Specific Modeling Research Articles

Signal quality enhancement in multiplexed communication systems based on the simulation model of the optimum technical specifications of Raman fiber optical amplifiers

Abstract Raman optical amplifiers have more reliability than the repeater in the optical communication networks. In the optical amplifier, the transmission equals the gain bandwidth of the amplifiers. The optical amplifier has functionalities such as WDM amplification (gain equalization), gain control (rapidly variation of the gain), inter-stage access (compensation in the dispersion rate). This paper presents the optimum technical specifications of fiber Raman optical amplifiers (FROAs) with average power system for signal quality improvement in multiplexed systems. The signal has upgraded to a max. Q-factor of 11.47 applied along 1000 m as a range through the free-space optical (FSO) communication channel that has an attenuation rate of 10 dB/km. Our suggested system has clarified the best Q-factor that is greatly increased to reach 49.36 in the presence of the pump laser. The multiplexed communication systems signal quality is enhanced by the percentage ratio of 39.65%.

From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata

Autonomous systems control many tasks in our daily lives. To increase trust in those systems and safety of the interaction between humans and autonomous systems, the system behaviour and reasons for autonomous decision should be explained to users, experts and public authorities. One way to provide such explanations is to use behavioural models to generate context- and user-specific explanations at run-time. However, this comes at the cost of higher modelling effort as additional models need to be constructed. In this paper, we propose a high-level process to extract such explanation models from system models, and to subsequently refine these towards specific users, explanation purposes and situations. By this, we enable the reuse of specification models for integrating self-explanation capabilities into systems. We showcase our approach using a running example from the autonomous driving domain.

Current directions in visual perceptual learning.

The visual expertise of adult humans is jointly determined by evolution, visual development, and visual perceptual learning. Perceptual learning refers to performance improvements in perceptual tasks after practice or training in the task. It occurs in almost all visual tasks, ranging from simple feature detection to complex scene analysis. In this Review, we focus on key behavioral aspects of visual perceptual learning. We begin by describing visual perceptual learning tasks and manipulations that influence the magnitude of learning, and then discuss specificity of learning. Next, we present theories and computational models of learning and specificity. We then review applications of visual perceptual learning in visual rehabilitation. Finally, we summarize the general principles of visual perceptual learning, discuss the tension between plasticity and stability, and conclude with new research directions.

Modeling and Optimization Analysis of Ancient Building Construction Rule Components Based on Deep Learning

Chinese culture is broad and profound, and successive dynasties have left many cultural treasures. Ancient architecture is a significant treasure, and it is also the core content of the inheritance of Chinese culture. Every Chinese ancient building has its own characteristics, and the creative components of each ancient building are an important part of ancient buildings. As a new learning mode of current scientific inquiry, the deep learning model includes high-level and high-stage cognitive processing ability and innovative thinking ability. Under the background of the above modeling and optimization analysis of ancient building construction rule components and the development of deep learning mode, this paper proposes the modeling and optimization analysis of ancient building construction rule components about deep studying. The results of the experiment are as follows: (1) about the concept of the deep learning technology model and the vacancy problems existing in the current situation of the design framework and optimization of ancient building construction rule components, the research direction of the experiment is determined, and through the investigation and analysis of the modeling and optimization of ancient building construction rule components based on the deep learning model, the technical guarantee is provided for the research of this paper; (2) the convolution neural network algorithm, inversion model algorithm, loss function algorithm, and optimization algorithm are used to calculate, evaluate, and analyze the research problems, and the investigation contents are identified and analyzed through experimental research. It can not only analyze the root of the research problems but also improve the specific modeling optimization problems of ancient buildings, to reduce the unnecessary loss of time and resources.

Regional well-being inequalities arising from healthcare expenditure public policies in Spain.

