Reinforcement Learning for Interference Coordination Stackelberg Games in Heterogeneous Cellular Networks

In future heterogeneous cellular networks with small cells, such as D2D and relay, interference coordination between macro cells and small cells should be addressed through effective resource allocation and power control. The two-step Stackelberg game is a widely used and feasible model for resource allocation and power control problem formulation. Both in the follower games for small cells and in the leader games for the macro cell, the cost parameters are a critical variable for the performance of Stackelberg game. Previous studies have failed to adequately address the optimization of cost parameters. This paper presents a reinforcement learning approach for effectively training cost parameters for better system performance. Furthermore, a two-stage pretraining plus ε -greedy algorithm is proposed to accelerate the convergence of reinforcement learning. The simulation results can demonstrate that compared with the three beachmarking algorithms, the proposed algorithm can enhance average throughput of all users and cellular users by up to 7% and 9.7%, respectively.

Methodology of making organizational and technological decisions at the stage of operational management of construction operations based on the forecasting system

The article discusses the issues of forecasting two key parameters of an investment and construction project: time and cost, while the building company is considered as a complex dynamic system. Taking into account the long-term nature of the creation of construction products and, as a consequence, the high level of work in progress, the need to use forecasting models is justified, both at short-term planning intervals (week, month) and at longer intervals (quarter, year). The article examines the formalized forecasting methods, gives a characteristic of the methods most widely used in practice. These methods include forecasting based on ARIMA models. DSTU has developed a pilot software package for an intelligent construction management system, which includes a software package for forecasting the time and cost parameters of a construction object at the stages of operational and current management.

Reliability Analysis for Unrepairable Automotive Components

The analysis of the reliability parameters of a technical object and the determination of the change in the reliability of the object over time, requires the knowledge of the functional characteristics and reliability parameters of the elements included in a system. On the basis of the failure data of the selected element of the object, in this case the vehicle, it is possible to determine the average working time to failure of the element and the appropriate form of distribution that characterizes the reliability and durability parameters of the tested element. The main purpose of the research presented in the article was to develop a method of assessing the reliability of an electronic component of a vehicle-a boot lid contactor. This paper also presents three possible methods of repairing the boot lid contactor (sealing the housing with adhesive with better way, replacing the element with a new one or the most time-consuming solution, changing the shape of the boot lid). The authors also decided to determine the reliability and cost parameters that will allow preventive replacement of this element. The tests were carried out on a fleet of 61 vehicles of the same model, but with different body structures. Contactor failures were reported in 41 cases, of which 29 were in the hatchback construction and 12 in the estate type. The analysis of the distribution selection for the tested part of the passenger car-the boot lid contactor-was performed using the Likelihood Value (LKV) test to determine the rank of distributions. Also the maximum likelihood (MLE) method was used to estimate the distribution parameters. The three-parameter Weibull distribution was the best-fitted distribution in both cases. It was clearly defined that one model of car with two different types of body have vastly different reliability characteristic. Based on the reliability characteristic and parameters, the appropriate preventive actions can be taken, minimizing the risk of damage, thus avoiding financial losses and guaranteeing an appropriate level of vehicle safety.

Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

Combining the right--potentially invasive and expensive, markers at the appropriate time is critical to obtain reliable yet economically sustainable decisions in the preclinical diagnosis of dementia. We propose a data-driven analytical framework to individualize the selection of prognostic biomarkers that balance accuracy, costs of opportunity due to delaying the decision, and cost of acquisition depending to prescribed cost parameters. We compared sequential and non-sequential decision strategies based on a linear mixed-effects classification model that integrates irregular, multi-variate longitudinal data. The framework was applied to separate participants that progress to Alzheimer's disease from the ones that do not within a time interval of three years. As expected, the highest accuracy was obtained by combining all available data from 20.9 measurements per subject on average that were acquired over 4.8 years on average. The proposed sequential algorithm empirically outperformed alternative methods by having lowest costs for a range of tested cost parameters. With the default cost parameters, the sequential algorithm reached an accuracy of 0.84, specificity of 0.86, and sensitivity of 0.82 (0.89, 0.91, and 0.88 with all available data, respectively) while requiring only 2.9 measurements on average (86 percent less observations than all available data) and a time interval of half a year on average (89 percent shorter than all time points). Our sequential algorithms established the decision based on individualized sequences of measurements with reduced process costs compared to non-sequential classification strategies while maintaining competitive accuracy.

