Real Examples Research Articles

INTER-CIVILIZATIONAL ISSUES AND THE FACTORS AFFECTING INTERFAITH RELATIONSHIPS: A STUDY OF MUHAMMAD HAMIDULLAH’S CONTRIBUTIONS

Interesting issues related to cross civilizations matters for linking the past with future. It becomes more significant when East meets the West. Contrast in civilizations and one’s exposure to this develops a different personality and Dr. Muhammad Hamidullah is one of them who is known for such intellectual contributions that has made an impact on the inter and intra civilisational issues. This paper explores the factors influencing interfaith relationships with a specific focus on the contributions of Dr. Muhammad Hamidullah. In an era of globalisation, multicultural and multi-religious societies are increasingly common. Dr. Hamidullah, having spent a significant portion of his life interacting with followers of various religions, provides valuable insights into fostering inter-civilisational harmony. His scholarly work and personal interactions highlight how different faiths can coexist peacefully within the same society. This study investigates the factors that enhance or hinder interfaith relationships and discusses the societal consequences of interfaith harmony. The research is framed around Dr. Hamidullah’s experiences and contributions, particularly during his time in France, where he engaged with people from diverse religious and cultural backgrounds. By examining his approach to interfaith dialogue and understanding, the paper aims to provide guidance on improving interfaith relations in contemporary society. This study employs a qualitative and narrative research methodology. It focuses on identifying and analysing the factors affecting interfaith harmony by interpreting the teachings and actions of Dr. Muhammad Hamidullah. The narrative approach is used to present real-life examples from his experiences, highlighting the challenges and opportunities in interfaith dialogues. The research is grounded in a detailed analysis of Dr. Hamidullah’s scholarly contributions and personal interactions with people of different faiths, emphasising practical solutions to improve interfaith relationships in today’s globalised world.

Analysis of Albert Bandura's Social Cognitive Theory and Its Development in Islamic Religious Education

This research aims to explain the concept of Albert Bandura's theory and its development in Islamic religious education. The research is motivated by many other learning theories that have not focused on the origin of behavior, especially those that come from outside a person. On the other hand, Albert Bandura, with the concept of self-efficacy and imitation, has discussed this in detail in his social cognitive theory. The method used is qualitative with the type of literature study or library research. The results of the study stated that Albert Bandura's theory is famous for its social learning theory that emphasizes cognition and evaluation. It includes modeling (live and symbolic) and self-efficacy, as well as research on the aggressive behavior of children who imitate aggression from adult models. In “Aggression: A Social Learning Analysis,” Bandura explains that aggression is learned through observation and reinforcement, as shown in the Bobo Doll experiment. Bandura's social cognitive theory emphasizes the function of self-regulation, learning in the social environment, visual and verbal learning outcomes, and the importance of imitation and role models, which requires educators, parents, and community leaders to be good examples. In Islamic Religious Education (PAI), Bandura's theory can be applied through the principles of modeling, self-efficacy, and mentoring. The Prophet is the ultimate role model, and the family should be the real example for children. PAI should teach the principles and resilience of faith to avoid the negative influence of the digital era with the guidance of parents and teachers so that the content absorbed by students is positive.

Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding: Non‐Gaussian and Time‐to‐Event Outcomes

ABSTRACTIn epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time‐to‐event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model‐based sensitivity analysis method for non‐Gaussian and time‐to‐event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples.

Assessing the role of artificial intelligence in enhancing customer personalization: A study of ethical and privacy implications in digital marketing

Technological evolution, especially in artificial intelligence (AI), has significantly changed digital marketing, specifically customer-targeted experience. Machine learning, recommendation systems, and predictive analytics have made it possible for enterprises to personalize content, products, and services, among other things, on a very large scale. Nevertheless, all these advances have come with large ethical and privacy concerns that are yet to be met. This paper discusses how AI can enhance customers' personalization to analyze the moral and privacy considerations involved. The research examines how machine learning marketing strategies work in the digital environment and real-life examples of organizations that successfully implement these approaches and potential issues. It highlights how individuals might buy more products and make businesses more profitable with customized advertising techniques. However, the paper also describes the moral problems of AI that use personal data without permission, artificially intelligent algorithms that affect decision-making, and artificial intelligence decision-making. At the same time, its work has yet to be well known. The paper looks into privacy risks like data breach incidents, privacy violations, GDPR, and CCPA. This paper adopts an exploratory case study research design as it gathers academic papers, industry examples, and expert opinions to present a balanced view of the effects of AI. Based on the research, conclusions can be drawn that great opportunities AI opens for personalization should be met with safety considerations and working ethical guidelines. In the end, the paper presents guidelines that should help businesses integrate trust and compliance into creating and using AI technologies.

