Dynamic Data Analysis Research Articles

Direct Discriminative Decoder Models for Analysis of High-Dimensional Dynamical Neural Data.

With the accelerated development of neural recording technology over the past few decades, research in integrative neuroscience has become increasingly reliant on data analysis methods that are scalable to high-dimensional recordings and computationally tractable. Latent process models have shown promising results in estimating the dynamics of cognitive processes using individual models for each neuron's receptive field. However, scaling these models to work on high-dimensional neural recordings remains challenging. Not only is it impractical to build receptive field models for individual neurons of a large neural population, but most neural data analyses based on individual receptive field models discard the local history of neural activity, which has been shown to be critical in the accurate inference of the underlying cognitive processes. Here, we propose a novel, scalable latent process model that can directly estimate cognitive process dynamics without requiring precise receptive field models of individual neurons or brain nodes. We call this the direct discriminative decoder (DDD) model. The DDD model consists of (1) a discriminative process that characterizes the conditional distribution of the signal to be estimated, or state, as a function of both the current neural activity and its local history, and (2) a state transition model that characterizes the evolution of the state over a longer time period. While this modeling framework inherits advantages of existing latent process modeling methods, its computational cost is tractable. More important, the solution can incorporate any information from the history of neural activity at any timescale in computing the estimate of the state process. There are many choices in building the discriminative process, including deep neural networks or gaussian processes, which adds to the flexibility of the framework. We argue that these attributes of the proposed methodology, along with its applicability to different modalities of neural data, make it a powerful tool for high-dimensional neural data analysis. We also introduce an extension of these methods, called the discriminative-generative decoder (DGD). The DGD includes both discriminative and generative processes in characterizing observed data. As a result, we can combine physiological correlates like behavior with neural data to better estimate underlying cognitive processes. We illustrate the methods, including steps for inference and model identification, and demonstrate applications to multiple data analysis problems with high-dimensional neural recordings. The modeling results demonstrate the computational and modeling advantages of the DDD and DGD methods.

A geospatial decision support system for ecotourism: a case study in the Campania region of Italy

Nowadays, the business of ecotourism and rural tourism is a very important activity for many inland areas, enabling them to both produce important income and sustain the rural economy. Moreover, sustainable tourism is included in the UN 17th Sustainable Development Goal, which is to be achieved by 2030 (SDG 8.9 and SDG 12). Recent progress in digital tourism promises to deliver important changes to this activity, but most of this progress is addressed at well-known tourist destinations, so not dealing with the challenge of inland, rural and ecotourism. It is within this framework that this paper attempts to demonstrate that a new type of geoSpatial Decision Support System (S-DSS), developed on a Geospatial Cyberinfrastructure (GCI) and with a substantial interdisciplinary core, could provide a valuable web-based operational tool which may be offered to both ecotourism and rural tourism end-users, planners and policy makers, so that they might better plan and manage this type of sustainable tourism. The S-DSS platform has also been designed to encourage use by the multi-user community (farmers, tourism enterprises, associations and public bodies). The methodology is linked to the creation of a GCI platform (www.landsupport.eu) that supports the acquisition, management, processing and analysis of both static (e.g. soil, geology) and dynamic data (e.g. environmental and daily climatic data), together with data visualization and computer on-the-fly applications, in order to perform modelling, all of which is potentially accessible via the Web. The S-DSS tool known as EcoSmarTour is demonstrated through a case study of the Campania region (South Italy) and, by connecting database and modelling, it aims to deliver a large amount of information that will improve knowledge of the territory, manage scenario analysis, produce maps and evaluate potential ecotourism footpaths or areas of interest, thus enabling the provision of better information on the entire ecotourism sector. The tool will also be demonstrated through reference to a short selection of additional use cases from elsewhere in Europe. Most importantly, the approach adopted is highly transferable because it relies on very general algorithms that can be easily applied wherever the necessary data are available.

