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Explainable AI for Enhancing Awareness of Academic Stress Among International University Students
No full textAcademic stress is a common challenge in higher education, especially for international university students who must adapt to new academic systems, expectations, and learning environments. In recent years, artificial intelligence has been increasingly used to analyze academic data and estimate student stress. However, most AI-based systems prioritize prediction accuracy over providing valuable support for student understanding. As a result, students may receive stress-related indicators without a clear explanation of how these results relate to their academic tasks or activities. This state-of-the-art review discusses current research on explainable artificial intelligence in the field of academic stress and student awareness. Based on literature published between 2020 and 2025, this review synthesizes work from educational technology, learning analytics, and explainable AI from a Human–Computer Interaction perspective. The analysis focuses on the representation of academic stress, the design of explanatory frameworks, and the extent to which existing systems facilitate students’ ability to interpret and reflect on their work. The review finds that awareness is rarely treated as an explicit outcome in existing research. Although explainable models are increasingly used, the explanations they produce are often technical and not student-oriented. International students are an underrepresented group in the literature, despite the apparent differences in their academic preparation, linguistic ability, and expectations. Consequently, these shortcomings limit the effectiveness of artificial intelligence systems as tools for enhancing student awareness. This review highlights the need to shift from prediction-oriented approaches toward awareness-oriented explainable AI systems that prioritize student understanding. By emphasizing human-centered explanation design and inclusive evaluation, future research can better support students in making sense of academic stress within diverse higher education environments.This article is published as Ahmed Almathami and Richard Stone. “Explainable AI for Enhancing Awareness of Academic Stress Among International University Students”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.1 (2026). doi: http://dx.doi.org/10.14569/IJACSA.2026.0170191
A bimodal model for thermal conductivity as a function of matric potential incorporating adsorption and capillarity
No full textEstimating soil thermal conductivity (λ) in relation to soil water matric potential (h) is important to understand energy transfer, water movement, and ecological processes in soils. However, there is a lack of universally applicable λ(h) relationships that characterize soil thermal behavior across the full h range, particularly for soils with diverse textural classes and varying bulk density. In this study, we present the Boltzman-Tani (BoT) model, a physically based and continuous framework that captures the bimodal pattern of λ (pF, the common logarithm of |h|) relationship over the entire h range by incorporating both adsorption and capillarity. The BoT model parameters pFads and pFcap, which mark the inflection points in the adsorption and capillary domains, respectively, have clear physical meanings and are linked to key soil thermal and hydraulic properties, i.e., critical and hydraulic continuity water contents. We evaluate the BoT model using a λ(pF) dataset comprising 246 observations from 18 soil samples, representing a wide range of texture and bulk density. The BOT model outperforms two existing models: the unified exponential model and the two-segment power model, achieving the least root mean square error (0.043 W m−1 K−1) and mean error (0.033 W m−1 K−1), the lowest Akaike Information Criterion (−82.2), and the highest coefficient of determination (0.983). The changes in λ are driven by variations in the adsorption and capillary components, with adsorptive force dominating at higher pF and capillary force becoming more influential at lower pF, validating the model’s theoretical framework. Furthermore, we develop pedo-transfer functions to estimate the BoT model parameters from soil texture and bulk density, which facilitates its broader and practical application such as soil-climate feedback and optimizing land management. Future work should focus on validating the model with larger, independent datasets and exploring its integration with advanced machine learning techniques to expand its predictive capabilities.This article is published as Fu, Yongwei, Wenjie Li, Yili Lu, Hu Zhou, Tusheng Ren, Joshua Heitman, and Robert Horton. "A bimodal model for thermal conductivity as a function of matric potential incorporating adsorption and capillarity." Geoderma 466 (2026): 117677. https://doi.org/10.1016/j.geoderma.2026.117677This research was supported by the National Natural Science Foundation of China (Grant Number: 42407414), Chinese Universities Scientific Fund (Grant Number: 15055005), US National Science Foundation (Grant Number: 2037504) and USDA-NIFA Multi-State Projects 4188 and 5188
HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs
No full textAdvances in hyperspectral imaging (HSI) and 3D reconstruction have enabled accurate, high-throughput characterization of agricultural produce quality and plant phenotypes, both essential for advancing agricultural sustainability and breeding programs. HSI captures detailed biochemical features of produce, while 3D geometric data substantially improves morphological analysis. However, integrating these two modalities at scale remains challenging, as conventional approaches involve complex hardware setups incompatible with automated phenotyping systems. Recent advances in neural radiance fields (NeRF) offer computationally efficient 3D reconstruction but typically require moving-camera setups, limiting throughput and reproducibility in standard indoor agricultural environments. To address these challenges, we introduce HSI-SC-NeRF, a stationary-camera multi-channel NeRF framework for high-throughput hyperspectral 3D reconstruction targeting postharvest inspection of agricultural produce. Multi-view hyperspectral data is captured using a stationary camera while the object rotates within a custom-built Teflon imaging chamber providing diffuse, uniform illumination. Object poses are estimated via ArUco calibration markers and transformed to the camera frame of reference through simulated pose transformations, enabling standard NeRF training on stationary-camera data. A multi-channel NeRF formulation optimizes reconstruction across all hyperspectral bands jointly using a composite spectral loss, supported by a two-stage training protocol that decouples geometric initialization from radiometric refinement. Experiments on three agricultural produce samples demonstrate high spatial reconstruction accuracy and strong spectral fidelity across the visible and near-infrared spectrum, confirming the suitability of HSI-SC-NeRF for integration into automated agricultural workflows.This is a preprint from Ku, Kibon, Talukder Z. Jubery, Adarsh Krishnamurthy, and Baskar Ganapathysubramanian. "HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs." arXiv preprint arXiv:2602.16950 (2026). doi: https://doi.org/10.48550/arXiv.2602.16950.This work was supported by the AI Institute for Resilient Agriculturen (USDA-NIFA 2021-67021-35329), NSF 2412929/2412928 and Iowa State University’s Plant Science Institute
Advanced ionic liquid-derived gas chromatography stationary phases for the temperature dependent separation of olefins and polyfluoroalkyl substances
No full textThis dissertation presents a comprehensive investigation into ionic liquid (IL)-derived gas chromatography (GC) stationary phases, strategies for polymerizing ILs, and incorporation of metal ions to enhance GC separations. Among the studies reported, the influence of metal–olefin interactions, polymerization and column-preparation routes, structural features of IL and polymeric ionic liquid (PIL) cations and anions, temperature-dependent retention behavior of IL stationary phases, and separations of fluorinated organic compounds on fluorinated ILs were systematically explored. In Chapter 2, imidazolium-based ILs and structurally analogous PILs were compared with regard to olefin–silver(I) interactions revealed that reduction of Ag+ ion during in-column polymerization led to diminished olefin retention, whereas PILs polymerized before salt incorporation achieved stronger olefin retention at equivalent Ag+ ion loadings, albeit with lower thermal stability than IL analogues. Chapter 3 expanded the work to copper(I) and copper(II) ion-containing IL and PIL phases. Despite the cost-advantage of Cu+ ions, its weaker metal–olefin complexation is a limitation; however, the introduction of Cu2+ ion into a [C6MIM+][NTf2⁻] IL enabled reversible and strong metal–olefin interactions, yielding higher retention factors for a range of unsaturated hydrocarbons compared to Cu+ ion systems. Following water-removal treatment, the Cu2+–IL phase exhibited markedly enhanced olefin retention; in contrast, reducing agents diminished Cu+-olefin interactions, and crosslinked PILs loaded with Cu+ ions outperformed non-crosslinked analogues under reducing conditions. Chapter 4 introduced crosslinked PIL networks bearing combinations of Ag+ and Cu2+ ions; crosslinking significantly improved thermal stability and hydrogen-reduction resistance while maintaining selective metal–olefin interactions, establishing crosslinking as an effective method to stabilize metal ion environments in GC stationary phases. Chapter 5 investigated temperature-dependent chromatographic metrics for trihexyl(tetradecyl)phosphonium-based IL phases compared to conventional polymer phases within the 28–52 °C range, using van’t Hoff analysis to reveal single-phase chromatographic behavior and demonstrating superior separation performance for the IL phases under optimized temperature conditions. Finally, Chapter 6 addressed the separation of fluorinated organic compounds on IL phases varying in fluorination and alkyl-thio substituents; retention and selectivity of alcohols, fluorotelomer alcohols, perfluoroalkenes and CF3-substituted aromatics were found to depend collectively on IL fluorination, alkyl chain branching and column length. GC×GC analyses revealed stronger interactions of the fluorinated IL phase with fluorotelomer alcohols compared with a commercial poly(trifluoropropylmethyl siloxane) stationary phase and uncovered complementary selectivity patterns between fluorinated and non-fluorinated ILs, enabling the detection of subtle fluorophilic, dispersive, hydrogen bonding and π–π interactions of analytes with stationary phases
