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A cloud load balancer should be proficient to modify it's approach to handle the various task kinds and the dynamic environment. In order to prevent situations where computing resources are excess or underutilized, an efficient task scheduling system is always necessary for optimum or efficient utilization of resources in cloud computing. Task Scheduling can be thought of as an optimization problem. As task scheduling in the cloud is an NP-Complete problem, the best solution cannot be found using gradient-based methods that look for optimal solutions to NP-Complete problems in a reasonable amount of time. Therefore, the task scheduling problem should be solved using evolutionary and meta-heuristic techniques. This study proposes a novel approach to task scheduling using the Cuckoo Optimization algorithm. With this approach, the load is effectively distributed among the virtual machines that are available, all the while keeping the total response time and average task processing time(PT) low. The comparative simulation results show that the proposed strategy performs better than state-of-the-art techniques such as Particle Swarm optimization, Ant Colony optimization, Genetic Algorithm and Stochastic Hill Climbing. [ABSTRACT FROM AUTHOR]

ABSTRACT Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design. [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Till date it is a great challenge to synthesize CdS photocatalyst with a high stability. In this study, a stable CdS photocatalyst was successfully synthesized by a simple, cost‐effective hydrothermal route via cadmium acetate and thiourea as Cd and S precursors, respectively. The effect of different processing times (8 h and 24 h) and molar ratios on the photocatalytic activity and stability were mainly investigated. The samples were characterized in detail by XRD, FT‐IR, XPS, UV‐vis DRS, Photolummiscence spectroscopy and Surface photovoltage. The photocatalytic activity of the as‐obtained nanostructures was examined for methylene blue (MB) dye degradation under visible light irradiation (λ≥420 nm). After 70 min of irradiation, nearly 82 % of MB was degraded by CdS nanostructures, display an improved photocatalytic activity for MB degradation with an apparent rate constant (k) of 0.019 min−1. Additionally, CdS nanostructures sustained a good photostability after five consecutive cycles. The higher photocatalytic activity of CdS nanostructure is attributed to an efficient separation of photogenerated electron‐hole pairs stimulated by its optimal molar ratio, optimal temperature and surface morphology. Our synthesized stable nanostructures have potential for dye contaminated waste‐water treatment. [ABSTRACT FROM AUTHOR]

Objectives: In cell-free and concentrated ascites reinfusion therapy (CART), the protein recovery rate decreases when the filtration membrane gets clogged. Employing a device with a filtration membrane washing feature prevents clogging, but it leads to the loss of ascites within the filter, resulting in reduced protein recovery. This study employed a device with a membrane washing function to investigate the relationship between protein recovery rate and the quantity of washing solution used, depending on the selected washing method. Methods: We analyzed cases of CART conducted at Fujita Health University Hospital between May 2021 and November 2022. The cases were divided and compared between two groups: one using flush and rinse as the washing method (flush+rinse group) and another using only flushing (flush group). Results: We identified nine cases and 16 sessions. In the flush+rinse group, the median amount of washing solution used per membrane washing was 259 mL per cycle, whereas it was 54 mL per cycle in the flush group. This difference was statistically significant (p

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scheduling is one of the many skills required for advancement in today's modern industry. The flow-shop scheduling problem is a well-known combinatorial optimization challenge. Scheduling issues for flow shops are NP-hard and challenging. The present research investigates a two-stage flow shop scheduling problem with decoupled processing and setup times, where a correlation exists between probabilities, job processing times, and setup times. This study proposes a novel heuristic algorithm that optimally sequences jobs to minimize the makespan and eliminates machine idle time, thereby reducing machine rental costs. The proposed algorithm's efficacy is demonstrated through several computational examples implemented in MATLAB 2021a. The results are compared with the existing approaches such as those by Johnson, Palmer, NEH, and Nailwal to highlight the proposed algorithm's superior performance. [ABSTRACT FROM AUTHOR]

