International Journal of Environment, Engineering and Education
Not a member yet
103 research outputs found
Sort by
Compact Bi-slot Patch Antenna with Tapered Edges for Ka-Band Applications Featuring Machine Learning-Assisted Performance Prediction
Full text linkMicrostrip patch antennas are vital for Ka-band communication owing to their compact size and high performance. This study introduces a modified patch design at 28 GHz featuring two corner truncations and dual-slot integration to enhance impedance matching and broaden the operational bandwidth. The objective of this work is to investigate whether geometrical modifications combined with intelligent modelling can yield improved performance metrics while accelerating the performance evaluation phase through a data-driven surrogate model. The proposed antenna was developed through parametric optimization in Ansys HFSS, in which its structure was systematically varied to achieve stable resonance and improved radiation performance. The optimized prototype achieves a simulated return loss of −67.11 dB, a bandwidth of 3.8 GHz, a VSWR of 1.0009, a peak gain of 7.65 dB, and an input impedance of 50.01 Ω, all indicating strong simulated electromagnetic performance. The design demonstrates a deep resonance corresponding to a high quality (Q) factor, making it a suitable candidate for applications where precise frequency selectivity is paramount. To accelerate evaluation, a machine learning framework was integrated, using 65,682 simulated samples to train regression models for predicting return loss. Among the tested algorithms, the Random Forest Regressor demonstrated the highest accuracy with a mean absolute error of 0.0471 dB and an R² of 0.9995. The integration of electromagnetic simulation and ML-assisted performance prediction demonstrates a reliable pathway for rapid evaluation of Ka-band antennas, offering strong potential for next-generation satellite and wireless communication systems
Mapping the Intellectual Core of Technology Adoption in Digital Startups: A Bibliometric Analysis via Bibliographic Coupling and Co‑Word Networks
Full text linkDigital startups are reshaping markets through the use of AI, cloud computing, and blockchain; however, scholarship on how these firms adopt technology remains fragmented. This study systematically maps the intellectual structure and thematic fronts of research on technology adoption in digital startups. A field-tagged Scopus search conducted in September 2025 (coverage 2000–2025) was cleaned and harmonized using a VOSviewer. After de-duplication, 2,243 documents were analyzed via bibliographic coupling (knowledge structure) and co-word analysis (thematic). Four coherent clusters emerge. Strategic innovation and leadership function as the governance backbone that shapes adoption decisions and risk appetite. Sustainable, data-driven business models translate adoption into performance outcomes through analytics capability and value capture. Corporate entrepreneurship within innovation ecosystems bridges firm-level capability with external partners, investors, and accelerators, linking adoption speed to ecosystem embeddedness. Digital business transformation operationalizes AI/cloud investments into processes and customer journeys. Cross-cutting co-word foci, such as perceived usefulness/user experience and organizational readiness, act as mechanisms connecting individual cognition with organizational capability. Emergent topics in policy, regulation, and platform governance appear as boundary conditions that enable or constrain adoption trajectories. The mapping provides an integrative lens organized along two axes: cognitive evaluation and organizational capability that jointly explain adoption in digital startups. It identifies gaps in external enablers and capability maturation paths. A forward-looking agenda is proposed, featuring multi-level models that link cognition, capability, and growth, as well as quasi-experimental evaluations of interface simplification and onboarding, cross-country comparisons of regulatory regimes, and longitudinal tracking of platform transitions
AI-Powered Approaches for Sustainable Environmental Education in the Digital Age: A Study of Chongqing International Kindergarten
