Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Neural Speech Recognizer for Quranic Recitations

In this paper, we build on our recent efforts to build a Neural Speech Recognizer (NSR) for Quranic recitations that can be utilized by individuals regardless of their gender, age, or experience. In our previous papers, we presented the Quran recitations by females and males (QRFAM) dataset, which is a large benchmark dataset of audio recordings made by male and female reciters from different age groups and proficiency levels. Along with this dataset, we created several baseline NSR systems based on Mozilla's DeepSpeech model using different subsets of the QRFAM dataset for training, validation and testing purposes. In this work, we present our efforts to optimize and improve these baseline models. We show how to improve the WER by up to $27.34{\%}$ compared to prior work.
Transcriptomic Analysis of Starvation on the Silkworm Brain

Simple Summary To shed light on the influence of starvation on animal brains, a comprehensive transcriptome analysis was carried out on silkworms. The subsequent enrichment analysis unveiled noteworthy changes in pathways associated with tissue structure, hormone metabolism, longevity, nucleic acid metabolism, immune response, and disease within the silkworm brain after the period of starvation. Abstract Starvation imposes significant stress on animal survival and development, resulting in organ damage within the organism. The brain, being one of the most vital organs in animals, plays a crucial role in coordinating the physiological functions of other organs. However, performing brain experiments on the human body is challenging. In this work, we selected the silkworm, a model Lepidoptera organism, due to its favorable characteristics. A comprehensive transcriptome analysis was conducted on the brain of silkworm subjected to starvation treatment. The analysis of differentially expressed genes revealed significant alterations in 330 genes following the period of starvation. Through an enrichment analysis, we successfully identified pathways associated with metabolism, hormones, immunity, and diseases. Our findings highlight the transcriptional response of the brain to starvation, providing valuable insights for comprehending the impact of starvation stress in other animals.
A new estimator of proportion with a linear function using data from two-decks randomized response model

ABSTRACT A new estimator for estimating the proportion of a potentially sensitive attribute in survey sampling has been introduced by solving a linear equation. The proposed estimator has been compared with the estimator proposed by Odumade and Singh (2009) with equal protection to all of the respondents. The asymptotic properties of the proposed estimator are investigated through exact numerical illustrations for different choices of parameters. A non randomized response approach has been suggested. A scope for further research has also been pointed out.
Two-Photon Excited Fluorescence Lifetime Imaging of Tetracycline-Labeled Retinal Calcification

Deposition of calcium-containing minerals such as hydroxyapatite and whitlockite in the subretinal pigment epithelial (sub-RPE) space of the retina is linked to the development of and progression to the end-stage of age-related macular degeneration (AMD). AMD is the most common eye disease causing blindness amongst the elderly in developed countries; early diagnosis is desirable, particularly to begin treatment where available. Calcification in the sub-RPE space is also directly linked to other diseases such as Pseudoxanthoma elasticum (PXE). We found that these mineral deposits could be imaged by fluorescence using tetracycline antibiotics as specific stains. Binding of tetracyclines to the minerals was accompanied by increases in fluorescence intensity and fluorescence lifetime. The lifetimes for tetracyclines differed substantially from the known background lifetime of the existing natural retinal fluorophores, suggesting that calcification could be visualized by lifetime imaging. However, the excitation wavelengths used to excite these lifetime changes were generally shorter than those approved for retinal imaging. Here, we show that tetracycline-stained drusen in post mortem human retinas may be imaged by fluorescence lifetime contrast using multiphoton (infrared) excitation. For this pilot study, ten eyes from six anonymous deceased donors (3 female, 3 male, mean age 83.7 years, range 79–97 years) were obtained with informed consent from the Maryland State Anatomy Board with ethical oversight and approval by the Institutional Review Board.
Decisional Needs of People From Minority Ethnic Groups Around Living Donor Kidney Transplantation: A UK Healthcare Professionals’ Perspective

Despite improved patient and clinical outcomes, living donor kidney transplantation is underutilized in the United Kingdom, particularly among minority ethnic groups, compared to deceased donor kidney transplantation. This may in part be due to the way in which kidney services present information about treatment options. With a focus on ethnicity, semi structured interviews captured the views of 19 kidney healthcare professionals from two renal centres in West Yorkshire, about the decisional needs and context within which people with advanced kidney disease make transplant decisions. Data were analysed using thematic analysis. Themes were categorized into three groups: 1) Kidney healthcare professionals: language, cultural awareness, trusted personnel, and staff diversity, 2) Patient information resources: timing and setting of education and suitability of patient-facing information and, 3) People with advanced kidney disease: knowledge, risk perception, and cultural/religious beliefs. To our knowledge, this is the first study in the United Kingdom to investigate in depth, healthcare professionals’ views on living donor kidney transplantation decision making. Six recommendations for service improvement/delivery to support decision making around living donor kidney transplantation among minority ethnic groups are described.
Approximate Measurement Invariance

