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Wire arc additive manufacturing (WAAM) is a potential method of producing complicated metal parts directly from computer-aided design (CAD) models. In this experiment, stainless steel 308 L alloy was deposited with a GMAW-interfaced three-axis special purpose Cartesian robot. The major goal was to understand the behaviour of deposited material and, later, its dissolution in specific concentrations of chemical combinations. The deposited samples were investigated for better understanding of the impact of welding current on bead properties, surface roughness, and microstructure. Furthermore, the dissolving behaviour of deposited samples was investigated in various acidic bath mixes to better understand the rate of dissolution. The results reveal that WAAM parameters, particularly current, have a considerable impact on bead properties, roughness, and microstructure. On the other hand, dissolution experiments demonstrated that the faster breakdown of WAAM deposited stainless steel occurs when a 1:1 ratio mixture of acids is used. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) significantly impacts millions globally, causing progressive memory loss and cognitive decline. While a cure remains elusive, early detection can mitigate effects and improve quality of life. Recent AD research has shown promise using deep learning algorithms on brain MRI images for stage prediction. This paper introduces a novel approach integrating two CNN algorithms, ResNet and EfficientNet, with a post-processing algorithm to enhance AD diagnosis. Empirical analyses on public datasets ADNI and OASIS evaluate the technique, leveraging the complementary strengths of the CNN models and a weighted averaging ensemble learning method. The proposed approach's uniqueness lies in combining multiple CNN architectures with a specialised post-processing algorithm. Notable accuracies achieved are 98.59% for EfficientNet, 94.59% for ResNet, and 98.97% with post-processing on ADNI, and 97.25% for EfficientNet, 99.36% for ResNet, and 99.41% with post-processing on OASIS. This work addresses existing methods' limitations and demonstrates superior predictive performance, contributing to AD diagnosis advancements and highlighting deep learning's potential in healthcare applications. [ABSTRACT FROM AUTHOR]

The Hirtisation® process is an electro-chemical process, wherein the electrolyte can easily access both external and internal surfaces. The process shows promising results in both support structure removal and reduction of surface roughness, and has the potential to solve the productivity and quality trade-off in powder bed fusion – laser beam (PBF-LB) processing. In this study, the role of the microstructure and surface topography on the capability of the Hirtisation® process to lower the PBF-LB produced surface roughness, has been investigated. A detailed microstructure analysis by SEM was used to determine the effect of the Hirtisation® process on removal of sintered powder, the effects on melt pool boundaries and grain boundaries, and thus the final surface quality. The Hirtisation® process significantly reduced surface roughness thanks to the complete removal of sintering powder from the as-built surface. Additionally, preferential material removal was detected along melt pool boundaries, leading to creation of notches of up to 10 µm in depth. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) significantly impacts millions globally, causing progressive memory loss and cognitive decline. While a cure remains elusive, early detection can mitigate effects and improve quality of life. Recent AD research has shown promise using deep learning algorithms on brain MRI images for stage prediction. This paper introduces a novel approach integrating two CNN algorithms, ResNet and EfficientNet, with a post-processing algorithm to enhance AD diagnosis. Empirical analyses on public datasets ADNI and OASIS evaluate the technique, leveraging the complementary strengths of the CNN models and a weighted averaging ensemble learning method. The proposed approach’s uniqueness lies in combining multiple CNN architectures with a specialised post-processing algorithm. Notable accuracies achieved are 98.59% for EfficientNet, 94.59% for ResNet, and 98.97% with post-processing on ADNI, and 97.25% for EfficientNet, 99.36% for ResNet, and 99.41% with post-processing on OASIS. This work addresses existing methods’ limitations and demonstrates superior predictive performance, contributing to AD diagnosis advancements and highlighting deep learning’s potential in healthcare applications.

In late year general crime percentage is expanding step by step and adapt up to this implementation office track down the ways of accelerating the general cycle one way we are involving face acknowledgment innovation for recognizing and checking the crook. The proposed criminal recognizable proof framework influence video live transfers for ongoing reconnaissance and examination the cycle starts with change of video content into individual edges which go through pre- handling methods to improve quality and standardize information it gathers the information for highlights like facial elements and dress subtleties picture video we are utilizing two kinds of profound learning models we are utilizing standard convolution brain network(CNN) design and pre-prepared CNN engineering specifically VGG-16 and VGG-19 calculations the post handling steps are coordinated to refine result alleviating bogus positive and upgrading by and large exactness in this contemplating giving the 95% exactness. [ABSTRACT FROM AUTHOR]

