Real Examples Research Articles

GANs for Image Security Applications: A Literature Review

Generative Adversarial Networks (GANs) have earned significant attention in various domains due to their generative model’s compelling ability to generate realistic examples probably drawn from sample distribution. Image security indicates the process of protecting digital images from unauthorized access, modification, or distribution. This requires a guarantee of image privacy, integrity, and authenticity to prohibit them from being exploited by malicious attacks. GANs can also be utilized for improving image security by exploiting its generation ability in encryption, steganography, and privacy-preserving tech-niques. This paper reviews GANs-based image security techniques providing a systematic overview of current literature and comparing the role of GANs in image encryption, image steganography, and priva-cy preserving from multiple dimensions. Additionally, it outlines future research directions to further explore the potential of GANs in addressing privacy and image security concerns

Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference.

Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.

The Impact of Blockchain on Critical Transport Infrastructure – The Case of Aviation

Abstract Critical Infrastructures produce critical goods and services for society and cover a wide range of sectors according to the governance frameworks in place in the United States, in the European Union (EU) and in EU Member States including Romania. Aviation is a particular case for the critical transport infrastructure, with a high degree of technical complexity, economic productivity and complex supply and production chains. Blockchain or Distributed Ledger is an already famous digital emerging technology with the possibility of disintermediating numerous system processes, thereby reducing costs, increasing security and reducing risk in a world where cumbersome and expensive intermediaries are required for trust in all sorts of transactions. This article provides a brief overview of the critical air transport infrastructure and of the blockchain technology, traces the possible uses of blockchain in aviation, gives real world examples and concludes with recommendations for practitioners moving forward.

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.

In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly used penalties such as the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP), the classical Newton-Raphson algorithm cannot be used. In this article, we propose a cubic Hermite interpolation penalty (CHIP) with a smoothing thresholding operator. Theoretically, we establish the nonasymptotic estimation error bounds for the global minimizer of the CHIP penalized high-dimensional linear regression. Moreover, we show that the estimated support coincides with the target support with a high probability. We derive the Karush-Kuhn-Tucker (KKT) condition for the CHIP penalized estimator and then develop a support detection-based Newton-Raphson (SDNR) algorithm to solve it. Simulation studies demonstrate that the proposed method performs well in a wide range of finite sample situations. We also illustrate the application of our method with a real data example.

Boosting co-creation of Nature‐based Solutions within Living Labs: Interrelating enablers using Interpretive Structural Modelling

Co-creation is at the intersection of Nature-based Solutions and Living Lab concepts, which adopt collaboration and innovation amongst different stakeholders to address complex issues such as climate change. Nature-based Solutions have been increasingly recognized for their potential to address diverse societal challenges in a multifunctional, action-oriented, and contextualized manner. Living Labs have proliferated as a participatory approach for the co-development and co-testing of Nature-based Solutions, particularly in Europe. Nevertheless, there is a persistent challenge regarding enhancing stakeholder engagement, which is recognized as crucial for the implementation of Nature-based Solutions. In this article, we examined ten empirical cases of Living Labs applying Nature-based Solutions to increase climate resilience in European coastal cities. We conducted an Interpretive Structural Modelling and Cross-impact Matrix Multiplication Applied to Classification analysis methodology to systematically analyse and interrelate co-creation enablers. We have identified fifteen enablers to co-create Nature-based Solutions in Living Labs across five levels of influence among each other. A key finding is that ‘sharing and learning from real-life examples’ and ‘opening opportunities for informal inputs’ have the most driving power among the studied enablers. Having a robust engagement toolbox also appears to be a crucial point in enhancing co-creation. Exploring the potential of such enablers seems vital to fostering the co-creation of Nature-based Solutions within Living Labs. By understanding how enablers are interlinked and can be prioritized based on their driving and dependence powers, environmental managers can better use their resources and engage with their key stakeholders more successfully.

Homogeneity Test of Ratios of Two Proportions in Stratified Bilateral and Unilateral Data

In paired-organ studies such as ophthalmology, otolaryngology, and rheumatology, etc., various approaches take highly correlated bilateral data into account for homogeneity tests but are less likely to focus on combined bilateral and unilateral data structures. Also, it is necessary and important to adjust the effect of confounders on stratified combined bilateral and unilateral data since, in these data structures, ignoring intra-class correlation and confounding effects can cause biased statistical inference. This article derived three homogeneity tests (the likelihood ratio test, the Wald test, and the score test) concerning these cooperative structure data to detect if ratios of proportions retain consistency across strata. Simulation shows that the score test provides a robust Type I error rate and satisfactory power performance. Finally, a real example is applied to demonstrate the application of these three proposed tests.

