Miracle-dz/UniFAR-FTUs huggingface dataset:
Epileptic Seizure Classification using Multidimensional EEG Spectrograms

Electroencephalography (EEG) is widely used to monitor seizure activity in epileptic patients. There are various types including scalp and intracranial EEG, further EEG is also used in deep brain stimulation devices for seizure management. In many cases, the EEG recordings are over a long period of time and manual reading for locating seizure activity can be labor intensive and prone to error. Machine learning based methods could aid in detecting seizure activity from these long EEG recordings and hence improve patient outcomes. We propose an auto-encoder deep learning method for classifying seizure/non-seizure activity in EEG signals. Towards this, we generate two- and three-dimensional spectrograms for training our deep learning model. Our experimental results show state-of-the-art performance in seizure classification when evaluated on a publicly available EEG dataset. 2D spectrograms can effectively incorporate both time and frequency domain information, and by combining EEG channels for generating 3D spectrograms, we are able to achieve superior performance with a classification accuracy of 99.7 %. We also performed analysis on various EEG segment lengths and channel-wise prediction using our proposed method.
Morphological Patterns of the Long Limb Bones in the Lesser Bandicoot Rat, Bandicota Bengalensis (Rodentia: Muridae)

We examined the morphological patterns of long limb bones in the Lesser Bandicoot rat (Bandicota bengalensis) using univariate, bivariate and multivariate statistical analyses. A total of 18 morphometric measurements were taken using 30 adult specimens (11 males and 19 females) of B. bengalensis. The univariate analysis revealed non-significant right-left difference in the longest limb bones. Therefore, right side limb bones were used for further analyses. For most of the limb bone measurements, the mean values of the males were slightly larger than that of the females. However, the coefficient of variation did not differ significantly between the sexes. Most measurements of the fore limb and hind limb bones were significantly correlated with the length of respective bones. Allometric analysis exhibited isometry for many of the variables of humerus, ulna, femur, and tibia against the length of respective bones. These trends were also implied by the principal component analysis, as high factor loadings were observed for 80% and 75% variables of the forelimb and hindlimb, respectively. The epicondylar regions of the stylopodial bones (humerus and femur) and the width of radius showed non-significant correlation and/or negative allometry. Finally, our results suggest slightly male-biased sexual size dimorphism in the long limb bones and the growth patterns of the bone parts are greatly variable might be linked to the individual bone functions. Bangladesh J. Zool. 51(2): 191-203, 2023
Site-to-site and year-to-year variation in Triticum aestivum–Aegilops cylindrica interference relationships

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winter Triticum aestivum L. (winter wheat)–Aegilops cylindrica Host. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures of A. cylindrica density: fall seedling, spring seedling, and reproductive tiller densities. Two parameters: i, the slope of the yield loss curve as A. cylindrica density approaches zero, and a, the maximum percentage yield loss as A. cylindrica density becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameter a varied significantly among years, but parameter i did not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameter i. Site-to-site and year-to-year variation in winter T. aestivum–A. cylindrica yield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winter T. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.
High Q-Factor Compact Permittivity Sensor Based on Coupled SRR-ELC Metamaterial Element and Metasurfaces Shield

This article describes the development of a microwave sensor with a high Q-factor for measuring permittivity. The compact size of the sensor is deemed essential, leading to the utilization of a coupled line with two coupled split-ring resonators (SRRs) as the main section of this sensor. The coupling between the SRR and two transmission line resonators creates a trap for the electrical field, forming a hot spot and resulting in a high Q-factor sensor. To further improve the Q-factor, the electric inductive capacitive (ELC) element in the ground layer is utilized. However, losses due to the dielectric and surface wave effects are common problems faced in obtaining optimal results. To address these challenges, electromagnetic band gap (EBG) unit-cells are used as metasurfaces to enclose the main sensor element. This set-up establishes an electromagnetic shield, minimizing losses and improving the overall performance of the sensor. The sensor is specifically tuned to operate at 3.36 GHz. The study employs simulation using the finite element method (FEM) and compares the results with experimental data. The material under test (MUT) in this study is FR-4 substrates with photonic band gap (PBG) properties, having a permittivity range from 2 to 4.2. The obtained outcomes are then compared with the effective permittivity derived from the Maxwell–Garnett equation. The measured average sensitivity (<inline-formula> <tex-math notation="LaTeX">${S}_{\text {avg}}$ </tex-math></inline-formula>) of the developed sensor is determined to be 4.68%. Despite its high sensitivity, the sensor maintains a compact size, with total dimensions of <inline-formula> <tex-math notation="LaTeX">$0.4\lambda _{{0}} \times 0.4\lambda _{{0}}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\lambda _{{0}}$ </tex-math></inline-formula> represents the free-space wavelength at the operating frequency.
EMG Classification of Hand and Wrist Force Tasks Using Fractal Algorithms

