Hasil untuk "Islam"

Wasikul Islam, Sergei Chekanov, Nicholas Luongo

This work presents advancements in model-agnostic searches for new physics at the Large Hadron Collider (LHC) through the application of event-based anomaly detection techniques utilizing unsupervised machine learning. We discuss the advantages of the anomaly detection approach, as demonstrated in a recent ATLAS analysis, and introduce ADFilter, a web-based tool designed to process collision events using autoencoders based on deep unsupervised neural networks. ADFilter calculates loss distributions for input events, aiding in determining the degree to which events can be considered anomalous. Real-life examples are provided to demonstrate how the tool can be used to reinterpret existing LHC results, with the goal of significantly improving exclusion limits. Furthermore, we present a comparative study between anomaly detection and supervised machine learning techniques, using the search for heavy resonances decaying into two or more Higgs bosons as a representative case to demonstrate the application and effectiveness of these methods.

Vera Pavlova, Mohammed Makhlouf

Despite recent advancements in Multilingual Information Retrieval (MLIR), a significant gap remains between research and practical deployment. Many studies assess MLIR performance in isolated settings, limiting their applicability to real-world scenarios. In this work, we leverage the unique characteristics of the Quranic multilingual corpus to examine the optimal strategies to develop an ad-hoc IR system for the Islamic domain that is designed to satisfy users' information needs in multiple languages. We prepared eleven retrieval models employing four training approaches: monolingual, cross-lingual, translate-train-all, and a novel mixed method combining cross-lingual and monolingual techniques. Evaluation on an in-domain dataset demonstrates that the mixed approach achieves promising results across diverse retrieval scenarios. Furthermore, we provide a detailed analysis of how different training configurations affect the embedding space and their implications for multilingual retrieval effectiveness. Finally, we discuss deployment considerations, emphasizing the cost-efficiency of deploying a single versatile, lightweight model for real-world MLIR applications.

Ashhadul Islam, Samir Brahim Belhaouari, Amine Bermak

The exponential growth of large language models (LLMs) like ChatGPT has revolutionized artificial intelligence, offering unprecedented capabilities in natural language processing. However, the extensive computational resources required for training these models have significant environmental implications, including high carbon emissions, energy consumption, and water usage. This research presents a novel approach to LLM pruning, focusing on the systematic evaluation of individual weight importance throughout the training process. By monitoring parameter evolution over time, we propose a method that effectively reduces model size without compromising performance. Extensive experiments with both a scaled-down LLM and a large multimodal model reveal that moderate pruning enhances efficiency and reduces loss, while excessive pruning drastically deteriorates model performance. These findings highlight the critical need for optimized AI models to ensure sustainable development, balancing technological advancement with environmental responsibility.

S M Asiful Islam Saky, Md Rashidul Islam, Md Saiful Arefin et al.

Respiratory diseases remain major global health challenges, and traditional auscultation is often limited by subjectivity, environmental noise, and inter-clinician variability. This study presents an explainable multimodal deep learning framework for automatic lung-disease detection using respiratory audio signals. The proposed system integrates two complementary representations: a spectral-temporal encoder based on a CNN-BiLSTM Attention architecture, and a handcrafted acoustic-feature encoder capturing physiologically meaningful descriptors such as MFCCs, spectral centroid, spectral bandwidth, and zero-crossing rate. These branches are combined through late-stage fusion to leverage both data-driven learning and domain-informed acoustic cues. The model is trained and evaluated on the Asthma Detection Dataset Version 2 using rigorous preprocessing, including resampling, normalization, noise filtering, data augmentation, and patient-level stratified partitioning. The study achieved strong generalization with 91.21% accuracy, 0.899 macro F1-score, and 0.9866 macro ROC-AUC, outperforming all ablated variants. An ablation study confirms the importance of temporal modeling, attention mechanisms, and multimodal fusion. The framework incorporates Grad-CAM, Integrated Gradients, and SHAP, generating interpretable spectral, temporal, and feature-level explanations aligned with known acoustic biomarkers to build clinical transparency. The findings demonstrate the framework's potential for telemedicine, point-of-care diagnostics, and real-world respiratory screening.

