Analytical Modeling Approach Research Articles

Dynamic model of the temperature downstream to an indirect charge-air cooler considering heat losses to the environment

Thermal management plays an increasingly important role for internal combustion engines due to the high demands on the transient response of vehicles and the legal requirements for pollutants. In particular, the goal to reduce exhaust-gas raw emissions while optimizing fuel consumption can be supported with the help of on-demand temperature control. One strategy to meet the thermal requirements connected to this consists of model-based approaches. For an implementation of such methods on an automotive electronic control unit, a sufficiently high accuracy has to be ensured by the underlying model, alongside the fulfillment of the real-time operation demand. In this publication, we suggest two analytical modeling approaches for heat exchangers with poor thermal isolation with respect to the surrounding. Here we lay a special focus on the real-time capability of the given algorithm for an automotive on-board application. To this end, we utilize the Hammerstein method which allows to divide the overall physical system into a nonlinear stationary part and a dynamical linear one. The former are based on the well-known concept for the dimensionless temperature change of heat exchangers, where we generalize this approach in order to take account of the heat-losses to the surrounding. We demonstrate our model for an indirect charge-air cooler in an internal combustion engine. For both models we find excellent accuracy, with an overall mean-absolute error of 1.23 K and 1.33 K respectively, when compared to experimental data sets containing measurements from the Worldwide harmonized Light Duty Test Procedure.

Optimum coupling of photovoltaic devices and Peltier coolers for improved performance and stability

ABSTRACT Photovoltaics (PV) have admittedly seen substantial advancements during the last decades. Improvements in the performance of conventional technologies as well as the emergence of cost effective alternatives facilitated its establishment as a major contributor towards the adoption of a green power production model. Nevertheless, photovoltaics still suffer from efficiency issues, mainly related to performance dependency upon operating temperature. For perovskite cells, it is also crucial to maintain temperature below a defined threshold to avoid premature degradation. Photovoltaic-thermoelectric hybridization has been recently proposed as a means of restricting excessive temperature increase, reducing recombination and improving the overall efficiency response. In this work, the performance of a hybrid photovoltaic-Peltier system is examined through an analytical modelling approach. Different thermoelectric coolers (TECs) and PV cell types have been studied. Furthermore, a sensitivity analysis has been conducted in order to identify the most critical parameters for obtaining optimum coupling between PV and TEC technologies.

Economic order quantity determination model

Order processing and inventory management are critical to inventory costs. Therefore, this research aims to reduce cost and lead time for case study construction material retailer. The objectives are to 1) study the order processing and inventory management of construction material retailer; and 2) to study inventory analysis techniques in decision making to determine the appropriate order quantity. 30 employees are interviewed for collecting data and a case study company inventory raw material data from January – December, 2021 is used as data. ABC analysis and EOQ are used to calculate quantities for the right order and quantities as the high demand. The data in the analysis is collected from samples of products that are important to production process, high value and high demand. The result of comparison between a case study of construction materials retailer’s inventory management model and ABC analysis and EOQ approach in group “A” material found that EOQ obtain cost 2,404,148.24 Baht and a case study of construction materials retailer’s inventory management model obtain cost 3,314,648.93 Baht. Therefore, EOQ saves cost than a case study of construction materials retailer’s inventory management model for 910,500.69 Baht per year.

Evaluating combined effect of different coated urea with PGPR on yield and nitrogen use efficiency in lowland rice by regression model and principal component analysis approach

Nitrogen (N) loss is a key problem in rice fields, which results in low nitrogen use efficiency (NUE) of applied N and could be a major limitation from economic and environmental perspectives. In order to improve the NUE, use of slow or controlled released N fertilizers could be a potential alternative to the conventional N inputs. With this objective to evaluate the effect of different slow-release fertilizers on NUE and grain yield of rice, a pot experiment was conducted during kharif 2017 at Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Banaras Hindu University Varanasi. The experiment comprised thirteen treatments including control, Polymer (PCU) and Neem coated urea (NCU) and their combination with Farm yard manure (FYM) and Plant growth promoting rhizobacteria (PGPR). The combined application of PCU, NCU, FYM and PGPR in treatment T12 provided highest grain yield 47.98 gpot−1and followed by T8 46.60 gpot−1. The result showed that, compared with the PCU treatment T2 (PCU100 Split), the Agronomic efficiency (AE) and Apparent nitrogen recovery efficiency (ANR%) of the mixed doses of coated urea treatment T12 (PCU50 NCU50 FYM10 PGPR Basal) increased by 20.38% and 28.48% respectively. Therefore, application of FYM and PGPR with coated urea improved rice yield, nutrient uptake and their use efficiency and potentially recommended in eastern plain of the Indo-Gangetic Plain for the sustainable production of rice crop.

