Longest Processing Time Research Articles

Functional performance comparison of long cane and secondary electronic travel aids for mobility enhancement

This study investigated the impact of secondary electronic travel aids (ETAs) on the mobility performance of individuals with profound visual impairment, specifically focusing on object detection and obstacle avoidance tasks. Seven experienced long-cane users were recruited for the study. The ETAs tested as secondary aids to the long cane included the MiniGuide, utilizing ultrasonic echo location, and the vOICe (Vision-to-Auditory Sensory Substitution Device), which translates visual information into auditory signals. Two software modes of the vOICe were examined: the standard contrast mode and a novel algorithm utilizing relative depth information. Notably, 30 min of familiarization was provided before the trials. Results indicated that while the use of the secondary ETAs led to increased awareness of the surrounding environment, it also resulted in longer processing times and more hesitations, compared with using the long cane alone. Participants demonstrated improved object detection and larger detection and safety ranges during obstacle avoidance. The study also introduced the concept of margin of stability (MoS) as a novel metric for evaluating gait stability while using ETAs. Both MiniGuide and vOICe users exhibited consistent and stable movements, suggesting that these additional aids did not negatively impact overall gait stability. The findings highlight the trade-offs between efficiency and effectiveness when using secondary mobility aids, with longer training periods potentially mitigating these issues. In addition, the study emphasizes the need for ongoing research to optimize the design and training programmes for new ETAs. Future studies may explore the impact of extended training periods on mobility performance. Overall, this research contributes to a deeper understanding of how ETAs can enhance the mobility of individuals with visual impairments while also highlighting some of the challenges associated with their use. Optimizing ETA design and training programmes is crucial for maximizing benefits for individuals with impaired vision.

Drone-based inspection of wind turbine blades: a comparative study of deep learning models

Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection. This paper offers a comparative study of five advanced deep learning models: Residual Networks (ResNet), Inception Networks (InceptionV3), YOLO (You Only Look Once), EfficientNet, and Vision Transformers (ViTs) applied to the DTU Drone Inspection Images of Wind Turbine dataset. The objective is to determine the most effective model for detecting defects in wind turbine blades, which is critical for ensuring the efficiency and safety of wind energy systems. The models were assessed using key performance metrics, such as accuracy, precision, recall, F1-score, and inference time. EfficientNet emerged as the top performer with the highest accuracy and balanced efficiency, while YOLO demonstrated the fastest inference time, making it ideal for real-time applications. ResNet and InceptionV3 also performed well, particularly in detecting subtle defects, whereas ViTs showed strong capabilities in capturing complex global features despite longer processing times. The findings provide valuable insights for selecting appropriate deep learning models in drone-based wind turbine inspections, contributing to more effective and reliable maintenance practices.

Effects of different types of starches on heat penetration and physicochemical characteristics in alfredo sauce

Corn starch is frequently used as a thickening agent in food products because of its stability under thermal processing conditions. However, with fluctuating raw material costs due to weather conditions, disruptions in supply chains, and geopolitical challenges, there is an increasing need to explore alternative sources of starch. This study examines the effect of retort processing on the heat penetration and physicochemical aspects of modified corn, tapioca, and potato starches in Alfredo sauce. Modified corn starch, commonly used in commercial sauces, was compared with tapioca and potato starches to evaluate their effects on viscosity, color, and processing times required to achieve a F0 = 6 for Clostridium botulinum. Using the reference starch concentration of 1.90%, modified corn starch showed a significant increase in viscosity from 3,328 cP to 13,296 cP with a processing time of 31.33 min. Modified tapioca starch, at the same concentration, increased from 3440 cP to 8176 cP over 38.38 min, indicating superior thermal stability being the longest cooking time evaluated between all samples. For modified potato starch, using the reference concentration, viscosity increased from 1,200 cP to 4,656 cP, with a processing time of 31.93 min. Adjusting the starch concentration to 3% for modified tapioca starch and 2.38% for modified potato starch increased viscosity to 14,400 cP and 16,336 cP, respectively. These adjustments resulted in a darker Alfredo sauce, with the L value decreasing from 92.27 to 83.67 due to the longer cooking time. Heat penetration findings suggest that modified potato starch can replace modified corn starch safely using the reference concentration (1.90%) without altering the process holding time but requiring further adjustments to match commercial viscosity standards, while modified tapioca starch, although requiring higher concentrations and longer processing times, was tested for retention time at 3.00% and to be a feasible alternative. This study offers valuable insights to the food industry in selecting appropriate starches to maintain the quality and safety of thermally processed Alfredo sauce.

