Comprehensive Analysis Framework Research Articles

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source–flow–sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Neural network approach for cholera dynamics: Integrating deterministic and stochastic insights

In this article, a comprehensive qualitative analysis framework is presented that integrates stochastic and deterministic models to explore the transmission dynamics of Cholera disease. The significance of the reproduction number in understanding the spread of the disease and in formulating effective control strategies is emphasized. The sensitivity analysis of model parameters is conducted to identify those that critically influence transmission. By converting the deterministic model into a stochastic form, Brownian motion and stochastic parameters are incorporated to capture the inherent randomness in disease dynamics. This approach is further enriched by the application of neural networks (NN) to simulate and validate the complex interactions involved in Cholera transmission. The analysis extends to the global positive solution, stochastic reproduction number, and virus-free stochastic dynamics, providing an improved understanding of the disease's behavior. The critical conditions necessary for the extinction and long-term elimination of the virus are also addressed, highlighting the importance of controlling its spread. The stationary distribution of the disease is examined through the lens of a regular Markov process, enhancing the understanding of the stochastic model's dynamics. The results verified by neural networks, compared with those obtained from conventional numerical methods, underscore the accuracy and versatility of neural networks in epidemiological research, offering new perspectives and methodologies for tackling this global health challenge.

Research on the Application of Machine Learning Algorithms in Green Economy Indicator Modeling and Sustainable Development Evaluation

This study presents a comprehensive modeling and analysis framework for investigating the impact of green GDP on climate factors and its potential for promoting sustainable development. Our model utilizes the entropy weight method (EWM) and grey correlation analysis (GRA) to optimize and process data, while employing a BP neural network for model training. Results indicate that green GDP leads to smaller global climate damage compared to traditional GDP, indicating its potential to alleviate the global climate crisis and promote green development.Furthermore, we built a green GDP prediction model using LSTM to forecast future changes. From the perspective of economy and management, the results indicate that transitioning from GDP to green GDP initially leads to a decrease in the index, followed by an increase. This suggests that green GDP development has short-term drawbacks but long-term benefits, including reduced resource consumption, improved economic conditions, and contribution to sustainable development. Finally, we analyzed the learning rate, number of iterations, and thresholds of related parameters through Logistic binary classification. Results demonstrate that our model is relatively stable, highlighting how green GDP addresses ecological challenges while promoting economic prosperity. These findings underscore the urgency of adopting the concept of green GDP in the contemporary era for achieving sustainable development goals.Overall, this research provides critical insights on the potential benefits and feasibility of green GDP as a crucial tool for mitigating climate risks and enhancing global prosperity.

Discursive Strategies in China’s Live Commerce Anchors Multimodality

At present, discourse analysis is widely used in news reports and TV talk shows in China, but there is a lack of discourse analysis research on live streaming. Against this backdrop, this study adopts a multimodal discourse analysis approach, drawing upon the comprehensive theoretical framework for multimodal discourse analysis and Kress and Van Leeuwen's seminal visual grammar theory as the theoretical basis, taking quality and quantity method to analyze data. Our research aims to investigate the intricate interplay between anchors and the amalgamation of diverse modalities in the process of constructing their image. Furthermore, we endeavor to analyze potential strategies that can be employed to enhance the efficacy of live broadcasting and augment viewers' engagement and consumption. In this paper, we decompose the multimodal discourse of the selected live video of “Oriental selection” into verbal and non-verbal modes, and analyze the process and role of how anchors use multimodality to form interactions and promote construction.

Emotion Analysis and Expression Algorithm of Dance Action Based on Machine Learning

Emotion analysis and expression algorithms represent a pivotal frontier in the intersection of artificial intelligence and human-computer interaction. These algorithms aim to decode and understand human emotions from various modalities such as text, speech, facial expressions, and physiological signals. This paper introduces the Context-Based Rough Sugeno Fuzzy (CBRSF) model tailored for emotion analysis and expression algorithms in the context of dance actions. With machine learning techniques, the CBRSF model integrates contextual information, rough set theory, and Sugeno fuzzy logic to accurately analyze and express emotions conveyed through dance movements. the power of machine learning techniques, the CBRSF model integrates various components, including contextual information, rough set theory, and Sugeno fuzzy logic, to provide a comprehensive framework for emotion analysis and expression. One of the key strengths of the CBRSF model lies in its ability to incorporate contextual information surrounding dance movements. Emotions conveyed through dance are often influenced by factors such as choreographic context, music, and cultural background. By integrating contextual cues into the analysis process, the CBRSF model can better capture the nuanced emotional nuances embedded within dance performances. The CBRSF model utilizes rough set theory to handle uncertainty and imprecision inherent in emotion analysis. Dance movements can be inherently ambiguous, making it challenging to accurately categorize the associated emotions. Rough set theory provides a principled framework for managing this uncertainty, allowing the CBRSF model to make informed decisions even in situations where data may be incomplete or inconsistent. Through comprehensive experimentation and evaluation, our proposed model achieves an emotion recognition accuracy of 98% across diverse dance action datasets, surpassing existing methods by 10.2%. Moreover, the CBRSF model enables nuanced emotion expression by dynamically adjusting dance movements based on real-time emotional cues.