One of the common issues in production scheduling is the flow shop sequencing issue. A specific type of manufacturing process follows a predetermined linear structure in a flow shop. To solve a flow shop problem in a static workshop, the goal of this study is to determine the shortest possible total processing time (make-span) for a sequence of 'n' tasks on 'm' machines. The suggested method is based on how each work slopes as it moves from the first to the last machine. This method is contrasted with four popular heuristics (Palmer, CDS, Dannenbring, NEH). The findings of the study for 'n' x 'm' flow shop sequencing problems show that the proposed method is effective and shows a promising amount of progress when compared to the other four heuristic strategies. [ABSTRACT FROM AUTHOR]

Background. Red blood cells are one of the components of blood. Blood is an important fluid in the human body. Knowing the blood groups is essential in blood transfusion operations, which depend on fixed conditions to avoid fatal errors. The method that is used to determine the blood groups is a traditional method that relies on medical laboratory technicians, as it is subject to human errors. Objective. This paper aims to design and implement a prototype to detect and classify blood groups to avoid human error in blood group detection. The proposed system employs image processing and machine learning algorithms for blood group detection and classification. Methods. The system consists of three stages. First, samples were collected from volunteers. Second, images of the samples were captured using a camera. Third, the images were analyzed using two methods: image proces­sing via MATLAB and machine learning algorithms via Orange, for blood group detection and classification. Results. The accuracy in processing images using the MATLAB program reached 100%, with processing time ranged from 1.5 to 1.6 seconds. Additionally, using machine learning with neural networks in the Orange program, the accuracy was 99.7%, with training times of 13.7 seconds and testing times of 1.2 seconds. Neural networks outperformed other models, as shown in the experimental results. The study concluded that automated blood type detection using image processing and machine learning methods is effective and feasible compared to manual methods. The proposed system outperformed previous studies in terms of accuracy, processing time, training time, and testing time using both methods. Conclusions. This study underscores the urgent need for precise blood type determination before emergency blood transfusions, which currently relies on manual inspection and is susceptible to human errors. These errors have the potential to endanger lives during blood transfusions. The main goal of the research was to develop an approach that combines image processing and machine learning to accurately classify blood groups.

Artikkelen redegjør for hvordan synet på allmennhetens innsynsrett og behovet for å redusere forvaltningens arbeidsbyrde har endret seg over tid. I dag kommer spenningen mellom innsynsretten og forvaltningens arbeidsbyrde opp i form av spørsmålet om hvilke krav som stilles til forvaltningens saksbehandlingstid for innsynskrav. I Norge er reglene som regulerer kravene til forvaltningens saksbehandlingstid, skjønnsmessig utformet. Dette har gitt opphav til en rekke uttalelser fra Sivilombudet. Artikkelforfatteren tar til orde for at Norge bør innføre regler om maksimal saksbehandlingstid for innsynskrav etter mønster av Europaparlaments- og rådsdirektiv 2019/1024 artikkel 4 nr. 2.

Exercise therapy is a prominent method for recovering from musculoskeletal disorders like Work-related Musculoskeletal Disorder (WMSD), Classroom-related Musculoskeletal Disorder (CMSD). This method requires the monitoring of a physiotherapist to advise the performer to do the exercise poses correctly. This process is costly and requires the presence of a physiotherapist. In this work, we plan to develop software for the real-time detection of exercise poses with the help of AI. This work replaces the physiotherapist and provides good real-time feedback on the users' wrist extension and flexion exercise posture. Artificial Intelligence (AI) is the most popular and widely used technique for real-time image analysis. In real-time the user takes at least 1 s to perform the exercise pose. Our model classifies and provides feedback within 0.79 s. The frame processing rate of our model is ~ 21 frames per second. Experimentation of this framework was done through CNN DenseNet with 2-level architecture. We have experimented with three different ways of outcomes. Our mode achieved 100% accuracy with 106 samples of data and 99.86% accuracy with 2160 samples. Finally, with 144 cross-person sample datasets achieved 83.33% accuracy. This technique performs well for evaluating wrist extension exercise poses for recovery and gives participants immediate feedback on whether their wrist extension is correct or incorrect. [ABSTRACT FROM AUTHOR]

