Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of a mesh on boundary layer transitions induced by free-stream turbulence and an isolated roughness element

Streamwise streaks, their lift-up and streak instability are integral to the bypass transition process. An experimental study has been carried out to find the effect of a mesh placed normal to the flow and at different wall-normal locations in the late stages of two transitional flows induced by free-stream turbulence (FST) and an isolated roughness element. The mesh causes an approximately 30 % reduction in the free-stream velocity, and mild acceleration, irrespective of its wall-normal location. Interestingly, when located near the wall, the mesh suppresses several transitional events leading to transition delay over a large downstream distance. The transition delay is found to be mainly caused by suppression of the lift-up of the high-shear layer and its distortion, along with modification of the spanwise streaky structure to an orderly one. However, with the mesh well away from the wall, the lifted-up shear layer remains largely unaffected, and the downstream boundary layer velocity profile develops an overshoot which is found to follow a plane mixing layer type profile up to the free stream. Reynolds stresses, and the size and strength of vortices increase in this mixing layer region. This high-intensity disturbance can possibly enhance transition of the accelerated flow far downstream, although a reduction in streamwise turbulence intensity occurs over a short distance downstream of the mesh. However, the shape of the large-scale streamwise structure in the wall-normal plane is found to be more or less the same as that without the mesh.
MSM: Mobility-Aware Service Migration for Seamless Provision: A Data-Driven Approach

Mobile-edge computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become nontrivial in MEC. In addition, the service migration issue is difficult to be solved in real time due to user mobility and dynamic network conditions. In this article, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. First, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Second, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision making by using user association patterns. Finally, we formulate the service migration as a 2-D-Markov decision process and devise a deep reinforcement learning (DRL)-based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.
Celebrating Einstein’s General Theory of Relativity: LIGO Has Detected Gravitational Wave Predicted by Einstein 100 Years Ago

On February 11, 2016, Laser Interferometer Gravitational-wave Observatory (LIGO) announced the detection of gravitational waves from two merging black holes. We at Prespacetime Journal celebrate Einstein’s General Theory of Relativity and congratulate LIGO and all the people and agencies involved for this landmark discovery predicted by Einstein 100 years ago. There is no doubt that this discovery marks the beginning of a new era in astronomy, cosmology and even quantum gravity.
On excision in periodic cyclic cohomology

On generalise une notion introduite par Wodzicki en disant qu'un algebre J est approximativement H-unifere si le complex (.) est acyclique. A toute suite exacted 0→J→A→A/J→0 ou J est approximativement H-unifere on peut associer une suite exacte a six termes qui relie la cohomologie cyclique periodique de J, A et A/J. On prouve que la plupart des algebres rencontrees dans les applications sont approximativement H-uniferes. C'est vrai en particulier pour les ideaux Ia et qA dans l'extension universelle et and l'extension scindee universelle de A
Use of a Machine Learning model for the reduction of BackOrders in the Cross Docking sales process for the Homecenter Order Service

In this work, it is necessary to analyze the increase of Back Order in the attention of crossdocking orders in the attention of Homecenter customers due to the lack of definition of purchase planning processes, resulting in logistics costs, fill rate charges and low service level. Thus, it is intended the companies that handle high volumes of inventory and constant orders should have a forecast plan to cover possible stock-outs. The main purpose of the research is to explain a way to prevent stock-outs using an artificial intelligence model, based on historical sales data of a medium-sized company that manages inventories, as well as to determine the machine earning model to predict and reduce backorders. For the data analysis, the Orange software was used, where the data was trained with different artificial intelligence models such as Decision Tree, Support Vector Machine, Random Forest, and neural networks. The most accurate model was defined according to numerical indicators such as the confusion matrix, the area under the curve (AUC) and the ROC curve analysis. Thus, we opted for the neural network model, which presented the most accurate data. Finally, the results are presented and a suggestion is made at the management level regarding decision making in the supply process. For this purpose, itis considered pertinent to delve into the subject of the variables that influence the accumulation of backorders.
Experimental optimization of photon-counting dual-energy thoracic imaging

