The Ga2S3-Sb2S3 quasi-binary system has been investigated for its potential to yield stable chalcogenide glasses with tailored thermal and structural properties. Using melt-quenching techniques, a series of (Ga2S3)x(Sb2S3)(1-)x compositions (0.0 <= x <= 0.5) were synthesized, and their glass-forming domain was mapped. The latter extends up to approximately x <= 0.40, as confirmed by X-ray diffraction and DSC analyses, with the x = 0.4 composition exhibiting a glass-ceramic character. Density measurements, combined with calculations of molar volume and packing density, revealed a continuous structural densification as Ga2S3 content increased. Differential scanning calorimetry showed an increase in glass transition temperature (T-g), with the best thermal stability observed for x = 0.2, as assessed by the Hruby criterion. Electrical conductivity measurements demonstrated thermally activated behaviour following the Arrhenius law, with maximum activation energy also centred at x = 0.2. Raman spectroscopy and DFT modelling were used to decipher the structural contributions of Sb-S and Ga-S bonding. The emergence of vibrational modes characteristic of Ga-based structural units, especially beyond x > 0.2, suggests a structural reorganization from Sb-centred pyramidal units to Ga-centred tetrahedral. This was corroborated by high-energy X-ray diffraction, which showed significant changes in intermediate-range order with increasing Ga content, particularly in the first sharp diffraction peak and partial coordination environments.
Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in large-scale missions such as search and rescue (SAR), environmental monitoring, and infrastructure inspection. These missions are often time- and energy-constrained, requiring cooperative planning strategies that maximize mission utility while ensuring safety and efficiency. Building on our prior single-UAV Energy-Aware Informative Path Planner with two-step lookahead (EA-IPP2n), this paper introduces VAP & EA-MIPP, a multi-UAV framework that integrates V-shape Area Partitioning (VAP) with Energy-Aware Multi-UAV Informative Path Planning (EA-MIPP). VAP partitions Regions of Interest (ROIs) into balanced angular sectors according to their spatial distribution, reducing redundant coverage while ensuring balanced informative workloads among UAVs.Within each sector, EA-MIPP extends EA-IPP2n to cooperative missions by incorporating dual time-energy constraints, two-step lookahead planning, and explicit handling of static obstacles and No-Fly Zones (NFZs). Extensive experiments evaluate both partitioning and planning components. In the partitioning stage, compared with five methods (DARP, PPS, AIS-EP, CVRP, and IM2GWO), the proposed VAP achieves the most balanced ROI allocation, reducing the average coefficient of variation of workload to only 0.60%, compared with 3.99%–30.17% for the competing methods. For path planning, across five cooperative UAV missions, EA-MIPP consistently outperforms the baseline Hierarchical Informative Path Planning (HIPP), achieving up to 27% higher total rewards, improved node coverage, and 20–30% lower planning time, while satisfying strict time and energy constraints. These results demonstrate that the proposed framework provides balanced workload distribution together with scalable, efficient, and energy-aware cooperative informative path planning for resource-constrained UAV missions.
This paper presents a bibliometric analysis of research trends and gaps in battery management systems (BMS) for electric aviation, spanning 1997 to December 2024. Based on 500 publications, the study identifies academic collaborations, regulatory frameworks, thematic evolutions, and research hotspots. It also highlights the field's most critical documents, countries, authors, and affiliations. The results reveal three major research phases: foundational development (1997–2016), rapid growth (2017–2022), and advanced multidisciplinary exploration (2023–December 2024). UAVs emerge as a central focus throughout, with increasing emphasis on electrical take-off and landing (eVTOL) aircraft in the later stages. Machine learning (ML), fuzzy logic approaches for power and thermal control, and predictive models for state estimation and problem diagnostics are among the most significant breakthroughs in the domain. However, significant challenges persist in this research domain, including the absence of clear regulatory frameworks for ML integration, the scarcity of onboard BMS models for large-scale aircraft, and insufficient predictive models for thermal management in eVTOL aircraft. Additionally, limited international collaboration, particularly from the United States, restricts the potential for data diversity and global innovation. This review provides a roadmap for future research, emphasizing the need for adaptive, real-time BMS models, stronger international partnerships, and policy standardization to accelerate the field of electric aviation.
Smart technological systems have emerged in the past years to aid in medication management among individuals, allowing patients to store and track their daily medication and aiding those prone to forgetfulness of dosage requirements. This paper reviews three areas concerning medication support systems, namely Internet-of-Things-based pill dispenser systems, medical paper digitization systems, and intelligent medication support systems. This paper’s main focus lies in reviewing the techniques adopted within each of the reviewed medication support areas to highlight their effectiveness, and identify common trends. For the Internet-of-Things-based pill dispenser systems, three major areas are reviewed: the pill dispensing mechanism, pill scheduling method, and the employed alarm systems. Regarding medical paper digitization systems, the reviewed methodologies are categorized into three key aspects, including those that rely on visual information processing, text and sequence modeling, and decision support systems. As for intelligent medication support systems, techniques such as dialog-based, reminder-based, and treatment supervision systems are reviewed.
This paper addresses the common issue of laparoscopic lens fogging (LLF), which impairs the surgeon’s visibility and prolongs surgical procedures. To overcome this challenge, an advanced electrical defogging system is proposed, utilizing a thin, transparent conductive layer configured in various geometries, including circular disks and concentric spirals. These designs enable uniform heating across the lens surface, thereby minimizing the temperature gradient between the lens and the body’s internal environment. Preliminary models employing a 14-ring Indium Tin Oxide (ITO) configuration achieved a stable lens temperature of 310 K (37 ℃). Building on this, the study investigates graphene as an alternative heating material. The graphene-based design demonstrated comparable thermal performance to ITO while operating at significantly lower voltages. Through comprehensive simulations and modeling, the results confirm the effectiveness of both materials in preventing fogging, ultimately reducing procedure time and enhancing surgical precision and patient safety.
