Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature extraction and fusion using deep convolutional neural networks for Covid-19 detection using CT and X-RAY images

COVID-19 represents a novel variant of the coronavirus disease, having rapidly disseminated across the globe. In recent studies pertaining to computer vision, image processing, and classification techniques, numerous methodologies have been introduced employing chest X-ray images and computerized tomography (CT) images. This investigation introduces novel fused deep features founded on CT and X-ray image classification, with the aim of detecting COVID-19 disease. The proposed approach encompasses three key phases: deep feature generation, iterative feature selection, and classification. During the feature generation phase, well-known pre-trained deep convolutional neural networks, namely DenseNet201, MobileNetV2, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were leveraged. Each network model generated 1000 features, which were subsequently fused, culminating in the acquisition of a final feature vector comprising 7000 dimensions. In order to distill the most pertinent information, the ReliefF and iterative maximum relevance minimum redundancy (RFImRMR) feature selection techniques were employed in the generation of the ultimate feature vector. To evaluate the performance of the proposed method, publicly available datasets of CT images and X-ray images were employed. Notably, the suggested deep learning approach attained accuracy rates of 99.33% and 93.10% for COVID-19 detection using CT and X-ray images, respectively. These achieved results serve as compelling evidence substantiating the efficacy of the proposed fused deep features and RFImRMR-based COVID-19 detection.
Penalized Regression Versus Random Forest Model in Analyzing High Dimensional Proteomic Data: Diagnosis of IgA Nephropathy

Background: Immunoglobulin A nephropathy (IgAN) is considered a chronic renal disease and the most prevalent glomeru-lonephritis throughout the world. In order to model a large number of extracted biomarkers and identify the most eﬀective biomarkers on IgAN disease, the researchers implemented 2 methods of penalized regression, known as LASSO and MCP logistic regression versus random forest method, which are appropriate for high dimensional and low sample size problems. Methods: Urinary protein proﬁles for both groups were composed of 493 proteins. Data were obtained in the case group (13 patients) using an experiment on urinary protein proﬁle of patients with IgAN and in the control group (8 healthy individuals) using nanoscale liquid chromatography with tandem mass spectrometry. Mann Whitney test as univariate analysis, and LASSO, MCP and random forest as multivariate analysis were used to evaluate the simultaneous eﬀect of biomarkers on IgAN in a high dimensional and low sample size setting. All the statistical analyses were performed in the R 3.3. 2 software. Results: AlthoughMannWhitneytestshowedthat144outof 493proteinsweresigniﬁcantlydiﬀerentbetweenthe2groups,LASSO, MCP, and random forest showed only 7, 3, and 5 biomarkers as eﬀective factors in IgAN diseases, respectively. The most eﬀective biomarker was SULF2 (OR = 0.28) and ALBU (OR = 2.66) in LASSO, A1AT (OR = 73.7) in MCP, and GOLM1 and IBP7 in the random forest method. Conclusions: Becauseallthe3modelswereabletotrulydiﬀerentiatealltheIgANpatientsfromthecontrolgroups,theresearchers suggest the proposed model for high dimensional and low sample size datasets.
Titanium Culture Vessel Presenting Temperature Gradation for the Thermotolerance Estimation of Cells

Hyperthermia can be induced to exploit the thermal intolerance of cancer cells, which is worse than that of normal cells, as a potential noninvasive cancer treatment. To develop an effective hyperthermia treatment, thermal cytotoxicity of cells should be comprehensively investigated. However, to conduct such investigations, the culture temperature must be accurately regulated. We previously reported a culture system in which the culture temperature could be accurately regulated by employing metallic culture vessels. However, appropriate temperature conditions for hyperthermia depend on the cell species. Consequently, several experiments need to be conducted, which is a bottleneck of inducing hyperthermia. Hence, we developed a cell culture system with temperature gradation on a metallic culture surface. Michigan Cancer Foundation-7 cells and normal human dermal fibroblasts were used as cancer and normal cell models, respectively. Normal cells showed stronger thermal tolerance; this was because the novel system immediately exhibited a temperature gradation. Thus, the developed culture system can be used to investigate the optimum thermal conditions for effective hyperthermia treatment. Furthermore, as the reactions of cultured cells can be effectively assessed with the present results, further research involving the thermal stimulation of cells is possible.
DF-Mask R-CNN: Direction Field-Based Optimized Instance Segmentation Network for Building Instance Extraction

