The Effects Green Human Resource on Employees’ Green Voice Behaviors Towards Green Innovation

This study investigates the impact of Green Human Resource (GHR) practices on employees’ green voice behaviors towards green innovation in Jordanian Manufacturing Companies (JMC). A survey design was employed to collect data from 187 workers in JMC using closed-ended questions on employees’ attitudes towards GHR practices and green innovation. The study used the SmartPLS4 tool to conduct structural equation modeling and path analysis to examine the direct and indirect effects of GHR practices on employees’ green voice behaviors towards green innovation. The study found that GHR practices and employee involvement positively influence green voice behaviors and green innovation, with green voice behaviors mediating the effect of GHR practices and employee involvement on green innovation. The study highlights the importance of GHR practices and employee involvement in promoting sustainable development and environmental performance in JMC, with practical implications for organizations, policymakers, and regulatory bodies. Overall, the study provides important insights into how organizations can promote sustainability and innovation through effective HR practices and employee involvement strategies

Not all pipelines are created equal. Pipelines have different characteristics, and would therefore show different levels of integrity and fail differently. The failure mode and cause of a given pipeline depends on several factors including the design, operating and environmental parameters. A new tool was developed to evaluate pipeline integrity and assess its potential failure mode, patterns, and rate based on the critical pipeline parameters. These parameters include the pipeline material of construction, wall thickness, operating pressure, service material, backfill medium/material, age, coating, pipeline size and other relevant parameters. The new tool was developed using pipeline data collected from the European Union, UK, and USA for pipeline failures over four decades. Failure models and patterns were analyzed, and over 60,000 failure modes/pattern combination were identified. The tool predicts the failure mode and patterns in terms of failure rate distribution by size of leak and its causes. It also shows the relative Pipeline Risk Index, defined as the pipeline’s potential failure rate relative to average pipeline population in the industry within similar pipeline categories. Ignition probabilities for pipeline failures were also analyzed and are predicted by this tool for each pipeline leak depending on the leak characteristics.

PresentationNot all pipelines are created equal. Pipelines have different characteristics, and would therefore show different levels of integrity and fail differently. The failure mode and cause of a given pipeline depends on several factors including the design, operating and environmental parameters. A new tool was developed to evaluate pipeline integrity and assess its potential failure mode, patterns, and rate based on the critical pipeline parameters. These parameters include the pipeline material of construction, wall thickness, operating pressure, service material, backfill medium/material, age, coating, pipeline size and other relevant parameters. The new tool was developed using pipeline data collected from the European Union, UK, and USA for pipeline failures over four decades. Failure models and patterns were analyzed, and over 60,000 failure modes/pattern combination were identified. The tool predicts the failure mode and patterns in terms of failure rate distribution by size of leak and its causes. It also shows the relative Pipeline Risk Index, defined as the pipeline’s potential failure rate relative to average pipeline population in the industry within similar pipeline categories. Ignition probabilities for pipeline failures were also analyzed and are predicted by this tool for each pipeline leak depending on the leak characteristics

The Role of HRM Practices on the Talent Management: Evidence from Jordanian Commercial Banks

This study aims to examine how human resource management (HRM) practices affect talent management in Jordanian commercial banks. Information was provided by 120 administrators and staff members working for Jordanian commercial banks. The questionnaire used in this study was developed based on a comprehensive review of relevant literature and underwent validity assessments by experts in the field. To ensure data quality, clear instructions were provided, and data collection staff were available for support. Steps were also taken to address common method bias, including participant anonymity, separate data collection points, and the inclusion of control variables in the analysis. These measures contribute to enhancing the validity and reliability of the study’s findings. The findings showed that administrative innovation is positively impacted by human resource management practices, while the study also showed that there is a positive, statistically significant impact of the dimensions of HRM practices (training and development, wages and incentives, performance appraisal) on administrative innovation in Jordanian commercial banks. By giving field indications on the nature of the interaction between these two variables in the workplace, the current research contributes to the development of the literature addressing the relationship between HRM practices and administrative creativity.  Lastly, this research recommends the need for officials in Jordanian commercial banks to pay attention to human resource management practices as an important factor contributing to administrative creativity, and to focus on training practices as training is a strategic option for preparing creative human cadres

Development of Empirical Method to Calculate Natural Gas Pipelines Rupture Exposure Radius

PresentationNatural Gas pipeline location classification are designed following an approach similar to ASME B31.8, which considers segmenting the pipeline length and count the population in each segment within a given distance from the pipeline (width of segment). ASMEB 31.8 utilizes fixed distance of 400m for the segment width, while other operators use the pipeline Rupture Exposure Radius (RER). This is a distance determined by the consequences modeling for pipeline full rupture. Since, the population density within the segment width affects the design factors of the pipeline, i.e. wall thickness requirements, over-predicting the distance can have significant cost implications. Some operators use default RER values on conservative estimates, while industrial best practices allow for detailed dispersion to calculate representative RER distances. Detailed dispersion modeling was performed for a large number of Natural Gas Pipeline scenarios, and an empirical formula was developed to estimate the RER for these pipelines as a function of the pipeline diameter and pressure. The dispersion calculations results show that the default RER values current used by some operators are very conservative, and that the cost of pipeline design/construction can be optimized by using the empirical formula developed in this work. The formula, which produces the RER value in terms of the distance from the pipeline to the point of 1⁄2 lower flammable limit is easy to use, and accurately represents the dispersion results. This eliminates the need to using sophisticated modeling software/tools to assess the RER values of Natural Gas pipelines. The formula also uses minimum number of data/information available about the pipelines (diameter and pressure only) increasing its effectiveness as a tool replacing the modeling software. In addition, for pipeline projects, lower RER distances result in more flexibility in route selection, lower pipeline location class and hence thinner wall thicknesses, less emergency isolation valves required and longer span between sectionalizing valves, which all translate to cost savings and reduces potential sources of leak (sectionalizing valves)

