European Journal of Science, Innovation and Technology

The Role of Digital Pedagogy in Enhancing Student Engagement and Learning Outcomes

2024-12-19T13:29:29+02:00

The advent of digital technology has significantly transformed educational practices, offering innovative approaches to teaching and learning. This paper explores the role of digital pedagogy in enhancing student engagement and learning outcomes. Digital pedagogy, encompassing a wide array of tools and strategies such as online platforms, interactive multimedia, and virtual collaboration, has the potential to create dynamic and immersive learning environments. By integrating digital resources into the curriculum, educators can cater to diverse learning styles, foster critical thinking, and promote active participation among students. This study examines various digital pedagogical methods, their impact on student motivation, and the consequent improvement in academic performance. Through a comprehensive review of recent literature and case studies, we analyse the effectiveness of digital pedagogy in various educational settings. The findings suggest that when effectively implemented, digital pedagogy not only enhances student engagement but also leads to better retention of knowledge and higher overall achievement. The paper concludes with recommendations for educators and institutions on adopting and optimizing digital pedagogical practices to meet the evolving needs of learners in the digital age.

Proposed Integration of Building Information Modeling and Database Management Systems for Start-Up Construction Firms

2024-12-23T15:09:41+02:00

This article investigates the potential integration of Building Information Modeling (BIM) and Database Management Systems (DBMS) to enhance the operational efficiency and competitiveness of start-up construction firms in the Philippines. The research aims to assess the current levels of awareness and utilization of these technologies among start-ups, identifying both the benefits and the barriers to their adoption. Using a mixed-methods approach, including quantitative surveys and qualitative interviews with construction professionals, this study seeks to gather comprehensive data on the subject. The proposal introduces a phased integration framework tailored to address the specific challenges faced by start-ups, such as cost and complexity, and proposes a hybrid satisfaction-challenge index to quantitatively measure the impact of BIM and DBMS on project management efficiency. The expected outcomes include valuable insights and practical guidance for start-up construction firms, construction managers, technology providers, policymakers, and the academic community, with the ultimate goal of optimizing construction management practices and fostering sustainable development in the Philippine construction industry.

Machine Learning-Enhanced Turbulence Prediction and Flow Optimization for Advanced Aerodynamic Design in High-Speed Regimes

2024-12-26T23:36:26+02:00

The design of high-speed aerodynamic systems is challenged by the need for accurate turbulence prediction and efficient flow optimization. Traditional Computational Fluid Dynamics (CFD) methods often fall short due to their computational intensity and limitations in resolving high-frequency turbulent structures. Recent advancements in Machine Learning (ML) have demonstrated significant potential in revolutionizing this field. By leveraging deep learning, reinforcement learning, and other advanced algorithms, ML models address the inherent complexities of turbulence with enhanced accuracy and computational efficiency.

This study explores the integration of ML techniques in turbulence prediction and flow optimization for high-speed aerodynamic applications. It identifies key challenges in traditional CFD methods, such as their reliance on Reynolds-averaged Navier-Stokes equations and large eddy simulations, which struggle with multi-scale turbulence phenomena. ML-based approaches, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), offer innovative solutions for subgrid-scale modeling, flow field reconstruction, and real-time monitoring.

Additionally, the research highlights ML's role in aerodynamic optimization, employing reinforcement learning and genetic algorithms to automate design processes and achieve multi-objective optimization. Case studies demonstrate a 20% improvement in prediction accuracy for supersonic flows and substantial computational time savings. These findings underscore the transformative potential of ML in high-speed aerodynamic engineering, paving the way for next-generation aerospace technologies.

AI-Driven Aerodynamic Design Optimization for High-Efficiency Wind Turbines: Enhancing Flow Dynamics and Maximizing Energy Output

2024-12-26T23:41:20+02:00

The integration of artificial intelligence (AI) in aerodynamic design optimization marks a significant advancement in the quest for sustainable energy solutions, particularly in enhancing wind turbine efficiency. This study delves into the innovative application of AI techniques, including machine learning (ML) algorithms and computational fluid dynamics (CFD) simulations, to revolutionize the design and functionality of wind turbine blades. By focusing on optimizing blade geometries, the research aims to minimize aerodynamic drag and turbulence while simultaneously maximizing energy output under varying environmental conditions. Leveraging vast datasets and advanced simulation tools, the study bridges the gap between theoretical models and practical implementations. The insights gained from this research provide a comprehensive roadmap for developing next-generation wind turbines with superior energy-harvesting capabilities, ultimately contributing to the global transition toward renewable energy sources and reducing the carbon footprint of power generation systems.

