Traditional Linear Model Research Articles

Transitioning to a Circular Economy: Business Models and Sector-Specific Practices

This paper explores the concept of circular economy models in business, contrasting them with traditional linear models. It introduces three key players in the circular economy: Product Manufacturers, Problem Solvers, and Facilitators, and identifies six general business models, including Circular Supply Chain, Sharing Platforms, Product as a Service, Product Life Extension, Resource Recovery, and Product Incentivized Return. The text also discusses sector-specific adaptations, highlighting permaculture in agriculture as an example of circular economy principles tailored to a specific industry. The content provides a framework for understanding how circular economy models can be implemented and adapted across various sectors, emphasizing the need for detailed analysis and tailored approaches in future research. Keywords: Circular Economy, Business Model, Sustainability

Photoelectric sensor array-based measurement method for the key motion parameters of the small flying object considering non-uniform curve trajectory

Purpose The paper aims to accurately measure the key motion parameters, such as velocity, azimuth and pitch angle, of the small flying object with a non-uniform curve trajectory. It proposes a measurement method and its calculation model of non-uniform curve trajectory using a photoelectric sensor array. Design/methodology/approach First, the basic composition of the measurement system and mechanism of photoelectric sensor array are described, respectively. Second, a non-uniform curve mathematical measurement model is constructed differently from the traditional linear trajectory, taking into account the influence of gravity and air resistance. Third, the measurement error of the system is analyzed through numerical simulation. Finally, the accuracy and feasibility of the approach are verified by live-ammunition experiments. Findings The results show that the systematic error of the hitting point coordinates can be reduced by 9% compared to the traditional linear measurement model. Consequently, this method can meet the higher measurement requirement for the key motion parameters of the small flying object under the non-uniform curve trajectory. Research limitations/implications (if applicable)- although the approach itself is generalizable, the method is unable to detect the motion parameters of multiple small flying objects. Research limitations/implications Although the approach itself is generalizable, the method is unable to detect the motion parameters of the multiple small flying objects. Practical implications It is evident that the proposed non-uniform curve measurement model is more precise in quantifying the essential characteristics of the small flying object, particularly in consideration of the environmental conditions. Social implications The precise measurement of the key motion parameters of the small flying object can facilitate the enhancement of the protective performance of protective materials. Originality/value A novel approach to measurement is proposed, which differs from the conventional uniform trajectory model. To this end, the space construction of the photoelectric sensor array is optimized. The number of the sensors is revised.

MODELING COMMERCIAL BANKS' KEY DRIVERS FOR FUND MOBILIZATION IN NIGERIA USING ARTIFICIAL NEURAL NETWORKS

This study explores the core role driving the willingness of commercial banks to mobilise funds for economic development based on the Multilayer Perceptron (MLP) of the artificial neural network (ANN) technique. This approach allows for a nuanced understanding of interdependencies that traditional linear models may overlook, making it particularly suited for analyzing intricate financial systems in emerging economies like Nigeria. This study is critically supported by the Financial Intermediation Theory, which explains the role of financial institutions as intermediaries that facilitate the flow of funds from savers to borrowers. Data used for the analysis and prediction is purposively obtained from 10 commercial banks in Nigeria. The results show that financial institutions are mostly driven to mobilize funds because of their commitment to ensuring an adequate flow of money to serve the deficit sectors of the economy compared to any other underlying reasons. The prediction performs optimally with an r2 value of 86.5% with a cubic predictability model of ????. ???????????????? +????. ???????????? + ????. ???????????????? − ????. ???????????????? − ????. ???????? = ????.????????????????, and the Sum of Square Error (SSE) of 0.002 is minimal based on practice. This study is significant as it could enable financial institutions to make future role predictions relating to this concept in Nigerian settings or other settings analogous to Nigeria using the derived ANN model. These insights provide a basis for banks in Nigeria and similar economies to make strategic financial decisions, supporting the application of ANN models to predict and enhance financial institutions' roles in economic development.

Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R2 = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection.