Blockchain and FL-based Network Resource Management for Interactive Immersive Services

Advanced services leveraged for future smart cities have played a significant role in the advancement of 5G networks towards the 6G vision. Interactive immersive applications are an example of those enabled services. Such applications allow for the interaction between multiple users in a 3D environment created by virtual presentations of real objects and participants using various technologies such as Virtual Reality (VR), Augmented Reality (AR), Extended Reality (XR), Digital Twin (DT) and holography. These applications require advanced computing models which allow for the processing of massive gathered amounts of data. Motions, gestures and object modification should be captured, added to the virtual environment, and shared with all the participants. Relying only on the cloud to process this data can cause significant delays. Therefore, a hybrid cloud/edge architecturewith an intelligent resource orchestration mechanism, that is able to allocate the available capacities efficiently is necessary. In this paper, a blockchain and federated learning-enabled predicted edge-resource allocation (FLP-RA) algorithm is introduced to manage the allocation of computing resources in B5G networks. It allows for smart edge nodes to train their local data and share it with other nodes to create a global estimation of future network loads. As such, nodes are able to make accurate decisions to distribute the available resources to provide the lowest computing delay

Digital twin for healthcare immersive services: fundamentals, architectures, and open issues

Digital Twin (DT) and Immersive Services (XR) technologies are revolutionizing the medical sector through designing applications that support virtual representation and interactive reality. Both technologies leverage one another to advance healthcare services and provide professionals a virtual environment where they can interact with the digital information of their patients more conveniently. The integration of DT and XR technologies enables the creation of advanced 3D models of patients (e.g., organs or body) based on their accurate real data gathered and processed by the DT improving traditional healthcare treatments such as telemedicine, training, and consultation. This chapter introduces the DT technology in immersive healthcare services and presents its benefits to the medical sector. It discusses the various requirements and protocols to build immersive models of the DT using advanced Artificial Intelligence (AI) and Machine Learning (ML)-based mechanisms. The chapter also proposes various paradigms that can be used to enable rapid deployment of these models, meeting the strict demands of the medical sector in terms of efficiency, accuracy, and precision

Edge Intelligence for Empowering IoT-based Healthcare Systems

The demand for real-time, affordable, and efficient smart healthcare services is increasing exponentially due to the technological revolution and burst of population. To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area. In this regard, edge computing technology can reduce latency and energy consumption by moving processes closer to the data sources in comparison to the traditional centralized cloud and IoT-based healthcare systems. In addition, by bringing automated insights into the smart healthcare systems, artificial intelligence (AI) provides the possibility of detecting and predicting high-risk diseases in advance, decreasing medical costs for patients, and offering efficient treatments. The objective of this article is to highlight the benefits of the adoption of edge intelligent technology, along with AI in smart healthcare systems. Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems. Additionally, the paper discusses issues and research directions arising when integrating these different technologies together.Comment: This paper has been accepted in IEEE Wireless Communication Magazin

Blockchain-assisted Decentralized Virtual Prosumer Grouping for P2P Energy Trading

© 2020 IEEE. Energy trading systems have revolutionized by taking advantage of energy users who produce surplusenergy. In the cyberphysical energy sharing systems, the participation of such consumers who can also sell their residuum energy for profit, namely prosumers, is critical for the sustainable and efficient energy sharing procedure and requires improved prosumer management. The idea of grouping the prosumers for better profits is a promising approach for prosumer management which is currently carried out in centralized manner; that face trust, security and scalability issues. Hence, a strong tool that can protect the prosumer privacy; log the changes for audit purposes and eventually improve the performance of the system is necessary. This paper proposes a blockchain-assisted approach using smart contracts for improved scalability and decentralization of the prosumer grouping mechanism in the context of P2P energy trading. The results show around 38.7% improvement in the performance and scalability of the system

An Adaptive UAV Positioning Model for Sustainable Smart Transportation

Several research works are being considered to adopt the use of UAVs to support smart transportation systems due to their movement flexibility. In this article, a UAV-supported vehicular network solution is developed which considers both power and coverage limitations of UAVs to attain the vision of sustainable smart cities. Nodes communicate with each other through the 5G connection and ad-hoc links. The solution is solved for as a predictive optimization problem that determines the height of the UAV to dynamically change it to ensure the optimal communication coverage of vehicular nodes. Moreover, the solution considers UAV energy consumption constraints when setting the optimal height of the UAV. Additionally, the optimal distance between every two adjacent UAVs is also considered to avoid any coverage overlapping while protecting their connectivity. Extensive evaluations were considered in terms of both implementation and simulation to test the proposed model. Evaluation results show that the proposed solution can predict vehicle traffic patterns accurately to ensure proper adjustments of the UAV height. Moreover, network coverage is ensured for areas with and without fixed BS availability with the support of the self-positioning UAVs while adhering to QoS requirements