This study presents a novel transfer learning approach and data augmentation technique for mental stability classification using human voice signals and addresses the challenges associated with limited data availability. Convolutional neural networks (CNNs) have been employed to analyse spectrogram images generated from voice recordings. Three CNN architectures, VGG16, InceptionV3, and DenseNet121, were evaluated across three experimental phases: training on non-augmented data, augmented data, and transfer learning. This proposed transfer learning approach involves pre-training models on the augmented dataset and fine-tuning them on the non-augmented dataset while ensuring strict data separation to prevent data leakage. The results demonstrate significant improvements in classification performance compared to the baseline approach. Among three CNN architectures, DenseNet121 achieved the highest accuracy of 94% and an AUC score of 99% using the proposed transfer learning approach. This finding highlights the effectiveness of combining data augmentation and transfer learning to enhance CNN-based classification of mental stability using voice spectrograms, offering a promising non-invasive tool for mental health diagnostics.
Calculations have been made for the double differential cross section (DDCS) for the ionization of metastable hydrogen atoms in the 3S state by electron and positron impact at energies of 150 eV and 250 eV. The authors implemented the second Born approximation to the multiple scattering theory as their model, evaluated the corresponding analytical expressions using Bethe and Lewis integrals, and numerically computed them using MATLAB. The generated DDCS captures the features of both recoil and binary fragmentation and, at the same time, provides an overall qualitative consistency with earlier studies, although some differences can be found between them at certain emission angles. The present work, therefore, supplies new theoretical reference levels for ionization investigations in hydrogen-like systems, now that no experimental data are available for the metastable 3S state.
This investigation is a rigorous theoretical study of the Single Differential Cross Section (SDCS) for the ionization of hydrogen in the 3s state by electron impact computed by means of the First-Born Approximation. The transition matrix has been found by means of the integral process of Bethe-Lewis. The effect of the Coulomb attractive force and the continuum of outgoing radiation have been taken into account in conjunction with the hypergeometric function, which has been used to denote the states of collision. It has hence been possible to deduce the SDCS. for a considerable range of respective incoming electron energies (100 eV to 250 eV). The results show a very distinct marked peak in the rate of ionization taking place for these energies (about 200 eV) with a gradual fall-off after with an increase in energy. The diffuse structure of the wave function in the 3s state serves to a certain extent to make variations in the rate of ionization with that of the incoming electron increased. The final results have been arrived at by means of numerical integrations done so by means of a MATLAB computer, which has yielded very accurate numbers for the cross sections. The results show up very fatally, with experimental results existing at the present date and with theoretical deductions validating the procedure of the FBA. in giving the results of the ionization of excited hydrogen atoms within the field of ionizing processes of electron-atom impact systems. The work gives a most complete basis for the further study of those systems where the processes of ionization and the complexities of the scattering process take place in excited states.
Entanglement generation in long-distance quantum networks is a difficult process due to resource limitations and the probabilistic nature of entanglement swapping. To maximize success probability, existing quantum routing algorithms employ computationally expensive solutions (e.g., linear programming) to determine which links to entangle and use for end-to-end entanglement generation. Such optimization methods, however, cannot meet the delay requirements of real-world quantum networks, necessitating swift yet efficient real-time optimization models. In this paper, we propose reinforcement learning (RL)-based models to determine which links to entangle and proactively swap to meet connection requests. We show that the proposed RL-based approach is 20x faster compared to linear programming. Moreover, we show that one can take advantage of the longevity of entanglements to (i) cache entangled links for future use and (ii) proactively swap entanglement on high-demand path segments, thereby increasing the likelihood of request success. Through comprehensive simulations, we demonstrate that caching unused entanglements leads to a 10-15% improvement in the performance of state-of-the-art quantum routing algorithms. Complementing caching with proactive entanglement swapping further enhances the request success rate by up to 52.55%.
This research examines the istijrar contract as a potential solution to the operational and Shariah compliance challenges commonly encountered in murabahah-based financing, particularly in scenarios involving continuous and repeated purchases. Such flexible purchasing arrangements often generate complexities and elevate compliance risks. To address these issues, the study adopts a structured literature review methodology, implemented in three stages. First, it reviews scholarly debates concerning the Shariah compliance of the istijrar contract. Second, it assesses existing global practices. Third, it analyzes Turkish practices and legal regulations, critically examining the discussions and models at each stage to identify the most suitable adaptation for implementation in Türkiye. Key findings highlight the absence of standardized frameworks in both global and Turkish contexts, along with the limited practical application of istijrar in Türkiye’s Islamic banking sector. In response, this research synthesizes these insights to develop a structured and independent istijrar model specifically tailored to the Turkish Islamic banking environment. It is anticipated that this model will enable Islamic banks to more effectively finance customers engaged in production and trade activities, thereby supporting the growth of the real economy.
