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Digital subscriber line (DSL) is a service that offers much faster Internet connection than a standard dial up connection. Crosstalk is a major impairment in DSL telecommunication networks. It degrades the upstream and downstream data rates and risks the DSL service providers ability to meet their service level agreement requirements. A novel approach to solve the upstream and downstream data rate problems is presented in this paper. The approach considered is to control the power spectral density and aggregate power level of the DSL access multiplexer.

This research aims to examine two constructs that define loyal brand relationships – greater resistance to changing their brand beliefs and attitudes (resistance to change) and greater willingness to shun competitor brands (determinism). The paper develops and tests psychometrically sound scales to measure these two constructs and proposes and tests a model that identifies antecedent and outcome linkages between resistance and determinism and other brand relationship measures across two studies.

Scale development was done following the guidelines recommended by Nunnally and Bernstein (1994) and Churchill (1979). Factor analysis was used to test scale item fit. Discriminant validity, scale reliability tests, nomological and predictive validity tests were conducted.

This study adds to the literature by identifying the specific dimensions that compose the resistance concept. It finds that strong resistance leads to determinism at the high end. It also finds that brand engagement is an outcome of resistance. In addition, three sub-components of the resistance dimension – resistance to new products that offer higher value, resistance to value improvements of competing offerings and resistance to quality deterioration for the focal or object brand – are also identified and scale items developed to measure them.

Resistance to change and determinism are less understood and researched concepts in the context of brand relationships. Further, these two dimensions are of particular importance in emerging markets such as India because of the changes taking place in these markets with the introduction of many global brands competing with traditional strong brands. For new brands, a key question is how to reduce the resistance that consumers exhibit toward trying out new brands. For existing brands, the key question is how to leverage the resistance that consumers have in trying out a new brand so that greater determinism is built and defections are minimized. This research helps answer some of these questions.

The primary purpose of the study was to identify the specific dimensions that compose the resistance concept. An important aspect of brand relationships is the ability to foster greater strength in the relationship. While the important of resistance has been known, a good scale to measure it has been lacking.

This paper aims to explore narratives of insecurity to understand how the casualisation of the employment relationship makes life more fragile and precarious. The authors engage in an inquiry about how multinational enterprises (MNEs) structure precariousness for workers in emerging economies. The authors attempt to understand how workers analyse their experiences of precariousness and what form their resistance takes as a result of their analysis.

The authors engage with the narratives of eight Indian workers/trade union activists working in different marginal spaces of the Indian economy to uncover a commons where we are the multitude. By commons, the authors imply shared forms of property, which stand against the concept of private property that is central to the social relations of capitalism. The authors are performing the data of workers by interspersing them in an analysis of angst and hope.

Workers understand their experiences of precariousness as emerging from a complex political economy structured by MNEs, which involves multiple fronts of marginalisation. Workers realize that they need to engage in comprehensive forms of resistance to undo the regimes of precariousness. Workers create shared universes of grief to relate to each other’s experiences of precariousness. The unfreedoms experienced by workers lead to a sharing of the social relations of commons where workers can resist by expressing solidarity with each other.

The authors contribute to practice by arguing that workers’ collectives should not accept the naturalisation of precariousness. By staging a dialogue about the injuries of precariousness, they can craft a politics of resistance that begins the process of commoning.

Workers’ politics of resistance can significantly democratise the global political economy in important ways by advancing the potential for commons.

The authors make an original contribution to the study of precariousness in the context of international business by arguing that the experience of precariousness can lead to a commons where workers resist structures of injustice.

The partially prestressed concrete beam with unbonded tendon is still an active field of research because of the difficulty in analyzing and understanding its behavior. The finite-element (FE) simulation of such beams using numerical software is very scarce in the literature and therefore this study is taken to demonstrate the modeling aspects of unbonded partially prestressed concrete (UPPSC) beams. This study aims to present the three-dimensional (3-D) nonlinear FE simulations of UPPSC beams subjected to monotonic static loadings using the numerical analysis package ANSYS.

The sensitivity study is carried out with three different mesh densities to obtain the optimum elements that reflect on the load–deflection behavior of numerical models, and the model with optimum element density is used further to model all the UPPSC beams in this study. Three half-symmetry FE model is constructed in ANSYS parametric design language domain with proper boundary conditions at the symmetry plane and support to achieve the same response as that of the full-scale experimental beam available in the literature. The linear and nonlinear material behavior of prestressing tendon and conventional steel reinforcements, concrete and anchorage and loading plates are modeled using link180, solid65 and solid185 elements, respectively. The Newton–Raphson iteration method is used to solve the nonlinear solution of the FE models.

The evolution of concrete cracking at critical loadings, yielding of nonprestressed steel reinforcements, stress increment in the prestressing tendon, stresses in concrete elements and the complete load–deflection behavior of the UPPSC beams are well predicted by the proposed FE model. The maximum discrepancy of ultimate moments and deflections of the validated FE models exhibit 13% and −5%, respectively, in comparison with the experimental results.

The FE analysis of UPPSC beams is done using ANSYS software, which is a versatile tool in contrast to the experimental testing to study the stress increments in the unbonded tendons and assess the complete nonlinear response of partially prestressed concrete beams. The validated numerical model and the techniques presented in this study can be readily used to explore the parametric analysis of UPPSC beams.

The developed model is capable of predicting the strength and nonlinear behavior of UPPSC beams with reasonable accuracy. The load–deflection plot captured by the FE model is corroborated with the experimental data existing in the literature and the FE results exhibit good agreement against the experimentally tested beams, which expresses the practicability of using FE analysis for the nonlinear response of UPPSC beams using ANSYS software.

The purpose of this paper is to examine the crucial antecedents of satisfaction and loyalty for the insurance industry and discuss how customer satisfaction and loyalty can be increased.

A large-scale global survey is conducted among 11,736 insurance customers. To analyze the data, structural equation modeling is used to estimate the influence of the identified antecedents of satisfaction and loyalty.

The results reveal a positive relationship between satisfaction and attitudinal loyalty for the insurance industry. In addition, the individual cognitive-based dimensions of anticipated regret and product category knowledge significantly affect satisfaction. Furthermore, co-production and trust both positively affect satisfaction and attitudinal loyalty.

This study identifies relevant antecedents of satisfaction and attitudinal loyalty for the emerging literature of insurance marketing. Future studies in this domain should examine the influence of additional dimensions which were not part of the present work, such as perceived price fairness, service quality, and switching costs.

By installing and maintaining efficient two-way communication channels, insurers can decrease customers’ anticipated regret and enhance product category knowledge, which, in turn, can increase customer satisfaction. In addition, frequent and honest communication should be used to build up trust and induce co-production, which positively affect satisfaction and attitudinal loyalty.

This paper identifies crucial antecedents of satisfaction and attitudinal loyalty for insurance companies. This research is timely, as previous works have largely neglected the idiosyncrasies of the insurance sector.

The purpose of this study is to determine if brand passion shapes attitudinal brand loyalty while driving a series of important brand-related outcomes (i.e. brand advocacy, social media following, sense of community, willingness to pay a premium price and alternative devaluation). These aspects are explored for sports apparel brands after considering the perceptions of Iranian consumers.

This study is based on the analysis of survey data gathered online and face-to-face from a sample of Iranian consumers of sports apparel brands that were analysed using partial least square path modelling.

The key empirical findings obtained confirm that brand passion underpins attitudinal brand loyalty and several important brand-related outcomes. Furthermore, the findings show that attitudinal brand loyalty explains the impact of brand passion on most of the outcomes considered, except for social media following.

This study advances knowledge of brand passion by illustrating its “power” as a strong nuance of relationships between consumers and brands. In particular, this study highlights the importance of brand passion in shaping attitudinal brand loyalty, as well as a driver of several outcomes of theoretical and managerial relevance.

By establishing strategies aimed at enhancing brand passion, brand managers can increase attitudinal brand loyalty, attain important goals such as brand advocacy, premium price and social media following, as well as the devaluation of competing brands.

This study uses a unidimensional theorisation of brand passion to increase the understanding of its role as predictor of attitudinal brand loyalty and driver of relevant outcomes. It also examines the mediating effect of attitudinal brand loyalty, thus illustrating important conceptual links between brand passion and brand loyalty in the context of sports apparel brands in a growing economy (Iran).

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.