Well-being inequalities arising from different healthcare expenditure public policies is currently a hot topic at a national scale, but especially so at a sub-national level because the inequalities in question are among citizens of the same country. Spain is an optimal study area to carry out research on this topic because it is considered to have one of the best health systems in the world, it is one of the top-ranking countries in terms of life expectancy rates (the indicators we use for well-being), and it has a decentralized public health system with significantly different regional healthcare expenditure public policies. Given that the factors involved in the complex direct, indirect, and second-order relationships between well-being and health spending are latent in nature, and that there are more hypotheses than certainties regarding these relationships, we propose a partial least squares structural equation modeling specification to test the research hypotheses and to estimate the corresponding impacts. These constructs are proxied by a set of 26 indicators, for which annual values at a regional scale were used for the period 2005–2018. From the estimation of this model, it can be concluded that mortality, expenditure and resources are the factors that have the greatest impact on well-being. In addition, a cluster analysis of the indicators for the constructs included in this research reveals the existence of three clearly differentiated groups of autonomous communities: the northern part of the country plus Extremadura (characterized by the lowest well-being and the highest mortality rates), Madrid (with the best results in well-being and mortality, the lowest public health expenditure per inhabitant and percentage of pharmaceutical spending, and the highest percentage in specialty care services and medical staff spending), and the rest of the country (south-eastern regions, with similar well-being values to those of the first group but with less health expenditure). Finally, a principal component analysis reveals that “healthiness” and “basic spending” are the optimal factors for mapping well-being and health spending in Spain.

Substrate induced dynamical remodeling of the binding pocket generates GTPase specificity in DOCK family of guanine nucleotide exchange factors

Dedicator of cytokinesis (DOCK) family of guanine nucleotide exchange factors (GEFs) activate two members of Rho family GTPases, Rac1/Cdc42, to exert diverse cellular processes, including cell migration. As DOCK GEFs have been critically implicated in tumour cell migration, understanding their function and specificity is imperative for designing anti-metastatic drugs. Based on their GTPase specificity they have been classified as Rac, Cdc42 and dual specific GEFs. Despite extensive structural studies, the factors that determine GTPase specificity of DOCK GEFs have remained elusive. Here, we show that subtle dynamical coupling between GEF and GTPase structures modulate the binding interface to generate mutual specificity. To cluster the dynamically coupled residues in GEF-GTPase complexes a novel intra-residue backbone-torsion-angles based mutual information (TMI) technique was employed. TMI was calculated from 4500 trajectories obtained from a total of 4.5μs molecular dynamics simulations performed on members of all the three clades of DOCK GEFs. The obtained clusters suggest a specificity generation mechanism that involves optimization of the binding pocket for the crucial divergent residue at the 56th position of Rac/Cdc42 (FCdc42/WRac1). These clusters encompass five residues from the structural segment lobe C - α10 helix of the DOCK proteins and functional SWI region of GTPase, which induce orchestrated structural modulations to generate the specificity. Even the conserved residues from SWI region are seen to augment the specificity defining dynamical rearrangements. Furthermore, the proposed dynamical GTPase- DOCK GEF specificity model was verified using mutagenesis studies on Rac1 and dual GTPase specific Dock2 and Dock6, respectively. Thus the current study provides the generic substrate specificity determinants of DOCK GEFs, which are not apparent from the conventional structural analysis.

The spatial organization of transcriptional control.

In animals, the sequences for controlling gene expression do not concentrate just at the transcription start site of genes, but are frequently thousands to millions of base pairs distal to it. The interaction of these sequences with one another and their transcription start sites is regulated by factors that shape the three-dimensional (3D) organization of the genome within the nucleus. Over the past decade, indirect tools exploiting high-throughput DNA sequencing have helped to map this 3D organization, have identified multiple key regulators of its structure and, in the process, have substantially reshaped our view of how 3D genome architecture regulates transcription. Now, new tools for high-throughput super-resolution imaging of chromatin have directly visualized the 3D chromatin organization, settling some debates left unresolved by earlier indirect methods, challenging some earlier models of regulatory specificity and creating hypotheses about the role of chromatin structure in transcriptional regulation.

