Product Model Research Articles

Reflections on Three Decades of Building Information Modeling

In 1994, the first European Conference on Product and Process Modelling took place in Dresden, Germany. The entire field of construction information technology research has enthusiastically embraced building information modeling (BIM) as a method for creating an ever more detailed and ever more complex model of the product, which is produced through an increasingly complex process. During this successful evolution, some of the fundamental principles of design, design collaboration, and design representation have been sidelined and may not have been sufficiently addressed by the standards and even less so by the software. The symptoms of this are coordination and productivity problems that persist in the construction industry as well as an increasing amount of planning, organizational, and legal paperwork that is needed to support BIM-based processes. This paper analyzes the gap between foundational theory and practice, and it argues that BIM-related research and development should find a better balance between product and process modeling, semantics and pragmatics, modeling and informing, form and function and behavior, as well as computer-integrated construction and productivity. The latter element in the listed pairs is the one that, if better addressed, would lead to even more useful tools and better building.

A DUAL-CHANNEL SUPPLY CHAIN MODEL OF COMPLEMENTARY PRODUCTS BY CONSIDERING GREEN MANUFACTURING LEVEL

The development of technology and the internet has encouraged industry players to develop their businesses by adding online sales media, which previously only used offline sales media. By adding online sales media, industry players can expand their markets so they can increase profits. This study aims to modify the DCSC model for complementary products by considering the green manufacturing level to maximize the profit of a system consisting of two manufacturers and one retailer.. Based on the model that has been constructed, the optimal solution is determined so that maximum profits are obtained for each model actor in centralized scenarios. After that, the model is applied and sensitivity analysis of its parameters is carried out. Based on the research results, it appears that the policy that is more profitable for each actor in terms of system benefits is the centralized scenario. Analysis of changes in selling price elasticity values and cross price sensitivity of one of the complementary products sold in two channels influences the increase or decrease in profits of each actor and system profits. However, changes in the value of cross-price sensitivity between two complementary products indicate a decrease in the profits of each actor and the profits of the system. The change in the sensitivity value of the green manufacturing level for each product shows an increase in system profits and retailer profits, where when the sensitivity value of the green manufacturing level of product 1 is greater, the profit of manufacturer 1 is also greater, but the profit of manufacturer 2 is smaller. Conversely, when the green manufacturing level sensitivity value of product 2 becomes greater, the profit of manufacturer 2 also becomes greater, but the profit of manufacturer 1 becomes smaller.

EXPRESS: Product Returns and Assortment Decisions: a Strategic Analysis of Online and Offline Competition

We present a model of product returns and assortment decisions in the context of online-offline retail competition. In equilibrium, the online store optimally offers easy-to-fit products to reduce costly returns. The competing brick-and-mortar store faces a subtle trade-off between generating higher sales and attracting more foot traffic, and thus, it might use its limited store size to stock harder-to-fit products. Our model provides a rationale for the prevalence of relatively lower-quality products sold online and the steady growth of specialty stores within the changing footprint of traditional retailing. The model is extended to include possibilities such as a physical store opening an online channel to compete with the online store. We apply this framework to investigate the possibility and consequences of retailers collaborating to handle returns. Specifically, a recent “buy online and return in store” (BORS) return policy allows consumers to return online purchases directly to a competing brick-and-mortar store. In the short run, BORS increases the BMR’s foot traffic and the returns volume. In the long run, BORS expands the OR’s assortment and increases BMR’s foot traffic. BORS can be sustained in the long run if the BMR is small or if its cost-saving effect is large.

Online detection of potato drying stages based on improved YOLOv7-tiny model

To realize accurate online identification of different stages of the agricultural product drying process and overcome the limitations of empirical models, this study proposes a method for online identification of agricultural product drying stages based on machine vision, which enhances the YOLOv7-tiny model by adding an attention mechanism module to the feature layer and the up-adoption process. The recognition results were compared and evaluated with those of other versions of YOLO, Faster R-CNN, SSD, EfficientDet, and an unimproved YOLOv7-tiny network. The results showed that the average recognition accuracy of this method for the constant drying stage, first drying stage deceleration and second drying stage deceleration of potato slices reached 98.8%, which was superior to that of the model without the attentional mechanism module. This lays the foundation for the establishment of an on-line adaptive drying model for agricultural products.

A Review of SDLCs for Big Data Analytics Systems in the Context of Very Small Entities Using the ISO/IEC 29110 Standard-Basic Profile

Context: A Systems Development Life Cycle (SDLC) is a model of phases-activities, roles, and products systematically used to develop software with functional expected quality. Although SDLC is widely applied to various software types, it remains unusual in Big Data Analytics Systems (BDAS). Objective: To address this issue, several SDLCs for BDAS have been proposed, along with comparative studies, to guide interested organizations in adapting them. This research seeks a lightweight, balanced, and feasible for small development teams or organizations, taking advantage of favorable characteristics of the international ISO standard. Method and Materials: This study describes the knowledge gap by reporting a comparative analysis of four relevant SDLCs. A selective research method was applied (CRISP-DM, TDSP, BDPL, and DDSL), focusing on alignment with the recent ISO/IEC 29110-basic profilestandard. The goal was to identify which SDLC contributes and fits better from a lightweight approach. Results: From the rigorous approach Cross Industry Standard Process for Data Mining (CRISP-DM) showed the highest alignment with the standard, for the agile approach it was Domino Data Science Lifecycle (DDSL) being the closest of the four. Team Data Science Process (TDSP) stood out as the most agile of those analyzed but fell short of the required results. BDPL, which manages another standard, was too rigorous and more distant. Conclusions: Research on new SDLC for Big Data Project Lifecycle (BDPL) has been practically nonexistent in software engineering from 2000 to 2023. Only BDPL was found in the academic literature, while the other three came from gray literature. Despite the relevance of this topic for BDAS organizations, no adequate SDLC was identified

Building Narratives of Effectiveness and Customer Orientation at the Interface of Different Actors in the Lifestyle Guidance Service Process

Co-production is expected to lead to more efficient and effective services. In response, this study aims to describe and understand the construction of effectiveness and the manifestation of customer orientation among the actors involved in the service process of lifestyle guidance. A qualitative narrative study was designed, and data were collected through the thematic interviews (n = 9) with the management, employees and customers of a Finnish public healthcare and social welfare organisation. The main narratives were as follows: (1) Shared value creation narrative, (2) Organisational strategy and customer meeting narrative, (3) Service co-production narrative and (4) Effective service process narrative. Based on the context, input, process and product (CIPP) model, they construct a plot narrative about the customer's need for individual guidance and the goal of transferring responsibility to the organisation. They create cross-pressure on the service process of lifestyle guidance. Achieving the effectiveness of lifestyle guidance requires the service provider to ensure the service is implemented more flexibly between general, self-directed and individually supportive guidance.

A novel framework for increasing research transparency: Exploring the connection between diversity and innovation.

A split sample/dual method research protocol is demonstrated to increase transparency while reducing the probability of false discovery. We apply the protocol to examine whether diversity in ownership teams increases or decreases the likelihood of a firm reporting a novel innovation using data from the 2018 United States Census Bureau's Annual Business Survey. Transparency is increased in three ways: 1) all specification testing and identifying potentially productive models is done in an exploratory subsample that 2) preserves the validity of hypothesis test statistics from de novo estimation in the holdout confirmatory sample with 3) all findings publicly documented in an earlier registered report and in this journal publication. Bayesian estimation procedures that leverage information from the exploratory stage included in the confirmatory stage estimation replace traditional frequentist null hypothesis significance testing. In addition to increasing statistical power by using information from the full sample, Bayesian methods directly estimate a probability distribution for the magnitude of an effect, allowing much richer inference. Estimated magnitudes of diversity along academic discipline, race, ethnicity, and foreign-born status dimensions are positively associated with innovation. A maximally diverse ownership team on these dimensions would be roughly six times more likely to report new-to-market innovation than a homophilic team.

Multi-objective mixed-model assembly line balancing with hierarchical worker assignment: A case study of gear reducer manufacturing operations

Assembly lines, generally speaking, can reduce production costs, shorten cycle times, and achieve higher quality levels. Since the current market is characterized by increasing product variability, mixed-model assembly lines, in which similar product models can be assembled simultaneously, are more suitable to respond to varied market demands than traditional single-model assembly lines. In addition, in an assembly line, tasks often differ in processing requirements, and workers may have different qualification levels. This study, therefore, aims to construct models for the multi-objective mixed-model assembly line balancing problem with hierarchical worker assignment (MO-MALBP-HW). The goal is to generate a suitable plan for a mixed-model assembly line balancing problem considering the constraint of a hierarchical workforce, the cost of a hierarchical workforce, and production cycle time. When the problem is simple, it can be solved by a mixed integer programming (MIP) model. When the problem becomes complex, it can be solved by a multi-objective genetic algorithm (MOGA) and a non-dominated sorting genetic algorithm II (NSGA-II) to obtain a near-optimal solution. The implementation of this model can effectively manage the multi-objective mixed-model assembly line balancing plan, thereby improving plant efficiency and reducing cost.

Random Forest Development and Modeling of Gross Primary Productivity in the Hudson Bay Lowlands

Peatlands are a critical component of the global carbon cycle. Within Canada, the Hudson Bay Lowlands (HBL) has accumulated an estimated 33 Gt of carbon as peat because of a small but persistent difference between gross primary productivity (GPP) and ecosystem respiration over millennia. However, the impacts of disturbance and climate change on GPP are difficult to monitor across the HBL due to its vast size and remote location. This study evaluates the potential for random forest regression models to estimate GPP at five HBL eddy covariance flux monitoring sites using only optical data from MODIS (500 m, 8 day) or harmonized Landsat/Sentinel (HLS; 30 m, 16 day or more frequent). The results show that spatial resolution has less impact on modeled daily GPP compared to temporal resolution across model configurations. Using MODIS data, individual sites’ daily GPP could be simulated with minimal bias, R2 up to 0.89 and mean absolute error as low as 0.37 g C m−2 day−1. For annual GPP, MODIS (R2 = 0.84; mean absolute error 40.5 g C m−2 year−1) also outperformed the HLS models (R2 = 0.46; mean absolute error 86.4 g C m−2 year−1).

“YOU MUST BE LISTENED TO”: FACILITATORS FOR BABY BOOMERS’ SOCIAL PARTICIPATION IN CANADA AND SPAIN

Abstract In Quebec (Canada) and Spain in 2021, the baby boomers represented more than a quarter (26%) of the population. To better support a shift in healthcare and social services, this cohort requires a reassessment of their social participation, a key determinant of healthy and active aging. Despite their committement to participation, little knowledge about what influence the social participation of this generation. This presentation aims to report results of two qualitative studies exploring the facilitators for social participation in the old age of the baby boom generation. These two studies were both carried out in Spain and Quebec with 53 individual and 12 group interviews, involving a total of 107 baby boomers and 52 community representatives. Perceived facilitators were related to: a) accessibility (welcoming environment, physical and emotional proximity, virtual to overcome transportation difficulties); b) availability (flexible participation without high commitment, from a critical viewpoint towards the responsibility of cares); c) positive image (being recognized, required and respected in intergenerational settings); d) socioeconomic and health status (low cost of activities, adaptation to limitations focusing on what you can still do); and e) motivation (feeling useful, helping others and impacting on the society, deciding freely according to their values, but questioning productive ageing models). The challenges associated with supporting attractive forms and opportunities of participation adapted to the needs of this new generation were also highlighted. Responding to these challenges implies creating spaces for interactive exchanges between the baby boomers and key social agents, with real translation in health promotion policies.

Development of a Real-Time Flood Prediction and Warning System for Underground Excavation Sites Based on Nomographs

In August 2022, a localized torrential downpour in Seoul highlighted the growing frequency of extreme rainfall events linked to climate change. Simultaneously, rapid urbanization has led to higher population densities and an increase in underground space development and excavation projects, such as the Yeongdong-daero complex transit center and the construction of the Greater Seoul Metropolitan Express Railway (GTX). This study examines the risks to evacuation routes at ‘underground excavation sites’ during heavy rainfall. Based on this analysis, a system for calculating evacuation buffer time and providing real-time flood forecasting and warnings were developed. A testbed was established in a basin susceptible to recurrent flooding, driven by seawater, external water, and internal water. The study modeled excavation sites with depths of less than 10 m, which were the areas currently excluded from intensive underground safety regulations. The flood risks along the evacuation routes were quantified using a depth-velocity product model. To stimulate real-time flood risks, a rainfall scenario was created by combining observed and predicted rainfall data from the weather radar. The real-time flood forecasting and warning system developed in this study, if implemented nationwide in flood-prone areas, could provide timely warnings to site managers and officials, enabling immediate evacuation based on the risk assessments of evacuation routes in different rainfall scenarios.

Building capacity indicators for hotel management in Chinese vocational colleges: A social psychology perspective

Addressing the critical gap between hospitality education and industry demands, this study develops key capacity indicators for hotel management in Chinese vocational colleges from a social psychology perspective. A structured research method was adopted, and the evaluation indicators were determined using a literature review. The Modified Delphi Method (MDM) and the Analytic Hierarchy Process (AHP) were used to refine the indicators and analyze their importance, respectively, to establish a hierarchical evaluation system. In both methods, we selected 15 experts to participate. Grounded in the Context, Input, Process, Product (CIPP) model and Competency-Based Education (CBE) theory, the study integrates social psychological constructs such as motivation, cultural adaptability, and interpersonal skills into the capacity indicators. Engaging hotel management and vocational education experts through MDM, the study results in a hierarchical system with 4 primary indicators, 10 secondary indicators, and 21 tertiary indicators. AHP assesses the relative importance of each indicator, highlighting the significance of social and environmental factors in shaping educational outcomes. This framework offers a robust foundation for teaching evaluation and improvement in vocational colleges, providing insights for developing skilled professionals who meet industry demands and enhancing the quality of vocational hotel management education in China through social psychological lenses.

Structural and semantic features of English eponyms in the “artificial intelligence” terminology system

The paper is focused on the structural and semantic characteristics of eponyms in the sublanguage “Artificial Intelligence” in English. The study’s relevance is determined by the rapid advancement of artificial intelligence systems and information technology in almost all spheres of human activities, which in turn leads to the creation of new lexical units. This finding highlights the necessity of conducting both theoretical and applied studies in this area of lexicology.We have found that in the sublanguage “Artificial Intelligence”, the names and surnames of individuals who have made significant contributions to the formation and development of this field of knowledge are used as a productive basis for creating new terms. We have also succeeded in revealing that anthroponyms can both undergo certain morphological transformations during the deonymization process and be used in their initial form. The most productive word-formation models are the use of an eponym in the possessive case and the formation of an eponym-based adjective. In contrast to anthroponyms, toponyms and names of literary and mythological characters shift into the category of common nouns without changing their form, but acquiring a new meaning. The study’s findings highlight the significance of eponyms as fundamental components of the language of science and technology and their impact on the way artificial intelligence concepts are understood.

Bicycle Tourism Development Model to Increase Community Income in Tourism Villages the Case of Burai Ecotourism Village, Ogan Ilir Regency

Sports tourism is booming today fueled by the increasing awareness of health and fitness around the world, resulting in a wide array of sporting events and outdoor recreational activities. One of them is Bicycle tourism, which is developing into one of the increasingly recognized tourist activities. Although it is well-known globally, it has not been packaged well in tourist villages, this potential has not been adequately utilized by the people of tourism villages, furthermore, the completeness of data on the potential of tourism products is also an obstacle to the development of Bicycle tourism product models. This research aims to develop bicycle tourism itinerary products based on tourism village potential prepared with local communities in the Burai tourism village. This research uses a combination method or mixed method with a sequential explanatory design, while the data analysis used is the Miles and Huberman model steps which are interactive, namely, data collection, data reduction, data display, and conclusion. Then the population is tourists visiting tourist villages and the resource persons/informants are: (1) Community Drivers of the tourism sector; (2) Local authorities; and (3) tourists. Data was obtained quantitatively and sampled using the Random Sampling technique. The result of this research is the creation of a light-category and medium-category Bicycle tourism development model and becomes a packaging solution for tourist bicycle products in tourist villages that increase people's income in tourist villages. This research contributes to the form of diversification of tourism products in tourism villages, so that natural tourism attractions, cultural tourism and artificial tourism can collaborate into products that have been made so that the community and further research can be developed on the potential absorption of informal labour that may arise based on needs found in the field.

Embrace the Era of Drones: A New Practical Design Approach to Emergency Rescue Drones

To increase user satisfaction with emergency rescue drone products, a product modelling design method based on the fuzzy Kano-QFD-FBS model is proposed. First, the initial user requirements for the emergency rescue drone products are obtained through a questionnaire, and the fuzzy Kano model is utilised and combined with the better–worse coefficient method to categorise the attributes, define the priorities of the user requirements, and screen out the key user requirements. Second, the QFD model is used to construct the quality house, analyse the key user requirements quantitatively, and obtain the design elements and weights of the emergency rescue drone product. The obtained key design elements are subsequently imported into the FBS model to complete the mapping transformation from the functional elements to the structural elements of the emergency rescue drone products and realise the styling design of the emergency rescue drone products. Finally, the user satisfaction scale based on appearance, functionality, and interaction was developed and the System Usability Scale (SUS) was used to evaluate user satisfaction with the emergency rescue drone design scheme. The new design scheme scored higher and showed significant differences in satisfaction ratings compared to the previous scheme. Hefei Jiaxun Technology Co., Ltd. carried out product development for the design scheme. At present, physical products have been sold on the market and have achieved good results. Hefei Jiaxun Technology Co., Ltd. conducted a survey on consumer satisfaction with this product, and the results revealed that customer satisfaction increased by 11.9% compared with that of previous products. Compared with similar products in the market, the consumer satisfaction with this product increased by 13.5%, indicating that it has obvious market competitiveness. This study shows that the method of product styling design based on the fuzzy Kano-QFD-FBS model can comprehensively acquire and analyse user requirements, realise accurate mapping from user requirements to product design elements, and output the specific solution of the emergency rescue drone product styling design. The design scheme performs well in meeting user requirements, verifies the feasibility and effectiveness of the fuzzy Kano-QFD-FBS model in the styling design study of emergency rescue drones, and provides a new paradigm for emergency rescue product design.

Model Pengembangan Zakat Produktif Studi Komparatif di Indonesia dan Brunei Darussalam

This research examines the productive zakat development model in Indonesia and Brunei Darussalam through a comparative approach. Productive zakat is an effort to utilize zakat funds to improve the economic welfare of mustahik in a sustainable manner. In Indonesia, zakat is regulated by Law no. 23 of 2011 and managed by the National Amil Zakat Agency (BAZNAS) and various Amil Zakat Institutions (LAZ). Meanwhile, in Brunei Darussalam, zakat is managed by the Brunei Islamic Religious Council (MUIB) based on sharia law. This study found that the two countries have different approaches but both focus on empowering the mustahik economy through programs such as providing business capital, skills training and assistance with production equipment. Indonesia shows diversity in program implementation with extensive collaboration between various institutions, while Brunei Darussalam utilizes full government support for more focused and effective program implementation. Challenges facing Indonesia include unequal distribution and weak supervision, while Brunei faces challenges in economic diversification. However, both countries have great opportunities to increase the effectiveness of productive zakat programs through technological innovation and closer collaboration between the public and private sectors. This study provides insight into best practices and lessons that can be drawn from each country to improve the welfare of mustahik in a sustainable manner. The conclusion of this research emphasizes the importance of strengthening the legal and institutional framework, as well as increasing transparency and accountability in the management of productive zakat.

Eye Tracking and Semantic Evaluation for Ceramic Teapot Product Modeling

In addition to their practical and aesthetic qualities, ceramic teapots are highly decorative and stylish. Based on the theory of perceptual engineering, this study employs eye-tracking technology and semantic-difference methods to investigate user preferences for ceramic teapot shapes. Using eye-movement experiments, the study first determines users’ visual attention to different morphological regions. Using the orthogonal alignment method, nine styling samples were developed by combining expert classification of classic and traditional teapot styling elements. By combining a semantic perception questionnaire with a satisfaction questionnaire, the study evaluated users’ visual attention to these samples and their satisfaction with them. It was found that shapes characterized by classic, rounded, and proportional coordination were more in line with consumers’ aesthetic preferences despite the differences in semantic evaluations among consumers of different genders and ages, which led to relatively consistent consumer satisfaction. The purpose of this study is not only to provide a scientific basis for styling ceramic teapots but also to assist designers in grasping the laws of consumer preference in order to create better products.

Design optimization of vibration amplitude reduction based on virtual prototype and machine learning

The traditional design optimization of vibration amplitude reduction mainly has the disadvantages of low modeling and prediction accuracy as well as low optimization efficiency. Therefore, this paper presents a design optimization method for vibration amplitude reduction based on virtual prototyping and machine learning, which combines the high accuracy of numerical calculations with the efficiency of machine learning, overcoming the shortcomings of traditional methods. Firstly, sample points are collected through the design of experiments and virtual prototype simulation. Then, based on the sampled data, a prediction model for the relationship between the design parameters and the amplitude of the product is established using Genetic Algorithm-Support Vector Regression (GA-SVR). On the basis of the GA-SVR prediction model, a multi-objective optimization model of product is established, and Multiple Objectives Particle Swarm Optimization -entropy weight- Technique for Order Preference by Similarity to Ideal Solution (MOPSO-entropy weight-TOPSIS) is used to solve for the optimal design parameters. Finally, the washing machine suspension system is used as an example to verify the effectiveness of the model. The results show that, compared with the original design scheme, the design scheme obtained by the model can reduce the amplitude of the washing machine suspension system by 12.68%, and reduce the total weight of the counterweight by 7.35%. This method is conducive to the intelligent and efficient design optimization of vibration amplitude reduction, and is of great significance to product life cycle design.

Industrial Sufficiency: A Conceptual Methodological Framework

Implementing sustainability strategies is essential for the future viability of companies. While companies have been focusing intensively on the operationalization of efficiency and consistency for quite some time, sufficiency approaches are rare. As a result, there is a lack of fundamental understanding of the concept, its inherent potential, as well as a lack of basic implementation concepts for strategies, management systems, and product and process development. Based on a literature review using Scopus and Web of Science according to the PRISMA approach, this paper develops a definition for the concept of industrial sufficiency and presents three general industrial sufficiency strategies (frugality, longevity, and specificity) regarding three distinct business determinants (product, production, and business model). The investigation shows that not only can there be overlaps between the three general sustainability strategies (efficiency, consistency, and sufficiency) but that individual measures are also mutually dependent at different levels. In addition, significant conflicts of objectives for implementation in industrial practice are revealed.

Identification of feature selection techniques for software defect prediction by using BCF-WASPAS methodology based on Einstein operators

PurposeThis research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction. The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.Design/methodology/approachTo do so, we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets, which leads to the bipolar complex fuzzy WASPAS method. The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters. The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.FindingsIt is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods. The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria, which contributes to more accurate feature selection for software defect prediction. We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology. We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.Originality/valueThis research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction. The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process, which will be useful for software engineers and help them select the best feature selection techniques. This work also helps to enhance the overall performance of software defect prediction systems.