Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotations about nonorthogonal axes.

Given a desired three-axis reorientation, it is well known that there are, in general, twelve possible sequences of three successive rotations about orthogonal axes which will perform the reorientation. The present paper determines whether the maneuver can or cannot be accomplished with three successive rotations about arbitrary (not necessarily orthogonal) axes. This result is then employed to derive conditions on the axes which insure that every rotation may be so generated. The associated angles of all the possible three-legged slews are exhibited by a closed analytical procedure which requires no assumptions concerning the degree of nonorthogonal ity nor any approximations. The classical orthogonal case (Euler angles) is then an immediate consequence as a special case of the generalized solution. The development also includes the solutions of single-axis reorientations by two successive rotations about arbitrary fixed lines. Finally, Euler's kinematic equations relative to arbitrary axes are derived.
A method of robust model matching control for nonminimum phase discrete-time systems in the presence of unmodeled dynamics

A method for the design of a robust model matching controller for nonminimum phase discrete-time systems in the presence of unmodeled dynamics is proposed. This controller robustly stabilizes the nominal plant in the presence of unmodeled dynamics and achieves the desired model matching simultaneously. Furthermore, in this method, we introduce the output loop compensator for the unmodeled dynamics. Sufficient condition for stabilizing the nominal plant in the presence of unmodeled dynamics has been established. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.
Joint State of Charge and State of Health Estimation of Lithium-ion Battery Using Improved Adaptive Dual Extended Kalman Filter Based on Piecewise Forgetting Factor Recursive Least Squares

This work aims to improve the accuracy of state of charge estimation for lithium-ion battery, as well as to accurately estimate state of health. This study presents a piecewise forgetting factor recursive least squares method based on integral separation with a second-order resistor-capacitor model and uses a novel adaptive filter based on error covariance correction on the conventional dual extended Kalman filter. The experiments show that the error of SOC estimation is less than 0.61% and the error of SOH is less than 0.09% under different complex conditions, the proposed method can effectively improve the estimation accuracy and robustness.
Seeing the stolen starlight with Herschel

Abstract The Herschel Space Observatory is ESA's fourth Cornerstone mission and will be the largest, most sensitive telescope ever put into space. It will be the first space observatory to observe from the far-infrared to the submillimetre waveband, unveiling the cool, hidden universe for the first time. Herschel will observe stars and galaxies at the stage of formation and discover where all the cosmic dust polluting galaxies comes from. Given the huge public interest in large space missions such as Hubble and Spitzer, Herschel is an ideal opportunity to excite and inform the UK public during the International Year of Astronomy 2009. Here we present some of the education and outreach projects created by the Herschel Outreach Group (HOG).
COVID-19 and Diarylamidines: The Parasitic Connection

As emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants (Omicron) continue to outpace and negate combinatorial vaccines and monoclonal antibody therapies targeting the spike protein (S) receptor binding domain (RBD), the appetite for developing similar COVID-19 treatments has significantly diminished, with the attention of the scientific community switching to long COVID treatments. However, treatments that reduce the risk of “post-COVID-19 syndrome” and associated sequelae remain in their infancy, particularly as no established criteria for diagnosis currently exist. Thus, alternative therapies that reduce infection and prevent the broad range of symptoms associated with ‘post-COVID-19 syndrome’ require investigation. This review begins with an overview of the parasitic–diarylamidine connection, followed by the renin-angiotensin system (RAS) and associated angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSSR2) involved in SARS-CoV-2 infection. Subsequently, the ability of diarylamidines to inhibit S-protein binding and various membrane serine proteases associated with SARS-CoV-2 and parasitic infections are discussed. Finally, the roles of diarylamidines (primarily DIZE) in vaccine efficacy, epigenetics, and the potential amelioration of long COVID sequelae are highlighted.
Updating Models and Their Uncertainties. I: Bayesian Statistical Framework