Redefining Cybersecurity with Artificial Intelligence: Innovations in Data Networking Defense

2025-01-02T18:29:52+02:00

The integration of Artificial Intelligence (AI) in cybersecurity has emerged as a transformative approach to addressing the growing complexity of cyber threats in data networking environments. This study explores the impact of AI-powered systems on threat detection, response times, and network resilience. Through a mixed-method analysis of case studies from industries such as healthcare, finance, and IoT, the research reveals that AI systems outperform traditional cybersecurity methods in both detection accuracy (95% vs. 80%) and response times (15 seconds vs. 25 seconds). However, scalability remains a critical challenge, with AI systems showing efficiency declines in large, complex networks.

The study employs a qualitative and exploratory research design, analyzing literature, case studies, and descriptive statistics. Key findings underscore the effectiveness of AI in automating threat detection and mitigation, while highlighting limitations such as data quality dependency and scalability issues. Statistical tests, including t-tests and ANOVA, further validate the superiority of AI systems.

This research has significant implications for practice, advocating for tailored AI solutions in diverse industries, regulatory frameworks for ethical AI use, and hybrid models combining AI with human expertise. Limitations include a focus on short-term outcomes and limited industry representation, which open pathways for future research.

The findings emphasize the transformative potential of AI in redefining cybersecurity, providing organizations with enhanced tools to safeguard their data networks. By addressing existing challenges and fostering interdisciplinary collaboration, AI can become an indispensable asset in the fight against evolving cyber threats.

From Algorithms to Security: AI’s Impact on Data Networking Cyber Defense

2025-01-02T18:30:08+02:00

The growing sophistication of cyber threats has necessitated a paradigm shift in how organizations approach data networking defenses. Traditional methods, often reactive and reliant on predefined threat signatures, have proven insufficient against the adaptive tactics of modern cybercriminals. In response, Artificial Intelligence (AI) has emerged as a transformative solution, offering the ability to automate threat detection, response, and mitigation processes. This study explores the impact of AI-powered algorithms on cybersecurity, focusing on their role in enhancing data networking defenses. By employing a qualitative and exploratory research design, the study examines real-world applications across industries such as healthcare, finance, and IoT, each of which faces unique cybersecurity challenges due to their reliance on interconnected systems and sensitive data.

Key findings reveal that AI systems outperform traditional methods with superior detection accuracy, achieving a rate of 95% compared to the 80% typically observed in conventional frameworks. AI’s capability to detect anomalies and previously unknown threats makes it a critical tool for modern cybersecurity. Additionally, these systems significantly reduce response times, mitigating threats 40% faster than manual or traditional methods. However, scalability challenges persist, especially in large and complex networks characterized by diverse traffic patterns and high data volumes. Statistical analyses, including t-tests and ANOVA, validate these findings, highlighting both the strengths and limitations of AI. While dependency on data quality and the focus on short-term performance are noted as constraints, the potential of AI to revolutionize cybersecurity remains clear. Addressing these challenges will be pivotal in unlocking new possibilities for securing data networks against increasingly sophisticated threats, providing a roadmap for future research and the optimization of AI-driven cybersecurity systems.

Sustainable Forest Management in the Democratic Republic of Congo through Its Forestry Policy

2025-01-02T18:54:27+02:00

Nowadays, sustainable forest management (SFM) is a major concern for everyone. Steps are being taken to achieve this. It is through both binding and non-binding texts that international organizations and individual countries enact principles and laws that the will to manage forests sustainably is expressed. The Democratic Republic of the Congo, with its substantial forest resources and its desire for sustainable management, is committed to this approach. That's why it has signed up to several international conventions and ratified agreements. Internally, several texts regulate SFM. This study was carried out with the aim of highlighting the DRC's major concern regarding the sustainable management of its forest. It was thus found that, through international agreements, conventions, its fundamental law, its forestry code and a number of decrees, the DRC is very concerned about the sustainability of its forest resources. All that remains is to apply the relevant provisions.