Exploring interactive effects of environmental and microbial factors on food waste anaerobic digestion performance: Interpretable machine learning models

Biogas yield in anaerobic digestion (AD) involves continuous and complex biological reactions. The traditional linear models failed to quantitatively assess the interactive effects of these factors on AD performance. To further explore the internal relationship between target variables and AD performance, this study developed four machine learning models to predict biogas yield and consider the interaction among various factors. Results indicated that the highest prediction accuracy of AD performance was achieved by adding bacterial genera dataset with environmental factors. Random forest model exhibited the highest accuracy, with the testing coefficient of determination equal to 0.9879. Among two types of input features, the bacterial genera accounted for 89.9 % of the impact on biogas yield, followed by environmental factors. The results revealed Keratinibaculum and Acetomicrobium as critical bacteria. The volatile fatty acid controlled below 2000 mg/L and the improved stirring system in AD process were recommended to achieve maximum biogas yield.

Artificial Neural Network-based Model for Predicting Cardiologists’ Over-apron Dose in CATHLABs

Abstract Aim: The radiation dose that cardiologists receive in the catheterization laboratory is influenced by various factors. Handling high-stress tasks in interventional cardiology departments may cause physicians to overlook the use of dosimeters. Therefore, it is essential to develop a model for predicting cardiologists’ radiation exposure. Materials and Methods: This study developed an artificial neural network (ANN) model to predict the over-apron radiation dose received by cardiologists during catheterization procedures, using dose area product (DAP) values. Leveraging a validated Monte Carlo simulation program, we generated data from simulations with varying spectra (70, 81, and 90 kVp) and tube orientations, resulting in 125 unique scenarios. We then used these data to train a multilayer perceptron neural network with four input features: DAP, energy spectrum, tube angulation, and the resulting cardiologist’s dose. Results: The model demonstrated high predictive accuracy with a correlation coefficient (R-value) of 0.95 and a root mean square error (RMSE) of 3.68 μSv, outperforming a traditional linear regression model, which had an R-value of 0.48 and an RMSE of 18.15 μSv. This significant improvement highlights the effectiveness of advanced techniques such as ANNs in accurately predicting occupational radiation doses. Conclusion: This study underscores the potential of ANN models for accurate radiation dose prediction, enhancing safety protocols, and providing a reliable tool for real-time exposure assessment in clinical settings. Future research should focus on broader validation and integration into real-time monitoring systems.

The Effect of Measurement Error on Hypothesis Testing in Small Sample Structural Equation Modeling: A Comparison of Various Estimation Approaches

Researchers seeking valid statistical inference in the presence of measurement error often apply approaches that ignore measurement error. This may result in biased estimates, inflated type I error rates, diminished power, and therefore, increases the risk of drawing erroneous conclusions. However, current advice on accounting for random measurement error is limited to large samples and traditional linear models. This article aims to address this gap for small samples and recent estimation approaches in structural equation modeling. Our results show substantial type I error rate inflation for approaches that ignore measurement error when the model contains correlated latent predictors.

On the value of Fano resonance in wave energy converters

The article evaluates the potential of the Fano resonance operational principle in wave energy converters (WECs), using a 2-body loosely moored self-referenced WEC as an illustrative example. By leveraging Fano resonance, the point absorber buoy can remain relatively stationary with low loading on mooring lines, serving as an efficient wave energy transmitter while concurrently achieving resonance within the internal power take-off (PTO) system. This arrangement reduces the motion of the point absorber hull, thereby decreasing loads on the WEC structure, mooring lines, and anchors. As a result, operational and structural costs are minimised, further reducing the levelised costs of generated energy. Additionally, by ensuring minimal fluctuations in the WEC, confidence in using traditional linear mathematical models is increased, as commonly employed for WEC performance assessment and control design.The article presents a resonance study and introduces newly derived solutions in the frequency domain for the proposed operational concept. It analytically demonstrates the viability of employing the Fano resonance operational strategy for WECs, suggesting that this strategy has the potential to compete with traditional methods of wave energy transformation. Furthermore, the insights gained from the study contribute to identifying optimal parameters for a PTO system, as well as optimising the design of the heaving buoy.

Determining the Influence of Real Estate Features on Prices with Partial Dependence Plots: A Case Study in Szczecin, Poland

Abstract The study explores the application of Partial Dependence Plots (PDP) in the analysis of real estate features. The study centers on a selected real estate market in Szczecin, Poland, aiming to highlight the efficacy of PDP in understanding and interpreting the complex relationships between various features and property prices. The primary objective is to showcase the potential of PDP in capturing the nuanced interactions between real estate attributes and their impact on market prices. The CatBoost model, known for its robust handling of categorical features and strong predictive capabilities, is employed as the machine learning algorithm for this analysis. The performance of this model will be compared against a traditional multiple linear regression model, providing insights into the advantages of leveraging advanced machine learning techniques in real estate analysis. Results obtained from the analysis will be presented and discussed, shedding light on the interpretability and accuracy of the CatBoost model compared to the traditional linear regression approach. The presentation will conclude with implications for real estate practitioners and researchers, emphasizing the potential for PDP to enhance the transparency and understanding of complex models in the real estate domain. This research contributes to the growing body of knowledge on the application of advanced machine learning techniques in real estate analysis.

Online Investor Sentiment via Machine Learning

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio.

A gender perspective on the circular economy: A literature review and research agenda

AbstractThe circular economy (CE) challenges the traditional linear economic model by emphasizing the importance of resource efficiency, recycling, and regeneration in society. However, the role of gender in CE practices remains underexplored. This article presents a systematic literature review on how gender dynamics intersect with CE, highlighting the implications of gendered labor distribution, access to resources, and decision‐making processes. Women, often pivotal in household and community resource management, are underrepresented in policy and research. Gender perspectives are therefore crucial for addressing social, environmental, and justice dimensions within CE. The study employs the lens of care labor to reveal power imbalances and the exclusion of marginalized groups, advocating for inclusive policies to achieve socially sustainable and equitable CE futures. The review identifies the overrepresentation of women in undervalued, informal activities and the dominance of men in highly valued, technologically advanced CE roles. It also underscores the benefits of gender diversity in corporate governance, which enhances sustainability practices. The article calls for a deeper examination of gender's impact on CE, suggesting a research agenda to integrate gender perspectives, enhance CE effectiveness, and promote equity. The findings indicate that addressing gender inequalities in CE is essential to prevent replicating the shortcomings of the linear economy and to foster innovative, inclusive, and sustainable development.

Exploring the Relationship Between Challenge and Reward for Game Based Learning

The aim of this research is to investigate how problem-solving challenges affect young people’s motivation for learning computer programming. It explores the relationship between difficulty and reward as part of a game-based learning application and evaluates the effectiveness of the ARCS learning model when teaching primary school children. Research in game-based learning is based, in part, on the study of the impact of games on students' learning motivation, engagement and outcomes. The ARCS model, developed by John Keller in 1987, is a widely recognized model for designing effective instruction that motivates and engages learners. The model is a cognitive psychology-based learning model that consists of four main elements: Attention, Relevance, Confidence, and Satisfaction. A pilot study comprising primary school teachers was conducted to evaluate the effectiveness of the ARCS model. Two experiments were conducted: the first an experimental group based on the design of the game focussing on the player’s experience and evaluation of the difficulty and reward mechanisms. For this group, the game dynamically changes in terms of difficulty and rewards. This is achieved by adjusting the difficulty and rewards through a dynamic player score to maintain player attention, confidence, and satisfaction. The second experiment involved a control group, where the game adopts a traditional linear learning model where difficulty and reward are fixed. For both experiments, data was collected from a feedback questionnaire. The questionnaire was designed to be closely aligned and mapped to the ARCS learning model’s four elements. The scores of the results were based on the Likert scale and t-test to determine the significance of the difference in scores between the experimental and control groups. Results based on data analysis of the user study show that the overall mean score of the experimental group is higher (= 4/5) compared to the control group (= 2.2/5) on all four ARCS elements. Further analysis shows that the most prominent teaching impact is on players’ satisfaction.

Prognostic insights after surgery for advances in understanding signet ring cell gastric cancer: a machine learning approach

BackgroundSignet ring cell (SRC) gastric carcinoma is traditionally associated with a poor prognosis. However, the literature has presented contradictory results. Linear models are the standard statistical tools typically used to study these conditions. However, machine learning (ML) models have the potential to replace or even outperform linear models in terms of predictive performance. MethodsThis study analyzed 608 patients diagnosed with gastric cancer at our institution. The analysis compared traditional linear models and ML models. Variables examined included demographic data, presence of an SRC component, lymph nodes (LNs) resected and affected (ratio), stage of the disease, body mass index, pathologic features, type of surgery, tumor location, and carcinoembryonic antigen levels to evaluate their influence on 5-year mortality and 2-year recurrence rates. ResultsSRC carcinoma was associated with poorer prognosis in terms of 5-year overall survival than non-SRC carcinoma. In addition, SRC exhibited higher rates of LN metastasis and a higher LN ratio (resected/affected) and was more prevalent in younger patients (<65 years). However, SRC was not an independent factor in the multivariate analysis. Linear models showed worse predictions for 5-year mortality and 2-year recurrence than ML models. The ML models did not consider the presence of the SRC component as an important variable. ConclusionSRC gastric carcinoma continues to present an uncertain prognosis. ML models can evaluate prognosis more accurately than traditional linear models. Large-scale studies using ML algorithms are necessary to elucidate the predictive potential of such models.

Predicting OCD severity from religiosity and personality: A machine learning and neural network approach

Obsessive-compulsive disorder (OCD) affects a significant portion of the United States population. The present study investigated the complex relationships among OCD severity, personality traits, religiosity, and spirituality with a dataset of 229 participants. We applied advanced machine and deep learning techniques to identify key predictors of OCD severity, uncovering nuanced relationships and unexpected findings. Notably, item-level features were more influential than aggregate scores, challenging traditional analytical approaches. Moreover, a neural network model, despite not surpassing a linear regression in predictive accuracy, provided a more comprehensive understanding of OCD’s heterogeneity and of the nonlinear relationships between our variables. The inclusion of demographic factors provided further explanatory power for predicting OCD severity, emphasizing the multifaceted nature of the disorder. Our results show that machine learning models can nearly match traditional linear models in predictive power while retaining nonlinear relationships essential to understanding OCD. Our study advocates for the adoption of sophisticated predictive models in examining complex psychological phenomena, encouraging a reevaluation of conventional analytical approaches when prediction is central to research questions.

High-fidelity predictions of diffusion in the brain microenvironment

Multiple-particle tracking (MPT) is a microscopy technique capable of simultaneously tracking hundreds to thousands of nanoparticles in a biological sample and has been used extensively to characterize biological microenvironments, including the brain extracellular space (ECS). Machine learning techniques have been applied to MPT data sets to predict the diffusion mode of nanoparticle trajectories as well as more complex biological variables, such as biological age. In this study, we develop a machine learning pipeline to predict and investigate changes to the brain ECS due to injury using supervised classification and feature importance calculations. We first validate the pipeline on three related but distinct MPT data sets from the living brain ECS—age differences, region differences, and enzymatic degradation of ECS structure. We predict three ages with 86% accuracy, three regions with 90% accuracy, and healthy versus enzyme-treated tissue with 69% accuracy. Since injury across groups is normally compared with traditional statistical approaches, we first used linear mixed effects models to compare features between healthy control conditions and injury induced by two different oxygen glucose deprivation (1) exposure times. We then used machine learning to predict injury state using MPT features. We show that the pipeline predicts between the healthy control, 0.5 h OGD treatment, and 1.5 h OGD treatment with 59% accuracy in the cortex and 66% in the striatum, and identifies nonlinear relationships between trajectory features that were not evident from traditional linear models. Our work demonstrates that machine learning applied to MPT data is effective across multiple experimental conditions and can find unique biologically relevant features of nanoparticle diffusion.

The effect of ESG divergence on the financial performance of Hong Kong-listed firms: an artificial neural network approach

This paper applies an advanced machine learning algorithm, the Artificial Neural Network (ANN), to examine both linear and nonlinear effects between firm-level characteristics and ESG performance of all firms listed on the Hong Kong Stock Exchange (HKEX) with ESG scores during 2019-2021. To mitigate the problem of data-specific findings due to rating bias from a single rating agency, we employ novel iScore (divergence-adjusted ESG measure). The documented findings indicate the unsuitability of traditional linear regression models to capture the nonlinear effects and to detect some linear relationships. Furthermore, the results show the superiority of the self-organising map (SOM) ANN framework in explaining the impact of firm-level factors on ESG performance.