When comparing data from different countries, time points, or groups, we run into at least two problems. First, we want to avoid large measurement artifacts that lead to erroneous substantive conclusions [1, 2]. For example, when comparing Finnish to Columbian survey answers, we may want to account for any differences in exuberance. Second, we want to ignore the – likely plentiful – small measurement artifacts whose effect on substantive conclusions is negligible [3, 4]. For example, when comparing Finns in 2002 with Finns in 2004 on an income question, most of the differences found are likely to be substantive; we would not want to spend an inordinate amount of time and modeling power on identifying all the small measurement differences between these already highly comparable groups. Tests for the presence or absence of measurement differences are typically called measurement invariance tests, sometimes also known as tests of differential item functioning [5] or item bias [6, 7]. Techniques to test for measurement invariance are numerous [8] but, for the purposes of this chapter, can be described as broadly falling into one of two categories: exact and approximate. In the exact methods (see Refs. [9–11] and Chapter 40, this volume), the researcher looks for a measurement model in which any small measurement Approximate Measurement Invariance Kimberley Lek, Daniel Oberski, Eldad Davidov , Jan Cieciuch, Daniel Seddig, and Peter Schmidt
Optimasi Deep Convolutional Neural Network (Deep CNN) untuk Deteksi Aritmia Melalui Sinyal EKG Menggunakan Arsitektur Conv1D

Cardiovascular disease (CVD) adalah penyebab signifikan morbiditas dan mortalitas di seluruh dunia. Aritmia menjadi salah satu kondisi yang paling parah dari CVD. Penyakit ini mengacu pada ketidakteraturan denyut atau irama jantung. Penyakit aritmia dapat diidentifikasi melalui rekaman sinyal elektrokardiogram (EKG). Pada penelitian ini, dilakukan data cleaning yang bertujuan untuk menghilangkan missing value pada dataset. Adapun metode yang digunakan untuk klasifikasi adalah Deep Convolutional Neural Network (Deep CNN) dengan arsitektur Conv1D. Oleh karena itu, dilakukan perubahan dimensi input menjadi satu dimensi. Data hasil perubahan dimensi dilanjutkan dengan proses normalisasi data menggunakan metode standar (z-score normalization) dan pembobotan dengan menggunakan WeightedRandomSample. Hal ini dilakukan agar didapatkan keseimbangan data. Selanjutnya, dataset diekstrasi dan diklasifikasikan dengan Deep CNN. Pemilihan Deep CNN dalam ekstraksi fitur karena dapat mengektraksi fitur yang sangat kompleks dari sinyal EKG sehingga dapat mencegah kehilangan informasi-informasi penting pada dataset. Selanjutnya dilakukan evaluasi menggunakan Confusion Matrix. Dari hasil evaluasi Deep CNN dengan arsitektur Conv1D pada deteksi arimtia menghasilkan akurasi 98,88% yang lebih tinggi dari metode sebelumnya.
Approximate finite‐time control for a class of uncertain nonlinear systems with dynamic compensation

In this work, a new robust nonlinear feedback control method with dynamic active compensation is proposed; the active control method has been applied to an integral series of finite‐time single‐input single‐output nonlinear system with uncertainty. In further tracking the closed‐loop stability and nonlinear uncertainty, an extended state observer has been employed to solve the immeasurability and nonlinear uncertainty within a nonlinear system. A singular perturbation theory has been used to solve for the finite‐time stability of the closed‐loop system; furthermore, numerical simulations showed that the robust nonlinear feedback controller tracked the uncertainty in a nonlinear Duffing‐type oscillator and has proven the effectiveness of the approximate finite‐time control strategy proposed. By using an approximate finite‐time control approach with active compensation, the uncertainty in a nonlinear system could be accurately estimated and controlled. Copyright © 2017 John Wiley & Sons, Ltd.
A Multi-Level Label-Aware Semi-Supervised Framework for Remote Sensing Scene Classification

