Search

The use of cosmetics is ubiquitous, signaling the importance of aesthetics for humans. Yet, little is known on drivers of cosmetic consumption. In a set of multiple surveys among over 5,000 women across seven countries in three continents (Study 1), we consistently show that heightened perceived pressure to change appearance is associated with higher cosmetic consumption. Further, perceived pressure (and subsequently cosmetic consumption) is influenced by implicit theories of human beauty, such that holding an entity beauty belief—that is, beauty beauty is fixed and unalterable—counterintuitively is associated with heightened perceived pressure to change appearance, which in turn is associated with higher cosmetic consumption. This pattern of results remains in Study 2 where an entity (vs. incremental) beauty belief was experimentally induced. Study 3 further shows that an entity (vs. incremental) beauty belief leads to higher intention to consume cosmetic products but only in a social, not a private, context. Our studies thus provide evidence that beauty-related consumption phenomena could be directly influenced by perception and indirectly influenced by deep-seated, almost philosophical, implicit theories. [ABSTRACT FROM AUTHOR]

Previous studies have primarily focused on understanding why people believe conspiracy theories, especially during societal crises (e.g., the COVID‐19 pandemic). The investigation of how such conspiracy beliefs would influence people's mental well‐being has just begun recently. The present research aims to address this crucial question by testing the relationships between psychological distress and COVID‐19 conspiracy beliefs with a five‐wave longitudinal study. On the one hand, COVID‐19 conspiracy beliefs could be more appealing to people with heightened distress, as these theories apparently help people to make sense of the uncertainty and life‐threatening disease outbreak. On the other hand, conspiracy theories could be a source of existential threat and thus, would induce rather than reduce psychological distress. We tested these possibilities empirically by a series of cross‐lagged model analyses. Using the random intercept cross‐lagged panel model analysis, we only found a between‐person association but not a cross‐lagged within‐person relationship between the two. COVID‐19 conspiracy beliefs was predicted by being more politically conservative. These findings were further corroborated by the supplementary latent growth curve analyses. Overall, our findings suggest that conspiracy beliefs may not induce or reduce psychological distress in the context of COVID‐19. [ABSTRACT FROM AUTHOR]

Wind is an essential, clean and sustainable renewable source of energy; however, wind speed is stochastic and intermittent. Accurate wind power generation forecasts are required to ensure that power generation can be scheduled economically and securely. This work proposes a hybrid deep learning technique that incorporates a quantum-inspired neural network to predict wind speeds 24 h in advance. An innovative neural network technique is implemented by cascading parallel convolutional neural networks (CNNs) with a long short-term memory (LSTM) and a quantum-inspired neural network (QINN). The proposed hybrid model is optimized using two iterative loops: the outer loop is implemented by using quantum particle swarm optimization (QPSO) to tune the structure of model and other hyperparameters/parameters. The inner loop uses an Adam optimizer to tune the weights and biases of the proposed model. Spatiotemporal wind speeds at various locations provide the 2D input data. Simulation results reveal that the proposed method outperforms other methods for 24 h-ahead wind speed forecasting. [ABSTRACT FROM AUTHOR]

Understanding why people believe conspiracy theories related to disease outbreaks and the consequences of such beliefs is critical for combating both the COVID-19 pandemic and its corresponding "infodemic." In the introduction to this special issue on conspiracy theories about infectious diseases, the authors first provide a brief overview of the narratives of conspiracy theories related to COVID-19, followed by a review of extant theoretical frameworks regarding the psychology of conspiracy beliefs. Specifically, they discuss how epistemic, existential, and social needs contribute to the holding of conspiracy beliefs. Then, the authors summarize the major findings from the nine empirical articles featured in this issue, particularly how they shed light on the antecedents and consequences of disease-related conspiracy beliefs. They conclude by discussing future directions for the study of disease-related conspiracy beliefs. [ABSTRACT FROM AUTHOR]

How infectious diseases shape individual minds and behaviors has been of interest to researchers. We conducted four studies to examine whether the threat perception of the COVID-19 pandemic was positively related to pro-environmentalism. Study 1 (N = 1,508) showed that individuals' threat perception of the pandemic was correlated with their pro-environmental behaviors. Study 2 (N = 241) clarified the causality by manipulating threat perception and found that individuals with high (vs. low) threat perception reported higher pro-environmental willingness. Study 3 (N = 406) revealed that awe for nature mediated this relationship. Study 4 (N = 405) replicated Study 3 more than two years after the outbreak and demonstrated the findings were robust regardless of decreases in infection fear. These findings suggest that the COVID-19 pandemic provides individuals with an opportunity to reconsider the way they treat nature. [ABSTRACT FROM AUTHOR]