A novel approach for perfusion process design based on a "Grey-Box" kinetic model.

Perfusion cell-culture mode has caught industrial interest in the field of biomanufacturing in recent years. Thanks to new technology, perfusion-culture processes can support higher cell densities, higher productivities and longer process times. However, due to the inherent operational complexity and high running costs, the development and design of perfusion-culture processes remain challenging. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells. Initially, four batches of bench-top reactor continuous-perfusion-culture data were used to fit the model parameters. Then, we proposed the model-based process design approach, aiming to quickly find out the "theoretically optimal" operational parameters combinations (perfusion rate and the proportion of feed medium in perfusion medium) which could achieve the target steady-state VCD while minimizing both medium cost and perfusion rate during steady state. Meanwhile, we proposed a model-based dynamic operational parameters-adjustment strategy to address the issue of cell-growth inhibition due to the high osmolality of concentrated perfusion medium. In addition, we employed a dynamic feedback control method to aid this strategy in preventing potential nutrient depletion scenarios. Finally, we test the feasibility of the model-based process design approach in both shake flask semi-perfusion culture (targeted at 5 × 107 cells/ml) and bench-top reactor continuous perfusion culture (targeted at 1.1 × 108 cells/ml). This approach significantly reduces the number of experiments needed for process design and development, thereby accelerating the advancement of perfusion-mode cell-culture processes.

Real time detection of fatigue cracks on steel structures by applying square wave induction

For years the Eddy currents principle together with inductive thermography have been applied to detect cracks or defects in metallic machine components. In case a component has a crack, by inducing Eddy currents within a coil, it is possible to cause a temperature increase in the edges of the crack. This temperature variation can be observed by using an infrared camera. Thus, the crack is registered and observed in the infrared images, which are later analyzed. However, currently used systems require an enormous amount of power and a long time for image processing. For this reason, applying this method to large steel structures such as bridges, cranes, wind power towers, pipe lines or offshore oil stations has been much more complex. To overcome these limitations, a light portable system that optimally recreates the Eddy currents principle has been developed and used in this research. This system generates a square wave AC voltage, it uses considerably less power and it is capable of detecting cracks in real time. This paper presents the simulations and experiments carried out on two different structural steel specimens to demonstrate the efficiency of the system and its potential in the field of nondestructive testing.

Dicing Process for 4H-SiC Wafers by Plasma Etching Using High-Pressure SF<sub>6</sub> Plasma with Metal Masks

Since SiC is hard and brittle, dicing by normal grinding process not only requires a long time for processing, but also reduces chip strength due to microcracks. The use of highly efficient and damage-free etching with high-pressure plasma as a chemical processing method for dicing rather than mechanical processing was investigated. The results of groove processing using a combination of a metal mask with slit-like apertures and plasma etching with high-pressure SF6 plasma showed that the processing speed decreased with decreasing slit width and increasing groove depth. The results of electrostatic field calculations suggest that this is due to a decrease in plasma intensity caused by electric field decreasing.

