Energy-efficient UAV communication: A NOMA scheme with resource allocation and trajectory optimization.

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes.

Priority setting for health technology adoption at the national level: Lessons learned over 25 years' experience.

BACKGROUND: Limited health budgets and continual advancement of health technologies require mechanisms for prioritization. Israel, with a publicly funded health service basket, has implemented and optimized such a health technology assessment process since 1999.We describe the process of evaluating technologies according to the Israeli model, analyze its outputs and benefits over two decades of implementation, and compare its key features with international experience. METHODS: Retrospective data were collected between 1998 and 2023, including work processes, committee composition, number of applications submitted and approved by a clinical domain, and yearly cost of the basket. Features were evaluated within the evidence-informed deliberative process (EDP) framework. RESULTS: This national model involves relevant stake holders in a participatory and transparent process, in a timely manner, and is accepted by the public, health professionals, and policy makers, facilitating early adoption of the newest medical technologies. Between 11 and 19 percent of applications are approved for reimbursement annually, mostly pharmaceuticals. On average 26 percent of approved technologies are added to the list without additional budget. Major domains of approved technologies were oncology, cardiology, and neurology. CONCLUSIONS: Israel created a unique model for the expansion of the health service basket. Despite an increasing number of applications and rising costs, the mechanism enables a consensus to be reached on which technologies to fund, while remaining within budget constraints and facilitating immediate implementation. The process, which prioritizes transparency and stake holder involvement, allows just a resource allocation while maximizing the adoption of novel technologies, contributing to an outstanding national level of health despite relatively low health spending.

Orthopaedic Trauma Never Sleeps: Resource Allocation Even During a Non-trauma Crisis.

This study aimed to describe hospital resource utilization of an orthopaedic trauma service and the injury epidemiology during the 2019-2020 coronavirus pandemic to help plan future non-trauma crises. A retrospective chart review was performed on adult patients > 18 years of age who presented to our Level I Trauma Center for musculoskeletal trauma from March 30, 2020 to May 8, 2020 (stay-at-home order) and from March 30, 2019 to May 8, 2019 (comparison group). There were 182 patient encounters and 274 fractures in the 2020 stay-at-home period, and there were 210 patient encounters and 337 fractures in the 2019 control group. There was no statistical difference found comparing the proportion of patient encounters in the stay-at-home period to the control period (p > 0.05). The similar volume of consultations and surgeries justifies maintenance of standard resource allocation. (Journal of Surgical Orthopaedic Advances 32(2):102-106, 2023).

A systematic review of whole disease models for informing healthcare resource allocation decisions.

BACKGROUND: Whole disease models (WDM) are large-scale, system-level models which can evaluate multiple decision questions across an entire care pathway. Whilst this type of model can offer several advantages as a platform for undertaking economic analyses, the availability and quality of existing WDMs is unknown. OBJECTIVES: This systematic review aimed to identify existing WDMs to explore which disease areas they cover, to critically assess the quality of these models and provide recommendations for future research. METHODS: An electronic search was performed on multiple databases (MEDLINE, EMBASE, the NHS Economic Evaluation Database and the Health Technology Assessment database) on 23rd July 2023. Two independent reviewers selected studies for inclusion. Study quality was assessed using the National Institute for Health and Care Excellence (NICE) appraisal checklist for economic evaluations. Model characteristics were descriptively summarised. RESULTS: Forty-four WDMs were identified, of which thirty-two were developed after 2010. The main disease areas covered by existing WDMs are heart disease, cancer, acquired immune deficiency syndrome and metabolic disease. The quality of included WDMs is generally low. Common limitations included failure to consider the harms and costs of adverse events (AEs) of interventions, lack of probabilistic sensitivity analysis (PSA) and poor reporting. CONCLUSIONS: There has been an increase in the number of WDMs since 2010. However, their quality is generally low which means they may require significant modification before they could be re-used, such as modelling AEs of interventions and incorporation of PSA. Sufficient details of the WDMs need to be reported to allow future reuse/adaptation.

Evaluating prediction model performance.

