Trine University is a private university in Angola, Indiana. It was founded in 1884 and offers degrees in the arts and sciences, business, education, and engineering. Trine University is accredited by the Higher Learning Commission..
This research investigates the influence of pearl millet husk microfiber and porous biocarbon incorporation into an epoxy resin matrix on the mechanical, thermal, dielectric, EMI shielding, and flammability behavior of the resulting composites. The results indicate that the addition of natural fiber and biocarbon modifies the functional performance of the epoxy system depending on filler composition. Among the variants, EPB1 exhibited the most favorable dielectric response with a permittivity of 4.4 and dielectric loss of 0.77, indicating improved energy storage and dissipation capability compared to neat epoxy. The composite EPB2, containing 56 vol.% epoxy, 40 vol.% pearl millet husk microfiber, and 4 vol.% porous biocarbon, showed enhanced thermal and flame-retardant behavior, with a thermogravimetric mass retention of 96%, a UL-94 V-0 rating, and a minimum flame spread rate of 5.31 mm/min. The overall findings demonstrate that filler content plays a critical role in controlling thermal stability, dielectric response, and fire resistance, while mechanical improvements depend on optimized phase interaction. This work highlights the potential of agricultural waste-based fillers for multifunctional epoxy composites with balanced thermal, electrical, and fire-safety characteristics.
This paper presents an explainable AI analysis of brake control in CARLA closed-loop driving. Longitudinal braking is studied through a threshold policy, a risk-aware reference policy, and a distilled policy learned from reference rollouts. The framework combines structured traffic-state features, high-fidelity XGBoost surrogates, and SHAP to analyze deployed brake behavior across Town10HD and Town05. Results show that brake generation is consistently dominated by front-vehicle distance, relative speed, and time to collision, while lateral variables contribute weakly. The distilled controller retains a forward-risk-oriented explanation structure rather than behaving as an arbitrary black box, but the degree of apparent semantic alignment with the reference policy is environment-dependent. Fallback-aware analysis shows that this alignment is substantially reinforced by the deployed safety fallback in Town10HD, while Town05 provides a cleaner view of the learned component itself. These findings show that learned brake control can remain interpretable when grounded in semantically structured state representations.
Underwater systems play a pivotal role in subaquatic exploration and navigation. This paper presents an in-depth analysis of the advancements and prospects of underwater systems from several technological viewpoints, aiming to shift the existing paradigms in underwater technology. The paper begins by discussing the challenges posed by traditional underwater technologies, focusing on sensing and navigation techniques, as well as underwater communication methods. Subsequently, it explores the potential of emerging technologies in transforming the field of underwater systems. These include transformative sensing technologies such as underwater light detection and ranging (LiDAR), differential Global Positioning System (GPS), and synthetic aperture sonar, as well as the integration of perception, quantum technology, and artificial intelligence for enhanced data processing, object detection, and multi-sensor fusion. The paper also highlights the importance of advanced communication networks in enabling efficient and reliable underwater communication. Furthermore, critical considerations such as technology management, acceptance range, and security issues are addressed to ensure the successful adaptation of these new technologies. This paper serves as a comprehensive guide for researchers and practitioners towards the next generation of underwater systems, highlighting the road to paradigm-shifting the subaquatic frontier through the integration of these advancements.
The integration of explainable artificial intelligence (XAI) with machine learning (ML) provides a foundation for transparent, data-driven decision-making in intelligent and resilient transportation systems. This paper presents an XAI-enhanced framework for short-term trafficvolume forecasting along the I-94 corridor, enabling proactive route optimization for autonomous vehicles (AVs). Three representative ML architectures including Decision Tree, Random Forest, and Deep Multilayer Perceptron (DeepMLP) were developed and systematically compared. By incorporating SHapley Additive exPlanations (SHAP), the study quantifies feature-level contributions and reveals how temporal and weather-related factors drive traffic variations. Moreover, the DeepMLP model achieves high predictive accuracy for traffic forecasting while attaining a remarkable level of interpretability through SHAP-based feature decomposition. These insights bridge the gap between model performance and human understanding, transforming traffic forecasting from a reactive process into an explainable, proactive tool for sustainable and intelligent AV routing.
Microplastics (MPs), both conventional and biodegradable, are emerging contaminants of significant concern in marine ecosystems. While biodegradable plastics such as polylactic acid (PLA) and polybutylene adipate-co-terephthalate (PBAT) are marketed as eco-friendly alternatives, growing evidence suggests that their degradation products may exhibit comparable toxicity to conventional polymers like polyethylene (PE) and polystyrene (PS). This review critically examines the physicochemical properties, degradation behavior, and ecotoxicological impacts of both biodegradable and conventional MPs on the marine microalga Chlorella vulgaris. The analysis highlights that both MP types inhibit algal growth, disrupt photosynthesis, and induce oxidative stress through reactive oxygen species (ROS) generation, enzymatic imbalance, and cellular membrane damage. Mechanistic pathways involve particle aggregation, surface adsorption, and the release of toxic leachates and nano-sized derivatives, amplifying their ecological risks. Furthermore, biodegradable MPs often fragment faster, increasing MP particle counts and pollutant adsorption potential. The findings challenge the perceived environmental safety of “biodegradable” plastics and underscore the need for realistic field-based assessments. Understanding MP-algae interactions is crucial, as disruptions at this trophic level may cascade through marine food webs, affecting productivity and biogeochemical cycling. The review calls for comprehensive ecotoxicological frameworks to guide sustainable polymer design and effective marine pollution management.
