Miracle-dz/UniFAR-FTUs huggingface dataset:
The Different Impacts of a Strong Dollar on Developed and Emerging Market Economies

The US dollar index has continued to rise and has reached its highest level since 2022. Based on the US dollar's status as the dominant international currency, this round of the dollar's significant and rapid appreciation has a significant impact on the global economic and financial situation. This study aims to examine how this round of dollar appreciation affects the major global economies and the extent of its impact. This paper analyzes the impact of a strong dollar on emerging economies and non-US developed economies and draws a comparison between the two impacts. The findings of this study reveal that the negative impact of the current round of strong dollar on the global economy outweighs the positive impact. This impact is evident in the form of hyperinflation, international foreign debt, and other adverse outcomes. Finally, this paper provides informative comments for emerging economies and non-US developed economies to better mitigate the challenges posed by the current round of dollar appreciation.
Cutaneous Fungal Infections Associated with Pediatric-onset Diabetes: A Case-control Study in the All of Us Research Program

Introduction: With the rapidly increasing incidence of pediatric diabetes mellitus (DM) in the United States, an understanding of the risk of long-term cutaneous consequences, particularly the risk of cutaneous fungal infections, is important. In this study, we evaluate the association between pediatric-onset Type 1 diabetes (T1D) and Type 2 diabetes (T2D) with the later development of cutaneous fungal infections. Methods: Through the All of Us electronic health record database, 300 de-identified participants with a diagnosis of T1D or T2D before the age of 18 were selected at random. These 300 participants, composing our pediatric-onset diabetes cohort, were diagnosed with T1D and/or T2D before the age of 18 and developed cutaneous fungal infections between less than 1 and 24 years later. Each case was age-, race-, and sex-matched to four control participants without T1D or T2D diagnoses, and we compared cutaneous fungal infections between pediatric-onset diabetic cases and controls. Results: Compared to the control cohort, participants with pediatric-onset diabetes were significantly more likely to present in adulthood with candidiasis of the mouth, onychomycosis, pityriasis versicolor, candidiasis of urogenital sites, and unspecified superficial mycosis, as well as dermatophytosis of the body, feet, and perianal regions than their non-diabetic counterparts. Conclusion: With the increasing incidence of pediatric DM, it will be important for clinicians to monitor the long-term cutaneous complications, including the risk of fungal infections, to improve dermatology patient outcomes. Further research is warranted to investigate the role of childhood diabetes intervention and glycemic control in mitigating dermatologic fungal complications through adulthood.
High-Energy Aspects of Inflationary Cosmology

Since the discovery of the cosmic microwave background (CMB), our understanding of the cosmos has been rapidly evolving. Detailed measurements of the CMB temperature fluctuations have led to a standard cosmological model, which traces the origin of the large-scale structure of the universe to quantum fluctuations during inflation. Although the basic framework of inflationary cosmology is now well-established, the microphysical mechanism responsible for the accelerated expansion remains a mystery. In this thesis, we describe how the physics underlying inflation can be probed using two cosmological observables: higher-order correlations of primordial density perturbations (non-Gaussianity) and primordial gravitational waves (tensor modes). In the first part of the thesis, we explore novel signatures of high-energy physics in higherorder correlation functions of inflationary perturbations. First, we use causality and unitarity to make connections between cosmological observations and the underlying short-distance dynamics of single-field inflation. We obtain a constraint on the size and the sign of the four-point function in terms of the amplitude of the three-point function. We then study the imprints of extra massive particles of arbitrary spin on the three-point function. We classify the couplings of these particles to inflationary scalar and tensor perturbations and derive explicit shape functions for their threepoint functions that can serve as templates for future observational searches. Establishing the particle content during inflation would provide important hints for the microscopic theory of inflation. In the second part, we study ways of testing the nature of inflation using inflationary tensor modes. We consider effects of gravitational corrections to Einstein gravity in models of high-scale inflation. We show that these scenarios can lead to a violation of the tensor consistency condition (i.e. the relation between the amplitude and the scale-dependence of the tensor two-point function) that is satisfied by canonical single-field inflationary models. Finally, we consider the prospects for measuring the inflationary superhorizon signature in future observations. We define an estimator that captures superhorizon correlations and present forecasts for the detectability of the signal with future CMB polarization experiments.
Antimicrobial Solutions for Endotracheal Tubes in Prevention of Ventilator-Associated Pneumonia

