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Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

Abstract: Technology advancement since last few decades creates cyber attack a critical issue. Cyber security has become an important part today. It has also become an important and crucial subject in the field of forensic science. Increased in the growth of internet technology and internet devices have increased the risk of cyber attack. Almost every organization today are depends on the internet and devices. There are many types of cyber attack. This paper is the detailed review about Ransomware attack. This paper is consisted about vast of the information about What is Ransomware Attack, how does it work, how ransomware attack emerged. After reading this paper you will learn about the ransomware attacks in history of cyber world. This will help you to learn and understand about ransomware attack, how to prevent yourself from ransomware attack. As a forensic science student, it is always important to be aware about the attacks that have happened in the history of cyber world. Before writing this paper, I have read and analyze many research paper and internet articles, so that I can write a detailed review paper which can help students and for the forensic awareness. Keywords: Cyberattack, Hacking, Ransomware, cyberworld, cyber security, ransomware, forensic, network security

Urban shrinkage is a critical issue in local small- and medium-sized cities in Korea. While there have been several studies to analyze the causes and consequences of vacancy increases, most have only focused on socioeconomic associations at larger scale and failed to consider individual housing level characteristics, primarily due to a lack of appropriate data. Based on data including 52,400 individual parcels, this study analyzes the primary contributors to vacant properties and their spatial distribution through a multilevel model design based on data for each parcel. Then, we identify areas at high risk of vacancy in the future to provide evidence to establish policies for improving the local environment. Results indicate that construction year, building structure, and road access conditions have a significant effect on vacant properties at the individual parcel level, and the presence of schools and hypermarket within 500 m are found to decrease vacant properties. Further, prediction outcomes show that the aged city center and areas with strict regulations on land use are expected to have a higher vacancy rate. These findings are used to provide a set of data-based revitalization strategies through the development of a vacancy prediction model.

Employee engagement has become a critical issue in Japanese companies. One way to develop it is to improve the relationship among employees through gratitude expressions. In the post-COVID-19 remote work environment, digital devices are essential. This paper confirms that expressions of gratitude delivered via digital devices enhance the relationship between employees. We experimented in a small-town government office where participants (n = 88) were asked to (1) use the Thanks App, an app we developed to express gratitude, for two months and (2) respond to an engagement survey we developed before and after the experimental period. Through cross-analysis of the data from the app and questionnaire, we found that the “trust in colleagues” factor had a strong correlation (r = 0.80, p < 0.001) with our new index computed by the app’s data. The results suggest that the use of the Thanks App may help visualize the trust relationship among teams. This study has a practical value in providing a new team management tool for visualizing team trust. In addition, it provides a new research method for emotional and social psychology using digital devices.

Student retention is a critical issue for universities, and nearly half of the students who start degree programs in science, technology, engineering, and mathematics (STEM) do not complete them. The current study tracks the progress of STEM students taking part in an entry-to-graduation program designed to build community, provide academic and social support, and promote engagement in academically purposeful activities. Although it had no effect on the number of students who changed their major, the program more than doubled the number of students who graduated in their original major. Black or Hispanic students taking part in the program also graduated at twice the rate of comparator students, largely attributable to the success of women in these groups. The results provide needed real-world insights into how to create an equitable environment that promotes the persistence and graduation of students, including those from groups historically underrepresented in STEM.

The transition from surface mining to underground is a critical issue for metal mines. The commonly cited procedure cored by ultimate-pit-limit (UPL) methodology is restricted to maximize the profit from both surface and underground mining, due to the absence of the integration of the profit from either of them. Under the target for such maximization, this study proposes a new optimization approach, which directly relates the design of open-pit limit and underground stopes, by equalizing the marginal profit from either surface or underground mining. The variation of the crown pillar size is involved in this approach. The proposed approach is applied to the Dagushan iron mine, and results show the total profit increased from 3.79 billion CNYs (original design by conventional UPL methodology) to 4.17 billion CNYs (optimal design by the proposed approach), by 9.91%. Moreover, the marginal profit from surface and underground mining, as well as total profit, of all possible designs of surface-to-underground mining transition in Dagushan iron mine is calculated to validate the proposed approach. When the marginal profits satisfy the criterion of the proposed approach, the maximum value of the total profit appears, and this demonstrates the proposed approach is robust to maximize the total profit in surface-to-underground mining transition. This work contributes to existing literature studies primarily from practical aspect, by providing a unified approach to optimize the transition from surface to underground mining.

