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Wearable health technologies (WHTs) show promise in improving the health and well-being of the aging population because they promote healthy lifestyles. They can be used to collect health information from users and encourage them to be physically active. Despite potential benefits of WHTs, recent studies have shown that older people have low continued use intention toward WHTs. Previous work on this topic is disjointed, and new theoretical viewpoints are required.

The authors propose an enablers and inhibitors perspective to model factors influencing continued use intention of WHTs among the elderly. To test the model, we collected data from Chinese elderly (N = 295) who had prior experience using WHTs.

The study results show that social value is the strongest enabler of continued WHT use, and emotional and epistemic values and device quality also increase use continuance. Inertia and technology anxiety were identified as significant inhibitors. A post hoc importance performance map analysis revealed that while emotional value is a highly significant predictor of continued WHT use, existing WHTs do not stimulate such value in our sample.

The research findings illustrate the importance of incorporating user resistance in technology acceptance studies in general and WHT usage studies in particular. This study contributes by providing an integrative model of technology continued use intention for the elderly along with practical implications for policymakers.

A limited number of prior studies have taken both enablers and inhibitors into account when explaining continued WHT use intention among the elderly. This paper fills this research gap and contributes to the WHT literature by considering both enablers and inhibitors in the same model. Moreover, this study contributes to the ongoing research on WHT, and more broadly, gerontechnology use among the elderly.

The tremendous growth of wireless applications and the demand for high data rate, the spectrum utilization improvement has been the most crucial challenges for wireless communication. Adapting cognitive radio with orthogonal frequency division multiplexing or offset quadrature amplitude modulation (OFDM/OQAM) improves the spectrum and energy efficiencies.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from secondary user (SUs) to primary user (PUs) and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high peak to average power ratio (PAPR) phenomenon is a common issue in the majority of the multicarrier modulation systems and thus OFDM/OQAM is no exception in this case.

Therefore, this paper aims to examine the effect of integrating the Walsh–Hadamard Transform (WHT) on the power spectral density and investigates the problem of PAPR in the WHT/OQAM system.

Thus, it overcomes the spectral leakage problem at the transmitter side and leads to less interference from SUs to PUs and between the SUs in cognitive radio technology. The benefit of exploiting pulse shape filtering in the OFDM/OQAM is to not only eliminate the requirement of the guard bands but also reduce the out of band energy transmission, which also improves the spectral isolation from the neighboring systems. But the high PAPR phenomenon is a common issue in the majority of the multicarrier modulation systems thus OFDM/OQAM is no exception in this case.

The reliability and maintainability of tunnel infrastructure and systems is an important factor in assuring normal operation of a tunnel. Evaluating availability of a large‐scale tunnel that includes civil, electrical, mechanical and electronic systems is a difficult task. The purpose of this paper is to present a methodology for performing such assessments, featuring the use of the Markov model.

The methodology involves application of failure mode, effect and criticality analysis (FMECA), state space diagram construction, formulation of state space equations, and development of transitional matrices. It also involves transformation of multi‐state models into two‐state models (each comprises of an “up” state and a “down” state) through the use of the frequency and duration method for determining the failure and repair rates, as well as the mean‐time‐between‐failures (MTBF) of the entire tunnel. By using the proposed bottom‐up approach, a MTBF tree linking the availability measures of individual equipment with those of sub‐systems, and ultimately the whole tunnel can be developed.

The tunnel availability measures obtained by this analysis can be used in making comparisons between different tunnel designs so as to determine the value for money of various options. Furthermore, weaknesses in a tunnel design can be identified in the analysis. The information obtained from this method can also be used to evaluate adequacy, security and maintainability of a tunnel.