Rahmat Rahmat, Muhammad Hammam Fadlurahamn, Andi muhammad Afdhal

Situationally and conditionally plural reality in Indonesia is not a new thing, but has long grown and developed and cultured in the archipelago. Something like this should have been exceeded by all of us. However, today, extremist movements are still entrenched which if left unchecked, will have fatal consequences for the nation's sovereignty. This research will discuss the concept of Gus Muwafiq's interpretation with the idea of his understanding in caring for the archipelago, through the approach of historicity and culturality of the archipelago typical of Indonesia. This research is a research with primary sources of Youtube videos and supported by related books and articles. The data in this study was collected by qualitative methods and using a phenomenological approach where the object of the phenomenon was Gus Muwafiq as an interpreter, in the context of answering the challenges of plurality in Indonesia. The results of this study show that Indonesia holds a big concept that comes from Islam itself, namely the concept of raiyyah (people) which is intended to be mutually responsible in building and caring for the nation. This idea is in line with the idea of inclusive theology and views that plurality is a reality that must be accepted absolutely.

Md Abu Shahid Chowdhury, Muhammad Muinul Islam, Mamun Jamal

The most prominent inorganic antibiotic alternatives are metallic or metal oxide nanoparticles. Green-synthesized nickel oxide nanoparticles (NiONPs), known for their anti-inflammatory, antibacterial, and potential anticancer properties, hold significant promise for novel biomedical applications. In this work, the extract of Allium cepa stalks was used as a precursor to synthesize NiONPs from nickel nitrate using the co-precipitation method. The antibacterial activity of NiONPs was examined after confirming the formation of NiONPs by investigating different physicochemical properties using UV–Vis spectroscopy, SEM, XRD, EDX, and FTIR. An absorption peak at 318.467 nm in the UV–Vis spectrum confirmed the formation of NiONPs. A highly crystalline face-centered cubic lattice, having an average size of 18.96 ± 1.6072 nm, was determined by XRD. SEM analysis revealed the surface morphology of the synthesized NiONPs, which was spherical in shape. A prominent peak at 509.21 cm−1 in the FTIR spectrum confirmed NiO formation, while the same analysis also identified functional groups responsible for stabilizing and capping the NiONPs. The antibacterial activity of NiONPs was quantitatively assessed against a gram-positive (Streptococcus aureus) bacterial strain and observed mortality rates of bacteria were 53.18 % after 20 h, whereas it reached 85.43 % after 24 h. This green synthesis approach using Allium cepa stalks successfully produced NiONPs exhibiting strong antibacterial activity against Gram-positive bacteria.

Johirul Islam, Ijaz Ahmad, Shakthi Gimhana et al.

Private 5G networks provide enhanced security, a wide range of optimized services through network slicing, reduced latency, and support for many IoT devices in a specific area, all under the owner's full control. Higher security and privacy to protect sensitive data is the most significant advantage of private networks, in e.g., smart hospitals. For long-term sustainability and cost-effectiveness of private 5G networks, analyzing and understanding the energy consumption variation holds a greater significance in reaching toward green private network architecture for 6G. This paper addresses this research gap by providing energy profiling of network components using an experimental laboratory setup that mimics real private 5G networks under various network conditions, which is a missing aspect in the existing literature.

Rishad Islam, Arielle Carr, Colin Jacobs

Generally, discretization of partial differential equations (PDEs) creates a sequence of linear systems $A_k x_k = b_k, k = 0, 1, 2, ..., N$ with well-known and structured sparsity patterns. Preconditioners are often necessary to achieve fast convergence When solving these linear systems using iterative solvers. We can use preconditioner updates for closely related systems instead of computing a preconditioner for each system from scratch. One such preconditioner update is the sparse approximate map (SAM), which is based on the sparse approximate inverse preconditioner using a least squares approximation. A SAM then acts as a map from one matrix in the sequence to another nearby one for which we have an effective preconditioner. To efficiently compute an effective SAM update (i.e., one that facilitates fast convergence of the iterative solver), we seek to compute an optimal sparsity pattern. In this paper, we examine several sparsity patterns for computing the SAM update to characterize optimal or near-optimal sparsity patterns for linear systems arising from discretized PDEs.