Ventilator-associated pneumonia is one of the most frequently encountered hospital infections and is an essential issue in the healthcare field. It is usually linked to a high mortality rate and prolonged hospitalization time. There is a lack of treatment, so alternative solutions must be continuously sought. The endotracheal tube is an indwelling device that is a significant culprit for ventilator-associated pneumonia because its surface can be colonized by different types of pathogens, which generate a multispecies biofilm. In the paper, we discuss the definition of ventilator-associated pneumonia, the economic burdens, and its outcomes. Then, we present the latest technological solutions for endotracheal tube surfaces, such as active antimicrobial coatings, passive coatings, and combinatorial methods, with examples from the literature. We end our analysis by identifying the gaps existing in the present research and investigating future possibilities that can decrease ventilator-associated pneumonia cases and improve patient comfort during treatment.
HUMAN ACTIVITY RECOGNITION SYSTEM (HARS) USING HISTOGRAM OF NORMALIZED FOURIER DESCRIPTOR

This paper presents a system, which is able to recognize different continuous human activities in real time from videos using a single stationary camera. The proposed system has a motion descriptor, Called histogram of normalized Fourier descriptor of object contour. After image segmentation the proposed system extract the object contour and estimate the FD ”Fourier descriptor” of that contour then normalize FD to cancel the effect of starting point variation ,Rotation and Scale. The histogram of the normalized Fourier descriptor is used as a feature vector.The authors used three methods for classification Support Vector Machine,One versus all support vector machine and Naive Bayes classifier. The classification by using SVM shows better performance than other methods. Experimental results on two data sets weizman and KTH validate the proposed system reliability and efficiency.
Evaluation of pre-training impact on fine-tuning for remote sensing scene classification

ABSTRACT Fine-tuning a Deep Convolutional Neural Network (DCNN) model obtained from complicated and tedious pre-training on large-scale datasets seems to have become a standard baseline for various tasks with scale-limited datasets. However, there is little work done to investigate into the pre-training’s impact on fine-tuning. In this letter, we systematically verify that pre-training’s impact originating from large-scale datasets in other domain is still insurmountable for full-training with mainstream datasets in the Remote Sensing Scene Classification (RSSC) domain. However, we confirm that this impact is task-related. That is, once the need of pre-training is satisfied, excessive pre-training may lead to generalization degradation of following fine-tuning. This counter-intuitive finding explains why identical pre-training results in different generalization disparity in various tasks. We also provide experimental evidence to show that excessive or noisy pre-training is possible to make fine-tuning perform worse than full-training in some RSSC tasks. It is worth noticing that possible factors, including dataset, initialization, optimization and domain gap are taken into consideration, and more enlightening conclusions are given through this evaluation.
Enhancing Contextualized GNNs for Multimodal Emotion Recognition: Improving Accuracy and Robustness

Emotion recognition from facial expressions is an important research area in the field of artificial intelligence. In this study, a novel deep-learning model is proposed for emotion recognition from facial expressions. The model uses a combination of modifications in COGMEN (Contextualized GNN-based Multimodal Emotion Recognition) to extract features from facial images and other modalities and capture temporal dependencies between frames. The model was evaluated on the IEMOCAP dataset, achieving an accuracy of 87.8%, outperforming (mostly) existing state-of-the-art methods. Furthermore, the model has a lower computational complexity compared to other methods, making it more practical for real-time applications. A high F1 score was reported for each emotion category, indicating good performance across all classes. The results demonstrate the potential method for improving emotion recognition from multiple modalities (i.e.: Audio, Visual & Text) and have implications for a wide range of applications such as affective computing, human-computer interaction, and mental health monitoring.
Curvature based criteria for slow invariant manifold computation: from differential geometry to numerical software implementations for model reduction in hydrocarbon combustion