The problem of updating a structural model and its associated uncertainties by utilizing dynamic response data is addressed using a Bayesian statistical framework that can handle the inherent ill-conditioning and possible nonuniqueness in model updating applications. The objective is not only to give more accurate response predictions for prescribed dynamic loadings but also to provide a quantitative assessment of this accuracy. In the methodology presented, the updated (optimal) models within a chosen class of structural models are the most probable based on the structural data if all the models are equally plausible a priori. The prediction accuracy of the optimal structural models is given by also updating probability models for the prediction error. The precision of the parameter estimates of the optimal structural models, as well as the precision of the optimal prediction-error parameters, can be examined. A large-sample asymptotic expression is given for the updated predictive probability distribution of the uncertain structural response, which is a weighted average of the prediction probability distributions for each optimal model. This predictive distribution can be used to make model predictions despite possible nonuniqueness in the optimal models.
Modeling and Simulation of Inverters for Electric Vehicle Application

This work deal with modeling and simulation based case study for the electric vehicles inverters. Several type of the the inverters have been investigated in this study. Inverters are playing a key role in the fie4ld of electric vehicle applications. Due to the widespread use of vehicles that run on fossil fuels, the transportation sector is a big cause of climate change but also health problems. Countries around the world are trying to switch from vehicles that run on fossil fuels to electric cars (EVs) to cut down on pollution. EVs do have some practical problems, though. One is ”range anxiety,” which is caused by the lack of charging stations, the high cost of looooong EVs, and the short range of affordably priced EVs. One possible solution to the problem of range anxiety is to use range extenders [4], which allow EVs to drive farther while keeping costs and performance as low as possible. Through a range extender, this paper will try to simulate the model of a larger battery for an existing electric vehicle to turn it into a series hybrid vehicle. The main goal of the project is to keep the SOC of the rechargeable battery within a range that the user has already set. The advantages provided by the range extender are in terms of safety and comfort. The use of range extender if properly implemented in affordable EVs Fig. 1. Inverter classifications in published literature has the potential to make the vision of mass market EVs a reality.
Metasurface with Directional Controlled Asymmetric Transmissions

We present a dual-functional metasurface with an asymmetric bidirectional transmission phase. This proposed metasurface comprises bi-layered structures shaped like double arrows with a sub-wavelength metal grating. It enables both linear polarization (LP) conversion for waves incident from opposing directions and independent phase control achieved by introducing rotation and adjusting the geometrical parameters of the double-arrow-shaped structures.
Deep Learning to Predict At-Risk Students’ Achievement in a Preparatory-year English Courses

Predicting learners’ final course achievement is most of the time based on the grades they get on their graded course activities. Thus, it is of great importance for both students and higher education institutions to detect risk instances which can be addressed by the academic institution to support students’ success and academic advancement. In this context, Learning Analytics (LA), which represents learners’ behavior inside Learning Management Systems (LMS), and Deep Learning (DL) techniques come into play as academic data, which can be used to predict learners’ future achievements. It is not surprising that at-risk profiling becomes necessary when there are large numbers of students taking a preparatory course online, for example, where instructors fail to monitor their progress in real-time. Thus, the proposed study aims to utilize neural networks (vRNN, LSTM, and GRU); to build models that predict students’ final grade by classifing them as pass or fail based on their assessment grades. In the training process, the three models, alongside a baseline Multilayer Perceptron (MLP) classifier, were trained on four datasets illustrating students’ LMS activity and final grade results in a two-module English preparatory course in King Abdulaziz University (KAU). The datasets were collected during the first and second semesters of 2021. Results indicate that though all of the three DL models performed better than the MLP baseline, the GRU model achieved the highest classification accuracy on three datasets: (ELIA 103-1, 103-2, and 104-1) with the accuracy values of 92.21%, 97.75%, and 94.34%, respectively. On ELIA 104-2 dataset, both vRNN and LSTM achieved 99.89% accuracy. Considering the prediction of at-risk students, the three DL models achieved high recall values ranging from 65.38% to 99.79. %
Microstructure-Property Correlation and Its Evolution during Aging in an Al4.4Co26Cr19Fe18Ni27Ti5.6 High-Entropy Alloy

