Using a combination of \textit{Hubble Space Telescope} and \textit{James Webb Space Telescope} imaging, a runaway supermassive black hole (RBH-1) was recently identified with an inferred velocity of $954^{+110}_{-126}\,\mathrm{km\,s^{-1}}$, likely ejected from a compact star-forming galaxy (denoted as GX) at $z \approx 0.96$. Assuming the runaway black hole was the outcome of the gravitational-wave-driven merger of two black holes, we use its measured runaway velocity together with gravitational-wave recoil predictions from numerical relativity and black hole perturbation theory to constrain the mass ratio and spin configuration of the progenitor SMBHs that overcame the final-parsec problem and merged $\sim 70$~Myr ago. We find that the progenitor binary must have been precessing, with a mass ratio $m_1/m_2\lesssim 6$, and that the more massive SMBH must have possessed a high spin (dimensionless spin magnitude $\sim 0.75$) in order to generate a recoil of this magnitude. This has important astrophysical implications as similar SMBH mergers can be an interesting source population for the upcoming LISA mission with signal-to-noise ratios $\gtrsim$ 1000. Furthermore, the progenitor SMBH properties imply that GX was likely formed through a major, gas-rich (``wet'') merger between two galaxies of comparable mass, with a mass ratio $\lesssim 4$.
Dhandeep Challagundla, Ignatius Bezzam, Riadul Islam
In recent years, Compute-in-memory (CiM) architectures have emerged as a promising solution for deep neural network (NN) accelerators. Multiply-accumulate~(MAC) is considered a {\textit de facto} unit operation in NNs. By leveraging the inherent parallel processing capabilities of CiM, NNs that require numerous MAC operations can be executed more efficiently. This is further facilitated by storing the weights in SRAM, reducing the need for extensive data movement and enhancing overall computational speed and efficiency. Traditional CiM architectures execute MAC operations in the analog domain, employing an Analog-to-Digital converter (ADC) to convert the analog MAC values into digital outputs. However, these ADCs introduce significant increase in area and power consumption, as well as introduce non-linearities. This work proposes a resonant time-domain compute-in-memory (TDC-CiM) architecture that eliminates the need for an ADC by using a time-to-digital converter (TDC) to digitize analog MAC results with lower power and area cost. A dedicated 8T SRAM cell enables reliable bitwise MAC operations, while the readout uses a 4-bit TDC with pulse-shrinking delay elements, achieving 1 GS/s sampling with a power consumption of only 1.25 mW. In addition, a weight stationary data mapping strategy combined with an automated SRAM macro selection algorithm enables scalable and energy-efficient deployment across CNN workloads. Evaluation across six CNN models shows that the algorithm reduces inference energy consumption by up to 8x when scaling SRAM size from 32~KB to 256~KB, while maintaining minimal accuracy loss after quantization. The feasibility of the proposed architecture is validated on an 8~KB SRAM memory array using TSMC 28~nm technology. The proposed TDC-CiM architecture demonstrates a throughput of 320~GOPS with an energy efficiency of 38.46~TOPS/W.
The rapid advancement of generative image models has transformed digital media to the point where AI generated images can no longer be reliably distinguished from authentic photographs by human observers or many conventional detection methods. Modern text to image systems such as Stable Diffusion and DALL E can now generate images so realistic that they often appear completely natural, leaving little to no visible artifacts for traditional deepfake detectors to rely on. This challenge has practical consequences for misinformation control, institutional identity verification, and digital trust in political and legal contexts. Instead of searching for hidden pixel level traces, we take a different approach: we observe how an image responds when it is gently disturbed and reconstructed by a diffusion model. We call this behavior diffusion snap back. By tracking how perceptual similarity measures (LPIPS, SSIM, and PSNR) change across different reconstruction strengths, we capture compact and interpretable signals that reveal how closely an image aligns with the diffusion model's learned denoising behavior. Evaluated on a balanced dataset of 4,000 human and AI generated images, the proposed method achieves an AUROC of 0.993 under stratified five fold cross validation and 0.990 on a holdout split using only logistic regression. Initial robustness tests show that the method remains stable under common real world distortions such as image compression and added noise. Although our experiments were conducted using a single diffusion backbone, the results indicate that reconstruction behavior can serve as a reliable and scalable foundation for synthetic media detection as generative models continue to grow more realistic.
