Engineering

Abstract: This study assessed the current status of irrigation systems and water management practices in Rwanda’s irrigated agricultural zones focusing on Nasho Government Funded Irrigation (GFI) scheme in Kirehe District and Kagitumba Irrigation Scheme in Nyagatare District. A mixed descriptive approach was applied combining field observation with structured questionnaires administered through Kobo Toolbox to 224 respondents in Nasho and 188 respondents in Kagitumba. Field observations were used to evaluate the physical condition and functionality of irrigation infrastructure while questionnaires captured stakeholder perceptions, water management practices, institutional arrangements and operational challenges. Results show that both irrigation schemes are operational but function below optimal efficiency due to multiple constraints. In Nasho, irrigation performance is mainly affected by sedimentation in canals and reservoirs, pump inefficiencies and inadequate maintenance practices leading to unreliable water delivery. In Kagitumba, despite the use of modern center pivot systems performance is constrained by pipeline corrosion, pressure losses, sediment-laden water and uneven water distribution. Across both schemes, more than 80% of respondents reported frequent system failures while over 95% indicated the absence of formal irrigation scheduling practices. Water management remains largely reactive with limited preventive maintenance and weak technical capacity among users and institutions. The study concludes that improving irrigation efficiency in Rwanda requires integrated interventions combining infrastructure rehabilitation, strengthened maintenance systems, improved water governance and farmer capacity development to enhance sustainable water use and agricultural productivity.

Abstract: The supercritical CO₂ extraction of essential oils from Origanum vulgare L., Matricaria chamomilla L., and Moringa oleifera Lam. was kinetically interpreted using a logistic mass transfer approach under different combinations of pressure and temperature. Extractions were performed in a fixed-bed SFE system operated for 210 min using high-purity CO₂ under pressures ranging from 100 to 500 bar and temperatures between 30 and 60 °C, depending on the vegetable matrix. The logistic model was parameterized through the total extractable mass (m_t), the characteristic time associated with the maximum extraction rate (t_m), and the kinetic slope parameter b. The highest extraction yields were obtained at 300 bar and 45 °C for oregano (2.807 g), 100 bar and 40 °C for chamomile (5.006 g), and 500 bar and 60 °C for moringa (5.433 g). Simultaneously, increasing pressure and temperature systematically reduced, decreasing from 16.737 to 8.75 min in oregano and from 15.01 to 9.73 min in moringa, indicating an intensification of convective-diffusional transport mechanisms. The model adequately reproduced the experimental extraction curves, particularly in Oregon, where SSD values remained below 0.03 under all evaluated conditions. Unlike highly parameterized phenomenological approaches, the proposed logistic formulation represented the extraction dynamics using kinetically interpretable parameters without requiring experimentally inaccessible internal coefficients. The results demonstrate that logistic modeling constitutes a mathematically simplified but kinetically robust alternative for the comparative analysis and preliminary optimization of supercritical extraction systems applied to aromatic and medicinal plant matrices.

Abstract: Alzheimer's disease (AD), a leading cause of dementia worldwide, is a neurological disorder characterized by progressive cognitive decline. AD is also considered a significant socioeconomic burden. While definitive diagnostic tools such as positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) biomarker analysis offer high sensitivity and specificity, they are limited by high cost, invasiveness, and limited accessibility. Consequently, these gold standard approaches hinder their applicability for large-scale screening and longitudinal follow-up. Recent advances in blood-based biomarkers hold promise in capturing systemic molecular changes associated with AD. In particular, transcriptomic signatures derived from RNA sequencing (RNA-seq) are promising in capturing systemic molecular changes associated with AD. Gene expression profiles in peripheral blood reveal underlying pathological processes. These pathological processes can be listed as synaptic dysfunction, neuroinflammation, and metabolic dysregulation. Together with the high-dimensional datasets and AI approaches enable the identification of robust predictive models which has the assistance of estimating AD-related biomarker status. We further discussed the integration of multiple omics data, including genomics, proteomics, and metabolomics to improve biomarker robustness. We also addressed key challenges related to reproducibility, repeatibility, cohort heterogeneity, and clinical application. And we outline future directions of standardized, scalable, and clinically applicable diagnostic machineries.