Short-term electric load forecasting is essential for the operation of power systems and the power market, including economic dispatch, unit commitment, peak load shaving, load management and bidding strategy development. This paper presents a novel method that uses a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, to conduct 24h-ahead electric load forecasting in power systems. Spearman's rank-order correlation, which serves as input size information for the CNN, was used to measure the cross-correlation between two sets of observations (chronological loads) of time shifts or lags. Both max and average pooling operations were utilized to extract data features in concatenation while spatial and conventional dropouts were employed to avoid overfitting in the hybrid CNN. The proposed method used two loops to yield the optimal CNN. The outer loop optimizes the structure and hyperparameters (such as kernel size) of the hybrid CNN by a proposed method of enhanced elite-based particle swarm optimization (EEPSO) while the inner loop optimizes the parameters (such as values of a kernel) and weights in neural networks using the Adam optimizer. The EEPSO is based on the mean search and chaotic descending inertia weight. The proposed method was used to study the 2018 and 2019 hourly load data in Taiwan Power Company. Simulation results show that the proposed method outperforms the well-known ARIMA, Radial Basis Function Network, Support Vector Regression, Long Short-term Memory and their hybrid variants. • Structure and hyperparameters of the CNN are optimized by enhanced elite-based PSO. • Both spatial and traditional dropouts are used to avoid overfitting in the CNN model. • Both max and average pooling operations are used to extract data features. • Hourly load data in Taiwan Power Company in 2018 and 2019 are studied. [ABSTRACT FROM AUTHOR]

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low-voltage ride-through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF-infinite bus power system and the IEEE 39-bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

As a result of the increasing penetration of renewables in power systems, wind farms (WFs) have to satisfy the low‐voltage ride‐through (LVRT) requirement to stay connected to the power grid under fault conditions. This study proposes a novel method that uses the static synchronous compensator (STATCOM) to improve the LVRT capability of WFs during grid faults. The proposed method is based on the Taguchi method which involves orthogonal experiments. The allocation (locations and sizes) and fuzzy control gains of STATCOM are determined by the analysis of mean (ANOM) technique as part of the Taguchi method to achieve a robust design that is insensitive to variations of operating conditions and faults. The optimal STATCOM that is obtained by the proposed method is validated in both a single WF‐infinite bus power system and the IEEE 39‐bus power system. The simulation results show that the proposed method can enhance the LVRT capability of WFs. [ABSTRACT FROM AUTHOR]

• The convolution and pooling operations are used to extract the wind power feature. • A double Gaussian activation function is proposed for Radial Basis Function network. • Coefficients of determination (r-square) of the results exceed 0.88 in 4 seasons. Wind power generation is always associated with uncertainties as a result of fluctuations of wind speed. Accurate predictions of wind power generation are important for the efficient operation of power systems. This paper presents a hybrid deep learning neural network for 24 h-ahead wind power generation forecasting. This novel method is based on a Convolutional Neural Network (CNN) that is cascaded with a Radial Basis Function Neural Network (RBFNN) with a double Gaussian function (DGF) as its activation function. The CNN is utilized to extract wind power characteristics by convolution, kernel and pooling operations. The supervised RBFNN, incorporating a DGF, deals with uncertain characteristics. Realistic wind power generations, measured on a wind farm, were used in simulations. The proposed method is implemented using TensorFlow and Keras Library. Comparative studies of different approaches are shown. Simulation results reveal that the proposed method is more accurate than traditional methods for 24 h-ahead wind power forecasting. [ABSTRACT FROM AUTHOR]

Continuing investing in a failing plan (i.e., the sunk-cost fallacy) is a common error that people are inclined to make when making decisions. It is impossible to get resources back that already have been invested. Hence, economic theory implies that decision makers’ decisions should only be guided by future gains and losses. According to the literature, the sunk-cost fallacy is driven by negative affect. Previous studies focused on negative incidental affect. We investigated, in contrast, whether the sunk-cost fallacy is caused by integral affect elicited by the specific decision context. Study 1 demonstrated a positive relationship between affective reaction and the sunk-cost fallacy. Study 2 replicated the finding in Study 1 in a within-subjects design, and demonstrated a full mediation of type of scenario (invest vs. non-invest) on the sunk-cost effect, mediated by integral affective reaction. A mediation using a within-subjects design additionally demonstrated that the effect is mediated by integral emotional responses experienced in relation to each scenario, and not by incidental emotional states that are unrelated to the scenarios. Study 3 replicated findings in the previous studies, and demonstrated that the relation between the sunk-cost fallacy and affect is moderated by justification. Participants who justified their decision were more resistant to the sunk-cost fallacy, and showed less negative affect elicited by the scenarios, than participants who did not justify their decision. Study 4 provided supporting evidence for our hypothesis by hindering conscious deliberation, and promoting reliance on affect, via cognitive load. The results showed that the relation between affect and the sunk-cost fallacy was stronger for participants under high cognitive load, than under low-load. The paper discussed how this research leads to new ways to protect against the sunk-cost fallacy in the discussion. [ABSTRACT FROM AUTHOR]

