Average Utility Research Articles

Dynamic UAV Deployment for Differentiated Services: A Multi-Agent Imitation Learning Based Approach

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

Stackelberg Game for Secure CR-NOMA Networks Against Internal Eavesdropper

The combination of CR-NOMA has enormous potential interest to improve spectral efficiency and increase system capacity in future wireless communication networks. Hence, according to the broadcast nature of the CR-NOMA wireless channel, handling the confidentiality of information is one of the most important requirements of the network. In this paper, PLS against internal Eve is investigated by the CR-NOMA under the limitations of joint delay and confidentiality rate. First, initial power allocation for SU based on the PU interference threshold is constructed. Then, a multi-objective problem based on different SUs demands is formulated based on Stackelberg game modeling. After that, the SE is studied and explored. At the final stage, an overall algorithm has been proposed whereby SE strategies are frequently updated to find the best parameters for the proposed system model. The simulation results show the superiority of the proposed scheme in achieving a higher average utility function, spectral efficiency, fairness index, up to 44%, 90%, and 54.7%, respectively, and reliability compared to the conventional scheme.

Behavior Analysis of User Interaction on Online Short Video Platform

Online short video platform (OSVP) can disseminate information in an intuitive and three-dimensional (3D) way, and achieve commercial value in a mobile, socialized, life-oriented manner. To fully understand the information dissemination mode of short videos, it is of practical significance to explore user behaviors and their interactive relationship. Therefore, this paper carries out a behavior analysis on user interaction on OSVP. Firstly, an OSVP user interaction model was established based on three aspects: activity, attention, and interactivity. Next, user preference for short videos was examined through cluster analysis and evaluation. Finally, the authors summarized the behavior features of OSVP user interaction and investigated user interaction to decide which videos should be pushed to users. The average utility and average cost of user interaction behavior were compared, indicators of degree centrality were summarized, and the validity of the proposed algorithm was verified by experimental results.

REVIEW ON CO-MORBIDITIES IN DIABETES MELLITUS

Aim and objectives: The aim of this study was to determine cost of illness and cost of utility in the study population and making recommendations to decrease the economic burden of the treatment by following the life style modifications. Methodology: It is a cross sectional pharmacoepidemiological study. Totally 132 diabetic patients both in-patients and out patients of medical and surgical departments in a secondary level referral hospital were included in this study which was conducted for a period of 6 months. All patients with diabetes type-II, continuing anti diabetic drugs (Metformin, Glibenclamide and Insulin,) for their diabetes management are included and patients who are diagnosed as Diabetic type-I and pregnancy and pediatric patients were excluded. Data was analyzed by Carlson comorbidities Index. Results: A total of 132 diabetic patients were included, in which male and famales were nearly equal in number. Based upon the comorbid conditions, it was found that the most of the subjects (69%) were suffering with diabetes along with hypertension and 9.9% are having comorbidity of hypertension &amp; acute renal failure along with DM and treatment costs more economic burden to the patients. The average utility of drugs is more for the combination of Metformin+Glibenclamide+Insulin than metformin+glibenclamide. Conclusion: Finally we concluded that study place is a rural area and most of people are with poor knowledge and having lack of awareness on both disease and treatment. By applying Charlson Co morbidities index we found the patient’s economic status by which we found that many of the diabetic patients facing economic burden, especially daily wager are feeling much difficulty to face even therapy cost also. In co morbidities side, we noticed 55% of the study population are having diabetes with hypertension. Although all the patients were provided with education regarding their disease and drugs for improving their quality of life, but it has an influence on very few people.

Incentive Mechanism for Improving Task Completion Quality in Mobile Crowdsensing

Due to the randomness of participants’ movement and the selfishness and dishonesty of individuals in crowdsensing, the quality of the sensing data collected by the server platform is uncertain. Therefore, it is necessary to design a reasonable incentive mechanism in crowdsensing to ensure the stability of the sensing data quality. Most of the existing incentive mechanisms for data quality in crowdsensing are based on traditional economics, which believe that the decision of participants to complete a task depends on whether the benefit of the task is greater than the cost of completing the task. However, behavioral economics shows that people will be affected by the cost of investment in the past, resulting in decision-making bias. Therefore, different from the existing incentive mechanism researches, this paper considers the impact of sunk cost on user decision-making. An incentive mechanism based on sunk cost called IMBSC is proposed to motivate participants to improve data quality. The IMBSC mechanism stimulates the sunk cost effect of participants by designing effort sensing reference factor and withhold factor to improve their own data quality. The effectiveness of the IMBSC mechanism is verified from three aspects of platform utility, participant utility and the number of tasks completed through simulation experiments. The simulation results show that compared with the system without IMBSC mechanism, the platform utility is increased by more than 100%, the average utility of participants is increased by about 6%, and the task completion is increased by more than 50%.