Abstract: The integration of generative artificial intelligence (AI) into agricultural extension services presents a transforma- tive opportunity to address the unique challenges faced by smallholder farmers, particularly in resource-constrainedsettings. While traditional extension services often struggle with scalability and personalized support, generative AI offers potential solutions through dynamic content generation, real-time decision-making assistance, and adaptive learning tools. This systematic literature review examines the efficacy of generative AI in enhancing agricultural extension services, focusing on its applications, benefits, and limitations for smallholder farmers. We synthesize existing research across multiple dimensions, including AI-driven farmer support, IoT-enabled monitoring, andclimate-smart agriculture, to identify gaps and trends in the current knowledge landscape. A rigorous methodol- ogy was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of both theoreticalframeworks and practical implementations. The findings reveal that generative AI can significantly improve access to tailored agricultural advice, optimize resource allocation, and mitigate climate-related risks; however, challengessuch as digital literacy, infrastructure limitations, and ethical concerns remain critical barriers to widespread adop- tion. The review also highlights the disproportionate focus on high-income regions, underscoring the need for moreinclusive research in low-resource agricultural systems. By consolidating these insights, we provide actionable rec- ommendations for policymakers, researchers, and practitioners to harness generative AI’s potential while addressingits socio-technical constraints, thereby fostering equitable and sustainable agricultural development.

Abstract: The rapid advancement of generative large language models (LLMs) has sparked significant interest in their poten- tial to transform higher education, particularly in fostering student engagement. While these models offer novelopportunities for personalized learning and interactive experiences, their integration into academic settings remains underexplored, with varying implications for pedagogy, ethics, and institutional policy. This systematic literaturereview examines the role of generative LLMs in enhancing student engagement across multiple dimensions, includ- ing their impact on learning outcomes, academic writing, subject-specific applications, and ethical considerations.We synthesize existing research to identify key trends, challenges, and gaps in the current understanding of how these technologies are reshaping educational practices. A rigorous methodological approach was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of the field. The findings reveal that generative LLMs can significantly influence student engagement by facilitating adaptive learning environments and supporting creative problem-solving; however, concerns about academic integrity, equitable access, and pedagogical alignment persist. The review also highlights emerging tools and systems designed to integrate LLMs into education, alongside institutional and student perspectives on adoption. Based on the synthesized evidence, we discuss future directions for research and policy, emphasizing the need for balanced frameworks that harness the benefits of generative AI while addressing its risks A. Chowdhury et al., 2025. This study contributes a structured overview of the current landscape, offering insights for educators, researchers, and policymakers navigating the evolving intersection of AI and higher education.

Abstract: The experimental determination of the relationships between the stress distribution zone in the soil layer and the parameters of tillage working bodies is a labor-intensive process. Therefore, preliminary mathematical modeling of this process is recommended to mi-nimize the total number of experiments. The research was conducted using the principles of classical mechanics and soil mechanics. Using an equation proposed by J. Boussinesq,a graphical-analytical method was developed to evaluate the stress state in the soil layer induced by a dihedral wedge. This method incorporates both the geometric parameters of the dihedral wedge and the physico-mechanical properties of the soil. A direct pro-portional relationshipwas established between the length of the dihedral wedge and the total area of the deformed soil mass. Specifically, increasing the length of the dihedral wedge by 83% (from 0.05 to 0.30 m) resulted in an 80% increase in the area of the de-formed soil mass (from 0.02 to 0.10 m²). The proposed graphical–analytical method can be employed in the design of tillage implements.

