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Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems' performance that the DL models' combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%. [ABSTRACT FROM AUTHOR]

Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models. [ABSTRACT FROM AUTHOR]

With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, We have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error. [ABSTRACT FROM AUTHOR]

A study of the spatial occurrence of iron precipitation in a high performance multicrystalline silicon (HPMC-Si) sample is presented. The separated effects of grain-boundaries, sparse intragranular dislocations, and dislocation clusters are investigated by combining the Fei imaging method with glow discharge mass spectroscopy, electron backscatter diffraction, and two iron precipitation models. While the area-averaged precipitation at grain boundaries is relatively minor, almost the whole iron precipitation occurs within the grains, despite the very low intra-granular dislocation density. The fraction of non-precipitated iron in the studied HPMC-Si material was found to be one to two orders of magnitude higher than that reported previously for standard materials. [ABSTRACT FROM AUTHOR]

The columnar-to-equiaxed transition (CET) is known to impact crack formation during the additive manufacturing of metallic alloys. While previous experiments have shown that CET is tunable via its alloying elements, a rigorous multicomponent model to demonstrate the impact of multi-alloying components on CET is still lacking. In this study, we developed a multicomponent model by fully coupling the phase diagram of the kinetic interface condition. Building upon the binary model reported by Gaumann et al. our model replaces the restrictive approach of calculating the non-equilibrium partition coefficient and liquidus slopes with kinetic phase diagram calculation. The extended multicomponent model was validated by comparing it with the Al–Cu results reported by Gaumann et al. CET transition curves were computed for two Al–Cu–Mg–Si–Zn alloys manufactured using laser powder bed fusion. The results are in qualitative agreement with our own and previously reported experimental results. These findings suggest that the proposed multicomponent CET model is a valuable tool for designing AM alloys and optimising processing parameters. The figure above shows the predicted columnar to equiaxed transitions curve of Al-1.6 wt%Cu-2.56 wt%Mg-1.2 wt%Si-5.44 wt%Zn (original AA7075) and Al-1.6 wt%Cu-2.56 wt%Mg-5.0 wt%Si-5.44 wt%Zn (Si rich AA7075) alloys. For a given temperature gradient, the addition of Si greatly reduces the critical interface growth rate for Columnar to Equiaxed Transition (CET) thus promoting CET. The prediction is in good agreement with the two alloys grain morphology shown with EBSD orientation map in the figure below. The EBSD orientation maps of (a) an original 7075 alloy sample and (b) Si-rich AA7075 alloy. The observed plane is parallel to the building direction. It is color-coded by inverse pole figure, and black lines indicate grain boundaries. [Display omitted] [ABSTRACT FROM AUTHOR]

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings. [ABSTRACT FROM AUTHOR]

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. [ABSTRACT FROM AUTHOR]

Highlights • Impulsive generalised high-order Recurrent Neural Networks with timevarying coefficient and continuously distributed delays have been studied. • The existence and the global exponential stability of piecewise weighted pseudo almost-periodic solutions have been established. • An illustrative example is given to prove the effectiveness of our results. Abstract In this paper, by employing fixed point theorem, generalized Gronwall–Bellman inequality and differential inequality techniques, some sufficient conditions are given for the existence and the exponential stability of the unique piecewise weighted pseudo almost-periodic solution of impulsive high-order recurrent neural networks with time-varying coefficients and mixed delays. An illustrative example is also given in the end of this paper to show the effectiveness of our results. [ABSTRACT FROM AUTHOR]

A critical issue restricting the application of Hillert-Agren-Liu solute-drag based model to predict interfacial conditions at a migrating ferrite-austenite interface is the lack of value for trans -interface diffusion parameter, i.e., the L parameter. Even an estimation of the parameter's order of magnitude is difficult due to its ambiguous physical interpretation. In this paper we extend a different solute-drag based model, namely the binary continuous growth model originally developed for rapid solidification, toward austenite to ferrite phase transformation to avoid this long-standing issue. The extensions consist of the treatments of multi-alloying components including interstitial carbon element and Gibbs-Thomson effect. The extended multi-component continuous growth model employs a physical parameter with clear physical meanings, i.e., interface diffusive speed in describing trans -interface energy dissipation, and can predict kinetic interface conditions and spontaneous diffusion-controlled to diffusion-less transition without using the ambiguous L parameter. The model is verified by the good agreement of its calculation results with those predicted by Hillert-Agren-Liu model for Fe–C alloys and Fe–C–Mn alloys. Further the model's advantages over Hillert-Agren-Liu model are demonstrated by calculating the kinetic interface condition phase diagrams of Fe–C–Mn–Ni alloys. It is concluded that the extended multi-component continuous growth model is valuable in unifying the efforts in addressing the common question of predicting deviations from local equilibrium at a fast-migrating interface during solidification and solid-state phase transformation. Fig. 1 Kinetic interface condition phase diagram for Fe-2.9%C-5.5%Mn–Ni alloys at v v D N i = 1.0 ; The dotted lines are equilibrium phase boundaries, and the straight lines are the kinetic phase boundaries.The manuscript deals with an important topic in phase transformation: austenite-ferrite Interface response to fast interface migrating rate for multi-component steels. By coupling with CALPHAD databases, we have extended Aziz's binary continuous growth model, originally developed for rapid solidification, toward austenite to ferrite phase transformation in multi-component alloys. This extension enables us to resolve the long-standing issues with the trans -interface diffusion parameter used in the classical Hillert-Agren-Liu solute drag model. The extension unifies the efforts in solidification community and solid-state phase transformation community in addressing the common question of predicting deviations from local equilibrium at a fast-migrating interface.The extended multi-component continuous growth model has been applied to Fe–C–Mn–Ni alloys to calculate the composition–temperature isopleths of Fe–C–Mn–Ni kinetic interface condition phase diagrams. The figure above shows the possible interface conditions with blue and red solid lines when a flat interface is moving at a given speed. The equilibrium phase boundaries are shown with red and blue dotted lines to set the references for the kinetic phase boundaries. It is difficult for the classical Hillert-Agren-Liu model to perform this case study as it requires the temperature dependent trans -interface diffusion parameter. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Instabilities in the Czochralski crystal growth process result in deviation from the desired crystal diameter i.e. uneven crystal surface. The excessive thermoelastic stress induced by diameter perturbation is studied by mean of numerical simulations. A set of 3D simulation has been performed for an axisymmetric crystal. The crystal anisotropy is taken into account. The influence of crystal diameter fluctuation on stress field inside the crystal has been studied. The resolved shear stresses have been calculated for 12 slip systems for three crystal orientations. Accumulated excess stress from its critical value is calculated. Simulation results suggest that crystal surface undulation affects both thermal field and stress distribution inside the crystal. The crystal region with high risk of dislocation generation is discussed for three crystal orientation. The stress level in [111] crystal orientation is found to be lower than other orientations. [Copyright &y& Elsevier]

Abstract: Variation of crystal radius during the growth process is an inevitable feature of Czochralski crystal growth method. Fluctuation in the system i.e. heating power, pulling rate and melt level perturbs the stability of the growth process during Czochralski silicon growth. Highly uneven topographies are likely to act as concentrators for thermally induced stresses and deformations. This effect has been underestimated and neglected in stress studies and the crystal is assumed perfectly cylindrical. A set of 2D simulation were performed for an axisymmetric crystal with isotropic properties. The crystal boundary is perturbed complying a sinusoidal function. The impact of amplitude and frequency of crystal radius fluctuation on stress field inside the crystal has been studied. Simulation result shows that crystal surface undulation affects both thermal field and stress distribution inside the crystal. Sharp angels and profound perturbation found to generate high stresses at the crystal periphery. [Copyright &y& Elsevier]

Abstract: The present work presents a 3D thermo-mechanical model and its application to the casting of square multi-crystalline silicon ingots. Stresses and deformations in the ingot are the results of the combined effects of thermal contractions and mechanical loads induced by the ingot-crucible interaction. The model accounts for plastic deformation at high temperature. Air gap formation, sticking and fracture of the coating layer are all considered while modelling the ingot-crucible contact. The model is applied to analyse the crystallisation and cooling process in a vertical Bridgman furnace. Several situations are studied to illustrate ingot-crucible interactions (air gap formation, partial sticking and loss of contact due to fracture of the coating layer) and their consequences on the residual stress and strain fields. The 3D evolution of air gap formation between the ingot and the crucible is also presented. [Copyright &y& Elsevier]