Nonlinear Dynamics of the Financial–Growth Nexus in African Emerging Economies: The Case of a Macroprudential Policy Regime

A panel data analysis of nonlinear financial growth dynamics in a macroprudential policy regime was conducted in a panel of 10 African emerging countries from 1983–2020, where it had been a non-prudential regime from 1983–1999 and a prudential regime from 2000–2020. The paper explored the validity of invented U-shape hypothesis in the prudential policy regime as well as the threshold level at which excessive finance boosts growth using the panel smooth transition regression (PSTR) model. The PSTR model was adopted due to its ability to address the problems of endogeneity and heterogeneity in a nonlinear framework. The results reveal evidence of a nonlinear effect between financial development and economic growth, where the minimum level of financial development is found to be 60.5% of GDP, above which financial development increases growth in African emerging countries. The findings confirmed a U-shaped relationship, contradicting the invented U-curve hypothesis. The focal policy recommendation is that the financial sector should be given adequate consideration and recognition by, for example, implementing appropriate financial reforms, developing a suitable investment portfolio, and keeping spending on technological investment in Africa’s emerging countries below the threshold. Again, caution is needed when introducing macroprudential policies at a low level of the financial system.

Data-driven mathematical modeling and quantitative analysis of cell dynamics in the tumor microenvironment

The tumor microenvironment (TME) exerts key effects on tumor development, progression and treatment. Therefore, a quantitative understanding of various cellular and molecular interactions in the TME is very important. In this study, we combined a dynamic model and data analysis to quantitatively explore how microenvironmental factors influence tumor growth and three phases of cancer immunoediting. First, we presented a model system of partial differential equations (PDEs) using four main types of cells within the microenvironment of solid tumors and used two sets of published experimental data to validate the model. Accordingly, the partial rank correlation coefficient (PRCC) was calculated to identify the sensitive parameters related to significant biological processes. Furthermore, numerical simulations indicated that the power of tumor proliferation exerts a substantial effect on the state of malignancy, but tumor control is achieved by adjusting sensitive microenvironmental factors, such as immune intensity and the proliferation of cancer-associated fibroblasts (CAFs). Moreover, we used two indicators to quantify three states, i.e., elimination, equilibrium and escape from cancer immunoediting. The quantitative analysis of the TME revealed that immune cells and CAFs dynamically guide the transition of the three states of immunoediting, namely, how these related factors affect the capacity of the immune system to eliminate developing tumor cells, hold them in an equilibrium state, or facilitate their expanded growth. These quantitative results provide new insights into how various microenvironmental changes mediate both natural and therapeutically induced cancer immunoediting responses.

