This study examines the Surau education pattern in Minangkabau, Indonesia, as a response to the growing disconnect between youth and their cultural heritage amidst modernization. This research aims to re-document the Surau education pattern, analyze its historical and cultural aspects, and propose a revitalization model that adapts it to contemporary challenges. The study seeks to expand the paradigm of Islamic education by incorporating local cultural heritage and practices, recognizing the significance of regional traditions in shaping educational experiences within Islamic contexts. Employing a descriptive qualitative approach with an ethnopedagogical framework, this research utilizes in-depth interviews with traditional leaders, religious figures, youths, and community representatives, and participatory observation and documentation study. Data analysis was executed using thematic analysis techniques, focusing on educational patterns, values imparted, and their implications for youth's social preparedness. The findings reveal that Surau education implements a contextual and practical learning model, enabling students to gain theoretical knowledge of religion and practice these values through various social and cultural activities. The Surau is an effective educational arena for instilling honesty, responsibility, deliberation, and preserving Minangkabau cultural identity. Moreover, Surau education fosters resilient mental character and social skills that support students' adaptation within broader society. Surau education significantly contributes to the holistic life readiness of Minangkabau youth, establishing Surau as a traditional education model relevant to future development. This model significantly shapes a morally grounded, religious, and socially prepared younger generation. It positively impacts Islamic value-based community education and revitalizes Surau as a relevant spiritual and social learning space in contemporary society.
Embryonic tissues deform across broad spatial and temporal scales and relax stress through active rearrangements. A quantitative link between cell-scale activity, spatial forcing, and emergent tissue-scale mechanics remains incomplete. Here, we use a vertex-based tissue model with active force fluctuations to study how motility controls viscoelastic response. After validation against experimental presomitic mesoderm relaxation dynamics, we extract intrinsic mechanical timescales using stress relaxation and oscillatory shear. The model captures motility-dependent shifts between elastic and viscous behavior and the coexistence of fast relaxation with long-lived residual stress. When subjected to spatially patterned, temporally pulsed forcing, tissues behave as mechanical filters: long-wavelength inputs are accumulated, whereas short-wavelength, cell-scale perturbations are rapidly erased, largely independent of motility. Simulations with localized motility hotspots, motivated by spatially confined FGF signaling reported in vertebrate limb development, produce sustained protrusive tissue deformations consistent with experimentally observed early bud-like morphologies. Together, these results establish a minimal framework linking motility-driven activity to wavelength-selective mechanical memory and emergent tissue patterning.
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.
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.
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.