Explicit Modeling of Multi-Product Customer Orders in a Multi-Period Production Planning Model

In many industries, companies receive customer orders that include multiple products. To simplify the use of optimization models for planning purposes, these orders are broken down, and the quantities of each product are grouped with the same products from other orders to be completed in the same period. Consequently, traditional production planning models enforce minimum demand constraints by product and period rather than by individual orders. An important drawback of this aggregation procedure is that it requires a fixed order fulfillment period, potentially missing opportunities for more efficient resource use through early completion. This paper introduces a novel mathematical formulation that preserves the integrity of customer orders, allowing for early fulfillment when possible. We compare a traditional linear programming model with a new mixed-integer programming approach using a sawmill case study. Although more complex than the traditional model, the proposed formulation reduces costs by approximately 6% by enabling early order completion and offers greater flexibility and control over the production process. This approach leads to better resource utilization and more precise order management, presenting a valuable alternative to conventional production planning models.

Exploring the Circular Economy in the Fashion Industry: Transforming Waste into Valuable Resources

The fashion industry, a major global economic sector, faces significant environmental challenges due to high resource consumption and waste generation. This paper evaluates the effectiveness of circular economy practices as a solution to these issues. Traditional linear models of production and disposal are insufficient for sustainable development, necessitating a shift towards circular economy principles that focus on resource efficiency, waste reduction, and recycling. Through detailed case studies of Patagonia and H&amp;M, this study highlights how circular economy practices, such as the use of renewable materials and comprehensive recycling systems, contribute to environmental sustainability and meet consumer expectations. Despite these advancements, barriers such as technological limitations, high initial investments, and the need for stronger policy support remain. Recommendations include increasing investment in recycling technologies, enhancing consumer education, and fostering industry-wide cooperation. Future research should expand to include a broader range of case studies to better understand global applications of circular economy practices.

The Role of Circular Economy Principles in Revolutionising Sustainable Manufacturing Practices

The increasing recognition of the limitations inherent in the traditional linear economic model [1] has spurred interest in the Circular Economy (CE) as a transformative framework for sustainable manufacturing practices [2]. This literature review systematically explores the integration of CE principles, focusing on strategies such as reuse, remanufacturing and recycling, which aim to minimise waste and optimise resource utilisation throughout product lifecycles. A thorough examination of eco-design methodologies highlights the importance of product longevity, reparability and material recovery, while advanced technologies—including digitalisation, Internet of Things (IoT) applications and material recovery systems—are identified as critical enablers of circularity in manufacturing processes. We also address consumer-centric design as a vital aspect of CE integration, emphasising the role of user engagement and education in fostering sustainable behaviours and product acceptance. Furthermore, the analysis includes financial mechanisms and investment strategies essential for supporting the transition to circular practices, alongside performance metrics and circularity indicators for assessing effectiveness. The role of small and medium enterprises (SMEs) in circular manufacturing, along with the challenges and barriers to implementation, is critically evaluated. Finally, the review identifies future prospects and opportunities within the CE landscape, underscoring the need for collaborative efforts among stakeholders to advance sustainable manufacturing. This comprehensive examination provides valuable insights into the potential of CE principles to revolutionise manufacturing practices and contribute to long-term sustainability goals.

Predicting the Bioaccessibility of Soil Cd, Pb, and As with Advanced Machine Learning for Continental-Scale Soil Environmental Criteria Determination in China.

Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with soil. Traditional batch experimental methods and linear models, however, are time-consuming and often fall short in precisely quantifying bioaccessibility. In this study, using 937 data points gathered from 56 journal articles, we developed machine learning models for three harmful inorganic elements, namely, Cd, Pb, and As. After thorough analysis, the model optimized through a boosting ensemble strategy demonstrated the best performance, with an average R 2 of 0.95 and an RMSE of 0.25. We further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence bioaccessibility. By utilizing the developed integrated models, we carried out predictions for 3002 data points across China, clarifying the bioaccessibility of cadmium (Cd), lead (Pb), and arsenic (As) in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting (IDW) interpolation method. Based on these findings, we further derived the soil environmental standards for metallurgical sites in China. Our observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd, Pb, and As in mining/smelting sites from 5, 58, and 14 to 1, 24, and 7, respectively. This research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.