The hand has excellent functional, aesthetic and social importance. However, Parkinson's disease, stroke, and other myopathies can cause motor impairments. Patients require a rehabilitation program to follow their progress, and one of the tools used to do that is the electromyographic (EMG) signals. This article proposes using algorithms to characterize and classify EMG signals during force exercises for the wrist and forearm. Eight healthy subjects participated in this study. They performed seven exercises, making five trials for each one. Signal features were analyzed in different time windows using a genetic algorithm and machine learning techniques to select the window that maximizes the classification. Combining four electrodes, seven exercises, and 14 algorithms achieved a classification accuracy of 92.41 % using the Multilayer Perceptron classifier. The study demonstrates a highly reliable method for classifying forearm and wrist exercises based on EMG signals, useful for exoskeletons or rehabilitation platforms. Future work will focus on implementing EMG signals to enhance motor rehabilitation therapy and provide findings that will help the scientific community investigate the combination of EEG signals for rehabilitation purposes.
Study of Haematocrit Level in Obstructive Sleep Apnea Syndrome

Obstructive Sleep Apnea Syndrome can lead to oxyhaemoglobin desaturation and has the possibility of stimulating erythropoiesis, leading to an increased haematocrit. The aim of this study is to identify the level of haematocrit in obstructive sleep apnea syndrome (OSAS) patients and to determine the correlation between haematocrit levels and the severity of obstructive sleep apnea syndrome (OSAS). This is a descriptive cross-sectional study at Hospital Universiti Sains Malaysia. All consented patients were interviewed and underwent a polysomnography (PSG) study. The severity of OSAS was based on the apnea–hypopnea index (AHI) and average oxygen desaturation. The blood haematocrit level was also obtained. Thirty-six OSAS patients were in the study group, and 17 patients were in the control group. The mean haematocrit level in the control group was 41.6 (2.5 SD), whereas in the study group the mean haematocrit level was 44.0 (3.27 SD). Pearson correlation analysis showed there was no significant correlation between AHI and haematocrit level (p-value: 0.392), however, there is a significant correlation between haematocrit levels and average oxygen saturation level (P-value: 0.022). The independent t-test between mean haematocrit level between OSAS group and control group showed that there was a significant difference between the mean haematocrit level among the control and OSAS groups. The increased level of haematocrit is significantly correlated with average oxygen desaturation but not with AHI in OSAS patient. Average oxygen desaturation can be considered as a parameter to assess severity and hypoxic stress in OSA patients.
Towards an integration management maturity

As industries continue to digitize at a rapid pace, the number of integrations, the connections between different digital systems, is increasing. As a consequence, integration management is becoming increasingly difficult, leading to scalability, stability, and system outage problems that directly affect company performance. For that reason the modern integration management platforms have emerged to offer efficient management of the growing number of integrations, as well as a new way to handle, enrich, and utilize data both inside and outside organizations.This research involved interviews with 20 software vendor professionals and 20 clients who had recently undergone an integration platform project. The study identified key drivers behind integration management projects and recognized different maturity levels in integration management. The results revealed that more mature companies are utilizing modern integration platforms to support their digital transformation. The study also highlighted the growing importance of data management and utilization in integration solutions.To address these findings, a maturity model was developed to help companies evaluate their current integration management status and find ways to progress in their integration management.
Stratospheric Temperature and Ozone Impacts of the Hunga Tonga‐Hunga Ha'apai Water Vapor Injection

The January 2022 eruption of the Hunga Tonga‐Hunga Ha'apai underwater volcano injected a large amount of water vapor into the mid‐stratosphere. This study uses model simulations to investigate the resulting stratospheric impacts out to 2031. Maximum radiatively‐driven model temperature changes occur in the Southern Hemisphere (SH) subtropics in April–May 2022, with warming of ∼1 K in the lower stratosphere and cooling of 3 K in the mid‐stratosphere. The radiative cooling combined with adiabatic cooling driven by the quasi‐biennial oscillation meridional circulation explains the near‐record cold anomaly observed in the SH subtropical mid‐stratosphere. Projected ozone responses maximize in 2023–2024 as the water vapor plume is transported globally throughout the stratosphere and mesosphere. The excess H2O increases the OH radical, causing a negative global ozone response (2%–10%) in the upper stratosphere and mesosphere due to increased odd hydrogen‐ozone loss, and a small positive ozone response (0.5%–1%) in the mid‐stratosphere due to interference of the NOx catalytic loss cycle by the additional OH. In the lower stratosphere, the excess H2O is projected to increase polar stratospheric clouds and springtime halogen‐ozone loss, enhancing the Antarctic ozone hole by 25–30 DU in 2023. Arctic impact is small, with maximum additional ozone loss of 4–5 DU projected in spring 2024. These responses diminish after 2024 to be quite small by 2031, as the excess H2O is removed from the stratosphere with a 2.5‐year e‐folding time. Given the year‐to‐year variability of the stratosphere, the magnitudes of these ozone responses may be below the threshold of detectability in observations.
Insights into Viral Zoonotic Diseases: COVID-19 and Monkeypox