This study investigates the impact of heat treatment on the mechanical, morphological, and dimensional properties of polyethylene terephthalate glycol (PETG), a commonly used thermoplastic in 3D printing. Taguchi L25 orthogonal array (OA) was employed to optimize 3D printing parameters, considering factors such as infill percentage (ranging from 20% to 100%), layer height (0.12 mm to 0.28 mm), layer width (0.32 mm to 0.62 mm), and infill pattern. Following ASTM D638 type IV standards, mechanical testing revealed that the optimal printing conditions included a 100% infill percentage, a layer height of 0.16 mm, a layer width of 0.32 mm, and an infill pattern of 5. Specimen 22, produced under these conditions, exhibited a notable stress-bearing capacity of 46.43 ± 1.394 MPa. These results are consistent with previous studies that underscored the significance of high infill percentages and finer layer dimensions in enhancing tensile properties. Subsequently, these optimized specimens were exposed to various heat treatment conditions. It was discovered that heat treatment at 85°C for 15 min yielded the most significant improvements, increasing the stress-bearing capacity to 53.462 ± 1.604 MPa, representing an impressive ∼16% enhancement compared to non-heat-treated specimens. However, this treatment also led to increased brittleness. Morphological analysis using Scanning Electron Microscopy (SEM) further substantiated the findings. Specimens subjected to heat treatment at 85°C exhibited fewer voids and porosities than those printed with lower infill percentages and larger layer dimensions. These observations underscored the importance of adequate infill density and finer printing details for optimizing strength. Regarding dimensional stability, dimensional changes were meticulously measured after heat treatment. Notably, specimens subjected to heat treatment at or near the glass transition temperature (Tg) of PETG experienced the most significant shrinkage. Specimen 6, treated at 85°C, displayed the highest shrinkage, with length and width reductions of 133.30 ± 3.85 mm and 25.90 ± 0.87 mm, respectively. [ABSTRACT FROM AUTHOR]

FDM, being the most popular AM technology, has a wide user base across the globe. The process makes use of a layer-by-layer approach, causing a staircase effect on the material's printed surface. Though it affects several mechanical properties, its effect on surface integrity is highly detrimental and needs to be addressed. Chemical vapour dip and immersion techniques can provide a rapid solution using solvents. The solvents employed in the current study were the polar solvents- acetone and ethyl acetate, the mid-polar solvent- tetrahydrofuran (THF), and the non-polar solvents- chloroform and dichloromethane (DCM). The superior surface finish obtained during the post processing of 3D printed parts was having mean roughness value (Ra) of 0.67 µm, originally 11.42 µm. It was obtained when experimentation was carried out with the chemical vapour technique using THF. The optimum surface finish was readily achieved with mid- and non-polar solvents, whereas the polar solvents were slow to react with the surface of the PLA. A number of variables, including the solvent's polarity, boiling point, vapour pressure, and water miscibility, have an impact on the final surface's appearance and strength. During vapour treatment, the tensile strength of the 3D printed parts got reduced between 11.6% (THF) to 43.78% (DCM). On the other hand, the chemical immersion technique has a even more impact on the material's strength and hardness. It reduces hardness to the maximum of 44.41% (THF), whereas the vapour evaporation technique reduces hardness by only up to 14.64% (chloroform). [ABSTRACT FROM AUTHOR]

A novel approach utilizing triangle toolpath has been developed in this study to improve the surface finish of parts created by extrusion-based additive manufacturing (AM). During the extrusion-based AM process, poorly oriented surfaces can result in inferior surface quality, failing to meet the desired quality requirements for the final part. The inherent staircase effect in extrusion-based AM limits the part surface quality of built parts, resulting in a substandard surface finish. To overcome this limitation, a triangle-based machining toolpath approach has been introduced to remove staircases from all directions and enhance the average surface roughness of parts produced through extrusion-based AM. The combined impact of AM and machining parameters on the outcome was investigated using statistical analysis techniques. Experimental results and surface profile images have validated the effectiveness of the proposed approach in achieving an improved surface finish. [ABSTRACT FROM AUTHOR]

