Suboptimal Scheme Research Articles

On the regulation of the fishery taking into account its effectiveness using the example of pollock in the northern part of the Sea of Okhotsk

The purpose of this work is to consider the question of the criteria for the effectiveness of fishing during the year using the example of pollock of the Severo-Okhotomorskaya subzone.Materials and methods: The work analyzed the effectiveness of the fishery based on the data of the fishery and statistical information of the Center for the Fishery Monitoring and Communications System of the Federal Agency for Fishery. The main indicators for the analysis were: pollock production by month, production per vessel-day of fishing, number of vessels per vessel, number of vessels per vessel-day of fishing, share of production beheaded + 25 in total production beheaded. The highly profitable pollock fishery of season “A” is observed in February, March and April, which is confirmed by reliably different indicators of the average production volume and the volume of production per ship-day of fishing during these months relative to season “B” (at p < 0.001).The results of the study: The results of the analysis of the considered indicators may indicate such reasons as the suboptimal amount of the total allowable catch, the development of which requires increased costs and, as a result, reduces the economic efficiency of the fishery, suboptimal schemes for the arrangement of the fleet.Practical significance: The results obtained during the work indicate the need to take into account economic and social criteria when assessing the efficiency of the exploitation of reserves of aquatic biological resources, which are currently not taken into account in fisheries management.

Comparison of common classification strategies for large-scale vegetation mapping over the Google Earth Engine platform

• We assessed the effect of various sample properties on classification performance. • The use of pixel time series from fusion data achieved optimal results. • Target class proportions and sample time frames affected accuracy. • Suboptimal methods had a larger impact on the accuracy of minority classes. • The combined use of Sentinel MSI and SAR data provided accurate vegetation maps. Vegetation resources have an essential role in sustainable development due to their close relationship with natural resource management and environmental protection. The monitoring of land use and cover is key for a more sustainable management of these resources, and Earth Observation satellites have provided an increasingly powerful platform for performing this task. To date, numerous classification algorithms have been developed for vegetation mapping, but a comparative evaluation of the strategies commonly used in large-scale applications (50000 km 2 and above) is lacking. We developed a classification framework based on random forests and Sentinel data within the Google Earth Engine platform to assess the performance of various strategies over a complex landscape with a wide range of vegetation classes, plot configurations, and agricultural practices. These strategies differed in key aspects related to the characteristics of the classifier and the sample, and were evaluated by class-specific and general accuracy metrics. We found that the use of pixel time series features from fusion data, class-balanced labels, and multi-season time frames enhanced overall performance by 1.3%–14.1% over alternative approaches, but in some cases also generated tradeoffs of 1.6%–8.5% between recall and precision. In general, suboptimal strategies were particularly ineffective for the detection of infrequent classes. Finally, the different parameter values used in the random forests did not have a significant influence over the results. Our results demonstrate the importance of algorithm design in the effective classification of multiple vegetation classes, corroborate the usefulness of Sentinel data to generate mapping products of high resolution and accuracy, and highlight the importance of cloud computing tools for the development of vegetation mapping tools in large-scale applications. These findings provide general guidelines for the design of future classification frameworks, which are necessary for facilitating a sustainable management of natural resources worldwide.

Abstract A004: Molecular adenoma features to predict metachronous colorectal cancer risk: A nested-case control study