Abstract: A set of global heat transfer simulations in Czochralski (Cz) furnace for producing silicon single crystals have been performed to investigate the effect of crystal and crucible rotations on melt convection and crystal/melt interface shape. The 2D axisymmetric simulations are carried out taking into account radiative and conductive heat transfers between furnace components, melt convection including thermocapillary forces (Marangoni) and gas flow. Melt flow pattern and temperature distribution have been studied for several combinations of crystal and crucible rotations and for different crystal heights. The result shows that crystal/melt interface shape and melt flow regime are strongly sensitive to the rate of rotations of both crystal and crucible. Interfaces with low deflection can be achieved for certain combinations of crystal and crucible rotation rates. [Copyright &y& Elsevier]

Abstract: The purpose of the present work is to present a thermo-mechanical model of the multi-crystalline ingot casting and its application to a laboratory-scale directional crystallisation furnace. In thermo-mechanical modelling, stresses and deformations in the ingot are the results of combined effects of thermal contractions and mechanical loads induced by the ingot–crucible contact. The model takes into account plastic relaxation at high temperature and is, therefore, capable of predicting the final residual stress and strain fields in the ingot. The contact between the ingot and the crucible is modelled using a variable stiffness model and simple coating fracture model. The model is applied to analyse the crystallisation and cooling process during directional crystallisation of small silicon ingots in a silica crucible. Several situations are studied to illustrate ingot–crucible interactions (air gap formation, sticking and loss of contact due fracture of the coating layer) and their consequences on the residual stress and strain fields. The simulation results show that the residual stress and strain fields due to sticking of the ingot to the crucible can be significantly higher than those due to the sole effect of thermally induced deformations. [Copyright &y& Elsevier]

Abstract: An existing two-phase mathematical model that addresses hot tearing formation in aluminium casting is extended to include microporosity formation and its effect on quantities associated with hot tearing formation. An application addressing the start-up phase of aluminium direct-chill casting reveals that thermally induced deformations increase the porosity level, and thus the hot tearing susceptibility if hot tearing is considered to be an “opening-up” of micropores caused by the deformation. [Copyright &y& Elsevier]

Exposure of human cells to genotoxic agents activates various signaling pathways involved in the execution of stress- and DNA-damage responses. Inappropriate functioning of the DNA-damage response to ionizing radiation is responsible for the human diseases ataxia telangiectasia (A-T) and Nijmegen breakage syndrome (NBS). Here, we show that ionizing radiation efficiently induces Jun/ATF transcription factor activity in normal human diploid fibroblasts, but not in fibroblasts derived from A-T and NBS patients. In normal cells, ionizing radiation was found to enhance the expression of c-Jun and, in particular ATF3, but in contrast to various other stress stimuli, ionizing radiation did not induce the expression of c-Fos and Fra1. Using specific inhibitors, we found that the ATM- and NBS-dependent activation of ATF3 does not require either p53 or reactive oxygen species, but is dependent on the p38 and JNK MAP kinases. Via these kinases, ionizing radiation activates ATF-2, one of the transcription factors acting on the atf3 promoter. As ionizing radiation does not activate ATF-2 phosphorylation in ATM- and NBS-deficient cells, both ATF-2 and ATF3 may play an important role in the protective response of human cells to ionizing radiation. [Copyright &y& Elsevier]

Breast cancer, a prevalent and life-threatening disease, necessitates early detection for the effective intervention and the improved patient health outcomes. This paper focuses on the critical problem of identifying breast cancer using a model called Attention U-Net. The model is utilized on the Breast Ultrasound Image Dataset (BUSI), comprising 780 breast images. The images are categorized into three distinct groups: 437 cases classified as benign, 210 cases classified as malignant, and 133 cases classified as normal. The proposed model leverages the attention-driven U-Net's encoder blocks to capture hierarchical features effectively. The model comprises four decoder blocks which is a pivotal component in the U-Net architecture, responsible for expanding the encoded feature representation obtained from the encoder block and for reconstructing spatial information. Four attention gates are incorporated strategically to enhance feature localization during decoding, showcasing a sophisticated design that facilitates accurate segmentation of breast tumors in ultrasound images. It displays its efficacy in accurately delineating and segregating tumor borders. The experimental findings demonstrate outstanding performance, achieving an overall accuracy of 0.98, precision of 0.97, recall of 0.90, and a dice score of 0.92. It demonstrates its effectiveness in precisely defining and separating tumor boundaries. This research aims to make automated breast cancer segmentation algorithms by emphasizing the importance of early detection in boosting diagnostic capabilities and enabling prompt and targeted medical interventions. [ABSTRACT FROM AUTHOR]

