Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning architectures for Brain Tumor detection: A Survey

Deep learning methods are used in various fields of medical imaging like medical image localization/Detection segmentation, classification, and registration. Various medical conditions can be detected, monitored, and treated with deep learning techniques. One such condition is brain tumors. The main cause of brain tumors is the fast and uncontrolled development of brain cells. Over time, numerous techniques for detecting brain tumors have been developed. Manual analysis of brain tumor images will be time-consuming and it requires expert radiologists. Deep learning techniques can solve this issue since it is a fully automated process. Various deep learning architectures are used nowadays for different pattern recognition tasks in medical imaging. This survey aims to deliver different recent deep learning models developed to detect brain tumors and to present the drawbacks of existing techniques.
A sequential linear least square algorithm for tracking dynamic shapes of smart structures

This paper presents a sequential linear least square algorithm for tracking dynamic shapes of piezoelectric smart structures. The dynamic shape discussed in this paper is defined as a host structural shape varying with time, and the tracking technique is to find an electric voltage history for each piezoelectric device over a time period so that the desired structural movements can be achieved. In the theoretical formulation, dynamic equations of piezoelectric smart structures are introduced by finite element analysis, and then a solution procedure for a set of time‐dependent electric voltages is derived by combining the linear least square method and the Houbolt numerical integration scheme. The formulation indicates that this algorithm can be used to find the time‐dependent voltages for tracking structural movements of piezoelectric smart structures. The present novel formulation is then demonstrated through numerical examples for tracking dynamic shapes of piezoelectric smart beams and plates. The numerical results for the smart beam are compared with the experimental ones. It is shown that the present sequential linear least square algorithm is capable of efficiently simulating dynamic shape tracking for smart structures. Copyright © 2006 John Wiley & Sons, Ltd.
Abundance of Transgene Transcript Variants Associated with Somatically Active Transgenic Helitrons from Multiple T-DNA Integration Sites in Maize

Helitrons, a novel type of mysterious DNA transposons discovered computationally prior to bench work confirmation, are components ubiquitous in most sequenced genomes of various eukaryotes, including plants, animals, and fungi. There is a paucity of empirical evidence to elucidate the mechanism of Helitrons transposition in plants. Here, by constructing several artificial defective Helitron (dHel) reporter systems, we aim to identify the autonomous Helitrons (aHel) in maize genetically and to demonstrate the transposition and repair mechanisms of Helitrons upon the dHel-GFP excision in maize. When crossing with various inbred lines, several transgenic lines produced progeny of segregated, purple-blotched kernels, resulting from a leaky expression of the C1 gene driven by the dHel-interrupted promoter. Transcription analysis indicated that the insertion of different dHels into the C1 promoter or exon would lead to multiple distinct mRNA transcripts corresponding to transgenes in the host genome. Simple excision products and circular intermediates of dHel-GFP transposition have been detected from the leaf tissue of the seedlings in F1 hybrids of transgenic lines with corresponding c1 tester, although they failed to be detected in all primary transgenic lines. These results revealed the transposition and repair mechanism of Helitrons in maize. It is strongly suggested that this reporter system can detect the genetic activity of autonomic Helitron at the molecular level. Sequence features of dHel itself, together with the flanking regions, impact the excision activity of dHel and the regulation of the dHel on the transcription level of the host gene.
Prediction and Analysis of Heart Attack using Various Machine Learning Algorithms

The healthcare industry is typically thought of as "information rich" yet "knowledge poor. " The healthcare systems include a vast amount of data. However, the lack of efficient analysis tools makes the employability challenging to uncover hidden linkages and patterns in the data. In the business and scientific realms, data mining and information retrieval have various applications. The use of data mining methods in the health service can produce insightful information. The possible applications for rule based, tree structure, Bayesian networks, and artificial neural networks classification are briefly discussed in this research based data mining approaches to a large volume of healthcare data. Huge amounts of healthcare data are gathered by the industry, but they are regrettably not "mined" to find hidden information. Heart attack is a primary causes of unexpected mortality, especially in women, heart attack prediction is crucial in nations with low incomes. Despite using common clinical techniques like electrocardiography and the research goal is to identify the finest machine learning algorithm for predicting heart attacks.
Multi-objective optimization through a novel Bayesian approach for industrial manufacturing of Polyvinyl Acetate

