Developed Model Research Articles

Study on Sustainability of Property Insurance

This paper is about developing and applying insurance models for historic landmarks that are in locations that experience extreme weather events. In order to make it,we devided this whole problem into 2 Questions as followed. Question 1 is about developing an insurance model for property owners and developers in areas that experience extreme weather events.The model has considered the risk level, feasibility, and desirability of the insurance, and use the Bayesian graph, maximum likelihood distribution, function graph, and entropy weight method to construct and evaluate the model.Question 2 is about applying the insurance model to a specific case study, which is the Cape Hatteras Lighthouse in North Carolina. We’ve assessed the value of the lighthouse, and compare and contrast the insurance options for the lighthouse before and after the relocation.All in all, the paper demonstrates the models using the Cape Hatteras Lighthouse as case study, and provides some results, implications, and recommendations for the stakeholders.

AI-based diagnosis of chronic obstructive pulmonary disease from low-dose CT images

Background: Chronic obstructive pulmonary disease (COPD) is a group of diseases characterized by airflow blockage. It is one of the leading causes of global mortality and is primarily attributed to smoking. COPD patients are usually diagnosed by spirometry test. Although regarded as the gold standard for COPD diagnosis, the spirometry test carries contraindications, thus prompting the development of low-dose computed tomography (low-dose CT) scan as an alternative for COPD screening. However, a practical limitation of diagnosing COPD from CT images is its reliance on the expertise of a skilled radiologist. Objective: To address this limitation, we aimed to develop a deep-learning model for the automated classification of COPD and non-COPD from low-dose CT images. Materials and methods: We examined the potential of a convolutional neural network for identifying COPD. Our dataset consisted of 10,000 low-dose CT images obtained from a lung cancer screening program, involving both ex-smokers and current smokers deemed at high risk of lung cancer. Spirometry data served as the ground truth for defining COPD. We used 90% of the datasets for training and 10% for testing. Results: Our developed model achieved notable performance metrics: an area under the receiver operating characteristic curve (AUC) of 0.97, an accuracy of 0.89, a precision of 0.85, a recall of 0.96, and an F1-score of 0.90. Conclusion: Our study demonstrates the potential of deep learning models to augment clinical assessments and improve the diagnosis of COPD, thereby enhancing diagnostic accuracy and efficiency. The findings suggest the feasibility of integrating this technology into routine lung cancer screening programs for COPD detection.

Multicentral Agent-Based Model of Six Epidemic Waves of COVID-19 in the Nizhny Novgorod Region of Russian Federation

Relevance. To investigate the characteristics of the COVID-19 pandemic and introduce timely and effective measures, there is a need for models that can predict the impact of various restrictive actions or characteristics of disease itself on COVID-19 spread dynamics. Employing agent-based models can be attractive because they take into consideration different population characteristics (e.g., age distribution and social activity) and restrictive measures, laboratory testing, etc., as well as random factors that are usually omitted in traditional modifications of the SIR-like dynamic models. Aim. Improvement of the previously proposed agent-based model [23,24] for modeling the spread of COVID-19 in various regions of the Russian Federation. At this stage, six waves of the spread of COVID-19 have been modeled in the Nizhny Novgorod region as a whole region, as well as in its individual cities, taking into account restrictive measures and vaccination of the population. Materials and Methods. In this paper we extend a recently proposed agent-based model for Monte Carlo-based numerical simulation of the spread of COVID-19 with consideration of testing and vaccination strategies. Analysis is performed in MATLAB/ GNU Octave. Results. Developed multicentral model allows for more accurate simulation of the epidemic dynamics within one region, when a patient zero usually arrives at a regional center, after which the distribution chains capture the periphery of the region due to pendulum migration. Furthermore, we demonstrate the application of the developed model to analyze the epidemic spread in the Nizhny Novgorod region of Russian Federation. The simulated dynamics of the daily newly detected cases and COVID-19-related deaths was in good agreement with the official statistical data both for the region as whole and different periphery cities. Conclusions. The results obtained with developed model suggest that the actual number of COVID-19 cases might be 1.5–3.0 times higher than the number of reported cases. The developed model also took into account the effect of vaccination. It is shown that with the same modeling parameters, but without vaccination, the third and fourth waves of the epidemic would be united into one characterized by a huge rise in the morbidity rates and the occurrence of natural individual immunity with the absence of further pandemic waves. Nonetheless, the number of deaths would exceed the real one by about 9–10 times.