Tunazzina Islam, Dan Goldwasser

Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups. This study presents a post-hoc analysis of microtargeting practices within climate campaigns by leveraging large language models (LLMs) to examine Meta (previously known as Facebook) advertisements. Our analysis focuses on two key aspects: demographic targeting and fairness. We evaluate the ability of LLMs to accurately predict the intended demographic targets, such as gender and age group. Furthermore, we instruct the LLMs to generate explanations for their classifications, providing transparent reasoning behind each decision. These explanations reveal the specific thematic elements used to engage different demographic segments, highlighting distinct strategies tailored to various audiences. Our findings show that young adults are primarily targeted through messages emphasizing activism and environmental consciousness, while women are engaged through themes related to caregiving roles and social advocacy. Additionally, we conduct a comprehensive fairness analysis to uncover biases in model predictions. We assess disparities in accuracy and error rates across demographic groups using established fairness metrics such as Demographic Parity, Equal Opportunity, and Predictive Equality. Our findings indicate that while LLMs perform well overall, certain biases exist, particularly in the classification of male audiences. The analysis of thematic explanations uncovers recurring patterns in messaging strategies tailored to various demographic groups, while the fairness analysis underscores the need for more inclusive targeting methods. This study provides a valuable framework for future research aimed at enhancing transparency, accountability, and inclusivity in social media-driven climate campaigns.

Saiful Islam, Md. Nahid Hasan

Cancer is a highly heterogeneous disease with significant variability in molecular features and clinical outcomes, making diagnosis and treatment challenging. In recent years, high-throughput omic technologies have facilitated the discovery of mechanisms underlying various cancer subtypes by providing diverse omics data, such as gene expression, DNA methylation, and miRNA expression. However, the complexity and heterogeneity of multi-omics data present significant challenges for their integration in exploring cancer subtypes. Various methods have been proposed to address these challenges. In this paper, we propose a novel and straightforward approach for identifying cancer subtypes by integrating patient-specific subnetworks features from different omics data. We construct patient-specific induced subnetwork using a random walk with restart algorithm from patient similarity networks (PSNs) and compute nine structural properties that capture essential network topology. These features are integrated across the three omic datasets to form comprehensive patient profiles. K-means clustering is then applied for cancer subtype identification. We evaluate our approach on five cancer datasets, including breast invasive carcinoma, colon adenocarcinoma, glioblastoma multiforme, kidney renal clear cell carcinoma, and lung squamous cell carcinoma, for three different omic data types. The evaluation shows that our method produces promising and effective results, demonstrating competitive or superior performance compared to existing methods and underscoring its potential for advancing personalized cancer diagnosis and treatment.

Ali Imran Sinaga, Mohd Nizam Sahad, Mohammad Amir Wan Harun

Often in commentaries on the Aceh Code, researchers relate it to the status of existing applicable laws and regulations as well as the principles of Human Rights (HAM). As a result, the Aceh Code has been criticized as not being in line with the law; it is even seen as radical, extreme, and contrary to human rights values. However, it is a law built based on Islamic principles. In order to counter this perception of the Aceh Code, this study analyzes the relationship between the Aceh Code and the practice of takzir punishment by 'Umar bin al-Khattab. The researcher used a qualitative method with a documentary research design to collect data from primary documents, namely the Acehnese Qanun and various legal jurisprudence reference books that contain the practice of 'Umar's takzir. Next, an analysis was made to compare the Aceh Code with the practice of takzir punishment by 'Umar bin al-Khattab. The results of the document analysis found that there are similarities between the Aceh Code and 'Umar's takzir practice in the execution of punishments, including flogging, imprisonment, and fines. The Acehnese Qanun even implements these punishments by adapting them to the conditions, culture, and local traditions of the Acehnese community. In conclusion, this study highlights the Aceh Code’s strong foundation in Islamic jurisprudence by drawing parallels between its punishments and the takzir practices of 'Umar bin al-Khattab. Contrary to common criticisms, the Aceh Code is not inherently radical or extreme but reflects a careful adaptation of Islamic legal principles to the cultural and societal context of Aceh. By demonstrating the Code's alignment with both traditional Islamic law and local practices, this research provides a deeper understanding of how Shariah-based laws can be integrated within contemporary legal frameworks while maintaining respect for cultural diversity.   Abstrak   Seringkali dalam ulasan mengenai Qanun Aceh, para penyelidik mengaitkannya dengan status undang-undang dan peraturan terpakai sedia ada serta prinsip-prinsip Hak Asasi Manusia (HAM). Akibatnya, Qanun Aceh telah dikritik sebagai tidak selaras dengan undang-undang tersebut, bahkan ia dilihat sebagai radikal, ekstrim, dan bertentangan dengan nilai-nilai HAM, sedangkan ia adalah undang-undang yang dibina berdasarkan prinsip Islam. Bagi menangkis persepsi ini terhadap Qanun Aceh, kajian ini menganalisis hubungan Qanun Aceh dengan amalan hukuman takzir ‘Umar bin al-Khattab. Pengkaji menggunakan metode kualitatif dengan reka bentuk kajian dokumentar untuk mengumpulkan data daripada dokumen primer, iaitu Qanun Aceh dan pelbagai buku rujukan fiqh undang-undang yang mengandungi amalan takzir ‘Umar. Seterusnya analisis dibuat bagi membandingkan Qanun Aceh dengan praktik hukuman takzir ‘Umar bin al-Khattab. Hasil analisis dokumen mendapati bahawa wujud persamaan antara Qanun Aceh dengan praktik takzir Umar dalam pelaksanaan hukuman merangkumi hukuman sebatan, penjara dan denda. Bahkan Qanun Aceh melaksanakan hukuman-hukuman ini dengan turut menyesuaikannya dengan kondisi, budaya, dan tradisi setempat masyarakat Aceh. Sebagai kesimpulan, kajian ini menekankan asas kukuh Qanun Aceh dalam jurisprudens Islam dengan menunjukkan persamaan antara hukuman-hukumannya dan amalan takzir yang telah dilaksanakan oleh 'Umar bin al-Khattab. Qanun Aceh bukanlah radikal atau ekstrem namun mencerminkan adaptasi dan penyesuaian prinsip undang-undang Islam kepada konteks budaya dan masyarakat Aceh. Kajian ini memberikan pemahaman yang lebih mendalam tentang bagaimana undang-undang berasaskan Syariah dapat diintegrasikan dalam rangka perundangan moden sambil kekal menghormati kepelbagaian budaya.

