Miracle-dz/UniFAR-FTUs huggingface dataset:
TEXT CLASSFICATION USING CNN AND CNN-LSTM

Text classification, language modelling, and machine translation are some of the applications. CNNs excel at retrieving local and position-invariant characteristics, but RNNs excel at classification based on a long-range semantic dependency instead of local key-value pairs. It shows out that CNNs perform admirably when used to NLP problems. The basic (Bow) model is a clear oversimplified based on faulty beliefs, but it has been the usual technique for years and has produced reasonable accuracy. The speed of CNNs is a major selling point. In this paper, Text classification is carried out by using a deep learning model that is CNN and a hybrid model using CNN-LSTM and compare the performance of two models.
The Prevalence of Fallopian Tube Occlusion in Women with Polycystic Ovary Syndrome Seems Similar to Non-Subfertile Women: A Retrospective Cohort Study

There are limited data on how non-infectious risk factors influence tubal patency in women with subfertility. With hormonal shifts influencing tubal secretions, it has been argued that subfertile women with polycystic ovary syndrome (PCOS) have lower tubal patency. In a retrospective study, 216 women, who underwent diagnostic evaluation for PCOS and infertility, were included. Fallopian tube patency was tested using HSG, HyCoSy, and laparoscopic chromopertubation in 171 (79.2%), 28 (13.0%), and 17 (7.9%), respectively. Bilateral patency was found in 193 women (89.4%), unilateral patency in 13 (6.0%) and bilateral occlusion in 10 (4.6%) patients. Women with PCOS phenotypes C (odds ratio, OR 0.179, 95% CI: 0.039–0.828) and D (OR 0.256, 95% CI: 0.069–0.947) demonstrated lower risks for Fallopian tube occlusion. In conclusion, our data suggest that about 5% of infertile women with PCOS also have bilateral tubal occlusion, which seems similar to the rate in non-subfertile women. With 11% of participants having unilateral or bilateral tubal occlusion, this should reassure women with PCOS that their hormonal challenges do not seem to increase their risk for tubal factor subfertility.
On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy

Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.
A Degradation Empirical-Model-Free Battery End-of-Life Prediction Framework Based on Gaussian Process Regression and Kalman Filter

Predicting the battery’s end-of-life (EOL) with uncertainty quantification is critical for ensuring system safety and reliability. This article presents a hybrid framework for battery EOL prediction and its uncertainty assessment based on Gaussian process regression (GPR) and Kalman filter (KF). First, a KF-based empirical-model-free state tracking phase is applied for the available partial battery degradation data. Then, the original time series forecasting problem of degradation curves is converted to the prediction of the virtual degradation rate and acceleration. Next, the prediction of the virtual degradation rate and acceleration is executed by the iterative GPR multistep ahead prediction strategy with moving sliding windows (SWs). Finally, the uncertainty assessment is carried out based on the SW length determination process. The effectiveness of our proposed method is validated on the open-source lithium-ion battery degradation dataset from the University of Oxford. Extensive EOL prediction tests have been carried out from 40% (early-stage), 60% (middle-stage), and 80% (late-stage) of the dataset, respectively. Compared with the popular EOL prediction method within particle filter (PF) framework, the predicted mean EOL cycle by our method is closer to the true value with a smaller range of prediction uncertainty.
The Effects of Alpha-Glycerylphosphorylcholine on Heart Rate Variability and Hemodynamic Variables Following Sprint Interval Exercise in Overweight and Obese Women