The onset of the COVID-19 pandemic in 2019 and the sporadic occurrences of monkeypox virus infections have presented substantial hurdles to global healthcare. The appearance of these viruses underscores the persistent menace of infectious diseases. Despite notable strides in medical breakthroughs, encompassing the discovery of novel therapeutics, vaccine advancements, and contemporary surveillance approaches, viral outbreaks persist, leading to millions of infections and fatalities. The consecutive nature of these outbreaks compels us to reassess the depth of our comprehension regarding how viruses transmit, their pathogenicity, and their remarkable adaptability, enabling swift survival. This sequence of events also prompts us to scrutinize the effectiveness of existing therapeutic options, the progress of vaccines, and monitoring procedures within the healthcare sector. This review aims to provide an overview of COVID-19 and monkeypox, delving into their pathogenicity, clinical features, treatment modalities, and challenges.
Novel Oral Anticoagulants in Cardiovascular Practice

In the recent era of medicine, Novel Oral Anticoagulants (Apixaban, Dabigatran, Edoxaban, and Rivaroxaban) have become the preferred drugs for long-term anticoagulation therapy in the majority of cardiovascular conditions, along with non-cardiac co-morbid conditions with few necessary exceptions. This preference is based on their easy availability, therapeutic efficacy, all-cost effectiveness, safety profile, and more convenient usage for both patients and clinicians. Novel Oral Anticoagulants (NOACs) have different pharmacokinetics and pharmacodynamics than oral vitamin K antagonists. This article highlights the basic pharmacology, common complications, available antidotes, and the utility of NOACs in different common cardiovascular diseases requiring long-term oral anticoagulation, including stroke prevention in valvular and non-valvular atrial fibrillation, coronary artery disease, myocardial infarction, left ventricular thrombus and cerebrovascular attacks. NOACs are still underutilized in cardiovascular practice because the concomitant co-morbid conditions hinder a clinician from prescribing these drugs confidently. This manuscript will provide a brief critical overview to help clinicians prescribe NOACs more conveniently.
Viscosity and Density Estimation using Centrifugal Pump Mechanical Vibration and Neural Networks

Centrifugal pumps handle fluids with properties that evolve due to changes in composition. Essential pump fluid properties, such as viscosity and density, significantly influence the performance of multi-stage pumps. This study establishes a correlation between vibration levels and fluid properties. It introduces an approach based on feature extraction and Artificial Neural Networks (ANN) to predict these properties.The methodology begins by gathering mechanical vibration data from a nine-stage pump operating with water or oil. Mechanical vibration signal processing involves applying the Discrete Fourier Transform, selecting frequency bands, and extracting the Root Mean Square as a feature for each frequency band. Extracted features, the pump pressure gain, and the pump rotation speed are the input selected features for an ANN model.The ANN architecture is a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN). The model hyperparameters selection consists of establishing an optimization space and the optimization implementation based on a Tree-structured Parzen Estimator algorithm. Hyperparameter includes the number of layers, the activation functions kind for each layer, and neurons per layer. The obtained fitting results demonstrate errors of less than 3 [cP] within a viscosity range of 1 to 200 [cP] and errors less than 9 [kg/m3] within a density range of 860 to 1000 [kg/m3].
Stratigraphic, geographic, and evolutionary development of the conodont genus Gondolella

The genus Gondolella has been defined as a gondola-shaped platform conodont and is known to be present in rocks of Pennsylvanian, Permian, and Triassic age in many parts of the world. Some 33 names have been proposed of which 19 are considered valid. The earlier species described are from the Desmoinesian Series of Midcontinent North America and the latest are from the Upper Triassic of North America, Europe and Asia. Reduction in size, loss of ridged platform, and narrowing of basal groove accompanied by modification of basal pit are the evolutionary trends noted. All known species have short stratigraphic ranges.
Manufacturing and reliability of a fiber Bragg grating strain sensor designed for uniaxial compression test of rocks

