S-curve Model Research Articles

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

The Technology Innovation of Hybrid Electric Vehicles: A Patent-Based Study

A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the progress of HEV technologies from their inception and patenting, through their market adoption, and to the expiration of their patent protection. In this study, we aimed to evaluate the technology lifecycle of the HEV industry using the growth S-curve method. The purpose of this study is to describe the technological lifecycle trajectory and current stage of the HEV industry, as well as the technical stages of each sub-technology, to facilitate better decision making. As part of this study, we used patent family data collected from the Derwent Innovation Index database from 1975 to 2022 and established an S-curve model for HEVs and their sub-technologies using logistic regression. In 2022, the technological maturity of HEVs reached 44%. The sub-technologies with the most substantial diffusion capabilities are energy management, propulsion systems, and cooling circuits. According to predictions, the saturation period for the patent family quantity related to HEVs is estimated to be around 53 years.

Physical characterization and mass modelling of Wood apple (Aegel marmelos L.)

Engineering properties of fruits and vegetables are useful in designing and development of agro processing machinery. In this study, the physical and mechanical properties of Wood apple were determined. Furthermore, the investigation include mass modelling based on physical properties of Wood apple. Fruits are graded by mass rather than size are more useful for developing the automated sorting and grading machines. Four different mathematical models (Linear, Quadratic, S-curve, and Power model) were applied to predict mass, volume and projected area of Wood apple. The data was evaluated and compared for graded and ungraded fruits. The mass of ungraded Wood apple was best predicted via arithmetic mean diameter (Da) as mean diameter in the S-curve model (M = 0.001+ (2.848/Da); R2 = 0.995; SEE = 0.048). Whereas, the ellipsoid volume (Vellip) of fruit was also best predicted in the S-curve model based on volume (M= 0.002+ (0.951/Vellip); R2 = 0.995; SEE = 0.050) for ungraded fruits.

A Practical Swelling Constitutive Model of Anhydrite and Its Application on Tunnel Engineering

Swelling of anhydrite rock causes serious damage to the tunnel and generates high additional costs in the process of tunnel construction and operation and has gradually become one of the main factors that threaten the safety of the tunnel. It is extremely difficult to predict swelling pressures and deformations accurately based on conventional swelling constitutive models. Thus, a new practical swelling constitutive model of anhydrite for tunnel engineering has been developed. First, swelling tests of natural anhydrite samples focusing on the time effect have been designed and conducted, whose test results show that swelling strain-time can be described by the S-curve model and that swelling stress-strain can be described by the quadratic model. Second, a swelling constitutive model with considering the time effect has been developed to reproduce the swelling behavior of anhydrite observed in swelling tests. This model can track the evolution of swelling activity in tunneling, which has practical significance for process simulation and process control of swelling disaster. Then, this model has been implemented within ANSYS for numerical simulation of the Lirang tunnel. Based on simulation results, useful measures have been proposed. Satisfactory results have been achieved according to the feedback from the site.

THE NONLINEAR IMPACT OF RESEARCH AND DEVELOPMENT INVESTMENTS ON FIRM PERFORMANCE: A SIGMOID (S) CURVE APPROACH IN G20 COUNTRIES

The interaction between research and development (R&D) investments and firm performance has been extensively studied in the academic literature. However, these studies have produced different findings by proposing various models to explain the relationship between R&D and firm performance. The inconsistency in findings can be partly attributed to limitations in the use of traditional linear modeling methods that do not fully capture the complex dynamics of this relationship. Using data on 31,205 firms and 434,016 observations in G20 countries over the period 2000-2022, this paper considers the Sigmoid (S) curve model, which aims to reconcile the differences in the literature and provide a more comprehensive view of the R&D performance relationship. The findings reveal a non-linear relationship between R&D intensity and firm performance and the validity of the S-curve model. Specifically, the R&D-performance relationship shows a negative slope at low levels of R&D investment, a positive slope at medium levels, and a negative slope again at high levels. These results suggest that the impact of R&D investments on firm performance varies depending on the level of investment. Moreover, the study reveals that this 3-stage S-curve relationship is consistent for firms with low firm size and low leverage, and that such firms are particularly affected by an increase in R&D investment. Finally, the findings show that this relationship holds both between developed and developing countries and between countries following Anglo-Saxon and Continental European law. However, this relationship is more pronounced in Anglo-Saxon and developing countries than in Continental European and developed countries.