The current study examined the effects of Alpha-Glycerylphosphorylcholine (A-GPC) on heart rate variability (HRV) and hemodynamic responses following a sprint interval exercise (SIE) in women who were overweight or obese. Participants (n = 12, 31.0 ± 4.6 years; 29.4 ± 2.1 kg/m2) consumed 1000 mg of A-GPC or a placebo after eating breakfast in a randomized, double-blind cross-over design. After 60 min, participants performed two bouts of the SIE (30 s Wingate) interspersed with 4 min of active recovery (40 rpm). Hemodynamic variables and HRV domains were measured before and 60 min after the A-GPC consumption, immediately after SIE, and every 15 min up to 120 min during recovery. A-GPC consumption increased resting levels of both the time domain (Standard Deviation of RR wave intervals [SDNN] and percentage of interval differences of adjacent RR intervals greater than 50 ms [pNN50%]) and frequency domain (high frequency [HF] and low frequency [LF]) variables of HRV (p < 0.05). Moreover, HRV variables (except for LF/HF) decreased (p < 0.05) immediately after SIE in the A-GPC and placebo sessions. Systolic and diastolic blood pressure increased (p < 0.05) immediately after SIE in both trials. Both HRV and hemodynamic variables recovered (p < 0.05) faster in the A-GPC compared to the placebo session. We concluded that A-GPC consumption recovers HRV and blood pressure faster following strenuous exercise in overweight and obese women, and that it might favorably modify cardiac autonomic function.
Novel Self-adaptive Genetic Algorithm for Solving AC Security Constrained Short-term Hydrothermal Scheduling

In this paper, a novel Self-Adaptive Genetic Algorithm (SAGA) is proposed to solve the AC Security Constrained Short-term Hydrothermal Scheduling (SCSHTS) problem. The efficiency of the proposed algorithm is tested on the benchmark version of the IEEE 30 BUS system, which consists of four thermal power plants and two hydropower plants, as well as an optimization period of two intervals. The new proposed algorithm is then applied to a standard IEEE 30 BUS system, taking into account all necessary system and security constraints. The results obtained from the new proposed SAGA are compared with those obtained from some metaheuristic methods, such as Conventional Cuckoo Search Algorithm (CCSA), Effective Novel Cuckoo Search Algorithm (ENCSA), and Modified Cuckoo Search Algorithm (MCSA). The simulation results indicate that the proposed SAGA provides better solutions than the other optimization methods that have been applied to this optimization problem.
Heart Disease Prediction Model using Deep Learning
Algorithms

The second most common cause of mortality is heart disease. Heart-related illnesses require quick treatment from medical professionals. The requirement for the most recent wireless communication technology is enormous given the global medical technology industry's quick expansion and advancements, notably for the ongoing patient monitoring. Due to the increase in the number of unknown diseases like COVID, earlier disease diagnosis is one of the challenging scenarios for healthcare professionals like doctors and medical technicians, especially for a critical disease that requires immediate treatments. Some of the latest difficulties posed by medical practitioners and service providers in providing high-quality healthcare services opened the way for the implementation of innovative technologies like machine learning, the Internet of Things (IoT), artificial intelligence, and data analytics to improve clinical outcomes. Artificial intelligence is one of those technologies that provide fast solutions for medical diagnosis. This study's primary goal was to suggest a novel medical strategy for earlier illness diagnosis of cardiac problems that need urgent medical attention. This research suggests a deep learning method that helps medical professionals to expedite the diagnosis of patients who require urgent care. The experimental findings demonstrate that the proposed model delivers better result with the prediction accuracy of 91.48%. The model was compared with several existing deep learning models. Additionally, it was determined that the proposed prototype will be more suitable for early disease prediction, particularly for heart diseases and its related diseases.
Constraining the Galactic millisecond pulsar population using Fermi Large Area Telescope

