Recent advances in automatic speech recognition (ASR) and speech enhancement have led to a widespread assumption that improving perceptual audio quality should directly benefit recognition accuracy. In this work, we rigorously examine whether this assumption holds for modern zero-shot ASR systems. We present a systematic empirical study on the impact of Segment Anything Model Audio by Meta AI, a recent foundation-scale speech enhancement model proposed by Meta, when used as a preprocessing step for zero-shot transcription with Whisper. Experiments are conducted across multiple Whisper model variants and two linguistically distinct noisy speech datasets: a real-world Bengali YouTube corpus and a publicly available English noisy dataset. Contrary to common intuition, our results show that SAM-Audio preprocessing consistently degrades ASR performance, increasing both Word Error Rate (WER) and Character Error Rate (CER) compared to raw noisy speech, despite substantial improvements in signal-level quality. Objective Peak Signal-to-Noise Ratio analysis on the English dataset confirms that SAM-Audio produces acoustically cleaner signals, yet this improvement fails to translate into recognition gains. Therefore, we conducted a detailed utterance-level analysis to understand this counterintuitive result. We found that the recognition degradation is a systematic issue affecting the majority of the audio, not just isolated outliers, and that the errors worsen as the Whisper model size increases. These findings expose a fundamental mismatch: audio that is perceptually cleaner to human listeners is not necessarily robust for machine recognition. This highlights the risk of blindly applying state-of-the-art denoising as a preprocessing step in zero-shot ASR pipelines.
Pendidikan Islam kontemporer menghadapi tantangan serius berupa krisis moral, disorientasi nilai, dan lemahnya integrasi antara ilmu dan akhlak. Penelitian ini bertujuan untuk mengkaji konsep pendidikan menurut Ibnu Miskawaih dan Al-Ghazali serta menganalisis relevansinya terhadap kondisi pendidikan Islam saat ini. Metode yang digunakan adalah kualitatif dengan pendekatan studi pustaka, yaitu menelaah sumber-sumber primer dan sekunder yang berkaitan dengan pemikiran kedua tokoh. Hasil penelitian menunjukkan bahwa Ibnu Miskawaih menekankan pendidikan berbasis akhlak melalui pendekatan rasional dan pembiasaan moral,sementara Al-Ghazali mengusung pendekatan sufistik yang menitikberatkan pada dimensi spiritual dan penyucian jiwa. Meskipun berbeda pendekatan, keduanya sepakat bahwa pendidikan harus mengembangkan akal, hati, dan ruh secara seimbang. Pemikiran mereka terbukti relevan dalam pengembangan kurikulum pendidikan Islam yang holistik dan berbasis nilai. Simpulan penelitian ini menegaskan bahwa warisan pemikiran klasik Islam tetap kontributif dan solutif dalam menjawab tantangan pendidikan modern, terutama dalam membentuk karakter unggul dan membangun sistem pendidikan yang integratif antara ilmu, iman, dan akhlak.
Characterizing eccentricity in gravitational waveforms in a consistent manner is crucial to facilitate parameter estimation, astrophysical population studies, as well as searches for these rare systems. We present a framework to characterize eccentricity directly from gravitational waveforms for non-precessing eccentric binary black hole (BBH) mergers using common modulations that eccentricity induces in all spherical harmonic modes of the signals. Our framework is in the spirit of existing methods that use frequency modulations in the waveforms, but we refine the approach by connecting it to state-of-the-art post-Newtonian calculations of the time evolution of the eccentricity. Using 39 numerical relativity (NR) simulations from the SXS and RIT catalogs, as well as waveforms obtained from the post-Newtonian approximation and effective-one-body (EOB) formalism, we show that our framework provides eccentricity estimates that connect smoothly into the relativistic regime (even up to $\sim 2M$ before merger). We also find that it is necessary to carry existing post-Newtonian calculations to an extra $0.5$PN order to adequately characterize existing NR simulations, and provide fits to the extra coefficient for existing simulations. We make the framework publicly available through the Python-based \texttt{gwModels} package.
Background. The issue of women’s inheritance rights has long been a subject of discussion in Islamic law. Although the Qur’an establishes a ratio of two-to-one between male and female heirs, many modern scholars and feminists challenge this provision, arguing for equal inheritance based on justice and social changes.Aim. This study aims to analyze the development of female inheritance rights within the framework of Islamic law, focusing on socio-historical factors and debates that have shaped its evolution, and to predict possible directions for its future interpretation.Methods. This research employs a library-based qualitative approach (library research) by examining classical fiqh literature from the four schools of thought and comparing them with contemporary interpretations and gender justice discourses.Results. The study finds that the classical formulation of 2:1 emerged during the socio-historical context of a patriarchal society, in which women had minimal public and economic roles. However, in contemporary contexts, where women increasingly contribute to family and public life, scholars propose more egalitarian approaches, either through reinterpretation or consensual redistribution mechanisms such as hibah and shadaqah. The study recommends a mediating framework between textual and contextual readings to achieve both fidelity to scripture and social justice.
