Miracle-dz/UniFAR-FTUs huggingface dataset:
Fault Tolerance and Cost Effectiveness Analysis of Low-Powered 3D-TESH Network

Exa-scale supercomputing is the prime target for the supercomputers. To reach at that point, supercomputers require redesigning the interconnection networks to support high network scalability, suitable network performance, low cost-performance ratio, low power usage, high fault-tolerance, low network congestion and high throughput with low latency. Among the thousand of interconnection networks, one of the most capable one is 3D-TESH, is a hierarchical interconnection network (HIN) interconnected with multiple basic modules (basic modules are basically treated as single on-chip network), where each on-chip network is similar to a 3Dmesh network and off-chip network are built using the 2Dtorus connections. Some static network parameters as well as dynamic communication performance (on-chip level) had been studied earlier for 3DTESH, which ensured the high significance of this network in comparing with others. Moreover, in case of power consumption, it requires about 41% less router power than 5Dtorus at the on-chip level. In this paper, we like to evaluate some other important static parameters like- packing density, fault tolerance, wiring complexity, cost effectiveness factor and etc. Here, we found that 3D-TESH yields much better results than network like - 3Dmesh, 2Dmesh, 2Dtorus, TESH and even the 3Dtorus.
An intelligent unsupervised anomaly detection in videos using inception capsule auto encoder

ABSTRACT The feature extraction model focuses on extracting the spatio-temporal features using an Inception-CAE. The extracted features have been enriched with essential data to achieve the abnormal events. In addition, the reconstruction error between the initial and the reconstructed video frames is estimated. Finally, the normality score is calculated and related to the threshold calculated using the CTOA. The proposed system is implemented in Python by initiating datasets from CUHK Avenue, UCSD Ped2 and LV. Performances are evaluated in terms of ROC, AUC, accuracy, loss, model size and time complexity. Then the performance of the proposed system is compared to recent existing techniques. The maximum AUC obtained by the proposed system is 99.1% for CUHK Avenue, 99% for UCSD Ped2 and 99.2% for LV datasets. Thus, the simulated outcomes clearly showed that the proposed system had achieved better performance against classical techniques for detecting the anomaly in videos.
3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video

This study aims to achieve accurate three-dimensional (3D) localization of multiple objects in a complicated scene using passive imaging. It is challenging, as it requires accurate localization of the objects in all three dimensions given recorded 2D images. An integral imaging system captures the scene from multiple angles and is able to computationally produce blur-based depth information about the objects in the scene. We propose a method to detect and segment objects in a 3D space using integral-imaging data obtained by a video camera array. Using objects’ two-dimensional regions detected via deep learning, we employ local computational integral imaging in detected objects’ depth tubes to estimate the depth positions of the objects along the viewing axis. This method analyzes object-based blurring characteristics in the 3D environment efficiently. Our camera array produces an array of multiple-view videos of the scene, called elemental videos. Thus, the proposed 3D object detection applied to the video frames allows for 3D tracking of the objects with knowledge of their depth positions along the video. Results show successful 3D object detection with depth localization in a real-life scene based on passive integral imaging. Such outcomes have not been obtained in previous studies using integral imaging; mainly, the proposed method outperforms them in its ability to detect the depth locations of objects that are in close proximity to each other, regardless of the object size. This study may contribute when robust 3D object localization is desired with passive imaging, but it requires a camera or lens array imaging apparatus.
Formulation and Physical Stability of High Total Solids Lentil Protein-Stabilised Emulsions for Use in Plant Protein-Based Young Child Formulae

The demand for high-quality plant protein products is increasing and the aim of this work was to evaluate the impact of increasing the total solids content on the formation and stability of lentil protein stabilised oil-in-water emulsions. A series of emulsions were formulated using different proportions of total solids: 23, 26, 29, 32, and 35% (w/v). The emulsions were formulated using three ingredients—lentil protein, sunflower oil, and maltodextrin—which made up 15.85, 27.43, and 56.72% (w/w) of the total solids, respectively. The changes in apparent viscosity, particle size distribution, and colour during thermal processing were evaluated, with the physical stability investigated using an analytical centrifuge. The apparent viscosity of the solutions increased with total solids content (25.6 to 130 mPa.s−1), as did redness colour intensity (a* value increased from 5.82 ± 0.12 to 7.70 ± 0.09). Thermal processing resulted in greater destabilisation for higher total solids samples, as evidenced by greater changes in particle size, along with decreased redness colour. These results bring a better understanding of high total solids plant protein emulsions and factors affecting their stability, which could be used for the development of cost-effective and sustainable processing solutions in the production of plant protein young child formulae.
Self-Reported Anxiety and Depression among Parents of Primary School Children during the COVID-19 Pandemic in Thailand, 2022

One significant concern during the COVID-19 pandemic is parents’ mental health, which may consequently affect children’s health and well-being. The objective of this study is to investigate generalized anxiety and depression in parents of primary-school-aged children and identify risk factors for mental health problems. A cross-sectional survey comprising 701 parents of primary school children in five of Thailand’s major provinces was carried out from January to March 2022. Generalized anxiety and depression levels were assessed using the GAD-7 and PHQ-9. Logistic regression was performed to determine the effects of independent variables on anxiety and depression. Results showed that the prevalence of generalized anxiety and depression was 42.7% and 28.5%, respectively, among Thai parents. Three strong associative factors included: (1) having a youngest child with mental health problems; (2) not assisting their children every day; and (3) drinking alcohol. These findings show that the parents must deal with several difficulties when trying to maintain work and parenting duties while being confined at home during emergency situations. The government should provide sufficient assistance to parents who lack skills in handling children with emotional and behavioral problems. Meanwhile, health promotion to reduce alcohol consumption should continue to be an area of focus.
The Central Nervous System Source Modulates Microglia Function and Morphology In Vitro

The regional heterogeneity of microglia was first described a century ago by Pio del Rio Hortega. Currently, new information on microglia heterogeneity throughout central nervous system (CNS) regions is being revealed by high-throughput techniques. It remains unclear whether these spatial specificities translate into different microglial behaviors in vitro. We cultured microglia isolated from the cortex and spinal cord and analyzed the effect of the CNS spatial source on behavior in vitro by applying the same experimental protocol and culture conditions. We analyzed the microglial cell numbers, function, and morphology and found a distinctive in vitro phenotype. We found that microglia were present in higher numbers in the spinal-cord-derived glial cultures, presenting different expressions of inflammatory genes and a lower phagocytosis rate under basal conditions or after activation with LPS and IFN-γ. Morphologically, the cortical microglial cells were more complex and presented longer ramifications, which were also observed in vivo in CX3CR1+/GFP transgenic reporter mice. Collectively, our data demonstrated that microglial behavior in vitro is defined according to specific spatial characteristics acquired by the tissue. Thus, our study highlights the importance of microglia as a source of CNS for in vitro studies.
Statistics and recognition for software birthmark based on clustering analysis

ABSTRACT The result of feature selection for software birthmark has a direct bearing on software recognition rate. In this paper, we apply constrained clustering to analyze the software features (SF). The within-class (homogeneous software) and between-class (heterogeneous software) distances of features are measured based on mutual information. Information gain functions and penalty functions are constructed using homogeneous and heterogeneous SF, respectively; and redundancy is measured with correlation coefficients. Then the software birthmark features with high class distinction and minimum redundancy are selected. The example of extracting and detecting framework of birthmark feature is also given. The algorithm is analyzed and compared with the similar algorithms, and it is shown the algorithm provide an effective approach for software birthmark selection and optimization.
Study on Characteristics of Partial Penetration of Projectile into Steel Panel

Study on partial embedment of projectile(PEP) is put forward with the development of modern science and technology.The outer part of PEP standing stably can form an obstacle or induce the selective object to attack it by waiting for the proper chance,or act as the loading point to realize the special application.The research on a new nose shape projectile was carried out by the numerical analysis on process and characteristics of PEP and material testing of rheological behavior.Meanwhile,the main dimensionless group parameters were obtained by dimensional method.The deformation of target and energy evolution of the whole system in the forward and rebound phases of projectile were analyzed.The exploratory development work on mechanics of the PEP action was performed.Ultimately,PEP on steel panel was verified by artillery experiment.
Image edge detection based on statistical estimation

In order to solve the problem that the traditional edge detection algorithms based on differential have poor noise robustness,the formation mechanism of the image edge was analyzed and an edge detection algorithm based on the statistical estimation was proposed according to the statistical estimation evaluation criteria.The algorithm divided the circular neighborhood of a pixel into two semi-circles in the θ direction and calculated the mean value and variance of pixel samples within two semi-circles separately.The effectiveness of the statistical estimation of the samples within two semi-circles was analyzed,and the effects of both noise and texture on the edge detection were eliminated.The edge refinement and connection were completed on the basis of the character that the statistical estimation bias of the pixel samples at both sides of the edge shows a roof-like distribution.The edge detected by noise-free Canny operator was used as the prior knowledge,and the edge detection evaluation function was established based on the conditional probability.The experimental results indicate that the image edge detection method based on the statistical estimation has high detection precision and strong noise robustness.
Measures of uniformity in experimental designs: A selective overview

ABSTRACT Discrepancies are measures which are defined as the deviation between the empirical and the theoretical uniform distribution. In this way, discrepancy is a measure of uniformity which provides a way of construction a special kind of space filling designs, namely uniform designs. Several discrepancies have been proposed in recent literature. A brief, selective review of these measures including some construction algorithms are given in this paper. Furthermore, a critical discussion along with some comparisons is provided, as well.
Nonlinear Transient Dynamics of Photoexcited Resonant Silicon Nanostructures

