623 research outputs found

    Contesting Ideologies Structuring Gender Transgression in the Swedish Marketplace

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    This paper interrogates how contesting state and marketplace ideologies in Sweden negotiates the construction of gender transgression in the mar- ketplace. There is always an incessant ideological battle among the differ- ent sides of a society – among the traditionalist and the more progressive, among those who embraces change and those who attempt to conserve corrupting the natural order. Between two such large ideological positions, consumers in Sweden are caught in the crossfires of different messages and strives to figure out their interpretive strategies. Given its status as a welfare State, Sweden is driven, to a large extent, by state– instigated double eman- cipation ideology. On the other hand, both traditional gender ideology and a more progressive gender transgressive ideology – that promotes gender inclusivity, neutrality and fluidity – also pervades consumption in the Swedish marketplace

    Social Imaginary of the Hijras: Dominant Cultural Narratives Mediating Ritualistic Consumption of Transgender and Gender Non-Binary Consumers in Bangladesh

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    This research interrogates ritualistic consumption of hijras, Transgender and Gender Non-Binary individuals in Bangladesh, manifested in a perpetual negotiation of ideologies, myths, religion, politico-legal and sociocultural imperatives. The study enacts social imaginaries of hijras to animate how hijras are oppressed but occasionally granted peripheral inclusion, journey through a liminal rite of passage into communitas and co-opt hegemonic ritualistic consumption, and has been relegated from being viewed in the trope of purity to pollution. The study contributes to prior CCT theories on marginalisation/stigmatisation and literature on intersectionality of gender

    Gender Transculturation : Navigating Market-Mediated Contesting Gender Ideologies in Consumer Acculturation

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    In this book, I advance the concept of gender transculturation to illustrate how migrant consumers navigate contesting gender ideologies in their host cultural marketplace. Taking a consumer cultural theoretical perspective, the study lies in the nexus of and unpacks an alternative understanding in the current research frontier of consumer acculturation, gender and ideologies. Acculturation of immigrants and gender have become increasingly critical issues in contemporary academic, socio-economic and politico-cultural debates. Rising mobility and migration from the Global South to the Global North have steered immigrants to cross transcultural borders into contexts that are ideologically diverse. South Asian respondents from the Bangladeshi diaspora in Sweden have been interviewed to understand their home, host and transcultural discourses about gender through their narratives about the marketplace and consumption.My findings develop the gender transculturation model that exhibits how respondents draw from four conflicting gender ideologies. They engage in perpetual and fluid navigation of ideological tensions through three modes of gender transculturation: ideological ossification, oscillation and osmosis. They demonstrate ossification by rigidifying patriarchal and Islamic and being resistant towards egalitarian and transgressive gender ideologies. Oscillation is embodied by retention of patriarchal and Islamic and reworking of egalitarian and transgressive gender ideologies. In osmosis, they reject patriarchal and romanticise and reflect on egalitarian and transgressive gender ideologies.This is a book for consumer researchers, marketers, managers, social actors, policy- makers, and consumers who want to know more about migrant consumers with vast ideological differences and their views and beliefs on gender in the marketplace

    Analyzing Robotics Software Vulnerabilities

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    Robots are widely used in our day-to-day life in various domains. For example, eldercare robots, such as CareO-Bots [1]are used to perform household tasks and provide mobility assistance [2]. Amazon uses manufacturing robots to accomplish manufacturing labor activities, such as welding and assembling equipment [2]. According to the International Data Corporation, spending on robotics is expected to reach USD 241.4 billion by the end of 2023 [4]. However, malicious users can exploit security vulnerabilities in hardware and software components of robotics systems to conduct security attacks and cause malfunction, i.e., deviate robots from their expected behaviors. Security attacks on robots can have serious consequences such as (i) bottlenecks and shutdowns in the assembly line, (ii) disruption in the food supply chain, (iii) incorrect treatment for patients, and (iv) unwanted military attacks injuring or killing civilians and military personnel [2]. Researchers [3] have observed a lack of awareness amongst practitioners related to security issues that can exist in robotics systems. Using qualitative analysis, the project aims to determine the software vulnerabilities that commonly appear in robotics systems. In this work in progress, we plan to discuss our initial findings using Robotics Vulnerability Database (RVD) repositories [5] the following questions – (i) what are the most frequent security vulnerabilities in robotics systems? (ii) what types of components are affected by the vulnerabilities? (iii) what categories of vulnerabilities exist and severity for robotics systems

    Optimal Configuration of Inspection and Rework Stations in a Multistage Flexible Flowline

