Total Processing Time Research Articles

Improvement and analysis of aircraft maintenance flow process using lean manufacturing, PDCA, PICA, and VSM for sustainable operation system

The aircraft maintenance process plays a crucial role in ensuring the safety and reliability of air travel. However, this process often encounters inefficiencies and waste, reducing productivity. This study aims to analyze the flow of the aircraft maintenance process and identify areas for improvement using Lean Manufacturing, Plan-Do-Check-Act (PDCA), Problem Identification and Corrective Action (PICA), and Value Stream Mapping (VSM) methods within a sustainability system. This study demonstrates how non-value-adding (NVA) and necessary but non-value-adding activities (NNVA) contribute to maintenance process delays, waste generation, and decreased process efficiency. It also underscores the impact of waste on process performance and proposes potential time savings through focused improvements. The result revealed that inventory wastage was the most prevalent issue, accounting for 9 out of 26 activities, followed by waiting for wastage with seven activities and defects with 5 activities. These wasteful activities are primarily concentrated in phases 3, 4, and 5, encompassing inspection, repair, and installation phases. Consequently, there is a pressing need to enhance efficiency in all phases, particularly phases 3, 4, and 5, by targeting non-value-added activities. Implementing these improvements can reduce the total process time from 18,229 minutes to 15,170.2 minutes, offering a detailed roadmap for enhancing the aircraft maintenance process. Thus, this study is an alternative consideration for the aircraft maintenance industry to ensure a continuous improvement process in facing the challenges of global industry innovation as the category of Sustainable Development Goals (SDGs).

Reducing Dandory Exchange Process Time on 1800 Ton Injection Molding Machine Using the Single Minute Exchange of Dies (Smed) Method at PT Takagi Sari Multi Utama

Takagi Sari Multi Utama is an automotive company engaged in injection molding, producing automotive products such as motor vehicle parts, car interiors, and exteriors. One of the main influences of the prolonged dandory process is the distance between the mold parking area and the machine, resulting in a total process time of 48.95 minutes, while the company's standard is set at 40 minutes. Minimizing the time can cause the dandory process of mold change using the Single Minute Exchange of Dies (SMED) method by converting the time of internal setup activities into external setup activities. The improvement results using the Single Minute Exchange of Dies (SMED) method reduced the time to 27.67 minutes or equivalent to 1,660.13 seconds. The reduction in dandory mold change process time on the 1800T injection molding machine is 21.29 minutes or equivalent to 1,277.16 seconds, with a percentage decrease of 44%

Sampling-Resilient Multi-Object Tracking

Multi-Object Tracking (MOT) is a cornerstone operator for video surveillance applications. To enable real-time processing of large-scale live video streams, we study an interesting scenario called down-sampled MOT, which performs object tracking only on a small subset of video frames. The problem is challenging for state-of-the-art MOT methods, which exhibit significant performance degradation under high frame reduction ratios. In this paper, we devise a sampling-resilient tracker with a novel sparse-observation Kalman filter (SOKF). It integrates an LSTM network to capture non-linear and dynamic motion patterns caused by sparse observations. Since the LSTM-based state transition is not compatible with the original noise estimation mechanism, we propose new estimation strategies based on Bayesian neural networks and derive the optimal Kalman gain for SOKF. To associate the detected bounding boxes robustly, we also propose a comprehensive similarity metric that systematically integrates multiple spatial matching signals. Experiments on three benchmark datasets show that our proposed tracker achieves the best trade-off between efficiency and accuracy. With the same tracking accuracy, we reduce the total processing time of ByteTrack by 2× in MOT17 and 3× in DanceTrack.

