Processing Time Research Articles

Micronization induced gelatinization of tapioca starch and its effects on starch physicochemical and structural properties.

The vibrating superfine mill (VSM) is a machine that belongs to the micronization technique. In this study, VSM was employed to produce micronized tapioca starch by varying micronization times (15, 30, 45, and 60min). The structural and physicochemical properties of the micronized starch were then examined. Scanning electron microscopy studies revealed that micronized starch was partially gelatinized, and the granule size dramatically increased when micronization time increased. X-ray diffraction patterns showed that the relative crystallinity was decreased from 24.67% (native) to 4.13% after micronization treatment for 15min and slightly decreased after that. The solubility of micronized starch significantly increased as the micronization time increased, which was associated with the destruction of the starch crystalline structure. Differential scanning calorimetry investigations confirmed that micronized starch was "partly gelatinized," and the degree of gelatinization increased to 81.27% when the micronization time was 60min. The weight-average molar mass was reduced by 15.0% (15min), 30.9% (30min), 55.7% (45min), and 70.5% (60min), respectively, indicating that the molecular structure was seriously degraded. The results demonstrated that the physicochemical changes of micronized starch granules were related to the destruction of the starch structure. These observations would provide details on micronized starch and its potential applications. PRACTICAL APPLICATION: These observations would provide details on micronized starch and its potential applications. Moreover, we believe that when the structures of starches were known, it is probable that the effect of VSM on the structural and physicochemical properties change of other starches might be predicted by adjusting the processing time.

An in-line dye tracer experiment to measure the residence time in continuous concrete processing

An in-line dye tracer experiment to measure the residence time in continuous concrete processing

Formation of Si nanopillars through partial sacrificing in super passivation reactive ion etching

The vertical gate-all-around (VGAA) metal-oxide-semiconductor field-effect transistor (MOSFET) holds remarkable potential in the three-dimensional (3D) integrated circuits (ICs), primarily owing to its capacity for vertical integration. The Si nanopillar, a crucial channel in the VGAA MOSFET, is conventionally shaped via the reactive ion etching (RIE) system employing SF6/O2. Past studies have indicated that high O2 gas conditions in RIE often result in Si grasses irregular nanostructures, such as nanospikes on the bottom surface, due to over-passivation. However, this study revealed that ultrahigh O2 proportions (>70%), especially when combined with low chamber pressure, inhibit the development of Si grasses in the RIE system (termed as super passivation). Nevertheless, this scenario leads to the segmentation of the Si nanopillar. To address this issue, a proposed partial sacrificing method, achieved by sacrificing the upper segment of the nanopillar through prolonged processing time and reduced mask size, successfully yielded Si nanopillars without Si grasses. Furthermore, an empirical model was developed to elucidate how experimental parameters influence etching characteristics, encompassing etching rate and Si nanopillar shape, through a systematic examination of the RIE etching process. This research significantly contributes to the production of VGAA MOSFETs and 3D ICs.

PADRE – A Repository for Research on Fault Detection and Isolation of Unmanned Aerial Vehicle Propellers

Unmanned aerial vehicles are being used increasingly in a variety of applications. They are more and more often operating in close proximity to people and equipment. This necessitates ensuring maximum stability and flight safety. A fundamental step to achieving this goal is timely and effective diagnosis of possible defects. Popular data-based methods require a large amount of data collected during flights in various conditions. This paper describes an open PADRE database of such measurements for the detection and classification of the most common faults - multirotor propeller failures. It presents the procedure of data acquisition, the structure of the repository and ways to use the various types of data contained therein. The repository enables research on drone fault detection to be undertaken without time-consuming preparation of measurement data. The database is available on GitHub at https://github.com/AeroLabPUT/UAV_measurement_data. The article also introduces new and universal quality indicators for evaluating classifiers with non-uniform parameters, are proposed. They allow comparison of methods tested for a variety of fault classes and with different processing times.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

Effects of temperature inversion on densification in chemical vapor infiltration

AbstractIn a classical chemical vapor infiltration (CVI) process, the competing effects of chemical kinetics and reagent gas transport lead to non‐uniform depositions such that outer layers of a preform densify faster leaving the core highly porous. Currently, CVI must be performed at a sufficiently low temperature to achieve good densification quality which leads to high processing time and cost. Volumetric heating of the preform, especially through microwaves, can create temperature inversion such that the core is hotter than the outer surface and potentially, overcome the challenges associated with isothermal CVI. Direct numerical simulations (DNS) of densification under various such temperature distributions indicate that microwave heating in CVI processing can lead to better (uniform) densification of porous preforms. The role of key parameters describing the temperature distributions on the densification behavior is investigated. Strategic temporal control of the temperature distribution shows that processing times can be reduced by almost half while maintaining a good densification quality similar to that of low‐temperature isothermal processing. Inside‐out densification due to the inverted temperature profile is a key distinguishing characteristic of microwave assisted CVI.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.

