Hasil untuk "Islam. Bahai Faith. Theosophy, etc."

Nadya Carissa Fernanda Putri, Nabila Kharimah Vedy

This study aims to examine the effects of service quality, customer perceived value, and trust on customer satisfaction, while also analyzing the meditating role of trust. Consumer shopping behavior has shifted alongside the growth of internet usage, leading to the rapid development of online food delivery (OFD) services. Online food delivery enables customers to conveniently order meals online and receive delivery directly to their address. In this highly competitive business environment, understanding the determinants of customer satisfaction is crucial. This study aims to examine service quality, customer perceived value, and trust in relation to customer satisfaction, both directly and to investigate the role of trust as a mediator. A quantitative research design was adopted and analyzed using PLS-SEM with the SmartPLS software. Data were collected through an online survey of 175 ShopeeFood users. The results reveal that service quality, customer perceived value, and trust significantly and positively influence customer satisfaction. Furthermore, trust is confirmed as a significant mediator in the relationship between service quality and customer perceived value toward customer satisfaction.

Mahmudul Islam, Killian Sheriff, Rodrigo Freitas

Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al., 2025) showed that the same dislocation motion both creates and destroys chemical short-range order (SRO), driving alloys into far-from-equilibrium SRO states. However, the connection between this dislocation-mediated SRO evolution and processing parameters remains largely unexplored. Here, we perform large-scale atomistic simulations of thermomechanical processing of equiatomic TiTaVW to determine how temperature and strain rate control SRO via competing creation ($Γ$) and annihilation ($λ$) rates. The simulations employ systems containing 2.4 million atoms and utilize a machine learning interatomic potential optimized to capture chemical complexity through the motif-based sampling technique. Using information-theoretic metrics, we quantify that the magnitude and chemical character of SRO vary systematically with processing parameters. We identify two regimes: a low-temperature regime with weak strain-rate sensitivity, and a high-temperature regime in which reduced dislocation density and increased screw character amplify chemical bias and accelerate SRO formation. The resulting steady-state SRO is far-from-equilibrium and cannot be produced by equilibrium thermal annealing. Together, these results provide a mechanistic and predictive link between processing parameters, dislocation physics, and SRO evolution in chemically complex alloys.

Mohammad Aghajani Asl, Behrooz Minaei Bidgoli

The advent of Large Language Models (LLMs) has revolutionized Natural Language Processing, yet their application in high-stakes, specialized domains like religious question answering is hindered by challenges like hallucination and unfaithfulness to authoritative sources. This issue is particularly critical for the Persian-speaking Muslim community, where accuracy and trustworthiness are paramount. Existing Retrieval-Augmented Generation (RAG) systems, relying on simplistic single-pass pipelines, fall short on complex, multi-hop queries requiring multi-step reasoning and evidence aggregation. To address this gap, we introduce FARSIQA, a novel, end-to-end system for Faithful Advanced Question Answering in the Persian Islamic domain. FARSIQA is built upon our innovative FAIR-RAG architecture: a Faithful, Adaptive, Iterative Refinement framework for RAG. FAIR-RAG employs a dynamic, self-correcting process: it adaptively decomposes complex queries, assesses evidence sufficiency, and enters an iterative loop to generate sub-queries, progressively filling information gaps. Operating on a curated knowledge base of over one million authoritative Islamic documents, FARSIQA demonstrates superior performance. Rigorous evaluation on the challenging IslamicPCQA benchmark shows state-of-the-art performance: the system achieves a remarkable 97.0% in Negative Rejection - a 40-point improvement over baselines - and a high Answer Correctness score of 74.3%. Our work establishes a new standard for Persian Islamic QA and validates that our iterative, adaptive architecture is crucial for building faithful, reliable AI systems in sensitive domains.

Tourjana Islam Supti, Israa Abuelezz, Aya Muhanad et al.

This study investigates the efficacy of role-taking and literacy-based interventions in reducing the influence of appearance cues, such as gender, age, ethnicity, and clothing style, on trust and risk-taking in social engineering contexts. A-4 (Group: Control, Literacy, Persuader, Persuadee) * 2 (Time: Pre, Post) mixed factorial design was implemented over two weeks with 139 participants. The control group received no material. The literacy group attended two sessions focused on how behavior can be similar regardless of appearance cues. The persuader group completed three sessions, learning how to use such cues to influence others. The persuadee group attended three sessions involving the selection, justification, and reflection on personas and scenarios. Scenarios centered on financial and rental advice. A one-week gap followed before post-intervention testing. In both pre- and post-tests, participants assessed personas combining appearance cues, offering mobile hotspots with potential risk. They rated trust and willingness to take the risk. Validated measures and scenarios were used, including word-of-mouth and issue involvement scales. It was expected that cue influence would diminish post-intervention. However, no significant within- or between-group differences emerged. Findings raise concerns about the effectiveness of debiasing efforts and call for reconsideration of approaches using literacy, role-taking, rehearsal, drama, and simulation.