Full text linkThe integration of technology into education has garnered significant interest, particularly in its potential to support environmental sustainability. This exploratory research investigates the role of artificial intelligence (AI) in early childhood education, with a focus on its implementation at Chongqing International Kindergarten to teach sustainability concepts. Guided by Mezirow’s Theory of Transformative Learning and Goleman’s concept of Ecological Intelligence, the study explores how AI-powered tools—including virtual ecosystems, real-time feedback systems, and eco-conscious behavior tracking mechanisms—enhance critical reflection, foster ecological intelligence, and promote environmentally responsible behaviors among young learners. A qualitative case study approach was employed, incorporating classroom observations, educator interviews, and pre-and post-assessments to evaluate engagement, environmental awareness, and behavioral changes. The findings reveal that AI tools significantly enhance environmental literacy, helping children understand the consequences of their actions and encouraging them to adopt sustainable practices. Interactive and personalized learning experiences AI provides stimulate critical thinking, transform sustainability-related values, and foster a deeper understanding of ecological interconnections. Students demonstrated improved awareness of sustainability concepts, such as resource conservation and biodiversity, alongside increased engagement in eco-friendly behaviors, including recycling and energy conservation. This research highlights the transformative potential of AI in early education, demonstrating its capacity to influence children's attitudes and behaviors toward environmental responsibility. Integrating AI-driven educational tools into sustainability curricula is crucial for cultivating a generation capable of making informed environmental decisions. The study concludes by recommending further exploration of AI's role in early education to optimize its impact on fostering long-term ecological intelligence and transformative learning
The Impact of Environmental Hazards on the Academic Performance of Public Secondary School Students
Full text linkEnvironmental factors significantly influence students' academic performance, with hazardous conditions posing notable impediments to educational outcomes. This study investigated the impact of environmental hazards—air pollution, noise pollution, and flooding—on the academic performance of students in public secondary schools in the South-South region of Nigeria. Three objectives and corresponding hypotheses guided the research. Employing a correlational survey design, the study targeted all principals of public secondary schools in the region. A purposive sampling technique was used to select a sample of 240 principals representing six states in South-South Nigeria. Data were collected using two self-designed instruments: the Environmental Hazards Questionnaire (EAQ) and the Academic Performance Questionnaire (APQ). Reliability testing of the instruments yielded coefficients of 0.91 and 0.88, respectively, indicating high reliability. Data analysis was conducted using simple regression to address the research questions, while hypotheses were tested using the t-test associated with simple regression at a 0.05 significance level. The results revealed that air pollution, noise pollution, and flooding have a significant and negative impact on students' academic performance. Specifically, air pollution contributes to health issues that disrupt students’ focus and attendance, while noise and flooding interfere with the learning environment and academic activities. The study concluded that mitigating these environmental hazards is essential for enhancing academic performance in public secondary schools. Recommendations include implementing policies to minimize air and noise pollution around school environments and adopting effective flood management strategies to safeguard academic infrastructure and activities
A Multidimensional Model of Pre-Writing Competence in Special Education Students: Validation Using Confirmatory Factor Analysis (CFA) Approach
Full text linkChildren with cerebral palsy (CP) face distinct challenges in pre-writing acquisition, yet existing assessment tools often prioritize motor deficits while overlooking environmental barriers. This study aimed to validate a multidimensional measurement model of pre-writing challenges and supports for students with CP in Indonesian special schools. Grounded in the ICF-CY, HAAT, and Universal Design for Learning (UDL) frameworks, the model comprised four latent constructs: motor impairment to writing (MIW), pre-writing activity difficulties (PAD), inclusive learning environment (ILE), and adaptive pre-writing media (APM). A total of 108 students with CP in the pre-writing stage were rated by their teachers using a 5-point Likert questionnaire. Confirmatory Factor Analysis (CFA) was conducted with IBM SPSS AMOS to examine model fit, factor loadings, and reliability. After removing poorly performing indicators, the final model with four factors and 19 indicators demonstrated excellent fit (Chi Square= 1.01, p = 0.43, CFI = 0.998, TLI = 0.997, RMSEA = 0.007, PNFI = 0.79). All standardized factor loadings ranged from 0.59 to 0.94 (p < 0.001), and Cronbach’s alpha values for the four constructs were between 0.87 and 0.89, indicating high internal consistency. Moderate to strong inter-factor correlations were found, particularly between PAD and ILE, highlighting the close interplay among functional limitations, activity demands, and classroom conditions. The validated instrument offers a concise framework for identifying pre-writing challenges and environmental supports in students with CP. It can inform individualized intervention planning, the design of inclusive learning environments, and the development of adaptive pre-writing media in resource-constrained settings