Dual-energy (DE) thoracic imaging with photon counting detectors (PCDs) enables suppressing anatomic noise due to bone structures, potentially improving the detection of lung nodules. Relative to DE approaches based on the kV-switching method, a PCD-based, single-exposure approach may reduce the potential for motion artifacts and suppress electronic noise. In this work, we experimentally optimize the energy thresholds and tube voltages for single-exposure DE thoracic imaging implemented with PCDs. We used a phantom consisting of uniform acrylic slabs totaling 8.6 cm in thickness with a 16 cm air gap between slabs. A lung nodule was simulated by adding 2 mm of acrylic in a small region of interest in the center of the phantom. We imaged the phantom using a PCD with a 750-μm-thick cadmium telluride x-ray convertor, 100×100 μm2 detector elements, two user-defined energy thresholds, and analog charge summing for charge sharing correction. We acquire images for tube voltages ranging from 90 kV to 130 kV in 10-kV increments. For each tube voltage, we varied the energy threshold from 32 keV to 90% of the maximum beam energy. The contrast-to-noise ratio (CNR) of the simulated nodule was measured for each combination of tube voltage and energy threshold. Optimal energy thresholds range from 45 keV to 55 keV for tube voltages ranging from 110 kV to 120 kV. When optimized for the energy thresholds, there was relatively little variability in the CNR across tube voltages beyond that due to measurement error. Future work will compare the CNR of PCD-based DE radiography with the conventional kV-switching approach.
Extracellular Vesicle-Based Drug Delivery Systems for Head and Neck Squamous Cell Carcinoma: A Systematic Review

It is estimated that there are over 890,000 new cases of head and neck squamous cell carcinoma (HNSCC) worldwide each year, accounting for approximately 5% of all cancer cases. Current treatment options for HNSCC often cause significant side effects and functional impairments, thus there is a challenge to discover more acceptable treatment technologies. Extracellular vesicles (EVs) can be utilized for HNSCC treatment in several ways, for example, for drug delivery, immune modulation, as biomarkers for diagnostics, gene therapy, or tumor microenvironment modulation. This systematic review summarizes new knowledge regarding these options. Articles published up to 11 December 2022, were identified by searching the electronic databases PubMed/MEDLINE, Scopus, Web of Science, and Cochrane. Only full-text original research papers written in English were considered eligible for analysis. The quality of studies was assessed using the Office of Health Assessment and Translation (OHAT) Risk of Bias Rating Tool for Human and Animal Studies, modified for the needs of this review. Of 436 identified records, 18 were eligible and included. It is important to note that the use of EVs as a treatment for HNSCC is still in the early stages of research, so we summarized information on challenges such as EV isolation, purification, and standardization of EV-based therapies in HNSCC.
A Scientific Approach to Building an Image Classification model of brain MRI images for Brain Tumor detection

The Computer associated-learning is one of the most significant achievements in the field of medical imaging. Generally, we use different technologies like computer tomography (CT scan) to diagnose disease or injury; in the lungs, liver, brain, etc. For this research, we have used MRI image datasets for the identification of Brain tumor classification and non-tumor classification. Tumors are generally abnormal growth; if this type of growth occurs in the brain is called a brain tumor. Early detection and proper treatment may reduce the chances of cancer. Computer vision is the domain where image features and classification extraction can be done very efficiently. In this research, automatic types of MRI image data sets can be considered using CNN (convolution neural network), i.e., VGG 16 architecture. With the help of a pre-trained model classifying and detecting brain tumors of an existing data set can be done. After augmentation volume of the data set can be improved and using individual augmentation values of each class also can be found. Experimental result shows that an accuracy of 97 % of an online data set can be seen.
Detecting Moving Objects Based on Fuzzy Non- Symmetric Membership Function in Color Space

In this paper, we address the problem about detecting moving objects in a video stream obtained by a fixed camera. To detect the moving objects, a method is proposed using the fuzzy Non-symmetric membership function  in color space. To evaluate the performances of moving detection, the performance comparisons of different space and different functions are carried out. The experiment results show good performance of our method for detecting moving objects.
What are the Attitudes of Medical Students at Jazan University Toward Professionalism?