PENGGUNAAN STORYTELLING UNTUK MEMBANTU SISWA MTS HUSAINIYAH CICALENGKA MENGENAL PROFESI TEKNOLOGI INFORMASI

Mts Husainiyah is an educational institution located in Panenjoan Cicalengka, Bandung Regency with a total of 152 female students. In facing this digital era, teenagers who have just reached an early age will be faced with changes field in the rapidly evolving fin the field of information technology that is rapidly evolving. The purpose of this community service project as an initial introduction to professions related to the field of information technology, so it is hoped that it will be useful in this digital era. The method used to provide an introduction to any profession related to the field of information technology is the Storytelling Method, which is a method of telling stories and providing real examples based on the experiences of volunteers who work in the field of information technology. Students will write their goals on a goal tree sheet. Based on the recapitulation results of the dream tree, it turns out that 25.27% of the students chose professions related to the field of information technology as many as 25.27% of the 91 students who were given an introduction. The initial target for students who choose a profession in the field of information technology is 20%, so this exceeds the target and the Storytelling Method is effective for use in this community service activity.

Causal Inference and AI: Elevating Decision-Making in Uncertain Environments

Artificial intelligence (AI) has changed the way decisions are made in industries; however, its impact may be limited in situations with uncertainty where cause and effect relationships play a crucial role. While conventional AI models are good at spotting connections between variables they may struggle when it comes to predicting results in fields such as healthcare finance and self-driving vehicles where understanding causation is key. Causal inference an emerging area in AI goes beyond finding correlations and enables systems to make stronger and dependable decisions, in intricate settings. By incorporating reasoning into AI systems operations can enhance their predictive abilities by comprehending the fundamental mechanisms governing cause and effect connections This piece delves into the amalgamation of causal deduction in AI by examining its theoretical groundwork as well as practical implementations and the obstacles and advantages of merging causal understanding with machine learning. Through real life examples and new methodologies such as networks and counterfactual logic establishment this paper underscores how causal inference can enhance AIs decision making skills by rendering them more flexible and efficient, in ambiguous scenarios. Keywords: Artificial Intelligence, Causal Inference, Machine Learning, Decision-Making, Bayesian Networks, Counterfactual Reasoning, Uncertainty, High-Stakes Environments, Predictive Models, Causality.

Partially factorized variational inference for high-dimensional mixed models

Abstract While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We show that this is indeed the case for mixed models, proving that standard mean-field variational inference dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on mixed models with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent algorithm that is used to find it. Our proposed methodology is implemented in the R package vglmer. Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of our approach compared to various alternatives.

Inference of record failure times from unit generalized Rayleigh distribution

ABSTRACT In this paper, problems of estimation and prediction for record values are discussed for a two-parameter unit generalized Rayleigh distribution. Maximum likelihood estimators of model parameters along with their existence and uniqueness are established, and corresponding approximate confidence intervals are constructed based on asymptotic theory. In addition, alternative generalized inferential approach is provided for comparison based on the proposed pivotal quantities. Further, prediction intervals of remaining useful life are also presented under classical and generalized inferential methods when the unit generalized Rayleigh record values are available. Extensive numerical studies and two real-life examples are conducted to show the performance of the proposed methods. The results indicate that both classical and generalized estimation methods work satisfactorily and the proposed generalized inferential approach performs superior to traditional estimates in general.

Semi-supervised estimation of a single-index varying-coefficient model

We introduce a single-index varying-coefficient model for the Framingham heart disease data and propose a semi-supervised estimation method that effectively utilizes unlabeled data. The method outperforms traditional approaches in accuracy, as validated by simulations and real examples.

Exploring the Critical Success Factors Influencing the Outcome of Digital Transformation Initiatives in Government Organizations

This study investigates the previous studies on successful digital transformation initiatives in government organizations and deduces the tangible and intangible benefits to showcase some real-life examples and evidence. This article provides a thorough evaluation of the available literature on successful digital transformation initiatives. It analyzes 53 important success elements grouped across seven dimensions, giving a conceptual framework for executing digital transformation in government organizations. The research identifies key success elements that are crucial for digital transformation, emphasizing the importance of clear planning, flexibility, agility, and robust data security measures. This study provides practical insights for organizations aiming to undertake digital transformation initiatives, highlighting strategies to overcome hurdles and maximize benefits. This study contributes a proposed conceptual framework and empirical evidence to guide academics, professionals, and decision-makers in effectively navigating and leveraging digital transformation in a rapidly evolving digital landscape.

Deep Regression Learning with Optimal Loss Function

In this article, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficiency. Second, different from the traditional maximum likelihood estimation (MLE), the proposed method avoids the specification of the distribution, making it adaptable to various distributions such as heavy tails and multimodal or heterogeneous distributions. Third, the proposed loss function relies on probabilities rather than direct observations as in least square loss, contributing to the robustness of the proposed estimator. Finally, the proposed loss function involves a nonparametric regression function only. This enables the direct application of the existing packages, simplifying the computational and programming requirements. We establish the large sample property of the proposed estimator in terms of its excess risk and minimax near-optimal rate. The theoretical results demonstrate that the proposed estimator is equivalent to the true MLE where the density function is known in terms of excess risk. Our simulation studies show that the proposed estimator outperforms the existing methods based on prediction accuracy, efficiency and robustness. Particularly, it is comparable to the MLE with the known density and even gets slightly better as the sample size increases. This implies that the adaptive and data-driven loss function from the estimated density may offer an additional avenue for capturing valuable information. We further apply the proposed method to four real data examples, resulting in significantly reduced out-of-sample prediction errors compared to existing methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Autoinhibition in (Bio)Chemistry: Identification and Mechanistic Classification.

Autoinhibition is a frequently invoked self-regulatory mechanism involved in various cellular processes to interpret clearly how these cells may control their complex functioning. This type of temporal behavior generally results in self-retardation or even in complete shuts down of the undesired reactions to occur meaning that the rate of a certain biochemical reaction is partially or completely retarded. Precise characterization and classification of a complex system where deceleration of the reaction rate is found, however, requires special circumspection to avoid false interpretation. Hereby, it was clearly demonstrated that the retardation effect of an inhibitor is unexpectedly often misidentified as autoinhibition, especially in complex biochemical enzymatic systems. It prompted us to clarify unambiguously the difference between inhibition and autoinhibition. The latter kinetic phenomenon is a special type of inhibition where the inhibitor forms by the result of a chemical or biochemical event exerting the self-decelerating effect on the rate of its own formation resulting thus in significantly different temporal patterns compared to the ones observed in the case of simple inhibitions. Kinetic activity of autoinhibitor towards the species involved in the given system allowed us to classify direct, indirect and dual autoinhibitions to be supported by real chemical examples.

Multivariate robust linear models for multivariate longitudinal data

Linear models commonly used in longitudinal data analysis often assume a multivariate normal distribution. This assumption, however, can lead to biased mean parameter estimates in the presence of outliers. To address this, alternative linear models based on multivariate t distributions have been developed. In this paper, we review the commonly used multivariate distributions applicable to multivariate longitudinal data and introduce multivariate Laplace linear models (MLLMs) that are designed to handle outliers effectively. These models incorporate a scale matrix that is autoregressive, heteroscedastic, and positive definite, using modified Cholesky and hypersphere decompositions. We conduct simulation studies and apply these models to a real data example, comparing the performance of MLLMs with multivariate normal linear models (MNLMs) and multivariate t linear models (MTLMs), and providing insights on when each model is most appropriate.

Analyzing chaotic systems with multi-step methods: Theory and simulations

Identifying and analyzing fixed points plays a crucial role in chaos theory for grasping the system behavior and advancing the understanding of its fundamental mechanics. This study explores new chaotic nonlinear integro-differential systems with four variables, employing Caputo–Fabrizio and Atangana–Baleanu derivatives. We confirm the presence and reliability of solutions and offer a real-life example. Additionally, we implement the suggested multi-step techniques on different nonlinear chaotic systems to demonstrate their accuracy.

International armed conflicts and their impact on aviation safety

Aviation transport is an integral part of the global infrastructure that ensures fast and efficient movement of people and cargo around the world. However, international armed conflicts create serious challenges for the aviation industry, affecting the safety of flights, the economic stability of airlines, as well as the international aviation community as a whole. This article is devoted to the analysis of the impact of armed conflicts on air traffic, taking into account real examples from different regions of the world. One key aspect of the research is examining the effects of airspace closures over conflict zones, which force airlines to change routes, increasing fuel and operating costs. In addition, the issue of flight security in conflict zones, where commercial aircraft are at increased risk of accidental or deliberate attacks, is analyzed. An important aspect of the article is also the assessment of economic losses suffered by airlines and airports due to conflicts. In particular, cases of flight cancellations, a decrease in passenger traffic, as well as an increase in insurance premiums, which significantly affect the financial performance of companies, are considered. The article also touches on issues of international cooperation in the field of aviation security, which is critically important for minimizing risks and ensuring the stability of air traffic in armed conflicts. In particular, the role of international organizations such as ICAO and the effectiveness of existing international agreements and treaties aimed at coordinating actions between states during crisis situations are analyzed. The article also focuses on developing practical recommendations for airlines and governments to improve their conflict preparedness, including route planning, risk management and improved information sharing systems. Given the complexity and multifaceted nature of the issue, the authors emphasize the need for further scientific research in this area in order to develop new strategies and tools to ensure the safety and stability of the global aviation industry in the face of armed conflicts. Thus, the article makes a significant contribution to the understanding of the impact of international armed conflicts on air traffic and offers a comprehensive approach to solving this problem.

Unsupervised Ground Roll Attenuation via Implicit Neural Representations

Coherent noise attenuation presents a significant challenge compared to incoherent noise, particularly concerning ground roll in land seismic data. Traditional methods encounter limitations with the substantial overlap of ground roll and reflections, resulting in a compromise between signal distortion and residual noise. Recent deep learning strategies, while promising, predominantly rely on supervised learning, necessitating extensive paired training#xD;data, which is frequently scarce in real-world scenarios. Unsupervised approaches often struggle with convergence issues or excessive parameter tuning. We introduce an unsupervised deep learning framework for separating reflections from ground roll to address these hurdles. Leveraging the inherent low-frequency bias of implicit neural representations (INR), our method prioritizes extracting self-similarity features during training. For seismic data, the network learns self-similarity flattened events before those with deep dips and other incoherent noise. To enhance the self-similarity of reflections, we first apply normal moveout (NMO) correction to flatten reflections and then use the network to extract these flattened reflections from the NMO-corrected data. Moreover, to ensure convergence, we integrate a horizontal derivative regularization term into the loss function, penalizing horizontal variations. This regularization prevents the extraction of unflattened events through extended training, ensuring convergence with high fidelity of flattened reflections. It additionally streamlines parameter tuning, yielding stable outputs and obviating the need for early stopping. We validate our method using synthetic and real land data examples, comparing it with traditional f-k filters with real data and demonstrating its superiority in noise attenuation and signal preservation.

Monitoring bivariate autocorrelated process using a deep learning-based control chart: A case study on the car manufacturing industry

Due to the high frequency of sensor data collection in modern industries, consecutive observations are potentially autocorrelated. Failing to address this issue adequately when designing control charts can lead to numerous false alarms, thereby compromising the efficiency of the monitoring technique. On the other hand, thanks to the evolution of measurement equipment, nowadays more than one quality characteristics are often measured simultaneously. The Hotelling T2 chart is the most used approach to detect abnormal (or OOC) conditions of processes with multiple quality characteristics. The OOC condition could be due to factors such as shifts in process mean, variance–covariance matrix, outliers, or trends. Conventional statistical control charts have weaknesses in the detection of complex OOC scenarios, while the occurrence of such scenarios is particularly prevalent in today’s modern industries. This paper presents a novel approach for developing control charts using machine learning techniques, specifically LSTM, to monitor bivariate autocorrelated processes. Utilizing a deep memory information structure and novel input features can efficiently capture the complex interdependencies and temporal patterns in autocorrelated processes, leading to suboptimal performance detecting process deviations. The proposed methodology is evaluated using Monte Carlo simulations, demonstrating improved performance compared to traditional control charts. A new practical usage is discovered for monitoring wheel alignment in a car manufacturing assembly using the ‘x-wheel’ device data, specifically designed to examine essential wheel angles. This real-life example demonstrates the feasibility of implementing the proposed method on production lines to oversee a correlated process with two variables.

