• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.223-228
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• 2022

Femtocells are being incorporated into heterogeneous networks in order to increase the network capacity. However, intensive deployment of femtocells results in undesired interference, which lowers the system's performance. Controlling the femtocell transmission power is one of of the aspects that can be addressed in order to mitigate the negative effects of the interference. It may also be utilized to facilitate the auto-configuration of the network's conductance, if necessary. This paper proposes the use of an auto-configuration technique for transmission power. The suggested technique is based on the transmission power of macrocells and the coverage provided by femtocells. The simulation findings show that the network's capacity has increased, and the amount of interference has decreased.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.6
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• pp.61-80
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• 2021

As the business environment becomes more rapid and unpredictable change, greater diversity, increased complexity, and intensified competitive pressures, supply chain risk management has been growing attention over the past several decades. However, little of known about how absorptive capacity can mitigate supply chain risk for improving operational performance despite its important role in responding to supply chain risk. Therefore, we aim to examine the role of organizational-level absorptive capacity on operational performance, and further identify how the interplay of individual-level and organizational-level absorptive capacity results in operational performance. Our results represent not only direct but also indirect effects of supply chain risk on operational performance, mediated by organizational-level absorptive capacity. Furthermore, this study reveals that individual-level absorptive capacity enhances the effect of organizational-level absorptive capacity on operational performance.

• Structural Engineering and Mechanics
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• v.83 no.1
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• pp.31-43
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• 2022

A nonlinear gas-spring tuned mass damper is proposed to mitigate the seismic responses of the multi-degree-of-freedom (MDOF) structure, in which the nine-story benchmark model is selected as the controlled object. The nonlinear mechanical properties of the gas-spring are investigated through theoretical analysis and experiments, and the damper's control parameters are designed. The control performance and damping mechanism of the proposed damper attached to the MDOF structure are systematically studied, and its reliability is also explored by parameter sensitivity analysis. The results illustrate that the nonlinear gas-spring TMD can transfer the primary structure's vibration energy from the lower to the higher modes, and consume energy through its own relative movement. The proposed damper has excellent "Reconciling Control Performance", which not only has a comparable control effect as the linear TMD, but also has certain advantages in working stroke. Furthermore, the control parameters of the gas-spring TMD can be determined according to the external excitation amplitude and the gas-spring's initial volume.

• Journal of Smart Tourism
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• v.1 no.1
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• pp.9-17
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• 2021

Smart tourism research and development have mainly focused on the benefits of smart tourism technologies to certain stakeholders with transactional relationships in destinations. However, smart technologies in destinations could also cause several negative outcomes, leading to social, ethical, and moral issues. Such issues arise from the power imbalance between different stakeholders of smart tourism development. To mitigate the adverse effects of smart technologies, destinations need to enunciate the essential moral and ethical principles when developing smart tourism. Therefore, adopting descriptive and normative approaches to stakeholder theory, this paper proposes a framework to showcase several methods to address the issues.

• Journal of Hospice and Palliative Care
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• v.24 no.4
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• pp.245-253
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• 2021

Purpose: This study aimed to explore the experiences of hospice and palliative care (HPC) nurses at inpatient hospice centers in South Korea during the coronavirus disease 2019 pandemic. Methods: Data collection was conducted through individual interviews with 15 HPC nurses using face-to-face interviews, telephone calls, or Zoom videoconferencing. Data were analyzed using the thematic analysis method. Results: This study found that HPC nurses experienced practical and ethical dilemmas that reinforced the essential meaning and value of hospice and palliative care. The participants emphasized their practical roles related to compliance with infection prevention measures and their roles as rebuilders of hospice and palliative care. Conclusion: The findings of this study indicate that inpatient hospice centers must mitigate the practical and ethical dilemmas experienced by nurses, consider establishing explanation nursing units, and provide education to support nurses' highlighted roles during the pandemic. This study can be used to prepare inpatient hospice centers and the nurses that work there for future infectious disease outbreaks.

• ETRI Journal
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• v.44 no.2
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Journal of Industrial Technology
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• v.42 no.1
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• pp.19-27
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• 2022

This review will discuss the effort to understand the interfacial reactions at the anode and cathode sides of all-solid-state batteries. Antiperovskite solid electrolytes have received increasing attention due to their low melting points and anion tunability which allow controlling microstructure and crystallographic structures of this material system. Antiperovskite solid electrolytes pave the way for the understanding relationship between critical current density and mechanical properties of solid electrolytes. Microstructure engineering of cathode materials has been introduced to mitigate the volume change of cathode materials in solid-state batteries. The hollow microstructure coupled with a robust outer oxide layer effectively mitigates both volume change and stress level of cathode materials induced by lithium insertion and extraction, thus improving the structural stability of the cathode and outer oxide layer, which results in stable cycling performance of all-solid-state batteries.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.1-4
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• 2022

The growth in global population and industrialization has led to an increasing demand for electricity. Accordingly, the electricity providers need to increase the electricity generation. Due to the economical and environmental concerns associated with the generation of electricity from fossil fuels. Alternative power recourses that can potentially mitigate the economical and environmental are of interest. Renewable energy resources are promising recourses that can participate in producing power. Among renewable power resources, solar energy is an abundant resource and is currently a field of research interest. Photovoltaic solar power is a promising renewable energy resource. The power output of PV systems is mainly affected by the solar irradiation and ambient temperature. this paper investigates the utilization of machine learning unsupervised neural network techniques that potentially improves the reliability of PV solar power systems during integration into the electrical grid.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2407-2424
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• 2022

Recently, Transformer has made great progress in video retrieval tasks due to its high representation capability. For the structure of a Transformer, the cascaded self-attention modules are capable of capturing long-distance feature dependencies. However, the local feature details are likely to have deteriorated. In addition, increasing the depth of the structure is likely to produce learning bias in the learned features. In this paper, an improved Transformer structure named TransDCS (Transformer with Dynamic Convolution and Shortcut) is proposed. A Multi-head Conv-Self-Attention module is introduced to model the local dependencies and improve the efficiency of local features extraction. Meanwhile, the augmented shortcuts module based on a dual identity matrix is applied to enhance the conduction of input features, and mitigate the learning bias. The proposed model is tested on MSRVTT, LSMDC and Activity-Net benchmarks, and it surpasses all previous solutions for the video-text retrieval task. For example, on the LSMDC benchmark, a gain of about 2.3% MdR and 6.1% MnR is obtained over recently proposed multimodal-based methods.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.89-96
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• 2022

Security is still a great concern to this day, albeit we have come a long way to mitigate its numerous threats. No-SQL databases are rapidly becoming the new database de-facto, as more and more apps are being developed every day. However, No-SQL databases security could be improved. In this paper, we discuss a way to improve the security of XML-based databases with the use of trust labels to be used as an access control model.