Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Super Resolution on detecting Covid-19 from CT Scans using VGG-16 Based Learning

With the recent outbreak of the novel Coronavirus (COVID-19), the importance of early and accurate diagnosis arises, as it directly affects mortality rates. Computed Tomography (CT) scans of the patients’ lungs is one of the diagnosis methods utilized in some countries, such as China. Manual inspection of CT scans can be a lengthy process, and may lead to inaccurate diagnosis. In this paper, a Deep Learning strategy based on VGG-16 is utilized with Transfer Learning for the purpose of binary classification of CT scans; Covid and NonCovid. Additionally, it is hypothesized in this study that Single Image Super Resolution (SISR) can boost the accuracy of the networks’ performance. This hypothesis is tested by following the training strategy with the original dataset as well as the same dataset scaled by a factor of ×2. Experimental results show that SISR has a positive effect on the overall training performance.
Clustering of Public Opinion on Natural Disasters in Indonesia Using DBSCAN and K-Medoids Algorithms

Natural disasters are disasters caused by events or series of events caused by nature such as earthquakes, tsunamis, volcanic eruptions, floods, tornadoes, and landslides. Some of these natural disasters have taken a lot of public attention, from empathy, sadness and criticism that form an opinion on social media. One of the most popular social media used by the public is Twitter. Opinions written by Twitter users are called tweets. A collection of tweets can be processed to obtain information by using data mining techniques namely Text Mining. In this study, the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm and K-Medoids were used. The result of this study shows that DBSCAN is the best algorithm because it has the Silhouette Index (SI) validity of 0.9140 and the average execution time in RapidMiner Studio is 83.40 seconds. Meanwhile, the K-Medoids algorithm has a Silhouette Index (SI) validity of 0.2259 and an average execution time in RapidMiner Studio 849.93 seconds. The frequency of the word “earthquake” dominates for the positive category, the word “disaster” dominates the negative category, and the word “flood and earthquake” dominates the negative category.
Mycoflora and DNA sequencing analysis of radioresistant fungi isolated from irradiated vehicular air conditioning filters collected in São Paulo, Brazil

Fungal bioburdens in filters from car air conditioning systems represent a potential risk of respiratory or pulmonary diseases to both passengers and drivers. The present study demonstrated high fungal contamination in air conditioning filters collected from fifty-seven vehicles from the city of São Paulo, Brazil. The filters samples were irradiated with 10, 15 and 20 kGy gamma ray doses with a cobalt-60 source. The presence of Aspergillus flavus, Penicillium glabrum and Fusarium incarnatum-equiseti remaining after the ionizing treatment with 10 and 15 kGy. The radioresistant pathogenic species was confirmed through genetic sequencing of the ITS, β-tubulin and calmodulin gene rDNA regions. The 20 kGy dose was efficient in inhibiting pathogenic fungi growth in all samples but promoted the fungal decontamination in 79% of the samples. These results and other efforts will enable ionizing radiation to become an important tool in contributing to the recycling of automotive vehicle air filters and in ensuring indoor air quality for both drivers and passengers.
On the use of acquisition function‐based Bayesian optimization method to efficiently tune SVM hyperparameters for structural damage detection

This article presents a novel application of an acquisition function‐based Bayesian optimization (BO) method to optimally tune the hyperparameters of soft margin support vector machine (SVM) for structural damage detection. The performance of SVM classifiers is known to depend heavily on the selection of hyperparameters. The optimal hyperparameters yield the minimum cross‐validation error. However, no explicit functional relationship exists between the hyperparameters and the cross‐validation error which can be used as an objective function. This BO approach uses the Bayes rule along with the concept of Gaussian process regression to estimate the objective function and an acquisition function to efficiently search the hyperparameter space. The performance of this methodology has been validated by training an optimally tuned SVM to carry out damage detection on an experimental benchmark structure using four different datasets. It was observed that the number of function evaluations to calculate the optimal values of hyperparameters turned out to be below 25 for each of the datasets, which is typically the number of function evaluations required (i.e., the swarm size) in each iteration of particle swarm optimization (PSO). A comparison of BO with PSO in terms of the number of function evaluations revealed its efficiency for the benchmark problem. This result signifies the practical importance of BO in speeding up the process of hyperparameter selection. Finally, some interesting observations regarding the obtained values of box constraint, support‐vector‐location, and the relationship between the convergence curve of BO and learning curves of SVM are discussed.
Pulse energy limitation of high-power nanosecond lasers due to plasma production in spatial filters