Abstract Introduction: In colorectal cancer (CRC) surveillance, adenoma characteristics such as size, number of adenomas, dysplasia, and villous components, are used as indicators for risk of developing metachronous cancer and guide the surveillance interval. The current risk groups (e.g., advanced adenomas) cannot optimally distinguish high risk from low risk, which may result in suboptimal surveillance strategies. Specific DNA copy number aberrations are associated with risk of colorectal adenoma-to-carcinoma progression, but these aberrations are present in only a subset of advanced adenomas. Therefore, we hypothesize that specific DNA copy number aberrations may better predict the risk of CRC than advanced adenoma. Aim: To evaluate whether a molecularly-defined high-risk adenoma is a better risk factor for CRC than the presently used advanced adenoma. Materials and methods: DNA copy number profiles were determined, by means of low-coverage whole genome sequencing, on a series of 529 adenomas, selected from a Norwegian adenoma cohort. We retrieved detailed information on adenoma characteristics and whether adenoma patients were subsequently diagnosed with CRC or not. By univariate and multivariate regression analysis we estimated the odds ratio for association between the development of CRC and baseline presence of advanced adenoma versus presence of a molecularly-defined high-risk adenoma, respectively. Results: Molecular high-risk features were observed in 85/267 (32%) of advanced adenomas and in 27/262 (10%) of non-advanced adenomas. The odds ratio for developing metachronous CRC was 3.58 (p=2.84E-8) and 1.90 (p=0.012), when an advanced adenoma or molecular high-risk adenoma was detected at the baseline colonoscopy, respectively. In the multivariate regression analysis only advanced adenoma was as a significant risk factor for CRC. Conclusion: Molecularly-defined high-risk adenomas are associated with an increased risk of CRC. However, the advanced adenoma is a stronger predictor for risk of CRC. Citation Format: Henriette C. Jodal, Eddymurphy U. Akwiwu, Leticia G. Leon, Margriet Lemmens, Pien Delis-van Diemen, Dagmar Klotz, Meike de Wit, Remond Fijneman, Monique van Leerdam, Evelien Dekker, Manon C. W. Spaander, Gerrit A. Meijer, Michael Bretthauer, Magnus Løberg, Veerle M. H. Coupé, Mette Kalager, Beatriz Carvalho. Molecular adenoma features to predict metachronous colorectal cancer risk: A nested-case control study [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer; 2022 Oct 1-4; Portland, OR. Philadelphia (PA): AACR; Cancer Res 2022;82(23 Suppl_1):Abstract nr A004.

Acute fuelling and recovery practices of academy soccer players: implications for growth, maturation, and physical performance

ABSTRACT Academy soccer players frequently train in the evening (i.e. 1700-2000 h), hence limited time to nutritionally prepare and recover due to schooling, travel and sleep schedules. Accordingly, we assessed timing and quantity of energy intake in the pre-training and post-training period. Over a 3-day in-season training period, male players (n=48; n=8 from under (U) 12, 13, 14, 15/16, 18 and 23 players) from an English Premier League academy self-reported dietary intake and physical activity levels (via the remote food photography method and activity diary, respectively) in the four hours pre- and post-training. Timing of pre-training energy intake ranged from 40 ± 28 mins (U15/U16 players) to 114 ± 71 mins (U18) before training and mean carbohydrate (CHO) intake ranged from 0.8±0.4 g.kg-1 (U23) to 1.5±0.9 g.kg-1 (U12). Timing of post-training energy intake ranged from 39 ± 27 mins (U14) to 70 ± 84 mins (U23) and mean CHO intake ranged from 1.6±0.8 g.kg-1 (U12) to 0.9±0.5 g.kg-1 (U14). In contrast to CHO, all age groups consumed sufficient protein intake in the post-training period (i.e. > 0.3 g.kg-1). We conclude academy soccer players habitually practice sub-optimal fuelling and recovery strategies, the consequence of which could impair growth, maturation and physical performance.

Richter transformation in Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukemia can evolve to an aggressive lymphoma-in most of the cases diffuse large B cells lymphoma, rarely Hodgkin lymphoma-and this complication is defined Richter syndrome (RS). Immunogenotypic features that characterize RS include unmutated IgHV status with high prevalence of IgHV4-39/D6-13/J5 sequence; deletion of chromosome 17p or 11q; activation of oncogenes as NOTCH1 and c-MYC; inactivation of onco-suppressors as TP53 and CDKN2A; high expression of CD38 in lymph-nodes. The prognosis of this condition is very poor: patients experience a rapid clinical deterioration with frequent therapeutic failure since the current options include suboptimal strategies as standard chemo-immunotherapy followed by hematopoietic stem cells transplantation or enrollment in clinical trials which investigate the efficacy of target drugs. Understanding the biology of such a heterogeneous condition is crucial to personalize the treatment and improve patient's survival.