Throughout many decades, the Schlieren visualization method has been mainly used as means to visualize transparent flows in a qualitative manner. The images recorded provide data regarding the existence of the flow, or illustrate predicted flow geometries and details. The colored Schlieren method has been developed in the late 1890s and has always had the intent to provide quantitative data rather than qualitative pictures of the studied phenomena. This paper centers on applying a quantitative color Schlieren method to help determine the gasodynamic parameters of an H2–O2 exhaust jet, developing in air. A comparison between the parameters obtained through calibrating the color filter for the Schlieren method and the results from a CFD simulation is performed to assess the range of the CS (color Schlieren) measurement. This paper's findings address the issues of calibrated color filter Schlieren encounter during its implementation and discusses possible errors appearing when the method is applied to a 3D flow. While the qualitative Schlieren images are still impressive to observe, the quantitative Schlieren presents challenges and a low measurement accuracy (75%) when applied to 3D flows and compared to 2D cases found in the literature (97–98%). [ABSTRACT FROM AUTHOR]

An accurate estimation of the water requirement of saffron, as the most strategic product in the eastern regions of Iran, is inevitable. Considering the field limitations in measuring the water requirement, applying empirical models has always been of interest. However, since each estimation model has unique strengths and weaknesses, relying only on an experimental model cannot obtain a reliable estimate for water requirements. This study intends to evaluate different combined methods' ability to merge the saffron water requirements simulations and obtain an improved output. Six empirical models and four other combination techniques were applied to get some skilful simulations about saffron water requirements in arid regions. Results indicate that the evapotranspiration prediction under the Abtew method (ABM) has more proficiency, such that its RMSE was 0.13 mm. Also, the different comparative tests show that the outputs of combined techniques such as Multi Model Super Ensemble ‘MMSE’ and Modified MMSE ‘M3SE’ outperform others.

Fused deposition modeling (FDM) is a technology with potential benefits such as material versatility, user-friendliness, and cost-effectiveness. However, the mechanical properties of FDM-printed specimens are relatively low. To address this issue, post-processing techniques such as shot peening can be employed. Shot peening is utilized as a post-treatment for metalbased and ABS materials, but its application to PLA material has not been explored yet. This study aimed to evaluate the effect of shot peening on the mechanical properties of FDM-printed PLA samples. A full factorial experimental design was employed with shot peening speed, duration, and number of outer shells as parameters. The tensile strength of 72 samples after the shot peening process was measured and evaluated. Analysis of variance (ANOVA) revealed that all parameters had a significant influence on tensile strength. Additionally, the interaction of speed*time and speed*number of shells also had a significant influence. Meanwhile, the interaction of time*number of shells and speed*time*number of shells had no significant influence. The findings of this study demonstrate the potential of post-processing techniques to enhance the mechanical strength of FDM components, particularly those composed of PLA material. [ABSTRACT FROM AUTHOR]

The poor strength and surface texture of the additively manufactured components has restricted its usage as a functional part. The post processing methods can overcome this limitation and open new opportunities in different application areas. In the present study, chemical finishing process has been employed for the flexible components build through material extrusion based additive manufacturing technology. Thermoplastic polyurethane (TPU) has been chosen as the material for printing flexible parts. Taguchi orthogonal array‐based design of experiments has been employed. Three process parameters that is, solvent type, infill density and immersion time have been selected for the study. The percentage improvement in the surface roughness as physical property and compressive strength as mechanical property has been chosen as the response parameters. The dimensional deviation was investigated by measuring the change in the dimensions of the manufactured components. It has been observed from ANOVA analysis that the improvement in surface roughness and compressive strength depends significantly on the immersion time. The Dimethyl sulfoxide (DMSO) solvent and immersion time of 125 s has been found significant for different properties of the chemically treated TPU parts. [ABSTRACT FROM AUTHOR]

Alumina (Al 2 O 3) ceramics are one of the most widely employed advanced ceramics by industries. However, traditional forming methods have not been able to meet the demands of fine shape design and high-precision fabrication for advanced functional applications. 3D printing technologies are progressively gaining consideration in many manufacturing sectors for their ability of designing complex shapes and customized parts. Extrusion-based 3D printing technology, known as direct ink writing (DIW), is an adaptable, simple, and eco-friendly forming process and amongst the most used for ceramics. In the last several years, significant advancements have been made in alumina ceramics with complex designs by the DIW forming technique. This review presents recent progresses in creating dense and porous alumina ceramics using DIW. It provides a detailed description of printable ink formulation strategies and their effects on the sinterability of dense and porous alumina shapes. Ink composition and printing parameter effects on the final properties of DIW alumina are discussed. Concurrently, key ink rheology and post-processing parameters for desired alumina shapes are identified. The review also provides a pathway to overcome the technical challenges in achieving a high density or lightweight alumina monolith by DIW. [ABSTRACT FROM AUTHOR]

Throughout many decades, the Schlieren visualization method has been mainly used as means to visualize transparent flows in a qualitative manner. The images recorded provide data regarding the existence of the flow, or illustrate predicted flow geometries and details. The colored Schlieren method has been developed in the late 1890s and has always had the intent to provide quantitative data rather than qualitative pictures of the studied phenomena. This paper centers on applying a quantitative color Schlieren method to help determine the gasodynamic parameters of an H2–O2 exhaust jet, developing in air. A comparison between the parameters obtained through calibrating the color filter for the Schlieren method and the results from a CFD simulation is performed to assess the range of the CS (color Schlieren) measurement. This paper’s findings address the issues of calibrated color filter Schlieren encounter during its implementation and discusses possible errors appearing when the method is applied to a 3D flow. While the qualitative Schlieren images are still impressive to observe, the quantitative Schlieren presents challenges and a low measurement accuracy (75%) when applied to 3D flows and compared to 2D cases found in the literature (97–98%).

Nickel based alloys are the most advanced alloys in terms of mechanical, thermal and corrosive properties. Therefore, they are well suited for applications in aviation, energy and marine industries. This work evaluates tool wear and workpiece quality in the post-process machining of additively manufactured (AM) Inconel 718 parts compared to forged Inconel 718. Five types of coated cemented carbide milling tools with different tool parameters were investigated under constant cutting parameters when down-milling. The results show a strong dependence on tool parameters e.g. tools with negative rake angles exhibited higher tool wear rates. [ABSTRACT FROM AUTHOR]

This work presents the influence of post processing on morphology, thermal and electrical properties of indium tin oxide (ITO) thin films annealed at 400 °C in different atmospheres. The commercially available 170 nm thick ITO layers deposited on glass were used as a starting material. The X-ray diffraction measurements revealed polycrystalline structure with dominant signal from (222) plane for all samples. The annealing reduces the intensity of this peak and causes increase of (221) and (440) peaks. Atomic force microscopy images showed that the surface morphology is typical for polycrystalline layers with roughness not exceeding few nm. Annealing in the oxygen and the nitrogen-hydrogen mixture (NHM) changes shapes of grains. The electrical conductivity decreases after annealing except the one of layer annealed in NHM. Thermal conductivities of annealed ITO thin films were in range from 6.4 to 10.6 W·m−1·K−1, and they were higher than the one for starting material—5.1 W·m−1·K−1. Present work showed that annealing can be used to modify properties of ITO layers to make them useful for specific applications e.g., in ITO based solar cells. [ABSTRACT FROM AUTHOR]

An accurate estimation of the water requirement of saffron, as the most strategic product in the eastern regions of Iran, is inevitable. Considering the field limitations in measuring the water requirement, applying empirical models has always been of interest. However, since each estimation model has unique strengths and weaknesses, relying only on an experimental model cannot obtain a reliable estimate for water requirements. This study intends to evaluate different combined methods' ability to merge the saffron water requirements simulations and obtain an improved output. Six empirical models and four other combination techniques were applied to get some skilful simulations about saffron water requirements in arid regions. Results indicate that the evapotranspiration prediction under the Abtew method (ABM) has more proficiency, such that its RMSE was 0.13 mm. Also, the different comparative tests show that the outputs of combined techniques such as Multi Model Super Ensemble 'MMSE' and Modified MMSE 'M3SE' outperform others. [ABSTRACT FROM AUTHOR]

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally.

Focused electron beam induced deposition (FEBID) is one of the few additive, direct-write manufacturing techniques capable of depositing complex 3D nanostructures. In this work, we explore post-growth electron beam curing (EBC) of such platinum-based FEBID deposits, where free-standing, sheet-like elements were deformed in a targeted manner by local irradiation without precursor gas present. This process diminishes the volumes of exposed regions and alters nano-grain sizes, which was comprehensively characterized by SEM, TEM and AFM and complemented by Monte Carlo simulations. For obtaining controlled and reproducible conditions for smooth, stable morphological bending, a wide range of parameters were varied, which will here be presented as a first step towards using local EBC as a tool to realize even more complex nano-architectures, beyond current 3D-FEBID capabilities, such as overhanging structures. We thereby open up a new prospect for future applications in research and development that could even be further developed towards functional imprinting. [ABSTRACT FROM AUTHOR]

