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Given how smart today’s malware authors have become through employing highly sophisticated techniques, it is only logical that methods be developed to combat the most potent threats, particularly where the malware is stealthy and makes indicators of compromise (IOC) difficult to detect. After the analysis is completed, the output can be employed to detect and then counteract the attack. The goal of this work is to propose a machine learning approach to improve malware detection by combining the strengths of both supervised and unsupervised machine learning techniques. This study is essential as malware has certainly become ubiquitous as cyber-criminals use it to attack systems in cyberspace. Malware analysis is required to reveal hidden IOC, to comprehend the attacker’s goal and the severity of the damage and to find vulnerabilities within the system.

This research proposes a hybrid approach for dynamic and static malware analysis that combines unsupervised and supervised machine learning algorithms and goes on to show how Malware exploiting steganography can be exposed.

The tactics used by malware developers to circumvent detection are becoming more advanced with steganography becoming a popular technique applied in obfuscation to evade mechanisms for detection. Malware analysis continues to call for continuous improvement of existing techniques. State-of-the-art approaches applying machine learning have become increasingly popular with highly promising results.

Cyber security researchers globally are grappling with devising innovative strategies to identify and defend against the threat of extremely sophisticated malware attacks on key infrastructure containing sensitive data. The process of detecting the presence of malware requires expertise in malware analysis. Applying intelligent methods to this process can aid practitioners in identifying malware’s behaviour and features. This is especially expedient where the malware is stealthy, hiding IOC.

Instantaneous unloading with equal force is usually used to simulate the sudden failure of cables. This simulation method with equivalent force requires obtaining the magnitude and direction of the force for the failed cable in the normal state. It is difficult, however, to determine the magnitude or direction of the equivalent force when the shape of the cable is complex (space curve). This model of equivalent force may be difficult to establish. Thus, a numerical simulation method, the instantaneous temperature rise method, was proposed to address the dynamic response caused by failures of the cables with complex structural form.

This method can instantly reduce the cable force to zero through the instantaneous temperature rise process of the cable. Combined with theoretical formula and finite element model, the numerical calculation principle and two key parameters (temperature rise value and temperature rise time) of this method were detailed. The validity of this approach was verified by comparing it with equivalent force models. Two cable-net case with saddle curved surfaces were presented. Their static failure behaviors were compared with the dynamic failure behaviors calculated by this method.

This simulation method can effectively address the structural dynamic response caused by cable failure and may be applied to all cable structures.

An instantaneous temperature rise method (ITRM) is proposed and verified. Its calculation theory is detailed. Two key parameters, temperature rise value and temperature rise time, of this method are discussed and the corresponding reference values are recommended.

The calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.

On the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.

The results applied to the case show that the CO₂ emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO₂ emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO₂ reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.

This research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

This study clarifies the operational performance of fashion companies during the coronavirus pandemic. Meanwhile, improvement strategies have been provided in the post-pandemic era.

The static and dynamic perspectives were combined to comprehensively analyze the operational performance of fashion companies before, during and after the COVID-19 outbreak. A comparative analysis among five representative countries was conducted to achieve global conclusions. Additionally, data envelopment analysis (DEA) theory and various DEA models were employed for the analysis.

The fashion industry has not achieved overall effectiveness. American companies have the best operational performance, followed by European and Chinese companies. In contrast, the impact of the pandemic on American companies was severe, whereas Chinese and European companies showed operational resilience. In addition, the pandemic had a devastating influence on the global fashion industry. This resulted in a decline in total factor productivity, and the main reason was technological regress. Furthermore, labor redundancy is a critical issue for the fashion industry in the post-pandemic era, even if it shows a decrease because of the pandemic.

The existing theory on the fashion industry during the pandemic was improved by expanding the time and geographical dimensions and integrating the advantages of various DEA models. Scientific improvement strategies were presented in the post-pandemic era with application value.

This paper aims to propose an interpretive framework to understand how machine learning (ML) affects the way companies interact with their ecosystem and how the introduction of digital technologies affects the value co-creation (VCC) process.

This study bases on configuration theory, which entails two main methodological phases. In the first phase the authors define the theoretically-derived interpretive framework through a literature review. In the second phase the authors adopt a case study methodology to inductively analyze the theoretically-derived domains and their relationships within a configuration.

ML enables multi-directional knowledge flows among value co-creators and expands the scope of VCC beyond the boundaries of the firm-client relationship. However, it determines a substantive imbalance in knowledge management power among the actors involved in VCC. ML positively impacts value co-creators’ performance but also requires significant organizational changes. To benefit from VCC via ML, value co-creators must be aligned in terms of digital maturity.

The paper answers the call for more theoretical and empirical research on the impact of the introduction of Industry 4.0 technology in companies and their ecosystem. It intends to improve the understanding of how ML technology affects the determinants and the process of VCC by providing both a static and dynamic analysis of the topic.

This paper aims to focus on the spatial docking task of unmanned vehicles under ground conditions. The docking task of military unmanned vehicle application scenarios has strict requirements. Therefore, how to design a docking robot mechanism to achieve accurate docking between vehicles has become a challenge.