Comparative Study of Microwave, Pulsed Electric Fields, and High Pressure Processing on the Extraction of Antioxidants from Olive Pomace

Olive oil production is characterized by large amounts of waste, and yet is considerably highly valued. Olive pomace can serve as a cheap source of bioactive compounds (BACs) with important antioxidant activity. Novel technologies like Pulsed Electric Fields (PEF) and High Pressure (HP) and microwave (MW) processing are considered green alternatives for the recovery of BACs. Different microwave (150–600 W), PEF (1–5 kV/cm field strength, 100–1500 pulses/15 µs width), and HP (250–650 MPa) conditions, in various product/solvent ratios, methanol concentrations, extraction temperatures, and processing times were investigated. Results indicated that the optimal MW extraction conditions were 300 W at 50 °C for 5 min using 60% v/v methanol with a product/solvent ratio of 1:10 g/mL. Similarly, the mix of 40% v/v methanol with olive pomace, treated at 650 MPa for the time needed for pressure build-up (1 min) were considered as optimal extraction conditions in the case of HP, while for PEF the optimal conditions were 60% v/v methanol with a product/solvent ratio of 1:10 g/mL, treated at 5000 pulses, followed by 1 h extraction under stirring conditions. Therefore, these alternative extraction technologies could assist the conventional practice in minimizing waste production and simultaneously align with the requirements of the circular bioeconomy concept.

The Effects of Displaying the Time Targets of the Manchester Triage System to Emergency Department Personnel: Prospective Crossover Study.

The use of triage systems such as the Manchester Triage System (MTS) is a standard procedure to determine the sequence of treatment in emergency departments (EDs). When using the MTS, time targets for treatment are determined. These are commonly displayed in the ED information system (EDIS) to ED staff. Using measurements as targets has been associated with a decline in meeting those targets. This study investigated the impact of displaying time targets for treatment to physicians on processing times in the ED. We analyzed the effects of displaying time targets to ED staff on waiting times in a prospective crossover study, during the introduction of a new EDIS in a large regional hospital in Germany. The old information system version used a module that showed the time target determined by the MTS, while the new system version used a priority list instead. Evaluation was based on 35,167 routinely collected electronic health records from the preintervention period and 10,655 records from the postintervention period. Electronic health records were extracted from the EDIS, and data were analyzed using descriptive statistics and generalized additive models. We evaluated the effects of the intervention on waiting times and the odds of achieving timely treatment according to the time targets set by the MTS. The average ED length of stay and waiting times increased when the EDIS that did not display time targets was used (average time from admission to treatment: preintervention phase=median 15, IQR 6-39 min; postintervention phase=median 11, IQR 5-23 min). However, severe cases with high acuity (as indicated by the triage score) benefited from lower waiting times (0.15 times as high as in the preintervention period for MTS1, only 0.49 as high for MTS2). Furthermore, these patients were less likely to receive delayed treatment, and we observed reduced odds of late treatment when crowding occurred. Our results suggest that it is beneficial to use a priority list instead of displaying time targets to ED personnel. These time targets may lead to false incentives. Our work highlights that working better is not the same as working faster.

IMMERSIVE AUGMENTED REALITY APPLICATIONS IN JAW AND KNEE REPLACEMENT SURGERY: ACCURACY AND PROCESSING TIME RESULTS

In medicine and healthcare, augmented reality (AR) has been used by physicians during surgical procedures. It has provedhelpful in preoperative planning and procedure navigation by allowing them to display in-depth information and visualizedetails in real time during surgery, prioritizing patient safety and healthcare. Due to the critical nature of surgical procedures,extreme accuracy is required when using ar technology to maintain patients' health. A few years ago, ar faced severalchallenges and limitations in surgeries, such as noise in real-time images, cutting errors, navigation errors, wrong implantplacement, overlay errors, navigating narrow areas, geometric accuracy limitations, image alignment, image registration,and occlusion handling. This paper reviews several recently published articles exploring ar technology usage in jaw andknee replacement surgery, focusing on identifying the newest technologies, methods, and solutions for the abovementionedlimitations. Based on data collected from the published papers, the results were compared for each problem solved in eacharticle regarding accuracy and processing time.

Fault detection through discrete wavelet transforms and radial basis function neural network in shunt compensated distribution systems

This paper discusses a critical study of fault detection, classification, and identification in distribution systems compensated through a distribution static synchronous compensator (D-STATCOM) using discrete wavelet transforms (DWT) and a radial basis function neural network (RBFNN). The efficacy of the proportional integral (PI) controller is discussed in the simulation during normal, voltage sag, and swell conditions. Then, the Daubechies mother wavelet (db4) is used here to extract and decompose the fault current signals because of its high accuracy of detection with less processing time. The models are subjected to different conditions of faults, such as line to ground (LG), line to line (LL), double line to ground (LLG), and three phases (LLL and LLLG) with different fault resistance. The novelty of the scheme is that DWT and RBFNN techniques were compared and proven effective in demonstrating the faulty conditions. To validate the proposed approach, a simulation study is carried out in MATLAB with various operating conditions, and it is shown that the proposed method can depend on abundantly protecting the distribution grid from faulty conditions.

Generation of Virtual Ground Control Points Using a Binocular Camera

This paper presents a methodology for generating virtual ground control points (VGCPs) using a binocular camera mounted on a drone. We compare the measurements of the binocular and monocular cameras between the classical method and the proposed one. This work aims to decrease human processing times while maintaining a reduced root mean square error (RMSE) for 3D reconstruction. Additionally, we propose utilizing COLMAP to enhance reconstruction accuracy by solely utilizing a sparse point cloud. The results demonstrate that implementing COLMAP for pre-processing reduces the RMSE by up to 16.9% in most cases. We prove that VGCPs further reduce the RMSE by up to 61.08%.

Probabilistic Task Offloading with Uncertain Processing Times in Device-to-Device Edge Networks

D2D edge computing is a promising solution to address the conflict between limited network capacity and increasing application demands, where mobile devices can offload their tasks to other peer devices/servers for better performance. Task offloading is critical to the performance of D2D edge computing. Most existing works on task offloading assume the task processing time is known or can be accurately estimated. However, the processing time is often uncertain until it is finished. Moreover, the same task can have largely different execution times under different scenarios, which leads to inaccurate offloading decisions and degraded performance. To address this problem, we propose a game-based probabilistic task offloading scheme with an uncertain processing time in D2D edge networks. First, we characterize the uncertainty of the task processing time using a probabilistic model. Second, we incorporate the proposed probabilistic model into an offloading decision game. We also analyze the structural properties of the game and prove that it can reach a Nash equilibrium. We evaluate the proposed work using real-world applications and datasets. The experimental results show that the proposed probabilistic model can accurately characterize the uncertainty of completion time, and the offloading algorithm can effectively improve the overall task completion rate in D2D networks.

Dried Beetroots: Optimization of the Osmotic Dehydration Process and Storage Stability

In this study, beetroots were osmotically dehydrated in sugar beet molasses. The input parameters of the drying process were varied: temperature (20 °C, 40 °C, and 60 °C), time (1 h, 3 h, and 5 h), and concentration of sugar beet molasses (40%, 60%, and 80%). Basic quality indicators were determined for the dried beetroot samples: dry matter content, water loss, solid gain, mineral and betaine content, and phenols and flavonoids, as well as antioxidant potential. After optimizing the results, favorable drying parameters were selected: temperature 60 °C, molasses concentration 70%, and processing time 5 h. According to the optimal drying conditions, the beetroots were dried and stored at 4 °C for 28 days. Half of the dried samples were coated with an edible biopolymer coating based on Camelina sativa oilcake, while the other half of the samples remained uncoated. The sustainability study aimed to confirm the effects of the biopolymer coating on the quality and sustainability of the osmotically dried beetroots.

Development and Validation of an Android-based Trail Making Test Application (TMT App) for Cognitive Evaluation in Healthy and Depressed Individuals

Background: The Trail Making Test (TMT) is a famous neuropsychological test that is frequently used. The structure of a paper-and-pencil-based TMT is highly likely to be adapted to a mobile app. Objective: The current study aimed to develop and validate an Android-based tablet version of TMT. Methods: The application (TMT App) was developed using an Android-based platform. Healthy and depressed individuals ( n = 133) were assessed on both the TMT versions (paper-based version and app-based version) in a random cross-over design. The device’s usability was ascertained using the system usability scale (SUS) in a subset of individuals ( n = 65). Results: There was a significant positive correlation between the individual processing times for the paper-based TMT-A and the app-based TMT-A in both healthy and depression groups [r(63) = 0.55, p < .001; and r(66) = 0.77, p < .001, respectively]. The individual processing times of the paper-based TMT-B and the app-based TMT-B also showed a significant positive correlation in both healthy control and depression groups [r(63) = 0.67, p < .001; and r(66) = 0.89, p < .001, respectively]. There was a positive correlation of age with TMT-A and TMT-B for either version. Both groups had similar positive responses to the usability of the TMT App. Conclusion: The preliminary validation results for the TMT App suggest that it is significantly correlated with existing paper-and-pencil methods, and that it is user friendly.