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches.

From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

Analytical modeling approach to drain current characterization of ionization irradiated dual material trigate TFET

Drain current modeling of dual material (DM) trigate TFET under the influence of radiation induced defects characterized by oxide trapped charges and ionized interface traps is formulated in this present research endeavor. An analytical methodology incorporating three dimensional Poisson's equation and Kane's approach introducing the impact of charge traps is derived here to explore the radiation response of the device. Radiation induced device characterization is then investigated in terms of electrostatic potential distribution, electric field profile and drain current performance of DM trigate TFET. Charge trapping and device parameters variation are further examined to capture the device response to radiation effect. Relevant simulated data extracted by ATLAS device simulator is used to substantiate the derived analytical model presented in this paper.

Anisotropic double‐Gaussian analytical wake model for an isolated horizontal‐axis wind turbine

AbstractAn anisotropic double Gaussian (DG) model for analytical wake modeling to predict the streamwise wake velocity behind an isolated non‐yawed horizontal‐axis wind turbine is proposed. The proposed model is based upon the conservation of mass and momentum inside a streamtube control volume. The wake growth rate parameters to distinguish the wake expansion rate between lateral and vertical directions were tuned based on numerical and measurement data of utility‐scale turbines. It was found that the proposed model can give feasible predictions within the full‐wake region under different inflow conditions. In addition, the other analytical models based on top‐hat shape and single Gaussian approaches were evaluated for comparison. The root‐mean‐square error statistical analysis was used to evaluate the performance of each examined model under different flow conditions. In general, the proposed model outperformed the other examined models in all wake region categories, particularly within the near‐wake region and the onset of the far‐wake region, which are beyond the scope of the conventional approach for analytical wake modeling. This advantage gives the potential for the proposed model to provide a better prediction for the wake flow estimation within tightly packed wind farms.

Microstructure-sensitive investigation on the mechanical behavior of CNT-reinforced composites considering debonding damage based on cohesive finite element method

In this paper, an analytical modeling and numerical approach based on the finite element (FE) method are introduced to study the influence of the real morphology (e.g., length and waviness) and distribution of carbon nanotubes (CNTs) on the mechanical behavior of the CNT-reinforced composites. The analytical model has considered the real morphology effect using the equivalent curved CNTs with perfect bonding. The equivalent chord length, diameter, and waviness have been computed using the abundance of different values of measured parameters in an actual microstructure of the CNTs in the matrix. In the FE method, a computational tool is used to map an actual composite microstructure onto a FE representative volume element (RVE) with the exact shape, size, distribution, and waviness of CNTs. FE analysis has been carried out in both well-bonded and interface-debonding conditions. The cohesive zone model (CZM) with bilinear traction-separation law for both opening and sliding modes has been utilized to capture the debonding at the CNT-matrix interface. Results indicate that in the presence of weak bonding, CNT waviness can enhance the composite stiffness rather than reduce it. A quite different conclusion to what can be obtained with the assumption of perfect adhesion. In the presence of poor bonding, the curvature has a positive effect on composite properties and improves the load transferring from the matrix. FE analysis for different RVEs highlights that the curvature and length of the CNTs are essential features of the microstructure, and they can effectively influence the mechanical properties of the material. Results demonstrate that the equivalent curved CNTs cannot appropriately represent the real microstructure.