This article highlights important performance metrics to consider when evaluating models developed for supervised classification or regression tasks using clinical data. When evaluating model performance, we detail the basics of confusion matrices, receiver operating characteristic curves, F1 scores, precision-recall curves, mean squared error, and other considerations. In this era, defined by the rapid proliferation of advanced prediction models, familiarity with various performance metrics beyond the area under the receiver operating characteristic curves and the nuances of evaluating model value upon implementation is essential to ensure effective resource allocation and optimal patient care delivery.

The impact of resource allocation during the COVID-19 pandemic on cardiac surgical practice and patient outcomes: a systematic review.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has shaken the world and placed enormous strain on healthcare systems globally. In this systematic review, we investigate the effect of resource allocation on cardiac surgery programs and the impact on patients awaiting elective cardiac surgery. METHODS: PubMed and Embase were systematically searched for articles published from 1 January 2019 to 30 August 2022. This systematic review included studies investigating the impact of the COVID-19 pandemic on resource allocation and the subsequent influence on cardiac surgery outcomes. A total of 1676 abstracts and titles were reviewed and 20 studies were included in this review. RESULTS: During the COVID-19 pandemic, resources were allocated away from elective cardiac surgery to help support the pandemic response. This resulted in increased wait times for elective patients, increased rates of urgent or emergent surgical intervention and increased rates of mortality or complications for patients awaiting or undergoing cardiac surgery during the pandemic. CONCLUSIONS: While the finite resources available during the pandemic were often insufficient to meet the needs of all patients as well as the influx of new COVID-19 patients, resource allocation away from elective cardiac surgery resulted in prolonged wait times, more frequent urgent or emergent surgeries and negative impacts on patient outcomes. Understanding the impacts of delayed access to care with regards to urgency of care, increased morbidity and mortality and increased utilization of resources per indexed case needs to be considered to navigate through pandemics to minimize the lingering effects that continue to negatively impact patient outcomes.

Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks.

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.

Optimal resource allocation method for energy harvesting based underlay Cognitive Radio networks.

This paper proposes an optimal resource allocation method. The method is to maximize the Energy Efficiency (EE) for an Energy Harvesting (EH) enabled underlay Cognitive Radio (CR) network. First, we assumed the Secondary Users (SUs) can harvest energy from the surrounding Radio Frequency (RF) signals. Then, we modelled the EE maximisation problem as a joint time and power optimization model. Next, the optimal EH time allocation factor can be calculated. After that the optimal power allocation strategy can be obtain by the fractional programming and Lagrange multiplier method. Finally simulation results show that the proposed iterative method can be better performance advantages compared with the exhaustive method and genetic algorithm. And the EE of this system model is significantly improved compared to the EE model without considering EH.

Deep Reinforcement Learning for Physical Layer Security Enhancement in Energy Harvesting Based Cognitive Radio Networks.

The paper studies the secrecy communication threatened by a single eavesdropper in Energy Harvesting (EH)-based cognitive radio networks, where both the Secure User (SU) and the jammer harvest, store, and utilize RF energy from the Primary Transmitter (PT). Our main goal is to optimize the time slots for energy harvesting and wireless communication for both the secure user as well as the jammer to maximize the long-term performance of secrecy communication. A multi-agent Deep Reinforcement Learning (DRL) method is proposed for solving the optimization of resource allocation and performance. Specifically, each sub-channel from the Secure Transmitter (ST) to the Secure Receiver (SR) link, along with the jammer to the eavesdropper link, is regarded as an agent, which is responsible for exploring optimal power allocation strategy while a time allocation network is established to obtain optimal EH time allocation strategy. Every agent dynamically interacts with the wireless communication environment. Simulation results demonstrate that the proposed DRL-based resource allocation method outperforms the existing schemes in terms of secrecy rate, convergence speed, and the average number of transition steps.

Predicting the impact of temperature on metabolic fluxes using resource allocation modelling: Application to polyphosphate accumulating organisms.

