This study investigates the legality, socio-environmental implications, and policy relevance of imported second-hand clothing (thrifting import) using an integrative theoretical framework that explicitly combines ecotheology, fiqh sosial Kiai Sahal Mahfudh, and fiqh muamalah. The research addresses the central problem of how thrifting imports intersect with Islamic legal norms, sustainability ethics, and state regulations amidst rising concerns over waste, illegal trade, and threats to local industries. Using qualitative library research supported by hermeneutic and content analysis methods, this study examines classical and contemporary fiqh sources, Qur'anic exegesis on muamalah, literature on circular economy and sustainable consumption, as well as regulatory documents and statistical reports. The findings show that thrifting import does not fulfill key requirements of a valid sale (ma‘qūd ‘alayh), contains elements of gharar and bāṭil, contributes to ecosystem degradation, and disrupts the textile industry. The study also formulates policy implications by proposing a maqāṣid-oriented regulatory design, including empowerment strategies for affected small traders, strengthening domestic industry, and environmental sustainability measures. These recommendations highlight the importance of integrating Islamic legal ethics with national economic and ecological policies.
The tradition of the Prophet's Birthday on Bawean Island embodies the intersection of Islamic values and local culture, shaping the distinctive religious practices of the island's people. This tradition is a religious celebration and a means of preserving cultural identity. This study formulates two main questions: (1) How is the sacred construction of the Prophet's Birthday tradition in Bawean formed through the interaction of Islam and local culture? Moreover, (2) How do people across generations interpret this tradition? The study used a qualitative approach with Husserl's phenomenology to explore the community's religious experiences. Data were collected through in-depth interviews, participant observation, and documentation in the form of archives, prayers, and photographs of the procession. Informants were selected purposively, including religious leaders, cultural figures, the Maulid committee, the general public, and the younger generation. Data analysis used phenomenological stages: bracketing, intuiting, analyzing, and describing. The findings indicate that the sacredness of the Maulid in Bawean is understood as a space for strengthening faith, social solidarity, and cultural inheritance. This tradition represents a harmonious construction of archipelagic Islam: Islamic in its values, local in its expression, and sacred in the spiritual experience of its people.
This paper presents an in-depth survey on the use of multimodal Generative Artificial Intelligence (GenAI) and autoregressive Large Language Models (LLMs) for human motion understanding and generation, offering insights into emerging methods, architectures, and their potential to advance realistic and versatile motion synthesis. Focusing exclusively on text and motion modalities, this research investigates how textual descriptions can guide the generation of complex, human-like motion sequences. The paper explores various generative approaches, including autoregressive models, diffusion models, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models, by analyzing their strengths and limitations in terms of motion quality, computational efficiency, and adaptability. It highlights recent advances in text-conditioned motion generation, where textual inputs are used to control and refine motion outputs with greater precision. The integration of LLMs further enhances these models by enabling semantic alignment between instructions and motion, improving coherence and contextual relevance. This systematic survey underscores the transformative potential of text-to-motion GenAI and LLM architectures in applications such as healthcare, humanoids, gaming, animation, and assistive technologies, while addressing ongoing challenges in generating efficient and realistic human motion.
Teague McMillan, Gabriele Dominici, Martin Gjoreski
et al.
Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especially problematic: explanations that omit salient clinical cues or mask spurious shortcuts can undermine clinician trust and lead to unsafe decision support. We study how inference and training-time choices shape explanation faithfulness, focusing on factors practitioners can control at deployment. We evaluate three LLMs (GPT-4.1-mini, LLaMA 70B, LLaMA 8B) on two datasets-BBQ (social bias) and MedQA (medical licensing questions), and manipulate the number and type of few-shot examples, prompting strategies, and training procedure. Our results show: (i) both the quantity and quality of few-shot examples significantly impact model faithfulness; (ii) faithfulness is sensitive to prompting design; (iii) the instruction-tuning phase improves measured faithfulness on MedQA. These findings offer insights into strategies for enhancing the interpretability and trustworthiness of LLMs in sensitive domains.