Purpose We assessed the attitudes of Jazan University medical students toward professionalism and evaluated the associations of those attitudes with demographic factors. Materials and Methods Seven hundred ninety-seven respondents (88.85% response rate) out of 898 students who were invited to this study (50.7% were men and 49.3% were women) were studied by their response to the Learner’s Attitude of Medical Professionalism Scale (LAMPS) that assess their level of agreement with 28 items, each reflecting a single professionalism competency within the subscales of Altruism, Excellence, Duty/Accountability, Honor/Integrity, and Respect for Others. Results Of 797 respondents (88.85% response rate), 50.7% were men and 49.3% were women. Subscales with high agreement scores were Excellence (median, 21.0; range, 6–30) and Duty/Accountability (median, 21.0; range, 7–35). Subscales with slightly lower agreement scores were Respect for Others (median, 15.0; range, 5–25), Altruism (median, 15.0; range, 5–25), and Honor/Integrity (median, 12.0; range, 5–25). No sex differences were observed for most subscales; however, men gave higher agreement scores on the Excellence subscale, and compared with year 3 respondents (preclinical phase), year 6 respondents (clinical phase) gave higher agreement scores on the Excellence, Altruism, and Respect for Others subscales. Conclusion Overall, medical students at Jazan University indicated high agreement with all attitudes to professionalism subscales on the LAMPS.
Idiopathic spontaneous bilateral subdural hematoma in a 28-year-old male: A comprehensive literature review and case report

Background: Subdural hematoma (SDH) is considered spontaneous in the absence of trauma history. It is a common presentation in the geriatric age group due to the many influential factors. Most cases have a known underlying etiology that explains the incidence, otherwise, they are considered idiopathic. We present a rare case report of idiopathic nontraumatic bilateral SDH with a literature review of similar cases. Case Description: A 28-year-old fit individual presented to the Emergency Department with a chronic history of severe headache and neck pain, associated with behavioral changes in the last 2 days. He reported that his symptoms started after lifting a heavy object 2 months ago; however, they became worse after a neck chiropractor therapy session. He is not on any chronic medications, and there was a negative history of trauma, seizure, hematological diseases, family history of neurological conditions, smoking, alcohol, or drug abuse with an unremarkable systemic review. A brain computerized tomography (CT) showed bilateral SDHs, for which he underwent bilateral mini craniotomies and drainage. The symptoms improved after surgery and the follow-up brain CT showed no recurrence. Conclusion: Idiopathic spontaneous bilateral SDH in the adult age group is an extremely rare pathology and the literature review only revealed three similar cases. With the absence of any pathology that would explain the presentation after extensive investigations, we hypothesized that a sudden rise in vascular pressure was the underlying etiology, especially with the fact that the symptoms became unbearable after the neck chiropractor therapy session.
Cell2Voxel: a novel, cell-based 3D tissue model from 2D multiplex tissue scans

For many applications in oncology, pathologists inspect 2D tissue sections of resected tumor samples to assess tumor morphology, identify infiltrating lymphocytes, and predict disease progression. While 3D tissue models obtained with confocal or light-sheet microscopes promise added value over 2D scans, data-handling and visualization of whole-slide scans at cell-resolution becomes computationally demanding and sometimes even impossible on consumer-grade hardware. To enable the simultaneous display of large tissue volumes and individual cell phenotypes with a small memory footprint, we created Cell2Voxel, a light-weight, cell-based 3D tissue model in which every cell is represented by a single voxel on a coarse, regular grid. As an alternative to confocal and light-sheet microscopy, we present a workflow including widefield imaging of multiplex 2D tissue sections, cell segmentation and phenotyping, image registration, conflict-free assignment of cell-voxels to the target grid, and the creation of the final 3D tissue model. As a showcase, we present 3D models of human colon cancer samples and murine pancreatic islets. We show that Cell2Voxel can reduce the memory footprint compared to other methods by 2-3 orders of magnitude and enables the simultaneous visualization of millions of individual cells in whole-slide tissue volumes on conventional computer hardware. While our final 3D model can be inspected using open-source software on all major operating systems, we additionally present an interactive data viewer tailored to our data format. Cell2Voxel can be created from any 2D widefield image stack, making it an inexpensive, easy-to-use alternative for large-volume 3D tissue visualization.
Note on bi-Lipschitz embeddings into normed spaces

Let (X, d), (Y, ρ) be metric spaces and f : X → Y an injective mapping. We put k f k Lip = sup{ρ(f(x), f(y))/d(x, y); x, y ∈ X, x 6 y}, and dist(f) = k f k Lip.k f 1 k Lip (the distortion of the mapping f). We investigate the minimum dimension N such that every n-point metric space can be embedded into the space l N with a prescribed distortion D. We obtain that this is possible for N ≥ C(log n)2n3/D, where C is a suitable absolute constant. This improves a result of Johnson, Lindenstrauss and Schechtman (JLS87) (with a simpler proof). Related results for embeddability into lN are obtained by a similar method.
People’s Response to Disaster: A Population Based Study of the Victims of 2018 Flood in Kerala, South India