The Effect Of Adaptive Learning Experience (Adlx) On Mathematics Learning Outcomes Of High School Students In Grade Xi Based On Learning Motivation

This study aims to analyze the effect of the use of ADLX (Adaptive Learning Experience) on the mathematics learning outcomes of grade XI high school students by considering learning motivation. Using a quasi-experimental method with a non-equivalent control group design, this study involved several 11th grade high school students in X City who were divided into two groups: an experimental group using ADLX and a control group using conventional methods. The research instruments include mathematics learning outcome tests and learning motivation questionnaires validated by experts. The data were analyzed using the variance analysis technique (ANOVA) to test the hypothesis. The results showed significant differences in mathematics learning outcomes between the experimental and control groups, with the experimental group using ADLX showing better results. The data analysis also revealed that learning motivation interacted significantly with ADLX use, suggesting students with high learning motivation benefited more from ADLX use than students with low learning motivation. In general, students report that their learning motivation is influenced by the material's relevance to their daily lives and interactive and engaging teaching approaches. The use of ADLX, which involves hands-on experience and real-life examples, is considered effective in increasing understanding and motivation for learning math. This shows that the contextual learning approach is to improve students' understanding of mathematics. The conclusion of this study is that the use of ADLX can improve the mathematics learning outcomes of high school students in grade XI, especially for students with high learning motivation. These findings emphasize the importance of integrating adaptive technology in learning and the need for strategies to increase student learning motivation to achieve optimal learning outcomes.

An Exploratory Study of the Use of ICT for Control Measures in Organization Across Industries

In business management, a control function refers to the process of monitoring, measuring, and regulating various aspects of an organization's activities to ensure that they align with the established goals and objectives. This function is crucial for achieving efficiency, effectiveness, and accountability within an organization. Information and Communication Technology (ICT) has had a profound impact on the control functions of various industries. These control functions encompass a range of activities including monitoring, automation, decision-making, and overall management of industrial processes. For the sake the research paper and better understanding for the readers we have divided Control Function into 5 components that are – Quality Control, Feedback, Finance, Supply Chain,Market and Competitor Analysis and Customer Relationship Management The paper will attempt to explain how each of the given components are impacted by ICT and provide a Real-life industry example for each.

Random forests regression for soft interval data

Analyzing soft interval data for uncertainty quantification has attracted much attention recently. Within this context, regression methods for interval data have been extensively studied. As most existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and the development of nonlinear regression tools for interval data is crucial. This paper proposes an interval-valued random forests model that defines the splitting criterion of variance reduction based on an L 2 type metric in the space of compact intervals. The model simultaneously considers the centers and ranges of the interval data as well as their possible interactions. Unlike most linear models that require additional constraints to ensure mathematical coherences, the proposed random forests model estimates the regression function in a nonparametric way, and so the predicted interval length is naturally nonnegative without any constraints. Simulation studies show that the new method outperforms typical existing regression methods for various linear, semi-linear, and nonlinear data archetypes and under different error measures. To demonstrate the applicability, a real data example is presented where the price range data of the Dow Jones Industrial Average index and its component stocks are analyzed.

A Robust High-Dimensional Test for Two-Sample Comparisons

The Hotelling T2 statistic is used to compare the mean vectors of two independent multivariate Gaussian distributions. Nevertheless, this statistic is highly sensitive to outliers and is not suitable for high-dimensional datasets where the number of variables exceeds the sample size. This study introduces a robust permutation test based on the minimum regularized covariance determinant (MRCD) estimator to address these limitations of the two-sample Hotelling T2 statistic. Simulation studies were performed to evaluate the proposed test’s empirical size, power, and robustness. Additionally, the test was applied to both uncontaminated and contaminated Alzheimer’s Disease datasets. The findings from the simulations and real data examples provide clues that the proposed test can be effectively used with high-dimensional data without being impacted by outliers. Finally, an R function within the “MVTests” package was developed to implement the proposed test statistic on real-world data.

Application of artificial intelligence in the field of engineering management

The primary topic of this work is the utilization of artificial intelligence (AI) in the domain of engineering management. This paper provides an overview of the application history, development trend, and research goal and significance of artificial intelligence in the field of engineering management. This paper, through a method of literature review, thoroughly examines the theoretical foundation of artificial intelligence in engineering management and showcases its practical implementation in intelligent decision support and intelligent monitoring using real-life examples, and the effectiveness of these applications is assessed qualitatively. Furthermore, this paper examines the challenges faced by artificial intelligence in engineering management applications, including data collecting and model optimization, and suggests appropriate risk response tactics. The research concludes by summarizing the primary effort, achievements, and breakthroughs, and provides a prospective outlook on the future development of artificial intelligence in the field of engineering management.