Background Surveillance is the backbone of infectious diseases control but an outbreak of a new pathogen to a developing country may have devastating consequences given less prepared healthcare systems in such countries. In October 2015, there was a sharp rise of febrile illnesses reported in Dairot Fever Hospital which prompted the general department for fever hospitals in the Egyptian ministry of health (MOH) to constitute a scientific committee for field visit study in Dairot fever hospital. Methods The committee held meetings at Dairot fever hospital, educated local healthcare providers, examined all isolated patients and requested samples of 118 isolated patients to be sent to central laboratories of MOH. Entomological services were also part of the committee and surveillance was started in the affected area. Results Out of 118 samples, 28 came back positive for Dengue virus type 1 by ELISA and PCR. Entomological surveillance revealed the presence of Aedes aegypti larvae and adult mosquito at the sites where cases were living; consequently, entomological control measures for dengue vector were immediately set leading to a dramatic decline in the density of adult mosquito (from 23% to 0%) and larvae (from 25% to 0.5%). Conclusion The prompt response of the MOH in Egypt led to rapid control of Dengue fever outbreak but educating healthcare workers about possible imported infectious diseases would have halted the outbreak much earlier which shows the importance of Infectious Diseases training in developing countries. Disclosures All authors: No reported disclosures. [ABSTRACT FROM AUTHOR]

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE). [ABSTRACT FROM AUTHOR]

Highlights • The gas in a pinhole was Ar, which likely came from the furnace atmosphere. • The pinholes did not affect the arrangement of dislocation etch pits. • Pinhole frequency before structure loss is not higher than without structure loss. • There were no indication that pinholes caused structure loss in seven ingots. • Modeling gives a stress concentration factor of at most 2.1 around pinhole. Abstract In the production of monocrystalline silicon by the Czochralski process, structure loss frequently reduces yield and increases production time. Structure loss means the transition from monocrystalline to multicrystalline structure. It is typically preceded by generation of dislocations and slip. Previous work concluded that a pinhole (i.e. a cavity from gas bubble in the melt) was the main cause of structure loss in an n-type Czochralski ingot. In the present study, seven industrial n-type Czochralski silicon ingots were investigated to assess whether pinholes frequently contribute to cause structure loss. Pinholes were found within 5 cm above the position where dislocations were generated in four out of the seven ingots. Slices were cut with the pinholes at one surface and etched with Sopori etchant. The pinholes did not appear to have affected the arrangements of etch pits in these samples and investigation of pinholes did not reveal the cause of structure loss. Furthermore, there were not significantly higher pinhole frequency before structure loss compared to ingots without structure loss among 4041 industrial ingots. Modeling of thermal stresses showed stress concentration around a pinhole, and it gives new insight into the level of stress the ingot can endure without generating dislocations and slip. [ABSTRACT FROM AUTHOR]

Monitoring the Earth's surface and objects is important for many applications, such as managing natural resources, crop yield predictions, and natural hazard analysis. Remote sensing is one of the most efficient and cost-effective solutions for analyzing land-use and land-cover (LULC) changes over the Earth's surface through advanced computer algorithms, such as classification and change detection. In the past literature, various developments were made to change detection algorithms to detect LULC multitemporal changes using optical or microwave imagery. The optical-based hyperspectral highlights the critical information, but sometimes it is difficult to analyze the dataset due to the presence of atmospheric distortion, radiometric errors, and misregistration. In this work, an artificial neural network-based post-classification comparison (ANPC) as change detection has been utilized to detect the muti-temporal LULC changes over a part of Uttar Pradesh, India, using the Hyperion EO-1 dataset. The experimental outcomes confirmed the effectiveness of ANPC (92.6%) as compared to the existing models, such as a spectral angle mapper (SAM) based post-classification comparison (SAMPC) (89.7%) and k-nearest neighbor (KNN) based post-classification comparison (KNNPC) (91.2%). The study will be beneficial in extracting critical information about the Earth's surface, analysis of crop diseases, crop diversity, agriculture, weather forecasting, and forest monitoring. [ABSTRACT FROM AUTHOR]