Social media platforms play a pivotal role in shaping political discourse, but analyzing their vast and rapidly evolving content remains a major challenge. We introduce an end-to-end framework for automatically inducing an interpretable topic taxonomy from unlabeled text corpora. By combining unsupervised clustering with prompt-based inference, our method leverages large language models (LLMs) to iteratively construct a taxonomy without requiring seed sets (predefined labels) or domain expertise. We validate the framework through a study of political advertising ahead of the 2024 U.S. presidential election. The induced taxonomy yields semantically rich topic labels and supports downstream analyses, including moral framing, in this setting. Results suggest that structured, iterative labeling yields more consistent and interpretable topic labels than existing approaches under human evaluation, and is practical for analyzing large-scale political advertising data.
The printing and labeling industries are struggling to meet the need for more complex and dynamic design requirements coming from the customers. It is now crucial to implement technological advancements to manage workflow, productivity, process optimization, and continual improvement. There has never been a time when the imagery and embellishments of apparel has been more commercially viable as it is now. Images and text are fused directly to fabric by heat transfer printing and labeling. For screen development which is required for heat transfer label mass production, many industries are still using the conventional method of screen development process. A CTS (computer-to-screen) innovates the printing and labeling industries by enhancing workflow, lowering consumable consumptions and chemical usage, speeding up setup, guaranteeing flawless design, and raising the print quality of the producing screens. The study's objective is to assess how CTS machines are used and how they affect existing heat transfer screen development processes in one of Bangladesh's leading printing and labeling companies. The study's primary goal is to highlight and analyze how the use of CTS machines reduces material and operational costs by optimizing the process. Costs for CapEx and OpEx are computed and compared for using CTS technology before and after adoption. Savings data such as material, consumable, and operating cost savings versus depreciation and machine payback period analysis were taken into consideration. It is clear from this study that CTS machines in the printing and labeling industries can guarantee profitability on top of Capital Expenditures.
Machine learning (ML) in Internet of Vehicles (IoV) applications enhanced intelligent transportation, autonomous driving capabilities, and various connected services within a large, heterogeneous network. However, the increased connectivity and massive data exchange for ML applications introduce significant privacy challenges. Privacy-preserving machine learning (PPML) offers potential solutions to address these challenges by preserving privacy at various stages of the ML pipeline. Despite the rapid development of ML-based IoV applications and the growing data privacy concerns, there are limited comprehensive studies on the adoption of PPML within this domain. Therefore, this study provides a comprehensive review of the fundamentals, recent advancements, and the challenges of integrating PPML into IoV applications. We first review existing surveys of various PPML techniques and their integration into IoV across different scopes. We then categorize IoV applications into three key domains and analyze the privacy challenges in leveraging ML in these application domains. Building on these fundamentals, we review recent advancements in integrating various PPML techniques within IoV applications, discussing their frameworks, key features, and performance in terms of privacy, utility, and efficiency. Finally, we identify current challenges and propose future research directions to enhance privacy and reliability in IoV applications.
The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.
We present a comprehensive analysis of late-time tails in gravitational radiation from merging spin-aligned eccentric binary black holes, using high-accuracy point-particle black hole perturbation theory simulations. We simulate the late-time evolution of 15 binary black hole mergers with mass ratio $q = 1000$, dimensionless spins $χ= [-0.9, -0.6, 0.0, 0.6, 0.9]$ and eccentricity at the last stable orbit $e_{\rm LSO} = [0.8, 0.9, 0.95]$. We track the tail amplitudes and exponents up to a retarded time coordinate $t = 9000M$ after merger for the six spin-weighted spherical harmonic modes $(2,1)$, $(2,2)$, $(3,2)$, $(3,3)$, $(4,3)$, and $(4,4)$ employing both frequentist and Bayesian approaches. We note that the tails are increasingly pronounced for binaries with high eccentricity $e_{\rm LSO}$ and large negative spin $χ$. We find that the overall late-time exponents closely approach their predicted asymptotic values ($p=-\ell-4$ for Weyl curvature scalar $ψ_{4,\ell m}$ where $\ell$ is the spin-weighted spherical harmonic index), while estimates restricted to the latest portion of the data exactly recover them. We further verify numerically that modes with the same spherical index $\ell$ share identical tail exponents, while variations in $m$ do not affect the tail behavior. Our analysis framework is publicly available through the gwtails Python package.
Islam Sayah, Ibtissem Chakroun, Claudio Gervasi
et al.