Extracting building instances from remote sensing images has various applications. However, existing instance segmentation methods have difficulty maintaining regular and angular building boundaries, corrupting the mask quality. This letter proposes a boundary-optimized instance segmentation method based on the direction field to cope with the low boundary quality. The proposed method develops a DF-Mask head to improve the mask quality of the primary instance segmentation network (Mask R-CNN) through boundary optimization. Within the DF-Mask head, first, a gated directional context-aware module (GDCAM) improves the mask features through the directional context and a designed gating mechanism. Then a direction field rectification module (DFRM) predicts the direction field and iteratively rectifies the mask. To the best of our knowledge, this is the first time that direction field rectification has been developed to the instance segmentation method and applied to building instance extraction. Experimental results on the Vaihingen and Massachusetts datasets suggest that our method outperforms the eight state-of-the-art methods. Compared with Mask R-CNN, our method improves APs by 4.7% and 2.2% on the two datasets, respectively.
Analysis of Facial Expressions to Estimate the Level of Engagement in Online Lectures

The present study aimed to develop a method for estimating students’ attentional state from facial expressions during online lectures. We estimated the level of attention while students watched a video lecture by measuring reaction time (RT) to detect a target sound that was irrelevant to the lecture. We assumed that RT to such a stimulus would be longer when participants were focusing on the lecture compared with when they were not. We sought to estimate how much learners focus on a lecture using RT measurement. In the experiment, the learner’s face was recorded by a video camera while watching a video lecture. Facial features were analyzed to predict RT to a task-irrelevant stimulus, which was assumed to be an index of the level of attention. We applied a machine learning method, light Gradient Boosting Machine (LightGBM), to estimate RTs from facial features extracted as action units (AUs) corresponding to facial muscle movements by an open-source software (OpenFace). The model obtained using LightGBM indicated that RTs to the irrelevant stimuli can be estimated from AUs, suggesting that facial expressions are useful for predicting attentional states while watching lectures. We re-analyzed the data while excluding RT data with sleepy faces of the students to test whether decreased general arousal caused by sleepiness was a significant factor in the RT lengthening observed in the experiment. The results were similar regardless of the inclusion of RTs with sleepy faces, indicating that facial expression can be used to predict learners’ level of attention to video lectures.
Exact solutions of topologically massive gravity

We study exact solutions of three-dimensional gravity with a cosmological constant and a gravitational Chern–Simons term: the theory known as topologically massive gravity. General techniques and classifications of solutions are reviewed. We show that biaxially squashed AdS3 solutions follow from assuming algebraic type D curvature for which the one-dimensional eigenspace of the traceless Ricci tensor is aligned with a Killing vector. We find the general Kundt solutions: spacetimes admitting an expansion-free geodesic null congruence. These provide the first examples of solutions with algebraic types III and II. We find that a Kundt solution of algebraic type D must be biaxial spacelike-squashed AdS3. We present explicitly the Kundt solutions for which all scalar polynomial curvature invariants are constant, and find that all Kundt solutions with algebraic types D, N and III have this property. These constant scalar invariant Kundt solutions are deformations of round or biaxial spacelike-squashed AdS3, and include AdS pp-waves. We also provide a comprehensive review of the literature, showing that most known solutions are locally equivalent to biaxially squashed AdS3 or AdS pp-waves.
Amplitude-versus-angle inversion based on the L1-norm-based likelihood function and the total variation regularization constraint

