Minimum Maintenance Cost Research Articles

Use of recycled tyre segments to enhance the stability of ballasted track by increased confinement

The most common railway ballast is produced by quarrying, and its mechanical characteristics are crucial for both stability and drainage for safer and faster rail operations. Ballasted tracks have certain drawbacks, primarily because ballast starts to degrade over time. In this regard, reducing the rate of ballast degradation is vital to enhance track longevity and minimise maintenance costs. This study demonstrates how segments of huge waste rubber tyres (e.g., 3 m in diameter) from the mining industry can be used to improve the stability of tracks, while contributing to reduced ballast deformation and degradation. By placing arched segments cut from these tyres along the track shoulders beyond the edge of sleepers (i.e., the plan view gives a schematic impression of a caterpillar), the in-situ lateral confining pressure can be increased from 20–25 (standard track) to 40–50 kPa. This novel idea of confined-caterpillar track (CCT) was tested at a prototype physical model (1:1 scale) at the National Facility for the Heavy-haul Railroad Testing, and the experimental outcomes were compared with the performance of a conventional track. Apart from constributing to at least 25% saving of quarried aggregates, the test results prove that the CCT concept can curtail the lateral displacement and settlement of the ballast layer, while reducing particle breakage and affecting significant stress reduction in the underlying substructure layers.

A new integrated strategy for optimising the maintenance cost of complex systems using reliability importance measures

ABSTRACT With the aging of production systems, failure modes become more common resulting in additional maintenance costs. To reduce these costs excess, flexible and smart maintenance strategies should be considered. This article proposes an integrated condition-based maintenance method associating opportunistic and importance measure concepts (IMC). The objective of an IMC-based model is to determine the contribution of the system’s components according to their criticality degree for reliability improvement and maintenance planning. The identification of the best maintenance planning consists of determining the expected minimal cost that guarantees the repair actions of a group of critical components in one shot. Therefore, determining IMC values is not so easy for complex systems, it requires knowing their operational structure, the determination of the reliability value of each configuration and finally calculating each component's IMC degree and ranking them for the prioritisation selection. For testing the proposed method, an industrial case study has been used, regarding a complex system whose components fail at random times. The system undergoes minimal repairs if one or more components fail accidentally or by decision after inspection actions. The numerical results show that the developed approach incurs minimal maintenance costs, and can be integrated as a decision-aid solution for manufacturers.

Input parameters and nomograms for service life-based design of reinforced concrete structures exposed to chlorides

Civil engineers are trying to promote the service-life-based design of reinforced concrete (RC) structures to ensure long corrosion-free service life with minimal maintenance, repair, rehabilitation, and life cycle costs. This paper highlights the difficulties in adopting the existing tools as a general practice, especially for high-performance concrete with novel materials. To overcome these difficulties, “SL-Chlor,” a MATLAB® computer program is developed to estimate service life, which uses Fick’s 2nd Law of Diffusion to model chloride ingress, a major factor causing corrosion. Based on SL-Chlor, user-friendly nomograms are then developed to estimate the service life of RC structures located at various distances from the seashore. With the development of nomograms, this paper attempts to bring a new perspective to promote service life-based design with the help of user-friendly tools. The use of nomograms is demonstrated using case studies with multiple input parameters. The effect of different input parameters on service life is found to be synergistic, in the following descending order of influence: cover depth (d) > chloride diffusion coefficient (DCl) > ageing coefficient (m) [maturity constant ofDCl)] > chloride threshold of the steel-cementitious system (Clth). These findings encourage not only the use of blended cements but also the implementation of 3C’s (adequate cover depth, compaction, and curing, which inherently influences d, DCl, and m) to help structures achieve the desired service life.