A branch-and-price algorithm for unrelated parallel machine scheduling with machine usage costs

This paper considers unrelated parallel machine scheduling involving machine usage costs, in addition to classic job completion time-related costs. The usage cost of each machine is made up of a fixed usage cost and a variable usage cost proportional to the total processing time of the jobs assigned to it. These features model many practical situations where machine usage costs include, for example, rental fees when the machines are not owned but rented. To tackle this problem, four mathematical models based on the Shortest Weighted Processing Time (SWPT) rule are introduced. Additionally, the problem is formulated into a set-partitioning model, for which a branch-and-price algorithm is proposed with an appropriate branching strategy. This facilitates the development of an efficient pseudo-polynomial dynamic programming algorithm and a polynomial-time heuristic to solve the pricing problem. Extensive numerical experiments demonstrate the superior performance of the proposed branch-and-price algorithm over the four SWPT-based mathematical formulations and an existing branch-and-price algorithm designed for a special case. Notably, it can optimally solve instances involving up to 225 jobs and 15 machines within one hour. Moreover, statistical analyses reveal that the proposed polynomial-time heuristic significantly reduces the computation time, and the mathematical model based on the contribution of every job to the total weighted completion time exhibits the best overall performance.

A Fully Integrated Microfluidic Device with Immobilized Dyes for Simultaneous Detection of Cell-Free DNA and Histones from Plasma Using Dehydrated Agarose Gates.

Sepsis, a life-threatening condition resulting from a failing host response to infection, causes millions of deaths annually, necessitating rapid and simple prognostic assessments. A variety of genomic and proteomic biomarkers have been developed for sepsis. For example, it has been shown that the level of plasma cell-free DNA (cfDNA) and circulating histones increases considerably during sepsis, and they are linked with sepsis severity and mortality. Developing a diagnostic tool that is capable of assessing such diverse biomarkers is challenging as the detection methodology is quite different for each. Here, a fully integrated microfluidic device capable of detecting a genomic biomarker (cfDNA) and a proteomic biomarker (total circulating histones) using a common detection platform has been demonstrated. The microfluidic device utilizes dehydrated agarose gates loaded with pH-specific agarose to electrophoretically trap cfDNA and histones at their respective isoelectric points. It also incorporates fluorescent dyes within the device, eliminating the need for off-chip sample preparation and allowing the direct testing of plasma samples without the need for labeling DNA and histones with fluorescent dyes beforehand. Xurography, which is a low-cost and rapid method for fabrication of microfluidics, is used in all the fabrication steps. Experimental results demonstrate the effective accumulation and separation of cfDNA and histones in the agarose gates in a total processing time of 20 min, employing 10 and 30 Volts for cfDNA and histone accumulation and detection, respectively. The device can potentially be used to distinguish between the survivors and non-survivors of sepsis. The integration of the detection of both biomarkers into a single device and dye immobilization enhances its clinical utility for rapid point-of-care assessment of sepsis prognosis.

Examining the Effect of Freezing Temperatures on the Survival Rate of Micro-Encapsulated Probiotic Lactobacillus acidophilus LA5 Using the Flash Freeze-Drying (FFD) Strategy.

This work proposes a novel drying method suitable for probiotic bacteria, called flash freeze-drying (FFD), which consists of a cyclic variation in pressure (up-down) in a very short time and is applied during primary drying. The effects of three FFD temperatures (-25 °C, -15 °C, and -3 °C) on the bacterial survival and water activity of Lactobacillus acidophilus LA5 (LA), previously microencapsulated with calcium alginate and chitosan, were evaluated. The total process time was 900 min, which is 68.75% less than the usual freeze-drying (FD) time of 2880 min. After FFD, LA treated at -25 °C reached a cell viability of 89.94%, which is 2.74% higher than that obtained by FD, as well as a water activity of 0.0522, which is 55% significantly lower than that observed using FD. Likewise, this freezing temperature showed 64.72% cell viability at the end of storage (28 days/20 °C/34% relative humidity). With the experimental data, a useful mathematical model was developed to obtain the optimal FFD operating parameters to achieve the target water content in the final drying.

Heat 'n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour.

Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.

Bounded scheduling two-component jobs simultaneously or hierarchically

This paper studies the multi-objective problem of scheduling n two-component jobs on a fabrication machine with bounded batch capacity in terms of the number of jobs. Each job consists of two components, one is common among all jobs (called standard component) and the other is unique to itself (called specific component). The machine processes all components so that it might become a capacitated bottleneck machine. Standard components have equal processing times. They are fabricated in batches sequentially on the machine, and a setup time is required to fabricate a batch. Each batch contains the standard components of at most b(b < n) jobs. Specific components are manufactured individually, therefore their associated setup times could be absorbed into their processing times. Standard components in a batch are not available until all these components are completed (meaning that the processing time of a batch is equal to the total processing time of the standard components in it), whereas a specific component is available on completion of its processing. A job is completed when both its two components are completed and available. For simultaneous optimization of makespan and maximum lateness, an O(n3)-time algorithm is obtained. Through a careful use of the heap data structure, the time bound can be improved to O(n2 log n). For hierarchical optimization of two maximum lateness objectives and makespan, an O(n5)-time algorithm is obtained, which can be improved to run in time O(n4).