A Two-Dimensional Numerical Study of Evaporation by Mixed Convection of an Inclined Damp Flat Plate: A Lean Engineering Approach Using DMADV Methodology
Full text linkThis paper presents a two-dimensional numerical study of the evaporation by mixed convection of an inclined damp flat plate subjected to a constant heat flux density. Airflow, heat, and mass transfers are governed by the equations of continuity, motion, energy, and diffusion, to which boundary layer approximations are applied. Adimensionalization, implicit finite difference method, and programming on MATLAB are used to solve the equations. The methodology is designed using the FAST (Function Analysis System Technique) method and reinforced with DWADV (Define, Measure, Analyze, Design, Verify) by applying Lean Engineering and Six Sigma. The approximation in the boundary layer makes it possible to reduce the number of terms in the equations of the problem. Adimensionalization links the parameters together and reduces their number. The quantities studied no longer depend on the measurement system. Comparison with other studies allowed us to validate our results. The work ends with presenting results about the influence of the Richardson number and the flat’s inclination on non-dimensional velocity, non-dimensional temperature, non-dimensional concentration, and coefficients of exchange associated with mixed convection: friction coefficient, Nusselt and Sherwood number. The increase in the value of the Richardson number generates the opposite effect of the increase in the inclination of the plate on the parameters of mixed convection and the exchange coefficients
Ethno-Pedagogical Module: A Theoretical Exploration of Knowledge Transmission in Ethnobiological Systems
Full text linkThe vertical transmission of ethnobiological knowledge across generations is crucial in preserving biodiversity and sustaining biocultural diversity. However, the mechanisms underpinning this process remain insufficiently studied. This paper introduces the "ethno-pedagogical module" as a novel interdisciplinary framework for analyzing and enhancing the understanding of traditional ecological knowledge (TEK) transmission. The module integrates perspectives from cultural anthropology, education theory, and ethnobiology. Cultural anthropology contributes insights into the role of rituals, oral traditions, and communal practices in knowledge dissemination. Education theory offers methodologies for understanding experiential and participatory learning processes essential to TEK. Ethnobiology provides the foundation for examining the practical and symbolic human-environment relationships embedded in traditional practices. This framework facilitates the disaggregation of TEK systems and enables cross-cultural comparisons, revealing how diverse communities sustain biocultural diversity through unique adaptive practices and learning processes. It also offers practical applications by guiding the integration of TEK into formal and informal education systems to foster environmental stewardship and cultural resilience among younger generations. By addressing the complexities of TEK transmission, the ethno-pedagogical module presents a structured approach for preserving traditional knowledge amidst global environmental and cultural changes. The module bridges traditional ecological practices with contemporary educational strategies, promoting resilience and innovation in sustaining biocultural diversity. This interdisciplinary approach underscores the importance of collaboration in safeguarding cultural heritage and biodiversity in an increasingly interconnected and rapidly changing world
Upcycling C&D Waste via Mechanical Abrasion: Balancing Aggregate Quality Enhancement against Process-Induced Damage