Stress Analysis of Belt Conveyor Roller Tube

Material transmission plays a crucial role in the industrial sector's economic landscape, and belt conveyors have emerged as highly efficient systems for continuous material transportation. These conveyors utilize rubber belts that facilitate the horizontal or inclined movement of materials. The primary objective is to transport materials from the loading point to the unloading point. This study focuses on the analysis of belt conveyors, specifically examining the utilization of reels and the significance of selecting suitable belt speeds. By employing advanced simulation software such as Ansys, the behavior of the reel under specific conditions was analyzed, and exaggerated images were utilized to visualize reel deformations. The findings indicate that the most significant deformation occurs at the center of the roller, while the highest stress values are concentrated near the bearings. To ensure efficient material transportation, it is imperative to understand and optimize the performance of belt conveyors. The selection of appropriate reels, belt speeds, and materials is crucial to maintain smooth operations and extend the system's service life. The insights gained from this research can guide decision-making processes, allowing industries to enhance efficiency and minimize maintenance costs. In conclusion, the selection of suitable components and a comprehensive understanding of reel behavior are key factors in improving belt conveyor systems. By employing advanced simulation tools and implementing effective strategies, industries can optimize material transportation processes, increase productivity, and minimize operational disruptions. Continuous improvement in design and maintenance practices will lead to streamlined operations and improved performance of belt conveyors.

Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model

The pumping system is a critical component in various industries and consumes 20% of the world’s energy demand, with 25–50% of that energy used in industrial operations. The primary goal for users of pumping systems is to minimise maintenance costs and energy consumption. Life cycle cost (LCC) analysis is a valuable tool for achieving this goal while improving energy efficiency and minimising waste. This paper aims to compare the LCC of pumping systems in both healthy and faulty conditions at different flow rates, and to determine the best AI-based machine learning algorithm for minimising costs after fault detection. The novelty of this research is that it will evaluate the performance of different machine learning algorithms, such as the hybrid model support vector machine (SVM) and the hidden Markov model (HMM), based on prediction speed, training time, and accuracy rate. The results of the study indicate that the hybrid SVM-HMM model can predict faults in the early stages more effectively than other algorithms, leading to significant reductions in energy costs.

Wheel Defect Detection Using a Hybrid Deep Learning Approach.

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques' cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

A review of sewage sludge dewatering and stabilisation in reed bed system: towards the process-based modelling

AbstractThe sludge treatment reed bed is a sustainable sewage sludge management technology that offers promising sludge dewatering and stabilisation, due to its low energy and labour requirements, and minimal operating and maintenance costs. Despite numerous existing studies and increasing applications of sludge treatment reed bed in both developed and developing countries, the lack of standard system configurations and operating regimes led to several operational problems, including bed clogging and poor dewatering and mineralisation efficiency. Sludge dewatering and stabilisation in STBRs are complex processes governed by system- and operation-related factors such as the substrate media, macrophytes, loading regime, sludge characteristics, and climate conditions. Rather than the complex experimental study, process-based modelling has become a promising approach to investigating the influence of specific factors on the efficiency of sludge dewatering and stabilisation. This paper presents an overview of the critical system and operating parameters in sludge treatment reed beds and a summary of the interactions between the factors. Then, a framework of process-based modelling is proposed, which provides a useful platform to study the interactions of operating parameters and other factors in the complex processes of sludge dewatering and stabilisation, which supports the system design and optimisation of sludge treatment reed beds.

Analisis Penentuan Perawatan Mesin Roll Table 5Q Dengan Metode Reliability Centered Maintenance (RCM) II Dan Life Cycle Cost (LCC) Di PT XYZ

Machine maintenance must be carried out as a support for the reliability of a machine so that the production system can run smoothly to meet production needs. The fundamental problem of this study is in PT XYZ with the Roll Table 5Q machine, which frequently experiences sudden damage caused by critical components. The goal of the study was to determine the maintenance time interval of the 5Q Roll Table machine, the optimal age of the machine, and the number of mechanics based on the minimum total maintenance cost in an effort to improve the reliability of the machine using the Reliability Centered Maintenance (RCM) II methods and Life Cycle Cost (LCC) methods. From the results of research for critical components As Roll Table obtained interval value of 331 hours / 13 days once a year with the selection of action is Scheduled restoration task and for Bearing components obtained interval value of 109 hours/5 days once a year with the selection of action is Scheduled on-condition task. From the calculation of the total life cycle cost of Rp. 3,720,459,386 with the optimal number of mechanics on the Roll Table machine as many as 3 mechanics with an efficiency level of 9.63% with an optimal age for the Roll Table machine 5Q is 12 years.