Health Utilities Index Mark 3 scores for children and youth: Population norms for Canada based on cycles 5 (2016 and 2017) and 6 (2018 and 2019) of the Canadian Health Measures Survey.

Utility scores are an important tool for evaluating health-related quality of life. Utility score norms have been published for Canadian adults, but no nationally representative utility score norms are available for children and youth. Health Utilities Index Mark 3 (HUI3) data from two recent cycles of the Canadian Health Measures Survey (i.e., 2016 and 2017, and 2018 and 2019) were used to provide utility score norms for children aged 6 to 11 years and adolescents aged 12 to 17 years. Children younger than 14 years answered the HUI3 under the supervision of an adult, while older children answered without supervision. Utility scores were reported as a weighted average (95% confidence intervals [CIs]) and median values (interquartile range). Utility scores were stratified by sociodemographic and medical characteristics of the child or adolescent. Regression analyses were used to identify predictors of utility scores. All results were weighted using sampling weights provided by Statistics Canada. Among the 2,297,136 children aged 6 to 11 years and the 2,329,185 adolescents aged 12 to 17 years in the weighted sample, the average utility scores were 0.95 (95% CI: 0.94 to 0.95) and 0.89 (95% CI: 0.87 to 0.90), respectively. Approximately 60% of the children and 34% of the adolescents had a utility score of 1.00. Analyses identified several factors associated with utility scores (e.g., age, chronic condition and income levels), although differences were observed between children and adolescents. This study provides utility score estimates based on a nationally representative sample of Canadian children and youth. Further research examining the determinants of utility scores of children and adolescents is warranted.

Optimization of 3D Spectrum Management in Future Wireless Networks

In this paper, we propose an application customized network slicing mechanism to cope with dynamic traffic in future networks and various service requirements for network users, namely, context-aware network slicing (CANS). The resource allocation is carried out in frequency (f), space (s) and time (t) domains (referred to as three domains (3D) resource allocation) while the customization targets at low latency and high reliability, characteristic for IoT applications. We first formulate CANS as a two-step matching game with unknown preferences for access and back-haul networks. To dynamically resize mobile users' network slices (NSs), we propose application customized CANS++ method based on a coalition game to minimize the hand-off risk in the network. We then develop two-step stable matching (TSSM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -Pareto stability techniques to find the solutions to CANS and CANS++, respectively. To improve energy and cost efficiency, an artificial intelligence-based Dynamic Network Architecture (DNA) with ON/OFF scheduling (AI-DOS) is proposed to dynamically maximize NS utilization. Finally, we carry out extensive numerical analysis and demonstrate that context-aware 3D network slicing and resizing schemes can improve at least three times the users' utilities. These utilities are enhanced up to 180% by minimizing their hand-off risk. Furthermore, NS shrinking technique can reduce the number of active nodes (controlled in the s-domain) by factor five while the average utility of macro base stations, controlled in the t-domain, is improved between 70% and 500%.

P688 Patient preferences in medication to treat Ulcerative Colitis: a discrete choice experiment

Abstract Background Since the number of treatment options for Ulcerative Colitis (UC) has expanded, patients and physicians face challenges regarding decisions about the medication options. In shared decision making, treating physicians help patients understand the trade-offs and their own preferences. We aimed to identify patients preferences about the relative risks and benefits of UC treatment options in patients with UC in the Netherlands. Furthermore, we assessed after how many failed treatment options, patients are willing to consider surgical treatment. Methods We conducted a web-based, discrete choice experiment (DCE) among adult UC patients. Patients were repeatedly asked to choose between two hypothetical treatment options with varying combinations of attribute levels (fig 1). Attributes were based on consecutively a literature search, patient interviews, surveys and a focus group. The six included attributes were: administration route, administration location, chance on symptom reduction (short and long term), chances on infection and on adverse events. A balanced overlap design was used. Patients were recruited via the patient association Crohn &amp; Colitis NL and in two academic hospitals. Data were analyzed by using Hierarchical Bayes estimation. Results A total of 172 patients participated in the DCE. Median disease duration was 8 (IQR 3-16) years. Most patients were anti-TNF experienced (52.9%). Symptom reduction after one year (relative importance (RI) 27.7 (95% CI 25.6-29.7)) was the most important when choosing between treatment options (table 1), followed by the chance of infection (22.3 (21.2 - 23.4)) and chance of symptom reduction after eight weeks (RI 19.5 (18.1 - 20.9)). The location of administration of the treatment and the chance of adverse events were the least important (8.7 (7.7 - 9.79) and 8.5 (7.9 - 9.2), respectively). Patients preferred oral administration over subcutaneous injections. Intravenous route of administration was least favorite. The differences in average utilities of patients previous treated with biologic therapy or small molecules or naïve patients are displayed in table 2. 133 patients filled in the trade off questions regarding surgery. Forty-four patients (33.1%) responded with unknown. Nineteen patients (14.3%) would not even consider surgery after failing all treatment options without any new available therapies left. Nine patients would consider surgery before trying any treatment options. Of the remaining patients, nine (6.8%) would consider surgery after failing six treatment options. Conclusion We found that symptom reduction after one year was the most important treatment attribute in UC patients. These outcomes can help understand the trade-offs and preferences of UC patients.