The Fermi Large Area Telescope (Fermi-LAT) has recently revealed a large population of gamma-ray emitting millisecond pulsars (MSPs) in our Galaxy. We aim to infer the properties of the Galactic population of gamma-ray emitting MSPs from the samples detected by the Fermi-LAT. We developed a Monte Carlo model to predict the spatial and gamma-ray luminosity distribution of the Galactic MSP population. Based on the estimated detection sensitivity of Fermi-LAT, we split the model population into detectable and undetectable samples of MSPs. Using a maximum likelihood method, we compared the detectable sample to a set of 36 MSPs detected by Fermi-LAT, and we derived the parameters of the spatial distribution and the total number of gamma-ray emitting MSPs in the Galaxy. The corresponding undetectable sample provided us with an estimate for the expected diffuse emission from unresolved MSPs in the Milky Way. We also applied our method to an extended sample of 66 MSPs that combines firmly detected MSPs and gamma-ray sources that show characteristics reminiscent of MSPs. For the first time our analysis provides gamma-ray based constraints on the Galactic population of MSPs. The radial scale length and vertical scale height of the population is consistent with estimates based on radio data. Our analysis suggests that MSPs do not provide any significant contribution to the isotropic diffuse gamma-ray background emission.
Maternal Nutrition, Body Composition and Gestational Weight Gain on Low Birth Weight and Small for Gestational Age—A Cohort Study in an Indian Urban Slum

Maternal nutritional status and care during pregnancy are essential for adequate birth weight. In this prospective cohort study (N = 1061) in an urban slum, we investigated the association of maternal anthropometry, body composition, gestational weight gain and dietary intakes with low birthweight (LBW, <2.5 kg). About one-third of the women were short (<150 cm), 35% were underweight (<45 kg), 23% suffered from chronic energy deficiency (CED, BMI < 18.5 kg/m2) and another 30% were overweight/obese. The mean age and BMI were 23 years and 21.7 kg/m2, respectively, and haemoglobin was 10.73 g/dL. The mean birthweight (N = 605) was 2.81 ± 0.5 kg, and the average gestational age was 38 ± 2 weeks. About 15% of infants had LBW, and 48% were small for gestational age (SGA). Maternal body composition was assessed by skinfold thickness (SFT) in all trimesters. In the first trimester (N = 762), we found that mean fat-free mass (FFM), fat mass (FM) and body fat percentage (% BF) were 38.86 kg, 11.43 kg and 21.55%, respectively. Low birthweight was significantly associated with preterm deliveries (p < 0.001) and less fat free mass (p = 0.02) in the third trimester. Among other factors were age (p = 0.017), maternal anthropometry (height: p = 0.031; weight: p = 0.059) and fewer antenatal check-ups (p = 0.037). Small size (SGA) was consistently associated with maternal bodyweight at all trimesters (term I, p = 0.013, term II, p = 0.003 and term III, p < 0.001), fat mass in the third trimester (p < 0.001) and maternal height (p = 0.003).
Evaluating the Efficacy of Eravacycline and Omadacycline against Extensively Drug-Resistant Acinetobacter baumannii Patient Isolates

For decades, the spread of multidrug-resistant (MDR) Acinetobacter baumannii has been rampant in critically ill, hospitalized patients. Traditional antibiotic therapies against this pathogen have been failing, leading to rising concerns over management options for patients. Two new antibiotics, eravacycline and omadacycline, were introduced to the market and have shown promising results in the treatment of Gram-negative infections. Since these drugs are newly available, there is limited in vitro data about their effectiveness against MDR A. baumannii or even susceptible strains. Here, we examined the effectiveness of 22 standard-of-care antibiotics, eravacycline, and omadacycline against susceptible and extensively drug-resistant (XDR) A. baumannii patient isolates from Cooper University Hospital. Furthermore, we examined selected combinations of eravacycline or omadacycline with other antibiotics against an XDR strain. We demonstrated that this collection of strains is largely resistant to monotherapies of carbapenems, fluoroquinolones, folate pathway antagonists, cephalosporins, and most tetracyclines. While clinical breakpoint data are not available for eravacycline or omadacycline, based on minimum inhibitory concentrations, eravacycline was highly effective against these strains. The aminoglycoside amikacin alone and in combination with eravacycline or omadacycline yielded the most promising results. Our comprehensive characterization offers direction in the treatment of this deadly infection in hospitalized patients.
Free Vibration Analysis of Centrifugally Stiffened Non Uniform Timoshenko Beams