Language Vitality Case of Higaonon Language in Rogongon, Iligan City

2025-01-02T19:13:07+02:00

In a globalized world today, the Indigenous Peoples’ languages have been impacted by the avalanche of socio-politico-economic factors which result to language instability. One of the IP languages in Mindanao, Philippines is the Higaonon language which is threatened with various social impacts. This study investigated the vitality of the Higaonon language using the EGIDS by Lewis & Simons (2010) as a tool and interviewed key informants. Qualitative method and descriptive analysis were used. Given the gap on the unmatched finding of ethnologue about the language domains and the DepEd policy in supporting the IP language, the researchers immersed themselves in a select area where Higaonon is used. Using the EGIDS, it was revealed that the Higaonon language is at Level 6b, threatened and vulnerable. However, some responses from informants indicated that the Higaonon language is vehicular somehow showing that is used in wider communication, education, and trade. On the other hand, it is shifting due to various factors that affect language progress. Important ideas from the narratives of the key informants are: mostly domestic use of the language, speakers’ discrimination due to the use of the language, inferiority of one’s ethnic identity, and motivation on sustaining the language use even outside the Philippines. Due to this, local policies and programs are enumerated to facilitate the survival and sustainability of the Higaonon language. The findings might be limited due to immersion into one area, therefore further investigations in various periphery areas where Higaonon is used are recommended.

How Sri Lanka Promotes Cultural Values through Social Media (Especial Reference to Selected YouTube Channels)

2025-01-07T10:55:49+02:00

This study examined how Sri Lanka portrayed culture in perspective of world tourism through YouTube. Central to the study is to find out what ways videos and information are used on promoting Sri Lankan culture through YouTube. YouTube videos which were published in 2023/2024 analyzed YouTube channels and the selected YouTubers were interviewed. An analysis of traveling videos of YouTubers was done to ascertain how Sri Lanka is portrayed in context of global tourism. The interviews and text analysis provided data needed to answer the research questions. Previous research findings and the text analysis revealed that the Sri Lanka YouTubers mostly introduce Sri Lanka as a destination for diving area and didn’t create a special channel for traveling. In conclusion the study suggested different areas for improvement so as to make Sri Lankan YouTubers more effective on advertising the tourism industry of the country globally as well as constructively attracting local people to engage as operators in tourism related business, while at the same time painting the image of the country so as to attract more foreign tourists.

International Brands: A Postcolonial Critical Discourse Analysis

2025-01-07T17:18:54+02:00

This study investigates the language of the taglines used by 100 brands to advertise for their products and the relation of the linguistic representation with the postcolonial identity that is envisaged to be constructed afterwards. The work uses Fairclough’s 1992 critical discourse analysis framework which is composed of the textual description of the discourse chosen first, then the interpretation of the discursive practices encapsulated in the case study and thirdly, the evaluation of the sociocultural meanings and ideas supported by the discourse. The corpus analysis points to the existence of an aspirational messaging and emotional appeal. It also highlights the ideas of a brand differentiation and a value proposition granted by the brands. The idea of the interconnectedness between the product and self-esteem realization fostered the consumerism effect and stressed the importance of technological advancement in a globalizing material world. Industrialization effect on the East-West relation is still encrypted in the post-colonial representation of the producer-consumer relation of knowledgeability, and hence, supremacy and dominance.

The Relationship between Banking Desertification and Poverty Rates: Iraq as a Case Study

2025-01-07T17:45:58+02:00

Economic and environmental factors often exacerbate the deterioration of financial services in certain regions, a phenomenon known as "banking desertification". This phenomenon could significantly impact local economies, potentially exacerbating poverty rates. The goal of the research is to understand the impact of banking desertification on poverty rates, as represented by the number of ATMs and bank branches. We selected Iraq as the case study for the period 2003-2022. We analyzed the multiple regression model using the SPSS software. The study indicates that banking deserts significantly impact poverty rates, and the number of ATMs decreases poverty rates.