Abstract: Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (Ca) separately from JDLink telematics, aligning model structure with each target's response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R²CV = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; Ca was retained for MLR (R²CV = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF's univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure.

Abstract: Emerging materials often face challenges in market adoption due to limited comparability and reliability of measurement-based material information, despite their potential to drive technological innovation. While standardization is widely recognized as an important mechanism for market diffusion, existing approaches provide limited insight into how material specifications facilitate the comparative evaluation of material characteristics and their use in market decision-making. This study proposes a complementary perspective that interprets standardization as an infrastructure for organizing the generation, sharing, and evaluation of measurement-based material information across industry, standard development organizations (SDOs), and markets. Within this framework, the study distinguishes between two complementary types of standards for material specifications. Type A standards enable the structured disclosure of measured characteristic values and associated measurement uncertainties, allowing application-specific evaluation without predefined acceptance criteria. In contrast, Type B standards define predefined characteristic values and compliance criteria, providing a basis for conformity assessment, certification, and quality assurance. These two types may be understood as complementary mechanisms that fulfill different functions of comparability and compliance under varying technological and market conditions in emerging material systems. Consequently, they contribute to both innovation-oriented market evaluation and quality-assured market acceptance.

Abstract: Double-sided solar greenhouses are recognized as energy-efficient agricultural facilities that significantly enhance land utilization and thermal performance through their unique double-sided lighting design, thereby promoting crop growth. However, challenges persist regarding insufficient heat storage capacity and suboptimal thermal environments within the shaded shed during the winter and spring seasons. To fully exploit the advantages of this greenhouse type, this study proposes a structural optimization methodology utilizing Computational Fluid Dynamics simulation. A CFD model was developed and validated against experimental data to ensure accuracy. Subsequently, the influence of key parameters, including roof geometry and wall thickness, on the internal photothermal environment was systematically analyzed. The results demonstrate that the 370 mm thick wall configuration achieves a daily peak temperature approximately 2°C lower than the 240 mm wall, indicating a more uniform spatial distribution, while exhibiting a nighttime temperature increase of up to 2.5°C, thereby confirming superior thermal insulation properties. Furthermore, the presence of a rear roof structure is critical for nighttime heat retention, maintaining a minimum temperature of approximately 5°C compared to 2°C in greenhouses lacking this feature, with a maximum temperature difference of 4.2°C, effectively optimizing temperature uniformity. Based on these findings, this research provides a robust theoretical foundation and technical support for the structural optimization of double-sided solar greenhouses and the advancement of facility agriculture.

Abstract: Events such as landslides and slope failures happen suddenly and can be catastrophic. To predict the onset of such events, as well as the flow and final deposition of the material, engineers make use of numerical modeling techniques. These events are associated with large deformation and mesh-based methods, such as the finite element method, are not capable of modeling them due to mesh distortion. The material point method (MPM) is a particle-based continuum method capable of modeling large deformation and material flow. In this paper, MPM is used to model the sudden and dynamic flow of material by modeling the collapse and runout of a non-cohesive sand column. The results from two- and three-dimensional models are compared to experiments, showing that MPM accurately predicts the free-surface profile of the material during collapse. Furthermore, the model accurately predicts the runout distance with an error of less than 5%.

Abstract: The current work uses Iterated Fission Probability (IFP) routine that was recently implemented in OpenMC to calculate reactor kinetics parameters. IFP is calculated from the product of the multiplication factors tracked across the L+1 generations of fission progenies. Since IFP is an excellent estimator of adjoint flux, it is able to calculate Λ_eff , β_eff and β_i of the reactor. OpenMC calculation of the reactor itself has k_eff = 1.01687 with an effective mean neutron generation time, Λ_eff = 44.82 μs. The effective delayed neutron fraction, β_eff that we get is 0.007235 or 723.5 pcm. Other calculations of β_eff using prompt methods for reactors with similar designs gave us values between 724 pcm to 752 pcm. Our own calculations using the prompt method in OpenMC gave us an effective delayed neutron fraction of 734.1 pcm. The group β_i that we obtain is 24 pcm, 131.4 pcm,124.1 pcm, 284.0 pcm, 112.7 pcm and 47.4 pcm respectively. If we strip away the influences of β_eff on β_i , by looking at only the abundances of each delayed neutron group, a_i ; we are able to see that the a_i is similar to the abundances of just ²³⁵U in the six group abundances of ENDF/B-VIII.0 evaluated cross section library. When we adopt a different evaluated cross section library in OpenMC, changes in β_i is due to the different β_eff and λ_i adopted in these libraries.