Twelve years of assessing the quality of preimplantation genetic testing for monogenic disorders.

Genomics Quality Assessment has provided external quality assessments (EQAs) for preimplantation genetic testing (PGT) for 12years for eight monogenic diseases to identify sub-optimal PGT strategies, testing and reporting of results, which can be shared with the genomics community to aid optimised standards of PGT services for couples. The EQAs were provided in two stages to mimic end-to-end protocols. Stage 1 involved DNA feasibility testing of a couple undergoing PGT and affected proband. Participants were required to report genotyping results and outline their embryo testing strategy. Lymphoblasts were distributed for mock embryo testing for stage 2. Submitted clinical reports and haplotyping results were assessed against peer-ratified criteria. Performance was monitored to identify poor performance. The most common testing methodology was short tandem repeat linkage analysis (59%); however, the adoption of single nucleotide polymorphism-based platforms was observed and a move from blastomere to trophectoderm testing. There was a variation in testing strategies, assigning marker informativity and understanding test limitations, some clinically unsafe. Critical errors were reported for genotyping and interpretation. EQA provides an overview of the standard of preimplantation genetic testing-M clinical testing and identifies areas of improvement for accurate detection of high-risk embryos.

Approximate optimal energy management with a high-precision vehicle speed prediction algorithm

A predictive energy management strategy is proposed for plug-in hybrid electric vehicles, which adopts a new high-precision vehicle speed prediction algorithm and a real-time reference state of charge (SOC) trajectory planning method. The speed prediction algorithm includes a modified Markov model based on vehicle speed features recognized by the self-organizing map neural network method, Fourier approximation, and moving average filtering. The prediction accuracy of the algorithm, measured by the root mean square error, is improved by 32.28% and 23.33%, respectively, over that of the standard Markov model when the prediction time is 5 and 10 s. To approach the optimal management strategy for improved vehicle fuel economy, the radial basis function neural network method is utilized to correlate the 10 s predicted vehicle speed with the trajectory slope of the suboptimal SOC for obtaining the reference SOC trajectory, which is applied to the adaptive equivalent consumption minimization strategy based on SOC feedback. The simulation indicates that the fuel consumption by using the proposed energy management strategy is very close to that by the theoretical suboptimal strategy with the shooting method, which leads to a superior fuel economy among the strategies studied.

An adaptive timing mechanism for urban traffic pre-signal based on hybrid exploration strategy to improve double deep Q network

With the increasing traffic congestion in cities, the priority of public transit has become a consensus for the development and management of urban transportation. The traffic pre-signal mechanism, which gives priority in time and space to buses by optimizing road right-of-way allocation, has gained wide attention and application. In order to broaden the action exploration range of the agent and avoid the pre-signal decision from falling into suboptimal strategy or local optimal strategy. For the exploration strategy of the DDQN algorithm, this paper reduces the probability of directly selecting the local optimal action and increases the probability of selecting non-greedy actions based on the principle that “the action with a larger value function is more likely to be selected.” This paper addresses the problem that the existing urban traffic pre-signal mechanism cannot adaptively adjust the advance time, and proposes a traffic pre-signal adaptive timing mechanism based on a Hybrid Exploration Strategy Double Deep Q Network (HES-DDQN) by combining the epsilon -greedy strategy and Boltzmann strategy. We have used the traffic simulation software VISSIM to conduct simulation experiments on an intersection. The experimental results show that, compared with the method of setting no pre-signal and the formula method of setting pre-signal, the HES-DDQN pre-signal mechanism can significantly reduce the average delay of buses, the waiting queue length, and the number of stops of social vehicles.

