Limited Coverage Research Articles

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain Improved Genetic Algorithm

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

Research on Multi-Sensor Fusion Fault Diagnosis Method Based on Spatiotemporal Attention Mechanism

As industrial systems become increasingly complex, real-time monitoring and intelligent management of industrial equipment have become imperative. However, the limitations in coverage and accuracy of single sensors make it challenging to comprehensively characterize the operational state of equipment, leading to reduced system reliability and increased pressures on data transmission and storage. To address these challenges, this study presents a novel fault diagnosis method based on multi-sensor fusion using a spatio-temporal attention mechanism. Initially, one-dimensional convolutional neural networks (1D-CNN) are employed to extract features from raw signals, effectively capturing local characteristics and ensuring the integrity and validity of fault signals. Subsequently, the spatiotemporal attention mechanism adjusts the feature weights based on the temporal and spatial correlations of different sensors, as well as their respective importance, thereby capturing the spatio-temporal dependencies across multiple sensors and enhancing the efficacy of information fusion. Finally, the proposed method is validated through experiments on a nickel flash smelting furnace system. The results demonstrate that the method achieves a fault diagnosis accuracy exceeding 97.78%, significantly enhancing fault detection and decision-making performance.

7831 Unveiling Drug Resistance Mechanisms In Triple-Positive Breast Cancer: Insights From Long-Term Estrogen Deprivation Models And Innovative Mutation Detection Strategies

Abstract Disclosure: S. Bahnassy: None. H.S. Andrews: None. L. Jin: None. B.F. Kohrn: None. S. Tam: None. D. Mobin: None. Y.J. Dunn: None. M. Balachandran: None. M. Podar: None. W. He: None. M.D. McCoy: None. R.A. Beckman: Consulting Fee; Self; AstraZeneca, Boehringer Ingelheim. Other; Self; Chief Scientific Officer of OncoMind, LLC, which owns patents on dynamic precision medicine. R.B. Riggins: None. Compelling evidence from preclinical and clinical data suggests that triple-positive (TP; HER2+/HR+) breast cancer (BC) has lower response rates to HER2-targeted therapy and a higher risk of recurrence to endocrine therapy (ET) than HER2+/HR−. Molecular mechanisms underpinning drug resistance in TP-BC are poorly understood, thereby limiting our ability to improve patient outcomes and prevent metastatic progression. In our recent publication, we established and characterized long-term estrogen deprivation (LTED) ET-resistant (ETR) TP-BC models that mimic resistance to aromatase inhibitors without using genetic manipulation approaches. We reported intrinsic subtype shifts towards non-luminal phenotypes, metabolic reprogramming, and acquiring mutations in genes encoding for transcription factors and chromatin modifiers, as potential mechanisms of ETR in our LTED models. The emergence of drug-resistant mutator subclones under treatment-induced selective pressures, coupled with limitations in shallow-depth whole genome sequencing coverage, highlight an unmet need to develop a strategy for visually monitoring, capturing, and mapping resistant subclones through a mutational phenotypic screen, an approach offering valuable insights for optimizing treatment schedules. To address this, we developed and applied an innovative dual fluorescence mutation detection system to detect mutator cells expressing both mCherry and green fluorescence protein (GFP). First, we validated if our system is working properly. Compared to parental cells, transduced BT-474 and MDA-MB-361 LTEDs showed a drastic increase in GFP+ cells, suggesting the presence of resistant mutator cells. Consistent with the increase in GFP signal, TP-LTEDs showed increased mutation number and frequency in two Duplex Sequencing targeted capture panels (neutral loci and DNA polymerase-focused), used to perform ultra-deep, highly accurate sequencing. We then treated parental cells with single, dual, and triple combinations of anti-HER2, ET and CDK4/6 inhibitors to track the emergence of GFP+ mutator cells. Nine-day treatments with triple combinatorial therapies were best at inhibiting the growth of both BT-474 and MDA-MB-361 parental cell lines, compared to single- and double-agent targeted therapies. However, no GFP signal was detected within treated cells, suggesting that longer treatment durations are needed for drug-resistance mutator subclones to be discovered. Ongoing studies monitoring TP-drug resistance mutator subclones with long-term conventional targeted-therapies will improve our understanding of drug resistance mechanisms in this understudied subtype of BC. Importantly, these studies will enable dynamic precision medicine simulations to empower for a more tailored and effective therapeutic administration to prevent drug resistance and metastatic progression of TP-BC. Presentation: 6/3/2024

7831 Unveiling Drug Resistance Mechanisms in Triple-Positive Breast Cancer: Insights from Long-Term Estrogen Deprivation Models and Innovative Mutation Detection Strategies