Presentation of Financial Assets in the Monetary Statistics

The purpose of the study. Currently, the issues of determining, presenting and analyzing indicators of economic assets, in particular, financial ones, are relevant, since financial assets, along with nonfinancial ones, constitute the most important characteristic of national wealth as a level of development of the country and are the basis for constructing national accounts, indicators of monetary statistics and other sections of macroeconomic statistics. In modern conditions, the role of these constructions in making effective management decisions at the macro level, individual sectors, economic entities, on the development of the national economy as a whole is increasing. The aim of the work is to systematize theoretical and practical developments on the definition and presentation of data on financial assets in modern monetary statistics of Russia, to identify areas for improving methods for assessing financial assets, a system of indicators characterizing their presence, structure and dynamics.Materials and methods. In the paper, the authors considered the essence, functions, classifications of financial assets and liabilities, applied structural and dynamic data analysis, as well as methods of theoretical research in the form of generalization, comparison and special analytical procedures based on official statistics from Rosstat, the Bank of Russia, the Ministry of Finance and international statistical organizations.Results. The work formulates the main directions of the study of financial assets, based on modern international statistical standards, taking into account domestic practice. Topical issues of the theory and practice of statistical collection and presentation of data on financial assets in monetary statistics, the application of definitions and classifications of international standards to Russian official statistics are considered. The features of the reflection of data on financial assets at the macro level, in the system of indicators of monetary statistics, have been formulated. The authors carried out a quantitative assessment of the structure and dynamics of indicators of monetary statistics in Russia, which characterize the state and movement of financial assets and liabilities.Conclusion. A statistical study of transactions with financial assets and in connection with the formation of monetary aggregates makes it possible to identify not only the main trends in the development of these processes, but also makes it possible to characterize the contribution of a particular subsector to the total value of financial intermediation, to analyze the relationship between the financial corporations’ sector and other sectors, and also a comprehensive change in the volume and composition of stocks and flows of financial assets as a result of exchanges between residents and non-residents.The authors comprehensively reviewed the methodological framework for constructing money aggregates and reviews of the financial corporations’ sector, incl. in the context of financial instruments. Statistical information on financial assets in monetary statistics is presented in terms of the formation of the money supply, other aggregates, the base, as well as the interaction between the institutional subsectors of the financial sector. A comprehensive description of the presence and movement of financial assets in the composition of monetary statistics can be obtained from the monetary surveys, containing data on the flows and stocks of financial assets and liabilities of financial corporations (residents) in relation to all other sectors (residents), as well as non-residents.All considered data are comparable [17,18] since basically, one classification of financial instruments is applied, a different degree of detail can be applied in practice for different constructions, but the base is the same. Questions of the theory and practice of presenting indicators of financial assets in the considered sections of macrostatistics and their analysis are a relevant and promising direction for improving national statistical accounting. Work in this direction continues, and the process of actively introducing international standards into domestic statistics has been going on for quite a long time. In addition, telecommuting, collecting information via the Internet, the emergence of a large number of new financial instruments and transactions, including modified ones, in the business turnover of institutional units complicate their identification and classification.The authors of the presented study have previously considered in their articles the problems of macroeconomic calculations, which allowed them in their work to consider in more detail the topical issues related to the processes of reflection of financial assets in monetary statistics.

Abstract P1-04-02: Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial

Abstract Background - In the NeoTRIPaPDL1 phase III trial, triple-negative breast cancer (TNBC) patients were randomized to receive nab-paclitaxel/carboplatin for 8 cycles (CT arm) with or without atezolizumab (CT/A arm). We previously reported that the majority of patients (65%) with PD-L1- at baseline converted to PD-L1+ (the majority with IC2/IC3) after the first cycle of treatment in the atezolizumab arm. Here we studied treatment associated changes in the molecular tumour features and immune microenvironment by PD-L1 expression. Methods – A total of 158 (56.4%) of patients enrolled in the NeoTRIPaPDL1 trial (76 and 82 from CT/A and CT arm, respectively) were included in this analysis by satisfying the following criteria: i) availability of a paired core biopsy at baseline and at day 1 of the second treatment cycle (D1C2); ii) evaluation of stromal TILs (sTILs) and staining for PD-L1 (Ventana SP142); iii) successful RNA-seq gene expression profiling. PD-L1 groups were defined as IC0/IC1 (<5% PD-L1low) vs IC2/IC3 (>=5% PD-L1high). Presence of different immune cell populations was quantified by gene expression profile deconvolution using ConsensusTME R package. Cancer hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package. Differences in score distribution were evaluated by 2-sided t-test. Results At baseline, both sTILs and immune cell signatures were upregulated in PD-L1high compared to PD-L1low tumours (p<0.001). No significant differences were found between the two treatment arms in the PD-L1high subpopulation. In the PD-L1low cases, both sTILs and immune related signatures were slightly downregulated in the CT/A arm (p<0.05), suggesting a modest unbalance among treatment arms with CT arm being slightly more inflamed than CT/A arm.At D1C2, PD-L1high tumours in the CT arm systematically had high sTILs (median=70%, range=30-90%), while PD-L1low tumours receiving CT/A had low sTILs in a significant proportion of cases (median=30%, range=0-90%; p<0.001). Similarly, at D1C2 several gene expression-estimated immune cell populations and immune-related signatures were upregulated in the CT arm compared to CT/A arm in PD-L1high tumours, with the weakest association observed for M2 macrophages (p=0.058). No tumour-related signatures were differentially expressed among the two treatment arms within groups with PD-L1high, suggesting that different treatment. modulate PD-L1 by engaging a different immune mileau instead of modulating tumor related features.Considering PD-L1low groups at D1C2, in the CT arm 21 tumors had >30% sTILs (n=21/69, 30.4%), while in the CT/A arm only 2 had sTILs >30% (2/27, 7.4%) (p<0.001). Analysis of gene expression data identified IFN-related signatures as the most upregulated in the CT compared to CT/A arm in PD-L1low cases at D1C2 (p<0.05). Conclusions Integrated dynamic analysis of PD-L1 expression and gene expression data highlighted significant treatment-specific changes of the immune landscape according to PD-L1 expression, when this biomarker is assessed during treatment. This indicates that the immune milieu associated with PD-L1 status is strongly dependent on the time of assessment in relationship to treatment received. Such observation may explain why in the NeoTRIP trial, baseline PD-L1 but not on-treatment PD-L1 was predictive of pCR in CT/A arm. In addition, our findings could have implications related to the use of PD-L1 as a predictive biomarker in pre-treated patients, especially when assessed early on during treatment. Citation Format: Maurizio Callari, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Matteo Dugo, Marc Thill, Antonio Anton, Marco Barreca, Stefania Russo, Eva Maria Ciruelos, Richard Greil, Stefania Zambelli, Balázs Gyorffy, Chanel Smart, Olivia Biasi, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-04-02.