Data mining is one of the emerging technologies used in many applications such as Market analysis and Machine learning. Temporal data mining is used to get a clear knowledge about current trend and to predict the upcoming future. The rudimentary challenge in introducing a data mining procedure is, processing time and memory consumption are highly increasing while trying to improve the accuracy, precision or recall. As well as, while trying to reduce the processing time or memory consumption, accuracy, precision and recall values are reducing significantly. So, for improving the performance of the system and to preserve the memory and processing time, Three-Dimensional Fuzzy FP-Tree (TDFFPT) is proposed for Temporal data mining. Three functional modules namely, Three-Dimensional Temporal data FP-Tree (TTDFPT), Fuzzy Logic based Temporal Data Tree Analyzer (FTDTA) and Temporal Data Frequent Itemset Miner (TDFIM) are integrated in the proposed method. This algorithm scans the database and generates frequent patterns as per the business need. Every time a client purchases a new item, it gets stored in the recent database layer instead of rescanning the entire records which are placed in the old layer. The results obtained shows that the performance of the proposed model is more efficient than that of the existing algorithm in terms of overall accuracy, processing time, reduction in the memory utilization, and the number of databases scans. In addition, the proposed model also provides improved decision making and accurate pattern prediction in the time series data. [ABSTRACT FROM AUTHOR]

The paper presents the influence of the waiting time of jobs in a 2 machine k-job Flow Shop Scheduling (FSS) problem. The main intention of the study is to find a sequence of jobs that delivers the least sum of the time of waiting for jobs. A heuristic approach has been adopted to achieve the desired objective. The experiments are conducted for more than 2000 problems of various sizes for the problems with special structures and problems with random times of processing. The weighted mean absolute error (WMAE) for the average of the sum of the waiting times of jobs is computed for both kind of problems after comparing with the optimal solutions. WMAE has been obtained less than 0.0075 for problems with special structures and less than 0.087 for problems with random times of processing. The WMAE is also reducing significantly with the increase in job size. The results demonstrate that the presented step-by-step procedure of the heuristic delivers significantly close to optimal solutions. [ABSTRACT FROM AUTHOR]

Microwave sterilization is a novel potential sterilization technology to improve food quality. An industrial microwave sterilization system was used to sterilize imitation crab meat under thermal processing intensity F0 = 1, 2, 3. The characteristics of the microwave process, such as heating rate, processing time, and C100, were calculated. In addition, the quality of processed imitation crab meat was investigated. Compared with the conventional retort method, microwave sterilization significantly shortened the processing time of imitation crab meat by 63.71% to 72.45%. Under the same thermal processing intensity, microwave sterilization has demonstrated better results than retort sterilization in terms of water-holding capacity, color, and texture. Furthermore, microwave-treated imitation crab meat ingredients had a greater capacity to bind water molecules and obtained a more appropriate secondary protein structure. In addition, microwave technology can better preserve the unsaturated fatty acids (UFA) of imitation crab meat, which are 9.14%, 1.19%, and 0.32% higher than the traditional method at F0 = 1, 2, 3. The results would provide useful data for the subsequent research and development of ready-to-eat surimi products. [ABSTRACT FROM AUTHOR]

Background and Objectives: Fine-grained soils often present challenges such as low load-bearing capacity, limited shear resistance, compaction difficulties, and settlement issues. Consequently, the stabilization of finegrained soils using additives has consistently garnered attention among road construction engineers. Moreover, the utilization of waste materials for soil stabilization has gained popularity due to its economic viability. Within the scope of this study, granite stone powder, a byproduct generated from stone-cutting workshops, was employed to stabilize fine-grained soils. Materials and Methods: Clay samples categorized as low plasticity (CL) were mixed with varying proportions (0%, 3%, 5%, 10%, 15%, and 20%) of granite stone powder in comparison to the dry soil. The processing time for soil and stone was considered at 7, 28, and 90 days. Subsequently, geotechnical properties, including grain size, Atterberg limits, and either compressive strength or CBR, were examined. Granulation characteristics were assessed using the sieve method, Atterberg limits were determined using the Cassagrande and paste wick method, and compressive strength was evaluated using the California loading test and ASTM standards. These investigations were carried out in the soil mechanics laboratory of Gorgan University of Agricultural Sciences and Natural Resources. Results: The results revealed that the addition of stone powder led to an increase in the contribution of the coarse soil fraction. Specifically, when 20% of stone powder was added and after 28 days, more than 60% of the weight in each sample consisted of particles with a diameter greater than 0.1 mm. In terms of plastic limit, plasticity index, and load-bearing capacity, there was no significant difference between the processing times of 28 days and 90 days. However, these values differed significantly from those recorded after 7 days of processing. The addition of rock powder to the soil samples resulted in a reduction in the liquid limit and plasticity index. With the inclusion of 20% stone powder, the soil samples transitioned from a plastic state (plasticity index = 25) to a moderately plastic state (plasticity index = 8). Furthermore, the highest value for CBR (19%), indicating soil load-bearing capacity, was achieved when 20% of rock powder was added. Conclusion: The research findings demonstrated that the inclusion of travertine stone powder at a weight percentage of 20% and a processing time of 28 days had the most significant positive impact on the mechanical strength of low-plasticity clay soil. From a practical standpoint, this optimal amount can be added to forest road soil, mixed thoroughly using disking machinery to a depth of 20 cm, and subsequently compacted using a roller. By following this procedure, waste stone powder can effectively stabilize and repair areas prone to instability and settlement on forest roads. [ABSTRACT FROM AUTHOR]