Semi-supervised learning (SSL) is a promising approach to reduce the labeling burden in remote sensing scene classification tasks. However, most semi-supervised methods typically exploit the single-level semantic information of unlabeled data, ignoring the multi-level semantic structure prevalent in remote sensing data. The multi-level semantic structure, which contains the correlation of different categories and the multi-granularity semantic information, can help the scene classification model to more accurately measure the feature distance between different categories and more effectively utilize unlabeled data. Therefore, this article proposes a multi-level label-aware (MLLA) semi-supervised scene classification framework, MLLA, which extends the semantic information captured in unlabeled data from single-level to multi-level to improve the scene classification performance. Specifically, we first propose a multi-level prototype awareness module to capture the multi-level semantic structure underlying remote sensing data. Then, based on this structure, a multi-level pseudo-label generation module is designed to assign multi-level pseudo-labels to the unlabeled data. Finally, by combining the labeled samples and the multi-level pseudo-labeled samples, the scene classification model is progressively trained. The experimental results on three benchmark datasets show that the proposed MLLA achieves excellent performance compared to other semi-supervised classification methods.
Analysis of Sexually Transmitted Infections Prevention Campaigns between 2008 and 2020

Abstract This study aimed to analyze the sexually transmitted infections (STIs) prevention campaigns promoted by the Brazilian Ministry of Health between 2008 and 2020. Fifty-three campaigns from the digital archive were included in the directed content analysis. The analysis was conducted based on the concepts of Combination Prevention and the Theory of Planned Behavior (TPB). Condom use and STI testing were constantly promoted, to the detriment of alternative preventive behaviors. The TPB concepts in the campaigns suggest the compatibility of the materials with the theory. We conclude that the campaigns present the desirable aspects of prevention. However, recent campaigns focused on aversive materials, which goes against studies that report negative effects from these strategies and is associated with the rise of moralist perspectives on prevention.
Frequency-Agile SIW-Based Filtenna Using Evanescent-Mode Cavity Resonators

A miniaturized second-order tunable filtenna using substrate-integrated waveguide (SIW) technology is presented in this letter. The coupled-resonator theory for bandpass filter synthesis is applied to combine high-$Q$ evanescent-mode (EVA-mode) cavity resonators with a radiating slot antenna into a second-order filtenna unit. External long-range linear actuators are deployed to achieve frequency reconfiguration. The capacitive loading of the resonator is varied by changing the critical gap size between the posttop and tuning disc of the EVA-mode cavity. Using a contactless capacitive tuning scheme, the filtenna's center frequency is varied from 2 to 2.6 GHz (26%). The manufactured filtenna prototype exhibits a realized gain ranging from 2.7 to 5.2 dB across its tuning range. In addition to maintaining a compact module, the filtenna also achieves higher out-of-band radiation suppression due to the integrated filtering capability. To the best of authors' knowledge, the proposed filtenna is the first tunable cavity-based SIW filtenna presented in the literature with complete experimental validation.
CHALLANGES AND PROSPECTS FOR TOMATO PRODUCTIVITY IN RESPONSE TO CLIMATIC VARIATIONS: EVIDNECES FROM KHYBER PAKHTUNKHWA-PAKISTAN

Climate change is the momentous and persisting change in the world’s temperature, precipitation, humidity, and other climatic variables. This study, therefore estimated the impact of climatic variations on tomato productivity across agro ecological zones of Khyber Pakhtunkhwa, Pakistan. Panel data for 28 years (1991-2018) across the six districts of the agro ecological was used due to availability of data on tomato productivity and climatic variables. Yield of tomato, area, maximum temperature and rainfall were included in the final estimated model. The results indicate that the average maximum temperature and average maximum temperature square have a significant impact on tomato yield. Average maximum temperature has positive coefficient while the average maximum temperature square has a negative coefficient. This demonstrates that, at first, the tomato yield increases as the temperature rises. It reaches the maximum at the critical temperature (34.95ºC) but shows a decline once the temperature rises from the critical value.
Closed combination of context-embedding iterative strategies

This work is motivated by the challenging problem of the computer-aided generation of approximations (viewed as a series of transformations) of partial derivative equations. In this framework, the approximations posed over complex settings are incrementally constructed by extending an approximation posed on a simple setting and combining these extensions. In order to formalize these extensions and their combination, we introduce a class of rewriting strategies, called context-embedding iterative strategies (CE-strategies, for short). Roughly speaking, the class of CE-strategies is constructed by means of adding contexts and an iteration operator allowing the definition of recursive strategies. We show that the class of CE-strategies is closed under combination with respect to a correctness-completeness criterion. It turns out that the class CE-strategies enjoy nice algebraic properties, namely, the combination is associative, has a neutral element, and all the elements are idempotents.
Quantifying barotropic and baroclinic eddy feedbacks in the persistence of the Southern Annular Mode