The understanding of microbial communities and the biological regulation of its members is crucial for implementation of novel technologies using microbial ecology. One poorly understood metabolic principle of microbial communities is resource allocation and biosynthesis. Resource allocation theory in polyphosphate accumulating organisms (PAOs) is limited as a result of their slow imposed growth rate (typical sludge retention times of at least 4 days) and limitations to quantify changes in biomass components over a 6 hours cycle (less than 10% of their growth). As a result, there is no direct evidence supporting that biosynthesis is an exclusive aerobic process in PAOs that alternate continuously between anaerobic and aerobic phases. Here, we apply resource allocation metabolic flux analysis to study the optimal phenotype of PAOs over a temperature range of 4 °C to 20 °C. The model applied in this research allowed to identify optimal metabolic strategies in a core metabolic model with limited constraints based on biological principles. The addition of a constraint limiting biomass synthesis to be an exclusive aerobic process changed the metabolic behaviour and improved the predictability of the model over the studied temperature range by closing the gap between prediction and experimental findings. The results validate the assumption of limited anaerobic biosynthesis in PAOs, specifically "Candidatus Accumulibacter" related species. Interestingly, the predicted growth yield was lower, suggesting that there are mechanistic barriers for anaerobic growth not yet understood nor reflected in the current models of PAOs. Moreover, we identified strategies of resource allocation applied by PAOs at different temperatures as a result of the decreased catalytic efficiencies of their biochemical reactions. Understanding resource allocation is paramount in the study of PAOs and their currently unknown complex metabolic regulation, and metabolic modelling based on biological first principles provides a useful tool to develop a mechanistic understanding.

Medical resource allocation planning by integrating machine learning and optimization models.

Patients' waiting time is a major issue in the Canadian healthcare system. The planning for resource allocation impacts patients' waiting time in medicare settings. This research focuses on the reduction of patients' waiting time by providing better planning for radiological resource allocation and efficient workload distribution. Resource allocation planning is directly related to the number of patient-arrival and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different modalities and different timeframes which makes the patient-arrival prediction challenging. In this research, a new three-phase solution framework is proposed where a new multi-target machine learning technique is integrated with an optimization model. In the first phase, a novel Ensemble of Pruned Regressor Chain (EPRC) model is developed and trained offline to predict uncertain parameters, such as patients' arrival. The proposed model is then compared with two popular multi-target prediction methods to evaluate the model's accuracy. In the second phase, the trained model is deployed in the real-time environment to forecast patients' arrival, miss Turn Around Time (miss-TAT) rate, and probable workload count. The forecasted data is used in phase three where a new multi-objective optimization model is developed to determine workload allocation. The Weighted-sum method is used to get efficient solutions. The proposed model is deployed in a Canadian healthcare company and evaluated using real-time healthcare data. It is observed in terms of accuracy, the proposed EPRC model performed 10.81 % better compared to the other multi-target models considered in this study. It is also noticed that the forecasting results have a direct impact on the workload distribution, where the proposed model decreases the total workload by approximately 25 %. Besides, the result shows the efficient workload distribution provided by the proposed framework can reduce the average patients' waiting time by 8.17 %.

Multi-Omics Profiling Reveals Resource Allocation and Acclimation Strategies to Temperature Changes in a Marine Dinoflagellate.

Temperature is a critical environmental factor that affects the cell growth of dinoflagellates and bloom formation. To date, the molecular mechanisms underlying the physiological responses to temperature variations are poorly understood. Here, we applied quantitative proteomic and untargeted metabolomic approaches to investigate protein and metabolite expression profiles of a bloom-forming dinoflagellate Prorocentrum shikokuense at different temperatures. Of the four temperatures (19, 22, 25, and 28°C) investigated, P. shikokuense at 25°C exhibited the maximal cell growth rate and maximum quantum efficiency of photosystem II (Fv/Fm) value. The levels of particulate organic carbon (POC) and nitrogen (PON) decreased with increasing temperature, while the POC/PON ratio increased and peaked at 25°C. Proteomic analysis showed proteins related to photoreaction, light harvesting, and protein homeostasis were highly expressed at 28°C when cells were under moderate heat stress. Metabolomic analysis further confirmed reallocated amino acids and soluble sugars at this temperature. Both omic analyses showed glutathione metabolism that scavenges the excess reactive oxygen species, and transcription and lipid biosynthesis that compensate for the low translation efficiency and plasma membrane fluidity were largely upregulated at suboptimal temperature. Higher accumulations of glutathione, glutarate semialdehyde, and 5-KETE at 19°C implied their important roles in low-temperature acclimation. The strikingly active nitrate reduction and nitrogen flux into asparagine, glutamine, and aspartic acid at 19°C indicated these three amino acids may serve as nitrogen storage pools and help cells cope with low temperature. Our study provides insights into the effects of temperature on dinoflagellate resource allocation and advances our knowledge of dinoflagellate bloom formation in marine environments. IMPORTANCE Marine phytoplankton is one of the most important nodes in global biogeochemical cycle. Deciphering temperature-associated marine phytoplankton cell stoichiometric changes and the underlying molecular mechanisms are therefore of great ecological concerns. However, knowledge of how phytoplankton adjust the cell stoichiometry to sustain growth under temperature changes is still lacking. This study investigates the variations of protein and metabolite profiles in a marine dinoflagellate across temperatures at which the field blooms usually occur and highlights the temperature-dependent molecular traits and key metabolites that may be associated with rapid cell growth and temperature stress acclimation.