Thermodynamic investigation of an innovative solar-driven trigeneration plant based on an integrated ORC-single effect-double lift absorption chiller

Maximizing the synergy between renewable resources and efficient multi-generation systems is crucial for transitioning the energy sector towards carbon-free production. The objective of this research paper is to investigate and optimize the performance of an integrated solar trigeneration plant. Electrical load and domestic heating supply are provided through an organic Rankine cycle and heat exchanger respectively. Cooling needs are provided using a single-effect double-lift absorption chiller (SE-DL ACH), a novel combination seldom encountered within multigeneration systems. Various collectors are evaluated, and an initial screening of organic fluids is done. A comprehensive analysis framework is developed, considering energy, exergy, and the sequence of energy usage, which is clarified into sequential (SSI) and parallel (PSI) integration.The final result indicates that n-decane is the most appropriate fluid, yielding thermal and electrical efficiencies of 11.93 % and 5 kW, respectively. Among the collectors evaluated, EFPC emerges as the most promising. SSI delivers electrical, cooling, and heating output of 5 kW, 105.7 kW, and 29.25 kW, with energy and exergy efficiencies of 45.6 % and 41.3 %. PSI demonstrates an improvement of 11.92 % and 43.08 % in cooling and heating generation. It exhibits an energy and exergy efficiency of 43.03 % and 37.26 %. Integrating the SE-DL parallel to the heat source has improved the potential of heat recovery by 22.22 %.

Data-driven health state estimation and remaining useful life prediction of fuel cells

Proton exchange membrane fuel cells (PEMFCs) can revolutionise transportation energy and promote environmentally friendly development. The purpose of this study is to predict the state of health (SOH) of PEMFCs and provide guidance for fuel cell maintenance. Under changing power demand situations, a practical method based on the Fréchet distance is proposed to predict the SOH, along with an empirical model to differentiate between the running-in and degradation periods. The proposed method does not require complex and expensive testing instruments and has a relative error of approximately 4.3 %. A voltage drop prediction model is established for steady power demand situations using the particle swarm optimisation-extreme learning machine (PSO-ELM) algorithm. Different activation functions and hidden layer neurons are investigated to enhance prediction accuracy. This study shows that the model effectively tracks the decreasing trend in the transmission voltage of the PEMFC stack. Additionally, a comprehensive analysis framework is developed to address the issue of the possibility of missing system parameters in practical applications. The influence of the system parameters on voltage drop prediction is thoroughly analysed, and the necessary parameters for accurate prediction are defined, providing theoretical guidance for practical monitoring and data collection.

Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification

Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the identification and quantification of metabolites. However, analyzing MS data poses challenges such as batch effects, outliers, and high-dimensional data. In this paper, we propose a comprehensive framework for MS data analysis. The framework integrates data calibration, outlier detection, and automatic classification modules. Data calibration is performed using a deep autoencoder to remove batch effects. Outlier detection combines multiple algorithms through ensemble learning to identify and remove outliers. Automatic classification utilizes a transformer model to handle high-dimensional data and capture global feature relationships. Experimental results on myocardial infarction (MI) and coronary heart disease (CHD) datasets demonstrate the effectiveness of the framework. It outperforms traditional classification models and achieves higher accuracy. The proposed framework provides a robust solution for MS data analysis, facilitating more accurate classification and enabling reliable biological insights in metabolomics research.

AI and crisis leadership: Using the POP-DOC Loop to explore potential implications and opportunities for leaders.

In the evolving landscape of crisis leadership and emergency management, artificial intelligence (AI) emerges as a potentially transformative force with far-reaching implications. Utilizing the POP-DOC Loop, a comprehensive framework for crisis leadership analysis and decision-making, this paper delves into the diverse roles that AI is poised to play in shaping the future of crisis planning and response. The POP-DOC Loop serves as a structured methodology, encompassing key elements such as information gathering, contextual analysis informed by social determinants, enhanced predictive modeling, guided decision-making, strategic action implementation, and appropriate communication. Rather than offer definitive predictions, this review aims to catalyze exploration and discussion, equipping researchers and practitioners to anticipate future contingencies. The paper concludes by examining the limitations and challenges posed by AI within this specialized context.