Bacterial nanocellulose (BNC) has gained considerable interest over the last decade due to its unique properties and versatile applications. However, the low yield and the high production cost significantly limit its industrial scalability. The proposed study explores the isolation of new BNC producers from date palm sap and the use of date waste extract as a sustainable carbon source to improve BNC productivity. Results revealed three potential BNC producers identified as <i>Komagataeibacter</i> sp. IS20, <i>Komagataeibacter</i> sp. IS21, and <i>Komagataeibacter</i> sp. IS22 with production yield of 1.7 g/L, 0.8 g/L and 1.8 g/L, respectively, in Hestrin-Schramm (HS) medium. The biopolymer characterization indicated the presence of type I cellulose, a high thermal stability, and a highly dense network made of cellulose nanofibrils for all BNC samples. The isolate IS22, showing the highest productivity, was selected for an optimization procedure using a full factorial design with date waste extract as a carbon source. The BNC yield increased to 6.59 g/L using 4% date waste extract and 2% ethanol after 10 days of incubation compared to the standard media (1.8 g/L). Two probiotic strains, including <i>Bacillus subtilis</i> (BS), and <i>Lactobacillus plantarum</i> (LP) were successfully encapsulated into BNC matrix through a co-culture approach. The BNC-LP and BNC-BS composites showed antibacterial activity against <i>Pseudomonas aeruginosa</i>. BNC–probiotic composites have emerged as a promising strategy for the effective delivery of viable probiotics in a wide range of applications. Overall, this study supports the use of date waste extract as a sustainable carbon source to enhance BNC productivity and reduce the environmental footprint using a high-yielding producer (IS22). Furthermore, the produced BNC demonstrated promising potential as an efficient carrier matrix for probiotic delivery.
The development of digital learning media has grown rapidly over the past decade; however, socioeconomic background has a strong influence on its use. Due to the vast diversity of users, there is no effective map. This study provides a literature review (SLR) on the use of digital technology in mathematics learning for early childhood through an analysis of 16 recent studies (studies that meet the exclusion and inclusion criteria in the PICOS framework). This paper attempts to bridge the gap between digital technology advancements and meaningful mathematics education practices. The conclusion is that game-based educational applications and interactive tools have become a primary approach proven effective in improving the understanding of basic mathematical concepts. However, behind this great potential, real challenges are found in their implementation - from the limitations of long-term studies to disparities in access across socioeconomic backgrounds. This study also highlights the need for synergy between technological innovation and appropriate pedagogical approaches. For educators, these findings provide practical guidance on selecting and implementing learning technologies, while for researchers, several critical areas are identified that require further exploration.
Bani Brota Biswas, Md. Nazmul Islam, Aishwarja Dey
et al.
Antibacterial resistance, fueled by the overuse and misuse of antibiotics, has become a global health concern, undermining the effectiveness of standard treatments against resistant bacterial strains. Pathogenic bacteria utilize mechanisms such as quorum sensing (QS) and biofilm formation to evade antimicrobial treatments, making these processes attractive therapeutic targets. This study investigates Sonneratia caseolaris, a mangrove plant, as a source of bioactive compounds to combat multidrug-resistant bacteria. Phytochemical analysis of the crude extract and its ethyl acetate and water fractions revealed the presence of carbohydrates, saponins, tannins, alkaloids, terpenoids, flavonoids, and steroids. Notably, significant antibiofilm activity (76.82 %, 78.84 %, and 76.06 %), pyocyanin inhibition (77.32 %, 70.73 %, and 67.28 %), and swarming motility inhibition (77.42 %, 78.74 %, and 77.18 %) were observed in the bioactive crude extract, ethyl acetate, and water fractions, respectively, at a concentration of 4 mg/mL. The compound Bis(2-Ethylhexyl) phthalate, isolated from the bioactive ethyl acetate fraction, was identified and characterized through GC-MS analysis. Microbiological assays revealed its potent antibiofilm (62.83 %), pyocyanin inhibition (52.18 %), and swarming motility inhibition (72.5 %) activities. Molecular docking further confirmed its QS inhibitory potential, with strong binding affinities to QS-regulatory proteins. These findings underscore the therapeutic potential of S. caseolaris as a source of novel QS and biofilm inhibitors. By targeting bacterial virulence rather than viability, this study offers a promising and sustainable strategy to address the growing threat of antibacterial resistance, laying the foundation for developing next-generation antimicrobial agents.
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.
Depression, a prevalent and complex mental health issue affecting millions worldwide, presents significant challenges for detection and monitoring. While facial expressions have shown promise in laboratory settings for identifying depression, their potential in real-world applications remains largely unexplored due to the difficulties in developing efficient mobile systems. In this study, we aim to introduce FacePsy, an open-source mobile sensing system designed to capture affective inferences by analyzing sophisticated features and generating real-time data on facial behavior landmarks, eye movements, and head gestures -- all within the naturalistic context of smartphone usage with 25 participants. Through rigorous development, testing, and optimization, we identified eye-open states, head gestures, smile expressions, and specific Action Units (2, 6, 7, 12, 15, and 17) as significant indicators of depressive episodes (AUROC=81%). Our regression model predicting PHQ-9 scores achieved moderate accuracy, with a Mean Absolute Error of 3.08. Our findings offer valuable insights and implications for enhancing deployable and usable mobile affective sensing systems, ultimately improving mental health monitoring, prediction, and just-in-time adaptive interventions for researchers and developers in healthcare.
We demonstrate that the submonoid membership problem and the rational subset membership problem are equivalent in Artin groups. Both these problem are undecidable in a given Artin group if and only if the group embeds the right-angled Artin groups of rank 4 over a path or a square; and this can be characterized using only the defining graph of the Artin group. These results generalize the ones by Lohrey - Steinberg for right-angled Artin groups. Moreover, both these decision problems are decidable for a given Artin group if and only if the group is subgroup separable. This equivalence for right-angled Artin groups is provided by Lohrey - Steinberg and Metaftsis - Raptis. The equivalence for general Artin groups comes from some observations here and the characterization of separable Artin groups by Almeida - Lima.
Additive manufacturing holds more potential to enable the development of ceramic-based components. Ceramics offer high resistance to heat, high fracture toughness, and are extremely corrosion resistant. Thus, ceramics are widely used in sectors such as the aerospace industry, automotive, microelectronics, and biomedicine. Using various additive manufacturing platforms, ceramics with complex and uniquely designed geometry can be developed to suit specific applications. This project aims at innovating high-temperature thermocouples by embedding conductive metal pastes into a ceramic structure. The paste used includes tungsten, molybdenum, and antimony. The metal pastes are precisely extruded into a T-shaped trench inside the ceramic matrix. Following specific temperature ranges, the ceramic matrix is sintered to improve the properties of the material. The sensors produced can function at extremely high temperatures and are thereby suitable for high-temperature environments. Comparative testing of the 3D sintered sensors with conventional temperature sensors shows high correlation between the two classes of sensors. The resulting R-squared value of 0.9885 is satisfactory which implies the reliability and accuracy of 3D sintering sensors are satisfactory in temperature sensing applications.
Teti Ratnawulan, Ricky Yoseptry, Iis Kusmiati
et al.
The background of this research is based on the need to understand the influence and implications of utilizing Information and Communication Technology (ICT) in the learning processes of 10th-grade students at SMAN 1 Banjaran. The implementation of technology in the educational context has become an integral part of enhancing learning quality and influencing students' motivation to learn. A qualitative method was employed. Data were gathered in the field through in-depth, structured interviews, and from literature sources. Findings from the literature review and field study are presented as research outcomes. The presented data are then abstracted with the objective of showcasing facts. In the subsequent stage, this data is interpreted to produce information or knowledge. The study concludes that the use of ICT at SMAN 1 Banjaran, especially by 10th-grade students, has a significant impact on the learning context. Although the majority of students actively use the internet for learning, challenges such as unstable connections and inadequate devices remain barriers. The majority of students show good learning motivation, but there is a small group that is less motivated, emphasizing the importance of a more inclusive learning approach. Nonetheless, the use of technology contributes positively to student engagement. Despite challenges such as access difficulties and technological limitations, more effective utilization of ICT is expected to enhance the overall learning motivation of students at SMAN 1 Banjaran
Technological advances and the development of globalization have led to the growth of global and regional media which ultimately encouraged the emergence of K-Pop culture in Indonesia. The purpose of this study was to determine the influence of watching Korean dramas on the imitation behavior of Pekanbaru students. The theory used is the S-O-R (Stimulus, Organism and Response) theory to explain the data and analysis of the stages of students' imitation behavior towards Korean dramas. The research methodology used is a descriptive qualitative method reinforced by interview data sources with references in the form of relevant articles. The research informants used were students of Pekanbaru Telecommunication Middle School, which were determined through purposive sampling techniques. The results of the study showed that based on the analysis of the SOR theory carried out, there were behaviors that were imitated by Pekanbaru Telecommunication Middle School students from the Korean dramas they watched. The habits imitated by Pekanbaru students were related to fashion, including makeup and skin care, eating habits, and language style or terms in Korean.
