Mobile applications are getting a great deal of interest among researchers due to their proliferation and pervasiveness, especially in the context of digital libraries of educational institutes. However, their low acceptance and usage are observed, hence, in-depth investigations are required in order to understand the factors behind low acceptance and intention to use mobile library application (MLA). Therefore, the aim of this work is to empirically explore the acceptance of MLA with a proposed model that is evolved from the technology acceptance model (TAM). The study objects to deliver empirical provision on acceptance of MLA. A self-administrated cross-sectional survey-based study was conducted to gather data from 340 users of MLA. Structural equation model (SEM) with an analysis of moment structure (AMOS) software was conducted to examine quantitative data. Results revealed that perceived usefulness and perceived ease of use are direct significant predictors with the intention to use MLA whereas system quality and habit are the influencing factors toward the usage intention of MLA. The findings help as a guide for effective decision in the design and development of MLA. Further, the outcomes can be utilized in the resource allocation process for ensuring the success of the library's vision and mission.

The control of spreading of COVID-19 in emergency situation the entire world is a challenge, and therefore, the aim of this study was to propose a spherical intelligent fuzzy decision model for control and diagnosis of COVID-19. The emergency event is known to have aspects of short time and data, harmfulness, and ambiguity, and policy makers are often rationally bounded under uncertainty and threat. There are some classic approaches for representing and explaining the complexity and vagueness of the information. The effective tool to describe and reduce the uncertainty in data information is fuzzy set and their extension. Therefore, we used fuzzy logic to develop fuzzy mathematical model for control of transmission and spreading of COVID19. The fuzzy control of early transmission and spreading of coronavirus by fuzzy mathematical model will be very effective. The proposed research work is on fuzzy mathematical model of intelligent decision systems under the spherical fuzzy information. In the proposed work, we will develop a newly and generalized technique for COVID19 based on the technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under spherical fuzzy environment. Finally, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested method, along with a sensitivity analysis and comparative analysis, showing the feasibility and reliability of its results. Keywords Spherical fuzzy set • Intelligent decision support systems • Emergency decision making of COVID-19 • Critical path problems 1 Introduction The situation of the world for the people is very risky to spend the peaceful life due to the spreading of the COVID-19. The COVID-19 is viral disease, a pandemic and the world Communicated by Valentina E. Balas.

Nowadays, rumor spreading has gradually evolved into a kind of organized behaviors, accompanied with strong uncertainty and fuzziness. However, existing fuzzy detection techniques for rumors focused their attention on supervised scenarios which require expert samples with labels for training. Thus they are not able to well handle unsupervised scenarios where labels are unavailable. To bridge such gap, this paper proposes a fuzzy detection system for rumors through explainable adaptive learning. Specifically, its core is a graph embedding-based generative adversarial network (Graph-GAN) model. First of all, it constructs fine-grained feature spaces via graph-level encoding. Furthermore, it introduces continuous adversarial training between a generator and a discriminator for unsupervised decoding. The two-stage scheme not only solves fuzzy rumor detection under unsupervised scenarios, but also improves robustness of the unsupervised training. Empirically, a set of experiments are carried out based on three real-world datasets. Compared with seven benchmark methods in terms of four metrics, the results of Graph-GAN reveal a proper performance which averagely exceeds baselines by 5% to 10%.

Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

The emergency situation of COVID-19 is a very important problem for emergency decision support systems. Control of the spread of COVID-19 in emergency situations across the world is a challenge and therefore the aim of this study is to propose a q-linear Diophantine fuzzy decision-making model for the control and diagnose COVID19. Basically, the paper includes three main parts for the achievement of appropriate and accurate measures to address the situation of emergency decision-making. First, we propose a novel generalization of Pythagorean fuzzy set, q-rung orthopair fuzzy set and linear Diophantine fuzzy set, called q-linear Diophantine fuzzy set (q-LDFS) and also discussed their important properties. In addition, aggregation operators play an effective role in aggregating uncertainty in decision-making problems. Therefore, algebraic norms based on certain operating laws for q-LDFSs are established. In the second part of the paper, we propose series of averaging and geometric aggregation operators based on defined operating laws under q-LDFS. The final part of the paper consists of two ranking algorithms based on proposed aggregation operators to address the emergency situation of COVID-19 under q-linear Diophantine fuzzy information. In addition, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.

Future Internet of Things (IoT) will utilize IEEE 802.15.4 based low data rate communication for various applications. In the IEEE 802.15.4 standard, nodes send data to their Personal Area Network (PAN) coordinator using the Guaranteed Time Slot (GTS). The standard does not meet the adaptive data requirements of GTS requesting nodes in an efficient manner. If requesting GTSs in an active period are more or less than the available limit, either the requested nodes will not be entertained or GTSs remain underutilized. Consequently, it may cause unnecessary delay or poor GTS utilization. In this paper, an Optimal GTS allocation Mechanism for Adaptive Duty cycle (OGMAD) is proposed that adapts the active period of the superframe in accordance with the requested data. OGMAD also reduces GTS size to improve link utilization as well as accommodate more GTS requesting nodes. Simulation results verify that OGMAD improves link utilization, reduces network delay and offers more nodes to transmit their data as compared to the standard.INDEX TERMS IEEE 802.15.4, Internet of things, wireless sensor networks, MAC protocol.

Abstract-Millions of devices are going to participate in 5G producing a huge space for security threats. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against Quantum computers. Therefore, in this paper, we propose a security protocol called Quantum Key GRID for Authentication and Key Agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in Long Term Evolution (LTE) architecture without any significant modifications in the underlying base system. The proposed QKG-AKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes.

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