Scheduling is one of the many skills required for advancement in today’s modern industry. The flow-shop scheduling problem is a well-known combinatorial optimization challenge. Scheduling issues for flow shops are NP-hard and challenging. The present research investigates a two-stage flow shop scheduling problem with decoupled processing and setup times, where a correlation exists between probabilities, job processing times, and setup times. This study proposes a novel heuristic algorithm that optimally sequences jobs to minimize the makespan and eliminates machine idle time, thereby reducing machine rental costs. The proposed algorithm’s efficacy is demonstrated through several computational examples implemented in MATLAB 2021a. The results are compared with the existing approaches such as those by Johnson, Palmer, NEH, and Nailwal to highlight the proposed algorithm’s superior performance.

Objective To analyze the application effect of intelligent information system of transfusion reactions(referred to as the system) in the supervision and management of transfusion reactions. Methods A retrospective analysis was conducted on the clinical data of a total of 67 914 blood transfusion patients in our hospital from January 2018 to August 2023. The patients were divided into the control group (n =36 367, with conventional transfusion management) and the observation group (n =31 547, with management by the system).The incidence of adverse reactions, the supervision and management scores, reporting time and processing time of adverse reactions, and patient satisfaction with blood transfusion in the two groups was observed. Results The incidence of transfusion reactions in the observation group and the control group was 0.23% (72/31 547) and 0.24% (86/36 367), with no statistically significant difference (P>0.05). The quality scores of the reporting, registration, processing and supervision, and the total scores of the observation group were higher than those of the control group:(21.46±2.76) vs (17.38±2.74) points, (21.65±2.80) vs (16.98±3.20) points, (22.30±2.42) vs (18.80±3.15) points, (22.85±2.10) vs (18.55 ± 3.06) points, and (90.38±5.96) vs (75.26 ± 6.10) points, with statistically significant difference(P

AbstractThailand is among high-ranking coffee-exporting countries in the world. This study investigated, analyzed and proposed ideas for improved production of coffee to meet the demands from the local and international market. Using data collected from site visits, observation and interviews, value stream mapping analysis was used to identify the activities in the coffee production process. Automated machine deployment was used to improve the performance of coffee production. From the findings, it was shown that the total cycle time could be reduced by more than 82 %, the profit of sorted cherry coffee could be increased by 80%, and the processing time of green bean coffee could be reduced by almost 97%. An automated color sorting machine was deployed to improve the production process. With integrated lean and automated mechanization techniques, farmers can gain several benefits and become more responsive to future customer demands.

This paper addresses the issue of efficient resource allocation in a Mobile Edge Computing (MEC) system, taking into account the trade-off between energy consumption and operation latency. The increasing deployment of connected devices and data-intensive services in the Internet of Things (IoT) poses significant challenges in terms of managing computational resources. In this study, we propose a MEC system model that considers energy constraints and the need to minimize latency to ensure optimal performance. We formulate the resource allocation problem in terms of a trade-off between energy consumption and latency, and explore solutions based on heuristic task offloading techniques. Our experiments demonstrate that our approach achieves improved latency performance while reducing energy consumption. We also evaluate the impact of various parameters, such as workload and resource availability, on the energy-latency trade-off. [ABSTRACT FROM AUTHOR]