Atmospheric currents, known as winds, are among the most important fields of study in different disciplines of science. One of the most important characteristics of wind is gustiness. Wind gust, among many other characteristics of the wind field, is studied extensively due to severe impacts that it may have on many aspects of human socio-economic activities. There are several models to predict wind gust speed. The results of these models always contain random and systematic errors that reduce the accuracy of predictions due to the lack of topographic resolution as well as the deficiencies of different physical schemes in the models. Consequently, post-processing is the most important process in the course of simulation and prediction using different types of models. Artificial neural network is one of the available tools that may be used to reduce errors of models by matching their outputs and observations. The aim of this study was to evaluate the performance of two models and artificial neural network in forecasting wind gust in Iran. First, a study was designed to examine two methods of the nonconvective wind gusts forecasting, i.e., the UK Meteorological Office (MOA) and WRF post-process diagnostic of wind gusts (WPD) performances. To investigate the performace of two methods, 1880 cases of non-convective wind gust observations of 32 synoptic stations in Iran, between 2013 and 2018, were studied. Four RMSE, MAE, MSE and R were used to measure the performace of those two methods. The results for WPD and MOA were 3.89, 3.07, 15.2, 0.66 and 4.37, 3.43, 19.1, 0.55, respectively. The results showed that the WPD method performed better than the MOA method. To post-process the wind gust forecasts with an artificial neural network, a feedforward multilayer perceptron with the back-propagation learning algorithm was designed. The model had a hybrid structure with a sigmoid activation function for the hidden layer and a linear transfer function in the output layer. Three training algorithms were used in the implementation of the model. Various combinations of normalized output variables of the WRF were used as input for network training and the target was observational wind gust speed. Seventy percent of the data were used for training, fifteen percent for testing and fifteen percent for validation. The results showed that the best way to combine the input parameters is to use 10m wind, sea level pressure, temperature and relative humidity resulting from the output of the WRF model and the wind gust speed resulting from both methods mentioned above. Also, the best algorithm for neural network training was the Levenberg-Marquardt algorithm. Finally, the implemented artificial neural network was able to improve the results of both wind gust speed prediction methods (WPD and MOA). Due to the relatively higher accuracy of the WPD method compared with MOA method in predicting the wind gust speed in Iran, the artificial neural network that assumed the prediction of this method as input, was more accurate than MOA method (RMSE, MAE, MSE and R were 2.05, 1.6, 4.21, 0.83 and 2.37, 1.86, 5.2, 0.77, respectively). [ABSTRACT FROM AUTHOR]

Background: Identification in the anthropology of the forensic world using radiographic methods can be evaluated from variations in the human skeleton. The skull is the most dimorphic part of the skeleton, precisely in the temporal bone, namely the mastoid process. The use of advanced radiological technology in the evaluation of the mastoid process in humans can be done using the CT scan modality. Purpose: To determine the measurement results of the total area of the mastoid triangle using post processing volume rendering and multi planar reformation. Method: This type of research is an analytic observational study with a retrospective approach. The number of samples used were 152 raw data of patients with criteria aged 26-65 years and without any pathological changes in the mastoid process area. This research uses simple random sampling technique. Measurements were carried out 10 times by using 2 gauges, and using 3D slicer software on volume rendering and multi planar reformation reconstruction data. Data were analyzed using the free sample T test. Result: Measurer A produces total area of mastoid triangle using volume rendering is 1165.72 ± 1.2506 and multi planar reformation is 1145.84 ± 1.46512, with a significance value of 0.204 (p > 0.05). Similar results were obtained by measuring B. Measurer A produced a total area of mastoid triangle using volume rendering is 1159.91 ± 1.29691 and an multi planar reformation is 1146.56 ± 1.4606, with a significance value of 0.400 (p > 0.05). Conclusion: The results indicated that there was no significant difference in the results of measuring the total area of mastoid triangle using post processing volume rendering and multi planar reformation.

The final thermo-mechanical properties of structural parts fabricated by masked stereolithography (MSLA) are highly determined not only by the processing parameters, but also by the post-processing methods. Improper implementation of post-treatment often leads to underperforming printouts. A novel tool for complex characterization of 3D printed bodies was developed and systematically demonstrated on a commercial free-radical photopolymerization (FRP) resin. The method relies on superimposed static and oscillatory mechanical test combining the heat deflection temperature (HDT) measurement together with the dynamic mechanical analysis (DMA) in a single test for fast and reliable characterization of parameters determining the curing behaviour of the photopolymer. The influence of post-curing time was addressed with a special focus on network density. Furthermore, the print orientation, having a high impact on mechanical properties, is discussed with a particular regard on the residual stress mitigation in future applications, such as 3D-printed cellular bodies.