The reliability and maintainability of tunnel infrastructure and systems are crucial factors for ensuring safety of tunnel operation. Unsafe conditions will cause closure of a tunnel. Efforts to improve availability of a tunnel often increase the tunnel's construction cost. Due to the complexity of tunnel systems, it is difficult to compare different tunnel designs, and trade‐off analyses to strike a balance between target availability and construction cost of a tunnel design are seldom performed. This paper presents a systematic methodology to address these issues. This methodology allows tunnel management to evaluate the adequacy, security and maintainability of a tunnel so that design weaknesses can be identified and the value of design improvements can be determined. The methodology can also be used to evaluate designs of other complex systems such as power generation or petrochemical processing plants.

A worked example demonstrating the application of the proposed methodology is presented in this paper.

Enterprise, Strategy

This case study documents the history of e-commerce adoption and usage in a fabric and garment manufacturing firm operating in an African country. Lessons drawn from the case could be applied to understanding the achievement of e-commerce benefits through the complex interrelationships between firm-level, national and global resources.

The case study presents a summary of e-commerce capabilities in the firm, the key resources developed and actions taken to deploy e-commerce capabilities and the notable benefits obtained through these e-commerce capabilities. The study shows that, first, the ability to access information and communication technology (ICT) infrastructure matters in developing countries, but managerial capabilities matter more. Managerial capabilities enable firms to find external resources (both in-country and globally) to substitute for internal resource deficiencies. Second, intangible social resources – trust, reputation and credibility – play a critical role in determining whether the e-commerce strategies of firms are successful or not.

An understanding of how managerial capabilities influence the creation of e-commerce capabilities and the achievement of e-commerce benefits, especially in an African or Ghanaian context. Learners can also draw lessons that could be applicable to understanding how a firm's strategic orientation, resource portfolio and the nature of its target market differentiate the extent of integration or adoption and usage of e-commerce in the firm.

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This study examined measures of service efforts and accomplishments for Texas public schools. There were differences in both outcomes and inputs depending on size of the school district. Input measures were useful for modeling outcomes of school districts by size. Student background measures--specifically attendance and proportion of economically disadvantaged students--were the most consistent explanators of school outcomes. Measures of the teaching environment, financial policy, efficiency, and funding levels were of less importance in explaining school effectiveness.

This paper aims to obtain fire resistance of semi-rigid joints for concrete-filled steel tubular (CFST) composite frames and temperature filed distribution of composite joints in fire.

The temperature filed model of semi-rigid joints to CFST columns with slabs was made by using ABAQUS finite element (FE) software, in considering temperature heating-up stage of fire modelling. The effects of composite slab, fire type and construction location were discussed, and the model was verified by the test results. The temperature distribution of composite joint under three-side or four-side fire condition was studied by the sequentially coupled thermal analysis method. The temperature versus time curves and temperature distribution of various construction and location were analyzed.

The paper provides FE analysis and numerical simulation on temperature field of semi-rigid joints for CFST composite frames in fire. The effects of composite slab, fire type and construction location were discussed, and the model was verified by the test results. It suggests that the temperature distribution of composite joint in three- or four-side fire condition showed a different development trend.

Because of the chosen FE analysis approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

The research results will become the scientific foundation of mechanical behavior and design method of semi-rigid CFST composite frames in fire.

This paper fulfils an identified need to study the temperature field distribution of the semi-rigid joints to CFST columns and investigate the mechanical behavior of the semi-rigid CFST joints in fire.

Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm using learned dictionary-based visual saliency.

First, the test fabric image is splitted into image blocks, and the learned dictionary with normal samples and defective sample is constructed by selecting the image block local binary pattern features with highest or lowest similarity comparing with the average feature vector; second, the first L largest correlation coefficients between each test image block and the dictionary are calculated, and other correlation coefficients are set to zeros; third, the sum of the non-zeros coefficients corresponding to defective samples is used to generate saliency map; finally, an improve valley-emphasis method can efficiently segment the defect region.

Experimental results demonstrate that the generated saliency map by the proposed method can efficiently outstand defect region comparing with the state-of-the-art, and segment results can precisely localize defect region.

In this paper, a novel fabric defect detection scheme is proposed via learned dictionary-based visual saliency.