Abstract: This paper presents the complete design and development of a dried whole blood cartridge designed for point-of-care (POC) clinical diagnostics. The system integrates a near-infrared (NIR) spectroscopy sensor with a disposable multilayer paper cartridge capable of collecting and analyzing small, controlled volumes of capillary blood (20 μL). The work emphasizes a technical and iterative design approach that combines product design with both additive and subtractive prototyping, supported by experimental validation. The development process involved multiple design iterations focusing on fluid transport, capillary dynamics, usability, and optical integration. Several materials and manufacturing processes, such as CNC (Computer Numerical Control) machining and Material Jetting (MJ), were explored to optimize channel geometry and flow behavior. Experimental results guided successive refinements, leading to a cartridge configuration that ensures efficient capillary action, minimal coagulation, and consistent optical alignment with the sensor’s analysis zone. The study underscores the importance of an integrated engineering approach that unites design methodology, material selection, and manufacturing processes to achieve a reliable and reproducible cartridge for point-of-care blood diagnostics. It demonstrates how iterative design, supported by experimenal testing, can effectively bridge the gap between experimental prototyping and practical implementation in medical device development.

Abstract: This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices (imageability, enclosure, human scale, greenness, and walking index) were quantified to assess their influence on pedestrian experience. Street width was explicitly incorporated as a morphological variable to examine its relationship with perceptual qualities using spearman correlation analysis and visual trend analysis using Pearson correlation. The results reveal consistent relationships between visual composition and perceptual outcomes, particularly strong associations between imageability, enclosure, and vegetation structure, as well as trade-offs between enclosure and sky visibility. In contrast, variables such as human scale and walking index show weak or negligible associations with street width, suggesting that pedestrian presence and activity patterns in heritage contexts are more strongly influenced by landscape elements, water features, and spatial continuity than by dimensional factors alone. Findings highlight how urban renewal strategies, such as streetscape enhancement and cultural preservation, shape pedestrian movement and spatial perception. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a robust alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasizing morphological continuity, human-scale design, and green infrastructure as critical determinants of walkability. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which affect pedestrian comfort and use of space. These findings support evidence-based policy and design strategies for optimizing historic urban streetscapes, with implications for balancing heritage conservation and modern pedestrian needs. Future research may refine perceptual metrics and extend the approach across diverse urban contexts.

Abstract: The Sultanate of Oman enjoys plenty of solar energy and wind energy; both have been exploited successfully in the country. However, geothermal energy has not been exploited yet in Oman. This natural heat source deserves more studies to assess its technical potential and economic feasibility compared to other electricity generation technologies in Oman. The current study fills this gap by presenting a techno-economic assessment (TEA) of a small 30-MW geothermal power plant in Oman, operating on a binary (two-fluid) cycle, with a drilling depth of 2 km. The analysis was performed using the renowned software tool SAM (System Advisor Model) of the United States National Renewable Energy Laboratory (NREL). The current results suggest a levelized cost of energy (LCOE) of 8.68 cents/kWh (0.0868 US$/kWh) or 33.4 baisa/kWh (0.0334 OMR/kWh). When compared with electricity tariff or solar photovoltaic (PV) power purchase agreement (PPA) rates in Oman, it was found that geothermal-based electricity is too expensive. Furthermore, the estimated geothermal LCOE is more than three times the LCOE value of self-owned photovoltaic (PV) power systems in Oman, which is around 10 baisa/kWh (0.010 OMR/kWh). The estimated first-year electricity generation for the geothermal power plant model is 261.268 GWh/year, leading to a specific electricity generation of 8,709 kWh/kW/year. This is about five times the specific power generation from PV power plants. The study is augmented by sensitivity analyses and regression models to help understand the impact of multiple input parameters. The study provides novel results regarding decision-making for geothermal power investment in Oman.

Abstract: The Automobile industry shifts from linear to circular economy for sustainability on a global level with respect to the industrial revolution 5.0, but it faces challenges when establishing circular economy. Circular supply chain implementation is dependent on multiple barriers and enablers, including economic managerial, technological, regulatory and social domains, making it ineffective for single factor solution. The purpose behind this review is to conduct a systematic literature review to develop an understanding how these interconnected barriers and enablers can together shape the circular supply chain implementation and their performance, specifically inside the automotive sector which is still remain a little known. By applying the PRISMA framework on 150 peer reviewed articles, research papers. The research shows that literature focuses on primarily on electric vehicle barriers within developing economies. circular supply chain implementation is governed not only by isolated barriers but by complex systematic interdependencies between enablers as well. This interdependencies are of enablers and barriers can be further classified into economical and financial, managerial and organizational, technological and infrastructure, policy and regularity and market and social. The study shows two systematic patterns, driving the transition technology- policy interdependence and conflicting relationship between large scale production and value extraction. The findings also presented a research agenda focusing on strategic value creation through material streams of automotive electronics, plastics and composites with high potential value and further insights are needed. Circular supply chain as a strategic approach for securing critical material supplies, while policymakers could leverage the use of digital tools as the foundational infrastructure for subsidies allocation and prevent the fraud.

Abstract: Additive manufacturing (AM), commonly known as 3D printing, has rapidly transformed modern manufacturing, creating a growing demand for engineers with both theoretical knowledge and practical skills. Despite its increasing relevance, AM is often incorporated into engineering curricula as a supplementary tool rather than a fully integrated subject, limiting students’ understanding of fundamental material–process–performance relationships. This study presents the development, implementation, and assessment of an integrated lecture–laboratory framework for AM education at the New Jersey Institute of Technology (NJIT). Two complementary courses were developed: an undergraduate course (Introduction to 3D Printing, CHE 415) and a graduate course (Additive Manufacturing and Applications, CHE 722). The curriculum integrates instruction in AM technologies, materials, and digital workflows with hands-on design challenges, team-based projects, and structured literature review, enabling students to engage in the complete design-to-fabrication process. Student learning outcomes were evaluated over multiple academic years using ABET-aligned assessments, grade distributions, and student self-assessments. Results demonstrate consistently high levels of student proficiency and engagement, with strong performance in design, problem-solving, and communication skills. The courses also attracted students from diverse disciplines, underscoring the interdisciplinary nature of AM education. While limitations remain in providing hands-on exposure to a broader range of AM technologies, ongoing expansion of laboratory infrastructure is expected to address these challenges. Overall, this work demonstrates that an integrated, project-based approach effectively bridges theory and practice and provides a scalable model for incorporating AM into engineering curricula.

Abstract: The research profession is undergoing its most profound transformation since the industrialization of R&D. As artificial intelligence automates the mechanics of technical work, professional value is migrating from execution to architecture, from knowledge recall to creative problem formulation. Operating under the materialist premise that human cognition is ultimately replicable, we argue the current ``human-in-the-loop'' paradigm is transient. The profession is increasingly moving towards Extremistan, where scalability creates winner-take-all dynamics and only transcendent outliers remain visible. Institutions face a Jevons Paradox: as intelligence becomes cheaper, demand for orchestration explodes, creating bottlenecks at the architectural layer. The central message: competence is no longer sufficient. Survival demands cultivating what machines currently lack—physics intuition, scientific taste, and the ability to formulate problems worth solving.