Oxygen and carbon are the most common impurities in multi-crystalline silicon. The general mechanism for formation and transport of O and C in the solidification furnace is as follows: oxygen from the silica crucible comes into the melt and combines with a silicon atom and evaporates at the gas/melt interface in the form of silicon oxide (SiO). Argon inert gas, injected into the furnace chamber, carries the SiO to the hot graphite fixtures, where it reacts with carbon to form carbon monoxide (CO) and silicon carbide (SiC). CO is carried by the gas to the melt free surface, where it dissociates into carbon and oxygen. Finally, during solidification oxygen and carbon are incorporated into the crystal. A global furnace model accounting for heat transfer, melt flow, gas flow and impurity transport has been applied to investigate the oxygen and carbon formation and transport in a vertical Bridgman furnace during the holding phase when the furnace is at its hottest. A case study is performed to investigate the effect of the applied heating power on the carbon and oxygen concentrations in the melt prior to solidification. [ABSTRACT FROM AUTHOR]

Solid-liquid Interface response function or the analysis of kinetic interfacial conditions under rapid solidification setting occupies a central role in solidification research. With the concepts of solute drag and solute trapping and on the base of irreversible thermodynamics, the calculation of kinetic interface condition phase diagram for binary alloys has been well established owing to the continuous growth model by Aziz et al. Motivated by the fast development of various additive manufacturing (AM) technologies, clear needs to extend the kinetic phase diagram calculation towards multi-component alloy systems emerge to meet the challenges of processing parameter optimization encountered under those sub-rapid or rapid AM solidification conditions for new alloys. In this paper the irreversible thermodynamics analysis and the binary continuous growth model are reformulated into a form suitable for the coupling with the multi-component CALPHAD databases. Then the numerical solution to the CALPHAD-coupled multi-component model is described. The model and its numerical solution are verified by comparing its calculation results with those reported in literature for A-B hypothetical ideal solution phases, Ag–Cu and Al–Be alloys. The model predictive power is demonstrated by calculating the kinetic diagram of Al–Ti, Fe–Cr–Ni and Al–Cu–Mg–Si–Zn alloys. To illustrate one of the practical values of the proposed model, kinetic growth restriction factor is calculated from the predicted Al–Ti and Al–Cu–Mg–Si–Zn kinetic phase diagram. It is concluded that the proposed multi-component model and its numerical solution can calculate kinetic phase diagram of any multi-component alloys. Moreover, the proposed model can be used in evaluating solute effect for grain refinement under sub-rapid or rapid solidification conditions, which is great value to understand the solidification phenomenon in AM. The model is expected to be useful in many scenarios to guide the optimization of AM processing parameters and alloys design. [ABSTRACT FROM AUTHOR]

Abstract: The shape of the melt/crystal interface in Czochralski grown silicon crystal is a result of the interaction of several phenomena. In particular, melt convection plays a key role as it affects the heat and mass transfer inside the melt. Depending on the growth conditions and heat transfer in the melt, the interface shape can evolve from a purely concave or convex shape to a W-shape. The aim of this study is to explore the heat transfer conditions and melt flow patterns leading to the formation of W-shape interfaces. The 2D simulations are performed for 6in. diameter silicon in order to identify conditions leading to W-shape formation. The numerical results show that the formation and development of W-shape interfaces is associated with growth conditions favoring the appearance of a vortex underneath the crystal. This vortex significantly influences the thermal field within the crystallization zone and locally alters the interface deflection. This study suggests that the formation of the W-shape interfaces influences significantly the grown-in defects and crystal quality. [Copyright &y& Elsevier]

Purpose: This study aims to introduce a novel approach in form of a comprehensive software suite to help understanding and optimizing the build orientation toward maximizing the fatigue lifetime of complex geometries. The objective is to find an optimized build orientation under a given in-service loading state, which brings on smoother surfaces in stressed regions, mitigated roughness-induced stress concentration and deferred crack initiation stage. The solution addresses scenarios that no post-build surface treatment can be applied. Design/methodology/approach: To account for the surface topography, the staircase induced surface roughness is registered as a function of build angle using the white light interferometry characterization, based on which the stress concentration factor (kt) is calculated. Thereafter, the developed module in "Fatlab toolbox" is used to find the optimum build angle, considering the integrated surface orientations and stress analysis under a given loading condition. Findings: Surface topography creates local stress concentrations upon loading, directly influencing the fatigue lifetime. It is a well-established fact that the conditions of the staircase geometry and surface roughness affect the magnitude of the stress concentration upon loading, which is influenced by the build orientation of the component. The proposed solution suggests the best build orientation that mitigates staircase-related surface roughness. Originality/value: The suggested numerical approach assists the designers with positioning of the part on the build plate to minimize the build orientation-induced surface roughness and improve the as-built fatigue lifetime of the component. [ABSTRACT FROM AUTHOR]

In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches. [ABSTRACT FROM AUTHOR]