Spleen Stiffness on Magnetic Resonance Elastography for the Detection of Portal Hypertension: A Systematic Review and Meta-Analysis.

Chronic liver disease, complicated by portal hypertension (PH), may alter the extra-hepatic hemodynamic condition and spleen stiffness (SS). We aimed to evaluate the diagnostic accuracy of MRI-based elastographic methods (MRE) for detecting PH. Seven studies were included with reference to SS with regard to the prediction of PH patients. Major outcomes considered for data extraction were diagnostic parameters for MRI for concluding mild PH, clinically significant PH and severe PH. PubMed, Scopus, Google Scholar, Cochrane and Science Direct databases were used to extract the published literature through to May, 2021Using the Rayyan Zotero and R softwares. Out of 587 studies extracted, 7 were selected based on inclusion and exclusion criteria. A QUADAS-2 assessment showed that all studies were clear in terms of patient selection and reference standard. A funnel plot showed that all the selected studies were outliers, indicating a low level of accuracy for the studies included. Subgroup analysis, with reference to SS as a predictor of PH, revealed raw mean difference (RMD) of 7.78% (95% CI 5.23-10.34, P<0.01). The corresponding RMD observed for <60 years and >60 yr were 34.26% (95% CI 9.33- 59.20, I2=100%, τ2=646.7688, P=0), and 46.92% (95% CI 20-59.33, I2=97%, τ2=1003.023, P=0) respectively. The specificity and sensitivity noted for MRI in determining SS were 0.721 and 0.747, respectively with an area-under the curve of 0.788. The estimated random effect models for specificity and sensitivity were 0.938 and 0.842, respectively. The real-time MRE has acceptable specificity and sensitivity for diagnosing SS.

A Tool for Supporting the Teaching and Modeling of Software Ecosystems Using SSN Notation

Software Ecosystems (SECO) are a set of actors and components that work as a unit, which establish relationships based on common interest to provide solutions or services for the software industry. As a company or organization expands its relationships and begins to interact with external actors, a network is formed and SECO includes both the actors and the involved artifacts. However, SECO is not commonly taught in the Software Engineering disciplines. The activity of modeling a SECO can assist in a better visualization and understanding of relationships. However, there is no official modeling standard for SECO, and the notations are quite varied. In this scenario, SSN (Software Supply Network) notation emerged to try to fill this gap. An eminent problem in the literature is the lack of support for modeling using SSN notation, the lack of available models and maintenance of models in general. In this context, this work aims to present a tool to support the teaching and modeling of SECO using the SSN notation, and thus alleviate the problem of the lack of specific modeling tools for SECO. In this work, the tool was applied in Software Engineering classes, where students filled out a questionnaire and a qualitative analysis was performed on the results. In general, the tool pleased both in terms of usability and understanding of SECO. Qualitative analysis revealed that the tool collaborates for SECO modeling, but it can improve usability and design, and there is a need for documentation and support for SECO teaching and modeling.

Some thoughts on analytical choices in the scaling model for test scores in international large-scale assessment studies

International large-scale assessments (LSAs), such as the Programme for International Student Assessment (PISA), provide essential information about the distribution of student proficiencies across a wide range of countries. The repeated assessments of the distributions of these cognitive domains offer policymakers important information for evaluating educational reforms and received considerable attention from the media. Furthermore, the analytical strategies employed in LSAs often define methodological standards for applied researchers in the field. Hence, it is vital to critically reflect on the conceptual foundations of analytical choices in LSA studies. This article discusses the methodological challenges in selecting and specifying the scaling model used to obtain proficiency estimates from the individual student responses in LSA studies. We distinguish design-based inference from model-based inference. It is argued that for the official reporting of LSA results, design-based inference should be preferred because it allows for a clear definition of the target of inference (e.g., country mean achievement) and is less sensitive to specific modeling assumptions. More specifically, we discuss five analytical choices in the specification of the scaling model: (1) specification of the functional form of item response functions, (2) the treatment of local dependencies and multidimensionality, (3) the consideration of test-taking behavior for estimating student ability, and the role of country differential items functioning (DIF) for (4) cross-country comparisons and (5) trend estimation. This article’s primary goal is to stimulate discussion about recently implemented changes and suggested refinements of the scaling models in LSA studies.

