Modeling Analysis Research Articles

On the stability and accuracy of TRT Lattice-Boltzmann method for non-Newtonian Ostwald-de Waele fluid flows

This paper brings a numerical analysis of the TRT modeling for the Lattice-Boltzmann method when solving flow for dilatant and pseudoplastic power-law fluids. Firstly, the method was reviewed to describe the required simulation parameters and the numerical methodology. Secondly, a mathematical procedure was performed to identify the characteristic relaxation frequency as a function of both flow and fluid properties and to work as a guide parameter for LBM operation. Then, a simple shearing Poiseuille flow was employed so its characteristic shear rate could be calculated as a function of fluid properties, given the flow was characterized by the Reynolds and Mach numbers. For this test case, convergence was then explored for a broad range of parameters, and its non-monotonic dependency on the Mach number for a given convergence criterion was shown. Then, stability maps were constructed based on the characteristic relaxation frequency, which showed a strong dependency between consistency and flow index so the simulation could converge. This was explored against the results from the converged tests, which pointed out the usefulness of the characteristic relaxation frequency in predicting stable solutions. Finally, quantitatively, it was shown that for this power-law fluid flow, the ||L2|| relative error depends on the Mach number to the power of 2(2−n) being now a function of the flow index, extending the previously reported dependency of the Mach number to the power of 2 for plane-Poiseuille flow.

A Compact Vulnerability Knowledge Graph for Risk Assessment

Software vulnerabilities, also known as flaws, bugs or weaknesses, are common in modern information systems, putting critical data of organizations and individuals at cyber risk. Due to the scarcity of resources, initial risk assessment is becoming a necessary step to prioritize vulnerabilities and make better decisions on remediation, mitigation, and patching. Datasets containing historical vulnerability information are crucial digital assets to enable AI-based risk assessments. However, existing datasets focus on collecting information on individual vulnerabilities while simply storing them in relational databases, disregarding their structural connections. This article constructs a compact vulnerability knowledge graph, VulKG, containing over 276 K nodes and 1 M relationships to represent the connections between vulnerabilities, exploits, affected products, vendors, referred domain names, and more. We provide a detailed analysis of VulKG modeling and construction, demonstrating VulKG-based query and reasoning, and providing a use case of applying VulKG to a vulnerability risk assessment task, i.e., co-exploitation behavior discovery. Experimental results demonstrate the value of graph connections in vulnerability risk assessment tasks. VulKG offers exciting opportunities for more novel and significant research in areas related to vulnerability risk assessment. The data and codes of this article are available at https://github.com/happyResearcher/VulKG.git .

Modeling and performance analysis of a new integrated solid oxide fuel cell and photovoltaic‐thermal energy supply system by heat current method

AbstractEfficient and reliable utilization of renewable energy at the user's end is the key to achieving a low‐carbon life. This paper proposed a new distributed energy system around the comprehensive utilization of solar energy by integrating solid oxide fuel cell (SOFC), energy storage equipment, photovoltaic thermal (PVT) collector, and heat pump. By integrating the use of SOFC and PVT, we can further minimize reliance on fossil fuels, while employing the coupling of PVT and heat pump effectively mitigates the inherent challenges of solar energy's variability and intermittency, all while enhancing overall system efficiency. On this basis, we apply the heat current method to construct a cross‐scale heat current model of the components and the system by considering the energy transfer, conversion, and storage characteristics of the system. By employing this model, we simulate the system's operation throughout an entire typical day, assess the COP enhancement of the PVT‐coupled heat pump system, analyze the influence of diverse operating conditions on daily system performance, and evaluate the economy of the energy storage devices in the system.

Dynamics and stability analysis of enzymatic cooperative chemical reactions in biological systems with time-delayed effects

The mathematical modeling and dynamic analysis of time-delayed enzymatic chemical reactions in biological systems are presented in this research. The objective is to examine the function of time lags in these reactions and to get a complete knowledge of the behavior of biological systems in a reaction to modifications in the quantity present of reactants and products. The model, which is based on delay differential equations, includes a time delay term to account for the lag between changes in the concentration of reactants, reaction rate constants and product responses. The findings give insight into how enzymatic processes behave dynamically and how stability is impacted by time lags, oscillation and general efficiency of the system. These results have significant importance for our comprehension of how biological processes are regulated and for the creation of biological control structures.