Over the past years highly effective phytopreparations with antibacterial activity have been created in Russia. Macleaya x kevensis Turill, which is a hybrid between heart-shaped Macleaya (Macleaya cordata) and small-fruited Macleaya (Macleaya microcarpa), can serve as a raw material for their production. To provide the pharmaceutical industry with this type of raw material in the conditions of the North Caucasian branch of VILAR, plantations of this crop have been created and research is being carried out to create a zonal technology for its cultivation. The basis of cultivatiov is the exogenous regulation of the processes of growth, development and bioproductivity of the plant organism through the use of organomineral and microfertilizers. In 2019–2022 experiments on testing humic fertilizers (Potassium Humate, Lignohumate) and their complexes with silicon-containing microfertilizer Siliplant were carried out on the Macleaya plantation. Treatments with these preparations were carried out: in the 1st year of the growing season – at the beginning of regrowth and after 30 days, in the 2nd and 3rd years of the growing season – at the beginning of the growing season and after the first harvesting of raw materials for growing plants. As a result of the tests, an increase in the growth and development of Macleaya plants was observed. The activation of growth processes contributed to an increase in crop yield in the first year with a single cut by 0.18–0.19 t/ha (17–18%), in the 2 and 3 years of the growing season by the sum of two cuts by 1.50–1.76 t/ha (20–24%) and the collection of alkaloids per hectare by 21–27%.

Double-sided lapping process with fixed abrasive shows a strong capability of achieving high efficiency and high surface quality due to its controllable movement of the abrasive and determined material removal modes, and it can be as an alternative technology for fabricating ultra-precision optical components. However, the formation and evolution of the surface topography and roughness (Ra) were rarely clarified in the previous research due to the complicity of the double-sided lapping process. This paper established a new mathematical analytical model to provide a better scientific understanding of the formation and evolution of the surface topography and roughness, in which the spatial location and protrusion height of abrasive particles, each abrasive-material interaction, kinematics trajectory of abrasive particles, and force balance on the workpiece were considered. A series of lapping experiments on the Y3Al5O12 (YAG) wafer were conducted to verify the feasibility of the proposed model and analyze the influence of processing parameters on surface topography and roughness evolution. The results indicated that the proposed analytical model can be effectively used to predict surface topography and optimize the process parameters of double-sided planetary lapping for better surface roughness. The variation of processing time and rotational speed of the lapping plate (pad) possesses a significant influence on surface topography and Ra values. Better surface Ra values can be achieved by selecting the proper processing time and rotational speed of the lapping plate. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article analyses the review, acceptance and publication dates of a sample of 21,890 articles from 326 Ibero‐American scientific journals from all subject areas and countries included in the Latindex Catalogue 2.0 and published between 2018 and 2020 (freely available as an open access dataset). The aim is to discover evaluation and publication times. The evaluation process takes a median of 110 days, the publication process, a median of 82 days, and the whole process, a median of 224 days. Statistical differences are found according to periodicity, subject areas, countries, existence of a printed version and article type (Call for Papers or General articles). From the data we find that the delay in publication is longer than publishers themselves report to the DOAJ. STEM areas present the most similarity in publication patterns, having a higher number of evaluation days (Ed) than publication days (Pd); Arts and Humanities present the opposite pattern, with a higher Pd than Ed. In the case of Social Sciences, the times are similar. General articles and Call for Papers articles differ in terms of Ed, but not Pd, indicating that Call for Papers revisions are faster. [ABSTRACT FROM AUTHOR]

In the process of acquiring and transmitting images, some pixel values may become inaccurate due to equipment errors in image sensors, memory storage devices, and so forth. These damaged pixels reduce the reliability of image processing tasks such as edge detection, region segmentation, and object recognition. Thus, performing noise removal is essential in image processing. Although many nonlinear filters have been proposed to remove such impulse noise, for images with a wide range of pixel values, determining which pixels should be considered noise is challenging owing to the large standard deviation of the original image. Therefore, this study proposes a filter designed to detect random impulse noise according to image regions by analyzing the local characteristics of a local mask. The proposed filter determines special locals through two rounds of noise differentiation and uses the standard deviation to identify the noise in these locals. Furthermore, noisy images are reconstructed using the weighted average. [ABSTRACT FROM AUTHOR]

