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A theoretical analysis is given of the peeling test for Redux‐bonded joints, as devised by Aero Research Ltd. and generally accepted as a standard quality control test for metal‐bonding processes. Numerical values derived from computations appear to be in reasonable agreement with experiments, but more test‐data and better knowledge of the clastic and plastic behaviour of both adhesive and adherent are necessary to make the method more reliable in this respect. The practical value of the method, however, is the indication it gives about the sensitivity of the test for the variables involved. This may make it feasible to introduce corrections for variations of some parameters, thereby improving the reproducibility of the test.

Purpose – Revisiting Fornell et al.'s (1996) seminal study, this chapter looks at the evidence for observed and unobserved heterogeneity within data underlying the American customer satisfaction index (ACSI) model. Examining data for two specific industries (utilities and hotels) reveals only modest differences. However, we suppose that unobserved heterogeneity critically affects the results. These insights provide the basis for shaping further differentiated ACSI model analyses and more precise interpretations.

Methodology/approach – This study applies the partial least squares (PLS) path modeling method and uses empirical data to estimate and compare the ACSI model results on the aggregate and industry-specific data levels. In addition, the finite mixture PLS path modeling (FIMIX-PLS) method is employed to further examine across industry similarities and within industry differences.

Findings – This research uncovers unobserved heterogeneity that guides forming three segments of customers within each industry. The major segment in each industry represents customers that are fairly loyal (i.e., neither disloyal nor extremely loyal) while the other two smaller segments are not as similar across the two industries. Our study identifies substantial differences across these segments within each industry. An importance-performance map analysis illustrates these differences and provides the basis for managerial implications.

Originality/value of the chapter – The unobserved heterogeneity revealed within industries in a given country (i.e., the United States of America) underlines the need to be open to differences within populations, beyond the observed heterogeneity across distinct groups or cultures, and the need to reconsider reporting requirements in academic research.

The management of children′s literature is a search for value and suitability. Effective policies in library and educational work are based firmly on knowledge of materials, and on the bibliographical and critical frame within which the materials appear and might best be selected. Boundaries, like those between quality and popular books, and between children′s and adult materials, present important challenges for selection, and implicit in this process are professional acumen and judgement. Yet also there are attitudes and systems of values, which can powerfully influence selection on grounds of morality and good taste. To guard against undue subjectivity, the knowledge frame should acknowledge the relevance of social and experiential context for all reading materials, how readers think as well as how they read, and what explicit and implicit agendas the authors have. The good professional takes all these factors on board.

A study is performed on the properties of jute-pineapple leaf fiber (PALF) blended yarn and 100% jute yarn. The jute-PALF blend ratios of two counts of yarn (5 and 7 lbs/spy) are 70:30 and 80:20 respectively. The physical properties of the blended yarns such as load at break, strain at break, tenacity at break, tensile modulus and quality ratio are tested and measured. It is observed from the test results that the physical properties of the blended yarns are better than those of the 100% jute yarn. The experimental results also show that the physical and structural properties of the blended yarn changes with the increase of PALF in the blend ratio. So the blending of PALF has a positive impact on yarn properties. Another study is done, in which it is found that surface appearance properties like color strength (K/S value) and whiteness, yellowness and brightness indices of the blended yarns are almost the same as those of the jute yarn due to the blending of jute-PALF. Therefore, the blending ratio does not cause any notable changes in the natural color of the jute yarn.

SOCIALLY, the two conferences can only be described as a huge success. The local arrangements for the entertainment of delegates were complete, and the receptions, banquets and excursions gave great pleasure to all who took part in them. To most of the English‐speaking delegates the trip to Brussels possessed the additional charm of novelty, and thus the week's proceedings assumed a holiday character. Save those who were suffering from mislaid baggage, and blistered feet caused by the trottoirs économiques de Bruxelles, a general note of gaiety prevailed, particularly among the British and Canadian representatives. Most of the American delegates were ladies, and they were all looking more or less tired, or were tormented by the thought of lost Saratoga trunks, which gave them a serious and detached appearance. The absence of attentive male librarians may also have contributed to the gloomy aspect which so many of them wore. Is it possible that the overwhelming feminine note in American librarianship is the key to the many discrepancies in library policy and work which have been observed by different writers? However that may be, it was distinctly noticeable that in comparison with their English, Dutch, Swedish and Belgian sisters, the American lady librarians were a tired and unhappy company. There were one or two noble exceptions, but the memory of these we prefer to hug in secret as a precious treasure.

Should we include cryptocurrency in risky portfolio investing? Bitcoin, given its status as the leader of cryptocurrencies and a speculative asset due to its non-dividend-paying trait and high volatility as well as high returns, poses an interesting question whether it can also be beneficial in a portfolio of risky assets. In order to find an answer, we revisit the conventional dual objective of minimizing risk and maximizing expected return for risky assets. Various models are tested to analyze the risk-return trade-off of risky portfolios including Bitcoin. Given an initial budget for a finite portfolio, the cumulative filtration yields the expected return and the covariance matrix. With the addition of Bitcoin, we compare the performance of the portfolio generated from the optimization models and technical analysis. The main implications are follows: (1) risk tolerance and diversification constraints are the key factors in portfolio optimization; (2) including cryptocurrency enhances portfolio returns; and (3) the Markowitz model (Kataoka’s and conditional value-at-risk models) recommends to fully weigh (unload) Bitcoin in (from) the portfolio.

Presents over sixty abstracts summarising the 1999 Employment Research Unit annual conference held at the University of Cardiff. Explores the multiple impacts of globalization on work and employment in contemporary organizations. Covers the human resource management implications of organizational responses to globalization. Examines the theoretical, methodological, empirical and comparative issues pertaining to competitiveness and the management of human resources, the impact of organisational strategies and international production on the workplace, the organization of labour markets, human resource development, cultural change in organisations, trade union responses, and trans‐national corporations. Cites many case studies showing how globalization has brought a lot of opportunities together with much change both to the employee and the employer. Considers the threats to existing cultures, structures and systems.

This paper tactics to implement the attack detection in medical Internet of things (IoT) devices using improved deep learning architecture for accomplishing the concept bring your own device (BYOD). Here, a simulation-based hospital environment is modeled where many IoT devices or medical equipment are communicated with each other. The node or the device, which is creating the attack are recognized with the support of attribute collection. The dataset pertaining to the attack detection in medical IoT is gathered from each node that is considered as features. These features are subjected to a deep belief network (DBN), which is a part of deep learning algorithm. Despite the existing DBN, the number of hidden neurons of DBN is tuned or optimized correctly with the help of a hybrid meta-heuristic algorithm by merging grasshopper optimization algorithm (GOA) and spider monkey optimization (SMO) in order to enhance the accuracy of detection. The hybrid algorithm is termed as local leader phase-based GOA (LLP-GOA). The DBN is used to train the nodes by creating the data library with attack details, thus maintaining accurate detection during testing.

This paper has presented novel attack detection in medical IoT devices using improved deep learning architecture as BYOD. With this, this paper aims to show the high convergence and better performance in detecting attacks in the hospital network.

From the analysis, the overall performance analysis of the proposed LLP-GOA-based DBN in terms of accuracy was 0.25% better than particle swarm optimization (PSO)-DBN, 0.15% enhanced than grey wolf algorithm (GWO)-DBN, 0.26% enhanced than SMO-DBN and 0.43% enhanced than GOA-DBN. Similarly, the accuracy of the proposed LLP-GOA-DBN model was 13% better than support vector machine (SVM), 5.4% enhanced than k-nearest neighbor (KNN), 8.7% finer than neural network (NN) and 3.5% enhanced than DBN.

This paper adopts a hybrid algorithm termed as LLP-GOA for the accurate detection of attacks in medical IoT for improving the enhanced security in healthcare sector using the optimized deep learning. This is the first work which utilizes LLP-GOA algorithm for improving the performance of DBN for enhancing the security in the healthcare sector.