Russian tourism industry: consequences of the COVID-19 pandemic

Aim. The presented study aims to statistically assess the scale of the pandemic’s negative impact on the major indicators of the tourism industry in the Russian Federation.Tasks. The author shows the dynamics of the key indicators of the tourism industry before and during the pandemic; assesses the impact of the crisis on the activities of travel agencies; compares the amount of funds received from the sale of tourist products in Russian regions before and during the pandemic; assesses the statistical significance of changes in funds received from the sale of tourist products per company before and during the pandemic in the federal districts of Russiа.Methods. This study presents and analyzes data of the Federal State Statistics Service on the major activity indicators of organizations in the Russian travel industry from 2017 to 2020. The data mart of the Federal State Statistics Service serves as a source of information about the activities of travel agencies in the constituent entities of the Russian Federation. In the course of the study, the author uses the methods of grouping, variation, structural and dynamic data analysis. To obtain region-specific information, a corresponding analysis was performed in 2019–2020 in 85 constituent entities of the Russian Federation. A hypothesis about data distribution normality is tested using the Shapiro–Wilk test. A hypothesis about the significance of the differences in tourist activity indicators before and during the pandemic is tested using the non-parametric Wilcoxon test, since the nature of data distribution is different from normal.Results. It is found that the pandemic-related crisis has had a negative impact on all key indicators of the tourism industry. There have been significant job cuts, and the seasonality that is typical of the ‘healthy’ stage of the tourism industry’s development has been interrupted. The biggest decline in the number of employees is observed in the summer months of 2020. The impact of the crisis on average wages is manifested in a significant decline in April 2020 and fluctuations in the level of yearly average wages becoming more pronounced during the crisis. Quarantine measures contributed to the withdrawal of 227 companies from the Russian tourist services market, a 3.7-fold decrease in the number of tourist trips, and a 54.4% reduction in funds received from the sale of tourist products. The study presents the results of estimating the statistical significance of changes in the amount of funds received from the sale of tourist products per company before and during the pandemic in the federal districts of Russia.Conclusions. The impact of the pandemic is expressed in the breakdown of tourism development trends that preceded it, creating prerequisites for the restructuring of the industry and the emergence of new trends. A set of measures and joint actions of the state and tourism enterprises are needed to overcome the consequences of the pandemic in the tourism sector.

