A Routing Algorithm Based on Ant Colony, Local Search and Fuzzy Inference to Improve Energy Consumption in Wireless Sensor Networks

Wireless sensor network is a new generation of networks in which the main aim is to collect data from the surrounding environment of network sensors. The major differences of wireless sensor networks with other networks are limited energy resources and relatively low processing capabilities. Therefore, managing power and reducing energy consumption are of great importance in these networks. In this paper, there was presented a mechanism for Wireless sensor network routing which can be more effective regarding the criteria of route length, end–to–end delay and network node energy for the quality of mechanism service. The proposed method used ant colony–based routing algorithm and local enquiry to find optimal routes. Also, a fuzzy inference system was used to determine the route quality which showed better performance compared with equation of route quality. The results of simulating mechanism showed that energy consumption and network efficiency had improved compared with those of previous methods.DOI:http://dx.doi.org/10.11591/ijece.v3i5.362

Assessing the Impact of Sustainable Practices on Organizational Performance

Over the past few decades, there has been a growing pressure on organizations to be fully responsible for their business operations in order to minimize their environmental impact. These pressures have evolved the green practice adoption procedures that companies are undertaking. In this thesis, we study the overall impact of adoption of various green practices on firms’ performance outcome. The green practices included in the study are: Internal environmental management, green design and product development, green purchasing and procurement, green distribution, investment recovery, reverse logistics and finally socially sustainable practices. The effectiveness of each of these seven practices is examined against environmental and financial performance of organizations. The influence level of different environmental drivers in form of regulatory pressure and other non-coercive pressures is also investigated. A survey among 45 Canadian firms is conducted for this purpose. The data obtained is subject to partial least square structural equation modeling using SmartPLS software for performing of statistical analysis. The model results show that internally oriented environmental practices such as internal environmental management and socially sustainable practices impact more the environmental and financial performance of companies than other practices. Furthermore, no significant relationship between product recovery practices and environmental and financial performance was observed. Keywords: Green supply chain management, environmental performance, green practices, PLS-SEM, survey questionnaire

Measuring the Effectiveness of Microsoft Authenticode: A Systematic Analysis of Signed Freeware

Recent studies have shown that Authenticode, the Windows code signing standard for portable executable files, can be abused by potentially unwanted programs (PUP) and malware to evade detection and bypass Windows protections. These studies discuss improper signature checks by frameworks (e.g., anti-virus programs), key mismanagement, improper verification by certificate authorities (CAs) and underground certificate trade as weaknesses that can be abused in Windows code signing public key infrastructure (PKI). We explore the Authenticode signatures of supposedly benign applications in the wild to gain a clearer understanding of this mechanism so that we can identify potential issues that can undermine trust in Authenticode. For studying the blackbox of the Authenticode, we tackle the main challenge of doing a measurement study on Authenticode, lack of a comprehensive corpus of Windows code signing certificates. As placing trust in the freeware that is downloaded from web is one significant use case of code signing, we target eight popular download portals as source of our dataset and collect 106K Windows applications. We present an analysis framework for studying code signing certificates and extract 27K certificates from signed executable applications. This framework provides a crawler for automated download of applications from download portals. Furthermore, as part of our analysis framework, we develop a linter that is specifically designed for Authenticode certificates. Both of our tools are in the process of release for public use of researchers. Our results identify issues in the code signing certificates that the Authenticode validation fails in preventing them. Usage of inadequately secure hash and public key algorithms such as MD5, SHA1 and 1024-bit RSA, missing or invalid Key Usage and Extended Key Usage, missing revocation information, non-critical Basic Constraints for CA certificates are examples of the issues that potentially undermine both integrity and authenticity assurance that Authenticode provides

Desafíos de la criminalización de la protesta y la corrupción a través de documentos internacionales