The efficient energy use in multiple sectors of modern industry is partly based on the efficient use of high-strength, high-performance alloys that retain remarkable mechanical properties at elevated and high temperatures. High-entropy alloys (HEAs) represent the most recent class of these materials with a high potential for high-temperature high-strength applications. Aside from their chemical composition and microstructure-property relationship, limited information on the effect of heat treatment as a decisive factor for alloy design is available in the literature. This work intends to contribute to this research topic by investigating the effect of heat treatment on the microstructure and mechanical performance of an Al4.4Co26Cr19Fe18Ni27Ti5. 6 HEA. The solution annealed state is compared to aged states obtained at different heat treatment times at 750 °C. The temporal evolution of the matrix and the γ’-precipitates are analyzed in terms of chemical composition, crystallography, size, shape, and volume fraction by means of scanning electron microscopy, transmission electron microscopy, and atom probe tomography. The yield strength evolution and strength contributions are calculated by classical state-of-the-art models as well as by ab-initio-based calculations of the critical resolved shear stress. The findings indicate promising mechanical properties of the investigated alloy and provide insight not only into possible strengthening mechanisms but also into the evolution of main phases during the heat treatment.
Bacterial Assessment of Stethoscopes Used by Healthcare Workers at a Tertiary Care Government Hospital in Bharatpur, Nepal

The transmission of healthcare-associated infections (HCAIs) in healthcare settings is a serious challenge in the medical fraternity. Medical devices, such as stethoscopes used by healthcare workers (HCWs), are likely to harbor a considerable number of pathogenic microbes, which may result in the transmission of HCAIs. This study sought to investigate bacterial contamination of stethoscopes used by HCWs at Bharatpur Hospital, Nepal. During the study period of 3 months from December 2019 to February 2020, a total of 87 stethoscopes were examined; bacterial pathogens were isolated and identified by culture and biochemical tests, and their susceptibilities against different antibiotics were determined using standard protocols of the Clinical and Laboratory Standards Institute (CLSI). The disc diffusion method was used primarily to screen for extended-spectrum beta-lactamase (ESBL)- and metallo-beta-lactamase (MBL)-producing isolates, followed by their confirmation using cephalosporin/clavulanate combination discs and the disc potentiation methods, respectively. In addition, molecular detection of blaCTX-M and blaVIM genes was performed using conventional polymerase chain reaction (PCR). Of the 87 stethoscopes examined, more than a quarter (28.7%) were colonized with different pathogenic bacteria. Bacterial contamination of stethoscopes was found to be significantly associated with various factors, such as disinfecting routine, method of disinfection, and department of the hospital (p < 0.05). A higher rate of bacterial contamination was observed on the diaphragm of the stethoscope (12.64%) and among HCWs who overlooked hand hygiene practices (45.45%). The prevalence of methicillin-resistant S. aureus (MRSA) was 44.44%, and approximately half of the Gram-negative isolates (47%) were multidrug resistant (MDR). Imipenem (81.25%) and chloramphenicol (83.33%) were found to be the most effective antibiotics for Gram-negative and Gram-positive bacteria, respectively. Phenotypic screening showed that 43.75% of isolates were ESBL producers, and 18.75% were MBL producers, but blaCTX-M and blaVIM genes were detected in only 31.25% and 6.25% of isolates, respectively. The results of the study call for effective stethoscope disinfection practices along with the judicious use of antibiotics by HCWs in order to minimize cross-contamination, emergence of resistance, and spread of nosocomial infections in clinical settings.
Evaluation of Pulse Transit Time for Different Sensing Methodologies of Arterial Waveforms