Research on the optimal planning method of hydrogen-storage units in wind–hydrogen energy system considering hydrogen energy source

Utilizing wind power (WP) for hydrogen production can alleviate wind curtailment and improve wind energy utilization. The optimal planning of hydrogen-storage units(HSUs) in wind–hydrogen energy system(W-HES) will affect its economic operation. This paper proposes a W-HES model comprising WP, HSUs, and loads. We then utilize clustering algorithm-based WP and power load scenario division method. And taking the minimum investment, operation, and maintenance cost of W-HES as the objective function, use the particle swarm optimization(PSO) algorithm to optimize the planning of HSUs of W-HES. Finally, we investigate the impact of diverse hydrogen energy sources and daily hydrogen load variations on the optimal planning outcomes of HSUs. It will provide a valuable insights for the planning and configuration of HSUs in W-HES.

Stochastic optimization – based economic design for a hybrid sustainable system of wind turbine, combined heat, and power generation, and electric and thermal storages considering uncertainty: A case study of Espoo, Finland

This work-study deals with the economic planning of an island sustainable system that consists of a wind turbine, battery, combined heat and power system, and thermal storage to meet electrical and thermal loads at the same time. The approach attempts to find minimum capital cost, repair and maintenance cost, and operating cost, as well as minimize the pollution of these elements. It is also constrained by the operation-planning model of the mentioned resources and storage, provided that a wind turbine and combined heat and power system have priority in electrical load supply, a combined system of heat and power has priority in thermal load supply, and batteries and storage devices have the task of filling the gap between supply and demand profiles. Uncertainty parameters of the scheme include electrical and thermal load and power output of the wind system. The scheme adopts the unscented transformation method to provide proper modeling of the mentioned uncertainties. Moreover, the essence of the problem is integer nonlinear programming, which uses the combined gray wolf optimization and honey bee mating optimization algorithm to achieve the desired solution. Simultaneous supply of electrical and thermal loads, modeling of uncertainties, and adoption of a combined solver in the proposed scheme are among the contributions. Finally, by implementing this scheme on data from the city of Espoo, Finland, the numerical results indicate a reduction of 10%–11% in the planning cost of the proposed scheme compared to a design that only supplies electricity.

Accuracy of a simplified sequencing and bioinformatics platform on genetic variant detection in patients with prostate cancer.

e17061 Background: Consensus guidelines suggest that genetic testing is clinically indicated for prostate cancer (PCa) patients who meet certain family history, stage and histopathological grade criteria. Current next generation sequencing (NGS) diagnostics rely on capital and maintenance intensive sequencers which require highly trained technical personnel to operate. This may restrict availability of testing where specimen transport or access to personnel/capital is limited. We sought to compare results obtained using a simpler sequencer coupled with a bioinformatics platform which requires minimal maintenance and capital cost with those from more traditional NGS equipment. Methods: We compared results in detection of 10 genes recommended by NCCN PCa guidelines (ATM, BRCA1, BRCA2, CHEK2, MLH1, MSH2, MSH6, PALB2, PSM2, and HOXB13) between the Oxford Nanopore Technologies MinION to the Illumina NextSeq sequencer. All samples were sequenced in a depth greater than 50x. Bioinformatics for the Illumina sequencer was performed using Illumina’s TruSight Hereditary Cancer panel. As the MinION reads long DNA fragments, and cancer diagnostics is focused on short exon sequencing, we used the CorrSeq Dx by Foresee Genomics to create long DNA molecules consisting of several tandem repeats of short DNA fragments separated by sequences recognized by automated bioinformatics software. Statistical analysis was performed using Cohen’s kappa test. This IRB-exempt study was conducted in accordance with the Declaration of Helsinki. Results: We detected using 380 variants in 13 patients on the Illumina platform and 379 (3 false negatives, 2 false positives) variants on the same patients using the MinION sequencer with CorrSeq Dx assay. The positive predictive value and sensitivity for the simplified platform was 99.5% (375/(375+2)) and 99.2% (375/(375+3)), respectively. The demonstrated accuracy of the simplified process was 99.9% (Cohen's kappa > 0.99). Conclusions: Thisl data suggests that a simpler sequencing solution with appropriate bioinformatics could allow clinical laboratories with limited technical and capital resources to perform germline and possibly somatic testing in-house; use of this technology may also serve to reduce costs for labs currently performing these tests. While some labs or clinics can outsource their NGS diagnostics if such commercial options are available, these options may not be possible where transport of specimens is logistically difficult or if access to technology is limited. A simpler process for NGS testing could improve access to guideline-based care in both remote and economically disadvantaged areas, resulting in better treatment outcomes as well as increased global healthcare equity. Further testing of this technology in prostate cancer as well as other disease states is warranted to fully understand its potential impact on patient care.