An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy Management

Reinforcement learning based energy management strategy has been an active research subject in the past few years. Different from the baseline reward function (BRF), the work proposes and investigates a multi-stage reward mechanism (MSRM) that scores the agent’s step and final performance during training and returns it to the agent in real time as a reward. MSRM will also improve the agent’s training through expert intervention which aims to prevent the agent from being trapped in sub-optimal strategies. The energy management performance considered by MSRM-based algorithm includes the energy balance, economic cost, and reliability. The reward function is assessed in conjunction with two deep reinforcement learning algorithms: double deep Q-learning network (DDQN) and policy gradient (PG). Upon benchmarking with BRF, the numerical simulation shows that MSRM tends to improve the convergence characteristic, reduce the explained variance, and reduce the tendency of the agent being trapped in suboptimal strategies. In addition, the methods have been assessed with MPC-based energy management strategies in terms of relative cost, self-balancing rate, and computational time. The assessment concludes that, in the given context, PG-MSRM has the best overall performance.

Joint Transmit Beamforming and Receive Filters Design for Coordinated Two-Cell Interfering Dual-Functional Radar-Communication Networks

Dual-functional radar-communication (DFRC) has been viewed as a promising component in future networks. The considered two-cell interfering DFRC network involves two multi-antenna base stations (BSs), and each BS serves multiple single-antenna users and receives echo signals to detect the target. An optimization framework for the joint transmit beamforming and receive filters design of a coordinated two-cell network is formulated. Specifically, we minimize the transmit power at two BSs subject to the signal-to-interference-and-noise ratio constraints. To solve the formulated non-convex optimization problem, an alternating optimization-based method is invoked. For the transmit beamforming design, a locally optimal successive inner convex approximation (SICA)-based method with fast convergence is firstly proposed. To further reduce the computational complexity, we also provide three zero-forcing (ZF)-based sub-optimal methods for practical consideration. For the receive filters design, a power iterations-based method is proposed. Simulation results validate the effectiveness of our proposed SICA-based locally optimal and ZF-based sub-optimal schemes.

Effectiveness of the Reconstructed MODIS Typical-Angle Reflectances on Forest Biomass Estimation

Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such as aboveground biomass (AGB)—a potential theoretically recognized but rarely explored. In this paper, we examined the effectiveness of the reconstructed MODIS typical-angle reflectances—reflectances observed from the hotspot, darkspot, and nadir directions—for estimating forest AGB from both theoretical and practical perspectives. To gain theoretical insights, we first tested the sensitivities of typical-angle reflectances to forest AGB through simulations using the 4-scale bidirectional reflectance distribution function (BRDF) model. We then built statistical models to fit the relationship between MODIS multi-angle observations and field-measured deciduous-broadleaf/mixed-temperate forest AGB at five sites in the eastern USA, assisted by a semivariogram analysis to determine the effect of pixel heterogeneity on the MODIS–AGB relationship. We also determined the effects of terrain and season on the predictive relationships. Our results indicated that multi-angle reflectances with fewer visible shadows yielded better AGB estimates (hotspot: R2 = 0.63, RMSE = 54.28 Mg/ha; nadir: R2 = 0.55, RMSE = 59.95 Mg/ha; darkspot: R2 = 0.46, RMSE = 65.66 Mg/ha) after filtering out the effects of complex terrain and pixel heterogeneity; the MODIS typical-angle reflectances in the NIR band were the most sensitive to forest AGB. We also found strong sensitivities of estimated accuracies to MODIS image acquisition dates or season. Overall, our results suggest that the current practice of leveraging only single-angle MODIS data can be a suboptimal strategy for AGB estimation. We advocate the use of MODIS multi-angle reflectances for optical remote sensing of forest AGB or potentially other ecological applications requiring forest structure information.

Efficient stabilization of imprecise statistical inference through conditional belief updating.

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behaviour. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence and explains human behaviour better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations but also, counterintuitively, increases the accuracy of resulting decisions.

