Improvement In Processing Time Research Articles

Ultraviolet line structuring of large solar wafers

AbstractUltrashort‐pulsed lasers are ideal for material processing by welding, marking, and cutting. Scanning systems are crucial for large substrates. We introduce a novel UV line structuring method using deformable mirror technology and freeform optics. This approach aims to improve processing times on large substrates, especially for M12 solar wafer processing. By using a freeform lens and a Zwobbel deformable mirror, we achieve extended processing fields and high scan frequencies. The paper explores optical designs, their robust optomechanical implementation for prototyping, and setup characterization.

CMK: Enhancing Resource Usage Monitoring across Diverse Bioinformatics Workflow Management Systems

The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing.

Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Objective. This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden—even when executed on modern multi-core computing servers. Cloud computing allows simulation tasks to be highly parallelized and considerably accelerated. Approach. We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between 105 and 2·1010 simulated decays. We deployed two popular MC simulation frameworks—SimSET and GATE—within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service. Main results. Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 109 decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs. Significance. Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably improve processing times and overall workflow efficiency, with future research exploring additional enhancements through optimized configurations and computational methods.

Impact of COVID-19 pandemic-induced surgical restrictions on operational performance: a case study at the University Hospital of Ulm.

The operating room (OR) is a high-cost and high-revenue area in a hospital comprising extremely complex process steps to treat patients. The perioperative process quality can be optimized through an efficiency-oriented central OR management based on performance indices. However, during the COVID-19 pandemic with the corresponding OR restrictions, there was a significant nation- and worldwide decline in the performance, which may have a lasting impact. Therefore, we proposed the hypothesis that COVID-19 pandemic-related OR restrictions could reduce operative performance in the long term. A retrospective, descriptive analysis of perioperative processing times was conducted exemplarily at the University Hospital Ulm using a pre-post design, examining the corresponding second quarters of 2019 to 2022. In total, n = 18,489 operations with n = 314,313 individual time intervals were analyzed. The statistical analyses included the Kruskal-Wallis test adjusted for multiple testing, and the significance level was set at p < 0.01. The results revealed not only a significant decrease in the case volume by 31% (2020) and 23% (2021) during the COVID-19 crisis years, but also significant time delays in various process steps; e.g. the median patient's OR occupancy time (column time) rose from 65min (2019) to 87min (2020) and remained elevated (72min in 2021 and 74min in 2022, respectively). Even in 2022, beyond the pandemic, the net anaesthesia time was permanently enhanced by 9min per case. Furthermore, both, the incision-to-closure time and surgeon attachment time were each significantly prolonged by 7 additional minutes, and the time from the end of anaesthesia to the release of the next patient was extended by 4min. Selected standardized index operations showed only a trend towards these changes, even with a decrease in the incision-to-closure time over time. Overall, long-term changes were found in essential perioperative process times even after retraction of the COVID-19 restrictions, indicating some processual "slow down" after the Covid-19-induced "shut down". Further analyses are needed to determine the appropriate targeted control measures to improve processing times and increase the process quality.

Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen

Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction.

Molecular radiation biomarkers are an emerging tool in radiation research with applications for cancer radiotherapy, radiation risk assessment, and even human space travel. However, biomarker screening in genome-wide expression datasets using conventional tools is time-consuming and underlies analyst (human) bias. Machine Learning (ML) methods can improve the sensitivity and specificity of biomarker identification, increase analytical speed, and avoid multicollinearity and human bias. To develop a resource-efficient ML framework for radiation biomarker discovery using gene expression data from irradiated normal tissues. Further, to identify biomarker panels predicting radiation dose with tissue specificity. A strategic search in the Gene Expression Omnibus database identified a transcriptomic dataset (GSE44762) for normal tissues radiation responses (murine kidney cortex and medulla) suited for biomarker discovery using an ML approach. The dataset was pre-processed in R and separated into train and test data subsets. High computational cost of Genetic Algorithm/k-Nearest Neighbor (GA/KNN) mandated optimization and 13 ML models were tested using the caret package in R. Biomarker performance was evaluated and visualized via Principal Component Analysis (PCA) and dose regression. The novelty of ML-identified biomarker panels was evaluated by literature search. Caret-based feature selection and ML methods vastly improved processing time over the GA approach. The KNN method yielded overall best performance values on train and test data and was implemented into the framework. The top-ranking genes were Cdkn1a, Gria3, Mdm2 and Plk2 in cortex, and Brf2, Ccng1, Cdkn1a, Ddit4l, and Gria3 in medulla. These candidates successfully categorized dose groups and tissues in PCA. Regression analysis showed that correlation between predicted and true dose was high with R2 of 0.97 and 0.99 for cortex and medulla, respectively. The caret framework is a powerful tool for radiation biomarker discovery optimizing performance with resource-efficiency for broad implementation in the field. The KNN-based approach identified Brf2, Ddit4l, and Gria3 mRNA as novel candidates that have been uncharacterized as radiation biomarkers to date. The biomarker panel showed good performance in dose and tissue separation and dose regression. Further training with larger cohorts is warranted to improve accuracy, especially for lower doses.