A big volume of new data is generated in every moment of the day by different devices and domains as social network, mobile and desktop devices, financial transaction, online websites, different search engines and a lot of smart home devices. The generated data is diversified and can be structured or unstructured. Clustering is the process of categorizing a dataset in groups of records that are similar and are called clusters, the grouping process being performed using a specific criterion. The K-means clustering algorithm is still popular after many years. Different versions of the K-means algorithm emerged along the time and were focused on improving the K-means algorithm by performing some preprocessing steps or by reducing the number of iterations having as a final objective the improvement of the processing time. This paper presents a way of improving the resulted clusters generated by the K-means algorithm by post processing the resulted clusters with a supervised learning algorithm. The proposed approach is focused on improving the quality of the resulting clusters and not on reducing the processing time. [ABSTRACT FROM AUTHOR]

The real-time video surveillance system has become an integral part of our life and Highways play a very crucial role in transportation. For a transportation system to work, the management of highways are necessary. It also prevents accident and other challenging issues on highways. Various machine learning and artificial intelligence based techniques are evolving with numerous advancement in this domain. These algorithms are efficient and very less time consuming. So the use of machine learning and artificial intelligence in transportation systems and highways could be very beneficial. In this paper, various approaches related to moving vehicle detection for the transportation system especially for highways are considered. The literature also reveals for existing research for the machine learning and AI based methodologies to resolve more complex real-time problems. The proposed work is also compared with the existing peer methods and demonstrated better performance achieved experimentally. [ABSTRACT FROM AUTHOR]

Because of the low surface roughness of three-dimensional (3D) printed microchannels, in some environments, the use requirements cannot be met, and, at the same time, they are difficult to process. Therefore, a surface roughness analysis of 3D printed microchannels and post-processing using abrasive flow technology are proposed. The value of the inner surface roughness of the straight pipe part was calculated by using the least-squares method combined with the definite integral. Using the equal area principle, MATLAB curve fitting was used to numerically calculate the semicircular pipe section, and the relationship between the value of surface roughness and the bending radius of the scanning layer thickness is given. Use MATLAB image processing technology to study the processing area. The roughness of the inner wall of the pipeline was analyzed by laser confocal, stereo, and scanning electron microscopes. The results show that the roughness of the inner surface of the pipe increases with an increase in the thickness of the sweeping layer, an increase in the inclination angle, and a decrease in the radius of curvature. In the case of abrasive flow processing, the longer the processing time, the greater the grinding amount and the greater the amount of grinding outside the pipe wall in each one-way machining; further, the processing has obvious directionality. [ABSTRACT FROM AUTHOR]

In this work, we consider an online enrichment procedure in the context of the Generalized Multiscale Finite Element Method (GMsFEM) for the two-phase flow model in highly heterogeneous porous media. The coefficient of the pressure equation is referred to as the permeability and is the main source of heterogeneity within the model. The elliptic pressure equation is solved using online GMsFEM, which is coupled with a hyperbolic transport equation where local conservation of mass is necessary. To satisfy the conservation property, we aim at constructing conservative fluxes within the space of multiscale basis functions through the use of a postprocessing technique. In order to improve the accuracy of the pressure and velocity solutions in the online GMsFEM, we apply a systematic online enrichment procedure. The increase in pressure accuracy due to the online construction is inherited by the conservative flux fields and the desired saturation solutions from the coupled transport equation. Despite the fact that the coefficient of the pressure equation is dependent on the saturation which may vary in time, we construct an approximation space using the absolute permeability field (λ(S) = 1) and no further basis updates follow. Numerical results corresponding to three different types of heterogeneous permeability coefficients are exhibited to show the performance of the proposed methodology. [ABSTRACT FROM AUTHOR]