Background: Studies have identified healthcare providers as an important determinant of vaccination acceptance. However, knowledge and attitudes toward vaccination have not been sufficiently studied in Saudi Arabia, especially among medical students. Therefore, we conducted this study to explore vaccination knowledge and attitudes among medical students at a large Saudi university. Methods: A cross-sectional survey was conducted on 182 Saudi medical students between February 2019 and May 2019. Participants were invited to fill out a self-administered questionnaire assessing knowledge and attitudes toward vaccination. The statistical analysis included descriptive analysis, Chi-square test, independent samples t-test, and analysis of variance (ANOVA). The relationship between knowledge and attitudes was assessed using Pearson's correlation test. Results: A total of 182 respondents completed the questionnaires, giving a response rate of 91%. The study included male (52.7%) and female (47.3%) students from study years 2, 3, 4, 5, and 6. The overall mean knowledge score was under average [3.05/9, standard deviation (SD) = 1.86] and the respondents showed generally moderate attitudes toward vaccination (mean = 30.60/45, SD = 6.07). While there was no sex difference in both the scores on knowledge and attitudes domains, the year of study was significantly associated with the mean knowledge score (F = 6.48, P < 0.01) and attitudes score (F = 7.12, P < 0.01). As predicted, there was a significant linear relationship between vaccination knowledge and attitudes (r = 0.71, P < 0.01). Conclusion: The study revealed generally moderate attitudes of Saudi medical students toward vaccination. However, several knowledge gaps were detected. The implications of the current findings are discussed. [ABSTRACT FROM AUTHOR]

Abstract Existence and uniqueness of pseudo almost automorphic solutions for a class of high-order Hopfield neural networks are established by employing a suitable fixed point theorem and differential inequality. Moreover, the attractivity and global exponential stability of the pseudo almost automorphic solution are also considered for the system. Two Numerical examples with graphical illustrations are given to illuminate our main results. [ABSTRACT FROM AUTHOR]

We report results from a national project about impurities in high performance multicrystalline silicon: Contamination sources, transport routes, interaction with crystal defects and impact on solar cell efficiency parameters. Several ingots were produced in a lab scale furnace. Growth parameters and crucible types were varied, and high purity quartz crucibles were compared to novel silicon nitride crucibles. The material was characterized by a range of methods including FTIR, GDMS, NAA, EBSD, dislocation etching, lifetime analysis. Wafers were processed, and lifetime was measured during the cell processing to analyse the effect of separate processing steps. Models were constructed to simulate the impurity transport during the crystallization. The diffusivity in quartz and silicon nitride crucibles required in the modelling is not generally known, and experiments were performed to evaluate them. Variations in dislocation density have the strongest impact on lifetime, even for this high quality material. The quartz crucible has the highest contamination potential, but the uncertainty in diffusion parameters makes it difficult to conclude if this potential is reached, and for which elements. We therefore make an overview showing which value of the diffusion coefficient shift the contamination dominance between feedstock/crucible/coating. The use of high purity crucibles enables comparable or even higher lifetime material to be made in lab scale compared to industrial settings. Our results indicate that a thin layer of higher purity, lower porosity quartz on the inside of the crucible can be an effective method to transfer this improvement to industrial conditions. The cell processing indicates that the diffusion/gettering sequence profoundly decreases the lifetime by rendering the random high angle grain boundaries electrically active. However, this effect is reversed after firing of the anti-reflective coating, when the remaining active defects are dislocation clusters and CSL boundaries. Silicon nitride crucibles appear to be a promising low oxygen alternative to traditional quartz crucibles in terms of purity. [ABSTRACT FROM AUTHOR]

Brazing experiments on commercially-pure copper plates, using brazing filler metal (MBF-2005), are conducted at temperatures in the range of 650–750 °C for time-durations in the range of 5–15 min. Shear tests for braze-joints involved use of a universal testing machine. Based on the shear-test results, a new brazing cycle has been developed that corresponds to the greatest shear strength of the braze-joint. The brazing cycle has been performed under a controlled dry-argon atmosphere in a tube furnace. Microscopic observations were made by use of both optical and electron microscopes; whereas surface roughness measurements were made by using a TR100 Surface Roughness Tester . It is found that successful brazing and good wetting can be achieved by the least voids by using an average surface roughness ( R a value) for the base material. [ABSTRACT FROM AUTHOR]

Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features' importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques. [ABSTRACT FROM AUTHOR]