Mathematical models for chemical kinetics are multiple times scale dynamical systems based on ordinary differential equations and can be reduced in order to decrease the dimensionality of the state space and the complexity of their numerical simulation by restriction to the slow flow. Some model order reduction techniques make use of the identification of low-dimensional, so-called slow invariant attracting manifolds. The focus of this work is on a discussion of a general viewpoint using differential geometric concepts in order to deal with slow invariant manifolds within an extended state space aiming to apprehend those manifolds as intrinsic geometric objects with curvature based criteria. In this context, a computational verifiable necessary condition for the slow invariant manifold graph as a reformulation of an invariance property is stated and proven in this work. Besides, an information theoretical viewpoint in a probability theoretical setting is constructed in order to quantify the information loss of a reduced system in comparison to the corresponding full system by using Shannon’s entropy notion. Moreover, a software package called AMMSoCK being an acronym for A Manifold Based Model Reduction Software for Chemical Kinetics is presented and a detailed documentation can be found in this work. The software can be used to reduce combustion mechanisms which is demonstrated by means of a hydrogen and a hydrocarbon combustion mechanism. The usage of AMMSoCK as well as the numerical results of those reductions are discussed.
STATISTICAL INFERENCE FOR MEASUREMENT EQUATION SELECTION IN THE LOG-REALGARCH MODEL

This article investigates the statistical inference problem of whether a measurement equation is self-consistent in the logarithmic realized GARCH model (log-RealGARCH). First, we provide the sufficient and necessary conditions for the strict stationarity of both the log-RealGARCH model and the log-GARCH-X model. Under these conditions, strong consistency and asymptotic normality of the quasi-maximum likelihood estimators of these two models are obtained. Then, based on the asymptotic results, we propose a Hausman-type self-consistency test for diagnosing the suitability of the measurement equation in the log-RealGARCH model. Finally, the results of simulations and an empirical study are found to accord with the theoretical results.
TransAMR: An Interpretable Transformer Model for Accurate Prediction of Antimicrobial Resistance Using Antibiotic Administration Data

Antimicrobial Resistance (AMR) is a growing public and veterinary health concern, and the ability to accurately predict AMR from antibiotics administration data is crucial for effectively treating and managing infections. While genomics-based approaches can provide better results, sequencing, assembling, and applying Machine Learning (ML) methods can take several hours. Therefore, alternative approaches are required. This study focused on using ML for antimicrobial stewardship by utilising data extracted from hospital electronic health records, which can be done in real-time, and developing an interpretable 1D-Transformer model for predicting AMR. A multi-baseline Integrated Gradient pipeline was also incorporated to interpret the model, and quantitative validation metrics were introduced to validate the model. The performance of the proposed 1D-Transformer model was evaluated using a dataset of urinary tract infection (UTI) patients with four antibiotics. The proposed 1D-Transformer model achieved 10% higher area under curve (AUC) in predicting AMR and outperformed traditional ML models. The Explainable Artificial Intelligence (XAI) pipeline also provided interpretable results, identifying the signatures contributing to the predictions. This could be used as a decision support tool for personalised treatment, introducing AMR-aware food and management of AMR, and it could also be used to identify signatures for targeted interventions.
Unraveling the Impact of Global Warming on Phragmites australis Distribution in Egypt