An improved resource allocation method for mapping service function chains based on A3C

AbstractNetwork function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

Experimental study of materials for the filtration of the intake air of the internal combustion engine of a motor vehicle

Experimental evaluation of the properties of filter materials in the selection of material for filtration of inlet air of internal combustion engines was carried out. The filtration characteristics were determined: filtration efficiency and accuracy, as well as flow resistance depending on the dust mass load. Filter materials based on cellulose, polyester, microfiber glass, and cotton and polyester nonwoven beds were studied. Two filter beds consisting of three base filter materials were designed: composite K1 (polyester-microglass-cellulose) and composite K2 (cellulose-microglass-cellulose), and their characteristics were performed. The air filtration quality factor was used to analyze the test results. The coefficient of filtration efficiency was defined, showing the effect of the preliminary stage on the total time of the filtration process. It was shown that the K1 composite has high (dpmax = 1.5÷3 µm) filtration accuracy, high initial filtration efficiency (99.8%), which shortens the pre-stage, and extends to 96-98% the duration of the main stage of the filtration process. The K1 composite obtained more than twice the mass loading of dust (kdK1 = 148,9 g/m2), which will allow the vehicle's mileage to be extended.

Spatial monitoring of hydrolysis in a plug-flow bioreactor: a support for flexible operation?

Hydrolysis at changing hydraulic retention time, recirculation, bedding straw content in the feed, bioaugmentation and the impact of those changes on gradient formation in the liquid phase in plug-flow reactors (PFRs) was examined. The pH-value, conductivity and oxidation–reduction potential (ORP) were monitored at three spots along the PFRs to study potential correlations to process performance during a total process time of 123 weeks. The on-line monitoring showed good correlations to acidogenesis: namely, the pH and ORP to the acidification, to butyric (and lactic) acid concentration and to the acid yield. The ORP (measured at the inlet) showed the most stable correlation to acidogenesis under dynamic operation, while the conductivity (at the outlet) correlated to the acid concentration in dependence on the feedstock. Multiple measurement spots as used in this study allow to gain more information about acidogenic fermentation than a single spot, simplifying process control and automation attempts with recalcitrant feedstock.Graphical

One-armed bandit problem and the mirror descent algorithm

We consider the application of the mirror descent algorithm (MDA) to the one-armed bandit problem in the minimax statement as applied to data processing. This problem is also known as the game with nature, where the player's payoff function is the mathematical expectation of the total income. The player must determine the most effective method of the two available and provide that it is predominantly used. In this case, the a priori efficiency of one of the methods is known. This article proposes a modification of the MDA that allows to improve the efficiency of control through the use of additional a priori information. The proposed strategy retains the characteristic property of strategies for one-armed bandits - if a known action is applied once, it will be applied until the end of the control. Modifications for the algorithm for one-by-one processing and for its batch version are considered. Batch processing is interesting in that the total processing time is determined by the number of batches and not the original amount of data, if it is possible to provide parallel processing of data in batches. For the proposed algorithms, using the Monte-Carlo simulation, the optimal values of the tunable parameters were calculated and the minimax risk estimates were obtained.

Online early work scheduling on parallel machines

We consider non-preemptive online parallel-machine scheduling with a common due date to maximize the total early work of all the jobs, i.e., the total processing time of the jobs (or parts) completed before the common due date. For the general case of m machines, we provide a parameter lower bound with respect to m. For the online algorithm, we first show that the tight competitive ratio of the classical list scheduling (LS) algorithm is 43. We then improve the upper bound on the competitive ratio for the previous algorithm, EFFm, to 1.2956. Additionally, we present a formula to compute the upper bound on the competitive ratio for any given m. For the case of three machines, we improve the lower bound to 1.1878 and propose an improved online algorithm with a tight competitive ratio of 1.2483.