Sk Injamamul Islam, Mohamed H. Hamad, Wanarit Jitsamai et al.

Clinostomum species, a parasitic pathogen of freshwater fish, is widely distributed and infects various host species. Recently, the pathological effect due to Clinostomum metacercarial infection was described in aquaculture in Thailand; however, the global genetic diversity and population structure of this species have not been studied yet. Therefore, this study aimed to provide a detailed description of genetic diversity and population dynamics of the digenean Clinostomum isolated from Trichopodus pectoralis with globally recorded Clinostomum species. The species was characterized molecularly by analyzing 18S rDNA and inter-transcribed spacer biomarker genes (ITS1 and ITS2). A BLAST search discovered that the 18S rDNA and ITS sequence had a 100% sequence similarity with Clinostomum piscidium isolated from India and Thailand. A comprehensive analysis revealed the presence of 12 distinct haplotypes among the Clinostomum populations. This study suggests that distinct patterns of genetic variation were identified by analyzing molecular variance, pairwise Fst, and employing structure analysis. It was observed that a gradient of genetic variation exists within continents, characterized by higher levels within different groups and lower levels of genetic differentiation. Additionally, a notable presence of mixed haplotypes was observed. The results of neutrality testing suggest that there has been a significant expansion in the populations of Clinostomum in India, America, and Kenya. The discoveries from this study will provide a valuable contribution to comprehending the genetics and evolution of Clinostomum species. Furthermore, key findings will be essential in developing efficient management approaches to prevent and control this parasite.

Wang L, Siau CS, Baharom N et al.