• Signals which overlaps in both time and frequency domain, are very difficult to separate out without distortion. • In multimedia content making, sound plays a vital role. • Deep Neural Networks (DNN)have been used to handle the full separation of overlapping sources. • DNNs for audio signal separation depends on factors such as the quality, size of the training data, architecture of the network, and the specific task at hand. Separating the signals from the mixture when the signals share same frequency and time domain pattern is always a matter of concern. In natural environment, sound events often appear simultaneously, increasing the complexity. Movie making and audio-visual content creation is an industry which covers a huge share of our economy. With the advancement of technology, it becomes important to give audience more realistic experience. But sometimes due to certain environmental conditions, recording devices capture sound signals which overlap with the character voice of the script or required ambience sound either in frequency domain or time domain or in both. To fix the problem, sound engineers mostly dub the sound in the studio and try to collect the ambience sound later on. Further mixing is done during post processing of sound. Although different software tools are available in market, but here aesthetic value and originality of the sound has been compromised. Because, it is very hard to get the exact voice parameters and same ambience in studio environment. This paper gives an overview of different approaches to deal with the overlapping sound by focussing the recently reported literature and highlights the key attributes which are catalysing the evolving scenario. [ABSTRACT FROM AUTHOR]

A critical challenge underpinning the adoption of Additive Manufacture (AM) as a technology is the postprocessing of manufactured components. For Powder Bed Fusion (PBF), this can involve the removal of powder from the interior of the component, often by vibrating the component to fluidise the powder to encourage drainage. In this paper, we develop and validate a computational model of the flow of metal powder suitable for predicting powder removal from such AM components. The model is a continuum Eulerian multiphase model of the powder including models for the granular temperature; the effect of vibration can be included through appropriate wall boundaries for this granular temperature. We validate the individual sub-models appropriate for AM metal powders by comparison with in-house and literature experimental results, and then apply the full model to a more complex geometry typical of an AM Heat Exchanger. The model is shown to provide valuable and accurate results at a fraction of the computational cost of a particle-based model. [ABSTRACT FROM AUTHOR]

• We explored two largest smartphone sensor-based datasets for classifying daily activities and transportation mode. • The impact and eficiency of different smartphone sensors have been analyzed for locomotion activities. • We proposed a post-processing classification approach (Mode technique) to refine classifier predictions. • We proposed window length optimization and smartphone orientation independent features. • We evaluated our proposed post-processing technique for both statistical machine learning and deep learning-based models. Recognition of daily human activities in various locomotion and transportation modes has numerous applications like coaching users for behavior modification and maintaining a healthy lifestyle. Besides, applications and user interfaces aware of user mobility through their smartphones can also aid in urban transportation planning, smart parking, and vehicular traffic monitoring. In this paper, we explored smartphone sensor-based two benchmark datasets (Sussex Huawei Locomotion (SHL) and Transportation Mode Detection (TMD)). Firstly, we demonstrated preprocesssing of sensor data, window length optimization based on Akaike Information Criteria (AIC), and introduced smartphone orientation independent features. We also provided an in-depth analysis of different smartphone sensors' importance for classifying daily activities and transportation modes. We justified the sensor relevance by showing the variation of performances with the number of sensors explored. For refining classifier predictions, we also proposed a post-processing approach named "Mode technique". This method primarily concentrates on the statistical analysis of transportation modes and improves the activity recognition rate in statistical classifiers: Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, Logistic Regression, Support Vectors Machine with RBF kernel, Random Forest, and deep learning-based methods: Artificial Neural Network and Recurrent Neural Network by smoothing the outputs of these classifiers. Besides, we showed the use of magnitude and jerk-based features to overcome the overfitting problem due to smartphone orientation. We obtained 97.2% accuracy in the SHL dataset and 99.13% accuracy in the TMD dataset. These results demonstrate that our approach can profoundly recognize various activities in advanced locomotion and transportation modes compared to existing methods in two large-scale datasets. [ABSTRACT FROM AUTHOR]

Background: Proton magnetic resonance spectroscopy or 1H MRS is a validated and non-invasive method used for studying liver fat. However, the metabolite spectra obtained by 1H MRS require a post-processing method for accurate liver fat quanti­fication. Various spectrum analysis software has been developed and is being used in many studies. To the best of our knowledge, no comparisons between spectrum analysis software for liver fat quantification have yet been completed. Objectives: To compare the post processing methods between java-based graphical for MR user interface packages (jMRUI) and totally automatic robust quantitation in NMR (TARQUIN) software for optimal liver fat quantification. Materials and methods: 1H MRS spectrum from the right lobe of the liver was obtained for post processing. Liver fat qualification was done by AMARES algorithms on jMRUI software, and automatic quantification algorithms was initiated by TARQUIN software. A total of 30 subjects participated in this study. Subjects were separated into a control group (n=15) and an overweight group (n=15) for liver fat quantification. Liver lipids at 0.9 ppm (-CH3 lipids) and 1.3 ppm (-CH2 lipids) were fitted and quantified. The results obtained from both jMRUI and TARQUIN post processing software packages for both groups were then compared. Results: A strong and moderate correlation of signal intensity between jMRUI and TARQUIN software was found (total lipids, r=0.836, p