Islam Nusantara, rooted in Indonesia's local traditions and culture, offers a moderate and inclusive approach to Islam, emphasizing social harmony and interfaith tolerance. Meanwhile, Islam Berkemajuan, often associated with the Muhammadiyah movement, focuses on modernization, rationality, and social progress through education and community development. This study aims to explore and analyze the differences and potential synergies between the concepts of Islam Nusantara and Islam Berkemajuan. The method used is a literature review with content analysis. The study data is derived from various books, articles, and other scholarly works relevant to the study's theme. The findings indicate that Islam Nusantara and Islam Berkemajuan are two complementary concepts that can create a harmonious and progressive society. By examining the theological, historical, and sociological foundations of these two concepts, the study provides deep insights into their contributions to the development of Islam in Indonesia in integrating traditional values with the demands of modern progress.
Recent scholarship has studied the extensive transformation of Turkey’s Directorate of Religious Affairs (Diyanet) over the past two decades as embodying a form of religious populism that mobilizes civilizational antagonisms. Based on a directed qualitative content analysis of Friday sermons, official publications, online material, broadcasts, and public statements by Diyanet leaders, this article makes three contributions. First, while confirming that the Diyanet promotes the civilizational unity of the ummah and casts Turkey as the spiritual custodian of a transhistorical Islamic world, the analysis shows that anti-elitist framings characteristic of populism are barely present in its rhetoric. Second, the article provides a detailed examination of <i>gönül coğrafyası</i> (geography of the heart), a widely invoked yet understudied concept through which the Diyanet reimagines Ottoman-Islamic heritage as a sacred topography of civilizational belonging and responsibility. Third, it examines how Europe is situated both outside and within this imagined geography: at once a constitutive and menacing “other” marked by Islamophobia and cultural decay yet also a moral frontier inhabited by Muslim diasporas through whom Turkish Islam extends its reach. By drawing such symbolic boundaries, the Diyanet frames Islam as both religious patrimony and ethical alternative to Western modernity, portraying itself as a key actor in the re-sacralization of modern life across borders.
Nicholas Steel, Clarissa Maria Mercedes Bauer-Staeb, John A Ford
et al.
Summary: Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy across all countries (overall mean –0·18 years [95% UI –0·22 to –0·13]), with all countries having an absolute fall in life expectancy except for Ireland, Iceland, Sweden, Norway, and Denmark, which showed marginal improvement in life expectancy, and Belgium, which showed no change in life expectancy. Across countries, the causes of death responsible for the largest improvements in life expectancy from 1990 to 2011 were cardiovascular diseases and neoplasms. Deaths from cardiovascular diseases were the primary driver of reductions in life expectancy improvements during 2011–19, and deaths from respiratory infections and other COVID-19 pandemic-related outcomes were responsible for the decreases in life expectancy during 2019–21. Deaths from cardiovascular diseases and neoplasms in 2019 were attributable to high systolic blood pressure, dietary risks, tobacco smoke, high LDL cholesterol, high BMI, occupational risks, high alcohol use, and other risks including low physical activity. Exposure to these major risk factors differed by country, with trends of increasing exposure to high BMI and decreasing exposure to tobacco smoke observed in all countries during 1990–2021. Interpretation: The countries that best maintained improvements in life expectancy after 2011 (Norway, Iceland, Belgium, Denmark, and Sweden) did so through better maintenance of reductions in mortality from cardiovascular diseases and neoplasms, underpinned by decreased exposures to major risks, possibly mitigated by government policies. The continued improvements in life expectancy in five countries during 2019–21 indicate that these countries were better prepared to withstand the COVID-19 pandemic. By contrast, countries with the greatest slowdown in life expectancy improvements after 2011 went on to have some of the largest decreases in life expectancy in 2019–21. These findings suggest that government policies that improve population health also build resilience to future shocks. Such policies include reducing population exposure to major upstream risks for cardiovascular diseases and neoplasms, such as harmful diets and low physical activity, tackling the commercial determinants of poor health, and ensuring access to affordable health services. Funding: Gates Foundation.
Md Rahatul Islam Udoy, Shamiul Alam, Md Mazharul Islam
et al.