Hub production scheduling problem with mold availability constraints

This research delves into a scheduling challenge inherent in the wheel hub casting process, a critical stage in automotive wheel manufacturing. The process involves shaping molten metal into specific wheel hub designs using dedicated molds. Diverse customer demands necessitate a unique mold for each wheel hub design, posing a scheduling challenge due to the limited availability of these expensive and long-lasting molds. There are 2 machines available for casting. Each wheel hub design requires a specific mold. These molds are expensive, long-lasting, and need periodic maintenance. The objective function is to minimize the total time (makespan) to complete all casting jobs. Only one job requiring a specific mold can be processed at a time (due to mold limitations). Molds are unavailable during maintenance periods. The challenge lies in scheduling jobs considering both machine availability and mold constraints to minimize the overall production time. We formulate a mathematical model using mixed integer programming to precisely represent the problem and its constraints. Inspired by the Longest Processing Time (LPT) rule, we propose a heuristic named LRTPT to efficiently schedule jobs on the two machines. For large-scale problems, we employ a novel SIAIS algorithm to find near-optimal solutions effectively. We also present a dedicated branch and bound algorithm specifically tailored to solve smaller-sized instances of the problem. This algorithm leverages two lower bounds and several dominance properties to expedite the search for the optimal solution. To evaluate the effectiveness of our proposed approaches, we conduct a series of comprehensive experiments. The results demonstrate that the branch and bound algorithm significantly outperforms the widely used optimization solver CPLEX for smaller problems. Furthermore, the SIAIS algorithm proves to be more efficient than existing scheduling heuristics.

Optimal PLA+ 3D Printing Parameters through Charpy Impact Testing: A Response Surface Methodology

Additive manufacturing (AM) has revolutionized the manufacturing sector, particularly with the advent of 3D printing technology, which allows for the creation of customized, cost-effective, and waste-free products. However, concerns about the strength and reliability of 3D-printed products persist. This study focuses on the impact of three crucial variables—infill density, printing speed, and infill pattern—on the strength of PLA+ 3D-printed products. Our goal is to optimize these parameters to enhance product strength without compromising efficiency. We employed Charpy impact testing and Response Surface Methodology (RSM) to analyze the effects of these variables in combination. Charpy impact testing provides a measure of material toughness, while RSM allows for the optimization of multiple interacting factors. Our experimental design included varying the infill density from low to high values, adjusting printing speeds from 70mm/s to 100mm/s, and using different infill patterns such as cubic and others. Our results show that increasing infill density significantly boosts product strength but also requires more material and longer processing times. Notably, we found that when the infill density exceeds 50%, the printing speed can be increased to 100mm/s without a notable reduction in strength, offering a balance between durability and production efficiency. Additionally, specific infill patterns like cubic provided better strength outcomes compared to others. These findings provide valuable insights for developing stronger and more efficient 3D-printed products using PLA+ materials. By optimizing these parameters, manufacturers can produce high-strength items more efficiently, thereby advancing the capabilities and applications of 3D printing technology in various industries.

The impact of Boric Acid tubes on quantitative urinary bacterial cultures in hospitalized patients

IntroductionThe accuracy of urine culture results can be affected by pre-analytical factors such as transport delays and storage conditions. The objectives of this study were to analyze urine collection practices and assess the impact of introducing boric acid tubes for urine collection on quantitative urinary bacterial cultures of hospitalized patients in medical wards.MethodsA quasi-experimental pre-post study conducted in an acute care facility. In the pre-intervention phase (2020–2021), urine samples were transported without preservatives at room temperature. In 2022 (post-intervention), we transitioned to boric acid transport tubes, evaluating its effect on significant bacterial growth (≥ 105 CFU/ml). Bivariate and multivariate analyses identified predictors of culture positivity.ResultsThroughout the duration of the study, a total of 12,660 urine cultures were analyzed. Date and time documentation was complete for 38.3% of specimens. Culture positivity was higher with longer processing times: positivity was 21.3% (220/1034) when specimens were processed within 4 h, 28.4% (955/3364) when processed in 4–24 h, and 32.9% (137/417) when processed after 24 h (p < 0.0001). For 4-24-hour processing, positivity decreased from 30.4% (704/2317) pre-intervention to 24.0% (251/1047) post-intervention (p < 0.001), with no significant changes in < 4 or ≥ 24-hour specimens. Stratified analysis by processing time revealed that the intervention was associated with reduced positivity only in cultures processed within 4–24 h (OR 0.80, 95% CI 0.67–0.94; p = 0.008).ConclusionThe introduction of boric acid transport tubes predominantly influenced cultures transported within a 4–24-hour window. This presents an opportunity to improve urine tract infection diagnostic practices in healthcare settings.

