy (FTIR), respectively. Results showed the film stiffness and tensile strength increased by incorporating either AgNPs or CuNPS. The interaction of AgNPs-agarose most likely involves physical bonds, while the interaction of CuNPs-agarose forms coordination bonds. An antibacterial test showed that the Ag-agarose nanocomposite inhibited the growth of Escherichia coli, Salmonella typhimurium, Staphylococcus aureus, Staphylococcus epidermidis, and Bacillus subtilis. In the meantime, Cu-agarose prevented the growth of Staphylococcus aureus. Overall, antibacterial activity was influenced by the interaction between metal nanoparticles and agarose, the concentration of metal nanoparticles, and the film's solubility. An agarose-based antibacterial film from Gracilaria sp. has the potential for use in various applications, including food packaging, pharmaceuticals, and other industries
Dynamic Capabilities and Technological Innovation for Firm Resilience: A Configurational Analysis
Get PDFFirm resilience is essential to manage response and rapid recovery from disruptive events for a firm. Moreover, there is limited literature that investigates the combined effects of dynamic capability and technological innovation that are interrelated with firm resilience. This study used the dimensions of firm resilience, which were investigated with both necessary condition analysis (NCA) and fuzzy-set Qualitative Comparative Analysis (fsQCA) methods using survey questionnaires from 308 respondents operating in Bangladeshi corporate industries that are currently facing uncertainties due to unforeseen crises. NCA results showed that visibility, market position, and digitalization achieved firm resilience as these antecedents reached the full percentile to achieve an optimal level of outcome. On the contrary, the influence of reserve capacity and big data analytics was not empirically significant for achieving firm resilience. Moreover, fsQCA results appreciated NCA results and showed four solutions that are sufficient for achieving a high level of firm resilience. The study reveals the configurational effects of dynamic capabilities and technological innovation to achieve firm resilience. The results show the necessary effects of configurational relationships that lead to outcomes. The configurational method is applied to identify the combined effects of antecedents that help managers predict high levels of firm resilience in a turbulent environment
Feature Transformation on Big Data for Species Classification in Machine Learning
No full textClassification of bacterial species, particularly for closely related taxa, remains a major challenge in many areas, e.g., public health, food industries, and many others. The issues are mainly caused by overlapping genetic features of organisms and data complexities. In this study, a bacterial taxonomic identification framework that integrates genome-derived motif sequences with machine learning was introduced. Two hundred and forty genome sequences from Salmonella enterica, representing six subspecies and ten serovars, were used for modelling. Sequence motifs were predicted from single-copy orthologous core genes of the downloaded genomes. Single nucleotide polymorphisms (SNPs) within these motifs were extracted and numerically encoded as machine learning features. The 20 top-most informative predictors from feature selections were used for model training in Random Forest and Support Vector Machine. Comparing the output from multiple analyses, the Random Forest model achieved the highest accuracy of 97.92%, demonstrating reliable differentiation of Salmonella at both subspecies and serovar levels. This research presents two key innovations: i) the use of sequence motifs as molecular signatures for bacterial classification; ii) a novel feature engineering method that transforms genome-derived data into machine learning-readable features. The proposed framework offers a practical and scalable solution for fine-level bacterial classification and has high potential to be applied for other microbial taxa
Enhance Multimodal Retrieval-Augmented Generation Using Multimodal Knowledge Graph
Get PDFLarge Language Models (LLMs) have shown impressive capabilities in natural language understanding and generation tasks. However, their reliance on text-only input limits their ability to handle tasks that require multimodal reasoning. To overcome this, Multimodal Large Language Models (MLLMs) have been introduced, enabling inputs such as images, text, video and audio. While MLLMs address some limitations, they often suffer from hallucinations because of over-reliance on internal knowledge and face high computational costs. Traditional vector-based multimodal RAG systems attempt to mitigate these issues by retrieving supporting information, but often suffer from cross-modal misalignment, where independently retrieved text and image content cannot align meaningfully. Motivated by the structured retrieval capabilities of text-based knowledge graph RAG, this paper proposes VisGraphRAG to address the challenge by modelling structured relationships between images and text within a unified MMKG. This structure enables more accurate retrieval and better alignment across modalities, resulting in more relevant and complete responses. The experimental results show that VisGraphRAG significantly outperforms the vector database-based baseline RAG, achieving a higher answer accuracy of 0.7629 compared to 0.6743. Besides accuracy, VisGraphRAG also shows superior performance in key RAGAS metrics such as multimodal relevance (0.8802 vs 0.7912), showing its stronger ability to retrieve relevance information across modalities. These results underscore the effectiveness of the proposed Multimodal Knowledge Graph (MMKG) methods in enhancing cross-modal alignment and supporting more accurate, context-aware generation in complex multimodal tasks