Abstract: This work demonstrated improvements in the photovoltaic performance metrics of a dye-sensitized solar cell (DSSC) through the application of Eu-doped strontium silicate – Sr2SiO4:Eu3+, luminescent downshifting (LDS) material. The material converted underuti-lized high energy ultraviolet (UV) photon into lower energy visible photon for better spec-tral responsivity in the DSSC. The LDS material was prepared by the conventional solid state technique. Surface morphology was examined by scanning electron microscope (SEM). Photoluminescence (PL) measurement was applied for the fluorescence emission. The photovoltaic performances of the bare and LDS enhanced devices were analyzed from the photovoltaic current – voltage measurement. Compared to the bare DSSC, the cell with Sr2SiO4:Eu3+ LDS phosphor material had an enhancement of 14.8 % in the short circuit current density (Jsc), from 0.243 – 0.279 mA/cm2. The open circuit voltage (Voc) yielded an improvement of 10 % from 580 – 638 mV. Maximum power output (Pmax) produced a boost of 26.5 % from 0.0136 – 0.0172 mW and the efficiency improvement at 26.6 % from 1.09 – 1.38 %. The coefficient of variation was introduced to evaluate device reproducibility. The device with the incorporation of Sr2SiO4:Eu3+ LDS phosphor, depicted a coefficient of variation of 8.5 %, suggesting good DSSC reproducibility consistency.

Abstract: Understanding climate-streamflow dependencies is crucial for evaluating reservoir impacts and adaptive water management. This study analyzed streamflow in two key Brazilian reservoirs, Três Marias (São Francisco Basin) and Serra da Mesa (Tocantins Basin), using monthly records from 1979 to 2020. A 12-month moving average temporal filter enhanced low-frequency climate signals to assess hydrological variability and memory. Temporal smoothing substantially clarified climate–streamflow dependencies, with correlation gains reaching 106% for PDO, 204% for ENSO, and more than 4,200% for the Antarctic Oscillation (AAO) in Três Marias. The filtered analysis revealed contrasting hydrological memory structures: Três Marias exhibited multi-year memory with maximum correlations at approximately 22–27 months, while Serra da Mesa showed faster response times of 4–12 months. To evaluate predictive implications, streamflow forecasting was performed using two deep learning architectures: LSTM (recurrent neural network baseline) and TCN (temporal convolutional network). TCN substantially outperformed LSTM in Três Marias (R² = 0.95 vs. 0.05), demonstrating that convolutional architectures effectively exploit low-frequency persistence when scale-aware preprocessing reveals it. These findings show that temporal filtering provides an effective framework for detecting climate–streamflow dependencies and hydrological memory, with direct implications for seasonal-to-decadal forecasting and climate-informed reservoir management under changing conditions.

Abstract: Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article presents a conceptual review informed by structured scoping searches across PubMed, Scopus, Semantic Scholar, Crossref, and selected policy sources covering January 2001–March 2026. The search component was used to map the field and identify representative frameworks, implementations, and technical advances rather than to estimate pooled effects. We synthesise the literature across four domains: conceptual foundations of integrated care, AI and multimodal analytics, implementation barriers, and digital-governance requirements. On that basis, we propose a five-level taxonomy ranging from disease-specific programmes to learning integrated care models and argue that most current deployments remain concentrated at digitally integrated but only weakly adaptive Type IV configurations. Across the literature, three recurrent constraints limit progression towards Type V learning systems: temporal blind spots, maintenance debt, and governance misalignment. Overall, the review positions AI-enabled integrated care less as a finished model than as an emerging design space requiring longitudinal data assets, stewarded model lifecycles, and accountable governance to support clinically useful, equitable, and trustworthy learning systems.