Muhammad Amiruddin, Hendy Rifki Saputra Arifin, Syahrul Ramadan
et al.
Perkembangan teknologi sedemikian rupa ini tidak terlepas dari banyaknya pemanfaatan teknologi yang memudahkan kehidupan manusia. Akibat dari perkembangan teknologi juga berdampak di dunia pendidikan yang salah satunya yaitu ujian yang berbasis computer di tingkat sekolah dasar sebagai pengganti dari ujian yang berbasis alat tulis. SDN 1 Gadingkulon merupakan salah satu sekolah dasar yang juga harus memanfaatkan teknologi berbasis computer untuk pembelajaran dan ujian namun banyak siswa yang belum dapat mengorasikannya dengan baik. oleh seban itu sosialisasi dan pendampingan oleh tim pengabdian KKN di SDN 1 Gadingkulon sangat diperlukan. Metode pengabdian ini menggunakan metode Scientific yang dirancang untuk mengkonsep sebuah pemahaman sekaligus praktik. hasilnya menunjukkan bahwa sosialisasi dilakukan pada siswa kelas 5 dan 6 SDN 1 Gadingkulon berjalan dengan baik. Dilanjutkan dengan pengenalan fitur Microsoft Word beserta fungsi dan penggunaan. Kesimpulan kegiatan pengabdian ini ialah sosialisasi dan pendampingan berjalan dengan efektif dan memberikan dampak yang signifikan pada siswa setelah terlaksananya kegiatan tersebut. Sebanyak 90 persen siswa telah mampu mengoperasikan computer terutama pada ujian sekolah yang sebelumnya siswa tidak mengetahui tata cara pengoperasian teknologi computer.
General Works, History (General) and history of Europe
In this paper we investigate the interplay of the Rashba spin-orbit coupling (RSOC) and a topological defect, such as a screw dislocation in an α-T3 Aharonov-Bohm quantum ring and scrutinized the effect of an external transverse magnetic field therein. Our study reveals that the energy spectrum follows a parabolic dependence on the Burgers vector associated with the screw dislocation. Moreover, its presence results in an effective flux, encompassing the ramifications due to both the topological flux and that due to the external magnetic field. Furthermore, we observe periodic oscillations in the persistent current in both charge and spin sectors, with a period equal to one flux quantum, which, however suffers a phase shift that is proportional to the dislocation present in the system. Such tunable oscillations of the spin persistent current highlights potential application of our system to be used as spintronic devices. Additionally, we derive and analyse the thermodynamic properties of the ring via obtaining the canonical partition function through Euler-Maclaurin formula. In particular, we compute the thermodynamic potentials, free energies, entropy, and heat capacity and found the latter to yield the expected Dulong-Petit law at large temperatures.
Victor Prokhorenko, Chadni Islam, Muhammad Ali Babar
An Operating System (OS) combines multiple interdependent software packages, which usually have their own independently developed architectures. When a multitude of independent packages are placed together in an OS, an implicit inter-package architecture is formed. For an evolutionary effort, designers/developers of OS can greatly benefit from fully understanding the system-wide dependency focused on individual files, specifically executable files, and dynamically loadable libraries. We propose a framework, DepEx, aimed at discovering the detailed package relations at the level of individual binary files and their associated evolutionary changes. We demonstrate the utility of DepEx by systematically investigating the evolution of a large-scale Open Source OS, Ubuntu. DepEx enabled us to systematically acquire and analyze the dependencies in different versions of Ubuntu released between 2005 (5.04) to 2023 (23.04). Our analysis revealed various evolutionary trends in package management and their implications based on the analysis of the 84 consecutive versions available for download (these include beta versions). This study has enabled us to assert that DepEx can provide researchers and practitioners with a better understanding of the implicit software dependencies in order to improve the stability, performance, and functionality of their software as well as to reduce the risk of issues arising during maintenance, updating, or migration.