IMPROVING A CIRCULAR REVERSE LOGISTICS TO HANDLE PRODUCT RETURN: INSIGHTS FROM A GLOBAL ELECTRONICS SERVICES PROVIDER

In the retail industry, consumers often return goods for various reasons, including damage, flaws, or plain dissatisfaction. The storing of returned goods can result in insufficient arrangement of the existing storage space and capacity. Such issue can cause problems like longer processing times, higher operating costs, and reduced customer satisfaction, hence necessitating prompt response. Therefore, this case study was conducted in a global electronics services provider to examine how circular reverse logistics can enhance the handling of product returns as well as developing a smart product return strategy. It is believed that creating a plan for product returns and integrating internal process improvements with external collaboration are highly important. The results showed that the organisation can establish a circular reverse logistic process to lower the cost and amount of product returns, enhance consumer service, and boost profitability. Such findings can benefit organisations wishing to establish circular reverse logistics procedures to handle product returns.

Assessing the effectiveness of static heuristics for scheduling lumber orders in the sawmilling production process

Although optimization models can be used to plan the production process, in most cases static heuristics, such as earliest due date (E), longest processing time (L), and shortest processing time (S), are used because of their simplicity. This study aims to analyze the production cost of the static heuristics and to determine how this cost relates to the size of the production orders in the sawmilling industry. We set a planning problem with different orders and due dates and solved it using two cost-minimization models to compare their solutions. The first was a planning model (PL) where orders were split up into products demand by period, and the second, a planning scheduling (PS) where the sequence of processing orders based on static heuristics was assumed as known. In the latter, the minimum production cost for each static heuristic was found. In both models, the same resource constraints were assumed. The costs showed no significant changes based on order sizes. However, 0,5 % of orders were delayed using PS-E, and 17 % of orders were delayed using PL. PL was an efficient solution method when changing the orders´ size and when looking for the best static heuristic to process the orders. However, PS-E showed the ability to reduce the backlog close to zero while the PL backlog ratio was 17 %. No penalties were applied to backlogs due to their subjective nature; however, when shortages occurred, the demand was unmet or backlogged with substantial costs. Thus, in case the proposed method is adopted using a conservative backlog cost, a sawmill producing under the cut-to-order environment that produces 300000 m3 /year would reduce backlogged orders by 51000 m3. If the holding lumber cost is 2 $/m3, annual savings would be $408000.

Influence of selected contaminations and melt filtration on the properties of ABS and ABS recyclate

AbstractPlastic recyclates can be contaminated with a variety of impurities due to their previous life, which cannot always be perfectly separated in recycling pretreatment steps. Hence melt filtration is an important step in reprocessing plastic recyclates to achieve material of sufficient purity, but it also leads to more stress on the material due to higher pressures and longer processing times. In our study, we investigated the influence of typical particulate impurities as may occur in recyclate on the mechanical and rheological properties of ABS. Even low amounts of contaminations lowered the key properties. Melt filtration of post‐consumer ABS recyclate with different mesh size melt screens showed little improvement of mechanical properties, but longer idle times and loss of material. Damage to the material caused by finer filtration could be shown by thermal measurements.Highlights Determination of properties of ABS contaminated with 0.5–2.0 wt.% of selected impurities (wood and rubber particles). Melt filtration of post‐consumer ABS recyclate with different melt screens. Investigation of process and properties of filtered ABS recyclate.

Synthesis of 58S bioactive glass based on a novel methodology employing microwave technology

The sol-gel method for bioactive glass synthesis offers advantages such as precise control over the chemistry, a straightforward way of modifying the composition and lower processing temperatures. However, longer processing times are required, increasing energy consumption.The present work successfully produced 58S bioactive glass employing microwave technology and a two steps synthesis route, saving significant energy and time (76 % and 99 % respectively) compared to conventional methods such as sol-gel and hydrothermal method, directly impacting on cost savings and competitiveness.Microwave pressure played a key role: higher pressure led to lower content of residual nitrates and could reduce final processing temperature. This pressure dependence allows for precise control of the glass properties. The resulting glass closely resembled the characteristics and properties of the same glass produced by conventional methods, demonstrating the effectiveness of microwave technology. Moreover, the glass reacted positively with Simulated Body Fluid, indicating good bioactivity.