The Predictive Value of Computed Tomography Findings for Poor Visual Outcome in Traumatic Eye Injury.

Background The prognosis of visual outcome is important for patients and healthcare providers and guides proper decision-making in traumatic eye injury. In this study, we have evaluated the predictive value of computed tomography (CT) scan findings for poor visual outcomes in patients with traumatic eye injuries. Methods In a retrospective survey, documents of 200 patients with traumatic eye injury who underwent a diagnostic orbital CT scan were reviewed. Disorganized or collapsed globe, intraocular foreign body or gas, increased or decreased anterior chamber size, hemorrhage in the anterior or posterior chamber, crystalline or intraocular lens dislocation, posterior sclera thickening, globe borders haziness, orbital fracture, orbital hemorrhage, and foreign body, optic canal, and optic nerve injuries are the diagnostic clues for eye injury in CT scan. The predictive value of CT scan findings for poor visual outcome was calculated by sensitivity, specificity, accuracy, predictive values, hazard ratios, and binary logistic regression model. Results The sensitivity, specificity, accuracy, and positive predictive values showed to be high. However, there was a low negative predictive value of CT findings for the prediction of poor vision. Among the investigated factors, disorganized/collapsed globe (HR 47.72, CI 6.13–371.62), increased/decreased anterior chamber size (HR 5.04, CI 2.57–9.88), hemorrhage in anterior/posterior chamber (HR 3.58, CI 1.900–6.774/3.62, CI 1.90–6.89), globe borders haziness (HR 3.06, CI 1.33–7.01), orbital foreign body (HR 3.66, CI 1.11–12.05), and optic canal/nerve injury (HR 21.62, CI 4.73–98.78) reached the statistical significance for increasing the hazard ratio for poor visual outcome in patients with a traumatic eye injury. Logistic regression analysis showed only evidence for disorganized/collapsed globe and optic canal/nerve injury in orbital CT scan as independent predictive factors for poor visual outcome. Conclusion CT scan findings can be used as prognostic factors for visual outcomes in patients with a traumatic eye injury.

Синтез управління стабілізацією геостаціонарного супутника у точці стояння

Розроблено методи та алгоритми балістичного забезпечення управління стабілізацією довготи точки стояння геостаціонарного супутника (ГСС). Еволюція поточних координат ГСС моделювалася диференціальними рівняннями руху матеріальної точки у центральному гравітаційному полі з урахуванням обмежених структурно-параметричних збурень відомої інтенсивності, викликаних загальним впливом Сонця, Місяця, відмінністю гравітаційного поля Землі від центрального тощо. Конкретне математичне моделювання цих збурень не розглядалося, використовувалися лише властивості їх загальної обмеженості за нормою. Для синтезу управління застосовувався відомий в теорії керування орієнтацією (кутовим положенням космічного апарата) аналог — метод декомпозиції загальної задачі синтезу на кінематичну і динамічну. Для їх вирішення використовувалися відомі узагальнення прямого методу Ляпунова дослідження стійкості розвʼязків диференціальних рівнянь на дослідження стійкості замкнених обмежених множин (конкретно багатовимірних еліпсоїдів) у їх фазових просторах. Поряд із методом синтезу управління стабілізацією довготи точки стояння ГСС запропоновано методи еліпсоїдального оцінювання, викликані впливом згаданих збурень помилок стабілізації, що встановилися. Методи засновані на побудові зовнішніх еліпсоїдальних оцінок множин досяжнос­ті та граничних множин у фазових просторах динамічних систем з обмеженими структурно-параметричними збуреннями. Запропоновано також метод отримання гарантованих інтервальних оцінок помилок стабілізації, що встановилися, по кожній з координат. Ефективність запропонованих методів керування ілюструється результатами компʼютер­ного моделювання.&#x0D;

