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Exploring healthcare providers\u27 perceptions of virtual reality in lung cancer treatment preparedness: a mixed-methods feasibility study for the development of EveryBreathMatters
No full textThis study examined healthcare providers\u27 perceptions of the benefits, challenges, and design preferences for Virtual Reality (VR)-based interventions to support treatment preparedness in lung cancer care. Our study involves 50 surveys and 10 interview responses, in a mixed-method design. We conducted descriptive statistics and thematic analysis through a hybrid inductive-deductive approach. Analysis of the quantitative data helped us capture demographic characteristics, VR familiarity, and perceived VR usefulness. Qualitative analysis gave us a deeper understanding of the VR tool design and Implementation. Descriptive statistics and Fisher\u27s exact tests were used to assess associations, while thematic analysis was conducted on interview transcripts. Only 28% of respondents were familiar with virtual reality. However, many of them admitted to its promise in potentially improving understanding (70%), managing expectations (56%), and engagement in care (68%). The respondents identified several challenges to VR use, including discomfort with technology (74%), usability-related issues (92%), and accessibility limitations (64%). They preferred that the content have short, video-based modules integrating multimodality of delivery (e.g., audio, avatars). Providers supported flexible delivery models that integrated both in-clinic and at-home delivery modes, emphasizing the importance of provider training and patient technical support. It is noteworthy that the respondents\u27 perceptions of VR usefulness were not associated with their demographic and specialty characteristics. Our findings provide valuable insight into the design of equitable and provider-informed VR tools, highlighting the need for real-world testing and Implementation, especially in cancer care
Artificial Intelligence-Integrated Biosensors for Antimicrobial Resistance Detection and Surveillance: A Review and Future Perspectives for Global Biosecurity
No full textAntimicrobial resistance (AMR) poses a critical threat to global health, undermining the efficacy of modern medicine. The escalating global epidemic of AMR jeopardizes the efficacy of contemporary medicine and undermines health systems globally. The swift, precise, and scalable identification of resistance determinants is essential for containment and stewardship initiatives yet, existing surveillance techniques are constrained by time, expense, and accessibility. Recent advancements in biosensor technology and artificial intelligence (AI) provide a revolutionary approach to decentralized, intelligent AMR monitoring. This review consolidates recent advancements in biosensor platforms-encompassing electrochemical, optical, piezoelectric, paper-based, and nanomaterial-based modalities-and their incorporation with AI and machine learning techniques for improved detection, signal interpretation, and predictive analytics. This study investigates the utilization of hybrid systems in clinical, veterinary, and environmental settings under the One Health surveillance framework. The research also examines the integration of AI-enabled biosensors within digital and Internet of Things (IoT) frameworks, emphasizing its capacity to produce real-time, data-intensive insights for public health decision-making. Critical analysis is conducted on key problems, including sensor repeatability, data scarcity, algorithmic transparency, and regulatory adaptation, in conjunction with socioeconomic and ethical considerations. The report delineates prospective avenues for research, policy, and implementation, highlighting open data standards, equitable access, and interdisciplinary collaboration. These breakthroughs collectively indicate the emergence of AI-driven biosensing networks, which provide predictive, adaptive, and globally coordinated AMR surveillance
LINKING MOLECULAR STRUCTURE EVOLUTION TO MECHANICAL DEGRADATION DURING ENVIRONMENTAL AGING IN CONVENTIONAL AND RECYCLABLE EPOXY SYSTEMS
No full textThe long-term performance of polymeric materials in extreme environments is governed by complex chemical and physical aging processes that alter their molecular structure and mechanical integrity. Conventional thermoset epoxies, while widely used in structural applications, are particularly susceptible to oxidative degradation at elevated temperatures, leading to embrittlement and loss of structural integrity. In the first part of this work, diffusion-limited oxidation (DLO) in a bulk structural epoxy was systematically investigated to establish the link between heterogeneous microstructural evolution and macroscopic mechanical degradation. High-temperature oxidation experiments were conducted in a controlled environmental chamber, and the resulting chemical, thermal, and mechanical changes were characterized using Fourier transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA), nanoindentation, and uniaxial tensile testing. The results revealed the formation of a distinct oxidized surface layer with elevated stiffness and increased carbonyl concentration, indicative of oxidative crosslinking. These localized chemical changes led to reduced viscoplastic deformation and an overall embrittlement of the bulk epoxy. Building on these findings, the second part of this work examined a recyclable epoxy vitrimer system to assess its environmental durability under oxidative and hydrolytic aging conditions. The virgin vitrimer, synthesized from DGEBA, glutaric anhydride, and zinc acetylacetonate, exhibited dynamic covalent adaptability while maintaining structural rigidity of a conventional thermoset. Accelerated aging experiments revealed distinct degradation mechanisms in oxidative versus hydrolytic environments. Hydrolysis involved an initial period of water uptake followed by reaction-driven mass loss and bulk erosion, while oxidation produced immediate degradation characterized by localized micro-porosity near the surface. Despite these differing mechanisms, both environments led to significant embrittlement and loss of mechanical integrity over time. Collectively, this work establishes fundamental structure–property relationships linking molecular-level aging phenomena to macroscopic mechanical degradation in both conventional and recyclable epoxy systems. The insights gained provide critical guidance for the design of more durable, recyclable polymer networks capable of sustaining structural performance in extreme environments
Global importance of nitrogen fixation across inland and coastal waters