Structural transformation and human capital
No full textIn recent decades, Korea, followed by China, has experienced structural shifts characterized by a prolonged hump-shaped trend in manufacturing, which has been significantly influenced by their openness to trade. In contrast, India's path in manufacturing has been shorter-lived, with the underlying causes still a subject of investigation. Our research explores human capital differences as a primary factor in understanding the distinct economic transformation patterns observed in these nations. This paper formulates an endogenous growth model of human capital as a driver of structural
change. By segmenting the economy into Agriculture, Manufacturing, and Services, and assessing the employment distribution between high-skilled and low-skilled workers in each sector, it becomes apparent that the contrasting growth rates of skilled labor segments contribute to the unique growth stories of China and India
From harm to healing: Identity, meaning, and support as mediators of the relationship between spiritual abuse and psychological outcomes in ex-Mormons
No full textSpiritual abuse (SA)—the misuse of spiritual authority through coercion, control, disempowerment, and fear-based messaging—has been linked to wide-ranging psychological harms, yet mechanisms of impact remain underspecified. High-cost religious contexts, characterized by intensive integration, clear in-/out-group boundaries, and strong demands for obedience and conformity, may exacerbate risk. This study tested whether three adaptive resources—identity coherence, presence of meaning, and social support—mediate associations between SA and mental-health outcomes in former members of The Church of Jesus Christ of Latter-day Saints (ex-Mormons). Using a latent structural equation model, I estimated direct and indirect effects of SA on symptoms of posttraumatic stress (PTS) and disturbances in self-organization (DSO), depression, anxiety, and well-being. Results indicated robust mediation: identity (i.e., the lack of coherence) emerged as the primary pathway linking SA to psychopathology, with meaning and support providing additional, outcome-specific routes; flourishing was largely explained by indirect effects through identity and meaning. Findings clarify theoretically coherent mechanisms by which SA undermines well-being and point to clinical leverage points—identity reconstruction and meaning-making—as priority targets for prevention and treatment, with social reconnection as a complementary intervention focus. Implications for assessment, case conceptualization, and intervention design in post-high-cost-religion populations are discussed
Using strategic zinc supplementation to enhance resilience and recovery from transportation stress in beef steers
No full textTransportation of cattle is an essential component of the U.S. beef production system, linking geographically dispersed cow–calf operations with the more concentrated feedlots in the Corn Belt and Great Plains. Cattle arriving at these feedlots are in transit for 8 hours on average, with some exceeding 14 hours (BQA, 2011). Longer duration times are associated with reduced feedlot performance and increased incidents of bovine respiratory diseases (Duff and Galyean, 2007), which are major concerns to cattle welfare and economic sustainability of the beef industry (NAHMS, 2013). Zinc (Zn) supplementation has been shown to support growth (MacDonald, 2000), appetite (Suzuki et al., 2011), and immune function (Kesler and Abuelo, 2024), suggesting a potential role in improving post-transport outcomes. The NASEM (2016) recommendation for Zn is 30 mg/kg dry matter (DM) for beef cattle, or 75-100 mg/kg DM for high-stressed calves consuming less than 1.90% of their body weight (BW) in DM. To evaluate the effects of pre-transit Zn supplementation and transport duration on physiological demand and performance, 80 Angus-cross steers were used in a 98-day study. Steers were stratified by BW on d -42 and assigned equally to one of two diets (DIET): no supplemental Zn (Zn0; 40 mg Zn/kg DM) or 100 mg supplemental Zn/kg DM (Zn100; ZnSO₄). Diets were fed for 42 days before transport, after which cattle were transported for either 8 (8H) or 18 hours (18H). Following transport, all steers received the Zn100 diet for the remaining 56 days, reflecting more typical industry receiving rations (Samuelson et al., 2016). Individual feed intake was collected via the GrowSafe bunk system based on feed disappearance. Individual BW were collected on d -42, -41, -1, 1, 7, 28, 55, and 56. Blood for trace mineral analysis was collected on d -42, -1, 1, 2, 7, 28, and 55. Blood for analysis of metabolites, inflammation, oxidative stress, and muscle fatigue were collected on d -1, 1, 2, and 7. Hair samples for hair cortisol analysis were collected on d -1, 28, and 55. Gait and attitude scores were collected on d -1 and 1. Data were analyzed using the Mixed procedure of SAS 9.4, except for gait and attitude score, which were analyzed using the GLM procedure of SAS 9.4. The model included the fixed effects of DIET, DUR and their interaction, with steer as the experimental unit.