We perform a novel comparative analysis between optically and mechanically derived pulse transit time (PTT) that is universally employed technique for cuffless blood pressure (BP) estimation. For data collection two inline photoplethysmogram (PPG) sensors were mounted at the distal and proximal phalanxes of the index finger of each subject and top each PPG sensor fixture a finger ballistocardiogram (BPP) sensors were clamped. The clamped stacking of the BPP sensors over the PPG sensors provided vertical aligned acquisition of the blood flow waveform through the radial artery for both sensors. The analysis of variance (ANOVA) between PTT derived from the BPP and PPG sensors resulted in a statistically significant difference at $p < 0.05$ . The PTT derived from the BPP sensors showed higher values,17.8 milliseconds on average, than the PTT derived from the PPG sensors. Higher accuracy PTT values will improve the estimation of cuffless BP and thus has the potential to revolutionize the technology.
Beamspace MIMO Systems With Reduced Beam Selection Complexity

Beamspace multiple-input multiple-output (B-MIMO) systems with proper beam selection (BS) promise to lower the radio-frequency (RF) chains overhead with no noticeable degradation in performance. Most of the existing BS schemes are not practical due to the computational cost arising from iterative search or alternating optimization. Hence, this letter examines the complexity reduction of the existing BS with incremental QR precoder (I-QR-P) and decremental QR precoder (D-QR-P). The proposed two-stage and three-stage algorithms reduce the complexity of D-QR-P and I-QR-P, respectively. Both aim to lower complexity by decreasing the candidate beam size by eliminating the beams with no contribution to any user and using matrix perturbation theory to update QR decompositions. Numerical results reveal that the proposed algorithms considerably reduce the complexity while maintaining a similar sum-rate with baseline algorithms.
Facial Emotion-based Music Recommender System using CNN

To find the music as per your emotion is a tedious and time taking task. The music recommendation system has gained a lot of popularity and saves time and energy for the person by playing the right music according to their emotions. Music services make vast volumes of music easily accessible. They are always trying to enhance music organization and search management, addressing the issue of choice, and making it easier to discover new music pieces. Recommendation systems are becoming increasingly common, allowing users to choose acceptable music for any circumstance. However, in terms of emotion-driven suggestions, there is still a gap. Music has a significant impact on humans and is commonly utilized for relaxation, mood control, stress relief, and mental and physical labor. Music therapy may be used in a variety of therapeutic contexts and practices to help people feel better. It is required to construct a suggestion service in addition to looking for expected music objects for customers. The Music Recommendation System (MRS) is designed in this paper to give a personalized music recommendation service using facial emotion-based analysis.
STRIDE: Systematic Radar Intelligence Analysis for ADRD Risk Evaluation With Gait Signature Simulation and Deep Learning

Abnormal gait is a significant noncognitive biomarker for Alzheimer’s disease (AD) and AD-related dementia (ADRD). Micro-Doppler radar (MDR), a nonwearable technology, can capture human gait movements for potential early ADRD risk assessment. In this article, we propose to design a systematic radar intelligence analysis for ARDR risk evaluation (STRIDE) integrating MDR sensors with advanced artificial intelligence (AI) technologies. STRIDE embeds a new deep learning (DL) classification framework. As a proof of concept, we develop a “digital twin” of STRIDE, consisting of a human walking simulation model and an MDR simulation model, to generate a gait signature dataset. Taking established human walking parameters, the walking model simulates individuals with ADRD under various conditions. The radar model based on electromagnetic scattering and the Doppler frequency shift model is employed to generate micro-Doppler signatures from different moving body parts (e.g., foot, limb, joint, torso, and shoulder). A band-dependent DL framework is developed to predict ADRD risks. The experimental results demonstrate the effectiveness and feasibility of STRIDE for evaluating ADRD risk.
Outcomes of Kidney Transplantation in Older Recipients