A remanufacturing inventory model to control the carbon emission using cap-and-trade regulation with the hexagonal fuzzy number

PurposeThe awareness for protecting the environment has resulted in remanufacturing and recycling policies in manufacturing industries. Carbon emission is one of the most important elements affecting the environment. Carbon emission due to production and transportation creates complicated situations for the manufacturing firms by affecting the manufacturer's carbon quota. The ecological consequences posed in a reverse logistic model are the subject of this study.Design/methodology/approachThe present study explores the fuzzy model of economic production for both remanufacturing and recycling with uncertain cost parameters under the cap-and-trade rule to control the carbon emission due to different modes of transportation. Due to imprecise cost parameters, the hexagonal fuzzy numbers are set to fuzzify the overall cost, which leads to correct decisions in a more confident way. The result is defuzzified by using graded mean integration.FindingsThis study offers an explicit condition to control the carbon emission of the manufacturer and reduce the optimum cost. The findings indicate that the collection of used goods that can be remanufactured must be increased. The model is validated numerically. Sensitivity analysis explores the various aspects of different parameters on net cost to accomplish the fuzzy production model.Originality/valueUnder fuzzy inference, the research offers a relevant contribution in the field of recycling with controlling carbon emission by using the cap-and-trade policy. This study provides a trading strategy for a manufacturer's decision to avoid losses.

Assessment of Potential Barriers to the Implementation of an Innovative AB-FB Energy Storage System under a Sustainable Perspective

The novel acid–base flow battery technology stores electrical energy using pH and salinity gradients in water and is suitable for different capacities (kWh–MWh scale) because of its scalable technology. Due to the flexibility of this system, it is predicted to provide technical, economic, and environmental benefits for supporting renewable energy integration, such as wind and solar energy, within the electricity generation system. However, its level of acceptance might be affected by additional conditioning factors in terms of policies and maintenance. To elucidate the relevance of the possible barriers to the implementation of the innovative AB-FB system, this work introduces an approach based on the analytic hierarchy process developed at three levels of hierarchy under a sustainability perspective. An exhaustive literature review as well as an assessment of experts’ evaluations were performed to identify the barriers in terms of technical, economic, environmental, policy, and maintenance aspects. Based on the results, the cost parameters (mostly attributed to the stack cost), followed by technical and environmental criteria, were deemed to be of the highest priority.

Circumventing Circumvention: An Economic Analysis of the Role of Education and Enforcement

The role of education and enforcement in ensuring compliance with a law or policy has been debated for more than a century now. We reopen this debate in the context of security circumvention by employees, currently a leading cause of information security and privacy breaches. Drawing on prior literature, we develop a microeconomic framework that captures employees’ circumventing behavior in the face of security controls. This allows us to obtain interesting insights that have implications for how an organization should employ anticircumvention measures. First, unless circumvention is rampant, education and enforcement often work better in combination, and not in isolation. Second, there are incentives for an organization to tolerate circumvention to an extent, even when education and enforcement are cheap. Finally, education and enforcement may be strategic complements or substitutes in different parts of the parameter space. When they are complements, if a change in cost parameters compels the organization to increase one, it would also require an increase in the other in lockstep. In contrast, when they are substitutes, an increase in one is associated with a decrease in the other. This paper was accepted by Chris Forman, information systems.

Specific Features of a Mini-Plant to Prepare Non-Explosive Components of Emulsion Explosives

Extensive engineering experience accumulated at AZOTTECH helps the company to develop and implement new types of equipment. In conditions when capital construction and infrastructure development are not possible in hard-to-reach areas of the country, this equipment provides mobility and availability in mining operations. This article presents a new type of equipment for preparation of non-explosive components of blasting compounds, i.e. a modular mobile plant or a mini-plant. The technological process and the main associated equipment are described. Calculation of efficient fuel consumption for autonomous heat and power supply is made. Recommendations on the optimal operation modes for this type of equipment with the distribution of the workload per the main process operations are provided. The mini-plant can be optimized both in terms of cost parameters, and customer process conditions. Its efficiency is on the same level with that of a modular plant for preparation of non-explosive components of blasting compounds. The advantages of a mobile plant offer more application possibilities in any mining conditions. The use of mini-plants will help not only large operations, but also small service companies to increase their competitiveness and reduce costs when planning blasting operations already at the beginning of the construction phase of hazardous production facilities, and in certain cases to avoid construction of large-size complexes.

ULTRASOUND-ASSISTED EXTRACTION OF OLIVE OIL

The objective of this work was to perform a preliminary economic analysis of the ultrasound-assisted extraction process of olive oil and compare it with the manufacturing costs of the conventional extraction process. The DWSIM software was used for economic modeling considering data found in the literature; the five cost parameters were considered. The results showed a cost of USD 126.50/kg of olive oil in the proposed process. Although expensive, about 54,350 bottles more of oil could be produced due to the higher yield that can be obtained. In parallel, about USD 11,350,000.00 could be earned in one year of operation. In this sense, more research must be done to demonstrate the economic viability of this type of industry, to improve the operations and the costs of manufacturing until the process becomes profitable.