Pre-transit performance was greater for Zn100 than Zn0 cattle. Longer transport (18H) increased physiological demand, as indicated by altered plasma metabolites, trace minerals, and markers of inflammation and oxidative stress compared with 8H. These alterations likely contributed to fatigue, as reflected by reduced time spent at the feed bunk by 18H cattle during the first 72 hours post-transport. Although Zn supplementation did not lessen the degree of metabolic demand, inflammation, or oxidative stress, it appeared to enhance recovery. Zn100–8H cattle consumed a greater percentage of pre-transit dry matter intake (DMI) by day 1 post-transport than Zn0-8H, while Zn100–18H DMI was not greater than Zn0-18H until day 4 and 6. Across treatments, attitude and gait scores increased post-transport, reflecting muscle fatigue; however, Zn100–8H cattle exhibited the least alteration to gait off-truck, suggesting greater Zn supplementation before transport may mitigate muscle fatigue after moderate transport duration. Zn100 cattle, also, maintained their DMI advantage through the first week post-transit even though all cattle were receiving the Zn100 diet. By d 7, most indicators of metabolic, inflammatory, and oxidative stress had returned to baseline, highlighting cattle resiliency when well prepared for transport. Post-transit, Zn0 cattle, now receiving the Zn100 diet, improved G:F and DMI by week 8, allowing them to match Zn100 performance so there was no difference in performance by the study’s end. Hair cortisol confirmed that neither transport duration caused prolonged or chronic stress. Collectively, these findings demonstrate that while longer transport imposes greater short-term physiological demands, cattle recover rapidly with appropriate preparation. Pre-transit Zn supplementation at 100 mg/kg DM enhances pre-transport performance and may enhance post-transport recovery, representing a valuable management tool for both cow–calf and feedlot operations
Global optimization of mixed-integer nonlinear programs via decision diagrams
No full textMixed-integer nonlinear programming (MINLP) provides a robust modeling framework for representing
real-world decision-making problems involving discrete and continuous variables under
nonlinear relationships. However, despite decades of progress in mixed-integer linear and convex
programming, global solution methods for general MINLPs remain limited in scalability and
scope, particularly when dealing with nonconvex, nonsmooth, or irregular problem structures that
challenge existing convexification techniques. This dissertation develops a graph-based global optimization
framework based on decision diagrams (DDs) that advances the state of the art in solving
complex MINLPs. The proposed approach departs from traditional algebraic formulations and
instead represents nonlinear constraints as structured graphs that capture variable dependencies
and enable the design of strong convex relaxations. The framework integrates DD-based reformulations,
linear outer approximations, and a spatial branch-and-bound algorithm with convergence
guarantees, providing the first general-purpose DD-based solution methodology for global MINLPs.