The multicultural experience (i.e., multicultural individuals and cross-cultural experiences) offers the intriguing possibility for (i) an empirical examination of how free-energy principles explain dynamic cultural behaviors and pragmatic cultural phenomena and (ii) a challenging but decisive test of thinking through other minds (TTOM) predictions. We highlight that TTOM needs to treat individuals as active cultural agents instead of passive learners. [ABSTRACT FROM AUTHOR]

The static synchronous compensator (STATCOM) has attracted considerable attention because it can stabilise severe transients that are caused by power system disturbances. This study presents a novel interval type 2 fuzzy control system (IT2‐FCS)‐based controller for the STATCOM to stabilise bus voltages that are caused by faults or forced wind farm outages in a smart grid. Two IT2‐FCSs are presented to tune increments of the proportional integral (PI) controller, which are optimised by the gradient descent method. The IT2 fuzzy rules, involving upper and lower membership functions, result in a fast and stable system response. Since many possible scenarios may arise in the power system, the Taguchi method is used to design experiments using an orthogonal array, in which all scenarios are mutually independent. A power system that consists of a wind farm and STATCOM is studied. Comparative studies show that the proposed method is superior to traditional PI and type 1 FCS methods. [ABSTRACT FROM AUTHOR]

Microgrid operation is challenging because the amount of electricity that is produced from renewables is uncertain and the inertia of distributed generation resources is very small. Energy storage systems can regulate energy, improve the reliability of the power systemand enhance the transient stability. This paper determines the optimal capacities of energy storage systems in an islanded microgrid that is composed of wind-turbine generators, photovoltaic arrays, and micro-turbine generators. The energy storage system can enhance the reliability of the microgrid and eliminate the unnecessary load shedding when a severe transient (such as a generator outage) occurs in the islanded microgrid. The studied problem is expressed as a multi-objective programming formulation, which is solved using an immunity-based algorithm. Four objective functions are optimized: minimum of energy storage capacity, minimum of load shedding, maximum of the lowest swing frequency, and minimum of the Customer Average Interruption Duration Index (CAIDI). These four objective functions are subject to both steady-state constraints and the transient-state equality constraint. The steady-state constraints include the total shed load limit, the feasible range of energy storage capacities while the transient-state equality constraint is expressed by the dynamic equation. The Pareto optimums are explored and optimality of the problem is investigated. The simulation results based on an islanded 15-bus microgrid show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) cells have attracted substantial interest recently owing to environmental concerns and the energy crisis. However, the efficiency of power generation by solar PV cells depends on various factors, such as climatic conditions and tilt angle. Therefore, maximum power point tracking (MPPT) for PV modules is important. This work proposes a novel robust design, which relies on various combinations of insolations, temperatures, and tilt angles, in a stand-alone PV system with MPPT. Orthogonal experiments are carried out using the Taguchi method, avoiding a full factorial experimental design. In the proposed Taguchi-based MPPT robust design of the PV system, insolation and temperature are regarded as an input and ‘noise’, respectively; insolation passage, tilt angle, and load resistance are factors that affect the results of the Taguchi experiments in multiple scenarios. A fuzzy logic controller, tuned by chaos particle swarm optimization (PSO) was utilized to implement MPPT. A stand-alone PV system was used to demonstrate the results that were obtained by the robust design of MPPT and applicability of the proposed method. Finally, a Digital Signal Processor-in-the-Loop simulation was performed using an OP5600 real-time digital simulator by Opal-RT to demonstrate the viability of the proposed scheme. [ABSTRACT FROM AUTHOR]

Research shows that parents' self-worth may be contingent on their children's performance, with implications for their interactions with children. This study examined whether such child-based worth is manifested in parents' recognition memory. Parents of school-age children in China (N = 527) reported on their child-based worth and completed a recognition memory task involving evaluative trait adjectives encoded in three conditions: self-reference, child-reference, and semantic processing. The more parents had child-based worth, the more they exhibited a child-reference effect – superior recognition memory of evaluative trait adjectives encoded with reference to the child rather than semantically. Parents exhibited the classic self-reference effect in comparisons of recognition memory between the self-reference and semantic processing conditions, but this effect was not evidenced among parents high in child-based worth. Only parents low in child-based worth exhibited the self-reference effect in comparisons between the self-reference and child-reference conditions. Findings suggest that when parents hinge their self-worth on children's performance, evaluative information related to children may be an elaborate structure in memory. [ABSTRACT FROM AUTHOR]