ABSTRACT Manufacturing long-chain branched polymers enhances the quality of products at the cost of production time, translating into high manufacturing costs. In this manuscript, we study the cost-versus-quality trade-off while optimizing the operating conditions for large-scale industrial production of Polyvinyl acetate (PVAc). PVAc polymerization is emulated using a large system of stiff differential equations resulting in time-expensive function evaluation. Thus, we aim to achieve the Pareto optimality using Multi-Objective Bayesian Optimization (MOBO) that introduces q-Expected Hyper-Volume Improvement (qEHVI) as the novel acquisition function. Comparison with Non-dominated Sorting Genetic Algorithm-II (NSGA-II), applied to the high-fidelity model, resulted in a similar Pareto front with only 1% of the high-fidelity calls required by NSGA-II. The results indicate the efficiency of MOBO for PVAc optimization and present a real-world application of a generic method that can be implemented to solve time-expensive multi-objective optimization in manufacturing processes
A note on exact calculation of the non central hypergeometric distribution

ABSTRACT Direct calculation of the non central hypergeometric (NH) distribution and its moments can present computational issues in both efficiency and accuracy. In response, several methods, both approximate and exact, for calculating the NH mean and variance have appeared in the literature. We add to this body of work, a straight-forward, exact method that is easily programed, efficient, and computationally stable. Specifically, by considering the logs of the values of the NH probability mass function (pmf) and then shifting the exponents so that, prior to normalization, the mode acquires a value of 1, concerns for overflow are eliminated.
The impacts of locus of crisis outcome control on responsibility attribution in hindsight: focusing on comparisons between the American and Chinese public

Abstract Through two equivalent national surveys in the United States (US) and China, this study investigated how people’s forethoughtful assessments of crisis outcomes affect their retrospective responsibility attribution and subsequent demand for regulatory intervention. The results revealed that when Americans and Chinese perceived that they could rely on the government as a proxy agent to achieve desirable crisis outcomes, they attributed more responsibility to the in-crisis company. The increased blame company attribution would spill over into their blame for social systems. These heightened attributional activities led to people’s calls for regulatory intervention. Inconsistent findings for the two countries are also discussed.
Sequence Characteristics and Expression Analysis of the Gene Encoding Sedoheptulose-1,7-Bisphosphatase, an Important Calvin Cycle Enzyme in Upland Cotton (Gossypium hirsutum L.)

Sedoheptulose-1,7-bisphosphatase (SBPase, EC 3.1.3.37) is a key enzyme in the plant Calvin cycle and one of the main rate-limiting enzymes in the plant photosynthesis pathway. Many studies have demonstrated that the SBPase gene plays an important role in plant photosynthetic efficiency, yield, and stress responses; however, few studies have been conducted on the function and expression of the GhSBPase gene in upland cotton. In this study, our results showed that the coding sequence (CDS) of GhSBPase gene was 1182 bp, encoding a protein with 393 amino acids. The GhSBPase protein had adenosine monophosphate (AMP) binding site and a FIG (FBPase/IMPase/glpX) domain, and had six Cys residues and a CGGT(A/Q)C motif that were involved in redox regulation in plants. Evolutionarily, the GhSBPase protein clustered into the dicotyledon subgroup and was most closely related to the tomato SlSBPase protein. Western-blot analysis further indicated that the GhSBPase gene was indeed the gene encoding the SBPase protein in upland cotton. The GhSBPase protein was localized in chloroplast, which was consistent with its function as a key enzyme in photosynthesis. The GhSBPase gene was specifically highly expressed in leaves, and its expression level was significantly lower in a yellow-green leaf mutant than in the wild type. Moreover, the GhSBPase expression was in response to drought, salt, high- and low-temperature stress, and exhibits different expression patterns. The GhSBPase promoter had the cis-acting elements in response to abiotic stress, phytohormone, and light. In addition, the GhSBPase expression was positively correlated with the chlorophyll fluorescence parameters, suggesting that changes in the expression of the GhSBPase had potential applicability in breeding for enhanced cotton photosynthetic efficiency. These results will help to understand the function of the GhSBPase gene in photosynthesis and the adaptability of plants to external stress and provide important gene information for the high-yield breeding of crops in the future.
Review of Various Machine Learning and Deep Learning Techniques for Audio Visual Automatic Speech Recognition

