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The purpose of this study is to examine the applicability of Watkins and Marsick’s model of learning organization in the school context and explore the relationship between the learning dimensions and perceived organizational outcomes.

Using the instrument, Dimensions of the Learning Organization Questionnaire (DLOQ), the study collected data from 322 teachers and professional staff in K-12 schools. Confirmatory factor analysis and structural equation modeling provide validity evidence for using the DLOQ in schools.

The study results indicate the learning organization is a multidimensional concept and the quality of the school as a learning organization is related to improved organizational performance as perceived by school personnel.

The study measured perceived organizational outcomes using a sample in an urban school district. Future research is encouraged to expand the study sample and to collect actual performance data to strengthen the findings.

The study provides reliability and validity evidence for an instrument that school leaders and practitioners can use to assist their evaluation of the school’s capacity as a learning organization to leverage improvement in school performance.

The study emphasizes an integrative approach in evaluating schools as learning organizations (SLOs) and extends the evidence base for the DLOQ studies. It offers empirical support for the significance of developing SLOs.

The increase in demand variability created by manufacturing enterprises presents new challenges for increasing resource usage and sharing flexibility. For this reason, it is of great importance to research manufacturing grids and their service modes. The purpose of this paper is to establish a systematic strategy and a system tool for manufacturing grid systems.

A manufacturing service oriented manufacturing grid (MSoMG) system is presented with open grid service architecture as the system architecture and GT3.9 as a development tool. A framework is proposed to support MSoMG by providing advisory tools and methods for uncertain information analysis and processing, multi-objective decision making of manufacturing grid service execution, manufacturing grid service performance prediction based on knowledge template, and flexible manufacturing grid service scheduling and solution. The methodology of the adopted rough set is discussed in detail. Finally, the design support strategies for MSoMG are investigated to guide the coordination of manufacturing activities.

Many conventional methods and models become very limited for manufacturing grid service with uncertain information. The processing of uncertain information and reasonable application flow can help to improve the completion rate and reliability of manufacturing grid services.

This research provides a solid foundation for manufacturing gird operations and can promote the use of a manufacturing grid mode.

A MSoMG system is presented. The manufacturing grid service with uncertain information is considered as well as design support strategies.

This paper offers insights into the consumption motives and purchasing behaviour of that market segment in Taiwan against the background of increasing consumption of luxury fashion brands by young female consumers in Asian countries.

Analysis of data collected using face-to-face semi-structured interviews with 23 fashion-conscious females aged 18-32 years was completed and new empirical insights are offered.

The study found a high level of involvement in the world of luxury fashion retailing. Asian consumers devoured media commentary, drew inspiration from female celebrities and treated information-seeking and discussion of luxury fashion brands with friends as a serious and enjoyable pursuit. The social status conferred by expensive fashion wear motivated them to spend on luxury brands even if their discretionary income was limited. Potential guilt in so doing was assuaged by rationalising that the quality was good and the purchase would be long lasting. Marketers targeting this valuable segment should communicate appeals to an aspirational lifestyle in traditional and social media, effective at reaching young women.

The study reported in this paper contributes to the limited published research into the luxury-marketing sector in Asia by examining the buying behaviour of female Strawberry Generation consumers in Taiwan. It is the first to research and investigate the meanings attached to luxury by these individuals in the collectivist culture of Taiwan, as well as their motivations, and the factors influencing their purchase of luxury fashions. The study thus contributes with new knowledge to the buying of luxury fashion products by young female Taiwanese consumers, which may be extended to other collectivist cultures in Asia.

The purpose of this paper is to present a novel strategy used for acceleration of free-vibration analysis, in which the hierarchical matrices structure and Compute Unified Device Architecture (CUDA) platform is applied to improve the performance of the traditional dual reciprocity boundary element method (DRBEM).

The DRBEM is applied in forming integral equation to reduce complexity. In the procedure of optimization computation, ℋ-Matrices are introduced by applying adaptive cross-approximation method. At the same time, this paper proposes a high-efficiency parallel algorithm using CUDA and the counterpart of the serial effective algorithm in ℋ-Matrices for inverse arithmetic operation.

The analysis for free-vibration could achieve impressive time and space efficiency by introducing hierarchical matrices technique. Although the serial algorithm based on ℋ-Matrices could obtain fair performance for complex inversion operation, the CUDA parallel algorithm would further double the efficiency. Without much loss in accuracy according to the examination of the numerical example, the relative error appeared in approximation process can be fixed by increasing degrees of freedoms or introducing certain amount of internal points.

The paper proposes a novel effective strategy to improve computational efficiency and decrease memory consumption of free-vibration problems. ℋ-Matrices structure and parallel operation based on CUDA are introduced in traditional DRBEM.

This study aims to investigate the factors responsible for substrate cracking reliability problem in through-glass vias (TGVs), which are critical components for glass-based 2.5 D integration.

Numerical models were used to examine the driving force for substrate cracking in glass interposers due to stress coupling during heating. An analytical solution was used to demonstrate how the energy release rate (ERR) for the glass substrate cracking is affected by the via design and the mismatch in thermal strain. Then, the numerical models were implemented to investigate the design factors effects, such as the pitch distance, via diameter, via pattern, via design, effect from a stress buffer layer and the interposer materials selection on the susceptibility to substrate cracking.