A REFINED METHOD FOR ANALYTICAL MODELING OF THE PROCESSES OF SPREADING VIRUS SOFTWARE TO ASSESS THE SECURITY OF INFORMATIZATION OBJECTS

Task. The purpose of writing this article is to improve the security of information in information systems. Model. The article proposes an improved method for analytical modeling of the process of spreading virus software in information systems in relation to the features of a special class of attacks implemented by the family of virus software - ransomware. Conclusions. The proposed method makes it possible to analytically calculate the intensity of infection of hosts of a protected information system with various types of virus software and use the obtained characteristics as initial data for the final assessment of the information security risk on the protected informatization object. Value. Modeling in the environment of the mathematical package PTC Mathcad Prime 7 showed the applicability of the proposed approach for analytical threat modeling.

Characterizing discourses about COVID-19 vaccines on Twitter: a topic modeling and sentiment analysis approach

ABSTRACT Background Evidence-based health communication is crucial for facilitating vaccine-related knowledge and addressing vaccine hesitancy. To that end, it is important to understand the discourses about COVID-19 vaccination and attend to the publics’ emotions underlying those discourses. Methods We collect tweets related to COVID-19 vaccines from March 2020 to March 2021. In total, 304,292 tweets from 134,015 users are collected. We conduct a Latent Dirichlet Allocation (LDA) modeling analysis and a sentiment analysis to analyze the discourse themes and sentiments. Results This study identifies seven themes of COVID-19 vaccine-related discourses. Vaccine advocacy (24.82%) is the most widely discussed topic about COVID-19 vaccines, followed by vaccine hesitancy (22.29%), vaccine rollout (12.99%), vaccine facts (12.61%), recognition for healthcare workers (12.47%), vaccine side effects (10.07%), and vaccine policies (4.75%). Trust is the most salient emotion associated with COVID-19 vaccine discourses, followed by anticipation, fear, joy, sadness, anger, surprise, and disgust. Among the seven topics, vaccine advocacy tweets are most likely to receive likes and comments, and vaccine fact tweets are most likely to receive retweets. Conclusions When talking about vaccines, publics’ emotions are dominated by trust and anticipation, yet mixed with fear and sadness. Although tweets about vaccine hesitancy are prevalent on Twitter, those messages receive fewer likes and comments than vaccine advocacy messages. Over time, tweets about vaccine advocacy and vaccine facts become more dominant whereas tweets about vaccine hesitancy become less dominant among COVID-19 vaccine discourses, suggesting that publics become more confident about COVID-19 vaccines as they obtain more information.

A Novel Analytical Modeling Approach for Quality Propagation of Transient Analysis of Serial Production Systems.

Production system modeling (PSM) for quality propagation involves mapping the principles between components and systems. While most existing studies focus on the steady-state analysis, the transient quality analysis remains largely unexplored. It is of significance to fully understand quality propagation, especially during transients, to shorten product changeover time, decrease quality loss, and improve quality. In this paper, a novel analytical PSM approach is established based on the Markov model, to explore product quality propagation for transient analysis of serial multi-stage production systems. The cascade property for quality propagation among correlated sequential stages was investigated, taking into account both the status of the current stage and the quality of the outputs from upstream stages. Closed-form formulae to evaluate transient quality performances of multi-stage systems were formulated, including the dynamics of system quality, settling time, and quality loss. An iterative procedure utilizing the aggregation technique is presented to approximate transient quality performance with computational efficiency and high accuracy. Moreover, system theoretic properties of quality measures were analyzed and the quality bottleneck identification method was investigated. In the case study, the modeling error was 0.36% and the calculation could clearly track system dynamics; quality bottleneck was identified to decrease the quality loss and facilitate continuous improvement. The experimental results illustrate the applicability of the proposed PSM approach.

Performance Investigation of Fiber Distributed Data Interface in Networking

Abstract: The extent of the ring, the number of stations on the ring, the number of stations waiting to communicate, and the frame size are all setup and workload characteristics that affect the performance of an FDDI LAN. In addition, the target token rotation time is a significant parameter that network management may regulate to optimise performance (TTRT). The influence of the TTRT on various performance indicators for different ring topologies was investigated using analytical modelling and simulation approaches. The performance of the FDDI network is the topic of this research. The functionality of the FDDI network was then assessed in terms of efficiency, reaction time, and network cost using various values of ring latency, fixed target token rotation time (TTRT), and number of stations. The entire project is done in MATLAB. Keywords: FDDI LAN, TTRT, Interface, Networking, MATLAB

Quantifying volume and high-speed technical actions of professional soccer players using foot-mounted inertial measurement units.