The Role of Empowering Leadership in Workplace Wellbeing

2025-01-07T17:52:13+02:00

This research sought to identify the role of empowering leadership in workplace well-being through its application in the Dean's Educational Group. The dimensions of empowerment leadership, which include enhanced work meaning, participation in decision-making, and confidence in high performance, were adopted. The dimensions of workplace well-being, which include job satisfaction, organizational respect, employer care, and work interference in private life, were also adopted. The Dean's Educational Group was chosen as the field of research, and a random sample of employees was selected through a questionnaire that included (125) individuals. The correlation relationship between two variables was assessed using the simple correlation coefficient (Spearman) and the regression model's significance was determined using the (t) test. The relative percentage contribution of the independent variable to the explanation of the dependent variable was also calculated using (R²). The researcher's main finding is that empowered leadership has a major influence on workplace well-being. The study ended with a number of suggestions, including Al-Ameed Educational Group's emphasis on empowering leadership as a key aspect in its work in general and workplace well-being in particular.

Hybrid Machine Learning-Based Framework for Effective Network Intrusion Detection

2025-01-07T18:19:08+02:00

Network security is a crucial area of research in computer networking, driven by the escalating rate and advanced nature of cyberattacks. This study explores the integration of machine learning (ML) approaches into Network Intrusion Detection Systems (NIDS) to improve their efficiency. Specifically, it combines K-means clustering and Random Forest algorithms to detect anomalies and threats within network traffic. An extensive literature review underscores the necessity for more comprehensive and accurate systems. The NSL-KDD and CICIDS-2017 datasets were adopted for training and testing the model. Preprocessing was performed to enhance dataset quality and facilitate effective model training. K-means clustering partitioned the dataset into five clusters, which were then employed to enhance the training of the Random Forest algorithm. Performance parameters such as accuracy, recall, precision, specificity, and F1 score were utilized to assess the models. The results indicate that the hybrid approach significantly improves detection accuracy, achieving an impressive 99.76%. Precision, and recall metrics further highlight the model's effectiveness, with values of 0.99, and 1.0. These outcomes demonstrate the potential of combining unsupervised and supervised learning methods to create robust NIDS. In conclusion, integrating K-means clustering and Random Forest offers a promising solution to the limitations of traditional intrusion detection methods. Future research should focus on optimizing computational efficiency, automating parameter tuning, and exploring real-time implementation to maximize the benefits of ML in enhancing network security.

Assessment of Tensile Strength, Hardness and Impact Toughness of API 5L Grade X65 Carbon Steel Pipe Welded Joints at the HAZ using TIG and SMAW Techniques

2025-01-10T14:50:31+02:00

This study examines the mechanical properties namely tensile strength, hardness and impact toughness of API 5L grade X65 carbon steel pipe at the Heat Affected Zone (HAZ) using Tungsten Inert Gas (TIG) and Shielded Metal Arc Welding (SMAW) processes. The welding parameters considered in this study were Voltage (V), Current (A) and Travel/Welding Speed (cm/min). The results obtained show that TIG welded joints at the HAZ had an optimum value for tensile strength, hardness and impact toughness of 5.879x10⁵kN/m², BHN 198.6 and 160 J/m²_,respectively. In contrast, the optimum for SMAW welded joints at the HAZ shows the tensile strength, hardness and impact toughness of 5.2235x10⁵kN/m², BHN 196.7 and 134 J/m²_,respectively. Equally too, Taguchi analysis ranked travel/welding speed, current and voltage as first, second and third, respectively, to be considered as significant to achieve optimum value at the HAZ for UTS when using TIG and SMAW techniques whereas, for hardness and impact toughness, consideration could be given to voltage first, closely followed by current and travel speed, respectively. Hence, the welding parameters considered in this study could influence the properties of API 5L grade X65 carbon steel pipe at the heat-affected zone.