Background: Natural disasters cause much hardship and suffering, loss of property, and increased morbidity and mortality amongst those affected. Timely and effective response for relief and rescue services go a long way in mitigating these consequences. Material and Methods: This population-based cross-sectional, descriptive study conducted in the immediate aftermath of the catastrophic flood that occurred in Kerala, South India, in 2018, documents the experiences of the victims, the community's preparedness, and response to the disaster. Results: Flood waters reached levels of over four feet within the premises of 55% of the houses and nearly 97% had water flooding inside their homes. More than 93% of the households were evacuated to safer locations and relief camps. The elderly and those with chronic illnesses were the worst sufferers, unable to access medical aid. Many families (62%) received help from neighbors. Conclusion: However, the loss of lives was minimal, and could be attributed to the immediate response of the local community in rescue and relief work. This experience underscores the vital importance of the local community as first responders, and their preparedness for disasters.
The Factors Attributed to Students’ Low Performance in Secondary Leaving Examination: The Case of Secondary Schools in Benadir Region of Somalia.

This research aims to identify the factors attributed to students’ low performance in secondary school leaving examinations in the Benadir region of Somalia. The study evaluated the tendency of students’ low performance in the examinations conducted from 2020 to 2022. Over the three years, academic performance has been below average, with a mean score rarely going above C-(below average). The study used a descriptive survey design analysis with basic random sampling techniques. The sample comprised 235 secondary school teachers, principals, and parents drawn from public and private secondary schools inside the Benadir region of Somalia. Data for the study was collected using a questionnaire, whereas Smart PLS 4.0 and SPSS 26.0 were used to test the hypothesis and analyse the respondents' profiles, respectively. Structural equation modelling (SEM) results were assessed to examine the causal associations between the independent and dependent variables through the mediator. According to the findings of structural equation modelling, the independent variables parent participation, school principals, and teacher qualification affect the incompetence of teacher’s school principals and parents as a mediating factor led to students’ low performance in secondary school leaving examination. The results also supported the hypothesis that the teachers, principals, and parents’ incompetence used in this study as mediating factors may have mediated the association between representatives' schools and students’ dismal academic achievement. The stakeholders were encouraged to create an environment enabling students to perform well in secondary school, leaving examinations by meeting the expectations of examining bodies.
Numerical and experimental analysis of local flow phenomena in laminar Taylor flow in a square mini-channel

The vertically upward Taylor flow in a small square channel (side length 2 mm) is one of the guiding measures within the priority program “Transport Processes at Fluidic Interfaces” (SPP 1506) of the German Research Foundation (DFG). This paper presents the results of coordinated experiments and three-dimensional numerical simulations (with three different academic computer codes) for typical local flow parameters (bubble shape, thickness of the liquid film, and velocity profiles) in different cutting planes (lateral and diagonal) for a specific co-current Taylor flow. For most quantities, the differences between the three simulation results and also between the numerical and experimental results are below a few percent. The experimental and computational results consistently show interesting three-dimensional flow effects in the rear part of the liquid film. There, a local back flow of liquid occurs in the fixed frame of reference which leads to a temporary reversal of the direction of the wall shear stress during the passage of a Taylor bubble. Notably, the axial positions of the region with local backflow and those of the minimum vertical velocity differ in the lateral and the diagonal liquid films. By a thorough analysis of the fully resolved simulation results, this previously unknown phenomenon is explained in detail and, moreover, approximate criteria for its occurrence in practical applications are given. It is the different magnitude of the velocity in the lateral film and in the corner region which leads to azimuthal pressure differences in the lateral and diagonal liquid films and causes a slight deviation of the bubble from the rotational symmetry. This deviation is opposite in the front and rear parts of the bubble and has the mentioned significant effects on the local flow field in the rear part of the liquid film.
COVID-19 Detection via Fusion of Modulation Spectrum and Linear Prediction Speech Features

The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48 h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes via a two-stage classification system. Experiments on three COVID-19 speech datasets show that the proposed two-stage system outperforms several state-of-the-art benchmarks, relies on interpretable features, as well as generalizes well to unseen datasets. Overall, the proposed system shows promise as an accessible, low-cost, at-home method for COVID-19 detection.
Solvability of linear and semilinear eigenvalue problems with $L^1$ data

We study the equation where A is a linear elliptic operator in divergence form, ~ is a real number and f is a function belonging to We find existence results similar to those obtained in the case f E L 2 (~l ). Furthermore, we study the Landesmann-Lazer, Dolph and Ambrosetti-Prodi problems for the operator A, always with L 1 (S2 ) data.