Based on the "Internet +" Era of Tourism and Hotel Industry Development Thinking

Amidst the surge of the "Internet Plus" era, the hospitality industry finds itself amidst unprecedented opportunities and challenges. This discourse revolves around an in-depth exploration of the current status, development models, and future strategies of the hospitality sector in the "Internet Plus" era. Presently, internet technologies are incessantly permeating various facets of the hospitality industry, profoundly influencing industry dynamics and operational models. The article begins by analyzing the fundamental status quo of the hospitality industry, unveiling industry characteristics and challenges under the backdrop of "Internet Plus." Subsequently, it delves into various development models within the Internet Plus hospitality sector, encompassing innovations in online booking and distribution models, optimization of intelligent services and experiences, as well as precision marketing and operational management driven by data. Additionally, the article proposes development strategies for the hospitality industry in the "Internet Plus" era, addressing technological innovation and digital transformation, enhancement of service quality and perceptual experiences, and the expansion of diversified business development pathways.

The impact of professional learning and development on primary and intermediate teachers’ digital technologies knowledge and efficacy beliefs

AbstractTo prepare young people for the increasingly complex and fluid world of their future, many countries have begun introducing digital technologies concepts and skills into their curricula. In 2017, the New Zealand National Curriculum was updated to incorporate digital technologies concepts in both the indigenous Māori-medium curriculum and the English-medium curriculum. This study investigated the long-term impact of three different models of digital technologies in professional learning and development on primary and intermediate schoolteachers’ knowledge and efficacy beliefs (value beliefs, self-efficacy beliefs and teaching efficacy beliefs). Professional learning and development were found to have a positive long-term impact on teachers’ digital technologies knowledge and efficacy beliefs, yet no one professional learning and development model was found to be more effective than another. Post professional learning and development, teachers were found to want more support on how to plan for Digital Technologies curricula implementation and fully adopt twenty-first-century pedagogical practices. School environments were shown to influence efficacy beliefs, and a lack of time in the classroom to plan for and upskill in digital technologies was reported. Limitations of the study are discussed, and areas for future research are identified.

Introducing the Occupational Health and Safety Potential Midpoint Impact Indicator in Social Life Cycle Assessment

Occupational health and safety (OSH) is crucial for sustainable development, recognized by corporations, the European Union, and Sustainable Development Goals. This study introduces a characterization model for OSH in the social life cycle assessment (S-LCA) to support the quantification of OHS along product supply chains and sustainable decision making. The characterization model aims to provide a practical approach for assessing OHS at the product level with actual working hours or recommends a secondary approach with monetary data, when working hours are unavailable, to calculate the Occupational Health and Safety Potential (OHSP). The developed model was tested in a theoretical case study on shirt production in Europe and globally. The case study shows that the European shirt value chain resulted in higher OHSP values than the global shirt values chain. In addition, the model shows which life cycle stages and organizations highly contributed to the OHSP results. In both approaches, the shirt production stage contributed highly. Differences in results emerged based on the calculation approach, underscoring the model’s versatility, because increasing the complexity of calculating the CFs with monetary values will affect the results based on sectorial monetary output. Additionally, the study mentions benefits to the operationalization of social impact assessment and limitations when the developed characterized model is employed. Last, this study aids in offering a tool for organizations to meet the demands of the new Corporate Sustainability Reporting Directive by quantifying and publicizing OHS data.

Flavonoid Oxidation Potentials and Antioxidant Activities-Theoretical Models Based on Oxidation Mechanisms and Related Changes in Electronic Structure

Herein, I will review our efforts to develop a comprehensive and robust model for the estimation of the first oxidation potential, Ep1, and antioxidant activity, AA, of flavonoids that would, besides enabling fast and cheap prediction of Ep1 and AA for a flavonoid of interest, help us explain the relationship between Ep1, AA and electronic structure. The model development went forward with enlarging the set of flavonoids and, that way, we had to learn how to deal with the structural peculiarities of some of the 35 flavonoids from the final calibration set, for which the Ep1 measurements were all made in our laboratory. The developed models were simple quadratic models based either on atomic spin densities or differences in the atomic charges of the species involved in any of the three main oxidation mechanisms. The best model takes into account all three mechanisms of oxidation, single electron transfer-proton transfer (SET-PT), sequential proton loss electron transfer (SPLET) and hydrogen atom transfer (HAT), yielding excellent statistics (R2 = 0.970, S.E. = 0.043).