Rotating beams – like structures are widely used in many engineering fields and are of great interest as they can be used to model blades of wind turbines, helicopter rotors, robotic manipulators, turbo-machinery and aircraft propellers. The governing differential equations of motion in free vibration of a non-uniform rotating Timoshenko beam, with general elastic restraints at the ends are solved using the differential quadrature method, (Bellman & Roth, 1986; Felix et al., 2008, 2009). The equations of motion are derived to include the effects of shear deformation, rotary inertia, hub radius, ends elastically restrained and non-uniform variation of the cross-sectional area of the beam. The presence of a centrifugal force due to the rotational motion is considered as Banerjee has developed, using Hamilton’s principle to capture the centrifugal stiffening arising in fast rotating structures, (Banerjee, 2001). With the proposed model, a great number of different situations are admitted to be solved. Particular cases with classical restraints can be deduced for limiting values of the rigidities. Also step changes in cross-section are considered (Naguleswaran, 2004).
Performance Evaluation of Lane Detection and Tracking Algorithm Based on Learning-Based Approach for Autonomous Vehicle

Disruptive technology, especially autonomous vehicles, is predicted to provide higher safety and reduce road traffic emissions. Lane detection and tracking are critical building blocks for developing autonomous or intelligent vehicles. This study presents a lane detecting algorithm for autonomous vehicles on different road pavements (structured and unstructured roads) to overcome challenges such as the low detection accuracy of lane detection and tracking. First, datasets for performance evaluation were created using an interpolation method. Second, a learning-based approach was used to create an algorithm using the steering angle, yaw angle, and sideslip angle as inputs for the adaptive controller. Finally, simulation tests for the lane recognition method were carried out by utilising a road driving video in Melbourne, Australia, and the BDD100K dataset created by the Berkeley DeepDrive Industrial Consortium. The mean detection accuracy ranges from 97% to 99%, and the detection time ranges from 20 to 22 ms under various road conditions with our proposed algorithm. This lane detection algorithm outperformed conventional techniques in terms of accuracy and processing time, as well as efficiency in lane detection and overcoming road interferences. The proposed algorithm will contribute to advancing the lane detection and tracking of intelligent-vehicle driving assistance and help further improve intelligent vehicle driving safety.
Progress of Research in In Situ Smart Hydrogels for Local Antitumor Therapy: A Review

Cancer seriously threatens human health. Surgery, radiotherapy and chemotherapy are the three pillars of traditional cancer treatment, with targeted therapy and immunotherapy emerging over recent decades. Standard drug regimens are mostly executed via intravenous injection (IV), especially for chemotherapy agents. However, these treatments pose severe risks, including off-target toxic side effects, low drug accumulation and penetration at the tumor site, repeated administration, etc., leading to inadequate treatment and failure to meet patients’ needs. Arising from these challenges, a local regional anticancer strategy has been proposed to enhance therapeutic efficacy and concomitantly reduce systemic toxicity. With the advances in biomaterials and our understanding of the tumor microenvironment, in situ stimulus-responsive hydrogels, also called smart hydrogels, have been extensively investigated for local anticancer therapy due to their injectability, compatibility and responsiveness to various stimuli (pH, enzyme, heat, light, magnetic fields, electric fields etc.). Herein, we focus on the latest progress regarding various stimuli that cause phase transition and drug release from smart hydrogels in local regional anticancer therapy. Additionally, the challenges and future trends of the reviewed in situ smart hydrogels for local drug delivery are summarized and proposed.
THz diffractive optics designed with neural networks

Application of the neural networks to the design of optical elements for the THz spectral range allows to obtain unique, counterintuitive phase delay maps, which can redirect the incident radiation into arbitrarily chosen intensity patterns. In this work, the neural network optimization of three structures is presented – a converging lens, a three-focal-spot lens, and a hologram of a square. The structures have been manufactured using the 3D-printing technique and verified experimentally. Proper functioning of such diffractive elements and high compliance with the numerical simulations has been proved. Proposed diffractive elements can be applied to many areas, including wireless telecommunication or imaging.
Cu2+-Ion-Substitution-Driven Microstructure and Microwave Dielectric Properties of Mg1−xCuxAl2O4 Ceramics