Intelligent Software Bug Prediction Framework with Parameter-Tuned LSTM with Attention Mechanism Using Adaptive Target-Based Pooling Deep Features

In recent years, various researchers have designed a software bug prediction model for classifying the nonfaulty and faulty modules in software that are correlated with software constraints. Software bug or defect prediction helps programmers or developers discover the possibilities of bugs and minimize maintenance costs. However, most approaches do not solve the class-imbalance issue regarding the software bug prediction model. To solve these issues, the latest software bug prediction model using enhanced deep-structured architecture is developed. Here, the software modules are obtained from online sources, which undergo pre-processing to remove unnecessary data. These pre-processed texts are considered for deep feature extraction, performed using a Convolutional Neural Network (CNN) with an adaptive target-based pooling method to get effective deep features. Here, the parameter tuning in CNN is performed using Hybrid Rat-Barnacle Mating Swarm Optimization (HR-BMSO) to enhance the prediction performance. These deep features are inserted into Adaptive Features-based Parameter-Tuned Attention Long Short Term Memory (AF-PTALSTM) for predicting the software bugs, in which the optimization of certain parameters takes place with the same HR-BMSO to get accurate predicted results. The accuracy and [Formula: see text]1-score of the designed AF-PTALSTM method attain 97% and 94% through analysis. Thus, the experimental analysis of the designed software bug prediction model depicts higher efficiency while estimating with traditional approaches.

Assessment of the Mechanical Properties of High Strength Mortar Incorporating Silica Fume and Graphene Nanoplatelets: Experimental and Mathematical Modeling

Cement-based mortar is recognized as a popular and cost-effective material for the rehabilitation and repair of reinforced concrete structures. However, the development of high-performance cement-based mortar is in high demand in order to not only enhance compressive strength but also to prolong the mortar lifespan and minimize maintenance costs as much as possible. In the current study, high-strength mortars incorporating both silica fume and graphene nanoplatelets (GNPs) were investigated and evaluated based on compressive and flexural strength. The graphene powder was added in amounts ranging from 0.5% to 2%, by cement weight, while silica fume was added as a partial replacement for cement (10%). The optimal content of the graphene was determined using response surface methodology (RSM). In addition, field emission scanning electron microscopy (FESEM) was used to assess the proposed mortar at the micro-scale level. The outcome revealed that the graphene-based mortar imparted superior mechanical properties compared to the control mixture. The compressive and flexural strength of the mortars containing 10% silica fume and 1% graphene increased by 33% and 35%, respectively. This positive result was attributed to the refinement of the nanopores and tiny cracks by the inclusion of GNPs, which was supported by microstructure testing. The RSM model was also shown to be capable of optimizing and predicting compressive and flexural strength with less error. It is possible to conclude that graphene-based high-strength mortar will serve as a sustainable material in the near future.

Usulan Penjadwalan Perawatan Mesin Filling Secara Preventive Dengan Modularity Design Dan LCC Di PT. Petrosida

PT. Petrosida is a manufacturing company engaged in pesticide production. The production system in this company is Make To Order (MTO) where the company will produce products after an order or order from consumers. In its production PT. Petrosida has problems with the main tools used for packaging, one of which is a 1-liter induction semi-automatic filling machine in the herbicide unit, where treatment is carried out after damage to a component. Critical components in filling machines experience an average of 20 times damage in 2022-2023 and the machine maintenance process is still not optimal with an average downtime of 5 hours / month. Therefore, the purpose of research is to be able to carry out preventive maintenance using the modularity design method in the hope that there will be no more breakdowns that hamper the production process so as to reduce machine maintenance costs and to find out the minimum total machine maintenance cost planning using the life cycle cost (LCC) method. By implementing preventive maintenance with modularity design, the total maintenance cost of Rp. 9,747,782 is lower than the company's total maintenance cost of Rp. 23,023,176 with an efficiency rate of 42.3%. And obtained the lowest total life cycle cost of Rp. 340,459,348, - with an optimal life on the machine of 3 years & the number of mechanics one person.