Urban political economy of elite education: international programs in Chinese elite public high schools

ABSTRACT This ethnographic data-driven article investigates the urban political economy of a new form of elite schooling, which is represented by the fee-charging international programs recently established by ‘key’ public high schools in metro China. Grounding the analysis in Harvey’s work on urbanization and neoliberalism, the literature on China’s development model, and Bourdieu’s theory of capital conversion, this article reveals that the construction of an international program is deeply implicated in the school’s social relationships with the state, real estate developers, and urban middle classes. While the program provides wealthy Chinese families with access to international higher education for accumulating desirable capitals at ‘world-class’ universities, the Chinese elite school generates its own capital. Through theorizing how the creation of the fee-charging international program draws together multiple forms of neoliberal profit-making and multi-scalar capital accumulation, this study adds novel conceptual and empirical insights to the emergent scholarship of elite and international education.

RBFNN-based UWB 4×4 MIMO Antenna Design with Compact Size, High Isolation, and Improved Diversity Performance for Millimeter-Wave 5G Applications

Abstract The millimeter-wave spectrum has emerged as a compelling solution to address the pressing need for high-datarate
capabilities in the development of 5G technology systems. Spanning between 20 GHz and 40 GHz, this spectrum
encompasses several prominent frequency bands crucial for advancing 5G applications. In light of this, our study
presents a thorough investigation into the design and performance of a compact cross-shaped slot broadband antenna,
complemented by a 4×4 Multiple-InputMultiple-Output (MIMO) configuration tailored for 5G operations at
28 GHz. The primary objective of this study is to develop an antenna system capable of achieving an extended bandwidth
ranging from 20 GHz to 40 GHz, effectively covering the crucial frequency bands essential for 5G millimeterwave(
mmWave) operations. To accomplish this, the optimization of antenna performance is meticulously carried out
using the Radial Basis FunctionNeuralNetworks (RBFNN) model. The RBFNNmodel serves as a robust tool for establishing
the intricate relationship between antenna dimensions, resonant frequency, and bandwidth. Subsequently,
the developed RBFNN model is employed to predict optimal antenna dimensions, ensuring resonance at 28 GHz and
meeting specified bandwidth targets. The single antenna is designed with a rectangular patch and a cross-shaped
slot and is constructed on the low loss Rogers RT Duroid 5880 substrate. This design reaches an outstanding bandwidth
of 19.5 GHz, and exhibits excellent radiation characteristics, with a high radiation efficiency of up to 99% and a
corresponding gain of 5.75 dB. The antenna’s design and performance are rigorously designed using HFSS software,
which is then compared to the results acquired using CST software. In addition, the proposed MIMO configuration
offers excellent performance in terms of key features such as small size (16 × 16.2 mm2), very wide bandwidth of 20
GHz, good gain of 6.75 high isolation exceeding 35 dB, and significant improvements in diversity performance measures
such as Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL), Total Active
Reflection Coefficient (TARC), and Mean Effective Gain (MEG). The potential of the proposed MIMO configuration
for high-speed applications is particularly remarkable. Practical verification of the MIMO configuration is carefully
carried out by fabrication andmeasurement. Experimental results strongly confirmthe effectiveness of the proposed
antenna design, establishing it as a competitive challenger for 5G technology.

Uniform Modeling of Heat Production Costs in Single-Family and Multi-Family Houses

To ensure a fair and extensive comparison of different heating systems four representative buildings including their heat demand for space heating and domestic hot water were defined. A model for the heat production costs has been developed based on existing methods like the VDI 2067 part 1 [1] and the simplified approach for the calculation of the "Levelised Cost of Heat" (LCoH) of the IEA SHC Task 54 "Price Cost reduction in solar thermal systems" [2]. The developed model includes the costs of purchase, installation, operation, maintenance and disposal and was implemented in an EXCEL calculation tool which enables the calculation of the total life cycle costs (LCC), the levelized cost of heat (LCoH) and the CO2 emissions over the lifetime of the system.

Unsupervised deep-learning framework for 5D seismic denoising and interpolation

We develop an unsupervised framework to reconstruct missing data from noisy and incomplete five-dimensional (5D) seismic data. Our method comprises two main components: a deep-learning network and the projection onto the convex sets (POCS) method. The model works iteratively, passing the data between the two components and splitting the data into a group of patches. The developed deep-learning model is inspired by the transformer architecture, wherein the extracted patches are flattened, and an embedded layer (fully connected layer) is used to transform the 1D flattened vector into the feature latent space. Afterward, an attention network is used to highlight the important information within these features, which improves the denoising performance of the deep-learning model. Furthermore, we use several skip connections between the fully connected layers to enhance the learning capability of the network. Next, the output of the POCS algorithm is considered the input of the deep-learning model for the following iteration. The developed model iteratively works in an unsupervised scheme wherein labeled data are not required. A performance comparison with benchmark methods using several synthetic and field examples shows that our method outperforms the traditional methods.

Examining the potential of Sigma-Thermic heat transfer fluid and its nanofluid in a natural circulation loop through CFD studies

Abstract This study investigates the impact of hot heat exchanger (HHX) inlet temperature on Sigma-thermic heat transfer fluid (STHF or STF) performance in a natural circulation loop. Various fluids, including STHF and STHF-based nanofluids, with volumetric concentrations ranging from 0.5% to 3%, are employed in the loop. The primary objective is to analyze the thermal performance of the loop under different conditions, focusing on temperature distribution, Nusselt number, friction factor, effectiveness, and mass flow rate. 3D numerical simulations are conducted, and the numerical model is validated against existing literature. The developed model incorporates considerations for viscous dissipation and axial conduction to predict the heat transfer potential of the loop. As the HHX inlet temperature increases, the mass flow rate rises. Notably, STHF/CuO nanofluids exhibit a more substantial enhancement than other nanofluids. At a 1 vol% concentration, the mass flow rate increases by 9.5%, 4%, and 2.7% for STHF/CuO, STHF/Al2O3, and hybrid nanofluids, respectively, compared to pure STHF. The study reveals significant improvements in mass flow rates and heat transfer efficiency with increasing HHX inlet temperatures, especially with STHF/CuO nanofluids. The total entropy generation reduction is notable, with percentages ranging from 2% to 18.5% for various nanofluids compared to pure STHF

Development of Teaching Performance Evaluation Application for Lecturers Using K-Nearest Neighbor Method with Manhattan Distance Approach

Based on the initial observation, there are several underlying issues that form the basis of this research. The teaching performance evaluation at Padang State University has limitations regarding the courses to be evaluated. Each student can only evaluate 5 (five) courses per semester, where these five courses are randomly selected by the system, allowing each student to evaluate different courses even in the same class. The evaluation of teaching performance at Padang State University is not specific to individual lecturers but to the courses. One course can be taught by several lecturers, so students evaluating the learning cannot provide assessments for each lecturer. This results in each lecturer not having their own performance results. Furthermore, the teaching performance evaluation at Padang State University does not have a classification for the filled evaluations, thus requiring a long time to calculate the final results. This study uses the Research and Development (R&D) method with the 4-D development model consisting of four stages: definition, design, development, and dissemination. The type of data used is primary data obtained from 3 media validators, the evaluation administration of Padang State University, and 46 students. The data analysis technique used is descriptive data analysis to describe the validity and practicality of the developed lecturer performance evaluation application. The results of this development study produced a lecturer performance evaluation with 202 training data, 47 test data, resulting in an accurate system with a precision value of 88.76%, a recall value of 89.93%, and a program accuracy value of 94.04%. The validity results of the web-based learning evaluation conducted by media experts obtained a score of 0.864 with a valid category. The practicality value of using web-based lecturer performance evaluation by students obtained a score of 87.42 with a practical category. Meanwhile, the practicality value obtained by the evaluation administration of Padang State University is 89.33 with a practical category.

A statistical model for estimating porosity based on various parameters of flow through porous media

ABSTRACT Flow through porous media is a process with a momentous impact on the environment. Hydraulic conductivity, which depends on the particle size, shape, and porosity of the media, has applications in different fields of science, e.g., groundwater, seepage, and soil stability. Precise estimation of porosity leads to accurate computation of the hydraulic conductivity of porous media. The present study investigates the effect of particle size on the porosity of porous media, i.e., crushed quartzite, river gravel, marble chips, and sand. The influence of the wall effect on packing and porosity of porous media based on the diameter ratio, i.e., the ratio of permeameter-to-particle size (Dp/dg) has also been investigated. The hydraulic conductivity and the porosity of porous media are observed to increase with an increase in the particle size. The study postulates a dependable relation between hydraulic conductivity and porosity. The experimentally obtained and the model-generated porosity values for crushed quartzite, river gravel, marble chips, and sand range between 0.38–0.42 and 0.37–0.43, respectively. Experimental data have been used to derive a statistical model based on the diameter ratio (5.08 ≤ (Dp/dg) ≤ 254) for estimating the porosity of media. The validation of the statistical model using experimental data establishes the efficacy of the developed model.