How the perfectionistic climate of a sports team predicts the athletic performance of elite athletes: a case study of the CUBAL women’s basketball team

ObjectiveIn competitive sports, understanding how the perfectionistic climate within teams influences the performance of elite female athletes can provide valuable insights for enhancing coaching practice and athletic achievement. Based on the cognitive appraisal theory of stress, this study constructs a dual-path model using stressors and coping strategies as mediators, referred to as the Perfectionistic Climate on Athletic Performance model (PCPM). The study explores the predictive role of the perfectionistic climate within sports teams on the athletic performance of elite female basketball players.MethodsThe empirical study the relationships among the variables in the model using a sample of 125 core players from the top-level women’s basketball teams in the 24th CUBAL24 tournament in 2022. A Structural Equation Modeling (SEM) analysis was conducted using AMOS 20.0, primarily employing the bias-corrected Bootstrap method to test the dual-path model.ResultsThe findings reveal double-edged paths towards a perfectionistic climate on athletic performance. In the positive pathway, a perfectionistic climate can positively predict athletic performance through challenge-related sources of stress and positive coping strategies. In the negative pathway, a perfectionistic climate can negatively predict athletic performance through threat-related sources of stress and negative coping strategies.ConclusionCoaches need to pay attention to athletes’ cognitive evaluations of the perfectionistic climate as a source of pressure. By setting challenging goals, coaches can guide athletes to view the perfectionistic climate of the sports team as a source of challenging pressure, thus unleashing their potential. Coaches should actively guide athletes in coping with the pressure brought about by the perfectionistic climate, enhancing their ability to handle stress. This will enable athletes to better adapt to the team’s perfectionistic climate and further improve individual and team athletic performance.

To Share or Not to Share: Examining Social Media in Public Relations Scholarship

ABSTRACT Social media has become an indispensable component of everyday life and had a transformative impact on strategic communication. This study reflects on the influence of social media in scholarship and examines social media research to identify strengths, gaps, and assumptions in this body of literature. Using structural topic modeling and network analysis, this research models every word published in six leading journals studying public relations from 2012 to 2022 in two separate studies. First, a census of the 2,675 articles, totaling 15926,460 words, is analyzed to establish the percentage of scholarship related to social media, methodologies used to study social media, and how social media interacts with other topics within public relations. Second, a subset of 666 articles that heavily emphasize social media are examined in order to identify the key social media topics within public relations scholarship. This research finds that social media is a major connecting topic within public relations scholarship; however, few social-media specific topics are being studied in public relations scholarship. Even research focused on social media is more heavily emphasizing existing public relations theory than social media-specific topics. We conclude with three propositions that detail a path forward for scholarship regarding social media.

Price Cannibalization Effect on Long-Term Electricity Prices and Profitability of Renewables in the Baltic States

This paper aims to evaluate price cannibalization effects in forecasts of long-term electricity prices and substantiate their relevance on the profitability of renewables in the Baltic States from 2024 to 2033. Statistical data analysis, literature review, scenario method, and PLEXOS modeling were applied. Five scenarios were analyzed for developing renewable energy sources (RES) and load in Lithuania. In contrast, scenarios for Estonia and Latvia were based on assumptions derived from the countries’ national RES strategies. The results showed that the increase in RES capacities will halve electricity market prices from around 130 EUR/MWh in 2024 to 58 EUR/MWh in Latvia, 60 EUR/MWh in Estonia, and 60–77 EUR/MWh in Lithuania in 2033. In time-waving, the absolute and relative price cannibalization effects of renewables were found. In 2033, the loss of revenue from solar photovoltaic (PV) generators was estimated to be 5.5–17.0 EUR/MWh in Lithuania, 7.1 EUR/MWh in Latvia, and 5.6 EUR in Estonia. The case of onshore wind demonstrated revenue losses of 10.5–22.0 EUR/MWh in Lithuania, 12.0 EUR/MWh in Latvia, and 10.0 EUR/MWh in Estonia. After 2029, revenues received by RES electricity generators could not guarantee project profitability; therefore, market flexibility options will be required. The key innovative strategy to mitigate the price cannibalization effect is the demand-side response when leveraging demand flexibility. Typically, this is achieved by sending price signals to the consumers who, if they have any, shift their demand to lower price periods. This is easily applied within HVAC systems, smart electric vehicle charging, and smart home appliance usage. Such behavior would allow the price cannibalization effect to be decreased.