Hessa Alawwad

This paper aims to address a gap in major Islamic topics by developing an ontology for the Book of Purification in Islam. Many authoritative Islamic texts begin with the Book of Purification, as it is essential for performing prayer (the second pillar of Islam after Shahadah, the profession of faith) and other religious duties such as Umrah and Hajj. The ontology development strategy followed six key steps: (1) domain identification, (2) knowledge acquisition, (3) conceptualization, (4) classification, (5) integration and implementation, and (6) ontology generation. This paper includes examples of the constructed tables and classifications. The focus is on the design and analysis phases, as technical implementation is beyond the scope of this study. However, an initial implementation is provided to illustrate the steps of the proposed strategy. The developed ontology ensures reusability by formally defining and encoding the key concepts, attributes, and relationships related to the Book of Purification. This structured representation is intended to support knowledge sharing and reuse.

Md. Adnanul Islam, Md. Faiyaz Abdullah Sayeedi, Md. Asaduzzaman Shuvo et al.

Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and reasoning for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language reasoning texts linked to each image. The YOLOv9s model achieves the highest precision of 0.92926 and mAP@50 of 0.92891 for object detection, while YOLOv11n-Seg reaches a segmentation precision of 0.91587 and mAP@50 of 0.79795. For reasoning generation, we tested a range of large vision-language models (LVLMs) in both zero-shot and few-shot settings. Our fine-tuned Mosquito-LLaMA3-8B model achieved the best results, with a final loss of 0.0028, a BLEU score of 54.7, BERTScore of 0.91, and ROUGE-L of 0.85. This dataset and model framework emphasize the theme "Prevention is Better than Cure", showcasing how AI-based detection can proactively address mosquito-borne disease risks. The dataset and implementation code are publicly available at GitHub: https://github.com/adnanul-islam-jisun/VisText-Mosquito

Khawar Islam, Naveed Akhtar

Generative diffusion models offer a natural choice for data augmentation when training complex vision models. However, ensuring reliability of their generative content as augmentation samples remains an open challenge. Despite a number of techniques utilizing generative images to strengthen model training, it remains unclear how to utilize the combination of natural and generative images as a rich supervisory signal for effective model induction. In this regard, we propose a text-to-image (T2I) data augmentation method, named DiffCoRe-Mix, that computes a set of generative counterparts for a training sample with an explicitly constrained diffusion model that leverages sample-based context and negative prompting for a reliable augmentation sample generation. To preserve key semantic axes, we also filter out undesired generative samples in our augmentation process. To that end, we propose a hard-cosine filtration in the embedding space of CLIP. Our approach systematically mixes the natural and generative images at pixel and patch levels. We extensively evaluate our technique on ImageNet-1K,Tiny ImageNet-200, CIFAR-100, Flowers102, CUB-Birds, Stanford Cars, and Caltech datasets, demonstrating a notable increase in performance across the board, achieving up to $\sim 3\%$ absolute gain for top-1 accuracy over the state-of-the-art methods, while showing comparable computational overhead. Our code is publicly available at https://github.com/khawar-islam/DiffCoRe-Mix

Yoga Adi Pratama, Muhammad Abdul Aris

This study aims to analyze the effects of capital intensity, audit quality, thin capitalization, and gender diversity on tax aggressiveness in state-owned enterprises (SOEs) listed on the Indonesia Stock Exchange (IDX) from 2020 to 2023. This research adopts a quantitative approach with an associative method, and the sample is selected using purposive sampling based on criteria such as SOEs listed on IDX, financial reports expressed in Indonesian Rupiah, and excluding the banking sector. The dependent variable is tax aggressiveness, measured using the Effective Tax Rate (ETR), while the independent variables are capital intensity, audit quality, thin capitalization, and gender diversity. Data analysis is performed using multiple linear regression and classical assumption tests to ensure the validity of the regression model. The findings indicate that capital intensity, audit quality, thin capitalization, and gender diversity significantly affect tax aggressiveness. This research has limitations, such as the restriction to SOEs and a three-year observation period. Future research is suggested to expand the scope by using the IDX-IC classification and extending the study period, as well as considering additional variables such as firm size and ownership structure.