Optically generated electron–hole plasma in high-index dielectric nanostructures was demonstrated as a means of tuning their optical properties. However, until now an ultrafast operation regime of such plasma-driven nanostructures has not been attained. Here, we perform pump–probe experiments with resonant silicon nanoparticles and report on dense optical plasma generation near the magnetic dipole resonance with an ultrafast (about 2.5 ps) relaxation rate. On the basis of experimental results, we develop an analytical model describing the transient response of a nanocrystalline silicon nanoparticle to an intense laser pulse and show theoretically that plasma-induced optical nonlinearity leads to ultrafast reconfiguration of the scattering power pattern. We demonstrate 100 fs switching to a unidirectional scattering regime upon irradiation of the nanoparticle by an intense femtosecond pulse. Our work lays the foundation for developing ultracompact and ultrafast all-optical signal processing devices.
A Method of Multi-Attribute Group Decision Making with Incomplete Hybrid Assessment Information

A new decision making approach is proposed for multi-attribute group decision making problem with incomplete hybrid assessment information.First,a conversion function is defined to transfer multifarious assessment information into intuitionistic fuzzy numbers.Then,based on the theory of information entropy,expert′s objection weight is judged by his hesitating degree.The D-S theory is used to deal with incomplete assessment information,and a possibility degree formula is conceived to rank alternatives.Finally,an example is given to show the feasibility and availability of the theory.
Novel Image Denoising Method Based on Discrete Fractional Orthogonal Wavelet Transform

The fractional wavelet transform extend the analysis method of wavelet transform about time-frequency domain to time-fractional frequency domain,can characterize signal features in time and fractional-frequency domain.Based on the theory of multiresolution analysis(MRA)of the fractional wavelet,the new forms about coefficient decomposition and reconstruction of discrete fractional wavelet transform(DFRWT)are given and made two-dimensional expansion.According to the feature of subband coefficient energy of image based on DFRWT varies with different p order,a novel image threshold denoising method based on DFRWT is proposed.The method in conditions of keeping the energy of low frequency subband for absolute great value,appropriate to raise the distribution of energy percentage,more beneficial for image noise suppression.The experimental results show that the performance of this method is better than the traditional wavelet threshold denoising method both in vision effect and peak signal to noise ratio.
Improvement of Encapsulation Technique of Organic Photovoltaics by UV-Curable Adhesive

This study develops a low-cost UV adhesive as a sealant and the associated encapsulation architecture, highly improving the stability of organic photovoltaic (OPV) device. In detail, this present study demonstrates how the composition and additive in the UV adhesive as a sealant affect the OPV stability. The face sealing and UV adhesive enable the OPV device to successfully meet three industrial criteria of accelerated tests of OPV stability: (1) T80 lifetime is more than 1000 h under the damp heat test of 65 °C/65% relative humidity (RH) environment, (2) T80 lifetime is more than 1000 h under the damp heat test of 85 °C/85% RH environment, and (3) T80 lifetime is larger than 1000 h under the continuous AM 1.5G solar illumination (i.e., light-soaking test).
SWIPT Enabled Cooperative Cognitive Radio Sensor Network With Non-Linear Power Amplifier

In this work, performance of a simultaneous wireless information and power transfer (SWIPT) enabled cooperative cognitive radio sensor network (CRSN) is explored over generalized Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> faded channels. A time switching (TS) protocol for SWIPT is used and the impact of TS factor on energy harvesting and information transmission phases, and information processing and broadcasting phases are considered. The cooperative CRSN consists of a pair of primary nodes (PNs) and sensor nodes (SNs), where one of the SWIPT enabled SN provides cooperative relaying for PNs communication by utilizing amplify-and-forward protocol. The overlay spectrum sharing protocol is used at the SN, which supports PNs communication along with its own transmission and resolves the spectrum scarcity issue. The impact of a non-linear power amplifier (NLPA) is also considered on the network performance. The analytical expressions of outage probability (OP), asymptotic OP, system throughput, and energy efficiency are obtained over Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> faded channels for arbitrary <inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula>. Further, the analytical expressions of ergodic capacity (EC) and asymptotic EC are obtained. The impact of NLPA and other system parameters on the performance of the considered network are highlighted. Finally, the obtained analytical expressions are validated through Monte-Carlo simulations.
SDS Electrophoresis on Gradient Polyacrylamide Gels as a Semiquantitative Tool for the Evaluation of Proteinuria