Climate change is a critical worldwide concern that affects every aspect of existence and all living beings. The study of climate change's impact on invasive species has piqued the interest of researchers worldwide due to the possible ramifications for biodiversity and vulnerable species protection. Phragmites australis (Cav.) Trin. Ex Steud. (Poaceae) is regarded as one of the most crucial invaders with a high tolerance for climate change impacts, particularly increased salinity, temperature, and atmospheric CO 2 , which may alter the surrounding ecosystems, and its uncontrolled spread may result in biodiversity deterioration. Because of its ecological importance and applications, common reed requires sustainable management to reap the majority of its benefits without damaging the environment, which requires a thorough understanding of its behavior in light of climate change. The following review article intends to investigate the response of the common reed to significant climate change factors including as salinity, temperature, and atmospheric CO 2 in Egypt, as well as to highlight rehabilitation solutions. Therefore, it is projected that the common reed population would continue to grow under the current climate change key drivers in Egypt, necessitating greater mitigation and adaption procedures from the government, academia, and society.
Dual-Feed Shared-Radiator Metal-Frame Full-Screen Mobile Phone Antenna for GPS and LTE Bands With a Dual-Function Capacitor

A dual-feed shared-radiator metal-frame full-screen mobile phone antenna is proposed for the global positioning system (GPS) L5 band and the long-term evolution (LTE) bands in this communication. A symmetrical T-slot element and an asymmetrical T-slot element are first separately designed to cover the desired GPS and LTE bands, respectively. With the help of a capacitor, the two individual elements are merged to share a common radiator and are excited by dual coupling feeds. The capacitor has the dual functions of acting as a frequency-adaptive switch and miniaturizing the antenna. The proposed antenna occupies a small clearance area of only 46 $\times $ 1 mm2. A prototype is fabricated and measured for verification. The experimental results illustrate that the −6 dB impedance operating band of the proposed antenna is 1172–1205 and 1410–2790 MHz, which can fully cover the GPS L5 band and the LTE B1/B2/B3/B32/B40/B41 bands with the isolation of better than 14.8 dB. The measured total efficiencies are 52.5%–63.9% for the GPS L5 band and 39.9%–61.5% for the LTE bands. With the merits of small clearance area, wide bandwidth, high isolation, and engineering feasibility due to the dual feeds, the proposed antenna is competitive for metal-frame full-screen mobile phones.
Performance Analysis Of SRAM and Dram in Low Power Application

All electronic systems must function quickly in the current environment, and 80 percent of electronic chips have memory components. SRAM (Static Random Access Memory) has thus become a major key component in many VLSI Chips in order to reduce the size of the memory chips, to increase the speed, to reduce leakage current, and to increase the power efficiency. Due to its high storage density and quick access time, it has also become a popular data storage device. SRAM has been given priority in the research community due to the recent sharp development in low power and low voltage memory devices. In this study, the design and performance of SRAM and DRAM cells were analyzed. This paper outlines the development and application of modified 6T SRAM cell with increased power efficiency.
Monte Carlo simulation-based analysis of unobtrusive PPG monitoring through clothes

The unobtrusive monitoring of vital parameters has recently gained significant importance in home care and the automotive industry to supervise the driver's awareness. In wearable devices, contact-based photoplethysmography (PPG) enables the estimation of heart rate, respiratory rate, blood pressure, and peripheral arterial oxygen saturation through the optical acquisition of blood volume variations in the skin. This work analyzes the feasibility of unobtrusive acquisition of the PPG signal through clothing using Monte Carlo simulations. For that, we suggest a male and a female model of the human back skin covered with six interchangeable clothing items with different tissue, color, and thickness. The arterial pulsation is simulated through blood volume variation in a modeled vessel during systole, diastole, and end-diastole. The absorption coefficients of each clothing were measured with an optical power meter and modelled accordingly. Nine increasing distances of 0 to 2.95 cm between a red (760 nm) or infra-red (900 nm) Light Emitting Diode (LED) and a photodiode (PD) are investigated. The quality of the PPG signal is evaluated by considering the detected light power, the order of the detected light intensities at systole, diastole and end-diastole, and the perfusion index. The simulations revealed that the ability to detect the PPG pulse through clothing depends on the thickness and type of fabric used, the LED-PD distance of the sensor, and its wavelength. The optimal LED-PD distance was found to be 1.2 to 1.8 cm. The infrared LED generally generates reproducible and better quality PPG signals for cotton and polyamide T-shirts, but not for a thick gray cotton hoodie and a thin acrylic red sweater. Besides, significant differences are reported between the male and the female models. Our results evidence the limitations of PPG monitoring through clothes and suppose a step forward in unobtrusive sensors' design for hospitals and smart cars.
Lumped Folded Michelson Interferometer Modulators With Broadband Technologies

