Backscattering of topologically protected helical edge states by line defects

The quantization of conductance in the presence of non-magnetic point defects is a consequence of topological protection and the spin-momentum locking of helical edge states in two-dimensional topological insulators. This protection ensures the absence of backscattering of helical edge modes in the quantum Hall phase of the system. However, our study focuses on exploring a novel approach to disrupt this protection. We propose that a linear arrangement of on-site impurities can effectively lift the topological protection of edge states in the Kane-Mele model. To investigate this phenomenon, we consider an armchair ribbon containing a line defect spanning its width. Utilizing the tight-binding model and non-equilibrium Green's function method, we calculate the transmission coefficient of the system. Our results reveal a suppression of conductance at energies near the lower edge of the bulk gap for positive on-site potentials. To further comprehend this behavior, we perform analytical calculations and discuss the formation of an impurity channel. This channel arises due to the overlap of in-gap bound states, linking the bottom edge of the ribbon to its top edge, consequently facilitating backscattering. Our explanation is supported by the analysis of the local density of states at sites near the position of impurities

Comparative Study of Growth Patterns for Three Strains of Broiler Chickens Using Mathematical Models

The aim of the current study was to investigate the growth pattern of three genetic strains of broiler chickens including Ross 308, Cobb and Arbor Acres by mathematical models. For this purpose, the body weight of 500 broilers for each strain was recorded weekly. Gompertz, Logistic and Richards functions were considered for data fitting. Three functions were compared by adjusted determination coefficient (R2) and root mean square error (RMSE). For all three models, R2 had high values, ranging from 0.987 to 0.999. The difference among the fitted functions by RMSE was significant compared to the R2. The Richards function had more appropriate description for the growth curve of the Cobb strain, because of having the minimum RMSE, 61.57 compared to 85.43 and 66.61, for Gompertz and Logistic functions, respectively. However, the Gompertz function with the maximum R2, and the minimum RMSE, 73.32 and 3237, respectively, was the most suitable function to describe the growth curve of Arbor Acres strain

A Reconsideration of the Number of the Prophet’s Wars

In hadith and historical sources, more than eighty wars, comprising ghazwas (the wars in which the Prophet was present) and sariyyas (the wars in which the Prophet was not present), have been attributed to the Prophet. Scholars of history have mentioned about twenty-seven ghazwas and more than fifty sariyyas, all of which took place during the ten years after the migration to Medina until the demise of the Prophet. Apart from the famous battles such as Badr, Uhud, Khandaq, Bani Qurayzah, Khaybar, Muta, Tabuk and Hunayn, many of these ghazwas and sariyyas are unknown except to some historians. This raises the question of why there must have been more than eighty wars in ten years, that is, almost one war every ninety days. This research tries to examine the real number of the Prophet's wars. The figure of eighty is greatly exaggerated.and was closer to twenty

An investigation on effects of perceived value on brand popularity and brand loyalty: A B2B case study

This research evaluates the effect of perceived value on brand popularity and brand loyalty for some organizations in business-to-business (B2B) domain under the effect of risk and e-service quality. The practical relationships among six different kinds of risks including performance, social, financial, time, psychological and safety with consideration of quality in e-commerce business on customer’s perceived value are evaluated and the effects of this perception of value on consequences of perceived value are measured. In this study, using the partial least square method as well as gathering the information of some Iranian firms that use electronic services, the study finds that there was a significant relationship between various types of risks and perceived value. There is also considerable influence of perceived value on satisfaction, brand popularity, and brand loyalty

A Survey on Deep Learning Role in Distribution Automation System : A New Collaborative Learning-to-Learning (L2L) Concept