[Analysis of the equity of health human resource allocation for echinococcosis control based on Gini index].

OBJECTIVE: To analyze the current status and evaluate the equity of health human resource allocation for echinococcosis control in Ganzi Tibetan Autonomous Prefecture, Sichuan Province, so as to provide the empirical evidence for optimizing the health human resource allocation for echinococcosis control. METHODS: A questionnaire survey was performed to collect the numbers of health human sources for echinococcosis control, including health workers, healthcare professionals, certified/assistant physicians and registered nurses, per 1 000 permanent residents, per 1 000 m2, per 1 000 residents screened using Bmode ultrasonography and per 1 000 echinococcosis patients in two highly endemic counties and three lowly endemic counties in Ganzi Tibetan Autonomous Prefecture, Sichuan Province from 2016 to 2019. The equity of health human resource allocation for echinococcosis control was evaluated by permanent residents and geographical areas using Lorenz curve and Gini index in Ganzi Tibetan Autonomous Prefecture from 2016 to 2019. RESULTS: The numbers of health workers, healthcare professionals, certified/assistant physicians and registered nurses per 1 000 permanent residents, per 1 000 permanent residents, per 1 000 m2, per 1 000 residents screened using B-mode ultrasonography and per 1 000 echinococcosis patients were 0.99-, 1.06-, 1.78- and 1.88-fold; 3.38-, 3.67-, 6.00- and 6.00-fold; 1.64-, 1.74-, 3.22- and 3.18-fold; and 64.92-, 70.39-, 139.34- and 117.44-fold more in lowly endemic counties than in highly endemic countries in Ganzi Tibetan Autonomous Prefecture, Sichuan Province, 2019. The Gini indexes of health human resource allocation for echinococcosis control were 0.371 to 0.397 by permanent residents and 0.477 to 0.591 by geographical areas in Ganzi Tibetan Autonomous Prefecture from 2016 to 2019, and the Gini indexes (0.469 to 0.730) for allocation of certified/assistant physicians and registered nurses were both higher than those of health workers and healthcare professionals (0.302 to 0.451) by both permanent residents and geographical areas. CONCLUSIONS: The health human resource allocation for echinococcosis control showed general equity by permanent residents and poor equity by geographical areas in Ganzi Tibetan Autonomous Prefecture, Sichuan Province from 2016 to 2019.

Decision criteria for resource allocation: an analysis of CONITEC oncology reports. / Critérios de decisão para a alocação de recursos: uma análise de relatórios da CONITEC na área de oncologia.

In health technology assessment (HTA), decision criteria are considered relevant to support the complex deliberative process that requires simultaneous consideration of multiple factors. The aim was to identify and analyze the decision criteria that have been used by the National Health Technology Assessment Commission (CONITEC) when recommending the incorporation of technologies for the treatment of cancer. Descriptive study, based on reports from CONITEC, between 2012 and 2018, on oncology technologies. The data were collected in a specific extraction form and analyzed using descriptive statistics. 39 reports were analyzed, 15 of them did not present any explicit decision criteria. Medicines were the most frequently evaluated type of technology. The most frequent types of cancers were: breast cancer, head and neck cancer, colorectal cancer, non-Hodgkin's lymphoma and lung cancer. The most frequently considered criteria were: financial impact and effectiveness. The study identified the decision criteria that have been most used in the area of ​​oncology, however, the lack of transparency in relation to the weight of these criteria makes it difficult to understand their influence on the result of the decisions taken.