Formation of Gurmukhi character/akshara from the recognized strokes in online handwriting recognition systems is a challenging task. In this paper, the task of character and akshara formation in an unconstrained environment have been addressed. After the recognition of online handwritten strokes the Gurmukhi akshara is formed using a hybrid approach. Two classifiers, namely, Support Vector Machine (SVM) and Recurrent Neural Network (RNN) have been experimented in this study. The classifier, yielded the maximum cross-validation accuracy has been utilized for stroke recognition. A total of 52,500 word samples have been collected from 175 writers in order to train the classifiers. Three post processing algorithms have been proposed in this article for improving the character and akshara recognition accuracy. The proposed methodology when tested on a dataset of 21,500 aksharas, written by 50 new writers, achieved average the accuracy rate of 97.1% and 87.1% for base character and akshara recognition, respectively. [ABSTRACT FROM AUTHOR]

For multi-ceramic materials based on the stereolithography (SL) principle, a 3D printing strategy was developed, and then an Al 2 O 3 -Si 3 N 4 functionally graded material (FGM) ceramic part was fabricated using this strategy. Six groups of mixtures, with a Si 3 N 4 content gradient of 20 vol% and a certain bimodal particle size distribution, were prepared using UV-curable pastes. A modified formula was proposed to evaluate the relationship between the actual minimum voidage of mixtures and the viscosities of their corresponding pastes. The viscosity of each paste was controlled using the prediction formula and optimization of dispersants. To design theprinting layer thickness, a mathematical relationship was established between Si 3 N 4 content and curing depth of paste. The Al 2 O 3 -Si 3 N 4 green body without deformation was printed using optimized parameters such as a layer thickness of 40 μm and a paste viscosity of ∼13,000 mPa·s. Finally, using debinding and sintering, denseparts having a complicated shape were obtained. [ABSTRACT FROM AUTHOR]

In the last years the industrial application of selective laser melting (SLM) has been increased. Main advantages of SLM are the reduction of production time for small batches and the freedom to generate complex designs. However, the rough surface quality of SLM parts currently limits their use and hinders the application in several industry sectors such as the hygienic and food industry. Due to the high complexity of SLM parts, the application of conventional processes is limited. Electropolishing (EP) is a promising process for reducing the roughness of simple SLM parts. However, it has not yet been explored intensively on complex SLM parts. Additionally, design constraints for the design of SLM parts to enable post processing are not yet existent. This experimental study investigates the resulting roughness of EP on different SLM design elements. Three types of EP were investigated, direct current, pulse/pulse reverse current and pulsed current EP. Additionally, the current density and processing time were varied. The SLM test sample (316L) contains internal pipe surfaces with different diameters in different orientations, overhangs and a lattice structure. To analyze the surface roughness, the arithmetic mean height of the roughness (Sa) was measured. At the end of the paper the findings on the process and the design constraints for SLM parts are summarized. The results show a significant improvement of the roughness on the external surfaces of the test sample for all parameter sets. The orientation of the surfaces with respect to the build direction influences the reduction of Sa. Increasing the pipe diameter leads to an increasing effect of internal EP. Finally, the DC parameter set showed a significantly better improvement of the Sa value on the internal lattice structure. [ABSTRACT FROM AUTHOR]

3D printing technology is used in a variety of industries without auxiliary tools because it is flexible in producing and reduces the waste of material. In this paper, the laser cutting process of polylactic acid sheets has been investigated by a 3D printer. The fused deposition modeling (FDM) method was used for printing the sheets. Production of sheets with a thickness of 2.3 mm by optimal conditions was conducted (each layer was perfectly solid with a thickness of 0.27 mm, and the extruder temperature of 226.62 °C). The laser used in this paper is a CO2 lowpower, continuous-wave laser. Laser input parameters including laser cutting speed, focal point position, and laser power were selected as the variables. By performing several experiments, the effective range of each parameter was evaluated. The upper and lower cut width, the angle of cone and the upper cut width ratio to the lower cut width of the process output parameters were selected. The optical microscope was used to examine the geometric characteristics of cutting kerf of the samples and then the images were measured using ImageJ software. The purpose of this paper is the laser cutting process to achieve cutting kerfs with good quality and proper setting of laser input parameters. [ABSTRACT FROM AUTHOR]