Prima Ayu Rizqy Mahanani, Fatma Dian Pratiwi, Fartika Ifriqia et al.

This article tends to analyze how Muslim women's activists, who are the members of Nasyiatul Aisyiyah and Fatayat NU in Kediri and Yogyakarta, build their digital literacy to prevent violence in social media. Mainly due to the digital divide between men and women, which causes imbalance and injustice when they access digital media. The method used in data collection was semi-ethnographic, in which the researcher participated in observing research objects when carrying out activities using digital technology, interviews, and documentation on 3 members of Nasyiatul Aisyiyah and Fatayat NU, both in Jogja and Kediri. The research findings show that what has been stigmatized to women so far is that they are powerless to master information and communication technology, which does not apply to members of Fatayat NU and Nasyiatul Aisyiyah. This research shows that women are also reliable in accessing the internet for the benefit of empowering women, especially KBGO issues. This research has provided a different understanding of the phenomenon of the massive use of internet-based technology by female activists

Mohsinul Kabir, Mohammad Ridwan Kabir, Riasat Siam Islam

We evaluated contemporary Islamic lifestyle applications supporting religious practices and motivation among Muslims. We reviewed 11 popular applications using self-determination theory and the technology-as-experience framework to assess their support for motivation and affective needs. Most applications lack features that foster autonomy, competence, and relatedness. We also interviewed ten devoted Muslim application users to gain insights into their experiences and unmet needs. Our findings indicate that existing applications fall short in providing comprehensive learning, social connections, and scholar consultations. We propose design implications based on our results, including guided religious information, shareability, virtual community engagement, scholarly question-answering, and personalized reminders. We aim to inform the design of Islamic lifestyle applications that better facilitate ritual practices, benefitting application designers and Muslim communities. Our research provides valuable insights into the untapped potential for lifestyle applications to act as religious companions supporting Muslims' spiritual journey.

Tousif Islam, Tejaswi Venumadhav

Using publicly available numerical relativity (NR) simulations for non-spinning eccentric binary black hole (BBH) mergers, Ref \cite{Islam:2024rhm} demonstrated that the eccentricity-induced modulations in the amplitudes and frequencies of different spherical harmonic modes are mutually consistent and can be modeled using a single time series modulation. We extend the validity of the results to all non-precessing binaries by using 83 NR simulations from the SXS, RIT, and MAYA catalogs for aligned-spin eccentric BBH mergers with mass ratios ranging from $1:1$ to $1:4$. Based on these phenomenological relations, we provide a framework named gwNRXHME to compute multi-modal eccentric non-precessing waveforms using two inputs: quadrupolar eccentric waveforms, and the corresponding multi-modal quasi-circular non-precessing waveforms. Furthermore, we compute an overall degree of departure in SXS, RIT, and MAYA NR data from these relations and find that SXS NR simulations generally adhere to these relations more strictly than RIT and MAYA data. We also show that these relations can offer a cost-effective way to filter out noisy higher-order spherical harmonic modes extracted from NR data. Our framework is publicly available through the gwModels package.

Baginda Sitompul, Afriani Manalu, Grace Metaria Sihombing et al.

In the Bible there is a lot of information that writes down the teachings that Jesus did. The fruit of Jesus' teaching is proof that He is a teacher who has a personality, broad insight, role model, both from His words and deeds. The works of Jesus are inseparable from the culture of learning that has been carried out since he was young. This is one factor that makes Him appear as the Great Teacher. After finishing studying from school, Jesus taught with creative and effective methods for His followers. For this reason, in the context of Christian Religious Education (PAK) which is taught in formal schools, it is necessary to refer to Christian Religious Education at the time of Jesus, so that the principles of Christian Religious Education in schools can be specifically maintained from time to time. The researcher will explain how Christian Religious Education was when Jesus was a Jewish boy, starting with His education in the midst of the family, education at Beit Safar, education at Beit Talmud, education at the Beit Midrash stage, to the implementation of Christian Religious Education in schools today. The purpose of writing is to study Christian Religious Education at the time of Jesus in schools and the implementation of Christian Religious Education in formal schools. The method used in writing is a qualitative research method with literature as the main source.