Influence of Anodic Aluminum Oxide Nanostructures on Resistive Humidity Sensing

Humidity nanosensors play a vital role in modern technology industries, including weather forecasts, industrial manufacturing, agriculture, food and chemistry storage. In recent years, research on humidity sensors has focused on different materials such as ceramics, polymers, carbon-based materials, semiconductors, MXenes or triboelectric nanogenerators, each with their own advantages and disadvantages. Among them, anodic aluminum oxide (AAO) is a well-known ceramic humidity sensor material with a long history of research and development. AAO humidity sensors offer advantages such as simple manufacturing processes, controllable nanostructures, high thermal stability and biocompatibility. However, traditional AAO fabrication still has disadvantages like high costs and longer process times. Hence, finding a low-cost and efficient method to fabricate AAO for controlling different nanostructures to meet the requirements is consistently a major research topic. From our previous studies, we have studied the relationship between the AAO capacitive humidity sensor and its nanostructures. In this paper, we explore the effect of an AAO nanoporous structure controlled by an anodization voltage of 20–40 V on the resistive-type humidity sensor performance instead of a capacitive one. We efficiently apply one-step hybrid pulse anodization at 25 °C to significantly reduce the processing time compared to the traditional two-step process under 0–10 °C. The AAO nanostructures and their impact on sensor measurements of humidity at 20–80 RH% will be discussed in detail. An electrical resistive sensing mechanism is established for further performance improvement by controlling anodization voltage.

Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

SummaryMost of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In this article, we propose a Processing‐in‐Memory (PIM) based localization approach for multilateration using a memristor crossbar array for performing faster matrix multiplication. The experiments were conducted on real‐time data collected through ultra‐wideband (UWB) chip. The simulation results demonstrate that the proposed model is around 50% faster than the fastest known localization algorithm considered for evaluation and exhibits an average of 55% improvement in localization accuracy.

Online processing and vocabulary learning in massed versus spaced repeated reading

The present exploratory study analyzed the effect of two different implementations of repeated reading (massed vs. spaced) on the online processing of the text and unknown vocabulary, as well as on incidental vocabulary learning. The relationship between processing and learning of target vocabulary in massed versus spaced repeated reading was also examined. A group of Spanish undergraduate students read the same text in English three times under either a massed (three times consecutively) or spaced (once a week) schedule, while their eye movements were recorded. Knowledge of a set of target words from the text was assessed in immediate and delayed multiple-choice, meaning-recognition tests. The results of this study suggest that repeated reading led to a decrease in total reading time and number of fixations in the massed group but not in the spaced group. However, there were no significant differences between the two conditions in the processing of target words or immediate vocabulary gains. Finally, it was observed that longer processing time on the target words was related to larger immediate vocabulary gains, but only in the spaced group.

Enhancing speaker verification accuracy with deep ensemble learning and inclusion of multifaceted demographic factors

&lt;span lang="EN-US"&gt;Effective speaker identification is essential for achieving robust speaker recognition in real-world applications such as mobile devices, security, and entertainment while ensuring high accuracy. However, deep learning models trained on large datasets with diverse demographic and environmental factors may lead to increased misclassification and longer processing times. This study proposes incorporating ethnicity and gender information as critical parameters in a deep learning model to enhance accuracy. Two convolutional neural network (CNN) models classify gender and ethnicity, followed by a Siamese deep learning model trained with critical parameters and additional features for speaker verification. The proposed model was tested using the VoxCeleb 2 database, which includes over one million utterances from 6,112 celebrities. In an evaluation after 500 epochs, equal error rate (EER) and minimum decision cost function (minDCF) showed notable results, scoring 1.68 and 0.10, respectively. The proposed model outperforms existing deep learning models, demonstrating improved performance in terms of reduced misclassification errors and faster processing times.&lt;/span&gt;

Real-time production scheduling using a deep reinforcement learning-based multi-agent approach