Li Wang,1,2,* Ching Sin Siau,2,* Nizam Baharom,3 Mohd Izzuddin Hairol,2 Lixin Huang,4 Lei Hum Wee2,5– 7 1Ya’an Polytechnic College, Ya’an, Sichuan, 625000, People’s Republic of China; 2Center for Community Health Studies (ReaCH), Faculty of Health Sciences, Universiti Kebangsaan Malaysia (UKM), Kuala Lumpur, 50300, Malaysia; 3Primer Healthcare Department, Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia (USIM), Nilai, Negeri Sembilan, 71800, Malaysia; 4Chengdu Sport University, Chengdu, Sichuan, 610000, People’s Republic of China; 5School of Medicine, Faculty of Health & Medical Sciences, Taylor’s University, Subang Jaya, Selangor, 47500, Malaysia; 6Non-Communicable Diseases and Public Health Research Group, Faculty of Health and Medical Sciences, Taylor’s University, Subang Jaya, 47500, Malaysia; 7Digital Health and Innovations Impact Lab, Taylor’s University, Subang Jaya, 47500, Malaysia*These authors contributed equally to this workCorrespondence: Ching Sin Siau, Klinik Psikologi Kesihatan, Block G, Jalan Raja Muda Abdul Aziz, Kuala Lumpur, 50300, Malaysia, Email chingsin.siau@ukm.edu.myPurpose: E-cigarettes (ECs) are gaining popularity among young people. This study aimed to assess university student vapers’ search frequency and source credibility of commonly used media in China, and their association with daily EC users’ motivation to quit EC use.Participants and Methods: This was a cross-sectional study involving an online quantitative survey in six universities in Chengdu, China. Participants answered questions on their EC use patterns, motivation to quit EC, use frequency and source credibility of media use. Hierarchical linear regression analyses were performed to determine the association between media search frequency and source credibility concerning EC health information with the motivation to quit EC.Results: There were a total of 325 participants (Mean age = 20.43, SD = 1.333). Video platforms and social media were ranked frequently used by the participants and were deemed to be more trustworthy. Perceived trustworthiness of online media was the most influential predictor of motivation to quit EC. Those who reported a higher frequency of accessing video platforms and medical health applications recorded higher motivation to quit EC use. Source credibility of news portals were associated higher motivation to quit EC. The association between higher nicotine dependence and lower motivation to quit EC was attenuated when media source credibility and trustworthiness of online media were added into the fully adjusted regression models.Conclusion: There is an association between media use frequency and source credibility to search for EC health information and motivation to quit vaping. More studies could be conducted to examine the effects of media use content on perceptions towards vaping and their motivation to quit vaping.Keywords: vaping, media search, health information, motivation to quit, college students

Tanvir Islam, Peter Washington

Stress is widely recognized as a major contributor to a variety of health issues. Stress prediction using biosignal data recorded by wearables is a key area of study in mobile sensing research because real-time stress prediction can enable digital interventions to immediately react at the onset of stress, helping to avoid many psychological and physiological symptoms such as heart rhythm irregularities. Electrodermal activity (EDA) is often used to measure stress. However, major challenges with the prediction of stress using machine learning include the subjectivity and sparseness of the labels, a large feature space, relatively few labels, and a complex nonlinear and subjective relationship between the features and outcomes. To tackle these issues, we examine the use of model personalization: training a separate stress prediction model for each user. To allow the neural network to learn the temporal dynamics of each individual's baseline biosignal patterns, thus enabling personalization with very few labels, we pre-train a 1-dimensional convolutional neural network (CNN) using self-supervised learning (SSL). We evaluate our method using the Wearable Stress and Affect prediction (WESAD) dataset. We fine-tune the pre-trained networks to the stress prediction task and compare against equivalent models without any self-supervised pre-training. We discover that embeddings learned using our pre-training method outperform supervised baselines with significantly fewer labeled data points: the models trained with SSL require less than 30% of the labels to reach equivalent performance without personalized SSL. This personalized learning method can enable precision health systems which are tailored to each subject and require few annotations by the end user, thus allowing for the mobile sensing of increasingly complex, heterogeneous, and subjective outcomes such as stress.

Amanul Islam, Fazidah Othman, Nazmus Sakib et al.

Graphical passwords are implemented as an alternative scheme to replace alphanumeric passwords to help users to memorize their password. However, most of the graphical password systems are vulnerable to shoulder-surfing attack due to the usage of the visual interface. In this research, a method that uses shifting condition with digraph substitution rules is proposed to address shoulder-surfing attack problem. The proposed algorithm uses both password images and decoy images throughout the user authentication procedure to confuse adversaries from obtaining the password images via direct observation or watching from a recorded session. The pass-images generated by this suggested algorithm are random and can only be generated if the algorithm is fully understood. As a result, adversaries will have no clue to obtain the right password images to log in. A user study was undertaken to assess the proposed method's effectiveness to avoid shoulder-surfing attacks. The results of the user study indicate that the proposed approach can withstand shoulder-surfing attacks (both direct observation and video recording method).The proposed method was tested and the results showed that it is able to resist shoulder-surfing and frequency of occurrence analysis attacks. Moreover, the experience gained in this research can be pervaded the gap on the realm of knowledge of the graphical password.

Durrotun Nabilah, Ismail, Elly Matul Imah

This research is aimed to describe the creative and imitative reasoning of students with reflective cognitive styles on math problem solving. The study uses a qualitative approach with a data collection instrument used of Matching Familiar Figure Tests, math ability test, mathematical problem-solving tasks, and interview guidelines. The research result studies indicate that a student with reflective cognitive style that have imitative reasoning in problem solving is less able to provide comprehensive information, still using a problem-solving strategy previously known, nor does he make a return check on the results received. It is different with reflective subjects that have proven creative reasoning capable of delivering well-known information, that can provide novel and thought-provoking solutions. Moreover, he also looking back the resulting solutions to the problem.

