Modeling Method Research Articles

Framework for Industry 4.0 Adoption Strategies in Indian Manufacturing Industries: A SWOT and N-ISM Approach

Considering the significance of industry 4.0 (I 4.0) in manufacturing and the impact of internal and external environments in achieving transformation, the organizations require a comprehensive framework to reap I 4.0 benefits. In this study, a systematic literature review is conducted to identify strengths, weaknesses, opportunities, threats (SWOT), and strategic factors that may affect I 4.0 implementation in the manufacturing sector in India. Based on SWOT analysis and expert panel discussion, ten strategies were finalized. The neutrosophic-interpretive structural modeling (N-ISM) method is further employed to evaluate the hierarchical relationships among the I 4.0 strategy. The structural model showed that the strategies “complete cost–benefit analysis of I 4.0 technologies (ST9)” and “government policies to support I 4.0 (S10)” play a significant role in I 4.0 adoption. The neutrosophic-Cross-Impact Matrix Multiplication Applied to Classification (N-MICMAC) also indicates that these strategies possess the highest driving power. The study contributes by developing a hierarchical framework for I 4.0 implementation strategies by identifying internal and external factors affecting its adoption. The current study also offers useful insights for managers and practitioners to acknowledge the challenges and opportunities of implementing I 4.0. Using this framework, they may understand interrelationships among strategies to build a comprehensive roadmap.

Degenerate phase-matching for multi-wavelength nonlinear mixing in aperiodic lattice lasers

Holographically designed aperiodic lattices (ALs) have proven to be an exciting engineering technique for achieving electrically switchable single- or multi-frequency emissions in terahertz (THz) semiconductor lasers. Here, we employ the nonlinear transfer matrix modeling method to investigate multi-wavelength nonlinear (sum- or difference-) frequency generation within an integrated THz (idler) laser cavity that also supports optical (pump and signal) waves. The laser cavity includes an aperiodic lattice, which engineers the idler photon lifetimes and effective refractive indices. The key findings are the following: (i) the nonlinear conversion efficiency reveals resonant enhancement at those idler frequencies where the photon lifetime is high; (ii) the resonant phase-matching (PM) process between the pump and idler waves has a one-to-one link with the engineered effective index dispersion; and (iii) in the absence of any other dispersion, the lowest threshold, multi-wavelength defect modes of the aperiodic lattice laser have degenerate phase-matched pump frequencies. This set of results will potentially have a significant impact on the wavelength multiplexing in electronically switchable THz-over-fiber communication systems [U.S. patent application 20,150,248,047A1 (3 September 2015)].

Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum

The geometric error distributed on components’ contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed. To determine the boundary of systematical error and random error, we used a cruciform boundary line method based on the K-space spectrum, achieving the optimal separation of the two with frequency difference. The effectiveness of the method was experimentally verified using two sets of machined surfaces. By comparing with current common random error filtering methods, the outstanding role of the proposed error separation method in separating random error and preserving processing features has been verified.

Features and Limitations of Fused Deposition Modelling (FDM) in Obtaining Textile-like Structures

In contemporary production processes and customisation, 3D printing has emerged as a prominent method for prototyping. It offers flexibility to create objects of diverse shapes, structures, sizes and materials. However, integrating this technology into the textile industry to achieve textile-like structures poses challenges. The fused deposition modelling (FDM) method is currently the closest approach to prototyping such structures due to its ability to extrude monofilaments resembling traditional threads. Regrettably, attempts to produce structures with properties akin to textiles, including flexibility, durability, breathability, lightness and fineness, have been unsuccessful due to various limitations inherent in the technical setups of 3D printers. This study analyses the key features of FDM printing that determine the feasibility of achieving authentic textile-like printed structures while clarifying the underlying logic behind their future purpose. Our aim was to assist future researchers in achieving the production of thin 3D-printed fibres (diameter ~0.1 mm) regardless of structure type.

Runoff prediction using combined machine learning models and signal decomposition

ABSTRACT Accurate forecasting of increasingly unpredictable river runoff is essential for effective water resource management in the face of climate change and human activities. This study uses four machine learning models of long short-term memory neural networks (LSTM), support vector machine (SVM), random forest, and artificial neural network models to improve runoff forecasting accuracy and explore combined forecasting models’ effectiveness. This study develops three advanced combined forecasting models (empirical mode decomposition (EMD)–LSTM, VMD–LSTM, wavelet analysis (WA)–LSTM) by combining preprocessing techniques of EMD, variational mode decomposition (VMD), and WA with the LSTM modeling method. These models use signal decomposition techniques to analyze 41 years of runoff data from the Huanren station (1980–2020). The findings reveal that the LSTM model outperforms the other three individual machine learning models when forecasting days with high runoff. Among the three decomposed combined models, the VMD–LSTM model demonstrates the best overall performance during the validation period, achieving root mean square error, Nash–Sutcliffe efficiency coefficient, and bias values of 52.14 m3/s, 0.96, and −0.002, respectively. The combination of LSTM with signal decomposition techniques shows promising potential for enhancing runoff prediction accuracy, with practical implications for water resource management and flood control strategies.

Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning

BackgroundAiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.MethodsThis paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices Ws and Wt that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model.ResultsTaking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the root mean square error of the proposed soft sensor model in predicting Pichia pastoris concentration and inulinase concentration is reduced by 48.7% and 54.9%, respectively.ConclusionThe proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method.

SSMBERT: A Space Science Mission Requirement Classification Method Based on BERT

Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities.

An effective Method For 3D Modelling of Urban Areas from Aerial Images and LiDAR data

Abstract. The aim of the paper is to identify a suitable methodology for the construction of 3D City Models (3DCM) from geospatial data. Recently, the use of hybrid sensors i.e. nadiral and oblique cameras supplemented by Light Detection And Ranging (LiDAR) sensors equipped on an aerial platform, are becoming useful instrumentation for mapping urban areas. In fact, the manuscript shows the use of such instrumentation for the production of a 3D City Model processed in the City Engine environment, starting from a geomatic dataset composed of a point cloud and a series of images covering a portion of the historic centre of Bordeaux (France). The results show the quality of the 3D models in LOD3, generated on the study area, in terms of architectural details, textures and semantic contributions.

Intelligent aerodynamic modelling method for steady/unsteady flow fields of airfoils driven by flow field images based on modified U-Net neural network

An intelligent modelling method driven by flow field images for predicting steady and unsteady flow filed around aerofoils has been developed. Signed Distance Field (SDF) images achieve dimensionality enhancement of aerofoil geometric information, and ‘synthesised images’ achieve dimensionality enhancement of the angle of attack of the aerofoil and Mach number. An intelligent aerodynamic model for steady flow field of aerofoils is constructed based on the U-Net neural network architecture, and further incorporating a long short-term memory (LSTM) module to construct a U-Net-LSTM neural network architecture to extract the temporal features. Typical NACA aerofoils results show that, the prediction error for steady flow is less than 1.98%, while the prediction error for unsteady flow is less than 2.56%. Additionally, the model demonstrates good generalization capability, with a generalization error for steady flow less than 2.45% and a generalization error for unsteady flow less than 3.34%. This research provides a new method for intelligent aerodynamic modelling based on physical representations. Compared to existing methods, this method avoids the need for extracting aerofoil geometry information and eliminates the necessity of predicting the flow field point by point, making it more concise and efficient. Highlights 1. An aerodynamic model was constructed using U-Net to rapidly predict the steady flow field around airfoils. 2. A Long Short-Term Memory (LSTM) module was incorporated to capture temporal information, enabling the rapid prediction of the unsteady flow field around airfoils. To address the problem of ‘dimension loss’ in the modelling datasets, effective data dimensionality enhancement was achieved using SDF images and ‘synthesized images’.

Innovative Web Design of Mobile Applications for Travel Planning: Improving the Quality of User Experience

The purpose of the study is to analyze and create a web design for a travel application that will provide effective and convenient user interaction with a digital resource. The research method: general scientific methods of analysis and synthesis (to analyze the current state of the problem and existing Ukrainian applications for travel planning), modeling method (development of a goal tree and a functional model of the problem) and prototyping method (development of a prototype of a web application for travel, taking into account modern design solutions and UX/UI approaches). The scientific novelty is the development of the most innovative and modern web application design that will meet modern user requirements and ensure effective customer interaction with the application. Conclusions. Developing a web design for a travel application that will meet modern user requirements and ensure effective interaction with a digital resource is a key component of a successful product on the market. The paper examines the current state of use of mobile travel applications, identifies their positive and negative characteristics. The practical significance of the obtained results lies in the development of a travel application interface that will provide the functions of route planning and planning, recommendations for places to visit, hotel and transport booking, organization of group trips, joint route planning with other users, obtaining detailed information about events and activities, organizing excursions, sharing tips and experiences with other users. The study results can be used to develop an intuitive, convenient and functional application interface that meets the needs of users in planning and organizing travel. The results of the study can be used to develop an intuitive, user-friendly and functional application interface. This will help to increase user satisfaction and the effectiveness of their interaction with the application in the process of planning and organizing travel.

Зеленый кнут? Силовой фактор и неполитические протесты в российских регионах на примере экологической мобилизации

Environmental activism is one of the important forms of civic activity in all types of political regimes. But under the conditions of autocracies it has a distinct specificity, which involves a conscious distancing from politics. The tendency to avoid “politicization” is an integral component of the strategy of eco-activists, who are well aware of the risks of protest actions in an authoritarian context. Environmental protest activity is often perceived as non-political by the ruling elites, but their reaction to it varies. In search of an answer to the question about the causes of such variations the authors elaborate a theoretical model, according to which the reaction of elites to environmental activism is determined by the power potential of the heads of executive government in combination with a high level of inequality, which signals the possible risks of eco-protests turning into broad political mobilization. The model is tested on data concerning environmental protests in Russian regions. By using the method of statistical modelling, the authors come to the conclusion, that the factor of socio-economic inequality does not affect the intensity of environmental mobilization, but has a key significance as a catalyst for the coercive reaction of the authorities. In the regions with high levels of inequality, the “stick” is systematically applied to environmentalists, while in regions with relatively moderate economic stratification environmental protests remain an acceptable form of expressing discontent. At the same time, the presence of a coercive component in the political capital of regional elites plays a crucial role in reducing the eco-protest potential. In addition to that, the research indicates that regional protest activity on environmental issues in Russia is leaving the format of non-political mobilization. As a result, the authorities’ response is increasingly based on suppression, similar to other types of protests, which potentially limits the opportunities for conveying an environmental agenda.

The Use of iPad LiDAR to Build a Digital Elevation Model in Defining Customary Zones (Case Study of Panglipuran, Bali)

Abstract. The LiDAR on the iPad device can be used for surveying and mapping. However, the accuracy and precision of the LiDAR scanner on the iPad are not as high as the LiDAR system on Terrestrial Laser Scanning (TLS), with a more limited range and smaller coverage area. Still, the LiDAR sensor integrated into the iPad has advantages when compared to professional LiDAR systems, including the iPad LiDAR as a low-cost portable 3D scanner, dynamic portability, speed, easy use at close range so that it can be used to scan objects with high density and can reach areas/parts of objects that are difficult to be covered by TLS. The 3D modelling method using iPad LiDAR is a solution for building 3D models.Since iPad’s point cloud data is not integrated with the global coordinate system, a georeferencing process is necessary to produce DEM (Digital Elevation Model), which will be used to define customary zones in Panglipuran Heritage Village. The georeferencing process is carried out by transforming the iPad’s LiDAR coordinates system to the global reference coordinates resulting from GNSS measurements. The scanning strategy must be well arranged. In addition, good control point design determines the quality of control point transformation results into a global reference coordinate system.This research contributes to obtaining an alternative low-cost 3D mapping method for cultural conservation areas using LiDAR technologies. This method is inexpensive but still considers geospatial quality aspects so it can be used for the decision-making process in spatial planning of cultural regions.

Examining the Impact of ict Use and Related Attitudes on Students’ Achievements in Science Using the pls-sem Model

Abstract Investigations were conducted into the relationships between science achievement and ict use in and out of the classroom, as well as the mediating effects of students’ interest in and perceptions of their ict proficiency. The primary focus of this study has been the association between ict and scientific achievement among Singaporean students as measured by the Programme for International Student Assessment (pisa) 2018. This study examined pisa 2018 data using partial least squares structural equation modeling (pls-sem) to evaluate the effect of ict use on students’ achievements in science. Complex cause-and-effect relationships in path models with latent variables can be estimated using the structural equation modeling method known as pls-sem. 6,676 15-year-old Singaporean students who participated in the 2018 Programme for International Student Assessment (pisa) were examined. Students’ interest in ict, their assessment of their ict competency, and their accomplishment in science were found to be correlated with how they used ict both inside and outside of the classroom. The findings also imply that the association between ict use and science accomplishment may be mediated by students’ perceived ict proficiency and interest. This research can contribute to advancing the theoretical knowledge of technology, learning, and the cultural environment, in addition to practical ict implementations in scientific classes that consider the variances in ict affects.

Energy Transition Planning in Achieving the Net Zero Emission 2060 Target in the Provincial Electricity Sector North Sumatra

This research discusses the energy transition towards the Net Zero Emissions (NZE) 2060 target in the electricity sector of North Sumatra Province. The challenges faced include dependence on fossil fuels, high greenhouse gas emissions, and limited renewable energy capacity. The study aims to model energy transition scenarios, evaluate the optimal renewable energy mix, and determine emission reduction strategies in the electricity sector using LEAP-NEMO software. This study uses a forecasting-based simulation modeling method with a mathematical approach through LEAP software and NEMO optimization framework. Three scenarios were analyzed, namely Business As Usual (BAU), Net Zero Emissions Carbon Capture Storage (NZE CCS), and Net Zero Emissions Full Renewable Energy (NZE FRE). The simulation results show that the NZE FRE scenario is able to achieve a 100% renewable energy mix by 2060, outperforming the BAU (42.1%) and NZE CCS (70.8%) scenarios. Solar, biomass, hydro, and geothermal energy are the main sources of energy, with solar energy as the largest contributor. Greenhouse gas emissions are reduced by up to 265.9 million tons of CO2 (70.5%) compared to the BAU scenario. These findings provide policy recommendations that support the implementation of renewable energy, reduction of greenhouse gas emissions, and fiscal policies that internalize environmental costs. The study also highlights the importance of investing in clean energy technologies to realize a sustainable electricity system in Indonesia.

Legal support for inclusiveness and equality for transgender employees

Transgender people in Ukraine face a lack of attention to their rights from both society and the law. To uphold the values of the rule of law, which ensures equality and dignity of all citizens, and to ensure compliance with international human rights standards, it is necessary to analyse the legal protection of their labour rights. The following methods were used in the study: dialectical technique, formal logical and systemic methodologies, formal logic, systemic technique, formal legal approach, comparative legal technique, and modelling method. The purpose of the study is to examine the current state of legislation and employment practices for transgender people to develop recommendations for improving the protection of their labour rights and supporting inclusiveness and equality in the workplace.

Deep Learning-Assisted Design of Novel Donor-Acceptor Combinations for Organic Photovoltaic Materials with Enhanced Efficiency.

Designing donor (D) and acceptor (A) structures and discovering promising D-A combinations can effectively improve organic photovoltaic (OPV) device performance. However, to obtain excellent power conversion efficiency (PCE), the trial-and-error structural design in the infinite chemical space is time-consuming and costly. Herein, a deep learning (DL)-assisted design framework for OPV materials is proposed. To effectively digitally represent the D and A structures, a structure representation method, polymer fingerprints, is developed, and a database of OPV materials is constructed. By applying an end-to-end graph neural network modeling method, high-precision DL models for predicting OPV performance are established. After combining the existing structures, ≈0.6 million virtual D-A combinations are generated. Then, the OPV performance of these candidate combinations is predicted by the well-trained models, and numbers of novel D-A combinations with high efficiency are identified. Experimental validations confirm that the prediction accuracy is greater than 93% and one of the screened combinations (i.e., D18:BTP-S11) exhibits an efficiency above 19.3% in single-junction organic solar cells. Finally, based on the structural gene analysis, the design rules to guide experimental explorations are suggested. The developed DL-assisted approach can accelerate the design of D-A combinations with ultrahigh efficiency and bring property breakthroughs for OPV devices.

Instructor's Emotional Intelligence Improves Pilot Student's Achievement at Akademi Penerbang Indonesia Banyuwangi

This study investigates the impact of Instructor Emotional Intelligence (IEI) on Pilot Student's Academic Achievement (PSA) at the Indonesian Aviation Academy in Banyuwangi. A quantitative approach using a survey method was employed to collect data from 20 pilot students selected through purposive sampling. The main research instrument was a questionnaire designed to measure relevant variables, which was validated for reliability and accuracy. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method with the SmartPLS 3 software, allowing for examination within a small sample and complex model structure. The findings indicate a significant relationship between instructor emotional intelligence and pilot student academic achievement. Indicators such as emotion management, empathy, and instructor motivation contribute positively to enhancing pilot students' academic performance. The loading factor and path coefficient values suggest that IEI has a strong influence on PSA, highlighting that students are more motivated and prepared to face academic challenges when guided by emotionally intelligent instructors. This study underscores the importance of instructors’ emotional skills in creating a supportive learning environment that can foster increased motivation and academic achievement among students. It recommends that aviation education institutions consider implementing emotional intelligence training for instructors to improve the quality of education and student learning outcomes.

A Fast Modeling Method for BOR–FDTD

Aiming at the inefficiency caused by the optimal design of rotationally symmetric horn feed models, a fast modeling method for rotationally symmetric structures is proposed, which is used to deal with the mesh generation of rotationally symmetric structures and the rapid establishment of computational models. In this paper, the body-of-revolution finite-difference time-domain (BOR–FDTD) method is employed to investigate the radiation performance of the horn feed. Due to the rotational symmetry of the horn antenna, modeling only requires the establishment of a two-dimensional cross-sectional mesh of the horn feed. An optimized Delaunay triangulation algorithm combined with the projection intersection method is utilized to triangulate the horn cross-section of arbitrary polygons and establish the BOR–FDTD computational mesh. Results from both single-medium and multi-medium triangulation algorithms and computational models verify the accuracy of this modeling method. The radiation patterns of a smooth-walled horn were calculated and compared with the modeling time of MATLAB 2017 and the simulation time of CST. The results reveal that the algorithm presented in this paper aligns well with the simulation results from CST; furthermore, the modeling time amounts to only 6.78% of the MATLAB program’s modeling time, while the total simulation time is 31.3% of CST, which demonstrates both the accuracy and efficiency of the proposed method.

Unlocking the habitat suitability of wild olive to improve its industrial potential: A comprehensive distribution modeling study

Wild olive (Olea europaea L.) is a highly significant forest tree species, both in Türkiye and globally. Its oil and other extracts from the fruits and leaves are vital to various industries, including culinary, cosmetics, pharmaceuticals, and healthcare, making it a valuable non-timber forest product. However, its natural distribution is restricted to Mediterranean climates, emphasizing the need for conservation efforts to support its growth and expansion. Potential distribution modelling is one of the best studies to be done to protect a species and ensure its survival. In this study, the MaxEnt method, which relies exclusively on presence data, was used to generate a potential distribution map for wild olive. The environmental variables to be included in the modeling method were determined using the Analytic Hierarchy Process (AHP), one of the multi-criteria decision-making methods. From an initial set of 29 variables, AHP selected the top 11 for the final model. The resulting model demonstrated high accuracy, with an AUC value of 0.922, successfully identifying and mapping the potential distribution areas for wild olive across Türkiye.

On Multicell-Interaction Chip: In Situ Observing the Interactions between the Astrocytes with Lysosomal Dysfunction and BBB Cells.

Lysosomes in astrocytes play vital roles in toxic protein degradation in the brain. Lysosomal dysfunction can lead to abnormal protein deposits, which further induce damage to neurons and the blood-brain barrier (BBB), and thereby affect the interaction between the nervous and vascular systems. Therefore, investigating the interactions between astrocytes with lysosomal dysfunction and BBB cells is of significant importance. However, the lack of effective in vitro models hinders the study of this complex system. Herein, an 8-well arrayed microfence multicell interculture chip (AMMIC) with a hydrophilically optimized surface is introduced for investigating the interactions between astrocytes and BBB cells. Then, a novel lysosome-targeted photosensitizer, IVQ-2Br, is synthesized for inducing controllable oxidative stress damage in the lysosomes of astrocytes. By the combination of the 8-well AMMIC and IVQ-2Br, a model for studying the interactions between astrocytes with lysosomal dysfunction and BBB cells has been constructed. Particularly, severe secondary injuries to BBB cells brought about by oxidative stress, including alterations in cell morphology and activity as well as notable DNA damage, are in situ observed on the 8-well AMMIC. The mediators involved in this oxidative stress injury-mediated intercellular communication are validated to be reactive oxygen species (ROS) and exosomes. This work not only presents an in vitro modeling method for studying cell-cell interactions but also demonstrates the potential of in vitro models constructed through the integration of complex microfluidic chip techniques and photosensitizers for advancing biomedical research.