No full textBiological nitrogen fixation is a key driver of global primary production and climate. Decades of effort have repeatedly updated nitrogen fixation estimates for terrestrial and open ocean systems, yet other aquatic systems in between have largely been ignored. Here we present an evaluation of nitrogen fixation for inland and coastal waters. We demonstrate that water column and sediment nitrogen fixation is ubiquitous across these diverse aquatic habitats, with rates ranging six orders of magnitude. We conservatively estimate that, despite accounting for less than 10% of the global surface area, inland and coastal aquatic systems fix 40 (30 to 54) teragrams of nitrogen per year, equivalent to 15% of the nitrogen fixed on land and in the open ocean. Inland systems contribute more than half of this biological nitrogen fixation
Enabling Memory-Augmented Neural Networks for Efficient Edge Applications
No full textDeep learning-based networks have achieved remarkable success in machine learning, demonstrating its effectiveness in numerous application domains, including computer vision, natural language processing, and big data analysis [1-3]. The precision of the systems based on deep learning relies on substantial computational resources as well as memory capacities in both the training and inference stages in these systems. More specifically, the use of deep neural networks (DNNs) involves computationally expensive training of the deep model, where millions of parameters are determined through a repeated parameter adjustment and fine-tuning process. The computations require significant memory capacities too. In the inference phase, the model computations for obtaining the output(s) based on the inputs should also be carried out. Again, the computation cost will be very high mainly due to, for example, high dimensionality of the input data (e.g., a high-resolution image or a long text) and significant numbers of tensor computations that need to be performed [4, 5]. In the inference phase, the calculations related to the model evaluation should be repeated. To improve the accuracy, increasingly more complex network architectures may need to be employed, which exacerbates the problem. These all make using DNNs in a hardware-implemented system a challenging task
MTU-LLM: LLM-based Multi-Robot Task Allocation and Path Planning for Heterogeneous Robots in Search and Rescue Operations
Get PDFUrban Search and Rescue operations after natural disasters involve locating and assisting victims in hazardous environments, which is challenging. Classical Multi-Robot Task Allocation (MRTA) and path planning approaches have been used to deploy heterogeneous robot teams in unsafe areas. However, existing methods often lack focus on workload balance and requirement fulfillment and struggle to generalize across different scenarios. To address these challenges, we propose Multi-robot Task allocation Utilizing LLMs (MTU-LLM), a framework designed to reduce the development time for task allocation and path planning approaches, enabling faster robot deployment. The framework uses an LLM-based “prompt engineering” approach that generates task allocation and path planning scripts for heterogeneous robot teams. This method is scalable, repeatable, and consistent across various environmental conditions, reducing lead time for MRTA algorithm development. The MTU-LLM approach is evaluated against classical MRTA and path planning methods using standard metrics. When tested on a standard environment map with varying robot teams and victim counts, the LLM-based approach demonstrates significantly higher computation time efficiency, better workload balance, and comparable requirement fulfillment percentage across numerous use cases compared to baseline methods
Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges
Get PDFParkinson’s disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive analysis offers valuable insights into the applications of ML in PD diagnosis. Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. We also discuss challenges, future directions, and outstanding issues
Optimizing biogas use in wastewater treatment plants for demand flexibility
No full textWastewater treatment plants are energy-intensive loads with operational flexibility, which has motivated interest in how these plants can support power grid operation through demand flexibility. Since electricity is often the largest single operating cost for wastewater treatment plants, leveraging demand flexibility could offer significant financial benefits. One approach to reduce and shift the wastewater treatment plant\u27s demand is through the use of biogas, a by-product of anaerobic digestion within the wastewater treatment process. Biogas (composed primarily of methane and carbon dioxide) is a renewable fuel that can be used to produce electricity to offset the plant\u27s demand from the grid. However, many wastewater treatment plants currently flare biogas. The goal of this paper is to determine the optimal use of an on-site biogas storage tank and generator to minimize the costs of a wastewater treatment plant participating in the frequency regulation market. To do this, we formulate the wastewater treatment plant optimization problem subject to biogas and frequency regulation constraints while managing biogas production uncertainty. We solve for the biogas generator schedule and frequency regulation capacity to minimize operational costs. In a case study using data from a California wastewater treatment plant, we demonstrate how our approach can exploit electricity rate structures to reduce electricity costs and effectively participate in the frequency regulation market
Upcycling of aluminum Twitch scrap via Shear Assisted Processing and Extrusion (ShAPE)
No full textAluminum is publicly perceived as recyclable, but mixed alloys and impurities in aluminum scrap require dilution via energy-intensive primary aluminum or downcycling to low-quality castings. In this study, cast billets of shredded aluminum scrap (Twitch) were blended with pre-consumer AA 6061, extruded into tubes via Shear Assisted Processing and Extrusion (ShAPE), and aged to T1 and T6 tempers. Microscopy reveals that ShAPE refined and distributed the deleterious AlFeSi phases. The Twitch extrusions had tensile properties comparable to AA 6061 yet without homogenizing or adding primary aluminum. Energy savings were 85% compared to conventional extrusion of primary aluminum alloys
An Energy Stable Local Discontinuous Galerkin Method for the Isothermal Navier-Stokes-Korteweg Equations
No full textIn this article, we develop an energy stable local discontinuous Galerkin (LDG) method for the isothermal Navier-Stokes-Korteweg (NSK) equations. Since the test and trial functions in LDG discretisations have to be in the same finite element space, it is difficult to obtain energy stable LDG discretizations for the isothermal NSK equations. To bridge this gap we first write the pressure into a free energy function form and introduce the velocity as a variable, then we use an extra auxiliary variable containing both the free energy function and the square of the velocity. These auxiliary variables are chosen in the stability analysis as test functions for the density and momentum balance equations. Using the Crank-Nicolson (CN) time integration method, we can prove then the stability of the CN-LDG method. Numerical experiments are provided to demonstrate the theoretical results, in particular on adaptive meshes