Workflow Applications Research Articles

Delve deep: Seismic revolution in acquisition and model building for exploration

Sparse-node acquisition is the latest approach to improve the earth model through the inclusion of ultra-long offsets and low-frequency generating sources. The new designs of node data collections with simultaneous shooting can be deployed on a regional basis, covering thousands of square miles in a cost-effective manner. In complex geologic settings such as salt environments, salt interpretation has a direct impact on subsalt imaging. However, salt interpretation can be quite time intensive and challenging. Full-waveform inversion (FWI), as a data-driven optimization algorithm with full-wavefield modeling, has become an essential tool for earth model building. However, its use in salt tectonic contexts, especially with streamer data collection, is limited because the frequencies acquired are not sufficiently low and the offsets are not sufficiently long. The recent advancement of the objective function enables the application of an FWI workflow, coupled with sparse-node acquisition in the deepwater settings of the Gulf of Mexico, to refine the salt geometry, correct the background velocity error in subsalt, and uncover a basin's structural framework. The increased frequency content of the FWI-updated fields enables us to extract the FWI-derived reflectivity (FDR), which is an image created by the FWI nonlinear iterative process with the full wavefield. Additionally, the classic extraction of amplitude-versus-angle/amplitude-variation-with-offset parameters employs the elastic Zoeppritz formulation to obtain rock-physics volumes and explain the amplitude on the prestack image gathers, which are typically generated by Kirchhoff or reverse time migration (RTM) depth migration. The next step is to substitute these migrations with FWI and output not only the poststack FDR but use it in a prestack mode to aid amplitude analysis at an early stage of model building.

An Android-based Augmented Reality Application Design for the Physics Book of Grade XII ; Chapter 7 Theory of Relatively

Generally speaking, there are still numerous institutions that employ learning methods that are relatively inefficient. For instance, they still rely on traditional book media. where, the use of book media is still relatively inefficient, especially in material that requires a high level of understanding. The Work System Design Method for this augmented reality application uses several software, namely Blender3D, Unity, and Vuforia. This system's design includes interfaces or display interfaces that depict application workflows described through Flowcharts. Through this final project, we have developed a mobile-based augmented reality application that boasts several key features. The Main Menu includes pointer buttons to other menus, while the AR Menu displays the 3D object. The Help and About menus are also available. Our conclusion is that this augmented reality application can assist students who are keen on learning, and it can alleviate difficulties in understanding complex subjects.

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Progress with Repository-based Annotation Infrastructure for Biodiversity Applications

Structure-Based Design of Small Imine Reductase Panels for Target Substrates.

Biocatalysis is important in the discovery, development, and manufacture of pharmaceuticals. However, the identification of enzymes for target transformations of interest requires major screening efforts. Here, we report a structure-based computational workflow to prioritize protein sequences by a score based on predicted activities on substrates, thereby reducing a resource-intensive laboratory-based biocatalyst screening. We selected imine reductases (IREDs) as a class of biocatalysts to illustrate the application of the computational workflow termed IREDFisher. Validation by using published data showed that IREDFisher can retrieve the best enzymes and increase the hit rate by identifying the top 20 ranked sequences. The power of IREDFisher is confirmed by computationally screening 1400 sequences for chosen reductive amination reactions with different levels of complexity. Highly active IREDs were identified by only testing 20 samples in vitro. Our speed test shows that it only takes 90 min to rank 85 sequences from user input and 30 min for the established IREDFisher database containing 591 IRED sequences. IREDFisher is available as a user-friendly web interface (https://enzymeevolver.com/IREDFisher). IREDFisher enables the rapid discovery of IREDs for applications in synthesis and directed evolution studies, with minimal time and resource expenditure. Future use of the workflow with other enzyme families could be implemented following the modification of the workflow scoring function.

Cost-Efficient Fault-Tolerant Workflow Scheduling for Deadline-Constrained Microservice-Based Applications in Clouds

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.

A customized anthropomorphic 3D-printed phantom to reproducibility assessment in computed tomography: an oncological case study.