Abstract Disclosure: S. Bahnassy: None. H.S. Andrews: None. L. Jin: None. B.F. Kohrn: None. S. Tam: None. D. Mobin: None. Y.J. Dunn: None. M. Balachandran: None. M. Podar: None. W. He: None. M.D. McCoy: None. R.A. Beckman: Consulting Fee; Self; AstraZeneca, Boehringer Ingelheim. Other; Self; Chief Scientific Officer of OncoMind, LLC, which owns patents on dynamic precision medicine. R.B. Riggins: None. Compelling evidence from preclinical and clinical data suggests that triple-positive (TP; HER2+/HR+) breast cancer (BC) has lower response rates to HER2-targeted therapy and a higher risk of recurrence to endocrine therapy (ET) than HER2+/HR−. Molecular mechanisms underpinning drug resistance in TP-BC are poorly understood, thereby limiting our ability to improve patient outcomes and prevent metastatic progression. In our recent publication, we established and characterized long-term estrogen deprivation (LTED) ET-resistant (ETR) TP-BC models that mimic resistance to aromatase inhibitors without using genetic manipulation approaches. We reported intrinsic subtype shifts towards non-luminal phenotypes, metabolic reprogramming, and acquiring mutations in genes encoding for transcription factors and chromatin modifiers, as potential mechanisms of ETR in our LTED models. The emergence of drug-resistant mutator subclones under treatment-induced selective pressures, coupled with limitations in shallow-depth whole genome sequencing coverage, highlight an unmet need to develop a strategy for visually monitoring, capturing, and mapping resistant subclones through a mutational phenotypic screen, an approach offering valuable insights for optimizing treatment schedules. To address this, we developed and applied an innovative dual fluorescence mutation detection system to detect mutator cells expressing both mCherry and green fluorescence protein (GFP). First, we validated if our system is working properly. Compared to parental cells, transduced BT-474 and MDA-MB-361 LTEDs showed a drastic increase in GFP+ cells, suggesting the presence of resistant mutator cells. Consistent with the increase in GFP signal, TP-LTEDs showed increased mutation number and frequency in two Duplex Sequencing targeted capture panels (neutral loci and DNA polymerase-focused), used to perform ultra-deep, highly accurate sequencing. We then treated parental cells with single, dual, and triple combinations of anti-HER2, ET and CDK4/6 inhibitors to track the emergence of GFP+ mutator cells. Nine-day treatments with triple combinatorial therapies were best at inhibiting the growth of both BT-474 and MDA-MB-361 parental cell lines, compared to single- and double-agent targeted therapies. However, no GFP signal was detected within treated cells, suggesting that longer treatment durations are needed for drug-resistance mutator subclones to be discovered. Ongoing studies monitoring TP-drug resistance mutator subclones with long-term conventional targeted-therapies will improve our understanding of drug resistance mechanisms in this understudied subtype of BC. Importantly, these studies will enable dynamic precision medicine simulations to empower for a more tailored and effective therapeutic administration to prevent drug resistance and metastatic progression of TP-BC. Presentation: 6/3/2024

The landscape of assisted reproductive technology access in India.

Historically, infertility has been stigmatized in the Indian society, primarily due to societal norms that equate marriage with procreation. In twentieth century, India focused primarily on over-fertility in its family planning programs, with little attention given to the complexities of infertility. The introduction of Assisted Reproductive Technology (ART) in the late 1970s made a global revolution, including in India, offering hope to infertile couples. Despite a significant rise in ART clinics offering a wide range of treatment options in the recent years, challenges remain, particularly regarding the affordability. In India, ART is typically dominated by the private sector as government support remains limited. Efforts to standardize ART practices, including the establishment of the National ART & Surrogacy Registry and ART act aim to regulate, improve outcomes and curb unethical practice. Despite these advancements, the high cost of treatment cycles and lack of insurance coverage limit many couples' ability to undergo fertility treatment. Addressing these issues requires a multifaceted approach, including policy reform, increased public awareness, and the development of affordable treatment options to ensure broader access to reproductive care across India.

Nationwide survey of the secondary findings in cancer genomic profiling: survey including liquid biopsy.