In this work, Cu-substituted MgAl2O4 ceramics were prepared via solid-state reaction. The crystal structure, cation distribution, and microwave dielectric properties of Mg1−xCuxAl2O4 ceramics were investigated. Cu2+ entered the MgAl2O4 lattice and formed a spinel structure. The substitution of Cu2+ ions for Mg2+ ions contributed to Al3+ ions preferential occupation of the octahedron and changed the degree of inversion. The quality factor (Qf) value, which is correlated with the degree of inversion, increased to a maximum value at x = 0.04 and then decreased. Ionic polarizability and relative density affected the dielectric constant (εr) value. The temperature coefficient of the resonant frequency (τf) value, which was dominated by the total bond energy, generally shifted to the positive direction. Satisfactory microwave dielectric properties were achieved in x = 0.04 and sintered at 1550 °C: εr = 8.28, Qf = 72,800 GHz, and τf = −59 ppm/°C. The Mg1−xCuxAl2O4 solid solution, possessing good performance, has potential for application in the field of modern telecommunication technology.
NON-DETECTION OF THE PUTATIVE SUBSTELLAR COMPANION TO HD 149382

It has been argued that a substellar companion may significantly influence the evolution of the progenitors of subdwarf B (sdB) stars. Recently, the bright sdB star HD 149382 has been claimed to host a substellar (possibly planetary) companion with a period of 2.391 days. This has important implications for the evolution of the progenitors of sdB stars as well as the source of the UV excess seen in elliptical galaxies. In order to verify this putative companion, we made 10 radial velocity measurements of HD 149382 over 17 days with the High Resolution Spectrograph at the Hobby–Eberly Telescope. Our data conclusively demonstrate that the putative substellar companion does not exist, and they exclude the presence of almost any substellar companion with P < 28 days and Msin i ≳ 1 MJup.
Measurement of the Neutron Cross Sections for the Reactions 169Tm(n, 2n)168Tm, 169Tm(n,3n)167Tm, and 181Ta(n,2n)180mTa

The cross sections of the (n, 2n) reaction of WZTm and of Y Ta at 14.61 + or - 0.31 MeV were determined relative to the known neutron cross section of the SXAl(n, alpha)SUNa reaction. The resulting values are 2014 + or - 93 and 1269 + or - 46 mb, respectively, when the Ryves et al. decay scheme is followed for the latter. The cross section of the (n, 3n) reaction of WZTm was determined at 18.21 + or - 0.24 MeV, relative to the WZTm (n, 2n) cross section, to be 618 + or - 25 mb. The shapes of the excitation curves for these three reactions were measured in the 12.3- to 18.3-MeV range. The (n, 2n) reactions were normalized at 14.61 MeV; the (n, 3n) reaction, at 18.21 MeV.
Online adaptive algorithm for optimal control with integral reinforcement learning

In this paper, we introduce an online algorithm that uses integral reinforcement knowledge for learning the continuous‐time optimal control solution for nonlinear systems with infinite horizon costs and partial knowledge of the system dynamics. This algorithm is a data‐based approach to the solution of the Hamilton–Jacobi–Bellman equation, and it does not require explicit knowledge on the system's drift dynamics. A novel adaptive control algorithm is given that is based on policy iteration and implemented using an actor/critic structure having two adaptive approximator structures. Both actor and critic approximation networks are adapted simultaneously. A persistence of excitation condition is required to guarantee convergence of the critic to the actual optimal value function. Novel adaptive control tuning algorithms are given for both critic and actor networks, with extra terms in the actor tuning law being required to guarantee closed loop dynamical stability. The approximate convergence to the optimal controller is proven, and stability of the system is also guaranteed. Simulation examples support the theoretical result. Copyright © 2013 John Wiley & Sons, Ltd.