THE CONTRIBUTION OF FINANCIAL DEVELOPMENT TO FORMALIZATION THROUGH ECONOMIC GROWTH: EVIDENCE FROM CROSS-COUNTRY DATA

The aim of the paper is to formulate empirical specification models to examine whether financial development stimulates economic growth and encourages formalization. Cross-country data analysis of 140 sample countries during the period from 2000 to 2018 were utilized, together with new indexes of financial development. The income decomposition method and the two-step estimation approach, together with the multiple linear regression with interactions effect, were employed as empirical methods. It was found that financial development can lower informal self-employment through economic growth. Financial development also contributes to economic growth. Therefore, development of financial markets and financial institutions should be an alternative policy to control informal employment.

Mutual Trust Influence on the Correlation between the Quality of Corporate Internal Control and the Accounting Information Quality Using Deep Learning Assessment.

There is a close correlation between internal control and accounting information quality in the process of enterprise management, and this correlation drives the effect of internal control on accounting information quality, thus forming the effect that internal control optimization promotes accounting information quality optimization. This paper firstly gives a brief description of internal control and accounting information quality and then evaluates the correlation between internal control and accounting information quality based on deep learning and proposes a specific modeling method. Through the correlation and promotion of internal control and enterprise accounting information quality, the optimization of enterprise accounting information quality is achieved. And in combination with the actual case analysis, it is found that the method of this paper can achieve 92% accuracy of correlation analysis, and the analysis is more efficient.

Case Study of Property Value Transfer Attributed to Transit: Spatial and Temporal Hedonic Price Impact of Light Rail in Minnesota’s Twin Cities

Does the proximity of transit increase property values, and can one example apply to another? Using a spatial and temporal hedonic price framework, this study of light rail transit’s impact on residential property values compares the station area property impacts of the Green and Blue Lines in Saint Paul and Minneapolis, Minnesota. The study also points to when “value transfer” (used here to describe the comparison and application of property value impacts near transit) is appropriate, and what practitioners should keep in mind to maximize the effectiveness of the exercise. In the case of value transfer for transit, the study finds that intimate local knowledge matters more than special modeling specifications to appropriately capture the impacted properties and transfer the return of investment.

Developing a cognitive model of L2 reading comprehension for evaluative purposes in an EMI context

There have been recurrent calls in the field of educational assessment to design theoretically substantiated and empirically supported cognitive models that could be used for assessment purposes. Cognitive models, a simplified depiction of problem-solving processes employed while working on a task of interest, may demonstrate knowledge and skills possessed by individuals at different learning and performance levels. Such models could have significant implications for the assessment of crucial abilities, such as reading comprehension, which is of utmost importance to academic and personal growth. As such, this qualitative study focuses on developing a cognitive model of L2 (second language) reading comprehension. The cognitive model blends the characteristics of three types of cognitive models (i.e., the cognitive model of test specifications, domain mastery, and task performance). The study also aims to offer insights into L2 reading assessments conducted in English-medium instruction settings, where the effective execution of L2 reading processes is crucial.

GFNet: Automatic segmentation of COVID-19 lung infection regions using CT images based on boundary features

In early 2020, the global spread of the COVID-19 has presented the world with a serious health crisis. Due to the large number of infected patients, automatic segmentation of lung infections using computed tomography (CT) images has great potential to enhance traditional medical strategies. However, the segmentation of infected regions in CT slices still faces many challenges. Specially, the most core problem is the high variability of infection characteristics and the low contrast between the infected and the normal regions. This problem leads to fuzzy regions in lung CT segmentation. To address this problem, we have designed a novel global feature network(GFNet) for COVID-19 lung infections: VGG16 as backbone, we design a Edge-guidance module(Eg) that fuses the features of each layer. First, features are extracted by reverse attention module and Eg is combined with it. This series of steps enables each layer to fully extract boundary details that are difficult to be noticed by previous models, thus solving the fuzzy problem of infected regions. The multi-layer output features are fused into the final output to finally achieve automatic and accurate segmentation of infected areas. We compared the traditional medical segmentation networks, UNet, UNet++, the latest model Inf-Net, and methods of few shot learning field. Experiments show that our model is superior to the above models in Dice, Sensitivity, Specificity and other evaluation metrics, and our segmentation results are clear and accurate from the visual effect, which proves the effectiveness of GFNet. In addition, we verify the generalization ability of GFNet on another “never seen” dataset, and the results prove that our model still has better generalization ability than the above model. Our code has been shared at https://github.com/zengzhenhuan/GFNet.