RAPIDprep: A Simple, Fast Protocol for RNA Metagenomic Sequencing of Clinical Samples.

Emerging infectious disease threats require rapid response tools to inform diagnostics, treatment, and outbreak control. RNA-based metagenomics offers this; however, most approaches are time-consuming and laborious. Here, we present a simple and fast protocol, the RAPIDprep assay, with the aim of providing a cause-agnostic laboratory diagnosis of infection within 24 h of sample collection by sequencing ribosomal RNA-depleted total RNA. The method is based on the synthesis and amplification of double-stranded cDNA followed by short-read sequencing, with minimal handling and clean-up steps to improve processing time. The approach was optimized and applied to a range of clinical respiratory samples to demonstrate diagnostic and quantitative performance. Our results showed robust depletion of both human and microbial rRNA, and library amplification across different sample types, qualities, and extraction kits using a single workflow without input nucleic-acid quantification or quality assessment. Furthermore, we demonstrated the genomic yield of both known and undiagnosed pathogens with complete genomes recovered in most cases to inform molecular epidemiological investigations and vaccine design. The RAPIDprep assay is a simple and effective tool, and representative of an important shift toward the integration of modern genomic techniques with infectious disease investigations.

Modified algebraic reconstruction technique based on circular scanning geometry to improve processing time in photoacoustic tomography

AbstractPhotoacoustic imaging is low‐risk, and noninvasive tool for imaging biological tissues that acoustically respond to absorbed irradiating nonionized short laser pulse by tissue, then the image of light energy absorption distribution in the tissue has been reconstructed by image reconstruction algorithms. Indeed, photoacoustic tomography is a hybrid imaging modality that combines pure optical and acoustic imaging methods to take advantage of them, therefore, it contains high optical contrast with good ultrasonic resolution. However, improving its main parts is challenging. One of the most important parts of photoacoustic imaging is image reconstruction using appropriate algorithms. There are various image reconstruction methods. Image reconstruction based on an algebraic algorithm can reconstruct a clear image without artifact, but the algorithm is typically time‐consuming. This paper deals with this drawback of the algorithm. Simulations demonstrate significant improvement in the processing time of reconstructed images achieved by our proposed algorithm as compared to the phase‐controlled algorithm. Based on the image reconstruction algorithm in circular scanning geometry presented in this article, the processing time required to create a 2D tomographic image is reduced from 60 to 3.89 s compared to our previous work, which was based on a phase‐controlled algorithm. This improvement was based on inspiration from the experiences of our past works in dealing with various image reconstruction algorithms, which ultimately led to improvement in the calculation time.

El empleo de metodologías de código abierto para las investigaciones costeras: comparativa de las técnicas de detección de cambios

Coastal studies have a great relevance historically. This increase in the last with the new technologies and the possible global change impact. In this context, open-source tools represent a key element in these investigations. The present project analyzes the advantages and problems of open-source tools for estimate coastal variations and volumetric changes. For this purpose, a small sector of the Galician coast was used as an example. The open-source approaches have produced similar results to those obtained with proprietary software (ratios ≥ 0.97). In some cases, these methods improve processing times and allowing to users better possibilities for customization and decision-making.

Color tone determination prior algorithm for depth variant underwater images from AUV’s to improve processing time and image quality

Color tone determination prior algorithm for depth variant underwater images from AUV’s to improve processing time and image quality