Michelson interferometer modulators (MIMs) have superior modulation efficiency but shortcomings in high-frequency performance. We combine broadband technologies, the inductive and T-coil peaking technologies, with folded Michelson interferometer modulators (FMIMs) to make the device footprint more compact and improve the bandwidth of these modulators. A series of FMIMs are explored with different additional electric structures and electrode distribution with only 250-μm modulation areas. The on-chip insertion loss is about 1.22 dB for all FMIMs. Under 2 V reverse bias, the calculated Vπ-Ls of the modulators with GSG and GS electrodes are 0.933 V·cm and 1.284 V·cm. Both broadband technologies can enhance the high-frequency performances of FMIMs. The measured electro-optical (EO) bandwidths can reach 28.4 GHz for inductive peaking FMIMs at −6 V bias, which is 44 GHz for T-coil peaking at –2 V. The T-coil peaking FMIMs with the GSG lumped electrode (LE) and a 30-ohm terminal resistor can achieve a better trade-off between various performances with the clearer eye diagrams of 60 GBaud 4-level pulse amplitude Modulation (PAM4) and 60 Gbps On-Off Keying (OOK).
PREDIKSI HARGA EMAS MENGGUNAKAN METODE LSTM DAN GRU

Prediksi harga emas sangat penting karena menjadi acuan bagi para investor untuk menentukan strategi yang tepat dalam berinvestasi. Tren metode prediksi dalam beberapa tahun terakhir adalah deep learning, yang merupakan subbidang machine learning dan populer dalam menangani masalah prediksi time-series. Dalam penelitian ini, kami membandingkan performa dua model deep learning, yaitu Long Short-Tern Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam memprediksi harga Emas. Hasil dalam penelitian ini menunjukkan bahwa model LSTM memiliki performa lebih baik dibanding model GRU dalam memprediksi harga Emas, dengan hasil perhitungan nilai eror LSTM lebih rendah yaitu nilai MAE sebesar 0.0389, RMSE sebesar 0.0475, dan MAPE sebesar 5.2047%. Dari hasil penelitian ini, kami menemukan bahwa LSTM adalah model yang lebih efektif dan akurat untuk memprediksi harga Emas dibanding LSTM.
ONTOLOGY-DRIVEN CONCEPTUAL MODEL AND DOMAIN ONTOLOGY FOR EGYPTIAN E-GOVERNMENT

: In recent years, online services have received considerable attention worldwide. One crucial online service during the coronavirus disease (COVID-19) pandemic was e-governance. In which governments provides various services to their citizens using information and communication technology. However, the residents of Arab countries have faced numerous of obstacles and have not received the full benefits of e-governance. One of the main reasons is the absence of integration and information sharing. Therefore, in this study, a novel domain ontology for the Egyptian e-government has been proposed. The developed ontology can be used to solve a variety of interoperability problems. The development process starts with building ontology-driven conceptual model using OntoUML. It is one of the most used ontology-driven conceptual modeling languages. The proposed model is then converted to a computable web ontology via the Web Ontology Language. The resulted ontology is evaluated by the OntoMetrics quality metrics. Results are compared with the metrics collected from 20 e-government ontologies and proved that the proposed ontology has better understandability measurements.