Understanding the persistence of the Southern Annular Mode (SAM) is important for the intraseasonal and decadal predictability of SAM. Using the ERA‐40 and ERA‐Interim reanalysis data, this study introduces a new method to quantify the relative roles of barotropic and baroclinic eddy feedbacks in the SAM persistence. Through a hybrid Eulerian‐Lagrangian Finite Amplitude Wave Activity diagnostic, it is found that (i) transient wave activity is important in driving the SAM, but it provides a negative feedback to the SAM persistence. (ii) Irreversible potential vorticity mixing, through barotropic processes in the upper troposphere, plays an important role in driving and sustaining the SAM variability. Particularly, following the poleward shift of the eddy‐driven jet, the reduction/enhancement in effective diffusivity on the jet's poleward/equatorward flank can be understood by a stronger/weaker zonal jet acting as a robust/leaky mixing barrier. (iii) Baroclinic eddy generation and vertical wave propagation mainly act to sustain the SAM variability.
Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB, ApoA1 and the ratio were determined using a Bayesian approach

ABSTRACT We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification.
The prognostic significance of KLRB1 and its further association with immune cells in breast cancer

Background Killer cell lectin-like receptor B1 (KLRB1) is an important member of the natural killer cell gene family. This study explored the potential value of KLRB1 as a breast cancer (BC) biomarker and its close association with the tumor immune microenvironment during the development of BC. Methods We examined the differential expression of KLRB1 in pan-cancer. Clinical and RNA-Seq data from BC samples were evaluated in The Cancer Genome Atlas (TCGA) and validated in Gene Expression Omnibus (GEO) datasets and by immunohistochemistry (IHC) staining. The relationship between KLRB1 and clinical parameters was explored through Chi-square tests. The diagnostic value of KLRB1 was evaluated using a receiver operating characteristic (ROC) curve. Survival analysis was tested by Kaplan-Meier curves to demonstrate the relationship between KLRB1 and survival. Univariable and multivariate cox regression analyses were carried out as well. The analysis of immune infiltration level and gene set enrichment analysis (GSEA) were conducted to examine KLRB1’s mechanism during the progression of BC. We used the Tumor Immune Estimation Resource (TIMER), the Cancer Single-cell Expression Map (CancerSCEM) database, the Tumor Immune Single-cell Hub (TISCH) database, and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method to explore KLRB1’s association with immune infiltration level and different quantitative distribution of immune cells. The relevant signaling pathways in BC associated with KLRB1 were identified using GSEA. Results The expression of KLRB1 was downregulated across the majority of cancers including BC. The lower KLRB1 expression group exhibited shorter relapse free survival (RFS) and overall survival (OS). IHC staining showed that KLRB1 staining was weaker in breast tumor tissues than in paratumors. Additionally, GSEA identified several pathway items distinctly enriched in BC. KLRB1 expression level was also positively related to the infiltrating number of immune cells in BC. Moreover, the CancerSCEM and TISCH databases as well as the CIBERSORT method demonstrated the close relationship between KLRB1 and immune cells, particularly macrophages. Conclusion Low KLRB1 expression was considered an independent prognostic biomarker and played an important role in the tumor immune microenvironment of BC patients.
A Multi-Connectivity Architecture with Data Replication for XR Traffic in mmWave Networks

mmWave communications are paving the way for next generation cellular networks due to their inherent ability to provide high data rates and mitigate interference. However, the intermittency of mmWave links and the advent of low-latency eXtended Reality(XR) applications presents a conundrum for satisfying strict QoS constraints. We propose a multi-tiered multi-connectivity network architecture which exploits mmWave macro-diversity to allow users (UEs) to connect to multiple base stations (gNBs) simultaneously and rapidly switch between them. The power of our proposed architecture lies in multiple tiers of multi-connectivity, wherein we selectively replicate UE data at a subset of associated gNBs in order to improve the response time to blockages. We evaluate the performance of XR traffic with standard scheduling algorithms. Our results show that connecting to multiple gNBs and enabling the scheduler to rapidly switch between them shields the UEs from higher handover delays and minimizes data plane interruptions. Although we show that our network architecture allows for much better performance even with conventional scheduling algorithms, we also highlight the need for better scheduling algorithms optimized for the multi-connectivity paradigm.