Full text linkThe construction industry is a significant consumer of natural aggregates and a major contributor to carbon emissions. Recycled Concrete Aggregates (RCA) derived from Construction and Demolition (C&D) waste offer a promising sustainable alternative. This study examines how mechanical abrasion affects RCA processed in a Los Angeles (LA) abrasion drum with revolutions ranging from 100 to 1000 to find an optimal treatment window that maximizes quality without causing aggregate damage. The results indicate that coarse RCA processed at 500–600 revolutions significantly improved specific gravity (~2.55 from ~2.3) and reduced water absorption (~2.0% from ~4-5%), meeting the standards for natural aggregates. This treatment effectively removed fine mortar particles and improved durability (soundness loss ~15%), surpassing untreated RCA, which exhibited soundness losses >30%. However, excessive abrasion beyond ~700 revolutions led to an increase in fines and micro-cracking, resulting in a soundness loss exceeding 23%, failing durability criteria. The optimal abrasion range (~500 revolutions) resulted in a coarse aggregate yield of about 50%, compared to only 27% at 1000 revolutions. The study shows that on-site processing of C&D waste at this optimal level produces high-value aggregates for structural concrete, supporting the circular economy by reducing dependence on virgin aggregates and diverting waste from landfills. Cost analysis indicates that moderate abrasion (~500 revolutions) maximizes net material value while minimizing energy use and dust production. These results emphasize the viability of mechanical abrasion as a sustainable upcycling method for RCA, balancing quality improvement with process-related damage
A Comparative Analysis of Python Text Matching Libraries: A Multilingual Evaluation of Capabilities, Performance and Resource Utilization
Full text linkPython text-matching libraries have become essential tools in data cleaning and natural language processing; however, researchers have not thoroughly examined their performance, accuracy, and resource efficiency across multilingual scenarios. This study evaluates five major libraries—FuzzyWuzzy, RapidFuzz, Difflib, Levenshtein, and Jellyfish—using a dataset of 50,000 test cases in English, Spanish, French, German, and Italian. We introduce controlled variations in text complexity, error types, and string lengths to measure processing speed, matching accuracy, and resource consumption. The experimental results reveal significant performance differences among the libraries. RapidFuzz processes text 40% faster than others while maintaining efficient memory usage. However, its performance varies depending on language and error type. Levenshtein achieves higher accuracy when handling non-Latin characters, while FuzzyWuzzy consistently performs well across different text lengths. Difflib, despite its built-in availability, runs slower and consumes more resources. Jellyfish specializes in phonetic matching but struggles with long text inputs. Memory usage fluctuates between 20 and 200 Megabytes for identical workloads, revealing substantial efficiency differences. These findings enable developers to select the most suitable library based on their specific needs and computational constraints. Our study introduces a standardized evaluation framework and a multilingual benchmarking dataset, enabling researchers to compare text-matching methods more effectively. By identifying key performance trade-offs, we provide a practical guide for optimizing text-matching efficiency in real-world applications. This research contributes to the broader field of natural language processing by offering data-driven insights and a structured methodology for evaluating text similarity techniques
Desulfurization of Zawia Refinery Diesel Using Adsorption Fixed Bed Process
Full text linkThe study focuses on the dynamic modeling of a fixed-bed adsorber for the adsorption of sulfur compounds in diesel fuel. The model considers non-ideal plug flow behavior and velocity variation along the column, providing a more realistic representation of the adsorption process. Additionally, internal mass-transfer resistances due to pore diffusion mechanisms are incorporated into the model. The study investigates adsorption performance by examining different flow rates (5 cc/min, 10 cc/min, 15 cc/min, and 20 cc/min) and inlet concentrations ranging from 586 to 100 ppm. The bed height is constant at 30 cm. The behavior of various parameters, such as bed utilization, breakpoint time, film mass transfer coefficient, and height of the adsorption zone, is analyzed. The results indicate that a sharp front of the breakthrough curve is observed, followed by the broadening of the tail of the breakthrough curve. The breakthrough curve represents the adsorbate concentration in the effluent stream over time. The investigation reveals that a high flow rate of 20 cc/min and a high inlet concentration yield better overall bed capacity utilization for the adsorption system. This means that the bed is more effectively utilized at higher flow rates and higher inlet concentrations, leading to improved adsorption performance. In conclusion, high flow rates and high inlet concentrations are favorable for enhancing the adsorption system's performance in terms of bed utilization. These results provide valuable insights for optimizing the design and operation of fixed-bed adsorbers that remove sulfur compounds from diesel fuel