Humans can often count unfamiliar objects by observing visual repetition and composition, rather than relying only on object categories. However, many exemplar-free counting models struggle in such situations and may overcount when objects contain symmetric components, repeated substructures, or partial occlusion. We introduce CountFormer, a controlled adaptation of a density-regression framework inspired by CounTR, where the image encoder is replaced with the self-supervised vision foundation model DINOv2. The resulting transformer features are combined with explicit two-dimensional positional embeddings and decoded by a lightweight convolutional network to produce a density map whose integral gives the final count. Our goal is not to propose a new counting architecture, but to study whether foundation-based representations improve structural consistency under a strictly exemplar-free setting. On FSC-147, CountFormer achieves competitive performance under the official benchmark (MAE 19.06, RMSE 118.45). Qualitative analysis suggests fewer part-level overcounting errors for some structurally complex objects, while overall error remains broadly consistent with prior approaches. Sensitivity analysis shows that evaluation metrics are strongly affected by a small number of extreme high-density scenes. Overall, the results highlight the role of representation quality in exemplar-free object counting.
Traditional neural networks employ fixed weights during inference, limiting their ability to adapt to changing input conditions, unlike biological neurons that adjust signal strength dynamically based on stimuli. This discrepancy between artificial and biological neurons constrains neural network flexibility and adaptability. To bridge this gap, we propose a novel framework for adaptive neural networks, where neuron weights are modeled as functions of the input signal, allowing the network to adjust dynamically in real-time. Importantly, we achieve this within the same traditional architecture of an Artificial Neural Network, maintaining structural familiarity while introducing dynamic adaptability. In our research, we apply Chebyshev polynomials as one of the many possible decomposition methods to achieve this adaptive weighting mechanism, with polynomial coefficients learned during training. Out of the 145 datasets tested, our adaptive Chebyshev neural network demonstrated a marked improvement over an equivalent MLP in approximately 8\% of cases, performing strictly better on 121 datasets. In the remaining 24 datasets, the performance of our algorithm matched that of the MLP, highlighting its ability to generalize standard neural network behavior while offering enhanced adaptability. As a generalized form of the MLP, this model seamlessly retains MLP performance where needed while extending its capabilities to achieve superior accuracy across a wide range of complex tasks. These results underscore the potential of adaptive neurons to enhance generalization, flexibility, and robustness in neural networks, particularly in applications with dynamic or non-linear data dependencies.
We study properties of "hyperbolic directions" in groups acting cocompactly on properly convex domains in real projective space, from three different perspectives simultaneously: the (coarse) metric geometry of the Hilbert metric, the projective geometry of the boundary of the domain, and the singular value gaps of projective automorphisms. We describe the relationship between different definitions of "Morse" and "regular" quasi-geodesics arising in these three different contexts. This generalizes several results of Benoist and Guichard to the non-Gromov hyperbolic setting.
As we build towards developing interactive systems that can recognize human emotional states and respond to individual needs more intuitively and empathetically in more personalized and context-aware computing time. This is especially important regarding mental health support, with a rising need for immediate, non-intrusive help tailored to each individual. Individual mental health and the complex nature of human emotions call for novel approaches beyond conventional proactive and reactive-based chatbot approaches. In this position paper, we will explore how to create Chatbots that can sense, interpret, and intervene in emotional signals by combining real-time facial expression analysis, physiological signal interpretation, and language models. This is achieved by incorporating facial affect detection into existing practical and ubiquitous passive sensing contexts, thus empowering them with the capabilities to the ubiquity of sensing behavioral primitives to recognize, interpret, and respond to human emotions. In parallel, the system employs cognitive-behavioral therapy tools such as cognitive reframing and mood journals, leveraging the therapeutic intervention potential of Chatbots in mental health contexts. Finally, we propose a project to build a system that enhances the emotional understanding of Chatbots to engage users in chat-based intervention, thereby helping manage their mood.
Rahul Islam, Tongze Zhang, Priyanshu Singh Bisen
et al.
MoodPupilar introduces a novel method for mood evaluation using pupillary response captured by a smartphone's front-facing camera during daily use. Over a four-week period, data was gathered from 25 participants to develop models capable of predicting daily mood averages. Utilizing the GLOBEM behavior modeling platform, we benchmarked the utility of pupillary response as a predictor for mood. Our proposed model demonstrated a Matthew's Correlation Coefficient (MCC) score of 0.15 for Valence and 0.12 for Arousal, which is on par with or exceeds those achieved by existing behavioral modeling algorithms supported by GLOBEM. This capability to accurately predict mood trends underscores the effectiveness of pupillary response data in providing crucial insights for timely mental health interventions and resource allocation. The outcomes are encouraging, demonstrating the potential of real-time and predictive mood analysis to support mental health interventions.