This paper focuses on a powerful and comprehensive overview of Deep Learning (DL) techniques on Distribution Automation System (DAS) applications to provide a complete viewpoint of modern power systems. DAS is a crucial approach to increasing the reliability, quality, and management of distribution networks. Due to the importance of development and sustainable security of DAS, the use of DL data-driven technology has grown significantly. DL techniques have blossomed rapidly, and have been widely applied in several fields of distribution systems. DL techniques are suitable for dynamic, decision-making, and uncertain environments such as DAS. This survey has provided a comprehensive review of the existing research into DL techniques on DAS applications, including fault detection and classification, load and energy forecasting, demand response, energy market forecasting, cyber security, network reconfiguration, and voltage control. Comparative results based on evaluation criteria are also addressed in this manuscript. According to the discussion and results of studies, the use and development of hybrid methods of DL with other methods to enhance and optimize the configuration of the techniques are highlighted. In all matters, hybrid structures accomplish better than single methods as hybrid approaches hold the benefit of several methods to construct a precise performance. Due to this, a new smart technique called Learning-to-learning (L2L) based DL is proposed that can enhance and improve the efficiency, reliability, and security of DAS. The proposed model follows several stages that link different DL algorithms to solve modern power system problems. To show the effectiveness and merit of the L2L based on the proposed framework, it has been tested on a modified reconfigurable IEEE 32 test system. This method has been implemented on several DAS applications that the results prove the decline of mean square errors by approximately 12% compared to conventional LSTM and GRU methods in terms of prediction fields.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

IoT-Enabled Operation of Multi Energy Hubs Considering Electric Vehicles and Demand Response

This paper introduces a novel Internet of Thing (IoT) enabled approach for optimizing the operation costs and enhancing the network reliability incorporating the uncertainty effects and energy management in multi-carrier Energy Hub (EH) and integrated energy systems (IES) with renewable resources, Combined Heat and Power (CHP) and Plug-In Hybrid Electric Vehicle (PHEV). In the proposed model, the optimization process of different carriers of Multi Energy Hubs (MEH) energy considers a price-based demand response (DR) program with MEH electrical and thermal demands. During the peak period, energy carrier prices are calculated at high tariffs, and other power hubs can help to reduce hub energy costs. The proposed model can handle the random behavior of renewable sources in a correlated environment and find optimal solution for turbines' communication in EHs. The simulation results show the high performance of the proposed model by considering the dependency between wind turbines in MEH structure, power exchange and heat among the EHs.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Study of use of pumice stone with sequencing batch reactor for treatment of dairy industries wastewate

Background and aims: Dairy industries generate a large amount of wastewater that contains high concentrations of carbonaceous and nitrogenous organic materials. Discharge of these wastewaters as untreated into environment leads to serious contaminations. The aim of this study was to evaluate the effectiveness of pumice stone application with sequencing batch reactor to remove organic compounds in dairy wastewaters. Methods: In this experimental study which was conducted at bench scale, two processes of sequencing batch reactor, conventional and equipped with pumice stone, were used as treatment models. In commissioning phase of systems, the sludge of municipal wastewater treatment plant underwent acclimatization for 9 days. Then, each reactor was inoculated with 2.5 liters of the sludge. In operation step, the reactors were continuously monitored for 36 days and the efficiency of the system for the removal of chemical oxygen demand (COD), biochemical oxygen demand (BOD5), proportion of total solids (TS) and total Kjeldahl nitrogen (TKN) was measured. Results: The mean removal efficiency of COD, BOD5, TS and TKN was respectively 61.8±9.4, 58.5±8.6, 64.9±2.5 and 46.5±22.5 for the reactor without batch and 67.5±9.4, 63.4±11.8, 66.7±3.2, 62.2±15.7 for the reactor equipped with batch. By these results, removal efficiency of COD and TKN by the batch-equipped system had a significant difference from removal efficiency of these parameters by the system without batch (p<0.05). Conclusion: This study demonstrated increased removal efficiency of COD, BOD5, TS and TKN in sequencing batch reactor when pumice stone is used as microbial growth batch in reactor. This model could be considered as a suitable choice for treatment of dairy wastewaters