This work aims to investigate the effect of driver response to relayed messages through Human Machine Interface (HMI) on the effectiveness of Human-Vehicle Interface (HVI) and to enable the optimized design of HMI. The investigation and mathematical modeling cover vehicles with Advanced Vehicle Assistant System (ADAS), which operates using Advanced Vehicle Control System (AVCS). The presented model uses driver response time and machine (electronics, sensors, processors) processing time to measure vehicular efficiency and driver interaction, which is also a function of the HMI design, and the way messages are passed to the driver. The produced model uses a probability function that can be used in the design and testing process to assess the effect of failure on the designed interface and relates the function to the driver's response time ratio and the vehicle electronics' processing time. The presented work concluded that as the driver response time increases, effective interaction decreases as a probability function. Also, as the driver response time deviates from the specified threshold, the effective interaction decreases, which also applies to the processing time. In addition, as the time ratio between the driver responses to the machine processing (Time Ratio) increases, the effective interaction parameter (R) value decreases. The work also proved that a more adaptive model is possible using a probability function correlated to the response time ratio to processing time. [ABSTRACT FROM AUTHOR]

Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.

Microwave sterilization is a novel potential sterilization technology to improve food quality. An industrial microwave sterilization system was used to sterilize imitation crab meat under thermal processing intensity F0 = 1, 2, 3. The characteristics of the microwave process, such as heating rate, processing time, and C100, were calculated. In addition, the quality of processed imitation crab meat was investigated. Compared with the conventional retort method, microwave sterilization significantly shortened the processing time of imitation crab meat by 63.71% to 72.45%. Under the same thermal processing intensity, microwave sterilization has demonstrated better results than retort sterilization in terms of water-holding capacity, color, and texture. Furthermore, microwave-treated imitation crab meat ingredients had a greater capacity to bind water molecules and obtained a more appropriate secondary protein structure. In addition, microwave technology can better preserve the unsaturated fatty acids (UFA) of imitation crab meat, which are 9.14%, 1.19%, and 0.32% higher than the traditional method at F0 = 1, 2, 3. The results would provide useful data for the subsequent research and development of ready-to-eat surimi products.

The load flow problem (LFP) in power distribution networks allows us to find the nodal voltage values within the electrical systems. These values, along with the system parameters, are useful to identify the (technical, economic, and environmental) operational indices and constraints that describe the system's behavior under an established load scenario. The solution of the LFP requires the implementation of numerical methods due to its mathematical model's nonlinear and non-convex nature. In the specialized literature, multiple classical and modern methods seek to improve the solutions achieved in terms of convergence and processing times. However, the most efficient method in both radial and meshed networks has not been determined. Consequently, this study identified the most widely used and efficient classical and modern methods reported in the literature: Newton–Raphson (NR), Gauss-Seidel (GS), Iterative Sweep (IS), Successive Approximations (SA), Taylor's Series (TS), and Triangular Method (TM). The analysis also identified and selected the most common test scenarios to validate the effectiveness of the proposed solution methods: 10-, 33-, and 69-node systems in radial and meshed topologies. The software employed to validate the processing times and convergence of the numerical methods was MATLAB. The results obtained by the different methods were compared, taking the NR methodology as the base case. Thanks to its convergence, this method is used in commercial software packages to solve the LFP, as is the case of DIgSILENT. After analyzing the results of this study, we can state that all the selected methods were suitable in terms of convergence. The greatest errors were 6.064×10−07 for power losses and 8.017×10−04 for nodal voltages, which are negligible values for practical purposes in radial and meshed networks. In this work, processing time was employed as the selection criterion, and TM was identified as the most efficient method for solving the AC power flow in radial and meshed topologies for all the scenarios analyzed.