Experimental Verification and Further Reflection on the Peak-End Rule

People make choices all the time, and sometimes the results of choices are unexpected, and the existence of the peak-end rule can explain the counterintuition in our decision evaluation. In fact, changes in "peak" and "end" experiences affect the choices people make all the time and greatly affect the evaluation of decisions. This article summarizes the characteristics of the Peak-End Rule and provides a large number of real-life examples to explain it. Based on this, three experiments were designed to verify the correctness of the Peak-End Rule from different perspectives. Among these experiments, Experiment 2 was the most important, and all participants participated in two stages of the experiment. In the first stage, they ran 100 meters with full effort, and in the second stage, they slowed down and ran 50 meters after running 100 meters with full effort. It was recorded that all participants chose to participate in the second stage of exercise, and more people chose to complete the second stage. This conclusion is contrary to common sense because the second stage covered an additional distance of 50 meters. But this result precisely confirms the conclusion of the Peak-End Theorem, because the end of the second stage run is easier, and more participants choose to run in the second stage. Meanwhile, through in-depth analysis of the data from Experiment 3, the author believes that the impact of the "endpoint" on decision-making is greater than that of the "peak".

Comparative Study of Seafloor Topography Prediction from Gravity–Geologic Method and Analytical Algorithm

Seafloor topography prediction can fill in sea areas without ship sounding data. However, the dependence of various topographic prediction algorithms on ship soundings varies significantly. Hence, this study explores the impact of the number and distributions of ship soundings on topographic prediction using the gravity–geologic method (GGM) and an analytical algorithm. Firstly, this study investigates the influence of ship sounding coverage on the two algorithms. The simulation results demonstrate that increasing coverage from 5.40% to 31.80%, coupled with more uniform distributions across the study area, substantially reduces the RMS error of the GGM. Specifically, the RMS error decreases from 238.68 m to 42.90 m, an improvement of 82.03%. The analytical algorithm maintains a consistent RMS error of 40.39 m because it does not depend on ship soundings. Furthermore, we select a 1° × 1° sea area (134.8°–135.8°E, 30.0°–31.0°N), and the ship soundings are divided into two control groups, Part I and Part II, with coverages of 8.19% and 33.19%, respectively. When Part II is used for calculation, the RMS error of the GGM decreases from 204.17 m to 126.95 m compared to when Part I is used, while the analytical algorithm exhibits an RMS error of 167.94 m. The findings indicate that the prediction accuracy of the GGM is significantly affected by ship soundings, whereas the analytical algorithm is more stable and independent of ship soundings. Based on simulation experiments and realistic examples, when the effective ship soundings coverage exceeds 30%, the GGM may have more advantages. Conversely, the analytical algorithm may be better. This suggests that effectively combining and utilizing different algorithms based on the ship sounding coverage can improve the accuracy of topographic prediction. This will provide a basis for integrating multiple algorithms to construct a global seafloor topography model.

A New Perspective for Scientific Modelling: Sparse Reconstruction Based Approach for Learning Time-Space Fractional Differential Equations

Abstract This paper studies a sparse reconstruction based approach to learn time-space fractional differential equations, i.e. to identify parameter values and particularly the order of the fractional derivatives. The approach uses a generalized Taylor series expansion to generate, in every iteration, a feature matrix which is used to learn the fractional orders of both, temporal and spatial derivatives by minimizing the LASSO operator using Differential Evolution algorithm. To verify the robustness of the method, numerical results for time-space fractional diffusion equation, wave equation, and Burgers? equation at different noise levels in the data are presented. Finally, the methodology is applied to a realistic example where underlying fractional differential equation associated to published experimental data obtained from an in-vitro cell culture assay is learned.

Deep Learning for Cybersecurity Classification: Utilizing Depth-Wise CNN and Attention Mechanism on VM-Obfuscated Data

With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks (CNNs) combined with a spatial attention mechanism to tackle VM-protected cybersecurity datasets. To address the scarcity of obfuscated malware samples, the dataset was generated using VMProtect to ensure the models were trained on real examples of modern obfuscated malware. The effectiveness of our approach is demonstrated through extensive experiments on both regular malware and obfuscated malware, where our model achieved accuracies of nearly 100% and 93.55% in classifying the regular malware and the obfuscated malware, respectively.

Bacterial Adhesion to Natural and Synthetic Fibre-Forming Polymers: Influence of Material Properties.