Efficient Privacy-Preserving Data Aggregation Scheme with Fault Tolerance in Smart Grid

As the traditional grid produces a large amount of greenhouse gas and cannot adapt to such new demands as dynamic electricity prices, data analysis, and early warning, smart grid with high efficiency and reliability is increasingly valued. It plays a key role in achieving carbon neutrality. Nonetheless, smart grid requires the collection of real-time power data, and personal privacy may be leaked through the frequent electricity measurement reports. With the requirements of data analysis and prediction while preserving users’ personal privacy, data aggregation schemes have emerged. However, existing schemes cannot resolve all the troubles well. Some schemes do not consider the failures for smart meters, and most of the schemes have expensive computation cost. In view of this, an efficient privacy-preserving data aggregation scheme with fault tolerance in smart grid is put forward in this paper. To be specific, the proposed scheme is lightweight due to the application of the symmetric homomorphic encryption technology and the elliptic curve cryptography. Even if some smart meters are destroyed, the proposed scheme can still successfully obtain aggregated data. Moreover, the proposed data aggregation scheme is proved to be secure, and all security requirements can be satisfied. Performance evaluation illustrates the relatively low computation cost and communication overhead of the proposed scheme compared to other related schemes.

Modeling and Analysis of Dynamic Data

Modeling and Analysis of Dynamic Data

Innovation in healthcare management: drug decriminalization for reducing the health damage from crime

The article focuses on the positive impact of drug legalization on reducing crime and violence's impact on health. The authors considered the potential of an innovative approach to crime prevention and health care improvement. Criminalization may reduce drug harm, but the current criminal-justice approach to drugs is not working enough. Drug use is still widespread, public. The personal harms are significantly large. There are a lot of short- and long-term health effects from crime and violence. The World Health Organization affirms that rates of drug use are unrelated to how effectively drug laws are enforced. Thus, it is actually to find new possibilities and develop new methods to reduce crime level and its negative influence. Drug decriminalization is one of such important issues. This article aims to investigate drug decriminalization for reducing the health damage from crime in the context of innovation in healthcare management. There is a comparative analysis of some drug legalization policies: decriminalization of the use and possession of all illicit drugs (with the control of their legal supply) or legalization of the use and supply of cannabis etc. The dynamic analysis of data for different types of crime, such as unlawful acts involving controlled drugs or precursors, intentional homicide, assault, kidnapping, sexual assault, and other violent crime both in two groups of countries – with criminal-justice and innovation health care (including drug decriminalization) approaches were taken. Portugal, Switzerland, Netherlands, Czechia are among the countries of the second group, which have decriminalized drug use and possession for personal use and have invested in harm reduction programs. The research consists of data for 25 European countries for 2008-2018 (the time limit of 2018 is determined by available statistics of the statistical service of the European Union, World Health Organization, UN Office on Drugs & Crime Databases, etc.). Based on the correlation and regression analysis, it is substantiated that drug decriminalization is an important factor in reducing the health damage from crime.

Using Oralstats for prosodic characterisation of speakers in different discourse genres

This paper presents the prosodic and morphosyntactic analysis of 437 intonational phrases, 2777 words and 12520 phones registered from the speech of four Spanish politicians in four different discourse genres (TV gathering, press conference, political rally and interview). Based on methods from forensic linguistics, sociolinguistics, phonetics and computational linguistics, a set of R scripts called Oralstats has been developed, which allows to carry out a multivariate, dynamic and interactive analysis of acoustic and textual data aligned with PRAAT. By doing so, a new computational approach is suggested for finding individual forensic marks, combining both phonetic and linguistic factors, such as lexical or morphosyntactic values. Data exploration comes from the visualization of prosodic and morphosyntactic variables collected through decision trees, heatmaps and boxplots. The results show how speakers behave in a significantly similar way when it comes to differentiating themselves by genre; however, in confrontational genres some unexpected phonic marks were found, such as low intensity or low pitch, (the expected behavior being the opposite), thus enabling us to single out some of the speakers.