AimsThe aims of the study were two-fold: i) examine the validity and reliability of high-speed kicking actions using foot-mounted inertial measurement unit’s (IMU), ii) quantify soccer players within-microcycle and inter-positional differences in both the frequency and speed of technical actions.MethodsDuring the in-season phase (25 weeks) of the UK domestic season, 21 professional soccer player ball releases, high-speed ball releases and ball release index were analysed. Pearson product-moment correlation coefficient and confidence intervals were used to determine the validity between the systems, whilst a general linear mixed model analysis approach was used to establish estimated marginal mean values for total ball releases, high-speed ball releases and ball release index.ResultsGood concurrent validity was observed for ball release velocity and high-speed kicks against a high-speed camera (r2- 0.96, CI 0.93–0.98). Ball releases, high-speed ball releases and ball release index all showed main effects for fixture proximity (p>0.001), playing positions (p>0.001) and across different training categories (p>0.001). The greatest high-speed ball releases were observed on a match-day (MD)+1 (17.6 ± 11.9; CI- 16.2 to 19) and MD-2 (16.8 ± 15; CI- 14.9 to 18.7), with MD+1 exhibiting the highest number of ball releases (161.1 ± 51.2; CI- 155.0 to 167.2) and ball release index (145.5 ± 45.2; CI- 140.1 to 150.9) across all fixture proximities. Possessions (0.3 ± 0.9; CI- 0.3 to 0.4) and small-sided games (1.4 ± 1.6; CI- 1.4 to 1.5), had the lowest values for high-speed ball releases with technical (6.1 ± 7.2; CI- 5.7 to 6.6) and tactical (10.0 ± 10.5; CI- 6.9 to 13.1) drills showing the largest high-speed ball releases.ConclusionsThe present study provides novel information regarding the quantification of technical actions of professional soccer players. Insights into absolute and relative frequency and intensity of releases in different drill types, provide practitioners with valuable information on technical outputs that can be manipulated during the process of planning training programmes to produce desired outcomes. Both volume and speed of ball release actions should be measured, when monitoring the technical actions in training according to fixture proximity, drill type and player position to permit enhanced training prescription.

Performance evaluation of gate engineered InAs–Si heterojunction surrounding gate TFET

In semiconductor industry, at nanoscale dimensions, numerous field effect devices have been proposed and investigated for further improvement in performance of low power circuit and system. In the present research report, a novel low power FET device structure namely: Surrounding Gate Triple Material Heterojunction Tunnel Field Effect Transistor (SGTM-heTFET) has been proposed with the analytical modeling approach. The benefits of surrounding gate and tunnel FETs are coupled to create a new structure, to decrease short channel effects. Three different gate materials with different work functions replace the gate material that surrounds the device. An analytical model of surface potential(ψ), electric field(E) and drain current (I DS ) have been developed for SGTM-heTFET. With the use of low work function material such as 4.0eV, 4.6eV and 4.0eV, the proposed model shows a better ON current of 10 −5 A/μm for a V GS of 0.7V, ON-OFF ratio of 10 10 with the sub-threshold swing of 50mV/dec. The developed model's for SGTM-heTFET shows excellent device characteristics and have been verified using TCAD simulation, ensuring the model's accuracy. • A Novel device structure called Surrounding Gate Triple Material Heterojunction TFET is proposed. • The electrical parameters are analytically calculated and also compared with the simulated output. • Along with triple material, hetero-dielectric (HfO 2 /SiO 2 ) arrangement is also proposed to reduce leakage current. • Improved ON current and I ON /I OFF ratio is achieved with SS less than 60mV/dec.

Are electricity storage systems in the Netherlands indispensable or doable? Testing single-application electricity storage business models with exploratory agent-based modeling

Electricity storage systems (ESS) are hailed by many scholars and practitioners as a key element of the future electricity systems and a key step toward the transition to renewables. Nonetheless, the global speed of ESS implementation is relatively slow, and among possible reasons is the lack of viable business models. We developed an agent-based model to simulate the behavior of ESS within the Dutch electricity market. We adopted an exploratory modeling analysis (EMA) approach to investigate the effects of two single-application business models over a period of twenty years on the value of ESS from the perspective of both investors and the government under uncertainties in the ESS technical and economics characteristics, and uncertainties in market conditions and regulations. Our results show ESS is not profitable in most scenarios, and generally “wholesale arbitrage” business model leads to more profit than “reserve capacity”. In addition, ESS economic and technical characteristics play more important roles in the value of ESS than market conditions, and carbon pricing.

Detecting COVID-19 Status Using Chest X-ray Images and Symptoms Analysis by Own Developed Mathematical Model: A Model Development and Analysis Approach

COVID-19 is a life-threatening infectious disease that has become a pandemic. The virus grows within the lower respiratory tract, where early-stage symptoms (such as cough, fever, and sore throat) develop, and then it causes a lung infection (pneumonia). This paper proposes a new artificial testing methodology to determine whether a patient has been infected by COVID-19. We have presented a prediction model based on a convolutional neural network (CNN) and our own developed mathematical equation-based algorithm named SymptomNet. The CNN algorithm classifies lung infections (pneumonia) using frontal chest X-ray images, and the symptom analysis algorithm (SymptomNet) predicts the possibility of COVID-19 infection from the developed symptoms in a patient. By combining the CNN image classifier method and SymptomNet algorithm, we have developed a model that predicts COVID-19 patients with an approximate accuracy of 96%. Ten out of the 13 symptoms were significantly correlated to the COVID-19 disease. Specially, fever, cough, body chills, shortness of breath, muscle pain, and sore throat were shown to be significantly related (r = 0.20; p = 0.001, r = 0.20; p < 0.001, r = 0.22; p < 0.001, r = 0.16; p < 0.001, r = −0.45; p < 0.001, r = −0.35; p < 0.001, respectively). In this model, the CNN classifier has an accuracy of approximately 96% (training loss = 0.1311, training accuracy = 0.9596, validation loss: 0.2754, and validation accuracy of 0.9273, F1-score: 94.16, precision: 91.33), and the SymptomNet algorithm has an accuracy of 97% (485 successful predictions out of 500 samples). This research work obtained promising accuracy while predicting COVID-19-infected patients. The proposed model can be ubiquitously used at a low cost and achieve high accuracy.

Decision-Making Models for Healthcare Supply Chain Disruptions: Review and Insights for Post-pandemic Era

The black swan event caused by the novel coronavirus (COVID-19) has majorly exposed the vulnerabilities of the supply chain of many of the firms. However, healthcare sector is one of the significant areas which majorly got disrupted during this pandemic event. The majority of the studies focus on the reviews on supply chain disruption modelling to analyse the risk and their effect on supply chains. This study provides a systemic literature review of various supply chain disruption models in the healthcare sector, analyses them based on different parameters identified from the literature. Some of our key findings include (1) analysis of quantitative and analytical modelling approaches used for dealing with disruption in healthcare sector; (2) managerial implications that derive potential future research avenues and insights for practitioners in the domain of healthcare supply chain disruption in post-pandemic era to improve international competitiveness and operational excellence.

A Multilevel Analytical Theory for Prediction of Ferroelectric Perovskite Oxide Properties from Composition.