Hengki Yandri, Dosi Juliawati, Anabelie V. Valdez
et al.
Bullying, loneliness, and academic stress are pressing psychological challenges frequently encountered by students in pesantren settings. This study aimed to investigate the complex interplay between loneliness, academic stress, and bullying among pesantren students, with particular attention to the implications for Islamic spiritual interventions. Employing a correlational research design, the study involved a sample of the students of Islamic boarding schools in Indonesia, drawn through a multistage random sampling technique from a population of 500 pesantren students. Data were gathered using validated and reliable instruments to assess levels of bullying, loneliness, and academic stress. To investigate the associations among the variables, correlation and regression analyses were employed. The results indicated statistically significant positive associations between loneliness and bullying, as well as between academic stress and bullying behavior, indicating that students who are bullied are more likely to experience loneliness and heightened academic stress. These results underscore the importance of addressing these interconnected psychological issues through holistic approaches. The study highlights the potential of Islamic spiritual responses, such as Islamic counseling and spiritual mindfulness practices, in mitigating the negative psychological effects of bullying, loneliness, and academic stress among students.
Md Rahatul Islam Udoy, Md Mazharul Islam, Elijah Johnson
et al.
This paper presents an innovative in-pixel contrast enhancement circuit that performs image processing directly within the pixel circuit. The circuit can be tuned for different modes of operation. In foreground enhancement mode, it suppresses low-intensity background pixels to nearly zero, isolating the foreground for better object visibility. In contrast enhancement mode, it improves overall image contrast. The contrast enhancement function is customizable both during the design phase and in real-time, allowing the circuit to adapt to specific applications and varying lighting conditions. A model of the designed pixel circuit is developed and applied to a full pixel array, demonstrating significant improvements in image quality. Simulations performed in HSPICE show a nearly 6x increase in Michelson Contrast Ratio (CR) in the foreground enhancement mode. The simulation results indicate its potential for real-time, adaptive contrast enhancement across various imaging environments.
Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana
et al.
People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigorous user evaluation with an additional 20 participants. The outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification.
Fly ash, a by-product of coal combustion in thermal power plants is utilized as a substitute for Portland Cement in concrete due to its pozzolanic properties. Mainly, class F fly ash—produced from the combustion of anthracite coal at approximately 1560˚C (according to SK SNI S15-1990-F)—contains less than 10% lime (CaO) and exhibits significant pozzolanic or filler properties. This research investigates the impact of varying fly ash proportions (0%, 10%, 15%, and 20%) on the compressive strength of concrete. The experimental setup included 108 cylindrical test specimens (10 cm in diameter and 20 cm in height), representing different brands of cement and fly ash mixtures, tested at 7, 14, and 28 days. The study was conducted at PT. Waskita Beton Precast Plant in Sadang, Purwakarta, West Java, targeting a concrete compressive strength of 40 MPa. Results indicate that at 0% fly ash, the compressive strength using Garuda Cement reached 76.66 MPa after 28 days. However, this strength decreased with increasing fly ash content, measuring 72.35 MPa, 69.07 MPa, and 68.13 MPa for 10%, 15%, and 20% fly ash, respectively. These findings highlight the influence of fly ash content on the structural integrity of concrete, suggesting a potential trade-off between sustainability and mechanical performance.
Keyword: Fly Ash, Compressive Strength of Concrete, Portland Cement
We explore the realization of BRST symmetry in the non-Lorentzian Yang-Mills Lagrangian within the context of Galilean and Carrollian Yang-Mills theory. Firstly we demonstrate the nilpotent property of classical BRST transformations and construct corresponding conserve charges for both cases. Then we analyze the algebra of these charges and observe the nilpotent properties at the algebraic level. The findings of this study contribute to a deeper understanding of BRST symmetry in non-Lorentzian Yang-Mills Lagrangians and provide insights into the algebraic properties of related conserve charges.
We study the movement of the living organism in a band form towards the presence of chemical substrates based on a system of partial differential evolution equations. We incorporate Einstein's method of Brownian motion to deduce the chemotactic model exhibiting a traveling band. It is the first time that Einstein's method has been used to motivate equations describing the mutual interaction of the chemotactic system. We have shown that in the presence of limited and unlimited substrate, traveling bands are achievable and it has been explained accordingly. We also study the stability of the constant steady states for the system. The linearized system about a constant steady state is obtained under the mixed Dirichlet and Neumann boundary conditions. We are able to find explicit conditions for linear instability. The linear stability is established with respect to the L-2 norm, H1-norm, and L-infinity norm under certain conditions.
Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.
Penelitian ini menguraikan tentang pelanggaran netralitas yang dilakukan oleh Kelompok Penyelenggara Pemungutan Suara (KPPS) pada Pemilihan Kepala Daerah (Pilkada) di Kota Tangerang Selatan (Tangsel) tahun 2020. Dalam menganalisis fenomena yang terjadi, penulis menggunakan teori electoral management bodies dari International Democracy and Electoral Assistance (IDEA, 2015) untuk mengidentifikasi dan menguraikan berbagai standarisasi tata kelola manajemen penyelenggara pemilu yang berintegritas. Penelitian ini menggunakan metode kualitatif dengan mencari berbagai data dari sumber pemberitaan cetak maupun elektronik dan berbagai sumber yang kredibel berkaitan dengan fenomena yang dikaji. KPPS sebagai lembaga ad-hoc yang memiliki peranan penting dalam menyelenggarakan pemilu maupun pilkada. Berdasarkan kewenangan yang dimiliki oleh KPPS, justru menjadi aktor utama dalam terciptanya malpraktik pemilu khususnya yang terjadi pada Pilkada Kota Tangsel tahun 2020. Temuan dari penelitian ini KPPS terbukti tidak independen, kurang efektifnya bimbingan teknis yang dilakukan oleh KPU, tidak profesional dan tidak transparannya kasus malpraktik ini diselesaikan mengenai prosedur substansial pelaksanaan pemilu.
International relations, Political science (General)
This study aims to determine the growth and development of Islamic education in the time of the Prophet Saw. This research uses a qualitative approach. In obtaining data as research material, researchers use library data (library research) with documentation methods. Based on the results of research analysis, it can be seen that in the time of the Prophet (peace be upon him) education had grown through his teachings and guidance from Allah Almighty through the intermediary of the angel Gabriel. This research reveals that in historical facts the Prophet (peace be upon him) educated in a surreptitious and overt way, due to unstable environmental conditions. The knowledge conveyed by the Prophet (saw) is based on the Qur'an as a guide book for Muslims.
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.
Surgical captioning plays an important role in surgical instruction prediction and report generation. However, the majority of captioning models still rely on the heavy computational object detector or feature extractor to extract regional features. In addition, the detection model requires additional bounding box annotation which is costly and needs skilled annotators. These lead to inference delay and limit the captioning model to deploy in real-time robotic surgery. For this purpose, we design an end-to-end detector and feature extractor-free captioning model by utilizing the patch-based shifted window technique. We propose Shifted Window-Based Multi-Layer Perceptrons Transformer Captioning model (SwinMLP-TranCAP) with faster inference speed and less computation. SwinMLP-TranCAP replaces the multi-head attention module with window-based multi-head MLP. Such deployments primarily focus on image understanding tasks, but very few works investigate the caption generation task. SwinMLP-TranCAP is also extended into a video version for video captioning tasks using 3D patches and windows. Compared with previous detector-based or feature extractor-based models, our models greatly simplify the architecture design while maintaining performance on two surgical datasets. The code is publicly available at https://github.com/XuMengyaAmy/SwinMLP_TranCAP.
In this paper, we propose an automatic brain tumor segmentation approach (e.g., PixelNet) using a pixel-level convolutional neural network (CNN). The model extracts feature from multiple convolutional layers and concatenate them to form a hyper-column where samples a modest number of pixels for optimization. Hyper-column ensures both local and global contextual information for pixel-wise predictors. The model confirms the statistical efficiency by sampling a few pixels in the training phase where spatial redundancy limits the information learning among the neighboring pixels in conventional pixel-level semantic segmentation approaches. Besides, label skewness in training data leads the convolutional model often converge to certain classes which is a common problem in the medical dataset. We deal with this problem by selecting an equal number of pixels for all the classes in sampling time. The proposed model has achieved promising results in brain tumor and ischemic stroke lesion segmentation datasets.
<p>Diploma verification is still done manually. During this time, the diploma is verified by displaying the original diploma. Because the campus does not match the diploma with the existing archives, diploma falsification is possible. IAIN Bukittinggi uses manual methods to verify the authenticity of diplomas. The goal of this research is to create a diploma verification system based on QR CODE to verify the authenticity of diplomas. Research and Development is the type of research employed (R&D). The system development model employs a waterfall approach and the System Development Life Cycle (SDLC). The validity test results are valid, with an average of 0.90. The average practicality test result is 92, indicating that the product is very practical. And the effectiveness test results with an average of 0.90, which is very high.</p>