The visual cues obtained from the face and mouth region of a speaker provide valuable information for speech per-ception. The idea of audio visual speech recognition is to combine visual information with acoustic speech signals to enhance the intelligibility of speech in the presence of ambient noises. In audio visual speech recognition lip image sequences of speakers are used along with acoustic signals to convert speech into text. Researchers are exploring ways to upgrade the performance of audio visual speech recognition and solve certain real life problems like designing voice dialling systems, highly secured biometric systems for authentication etc. A review of the latest research findings on audio visual automatic speech recognition using traditional machine learning, neural networks and other deep learning techniques is presented in this work. This paper describes future research opportunities through a comparative analysis of the various techniques used in the literature for the different stages of audiovisual speech recognition, including the region of interest detection, audio and visual speech feature extraction and fusion of the modalities.
Modeling Wave Run-up along a Sloping or a Moving Wall Boundary

Abstract In this study, a volume of fluid–based flow model is developed to calculate the wave run-up along a sloping bottom or a moving vertical wall. The model equations are continuity and momentum equations, which are solved numerically for the values of fluid velocity and pressure using the finite-difference scheme. The moving free surface is traced according to the concept of volume of fluid, with calculated net change of fluid flux for updating the new position of the free surface. This model is applied to study the piling up of a nonlinear free surface in front of an impulsively moving wall. The results in terms of free-surface elevation and pressure distribution for various moving-wall conditions are presented with the comparisons to available experimental data. Model application is also performed by simulating propagation of nonlinear shallow-water waves (solitary waves or cnoidal waves) and the process of wave run-up along a sloping bottom. The predicted wave elevations and run-ups are found to agree reasonably well with other published numerical solutions and measured data.
A Review of the Evaluation System for Curriculum Learning

In recent years, deep learning models have been more and more widely used in various fields and have become a research hotspot for various tasks in artificial intelligence, but there are significant limitations in non-convex optimization problems. As a model training strategy for non-convex optimization, curriculum learning advocates that models learn in the order of easier to more difficult data, mimicking the basic idea of gradual human learning as they learn curriculum. This strategy has been widely used in the fields of computer vision, natural language processing, and reinforcement learning; it can effectively solve the non-convex optimization problem and improve the generalization ability and convergence speed of models. This paper first introduces the application of curriculum learning at three major levels: data, task, and model, and summarizes the evaluators designed using curriculum learning methods in various domains, including difficulty evaluators, training schedulers, and loss evaluators, which correspond to the three stages of difficulty evaluation, training schedule, and loss evaluation in the application of curriculum learning to model training. We also discuss how to choose an appropriate evaluation system and the differences between terms used in different types of research. Finally, we summarize five methods similar to curriculum learning in the field of machine learning and provide a summary and outlook of the curriculum learning evaluation system.
Parallelization of discrete element simulation

Abstract The Discrete Element Method (DEM) is used for modeling discontinuous or non‐homogeneous materials and has been applied for various engineering applications. However, it often requires the use of a very large number of elements and very small time steps in order to ensure numerical stability. This in turn leads to considerable computational time requirements for each simulation of an actual engineering problem. To resolve this issue, this paper provides a systematic and general procedure for parallelizing a discrete element simulation system. Three strategies (the zone of interest, zoning, and interaction‐based geometric partitioning strategies) are proposed for hastening the simulation, at different simulation stages. An in‐house discrete element simulation framework, called VEDO (VErsatile Discrete Objects), is extended for parallel computation. Software utilizing this framework, named KNIGHT&IRIS 2.0, was developed to verify the feasibility of these strategies. Three numerical examples were tested using both shared memory and distributed memory platforms. These examples demonstrate that the strategies proposed in this study are effective and useful for researchers interested in developing a parallel discrete element simulation system.
Why 2D layout in 3D images matters: evidence from visual search and eyetracking