Keywords: care, maintenance, ste PERAWATAN DAN PEMELIHARAAN PADA TURBIN UAP DI PLTU REMBANG

Steam Power Plants (PLTU) currently have a strategic role in the electricity system in Indonesia. The maintenance system implemented by the steam power plant company will greatly affect the sustainability of the electricity produced. The steam power plant has several main parts in its production system, one of which is the turbine. The purpose of this research is to answer how to maintain the steam turbine at the Rembang Steam Power Plant (PLTU). The method used in this research is descriptive qualitative. Data collection was carried out through direct observation and interviews with related parties in the steam turbine maintenance section. The results show that care and maintenance on the steam turbine at the Rembang Steam Power Plant (PLTU) has proven to be effective and efficient to minimize various disturbances or problems that occur in the components of the steam turbine and can minimize maintenance costs and work safety can be guaranteed.
 Keywords: care, maintenance, steam turbine

Maintenance Cost Minimization for an Agricultural Harvesting Gripper.

A crucial aspect that has to be considered in all fields and, especially, in smart farming, a rapidly developing industry, is maintenance. Due to the costs generated by both under-maintaining and over-maintaining the components of a system, a balance has to be achieved. The paper is focused on presenting an optimal maintenance policy used to ensure cost minimization by determining the optimal time to make a preventive replacement of the actuators of a harvesting robotic system. First, a brief presentation of the gripper with Festo fluidic muscles used in a novel way instead of fingers is given. Then, the nature-inspired optimization algorithm, as well as the maintenance policy are described. The paper also includes the steps and the obtained results of the developed optimal maintenance policy applied for the Festo fluidic muscles. The outcome of the optimization shows that a significant reduction in the costs is obtained if one performs a preventive replacement of the actuators a few days before the lifetime provided by the manufacturer and the lifetime estimated using a Weibull distribution.

Wind Turbine Optimal Preventive Maintenance Scheduling Using Fibonacci Search and Genetic Algorithm

Maintenance scheduling is essential and crucial for wind turbines to avoid breakdowns and reduce maintenance costs. Many maintenance models have been developed for wind turbines' maintenance planning, such as corrective, preventive and predictive maintenance. Due to communities' dependence on wind turbines for electricity needs, preventative maintenance is the most widely used method for maintenance scheduling. The downside to using this approach is that preventive maintenance is often done in fixed intervals, which is inefficient. In this paper, a more detailed maintenance plan for a 2MW wind turbine has been developed. The paper's focus is to minimize a wind turbine's maintenance cost based on a wind turbine's reliability model. This study uses a two-layer optimization framework: Fibonacci and Genetic Algorithm (GA). The first layer in the optimization method (Fibonacci) finds the optimal number of preventive maintenance required for the system. In the second layer, the optimal times for preventative maintenance and optimal components to maintain have been determined to minimize maintenance costs. The Monte Carlo simulation estimates wind turbine component failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the overall corrective and preventive maintenance costs during the system's lifetime. Finally, an optimal preventive maintenance schedule is proposed for a 2MW wind turbine using the presented method. The method used in this paper can be expanded to a wind farm or similar engineering systems.

Perencanaan Jadwal Perawatan Komponen Mesin Roll Forming Baja Ringan Untuk Meminimumkan Biaya Perawatan

PT. Nadha Karya Utama Indonesia, a company engaged in the manufacturing service industry that produces mild steel, located on Jl. Cold Hamlet, RT 04 RW 07, Ngronggot, Kab. Nganjuk, East Java. The purpose of this study is to plan the Roll Forming machine maintenance schedule in order to minimize maintenance costs. The method used in this study is using the Preventive Maintenance method. The calculation results obtain the reliability value of each component on the Roll Forming machine, namely 0.5 for the reliability of the Roll component, 0.5 for the Gear Box component, 0.5 for the Main Engine component, 0.46 for Bearings, and 0.5 for reliability Roll Stand. Determination of maintenance time intervals is based on Peventive Maintenance on Roll components for 37 days, Gear Boxes for 108 days, Main Machinery for 108 days, Bearings for 53 days, and Roll Stands for 146 days. Based on the calculation results, the total cost of maintenance before carrying out preventive with reliability is known to be Rp. 19,340,000, after carrying out preventive calculations with reliability is Rp. 13,120,139.10, it can be seen that before and after carrying out preventive calculations there is a difference in costs of Rp. 6,219. 860.9. From these results it is known that preventive maintenance can reduce the costs incurred by PT. Nadha Karya Utama Indonesia from before the Preventive Maintenance calculations were carried out.

