Analysis of effective factors on the behavioral tendencies of customers of fast food restaurants

In this research, the effective factors on the behavioral tendencies of the customers of fast food restaurants are studied. Also, factors such as food quality, service quality, mental pictures, perceived value, behavioral tendency, faithfulness and satisfaction are considered. The research method with regard to data collection is descriptive survey and it is an applied research. In order to collect the required information and data for research hypotheses, questionnaires (primary resource) as well as books, articles, theses and data bases are used as secondary resources. The collected data from questionnaire were entered into SPSS software for descriptive research and Lisrel software was used for inferential statistics and also extracting and confirmatory factor analysis model and structural model was used. Statistical population of the present research includes all customers of Super Star chain restaurants in Tehran and the sample population regarding facilities and spatial limitations are 384 samples. This study aimed at analysis of the effect of parameters of mental picture and perceived value on customer satisfaction and then this research examines the effect of customer satisfaction on his behavior intentions in Buff chain restaurants. The results of this research show that all factors mentioned greatly affects customers' behavior intention; as a result managers of Buff chain restaurants should take into account customers' satisfaction on customers' behavioral intentions for the positive effect of factors such as service quality, food quality, mental picture of restaurant, the perceived value by the customer

Analysis of effective factors on the behavioral tendencies of customers of fast food restaurants

In this research, the effective factors on the behavioral tendencies of the customers of fast food restaurants are studied. Also, factors such as food quality, service quality, mental pictures, perceived value, behavioral tendency, faithfulness and satisfaction are considered. The research method with regard to data collection is descriptive survey and it is an applied research. In order to collect the required information and data for research hypotheses, questionnaires (primary resource) as well as books, articles, theses and data bases are used as secondary resources. The collected data from questionnaire were entered into SPSS software for descriptive research and Lisrel software was used for inferential statistics and also extracting and confirmatory factor analysis model and structural model was used. Statistical population of the present research includes all customers of Super Star chain restaurants in Tehran and the sample population regarding facilities and spatial limitations are 384 samples. This study aimed at analysis of the effect of parameters of mental picture and perceived value on customer satisfaction and then this research examines the effect of customer satisfaction on his behavior intentions in Buff chain restaurants. The results of this research show that all factors mentioned greatly affects customers' behavior intention; as a result managers of Buff chain restaurants should take into account customers' satisfaction on customers' behavioral intentions for the positive effect of factors such as service quality, food quality, mental picture of restaurant, the perceived value by the customer

Simultaneous measurement of rice grain friction coefficient and angle of repose using rotating cylinders

Several methods have been developed to measure the friction coefficient of grains and seeds as well as their angle of repose, independently. However, it seems that one single method capable of simultaneous measurement of these two important parameters of grain products is of importance. In this study, simultaneous measurement of brown rice grain friction coefficient and angle of repose is performed by using rotating cylinders method. The effects of rotational speed, cylinder diameter and filling degree on rice grains, also lower and higher angle of repose along with their friction properties were considered. Results showed that proper speed for reaching slumping and rolling motions depends on cylinder diameter and filling degree varying from 0.5 to 9.7 rpm and 2 to 34.6 rpm, respectively. An insignificant difference in the range of 1° to 4° was observed between lower and upper angles of repose which indicates the reliability of the proposed method to determine the dynamic angle of repose. Results also revealed that rotational speed of cylinders did not significantly affect the lower and upper angles of repose; however, they were not affected by filling degree. Finally, the friction coefficient of rice grains was affected by cylinder diameter and filling degree. The findings of this study may be applied in the design and mathematical modeling of grains’ motions in rotary dryers, mixers, and silos

Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch

The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While CNN has shown promising results, it does not extract explainable knowledge from the data. To address this limitation, a physics-inspired CNN model is developed by incorporating constraints of the ED problem into the CNN training to ensure that the model follows physical laws while fitting the data. The proposed method can significantly accelerate real-time economic dispatch of microgrids without compromising the accuracy of numerical optimization techniques. The effectiveness of the proposed data-driven approach for optimal allocation of microgrid resources in real-time is verified through a comprehensive comparison with conventional numerical optimization approaches

ADAPT: a price-stabilizing compliance policy for renewable energy certificates: the case of SREC markets

Currently most Renewable Energy Certificate (REC) markets are defined based on targets which create an artificial step demand function resembling a cliff. This target policy produces volatile prices which can make investing in renewables a risky proposition. In this paper, we propose an alternative policy called Adjustable Dynamic Assignment of Penalties and Targets (ADAPT) which uses a sloped compliance penalty and a self-regulating requirement schedule, both designed to stabilize REC prices, helping to alleviate a common weakness of environmental markets. To capture market behavior, we model the market as a stochastic dynamic programming problem to understand how the market might balance the decision to use a REC now versus holding it for future periods (in the face of uncertain new supply). Then, we present and prove some of the properties of this market, and finally we show that this mechanism reduces the volatility of REC prices which should stabilize the market and encourage long-term investment in renewables

Time-Domain Operational Metrics for Real-time Resilience Assessment in DC Microgrids