Studies on computed tomography (CT) reproducibility at different acquisition parameters have to take into account radiation dose administered and related ethical issues. 3D-printed phantoms provide the possibility to investigate these features deeply and to foster CT research, also taking advantage by outperforming new generation scanners. The aim of this study is to propose a new anthropomorphic 3D-printed phantom for chest lesions, tailored on a real patient CT scan, to investigate the variability of volume and Hounsfield Unit (HU) measurements at different CT acquisition parameters. The chest CT of a 75-year-old patient with a paramediastinal lung lesion was segmented based on an eight-compartment approach related to HU ranges (air lung, lung interstitium, fat, muscle, vascular, skin, bone, and lesion). From each mask produced, the 3D.stl model was exported and linked to a different printing infill value, based on a preliminary test and HU ratios derived from the patient scan. Fused deposition modeling (FDM) technology printing was chosen with filament materials in polylactic acid (PLA). Phantom was acquired at 50 mAs and three different tube voltages of 80, 100, and 120 kVp on two different scanners, namely, Siemens Somatom Force (Siemens Healthineers, Erlangen, Germany; same setting of real patient for 80 kVp acquisition) and GE 750 HD CT (GE Healthcare, Chicago, IL). The same segmentation workflow was then applied on each phantom acquisition after coregistration pipeline, and Dice Similarity Coefficient (DSC) and HU averages were extracted and compared for each compartment. DSC comparison among real patient versus phantom scans at different kVp, and on both CT scanners, demonstrated a good overlap of different compartments and lesion vascularization with a higher similarity for lung and lesion masks for each setting (about 0.9 and 0.8, respectively). Although mean HU was not comparable with real data, due to the PLA material, the proportion of intensity values for each compartment remains respected. The proposed approach demonstrated the reliability of 3D-printed technology for personalized approaches in CT research, opening to the application of the same workflow to other oncological fields.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud

Cloud computing offers resources to users for computing and storage needs on the policy pay-what-you-use model. A private cloud is an excellent cost-saving option because the user owns the resources and can be used free to execute workflow applications. However, when private cloud resources are insufficient to fulfill the needs of workflow applications, the public cloud is the only option. To this end, a hybrid cloud (HC) is an elegant way to combine public and private cloud resources to meet the requirements of workflow applications. Therefore, large enterprises prefer the HC, but task scheduling in the HC is complicated. Therefore, this study proposed a Deadline-constrained Cost-aware Workflow Scheduling (DCWS) algorithm to address task scheduling problems in the HC. Firstly, we will execute maximum workflow tasks on the private cloud owned by users under the deadline constraint. Secondly, send the unscheduled workflow tasks to the public cloud for execution. Lastly, we will ensure scheduling whole workflow tasks on the private cloud and the communication channels (for sending to the public cloud) to satisfy the task-precedence needs and the task deadline constraint while incurring a minimal monetary cost. Furthermore, we reduce task slack time with the help of the sub-deadline division technique and search for an ideal virtual machine (VM) to reduce further execution time and monetary cost. The experimental results prove that the DCWS algorithm outperformed existing algorithms.

Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data.

Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole. A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity. The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD. Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.

A fault tolerance aware green IoT workflow scheduling algorithm for multi-dimensional resource utilization in sustainable cloud computing

In recent times, the distributed cloud computing landscape has witnessed a remarkable surge in processing vast amounts of data and the crucial need to maintain service level agreements (SLAs) between providers and consumers. In this dynamic environment, cloud resources are inherently multidimensional, encompassing computing machines and communication connections susceptible to failures and energy-related considerations. However, given two-objective energy and reliability optimization, existing time allocation policies focus primarily on optimizing a single intent, which leads to system degradation in environments that generate problematic constraints from executable processing units. To handle the shortcomings of the application placement policies, we suggest a solution in the form of a three-phase bi-objective workflow scheduling issue called (Bi-OWSP). This three-phase approach aims to optimize workflow scheduling by simultaneously considering energy and reliability as two vital objectives. To achieve this, we employ two distinct algorithms. First is the stepwise dynamic voltage and frequency scale algorithm ensures energy efficiency by calculating optimal frequencies, reducing energy usage, and minimizing task mapping time. The second algorithm is the reliability-conscious heterogeneous fault tolerance approach, which emphasizes avoiding high-deficit servers and communication links to enhance system reliability. Furthermore, we introduce an energy-aware stepwise reliability maximization algorithm, which intelligently selects the best combination of task-server pairs to achieve energy minimization and reliability maximization. Through extensive simulation experiments on artificial and real-world workflow applications, we demonstrate the significance of Bi-OWSP in providing superior energy-reliability compensation solutions compared to competing algorithms.