LBA02-10 ACCURACY OF A SIMPLIFIED SEQUENCING AND BIOINFORMATICS PLATFORM ON GENETIC VARIANT DETECTION IN PROSTATE CANCER PATIENTS

You have accessJournal of UrologyCME1 Apr 2023LBA02-10 ACCURACY OF A SIMPLIFIED SEQUENCING AND BIOINFORMATICS PLATFORM ON GENETIC VARIANT DETECTION IN PROSTATE CANCER PATIENTS Deepak Kapoor, Jonathan Anderson, and Ann Anderson Deepak KapoorDeepak Kapoor More articles by this author , Jonathan AndersonJonathan Anderson More articles by this author , and Ann AndersonAnn Anderson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003361.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Consensus guidelines suggest that genetic testing is clinically indicated for prostate cancer (PCa) patients who meet certain family history, stage and histopathological grade criteria. Current next generation sequencing (NGS) diagnostics rely on capital and maintenance intensive sequencers and require highly trained technical personnel to operate. This may restrict availability of testing where specimen transport or access to personnel/capital is limited. We sought to compare a simpler sequencer coupled with a bioinformatics platform which requires minimal maintenance and capital cost with traditional NGS equipment. METHODS: We compared results in detection of 10 genes recommended by NCCN PCa guidelines (ATM, BRCA1, BRCA2, CHEK2, MLH1, MSH2, MSH6, PALB2, PSM2, and HOXB13) between the Oxford Nanopore Technologies MinION to the Illumina NextSeq sequencer. All samples were sequenced in a depth greater than 50x. Bioinformatics for the Illumina sequencer was Illumina’s TruSight™ Hereditary Cancer panel. As the MinION reads long DNA fragments, and cancer diagnostics is focused on short exon sequencing, we used the CorrSeq Dx™ by Foresee Genomics to create long DNA molecules consisting of several tandem repeats of short DNA fragments separated by sequences recognized by the automated bioinformatics software. Statistical analysis was performed using Cohen’s kappa test. This IRB-exempt study was conducted in accordance with the Declaration of Helsinki. RESULTS: We detected using 380 variants in 13 patients on the Illumina platform and 379 (3 false negatives, 2 false positives) variants on the same patients using the MinION sequencer with CorrSeq Dx assay. The positive predictive value and sensitivity for the simplified platform was 99.5% (375/(375+2)) and 99.2% (375/(375+3)), respectively. The demonstrated accuracy of the simplified process was 99.9% (Cohen's kappa >0.99). CONCLUSIONS: Our data suggests that this application could allow smaller clinical laboratories which are currently outsourcing their NGS diagnostics to perform genetic testing in-house. Use of a simpler process for NGS testing could improve access to guideline-based care in remote and economically disadvantaged areas, resulting in better treatment outcomes as well as increased global healthcare equity. Further testing of this technology is warranted to further assess its impact on patient care. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1190 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Deepak Kapoor More articles by this author Jonathan Anderson More articles by this author Ann Anderson More articles by this author Expand All Advertisement PDF downloadLoading ...

A Semantic Segmentation Method Based on Image Entropy Weighted Spatio-Temporal Fusion for Blade Attachment Recognition of Marine Current Turbines

Marine current turbines (MCTs) may exhibit reduced energy production and structural instability due to attachments, such as biofouling and plankton. Semantic segmentation (SS) is utilized to recognize these attachments, enabling on-demand maintenance towards optimizing power generation efficiency and minimizing maintenance costs. However, the degree of motion blur might vary according to the MCT rotational speed. The SS methods are not robust against such variations, and the recognition accuracy could be significantly reduced. In order to alleviate this problem, the SS method is proposed based on image entropy weighted spatio-temporal fusion (IEWSTF). The method has two features: (1) A spatio-temporal fusion (STF) mechanism is proposed to learn spatio-temporal (ST) features in adjacent frames while conducting feature fusion, thus reducing the impact of motion blur on feature extraction. (2) An image entropy weighting (IEW) mechanism is proposed to adjust the fusion weights adaptively for better fusion effects. The experimental results demonstrate that the proposed method achieves superior recognition performance with MCT datasets with various rotational speeds and is more robust to rotational speed variations than other methods.