Polymer-based textiles have a major impact on human well-being, as they provide the desired functional protection and aesthetic comfort when worn. However, natural and synthetic polymer-based textiles can also pose serious health risks, as they are surfaces that allow the adhesion of various bacteria, including pathogenic bacteria. To minimise these problems, antibacterial chemical treatments are generally applicable in the case of polymer-based textiles. However, to avoid the use of potentially toxic chemicals, sustainable approaches require the customised design of non-adhesive polymer-based textiles, considering their chemical, physicochemical, constructional, and textural properties. Before designing, several articles are required to gain sufficient knowledge of the described object. Despite the urgent need to combat bacteria (on polymer-based textiles), which pose a serious global health risk, only a few review articles have been published that address bacterial adhesion in the context of superhydrophobic and antibacterial textile materials, while only one review article holistically addresses the textile factors and their influence on this phenomenon. The aim of this review article is to expand the insufficient knowledge about bacterial adhesion to polymer-based textiles on the basis of theoretical findings and real examples through a high degree of structuring, simplification, holistic consideration, and visualization. Therefore, this review provides an insight into the mechanisms involved in bacterial adhesion and a comprehensive overview of the influence of different textile factors, such as chemical composition, hydrophilicity/hydrophobicity, surface charge, surface free energy, roughness, and porosity, on bacterial adhesion. To emphasise the importance of the synergistic effect of the combined textile factors, examples of the influence of hydrophilicity/hydrophobicity in combination with surface charge, surface roughness, and porosity are discussed. From the review, it can be concluded that the combination of hydrophilicity/hydrophobicity and the surface charge of textile fibres and bacteria is crucial for bacterial adhesion, with roughness and porosity being the most important factors among the constructive and textural properties of polymer-based textiles.

Boosting the HP filter for trending time series with long-range dependence

This article extends recent asymptotic theory developed for the Hodrick Prescott (HP) filter and boosted HP (bHP) filter to long-range dependent time series that have fractional Brownian motion (fBM) limit processes after suitable standardization. Under general conditions, it is shown that the asymptotic form of the HP filter is a smooth curve, analogous to the finding in Phillips and Jin for integrated time series and series with deterministic drifts. Boosting the filter using the iterative procedure suggested in Phillips and Shi leads under well-defined rate conditions to a consistent estimate of the fBM limit process or the fBM limit process with an accompanying deterministic drift when that is present. A stopping criterion is used to automate the boosting algorithm, giving a data-determined method for practical implementation. The theory is illustrated in simulations and two real data examples that highlight the differences between simple HP filtering and the use of boosting. The analysis is assisted by employing a uniformly and almost surely convergent trigonometric series representation of fBM.

Copula-based analysis of dependent current status data with semiparametric linear transformation model.

This paper discusses regression analysis of current status data with dependent censoring, a problem that often occurs in many areas such as cross-sectional studies, epidemiological investigations and tumorigenicity experiments. Copula model-based methods are commonly employed to tackle this issue. However, these methods often face challenges in terms of model and parameter identification. The primary aim of this paper is to propose a copula-based analysis for dependent current status data, where the association parameter is left unspecified. Our method is based on a general class of semiparametric linear transformation models and parametric copulas. We demonstrate that the proposed semiparametric model is identifiable under certain regularity conditions from the distribution of the observed data. For inference, we develop a sieve maximum likelihood estimation method, using Bernstein polynomials to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established. Finally, to demonstrate the effectiveness and practical applicability of our method, we conduct an extensive simulation study and apply the proposed method to a real data example.

Estimation of change-point in the covariate effects on restricted mean survival time

The restricted mean survival time (RMST) emerges as a crucial summary metric in survival analysis. In some cases, certain covariates may have a varying impact on RMST at one specific change-point. For instance, consider studies focusing on women’s health, where a notable shift in a patient’s lifespan might occur when the age at menopause surpasses a particular cutoff value. To tackle this complex phenomenon, we consider a regression analysis of RMST with change-point in the covariate effects. This involves building a generalized linear model that directly captures the relationship between RMST and associated covariates that may have a change-point. Using the smoothed estimating equation with pseudo-observations, we obtain parameter estimators and establish the large sample properties. Finally, we assess the finite sample performance of the proposed method through simulation studies and apply it to real data examples from primary biliary cirrhosis and rectal cancer studies.

Testing the ratio of two Poisson means based on an inferential model

The ratio of two Poisson means is commonly used in biological, epidemiological, and medical. In this article, we consider the problem of testing the ratio of two Poisson means and propose a valid and efficient test based on the inference model (IM). The simulation studies show that the type I error and power of the IM-based test are competitive or even better than the six recommended tests: The likelihood ratio, mid-p, logarithmic transformation, and three tests based on the method of variance estimates recovery (MOVER). For R < 1 , the Rao-MOVER and fiducial-MOVER tests are slightly liberal, the mid-p test is somewhat conservative. For R > 1 , the Rao-MOVER and fiducial-MOVER tests are slightly conservative. The likelihood ratio and logarithmic transformation tests cannot control the type I error well. Therefore, the IM test could be recommended for practical applications. A real numerical example is presented to illustrate the flexibility of the proposed test.