With the passage of the Islamic Penal Code of 1992, the legislator took a critical step contrary to the provisions of international documents such as the Political and Civil Covenant and the Universal Declaration of Human rights and other documents criminalize behaviors that are critical of human rights, both in terms of punishment and in non-compliance with the principles and principles of criminality. As provided in Article 286 for severe on-the-ground corruption with a view to development in various fields, the death penalty has been specified. The perpetrators of these crimes and deviations from the substantive principles of security crimes, such as riots and corruption on earth, present challenges that will be addressed in this article, first explaining the importance of the right to life and the death penalty in international documents and the Iranian legal system.Con la aprobación del Código Penal Islámico de 1992, el legislador dio un paso crítico contrario a las disposiciones de Los documentos internacionales como el Pacto Político y Civil y la Declaración Universal de Derechos Humanos y otros documentos penalizan comportamientos que son críticos de los derechos humanos, tanto en términos de castigo como en incumplimiento de los principios y principios de criminalidad. Según lo dispuesto en el Artículo 286 para la corrupción severa en el terreno con miras al desarrollo en varios campos, se ha especificado la pena de muerte. Los autores de estos crímenes y las desviaciones de los principios sustantivos de los crímenes de seguridad, como los disturbios y la corrupción en la tierra, presentan desafíos que se abordarán en este artículo, primero explicando la importancia del derecho a la vida y la pena de muerte en documentos internacionales y el sistema legal iraní

A Reconfigurable Color Reflector by Selective Phase Change of GeTe in a Multilayer Structure

It is shown that a phase change material (PCM), germanium telluride (GeTe), when integrated into a subwavelength layered optical cavity, can produce widely tunable reflective colors. It is shown that the crystallization temperature (Tx) of GeTe is dependent on the film thickness for thin films of less than â 20 nm, which is exploited for color tuning. Four colors from the same physical structure are demonstrated by electrical heating, through novel optical and thermal engineering of a thin film stack that includes two GeTe layers with only a single integrated joule heater element. The selective sensitivity to incident light angle and low polarization dependence, as well as the low static power consumption of this device make it a good candidate for potential consumer electronics applications.A subâ wavelength optical cavity consisting of multiple layers of germanium telluride (GeTe) is shown here to produce widely tunable reflective colors. The dependence of GeTe crystallization temperature on its film thickness is exploited to achieve four colors from the same physical multiâ layer structure. An integrated electrical heating approach is used to switch between different colors.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/1/adom201801214-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/2/adom201801214_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148263/3/adom201801214.pd

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

Comparative Analysis of the Impact of the Four Elements of Strategic Plan on Product and Market Development in Zinc Companies

Objective :This study was to analyze the impact of each of four elements of strategic plan (vision processing, analysis of external environment, inter- organization analysis and strategic control) on product and market development in zinc companies in Zanjan province.Methodology:Based on objective, this was a descriptive - explanatory study, while it could be defined a cross-sectional one based on time. According to the result, it was an applied research and a quantitative and qualitative based on variables. Finally, it was a surveying study according to data collection method. The target population members included 120 CEOs and business managers of companies active in the zinc industry in Zanjan Province, which were evaluated by census method. The data collection tool was a questionnaire and SPSS software was used for data statistical analysis. The hypotheses were also tested using single-sample t-test and ANOVA.Results : The "vision processing or building" does not affect the product and market development, but three factors of "external environment analysis, inter-organizational analysis and strategic control" affect the development of the market and the product of zinc companies. The most effective factor is "strategic control", and the least effective factor is "external environment analysis"

The Role of Human Resource Management in the Growth of Startups: A Multiple Case Study from the Perspective of Entrepreneurs and Employees

The purpose of the present study is to investigate the role of human resource management (HRM) in the growth of startups. Accordingly, the missions and practices of HRM in startups are identified from the perspective of entrepreneurs and employees. A multiple case study approach is used by focusing on two startups in Iran. Moreover, the data are collected by interviewing one entrepreneur and four employees in each company. Besides, qualitative data are analysed using thematic analysis. Results demonstrated the various types of HRM missions and practices in startups and reflect different perspectives of entrepreneurs and employees of human resources management in these companies. Finally, the results illustrated that HRM can play an important role in the growth of these companies. Although research in the field of startups has increased in recent years, the role of HRM in the growth of these companies has rarely been addressed. The present research helps to expand literature related to the role of HRM in the growth of startups by considering the different perspectives of startups' entrepreneurs and employees

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions