Comprehensive Model Research Articles

Development of Reciprocal Teaching-Based Interactive Multimedia to Enhance Students' Learning Outcomes of Database Fundamentals

Database subjects are one of the basic compulsory subjects for the Computer Engineering and Informatics program. For Software Engineering students, topics such as Data Definition Language (DDL) and Data Manipulation Language (DML) are crucial for understanding advanced lessons. However, learning outcomes have been low due to factors such as suboptimal media, methods, models, and teaching processes. This research aims to design reciprocal teaching-based interactive multimedia to enhance student learning outcomes in DDL and DML materials. The research method used is adapted from the Comprehensive Life Cycle model, with a sample of students from class X-RPL-1 at Bina Wisata Lembang Vocational High School.The results of this study are as follows: 1) An average percentage of 80% was obtained from media experts and 80% from subject matter experts, both categorized as “Very Good”. 2) The use of multimedia improved student learning outcomes, demonstrated by an average score increase from 34 to 73. Furthermore, based on the pretest and posttest results, an average normalized gain (n-gain) of 0.59 was obtained with a criteria of 'Moderate' effectiveness. 3) Students responses to the created multimedia resulted in an average percentage score of 86.41%, categorized as “Very Good”.

Estimating Yield Response Functions to Nitrogen for Annual Crops in Iran

Nitrate is a crucial element for crop growth, and its optimal application is essential for maximizing agricultural yield. In Iranian agriculture, there is a substantial gap between recommended nitrate usage and what farmers actually apply. In this study, our primary objective is to determine the most effective utilization of nitrate for crop cultivation. Simultaneously, we aim to analyze the factors that contribute to the disparity between optimal and current nitrate application practices. Furthermore, our research explores the impact of these differences on regional variations in crop yields. This is achieved using a quadratic yield response function model based on unbalanced panel data spanning the years 2000 to 2016, which includes a total of 14 crop activities and encompasses 31 administrative regions. The results show that rice exhibits the highest nitrogen usage, while rain-fed wheat demonstrates the lowest utilization at the optimal point. Depending on whether random- or fixed-effects estimation is found to be the most suitable specification, average yields corresponding to the optimal level of nitrogen use are calculated by region, or the average across all regions. In Iran, the top-performing regions for cereals like rain-fed wheat and irrigated barley can achieve yields of 1.33 and 3 t/ha, respectively. These yields represent a 31% and a 9% increase from the levels observed in 2016. The outcomes derived from the estimated yield response function will be integrated into comprehensive agricultural, economic, and environmental optimization models. These integrated models will facilitate the assessment of various fertilizer policies on fertilizer use, land allocation, farm-household incomes, and environmental externalities, such as nitrate leaching and nitrate balance. This study holds substantial scientific promise, given its exploration of the policy implications surrounding fertilizer usage, making it crucial not only for Iran, but also for many developing nations grappling with inefficient and unsustainable agricultural practices. It represents the first of its kind in the literature, providing estimations of optimal nitrogen use and crop yield points across all regions in Iran. This is achieved through advanced visualization using GIS maps.

Investigating the synergistic effects of immunotherapy and normalization treatment in modulating tumor microenvironment and enhancing treatment efficacy

We developed a comprehensive mathematical model of cancer immunotherapy that takes into account: i) Immune checkpoint blockers (ICBs) and the interactions between cancer cells and the immune system, ii) characteristics of the tumor microenvironment, such as the tumor hydraulic conductivity, interstitial fluid pressure, and vascular permeability, iii) spatial and temporal variations of the modeled components within the tumor and the surrounding host tissue, iv) the transport of modeled components through the vasculature and between the tumor-host tissue with convection and diffusion, and v) modeling of the tumor draining lymph nodes were the antigen presentation and the development of cytotoxic immune cells take place. Our model successfully reproduced experimental data from various murine tumor types and predicted immune system profiling, which is challenging to achieve experimentally. It showed that combination of ICB therapy and normalization treatments, that aim to improve tumor perfusion, decreases interstitial fluid pressure and increases the concentration of both innate and adaptive immune cells at the tumor center rather than the periphery. Furthermore, using the model, we investigated the impact of modeled components on treatment outcomes. The analysis found that the number of functional vessels inside the tumor region and the ICB dose administered have the largest impact on treatment outcomes.

Spectra-Based Machine Learning for Predicting the Statistical Interaction Properties of CO Adsorbates on Surface.

Theoretical analyses of small-molecule adsorption on heterogeneous catalyst surfaces often rely on simplified models of molecular adsorption with the most favorable configuration. Given that real-world experimental tests frequently entail multiple molecules interacting with the surface, there is a pressing need for a comprehensive multimolecule adsorption model to bridge the gap between theory and experiment. Using machine learning, we predict the average values of important adsorption properties from conformationally averaged, calculated infrared and Raman spectra and compare these values to those theoretically derived from the conformationally averaged ensemble. Remarkably, our approach yields excellent predictions even when faced with large and indeterminate numbers of surface molecules. These quantitative spectra-averaged property relationships provide a theoretical framework for extracting key interaction properties from the spectra of real chemical environments.

Quantitative characterization of the evolution of in-situ adsorption/free gas in deep coal seams: Insights from NMR fluid detection and geological time simulations

Accurate quantification of the coexistence of adsorbed and free gas content holds the utmost significance for estimating gas-in-place resources and predicting gas production dynamics. In this study, we conducted real-time isothermal adsorption experiments and NMR fluid monitoring on stress-confining core samples, from the Zhengzhuang Block's No.3 coal seam in the southern Qinshui Basin. Our focus was on assessing multi-phase methane gas contents within coal under various pressure and temperature (P/T) conditions. By integrating experimental findings with adsorption potential theory and the SDR adsorption model, we developed comprehensive models for adsorbed, free, and total gas contents as functions of P/T and water/gas volume saturation. Utilizing these models, we predicted vertical variations in adsorbed and free gas contents within the coal seam. Our results revealed that the interplay between positive reservoir pressure effects and adverse reservoir temperature effects influenced both adsorbed and free methane gases. With increasing burial depth, the influence of pressure on adsorbed gas diminished, while temperature effects became more pronounced. Conversely, free gas content responded noticeably to reservoir pressure, with temperature exerting a marginal influence. Additionally, we performed a numerical simulation to reconstruct the thermal history, burial trajectory, and evolution of reservoir pressure for the No.3 coal seam. The simulation results served as foundational data for understanding the evolution of free and adsorbed gas contents across different geological epochs within the in-situ reservoir. Our findings unveiled a four-stage evolutionary progression in both adsorbed and free gas contents, correlating with the uplift and subsidence of the coal seam. In conclusion, our study provides a conceptual model elucidating the intricate, deep-time evolution process and mechanisms governing the occurrence of multiphase gases across distinct geological epochs. The implications of this research are crucial for accurately evaluating gas-in-place resources and guiding the exploration and development of deep coalbed methane resources.

The double-edged sword effect of mega sport event strength on volunteer performance

Purpose This study aims to use event system theory and job demands–resources (JD-R) model to examine the double-edged sword effect of event strength of Beijing Winter Olympics (BWO) on volunteers’ in-role performance and proactive behavior as mediated by work overload and meaningfulness. Design/methodology/approach The sample included 193 volunteer–leader dyads working at one BWO venue. The authors conducted a multitime and multisource study using Mplus 8 to examine the hypotheses. Findings Volunteers’ perceived BWO event strength was positively related to work overload, which reduced in-role performance and proactive behavior. Volunteers’ perceived BWO event strength was also positively related to work meaningfulness, which promoted in-role performance and proactive behavior. Perceived organizational support served as a moderator by mitigating the positive relationship between perceived BWO event strength and work overload; however, it did not strengthen the positive relationship between perceived BWO event strength and work meaningfulness. Originality/value This study developed a comprehensive model of how BWO event strength affected volunteers’ performance from the perspective of event system theory and the JD-R model, which enriches theoretical application and research in the context of mega sport events.

Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics.

Multilingual sentiment analysis (MSA) involves the task of comprehending people's opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.

Application of Neural Radiance Fields (NeRFs) for 3D Model Representation in the Industrial Metaverse

This study explores the utilization of Neural Radiance Fields (NeRFs), with a specific focus on the Instant NeRFs technique. The objective is to represent three-dimensional (3D) models within the context of the industrial metaverse, aiming to achieve a high-fidelity reconstruction of objects in virtual environments. NeRFs, renowned for their innovative approach, enable comprehensive model reconstructions by integrating diverse viewpoints and lighting conditions. The study employs tools such as Unity, Photon Pun2, and Oculus Interaction SDK to develop an immersive metaverse. Within this virtual industrial environment, users encounter numerous interactive six-dimensional (6D) models, fostering active engagement and enriching the overall experience. While initial implementations showcase promising results, they also introduce computational complexities. Nevertheless, this integration forms the basis for immersive comprehension and collaborative interactions within the industrial metaverse. The evolving potential of NeRF technology promises even more exciting prospects in the future.

