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Gathering, analyzing and securing electronic data from various digital devices for use in legal or investigative procedures is the key process of computer forensics. Information retrieved from servers, hard drives, cellphones, tablets and other devices is all included in this. This article tackles the challenging problem of how to prioritize different kinds of computer forensics and figure out which kind is most useful in cases of cybercrime, fraud, theft of intellectual property, harassment and espionage.

Therefore, we first introduce enhanced versions of Hamacher power aggregation operators (AOs) within the framework of bipolar complex fuzzy (BCF) sets. These include BCF Hamacher power averaging (BCFHPA), BCF Hamacher power-weighted averaging (BCFHPWA), BCF Hamacher power-ordered weighted averaging (BCFHPOWA), BCF Hamacher power geometric (BCFHPG), BCF Hamacher power-weighted geometric (BCFHPWG) and BCF Hamacher power-ordered-weighted geometric (BCFHPOWG) operators. Employing the devised AOs, we devise a technique of decision-making (DM) for dealing with DM dilemmas with the BCF set (BCFS).

We prioritize different types of computer forensic by taking artificial data in a numerical example and getting the finest computer forensic. Further, by this example, we reveal the applicability of the proposed theory. This work provides a more elaborate and versatile procedure for classifying computer forensics with dual aspects of criteria and extra fuzzy information. It allows for better and less biased DM in the more intricate digital investigations, which may lead to better DM and time-saving in real-life forensic scenarios. To demonstrate the significance and impression of the devised operators and techniques of DM, they are compared with existing ones.

This research is the first to combine Hamacher and power AOs in BCFS for computer forensics DM. It presents new operators and a DM approach that is not encountered in the existing literature and is specifically designed to deal with the challenges and risks associated with the classification of computer forensics. The framework’s capacity to accommodate bipolar criteria and extra fuzzy information is a major development in the field of digital forensics and decision science.

This paper aims to study the influence of different seal structure parameters and working conditions on the air-oil two-phase flow characteristics and leakage characteristics of the seal cavity in the bearing cavity of the aero-engine spindle bearing tester.

In this paper, the VOF method and RNG k-ε turbulence model are used to explore the flow characteristics and leakage characteristics of the labyrinth seal cavity of an aero-engine spindle bearing tester under the condition of air-oil two-phase flow.

The distribution of the lubricating oil is related to the sealing clearance and the air-oil ratio. The amount of oil leakage increases with increasing of sealing chamber clearance, air-oil ratio and inlet velocity and decreases with increasing curvature and speed. The amount of air leakage increases with sealing clearance and inlet velocity.

In comparison to the pure air-phase flow field, the air-oil two-phase flow field can more accurately simulate the lubricating oil flow in the sealing chamber.

This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models.

An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between crypto-price and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision.

The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models.

The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies’ prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction. The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.

To do so, we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets, which leads to the bipolar complex fuzzy WASPAS method. The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters. The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.

It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods. The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria, which contributes to more accurate feature selection for software defect prediction. We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology. We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.

This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction. The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process, which will be useful for software engineers and help them select the best feature selection techniques. This work also helps to enhance the overall performance of software defect prediction systems.

This paper aims to focus on the research progress of intelligent self-healing anti-corrosion coatings in the aviation field in the past few years. The paper provides certain literature review supports and development direction suggestions for future research on intelligent self-healing coatings in aviation.

This mini-review uses a systematic literature review process to provide a comprehensive and up-to-date review of intelligent self-healing anti-corrosion coatings that have been researched and applied in the field of aviation in recent years. In total, 64 articles published in journals in this field in the last few years were analysed in this paper.

The authors conclude that the incorporation of multiple external stimulus-response mechanisms makes the coatings smarter in addition to their original self-healing corrosion protection function. In the future, further research is still needed in the research and development of new coating materials, the synergistic release of multiple self-healing mechanisms, coating preparation technology and corrosion monitoring technology.

To the best of the authors’ knowledge, this is one of the few systematic literature reviews on intelligent self-healing anti-corrosion coatings in aviation. The authors provide a comprehensive overview of the topical issues of such coatings and present their views and opinions by discussing the opportunities and challenges that self-healing coatings will face in future development.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

As the realm of artificial intelligence (AI) expands, its societal implications soar. Today, society actively debates its rollout – yet such discourse often permeates news articles and social media unobjectively and without rigorous peer review. The purpose of this paper is to discuss the most pressing concerns associated with AI development and deployment.

This paper proposes a tripartite framework as a strategic guide and guardrail for AI’s evolution.

This robust framework addresses challenges, mitigates potential risks and harnesses AI opportunities for our shared future.

A policy-techno-legal framework for understanding key AI challenges and foundations for building AI guardrails.

This paper investigates the spillover effect of firms’ social media engagement with investors on consumption market performance and examines the impact of balanced/imbalanced social media stakeholder engagement strategies on firms’ consumption market performance.

The study employs multi-source secondary data covering 3,856 quarterly observations of 188 firms in the Chinese retail industry over six years (2015–2020). Polynomial regression analysis and response surface methodology are used to test the hypotheses.

The study reveals that firms’ social media engagement with investors has a positive spillover effect on consumption market performance. Additionally, the authors find that a balanced social media engagement strategy, which allocates resources evenly between consumers and investors, is more likely to optimize firm performance than an imbalanced strategy.

The research reveals cross-stakeholder spillover effects of social media engagement, introduces balanced/imbalanced engagement strategy concepts and extends the balanced marketing perspective to the social media context, providing guidance for firms to optimize their social media strategies.

Recent global catastrophic events, such as the Russia–Ukraine War and the COVID-19 pandemic, have led to several policy uncertainties in the global economy. Due to increasing financialization, these policy uncertainties have had a ripple effect on the global financial system, urging investors to search for alternative safe-haven financial instruments. To understand how these uncertainties influence Indian green financial markets, the present study seeks to explore the impact of US monetary policy uncertainty, energy policy uncertainty, oil price uncertainty and climate policy uncertainty on the Indian BSE Green and BSE Carbon indexes.

In order to achieve the stated objective, this study employs the autoregressive distributed lag model alongside the Bayer and Hanck cointegration tests and the Granger causality test, leveraging monthly data spanning from December 2012 to April 2024.

Empirical evidence states that there exists a strong cointegration between the explanatory and outcome variables, and US monetary policy uncertainty, energy policy uncertainty, oil price uncertainty and climate policy uncertainty exert a positive and significant influence on the Indian BSE Green and BSE Carbon index. Furthermore, the Granger causality test confirms a unidirectional relationship between US monetary policy uncertainty, energy policy uncertainty, oil price uncertainty and the Indian BSE Green and BSE Carbon indexes, as well as a bidirectional relationship between the Indian BSE Green and BSE Carbon indexes and climate policy uncertainty.

This study offer practical implications by suggesting that investors can use Indian green index as a hedge and safe haven against the aforementioned uncertainties. Investors should consider these dynamics while constructing an optimum portfolio to avoid losses caused by rising uncertainties.

The study unveils a unique relationship between green indices and various uncertainties, a topic not previously explored in the literature. It provides valuable policy recommendations aimed at elucidating the implications of green markets for sustainable development and the formulation of risk mitigation strategies.