Mixture mean residual life model for competing risks data with mismeasured covariates

This paper proposes a mixture regression model for competing risks data, where the logistic regression model is specified for the marginal probabilities of the failure types and the mean residual lifetime (MRL) model is assumed for the failure time given the failure of interest. The estimating equations (EEs) are derived to infer the logistic regression and MRL model separately. We further consider the situation where the covariates are subject to measurement error. The presence of measurement error imposes extra challenges for the analysis of complex time-to-event data. By using the above EEs as the correction-amenable original estimating functions, we propose a corrected score estimation, which does not require specifying the distributions for unobserved error-prone covariates. The proposed estimators are shown to be consistent and asymptotically normally distributed. The performance of the method is investigated by intensive simulation studies and two real examples are presented to illustrate the proposed methods.

Lowering the carbon footprint of beer through waste breadcrumb substitution for malted barley: Life cycle assessment and experimental study

Beer is the most produced and consumed alcoholic beverage in the world, but the agricultural production of its most common ingredient, i.e. malted barley, is a significant contributor to the overall environmental footprint of beer. In addition, food wastage, particularly bread with millions of slices wasted daily, poses a waste management challenge across the globe. This study aims to address both issues through brewing beer with waste bread that would have otherwise ended up in landfill by replacing a portion of malted barley with waste bread. A sourdough pale ale was brewed at various bread percentages to understand how the inclusion of bread changed the sugar profile and fermentability of the beer. The samples were mashed at two different temperatures, 65 °C and 70 °C, to assess the impacts of mashing. It was found that the volume of alcohol produced declined with increasing bread amounts, but brewing with up to 60 wt% bread produced the same volume of alcohol as a standard beer. A life cycle assessment was performed to quantify the change in cradle to grave environmental impact for brewing beers with varying bread percentages with the view to conduct more targeted feasibility studies in the future with waste bread substitution. Significant reductions in emissions were observed as regards global warming potential, terrestrial ecotoxicity, acidification, eutrophication, ozone depletion, and abiotic depletion of fossil fuels. In particular, the global warming potential for the real-life example microbrewery studied in this work was decreased by 7.13% of the total carbon dioxide equivalent annually, demonstrating the environmental advantages of brewing beer with waste bread.

Space-time shape optimization of rotating electric machines

This paper is devoted to the shape optimization of the internal structure of an electric motor, and more precisely of the arrangement of air and ferromagnetic material inside the rotor part with the aim to increase the torque of the machine. The governing physical problem is the time-dependent, nonlinear magneto-quasi-static version of Maxwell’s equations. This multiphase problem can be reformulated on a 2D section of the real cylindrical 3D configuration; however, due to the rotation of the machine, the geometry of the various material phases at play (the ferromagnetic material, the permanent magnets, air, etc.) undergoes a prescribed motion over the considered time period. This original setting raises a number of issues. From the theoretical viewpoint, we prove the well-posedness of this unusual nonlinear evolution problem featuring a moving geometry. We then calculate the shape derivative of a performance criterion depending on the shape of the ferromagnetic phase via the corresponding magneto-quasi-static potential. Our numerical framework to address this problem is based on a shape gradient algorithm. The nonlinear time periodic evolution problems for the magneto-quasi-static potential is solved in the time domain, with a Newton–Raphson method. The discretization features a space-time finite element method, applied on a precise, meshed representation of the space-time region of interest, which encloses a body-fitted representation of the various material phases of the motor at all the considered stages of the time period. After appraising the efficiency of our numerical framework on an academic problem, we present a quite realistic example of optimal design of the ferromagnetic phase of the rotor of an electric machine.