Previous research has suggested that bribery is more normative in some countries than in others. To understand the underlying process, this paper examines the effects of social norm and gender on bribe-giving behavior. We argue that social norms provide information for strategic planning and impression management, and thus would impact participants’ bribe amount. Besides, males are more agentic and focus more on impression management than females. We predicted that males would defy the norm in order to win when the amount of their bribe was kept private, but would conform to the norm when it was made public. To test this hypothesis, we conducted two studies using a competitive game. In each game, we asked three participants to compete in five rounds of creative tasks, and the winner was determined by a referee’s subjective judgment of the participants’ performance on the tasks. Participants were allowed to give bribes to the referee. Bribe-giving norms were manipulated in two domains: norm level (high vs. low) and norm context (private vs. public), in order to investigate the influence of informational and affiliational needs. Studies 1 and 2 consistently showed that individuals conformed to the norm level of bribe-giving while maintaining a relative advantage for economic benefit. Study 2 found that males gave larger bribes in the private context than in the public, whereas females gave smaller bribes in both contexts. We used a latent growth curve model (LGCM) to depict the development of bribe-giving behaviors during five rounds of competition. The results showed that gender, creative performance, and norm level all influence the trajectory of bribe-giving behavior. [ABSTRACT FROM AUTHOR]

The installation of renewable energy generation resources (such as photovoltaic arrays and wind-turbine generators) in a microgrid is important because a microgrid can increase the penetration of renewable energies in a smart grid. A community may be a grid-tied microgrid in which an energy management system may dispatch elastic loads (such as air conditioning systems). This paper investigates the optimal sizing of renewable energy generation resources in a community microgrid. The cost of renewables and community welfare are optimized while the comfort zone of indoor temperature in all homes is maintained using air conditioning systems. Community welfare is ensured by minimizing the purchased power from and maximizing the sold power to the utility grid with different time-of-use electricity tariffs. Since the problem of interest involves a large number of variables and chronological constraints, Markov models of photovoltaic power generation, wind generation, load and temperature are utilized to reduce the numbers of variables and constraints. The Markov-based optimization problem is then solved using the interior-point algorithm. The simulation results, based on a smart community of 50 homes, reveal the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1′′ constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system. [ABSTRACT FROM AUTHOR]

High-impedance faults (HIFs) caused by downed conductors in electric power systems are in general difficult to be detected using traditional protection relays due to small fault currents. The energized downed conductor can pose a safety risk to the public and cause a fire hazard. This paper presents a new method for locating the line (feeder) section of the HIF with the help of limited measurements in electric power systems. The discrete wavelet transform is used to extract the features of transients caused by HIFs. A modified k-means algorithm associated with genetic algorithms is then utilized to determine the placement of measurement facilities. The signal energies attained by wavelet coefficients serve as inputs to the support vector machine for locating the HIF line section. The simulation results obtained from an 18-busbar distribution system show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

A deregulated power market utilizes wholesale and retail transactions as two of its major business models. Locational marginal prices (LMPs) originating from power markets and system operations imply electricity values in a location or at a bus. Market participants can develop bidding strategies according to vital information of LMPs. The security coordinator can conduct market redispatch for congestion management based on LMP serving as a crucial indicator. This work presents a novel method using the empirical mode decomposition (EMD) incorporating correlation coefficients and neural networks using the back-propagation (BP) algorithm for undertaking short-term LMP forecasting. The correlation coefficient identifies coherent LMPs that act as inputs for the BP-based neural network to forecast the next LMPs. Additionally, validity of the proposed method is verified using historical LMPs in the PJM market in USA. Simulation results demonstrate that the proposed method can forecast short-term LMP values more efficiently than the traditional autoregressive integrated moving average, BP-based network without EMD, BP-based artificial neural networks with multi-resolution analysis of the wavelet transform and recurrent neural network. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new energy ship is being developed to address energy shortages and greenhouse gas emissions. New energy ships feature low operational costs and zero emissions. This study discusses the characteristics and development of solar-powered ships, wind-powered ships, fuel cell-powered ships, and new energy hybrid ships. Three important technologies are used for the power system of the new energy ship: new-energy spatio-temporal prediction, ship power scheduling, and Digital Twin (DT). Research shows that new energy spatio-temporal prediction reduces the uncertainty for a ship power system. Ship power scheduling technology guarantees safety and low-carbon operation for the ship. DT simulates the navigational environment for the new energy ship to characterize the boundary of the shipboard's new energy power generation. The future technical direction for new energy ship power systems is also being discussed. [ABSTRACT FROM AUTHOR]