The purpose of this research is to investigate a novel three-stage flow shop scheduling problem with an ambiguous processing time. The uncertain information is characterized by Pentagonal fuzzy numbers. To solve the problem, in this paper, two different strategies are proposed; one relies on the idea of a ranking function, and the other on the close interval approximation of the pentagonal fuzzy number. For persons that need to be more specific in their requirements, the close interval approximation of the Pentagonal fuzzy number is judged to be the best appropriate approximation interval. Regarding the rental cost specification, these methods are used to reduce the rental cost for the concerned devices. In addition, a comparison of our suggested approach's computed total processing time, total machine rental cost, and machine idle time to the existing approach is introduced. A numerical example is shown to clarify the benefits of the two strategies and to help the readers understand it better. [ABSTRACT FROM AUTHOR]

In order to solve the dynamic rescheduling problem of flexible workshop under the disturbance of urgent order production, a dynamic rescheduling method based on forced evolutionary genetic algorithm and memory priority processing channel was proposed to shorten the overall processing time. Firstly, the dynamic rescheduling problem was described and modeled. Then, aiming at the static scheduling problem of flexible workshop, a method of genetic probability and operation range changing adaptively with individuals was designed, and a static scheduling algorithm of forced evolutionary genetic algorithm was proposed. For the dynamic rescheduling problem under the disturbance of urgent orders, a priority processing channel with memory ability was set up. Finally, taking the 6-Piece workshop as the experimental background, the dynamic rescheduling method of flexible workshop was experimentally verified, namely, the static scheduling performance based on forced evolutionary genetic algorithm and the dynamic rescheduling performance based on memory priority channel were verified respectively. The experiment results show that the average processing time of the static scheduling scheme of the forced evolutionary genetic algorithm is 39.4 min, which is 4.57% less than that of the improved genetic algorithm and 6.85% less than that of the standard genetic algorithm. Under the working conditions of different expediting times and different workpiece expediting, the expedited order production time and overall processing time of memory priority processing channel are less than those of the greedy adaptive method. The results show that the rescheduling scheme based on memory priority processing channel takes into account the processing time of emergency orders and the processing time of overall orders. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop an interactive classifier aided deep learning system to assist cardiologists for heart arrhythmia disease classification as it shows a health-threatening condition that can lead to heart-related complications. Therefore, automatic arrhythmia heart disease detection in an early stage is of high interest as it helps to reduce the mortality rate of cardiac disease patients. In this context, a deep learning architecture is propounded for automatic classification of the patient's electrocardiogram (ECG) signal into a specific class according to the ANSI–AAMI standards. Our proposed methodology is a multistage technique. The first stage combines an R–R peak extraction with a low pass filter applied on the ECG raw data for noise removal. The proposed second stage is a convolutional neural network (CNN) based Fully Connected layers architecture, using different networks optimizer. Different ECG databases have been used for validation purposes. The whole system is implemented on CPU and GPU for complexity analysis. For the predicted improved PTB dataset, the classification accuracy results achieve 99.37%, 99.15%, and 99.31% for training, validation, and testing, respectively. Besides, for the MIT-BIH database, the training, validation, and testing accuracies are 99.5%, 99.06%, and 99.34%, respectively. A top F1-score of 0.99 is obtained. Experimental results show a high achievement compared to the state-of-the-art models. The implementation on GPU confirms the low computational complexity of the system and the possible use in detecting disease events in real-time, which makes it a good candidate for portable healthcare devices. [ABSTRACT FROM AUTHOR]

A dataset with good quality is a valuable asset for a company. The data can be processed into information to help companies improve decision-making. However, the data increased more and more over time to decrease data quality. Thus, good data management is important to keep data quality meeting company standards. One of the efforts that can be done is conducting data cleansing to clean data from errors, inaccuracies, duplication, format discrepancies, etc. Apache Spark is an engine that can analyze large amounts of data. Oracle Database is a database management system used to manage databases. Both have their own reliability and can be used to analyze SQL-shaped data. This study compared Spark and Oracle performance based on query processing time. Both were tested on queries used to perform data cleansing of millions of rows of the dataset. The research focuses on finding out Spark and Oracle's performance through quantitative analysis. The results of this study showed that there were differences in query processing times on both tools. Apache Spark is rated better because it has a relatively faster query processing time than Oracle Database. It can be concluded that Oracle is more reliable in storing complex data models than in analyzing large data. For future research, it is suggested to add other comparison aspects such as memory and CPU usage. The researchers can also consider using query optimization techniques to enrich query experiments.