ERR for substrate cracking was found to be directly proportional to the via diameter and the thermal mismatch strain. When a via pattern is implemented for high-density integration, a coupling in the stress fields was identified. This coupling effect was found to depend on the pitch distance, the position of the vias, and the via arrangement, suggesting a via pattern-dependent reliability behavior for glass interposers. Changing the design of the via to an annular shape or a substrate-cored via was found to be a promising approach to reduce the susceptibility to substrate cracking compared to a fully filled solid via. Also, the use of a stress buffer layer, an encouraging design prospect presented for the first time for TGVs in this study, was found to significantly reduce cracking. Finally, alternative via and substrate materials showed lower tendency for substrate cracking, indicating that the reliability of glass interposers can be further enhanced with the implementation of such new materials.

This study signifies the first attempt to comprehensively evaluate the susceptibility to crack formation in glass interposers during heating. Therefore, this study provides new perspectives on how to achieve a significant potential reliability improvement for TGVs.

The trend of “Deep Learning for Internet of Things (IoT)” has gained fresh momentum with enormous upcoming applications employing these models as their processing engine and Cloud as their resource giant. But this picture leads to underutilization of ever-increasing device pool of IoT that has already passed 15 billion mark in 2015. Thus, it is high time to explore a different approach to tackle this issue, keeping in view the characteristics and needs of the two fields. Processing at the Edge can boost applications with real-time deadlines while complementing security.

This review paper contributes towards three cardinal directions of research in the field of DL for IoT. The first section covers the categories of IoT devices and how Fog can aid in overcoming the underutilization of millions of devices, forming the realm of the things for IoT. The second direction handles the issue of immense computational requirements of DL models by uncovering specific compression techniques. An appropriate combination of these techniques, including regularization, quantization, and pruning, can aid in building an effective compression pipeline for establishing DL models for IoT use-cases. The third direction incorporates both these views and introduces a novel approach of parallelization for setting up a distributed systems view of DL for IoT.

DL models are growing deeper with every passing year. Well-coordinated distributed execution of such models using Fog displays a promising future for the IoT application realm. It is realized that a vertically partitioned compressed deep model can handle the trade-off between size, accuracy, communication overhead, bandwidth utilization, and latency but at the expense of an additionally considerable memory footprint. To reduce the memory budget, we propose to exploit Hashed Nets as potentially favorable candidates for distributed frameworks. However, the critical point between accuracy and size for such models needs further investigation.

To the best of our knowledge, no study has explored the inherent parallelism in deep neural network architectures for their efficient distribution over the Edge-Fog continuum. Besides covering techniques and frameworks that have tried to bring inference to the Edge, the review uncovers significant issues and possible future directions for endorsing deep models as processing engines for real-time IoT. The study is directed to both researchers and industrialists to take on various applications to the Edge for better user experience.

The growing use of internet-based learning (IBL) platforms in institutions of higher education is producing profound changes in the traditional teaching learning process worldwide. This paper aims to identify and understand the ways in which higher education institutions draw benefits by the use of such means, synthesizing the literature research.

The study synthesized the literature research by using a mixed method approach in which both Web of Science (WoS) and bibliographic techniques were used to retrieve the relevant data base.

The comprehensive review of the literature suggests that communication technology (CT), massive open online courseware (MOOCs), social networking sites (SNSs), blogs, real simple syndication (RSS) and YouTube are creating new possibilities and avenues of collaborative learning by transforming the traditional class and teacher-centric system.

Multiplicity of the IBL platforms and rapid technological obsolesce are some of the limitations of this paper.

The findings of this study are highly useful in developing a strategic framework to accelerate the integration of IBL platforms to make teaching learning process more interactive and informative.

The study examines the presence of the pollution haven or pollution halo hypothesis in Brazil, Russia, India, China and South Africa (BRICS) and Next-11 economies. Hence, it empirically tests the direct impact of foreign direct investment (FDI) on the ecological footprint. Further, it explores the moderating role of green innovation on the nexus between FDI and ecological footprint.

The study uses the Driscoll–Kraay (DK) standard error panel regression technique to examine the long-run elasticities amongst the variables for the group of emerging countries, BRICS and Next-11, during the period of 1992 to 2018. Further, statistical robustness is demonstrated using the fully modified ordinary least squares technique.

The empirical finding shows that FDI degrades environmental quality by raising the ecological footprint. Thus, it proves that FDI is a source of pollution haven in BRICS and Next-11 countries. However, green innovation negatively moderates the relationship between FDI and ecological footprint. That means the joint impact of green innovation, and FDI proves the presence of the pollution halo hypothesis. Further, renewable energy consumption is reducing the ecological footprint, but economic growth and industrialisation are worsening the environmental quality.

This study offers policy implications for governments and policymakers to promote environmental sustainability by improving green innovation and allowing FDI that encourages clean and advanced technology.

No prior studies examine the moderating role of green innovation on the relationship between FDI and ecological footprint in the context of emerging countries.