Nonlinear vibration analysis for bolted CFRC plates based on displacement-dependent surface spring-damping model of bolted joint

Under the effect of external excitation, bolted structures are prone to stiffness weakening and damping energy dissipation at the bolted joint, which makes the structures show nonlinear vibration behavior. In this paper, the displacement-dependent surface springs and tangential surface damping expressed by the S-curve model are proposed to simulate the nonlinear mechanical properties of the bolted joint. The non-uniform distribution of the mechanical parameters for the bolted joint is given to simulate the actual joint interface pressure distribution. Based on these, a nonlinear semi-analytical model for bolted carbon fiber reinforced composites (CFRC) plates considering the nonlinear mechanical characteristics of the bolted joint is established. Combined with the experimental results for a bolted CFRC beam, the specific S-curve expressions for the relevant mechanical parameters of the bolted joint are obtained by the inverse identification technology and numerical fitting method. After that, the bolted joint model is transplanted to the dynamic model of a bolted CFRC plate for numerical calculation. The comparison with the corresponding measured results shows that the proposed modeling method can well reproduce the soft nonlinear vibration of the actual bolted CFRC plate, which verifies the correctness and applicability of the proposed modeling method.

Exploring Primary Aluminum Consumption: New Perspectives from Hybrid CEEMDAN-S-Curve Model

Aluminum is globally the most used nonferrous metal. Clarifying the consumption of primary aluminum is vital to economic development and emission reduction. Based on the signal decomposition tool and S-curve model, a new hybrid complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-S-curve model is proposed to analyze primary aluminum consumption of different countries for the last 100 years. The results show that: (1) Per capita primary aluminum consumption can be decomposed into low-frequency, medium-frequency, and high-frequency components, contributing over 70%, 2–17%, and less than 9% to variability of consumption series, respectively. This can be interpreted as economic development represented by GDP per capita, shocks from significant events, and short-term fluctuations, respectively. (2) The CEEMDAN-S-curve shows good applicability and generalizability by using this model in different countries. (3) A new strategy is provided to analyze and predict the consumption pattern of primary aluminum. Furthermore, some important topics related to primary aluminum consumption are discussed, such as CO2 emission and recovery. Based on the results, to meet economic development and achieve sustainable development goals, some measures should be implemented, such as making policies, encouraging resource recovery, and developing new technologies.

Carbon Sequestration Model Considering Forest Growth Cycle

According to the World Meteorological Organization, “record” greenhouse gas concentrations and heat accumulation have pushed the planet into uncharted territory, driving global warming. We need to figure out how to mitigate global warming by maximizing the overall benefits of forests based on the optimal deforestation period. This provides a basis for decision-making for forest managers. At the micro level, we only consider the carbon sequestration capacity of individual trees. AHP was used to determine the more important influencing factors. The S-curve model was fitted by single-factor and multi-factor analysis to obtain the carbon sequestration saturation point. It should be cut down at the saturation point of carbon sequestration. At the macro level, we consider the carbon sequestration capacity of forests. Therefore, from the economics perspective, we construct a carbon sequestration benefit model based on the optimal rotation period and deduce the optimal and maximum carbon sequestration benefits.

The technology life cycle of Persian lime. A patent based analysis

The citrus agro-industry is one of the world’s most important agricultural sectors. The Persian lime is one of six citrus fruit groups with economic significance. The technological lifecycle of Persian lime sector is assessed in this study using the growth S-curve approach. The objective is to depict the technology life cycle trajectory and current stage of the Persian lime citrus fruit, as well as each of its value chain phases, in order to facilitate better decision-making and technological strategies. The study uses technological patents collected from the World Intellectual Property Organization (WIPO) database from 1975 to 2009. The S-curve model of Persian lime and its value chain stages is generated using logistic mathematical regression. According to the findings of this study, Persian lime is in the maturity stage of the technology life cycle. As a result, the primary strategy could be cost reduction, process innovation, and price strategies to capitalize on market opportunities. When compared to other value chain phases, the transformation phase has the highest number of patents according to the value chain analysis and it is the unique value chain phase with statistical significance in the model. The transformation phase is also at the maturity stage of the technology life cycle. This creates opportunities in two ways: first, to adopt previously developed technologies in the transformation phase and improve process efficiency to reduce costs; and second, to reinforce innovative technological efforts in other phases of the Persian lime value chain, such as growth, harvest, or post-harvest.