The dynamic role of the Japanese property sector REITs in mixed-assets portfolio

PurposeThis study examined the dynamic role of the Japanese property sector, particularly the real estate investment trusts (REITs), in mixed-asset portfolios of stocks and bonds, as well as office, retail, hotel and residential REITs.Design/methodology/approachDaily data were retrieved from 01 January 2008 to 31 December 2019. The sample time frame consisted of in-sample and out-of-sample periods. The dynamic conditional correlation-generalised autoregressive conditional heteroskedastic (DCC-GJRGARCH) model was deployed to obtain the forecast estimates of time-varying volatility of REITs and correlations with other assets. The estimates were employed to construct out-of-sample portfolios based on the three assets for daily investment. The five sets of portfolios with each individual property sector REITs, as well as a portfolio of stocks and bonds that served as a benchmark, were produced. The average utility for each set of portfolios was estimated and compared with the average utility of the benchmark portfolio. The average transaction cost (TC) for portfolio rebalancing was calculated as well.FindingsThe forecast of volatility estimates for each property sector revealed that each asset displayed a similar pattern with the differences in the volatility magnitude. Notably, hotel and retail REITs were more volatile than other property sector REITs. The property sector REITs exhibited a positive correlation with stocks but negatively linked with bonds. The results unveiled the diversification benefits of incorporating property sector REITs. The portfolio with property sector REITs had higher risk-adjusted returns and utility, compared to portfolio consisting of stocks and bonds. The benefits outweighed the TC for portfolio rebalancing.Practical implicationsThis study highlights the importance of quantifying the conditional time-varying volatility and correlations of the property sector REITs with other asset returns, especially for investment decision, to select and include property sector REITs in mixed-asset portfolios. For fund managers seeking liquid assets in daily investment, this analysis suggests the inclusion of hotel and retail REITs to enhance REITs' portfolio performance.Originality/valueThis study is the first to investigate the dynamic characteristics of the volatility and correlation of each property sector REITs with other financial assets by employing the conditional framework that accounted for short- and long-run persistency in economic shocks. The reported outcomes shed light on the differences in the underlying properties that contribute to the variances in dynamic volatility of each sector REITs, as well as REITs' correlations with stocks and bonds. This application enables the authors to transmit the dynamics of variance-covariance matrix amongst each property sector REITs, stocks and bonds into asset allocation problem on a daily basis.

Playing against no-regret players

We consider n-player repeated games where one optimizer plays against no-regret players. In a 2-player game, the optimizer can always guarantee an expected average utility of at least the Stackelberg value per round. However, if there are several players, this result does not hold, and the optimizer can guarantee at least the correlated Stackelberg value per round. Finally, we can obtain the same result almost surely and not in expectation.

Complex dynamics of Cournot-Bertrand duopoly game with peer-induced fairness and delay decision

&lt;p style='text-indent:20px;'&gt;This paper establishes the Cournot-Bertrand duopoly game models with bounded rationality expectations under the assumption that the players are fair-minded agents who may adopt the delay decision. The existence and local stability of the equilibrium points are investigated. The effects of key parameters on the long-term dynamical evolution process of the models are investigated by numerical simulation. Furthermore, the performance of the players is examined using average profit and utility indexes. The results reveal that the excessively fast adjustment speeds and the higher fairness concern coefficients have a strong destabilizing impact on the system's stability. The product differentiation degree and delay strategy may be strong stabilizing or destabilizing factors for the game, which indicates that an appropriate product differentiation degree or delay weight is conducive to the system preserving its stability. In addition, when the system falls into periodic cycles or chaotic motions, the change trends of the players' performance may elevate or decline depending on the specific parameter conditions. Therefore, the chaos does not always necessarily detrimental to all the players in the Cournot-Bertrand duopoly game.&lt;/p&gt;