In the real-time scheduling (RTS) research field, it has been shown that employing multiple dispatching rules (MDRs) for the components in a flexible manufacturing system will improve production performance much more than a single dispatching rule (SDR). To fulfill the goal of Industry 4.0 in production control and improve production performance, this study deploys a deep reinforcement learning-based multi-agent (DRLBMA) approach for real-time scheduling. The proposed approach uses the MDRs strategy by integrating two main methodologies: an off-line learning module and a Deep Q-learning-based multi-agent module. The proposed method employs a two-level self-organizing map (SOM) to determine the system’s states. The proposed methodology determines the best MDRs decision. The approach is applied to a case study of a smart manufacturing system. The results of the proposed method are compared to different scheduling strategies, such as reinforcement learning (RL)-based real-time scheduling, two-level self-organized map (SOM), and continuous rescheduling using a single dispatching rule, such as earliest due date (EDD), shortest processing time (SPT), and longest processing time (LPT). The findings reveal that, in terms of the total weighted tardiness, throughput, and mean cycle time performance criteria, the proposed DRLBMA-based real-time scheduling is more efficient than these scheduling strategies.

Stochastic inactivation evaluation of foodborne pathogens during ohmic heating of poultry meat

Abstract The objective of this study is to stochastically assess the inactivation probabilities of four common foodborne pathogens (Listeria, Salmonella, Escherichia coli, and Campylobacter) in chicken meat during ohmic heating (OH) in a salt solution. A mechanistic model was used to accomplish this, coupling heat transfer, laminar fluid flow, and the electric field, and solved numerically using COMSOL Multiphysics® v5.6. The 3D model represented 1000 particles randomly placed on the meat’s surface to determine the 7-log reduction of bacterial load probability. These particles are virtual representatives of bacterial colonies in the model. The influence of uncertain input parameters (specific heat capacity and electrical conductivity) and OH conditions (salt concentration of the heating medium, applied voltage, and heating time) was explained using logistic regression. The same analysis was repeated for the slowest heating point of chicken meat, as well. According to the findings, cold spots are observed at the corners of the meat piece during OH, requiring additional attention to the meat surface temperature to prevent under-processing. Sensitivity analysis revealed that the applied voltage and brine concentration are the main factors affecting the inactivation probabilities of pathogenic bacterial cells on the chicken meat surface. Salmonella and Listeria may require higher electrical conductivity of chicken meat and longer processing times. The developed model enables predicting inactivation probabilities of microorganisms that can be found on the outer surface by measuring the core temperature of the meat. However, especially for bacteria with higher heat resistance, it is better to consider the cold spot temperature found in the corners of the food material during OH.

An power and bound-aware optimised scheduler for virtualized cloud computing

Cloud computing has become a crucial paradigm for large-scale data-intensive applications, but it also brings challenges like energy consumption, execution time, heat, and operational costs. Improving workflow scheduling in cloud environments can address these issues and optimize resource utilization, leading to significant ecological and financial benefits. As data centres and networks continue to expand globally, efficient scheduling becomes even more critical for achieving better performance and sustainability in cloud computing. Schedulers mindful of energy and deadlines will assign resources to jobs in a way that consumes the least energy while upholding the task’s quality standards. Because this scheduling involves a Non-deterministic Polynomial (NP)-hard problem, the schedulers are able to minimize complexity by utilizing metaheuristic techniques. This work has developed methods like Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for optimizing the scheduler. Local search and exploration are respectably supported by heuristic algorithms. The algorithm’s exploration and exploitation features must also be balanced. The primary objective is to optimize computation-intensive workflows in a way that minimizes both energy consumption and execution time while maximizing throughput. This optimization should be achieved without compromising the Quality of Service (QoS) guarantee provided to users. The focus is on striking a balance between energy efficiency and performance to enhance the overall efficiency and cost-effectiveness of cloud computing environments. According to the simulation findings, the suggested ABC has a higher guarantee ratio for 5000 jobs when compared to the GA, PSO, GA with the longest processing time, and GA with the lowest processing time, by 7.14 percent, 4.7 percent, 3.5 percent, and 2.3 percent, respectively. It is observed that the proposed ABC possesses qualities like high flexibility, great robustness, and quick convergence leading to good performance.