For a neodymium glass laser, which is used to pump the parametric amplifier of the PEARL facility, we investigate the problem of filling the pinholes of vacuum spatial filters with plasma resulting from the ablation of the pinhole surface by laser radiation. The time of plasma expansion is measured and the causes of its appearance are determined, among which the main ones are the spherical aberrations of the lenses of spatial filters, which increase the intensity at the pinhole edges. With the total spherical aberration of the spatial filters, which lowers the Strehl number to 0.15, two nanosecond pulses with energies of 130 – 140 J, delayed relative to each other by 1.8 ns and freely passing through the final spatial filter, are generated on the PEARL facility.
Peripheral facial features guiding eye movements and reducing fixational variability

Face processing is a fast and efficient process due to its evolutionary and social importance. A majority of people direct their first eye movement to a featureless point just below the eyes that maximizes accuracy in recognizing a person's identity and gender. Yet, the exact properties or features of the face that guide the first eye movements and reduce fixational variability are unknown. Here, we manipulated the presence of the facial features and the spatial configuration of features to investigate their effect on the location and variability of first and second fixations to peripherally presented faces. Our results showed that observers can utilize the face outline, individual facial features, and feature spatial configuration to guide the first eye movements to their preferred point of fixation. The eyes have a preferential role in guiding the first eye movements and reducing fixation variability. Eliminating the eyes or altering their position had the greatest influence on the location and variability of fixations and resulted in the largest detriment to face identification performance. The other internal features (nose and mouth) also contribute to reducing fixation variability. A subsequent experiment measuring detection of single features showed that the eyes have the highest detectability (relative to other features) in the visual periphery providing a strong sensory signal to guide the oculomotor system. Together, the results suggest a flexible multiple-cue approach that might be a robust solution to cope with how the varying eccentricities in the real world influence the ability to resolve individual feature properties and the preferential role of the eyes.
Rab25 acts as an oncogene and participates in the regulation of aerobic glycolysis via PKM2 in gastric adenocarcinoma

Background Rab25, a hub node of protein-protein interaction networks, has become one of the most popular tumor-associated proteins. Pyruvate kinase (PK) is the main rate-limiting enzyme in the glycolysis pathway and plays a significant role in the regulation of Warburg effect. In this study, we aimed to characterize the expression and function of Rab25 in gastric adenocarcinoma (GAC) and verify our hypothesis experimentally that Rab25 may participate in the regulation of aerobic glycolysis via PKM2 (a subtype of PK) in GAC. Methods The impact of Rab25 expression on patient overall survival was estimated using the Kaplan-Meier method. Rab25 expression was silenced or increased respectively by lentivirus-mediated transfection. To assess the role of Rab25 in cell viability in vitro, cell proliferation and migration experiments were performed. Levels of pyruvate and lactic acid were detected by kits. Immunofluorescence analysis and Co-Immunoprecipitation were performed to explore the interaction between Rab25 and PKM2 protein. Results High Rab25 expression was associated with reduced patient overall survival. Silencing Rab25 inhibits GAC cells proliferation and overexpressing Rab25 promotes cell proliferation and migration in gastric cells in vitro. The study revealed that Rab25 participates in the positive regulation of aerobic glycolysis in GAC cells. Rab25 protein and PKM2 protein co-located on the cell membrane and could bind to each other in GAC cells. Rab25 is a positive regulator of PKM2 and Rab25 up-regulation promotes phosphorylation of PKM2. Conclusions In human GAC, Rab25 predicts poor prognosis and regulates aerobic glycolysis via phosphorylating PKM2-Y105.
Improving Generalization Capability of Multiorgan Segmentation Models Using Dual-Energy CT

Multiorgan segmentation in computed tomography (CT) images is essential for a variety of clinical applications. Due to variations in acquisition protocols, clinical data differ in terms of soft-tissue contrast, noise, and artifacts. Devising automatic multiorgan segmentation approaches, which are generalized to data acquired using different CT protocols, are challenging and essential when conducting any multicenter/scanner analyses. In this study, we investigate the use of dual-energy CT (DECT) images to train a fully convolutional segmentation network which is generalized to CT images acquired using different protocols (i.e., at different energy levels and using a variety of reconstruction kernels) from different CT scanners. Furthermore, a novel image fusion approach in the frequency domain is proposed and compared to state-of-the-art fusion approaches, in terms of the segmentation quality achieved by the network. Overall, the experiments indicate that the generalization capability of the segmentation network is improved using DECT image fusion. The proposed fusion method outperforms all single-energy CT approaches. It provided a significant improvement in segmentation accuracy, ranging from 16.0% to 23.35% with $p \leq 0.03$ . Furthermore, two image fusion methods statistically significantly improve segmentation quality in the abdominal organs compared to simply using all available DECT data.
Machine Learning Control Based on Approximation of Optimal Trajectories

The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set.
Photocatalytic decontamination of pharmaceutical effluent in a double tube reactor by ZnO catalyst. Physico chemical characterization and optimization. 