Digital pixel sensor (DPS) has evolved as a pivotal component in modern imaging systems and has the potential to revolutionize various fields such as medical imaging, astronomy, surveillance, IoT devices, etc. Compared to analog pixel sensors, the DPS offers high speed and good image quality. However, the introduced intrinsic complexity within each pixel, primarily attributed to the accommodation of the ADC circuit, engenders a substantial increase in the pixel pitch. Unfortunately, such a pronounced escalation in pixel pitch drastically undermines the feasibility of achieving high-density integration, which is an obstacle that significantly narrows down the field of potential applications. Nonetheless, designing compact conversion circuits along with strategic integration of 3D architectural paradigms can be a potential remedy to the prevailing situation. This review article presents a comprehensive overview of the vast area of DPS technology. The operating principles, advantages, and challenges of different types of DPS circuits have been analyzed. We categorize the schemes into several categories based on ADC operation. A comparative study based on different performance metrics has also been showcased for a well-rounded understanding.
We study the phenomenology of non-spinning eccentric binary black hole (BBH) mergers using numerical relativity (NR) waveforms and \texttt{EccentricIMR} waveform model, as presented in Ref. \cite{Hinder:2017sxy} (Hinder, Kidder, and Pfeiffer, arXiv:1709.02007). This model is formulated by combining an eccentric inspiral, derived from a post-Newtonian (PN) approximation including 3PN conservative and 2PN reactive contributions to the BBH dynamics, with a circular merger model. A distinctive feature of \texttt{EccentricIMR} is its two-parameter treatment, utilizing eccentricity and mean anomaly, to characterize eccentric waveforms. We implement the \texttt{EccentricIMR} model in \texttt{Python} to facilitate routine use. We then validate the model against 35 eccentric NR waveforms obtained from both the SXS and RIT NR catalogs. We find that \texttt{EccentricIMR} model reasonably match NR data for eccentricities up to $0.16$, specified at a dimensionless reference frequency of $x=0.07$, and mass ratios up to $q=4$. Additionally, we use this model as a tool for cross-comparing eccentric NR data obtained from the SXS and RIT catalogs. Furthermore, we explore the validity of a circular merger model often used in eccentric BBH merger modelling using both the NR data and \texttt{EccentricIMR} model. Finally, we use this model to explore the effect of mean anomaly in eccentric BBH mergers.
Using adiabatic point-particle black hole perturbation theory, we simulate plausible gravitational wave~(GW) signatures in two exotic scenarios (i) where a small black hole is emitted by a larger one ('black hole emission') and (ii) where a small black hole is emitted by a larger one and subsequently absorbed back ('black hole absorption'). While such scenarios are forbidden in general relativity!(GR), alternative theories (such as certain quantum gravity scenarios obeying the weak gravity conjecture, white holes, and Hawking radiation) may allow them. By leveraging the phenomenology of black hole emission and absorption signals, we introduce straightforward modifications to existing gravitational waveform models to mimic gravitational radiation associated with these exotic events. We anticipate that these (incomplete but) initial simulations, coupled with the adjusted waveform models, will aid in the development of null tests for GR using GWs.
Mahbub Ul Islam Khan, Md. Ilius Hasan Pathan, Mohammad Mominur Rahman
et al.
Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-<inline-formula> <tex-math notation="LaTeX">$\phi $ </tex-math></inline-formula> inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.
This research adopts a quantitative research type in analyzing the influence of integrated marketing communication, Islamic service quality and complaint handling on customer loyalty with customer satisfaction as an intervening variable. The population in this study were all customers of the State Savings Bank (BTN) Syariah KCP Ungaran. In determining the sample, the researcher used purposive sampling techniques and the Lemeshow formula to determine the sample size so that 100 respondents were obtained. In testing the hypothesis, this research applies multiple linear regression analysis and path analysis using SPSS 23 software. Based on the results of the hypothesis test, integrated marketing communication has a negative effect on customer loyalty. Islamic service quality has no effect on customer loyalty. Complaint handling has a positive effect on customer loyalty. Integrated marketing communication has no effect on customer satisfaction. Islamic service quality has a positive effect on customer satisfaction. Complaint handling has a positive and significant effect on customer satisfaction. Customer satisfaction has a positive effect on customer loyalty. Customer satisfaction is unable to mediate the influence of integrated marketing communication on customer loyalty. Customer satisfaction is able to mediate the influence of Islamic service quality on customer loyalty. Customer satisfaction is unable to mediate the influence of integrated marketing communication on customer loyalty.