IOT Based Accident and Analysis System Using Edge Computing

The increasing number of road accidents and their associated consequences necessitate the development of advanced systems for timely detection and analysis. This paper proposes an Internet of Things (IOT)-based Accident Detection and Analysis System that leverages Edge Computing for real-time processing and decision-making. The system integrates smart sensors, edge devices, and cloud infrastructure to create a comprehensive framework for efficient accident detection and analysis. The proposed IOT-based Accident Detection and Analysis System enhances road safety by enabling quick response to accidents and providing valuable insights for preventive measures. The integration of edge computing optimizes resource utilization, minimizes latency, and enhances the overall efficiency of the system. This research contributes to the evolving field of smart transportation systems, addressing the critical need for advanced technologies accident detection and analysis.

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation.

Understanding the Sustainable Mechanisms of Poverty Alleviation Resettlement in China’s Developed Regions under the Background of Land Relocation: Drivers, Paths and Outcomes

In the context of land relocation, poverty alleviation resettlement (PAR) is considered an effective approach to improve the man–land relationship and development issues. However, current studies pay little attention to PAR and its spillover effects within developed regions. Furthermore, the complete mechanism chain has received little research concentration. Thus, employing a qualitative survey, this study aims to investigate the overall mechanisms of developed regions’ PAR in the context of land relocation. The study will deal with the following questions: Why does PAR occur in developed regions? How does the resettlement approach to poverty alleviation (i.e., paths)? What are the effects of resettlement on poverty alleviation, including its own effects and spillover effects? Through answering these questions, this study will highlight PAR in developed regions and investigate the spillovers from social, economic and ecological perspectives. Particularly, a comprehensive mechanism analysis framework for PAR will be presented to motivate future studies. Results indicate that PAR is generally caused by ecological poverty alleviation, geological disaster prevention and county town urbanisation promotion and that emigration and resettlement are the paths to PAR. In addition, the direct outcome is the overall rise in the number of resettlers over time, and the spillovers show the sustainable collaboration of economic, social and ecological dimensions. These findings will influence future land reform and housing initiatives.

Impacts of urban scale on low-carbon development: evidence from 265 cities in China

Introduction: With rapid urbanization and increasing greenhouse effect, low-carbon development is the integral factor of achieving sustainable development.Methods: A comprehensive analysis framework is built to quantify the impact of urban scale on low-carbon development and we conduct empirical research on 265 cities during 2009–2019.Results and Discussion: We find that: first, urban scale can significantly promote low-carbon development and this finding still holds after the robustness test. Specifically, for every 1% increase in urban scale, the low-carbon development level of the urban increases by 0.128%; second, urban scale promotes low-carbon development mainly through three paths: efficiency improvement, industrial structure upgrading, and innovation; third, there is heterogeneity in this effect among cities with different city levels, city structures, and traffic conditions. Therefore, we propose specific, targeted policy implications, including reducing restrictions on the expansion of large cities and promoting high-tech development.

Effects of Inflation, Ten-Year Bond Yield Rate, and VIX Index on the Stock Prices of Banks Across All Three Market Capitalizations in India

This study investigates the impact of critical economic factors—namely, inflation, the 10-year bond yield rate, and the VIX index—on the stock prices of banks operating across different market capitalization segments in India. Through a comprehensive regression analysis framework, this research quantifies the relationships between these economic factors and bank stock prices while accounting for potential variances across large-cap, mid-cap, and small-cap banks. Utilizing data from the past five years, this analysis not only provides a nuanced understanding of how these macroeconomic indicators influence bank stock prices but also explores the specific effects on banks of varying market capitalizations. The findings reveal that small-cap companies are predominantly influenced by internal management decisions and capital allocation, whereas the consumer price index significantly predicts and reflects stock price behavior. Conversely, the bond yield rate and VIX index show minimal impact on stock prices. This study offers valuable insights for investors, policymakers, and financial institutions, aiding in the development of informed investment strategies and risk management practices.

InVAErt networks: A data-driven framework for model synthesis and identifiability analysis

Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven architectures would suggest to extend this representation to other aspects of system analysis including model inversion and identifiability. We introduce InVAErt (pronounced invert) networks, a comprehensive framework for data-driven analysis and synthesis of parametric physical systems which uses a deterministic encoder and decoder to represent the forward and inverse solution maps, a normalizing flow to capture the probabilistic distribution of system outputs, and a variational encoder designed to learn a compact latent representation for the lack of bijectivity between inputs and outputs. We formally analyze how changes in the penalty coefficients affect the stationarity condition of the loss function, the phenomenon of posterior collapse, and propose strategies for latent space sampling, since we find that all these aspects significantly affect both training and testing performance. We verify our framework through extensive numerical examples, including simple linear, nonlinear, and periodic maps, dynamical systems, and spatio-temporal PDEs.

An agent-based modelling framework for performance assessment of search and rescue operations in the Barents Sea

In recent years, the increase in Arctic offshore activities has raised concerns about the search and rescue (SAR) operations as mitigative measures to ensure the safety of shipping and cruise activities. Performing SAR operations in the remote Arctic offshore environment is exceptionally challenging due to the severe environmental conditions, including low temperatures, high waves, strong winds, heavy snow showers, sea ice, spray icing, dense fog, limited visibility, and polar low pressures. Moreover, the scarcity of port infrastructure along the Arctic coastline exacerbates the difficulties faced during SAR operations. Tackling the aforementioned challenges necessitates a comprehensive modelling framework for analysis of SAR operations in the Arctic that is able to consider the dynamics and uncertain nature of Arctic harsh environmental conditions, and the constraints imposed by the limited capacity of Arctic SAR infrastructure. To this aim, this paper proposes an agent-based modelling (ABM) framework to assess the performance of SAR operations while tackling such challenges in the Arctic. A Monte Carlo simulation approach is used to model the dynamics and uncertainty of weather and sea conditions using historical data, using which some severity levels are determined. Expert judgement process is then used quantify the impacts of such severity levels on the performance of rescue agents, and thus, on the total rescue time. To validate the proposed framework, an illustrative case in the Norwegian Barents Sea is considered, where the performance of SAR infrastructure is examined under different rescue scenarios. According to the results of this paper, the total rescue time is the longest during December to February, and the shortest rescue time is associated with months May to August. Some recommendations are further proposed to improve the performance of SAR infrastructure in the Barents Sea.

Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.

Elastoplastic Solution of Cylindrical Cavity Expansion in Unsaturated Offshore Island Soil Considering Anisotropy

An elastoplastic analysis scheme for the cylindrical cavity expansion in offshore islands unsaturated soils considering anisotropy is established. The hydraulic properties and anisotropy caused by stress of unsaturated soils are coupled in an elastoplastic constitutive matrix for unsaturated soil to obtain the governing equations for the cylindrical cavity expansion problem, with an analytical solution that utilizes the original hydro-mechanical state of the soil as the initial conditions. Through a comparative analysis with other analytical solutions, the effectiveness of the new solution is verified. Moreover, the swelling response of the cylindrical cavity expansion in unsaturated soils is examined by systematically analyzing different parameters of the surrounding soil. The findings reveal that the development and rate of anisotropy in normal consolidated soil and over-consolidated soil exert a significant impact on the soil’s mechanical characteristics. Nevertheless, the alteration in the model constant h has little effect on the soil’s mechanical characteristics. The analytical solution introduces anisotropy and broadens the expansion theory of unsaturated soils to yield a more comprehensive theoretical framework for the comprehensive analysis of offshore islands’ unsaturated soils.

Building meanings through annual reports

PropósitoEste artículo pretende entender por qué y cómo se incluyen elementos paratextuales en los informes anuales y, cómo se construye el significado a través del funcionamiento del lenguaje y las imágenes.Diseño/metodología/enfoqueSe desenvolvió un marco de análisis global, que combina los elementos paratextuales de Genette y los conceptos de retórica y denotación y connotación de Barthes. Este marco se aplicó al estudio de caso del grupo dst, un grupo portugués de ingeniería y construcción.ResultadosEl estudio muestra el potential del informe anual como herramienta de comunicación entre una organización y sus grupos de interés. El marco de análisis evidencia que los elementos paratextuales resaltan y complementan la información contable y que los significados denotativos y connotativos asociados a ellos hacen visibles y realzan rasgos intangibles de la organización.Originalidad/valorEste artículo extiende teorías de otros campos interdisciplinarios a la comunicación contable y propone un marco global que combina los escritos de Genette y Barthes. Explorando el contexto portugués, insuficientemente investigado, también se añade a la literatura mediante el análisis de las motivaciones y opciones de los preparadores de los informes anuales sobre la inclusión de elementos paratextuales.

A comprehensive framework for explainable cluster analysis

Machine learning has proven to be a powerful tool for knowledge extraction from large data sets across different domains. Data quality and results interpretability are essential when applying machine learning to inform decision-making processes. This is especially true for clustering methods, which are frequently employed for extracting knowledge from large data sets, due to their unsupervised nature. Although there are significant recent developments in explainable artificial intelligence (XAI) applied to unsupervised problems, they focus primarily on cluster interpretability and often overlook data quality challenges. Moreover, these developments are typically designed to use specific clustering algorithms, limiting their adaptability to incorporate alternative techniques. We propose a novel and comprehensive four-step sequential framework for explainable cluster analysis on high-dimensional mixed-type data to address these limitations. The framework encompasses data preprocessing, dimensionality reduction, clustering, and classification to ensure robust and explainable results. The proposed methodology has also been implemented in an open-source Python package called Clust-learn, designed to be accessible and customizable for researchers and practitioners. The framework has been validated by applying a case study focusing on large-scale assessments in education, effectively illustrating the strength and usefulness of the methodology in extracting and synthesizing knowledge from complex real-world data.