We surveyed the status of the secondary finding (SF) disclosure in comprehensive genome profiling (CGP) in 2020. The situation has changed: increase in the number of hospitals that provide CGP, an update to the Comprehensive Tumor Genomic Profiling: Materials for Review of Secondary Findings (CTGPMRSF), and the addition of a liquid biopsy test, FoundationOne® Liquid CDx (F1L). Moreover, the actual situation was unclear because the 2020 survey did not include all designated and cooperative hospitals. Herein, we conducted a questionnaire survey of all designated-core, designated, and cooperative hospitals to identify the current status and challenges concerning SF in the CGP in 2022. A total of 82.1% of the hospitals responded and 77.7% of the response was from cooperative hospitals. Approximately 80% of the hospitals used CTGPMRSF. SF disclosure, confirmatory test implementation, and SF confirmation rates were 12.4%, 31.6%, and 46.6% for FoundationOne® CDx (F1CDx), respectively, and 6.8%, 31.8%, and 70.7% for F1L, respectively. The implementation rate of the confirmatory test was substantially higher in hospitals with genetic experts and in hospitals that could conduct confirmatory tests on the same day. Our survey provides insight into how SF is handled in Japan. The percentage of cases leading to confirmatory tests has gradually increased, although challenges such as insurance coverage limitations and varied understanding of SF among patients and healthcare providers persist. With the increasing use of whole-genome analysis, our findings will provide valuable insights into establishing an effective SF disclosure system.

Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R2 and 0.9 training R2). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

Potential of Non-Contact Dynamic Response Measurements for Predicting Small Size or Hidden Damages in Highly Damped Structures.

Vibration-based structural health monitoring (SHM) is essential for evaluating structural integrity. Traditional methods using contact vibration sensors like accelerometers have limitations in accessibility, coverage, and impact on structural dynamics. Recent digital advancements offer new solutions through high-speed camera-based measurements. This study explores how camera settings (speed and resolution) influence the accuracy of dynamic response measurements for detecting small cracks in damped cantilever beams. Different beam thicknesses affect damping, altering dynamic response parameters such as frequency and amplitude, which are crucial for damage quantification. Experiments were conducted on 3D-printed Acrylonitrile Butadiene Styrene (ABS) cantilever beams with varying crack depth ratios from 0% to 60% of the beam thickness. The study utilised the Canny edge detection technique and Fast Fourier Transform to analyse vibration behaviour captured by cameras at different settings. The results show an optimal set of camera resolutions and frame rates for accurately capturing dynamic responses. Empirical models based on four image resolutions were validated against experimental data, achieving over 98% accuracy for predicting the natural frequency and around 90% for resonance amplitude. The optimal frame rate for measuring natural frequency and amplitude was found to be 2.4 times the beam's natural frequency. The findings provide a method for damage assessment by establishing a relationship between crack depth, beam thickness, and damping ratio.

Deposit insurance and credit union earnings opacity

This study examines the impact of deposit insurance coverage on credit union earnings opacity. For identification, we employ the provisions outlined in Section 136 of the Emergency Economic Stabilization Act, which raised the upper limit of deposit insurance coverage from $100,000 to $250,000. Using variation in insured deposits brought about by the differential impact of the change to deposit insurance arrangements and a difference-in-differences approach, we find that credit unions experiencing a substantial rise in insured deposits tend to exercise more discretion over loan loss provisions, leading to an increase in earnings opacity. This is most evident for small and medium sized credit unions.

StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT

Due to limitations in network coverage and capacity, terrestrial networks cannot meet the growing demand for widespread intelligent applications in recent years. The increasingly popular Satellite-based Internet of Things (S-IoT), with its extensive and boundless coverage, has emerged as the optimal solution. However, the large-scale distributed communication and services have led to significant challenges in scalability and security. Blockchain can help mitigate security and privacy issues in data sharing of S-IoT. To address these challenges, we propose StarCross, a secure and lightweight data sharing framework for S-IoT based on redactable blockchain. Firstly, we develop a space-ground collaborative sharding blockchain network architecture. Low Earth Orbit (LEO) satellite network serves as the communication intermediary between shards. Secondly, StarCross employs an improved Proof of Work (PoW) consensus based on dynamic difficulty to mitigate the challenges of uneven computing power and centralization within each shard. To ensure the atomicity of cross-shard transactions, StarCross introduces a blockchain rewriting mechanism called RBCVC, which combines chameleon hash and voting-based consensus. Thirdly, we conducted theoretical analysis and experimental evaluations of StarCross. The results indicate that compared to existing solutions, StarCross achieves better scalability and security in S-IoT. StarCross’s TPS increased to 260.9% of the baseline, while the transaction confirmation latency decreased by 79.7%.