Background Access to kidney transplantation is limited for elderly patients with end-stage renal disease (ESRD), who often die while on the waiting list or receive kidneys from marginal deceased donors. In our transplantation center, most donated kidneys were from younger living relatives, in whom donations to elderly outcomes were not previously studied. In this study, we aimed to determine the short- and long-term outcomes of patients aged ≥65 years to justify the use of kidneys from younger donors in older recipients. We also compared the outcomes between those who received kidneys from living donors (LDs) and deceased donors (DDs). Material/Methods We analyzed the patients’ demographic data and the 1-, 5-, and 10-year patient and graft survival rates of patients aged ≥65 years who received kidney transplants between January 2005 and December 2020. Results Among 158 patients, 136 received kidneys from LD and 22 from DD. The mean age was 69 years old. In this cohort, the most common cause of ESRD was diabetes. The graft survival rates were 99%, 96%, and 94% after 1, 5, and 10 years, respectively. Patient survival was 94%, 83%, and 61% after 1, 5, and 10 years, respectively. Delayed graft function rates, 1-year patient survival, and 5- and 10-year graft survival rates were lower in the DD group. Ischemic heart disease and transplantation from DD were independent risk factors for mortality. Conclusions Our study demonstrated reasonably good patient and graft survival rates in older patients. Outcomes were better in patients who received kidneys from LD.
Performance Analysis of PV Fed DC-DC Converter for Simultaneous Buck-Boost Operation

The increase penetration of Renewable Energy based Power sources (REPS) has led the emphasis on the use of DC power at the load terminals. The most REPS generate DC power and this DC power can be directly used by utilizing DC converters (DCC). Hence, DCC are trustworthy for accomplish the application of REPS. In this work a DCC is designed which is capable of bi-directional current flow as well as voltage profile with diversity. The DCC proposed can function in buck and boost mode simultaneously to meet the power and voltage ratings of various sources as well as applications. The proposed converter is energized via PV-source which supplies the DC power as per the requirement. The switching requirement of the proposed DCC is also very low with two counts. Moreover, switches can be operated with low voltage ratings which in-turn reduces the switching losses. Also, it offers negligible interference even at high switching frequencies. The proposed DC converter is designed for low as well as high power ratings voltage rating. Hence the presented DCC has various advantages in terms of voltage rating, operational modes as well as high efficiency when operated with DC various voltage ratings at the same time.
Global Prescribed Performance-Based Fault-Tolerant Control for Trajectory Tracking of the ACV With Uncertainty

This paper focuses on a fixed-time prescribed performance-based Fault-tolerant controller that guarantees the global performance for the Trajectory tracking control of the air cushion vehicle (ACV). In the presence of faults, time-varying parameters and environmental disturbances, the proposed control system in this paper has good robustness and control performance. Firstly, an integrated observer for disturbances and faults is proposed to suppress all uncertainties in the system. Then a novel prescribed performance function is designed that does not need to be constrained by the initial value of the target errors while ensuring that it has fixed-time convergence performance. Global prescribed performance control has the advantage of separating the error initial conditions from the prescribed performance function. At the same time, combined with the global sliding mode control method, the fast convergence and robustness of the whole system are guaranteed. Finally, the simulation results verify the effectiveness of the control system.
Compact Nd:YAG and Nd:YVO/sub 4/ amplifiers end-pumped by a high-brightness stacked array

We analyze both theoretically and experimentally the performances of compact laser amplifiers longitudinally pumped by a high-brightness stacked array. Results are given for Nd:YAG and Nd:YVO/sub 4/ as the active media. A model of amplification which takes into account the particular pumping configuration is presented to fit the experimental results. With less than 5-mm-long crystals, single-pass small-signal gain-length products of /spl sim/1.6 on a 1.5 mm /spl times/1.5 mm aperture are demonstrated with optical-to-optical extraction efficiency of /spl sim/25%. The influences of the amplified spontaneous emission, crystal orientations, pump beam characteristics, and standing wave grating on the gain are also analyzed.