Operations in an electric distribution system become challenging because the penetration of renewables (such as large photovoltaic (PV) arrays) becomes high nowadays. The detection and classification of faults in PV arrays are crucial for energy management and the mitigation of financial losses. The convolutional neural network (CNN) is one of the most popular deep learning-based methods for solving detection and classification problems. The use of CNNs in various fields has yielded promising results owing to their ability to extract features of signals. This study presents a 3D CNN for PV fault detection and classification. Both direct current (DC) and alternating current (AC) signals in the PV system are converted to 3D images by using the Gramian Angular Field (GAF) transform for signal pre-processing. The proposed method yields promising results in terms of overall accuracy (OA) of testing data. Simulation results indicate that the proposed 3D CNN outperforms other machine learning (ML) methods, such as k-nearest neighbor, Random Forest, Decision Tree, and Support Vector Machine, when applied to the problem of interest. • 3D CNN is used to detect and classify faults in the PV system. • Each signal in the PV array is transformed to a 2D image by GAF. • Both AC and DC signals are utilized to improve accuracy. [ABSTRACT FROM AUTHOR]

The roles of reactive power control in a distribution system become essential due to the high penetration of distributed generations (DG) these days. Proper reactive power control can reduce real power losses and regulate the voltage profile in a power system. However, intermittent characteristics of DGs (e.g., renewable energies from wind and solar power) impose uncertainty on power generation in the power system. Therefore, this paper presents a novel fast probabilistic power-flow (FPPF) method based on the Gram–Charlier series expansion to deal with such uncertainty. The FPPF method only deals with stochastic variations of random variables with respect to the expected values, thus reducing the number of iterations. Moreover, the chaotic particle swarm optimization is used to adjust generator voltages, transformer taps, and static compensators to minimize the real power losses while the stochastic voltages satisfy the operational limits. Applicability of the proposed method is verified through simulation using an autonomous 25-bus (Penghu) system and the IEEE 118-bus system. Comparative studies considering traditional probabilistic power-flow methods are performed as well. [ABSTRACT FROM AUTHOR]

In this chapter, Kelly introduces the classic commitment model (CCM) and the cultural transmission model (CTM). Based on the entanglement thesis and the co-opt thesis, Kelly argues that "disgust is neither necessary nor sufficient to justify a moral judgment" (p. 147). In this book, Kelly presents arguments from advocates and skeptics and uses his own theories and models proposed in the first four chapters to analyze the arguments in detail. [Extracted from the article]

Underfrequency load shedding maintains the power system in a stable condition when a severe disturbance occurs. The underfrequency relay protects the power system against a blackout when the system frequency declines to predetermined settings. This paper determines the shed load at each stage for the underfrequency relay (81L) in a stand-alone microgrid considering multiple scenarios. In addition, the probabilities of all scenarios are defined based on historical meteorological data and the Markov two-state model. The shed load is minimized and the lowest swing frequency is maximized using the genetic algorithms. Due to many inequality constraints in the studied problem, an efficient tree-encoding chromosome and the penalty functions are presented as well. Moreover, applicability of the proposed method is demonstrated using a microgrid test bed with the gas-turbine, wind-turbine generator, and solar photovoltaic. [ABSTRACT FROM PUBLISHER]

Abstract: A stand-alone power system is an independent system that comprises micro-turbines, diesel generators, a wind park, solar photovoltaic (PV) modules and/or energy storages, etc. This paper presents a new method for dealing with the short-term active power scheduling of a stand-alone system. The fuel cost of diesel units and CO2 emission are minimized and all operation constraints are satisfied. The maximum wind and solar PV powers with uncertainties are modeled using fuzzy sets. Adaptive chaos clonal evolutionary programming (ACCEP) is employed to solve this problem, which is formulated as an interactive multi-objective problem. Different degrees of fuzziness, preferred reference levels, and priority list for diesel generators are discussed. Simulation results show the applicability of the proposed method. [Copyright &y& Elsevier]

Long-term cultural experiences influence neural response to one's own and friend's faces. The present study investigated whether an individual's culturally specific pattern of neural activity to faces can be modulated by temporary access to other cultural frameworks using a self-construal priming paradigm. Event-related potentials were recorded from British and Chinese adults during judgments of orientations of one's own and friend's faces after they were primed with independent and interdependent self-construals. We found that an early frontal negative activity at 220–340 ms (the anterior N2) differentiated between one's own and friend's faces in both cultural groups. Most remarkably, for British participants, priming an interdependent self-construal reduced the default anterior N2 to their own faces. For Chinese participants, however, priming an independent self-construal suppressed the default anterior N2 to their friend's faces. These findings indicate fast modulations of culturally specific neural responses induced by temporary access to other cultural frameworks. [ABSTRACT FROM AUTHOR]

The article discusses the crucial role that culture plays in influencing individual social behavior as an individual’s behavior encompasses more than the personality and the immediate social environment. Some of the different ways according to Michael Bond that culture can influence behaviors are described. Culture has been found to provide individuals with shared representations, that individuals, subsequently, use to impart meanings and display appropriate behaviors.