Resilience is emerging as an evolving notion, reflecting a system's ability to endure and adapt to sudden and catastrophic changes and disruptions. This paper spotlights the significance of the quantitative resilience indices of medium-voltage DC (MVDC) distribution technology in marine vessels, notably naval ships. Given the intricate electrical requirements of modern naval ships, the need for a robust power supply underlines the imperative of resilient DC microgrids. Addressing this, our study introduces a novel quantitative metric for operational resilience of DC microgrids based on the measured voltage of main DC bus. This metric not only fuses real-time tracking, compatibility, and computational efficiency, but also adeptly monitors multiple event phases based on time-domain analysis of dc bus voltage dynamics. The intricacies of the dc bus voltage, including overshoots and undershoots, are meticulously accounted for in the algorithm design. With respect to existing research that typically focuses on offline resilience assessments, the proposed index provides valuable real-time information for microgrid operators and identifies whether microgrid resilience is deteriorating over time

Economic Model Predictive Control of Water Distribution Systems with Accelerated Optimization Algorithm

Model predictive control (MPC) has emerged as an effective strategy for water distribution systems (WDSs) management. However, it is hampered by the computational burden for large-scale WDSs due to the combinatorial growth of possible control actions that must be evaluated at each time step. Therefore, a fast computation algorithm to implement MPC in WDSs can be obtained using a move-blocking approach that simplifies control decisions while ensuring solution feasibility. This paper introduces a least-restrictive move-blocking that interpolates the blocked control rate of change, aiming at balancing computational efficiency with operational effectiveness. The proposed control strategy is demonstrated on aggregated WDSs, encompassing multiple hydraulic elements. This implementation is incorporated into a multi-objective optimization framework that concurrently optimizes water level security of the storage tanks, smoothness of the control actions, and cost-effective objectives. A fair comparison between the proposed approach with the non-blocking Economic MPC is provided

Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

This paper aims to introduce a new statistical learning technique based on sparsity promoting for data-driven modeling and control of solar photovoltaic (PV) systems. Compared with conventional sparse regression techniques that might introduce computational complexities when the number of candidate functions increases, an innovative algorithm, named adaptive regulated sparse regression (ARSR) is proposed that adaptively regulates the hyperparameter weights of candidate functions to best represent the dynamics of PV systems. Utilizing this algorithm, open-loop and closed-loop models of single-stage and two-stage PV systems are obtained from measurements and are utilized for control design purposes. Moreover, it is demonstrated that the proposed data-driven approach can successfully be employed for fault analysis studies, which distinguishes its capabilities compared with other data-driven techniques. Finally, the proposed approach is validated through real-time simulations

Nonlinear Model Predictive Control for Navy Microgrids with Stabilizing Terminal Ingredients

This paper presents a novel control strategy for medium voltage DC (MVDC) naval shipboard microgrids (MGs), employing a nonlinear model predictive controller (NMPC) enhanced with stabilizing features and an intricate droop control architecture. This combination quickly regulates the output voltage and adeptly allocates supercapacitors for pulsed power loads (PPLs), while the battery energy storage system (BESS) and auxiliary generators handle the steady state loads. A key feature of this study is the formulation of terminal cost and constraints, providing recursive feasibility and closed-loop stability in the Lyapunov sense, that offers a more robust and effective approach to naval power and energy management. By comparing the proposed Lyapunov-based NMPC with conventional PI controller under fluctuating PPLs, the control robustness is validated

Physical properties and modeling for sunflower seeds

For designing the dehulling, separating, threshing, sizing and planting machines for sunflower, physical and mechanical properties of sunflower seeds should be known.  In this work some physical properties of three varieties of sunflower seeds, distance between the adjacent seeds on the sunflower head (SH), length, width, thickness, mass of the individual seeds,  1000- seeds mass, and changing these parameters with location of seeds on SH were measured.  Then shape properties, including geometric mean diameter, sphericity, surface area, projected area and volume of the seeds were calculated.  Variations of the shape properties of the seeds on the SH were studied.  Statistical indices for dimensional and shape parameters were calculated.  For Mikhi, Sirena, and Songhori varieties, true and bulk densities, porosity, angle of repose on wood and galvanized surfaces were calculated by using standard methods in the moisture of 9.15, 5.26 and 5.62% (w.b.), respectively.  The distribution of distance between adjacent seeds on SH was modeled by using three continuous statistical distributions namely Normal, two-parameter Log-normal and two-parameter Weibull distribution.  Size and mass of seeds were modeled with two-parameter Weibull distribution.  The parameters of the probability density functions (PDF) were estimated, then evaluated, and the predictive performances of the models were compared.  Log likelihood goodness of fit test was used to test how well different PDFs work for prediction of the distance between seeds on sunflower head, size and mass of seeds.   The results for three varieties showed that when the distance between locations of the seed from center of the sunflower head increased, size, shape properties and mass of seed, increased, too.  The values of true and bulk density, porosity and angle of repose on wood and galvanized surfaces for Mikhi variety were 497.500 kg/m3, 331.027 kg/m3, 33.46% , 25.08° and 22.23°, for Sirena were 580.368 kg/m3, 422.015 kg/m3, 27.28%, 26.80° and 23.86°, and for Songhori were 471.746 kg/m3, 319.346 kg/m3, 32.30%, 24.39° and 21.70° respectively.  Modeling result for the distance between adjacent seeds on SH showed that, Log-normal distribution model fits the empirical probability density well and two-parameter Weibull distribution had worst performance for prediction.  Also modeling result for the distance between adjacent seeds on showed that whenever Skewness and Kurtosis had negative value, Weibull distribution was best fit.  Statistical analyses for dimensional properties and mass showed that in most cases, both Skewness and Kurtosis had negative values.  Therefor for modeling dimensional properties and mass, Weibull distribution was used. Keywords: sunflower seed, normal modeling, two-parameter log-normal modeling, two-parameter Weibull distribution modeling, physical properties