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Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad.

Adjustments in the designs of water resources systems due to climate change and other nonstationarities are warranted because the benefits of effective adaptation are well recognized. Therefore, the time and resources invested in these analyses are well worth the effort. Before a major investment in an effort is made, however, it is reasonable to determine if the problem is of sufficient complexity or the value of additional information is high enough to warrant the inclusion of complex, sophisticated methods that explicitly include nonstationarity and associated decision-making under deep uncertainty. There exist several planning level conditions such as the lifetime of the project, its criticality, and its reversibility that may indicate detailed analysis is not needed. There are also sequential analysis and screening steps that can be applied to determine the complexity of the methodology needed. Finally, the use of decision analysis can also help determine if additional, detailed analysis, or data collection are necessary. The use of one or several of these methods should be considered as initial steps before undertaking a vulnerability assessment and developing an adaptation strategy for a water resources system.

Coal is still one of the major energy sources. It is used as a reducing agent in the metallurgical industry, in the cement industry coal is a source of energy and it is still used in power generation. Mukah coal is characterized through chemical and mineralogical properties determinations. XRD pattern of the coal shows that it is amorphous in nature and dominated by quartz and kaolinite. Mukah coal has about total carbon 97.98 wt% with SiO2, Al2O3 and Fe2O3 present as the most predominant oxides. The oxides make up approximately 1.58 wt% of the coal samples. The SEM image shows basically depicts coal particles of various irregular shapes and sizes. The mineral matter was not clearly seen on the surface of the coal particle as it was supposedly embedded inside the bulk of carbonaceous matter.

Recently, the safety issue of maritime autonomous surface ships (MASS) has become a hot topic. Preliminary hazard analysis of MASS can assist autonomous ship design and ensure safe and reliable operation. However, since MASS technology is still at its early stage, there are not enough data for comprehensive hazard analysis. Hence, this paper attempts to combine conventional ship data and MASS experiments to conduct a preliminary hazard analysis for autonomy level III MASS using the hybrid causal logic (HCL) method. Firstly, the hazardous scenario of autonomy level III MASS is developed using the event sequence diagram (ESD). Furthermore, the fault tree (FT) method is utilized to analyze mechanical events in ESD. The events involving human factors and related to MASS in the ESD are analyzed using Bayesian Belief Network (BBN). Finally, the accident probability of autonomy level III MASS is calculated in practice through historical data and a test ship with both an autonomous and a remote navigation mode in Wuhan and Nanjing, China. Moreover, the key influence factors are found, and the accident-causing event chains are identified, thus providing a reference for MASS design and safety assessment process. This process is applied to the preliminary hazard analysis of the test ship.

The preliminary analysis of network accuracy is the basis for assessing the correctness of network design. The next step should be to modify the structure of the network or adjust the a priori presupposed measurement accuracy. Such an analysis facilitates determining the target network accuracy model for results of adjustment of the observational set. A control network established to determine the displacement of civil engineering objects should be structurally sufficiently accurate, reliable, and flexible. These criteria can be met if the modular network technology is used. This paper reports a study that involved a comparative analysis of this measurement method (modular networks) and several variants of classic measurement methods (using a total station). Other indices of the technical quality of a geodetic network (determinability, reliability) were also considered. An attempt was made to develop a methodology for determining as realistic as possible values of mean observational errors as well. A procedure for estimating mean errors of observations is presented. The conclusions may help design control networks for investigating the stability of civil engineering objects, for example.

In the face of the increasing complexity of risk factors in the coal mining transportation system (CMTS) during the process of intelligent transformation, this study proposes a method for analyzing accidents in CMTS based on fault tree analysis (FTA) combined with Bayesian networks (BN) and preliminary hazard analysis (PHA). Firstly, the fault tree model of CMTS was transformed into a risk Bayesian network, and the inference results of the fault tree and Bayesian network were integrated to identify the key risk factors in the transportation system. Subsequently, based on the preliminary hazard analysis of these key risk factors, corresponding rectification measures and a risk control system construction plan are proposed. Finally, a case study was carried out on the X coal mine as a pilot mine to verify the feasibility of the method. The application of this method effectively identifies and evaluates potential risk factors in CMTS, providing a scientific basis for accident prevention. This research holds significant importance for the safety management and decision making of coal mine enterprises during the process of intelligent transformation and is expected to provide strong support for enhancing the safety and reliability of CMTS.

Fires are one of the main disasters in underground engineering. In order to comprehensively describe and evaluate the risk of underground engineering fires, this study proposes a UEF risk assessment method based on EPB-FBN. Firstly, based on the EPB model, the static and dynamic information of the fire, such as the cause, occurrence, hazard, product, consequence, and emergency rescue, was analyzed. An EPB model of underground engineering fires was established, and the EPB model was transformed into a BN structure through the conversion rules. Secondly, a fuzzy number was used to describe the state of UEF variable nodes, and a fuzzy conditional probability table was established to describe the uncertain logical relationship between UEF nodes. In order to make full use of the expert knowledge and empirical data, the probability was divided into intervals, and a triangulated fuzzy number was used to represent the linguistic variables judged by experts. The α-weighted valuation method was used for de-fuzzification, and the exact conditional probability table parameters were obtained. Through fuzzy Bayesian inference, the key risk factors can be identified, the sensitivity value of key factors can be calculated, and the maximum risk chain can be found in the case of known evidence. Finally, the method was applied to the deductive analysis of three scenarios. The results show that the model can provide realistic analysis ideas for fire safety evaluation and emergency management of underground engineering. The proposed EPB risk assessment model provides a new perspective for the analysis of UEF accidents and contributes to the ongoing development of UEF research.

PurposeA crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the technologies of artificial intelligence (AI). The purpose of this study is to propose a policy framework for adoption of AI in the finance sector by exploring the driving factors through systems approach.Design/methodology/approachBased on literature review and discussions with experts from both industry and academia, nine enablers were shortlisted, which were used in the questionnaire survey to determine ranks of enablers. Further, the study developed the interpretive structural model (ISM) with the help of experts.FindingsThe ISM digraph developed with the help of the experts, resulted in the enablers like anticipated profitability, contactless solutions, credit risk management and software vendor support as dependent factors and stood at the top of the ISM. On the other hand, factors like availability of the data, technical infrastructure and funds are the most driving factors, which lie on the bottom of the ISM.Research limitations/implicationsThe study provides implications and policy recommendations for the practicing managers and government agencies approaching the digital transformation towards the adoption of AI in the finance ecosystem.Originality/valueThe paper uses the systems approach for the development of the ISM of the enabling factors for the adoption of AI technology. On the basis of the results, the study proposes a policy framework to accelerate the functioning of the finance ecosystem with AI technology.

The EUSO-TA detector is a pathfinder for the JEM-EUSO project and is currently installed in Black Rock Mesa (Utah) on the site of the Telescope Array fluorescence detectors. Aim of this experiment is to validate the observation principle of JEM-EUSO on air showers measured from ground. The experiment gets data in coincidence with the TA triggers to increase the likelihood of cosmic ray detection. In this framework the collaboration is also testing the detector response with respect to several test events from lasers and LED flashers. Moreover, another aim of the project is the validation of the stability of the data acquisition chain in real sky condition and the optimization of the trigger scheme for the rejection of background. Data analysis is ongoing to identify cosmic ray events in coincidence with the TA detector. In this contribution we will show the response of the EUSO-TA detector to all the different typologies of events and we will show some preliminary results on the trigger optimization performed on such data.