Abstract: Power quality in distribution systems may deteriorate due to increased time-varying and nonlinear loads. The harmonic current is one of the probabilistic and nonlinear pollution sources causing voltage distortion. The harmonic currents and bus loads in a power system are non-deterministic in nature. The single-tuned passive filter is considered an economical but efficient approach in reducing harmonics. This paper presents a new method for studying passive filter planning using cumulants and the Adaptive Dynamic Clone Selection Algorithm (ADCSA). The harmonic voltages, calculated by cumulants, correspond to those whose values of cumulative probabilities are 0.95. ADCSA was employed because of its ability to search the global optimum. The number (integer) of capacitor bank and tuned resonance frequency (real number) of the single-tuned passive filter are considered unknowns. The simulation results from an 18-bus system with multiple time-varying loads and harmonic currents show the applicability of the proposed method. [Copyright &y& Elsevier]

The author share their view on the result of the study conducted by researcher Joan Y. Chiao and colleague on cultural neuroscience. They chronicle the foundation of the cognitive science and culture science, and examine how they have shown the magnitude of neural responses vary based on the individual's degree of culture-typical identity. The authors also recognize the researcher's experiment that offers enough evidence that support the relations of biology, behavior, and culture.

This paper presents a novel field-programmable gate array (FPGA) based method for empirical mode decomposition (EMD) in real time. Traditionally, EMD can be easily implemented and developed using a high-level computer language in a PC or DSP chip. However, it is difficult to implement EMD in a hardware environment. This paper develops EMD for real-time applications using a hardware-based FPGA. The proposed FPGA-based method calculates the upper and lower envelopes in EMD point by point by using a circular queue to temporarily store values of maxima and minima, from which the upper and lower envelopes in the EMD can be determined continuously. Additionally, an attempt is made to increase the efficiency of the computational process by cascading several identical modules as a serial pipeline structure in order to conduct an iterative loop for calculating the intrinsic mode functions in EMD. The fast process from the serial pipeline structure results in real-time computation with a sampling rate of up to 12.5 MHz and mitigation of the end effect. The proposed method is validated by the simulation results obtained by Quartus II and verified by FPGA (Altera Stratix III EP3SL150F1152C2) realization, revealing its effectiveness in real-time applications. [ABSTRACT FROM PUBLISHER]

For the operators of a power system, underfrequency load shedding is important for maintaining the system in a stable and reliable state whenever it suffers from a severe disturbance. The underfrequency relay protects the power system against a blackout when the system frequency declines to predetermined settings. This paper determines the shed load at each stage for the underfrequency relay (81L). The genetic algorithm (GA) is employed to minimize the shed load and maximize the lowest swing frequency. Penalty functions and chromosomes with varying lengths are utilized in the GA to determine the optimal shed loads at all stages. The uncertainty in bus loads is modeled by a fuzzy set. A stand-alone power system with diesel and wind-power generators is adopted to demonstrate the applicability of the proposed method. [ABSTRACT FROM PUBLISHER]

Techniques for distributed generations (DGs) have attracted increasing attention due to their effects on environmental sustainability and the reduction in traditional megawatt (MW) generation expansion. Wind farms are one of the DGs and have intermittent characteristics. This paper presents a method for static VAR planning considering existing wind generator voltages and transformer taps as controllers to regulate the voltage profile in a distribution system with wind farms. Wind power generations and bus loads are modeled with the Markov model. The probabilities and durations of the operation states are obtained. Through a quantum evolutionary algorithm, the cost of static VAR compensators and MW loss in the system are minimized and the operational constraints are fulfilled. The applicability of the proposed method is verified through simulations using a standalone 25-bus (Penghu) system and a 17-bus system. [ABSTRACT FROM PUBLISHER]

Abstract: Wind energy is currently one of the types of renewable energy with a large generation capacity. However, since the operation of wind power generation is challenging due to its intermittent characteristics, forecasting wind power generation efficiently is essential for economic operation. This paper proposes a new method of wind power and speed forecasting using a multi-layer feed-forward neural network (MFNN) to develop forecasting in time-scales that can vary from a few minutes to an hour. Inputs for the MFNN are modeled by fuzzy numbers because the measurement facilities provide maximum, average and minimum values. Then simultaneous perturbation stochastic approximation (SPSA) algorithm is employed to train the MFNN. Real wind power generation and wind speed data measured at a wind farm are used for simulation. Comparative studies between the proposed method and traditional methods are shown. [ABSTRACT FROM AUTHOR]

The article presents author's views on the lay theories of racial difference which make a difference. He presents his opinions on diversity science. According to the author, diversity science represents a new hybrid discipline, an integration of various fields that study intergroup relations with a focus on prejudice and discrimination. He feels there should be research work in future to connect various ideas and methodologies to build a strong diversity science.