Expanded S-Curve Model of Relationship between Domestic Water Usage and Economic Development: A Case Study of Typical Countries

Domestic water plays a growing role with the unprecedented economic development and rising urbanization. The lack of long-term evaluation of domestic water usage trends limits our understanding of the relationship between domestic water usage and economics. Here, we present a pragmatic approach to assess the long-term relationship between domestic water usage and economics through historical data of the last 100 years from 10 typical countries to establish an evaluation method for different economics. The relationship between domestic water usage and GDP per capita was described as an expanded S-curve model and the mathematical modeling was derived to simulate this relationship for four typical countries as case studies. The simulation results show that the expanded S-curve of different countries can be calibrated with three key points: takeoff point, turning point, and zero-growth point, and four transitional sections: slow growth, accelerated growth, decelerated growth, and zero/negative growth, corresponding to the same economic development level. In addition, other factors influencing domestic water usage are also discussed in this research, including urbanization, industrial structure, and technical progress. We hope to provide a case study of an expanded S-curve as a foundation for forecasting domestic water usage in different countries or in the same economy at different developmental stages.

The role of expectation in innovation evolution: Exploring hype cycles

Digitalisation has unleashed numerous and rapid technological, enterprise, and societal innovations. The complexity of social learning and action evidenced in these innovations (e.g., social media-based commerce) across the individual, organisational, and societal levels has multiplied since the introduction of superglue, radial tires, and televisions in the 1950s. Nevertheless, the diffusion S-curve model has remained dominant in innovation evolution research and practice for almost 60 years. Gartner's hype cycles introduce an alternative model and are increasingly used in high-tech management practice. Despite its popularity for over 20 years, the scant academic literature has offered little insight beyond verifying the existence of hype cycle phenomena. Building on the foundation of innovation diffusion and extant hype cycle literature and integrating it with perspectives across several diverse disciplines, this paper develops a conceptual framework for understanding hype cycles and connecting them with the S-curves. It establishes the role of expectation and presents its changes over the course of early-stage innovations leading to the initial adoptions. The paper concludes by highlighting contributions to and suggesting several directions for future research.

Used Car Price Estimation: Moving from Linear Regression towards a New S-Curve Model

A simple linear regression is commonly used as a practical predictive model on a used car price. It is a useful model which carry smaller prediction errors around its central mean. Practically, real data will hardly produce a linear relationship. A non-linear model has been observed to better forecast any price appreciation and manage prediction errors in real-life phenomena. In this paper, an S-curve model shall be proposed as an alternative non-linear model in estimating the price of used cars. A dynamic S-shaped Membership Function (SMF) is used as a basis to build an S-curve pricing model in this research study. Real used car price data has been collected from a popular website. Comparisons against linear regression and cubic regression are made. An S-curve model has produced smaller error than linear regression while its residual is closer to a cubic regression. Overall, an S-curve model is anticipated to provide a better and more practical estimate on used car prices in Malaysia.

Understanding the S-Curve of Ambidextrous Behavior in Learning Emerging Digital Technologies

Emerging digital technologies such as Internet of Things, artificial intelligence and machine learning, data analytics, and cloud technologies have been evolving rapidly and impacting the labor market. As new technologies proliferate, the availability of talent in these skills becomes a massive challenge for firms involved in digital transformation. As a result, firms go for an ambidextrous strategy via continuous in-house talent development for upcoming needs while using existing talent for ongoing projects. The success of a firm's ambidextrous strategy depends on the ambidextrous behavior of employees in terms of learning aspirations and actual learning behavior. This study is designed to analyze the employee's learning aspiration and reskilling resistance using the S-Curve model. Influencing factors are identified, and their influence on employee's ambidextrous behavior is analyzed. A case study of a multinational corporation is used to identify influencing factors involving focus group discussions, surveys, interviews, and ERP data. Subsequently, a nonlinear relationship is identified between employees' reskilling resistance and work experience in an S-Curve form. Further, it explains the moderation effect of the influencing factors on the exploration and exploitation behavior along the S-Curve.

Tax evasion in the EU countries following a predictive analysis and a forecast model for Slovakia

Research background: Tax evasion is an urgent challenge for governments, as reaching sufficient level of tax revenues enable adequate sustainable economic development. The motivation for the research was thus the identification of the situation in the EU countries. 
 Purpose of the article: The main research objective was to identify the extent of tax evasion in the EU countries, with a subsequent specific focus on the econometric predictive models and a forecast of their future development in the case of Slovakia as the poorest performing country of the V4 in this area. 
 Methods: The research was primarily based on testing selected statistical indicators in the field of tax evasions expressed on the basis of the VAT gap. The data for the research was obtained from the EUROSTAT database and the international system VIES for the period between 2000 and 2017. In addition to panel graphs, the research hypotheses were tested primarily using a cluster analysis, t-test, time series analysis, and an analysis of the time series trend with 4 basic models: linear trend, quadratic trend, growth curve model, and S-curve model. On the basis of the Mean Absolute Percentage Error (MAPE), the S-Curve model was selected as the determining model of predicting tax evasion. 
 Findings & value added: Based on the results of the cluster analysis, the EU countries were divided into five reference groups by the VAT gap value, using the VAT gap percentage share on the overall GDP value. The research also provides a unique methodological framework and a unique econometric model for predicting the future VAT gap in Slovakia as the poorest performing country of the V4 in this area, which is applicable to other V4 and EU countries. The research results also enable policy-makers in the EU countries and specifically also in Slovakia and other V4 countries to compare themselves explicitly with the reference countries of the EU in terms of tax evasion and subsequently adopt adequate measures to improve the effectiveness and performance in this field.