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

PurposeThis study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.Design/methodology/approachIn this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.FindingsThe experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.Originality/valueIn this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

An NFC application for the process mapping automation for SMEs

In the past decade, the focus on Process Reengineering (PR) and innovation management has driven enterprises to explore how new technologies can reshape their business models and redefine their operational processes. Business Process Management, the visual display of physical process and IT flow, provide a structured approach to understanding, analyzing, and improving business processes, leading to increased efficiency, productivity, and customer satisfaction. While small and medium-sized enterprises (SMEs) play a vital role in manufacturing economies, their limited technical and financial resources often hinder substantial investments in research and development (R&D). This research aims to address this challenge by providing SMEs with an affordable and user-friendly solution for automating business process mapping, calculating total process times, and determining phase durations without the need for external expertise. Through the utilization of Near Field Communication (NFC) technology, a comprehensive toolkit consisting of IoT devices with NFC tags has been developed. This toolkit can be seamlessly integrated into the local area network of the company. Rigorous testing in a laboratory environment has demonstrated the solution effectiveness and its seamless integration into the production environment of SMEs. By empowering SMEs with this cost-effective solution, they can gain valuable insights of their processes. Furthermore, by facilitating the easy and rapid development of their own business process mapping, SMEs have the potential to pave the way for effective and efficient reengineering. This research bridges the gap between limited resources and the growing demand for technological and organizational innovation, as well as cost savings in SMEs. It offers a practical and accessible approach to enhancing productivity and competitiveness in the ever-evolving business landscape.

The influence of current results in a event­oriented data collection system

The article discusses synchronous and asynchronous procedures in the implementation of the microservices management algorithm in an adaptive system for processing large data flows when collecting information on the main event-oriented approach in the implementation of the architecture of a software system that processes information in real-time. This approach is important when processing large volumes of data from heterogeneous information sources, especially when the task is to minimize the total processing time of large data streams and reduce the total number of calls to data sources. The proposed approach makes it possible, based on its adaptability, to manage the selection of the composition and the number of calls of microservices to the sources, by the events that occur during the collection of information. Thus, it is possible to determine the choice of sources based on the assessment of the effectiveness of obtaining relevant information from them. This is especially important when processing large data flows from heterogeneous information sources when the task is to minimize the total time of collection and processing of large data flows. In turn, this poses the task of minimizing the number of requests to information sources to obtain a sufficient number of data units that are relevant to the search query. The creation of effective big data processing systems requires constant development of approaches to the architecture of building software applications. The event-oriented microservice architecture of the system makes it possible to adapt the operation of the system to the loads on individual microservices and the efficiency of their work by forming and responding to relevant events based on the analysis of relevant events that occur during the collection and initial processing of the received data. Depending on the specific task, it is possible to use both a synchronous and an asynchronous microservice management algorithm. The article provides an analysis of the effectiveness of obtaining relevant data depending on the degree of consideration in the evaluation of results and the formation of events, both the depth of the previous history of the results of requests and the size of their quantitative impact. The use of event-oriented microservice architecture can be especially effective when developing various information and analytical systems that need, according to user requests, to access various sources of information, analyze their data for relevance according to the request, and process large data streams.

A plant disease classification using one-shot learning technique with field images

Early diagnosis of plant diseases is crucial for preventing plagues and mitigating their effects on crops. The most precise automatic methods for identifying plant diseases using images of plant fields are powered by deep learning. Big image datasets should always be gathered and annotated for these methods to work, which is often not technically or financially feasible. This paper offers one-shot learning (OSL) techniques for plant disease classification with limited datasets utilizing Siamese Neural Network (SNN). There are five different crop kinds in the dataset: grape, wheat, cotton, cucumber, and corn. Five sets of images showing both healthy and diseased crops are used to represent each of the new crops. The dataset's includes 25 classes with 875 leaf images. Data augmentation techniques are used to enhance the size and dimension of the plant leaf disease image dataset. To provide effective segmentation, this paper provides a unique method for region-based image segmentation that divides an image into its most prominent regions. It also addresses issues with earlier region-based segmentation methods. SVM-based classifiers have better generalization properties as their efficiency does not depend on the number of features. Such merit is beneficial in primary diagnostics decisions to check if the input image is included in the database or not to reduce the consumed time. OSL was applied and compared to standard fine-tuning transfer learning utilizing Siamese networks and triplet loss. Siamese provides superior classification accuracy and localization accuracy with minimal errors than other approaches. The proposed approach has a total processing time of 5 ms, which makes it appropriate for real-time applications. In terms of specificity, sensitivity, precision, accuracy, MCC, and F-measure, the proposed approach beats all current machine learning algorithms for small training sets.