Proteinuria is an important sign of kidney diseases. Different protein patterns in urine associated with glomerular, tubular and overload proteinuria may be differentiated using the immunochemical detection of indicator proteins or via urinary proteins electrophoresis. Our aim was to characterize sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) using commercially available 4–20% gradient gels as a method to detect and differentiate proteinuria. Our laboratory-based study used excess urine samples collected for routine diagnostic purposes from adult patients of a tertiary-care hospital, including patients with albumin/creatinine < 30 mg/g and patients with dipstick proteinuria. The limit of albumin detection was estimated to be 3 mg/L. In 93 samples with albumin/creatinine < 30 mg/g, an albumin fraction was detected in 87% of samples with a minimum albumin concentration of 2.11 mg/L. The separation of 300 urine samples of patients with proteinuria revealed distinct protein patterns differentiated using the molecular weights of the detected proteins: glomerular (albumin and higher molecular weights) and two types of tubular proteinuria (“upper” ≥20 kDa and “lower” with lower molecular weights). These patterns were associated with different values of the glomerular filtration rate (median 66, 71 and 31 mL/min/1.72 m2, respectively, p = 0.004) and different proportions of multiple myeloma and nephrological diagnoses. As confirmed using tandem mass spectrometry and western blot, the SDS-PAGE protein fractions contained indicator proteins including immunoglobulin G, transferrin (glomerular proteinuria), α1-microglobulin, retinol-binding protein, neutrophil gelatinase-associated lipocalin, cystatin C, and β2-microglobulin (tubular), immunoglobulin light chain, myoglobin, and lysozyme (overflow). SDS-PAGE separation of urine proteins on commercially available 4–20% gradient gels is a reliable technique to diagnose proteinuria and differentiate between its main clinically relevant types.
Face Expression Recognition Based on Convolutional Neural Networks

Emotion recognition plays an integral role in human-computer interaction systems. This process involves finding interesting facial areas in an image and classifying them into seven categories: anger, disgust, fear, happiness, neutrality, sadness and surprise. While many breakthroughs have been made in image classification and facial expression recognition, there is still massive scope for progress in this research area to address the need for greater accuracy. In this paper, we use a Convolutional Neural Network (CNN) for extracting features for facial expression recognition. Based on the characteristics of the dataset, deep learning algorithms are introduced in this paper to improve the classification task. With the optimization model proposed in the paper, I obtained very competitive results compared to state-of-the-art methods on the fer2013 dataset, which is exciting.
Orientation Invariant Tensor Completion In Both Spectual And Space Domains

The performance of most tensor completion algorithms heavily relies on the definition of tensor low-rankness. Among the various low-rank regularizations proposed in the last decade, the Tubal+Tucker Nuclear Norm (T2NN) firstly considers the low rankness both in spectral and space domains. However, this norm is unfortunately sensitive to the orientation, and thus fails to model low-rankness in multiple orientations. To this point, a new tensor norm, dubbed Orientation Invariant Hybrid Nuclear Norm (OIHNN), is first defined and then applied to formulate a new tensor completion model. To solve the model, an efficient algorithm is developed within the framework of Alternating Direction Method of Multipliers (ADMM). Effectiveness of our method is validated through experimental results on real datasets.
Viral Vector-Based Gene Therapy

Gene therapy is a technique involving the modification of an individual’s genes for treating a particular disease. The key to effective gene therapy is an efficient carrier delivery system. Viral vectors that have been artificially modified to lose their pathogenicity are used widely as a delivery system, with the key advantages of their natural high transduction efficiency and stable expression. With decades of development, viral vector-based gene therapies have achieved promising clinical outcomes. Currently, the three key vector strategies are based on adeno-associated viruses, adenoviruses, and lentiviruses. However, certain challenges, such as immunotoxicity and “off-target”, continue to exist. In the present review, the above three viral vectors are discussed along with their respective therapeutic applications. In addition, the major translational challenges encountered in viral vector-based gene therapies are summarized, and the possible strategies to address these challenges are also discussed.
Deep Learning-and Transfer Learning-based Models for COVID-19 Detection using Radiography Images

Image classification and segmentation techniques are still very popular in the medical field (for healthcare), in which the medical image plays an important role in the detection and screening of diseases. Recently, the spread of new viral diseases, namely Covid-19, requires powerful computer models and rich resources (datasets) to fight this phenomenon. In this study, we propose to examine the CNN Deep Learning algorithm and two Transfer Learning models, namely RestNet50 and MobileNetV2 using the pretrained model of the ImageNet database, experimented on the new dataset (COVID-QU-Ex Dataset 2022) offered by the University of Qatar. These models are tested to classify radiography images into two classes (Covid19 and Normal). The results achieved by CNN (Acc =95.97%), ResNet50 (Acc =95.53%) and MobileNetV2 (Acc =97.32%) show that these algorithms are promising in order to combat this Covid-19 disease by detecting it through thoracic images (Chest X-ray type).