Building on this foundation, the second part of the dissertation extends the graphical framework
to statistical estimation problems with nonconvex regularization functions, such as the smoothly
clipped absolute deviation (SCAD) and minimax concave penalty (MCP). These models, widely
used for inducing sparsity and improving estimator properties, have historically been intractable
for global solvers due to discontinuous or integral terms. The dissertation introduces the concept of
a scale function as a unifying representation of norm-bounding and nonconvex penalty functions,
which enables the development of a graph-based convexification technique that results in global optimality
without the need to add auxiliary variables or artificial bounds. Extensive computational
experiments on benchmark MINLPs and sparse regression problems demonstrate the capability
of the proposed frameworks to obtain global solutions for models that are otherwise inadmissible
to state-of-the-art solvers such as BARON and SCIP. Together, these contributions establish a new class of graphical global optimization methods that expand the reach of decision diagram
technologies to general nonlinear programming and modern statistical learning applications
Pedological investigation for enhancing corn productivity in the agroecological zones of southern highlands of Tanzania
No full textSoil acidity is part of the land degradation that limits crop productivity in the Southern Highlands Zone of Tanzania, a region characterized by highly weathered soils with low fertility and strong phosphorus fixation. Pedological characterization, phosphorus adsorption, and fertility evaluation using lime and fertilizer management options formed key themes of the study to understand the mechanisms governing the restoration of soil productivity. The pedology of the study areas indicates that the soils are predominantly acidic, rich in sesquioxides, with variable clay distribution and moderate structure. These features are associated with advanced degree of weathering and leaching processes in soil systems. Phosphorus adsorption experiments revealed a high P-fixation capacity associated with the abundance of sesquioxides of Al and Fe, which significantly reduced P availability to crops. The fertility evaluation showed a reduction in soil acidity, P-adsorption, and enhanced nutrient availability, as well as increased maize yield, following the use of lime in field factorial experiments that combined lime and fertilizer treatments. The interaction between lime and fertilizer revealed synergistic effects, where moderate lime rates coupled with balanced fertilization optimized soil chemical properties and crop performance. These findings underscore the significance of integrated soil fertility management in addressing constraints of production such as soil acidity in agroecosystems, through integration of fertility inputs particularly lime and fertilizers, and promoting sustainable crop production in the Southern Highland Zone of Tanzania
Development and validation of multi-sensor embedded system for low-cost pavement monitoring
No full textEffective pavement condition evaluation is essential for infrastructure management, yet the high costs of commercial monitoring systems create significant barriers for budget-constrained transportation agencies. Many small and medium-sized agencies cannot afford systematic pavement assessment programs, resulting in reactive maintenance approaches and inefficient resource allocation. Existing low-cost alternatives fail to provide the comprehensive capabilities and professional-grade accuracy needed for effective pavement management decisions.
This dissertation presents the development and validation of an integrated multi-sensor embedded system that delivers comprehensive pavement condition evaluation at substantially reduced costs while maintaining professional accuracy standards. The work establishes a systematic framework for low-cost embedded system development, creates a novel sensor fusion approach combining Inertial Measurement Units and Pulsed Coherent Radar for accurate roughness measurement, implements advanced computer vision methodologies with synthetic data augmentation for automated distress detection, and develops practical deployment strategies for transportation agencies.
The methodology combines embedded systems engineering, sensor fusion techniques, computer vision, and machine learning approaches. A comprehensive framework guides component selection for microcontrollers, single-board computers, and sensors based on performance requirements and budget constraints. The roughness measurement system integrates multiple sensors through advanced signal processing and optimized quarter-car modeling. For distress detection, the dissertation implements computer vision pipelines utilizing multiple state-of-the-art model architectures enhanced by synthetic data augmentation methodologies using StyleGAN3 models to address dataset imbalance challenges.
The developed system demonstrates exceptional performance with dramatic cost reductions compared to conventional commercial solutions while maintaining professional-grade accuracy. The sensor fusion approach achieved strong correlation with reference measurements across diverse road segments and test configurations. Synthetic data augmentation methodologies showed effectiveness across different computer vision architectures, with performance improvements varying based on model architecture and integration strategies. Advanced image processing techniques eliminated duplicate detections and provided precise distress quantification compatible with existing Geographic Information System workflows.
This dissertation addresses critical accessibility challenges in pavement condition evaluation by enabling broader access to sophisticated monitoring capabilities. The integrated system enables transportation agencies with limited resources to implement systematic pavement assessment programs previously available only to well-funded organizations. The work bridges the gap between academic developments and practical implementation by providing complete solutions that integrate seamlessly with existing agency workflows, offering improved decision-making capabilities and enhanced pavement management practices