MODELLING OF THE ROUTE NETWORK FOR PERISHABLE CARGO DELIVERY IN THE REGIONAL TRAFFIC ON THE BASIS OF PETRI NETS

The article is devoted to the features of the organization and technology of delivery of perishable goods in small batches by road in regional traffic. It has been established that when planning this type of cargo delivery, it becomes necessary to determine such routes bypassing specified points, at which the time of delivery of perishable goods to points of sale will be minimal. Thus, as a criterion that determines the rationality of building a route network for the delivery of perishable goods in regional traffic, has been defined the time of cargo’s delivery in small batches in regional traffic. The route network for the delivery of perishable goods with a minimum delivery time is considered rational. To analyze the execution time of each delivery phase and take into account the time and quantitative parameters that affect the delivery process, the study built a model which is based on the theory of Petri nets. The parameters of the model were established on the basis of the reporting data of the private enterprise "Samoilenko A.I.". The model took into account time parameters and quantitative factors affecting the process of delivery of perishable goods in regional traffic. An experiment was carried out using the developed model. The obtained value of the integral error of the data discrepancy throughout the system allows us to assume that the constructed model adequately reproduces the process of delivery of perishable goods in small batches by road in regional traffic. Taking into account the results of delivery process’ modeling, using the method of a short connecting network, a rational route network was built in the study, which consists of six routes. The effect was determined as a time difference for the delivery system along the network of rational route network and along the existing network, which amounted to 131 minutes.

Recognition Rate Advancement and Data Error Improvement of Pathology Cutting with H-DenseUNet for Hepatocellular Carcinoma Image

Due to the fact that previous studies have rarely investigated the recognition rate discrepancy and pathology data error when applied to different databases, the purpose of this study is to investigate the improvement of recognition rate via deep learning-based liver lesion segmentation with the incorporation of hospital data. The recognition model used in this study is H-DenseUNet, which is applied to the segmentation of the liver and lesions, and a mixture of 2D/3D Hybrid-DenseUNet is used to reduce the recognition time and system memory requirements. Differences in recognition results were determined by comparing the training files of the standard LiTS competition data set with the training set after mixing in an additional 30 patients. The average error value of 9.6% was obtained by comparing the data discrepancy between the actual pathology data and the pathology data after the analysis of the identified images imported from Kaohsiung Chang Gung Memorial Hospital. The average error rate of the recognition output after mixing the LiTS database with hospital data for training was 1%. In the recognition part, the Dice coefficient was 0.52 after training 50 epochs using the standard LiTS database, while the Dice coefficient was increased to 0.61 after adding 30 hospital data to the training. After importing 3D Slice and ITK-Snap software, a 3D image of the lesion and liver segmentation can be developed. It is hoped that this method could be used to stimulate more research in addition to the general public standard database in the future, as well as to study the applicability of hospital data and improve the generality of the database.

Velodrome: Out-of-Distribution Generalization from Labeled and Unlabeled Gene Expression Data for Drug Response Prediction

Data discrepancy between preclinical and clinical datasets poses a major challenge for accurate drug response prediction based on gene expression data. Different methods of transfer learning have been proposed to address this data discrepancy. These methods generally use cell lines as source domains and patients, patient-derived xenografts, or other cell lines as target domains. However, they assume that they have access to the target domain during training or fine-tuning and they can only take labeled source domains as input. The former is a strong assumption that is not satisfied during deployment of these models in the clinic. The latter means these methods rely on labeled source domains which are of limited size. To avoid this assumption, we formulate drug response prediction as an out-of-distribution generalization problem which does not assume that the target domain is accessible during training. Moreover, to exploit unlabeled source domain data, which tends to be much more plentiful than labeled data, we adopt a semi-supervised approach. We propose Velodrome, a semi-supervised method of out-of-distribution generalization that takes labeled and unlabeled data from different resources as input and makes generalizable predictions. Velodrome achieves this goal by introducing an objective function that combines a supervised loss for accurate prediction, an alignment loss for generalization, and a consistency loss to incorporate unlabeled samples. Our experimental results demonstrate that Velodrome outperforms state-of-the-art pharmacogenomics and transfer learning baselines on cell lines, patient-derived xenografts, and patients and therefore, may guide precision oncology more accurately.