Pyrolysis-gasification conversion of waste pharmaceutical blisters: Thermo-kinetic and thermodynamic study, fuel gas analysis and machine learning modeling

It delved into the pyrolysis-gasification behavior of waste pharmaceutical blisters (WPBs), exploring both kinetics and thermodynamics, and employing machine learning modeling. The decomposition of WPBs occurred in three stages, with temperature intervals of 140–350, 350–500, and 500–900 °C, respectively. Cyclization/aromatization reactions were hindered, as indicated by mass spectrometric analysis, which revealed the main evolved products, including CxHy, CH3OH, CH4, and H2. Apparent activation energy values obtained from FWO, KAS, Friedman, Cai & Chen models were comparable, showing a decreasing trend ranging from 264.9 to 48.4 kJ mol−1 at α ≤ 0.70, followed by a subsequent increase to 283.1 kJ mol−1 at a conversion of 0.80. The D1 model was more reliable in describing the pyrolysis-gasification process of WPBs. Machine learning modeling results indicated that the ANN19 with a topology structure of 5 ∗ 15 ∗ 1 and genetic programming models showed superior performance in predicting TG data.

Representing austerity: Baby Banks and news media

AbstractRising levels of hardship since the introduction of austerity have rendered essential items unaffordable for many on low incomes with young children. Baby Banks—organisations freely providing essential items and equipment to those with, or expecting, a baby or young infant—have grown. These organisations have received little academic attention and much of what is known about them comes from news media coverage. News media plays a critical role in raising public awareness, shaping public attitudes, and influencing policy formation. Drawing on 384 news articles, this paper explores the scale and nature of news article coverage of Baby Banks between 2009 and 2022 using sentiment analysis and topic modelling. Through employing these approaches, our research adds quantitative evidence to extant work on news media coverage under austerity. Our results show that the number of articles written about Baby Banks has grown since 2009, with peaks in coverage during the COVID‐19 pandemic in 2020, potentially reflecting the growing need for these organisations. While sentiment towards Baby Banks within news articles has predominantly been positive, since 2019 there has been a rise in negative coverage due to an increase in articles critical of the growing number of people requiring Baby Banks. Alongside discussions of political changes driving rising hardship, prominent underpinning narratives in articles have included charitable appeals for donations, praise for community work, the royal family, and mothers. By focusing on the sentiment and key discussions around Baby Banks, our research extends understandings of media coverage under austerity.

Exploring The Drivers of Entrepreneurial Intentions Among Farmers in Tanzania's Agricultural Sector

Purpose: This paper investigates the entrepreneurial intentions of orange farmers in Muheza, Tanzania, by validating the extended theory of planned behaviour (TPB). Design/Methodology/ Approach: The study used a quantitative method approach, with cluster and simple random sampling, to recruit 349 orange farmers. The survey was conducted on 349 small-scale orange farmers in Muheza. To test the model, the constructs of entrepreneurship training, farmers’ attitudes, subjective norms, perceived behaviour control and entrepreneurial intention inputs were validated, followed by factor analysis and structural equation modelling. Findings: The study's results revealed the crucial role of perceived behaviour control and attitudes in driving the entrepreneurial intention of orange farmers. This significant influence underscores the importance of these factors in the development of entrepreneurial intention among orange farmers. It was also found that attitudes and perceived behaviour control predicted the entrepreneurial intention of orange farmers but not subjective norms, providing valuable insights for future research and policy development. Research Limitation: This study primarily focused on orange farmers in the Tanga Region, specifically in the Muheza District. Practical Implication: The findings suggest a clear path for action. To foster and enhance the entrepreneurial intentions of orange farmers, the government and policymakers should concentrate on instilling positive attitudes and controlling perceived behaviour. By promoting a positive stance toward entrepreneurship, we can significantly boost the willingness of orange farmers to engage in commercial farming, thereby contributing to the growth and development of Tanzania's agricultural sector. Social Implication: The study recommends policy options focusing on cultivating positive attitudes and perceived behaviour control among orange farmers to increase entrepreneurial intention, requiring concerted efforts from the government and policymakers. Originality / Value/ Novelty: It contributes to the body of knowledge, addressing the gap regarding the drivers influencing orange farmers' entrepreneurial intentions. Furthermore, establishing validated drivers of entrepreneurial intention among Tanzanian orange farmers could significantly assist the government in promoting economic development and achieving its vision of becoming a semi-industrialized country.

Pyrolysis conversion of multi-layer packaging waste under a CO2 atmosphere: Thermo-kinetic study, evolved products analysis and artificial neural networks modeling

Packaging waste such as beverage carton forms a significant part of municipal solid waste and its pyrolysis behavior, kinetics, and thermodynamics were studied. A four-stage decomposition process was revealed: dehydration below 200 ℃, paperboard degradation at 200–400 ℃, polyethylene devolatilization at 400–550 ℃, and inorganic decomposition at 550–900 ℃. The evolved products included furans and acetic acid during stage II, followed by the presence of 2-butene and 1-pentene in the subsequent stage. Apparent activation energy (Ea) were determined using model-free models, revealing a notable level of comparability among these results. The average Ea was 123.6 kJ/mol within α range of 0.10–0.60, increasing to 233.3 kJ/mol beyond that range. The most probable reaction mechanism was determined, with the one-dimensional model proving more reliable. An artificial neural network model was developed to predict the thermal degradation. The selected topology of 5*15*1 displayed a robust ability to predict the thermal data.

Linking EFL students’ psychological needs to engagement in Informal Digital Learning of English: a structural equation modeling analysis

EFL students are increasingly engaging in self-directed English learning in extramural digital settings (Informal Digital Learning of English; IDLE). However, effective strategies for boosting IDLE engagement are largely unexplored. The Basic Psychological Needs (BPNs) theory, a fundamental element of the Self-Determination Theory, proposes that fulfilling three core needs—autonomy, competence, and relatedness—is crucial for human well-being and optimal functioning. Hence, this study aims to investigate the potential connection between in-class BPNs and receptive (consuming content) and productive (producing content) IDLE activities. Data from 472 Kazakhstani EFL students across seven universities were analyzed using structural equation modeling. Our findings showed that students with higher overall BPNs engaged more in IDLE activities, particularly those with higher competence in both receptive and productive IDLE, and those with higher autonomy in productive IDLE. Surprisingly, relatedness was not a significant factor in IDLE. Our study offers the first empirical evidence demonstrating a positive link between BPNs and IDLE engagement. Pedagogically, educators can develop strategies aligned with students’ BPNs to enhance their IDLE engagement.

Mapping the Science World with Correlated Topic Modeling Analysis from Science-Related Posts on the Reddit Platform

The main source of technology in today's world is science. The world of science is being reshaped every day with the continuation of studies in the fields of science and technology. However, the development of technology causes the amount of data that cannot be processed with traditional methods on technological platforms to increase day by day. Making meaningful data meaningful by processing unmeaningful data will enable companies, institutions, and organizations to transform big data into highly beneficial tools. One of the most effective data mining techniques for processing and making sense of data is topic modeling. In this study, semantic content analysis of science-related posts on the Reddit platform was conducted using corelated topic modeling (CTM), one of the topic modeling techniques. In the first nine months of 2022, the hidden meanings in Reddit posts and the correlation between these meanings were revealed, and the relevant results were shared. It is thought that the results obtained will be a source of ideas for people interested in science and scientists for their research.

The Thermodynamic‐Analysis‐Derived Structure Optimization on the Solar Thermal Localized Desalination and Water‐Treatments

AbstractThe solar thermal localization (STL) technology is promising and cost‐effective for desalination or wastewater treatments, but currently in an insufficient status on its structure optimization. This study emphasized the water‐cooled STL (WSTL) system and its step‐forward modifications, involving the air‐insulated WSTL (AWSTL) system and the vacuum WSTL (VWSTL) system, together with their thermodynamic modeling analysis on energy‐water conversions, heat‐mass transfers, and energy losses. Results indicated the best‐performing VWSTL mode can achieve a freshwater yield as high as 1.503 kg·m−2·h−1 at energy efficiency of 68.0% (the light intensity at 1500 W·m−2 indoor), and that of 4.31 kg·m−2·day−1 at energy efficiency of 47.5% (the 8‐h averaged solar light intensity at 769.7 W·m−2 outdoor per‐day) whose energy efficiency is exceeded all competitors in open literature. There likely is an improvement space in applying better evaporation materials.

Assessing economic investment required to scale up bariatric surgery capacity in England: a health economic modelling analysis

ObjectivesTo quantify the economic investment required to increase bariatric surgery (BaS) capacity in National Health Service (NHS) England considering the growing obesity prevalence and low provision of BaS in England...

Integrating district heating potentials into European energy system modelling: An assessment of cost advantages of renewable and excess heat

This paper takes a novel modelling approach by considering high spatial resolution heat generation potentials for district heating and integrating them into a European energy system model. Subsequently, a modelling analysis of an integrated energy system including district heating, electricity and hydrogen supply for 25 EU Member States and the year 2050 is carried out. In contrast to existing approaches, the modelling approach captures the heterogeneous resource availability in district heating. The results show multivalent district heating networks based on a wide range of renewable and excess heat sources used directly or in combination with large-scale heat pumps. The high spatial resolution of the heat generation potentials allows a detailed cost comparison of different possible future technology mixes in district heating. The paper finds that the use of heat pumps, geothermal energy and industrial excess heat offer slight cost advantages for the energy system as a whole. Geothermal heat can also provide cost advantages for district heating generation.