ABSTRACTIn prestack seismic data, outlier errors occur and can negatively influence the outcome of the amplitude-versus-angle (AVA) inversion process. Hence, their effect needs to be minimized during AVA inversion. AVA inversion based on the L2-norm-based likelihood function is highly sensitive to outlier errors. In comparison, AVA inversion based on the L1-norm-based likelihood function is less affected by outlier errors, and for this reason we have used it with the total variation regularization method used as a constraint to invert discontinuities from geologic bodies. To ensure that the inversion results contain low-frequency components, prior information constraints from model parameters are added to the inverse objective function, which is then solved by the iterative reweighted least-squares method. Results of numerical tests and real-data examples from the application of this method indicate that the algorithm is strongly robust against noise, especially abnormal outlier errors, and that the resul.
Diffuse Neurocysticercosis in a Survivor of Tuberculosis

Neurocysticercosis (NCC) is a prevalent parasitic infection of the central nervous system (CNS) caused by Taenia solium larvae. It is commonly found in developing countries with a high incidence of both NCC and CNS tuberculosis. In cases where patients have a history of pulmonary tuberculosis, the lesions arising from NCC can be misdiagnosed as tuberculomas. Furthermore, when papilledema (a sign of raised intracranial pressure) is present, CNS tuberculosis is often considered a more probable diagnosis compared to NCC. While NCC is common, disseminated cysticercosis is rare. In this article, we present a case of diffuse neurocysticercosis and discuss relevant management approaches.
A numerical study of parallel-plate and open-plate droplet transport in electrowetting-on-dielectrode (EWOD)

ABSTRACT Electrowetting-induced microwater droplet transports in parallel-plate and open-plate electrowetting-on-dielectrode (EWOD) have been studied numerically. The governing equations for three-dimensional transient microfluidic flow are solved by a finite volume scheme with a two-step projection method on a fixed computational domain. The free surface of the droplet is tracked by the coupled level set and volume-of-fluid (CLSVOF) method with the surface tension force determined by the continuum surface force (CSF) model. The results of the numerical model have been validated with published experimental data and the physics of droplet transport have been investigated. A parametric study has been performed in which the effects of voltage amplitude, channel gap, and electrode size have been examined.
MD 2197: Experimental studies of Landau damping by means of Beam Transfer Function measurements in the presence of beam-beam interactions and diffusive mechanisms

Beam Transfer Function (BTF) measurements are direct measurements of the stability diagrams that define the stability threshold of coherent beam instabilities driven by the impedance. At the LHC, some coherent instabilities at flat top energy are still not fully understood and the BTF measurements provide a method to experimentally probe the Landau damping of the proton beams. The BTF response is sensitive to the particle distribution changes and contains information about the transverse tune spread in the beams. The BTF system has been installed in the LHC in 2015 in order to investigate the Landau damping at different stages of the operational cycle, machine configurations (different octupole currents, crossing angles, tunes etc.) and in presence of beam-beam excited resonances that may provoke diffusion mechanisms with a subsequent change of Landau damping. Past MDs showed some difficulties for the reconstruction of the stability diagram from BTF measurements and several improvements on the BTF system have been put in place in order to improve the signal to noise ratio. In this note a detailed procedure of the MD is described together with some preliminary results of BTF measurements in the presence of linear coupling.
Induction treatment in high-grade B-cell lymphoma with a concurrent MYC and BCL2 and/or BCL6 rearrangement: a systematic review and meta-analysis