Wahyuni Nuryatul Choiroh, Munawir Munawir

Sunni scholars view Hadith as a normative source capable of embodying the essence of the Qur'an, considering Hadith's position as the second source of Islamic religious teachings after the Qur'an. The controversy between textual and contextual schools of thought regarding the study of Hadith has existed since the early development of Islam. Even to this day, textualist scholars continue to advocate for their principles. This study aims to uncover Yusuf al-Qaradhawi's thoughts on the methodology of understanding Hadith in his book "Kaifa Nata’amal ma’a al-Sunnah al-Nabawiyah" (How to Interact with the Prophetic Sunnah), enabling scholars of Hadith to understand Hadith properly. In this context, the author raises the issue of women's participation in politics as an implication of the offered methodology. This research adopts a qualitative approach with a library research method. The results of this study indicate that al-Qaradhawi proposes eight basic principles for understanding the Hadith of the Prophet Muhammad (peace be upon him). These principles include understanding Hadith in harmony with the Qur'an, combining several Hadiths on one topic, reconciling conflicting Hadiths, understanding Hadiths in their background, situation, and orientation, distinguishing between the inconsistency of the means and the consistency of the objectives of the Hadith, comparing literal and metaphorical expressions in understanding Hadiths, comparing the unseen with the visible, and validating the terminology of Hadith. The methodological framework proposed by al-Qaradhawi is expected to keep the study of Hadith relevant to the changing times. Regarding the implications and significance of his methodology regarding women's participation in politics through Hadith, it can be concluded that, according to al-Qaradhawi, women have the same rights to participate and engage in politics as men.

Melisa Paulina

During the online learning period there are obstacles and obstacles faced by students, even so learning must still be carried out, so students are expected to be able to convince themselves to be able to achieve online learning goals. The purpose of this study was to determine the effect of social support on selfefficacy in Islamic students. This study used a quantitative approach with a sample of 410 students, with 124 male students and 286 female students. The general self-efficacy scale-12 (GSES-12) developed by Bosscher & Smit (1998) is used by researchers to measure self-efficacy, and the multidimensional scale of perceived social support (MSPSS) created by Zimet et al. (1988) researchers used to measure social support. Test the validity of the construct measurement on each variable was carried out after data collection and before data analysis. In testing the validity of measuring instruments, researchers used confirmatory factor analysis (CFA) (Muthen & Muthen, 2017). Hypothesis testing was carried out using the software SPSS Version 24. The results showed a significance value of .000 (p < .05) thus, there was a significant influence between social support on the self-efficacy of Islamic students in online learning. The results of the regression analysis test obtained an R Square of .152 or 15.2% on the effect of social support on self-efficacy in Islamic students in online learning.

Ajaharul Islam, Lihua Dong, Yun Guo et al.

We introduce a method for reducing anisotropic heavy-quark potentials to isotropic potentials by using an effective screening mass that depends on the quantum numbers $l$ and $m$ of a given state. We demonstrate that, using the resulting 1D effective potential model, one can solve a 1D Schrödinger equation and reproduce the full 3D results for the energies and binding energies of low-lying heavy-quarkonium bound states to relatively high accuracy. This includes the splitting of different p-wave polarizations. The resulting 1D effective model provides a way to include momentum anisotropy effects in open quantum system simulations of heavy-quarkonium dynamics in the quark-gluon plasma.

Chadni Islam, Victor Prokhorenko, M. Ali Babar

Runtime software patching aims to minimize or eliminate service downtime, user interruptions and potential data losses while deploying a patch. Due to modern software systems' high variance and heterogeneity, no universal solutions are available or proposed to deploy and execute patches at runtime. Existing runtime software patching solutions focus on specific cases, scenarios, programming languages and operating systems. This paper aims to identify, investigate and synthesize state-of-the-art runtime software patching approaches and gives an overview of currently unsolved challenges. It further provides insights on multiple aspects of runtime patching approaches such as patch scales, general strategies and responsibilities. This study identifies seven levels of granularity, two key strategies providing a conceptual model of three responsible entities and four capabilities of runtime patching solutions. Through the analysis of the existing literature, this research also reveals open issues hindering more comprehensive adoption of runtime patching in practice. Finally, it proposes several crucial future directions that require further attention from both researchers and practitioners.