Fault-tolerant scheduling of graph-based loads on fog/cloud environments with multi-level queues and LSTM-based workload prediction

Workflow scheduling in the fog/cloud environment is a complex challenge, particularly with the increasing number of IoT devices. Many workflow applications have time constraints. The duplication approach is one of the methods that can help tasks to meet their deadline. This paper presents an architecture that uses a multi-criteria scheduling algorithm and multi-level queues. The proposed architecture determines a duplication coefficient for each queue, and tasks are placed in these queues using a triple-tuple approach composed of the tasks' priority, time pressure, and workload prediction of required resources. The proposed architecture uses various methods for calculating desired parameters. CPE is used to calculate the priority of tasks. Workload prediction is performed by Long-Short Term Memory (LSTM) neural network which is known for its forecasting capabilities. To evaluate the effectiveness of our proposed method, we conducted a software simulation study comparing it with four recent algorithms. The simulation results confirmed the superiority of our approach in different areas. The throughput is improved by 48.6%, communication cost was reduced by 56%. Also, make-span, waiting time, and the parallelism degree are improved between 15 to 62%.

Moving beyond the desktop: prospects for practical bioimage analysis via the web.

As biological imaging continues to rapidly advance, it results in increasingly complex image data, necessitating a reevaluation of conventional bioimage analysis methods and their accessibility. This perspective underscores our belief that a transition from desktop-based tools to web-based bioimage analysis could unlock immense opportunities for improved accessibility, enhanced collaboration, and streamlined workflows. We outline the potential benefits, such as reduced local computational demands and solutions to common challenges, including software installation issues and limited reproducibility. Furthermore, we explore the present state of web-based tools, hurdles in implementation, and the significance of collective involvement from the scientific community in driving this transition. In acknowledging the potential roadblocks and complexity of data management, we suggest a combined approach of selective prototyping and large-scale workflow application for optimal usage. Embracing web-based bioimage analysis could pave the way for the life sciences community to accelerate biological research, offering a robust platform for a more collaborative, efficient, and democratized science.

Reliability-Aware and Energy-Efficient Workflow Scheduling in IaaS Clouds

Nowadays, more and more workflow applications with different computing requirements are migrated to clouds and executed with cloud resources. Workflow scheduling becomes a critical problem in the cloud environment, which focuses on meeting various quality of service (QoS) constraints. Workflow reliability and energy consumption are two essential parts in clouds and minimizing energy consumption for scheduling workflow with the reliability constraint is a challenging issue. In response to the challenge, we propose a workflow scheduling algorithm named REWS to reduce energy consumption and satisfy workflow reliability constraints. In REWS, a new sub-reliability constraint prediction strategy is adopted to break down the workflow reliability constraint to task sub-reliability constraints and the effectiveness of this strategy is proved. Moreover, an update method is adopted to adjust the task sub-reliability constraint for reducing energy consumption. In addition, a brief system framework which consists of five parts: workflow analyzer, reliability decomposer, resource manager, workflow scheduler and feedback processer is built to support the algorithm implementation of REWS. We conduct the experiments using both synthetic data and real-world data to evaluate the proposed REWS approach. The results demonstrate the superiority of REWS as compared with the state-of-the-art algorithms. <i>Note to Practitioners</i>&#x2014;Workflow scheduling is a challenging issue in emerging trends of the cloud environment that focuses on satisfying various QoS constraints. In this paper, we investigate a reliability-aware and energy-efficient workflow scheduling problem in cloud computing. A novel workflow scheduling algorithm called REWS, is designed to reduce the energy consumption and meet the workfolw reliability constraint. The basic idea of REWS is to divide the workflow reliability constraint into task sub-reliability constraints and schedule tasks with an energy-efficient scheduling strategy. We conduct the experiments to evaluate the proposed REWS and the results demonstrate that REWS outperforms the state-of-the-art algorithms.

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterization, and active source seismology. Using laser-pulse techniques, DAS turns (commercial) fiber-optic cables into seismic arrays with a spatial sampling density of the order of meters and a time sampling rate up to one thousand Hertz. The versatility of DAS enables dense instrumentation of traditionally inaccessible domains, such as urban, glaciated, and submarine environments. This in turn opens up novel applications such as traffic density monitoring and maritime vessel tracking. However, these new environments also introduce new challenges in handling various types of recorded noise, impeding the application of traditional data analysis workflows. In order to tackle the challenges posed by noise, new denoising techniques need to be explored that are tailored to DAS. In this work, we propose a Deep Learning approach that leverages the spatial density of DAS measurements to remove spatially incoherent noise with unknown characteristics. This approach is entirely self-supervised, so no noise-free ground truth is required, and it makes no assumptions regarding the noise characteristics other than that it is spatio-temporally incoherent. We apply our approach to both synthetic and real-world DAS data to demonstrate its excellent performance, even when the signals of interest are well below the noise level. Our proposed methods can be readily incorporated into conventional data processing workflows to facilitate subsequent seismological analyses.

Workflow Scheduling in Serverless Edge Computing for the Industrial Internet of Things: A Learning Approach

Serverless edge computing is seen as a promising enabler to execute differentiated Industrial Internet of Things (IIoT) applications without managing the underlying servers and clusters. In IIoT serverless edge computing, IIoT workflow scheduling for cloud-edge collaborative processing is closely related to the service quality of users. However, serverless functions decomposed by IIoT applications are limited in their deployment at the edge due to the resource-constrained nature of edge infrastructures. Additionally, the scheduling of complex IIoT applications supported by serverless computing is more challenging. Therefore, considering the limited function deployment and the complex dependencies of serverless workflows, we model the workflow application as Directed Acyclic Graph (DAG) and formulate the scheduling problem as a multi-objective optimization problem. A Dueling Double Deep Q-Network (D3QN)-based solution is proposed to make scheduling decisions under dynamically changing systems. Extensive simulation experiments are conducted to validate the superiority of the proposed scheme.

Reliability, Rental-Cost and Energy-Aware Multi-Workflow Scheduling on Multi-Cloud Systems

Computationally intensive applications with a wide range of requirements are advancing to cloud computing platforms. However, with the growing demands from users, cloud providers are not always able to provide all the prerequisites of the application. Hence, flexible computation and storage systems, such as multi-cloud systems, emerged as a suitable solution. Different charging mechanisms, vast resource configuration, different energy consumption, and reliability are the key issues for multi-cloud systems. To address these issues, we propose a multi-workflow scheduling framework for multi-cloud systems, intending to lower the monetary cost and energy consumption while enhancing the reliability of application execution. Our proposed platform presents different methods (utilizing resource gaps, the DVFS utilized method, and a task duplication mechanism) to ensure each application's requirement. The Weibull distribution is used to model task reliability at different resource fault rates and fault behavior. Various synthetic workflow applications are used to perform simulation experiments. The results of the performance evaluation demonstrated that our proposed algorithms outperform (in the terms of resource rental cost, efficient energy consumption, and improved reliability) state-of-the-art algorithms for multi-cloud systems.

M-dimension hybrid algorithm for scientific workflow in cloud computing

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. With the emergence cloud computing, can benefit from virtually unlimited resources with minimal hardware investment. Scheduling the submitted Scientific Workflow Application (SWFA) tasks to the available computational resources while optimizing the cost of executing the SWFA is one of the most challenging processes of Workflow Management System (WfMS) in a cloud computing environment. Several cost optimization approaches have been proposed to improve the economic aspect of SWFS in cloud computing. The main goal of the paper is to present a new M-dimension hybrid algorithm, which uses a meta-heuristic algorithm such as Completion Time Driven Hyper-Heuristic (CTDHH), Hybrid Cost-effective Hybrid-Scheduling (HCHS), particle swarm optimization (PSO) and genetic algorithm (GA) and using heuristic algorithms such as the IC-PCPD2 and IC-Loss algorithms. Based on the results of the experimental comparison, the proposed method has proven to yield the most effective performance results for all considered experimental scenarios.

Fully automatic tracking of native glenohumeral kinematics from stereo-radiography

The current work introduces a system for fully automatic tracking of native glenohumeral kinematics in stereo-radiography sequences. The proposed method first applies convolutional neural networks to obtain segmentation and semantic key point predictions in biplanar radiograph frames. Preliminary bone pose estimates are computed by solving a non-convex optimization problem with semidefinite relaxations to register digitized bone landmarks to semantic key points. Initial poses are then refined by registering computed tomography-based digitally reconstructed radiographs to captured scenes, which are masked by segmentation maps to isolate the shoulder joint. A particular neural net architecture which exploits subject-specific geometry is also introduced to improve segmentation predictions and increase robustness of subsequent pose estimates. The method is evaluated by comparing predicted glenohumeral kinematics to manually tracked values from 17 trials capturing 4 dynamic activities. Median orientation differences between predicted and ground truth poses were 1.7∘ and 8.6∘ for the scapula and humerus, respectively. Joint-level kinematics differences were less than 2∘ in 65%, 13%, and 63% of frames for XYZ orientation DoFs based on Euler angle decompositions. Automation of kinematic tracking can increase scalability of tracking workflows in research, clinical, or surgical applications.

Resilience and load balancing in Fog networks: A Multi-Criteria Decision Analysis approach

The advent of Cloud Computing enabled the proliferation of Internet-of-Things (IoT) applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to serve distributed IoT application workflows using the concept of stateless micro Fog service replicas, which provides resiliency and maintains service availability in the face of failures. Load balancing supports this collaboration by optimally assigning workloads to appropriate services, i.e., distributing the load among Fog nodes to fairly utilize compute and network resources and minimize execution delays. In this paper, we propose using ELECTRE, a Multi-Criteria Decision Analysis (MCDA) approach, to efficiently balance the load in Fog environments. We considered multiple objectives to make service selection decisions, including compute and network load information. We evaluate our approach in a realistic unbalanced topological setup with heterogeneous workload requirements. To the best of our knowledge, this is the first time ELECTRE-based methods are used to balance the load in Fog environments. Through simulations, we compared the performance of our proposed approach with traditional baseline methods that are commonly used in practice, namely random, Round-Robin, nearest node, and fastest service selection algorithms. In terms of the overall system performance, our approach outperforms these methods with up to 67% improvement.

Recent Developments in Mass Spectrometry to Support Next-Generation Synthesis and Screening.

The complexity of new therapeutics continues to increase and the timeline for the discovery of these therapeutics continues to shrink. This creates demand for new analytical techniques to facilitate quicker discovery and development of novel drugs. Mass spectrometry is one of the most prolific analytical techniques that has been applied across the entire drug discovery pipeline. New mass spectrometers and the associated methods for sampling have been introduced at a rate that keeps pace with new chemistries, therapeutic types, and screening practices used by modern drug hunters. This microperspective covers application and implementation of new mass spectrometry workflows that enable current and future efforts in screening and synthesis for drug discovery.

4D Flow Cardiovascular Magnetic Resonance-Derived Flow Biomarkers for Early Detection of Flow Abnormalities: A Multicohort New Zealand Based Study

Four-dimensional (4D) flow cardiovascular magnetic resonance (4D flow CMR) imaging has evolved as a versatile imaging technique for the assessment of cardiovascular three-dimensional time resolved haemodynamics. This study aimed to examine the utility and metrics derived from 4D flow CMR in different patient cohorts. Controls (n=26), adult tetralogy of Fallot (rToF; n=14), and left ventricular hypertrophy (LVH; n=10) patients were scanned at the Centre of Advanced MRI, University of Auckland. Quantification of flow and examination of between-group differences were performed. Intraclass correlation coefficients (ICC) were used to correlate 4D and 2D CMR. Forward flow rates averaged in the pulmonary veins were significantly greater in LVH than controls (79.11±54.24 mL/sec vs 32.58±35.98 mL/sec, respectively, p<0.05), as was the pulmonary venous atrial reversal peak velocity (–0.44±0.23 m/sec vs –0.13±0.27 m/sec, respectively, p<0.05). Mitral indices of net flow and peak velocity expressed no significant between-group differences. In the rToF group, quantification of pulmonary artery and branches demonstrated a good correlation with 2D CMR. Greatest correlation was between pulmonary artery backward flow (difference of s–2.50±6.91 mL/cycle, ICC 0.96). In relation to pulmonary valve regurgitation categorisation, 1 of 14 cases were differently categorised using 4D flow (ICC 0.91). Quantification of flow with 4D flow CMR was feasible, with a single acquisition introducing an array of flow metrics that may be valuable for the evaluation of various conditions. Larger scale investigations of 4D flow CMR are required, with wider-scale application in routine clinical workflows.