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    Inspection and rework are two important issues of quality control. In this research, an N-stage flowline is considered to make decisions on these two issues. When defective items are detected at the inspection station the items are either scrapped or reworked. A reworkable item may be repaired at the regular defect-creating workstation or at a dedicated off-line rework station. Two problems (end-of-line and multistage inspections) are considered here to deal with this situation. The end-of-line inspection (ELI) problem considers an inspection station located at the end of the line while the multistage inspection (MSI) problem deals with multiple in-line inspection stations that partition the flowline into multiple flexible lines. Models for unit cost of production are developed for both problems. The ELI problem is formulated for determining the best decision among alternative policies for dealing with defective items. For an MSI problem a unit cost function is developed for determining the number and locations of in-line inspection stations along with the alternative decisions on each type of defects. Both of the problems are formulated as fractional mixed-integer nonlinear programming (f-MINLP) to minimize the unit cost of production. After several transformations the f-MINLP becomes a mixed-integer linear programming (MILP) problem. A construction heuristic, coined as Inspection Station Assignment (ISA) heuristic is developed to determine a sub-optimal location of inspection and rework stations in order to achieve minimum unit cost of production. A hybrid of Ant-Colony Optimization-based metaheuristic (ACOR) and ISA is devised to efficiently solve large instances of MSI problems. Numerical examples are presented to show the solution procedure of ELI problems with branch and bound (B&B) method. Empirical studies on a production line with large number of workstations are presented to show the quality and efficiency of the solution processes involved in both ELI and MSI problems. Computational results present that the hybrid heuristic ISA+ACOR shows better performance in terms of solution quality and efficiency. These approaches are applicable to many discrete product manufacturing systems including garments industry

    Throughput and Yield Improvement for a Continuous Discrete-Product Manufacturing System

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    A seam-welded steel pipe manufacturing process has mainly four distinct major design and/or operational problems dealing with buffer inventory, cutting tools, pipe sizing and inspection-rework facility. The general objective of this research is to optimally solve these four important problems to improve the throughput and yield of the system at a minimum cost. The first problem of this research finds the optimal buffer capacity of steel strip coils to minimize the maintenance and downtime related costs. The total cost function for this coil feeding system is formulated as a constrained non-linear programming (NLP) problem which is solved with a search algorithm. The second problem aims at finding the optimal tool magazine reload timing, magazine size and the order quantity for the cutting tools. This tool magazine system is formulated as a mixed-integer NLP problem which is solved for minimizing the total cost. The third problem deals with different type of manufacturing defects. The profit function of this problem forms a binary integer NLP problem which involves multiple integrals with several exponential and discrete functions. An exhaustive search method is employed to find the optimum strategy for dealing with the defects and pipe sizing. The fourth problem pertains to the number of servers and floor space allocations for the off-line inspection-rework facility. The total cost function forms an integer NLP structure, which is minimized with a customized search algorithm. In order to judge the impact of the above-mentioned problems, an overall equipment effectiveness (OEE) measure, coined as monetary loss based regression (MLBR) method, is also developed as the fifth problem to assess the performance of the entire manufacturing system. Finally, a numerical simulation of the entire process is conducted to illustrate the applications of the optimum parameters setting and to evaluate the overall effectiveness of the simulated system. The successful improvement of the simulated system supports this research to be implemented in a real manufacturing setup. Different pathways shown here for improving the throughput and yield of industrial systems reflect not only to the improvement of methodologies and techniques but also to the advancement of new technology and national economy

    Machine Learning-Oriented Predictive Maintenance (PdM) Framework for Autonomous Vehicles (AVs): Adopting Blockchain for PdM Solution

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    Autonomous Vehicles (AVs) refers to smart, connected and multimedia cars with technological megatrends of the fourth industrial revolution (Industry 4.0) and have gained huge strive in today\u27s world. AVs adopt automated driving systems (ADS) technique that permits the vehicle to manage and control driving points without human drivers by utilizing advanced equipment including a combination of sensors, controllers, onboard computers, actuators, algorithms, and advanced software embedded in the different parts of the vehicle. These advanced sensors provide unique inputs to the ADS to generate a path from point A to point B. Ensuring the safety of sensors by limiting maintenance costs has become a major challenge for AVs community. The predictive maintenance (PdM) approach has the potential to address the AVs failures. In this paper, we propose a novel, conceptual, and high-level domain-specific software architecture for the machine learning-oriented predictive maintenance (PdM) framework that shall enable predicting early malfunctioning, quality, safety, and performance deficiencies of AVs. The novel framework collects the data from sensors and major equipment and stores the collected data in immutable and transparent blockchain technology. Collected data shall be validated, extracted, and classified by adopting machine learning (ML) techniques. ML module shall predict the possible malfunctioning of the sensors while providing potential solutions from the stored data in the blockchain network. In this paper, our effort was to conduct a feasibility study, elicit and specify all the requirements for the proposed framework. In future research, we aim to extend the conceptual work and implement a prototype in real-world scenarios
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