Sumbul

The National Stock Exchange of India (NSE) has presented Nifty 50 Shariah and Nifty 500 Shariah indices to provide unconventional indices for Sharia-compliant companies. These indices follow Sharia laws and can be used in portfolios that are culturally dependable commodities for investors who do not wish to put their money into the undesired business. NSE witnessed big movements in the indices during the Covid-19 period. This study seeks to understand the association between Nifty 500 Sharia and Nifty 50 Sharia and their respective selected indexes, Nifty 500 and Nifty 50, during the Covid-19 pandemic. The period from 27/01/2020 to 31/05/2022 has been taken for this study. The techniques applied, like correlation, co-integration, GMM, etc. based on the objectives of this paper. We conclude that the return of Sharia indices is better compared to the other indices. Also, stocks compliant with Sharia Indices are less risky and a better alternative for the portfolio during pandemic times.

Tousif Islam

We investigate the $α$-$β$ mapping, as previously introduced by Islam et al.~\cite{Islam:2022laz}, which relates numerical relativity (NR) and adiabatic point-particle black hole perturbation theory (BHPT) waveforms in the comparable mass regime for quasi-circular, non-spinning binary black holes. This mapping involves scaling the amplitude of individual modes with different values of $α$ and the time (and therefore the phase) with a single parameter, $β$. In this paper, we demonstrate that this scaling, both in terms of time and orbital frequencies, also extends to the overall gravitational-wave energy flux. This means that we can find a single $α_{\mathcal{F}}$ that scales the BHPT flux and a single $β_{\mathcal{F}}$ (which matches the value of $β$) that scales the BHPT time such a way that it aligns with NR flux evolution. We then explore the connection between the scaling parameter $α_{\mathcal{F}}$ ($β_{\mathcal{F}}$) and the missing finite size correction for the secondary black hole within the BHPT framework.

Tousif Islam, Gaurav Khanna

We investigate the interplay between numerical relativity (NR) and point-particle black hole perturbation theory (ppBHPT) in the comparable mass ratio regime. In the ppBHPT framework, the secondary black hole is treated as a point particle, neglecting its finite size. Our study focuses on addressing the missing finite size effect in the ppBHPT framework and proposing a method for incorporating the size of the secondary into the perturbation theory framework. We demonstrate that by considering the secondary as a finite size object, the BHPT waveforms closely match NR waveforms. Additionally, we revisit the $α$-$β$ scaling technique, which was previously introduced by Islam et al, as a means to effectively match ppBHPT waveforms to NR waveforms. We further analyze the scaling procedure and decompose it into different components, attributing them to various effects, including the corrections arising from the finite size of the secondary black hole.

Tousif Islam, Gaurav Khanna

We investigate the interplay between numerical relativity (NR) and adiabatic point-particle black hole perturbation theory (ppBHPT) in the comparable mass regime for quasi-circular non-spinning binary black holes. Specifically, we reassess the $α$-$β$ scaling technique, previously introduced by Islam et al, as a means to effectively match ppBHPT waveforms to NR waveforms within this regime. In particular, $α$ rescales the amplitude and $β$ rescales the time (and hence the phase). Utilizing publicly available long NR data (\texttt{SXS:BBH:2265}~\cite{sxs_collaboration_2019}) for a mass ratio of $1:3$, encompassing the final $\sim 65$ orbital cycles of the binary evolution, we examine the range of applicability of such scalings. We observe that the scaling technique remains effective even during the earlier stages of the inspiral. Additionally, we provide commentary on the temporal evolution of the $α$ and $β$ parameters and discuss whether they can be approximated as constant values. Consequently, we derive the $α$-$β$ scaling as a function of orbital frequencies and demonstrate that it is equivalent to a frequency-dependent correction. We further provide a brief comparison between post-Newtonian (PN) waveforms and the rescaled ppBHPT waveform at a mass ratio of $q=3$ and comment on their regime of validity. Finally, we explore the possibility of using PN theory to obtain the $α$-$β$ calibration parameters and still provide a rescaled ppBHPT waveform that matches NR.

Shabaz Patel, Hassan Kane, Rayhan Patel

Large Language Models (LLMs) have demonstrated remarkable performance across numerous natural language understanding use cases. However, this impressive performance comes with inherent limitations, such as the tendency to perpetuate stereotypical biases or fabricate non-existent facts. In the context of Islam and its representation, accurate and factual representation of its beliefs and teachings rooted in the Quran and Sunnah is key. This work focuses on the challenge of building domain-specific LLMs faithful to the Islamic worldview and proposes ways to build and evaluate such systems. Firstly, we define this open-ended goal as a technical problem and propose various solutions. Subsequently, we critically examine known challenges inherent to each approach and highlight evaluation methodologies that can be used to assess such systems. This work highlights the need for high-quality datasets, evaluations, and interdisciplinary work blending machine learning with Islamic scholarship.