예비교사를 대상으로 한 서비스러닝의 전공 융합 및 실천공동체적 특징 분석

Purpose: The purpose of this study was to identify the significance of the characteristics of major convergence and community of practice on pre-service teachers' service-learning activities. Methods: To identify the characteristics of major convergence and community of practice in pre-service teachers' service-learning experiences, the interview data of six pre-service teachers who participated in a local problem-solving project at University A were analyzed using a topic modeling analysis technique. Results: The team that grew into a community of practice showed a reciprocal relationship in terms of smooth communication, willingness to take on insufficient roles, co-leadership, positive perspectives on major convergence, and attempts to converge majors in a transdisciplinary manner. It was also confirmed that pre-service teachers experienced problem-solving approaches from different academic backgrounds through the convergence of their majors and deepened their professionalism in community management through collaboration within the community of practice. Conclusion: Service learning for pre-service teachers needs to be incorporated into teacher education curricula so that pre-service teachers can contribute to real-world community problems and systematically learn how to solve educational problems through interdisciplinary collaboration.

Verification of Reliability of Finite Element Analysis Modeling for TMED Damper System

Verification of Reliability of Finite Element Analysis Modeling for TMED Damper System

Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia

Sea level rise (SLR) attributed to the melting of ice caps and thermal expansion of seawater is of great global significance to vast populations of people residing along the world’s coastlines. The extent of SLR’s impact on physical coastal areas is determined by multiple factors such as geographical location, coastal structure, wetland vegetation and related oceanic changes. For coastal communities at risk of inundation and coastal erosion due to SLR, the modelling and projection of future sea levels can provide the information necessary to prepare and adapt to gradual sea level rise over several years. In the following study, a new model for predicting future sea levels is presented, which focusses on two tide gauge locations (Darwin and Milner Bay) in the Northern Territory (NT), Australia. Historical data from the Australian Bureau of Meteorology (BOM) from 1990 to 2022 are used for data training and prediction using artificial intelligence models and computation of mean sea level (MSL) linear projection. The study employs a new double data decomposition approach using Multivariate Variational Mode Decomposition (MVMD) and Successive Variational Mode Decomposition (SVMD) with dimensionality reduction techniques of Principal Component Analysis (PCA) for data modelling using four artificial intelligence models (Support Vector Regression (SVR), Adaptive Boosting Regressor (AdaBoost), Multilayer Perceptron (MLP), and Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN-BiGRU). It proposes a deep learning hybrid CNN-BiGRU model for sea level prediction, which is benchmarked by SVR, AdaBoost, and MLP. MVMD-SVMD-CNN-BiGRU hybrid models achieved the highest performance values of 0.9979 (d), 0.996 (NS), 0.9409 (L); and 0.998 (d), 0.9959 (NS), 0.9413 (L) for Milner Bay and Darwin, respectively. It also attained the lowest error values of 0.1016 (RMSE), 0.0782 (MABE), 2.3699 (RRMSE), and 2.4123 (MAPE) for Darwin and 0.0248 (RMSE), 0.0189 (MABE), 1.9901 (RRMSE), and 1.7486 (MAPE) for Milner Bay. The mean sea level (MSL) trend analysis showed a rise of 6.1 ± 1.1 mm and 5.6 ± 1.5 mm for Darwin and Milner Bay, respectively, from 1990 to 2022.

A geospatial modelling for glacial lake outburst floods susceptibility analysis in the Central Himalayas

ABSTRACT Glacial lakes have increased throughout a significant portion of the Himalayan region, and hence the present study focuses on a geospatial modelling approach for analysing the susceptibility to glacier lake outburst flood (GLOF) in the central Himalayan region. Multitemporal satellite imageries such as Landsat-TM and Sentinel-2 were taken. The hydrodynamic compatibility tool, i.e. HEC-RAS 5.0, was used for GLOF prediction, and the lake volume and surface area were calculated using established empirical equations. The breach fails and flood flow were approximated, and arising uncertainties were analysed with three outburst scenarios. The number of lakes and their sizes have significantly increased according to the findings of the study. Of these, the Vasundhara Lake (VL), which is located at 4,676 m above the sea level, is assigned to the hazardous category for more examination. It has been found that the lake's area expanded over the past three decades (i.e. 1994–2020), rising from 0.15 to 0.29 km2. The study demonstrates that, in the worst-case situation, infrastructure along riverbanks may be susceptible to harm. The study provides information on the potential effects of GLOF events in the study region that might be used in early warning systems and water resource management plans.