Background: Dry specimen transport has shown equivalence to traditional liquid transport using a novel high-risk Human papillomavirus assay. Considering that dry transport might cross obstacles during cervical cancer screening in low and middle resource settings, this study was designed evaluate different processing time of dry specimen transport using the same isothermal amplification hrHPV assay. Methods: There were 564 women between the ages of 30–55 recruited from colposcopy clinic. For each patient, two endocervical samples were collected and placed into empty collection tubes by physician. Samples were stored at room temperature until analyzed for hrHPV using the AmpFire assay at two time points: 2 days and 2 weeks. 511 of the 564 participants with positive hrHPV were provided colposcopy exam and quadrant biopsy. Results: A total of 1128 endocervical samples from 564 patients were detected by the Ampfire assay. Good agreement was found between two time periods (Kappa ​± ​Standard error ​= ​0.67 ​± ​0.04). Sensitivity (2days/2weeks) for CIN2+ was 95.28% (95% CI: 92.14%–98.42%) vs 90.57% (CI (86.65%–94.49%) and specificity (2days/2weeks) was 22.47% (CI 19.33%–25.61%) vs 28.15% (CI 24.23%–32.07%) respectively. The difference for Ampfire HPV detection in sensitivity for CIN2+ for the two time periods was not significant (P ​= ​0.227), while the difference in specificity for CIN2+ was significant (P ​= ​0.001). The difference in Ct values 29.23 (CI 28.15–30.31) and 29.27 (CI 28.19–30.35) between two time points was not significant (P ​= ​0.164). Conclusion: Processing dry brush specimens can be delayed up to 2 weeks. Using the AmpFire assay platform which supports cervical cancer prevention programs in low-to-middle-income countries (LMICs).

Cities worldwide face a significant public health and environmental challenge in handling municipal solid waste (MSW). This research exposed an effective utilization of MSW as the source for hydrogen production via a supercritical water gasification process under 450–650 °C at 15–45 min processing time. The impacts of gasification temperature and processing time on the functional properties of hydrogen production are studied. Its results are compared to identify the optimum processing temperature and processing time to adopt the system. Integrating 3 wt% silicon dioxide (SiO 2) nanoparticles/3 wt% of potassium carbonate (K 2 CO 3) enhances hydrogen production by increasing the catalyst's surface area and improving the stability of active sites, leading to more efficient gasification reactions. Increasing the gasification temperature from 450 to 650 °C significantly raises the hydrogen molar fraction and gas yield with peak gasification efficiency (GE) and hydrogen efficiency (HE) values. The gasifier functioned with catalyst (3 wt% K 2 CO 3 /SiO 2) under 650 °C gasification temperature and 45min gasification time influenced better output responses like improved hydrogen gas yield of 63.7 mol/kg, higher gasification efficiency of 59.8 %, better hydrogen efficiency (63.4 %) and increased carbon conversion efficiency of 63.4 and 42.5 % respectively. [Display omitted] • Municipal solid waste is the source of hydrogen production. • Enriches the hydrogen yield by the adaptations of 3 wt% K 2 CO 3 /SiO 2 catalyst. • The SCWG processed with 3 wt% K 2 CO 3 /SiO 2 catalyst offered superior hydrogen yield. • The SCWG processed with 3 wt% K 2 CO 3 /SiO 2 catalyst maximum gasification efficiency. [ABSTRACT FROM AUTHOR]

Dual-layer displays (DLDs) can achieve high dynamic range by reducing light leakage via bonding two LC panels. However, the physical distance between the two LC panels introduces a parallax error when the display is viewed off-axis. This paper proposes a convolutional neural network-based image splitting algorithm for improving the display quality of a DLD. In order to improve processing speed, the network structure is designed relatively simply. The training dataset is established mainly based on TV signals. Different from the common image evaluation metrics based on PSNR and SSIM, the paper proposes to apply learned perceptual image patch similarity (LPIPS) to define the loss function for DLDs. The simulation and experiment results show that compared with the existing method, the proposed method has better performance in reducing parallax error and improving image details. The processing time can be shortened by 2.7 times. The proposed CNN-based method can greatly reduce the processing time while maintaining high display quality, which presents high practicability. [ABSTRACT FROM AUTHOR]