Jesse Islam, Maxime Turgeon, Robert Sladek et al.

In the context of survival analysis, data-driven neural network-based methods have been developed to model complex covariate effects. While these methods may provide better predictive performance than regression-based approaches, not all can model time-varying interactions and complex baseline hazards. To address this, we propose Case-Base Neural Networks (CBNNs) as a new approach that combines the case-base sampling framework with flexible neural network architectures. Using a novel sampling scheme and data augmentation to naturally account for censoring, we construct a feed-forward neural network that includes time as an input. CBNNs predict the probability of an event occurring at a given moment to estimate the full hazard function. We compare the performance of CBNNs to regression and neural network-based survival methods in a simulation and three case studies using two time-dependent metrics. First, we examine performance on a simulation involving a complex baseline hazard and time-varying interactions to assess all methods, with CBNN outperforming competitors. Then, we apply all methods to three real data applications, with CBNNs outperforming the competing models in two studies and showing similar performance in the third. Our results highlight the benefit of combining case-base sampling with deep learning to provide a simple and flexible framework for data-driven modeling of single event survival outcomes that estimates time-varying effects and a complex baseline hazard by design. An R package is available at https://github.com/Jesse-Islam/cbnn.

Taimeng Fu, Shaoshu Su, Yiren Lu et al.

Simultaneous Localization and Mapping (SLAM) stands as one of the critical challenges in robot navigation. A SLAM system often consists of a front-end component for motion estimation and a back-end system for eliminating estimation drifts. Recent advancements suggest that data-driven methods are highly effective for front-end tasks, while geometry-based methods continue to be essential in the back-end processes. However, such a decoupled paradigm between the data-driven front-end and geometry-based back-end can lead to sub-optimal performance, consequently reducing the system's capabilities and generalization potential. To solve this problem, we proposed a novel self-supervised imperative learning framework, named imperative SLAM (iSLAM), which fosters reciprocal correction between the front-end and back-end, thus enhancing performance without necessitating any external supervision. Specifically, we formulate the SLAM problem as a bilevel optimization so that the front-end and back-end are bidirectionally connected. As a result, the front-end model can learn global geometric knowledge obtained through pose graph optimization by back-propagating the residuals from the back-end component. We showcase the effectiveness of this new framework through an application of stereo-inertial SLAM. The experiments show that the iSLAM training strategy achieves an accuracy improvement of 22% on average over a baseline model. To the best of our knowledge, iSLAM is the first SLAM system showing that the front-end and back-end components can mutually correct each other in a self-supervised manner.

Ariani Barroroh Baried, A. Hanief Saha Ghafur, Mulawarman Hannase

The lack of records about the involvement and contribution of women in Sufism texts cannot be used as an excuse that women have a small role and position in the development and dissemination of Sufism teachings, doctrines and practices. So far, the well-known female Sufis, namely Rabi'ah al-Adawiyah (717-801) and Aishah al-Ba'uniyyah (c. 1456-1517), are two big names who prove that women have equal opportunities in spiritual attainment. Many researchers discuss female Sufis but focus on the eastern world and parts of India, only a few researchers have written about female Sufis in the West, therefore the authors are interested in discussing female Sufis in the West. This research is a library research. The author uses a literature review with the data sources used in this paper are secondary data originating from the literature such as books, journals, articles, and various sources that are relevant to the theme of the discussion in this paper.. The results of the study describe female Sufisin the contemporary era such as Hajjah Amina Adil and Hajjah Naziha Adil with their organizations engaged in philanthropy. Then another female Sufi is Nahid Angha who is known as one of the founders of the International Sufi Woman Organization, a world Sufi women's organization. This organization is concerned with peace programs and women's empowerment.The next female Sufi, namely Eva de Vitray in the path of Sufism, Eva de Vitray-Meyerovitch or Hawwa Hanim, took allegiance to the murshid of the Qadiriyah order from Morocco, Sheikh Hamzah al-Qadiri al-Boutchichi. Eva is also connected with Sheikh Khaled Bentounes, a murshid of the Syadziliyah-Alawiyahtarekat who lives in France