A simulation-based genetic algorithm for a semi-automated warehouse scheduling problem with processing time variability

For warehouse operations, efficiently scheduling the available resources is crucial to improve the productivity and customer satisfaction. This paper proposes a simulation-based evolutionary algorithm for order scheduling and multi-robot task assignment in a robotic mobile fulfillment system. The algorithm proactively deals with the effects of the processing time variability by evaluating schedules based on both its system performance as well as its robustness under uncertain conditions. The algorithm implements an efficient resource allocation method and a variance reduction technique to reduce the overall computational burden. The experimental results show that the techniques to reduce the computational time are effective and can significantly reduce the amount of simulations required for the fitness evaluation. If a candidate schedule is allocated insufficient simulation replications it can lead to an inaccurate estimate of its long-term average performance. This could lead to an average performance loss of 7.3 %. Furthermore, the proactive scheduler is able to generate schedules that are more robust compared to deterministically generated. A reduction in the average operational cost of about 5 % can be reached, compared to a deterministically generated schedule. The paper reveals the relevance of identifying and modeling uncertainty when designing schedules in an operational system, rather than looking for optimal schedules for ideal scenarios.

Critical Examination of 3D Building Modelling through UAV Frame and Video Imaging

Abstract. Data capture in UAV photogrammetry is carried out using two main methodologies: frame frame-based and video video-based. Frame Frame-based data gathering is the preferred method among UAV projects because to its inherent reliability in calibration. Nonetheless, circumstances involving moving objects or occlusions inside the measured region may produce unsatisfactory results utilizing this method. I In response to these challenges, video video-based data collecting appears as a potential option, capable of creating a series of successive images that together alleviate the constraints outlined above. In this study we aim to compare the usefulness of frame and video images in building 3D models, using both oblique and vertical image orientations. Rigorous evaluations produced many outputs, including dense point clouds, digital surface models (DSM), meshes, and orthophotographs. The evaluation criteria included da ta acquisition velocity, processing efficiency, calibration precision, distortion analysis, residual plots, scale correctness, and reprojection error. The empirical results demonstrated the advantages of video video-frame captures in improving the quality of the resulting 3D models. Notably, the use of video frames resulted in a significant reduction in reprojection error by 16%, calibration residuals by 36%, distortions by up to 51%, and processing time by 27%. Thus, it seems that integrating video frames improves data gathering accuracy and speeds up processing, replacing standard frame images with video counterparts for increased efficacy.

Spatiotemporal Properties of Common Semantic Categories for Words and Pictures.

The timing of semantic processing during object recognition in the brain is a topic of ongoing discussion. One way of addressing this question is by applying multivariate pattern analysis to human electrophysiological responses to object images of different semantic categories. However, although multivariate pattern analysis can reveal whether neuronal activity patterns are distinct for different stimulus categories, concerns remain on whether low-level visual features also contribute to the classification results. To circumvent this issue, we applied a cross-decoding approach to magnetoencephalography data from stimuli from two different modalities: images and their corresponding written words. We employed items from three categories and presented them in a randomized order. We show that if the classifier is trained on words, pictures are classified between 150 and 430 msec after stimulus onset, and when training on pictures, words are classified between 225 and 430 msec. The topographical map, identified using a searchlight approach for cross-modal activation in both directions, showed left lateralization, confirming the involvement of linguistic representations. These results point to semantic activation of pictorial stimuli occurring at ≈150 msec, whereas for words, the semantic activation occurs at ≈230 msec.

Optimal Scheme Models with Two Kinds of Errors

In this paper, we propose stochastic scheme models with two kinds of errors for a computer system. A highly reliable computer system needs greatly to be backed up successfully in case of computer system crashes due to hardware failures and malicious software infections. We aim to determine appropriate backup times in computer processing of constant and random tasks. For this purpose, the trade-off between backup times and data losses due to errors is considered. The total execution times until the computing processing completes are obtained, and optimal backup policies to minimize them are derived analytically. Furthermore, we take up extended models of random tasks with [Formula: see text] sub-tasks and analyze them.