Magnesium alloys are contemporary candidates for many structural applications of which medical applications, such as bioresorbable implants, are of significant interest to the community and a challenge to materials scientists. The generally poor resistance of magnesium alloys to environmentally assisted fracture, resulting, in particular, in faster-than-desired bio-corrosion degradation in body fluids, strongly impedes their broad uptake in clinical practice. Since temporary structures implanted to support osteosynthesis or healing tissues may experience variable loading, the resistance to bio-corrosion fatigue is a critical issue that has yet to be understood in order to maintain the structural integrity and to prevent the premature failure of implants. In the present communication, we address several aspects of the corrosion fatigue behaviour of magnesium alloys, using the popular commercial ZK60 Mg-Zn-Zr alloy as a representative example. Specifically, the effects of the testing frequency, surface roughness and metallic coatings are discussed in conjunction with the fatigue fractography after the testing of miniature specimens in air and simulated body fluid. It is demonstrated that accelerated environmentally assisted degradation under cyclic loading occurs due to a complicated interplay between corrosion damage, stress corrosion cracking and cyclic loads. The occurrence of corrosion fatigue in Mg alloys is exaggerated by the significant sensitivity to the testing frequency. The fatigue life or strength reduced remarkably with a decrease in the test frequency.

Precipitation intensity estimation is a critical issue in the analysis of weather conditions. Most existing approaches focus on building complex models to extract rain streaks. However, an efficient approach to estimate the precipitation intensity from surveillance cameras is still challenging. This study proposes a convolutional neural network known as the signal filtering convolutional neural network (SF-CNN) to handle precipitation intensity using surveillance-based images. The SF-CNN has two main blocks, the signal filtering block (SF block) and the gradually decreasing dimension block (GDD block), to extract features for the precipitation intensity estimation. The SF block with the filtering operation is constructed in different parts of the SF-CNN to remove the noise from the features containing rain streak information. The GDD block continuously takes the pair of the convolutional operation with the activation function to reduce the dimension of features. Our main contributions are (1) an SF block considering the signal filtering process and effectively removing the useless signals and (2) a procedure of gradually decreasing the dimension of the feature able to learn and reserve the information of features. Experiments on the self-collected dataset, consisting of 9394 raining images with six precipitation intensity levels, demonstrate the proposed approach’s effectiveness against the popular convolutional neural networks. To the best of our knowledge, the self-collected dataset is the largest dataset for monitoring infrared images of precipitation intensity.

Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization’s cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises’ security policies library, for consistent implementation of security controls. Significant and common cybersecurity policies of various enterprises are compared and explored in this study to provide robust and comprehensive cybersecurity knowledge that can be used in various enterprises. Several significant common security policies were identified and discussed in this comprehensive study. This study identified 10 common cybersecurity policy aspects in five enterprises: healthcare, finance, education, aviation, and e-commerce. We aimed to build a strong infrastructure in each business, and investigate the security laws and policies that apply to all businesses in each sector. Furthermore, the findings of this study reveal that the importance of cybersecurity requirements differ across multiple organizations. The choice and applicability of cybersecurity policies are determined by the type of information under control and the security requirements of organizations in relation to these policies.

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine learning model while locally preserving their private data. Recently, the FL in edge-aided unmanned aerial vehicle (UAV) networks has drawn an upsurge of research interest due to a bursting increase in heterogeneous data acquired by UAVs and the need to build the global model with privacy; however, a critical issue is how to deal with the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data while ensuring the convergence of learning. To effectively address this challenging issue, this paper proposes a novel and high-performing FL scheme, namely, the hierarchical FL algorithm, for the edge-aided UAV network, which exploits the edge servers located in base stations as intermediate aggregators with employing commonly shared data. Experiment results demonstrate that the proposed hierarchical FL algorithm outperforms several baseline FL algorithms and exhibits better convergence behavior.