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Personalizing Learning Pathways Through Deep Learning Models and Educational Data Analytics
No full textThe aim of this research is to investigate the role of deep learning and educational data analysis in personalizing learning paths. Relying on the capacity of deep learning models to identify behavioral patterns and predict learner performance, it is possible to design learning paths that meet individual needs. In this context, educational data analysis not only helps to improve the quality of education, but also paves the way for the development of adaptive and intelligent learning systems. The research also addresses the technical challenges, ethical considerations, and implementation limitations of this approach and offers solutions for the responsible use of new technologies in education
AIS underrepresents vessel traffic in Scotland's Marine Protected Areas
No full textMaritime traffic poses a variety of risks to both the marine environment and marine wildlife. To quantify and predict risk, accurate data on the distribution and densities of vessel traffic is required, yet currently there is no single data type that captures all vessel traffic. Most commonly, AIS (Automatic Identification System) vessel tracking data is used, despite awareness that AIS data does not fully capture all vessels present. Therefore, evaluations using only AIS likely underestimate the potential impacts. To estimate the scale of underestimation, vessel presence within six of Scotland's Marine Protected Areas (MPAs) were recorded during >1800 h of land-based and at-sea surveys, and compared with AIS data collected from a network of receivers deployed around Scotland. Non-AIS vessels were present within MPAs during 62 % of the surveyed period, with 64 % of vessels sighted not broadcasting AIS. AIS transmission rates varied between MPA, season and vessel type. Given that AIS data is the most commonly used data type for quantifying vessel activity and predicting associated impacts, consideration must be given to the volume of vessel traffic not represented within AIS datasets, particularly within MPAs. Underestimation of actual vessel traffic is likely leading to insufficient management or mitigation efforts within areas designated for protection
Introduction to artificial intelligence in chemical engineering
No full textThis book chapter examines how artificial intelligence (AI) has been used in chemical engineering, emphasizing its development, present uses, and prospects for the future. Examining machine learning (ML) and deep learning (DL) approaches for fault identification and process optimization is one of the goals, as is tackling data management issues. The main techniques used in this study are reinforcement learning for real-time process control, supervised learning for property prediction, and unsupervised learning for anomaly detection. Notably, the study uses methods like LIME for model interpretability and highlights the significance of explainable AI to promote trust in AI systems. According to the research, putting AI into practice can increase operational efficiency by up to 30%, lower expenses by about 20%, and increase safety by enabling proactive monitoring. The innovative aspect of this work is its all-encompassing framework, which incorporates AI techniques specifically designed to address the difficulties in chemical engineering. The creation of hybrid AI systems that integrate ML with process simulation tools is one example of future applications that will advance sustainable chemical manufacturing methods and allow for real-time decision-making. The chapter also emphasizes how important data management techniques, like feature engineering and data cleaning, are to the successful application of AI. Additionally, it tackles ethical issues, like AI bias and accountability, guaranteeing that AI solutions are not only efficient but also equitable and open. The study’s conclusions offer a fundamental understanding of how to use AI in the chemical industry with the ultimate goals of process optimization, innovation promotion, and navigating the intricacies of ethical and regulatory issues
Electrochemical Biosensor for Rapid Detection of Acute Rejection in Kidney Transplants
No full textKidney transplant recipients face a high risk of acute rejection (AR), where the immune system attacks the transplanted organ. Current diagnostics rely on invasive biopsies with procedural risks, costs, and limited temporal resolution. While urinary chemokines CXCL9 and CXCL10 are promising non-invasive AR biomarkers, clinical adoption is limited by labor-intensive detection and lack of point-of-care (POC) solutions. A rapid, label-free electrochemical biosensing platform for simultaneous quantification of CXCL9 and CXCL10 chemokines from 5 µL of unprocessed urine in 15 min, which for ELISA and biopsy is between 24–72 hrs, is presented. The system uses screen-printed carbon electrodes modified with a Ti 3C 2T x MXene-crosslinked bovine serum albumin hydrogel, offering high conductivity, nano-porosity, anti-fouling properties, and signal stability for up to 30 days. The platform enables single-digit pg/mL-level sensitivity, meeting clinical thresholds. In a prospective clinical study, biosensor-measured chemokine data trained a bootstrapped logistic regression classifier, achieving 83% AR classification accuracy. When combined with additional clinical and histopathological features, accuracy increased to 98%. This work integrates advanced materials, biosensor engineering, and machine learning to deliver a scalable, cost-effective POC solution for real-time, non-invasive AR monitoring. The platform will help reduce biopsy dependence, enable earlier intervention, and ultimately improve long-term transplant outcomes.</p
Risk aggregation and stochastic dominance for a class of heavy-tailed distributions
No full textWe introduce a new class of heavy-tailed distributions for which any weighted average of independent and identically distributed random variables is larger than one such random variable in (usual) stochastic order. We show that many commonly used extremely heavy-tailed (i.e., infinite-mean) distributions, such as the Pareto, Fréchet, and Burr distributions, belong to this class. The established stochastic dominance relation can be further generalized to allow negatively dependent or non-identically distributed random variables. In particular, the weighted average of non-identically distributed random variables dominates their distribution mixtures in stochastic order
Mathematical models for the EP2 and EP4 signaling pathways and their crosstalk
No full textG protein-coupled receptors EP2 and EP4 are both activated by the lipid messenger Prostaglandin E2 (PGE2) and induce the intracellular production of cyclic AMP (cAMP), ultimately affecting gene expression. Changes in cellular responses to PGE2 can have important consequences on immunity and disease, yet a detailed understanding of the EP2-EP4 signaling network is lacking. EP2 and EP4 are often co-expressed in cells but their specific contribution to cAMP production is poorly understood. Experimental data have shown that cAMP levels differ depending on whether PGE2 triggers EP2 or EP4, or both. To better understand the underlying mechanisms and predict cellular responses to PGE2, we developed mathematical models for EP2 and EP4 cAMP signaling, including receptor crosstalk. The mathematical models qualitatively reproduce the experimentally observed cAMP levels and provide mechanistic insight into both the differences and commonalities in EP2/EP4 signaling. We found that ligand binding dynamics play a crucial role for both single-receptor signaling and inter-receptor crosstalk. Inhibition of PGE2 signaling via receptor antagonists is gaining increasing attention in tumor immunology. These mathematical models could therefore contribute to the design of more effective anti-tumor therapies targeting EP2 and EP4
Optical Spin Effects Induced by Phase Conjugation at a Space‐Time Interface
No full textElectromagnetic temporal boundaries, emerging when the constitutive parameters of a medium undergo abrupt temporal variations, have garnered significant interest for their role in facilitating unconventional wave phenomena and enabling sophisticated field manipulations. A key manifestation is temporal reflection in an unbounded spatial domain, where a sudden temporal discontinuity induces phase‐conjugated backward waves alongside anomalous spin conversion. This study explores distinctive spin‐conversion dynamics at a time‐dependent spatial interface governed by Lorentz‐type dispersion, in which the plasma frequency undergoes rapid modulation over time. The interaction of a circularly polarized wave with a space‐time interface excites electromagnetic signals at the system's natural resonance, allowing precise control over polarization states. The scattered field stems from the combined influence of temporal and spatial boundaries, yielding a superposition of the original incident wave's polarization and its phase‐conjugated counterpart
Allelic diversity of the Zymoseptoria tritici effector Zt-11 leads to the loss of interactions with small, secreted proteins from wheat
No full textPathogen secreted effector proteins play a key role in host-pathogen interactions, however the evolutionary and structural mechanisms underlying their diversification remain poorly understood. Previously, Zt-11 a Zymoseptoria tritici pathogen effector was found to interact with small, secreted proteins from wheat (TaSRTRG6, TaSSP6 and TaSSP7). Deletion of Zt-11 delayed Septoria tritici blotch (STB) disease development in wheat. Here, we investigate the diversity of Zt-11 in 168 field isolates which revealed high allelic diversity in Zt-11, three distinct Zt-11 haplotype groups and signatures of positive selection. Structural predictions of these isoforms and the wheat host interacting protein TaSRTRG6, exhibit high structural confidence (pLDDT >80). Whereas the wheat interactors TaSSP6 and TaSSP7 are intrinsically disordered proteins with no reliable structural model. Molecular docking and yeast two-hybrid assays revealed haplotype-specific binding between Zt-11 and TaSRTRG6, with some isoforms showing loss of interaction in vivo. Finally, I1:H1 which no longer interact with small, secreted wheat proteins (TaSRTRG6, TaSSP6 and TaSSP7) also displayed increased disease symptoms during wheat infection assays. Together these findings suggest that Zt-11 undergoes evolution through positive selection and structural adaptation enabling evasion from wheat host small, secreted proteins
Process modelling and analysis of ikaite production for atmospheric CO<sub>2</sub> removal through ocean alkalinity enhancement
No full textThe production of ikaite, a metastable calcium carbonate hydrate, offers a promising pathway for atmospheric CO2 removal through ocean alkalinity enhancement. This study explores the feasibility of ikaite production through a three-step process, involving calcite (CaCO3) dissolution under elevated CO2 pressure, CO2 degassing from the calcium carbonate rich solution, and subsequent crystallisation. Here, a mathematical model was developed and validated against experimental data, and the effect of key operational parameters was examined. The calcite loading/dosage, particle size and CO2 pressure for dissolution, seed loading and particle size for crystallisation, and degassing pressure as critical factors have significant impact on process efficiencies. Under optimal conditions, involving CO2 pressures of 2 bar for dissolution, 0.01 bar for degassing, and 0.001 bar for crystallisation, with seed loading of 5 kg/m³ and seed particle sizes of 3 μm, the process achieved steady state ikaite production of 1.64 kg/m³ from a calcite feed of 0.83 kg/m³. This investigation demonstrates the technical viability of ikaite production through CO2 pressure swing and informs its future development as a potential contributor to climate change mitigation
Exploring spatiotemporal heterogeneity and nonlinear effects in electric vehicle crash risk prediction: A hybrid modeling approach
No full textElectric vehicle (EV)-related risk and uncertainty pose critical challenges for urban traffic management. Fine-grained crash risk prediction at 1 km × 1 km and hour-of-day resolution remains difficult due to rapidly evolving, strongly spatiotemporally heterogeneous crash patterns. Crash risk research spans risk measurement, prediction modeling, and factor selection, with a move toward interpretable nonlinear hybrid methods, yet temporal dynamics and local heterogeneity remain insufficiently modeled. This study addresses these limitations by first constructing a Spatio-Temporal Adaptive Network Kernel Density Estimation (ST-ANKDE) method that combines network-constrained proximity, cyclic time weighting, severity weighting, and adaptive bandwidths, and then developing a Multiscale Geographically and Temporally Weighted Regression–Extreme Gradient Boosting (MGTWR-XGBoost) method to learn local heterogeneity and nonlinear effects. To capture the influence of preceding periods and adjacent grids, we introduce temporal and spatial weighted crash risk variables (T-AccRisk and S-AccRisk). These are analyzed alongside road-network density, built-environment variables, socioeconomic variables, and EV-specific infrastructure variables. An empirical case study on 14,818 EV crashes shows that ST-ANKDE effectively captures crash risk dynamics, with a mean value of 6.57, and reveals pronounced spatiotemporal heterogeneity. The results show that MGTWR-XGBoost, enhanced by S-AccRisk and T-AccRisk to capture spatiotemporal dependence, achieves MAE = 1.54 and RMSE = 2.06 and outperforms standalone machine learning and other hybrid methods; road-network density, built-environment features, population density, and EV infrastructure coefficients exhibit significant spatiotemporal heterogeneity. Moreover, SHapley Additive exPlanations (SHAP) further analyzes nonlinear effects. These findings enable grid-level early warning, priority targeting of high-risk periods/locations, and data-driven deployment of enforcement and infrastructure for EV safety management
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