Nos processos de avaliação de tecnologias em saúde (ATS), critérios de decisão são considerados relevantes para apoiar o complexo processo deliberativo que requer consideração simultânea de múltiplos fatores. Objetivou-se identificar e analisar os critérios de decisão que têm sido utilizados pela Comissão Nacional de Avaliação de Tecnologias em Saúde (CONITEC) na recomendação de incorporação de tecnologias para o tratamento do câncer. Trata-se de um estudo descritivo, baseado em relatórios da CONITEC, entre 2012 e 2018, sobre tecnologias em oncologia. Os dados foram coletados em formulário de extração específico e analisados por meio de estatísticas descritivas. Foram analisados 39 relatórios, 15 deles não apresentavam nenhum critério de decisão explícito. Os medicamentos constituíram o tipo de tecnologia mais frequentemente avaliado. Os tipos de cânceres mais frequentes foram: de mama, de cabeça e pescoço, colorretal, linfoma não Hodgkin e de pulmão. Os critérios considerados com mais frequência foram: impacto financeiro e efetividade. O estudo identificou os critérios de decisão que têm sido mais utilizados na área de oncologia, entretanto, a falta de transparência em relação ao peso desses critérios torna difícil compreender a influência deles no resultado das decisões tomadas.

Disparate resource allocation during the COVID-19 pandemic among trauma centers: A Western Trauma Association national survey.

BACKGROUND: During the pandemic, hospitals implemented disaster plans to conserve resources while maintaining patient care. It was unclear how these plans impacted injury care and trauma surgeons. STUDY DESIGN: A 16 question survey assessing COVID-related hospital policy and resource allocation pre-COVID-19 peak (March), and a 19 question post-peak (June) survey was distributed to Trauma/Critical Care attending's via social media and the Western Trauma Association member email list. RESULTS: There were 120 pre- and 134 post-peak respondents. Most (95%) altered trauma PPE components, a nd 67% noted changes in their admission population pre-peak while 80% did so post-peak. Penetrating injury increased 56% at Level 1 centers and 27% at Level 2 centers. Altered ICU and transfusion criteria were noted with 25% relocating TBI patients, 17% revised rib fracture admission criteria, and 23% adjusted transfusion practices. Importantly, 12% changed their massive transfusion protocol, with 11% reducing the symptomatic transfusion threshold from 7 g/dL to 6 g/dL. Half (50%) disclosed impediments to patient care including PPE shortages and COVID test-related procedural delay (Fig. 2). While only 14% felt their institution was overwhelmed by COVID, the vast majority (81%) shared durable concerns about personal health and safety. CONCLUSIONS: Disparate approaches to COVID-19 preparedness and response characterize survey respondent facility actions. These disparities, especially between Level 1 and Level 2 centers, represent opportunities for the trauma community to coordinate best-practice planning and implementation in light of future consequence infection or pandemic care.

Nonuniformity of Whole-Cerebral Neural Resource Allocation, a Neuromarker of the Broad-Task Attention.

The neural basis of attention is thought to involve the allocation of limited neural resources. However, the quantitative validation of this hypothesis remains challenging. Here, we provide quantitative evidence that the nonuniform allocation of neural resources across the whole cerebral gray matter reflects the broad-task process of sustained attention. We propose a neural measure for the nonuniformity of whole-cerebral allocation using functional magnetic resonance imaging. We found that this measure was significantly correlated with conventional indicators of attention level, such as task difficulty and pupil dilation. We further found that the broad-task neural correlates of the measure belong to frontoparietal and dorsal attention networks. Finally, we found that patients with attention-deficit/hyperactivity disorder showed abnormal decreases in the level of the proposed measure, reflecting the executive dysfunction. This study proposes a neuromarker suggesting that the nonuniform allocation of neural resources may be the broad-task neural basis of sustained attention.