Trine University

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.

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.

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.

BACKGROUND:Artificial intelligence (AI) is increasingly embedded in healthcare businesses, promoted for its ability to enhance efficiency, reduce costs and optimize workflows. However, the intersection of profit-driven priorities with patient-centred values presents significant ethical and professional challenges for nurses, who serve as the frontline mediators between technology and patients. AIM:This study aimed to explore nurses lived experiences of AI integration in healthcare businesses, focusing on how they navigate tensions between institutional efficiency and their professional commitment to patient-centred care. METHODS:An interpretive phenomenological design was employed to capture the depth of nurses' perspectives. Data were collected between May and June 2025 through 26 semi-structured interviews and 1 focus group with 7 nurses, yielding a total of 33 participants from AI-integrated private hospitals. Transcripts were analyzed thematically, with trustworthiness ensured through member validation, audit trails and reflexive journaling. RESULTS:Four overarching themes emerged. Nurses reported emotional and ethical conflicts when AI recommendations contradicted clinical judgement, often leading to moral distress. Business imperatives were perceived to prioritize efficiency over individualized care, with nurses excluded from decision-making about AI adoption. Many participants expressed anxiety over role displacement and a diminishing sense of autonomy, although some redefined their professional identity as technology navigators. Inadequate training and lack of institutional support further amplified challenges, leaving nurses underprepared to manage AI tools effectively. CONCLUSION:While AI offers organizational advantages, its integration without inclusive planning and adequate training risks undermining holistic nursing practice. Strengthening institutional support, valuing nurses' input and balancing efficiency with empathy are essential to align technological innovation with compassionate, patient-centred care.

This paper presents a graphene-based multiband terahertz (THz) MIMO antenna developed to meet the stringent requirements of future high-speed wireless and biomedical systems. The antenna exhibits resonances at 2.7345, 3.2455, 3.739, and 4.243 THz, each offering a broad bandwidth greater than 0.21 THz. Graphene, chosen as the conducting element, and polyimide, selected as the substrate, provide an optimal material combination to ensure low losses and stable operation at terahertz frequencies. The antenna combines silicon decoupling structures with a low loss polyimide substrate, forming a composite material for improved isolation, stability and the terahertz performance. Performance evaluation demonstrates a radiation efficiency of 90.42%, a peak gain of 13.253 dB, and strong isolation of 33.026 dB between ports. Diversity performance is further validated through the calculation of envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), channel capacity loss (CCL), and mean effective gain (MEG), all of which confirm excellent MIMO characteristics and robust transmission quality. The antenna design progresses from a single-element configuration to a MIMO structure, where silicon-based parasitic decoupling structures (PDS) and defective ground slots (DGS) are incorporated to suppress mutual coupling while maintaining stable radiation patterns. The resulting low-profile and high-performance antenna demonstrates wide frequency coverage, strong gain, and reliable isolation, making it a promising solution for diverse applications. These include non-invasive biomedical imaging and spectroscopy, high-resolution material characterization, environmental and security monitoring, and nextgeneration 6G communication scenarios requiring ultra-fast, low-latency, and high-capacity data transmission. To enhance design accuracy and efficiency, machine learning (ML) driven regression algorithms are employed for performance prediction, achieving high consistency with simulation results and reducing the need for repeated design iterations. The combination of multiband operation, advanced material utilization, and intelligent performance optimization positions this antenna as a strong candidate for integration into future terahertzenabled systems.