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This paper addresses the problem of energy consumption management of a large population of plug-in hybrid electric vehicles (PHEVs). A PHEV can be derived either on its gasoline or the battery modes while traveling, and can be either charged from or discharged to the grid while parking at home, respectively. This feature is the main novel consideration in our system model, besides taking into account the constraints like traveling time and distance, and the bounds on the charging power and state of charge of the battery. On the basis of the proposed model of PHEV, the optimal battery-gasoline energy management strategy is obtained in order to minimize the cost of energy and battery degradation for each user. Such a strategy can also compensate the peak load. Since the strategy of each PHEV affects other users’ cost through the electricity price, we model such an interaction as a game problem and we prove that there exist a unique Nash equilibrium point of the game. Then, due to the fact that the effect of a single PHEV of a large population on the other users’ costs is negligible, the theory of mean filed (MF) game is utilized to develop a decentralized control strategy for PHEVs. The lightweight information exchange of the proposed MF algorithm ensures a low computational cost and communication overhead. The proof of convergence for the proposed algorithm is given and it is shown that as the number of PHEVs goes to infinity the MF optimal strategy of the players converges to the unique Nash equilibrium point of the game. In simulation results, the effectiveness of the proposed method in terms of demand side management, peak shaving, convergence rate, and robustness is studied. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between candidates’ online popularity and election results, as a step towards creating a model to forecast the results of Taiwanese elections even in the absence of reliable opinion polls on a district-by-district level. 253 of 354 legislative candidates of single-member districts in Taiwan’s 2016 general election had active public Facebook pages during the election period. Hypothesizing that the relative popularity of candidates’ Facebook posts will be positively related to their election results, I calculated each candidate’s Like Ratio (i.e. proportions of all likes on Facebook posts obtained by candidates in their district). In order to have a measure of online interest without the influence of subjective positivity, I similarly calculated the proportion of daily average page views for each candidate’s Wikipedia page. I ran a regression analysis, incorporating data on results of previous elections and available opinion poll data. I found the models could describe the result of the election well and reject the null hypothesis. My models successfully predicted 80% of winners in single-member districts and were effective in districts without local opinion polls with a predictive power approaching that of traditional opinion polls. The models also showed good accuracy when run on data for the 2014 Taiwanese municipal mayors election. [ABSTRACT FROM AUTHOR]

We introduce a new agent-based model of opinion dynamics in which binary opinions of each agent can be measured and described regarding both pre- and post-influence at both of two levels, public and private, vis-à-vis the influence source. The model combines ideas introduced within the q-voter model with noise, proposed by physicists, with the descriptive, four-dimensional model of social response, formulated by social psychologists. We investigate two versions of the same model that differ only by the updating order: an opinion on the public level is updated before an opinion on the private level or vice versa. We show how the results on the macroscopic scale depend on this order. The main finding of this paper is that both models produce the same outcome if one looks only at such a macroscopic variable as the total number of the individuals with positive opinions. However, if also the level of internal harmony (viz., dissonance) is measured, then significant, qualitative differences are seen between these two versions of the model. All results were obtained simultaneously within Monte Carlo simulations and analytical calculations. We discuss the importance of our studies and findings from three points of view: the theory of phase transitions, agent-based modeling of social systems, and social psychology. [ABSTRACT FROM AUTHOR]

Participative branding is a process by which DMOs, locals and visitors contribute, through their activity in social media, to the definition of the emotional values associated to a destination. From the communicative point of view it is particularly important that DMOs and local citizens work together in the transmission of a set of coherent values that compose a personalised identity. This paper presents a novel methodology, based on semantic similarity measures, that permits to make an automated analysis of the emotional values transmitted by official tourist offices and by local citizens through social media. A study of 54,000 tweets from 9 main European tourist destinations highlights the lack of a strategy towards the communication of a distinctive brand and a strong gap between the official view transmitted by the DMOs of the destinations and the one communicated by their residents. [ABSTRACT FROM AUTHOR]

In this paper, we derive mutual information based upper bounds on the number of nonadaptive group tests required to identify a given number of “non-defective” items from a large population containing a small number of “defective” items. In the asymptotic regime with the population size $N \rightarrow \infty$ , to identify $L$ nondefective items out of a population containing $K$ defective items, our results show that $\frac{C_s K}{1-o(1)} (\Phi (\alpha _0, \beta _0) + o(1))$ measurements are sufficient when the tests are reliable. Here, $C_s$ is a constant independent of $N, K$ , and $L$ , and $\Phi (\alpha _0, \beta _0)$ is a bounded function of $\alpha _0 \triangleq \lim _{N\rightarrow \infty } \frac{L}{N-K}$ and $\beta _0 \triangleq \lim _{N\rightarrow \infty } \frac{K}{N-K}$. In contrast, the necessary number of tests using the conventional approach of first identifying the $K$ defective items and picking the required number of nondefective items from the complement set grows with $N$ as $O\left(K \log N \right)$. We also derive upper bounds on the number of tests under both dilution and additive noise models. Our results are obtained under a very general sparse signal model, by virtue of which, they are also applicable to other important sparse signal based applications such as compressive sensing. [ABSTRACT FROM AUTHOR]

Contribution: This paper shows that participating in a year-round program for African–American male middle school students (ages 10–14) can positively impact their attitudes toward STEM, their confidence in problem solving and team work, and their interest in STEM careers, but may not impact the interest in taking STEM classes to the same extent. Background: Increasing the number of under-represented minorities pursuing degrees in science and engineering through formal and informal learning activities has been a focus in the United States for many years. Efforts to diversify the engineering workforce have often targeted students in high school and college (ages 15–22), with varying success. Intended Outcomes: This program was designed to increase participants’ content knowledge, interest in STEM courses, interest in STEM careers, interest in attending college, and improve their attitudes about STEM. Application Design: Approaches used included: 1) introducing software tools that encouraged critical thinking, creativity and independent learning; 2) integrating engineering and software design processes; 3) providing projects that were age- and culturally-appropriate; and 4) using ethnically matched mentors. Findings: After almost two years in the program participants show improved attitudes toward STEM, more interest and career choices in computing and engineering, and increased interest in attending college. These findings suggested that summer and academic year programs can be effective for cultivating interest in computing and engineering careers, but their impact on interest in studying related subjects may need further study. [ABSTRACT FROM AUTHOR]

Influence maximization has attracted a lot of attention due to its numerous applications, including diffusion of social movements, the spread of news, viral marketing and outbreak of diseases. The objective is to discover a group of users that are able to maximize the spread of influence across a network. The greedy algorithm gives a solution to the Influence Maximization problem while having a good approximation ratio. Nevertheless it does not scale well for large scale datasets. In this paper, we propose Matrix Influence, MATI, an efficient algorithm that can be used under both the Linear Threshold and Independent Cascade diffusion models. MATI is based on the precalculation of the influence by taking advantage of the simple paths in the node’s neighborhood. An extensive empirical analysis has been performed on multiple real-world datasets showing that MATI has competitive performance when compared to other well-known algorithms with regards to running time and expected influence spread. [ABSTRACT FROM AUTHOR]

Background: Violence against women by an intimate partner (DV) is a serious public health and human rights issue. Attitudes justifying DV strongly predict its perpetration and victimisation. This paper presents gendered ecological analyses of the societal acceptance of DV in 49 low- and middle-income countries (LMICs) across geographical regions. Methods and findings: We utilised data from 49 Demographic and Health Surveys conducted between 2005 and 2017, United Nations Statistics and topic-specific meta-databases. DV acceptance was measured as the justification of ‘wife-beating’ in at least one of five circumstances, and by the summative scale. Stepwise multiple linear regression examined country-level social, economic and political empowerment predictors of societal acceptance of DV amongst women, men, and the aggregate gender difference. Women were more likely than men to justify DV in Sub-Saharan Africa and South (east) Asia with societal acceptance of DV being more widespread in these regions compared with Latin America, the Caribbean, Central/West Asia and Europe. Political conflict and limited economic rights for women were associated with higher levels of DV acceptance amongst women and men. Men in more democratic countries were less likely to justify DV. Amongst women, higher national female literacy rates predicted lower levels of justification. There were higher levels of DV acceptance amongst women and a wider aggregate gender difference in countries with a larger representation of women in national parliament. Conclusion: Justification of DV is widespread amongst women and men in LMICs with acceptance rates varying across countries and regions. Gender differences in the impact of contextual factors on DV acceptance supports a gendered approach to national-level interventions. Our findings highlight the need for tailored interventions targeting DV acceptance in conflict-impacted societies. The emphasis of inter(national) policies on the ‘empowerment’ domains of widely-used gender (in)equality indices need to be coupled with strategies tackling discriminatory gender norms. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of childhood experiences of parental corporal punishment (CP) and neglectful parenting (NP) on Japanese university students’ endorsement of parental CP (EPP) to discipline children, in relation to subjective happiness (SH). A total of 536 undergraduate students who showed no physical symptoms completed anonymous paper-based questionnaires addressing demographic characteristics, undergraduate classes, and recent health conditions on SF-8 (PCS, MCS). It was found that the proportions of participants who experienced pervasive CP and NP were larger in men than in women (36.5% vs. 19.4% for CP; 22.1% vs. 9.7% for NP). Multiple regression analyses (n = 346) revealed that the CP score was associated with positive EPP (β = 0.310, p < 0.001). Further, students whose major was nursery education reported significantly lower level of EPP; however, neither SH nor good recent health conditions significantly reduced EPP. The NP score was inversely associated with the SH score (β = -0.253, p < 0.001) (n = 346). In conclusion, childhood experiences of parental CP may affect adolescents’ views related to their own parenting. Further investigation using internationally comparable methodologies, especially in prospective cohort studies, is warranted, not only in Japan but also in other Asian countries. [ABSTRACT FROM AUTHOR]

This paper examines social class-based differences in influence in online review contexts. We explore four mechanisms for how a review writer’s social class may affect readers’ evaluations of the organization. First, we argue that, via a “contagion” process, organizations reviewed by higher-class individuals will be evaluated more positively than organizations reviewed by lower-class individuals. Second, we expect that higher-class reviewers will be seen as more knowledgeable; thus, their opinions will be more influential in shaping others’ opinions. Third, we expect that reviewers will be seen more influential when they review organizations that match their social class. Fourth, we expect people to be more influenced by those who share their own class background. A large-scale observational study of reviews (N = 1,234,665) from finds support for the contagion, the organization-reviewer social class matching, and the reviewer-participant social matching hypotheses, but disconfirms the hypothesis that higher-class reviewers are always treated as having more expertise. Two experimental studies (N = 354 and N = 638) demonstrate that reviewer class plays a causal role in both a contagion process and in an assumption of higher-class knowledge process, but do not provide evidence for the reviewer-participant social matching hypothesis. [ABSTRACT FROM AUTHOR]

Expansion of the distribution network and close distance of distribution transformers lead to allocation of excessive surge arresters, while the number of arresters failures in the traditional routine may cause unwanted power outages. Therefore, this paper aims to propose a method to decrease the number of surge arresters as well as keep overvoltage protection of the network in the event of direct lightning strokes. According to different bus structures of the distribution network, elimination of allocated arresters is carried out by investigation of their protective distances at different stroke conditions. A guideline is presented for the IEEE 34-bus distribution system and the outcome is compared with the standard. The analysis is also carried out for a practical distribution network at West Mazandaran Power Distribution Company, Mazandaran, Iran. Possibilities for the elimination of surge arresters at the transformer station and installation of new transformers without allocation of new arresters are investigated in the analysis. Simulations are carried out based on the standard 8/20 lightning current wave at a transient analysis of DIgSILENT. [ABSTRACT FROM PUBLISHER]

This paper presents the impact of different explanatory variables, such as remote control availability and conducted preventive maintenance, among others, on failure statistics of a disconnector population in Sweden using the proportional hazard model. To do so, 2191 work orders were analyzed, which included 1626 disconnectors and 278 major failures. Here, the results show that the remote control availability for disconnectors—an example of such smart-grid technology—has a negative effect on the failure rate, whereas preventive maintenance has a positive impact. It is also shown that the disconnector age is not significant and that certain disconnector types have a significant and positive correlation toward failures when compared to other disconnector types. The results increase the understanding of disconnector failures to improve asset management. [ABSTRACT FROM PUBLISHER]

Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends). In this paper, I highlight the importance of collecting data on actual treatment diffusion in order to more accurately measure treatment interference. Furthermore, I show that with accurate measures of treatment interference, we can identify and estimate a series of causal effects that are previously unavailable, including the direct treatment effect, treatment interference effect, and treatment effect on interference. I illustrate the methods through a case study of a social network–based smoking prevention intervention. [ABSTRACT FROM AUTHOR]

In order to provide the ancillary service for smart grid, this paper proposes a modelling and control protocol design approach for the aggregation of heterogeneous thermostatically controlled loads (TCLs). A 2-D state bin is proposed to model the second-order TCL dynamics in a population model. Detailed procedure of calculating the transition probability in the system matrix is provided. In the controller design, a model predictive control (MPC) scheme is proposed to obtain the optimal control actions along the prediction horizon. In addition, implementation of the control signal for adjusting TCLs' statuses are also investigated with practical situations considered. Simulation results reveal the feasibility and efficacy of the proposed modelling and control approach when applied on a large population of TCLs. Some factors that may affect the service performance are also discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid algorithm based on the genetic algorithm (GA) and the evolution strategy (ES) for the electromagnetic optimization problem. The GA is not good enough at times in searching the optimal solution from the view point of the convergence speed and the solution quality, while the ES has the risk of being trapped in a local minimum. The hybrid algorithm is composed of GA and ES in order to make up for these defects. First, we reached the vicinity of optimal solution using the GA. Then, the ES is used to find the accurate optimal solution. The switching point can be a main issue, which is also resolved in this paper. First, the performance of the convergence speed and the solution accuracy are comparatively tested using the known functions. In addition, the optimized design of the 2.45 GHz coplanar waveguide-fed circularly polarized antenna is carried out as a practical application. Only the GA and the hybrid algorithm reach the satisfactory value, and the more rapid convergence can be shown by the ES in this hybrid method after 380 iterations. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology to determine a realistic time-dependent dielectric breakdown failure rate. The in-die constant voltage stress was performed to determine the chip level Weibull shape ( \beta \mathrm{die}) and voltage acceleration factor, while a voltage ramp (Vramp) is performed in production line (inline Vramp) to determine the via-to-line and line-to-line spacing distributions. We found that for the chip population with spacing ( $s$ ) smaller than 4 nm, the in-die voltage accelerations based on power-law and sqrt-V models do not lead to a significant difference in the lifetime prediction. For the chips with large spacing, the stress voltage ( $\sim 20$ V) is significantly higher than the operating voltage (1 V). The extrapolation using the power law results in an infinitely long lifetime, which could lead to an overoptimistic reliability prediction. In this paper, a new method is introduced for a more realistic failure rate calculation, which is the superposition of the failure rates of chips with small spacing ( $s<4$ nm) and the failure rates of chips with large spacing, by using different voltage acceleration models. [ABSTRACT FROM AUTHOR]

Power converters systems aimed at renewable energy applications have become a common option for sustainable electricity and distributed generation, since their performance has improved, and prices have steadily been reduced in the last years. However, there are still several drawbacks that hinder their widespread installation, such as the simultaneous minimization of cost and volume, efficiency maximization, size reduction, etc. Quite often, accomplishing these goals requires dealing with complicated optimization problems, which are difficult to solve by classical methods. Metaheuristic techniques provide a viable alternative to solve complex intricate optimization problems, such as those encountered in the development of power electronics converters. This paper presents a comprehensive coverage of metaheuristic methodologies applied in the area of power converters. The review includes a classification of the methodologies and main objective functions in each paper surveyed. An aim for this paper is to highlight the importance of the optimization tools, and the many benefits they provide to tackle the challenges encountered in the design, operation, and control of power converters. [ABSTRACT FROM AUTHOR]

Scheduling for directed acyclic graph (DAG) tasks with the objective of minimizing makespan has become an important problem in a variety of applications on heterogeneous computing platforms, which involves making decisions about the execution order of tasks and task-to-processor mapping. Recently, the chemical reaction optimization (CRO) method has proved to be very effective in many fields. In this paper, an improved hybrid version of the CRO method called HCRO (hybrid CRO) is developed for solving the DAG-based task scheduling problem. In HCRO, the CRO method is integrated with the novel heuristic approaches, and a new selection strategy is proposed. More specifically, the following contributions are made in this paper. (1) A Gaussian random walk approach is proposed to search for optimal local candidate solutions. (2) A left or right rotating shift method based on the theory of maximum Hamming distance is used to guarantee that our HCRO algorithm can escape from local optima. (3) A novel selection strategy based on the normal distribution and a pseudo-random shuffle approach are developed to keep the molecular diversity. Moreover, an exclusive-OR (XOR) operator between two strings is introduced to reduce the chance of cloning before new molecules are generated. Both simulation and real-life experiments have been conducted in this paper to verify the effectiveness of HCRO. The results show that the HCRO algorithm schedules the DAG tasks much better than the existing algorithms in terms of makespan and speed of convergence. [ABSTRACT FROM AUTHOR]

Modern engines feature a considerable number of adjustable control parameters. With this increasing number of degrees of freedom (DoFs) for engines and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated and efficient engine optimization approach is desired. In this paper, interdisciplinary research on a multiobjective evolutionary algorithm (MOEA)-based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the cosimulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2)-based multiobjective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500–2250 r/min) and indicated mean effective pressures (IMEPs) (3–4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. In this paper, the settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio $\lambda$. The cosimulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual. [ABSTRACT FROM PUBLISHER]

Pulsed electric field (PEF) treatment can be used for non-thermal inactivation of microorganisms. The aim of this paper is to investigate PEF treatment of yeast, Saccharomyces cerevisiae, using three different field waveforms: square; non-oscillating exponential and oscillating exponential. The PEF system used in this paper consists of a pulsed power supply and a parallel-plane metallic electrodes treatment cell located in an air-pressurised chamber. PEF treatment of the yeast was conducted using electric field impulses with magnitudes of 67 kV/cm and 80 kV/cm. The efficacy of the PEF treatment for inactivation of the yeast cells was assessed by comparison of the PEFtreated and untreated yeast populations. Results showed that ~3-log10 reduction in the yeast population can be achieved with 100 impulses using all tested waveforms. Amongst all three tested waveforms non-oscillating exponential impulses demonstrated improved PEF performance. The effect of duration of treatment and peak magnitude of the field on the PEF process is discussed. [ABSTRACT FROM PUBLISHER]

Energy storage systems (ESSs) play a significant role in performance improvement of future electric traction systems. This paper investigates an ESS based on supercapacitors for trams as a reliable technical solution with considerable energy saving potential. Operating the ESS onboard a tram brings the following benefits: reduction of peak power demands, decrease of power consumption, improvement of performance, and efficiency. Whereas the commonly used ESS power management (PM) has been dealing only with PM without vehicle position knowledge, the main emphasis of the presented research is put on position-based Takagi–Sugeno fuzzy (T-S fuzzy) PM for human-driven trams with an ESS. The tram position, located by, e.g., a Global Positioning System module, is used to predict future energy demands and power flows. This information is used in the preparation of the supercapacitor energy level and its power flow control. The optimal ESS power flow with respect to the tram position is a very complex problem. Therefore, soft computing techniques are used in this paper. The main part of the proposed ESS PM is the T-S fuzzy controller. The T-S fuzzy reduces problem dimensionality using expert knowledge represented by the rules. The output membership functions were optimized by differential evolution. The designed ESS PM was verified on real traffic data. [ABSTRACT FROM PUBLISHER]

This paper addresses the integrated volt-var control for distribution network operation via multiobjective optimization. This paper seeks to explore the problem of energy savings and peak demand relief through the voltage reduction procedure. Currently, due to the emergence of the distribution smart grids, these procedures are gaining renewed interest and attention. The proposal presented here is for the operation phase, with a strategy based on an hourly load forecast for the next day/week, taking into account the active power intake reduction, and the voltage deviation. Therefore, the result is a set of nondominated optimal solutions, and then one may decide when, where, and how to apply them to meet different goals. The obtained solutions, for two typical distribution networks, describe relevant economic and technical benefits. [ABSTRACT FROM AUTHOR]

Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Quantifying the impact of new technologies on a single distribution feeder is a tractable analysis problem. But the analysis of a single distribution feeder does not provide insight into the complexities and variations of a system-wide deployment. The inability to extrapolate system-wide impacts hinders the deployment of many promising new technologies. This paper presents a three-stage method of extrapolating technological impacts, either simulated or from a field demonstration, from a limited number of distribution feeders to the entire system. The size of the system can vary from the service territory of a single utility, to a region, or to an entire country. The paper will include an example analysis using the United States Department of Energy funded Smart Grid Investment Grant projects, extrapolating their benefits to a national level, and an example of a regionalized deployment of Volt-VAR optimization for peak demand reduction. Both of the example analyses utilize the presented three-stage extrapolation method. [ABSTRACT FROM PUBLISHER]

Evolutionary algorithms (EAs) have become the standards and paradigms for solving inverse problems. However, their two inherited operations, namely, the crossover and mutation operations, are complicated and difficult, both in theory and in numerical implementations. In this regard, increasing efforts have been devoted to EAs which are based on probabilistic models (EAPMs) to overcome the shortcomings of available EAs. The population-based incremental learning (PBIL) is an EAPM; moreover, it can bridge the gap between machine learning and the EAs, hence enjoying several merits compared with other EAs. However, lukewarm efforts have been devoted to PBILs, especially the real coded PBILs, in the study of inverse problems in electromagnetics. In this regard, a novel real coded PBIL is being proposed in this paper. In the proposed real coded PBIL, a probability matrix is proposed to randomly generate a population, and the updating formulas for this probability matrix using the so far searched best solution and the best solution of the current population are introduced to strike a balance between convergence performance and solution quality. The proposed real coded PBIL algorithm is numerically experimented on several case studies and promising results are reported in this paper. [ABSTRACT FROM PUBLISHER]

Design of electromagnetic devices has multimodal, multidimensional, and constrained characteristics. Metaheuristic approaches are good choices for tackling these design problems owing to their simulation-based property. As many electromagnetic design problems require a long computing time to solve (even on modern computers) and many heuristic approaches have been created, the major goal of this paper is to improve the effectiveness and robustness of existing approaches. This paper proposes an algorithm with the ensemble of two composite differential evolution (DE) ingredients. One ingredient is biased toward exploration and the other is biased toward exploitation. The probability of choosing which ingredient to search for new solutions is adaptively updated based on the previous performance of each ingredient. The algorithm is applied to solve a loudspeaker design problem with promising performance when compared with DE, artificial bee colony, two improved artificial bee colony algorithms, and a randomly choosing ingredients algorithm. [ABSTRACT FROM AUTHOR]

The impact of the electric vehicle (EV) charging on node voltages and feeder currents of an electric grid can be reduced by upgrading its feeder capacity or deploying onsite photovoltaic (PV) and battery energy storage (BES), or their appropriate combination. It, in turn, enhances the quality of service (QoS) of the charging. This paper proposes an approach, which can enable a distribution network operator/business owner in finding the appropriate combination of the solutions above for providing the best QoS at a minimum cost while reducing the grid impact. The developed approach takes into account the previously scarcely incorporated characterization aspects (whether it is a constant power, constant current, or constant impedance load) of the EV charging. It has been applied to an IEEE 37-bus system in conjunction with the parking lots of the University of Queensland for a case study. The numerical results show that the proposed approach can effectively find the appropriate combination of the solutions above and the corresponding optimal sizes of PV and BES for a given EV penetration level or a given QoS. [ABSTRACT FROM AUTHOR]

An optimum operating strategy is needed for early amortization of expensive hybrid electric vehicle powertrain parts. Especially in the operation of long-haul trucks, fuel costs have a large impact on the total cost of ownership, which is the key entrepreneurial figure in the transportation business. Combined with route information, a predictive powertrain control (PPC) system increases the fuel-saving. In a MATLAB/Simulink model-based generic approach, the operating strategy and the PPC are optimized using a genetic algorithm. The tradeoff between minimizing the fuel consumption and simultaneously maximizing the vehicle speed and gradeability to decrease time-related fixed costs has to be solved. This leads to a multi-optimization problem. The operating strategy is developed for a parallel hybrid topology that includes the fuel-saving functions of regenerative braking, boosting, shifting the load point, and electric drive only operation. The methodology developed answers the search for an optimum control parameter setup combining the operational strategy and the PPC system in long-haul operations. This paper describes the model building, simulation, and optimization of a rule-based control strategy. The route profile and fuel consumption of an internal combustion engine long haul truck were measured in a real-life test run. The recorded data are used for model building and to validate the simulation tool. With an optimized parameter setup, fuel-saving of up to 11% were achieved in theory with acceptable vehicle speed and gradeability. [ABSTRACT FROM AUTHOR]

The application of the cross entropy (CE) method to tone injection (TI) can make the peak-to-average power ratio (PAPR) of the orthogonal frequency division multiplexing (OFDM) signals reduced efficiently with great complexity savings. However, as a population-based search method, the CE method requires large samples to find a high-quality solution, which means higher complexity and more time consuming concerning convergence. To reduce the complexity while achieving promising PAPR performance, an improved CE based TI scheme is proposed in this paper. First, a structured extended constellation for rectangular quadrature amplitude modulation is designed to provide more freedom degrees. With proper parameter selection, the proposed extended constellation can offer more design flexibilities and achieve a better tradeoff between freedom degree and power increase. Then, by analyzing the impact of the subcarrier candidate population on the conventional CE based TI scheme, we derive an explicit expression for the determination of the size of the subcarrier candidate population. And, based on the prior information carried by the clipping signal, proper candidate subcarriers are selected. Finally, an improved CE based TI scheme is proposed, which can efficiently enhance the solution quality and greatly accelerate the convergence speed. Simulation results demonstrate that the improved CE based TI scheme achieves great improvements on the performance of PAPR, bit error rate, power increase and complexity compared with the conventional CE based TI scheme. [ABSTRACT FROM PUBLISHER]

Privacy concern in data sharing especially for health data gains particularly increasing attention nowadays. Now, some patients agree to open their information for research use, which gives rise to a new question of how to effectively use the public information to better understand the private dataset without breaching privacy. In this paper, we specialize this question as selecting an optimal subset of the public dataset for M-estimators in the framework of differential privacy (DP) in [1] . From a perspective of non-interactive learning, we first construct the weighted private density estimation from the hybrid datasets under DP. Along the same line as [2] , we analyze the accuracy of the DP M-estimators based on the hybrid datasets. Our main contributions are (i) we find that the bias-variance tradeoff in the performance of our M-estimators can be characterized in the sample size of the released dataset; (ii) based on this finding, we develop an algorithm to select the optimal subset of the public dataset to release under DP. Our simulation studies and application to the real datasets confirm our findings and set a guideline in the real application. [ABSTRACT FROM AUTHOR]

Improvements to overcome premature convergence of existing wind driven optimization algorithms are proposed. The specific measures being proposed include: 1) selection of the origin point of every parcel using dynamic and random tournament selection mechanisms to guarantee that there is a good balance between exploration and exploitation searches. In this paper, the “so far searched best solution” will be exploited to guide the movement of the randomly initialized parcels by introducing a newly designed mechanism; and a probabilistic mutation is designed; and 2) full utilization of the latest information accumulated from the searched history in order to guide the search toward the potential solutions to enhance convergences, and the “so far searched worst parcel” is used to shift the new parcel away from the parcel in issue. Numerical results on three case studies are reported to showcase the feasibility and the merit of the proposed method in solving both practical engineering design problems and mathematical test functions. [ABSTRACT FROM AUTHOR]

The optimization of electrical machines and energy-harvesting devices has been one of the hot topics of research in recent years. In this context, advanced computational intelligence algorithms are suitable design tools, since they are able to optimize complex systems with a large number of constraints and free design parameters. This paper presents the social network optimization (SNO): a new population-based algorithm developed to guarantee an effective and faster exploration of the solution domain with respect to traditional optimization techniques. The proposed technique will be described and validated with a benchmark case study. Finally, the optimization problem of a tubular permanent magnet linear generator will be formulated in the context of vehicular energy-harvesting systems. [ABSTRACT FROM PUBLISHER]

Nodal voltages and feeder currents are acquired by running load flows for electric vehicle (EV) charging purposes. The impact of the charging is analyzed by either visually inspecting the voltages and currents or calculating appropriate impact indices. Optimal charging, on the other hand, is performed by incorporating the voltages and currents in charging strategies. Though the impact indices are frequently used for assessing the impact, their usage for optimizing charging has been minimal. Therefore, this paper develops probabilistic impact indices for nodal voltage violations and feeder overloading, which can be applied not only for investigating the impact but also for optimizing charging. Also, aggregated EV load demand of a large EV population is modeled by using a new combined state of charge (SOC)-based charging strategy in time series, for assessing the applicability of the developed impact indices for practical purposes. The combined SOC dynamically integrates arrival and departure of EV and delivered energy. The numerical results show that the proposed impact indices in conjunction with the SOC-based charging have numerous advantages over the conventional charging strategies aided by the load flows. [ABSTRACT FROM AUTHOR]

Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks to directly represent and learn the cell invasion and mass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density, and tumor boundary derived from multimodal imaging data. The expansion network models the mass-effect from the growing motion of tumor mass. We also study different architectures that fuse the invasion and expansion networks, in order to exploit the inherent correlations among them. Our network can easily be trained on population data and personalized to a target patient, unlike most previous mathematical modeling methods that fail to incorporate population data. Quantitative experiments on a pancreatic tumor data set show that the proposed method substantially outperforms a state-of-the-art mathematical model-based approach in both accuracy and efficiency, and that the information captured by each of the two subnetworks is complementary. [ABSTRACT FROM PUBLISHER]

In this paper, several studies on total ionizing dose effects on pinned photodiode CMOS images sensors are presented. More precisely, the evolution of a parasitic signal called random telegraph signal (RTS) is analyzed through several photodiode designs. It is shown that the population of pixels exhibiting this fluctuation depends on the design variants. This population also increases in a different way with the dose: the effects are not the same considering a low or high X-rays irradiation. Moreover, a statistical analysis is realized in order to better characterize the defects responsible for RTS. It turns out that an electric-field enhancement signature can appear in some specific cases. [ABSTRACT FROM PUBLISHER]

Limiting the number of retransmissions in practical random access systems such as S-ALOHA and IEEE 802.11 systems has been used, since it was often considered as a way of preventing the system from being congested, by dropping packets in retransmission or letting users know network congestion after the maximum of retransmissions. Therefore, it can be expected that the smaller the maximum of retransmissions is allowed, the smaller the backlog size gets with a higher packet dropping probability. This paper explores how such a retransmission limit alters the shape of the stability and throughput regions of S-ALOHA systems. To do this, we first consider an S-ALOHA system, where two users with infinite queue length have different packet arrival rate and retransmission probability, which is called asymmetric user. Then, we obtain those results of the systems with $N$ symmetric users. [ABSTRACT FROM PUBLISHER]

In group testing, simple binary-output tests are designed to identify a small number $t$ of defective items that are present in a large population of $N$ items. Each test takes as input a group of items and produces a binary output indicating whether the group is free of the defective items or contains one or more of them. In this paper, we study a relaxation of the combinatorial group testing problem. A matrix is called $(t,\epsilon )$ -disjunct if it gives rise to a nonadaptive group testing scheme with the property of identifying a uniformly random $t$ -set of defective subjects out of a population of size $N$ with false positive probability of an item at most $\epsilon $ . We establish a new connection between $(t,\epsilon )$ -disjunct matrices and error correcting codes based on the dual distance of the codes and derive estimates of the parameters of codes that give rise to such schemes. Our methods rely on the moments of the distance distribution of codes and inequalities for moments of sums of independent random variables. We also provide a new connection between group testing schemes and combinatorial designs. [ABSTRACT FROM PUBLISHER]

In the classical non-adaptive group testing setup, pools of items are tested together, and the main goal of a recovery algorithm is to identify the complete defective set given the outcomes of different group tests. In contrast, the main goal of a non-defective subset recovery algorithm is to identify a subset of non-defective items given the test outcomes. In this paper, we present a suite of computationally efficient and analytically tractable non-defective subset recovery algorithms. By analyzing the probability of error of the algorithms, we obtain bounds on the number of tests required for non-defective subset recovery with arbitrarily small probability of error. Our analysis accounts for the impact of both the additive noise (false positives) and dilution noise (false negatives). By comparing with information theoretic lower bounds, we show that the upper bounds on the number of tests are orderwise tight up to a \log ^2K factor, where $K$ is the number of defective items. We also provide simulation results that compare the relative performance of the different algorithms and reveal insights into their practical utility. The proposed algorithms significantly outperform the straightforward approaches of testing items one-by-one, and of first identifying the defective set and then choosing the non-defective items from the complement set, in terms of the number of measurements required to ensure a given success rate. [ABSTRACT FROM PUBLISHER]

The hospital system is a complex service system in which patients and hospitals interact and make their decisions based on bounded rationality and information. In this paper, we develop a generative agent-based model to simulate the behavior of a hospital service system. Our model combines agent-based simulation and queueing models to mimic the hospital service processes. Our goal is to simulate and understand the growth and size distribution of hospitals. This simulation model includes agents for supply elements (i.e., hospitals with different resources and expansion strategies) and demand elements (i.e., patients with different preferences for their hospital selections) in a hospital service system. Three important questions are investigated: 1) what is the emergent size distribution of hospitals? 2) what key factors influence the size distribution? and 3) how sensitive is the size distribution to these key factors? Simulation results show that the size distribution is neither power law nor lognormal. Rather, the distribution is leptokurtic, and more skewed than normal but less skewed than lognormal. This result contradicts those in extant literature on the size distributions of human and natural systems, including cities, firms, power-grids, and citation network. We conduct a set of experiments to identify the generative mechanisms for the hospital size distribution and to test the robustness of the results. The model was validated empirically by using a U.S. hospital size dataset. [ABSTRACT FROM AUTHOR]

This paper presents a novel evolutionary strategy for multiobjective optimization in which a population’s evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence. [ABSTRACT FROM AUTHOR]

This paper proposes an improved hybrid method to compute the parameters of the two-diode model of photovoltaic (PV) module. Unlike previous methods, it attains the speed of the analytical approach by utilizing only datasheet information. Furthermore, its accuracy is not compromised as it does not require simplifications in its computation. Four parameters are determined analytically, while the remaining three are optimized by using an evolutionary algorithm, i.e., the differential evolution. The speed is improved because the parameters are optimized only once, i.e., at standard test condition, while the values at other conditions are computed analytically. Furthermore, a procedure to guide the initial conditions of the Newton–Raphson iteration is introduced. For validation, the algorithm is compared to other established computational methods for mono-, polycrystalline, and thin film modules. When evaluated against the experimental data, the mean absolute error is improved by one order of magnitude, while the speed is increased by approximately threefold. The standard deviation of the decision parameters over 100 independent runs is less than 0.1—which suggests that the optimization process is very consistent. Due to its speed and accuracy, the method is envisaged to be useful as a computational engine in PV simulator. [ABSTRACT FROM PUBLISHER]