Background and aim High-grade B cell lymphomas with concomitant MYC and BCL2 and/or BCL6 rearrangements (HGBCL-DH/TH) have a poor prognosis when treated with the standard R-CHOP-like chemoimmunotherapy protocol. Whether this can be improved using intensified regimens is still under debate. However, due to the rarity of HGBCL-DH/TH there are no prospective, randomized controlled trials (RCT) available. Thus, with this systematic review and meta-analysis we attempted to compare survival in HGBCL-DH/TH patients receiving intensified vs. R-CHOP(-like) regimens. Methods The PubMed and Web of Science databases were searched for original studies reporting on first-line treatment in HGBCL-DH/TH patients from 08/2014 until 04/2022. Studies with only localized stage disease, ≤10 patients, single-arm, non-full peer-reviewed publications, and preclinical studies were excluded. The quality of literature and the risk of bias was assessed using the Methodological Index for Non-Randomized Studies (MINORS) and National Heart, Lung, and Blood Institute (NHLBI) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Random-effect models were used to compare R-CHOP-(like) and intensified regimens regarding 2-year overall survival (2y-OS) and 2-year progression-free survival (2y-PFS). Results Altogether, 11 retrospective studies, but no RCT, with 891 patients were included. Only four studies were of good quality based on aforementioned criteria. Intensified treatment could improve 2y-OS (hazard ratio [HR]=0.78 [95% confidence interval [CI] 0.63-0.96]; p=0.02) as well as 2y-PFS (HR=0.66 [95% CI 0.44-0.99]; p=0.045). Conclusions This meta-analysis indicates that intensified regimens could possibly improve 2y-OS and 2y-PFS in HGBCL-DH/TH patients. However, the significance of these results is mainly limited by data quality, data robustness, and its retrospective nature. There is still a need for innovative controlled clinical trials in this difficult to treat patient population. Systematic review registration https://www.crd. york.ac. uk/prospero, identifier CRD42022313234.
Moment-Based Distributionally Robust Peer-to-Peer Transactive Energy Trading Framework Between Networked Microgrids, Smart Parking Lots and Electricity Distribution Network

This paper proposes a decentralized scheduling framework for peer-to-peer (P2P) transactive energy trading between prosumers and the local electricity market (LEM). In this approach, prosumers are modeled in the form of networked microgrids (NMG) and smart parking lots (SPL), which can exchange energy in a P2P framework. At the same time, each of the prosumers exchanges information and energy with the LEM in a decentralized approach based on the distribution locational marginal price (DLMP). The LEM is modeled by an active radial electricity distribution network (EDN), considering a high penetration of renewable energy sources (RES) and the feeder power losses in a convex optimization model. In proposed P2P transactive energy model, the alternating direction method of multipliers (ADMM) has been used to achieve the equilibrium point between peers. The distributionally robust optimization (DRO) method has been used to model the uncertain behavior of RES in NMGs, SPLs, and the EDN. In addition, the impact of applying smart charging strategies for electric vehicles (EV) in the proposed P2P transactive energy model has been investigated. To confirm the functionality of the model, 12-bus, 6-bus, and 4-bus networks have been used to model NMGs, while the EDN is embodied by a standard IEEE 33-bus and 123-bus test systems.
Non-Linear Time Varying Modeling for Phase Noise in Oscillators Based On a Discrete Recursive Approach

A unique approach for the modeling of phase noise is examined in this thesis. In previous work regarding phase noise theory, the memory property of phase is virtually ignored. The thesis introduces the Discrete Recursive Procedure (DRP): a systematic approach or methodology to predict phase noise using a discrete recursive algorithm taking into account the memory property of phase. This discrete recursive algorithm is a general extension of the Linear Time Varying (LTV) model and is referred to as the NonLinear Time Varying (NLTV) model. Simulations are performed using the DRP method. Phase fluctuation comparisons are made between the LTV and the NLTV models for an ideal oscillator. The simulation results show that the NLTV model taking into account the memory property of phase makes more realistic phase noise predictions than the LTV model for asymmetrical Impulse Sensitivity Function (ISF) cases. Phase noise simulation results using the NLTV model are given for a modified 810-MHz CMOS cross-coupled LC oscillator design. At 90kHz offset, the simulation prediction (-89 dBc/Hz) and the measurement readings (-93 dBc/Hz) are closely matched with a difference of approximately 4 dBc/Hz while the CAD simulation prediction ( -101. 8) has a difference of 9 dBc/Hz from the measurements. In the phase noise simulation for the 62-MHz BIT Colpitts oscillator design, the NLTV model predicts a -26 dBc/decade and -19.5 dBc/decade for the flicker noise and thermal noise regions in accordance with the theoretical -30 dBc/decade and -20 dBc/decade slopes.
Group Formation on Physics Exams