T. M. Haekal, Wahidmurni Wahidmurni, Indah Aminatuz Zuhriyah
This research aims to find out the influence of Islamic leadership, religiosity, and work discipline with work ethic on teacher performance in State Madrasah Aliyah throughout Malang Raya. This research uses a quantitative approach with a correlational type. The population consisted of 224 teachers and 144 samples (simple random sampling) who were given a questionnaire. Data analysis using Smart PLS V.3.2.8. The research results found that Islamic leadership and religiosity positively and significantly affected teacher performance. In contrast, work discipline did not have a significant effect. Work ethic has a positive and significant influence on teacher performance. Although Islamic leadership does not directly influence teacher performance through work ethic, religiosity does not directly influence teacher performance through work ethic. Work discipline, although indirect, has a positive and significant impact on teacher performance through work ethic. In conclusion, certified teachers meet teacher performance standards in madrasas. This research has implications for the management and development of human resources in madrasas, especially in improving teacher performance, which should be carried out in a planned, systematic and sustainable manner through the total involvement of all parties.
The recurrent nova RS Ophiuchi (RS Oph) underwent its most recent eruption on 8 August 2021 and became the first nova to produce both detectable GeV and TeV emission. We used extensive X-ray monitoring with the Neutron Star Interior Composition Explorer Mission (NICER) to model the X-ray spectrum and probe the shock conditions throughout the 2021 eruption. The rapidly evolving NICER spectra consisted of both line and continuum emission that could not be accounted for using a single-temperature collisional equilibrium plasma model with an absorber that fully covered the source. We successfully modelled the NICER spectrum as a non-equilibrium ionization collisional plasma with partial-covering absorption. The temperature of the the non-equilibrium plasma show a peak on Day 5 with a kT of approximately 24 keV. The increase in temperature during the first five days could have been due to increasing contribution to the X-ray emission from material behind fast polar shocks or a decrease is the amount of energy being drained from shocks into particle acceleration during that time period. The absorption showed a change from fully covering the source to having a covering fraction of roughly 0.4, suggesting a geometrical evolution of the shock region within the complex global distribution of the circumstellar material. These findings show the evidence of the ejecta interacting with some dense equatorial shell initially and with less dense material in the bipolar regions at later times during the eruption.
We consider a quantum ring of a certain radius R built from a sheet of the $α$-$T_3$ lattice and solve for its spectral properties in presence of an external magnetic field. The energy spectrum consists of a conduction band, a valence band and a zero energy flat band, all having a number of discrete levels therein which can be characterized by the angular momentum quantum number, m. The energy levels in the flat band are infinitely degenerate irrespective of the value of $α$. We reveal a two-fold degeneracy of the levels in the conduction band as well as in the valence band for $α$ = 0 and $α$ = 1. However, the m = 0 level for $α$ = 1 is an exception. Corresponding to an intermediate value of $α$, namely, 0 <$α$< 1, the energy levels become nondegenerate. The scenario remains unaltered when the ring is threaded by a magnetic flux which is an integer multiple of the flux quantum. We also calculate the persistent current which exhibits quantum oscillations as a function of the magnetic field with a period of one flux quantum at a particular Dirac point, which is often referred to as a valley. The total current oscillates with a periodicity of one flux quantum for any intermediate value of $α$. We have also explored the effect of a mass term (that breaks the sublattice symmetry) in the Hamiltonian. In the absence of a magnetic field, the energy levels in the flat band become dispersive, except for the m = 0 level in the case of $α$ = 1. In presence of the field, each of the flat band levels becomes dispersive for any $α\neq$ 0. Finally, we also see the effect of the mass term on the behaviour of the persistent current, which shows periodicity of one flux quantum, but the total current remains finite for all values of $α$.
Riffat Islam, Sanaa Rafique, Olga A. Bredikhina
et al.
University students' day-to-day lives largely depend on transportation. Public transit alternatives are not widely available in the United States' small-town, rural, and suburban collegiate environments (U.S.). This study disseminated an online survey among international students studying at The University of Alabama (UA) campus, a predominantly white institution in Tuscaloosa, Alabama. The objective of this research was to investigate and analyze international student travel experiences in a city with a significant college-affiliated population and highlight particular transportation issues in the area. The survey results show that international students find it difficult to travel within Tuscaloosa without a personal vehicle, as other modes of transportation, including walking, biking, and using public transportation, are not always convenient or reliable. The study findings may be of interest to transportation experts, city planners, university administrators, and college students who want to better understand travel-related challenges experienced by international students.