The benefits of immature cognitive control: How distributed attention guards against learning traps

Cognitive control allows one to focus one’s attention efficiently on relevant information while filtering out irrelevant information. This ability provides a means of rapid and effective learning, but using this control also brings risks. Importantly, useful information may be ignored and missed, and learners may fall into “learning traps” (e.g., learned inattention) wherein they fail to realize that what they ignore carries important information. Previous research has shown that adults may be more prone to such traps than young children, but the mechanisms underlying this difference are unclear. The current study used eye tracking to examine the role of attentional control during learning in succumbing to these learning traps. The participants, 4-year-old children and adults, completed a category learning task in which an unannounced switch occurred wherein the feature dimensions most relevant to correct categorization became irrelevant and formerly irrelevant dimensions became relevant. After the switch, adults were more likely than children to ignore the new highly relevant dimension and settle on a suboptimal categorization strategy. Furthermore, eye-tracking analyses reveal that greater attentional selectivity during learning (i.e., optimizing attention to focus only on the most relevant sources of information) predicted this tendency to miss important information later. Children’s immature cognitive control, leading to broadly distributed attention, appears to protect children from this trap—although at the cost of less efficient and slower learning. These results demonstrate the double-edged sword of cognitive control and suggest that immature control may serve an adaptive function early in development.

Mupirocin susceptibility of staphylococci 2022: Is it time for a change in MRSA decolonization protocols?

Nasal decolonization with mupirocin has been a common strategy for the prevention of surgical site infections (SSIs) and recurrent skin and soft tissue infections due to Staphylococcus aureus (SA). We recently noted an increase in SSIs due to SA, including a case of post-operative mupirocin-resistant methicillin-resistant SA (MRSA) infection despite attempted preoperative decolonization with mupirocin. We therefore evaluated the mupirocin susceptibility of SA at our hospital to determine the optimal regimen for decolonization. SA isolates were recovered from clinical and screening samples received in the microbiology laboratory. Mupirocin susceptibility was determined using e-tests and isolates were categorized as susceptible or resistant using a breakpoint MIC value of 4mcg/ml. 223 unique SA isolates from 218 patients were tested. Twenty-four SA isolates (10.8%) were resistant to mupirocin (20 MRSA and 4 methicillin-sensitive SA [MSSA]). MRSA strains were more likely to be resistant to mupirocin than MSSA strains (22.5% vs 3.0%, P < .001). The emergence of drug resistance makes the policy of decolonization with nasal mupirocin a suboptimal strategy for the prevention of MRSA infections. In our study, less than 80% of MRSA strains were mupirocin susceptible. In patients colonized with MRSA at high risk for infection (eg, total joint replacement), other regimens such as intranasal povidone iodine may be preferable to mupirocin for patient decolonization.

Optimal investment and reinsurance on survival and growth problems for the risk model with common shock dependence

This paper investigates goal-reaching problems regarding optimal investment and proportional reinsurance with two dependent classes of insurance business, where the two claim number processes are correlated through a common shock component. The optimization problems are formulated in a general form first, and then four criteria including maximum survival probability, minimum expected ruin penalty, minimum (maximum) expected time (reward) to reach a goal are fully discussed. By the technique of stochastic control theory and through the corresponding Hamilton–Jacobi–Bellman equation, the optimal results are derived and analyzed in different cases. In particular, when discussing the maximum survival probability with a target level U beyond the safe level (where ruin can be avoided with certainty once it is achieved), we construct ε-optimal (suboptimal) strategies to resolve the inaccessibility of the safe level caused by classical optimal strategies. Furthermore, numerical simulations and analysis are presented to illustrate the influence of typical parameters on the main results.

Progressive multi-scale fusion network for RGB-D salient object detection

Salient object detection (SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks. The depth map could provide additional spatial prior and boundary cues to boost the performance. Combining the depth information with image data obtained from standard visual cameras has been widely used in recent SOD works, however, introducing depth information in a suboptimal fusion strategy may have negative influence in the performance of SOD. In this paper, we discuss about the advantages of the so-called progressive multi-scale fusion method and propose a mask-guided feature aggregation module (MGFA). The proposed framework can effectively combine the two features of different modalities and, furthermore, alleviate the impact of erroneous depth features, which are inevitably caused by the variation of depth quality. We further introduce a mask-guided refinement module (MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection. Experiments on five challenging benchmarks demonstrate that the proposed method outperforms 11 state-of-the-art methods under different evaluation metrics.

Beyond the triangle of renewable energy acceptance: The five dimensions of domestic hydrogen acceptance

The ‘deep’ decarbonization of the residential sector is a priority for meeting national climate change targets, especially in countries such as the UK where natural gas has been the dominant fuel source for over half a century. Hydrogen blending and repurposing the national grid to supply low-carbon hydrogen gas may offer respective short- and long-term solutions to achieving emissions reduction across parts of the housing sector. Despite this imperative, the social acceptance of domestic hydrogen energy technologies remains underexplored by sustainability scholars, with limited insights regarding consumer perceptions and expectations of the transition. A knowledge deficit of this magnitude is likely to hinder effective policymaking and may result in sub-optimal rollout strategies that derail the trajectory of the net zero agenda. Addressing this knowledge gap, this study develops a conceptual framework for examining the consumer-facing side of the hydrogen transition. The paper affirms that the spatiotemporal patterns of renewable energy adoption are shaped by a range of interacting scales, dimensions, and factors. The UK’s emerging hydrogen landscape and its actor-network is characterized as a heterogenous system, composed of dynamic relationships and interdependencies. Future studies should engage with domestic hydrogen acceptance as a co-evolving, multi-scalar phenomenon rooted in the interplay of five distinct dimensions: attitudinal, sociopolitical, community, market, and behavioral acceptance. If arrived to, behavioral acceptance helps realize the domestication of hydrogen heating and cooking, established on grounds on cognitive, sociopolitical, and sociocultural legitimacy. The research community should internalize the complexity and richness of consumer attitudes and responses, through a more critical and reflexive approach to the study of social acceptance.

A Markovian decision model of adaptive cancer treatment and quality of life

This paper develops and analyzes a Markov chain model for the treatment of cancer. Cancer therapy is modeled as the patient’s Markov Decision Problem, with the objective of maximizing the patient’s discounted expected quality of life years. Patients make decisions on the duration of therapy based on the progression of the disease as well as their own preferences. We obtain a powerful analytic decision tool through which patients may select their preferred treatment strategy. We illustrate the tradeoffs patients in a numerical example and calculate the value lost to a cohort in suboptimal strategies. In a second model patients may make choices to include drug holidays. By delaying therapy, the patient temporarily forgoes the gains of therapy in order to delay its side effects. We obtain an analytic tool that allows numerical approximations of the optimal times of delay.

Adaptive Radar Detection in the Presence of Missing-data

This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation–maximization (EM) algorithm or its expectation–conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest.

Energy-Efficient NOMA Multicasting System for Beyond 5G Cellular V2X Communications With Imperfect CSI

The integration of non-orthogonal multiple access (NOMA) in vehicle-to-everything (V2X) communications has recently shown great potential to improve traffic efficiency, control, and reliability of beyond 5G transportation systems. In V2X communications, it is vital to inspect imperfect channel state information (CSI) because the high mobility of vehicles leads to more channel estimation uncertainties. This paper proposes an energy-efficient power allocation scheme for the road-side unit (RSU) assisted NOMA multicasting in beyond 5G cellular V2X networks. In particular, the energy efficiency maximization problem is investigated under the outage probability of vehicles under imperfect CSI, quality of services (QoS), and power limit constraints. Since the problem is non-convex and difficult to solve directly, we first convert outage probability constraint to non-probabilistic constraint through approximation and adopt a low complexity gradient assisted binary search (GABS) method to obtain the efficient power allocation at RSUs. Then, a successive convex approximation (SCA) technique is exploited to transform the power allocation problem of vehicles associated with each RSU into a tractable concave-convex fractional programming (CCFP) problem. The optimal solution to the CCFP problem is achieved through Dinkelbach and the dual decomposition method. The global optimal power allocation through the GABS-Exhaustive scheme act as a benchmark, which has considerable computational complexity. Simulation results unveil that the proposed suboptimal scheme (GABS-Dinkelbach) can achieve near-optimal performance with very low complexity.