Precise perception of three-dimensional (3D) images is crucial for a rewarding experience when using novel displays. However, the capability of the human visual system to perceive binocular disparities varies across the visual field meaning that depth perception might be affected by the two-dimensional (2D) layout of items on the screen. Nevertheless, potential difficulties in perceiving 3D images during free viewing have received only a little attention so far, limiting opportunities to enhance visual effectiveness of information presentation. The aim of this study was to elucidate how the 2D layout of items in 3D images impacts visual search and distribution of maintaining attention based on the analysis of the viewer’s gaze. Participants were searching for a target which was projected one plane closer to the viewer compared to distractors on a multi-plane display. The 2D layout of items was manipulated by changing the item distance from the center of the display plane from 2° to 8°. As a result, the targets were identified correctly when the items were displayed close to the center of the display plane, however, the number of errors grew with an increase in distance. Moreover, correct responses were given more often when subjects paid more attention to targets compared to other items on the screen. However, a more balanced distribution of attention over time across all items was characteristic of the incorrectly completed trials. Thus, our results suggest that items should be displayed close to each other in a 2D layout to facilitate precise perception of 3D images and considering distribution of attention maintenance based on eye-tracking might be useful in the objective assessment of user experience for novel displays.
Particle Image Velocimetry Study of the Flow over a Slender Delta Wing

The leading-edge flow separation over a sharp-edged 76o swept delta wing at a constant angle of attack of 20 o is investigated using Stereoscopic Particle Image Velocimetry (SPIV). The leading-edge vortex cross flow is measured at several planes perpendicular to the wing surface. The instantaneous flow field pattern and the substructures of the leading-edge vortex flow are investigated at three moderate Reynolds numbers 0.2 x 10 6 , 0.4 x 10 6 and 0.6 x 10 6 , respectively. Furthermore, the topology of the flow on the wing surface is studied using an oil-film flow visualization technique and PIV measurements on a plane parallel to the surface. The surface streamline pattern resolves the trace of the primary-attachment line and of the secondaryseparation line. Moreover, both techniques indicate a vortex instability to occur from about x/c = 0.7.
A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect’s input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development.
Analysis of SD-WAN Packets using Machine Learning Algorithm

In recent years, legacy networks have evolved to incorporate the use of programmability features with the aim of improving performance and resource utilisation. In achieving this goal, packets need to be monitored and classified. In this study, an optimal monitoring tool is used in capturing the packets or flows in an emulated Software Defined Wide Area Network using GNS3. The network architecture is implemented using two hosts communicating to a server integrated with a machine learning (ML) model (python based) to classify real network packets. The ML model is achieved using the Decision Tree algorithm based on python programming. The proposed implementation ensures the ML algorithm efficiently classifies and segments various packets in the network in a database structure. This testbed can be effectively implemented in a real network scenario, and packet data can be captured and analysed into a database structure which can be used for further analysis such as congestion window or throughput for improving network performance and resource utilisation.
Global luminescent oil-film skin-friction meter generalized to three-dimensional geometry and applied to FAITH Hill

This paper further develops the global luminescent oil-film skin-friction meter by considering its application to a three dimensional axisymmetric wall-mounted bump subjected to subsonic turbulent boundary layer flow. The geometry gives rise to a necklace vortex and large scale separation on its lee side. An oil-film is only usable by the global luminescent oil-film skin-friction meter if it meets certain criteria. Most notably, it must be sufficiently thick to provide a good signal but not so thick that the relationship between luminescent intensity and oil-film thickness becomes nonlinear. Different regions on the model give rise to a usable oil-film at different times in an experiment. This paper proposes a technique for the selection of which solutions from a sequence of “snapshot solutions” taken during the run should be averaged for a particular region on the model. A relative skin-friction distribution resulting from the techniques proposed herein is presented for each of four sections of the model: One down the model’s centerline, and three across the model in the spanwise direction.
GNN Model Based On Node Classification Forecasting in Social Network

In the time of ever-growing technology, engineering, and deep learning methods, one thing that has caught the attention of people is the invention of Neural Networks, also known as Artificial Neural Networks [1]. These are the subset of machine learning and are at the core of deep learning. Their structure and nomenclature are modeled after the human brain, mimicking the communication between biological neurons [2]. This work is presented and explained by ANN, Graphical Neural Network [3], which is a type of NN that works on graphs. In today’s world, one can see various real-life applications of GNNs like those in various social networks, prediction of molecules, and drug preparation in medical sciences, road traffic, etc.The article deals with the application of the GNN showing how can a GNN helps in forecasting information about a person in a social network based on various given datasets. In the end, one can easily forecast the information of a person using various tools like Pytorch, etc.