Makalenin konusu, Behşemiyye geleneğinin son mümessili olan Hâkim el-Cüşemî'nin, şefaati, günahkâr kimseler için ilahî afv ve ebedî saadet vesilesi olarak değerlendirip değerlendirmemesidir. Amaç, Cüşemî nazarında şefaatin nasıl bir içeriğe ve keyfiyete sahip olduğunu tespit etmektir. Önemi ise onun bu meseleye ilişkin yaklaşımını kapsamlı ve detaylı yansıtan müstakil bir çalışmaya şimdiye kadar rastlanmamış olmasıdır. Ayrıca bu makale, Cüşemî'nin meseleye yaklaşımı bağlamında farklı ve özgün sorular sorup bunlara yeni cevaplar üretmek suretiyle okuyucuya vizyonel ve ufuk açıcı perspektifler kazandırmayı hedeflemektedir. Müellif özelinde mesele irdeleneceğinden makalenin bazı belirlemeler ve sınırlandırmalar barındırması kaçınılmazdır. Bu sebeple makale; konu, kaynak ve yöntem açılarından daraltıcı ve sınırlı tutulacaktır. Bu doğrultuda yöntem olarak ilkin doküman analizi metodu kullanılmıştır. Bilhassa müellife ait olduğu tespit edilen Cüşemî'nin kendi teliflerine öncelik verilmiştir. Ardından geçmiş ve günümüz dünyasında mesele hakkında yazılan teliflere müracaat edilmiştir. Bu noktada söz konusu telifler; tarafsız, bütüncül ve sistematik olarak sorgulanıp değerlendirilmiştir. Başlıklar hâlinde düzenlenen konular, mukayese yöntemiyle içerik analizine tâbi tutulmuştur. Ayrıca literatürde mevcut olan bilgilerin anlatımında tanımlayıcı ve açıklayıcı metot izlenmiştir. Buna paralel olarak makalenin yazılış amacına muvafık olacak biçimde argümantasyon yöntemi kullanılmıştır. Nitekim Kur'ân'da şefaatin, Allah'ın iznine ve onayına bağlı olduğu ve bunun da O'nun sevdiği ve hoşnut olduğu kimseler için mümkün olacağı bildirilmesine rağmen bu kimselerin kimler olduğu hususunda açık ve kesin hükümlerin olmayışı, şefaatle ilgili ihtilaf ve tartışmaları beraberinde getirmiştir. Bu makalede şefaati, ebedî olarak cennette olacak müminlerin sevaplarının ziyadeleştirilmesi ve derece ve makamlarının yükseltilmesi olarak algılayan Cüşemî, Allah'ın razı olduğu bir kul olmak için büyük günahlardan uzak durmak gerektiğini yani büyük günahlardan kaçınmanın cennete girmek için vazgeçilmez bir şart olduğunu savunur. Ona göre şefaat, ilahî afv ve ebedî kurtuluş vesilesi olarak kabul edilemez. Zira şefaatin, cehennemden çıkarılıp cennete gönderme formunda anlaşılması, her şeyden önce dinî açıdan sahih ve muteber olmamakla beraber günahkâr kimseler için ilahî bir iltimas ve imtiyaz olacağı algısını çağrıştırmaktadır. Ayrıca Hz. Peygamber'in şefaatinin büyük günahı olan kimseler için olacağı hadîsi, Cüşemî açısından hem eksik metinli hem de kişiyi günah ve haramlardan kaçınmayı teşvik etmek yerine onları işlemeye isteklendirmekte ve cesaretlendirmektedir. Aynı zamanda onun nazarında bu hadîs, Allah'ın cezalandırmak üzere cehenneme gönderdiği kimselerin Hz. Peygamber'in savunması ve sahiplenmesi manasına gelir ki bu da O'nun affı, merhameti ve şefkatinin ikinci plana itilmesi demektir. Yine Allah'ın, kendisi katında sözün değiştirilmeyeceği yönündeki beyanı, Cüşemî'ye göre O'nun karar değiştirmesi manasına gelir ki bu durum, ilahî emir ve nehiylere karşı ilgisizliğe, gevşekliğe ve umursamazlığa sevk eder. Netice itibariyle büyük günah ile küfrün eşit görülüp aynı kefeye konduğu bu yaklaşımda, Allah'ın hakkaniyeti, rahmet, şefkati ve affediciliği ya gölgelenmekte ya da görmezden gelinmektedir. Oysa Allah, büyük günahı olan kimseleri mümin olarak nitelendirmektedir. Dolayısıyla Cüşemî, günahkâr kimseleri hâriç tutarak şefaatin kapsamını daraltmaktadır.
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.
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.
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.
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.
This study explores how live streaming shopping, twin event promotions, and E-WOM impact impulse buying behavior among Gen Z users of the Shopee e-commerce platform. It is intended to serve as a scholarly reference for future research. The study employed a quantitative descriptive approach, utilizing Partial Least Squares (PLS) for data analysis. The results suggest that while live streaming shopping and E-WOM do not affect impulse buying behavior among Gen Z users of Shopee in Surabaya, twin event promotions contribute to their impulse buying behavior.