Agro-Morphological Performance of Banana and Plantain Cultivars (Musa spp.) Collected and Cultivated in the Open Field, Maniema Province, DR Congo

2025-01-21T18:13:49+02:00

This study aimed to evaluate the agro-morphological performance of banana and plantain (Musa spp.) cultivars collected in 7 territories of the Maniema Province and cultivated in the open field, Democratic Republic of Congo. A single factorial design of elongated plots with 11 repetitions was set up. Eleven cultivars were used: Tala lola (Mbudi: AAB), Leese (Mukokoonde: AAB), Nguku (Kangindi: AAB), Kamboloso (Kakumbi: AAB), Litete (Bondjilo: AAB), Lokusu (Mbole: AAB), Losakala (Mudioko: AAB), Libanga likale (Kileka: AAB), Andula (Akondo: AAB) for plantains, Kamaramasenge (Kamera: AAB) and Yangambi Km 5 (IBOTA: AAA) for bananas. The field was 40 m x 40 m, the spacings chosen were 3 m x 3 m with a density of 121 feet. Thus, the results obtained showed a strong agro-morphological variability due to genetic differences and the adaptability of banana and plantain cultivars in relation to the influence of biotic and abiotic environmental factors. The growth parameters (height of the pseudo-stem, diameter at the collar, leaf surface, day at flowering and the number of suckers emitted per plant) and production (number of hands per bunch, number of fingers per hand, weight of a finger, bunch weight and yield) contribute very significantly to determining the agro morphological performance of banana and plantain cultivars. The best yields were obtained with the Yangambi Km 5 cultivar (43,7t/ha) for bananas and the Tala lola cultivar (30 t/ha), followed by the Losakala cultivar 23,7t/ha for plantains. Thus, for large-scale production for profit, we recommend using the Yangambi Km 5 cultivar for bananas and the Tala lola and Losakala cultivars for plantains.

Tolerance of Banana and Plantain Cultivars (Musa spp.) against the Epidemiology of BBTV, Grown in Open Fields, Maniema, DR Congo

2025-01-21T18:15:59+02:00

The objective of the study was to evaluate the expression of banana and plantain (Musa spp.) cultivars grown in the open field in the face of the epidemiology of BBTV, Maniema, DR Congo. Specifically, to determine which banana and plantain (Musa spp.) cultivars are resistant, tolerant and sensitive to the epidemiology of BBTV. To achieve the objective, methods relating to the latter were used. A single factorial design of elongated plots with 11 repetitions and 11 cultivars of banana and plantains was set up. The spacings chosen were 3 m x 3 m, i.e. a density of 121 feet in a field of 40 m x 40 m. Thus, the results obtained showed that from the TAS ELISA in the presence of the vector (Pentalonia nigronervosa) the plants of banana and plantain cultivars with or without symptoms of BBTD are sometimes carriers of the banana bunchy top virus (i.e. 24.28%). There is a strong correlation between plant-vector-disease-production, that is to say; plants carrying aphid colonies are likely to be more infected by BBTV and cause significant yield losses. The correlation between production, incidence and severity makes it possible to uncover the resistance and tolerance of cultivars to BBTV. The cultivars Kamboloso, Lokoka, Yangambi Km 5 and Tala lola having achieved excellent yields are said to be resistant to bunchy top disease, their severity in the TAS ELISA oscillated between 0-10% of the general scale for evaluating viral diseases. Furthermore, the cultivars Libanga likale, Leese, Andula, Lokusu, Losakala and Nguku are tolerant to BBTD given that they obtained good yields and their ELISA severity level evolved between 11-40% and finally, the Kamaramasenge cultivar is sensitive to BBTD because it achieved an intermediate yield and a severity level varying between 41-75% of the general scale assessment of viral diseases. This study clearly confirms that there is a positive effect of aphid bite inoculum on virus transmission in banana and plantain cultivars grown in the open field. We recommend that genetic improvement studies be carried out by other researchers to explore the resistance and tolerance genes found in the cultivars Kamboloso, Lokoka, Yangambi Km 5, Tala lola, Libanga likale, Leese, Andula, Lokusu, Losakala and Nguku for better cultural control of BBTV and an improvement in the productivity of bananas and plantains in the sub-region of Africa to the east of the RD Congo in particular and throughout the world in general.

Identification of Rice Insect Pests (Oryza sativa) Grown in Kindu: Case of the Lwama I Site

2025-01-21T18:17:55+02:00

The experiment was carried out in the DRC, Maniema Province, City of Kindu precisely in the Lwama I university site. The main objective of this study was to identify the insect pests of rice (Oryza sativa) grown in Kindu. Indeed, in a specific way, we have set the following objectives: to determine the types of insects that attack rice grown in Kindu, and to observe the damage caused by insect pests to the organs of rice grown in Kindu. To achieve these objectives and test our hypotheses, we used the experimental method with a randomized complete block design with 4 treatments and 4 replicates.

At the end of this study, the following results were obtained: (1) The order of orthoptera 29.4% and beetle with 24% were important; (2) 17 species of insect pests have been identified and described; (3) 4 species showed the high number of insects inventoried (Zonocerus variegantus with 38 insects, Orseola oryzivora with 34 insects, Diploxis falaxis with 31 insects and forficula auricularia 28 insects); (4) 3 species caused serious nuisance during the vegetative phase (Coccinella septempunctata with 60%, Cnapholocrosis medinalis and Shistocera gregaria attacked at 55%); (5) 3 species were important for the nuisance in the production phase (Forficula auricularia caused damage at 80% attack, Diaperasticus Erytrocephalus at 70% and Leptoglosus membranaceus at 56.1%).

In view of these results, the production of rainfed rice cultivation in Kindu reinforces a problem related to attacks, especially since each species causes losses on the different organs and on each phase of the plant's development. Losses caused by insects that cut the stubble and destroy the panicles are more formidable.

Comparative Study on the Production of Upland Rice (Oryza sativa) Grown in Kindu Varieties: KISASA, ONU, MOMBE and DIMENA, Lwama I Site

2025-01-21T18:19:49+02:00

The experiment was carried out in the DRC in the province of Maniema, city of Kindu, more precisely in the university site Lwama I. The general objective of this study was to conduct a comparative study on the production of upland rice (Oryza sativa) grown in Kindu in the case of the varieties: KISASA, ONU, MOMBE and DIMENA, Lwama I site. Indeed, it had the following specific objectives: to determine the growth-related behaviors of each variety studied, and to compare the production level of the varieties studied.

In the course of this study, the hypotheses were put forward: (a) The varieties of rainfed rice farmed in Kindu are said to have different behaviors related to growth, and (b) There are reportedly productive varieties of upland rice in the city of Kindu. To achieve this, we conducted a field experiment with a randomized complete block device with 4 treatments and 4 replicates.

The following results were obtained: (1) For the emergence rate, the ONU variety was the best with 79.33% followed by the DIMENA variety with 60.45%; (2) For tillering, the UN variety 36 tillers followed by the MOMBE variety with 14 tillers; (3) For the diameter at the collar, the ONU variety was the best with 3.40cm followed by the MOMBE variety with 2.69cm; (4) For the height of the plants, the variety DIMENA obtained the high height with 74.27cm followed by the variety MOMBE with 51.55cm; (5) For the number of grains per panicle, KISASA was the best with 209.91 followed by the DIMENA variety with 199.41; (6) For grain weight per plot, the MOMBE variety was better with 1.93. The highest yield was observed in T1 (DIMENA) with 835.64 Kg/ha and the lowest yield is 678.12 Kg/ha observed in T4 (KISASA). The average is 721.8 Kg/h. Statistically, no variety was superior to the other in terms of yield.

At the end of this study, we say that our hypotheses have been verified and our objectives have been achieved. To do this, we suggest the following: (1) It is up to the growers to use the DIMENA variety which was better in this study followed by the KISASA variety whose yield was satisfactory; (2) It is up to the researchers to deepen this study in order to address the other aspects that have not been covered.

Morphological Characterization of Cassava Accessions (Maniohot spp) Collected in Maniema (RDC)

2025-01-21T18:23:32+02:00

The objective of this work was to differentiate the cultivars of cassava produced in the Province of Maniema on the basis of descriptors selected among the descriptors of cassava. The fifteen cassava cultivars collected were evaluated in a field plant system. The descriptive analysis showed phenotypic differences in certain characters such as: color of the apical leaf, shape of the apical leaf, height, diameter at the collar. Ascending hierarchical classification (CHA) made it possible to structure these cultivars into 3 groups of agromorphological diversity. In conclusion, the cassava cultivars grown in the province of Maniema have a great diversity of forms which can constitute a good basis of selection for cassava producers in the Province in particular and in the Democratic Republic of Congo (DRC) in general.