As our classrooms become more active and collaborative, we need to consider ways that our assessments can take on the same active and collaborative spirit that our classes have. One way that we can accomplish this goal is through the use of group exams. In exams at Texas State University and East Carolina University, we have utilized a two-phase exam format allowing open collaboration—that is, allowing students to form their own groups. We have analyzed self-reported collaboration data and find that the room influences how students will associate with each other. We have also compared collaboration patterns evident in student responses for students in open collaboration settings to students in closed collaboration settings.
Transition dynamics and metastable states during premelting and freezing of ice surfaces

The premelting of ice is well known, but little is known about how the premelted and solid surfaces convert to each other. In this work, the transition dynamics between two phases are revealed with large-scale molecular dynamics simulations. Supercooling and superheating states exist in the transition, and are overcome by nucleation-like processes. The natural inhomogeneity of ice surfaces enhances nuclei formation, while it only accelerates premelting but not freezing. Furthermore, the complete freezing of ice surfaces may be hindered by the stacking order mismatch between nuclei. This work points out the importance of metastable states in premelting, and the necessity of a large system scale in describing its transition process.
Theory for Marchenko imaging of marine seismic data with free surface multiple elimination

The theory of data-driven true amplitude migration is presented for multicomponent marine seismic data. The Marchenko scheme is adapted to account for the ghost, free surface and internal multiple effects and works without the need to know the source wavelet. A true amplitude image is formed from the obtained focusing functions without ghost effects and artefacts from free surface and internal multiples. The resulting reflectivity at image times can be input for a final step of full waveform inversion. The numerical example shows the effectiveness of the method in a simple 1D problem.
Factors Affecting Continuance Intention of M-Government: An Empirical Study

Mobile government (m-government) can potentially provide substantial benefits. Nonetheless, the low level of use has prevented realizing the potential benefits of m-government. As a result, researchers have studied the factors affecting the acceptance of m-government. However, to date, no study has empirically investigated the factors affecting the continuance intention of using m-government. This article argues that investigating these factors will provide a greater insight into why the potential benefits have not been realized. The theoretical foundation of the proposed model builds on the expectation-confirmation model, information system success model, and the external factor trust. This study has adopted a quantitative methodology and conducted an online questionnaire. The data were collected from 553 m-government users in Saudi Arabia, who have used multiple m-government services. The results show that the proposed model has the capability to identify the factors affecting the continuance intention in m-government context.
Comparative Study on the Predominance of Lactobacillus spp. and Escherichia Coli in Healthy vs Colibacillosis Diseased Broilers

This study aims to identify relative proportions of beneficial and pathogenic bacteria in the gut of broilers and risk factors that may be contributing to the development of colibacillosis disease in broiler farms of District Kasur, Punjab, Pakistan. For this, 10 healthy and 10 colibacillosis affected broiler farms were surveyed for ileum and blood sample collection along with data regarding farm management, antibiotic use and hygiene practices. Lactobacillus and Escherichia coli number was estimated using Miles and Misra method and colibacillosis was confirmed by Congo red dye assay. Lactobacillus and E. coli were identified biochemically. For risk factors analysis chi-square analysis was performed to find any significant association between the health status of the farm and risk factors. Results showed during disease and healthy conditions Lactobacillus and Escherichia coli counts differ significantly ( p <0.05). E. coli counts (106-108 to 107-109) increased ( p <0.05) about three folds and Lactobacillus counts decrease (106-108 to 105-107) about four folds in disease conditions. Risk factor analysis showed colibacillosis disease was significantly associated ( p <0.05) with non-vaccinated flocks, natural ventilation systems, rodent presence and the lack of outfit disinfection or change by workers when moving between different houses. It is concluded that E. coli and Lactobacillus work antagonistically to each other. However, further research is necessary to determine the exact mechanisms by which E. coli and Lactobacillus influence the development of colibacillosis. While Lactobacillus as probiotic may help with prevention, good hygiene and management practices are still crucial in preventing the spread of disease.