Calculation of heating and cooling energy loads at the district scale: Development of MoDEM, a modular and technologically explicit platform

The rationalization of energy at the district scale requires the development of numerical tools capable of simulating not only the energy demand, but also energy loads at such a scale. However, such simulation remains difficult because of the computational cost and lack of reliable data. A common solution to both these problems is to simplify the model, generally at the expense of accuracy. To overcome these problems, a specific modeling and simulation platform called Modular District Energy Model (MoDEM) is developed. This platform enables the calculation of the heating and cooling load curves of each building, integrating the interactions between the buildings taking into account longwave radiative heat transfers between buildings external surfaces. Moreover, with different levels of models included in its calculation engine, MoDEM also enables the testing of modeling assumptions. MoDEM can also handle the geographical information system geometry and detailed solar radiation calculation. To validate the application of MoDEM, a district in Paris is modeled, comprising 900 flats built in the 1930s. The results demonstrate that 1) various models have little impact on energy consumption and 2) some physical phenomena usually not modeled appropriately, such as the longwave radiative exchange between buildings, can no longer be neglected while considering the load curve. It is worth mentioning that MoDEM is based on a co-simulation layer allowing parallel computing. From a broader point of view, this specific feature enables the easy and computationally efficient integration of various models like, for instance, specific electricity consumption, renewable energy production, grid management simulation and energy storage. • A Modular District Energy Model (MoDEM) was developed. • MoDEM can be adapted to various situations. • The district-scale model can reduce simplification and integrate statistical data.

The Binomial Distribution: Historical Origin and Evolution of Its Problem Situations

The increase in available probabilistic information and its usefulness for understanding the world has made it necessary to promote probabilistic literate citizens. For this, the binomial distribution is fundamental as one of the most important distributions for understanding random phenomena and effective decision making, and as a facilitator for the understanding of mathematical and probabilistic notions such as the normal distribution. However, to understand it effectively, it is necessary to consider how it has developed throughout history, that is, the components that gave it the form and meaning that we know today. To address this perspective, we identify the problem situations that gave origin to the binomial distribution, the operational and discursive practices developed to find solutions, and the conflicts that caused a leap in mathematical and probability heuristics, culminating in what is now known as the binomial distribution formula. As a result, we present five historical links to the binomial phenomenon where problem situations of increasing complexity were addressed: a case study using informal means (such as direct counting), the formalization of numerical patterns and constructs related to counting cases, specific probability calculus, the study and modeling of probability in variable or complex phenomena, and the use of the distribution formula as a tool to approaching notions such as the normal distribution. The periods and situations identified correspond to a required step in the design of binomial distribution learning from a historical epistemological perspective and when solving conflicts.

Cinco-Based Approach for Agent Petri Net Models

In this paper, a simplified simulation approach of the Agent Petri Nets (APN) model is proposed to facilitate and accelerate the development and evaluation of system models. The main idea is to apply the new trends of model-driven approach to the design of specific tools and provide facilities to evaluate quickly the developed system. The CINCO tooling suite is used to full generation of a specific graphical modelling tool for the APN model. The simulation code is simply added to the generated code. This tool allows users to build models based on Agent Petri Nets formalism and at the same time to execute these models. Combined APN with the CINCO tool greatly simplifies the development of modelling applications and allows users to get an early experience with a prototype system. To highlight the ability of the proposed tool, a real-life example of a traffic light adaptive system is considered and discussed here.