Task offloading and resource allocation for intersection scenarios in vehicular edge computing

Vehicular edge computing (VEC) is a promising paradigm to relieve the overload on corresponding edge servers by utilising the idle resources of nearby vehicles. However, due to the high mobility of vehicles, the vehicles perhaps drive out of the communication range of user equipments (UEs) during task processing. Therefore, it is important to select the appropriate vehicles as service nodes. In this paper, we propose a task offloading and resource allocation scheme for UEs near the intersection. We first study the availability of vehicles according to characteristics movement of vehicles at the intersection. Then, considering the delay constraint of tasks, and the computing capacity of vehicles and the edge server, a double deep Q-network approach is adopted to obtain the optimal policy of task offloading and resource allocation. Simulation results show that the proposed scheme has better performance in improving the average utility of UEs and task success rate.

AGENT BASED APPROACH FOR TASK OFFLOADING IN EDGE COMPUTING

Due to limited resource capacity in the edge network and a high volume of tasks offloaded to edge servers, edge resources may be unable to provide the required capacity for serving all tasks. As a result, some tasks should be moved to the cloud, which may cause additional delays. This may lead to dissatisfaction among users of the transferred tasks. In this paper, a new agent-based approach to decision-making is presented about which tasks should be transferred to the cloud and which ones should be served locally. This approach tries to pair tasks with resources such that a paired resource is the most preferred resource by the user or task among all available resources. We demonstrate that reaching a Nash Equilibrium point can satisfy the aforementioned condition. A game-theoretic analysis is included to demonstrate that the presented approach increases the average utility of the user and their level of satisfaction.

Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Unmanned Aerial Vehicle (UAV) clustering is promising for performing large-scale missions because of high mobility and easy deployment. However, the connectivity problems caused by the high mobility of UAVs and the interferences are mostly ignored in existing UAV mission executions. Rapid restoration of UAV network connectivity is essential for preventing communication disruption and improving the overall network performance. Thus, a graph coalition formation game that integrates the UAV time-varying topology graph with the coalition formation game is proposed to rapidly restore the connectivity of the UAV network in need. The method improves the utility of UAV clustering and ensures the network connectivity. Then, a graph coalition formation game algorithm based on the shortest path tree (SPT-GCF) rapidly forms an approximately optimal coalition structure. The simulation results show that the proposed approach increases the average utility of UAV clustering by 6.5% and 14.5% respectively when compared with the existing non-overlapping coalition formation game (NOCFG) and the coalition formation game without considering the cluster connectivity.

Optimization of behaviour strategies within the simulation model of a multi-agent socio-economic system

This article presents a new simulation model of a multi-agent socio-economic system (MA-SES), in which individual, including interproduct interactions are implemented. Within the MA-SES, the models of agent-producers and agents-consumers behaviour with their objective functions are studied. In particular, producers follow their own production strategies by choosing the moments for introducing new fixed assets and labour resources. Consumers participate in barter and monetary deals by interacting with other agents. The states of readiness of agents-producers to introduce new fixed assets and labour resources, as well as the states of readiness of agents-consumers to complete barter and monetary deals are set up for each time moment using lognormal distributions with given characteristics, which are the control parameters of the system. Important bi-objective optimisation problems are formulated for ensembles of agents-producers and agents-consumers. A new parallel hybrid genetic algorithm (MORCGA-MOPSO) was developed, in particular, providing the possibility of seeking the Pareto-optimal solutions for maximising the average (over an ensemble of agents) profit and the total number of agent-producers’ buyers, as well as maximising the average utility and monetary savings of agents-consumers. The parameters of log-normal distributions that determine the states of interacting agents that make individual decisions are computed. The features of the behavioural strategies of producers and consumers are determined, which make possible to achieve an improvement in the values of the objective functions through controlling the dynamics of the input of production resources and choosing the preferred types of interproduct interactions, in particular, barter, monetary etc.

Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

QOL-21. REAL-WORLD QUALITY-OF-LIFE AND HEALTH UTILITIES FOR PATIENTS WITH CNS CANCERS ON THE XCELSIOR PLATFORM

Abstract OBJECTIVE xCures operates a direct-to-patient, real-world evidence platform according to the nationwide observational research protocol XCELSIOR (NCT03793088), which aggregates medical data in an FDA-compliant electronic database. There is a relative scarcity of published results on real-world Quality-of-Life (QoL) outcomes and resulting health state utilities for patients with Central Nervous System (CNS) cancers. We sought to use the xCures platform to help address this issue. METHODS XCELSIOR participants were invited weekly to complete the EuroQol 5-Dimension (EQ-5D-5L) QoL survey. A EuroQol-endorsed model was used to estimate health state utilities from patient surveys. Survey results, utility estimates, and clinical information were analyzed to understand the influence of common mutations in glioma on patient reported outcomes. RESULTS 30 patients (21 glioblastoma, 4 oligodendroglioma, 2 diffuse midline glioma, 2 diffuse astrocytoma, 1 Schwannoma) completed at least one EQ-5D-5L survey over the course of 9 weeks. Health state utilities were positively correlated (p&amp;lt; .03) with Karnofsky Performance Score (KPS). The average utility for EGFR-mutated patients (N=6) was .578 (SD=.334) while for EGFR-wildtype patients (N=7) it was .747 (SD=.227). The average utility for MGMT-methylated patients (N=13) was .816 (SD=.139) while for non-methylated patients (N=10) it was .583 (SD=.379). The average utility for IDH-mutated patients (N=9) was .789 (SD=.153) while for non-mutated patients (N=17) it was .685 (SD=.324). The average utility for patients with a 1p/19q co-deletion (N=4) was .837 (SD=.126) while for those without a co-deletion (N=8) it was .744 (SD=.244). CONCLUSION The xCures platform has enabled the study of real-world QoL outcomes for patients with CNS cancers. This is the first report studying the influence of genetic mutations on QoL collected through patient reported outcomes. Future research aims to perform similar studies with larger number of patients.

An efficient hash map based technique for mining high average utility itemset

An efficient hash map based technique for mining high average utility itemset

Modifying the Composite Time Trade-Off Method to Improve Its Discriminatory Power

ObjectivesIn cost-effectiveness analysis of health technologies, health state utilities are needed. They are often elicited with a composite time trade-off (cTTO) method, particularly for the widely used EQ-5D-5L. Unfortunately, cTTO discriminatory power is hindered by (1) respondents’ nontrading (NT) of time for quality, (2) censoring of utilities at −1, and (3) poor correlation of negative utilities with state severity. We investigated whether modifying cTTO can mitigate these effects. MethodsWe interviewed online 478 students (February to April, 2021) who each valued the same 10 EQ-5D-5L health states in 1 of 3 arms. Arm A used a standard cTTO, expanded with 2 questions to explore reasons for NT and censoring. Arms B and C used a time trade-off with modified alternatives offered to overcome loss aversion, to unify the tasks for positive and negative utilities, and to enable eliciting utilities < −1. ResultsIn arms B and C, we observed less NT than in A (respectively, 4% and 4% vs 10%), more strictly negative utilities (38% and 40% vs 25%), and more utilities ≤ −1 (18% and 30% vs 10%). The average utility of state 55555 dropped to −2.15 and −2.52 from −0.53. Enabling finer trades in arm A reduced NT by 70%. Arms B and C yielded an intuitive association between negative utilities and state severity. These arms were considered more difficult and resulted in more inconsistencies. ConclusionsThe discriminatory power of cTTO can be improved, but it may require increasing the difficulty of the task. The standard cTTO may overestimate the utilities, especially of severe states.

Determining the impacts and recovery potentials of a modular designed residential building using the novel LCA-C2C–PBSCI method

Assessing the circularity and impact potentials of buildings has become a growing concern in the scientific community. Life cycle assessment (LCA) is a key tool used to evaluate the environmental impact potentials of buildings, but its adoption for assessing circularity is faced with several limitations. In this study, the novel LCA-cradle-to-cradle-predictive building systemic circularity indicator (LCA-C2C–PBSCI) method is adopted to evaluate the impact and recovery potentials of a modular designed residential building in China. Case scenarios based on the forecasted recyclable percentages of materials and recyclability plan of the case building were modeled and assessed using the PBSCI. The impact potentials of the case scenarios were also concurrently assessed using the LCA-C2C aspect of the method. It was found that, the average product utility, mass of materials and the adopted recyclability plan of the case building influences the rate of recyclability at end-of-life (EoL). Also, the chosen EoL allocation approach, the predicted mass recyclable and the adopted disposal scenarios hugely influence the impact potentials of the case building. The recommended scenario for the case building was one which had a high recoverable potential (85.3% of mass recyclable), but low impact potentials (432.021 kg CO2/m2). The adoption of this iterative method for assessing the impacts and recovery potentials of buildings does not only provide a means to formulate decisions based on the environmental impacts of materials or product system only, but also a way to consider the technical, social, and economic implications of such decisions towards the attainment of sustainable development.