ВЫРАЩИВАНИЕ МОЛОДНЯКА КОЗ С ИСПОЛЬЗОВАНИЕМ В ИХ РАЦИОНАХ НЕТРАДИЦИОННЫХ КОРМОВЫХ ДОБАВОК

The aim of the research is to determine the effectiveness of non-traditional feed additives in the rearing of young goats. To conduct the scientific and economic experiment, three groups of young goats were formed. Average daily rations of young goats met the feeding requirements in terms of exchange energy, dry matter, crude protein, except digestible protein, copper, and cobalt. The data analysis of live weight dynamics showed that there was no significant difference between the groups of animals at the beginning of the experiment. However, the animals of the experimental groups that consumed feed additives exceeded the live weight of goats from the control group starting from the age of 6 months. Upon reaching the age of 6 months, the animals from the control group were inferior in live weight to the goats of the experimental groups by 3.51% and 4.39%, at 8 months - by 3.72% and 6.69%, at 10 months - by 3.61% and 6.89% (P> 0.95), and at the age of 12 months - by 3.59% and 7.78%, respectively (P> 0.99). Data analysis of the average daily gains of the experimental animals showed the superiority of the goats of the experimental groups over the control group in terms of growth intensity. Animals of the experimental groups surpassed animals of the control group in terms of average daily gains of live weight at the age of 4-6 months by 20.93% and 25.58%, in the period of 6-8 months - by 4.89% and 19.52%, at 8-10 months - by 2.78% and 8.33%, and in the period of 10-12 months of age by 3.46% and 17.11%, respectively. Thus, application of non-traditional feed additives in rations of goats contributes to improvement of growth and development parameters without negative affect on the body.

Statistical assessment of the activity of collective accommodation facilities and their contribution to the economy of the Russian Federation regions

The purpose of the study. Tourism is a priority area of economic development. Short-term accommodation of tourists plays an important role in the tourism industry. The indicator of the number of persons placed in collective accommodation facilities is used to determine the tourist flow in the context of the regions of the Russian Federation. The approach we have used in this study aims to analyze the activity of collective accommodation facilities and assess their contributionto the economy of the Russian Federation regions based on current statistical information. The research was carried out at the expense of budgetary funds on the state assignment to the Financial University under the Government of the Russian Federation. The study focuses on:analysis of the activity of collective accommodation facilities on the territory of Russia,testing the hypothesis that the differences in the main indicators, characterizing the activity of collective accommodation facilities are due to the influence of the territorial factor,evaluate the efficiency of functioning of collective accommodation facilities and their profitability, and highlight the regions of Russia with low, medium and high levels of profitability of tourist accommodation facilities,assess the contribution of the activity of collective accommodation facilities for tourists to the economy of the regions of Russia. Materials and methods. In the paper, the authors considered definitions, classification, a system of indicators that form a methodological basis for statistical observation of the activity of collective accommodation facilities and their presentation in accordance with the current international and national statistical standards. We applied the grouping method, structural and dynamic data analysis. The hypothesis of the normal distribution of the data was tested on the basis of the Shapiro-Wilk Test. The hypothesis that the differences in indicators characterizing tourist activity are due to the influence of the regional factor was tested using the nonparametric Kruskal-Wallis Test. To assess the contribution of the activity of collective accommodation facilities to the economy of the Russian region, the authors calculated an integral index assessing the resources and results of tourism activities in the region. The analysis was carried out according to 5 indicators characterizing tourist activity in 82 regions of the Russian Federation in 2019.Results. The study found that the territorial factor has a statistically significant effect on the number of seats for receiving tourists, the number of accommodated persons, the number of overnight stays. At the same time, the differences in the distribution of the collective accommodation facilities over the territory of Russia are caused not by the belonging of the region of Russia to a certain federal district, but by other factors. The study also made it possible to come to the conclusion that, in terms of the profitability of collective accommodation facilities, the regions of Russia are extremely heterogeneous. The authors identified groups of Russian regions with profitability of collective accommodation facilities below average, above average and high. The integral index, based on the resources and results of tourist activity, made it possible to determine the regions of Russia, in which the results of tourist activities are ahead, correspond or lag behind the available resources for the implementation of tourist activities. Conclusion. The results obtained will make it possible to correlate the resources of each Russian region for the implementation of tourist activities with its results. An increase in the number of accommodations, an increase in their level of comfort, accessibility, and convenient location will give an impetus to the development of the tourism sector in Russia; arouse interest in tourist trips both among citizens of the country and among foreign guests. The regional aspect of the location of accommodation facilities is extremely important, since it is not only the basic position of the tourist infrastructure, but also has a direct impact on the contribution of tourist activity to the economy of the region of Russia. Therefore, the issue of the influence of accommodation facilities on the region’s economy requires further study.