This work focused on the study of the elimination of a non-steroidal anti-inflammatory drug (diclofenac) using heterogeneous photo catalysis as a treatment.The study investigated the effect of parameters (catalyst concentration, initial diclofenac concentration, ratio r r = . and volume flow) on the photocatalytic degradation of diclofenac. The analyses performed are pH, electrical conductivity, turbidity, surface tension, chemical oxygen demand, nitrite and diclofenac concentration.The study showed that: The heterogeneous photo catalytic degradation is influenced by :The concentration of the ZnO catalyst; The concentration of 1.5 g/L gives a better degradation in diclofenac with a percentage of elimination of 52%; The initial concentration of diclofenac; The concentration of 100 mg/L of diclofenac gives a better degradation with a percentage of elimination of 52%; The ratio r = ; The three ratios (1; 5; and 10%) give a total degradation after one hour of treatment; The volume flow rate of 25 mL/s gives a total degradation after one hour of treatment. Optimal conditions for heterogeneous photo-catalysis are : [ZnO] = 1.5 g/L, [diclofenac] = 100 mg/L, Volume flow rate = 25 mL/s For a volume flow rate of 20 mL/s, optimal conditions are met by adding H2O2.
A Portable Device for Hand Rehabilitation With Force Cognition: Design, Interaction, and Experiment

Introducing interactive system into portable robots for hand rehabilitation has always been a crucial topic. Moreover, hand rehabilitation with force cognition can make patients participate actively and improve the rehabilitation effect. In this article, we design a portable robotic device with an interactive system for patients to rehabilitate with force cognition. First, an exoskeleton glove is designed with a compact mechanical structure which is controlled by a real-time feedback system. The portable device allows patients to rehabilitate not only in the hospital. Next, an interactive system, including virtual environment and force cognition, is introduced to detect the hand motion and collision. Finally, clinical tests of our portable device are carried out with nine subjects after tendon injury to show the effective assistance with our device.
P‐wave Tomography and Azimuthal Anisotropy of the Manila‐Taiwan‐Southern Ryukyu Region

Taiwan is considered to be created by interactions between the Manila and Ryukyu subduction zones, which form a normal double‐slab subduction system. Because of the two opposite dipping subductions of the Eurasian Plate (EP) and the Philippine Sea Plate (PSP) around Taiwan, subduction polarity reversal beneath northern Taiwan is presumed, which is accounted for by a slab breakoff model. However, this model has not been convincingly supported by tomographic results so far. Moreover, details of the dynamic process and deformation in this normal double‐slab subduction system are still unclear. In this work, we determine high‐resolution 3‐D tomographic images of isotropic P‐wave velocity and azimuthal anisotropy beneath the Manila‐Taiwan‐southern Ryukyu region, which provide substantial observational constraints on this double‐slab subduction system. Our results clearly show the subducted EP breakoff beneath northern Taiwan. We propose two possible factors causing the slab breakoff: the subducted EP rollback under its own gravity after its subduction cessation and collision with the subducted PSP, which may jointly cause the slab breakoff beneath northern Taiwan after a longer collision history. In addition, the subducted EP rollback may be responsible for magmatism and basalts around Taiwan, as well as recent big earthquakes beneath the Taiwan Strait and the South China Sea (SCS). Our results also reveal anisotropy parallel with the SCS fossil ridge beneath the northernmost Luzon Island. We suggest that the fossil ridge and buoyant plateau subduction and/or the ridge‐parallel mantle flow during the SCS seafloor spreading may result in the ridge‐parallel anisotropy.
Charles Perreault. The Quality of the Archaeological Record (Chicago & London: The University of Chicago Press, 2019, ix and 254pp., 57 figs, 8 tables, pbk, ISBN 978-0-226-63096-0)

org/10.1073/pnas.1721372115 Jehne, M. 2017. Web Essay F: Caesar the Politician. In: K.A. Raaflaub, ed. The Landmark Julius Caesar Web Essays. New York: Pantheon Books, pp. 6–12. [online] [accessed 10 November 2020]. Available at: <http://thelandmarkcaesar.com> Jervis, A. 2017. Web Essay HH: The Gallic War as a Work of Propaganda. In: K.A. Raaflaub, ed. The Landmark Julius Caesar Web Essays. New York: Pantheon Books, pp. 236–40. [online] [accessed 10 November 2020]. Available at: <http://the landmarkcaesar.com> Keeley, L. 1996. War Before Civilization: The Myth of the Peaceful Savage. Oxford: Oxford University Press.
Developing and Evaluating Novel Treatments for Nonsuicidal Self-Injury Within Swedish Health Care