Past studies have empirically demonstrated a surprising agreement between gravitational waveforms computed using adiabatic-driven-inspiral point-particle black hole perturbation theory (ppBHPT) and numerical relativity (NR) following a straightforward calibration step, sometimes referred to as $α$-$β$ scaling. Specifically focusing on the quadrupole mode, this calibration technique necessitates only two time-independent parameters to scale the overall amplitude and time coordinate. In this article, part of a special issue, we investigate this scaling for non-spinning binaries at the equal mass limit. Even without calibration, NR and ppBHPT waveforms exhibit an unexpected degree of similarity after accounting for different mass scale definitions. Post-calibration, good agreement between ppBHPT and NR waveforms extends nearly up to the point of the merger. We also assess the breakdown of the time-independent assumption of the scaling parameters, shedding light on current limitations and suggesting potential generalizations for the $α$-$β$ scaling technique.
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.
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.
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. %These identified sub-structures can provide interpretations of GNN's behavior. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and failing to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons for spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, \tianxiang{we propose a new explanation framework with an auxiliary alignment loss, which is theoretically proven to be optimizing a more faithful explanation objective intrinsically. Concretely for this alignment loss, a set of different perspectives are explored: anchor-based alignment, distributional alignment based on Gaussian mixture models, mutual-information-based alignment, etc. A comprehensive study is conducted both on the effectiveness of this new framework in terms of explanation faithfulness/consistency and on the advantages of these variants.
This study explores the factors influencing teacher job satisfaction in Madrasah Tsanawiyah Negeri (MTsN) in Riau Islands Province. The study focused on participatory leader behaviour, achievement orientation, and work motivation as the main variables affecting teacher job satisfaction. The research method used is quantitative with a statistical analysis approach to test the hypothesis that has been set. Three schools with the same accreditation status (A) in the western, central, and eastern research areas were selected as samples through area sampling techniques. The total sampling technique was used to take 136 teachers from these schools. The results showed that participatory leader behaviour and achievement orientation significantly influenced teacher job satisfaction directly or indirectly through work motivation. The pathway analysis confirmed that participatory leader behaviour, achievement orientation, and work motivation significantly affected teacher job satisfaction in MTsN in Riau Islands Province. The implication is to develop effective and holistic management strategies to improve the welfare of educational organizations.
This study identifies the interplay between the engagement of Kasepuhan Ciptagelar, an indigenous community inhabiting the state forest in West Java, and tourism development in the area. Practising local spirituality rooted in an indigenous belief, tatali paranti karuhun, while administratively accepting Islam, the people have been struggling to deal with the nearby majority Sundanese who practices Islam and the establishment of the national park covering their living space. The study considers whether a minority group living in an area endowed with both natural and cultural tourism resources consciously chooses tourism as a selected ground to deal with policies which neglect them in terms of religious practices and land policy. Employing the ethnographic method, the study reveals that contesting identity in tourism also means the readiness to accommodate various outside elements. However, the strategy has led the Halimun Salak National Park authority to declare the area as a “special status area” for cultural tourism inside the state park since 2017. The study findings show that after engaging with tourism, various rituals and art performances rooted in the old Sundanese spirituality, which is not officially recognized by the state, can be freely performed for the sake of tourists. In this case, the community is not passive in dealing with external forces but has also enabled its own silent productivity, including its varying consequences.
AbstractThis article advocates for an increased attention to how piously striving Muslims learn about, cultivate, and experience nearness to God. The empirical material is taken from our current research on Danish Muslims’ engagement with Islamic teaching and learning. We examine examples of oral teachings that instruct the audience to be constantly aware of God and address him directly in prayer, examples of how an awareness of God is cultivated and practiced in everyday life, and individual narratives of experiences of being close to or helped by God. With inspiration from the anthropology of Christianity as well as Islam, we propose an analytical model for understanding the process whereby Muslim efforts to draw near to God can ‘work’. Three interrelated dynamics are crucial to this process, and we identify each through our reading of existing scholarship. The dynamics at play are, respectively, a taqwa-infused faith frame, its related semiotic ideology, and a relationship of experienced reciprocal agency with God.