Research on internal network data security monitoring method based on NB-IoT

In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node’s optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation.

Retraction Note: Image detection of mountain soil loss and human avoidance trajectory based on cloud computing and machine learning

With the continuous progress and development of modern social science management and network security technology in China, the wide application of new-generation technologies such as intelligent robotics learning has made it more and more extensive in the field of analysis. At present, it has fully played an important technical leading role in many application fields, such as analysis methods, collection and sorting ability, analysis of various large-scale business computing application statistical data of enterprises, mining theoretical knowledge of natural environment science, image recognition, retrieval of global geographic location information and so on. Through the simulation of soil erosion in mountainous areas in my country, dynamic data analysis describes the basic situation of soil erosion in various mountainous areas in my country and the whole process of its evolution and development and comprehensively analyzes and studies various soil erosion events in mountainous areas in my country. The corresponding solutions can effectively and accurately respond to emergencies of soil erosion in mountainous areas and effectively reduce the loss of soil and water crops and economic losses in natural disasters. In recent years, information technology companies related to computer and machine learning capabilities, especially based on machine cloud computing, learning science and technology based on the theory of human machine artificial intelligence, and combining related professional technologies, have been used in various areas to prevent floods, droughts, risks, and rain in various areas. Disaster reduction and other non-natural engineering construction technology prevention measures have gradually been applied. The main research question of this paper is to design a related human body trajectory image detection system for new human facial image processing based on machine learning, and information collection and detection management system, and conduct in-depth research on its application prospects.

FSATOOL 2.0: An integrated molecular dynamics simulation and trajectory data analysis program.

Molecular dynamics simulation is important in the computational study of the biomolecules. In this paper, we upgrade our previous FSATOOL to version 2.0. It is no longer a plugin as before. Besides the existed enhanced sampling and Markov state model analysis module, FSATOOL 2.0 has three new features now. First, it contains a molecular dynamics simulation engine on both CPU and GPU device. The engine works with an embedded enhanced sampling module. Second, it can do the free energy calculation by various practical methods, including the weighted histogram analysis method and Gaussian mixture model. Third, it has many subroutines to process the trajectory data, such as principal component analysis, time-structure based independent component analysis, contact analysis, and Φ-value analysis. Most importantly, all these calculations are integrated into one package. The trajectory data format is compatible with all the modules. With a proper input parameter file, users can do the molecular dynamics simulation and data analysis work by only a few simplified commands. The capabilities and theoretical backgrounds of FSATOOL 2.0 are introduced in the paper.

Prognostic and Therapeutic Significance of Circulating Tumor Cell Phenotype Detection Based on Epithelial-Mesenchymal Transition Markers in Early and Midstage Colorectal Cancer First-Line Chemotherapy.