In this paper, first, the docking mechanism system is described, and the inverse kinematics model of the docking robot based on Stewart is established. Second, the genetic algorithm-based optimization method for multiobjective parameters of parallel mechanisms including workspace volume and mechanism flexibility is proposed to solve the problem of multiparameter optimization of parallel mechanism and realize the docking of unmanned vehicle space flexibility. The optimization results verify that the structural parameters meet the design requirements. Besides, the static and dynamic finite element analysis are carried out to verify the structural strength and dynamic performance of the docking robot according to the stiffness, strength, dead load and dynamic performance of the docking robot. Finally, taking the docking robot as the experimental platform, experiments are carried out under different working conditions, and the experimental results verify that the docking robot can achieve accurate docking tasks.

Experiments on the docking robot that the proposed design and optimization method has a good effect on structural strength and control accuracy. The experimental results verify that the docking robot mechanism can achieve accurate docking tasks, which is expected to provide technical guidance and reference for unmanned vehicles docking technology.

This research can provide technical guidance and reference for spatial docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

This study aims to investigate the stability performance of partial journal bearings of 120° and 180° partial angles with micropolar lubricant.

To investigate the stability characteristics of partial journal bearing, a MATLAB source code is written. To solve the Reynolds’ equation, the finite element method is used. Stability performances of 120° and 180° partial journal bearings are computed for a wide range of non-dimensional micropolar fluid parameters and working eccentricities.

The presented results provide design data for stability parameters in terms of equivalent stiffness, whirl frequency ratio, critical mass and threshold speed of the rotor with respect to eccentricities and material size of the lubricant. The stability of 180° partial journal bearing is found to be higher than 120° partial journal bearing.

In open literature, it is rare to find the stability of a partial journal bearing lubricated with micropolar fluid. Very few researchers have studied the combined effect of eccentricities and micropolar lubricant parameters on the dynamic performance of such bearings. Hence, it is important to study the dynamic stability to explore the complete investigation of the performance of partial journal bearings with micropolar fluid.

Existence of working poverty reduces the effectiveness of the strategy of “increasing employment to reduce poverty”. Developed countries are already concerned about it but insufficient attention has been made by developing countries. Focusing on developing countries this study identifies (1) the effects of trade openness (TO) on working poverty and (2) whether the working poverty trap exists or not in developing countries. Both objectives are also analyzed for three subsamples of low income, lower-middle income and upper-middle income developing countries.

Panel data for 98 developing countries over the period of 2000–2016 have been collected for the study. Fixed effect and GMM methods are applied for static and dynamic analysis, respectively.

The study finds that TO significantly reduces working poverty rate (WPR) (mainly driven up by upper-middle income developing countries). The positive association between WPR with its previous year's rate proves the existence of working poverty trap.

The study's outcome is subject to selected time, countries and methods. Future research should use more improve methods and should identify the channels through which TO could affect working poverty.

Middle income and upper-middle income developing countries should increase TO to reduce the working poverty. Low income developing countries that have the highest working poverty should search the way to derive beneficial effects of trade on working poverty.

Working poverty is not only a developed country issue rather it is a global phenomenon. Hence, it is expected that the study will raise the social consciousness about this phenomenon in developing countries too.

The study fulfills the gaps of identifying the effects of TO on working poverty and existence of in-work poverty trap in developing countries.

The dynamic response of the nuclear power plants (NPPs) with pile foundation reinforcement have not yet been systemically investigated in detail. Thus, there is an urgent need to improve evaluation methods for nonlithological foundation reinforcements, as this issue is bound to become an unavoidable task.

A nonlinear seismic wave input method is adopted to consider both a nonlinear viscoelastic artificial boundary and the nonlinear properties of the overburden layer soil. Subsequently, the effects of certain vital parameters on the structural response are analyzed.

A suitable range for the size of the overburden foundation is suggested. Then, when piles are used to reinforce the overburden foundation, the peak frequencies in the floor response spectra (FRS) in the horizontal direction becomes higher (38%). Finally, the Poisson ratio of the foundation soil has a significant influence on the FRS peak frequency in the vertical direction (reduce 35%–48%).

The quantifiable results are performed to demonstrate the seismic responses with respect to key design parameters, including foundational dimensions, the Poisson Ratio of the soil and the depth of the foundation. The results can help guide the development of seismic safety requirements for NPPs.

This study aims to explore the relationship between tourism and women’s employment in 32 South American and Caribbean countries from 1996 to 2020.

In this paper, both static (fixed effects and random effects) and dynamic panel data models (system and differenced generalized method of moments) are used. In addition to gross domestic product, trade, education and urban population are also considered in this study.

According to the findings, a boost in tourism led to an increase in women’s engagement in the economy and service sectors. This paper also explores the efficiency of alternate methods to deal with various models of women labor force (WLF) involvement in various sectors. Women’s employment opportunities in the service sector expand as a result of tourism, but in the agricultural and industrial sectors, that employment opportunity is reduced.

This study investigated the impact of tourism on WLF participation and found that it had a significant impact. This study, on the other hand, specifically contributed to the tourism sector in some specific study areas, such as tourism and agriculture, service and industry sectors. This study also displays that female participation in South America and the Caribbean countries is increasing and women are shifting away from traditional economic sectors.

This is the pioneering study to discover tourism and female participation in employment in South American and Caribbean countries. The findings of this study have important implications for future studies and policy debates examining the consequence of the tourism industry on WLF.