Shelf-life stability of Asparagopsis bromoform in oil and freeze-dried powder

The idea of delivering bromoform from Asparagopsis using edible oil has gained momentum recently due to the improved processing time and that it is already a feed that many livestock producers use. The stability of bromoform in oil compared to freeze-dried product is still not well understood. To fill this gap, a systematic study was carried out to determine the effects of storage temperatures (40 °C, 25 °C, 4 °C and -20 °C), fluorescent light and exposure to open air, on the retention of bromoform in freeze-dried Asparagopsis (FD-Asp) and Asparagopsis oil (Asp-Oil) over 24-week period. In the absence of fluorescent light, Asp-Oil was a more effective way to preserve bromoform compared to FD-Asp due to either no change or higher Asp-Oil bromoform content (storage temperature dependent) after 24-week storage. Under the same conditions, FD-Asp bromoform content decreased by 74% at 40 °C, 53% at 25 °C, 6% at 4 °C, and no change of FD-Asp bromoform content at -20 °C. The presence of fluorescent light negatively affected Asp-Oil bromoform content at both 25 °C and 40 °C while the effect was insignificant on FD-Asp. The exposure of Asp-Oil to open air resulted in the decrease of bromoform content to below quantification limit (0.18 mg g−1) on week 8 for 40 °C sample and on week 16 for 25 °C sample. This study provides empirical evidence on the stabilising effect of oil in preserving bromoform extracted from Asparagopsis, confirming it is a more attractive medium to deliver bromoform compared to the freeze-dried powder form.

RETRACTED: Internet of Things security framework based on near field communication with Otway Rees protocol

AbstractInternet of Things (IoT) devices are crucial in different industries because they provide high‐functioning services to the IoT network connectivity. The IoT devices collect sensitive data, which comes under potential risk while transmitting data from one location to another. The data transmission is performed using various communication protocols that give security to the data. The intermediate attacks such as jamming, collision, flooding, worm‐hole, black‐hole and so forth create several security‐related issues. These intermediate attacks affect data privacy, trust, user identity, which completely affects the IoT device performance. So, this article introduces the near field communication (NFC) protocol with the Otway Rees protocol for making the secure communication process. In IoT applications based on short‐range communication, intermediate attacker issues have been reduced by combining NFC protocol features with Otway Rees protocol features. The NFC protocol provides baseline security trustworthiness while making the communication in the IoT environment cost‐effective. IoT communication is protected while cost‐effective using the security model, ensuring data integrity and confidentiality. In addition to this, the user and servers are authenticated for every session, which provides data security. Moreover, the discussed system is implemented using the NS2 simulation tool, ensuring security and trust with minimum complexity. Even in abnormal traffic conditions, our proposed method resulted in data transmission speeds (up to 289 Kb/s), and this feature can improve processing times.

A New U-Net Based Convolutional Neural Network for Estimating Caribou Lichen Ground Cover from Field-Level RGB Images

High-quality ground-truth data are critical for developing reliable Earth Observation (EO) based geospatial products. Conventional methods of collecting these data are either subject to an unknown amount of human error and bias or require extended time in the field to complete (i.e., point-intercept assessments). Digital photograph classification (DPC) may address these drawbacks. In this study, we first assess the performance of a DPC method developed through licensed software to estimate ground cover percentage (%) of bright lichens, a critical caribou forage in fall and winter when other food resources are scarce. We then evaluate the feasibility of replicating this workflow in an open-source environment with a modified U-net model to improve processing time and scalability. Our results indicate that DPC is appropriate for generating ground-truth data in support of large-scale EO-based lichen mapping within the boreal forests of eastern Canada. Our final open-sourced classification model, Lichen Convolutional Neural Network (LiCNN), is comparably accurate yet more efficient than the licensed workflow. Therefore, the LiCNN approach successfully addresses the mentioned shortcomings of conventional ground-truth data collection methods efficiently and without the need for specialized software.

IMPLEMENTATION OF VALUE STREAM MAPPING TO REDUCE WASTE IN WARRANTY CLAIM PROCESS AT COMMERCIAL VEHICLE AUTOMOTIVE COMPANY

In the automotive industry, after-sales service becomes one of the marketing strategies used. The obstacles arose when there was a problem with the products, but the services related follow-up decisions problem report from customers seems too long because the information delivered by the dealer has not been fulfilled. This research aims to identify waste and implement improvements that can reach the standard after-sales service time set by the company. This study uses the value stream mapping (VSM) method combined with a root cause analysis approach to reduce waste. The problem identification found that the most prolonged decision was the starting system. Then, the dominant waste is a waiting process. The VSM improved processing time efficiency by 34.47% or 33.97 hours (1.4 days). This result proved that the overall reporting time reached 60 hours (2 days) as the company target.

An efficient model for copy-move image forgery detection

PurposeThis study aims to solve problems of detecting copy-move images. With input images, the problem aims to: Confirm the original or forgery of the images, evaluate the performance of the detection and compare the proposed method’s effectiveness to the related ones.Design/methodology/approachThis paper proposes an algorithm to identify copy-move images by matching the characteristics of objects in the same group. The method is carried out through two stages of grouping the objects and comparing objects’ features. The classification and clustering can improve processing time by skipping groups of only one object, and feature comparison on objects in the same group improves accuracy of the detection. YOLO5, the latest version of you only look once (YOLO) developed by Ultralytics LLC, and K-means are applied to classify and group the objects in the first stage. Then, modified Zernike moments (MZMs) and correlation coefficients are used for the features extraction and matching in the second stage. The Open Images V6 data set is used to train the YOLO5 model. The combination of YOLO5 and MZM makes the effectiveness of the proposed method for copy-move image detection with an average accuracy of 94.26% for images of benchmark and MICC-F600 and 95.37% for natural images. The outstanding feature of the method is that it can balance both processing time and accuracy in detecting duplicate regions on the image.FindingsThe problem is then solved by doing the following steps: Build a method to detect objects and compare their features to find the similarity if they are copy-move objects; use YOLO5 for the object detection and group the same category objects; ignore the group having only one object and extract the features of the other groups by MZMs; detect copy-move regions using K-means clustering; and calculate and compare the detection accuracy of the proposed method and related methods.Originality/valueThe main contributions of this paper include: Reduce the processing time by using YOLO5 in objects detection and K-means in clustering; improve the accuracy by using MZM to extract features and correlation coefficients to matching them; and implement and prove the effectiveness of the proposed method for three copy-move data sets: benchmark, MICC-F600 and author-built images.

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

Deep learning based techniques for flame identification in optical engines

The mandatory migration from fossil to renewable energy sources requires the characterization of new alternative fuels. One important step in fuel characterization is the test in optical engines, which allows the morphological characterization of flames. This analysis requires the post treatment of images by using segmentation. In many cases, an automatic threshold presents shortcomings as the flames may present different regions with variable luminosity, as also reflections from valves and cylinder liner. Consequently, a time-consuming manual image processing is required and, therefore, an automatic procedure would be welcome. The use of deep learning techniques for image segmentation is a promising alternative for such task, which has showed excellent results in several applications. In this study, two different models were trained to identify flames in images obtained from an optical engine operating at various conditions. The dataset used to train the models was generated by using images from tests with several types of fuels and combustion modes. The effects of image resolution and the generalization capabilities for different fuels and combustion operation were investigated. After analyzing the results, the use of deep learning methods to identify and characterize flames was validated as a mean for improving processing time.

Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15

This paper looks at the impact of changing Spark’s configuration parameters on machine learning algorithms using a large dataset—the UNSW-NB15 dataset. The environmental conditions that will optimize the classification process are studied. To build smart intrusion detection systems, a deep understanding of the environmental parameters is necessary. Specifically, the focus is on the following environmental parameters: the executor memory, number of executors, number of cores per executor, execution time, as well as the impact on statistical measures. Hence, the objective was to optimize resource usage and minimize processing time for Decision Tree classification, using Spark. This shows whether additional resources will increase performance, lower processing time, and optimize computing resources. The UNSW-NB15 dataset, being a large dataset, provides enough data and complexity to see the changes in computing resource configurations in Spark. Principal Component Analysis was used for preprocessing the dataset. Results indicated that a lack of executors and cores result in wasted resources and long processing time. Excessive resource allocation did not improve processing time. Environmental tuning has a noticeable impact.

LSB-based Audio Steganographical Framework for Securing Data in Transit

The benefits that individuals and organizations derive from the digital era comes with its own challenges. Globally, data has become one of the greatest assets for decision making and operational improvements among businesses, government agencies and even individuals. Data on its own and at its source does not make so much contribution to business processes. Data is transmitted from one location to another towards attainment of its goal as a critical resource in decision making. However, data including sensitive or confidential ones are transmitted via public channels such as the Internet. The data so transmitted via the Internet is vulnerable to interception and unauthorized manipulation. This demands that data in transit is protected from the prying eyes of the malicious internet users. One of such strategies for transmitting data via public channels such as the Internet without attracting attention from intruders is steganography. In this paper, the least significant bit algorithm was used with an audio file for hiding data in transit. The algorithm used in this research proves to be one of the simplest ways of securing data using audio steganography. The method employed the LSB technique by using audio files as the stego object for the final implementation in the Java programming language. The experimental results proved to be one of the best methods of implementing steganography. The accuracy of the stego objects shows high quality, and similarity scores with an improved processing time.

Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.