Purpose Epithelial-mesenchymal transition (EMT) is related to the process of metastasis and challenges the detection of circulating tumor cells (CTCs) based on epithelial cell adhesion molecules. Circulating tumor cells (CTCs) have been proven to be a prognostic indicator of colorectal cancer (CRC). Although there is evidence that CTC heterogeneity based on EMT markers is associated with disease progression, no standard recommendations have been established for clinical practice. This study is aimed at evaluating the prognostic significance of dynamic CTC detection based on EMT for early and midstage colorectal cancer patients. Methods 101 patients with early to midterm CRC were admitted from January 2016 to September 2018. All patients underwent CRC radical surgery and standard chemotherapy. Patients in the postchemotherapy were able to epithelial mesenchymal transformed (EMT) CTC testing in peripheral blood using the CanPatrol™ system. Multiple CTC tests were performed according to patient's own condition and different follow-up time points. Based on patient's basic information and follow-up data, the Kaplan-Meier method was utilized to establish the progression-free survival model, and the log-rank test was utilized to compare the survival rates between the two groups. Result Total CTC change of the patient is the best method to predict whether progression-free survival progresses in tumor patients (Area = 0.857). The second detection of total number of CTCs (P < 0.01) detected after chemotherapy, epithelial CTCs (P = 0.032), the increased total number of CTCs (P < 0.01), and the increased number of mesenchymal CTCs (P = 0.015) are significantly related with patient's poor progression-free survival. Conclusion Analysis of the second CTC count and classification after follow-up are more related to the survival prognosis of the tumor. The joint analysis of CTC dynamic monitoring data is a good tool to judge patient's survival prognosis.

Numerical study of complex flow physics and coherent structures of the flow through a convoluted duct

The open source computational fluid dynamics (CFD) software OpenFOAM is used to investigate the flow through the Wellborn s-duct. Numerical simulations with two different modelling techniques are performed. Transient simulations with Unsteady Reynolds Averaged Navier Stokes (URANS) and Detached Delayed Eddy Simulation (DDES) formulations are compared and validated against experimental data. Given its nature, URANS results show a low unsteadiness level with an almost static shear layer. DDES simulations correspond closely to experimental data. Total pressure recovery is within 2.07% of values recorded in experiments. The highest standard deviation values of velocity components are located in the central interface plane region. Modal decomposition techniques are applied to determine coherent structures and the dynamic nature of the complex flow physics. One horizontal shifting mode and two vertical shifting modes are clearly identified. The two vertical shifting modes oscillate at very different frequencies of 460 and 70 Hz. Phase information and frequency of occurrence are determined through dynamic mode decomposition and frequency domain analysis of snapshot flow field data. Frequency maps of flow properties at the aerodynamic interface plane (AIP) are also presented. Phase analysis of modes in convoluted ducts are reported in the public domain for the first time and offer promise of a tailored, active flow control strategy.

ANALYSIS OF RESPONSIBILITY CENTERS PERFORMANCE IN BUSINESSES BY SYSTEM DYNAMICS METHOD

Today, the high dynamism of the business sector makes it difficult for managers to make decisions about the future of their businesses. Business managers need to keep their businesses and all units of them under control in all aspects in order to make effective decisions. In order to achieve this, responsibility centers and the data produced in these centers are used. Today, analyzing the data produced in the responsibility centers dynamically has become a necessity. System dynamics models that enable dynamic analysis of business data have been used successfully in recent years. System dynamics approach, which is also used in different business sectors, is a simulation method that can be used in solving dynamic problems and developing business strategies that traditional management accounting techniques have difficulty in responding. In this study, a model of a manufacturing enterprise has been created to measure the performance of responsibility centers within the enterprise. The data obtained from this model is evaluated with the help of the model and the future performance of the enterprise is tried to be predicted. Using this model, different scenarios were tried out and the results were compared, and it was revealed that the system dynamics could be used effectively in the evaluation of responsibility centers and business performance.

Dynamic model of beef availability for Rendang Industry in Payakumbuh City

The Payakumbuh City of Rendang is an icon that reflects one of the typical traditional foods with beef, coconut milk, and different kinds of spices as the basic ingredients. The availability of beef is very important for the rendang industry to ensure the production process. The roles of breeders, wholesalers, retailers, and their distribution need to be managed properly from upstream to downstream. This study aims to analyze the availability of beef for the rendang industry in Payakumbuh City using a dynamic model. Survey research methods and dynamic model data analysis method with Powersim software were utilized. The availability of beef to meet the demand for the rendang industry in Payakumbuh City is still very dependent on the results of slaughtered cattle in Payakumbuh City. Imported beef supply is currently used as an alternative option to fulfill the beef needs for the rendang industry in Payakumbuh City.