A Duistermaat-Guillemin-Gutzwiller trace formula for Dirac-type operators on a globally hyperbolic spatially compact stationary spacetime is achieved by generalising the recent construction by Strohmaier and Zelditch [Adv. Math. \textbf{376}, 107434 (2021)] to a vector bundle setting. We have analysed the spectrum of the Lie derivative with respect to a global timelike Killing vector field on the solution space of the Dirac equation and found that it consists of discrete real eigenvalues. The distributional trace of the time evolution operator has singularities at the periods of induced Killing flow on the manifold of lightlike geodesics. This gives rise to the Weyl law asymptotic at the vanishing period. A pivotal technical ingredient to prove these results is the Feynman propagator. In order to obtain a Fourier integral description of this propagator, we have generalised the classic work of Duistermaat and Hörmander [Acta Math. \textbf{128}, 183 (1972)] on distinguished parametrices for normally hyperbolic operators on a globally hyperbolic spacetime by propounding their microlocalisation theorem to a bundle setting. As a by-product of these analyses, another proof of the existence of Hadamard bisolutions for a normally hyperbolic operator (resp. Dirac-type operator) is reported.
Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional anti-malware systems using different obfuscation techniques. Therefore, only AI-enabled anti-malware system is robust against these techniques and can detect different features in the malware files that aid in malicious activities. In this study we review two AI-enabled techniques for detecting malware in Windows and Android operating system, respectively. Both the techniques achieved perfect accuracy in detecting various malware families.
The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it becomes very difficult to monitor and identify users activities on these networks. Moreover, such systems have strong defensive mechanisms to protect users against potential risks, including the extraction of traffic characteristics and website fingerprinting. However, the strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. As a result, a substantial body of research has been undertaken to examine and classify encrypted traffic using machine learning techniques. This paper presents a comprehensive examination of the existing approaches utilized for the categorization of anonymous traffic as well as encrypted network traffic inside the darknet. Also, this paper presents a comprehensive analysis of methods of darknet traffic using machine learning techniques to monitor and identify the traffic attacks inside the darknet.
Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy industries. Our goal in this paper is to analyze how those industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. In this work, we propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook. Finally, we release our stance dataset, model, and set of themes related to climate campaigns for future work on opinion mining and the automatic detection of climate change stances.
Our study introduces a new image-to-video generator called FashionFlow to generate fashion videos. By utilising a diffusion model, we are able to create short videos from still fashion images. Our approach involves developing and connecting relevant components with the diffusion model, which results in the creation of high-fidelity videos that are aligned with the conditional image. The components include the use of pseudo-3D convolutional layers to generate videos efficiently. VAE and CLIP encoders capture vital characteristics from still images to condition the diffusion model at a global level. Our research demonstrates a successful synthesis of fashion videos featuring models posing from various angles, showcasing the fit and appearance of the garment. Our findings hold great promise for improving and enhancing the shopping experience for the online fashion industry.
Islam wasathiyah diartikan dengan cara pandang tengah, adil, hidup harmoni di tengah masyarakat yang beragam serta dinamis. nilai humanis-dialogis senantiasa disajikan dalam Islam wasathiya, sehingga setiap individu memiliki tanggungjawab terhadap individu lain dengan solidaritas, simpati dan empati yang terbangun secara organik. Persaudaraan dan kebersamaan senantiasa dikedepankan daripada ego personal dan kelompok. Islam wasathiyah menjadi tawaran kepada masyarakat agar terhindar dari konflik berkepanjangan baik itu atas nama ideologi, kemanusiaan dan sampai kepada politisasi hukum sehingga misi kehadiran Islam sebagai rahmatan lil’alamin akan dirasakan oleh umat manusia bahkan seluruh makhluk di bumi pada akhirnya ketentram dan kedamaian bisa tercapai dalam kehidupan umat manusia
Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood
A novel Face Pyramid Vision Transformer (FPVT) is proposed to learn a discriminative multi-scale facial representations for face recognition and verification. In FPVT, Face Spatial Reduction Attention (FSRA) and Dimensionality Reduction (FDR) layers are employed to make the feature maps compact, thus reducing the computations. An Improved Patch Embedding (IPE) algorithm is proposed to exploit the benefits of CNNs in ViTs (e.g., shared weights, local context, and receptive fields) to model lower-level edges to higher-level semantic primitives. Within FPVT framework, a Convolutional Feed-Forward Network (CFFN) is proposed that extracts locality information to learn low level facial information. The proposed FPVT is evaluated on seven benchmark datasets and compared with ten existing state-of-the-art methods, including CNNs, pure ViTs, and Convolutional ViTs. Despite fewer parameters, FPVT has demonstrated excellent performance over the compared methods. Project page is available at https://khawar-islam.github.io/fpvt/