Prediction of properties from composition is a fundamental goal of materials science that is particularly relevant for ferroelectric perovskite oxide solid solutions where compositional variation is a primary tool for material design. Design of ferroelectric oxide solid solutions has been guided by heuristics and first-principles and Landau-Ginzburg-Devonshire theoretical methods that become increasingly difficult to apply in ternary, quaternary, and quintary solid solutions. To address this problem, a multilevel model is developed for the prediction of the ferroelectric-to-paraelectric transition temperature (Tc ), coercive field (Ec ), and polarization (P) of PbTiO3 -derived ferroelectric solid solutions from composition. The characteristics of the materials at different length scales, starting at the level of the electronic structure and chemical bonding of the constituent ions and ending at the level of collective behavior, are analytically related by using ferroelectric domain walls and cationic off-center displacements as the key links between the different levels of the model. The obtained composition-structure-property relationships provide a unified quantitatively predictive theory for understanding PbTiO3 -derived solid solutions. Such a multilevel analytical modeling approach is likely to be generally applicable to different classes of ferroelectric perovskite oxides and to other functional properties, and to materials and properties beyond the field of ferroelectrics.

Year-Round Dive Characteristics of Male Beluga Whales From the Eastern Beaufort Sea Population Indicate Seasonal Shifts in Foraging Strategies

Dive behavior represents multiple ecological functions for marine mammals, but our understanding of dive characteristics is typically limited by the resolution or longevity of tagging studies. Knowledge on the time-depth structures of dives can provide insight into the behaviors represented by vertical movements; furthering our understanding of the ecological importance of habitats occupied, seasonal shifts in activity, and the energetic consequences of targeting prey at a given depth. Given our incomplete understanding of Eastern Beaufort Sea (EBS) beluga whale behavior over an annual cycle, we aimed to characterize dives made by belugas, with a focus on analyzing shifts in foraging strategies. Objectives were to (i) characterize and classify the range of beluga-specific dive types over an annual cycle, (ii) propose dive functions based on optimal foraging theory, physiology, and association with environmental variables, and (iii) identify whether belugas undergo seasonal shifts in the frequency of dives associated with variable foraging strategies. Satellite-linked time-depth-recorders (TDRs) were attached to 13 male belugas from the EBS population in 2018 and 2019, and depth data were collected in time series at a 75 s sampling interval. Tags collected data for between 13 and 357 days, including three tags which collected data across all months. A total of 90,211 dives were identified and characterized by twelve time and depth metrics and classified into eight dive types using a Gaussian mixed modeling and hierarchical clustering analysis approach. Dive structures identify various seasonal behaviors and indicate year-round foraging. Shallower and more frequent diving during winter in the Bering Sea indicate foraging may be energetically cheaper, but less rewarding than deeper diving during summer in the Beaufort Sea and Arctic Archipelago, which frequently exceeded the aerobic dive limit previously calculated for this population. Structure, frequency and association with environmental variables supports the use of other dives in recovery, transiting, and navigating through sea ice. The current study provides the first comprehensive description of the year-round dive structures of any beluga population, providing baseline information to allow improved characterization and to monitor how this population may respond to environmental change and increasing anthropogenic stressors.

Independent assessment of a deep learning system for lymph node metastasis detection on the Augmented Reality Microscope

Several machine learning algorithms have demonstrated high predictive capability in the identification of cancer within digitized pathology slides. The Augmented Reality Microscope (ARM) has allowed these algorithms to be seamlessly integrated within the pathology workflow by overlaying their inferences onto its microscopic field of view in real time. We present an independent assessment of the LYmph Node Assistant (LYNA) models, state-of-the-art algorithms for the identification of breast cancer metastases in lymph node biopsies, optimized for usage on the ARM. We assessed the models on 40 whole slide images at the commonly used objective magnifications of 10×, 20×, and 40×. We analyzed their performance across clinically relevant subclasses of tissue, including breast cancer, lymphocytes, histiocytes, blood, and fat. Each model obtained overall AUC values of approximately 0.98, accuracy values of approximately 0.94, and sensitivity values above 0.88 at classifying small regions of a field of view as benign or cancerous. Across tissue subclasses, the models performed most accurately on fat and blood, and least accurately on histiocytes, germinal centers, and sinus. The models also struggled with the identification of isolated tumor cells, especially at lower magnifications. After testing, we reviewed the discrepancies between model predictions and ground truth to understand the causes of error. We introduce a distinction between proper and improper ground truth for analysis in cases of uncertain annotations. Taken together, these methods comprise a novel approach for exploratory model analysis over complex anatomic pathology data in which precise ground truth is difficult to establish.