Rapid assessment of the main economic value indicators in lupine flour samples using infrared spectroscopy

Background. A calibration model has been developed for rapid assessment of economic value indicators (protein, oil, and quinolizidine alkaloid contents) in the seeds of narrowleaf lupine accessions from VIR using near-infrared spectroscopy, with the help of which it is possible to decide on the further use of the accessions.Materials and methods. Biochemical quality indicators (protein, oil, and quinolizidine alkaloid content) were studied in the seeds of narrowleaf lupine (Lupinus angustifolius L.) grown in 2019 in the northwest of Russia. Calibration models for measuring protein, oil and alkaloids in lupine seeds (62 accessions) were developed using a MATRIX-I IR analyzer (Bruker Optics, Germany). To construct calibration models, we used the values obtained by chemical analysis methods accepted at VIR. The oil content in lupine seeds was assessed by the defatted dry residue technique in Soxhlet extractors, protein by the Kjeldahl method, and quinolizidine alkaloids by gas chromatography coupled with mass spectrometry. All indicators were recalculated on the dry-weight basis.Results and conclusion. Statistical significance of the developed models was verified according to the results of measuring the content of protein, oil and alkaloids in the seeds of the test batch. The protein and oil content data obtained using a calibration curve did not differ significantly from the results of chemical studies, in contrast to alkaloid indicators. Consequently, the developed calibration model for the MATRIX-I IR analyzer can be used for rapid assessment of protein and oil content in narrowleaf lupine flour samples, thus accelerating the process of obtaining data on the main economic value indicators. The analysis does not require reagents and is safe.

Dimensional Analysis based Prediction Model for Fuel Consumption in the Deutz Agrotrac Tractor

Background: Fuel consumption is a critical factor in the performance of an agricultural tractor. And it can be affected by numerous parameters. Farming practices is an essential aspect that affects the cost of operation and the overall efficiency of the tractor. Predictions of fuel consumption rates in tractors and the energy consumption parameters of primary tillage equipment under local conditions are therefore essential selection criteria in purchasing decisions. Methods: A prediction model for fuel usage during tillage operations was created using dimensional analysis. The model was established by incorporating experimental findings into an equation. The experiment was conducted in the El Ouracia region of Setif, Algeria in 2022/2023 crop season, with fuel usage per time unit determined through topping-up the tank method. The fuel experiment parameters were measured according to their respective standard procedures. Result: By simulating fuel usage with tillage depth, tillage width, travel speed, soil bulk density and cone index. The constant in the prediction model was obtained. This developed model has the potential to forecast the tractor fuel consumption during tillage operation.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study

BackgroundHospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.AimThis study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI.MethodsThis retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011–2012 and 2019–2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance.ResultsPatients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results.ConclusionThis study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Profiling population-wide exposure to environmental chemicals: A case study of naphthalene

Long-term exposure to environmental chemicals can detrimentally impact human health, and understanding the relationship between age distribution and levels of external and internal exposure is crucial. Nonetheless, existing methods for assessing population-wide exposure across age groups are limited. To bridge this research gap, we introduced a modeling approach designed to assess both chronic external and internal exposure to chemicals at the population level. The external and internal exposure assessments were quantified in terms of the average daily dose (ADD) and steady-state blood concentration of the environmental chemical, respectively, which were categorized by age and gender groups. The modeling process was presented within a spreadsheet framework, affording users the capability to execute population-wide exposure analyses across a spectrum of chemicals. Our simulation outcomes underscored a salient trend: younger age groups, particularly infants and children, exhibited markedly higher ADD values and blood concentrations of environmental chemicals compared to their older counterparts. This observation is due to the elevated basal metabolic rate per unit of body weight characteristic of younger individuals, coupled with their diminished biotransformation kinetics of xenobiotics within their livers. These factors collectively contribute to increased intake rates of environmental chemicals per unit of body weight through air and food consumption, along with heightened bioaccumulation of these chemicals within their bodies (e.g., blood). Furthermore, we augmented the precision of the external and internal exposure assessment by incorporating the age distribution across the population. The simulation outcomes unveiled that, to estimate the central tendency of the population's exposure levels, employing the baseline value group (age group 21-30) or the surrogate age of 25 serves as a simple yet dependable approach. However, for comprehensive population protection, our recommendation aligns with conducting exposure assessments for the younger age groups (age group 0-11). Future studies should integrate individual-level exposure assessment, analyze vulnerable population groups, and refine population structures within our developed model.

Non-Poissonian bursts in the arrival of phenotypic variation can strongly affect the dynamics of adaptation.

Modelling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modelled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be 'bursty' or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession, or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for 'monomorphic' populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behaviour in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

Comparative analysis of the prevalence 3-HIT concept in people living with HIV and seronegative patients with chronic conditions. Cross-3HIT Project.

ObjectivesThis study aimed to assess and compare the occurrence of 3-HIT in people living with HIV (PLWH) and seronegative patients. Additionally, the study investigated whether HIV infection could serve as...