Length-weight relationship of bigeye scad, Selar crumenophthalmus (Bloch, 1793): Regular vs hierarchical bayesian methods

Fisheries management or conservation requires information on length-weight relationship (LWR) for the fishing regulation and biomass estimation. This study aims to assess LWR estimation using two methods, regular and Bayesian hierarchical approached for big-eye Scad (Selar crumenophthalmus). Samples of big eye Scad were collected at several fish landings around Ambon Island from March to August 2020. Length-weight relationship measurement to obtain the parameters of W = a*Lb was tested using generalized linear model and t-test. The parameter b for monthly sampling was not significantly different (F = 0.77, df = 70, P = 0.89) and showed isometric growth b=3 (t = -1.13, df = 4, P = 0.32). Regular measurement resulted parameter log10(a) = -1.99 (±SD = 1.06) dan b = 3.06 (±SD = 0.084). Bayesian method produced parameter log10(a) = -2.07 (±SD = 0.2365) dan parameter b = 3.21 (±SD = 0.1497). Weight measurement from HB approach was significantly higher than the regular method (t = 1.65; df = 405; P <0.0001), and might produce over-estimated of weight from length data. Discrepancy of these methods was overcome by combining all information of LWR to obtain the best estimation on LWR parameters.

A Review on Data Discrepancy Factor Performance for Industrial Applications using Clustering Algorithms

DDF is the most significant measure among different bunch execution procedures to assess the immaculateness of any group component. Ordinarily, best groups are assessing by processing the quantity of information focuses inside a bunch. At the point when this tally is comparable to the quantity of required information focuses then this group is viewed as great. The greatness of the bunch system is fundamental not exclusively to discover the information check inside a group yet in addition to inspect it by totalling the information focuses these are (I) present inside a group where it ought not be and the other way around and (ii) not grouped for example anomalies (OL). The principle usefulness of DDF is that all bunch focuses can be gathered in comparative groups without exceptions, the current paper features on how contrasted with DDF more effective Clusters can be shaped through the Modern DDF. Further, we assess the exhibition of some grouping calculations, K-Means. As of late we, fostered the Modified K-Means Algorithm and Hierarchical Algorithm by utilizing the Data Discrepancy Factor (DDF).

Inverse Scattering Source Problems

The purpose of this chapter is to discuss some of the highlights of the mathematical theory of direct and inverse scattering and inverse source scattering problem for acoustic, elastic and electromagnetic waves. We also briefly explain the uniqueness of the external source for acoustic, elastic and electromagnetic waves equation. However, we must first issue a caveat to the reader. We will also present the recent results for inverse source problems. The resents results including a logarithmic estimate consists of two parts: the Lipschitz part data discrepancy and the high frequency tail of the source function. In general, it is known that due to the existence of non-radiation source, there is no uniqueness for the inverse source problems at a fixed frequency.

Reliability of a New Smartphone Accessory for Scoliosis Measurements

BACKGROUND: Idiopathic scoliosis is the most common pathology of the children’s spine, identified as spine lateral curvature with Cobb angle greater than 10°. The rapid development of technology allows, even mobile devices, to perform a quick and cheap diagnosis ensuring an accuracy similar to the Bunnell scoliometer. OBJECTIVE: The study aimed to validate the accuracy of a newly developed accessory for measuring the angle of trunk rotation (ATR) using mobile devices. METHODS: ATR measurements were carried out on a group of 30 adults using 3 diagnostic tools: the Bunnell scoliometer, Scolioscreen, and mScolio designed by the authors. Scolioscreen and mScolio were used together with a smartphone and the Clinometer app. Three measurements were performed using each of the methods. RESULTS: The greatest data discrepancy was obtained between the Bunnell scoliometer and Scolioscreen (-3.7°÷4.4°) and was twice larger than between the Bunnell scoliometer and mScolio (-1.8°÷2.1°). The excellent agreement was obtained for the mScolio device, where the concordance correlation coefficient was 0.9381 (95% CI of 0.9076÷0.9588) and the interclass correlation coefficient was 0.9501 (95% CI of 0.9242÷0.9671). CONCLUSIONS: Tests showed excellent reliability and validity of ATR using the mScolio solution. Obtained results show excellent consistency between mScolio and the Brunnell scoliometer measurements.

Quality of data on trade in goods between European Union member states

The objective of presented analysis is to assess quality of data on foreign trade within the Union. Data from Eurostat’s COMEXT database was used. The differences between declared export quantities of foods from a given country and data on imports from this country to other member states gathered by Eurostat have been analyzed. These differences partly result from the adopted statistical thresholds and reflect the quality of the collected data. The authors have compared EU member states based on convergence of data on dispatches and arrivals of goods from each country. Using data discrepancy measures member states were ranked with regard to statistical data quality, which is an innovation in foreign trade research.