Comparative Study on the Developmental Stages of Global CCS Technology Based on the S-Curve Model

This article uses the S-curve model to empirically explore global carbon capture and storage (CCS) technology development stages and conduct a comparative analysis for various countries. The results indicate that global CCS technology is currently in the growth stage and that the technological track will stabilize in 2042 and beyond. The United States is currently the leader in the field of CCS technology, but China and South Korea have the fastest development of CCS technology.

Emergent neologism: A study of an emerging meaning with competing forms based on the first six months of COVID-19

This paper investigates the emergence of COVID-19 neologisms. It focuses on the strategies used to coin emerging neologisms, the relationship between the strategies and the usage preferences, as well as the correlation between internet usage data and epidemiological data. The internet usage data were collected from December 2019 to June 2020 from the Baidu Index, covering the usage of all five categories of the COVID-19 name variants. The epidemiological data, from the Chinese Center for Disease Control and Prevention, are statistics of newly confirmed cases, newly suspected cases, new deaths, and currently suspected cases at a given time. The study identified three strategies in the coinage of neologisms: categorization, avoidance, and synthesis. In addition, a strong correlation between emergent neologisms and pandemic developments was discovered with a binomial model, and the emerging neologisms demonstrated a skewed S-curve life cycle, which is different from the established S-curve model of replacement changes. In sum, by leveraging internet usage data, this first study of the life cycle of emergent neologisms has several contributions: A theory of how new words emerge, the correlation between emergent neologisms and emerging events, and the potential of modeling language use for epidemiological predictions.

Double S-Curve Versus Cusp-Overlap Technique: Defining the Optimal Fluoroscopic Projection for TAVR With a Self-Expanding Device.

The purpose of this study was to assess the concordance between transcatheter aortic valve implantation angles generated by the "double S-curve" and "cusp-overlap" techniques. The "double S-curve" and "cusp-overlap" methods aim to define optimal fluoroscopic projections for transcatheter aortic valve replacement (TAVR) with a self-expandable device. The study included 100 consecutive patients undergoing TAVR with self-expanding device planned by multidetector computed tomography. TAVR was performed using the double S-curve model, as a view in which both the aortic valve annulus and delivery catheter planes are displayed perpendicularly on fluoroscopy. Optimal projection according to the cusp-overlap technique was retrospectively generated by overlapping the right and left cups on the multidetector computed tomography annular plane. The angular difference between methods was assessed in spherical 3 dimensions and on the left and right anterior oblique (RAO) and cranial and caudal (CAU) axes. The double S-curve and cusp-overlap methods provided views located in the same quadrant, mostly the RAO and CAU, in 92% of patients with a median 3-dimensional angular difference of 10.0° (interquartile range: 5.5° to 17.9°). The 3-dimensional deviation between the average angulation obtained by each method was not statistically significant (1.49°; p=0.349). No significant differences in average coordinates were noted between the double S-curve and cusp-overlap methods (RAO: 14.7 ± 15.2 vs. 12.9 ± 12.5; p=0.36; and CAU: 27.0 ± 9.4 vs. 26.9 ± 10.4; p=0.90). TAVR using the double S-curve was associated with 98% device success, low complication rate, and absence of moderate-to-severe paravalvular leak. The double S-curve and cusp-overlap methods provide comparable TAVR projections, mostly RAO and CAU. TAVR using the double S-curve model is associated with a high rate of device success and low rate of procedural complications.