The study of failure behavior of a diverse population of cables is challenging. Previous attempts have failed to capture the complexity of cable system failures due to an independent analysis of multiple failure causes or influential factors. In this paper, the multiple correspondence analysis (MCA) is proposed for simultaneous analyses of multiple variables responsible for the cable failures and classification of cables into homogeneous groups in terms of past failure behavior. The proposed classification method is less subjective as it gives equal consideration to all the cable features. The methodology has been applied to the main cable section and cable joint failure data of a diverse population of cables obtained from a Chinese utility company. The failure data have six categorical variables related to cable features and failure characteristics. The application of MCA provided an enriched view and understanding of failure behavior by allowing visual exploration of the failure patterns and associations. Based on the past failure, the cable sections and joints were classified into three and four groups, respectively. The failure trend of each classified group is evaluated separately. Results show that failure history and trend of each classified group is different. Thus, they must be analyzed and treated differently in the forecasting or maintenance planning procedures. [ABSTRACT FROM AUTHOR]

This paper outlines a novel method for calculating the probability that an individual exposed to an electrical hazard will experience ventricular fibrillation (v.f.) as a result. This method was developed to deal with high-impact low-likelihood (HILL) events where a minute percentage of the population is exposed to, or is susceptible to, the hazards created by the event. The consequences of a HILL event can be extreme, for example, death, but effective management of HILL events is a quandary for society since the likelihood of the extreme outcome is low. The framework presented here was developed to analyze and manage electrical hazards associated with ground potential rise during power system faults. It can, however, be applied to other events involving infrequent exposure of small percentages of the population to electrical hazards. Where electrical hazards occur as HILL events, the range of sensitivity in the population becomes critical so it is necessary to ensure that the more extreme individuals are explicitly considered. Most commonly used methods for estimating complex probabilities (for example, Monte–Carlo) are poorly suited to this application as they focus on the behavior of the “bulk” of probability distributions rather than characterizing the tails. The method outlined facilitates deliberate consideration of the complete range of sensitivities to electricity demonstrated by the human population. It also has the additional benefit of significantly reduced computational requirements while remaining intuitive in application. The validity of the method is confirmed by reproducing previous work investigating the probability of v.f. of step voltages using the safety criteria outlined in IEEE standard 80. [ABSTRACT FROM AUTHOR]

Poor sleep is increasingly being recognized as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics, which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists, and an unrepresentative sleeping environment. A home-based noncontact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an undermattress sleep monitoring system for noncontact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimized feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity, and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation dataset (71.9%, 87.9%, and 56% and 77.5%, 98%, and 57% is reported for younger and older subjects, respectively). These results compare favorably with similar research, however this system provides an ambient alternative suitable for long-term continuous sleep monitoring, particularly among vulnerable populations. [ABSTRACT FROM PUBLISHER]

The max-bisection problem consists in partitioning the vertices of a weighted undirected graph into two equally sized subsets so as to maximize the sum of the weights of crossing edges. It is an NP-hard combinatorial optimization problem that arises in many applications. In this paper, we present a memetic algorithm for the max-bisection problem, which integrates a new fast local search procedure, a crossover operator, and a pool updating strategy. These strategies achieve a balance between intensification and diversification. Extensive experiments were performed on a number of benchmark instances with 800 to 10,000 vertices from the literature. The proposed memetic algorithm improved the best known solutions for all benchmark instances tested in this paper. The improvement in terms of cut value over the CirCut by Burer et al. refid="ref34"/ ranging from 0.02 to 4.15 percent, and the average time of our proposed memetic algorithm is much lower than that of CirCut. It shows that the proposed memetic algorithm can find high quality solutions in an acceptable running time. [ABSTRACT FROM AUTHOR]

In the current DVB generation, satellite terminals are expected to be interactive and capable of transmission in the return channel with satisfying quality. Considering the bursty nature of their traffic and the long propagation delay, the use of a random access technique is a viable solution for such a Medium Access Control (MAC) scenario. In this paper, random access communication design in DVB-RCS2 is considered with particular regard to the recently introduced Contention Resolution Diversity Slotted Aloha (CRDSA) technique. This paper presents a model for design and tackles some issues on performance evaluation of the system by giving intuitive and effective tools. Moreover, dynamic control procedures that are able to avoid congestion at the gateway are introduced. Results show the advantages brought by CRDSA to DVB-RCS2 with regard to the previous state of the art. [ABSTRACT FROM AUTHOR]

This paper discusses the bias problem when estimating the population size of big data such as online social networks (OSN) using uniform random sampling and simple random walk. Unlike the traditional estimation problem where the sample size is not very small relative to the data size, in big data, a small sample relative to the data size is already very large and costly to obtain. We point out that when small samples are used, there is a bias that is no longer negligible. This paper shows analytically that the relative bias can be approximated by the reciprocal of the number of collisions; thereby, a bias correction estimator is introduced. The result is further supported by both simulation studies and the real Twitter network that contains 41.7 million nodes. [ABSTRACT FROM AUTHOR]