Toward Efficiency and Accuracy: Implementation of a Semiautomated Data Capture and Processing Model for the Construction of a Hospital-based Tumor Registry in Chile

Introduction: The innovative implementation of a Hospital-based cancer registry (HBCR) at the Arturo López Pérez Oncology Institute (FALP), showcasing the transition from a manual data extraction model to a semi-automation of the process. The purpose of this publication is to compare both methodologies by assessing their efficiency and accuracy. Methods: The analysis was conducted by comparing the complete dataset of the FALP HBCR from 2017 to 2021. The efficiency variable is analyzed, taking into account the total execution time of the registration process, and the precision variable was measured through the internal data consistency method using the IARCcrg Tools Software. Results: In terms of efficiency, the analysis reveals that in 2017, employing a manual approach without automation, it was necessary to analyze 13,061 cases over 144 weeks with an average of 4 registrars to achieve a total of 3,211 cases fully registered. In contrast, over the subsequent 4 years (2018 to 2021), with varying degrees of automation, 65,088 cases were analyzed within 115 weeks, employing an average of 8 registrars, resulting in 13,537 fully registered. This method demonstrated to be 3 times more efficient. Regarding precision, the manual approach exhibited a 5% error rate in registered cases, whereas the automated approach showed a 0.6% error rate during the 2018-2021 period. Conclusion: The obtained results highlight the significant impact of semi-automating the tumor registration process through the utilization of tools for data capture and processing, achieving a threefold increase in efficiency and reducing errors to 0.6%.

Effect of Ultrasonic-Assisted Enzymatic Hydrolysis on Functional Properties and Antioxidant Activity of Eri Silkworm Pupa Protein Isolate

Philosamia ricini (Eri silkworm) pupa protein isolate (EPI) was utilized to prepare pupa protein hydrolysate (EPIH) through enzymatic hydrolysis. Additionally, the isolate underwent ultrasonic treatment at 20 kHz to become ultrasound pretreated EPI (EPIU), which was then enzymatically hydrolyzed to obtain ultrasound pretreated protein hydrolysate (EPIUH). The physicochemical properties of these samples were investigated, including molecular weight, solubility, foaming and emulsion properties, water- and oil-holding capacity, antioxidant activity, and color. When compared to EPI (used as the control), EPIU exhibited a high degree of hydrolysis at 20 minutes ( DH = 29.24 % ). At a total process time of 20 minutes, the degree of hydrolysis for EPIH, EPIU, and EPIUH was found to be 13%, 29%, and 41%, respectively. SDS-PAGE analysis indicated no difference in molecular weight between EPI and EPIU (11–75 kDa). However, the molecular weight profiles of EPIH and EPIUH were reduced (8–45 kDa), resulting in changes in protein functionalities. The high DH value contributed to the enhancement of antioxidant activity, solubility, emulsion capacity, emulsion stability, and foam capacity of the protein isolate at pH 7. Furthermore, the ultrasonic pretreatment of the protein hydrolysate increased the lightness of the protein powder by reducing the enzyme activity of the polyphenol oxidase (PPO). These results suggest that ultrasonic pretreatment of the protein hydrolysate could be applied to improve the properties of Eri silkworm pupa protein for use in the food and beverage industry, such as protein-rich beverages or salad dressings.

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both p < 0.05). The average total processing time for semi-automatic segmentation was significantly shorter than that for manual segmentation (35 s vs. 92 s, p < 0.001). The BCa radiomics model based on semi-automatic segmentation method had a similar diagnostic performance as that of manual segmentation, while being less time-consuming and requiring fewer manual interventions.