In this work we present an optical Fiber Bragg Grating (FBG) strain sensor appropriate for uniaxial compression test of rocks. The rocks are a complex aggregate of materials and may exhibit in some cases mineral grains of size bigger than the conventional electrical strain gages used. Furthermore, commercial surface mountable optical FBG strain sensors are packaged in planar configuration, which is not appropriate for the cylindrical rock cores in uniaxial compression test. The optical FBG sensors were designed and manufactured manually fitted for cylindrical rock samples. A calibration process was carried out in order to stablish the mechanical performance of the FBG packaging; later, a compression test was conducted on hard rock sample and the strain was recorded by the traditional electrical strain gage (ESG) and FBG system. Under this comparison, the FBG sensors reaches a larger recording area, sensitivity under 100 microstrain and a reliable strain transfer, all of which is appropriate to measure strain for heterogeneous rock composition and minerals grain size.
Cell-Surface GRP78-Targeted Chimeric Antigen Receptor T Cells Eliminate Lung Cancer Tumor Xenografts

Lung cancer is one of the most common and intractable malignancies. It is associated with low survival rates despite existing treatments, indicating that new and more effective therapies are urgently needed such as the chimeric antigen receptor-T (CAR-T) cell immunotherapy. The cell-surface glucose-regulated protein 78 (csGRP78) is expressed in various hematological malignancies and solid tumor cells including lung cancer in response to cancer-related endoplasmic reticulum stress, while GRP78 is restricted to inside the normal cells. Here, we detected the prominent expression of csGRP78 in both lung cancer cell lines, A549 and H1299, as well as cancer stemlike cells derived from A549 by immunofluorescence. Next, a csGRP78-targeted CAR was constructed, and the transduced CAR-T cells were tested for their potency to kill the two lung cancer cell lines and derived stemlike cells, which was correlated with specific interferon γ release in vitro. Finally, we found that csGRP78 CAR-T cells also efficiently killed both lung cancer cells and cancer stemlike cells, resulting into the elimination of tumor xenografts in vivo, neither with any evidence of relapse after 63 days of tumor clearance nor any detrimental impact on other body organs we examined. Our study reveals the capacity of csGRP78 as a therapeutic target and offers valuable insight into the development of csGRP78 CAR-T cells as potential therapy for lung cancer.
Cascaded Model Predictive Control of Underactuated Bipedal Walking with Impact and Friction Considerations

This study demonstrates a cascaded model predictive control (C-MPC) method for input constrained control of underactuated planar bipedal walking with any predefined sta-bilizable trajectory. Our approach aims to increase the trajectory tracking performance of the system and produce realistic and applicable responses while respecting friction constraints and considering impact dynamics. Primarily focusing on zero dynamics with PD (ZD+PD) control, this proposed control method constitutes a second layer controller on top of the well-known trajectory tracking controllers for underactuated bipedal walking systems. We successfully implement this model-based controller and test against large modeling errors, noises, and disturbances, where the conventional ZD+PD control fails to maintain stability.
Magneto-Refractive Effect and Sensing Characteristics of Erbium-Doped Silica Fiber

The magneto-refractive sensing characteristics of erbium-doped silica fiber (EDF) demonstrate significant potential for magnetic field sensing in harsh environments. The refractive index (RI) distribution with different magnetic fields of EDF is investigated. The experiment shows that the RI changes of EDF core region is linear to the magnetic field, the magneto-refractive sensitivity of EDF is $\boldsymbol{-4.60\times 10^{-4}\text{RIU}/\text{mT}}$, which is about three orders of magnitude higher than the single mode fiber (SMF). All solid EDF magneto-refractive sensing measurement system was constructed to detect weak alternating current (AC) magnetic fields. The magnetic field sensing sensitivity in AC magnetic field (500Hz) was measured as $\boldsymbol{4.914\times 10^{-5}\text{rad}/\mu \mathrm{T}}$ with a resolution of 0.37 $\mu \mathbf{T}/\surd\mathbf{Hz}$. The EDF magneto-refractive sensor has good linearity and high magnetic field sensitivity and shows development prospect in all-fiber weak magnetic field detection.
MALL as a Language Learning Tool for Saudi EFL University Learners: An Empirical Study With Reference to TAM

Veteran US technology writer and publisher Fred Davis defined the Technology Acceptance Model (TAM) as characterized by perceived usefulness and usefulness by users. However, these attitudes are largely determined by external variables such as age, gender, and social set up. This study aims to apply an extended conceptual framework of TAM to examine the adoption of MALL by English-major students, encompassing variables such as perceived enjoyment, instructor support, and MALL interactivity as exogenous factors. A questionnaire was administered to 392 English-major students at King Saud University, employing a cross-sectional research design. Using AMOS version 26.0, results from confirmatory factor analysis (CFA) supported the convergent and discriminant validity of the study constructs. The structural model results demonstrated a statistically significant impact of external variables – perceived enjoyment, MALL interactivity, and instructor support – on students' perceptions of MALL usefulness and ease of use. As a result, students' perceived usefulness and ease of use significantly influenced their intention to adopt MALL for language learning. The model accounted for 62% of the variance in MALL tools adoption among Saudi English major students. In conclusion, the extended TAM framework effectively explains the adoption of MALL by Saudi English major students.