Penelitian ini membahas tentang eksistensi kedudukan fatwa DSN MUI terhadap keberlangsungan operasional bisnis di lembaga keuangan syariah. Dengan menggunakan metode penelitian deskriptif yang bersifat kepustakaan, penelitian ini menemukan fakta bahwa kedudukan fatwa DSN MUI menjadi salah satu prasyarat dalam menentukan ketetapan suatu produk di lembaga keuangan syariah. Ketika produk yang di ajukan oleh DPS masing-masing lembaga keuangan syariah mendapatkan ketentuan fatwa dari DSN MUI. Maka produk perbankan syariah itu telah sesuai dengan tuntunan syariat Islam serta ketentuan fatwa tersebut menjadi payung hukum dalam menjalankan produk yang menjadi daya tarik bagi para nasabah. Apalagi sampai dilegitimasi oleh undang-undang maupun Peraturan Bank Indonesia. Sehingga fatwa DSN MUI tersebut mejadi ketentuan hukum yang mengikat bagi seluruh lembaga keuangan syariah. Dengan demikian, ketentuan fatwa tersebut secara tidak langsung menjadi salah satu faktor yang menentukan keberlangsungan segala operasional bisnis di lembaga keuangan syariah yang memiliki manfaat dan keberkahan bagi semua pihak yang menjalankannya, baik itu bagi LKS sebagai otoritas yang memiliki penawaran produk maupun bagi nasabah sebagai pemakai produk
The purpose of this study was to determine how much influence organizational commitment and achievement motivation have on teacher performance at Muhammadiyah Vocational Schools throughout the city of Metro Lampung. This research is quantitative research with this type of research is causal-comparative research. The population in this study were all teachers of Muhammadiyah Vocational Schools throughout Metro City, totaling 207. The research sample was 67 teachers using the proportional cluster random sampling technique. This research instrument uses a questionnaire. The data analysis technique in this study uses statistical analysis, namely simple and multiple regression analysis and analysis of the coefficient of determination and the F test. The results of this study are organizational commitment and achievement motivation together have a positive and significant effect on teacher performance at SMK Muhammadiyah throughout – Metro City. This is evident from the probability value (p), which is 0.000 < 0.05. The value of the coefficient of determination obtained is 0.590. This means that achievement motivation affects teacher performance by 59.0% and is tested empirically at a significance level of 5%.
Mhafuzul Islam, Mashrur Chowdhury, Zadid Khan
et al.
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum-classical neural networks (NN), complex and high-dimensional features can be extracted by the classical NN to a reduced but more informative feature space to be processed by the existing quantum computers. In this study, we develop a hybrid quantum-classical NN to detect an amplitude shift cyber-attack on an in-vehicle control area network (CAN) dataset. We show that using the hybrid quantum classical NN, it is possible to achieve an attack detection accuracy of 94%, which is higher than a Long short-term memory (LSTM) NN (87%) or quantum NN alone (62%)
This research elaborated the definition of Online Learning and the online learning application in English Language Teaching. The methodology of research uses qualitative research in explaining the Online Learning Application for English Language Teaching. This research is still lacking research in online learning for English Language Teaching; therefore, it significant to be done. The finding of the research is followed: First, online learning is the worldwide technologies that use the web, e-mail, new groups and texts, audio, and video conferencing. Online learning is always connected to the internet. It can use for long-distance or not in one place. The students can use in certain conditions “distance learning”. Second, Online learning supported the proper application in English Language Learning, such as: Edmodo, Zoom, Blogs, WhatsApp Group, Email, SeeSaw, Sway, Google Classroom, ClassDojo, Show My homework, Surveymonkey in-app surveys, SmartSurvey.
Penelitian ini menjelaskan mengenai pembelajaran online (via Internet) dan pembelajaran online untuk mata kuliah bahasa Inggris. Metode yang dunakan adalah penelitian kualitatif untuk menjelaskan pembelajaran online (Aplikasi via Internet) untuk pembeljaran bahasa Inggris. Penilitian ini masih sedikit dilakukan, oleh karena itu tetap dilakukan. Pembahasan dari permasalahan penelitian ini adalah: Pertama, pembelajaran online merupakan teknologi yang digunakan oleh seluruh dunia yang menggunakan web, email, grup dan teks baru, konferensi audio dan video. Pembelajaran online selalu terhubung di internet. Pemebeljaran inidapat dilakukan dalam jarak jauh atau tidak di satu tempat. Siswa dapat menggunakan “pembelajaran jarak jauh” dalam kondisi tertentu. Kedua, pembelajaran online ini didukung dengan aplikasi yang mudah dalam pembelajaran Bahasa Inggris, seperti: Edmodo, Zoom, Blogs, WhatsApp Group, Email, SeeSaw, Sway, Google Classroom, ClassDojo, Show My homework, Surveymonkey in-app surveys, SmartSurvey.