Hyphenation as a compounding technique in English

Hyphenated compounds have largely been neglected in the studies of compounding, which have seldom analysed compounds in context. In this study, we argue that the hyphen use in compounds is strongly motivated. Hyphenation is used when words form a unit, which reduces the possibility of parsing them into separate units or other forms. The current study adopts a new perspective on contextual factors, namely, which part of speech (PoS) the compound as a whole belongs to and how people correctly parse a compound into a unit. This process can be observed and analysed by considering examples. This study therefore holds that hyphenation might have gradually become a compounding technique that differs from general compounding principles. To better understand hyphenated compounds and the motivation for using hyphenation, we conduct a quantitative investigation into their distribution frequency to explore how English hyphenated compounds have been used in over the last 200 years. Diachronic change in the frequency of the distribution for compounds has seldom been considered. This question is explored by using frequency data obtained from the three databases that contain hyphenated compounds. Diachronic analysis shows that the frequencies of tokens and types in hyphenated compounds have been increasing, and changes in both frequencies follow the S-curve model. Historical evidence shows that hyphenation in compounds, as an orthographic form, does not seem to disappear easily. Familiarity and economy, as suggested in the cognitive studies of compounding, cannot adequately explain this phenomenon. The three databases that we used provide cross-verification that suggests that hyphenation has evolved into a compounding technique. Language users probably unconsciously take advantage of the discriminative learning model to remind themselves that these combinations should be parsed differently. Thus the hyphenation compounding technique facilitates communication efficiency. Overall, this study significantly enhances our understanding of the nature of compounding, the motivations for using hyphenation, and its cognitive processing.

Referral Timing and Fundraising Success in Crowdfunding

Problem definition : Crowdfunding, a relatively new approach for raising capital for early-stage ventures, has grown by leaps and bounds in the past few years. Entrepreneurs launch a campaign on a web platform and solicit contributions from many potential backers. A primary way that entrepreneurs affect fundraising is by leveraging their social networks to drive traffic to their campaign. We address the following question: When should an entrepreneur send out referral links to impel traffic to the campaign web page? Academic/practical relevance : Prior capital accumulation serves as social proof of the project’s “quality,” which can result in herding. However, prior capital accumulation can also lead to crowding out and bystander effects. Entrepreneurs’ social networks strongly affect their chances of success, but they often do not know when to solicit contacts’ involvement. We investigate this question via a combination of empirical and analytical methods, providing guidance for platform owners and entrepreneurs. Methodology : The social proof/herding mechanism leads to a convex-shaped effect of current accumulation on future contributions, the crowding out scenario leads to a concave-shaped effect, and the initial dominance of herding giving way to the later dominance of crowding out leads to a sigmoidal effect (S-shaped curve). We use a Markov decision process model to derive three alternative optimal referral policies, which we fit to proprietary data from a large crowdfunding platform. We explore heterogeneity in relative model fit across different subsamples of our data, demonstrating that our conclusion is stable over a range of scenarios. Results : Using mathematical models, we identify optimal referral strategies under the concave, convex, and S-curve assumptions. Estimating these models on the proprietary data, we show that the S-curve model exhibits the best fit. Based on estimated model parameters, our simulations show that a nonoptimal (e.g., myopic) expenditure of referrals can lead to a substantially smaller accumulation of funds. Managerial implications : The results of this paper help inform both platform owners and entrepreneurs. Platform owners can perform this sort of analysis to provide guidance to entrepreneurs about referral strategy. The entrepreneurs, in turn, learn that in an environment similar to that represented in our data, they will benefit from concentrating their referrals earlier in the fundraising process, while retaining some portion for the final stages of fundraising. The mix of this early versus late referral allocation within the campaign duration may vary depending on the entrepreneurs’ social capital and referral cost.

Research trends in carbon capture and storage: A comparison of China with Canada

In order to effectively address climate change, academia and industry have paid much attention to the development trend of Carbon Capture and Storage (CCS). However, there is no mature CCS research trend monitoring system. China is likely to be the largest market for CCS technology in the future, while Canada is the first country to start the research and development of CCS. Existing studies have discussed the CCS research trends at the global level, in China, Canada, and in other countries. However, few comparative studies have been carried out in key countries. In this study, an integrated method of bibliometrics and S-Curve is proposed with the purpose of comparing CCS research trends between China and Canada. Firstly, the bibliometrics method was used to compare the conceptual structure and research route of CCS research in China and in Canada. Secondly, the key collaborators were indentified through the comparison of collaboration relationships. Finally, the S-Curve model was employed to forecast the CCS research output trend in China and Canada. This study found that China's CCS research had the advantage of pre-combustion carbon capture, while Canada's advantage lied in the ecosystem carbon sequestration. It was also shown that Canada’s CCS cumulative publication may reach saturation 15 years later than that in China. This paper provides insight into the CCS research and development optimization for China, Canada and globally. The CCS research trends comparison tools proposed in this study can benefit to monitor the CCS research by governments and enterprises.