Novel hierarchical bioinspired cellular structures with enhanced energy absorption under uniaxial compression

Biological thin-walled cellular structures, such as hierarchical structures of bone, exhibit unique internal structures that offer lightweight characteristics and high energy absorption capabilities. The compact bone shell can protect against local damage during high-impact events, while the porous cancellous bone is essential in absorbing energy. This research proposed five novel bioinspired cellular structures inspired by the well-arranged structure of femur bones. These structures comprise four distinct cell types: hexagonal honeycomb, re-entrant, hybrid, and hierarchical hybrid cells. A comprehensive numerical model, validated with experimental data, was employed to assess the performance of these structures under uniaxial compression. Some key characteristics were revealed, including peak elastic load, plateau load, energy absorption capacity, Poisson's ratio, and the effect of hierarchical cell size. The results demonstrated that the novel bioinspired structures surpassed the energy absorption performance of traditional designs, such as hexagonal design, re-entrant, and trabecular-bone-inspired structures. This enhanced performance was due to the progressive buckling and collapse mechanisms, showing promising implications for future engineering applications, particularly where energy absorption is of paramount importance.

Unifying heterogeneous and homogeneous relations for personalized compatibility modeling

In recent years, the fashion industry has developed rapidly, leading to an increasing demand for personalized clothing matching. Although noteworthy progress has been made by existing work with advanced graph neural networks, there still remain three prominent challenges: comprehensive entity relation modeling, adaptive neighbor node sampling, and thorough characteri-zation of fashion items based on multi-modalities. To solve the above problems, we propose a novel personalized compatibility modeling framework, which explores both heterogeneous (i.e., item-user and user-item) and homogeneous (i.e., item-item and user-user) relation graphs with multi-modalities. Moreover, we design a correlation-guided neighbor sampling strategy to learn the correlation-aware subgraphs, which plays a crucial role in facilitating the exploration of compatibility relationships or personalized preferences. Finally, we conduct extensive experiments on two publicly available fashion datasets to demonstrate the superiority of our proposed model over existing benchmark methods.

Detailed kinetics modeling of sulfur species evolution in alternating reducing/oxidizing atmosphere

Under conditions of deep load fluctuation, the furnace of a coal-fired boiler experiences alternating reduction/oxidation atmospheres, which directly impact the evolution of sulfur species. Building upon our previous mechanism (Model I), this study specifically considered the influence of alternating atmospheres and proposed a comprehensive reaction model (Model II), incorporating 8 additional elementary reactions. The relative error in predicting the concentration distribution of sulfur species was reduced from 18% (Model I) to 13% (Model II). In the alternating atmospheres, the predominant sulfur species remained as SO2 and H2S. Sensitivity analysis revealed that S, SH, SO, HSO, and HOSO were the critical free radicals. The new supplementary reactions (118) H2S + M = SH + H + M and (116) H2S + O2=HSO + OH played a significant role in the generation and consumption of H2S and SO2. Rate of production analysis demonstrated that the alternating atmosphere impacted the production and consumption primary free radicals like O/H/OH, thus potentially causing changes of S, SH, SO, HSO, and HOSO; moreover, it introduced new reaction pathways for SH and SO. Finally, a simplified reaction pathway for the sulfur species evolution was proposed. This study seeks to provide insights into the sulfur species evolution under the peak regulation conditions in coal-fired boilers.

A rapid simplified method for determining tsunami inundation extent based on energy conservation

AbstractThis paper develops a tsunami inundation model, filling the current void between industry applied simplified methods (bathtub and attenuation) and comprehensive numerical modeling. The proposed model utilizes two‐dimensional equations established on hydraulic principles (energy conservation and friction loss) to produce the finite‐difference, two‐dimensional model. While the sophistication of depth‐averaged numerical modeling has not been entirely replicated, the incorporation of hydraulic roughness and gravitational effects (slope) through the Bernoulli energy equation, accompanied by suitable assumptions, significantly improves performance over simplified bathtub and attenuation methods. This improvement not only retains excellent efficiency (completing case studies in under 100 s) but also yields inundation results comparable to traditional numerical modeling, which typically takes multiple hours to days. This is the first application of such equations to a two‐dimensional tsunami inundation model, leading to simulations at Gisborne and Christchurch, New Zealand, for validation against the numerical model, COMCOT. F1 scores produced from inundation extent comparisons were upwards of 84%. Inundation depth discrepancies recorded 77% within a 1 m range, while over 93% within a 2 m range.

Application of a comprehensive atmospheric boundary layer model to a realistic urban-scale wind simulation

Atmospheric Boundary Layer (ABL) is the region below the stratosphere that is significantly affected by Earth’s surface. In this article, an improved ABL model has been tested and validated with field measurements, while simultaneously highlighting the consequences of using an incorrectly formulated ABL model when performing pedestrian wind comfort and safety analysis using Computational Fluid Dynamics (CFD) in a full-scale, realistic setting. The improved ABL model was successfully validated on wind tunnel scale test cases previously, and this article provides evidence that the validation can be extended to realistic, urban-scale test cases as well. Steady, incompressible, Reynolds Averaged Navier–Stokes (RANS) simulations were performed and the results obtained from the incorrect ABL model are compared with those from an improved, consistent ABL model, and the main differences between the two formulations are pointed out in terms of wind velocities and turbulent kinetic energies at the pedestrian height level, and wind comfort maps. The analyses show that using an incorrectly formulated ABL model results in an over-prediction of near-ground velocities due to an artificial wind acceleration, a significant under-prediction of turbulent kinetic energy values, and consequently resulting in a significantly worse pedestrian wind comfort map that differs up to 3 comfort classes in some regions.

Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industry

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings.

A Comprehensive Comparison of Various Galactic Cosmic-Ray Models to the State-of-the-art Particle and Radiation Measurements

Galactic cosmic rays (GCRs) are the slowly varying background energetic particles that originate outside the solar system, are modulated by the heliospheric magnetic field, and pose ongoing radiation hazards to deep space exploration missions. To assess the potential radiation risk, various models have been developed to predict the GCR flux near Earth based on propagation theories and/or empirical functions. It is essential to benchmark these models by validating against the state-of-the-art measurements. In this work, a comprehensive model–observation comparison of the energy-dependent particle flux has been performed, by combining five typical GCR models and observational data from the Cosmic Ray Isotope Spectrometer on board the Advanced Composition Explorer spacecraft at relatively lower energies and data from the Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics and Alpha Magnetic Spectrometer at higher energies. The analysis shows that, out of the five models investigated in this study, the optimal model, characterized by minimal relative difference or reduced chi-square divergence from measurements, depends on the particle type, energy range, and epoch of interest. Furthermore, a silicon slab is applied to compute the absorbed dose rate using conversion factors applied to GCR model outputs, and the results are compared to measurements from the Cosmic Ray Telescope for the Effects of Radiation. The comparisons in this paper have implications for the strengths and limitations of individual GCR models, advance our comprehension of the underlying GCR transport mechanisms, and also have strong application aspects for mitigating space radiation risks.

Heat transfer enhancement of electrospray cooling with microencapsulated phase change material slurry (MPCMS): A comprehensive numerical model and experimental study

Electrospray cooling (ESC) technology is increasingly emphasized due to its efficient heat removal capacity, minor coolant consumption and accurate thermal control. Previous articles had focused on either experimental exploration on ESC performance or numerical methods on solitary electrospray such as Volume of Fluid [1], Boundary Element Method [2] and Lagrangian model [3]. However, there is no comprehensive model characterizing the whole ESC process. Moreover, there was barely investigation on ESC enhancement using phase change materials (PCM). Herein, this study has bridged this gap by developing a new comprehensive model to explore the ESC enhancement with microencapsulated phase change material slurry (MPCMS), and conducted experiment to verify it. The influences of applied voltage, mass concentration and inlet temperature of MPCMS on the ESC were discussed. Results revealed that MPCMS offered superior cooling performance during melting process due to latent heat absorption compared to deionized water. Nevertheless, as wall temperatures increased, the deposition, higher viscosity and lower thermal conductivity of MPCMS would compromise cooling efficiency. Moreover, higher applied voltage enhanced cooling performance by increasing droplet velocity, and reducing the possibility of droplet rebound. When the inlet temperature approached the peak melting temperature, the convective heat transfer coefficient reached its maximum.

Taking Dandelion as an Example: Exploring a Comprehensive Prediction and Evaluation Model for Invasive Species

With the rapid growth of global trade and tourism, the phenomenon of species invasion is becoming increasingly serious. Invasive species often have a significant impact on native ecosystems, disrupting ecological balance and threatening biodiversity. Therefore, it is essential to conduct in-depth research on invasive species. Dandelion, due to its strong reproductive ability, is considered a common invasive plant, but its application value in medicine, food, and ornamental uses cannot be ignored. Therefore, this article aims to establish a mathematical model to explore the relationship between invasive species and human activities and take Dandelion as an example to establish a comprehensive prediction and evaluation model for detailed analysis.

Numerical modelling of the structural response of a novel hybrid densified wood filled-aluminium tube dowel for structural timber connections

This paper deals with the finite element modelling of the structural response of timber connections assembled using a novel hybrid dowel, made of densified wood filled aluminium tube, for the first time. Predictive and comprehensive finite element models, using the LS-Dyna Software, are developed to thoroughly investigate the non-linear 3D mechanical behaviour and optimize the design of the aluminium-to-timber connections. The materials parameters of the models are, first, identified based on experimental data from three-point bending tests. Then the models are validated by comparison to experimental data from slotted-in aluminium plate timber connections assembled either using steel dowel or hybrid densified wood filled aluminium tube dowel. The results are compared in terms of load-slip curves as well as in terms of failure modes. In addition, a parametrical study is conducted using the validated finite element model to investigate the influence of some material and geometrical parameters. The results showed that the hybrid densified wood filled aluminium tube dowels can be a potential substitute for conventional steel dowels. The study highlights the efficiency of the proposed finite element model in terms of quality of results and efficiency of use upstream the design process of such structures.

A comprehensive assessment model for livelihood vulnerability using fuzzy TOPSIS technique: A case of 2022 flood in Pakistan

One of the most significant natural disasters that results in significant economic losses and human misery is flooding. Flooding can threaten people’s livelihoods because it damages homes, infrastructure, and other essential assets. This study’s objective is to evaluate the livelihood vulnerability (LHV) of those who have been relocated and displaced as a result of the recent flood in Pakistan’s Khyber Pakhtunkhwa (KP) province. The study was conducted in three districts of KP province, which are Charsadda, Nowshera, and Dera Ismail Khan (D.I. Khan). First, a comprehensive literature review was conducted to identify relevant criteria for LHV assessment. As a result, fourteen criteria were selected to measure LHV in the selected regions. Then, a group of experts was invited to prioritize the criteria and provide linguistic ratings for the alternatives in terms of the selected criteria. Finally, the fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) method was used to classify the targeted districts on the basis of their closeness coefficient (CC) values. To assess the robustness of the proposed model, a sensitivity analysis was also provided. The findings of the study revealed that communities residing in D.I. Khan district have the highest LHV compared to Charsadda and Nowshera districts. Besides, loss of harvested or stored crops, total workability, arable landholding, and medical and living expenditures were found to be the most dominant criteria for LHV evaluation. The study concluded that greater efforts are needed during the transition period to improve migrant workers’ employment skills and help them reestablish sustainable sources of income.

Examining Sustainable Overseas Investment Information-Sharing Model for Automobile Enterprises: A Multi-Modal Weight Network Approach

AbstractIn an era of globalization, automotive companies are increasingly looking to make overseas investments to expand their production capacity and explore foreign markets. However, the outcomes of such investments are often influenced by a myriad of factors, including policy changes, social dynamics, and market conditions. To address the need for a comprehensive overseas investment information-sharing model, this research proposes an innovative approach based on a multi-modal weight network. This model aims to provide users with a global perspective on overseas investment opportunities, encompassing policy insights, and market dynamics. It integrates data from various sources, offering multi-dimensional information on investment regions, scales, fields, motivations, and strategies. Real-time updates ensure the timeliness and accuracy of the information, enabling users to adapt to the rapidly changing international economic landscape. Challenges such as data collection, privacy concerns, investment diversity, advanced analytics, and real-time updates are carefully considered and addressed. The model incorporates sophisticated analytical methods to extract valuable insights from vast data, guiding sound decision-making for automotive enterprises. Experimental results demonstrate an impressive accuracy rate of 87.9% and an mAP value of 86.8%, highlighting the model’s effectiveness in providing precise and reliable investment information. This innovative multi-modal weight network model empowers automotive companies to navigate the complexities of international investments, enabling them to make informed decisions and achieve success in the global market.