Nonsuicidal self-injury is common and associated with subsequent adverse outcomes. Early interventions may prevent suicide among other outcomes. Available treatment options are comprehensive and time-consuming and may not be readily available to everyone in need. Emotion regulation group therapy (ERGT) and emotion regulation individual therapy (ERITA) were designed to address the need of effective short-term, focused, and easily implementable treatments for adults and adolescents with selfinjurious behaviors. A Swedish research program comprising five psychological treatment studies has been launched, among them the development of an online version of ERITA. Results from the first three studies provides promising information about feasibility, acceptability, and utility of ERGT and ERITA. Study IV and Study V will utilize randomized controlled designs to examine treatment efficacy of online ERITA. The present article reviews and discusses methodological considerations, results, experiences, and implications for future research.
The Impact of Discount Frame and Type of Product on Purchase Intention

In this modern era, shifting the type of product from physical to digital has been popular. As an example, a textbook that been used by students in university may be acquired in the digital form which is an e-book. Marketing promotion is not only for a textbook, yet e-book should be treated the same. Online seller in Indonesia does promotion which is price discount to attract customer. Accordingly, there are two types of price discount frames which are % form or absolute form. Both may attract customers differently. Therefore this research aimed to find the impact of discount frame and type of product on customer purchase intention. The sample number is 92 students who take management Master's Degree at Tarumanagara University and the object of this research is purchase intention. This research used experimental design 2x2 factorial. SPSS program is used to do an ANOVA analysis. The first independent variable is the discount frame with two sub-factorials which are % discount and Rp discount. The second independent variable is the type of product with two sub-factors which are textbooks and e-book. The result showed that type of product has a significant effect on customer purchase intention. There is no significant effect of the discount frame on purchase intention. The research result also showed that type of product does not moderate the relationship between discount frame and customer purchase intention. Pada zaman modern saat ini, perkembang bentuk produk dari bentuk fisik menjadi digital semakin banyak. Sebagai contoh, textbook yang dapat diakses digital dan dikenal dengan istilah e-book . Promosi marketing tidak hanya berlaku untuk textbook , melainkan e-book juga. Penjual online di Indonesia melakukan promosi yaitu diskon harga untuk menarik perhatian konsumen. Terdapat dua jenis discount frame yaitu diskon % atau potongan harga dimana dapat menarik perhatian konsumen secara berbeda. Maka dari itu, penelitian ini bertujuan untuk mengetahui pengaruh discount frame dan type of product terhadap minat beli konsumen. Jumlah sampel adalah 92 siswa yang mengambil gelar Magister Manajemen di Universitas Tarumanagara dan objeknya adalah minat beli konsumen. Penelitian ini menggunakan desain faktorial 2x2. Program SPSS digunakan untuk melakukan analisis ANOVA. Variabel independen pertama adalah discount frame dengan dua sub-faktorial yaitu diskon % dan diskon Rupiah. Variabel independen kedua adalah type of product dengan dua sub-faktor yaitu buku textbook dan e-book . Hasil analisa menunjukkan bahwa type of product memiliki efek yang siginifikan terhadap purchase intention . Tidak ada pengaruh yang signifikan antara discount frame dan purchase intention . Hasil analisa juga menunjukan bahwa type of product tidak memoderasi hubungan antara discount frame dan purchase intention.
A continual reassessment method without undue risk of toxicity

Abstract We revisit our earlier extension of the CRM and introduce a new version of the dose escalation procedure. Its performance is assessed by simulation, with respect to a template of five operating measures BEARS (Benchmark, Efficiency, Accuracy, Reliability, Safety) that was used in Zhang, Wang and Yang. Our goal is to correctly identify the maximum tolerated dose with a high probability while protecting trial subjects from undue risk of toxicity. Simulation results show that this may be achieved for some considered scenarios, selective models, and considered competing designs. We discuss the pros and cons of the new version of the dose escalation procedure in the face of treatment versus experimentation dilemma.
Reconciling the Size‐Dependence of Marine Particle Sinking Speed

Sinking particles are critical to the ocean's “biological pump,” sequestering carbon from the atmosphere. Particles' sinking speeds are a primary factor determining fluxes and subsequent ecological and climatic impacts. While size is a key determinant of particles' sinking speeds, observations suggest a variable size‐sinking relationship, affected by other particle properties, resulting in substantial spread in parameterizations of particle sinking and fluxes. We compile particle size‐sinking observations and apply hierarchical Bayesian statistical models to resolve the size‐sinking relationship while accounting for other factors. We find an overall scaling close to the general Navier‐Stokes drag equation, and differences between particle types, open ocean versus coastal/laboratory particles, and in situ versus ex situ methods. These results can help harmonize how Earth system models parameterize particle fluxes and support a weaker size‐dependence than often assumed, with implications for the flux contribution of small particles and the predicted future shrinking of marine particle populations.