Insights into the interaction of nitrobenzene and the Ag(111) surface: A DFT study

This study explores the potential of nitrobenzene as an anolyte material for nonaqueous redox flow batteries (RFBs) by theoretically examining its low-coverage adsorption behavior on neutral and charged Ag(111) model electrode surfaces. At the low coverage limit, DFT calculations show a preference for nitrobenzene to adsorb parallel to the surface, with the benzene ring and nitro group centered over HCP sites. Interactions between nitrobenzene and the surface were analyzed using induced charge density analysis, Bader charge analysis, and projected density of states (PDOS). It was found that nitrobenzene adsorbs primarily through van der Waals interactions with the surface. As nitrobenzene accumulates negative charge, the strength of adsorption diminishes. Understanding the electrode-electrolyte interface is crucial for enhancing RFB electrochemical performance, and this study sheds light on nitrobenzene's interaction with a model Ag electrode.

Perceived benefits and limitations of a psychoeducation program for patients with fibromyalgia: an interpretative phenomenological analysis.

To analyze the perceived benefits and limitations of a pain psychoeducation program as a non-pharmacological treatment for patients with fibromyalgia. An interpretative phenomenological analysis was applied to analyze the subjective experiences of 11 patients with fibromyalgia who participated in a pain psychoeducation program. This program includes educational sessions that address pain understanding, coping strategies, and relaxation techniques. Semi-structured interviews were conducted, transcribed, and analyzed using ATLAS.ti software. Patients reported significant improvements in cognitive-functional capacity and socio-emotional ability, including better disease understanding and management, emotional stability, and interpersonal relationships. Despite these benefits, they identified limitations in program individualization and insufficient coverage of certain topics, such as sexual health and legal aspects of disability. Enhanced self-management skills were evident, with observed shifts in disease perception and coping strategies. The psychoeducation program was viewed positively, influencing functional, cognitive, and emotional enhancements. Nonetheless, the need for increased program personalization and expanded socio-economic support was noted. Future research should focus on the long-term impacts of psychoeducation and the feasibility of tailored interventions.

Digital Advertising Trends and Effectiveness in the Modern Era: A Systematic Literature Review

This research aims to understand the latest trends in digital advertising and measure the effectiveness of video content and social media in increasing engagement, conversion, brand awareness, and customer loyalty. This research utilizes a quantitative study design with a cross-sectional survey to collect data from social media-active consumers exposed to video advertisements. Data analysis used descriptive and inferential statistical techniques to test the relationship between these variables. The findings showed that video content is highly effective in attracting attention and maintaining consumer interest longer than text or static images. The video also allows for more complex and emotional messaging. Social media facilitates two-way interaction between brands and consumers, strengthening relationships and increasing customer loyalty. Integrating video content with social media platforms such as YouTube, Facebook, Instagram, and TikTok significantly increases engagement and conversion. Personalization of ads through artificial intelligence (AI) technology has also been shown to increase campaign relevance and effectiveness. This research contributes to the digital advertising literature by demonstrating the importance of integrating video and social media content and using AI technology for personalization. The findings offer practical guidance for advertisers to improve the effectiveness of their campaigns. However, this study has limitations in sample coverage and quantitative analysis focus. Future research should expand the sample and incorporate qualitative approaches to gain more comprehensive insights.

ToxinPred 3.0: An improved method for predicting the toxicity of peptides

Toxicity emerges as a prominent challenge in the design of therapeutic peptides, causing the failure of numerous peptides during clinical trials. In 2013, our group developed ToxinPred, a computational method that has been extensively adopted by the scientific community for predicting peptide toxicity. In this paper, we propose a refined variant of ToxinPred that showcases improved reliability and accuracy in predicting peptide toxicity. Initially, we utilized a similarity/alignment-based approach employing BLAST to predict toxic peptides, which yielded satisfactory accuracy; however, the method suffered from inadequate coverage. Subsequently, we employed a motif-based approach using MERCI software to uncover specific patterns or motifs that are exclusively observed in toxic peptides. The search for these motifs in peptides allowed us to predict toxic peptides with a high level of specificity with poor sensitivity. To overcome the coverage limitations, we developed alignment-free methods using machine/deep learning techniques to balance sensitivity and specificity of prediction. Deep learning model (ANN - LSTM with fixed sequence length) developed using one-hot encoding achieved a maximum AUROC of 0.93 with MCC of 0.71 on an independent dataset. Machine learning model (extra tree) developed using compositional features of peptides achieved a maximum AUROC of 0.95 with MCC of 0.78. We also developed large language models and achieved maximum AUC of 0.93 using ESM2-t33. Finally, we developed hybrid or ensemble methods combining two or more methods to enhance performance. Our specific hybrid method, which combines a motif-based approach with a machine learning-based model, achieved a maximum AUROC of 0.98 with MCC 0.81 on an independent dataset. In this study, all models were trained and tested on 80 % of data using five-fold cross-validation and evaluated on the remaining 20 % of data called independent dataset. The evaluation of all methods on an independent dataset revealed that the method proposed in this study exhibited better performance than existing methods. To cater to the needs of the scientific community, we have developed a standalone software, pip package and web-based server ToxinPred3 (https://github.com/raghavagps/toxinpred3 and https://webs.iiitd.edu.in/raghava/toxinpred3/).