Abstract: This paper presents a method that integrates deterministic approach with fault-tree analysis for reliability assessment of a composite system (generation and transmission in power systems). The contingency screening is conducted in the first step. The results are further classified into three clusters in the second step: normal, local trouble and system trouble. The fault-tree analysis is used to assess the reliability of the composite system in the third step. Finally, Risk Reduction Worth is adopted as a measure of importance for identifying the crucial element that has significant impact on the reliability. In this paper, a composite system in Taiwan serves as an example for illustrating the simulation results attained by the proposed method. The simulation results, verified by Siemens PTI PSS/E TPLAN software package, show that the proposed method is applicable for large scale power systems. [Copyright &y& Elsevier]

This paper explores how the lay theory approach provides a framework beyond previous stereotype/prejudice research to understand dynamic personality processes in interracial/ethnic contexts. The authors conceptualize theory of race within the Cognitive–Affective Personality System (CAPS), in which lay people's beliefs regarding the essential nature of race sets up a mind-set through which individuals construe and interpret their social experiences. The research findings illustrate that endorsement of the essentialist theory (i.e., that race reflects deep-seated, inalterable essence and is indicative of traits and ability) versus the social constructionist theory (i.e., that race is socially constructed, malleable, and arbitrary) are associated with different encoding and representation of social information, which in turn affect feelings, motivation, and competence in navigating between racial and cultural boundaries. These findings shed light on dynamic interracial/intercultural processes. Relations of this approach to CAPS are discussed. [ABSTRACT FROM AUTHOR]

This paper applies new maximum-power-point tracking (MPPT) algorithms to a wind-turbine generator system (WTGS). In this paper, the WTGS is a direct-drive system and includes the wind-turbine, permanent-magnet (PM) synchronous generator, three-phase full bridge rectifier, buck-boost converter and load. The new MPPT method uses gradient approximation (GA) algorithm. Three methods based on GA for achieving MPPT are discussed in this paper: (1) full-sensor control with anemometer and tachometer, (2) rule-based method and (3) adaptive duty cycle method. The third method has merits of no PID parameters, proportional constant, anemometer, tachometer and characteristics of WTGS required. This method enables the permanent-magnet synchronous generator (PMSG) to operate at variable speeds to achieve good performance. Simulation results show that the tip-speed ratio (TSR) and power coefficient obtained by the adaptive duty cycle method with GA can be almost identical to the optimal values. [Copyright &y& Elsevier]

A microgrid including distributed generation sources (DGSs) is generally connected to a bulk power system to provide power to its local loads. The point of common coupling between the bulk power system and microgrid should meet relevant standards. The microgrid becomes a weak system with unbalanced and distorted voltages after it is disconnected from the bulk power system. A synchronisation controller for the DGS considering imbalanced three‐phase voltages and harmonic voltages is presented. The proposed method can be implemented without altering the traditional phase‐locked loop circuit. No filters and compensators are needed in the proposed method. Simulation results reveal that the proposed method is applicable. [ABSTRACT FROM AUTHOR]

This paper proposed a method for reliability assessment of the protection system for a switchyard by fault-tree analysis considering uncertainty of unavailability for an element. Unavailability of an element with uncertainty is expressed with the fuzzy set. The fault-tree analysis incorporated with the fuzzy set is employed to conduct the reliability assessment. The importance of elements influencing reliability can be achieved by the Fuzzy Importance Measure. Compared with traditional methods, the fault-tree analysis requires less computation. In this paper, a 345 kV switchyard in the 3rd nuclear power plant in Taiwan serves as an example for illustrating the results of the proposed method. [ABSTRACT FROM AUTHOR]

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and "epidemics, pandemics, and disasters" (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems. [ABSTRACT FROM AUTHOR]

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible "N-1" and "N-2" forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutually independent. A standalone 38-bus power grid with two wind turbines of 2 × 2 MW and photovoltaics of 7.563 MW was studied. For each event, the proposed method generally obtains a result with a smaller shed load and a smaller overshoot frequency than the utility and existing methods. These simulation results verify that the proposed method is practically applicable in a standalone power system with penetration of renewables. [ABSTRACT FROM AUTHOR]

Although the nature versus nurture debate about gender differences has persisted for decades, few studies have examined the implications of individuals' nature versus nurture beliefs. In the present research, we examined how women's beliefs that gender is biologically determined or socially constructed affected their self-stereotyping tendency. Specifically, we hypothesized that holding a biological gender theory would orient women to view possessing gender stereotypical characteristics as inevitable, and thus would be linked to stronger gender self-stereotyping (even for negative feminine traits) than would a social gender theory. In two studies, using both correlational and experimental designs, we found as predicted that the biological gender theory was linked to stronger gender self-stereotyping tendency (as reflected by greater endorsements of negative feminine traits and slower reaction time in denying stereotypic feminine traits). Moreover, this relationship holds even when the participants' sexist attitudes were statistically controlled. Implications of these findings for maintenance of gender inequality were discussed. [ABSTRACT FROM AUTHOR]

Abstract: Voltage sag, caused by faults, is an important power quality problem. Static transfer switches (STSes) are considered to be one of the efficient remedies for voltage sags. Determination of the installation locations for STSes in a distribution system, therefore, becomes essential for planning a system with sensitive loads. In this paper, the problem is formulated as a fuzzy multi-objective programming problem considering STS investment cost, sensitive bus voltage deviations and total voltage sags. Genetic Algorithms (GAs) using a vertex encoding/decoding are used to solve the problem. The vertex-based Prufer number is used in GAs for encoding/decoding the chromosomes (strings). The Prufer number ensures that the distribution system structure is radial in the iterations. The simulation results obtained from a 33-bus system are used to show the applicability of the proposed method. [Copyright &y& Elsevier]

The disastrous aftermath of Katrina brought to light a great rift between Blacks and Whites in the United States. Polls taken shortly after the disaster gave clear indication that many Blacks felt that the response to Katrina was slowed by racism. At the same time, many Whites felt that the residents of New Orleans were to blame for their predicament. To understand the causal role ethnic identity plays in shaping individuals’ perceptions, the present study experimentally manipulated Whites’ social identification and measured their perceptions of the Katrina disaster's aftermath. Our results indicate that White Americans exhibited greater prejudice when thinking of themselves as “American” (an identity seemingly inclusive of Blacks) than when identifying as “White American” or “European American” (an identity that seemingly excludes Blacks). This finding demonstrates a boundary condition to the Common Ingroup Identity Model, such that a dual identity is more conducive to positive intergroup relations when strong racial assumptions exist about the overarching identity. [ABSTRACT FROM AUTHOR]

Determination of feeder configuration involving the switch statuses is important for the operation in the distribution system. Minimization of the megawatt loss is crucial if the system is in a normal condition; the voltage drop is expected to be as low as possible while a fault occurs in the system. This paper presents a method based on genetic algorithms (GAs) to determine the network configuration. The proposed method, formulated as a fuzzy multiobjective problem, takes both the normal condition and the contingencies (faults) into account. The switch statuses are considered as preventive controls for multiple individual balanced faults. A vertex encoding based on the Prufer number is used in GA for encoding the chromosomes (strings). The Prufer number ensures that the system structure will be radial for the distribution system. The simulation results from two distribution systems are used to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Bidding competition is a main transaction approach in a deregulated market. Locational marginal prices (LMPs) resulting from bidding competition signal electricity values at a node or in an area. The LMP reveals important information for market participants to develop their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using fuzzy reasoning and recurrent neural networks (RNNs) for forecasting LMPs. The fuzzy rules are used to perform the linguistic reasoning about the contingencies. The reasoning results serve as a part of inputs to the RNNs for forecasting the LMPs. The historical LMPs in the PJM market are used to test the proposed method. It is found that the proposed neuro-fuzzy method is capable of forecasting LMP values efficiently. [Copyright &y& Elsevier]

The installed transformer capacity can be reduced when the transformer connects intermittent loads with low duty cycles. This paper proposes a new method based on genetic algorithms to determine the transformer capacities for intermittent loads. The costs of transformers are minimized while the voltage fluctuations (drops) satisfy relevant standards. The duration of the voltage drop can be derived from the Markov Process. The intermittent loads are modeled with discrete stochastic distribution; therefore, the generating function is used for aggregating multi-intermittent loads at a bus. Two practical factories with grinders and welders are used to show the applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Estimation of energy loss is an essential task for both operation and planning in electric distribution systems. The energy losses are considered to be uncertain due to measurements. A new method based on fuzzy-c-number clustering and fuzzy multiple linear regression analysis is proposed for developing energy loss formulas to estimate losses in this paper. The proposed method is implemented with three stages. A part of Taipower distribution system in Taipei is used for illustrating the performance of the proposed method. [Copyright &y& Elsevier]

The comparison of differences between cultural groups has been a dominant approach in cross-cultural psychology. This approach assumes behavioral influence of a relatively static, monolithic culture on members in a cultural group. This paper, however, calls for a dynamic constructivist approach to the study of culture. We begin by drawing a parallel between past research in cultural and personality psychology. We then highlight recent findings that attest to the value of a social-cognitive model of culture. Specifically, we discuss studies that have demonstrated how well established cross-cultural differences may appear or disappear depending on the availability, accessibility, and applicability of cultural theories. We present evidence that situational characteristics may render cultural theories more or less accessible and applicable, and propose a culture × situation interaction model for understanding inter- and intra-cultural variations. [Copyright &y& Elsevier]