Joint optimization of offloading strategy and resource allocation for multi-user in dynamic vehicular edge computing systems

Internet of Vehicles (IoV) relies heavily on its computing capability to facilitate various vehicular applications. Since the cloud computing or mobile edge computing (MEC) only cannot satisfy the latency requirement due to the limitation of the resource coverage, the cloud–edge-end cooperative computing has become an emerging paradigm. A comprehensive IoV architecture is considered and a joint optimization problem is formulated to minimize the system function value. To optimize the resource allocation and the task offloading strategy, the simulated spring system algorithm (SSSA) is designed where the initial problem is decoupled into two sub-problems with priority. The first one is to allocate computing resources based on KKT conditions, thus the individual optimal solution is achieved. The second one is solved based on the idea of simulated spring system such that the task offloading strategy is obtained. Two sub-problems iterate mutually to update each other until finishing the binary tree traversal. Thus, the proposed solution adapts to various conditions and the computational complexity is also reduced compared with traditional methods. Simulation verifies that the proposed algorithm reduces the maximum system function value by about 31% compared with the benchmark methods and performs efficiently in various road conditions.

"It Has Improved My Practice to Be Able to Offer Alternative Treatments": A Longitudinal Qualitative Study of Oregon Medicaid Back Pain Providers.

Introduction: This study aimed to understand health care providers' experiences implementing the Oregon Back Pain Policy (OBPP) over time. The Medicaid OBPP expanded coverage of evidence-based nonpharmacological therapy (NPT) for back pain and restricted access to opioid therapy and interventional approaches. Methods: The study included six online, asynchronous focus groups with providers in February 2020 (Time 1) and August 2022 (Time 2). Analysis was conducted with a longitudinal, recurrent cross-sectional approach. Analysis occurred in three stages: (1) An immersion/crystallization approach was used to analyze Time 1 focus group data, (2) reflexive thematic analysis was used to analyze Time 2 data, and (3) longitudinal analysis was used to integrate the findings across time points. Results: At Time 1, 48 clinicians and 44 NPT providers participated in the study. Time 2 included 63 clinicians and 59 NPT providers. The longitudinal analysis of the focus group data resulted in four themes: (1) general awareness of the policy, (2) providers support the policy and perceive a benefit to their patients, (3) barriers to NPT accessibility, and (4) barriers to referring patients to NPT. Conclusion: The goal of the OBPP was to improve back pain care for Oregon Medicaid members by increasing access to evidence-based NPT and decreasing reliance on opioid medications. This study revealed that, although clinicians and NPT providers supported the policy, they faced persistent implementation challenges related to referrals, prior authorizations, coverage limitations, low reimbursement rates, and a reduced workforce for NPT providers. In some cases, implementation barriers were removed during the COVID-19 pandemic, but other challenges were more prominent during the pandemic.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Adaptive Scheduling of High-Availability Drone Swarms for Congestion Alleviation in Connected Automated Vehicles

The Intelligent Transportation System (ITS) serves as a pivotal element within urban networks, offering decision support to users and connected automated vehicles (CAVs) through comprehensive information gathering, sensing, device control, and data processing. Presently, ITS predominantly relies on sensors embedded in fixed infrastructure, notably Roadside Units (RSUs). However, RSUs are confined by coverage limitations and may encounter challenges in prompt emergency responses. On-demand resources, such as drones, present a viable option to supplement these deficiencies effectively. This paper introduces an approach where Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies are integrated to formulate a high-availability drone swarm control and communication infrastructure framework, comprising the cloud layer, edge layer, and device layer. Drones confront limitations in flight duration attributed to battery limitations, posing a challenge in sustaining continuous monitoring of road conditions over extended periods. Effective drone scheduling stands as a promising solution to overcome these constraints. To tackle this issue, we initially utilized Graph WaveNet, a specialized graph neural network structure tailored for spatial-temporal graph modeling, for training a congestion prediction model using real-world dataset inputs. Building upon this, we further propose an algorithm for drone scheduling based on congestion prediction. Our simulation experiments using real-world data demonstrate that, compared to the baseline method, the proposed scheduling algorithm not only yielded superior scheduling gains but also mitigated drone idle rates.

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents’ decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance.