Modeling Techniques Research Articles

Climate Change and Urban Flooding: Assessing Remote Sensing Data and Flood Modeling Techniques: A Comprehensive Review

This review critically examines the current understanding of climate change impacts on urban flooding, synthesizing recent findings to provide a comprehensive overview of how rising temperatures, sea-level rise, and increased extreme weather events influence urban floods. Additionally, it discusses various flood modeling techniques, outlining their advantages and disadvantages. Covering a broad spectrum of studies published between 2007 and 2024, selected for their relevance and methodology, this review highlights several key findings. Climate change significantly exacerbates urban flood disasters, with remote sensing emerging as an indispensable tool for climate change and flood monitoring and prediction. Despite these advancements, notable gaps remain in the literature. There is a conspicuous lack of long-term impact studies and limited discourse on the efficacy of existing mitigation strategies. Additionally, the review underscores the deficiency of real-time flood monitoring systems specifically designed for urban areas. These gaps highlight an urgent need for targeted research and the development of adaptive management practices to enhance flood prediction and management in urban settings. Future research must focus on advancing real-time monitoring systems, integrating remote sensing technologies more effectively, and developing comprehensive flood models. The review also emphasizes the importance of interdisciplinary approaches that merge hydrology, climatology, urban planning, and technology. By consolidating existing research, this review serves as a valuable resource for researchers and policymakers. It underscores the critical need for innovative flood modeling techniques and adaptive management strategies to tackle the challenges posed by climate change. This synthesis not only enriches the current body of knowledge, but also provides clear directions for future investigations, emphasizing the imperative for improved prediction, preparedness, and response mechanisms in urban flood management.

The Synergy Between Industry 5.0 and Circular Economy for Sustainable Performance in the Chinese Manufacturing Industry

The industrial shift from Industry 4.0 to Industry 5.0 has transformed organizational thinking, moving the focus from purely technological implementation to a more human-centered approach. The current study has focused on the Industry 5.0 technological capabilities to bring into circular economy practices aligned with sustainable development goals, aiming to enhance sustainable performance. Moreover, the resource-based theory has grounded the development of the comprehensive framework on Industry 5.0 technological capabilities (artificial intelligence capabilities, big data analytical capabilities, Internet of Things capabilities, machine learning capabilities, and blockchain technology capabilities) and circular economy practices (eco-design, management system, and investment recovery) to achieve sustainable performance (environmental performance, social performance, and economic performance). Data have been collected from 179 respondents from the Chinese manufacturing industry. Additionally, data have been analyzed using the structural equation modeling technique. The results showed that Industry 5.0 technological capabilities directly affect sustainable performance. Moreover, circular economy practices played a dual, moderating, and mediating role between Industry 5.0 technological capabilities and sustainable performance. The current study has contributed to filling a gap in the literature on Industry 5.0 capabilities, especially in the circular economy and sustainable performance perspective. The practical contribution recommended is that if organizations focused on their Industry 5.0 technological capabilities, it would boost circular economy practices and sustainable performance to achieve sustainable development goals.

ASSESSMENT OF PHOTOGRAMMETRY TECHNIQUES FOR 3D MODEL ACQUISITION: A COMPARATIVE STUDY ON THE ACCURACY AND SUITABILITY OF SCANNED MODELS COMPARED TO CAD REFERENCES

This paper presents a comprehensive evaluation of the accuracy of 3D scanning methods using various devices and software to generate 3D models. The resulting models were compared to their corresponding Computer-Aided Design (CAD) models to quantify discrepancies. The primary objective was to assess potential errors, benefits, and limitations of each scanning method, providing insights for future research and development. A switch, chosen as the test object for its moderate complexity and size, enabled a thorough assessment under realistic conditions. The analysis reveals the strengths and limitations of each scanning method, guiding researchers and developers in selecting the most suitable techniques for specific applications. Additionally, the study underscores the importance of device and software parameters on the accuracy of 3D models, offering a comprehensive evaluation of effective techniques for 3D model.

A Raster-Based Multi-Objective Spatial Optimization Framework for Offshore Wind Farm Site-Prospecting

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea.

Malaria notifications in Legal Amazon, Brazil (2003-2022): A Comprehensive Dataset for research & surveillance

Malaria, a life-threatening disease, is preventable and curable. However, the World Health Organization reported alarming statistics for the year 2021, estimating 247 million cases of malaria worldwide, resulting in 619,000 deaths. In Brazil, cases of malaria are concentrated in the region of the Amazon rainforest, known as the Legal Amazon. In response to calls for more research to adapt malaria mitigation and eradication strategies to local conditions, this article presents a dataset based on data collected from the Malaria Epidemiological Surveillance Information System (SIVEP-Malaria) for the period from January 2003 to December 2022 on patients diagnosed with malaria. The pre-processed data set is composed of 1,251,309 records of confirmed malaria cases and included four attributes (Date, Municipality, Test results, and Notifications), attributes considered essential for the development and application of series modeling techniques temporal. By making this dataset available, it is intended to provide researchers with the necessary tools to develop effective strategies for mitigating and eradicating malaria in the region.

A New Frontier in Wind Shear Intensity Forecasting: Stacked Temporal Convolutional Networks and Tree-Based Models Framework

Wind shear presents a considerable hazard to aviation safety, especially during the critical phases of takeoff and landing. Accurate forecasting of wind shear events is essential to mitigate these risks and improve both flight safety and operational efficiency. This paper introduces a hybrid Temporal Convolutional Networks and Tree-Based Models (TCNs-TBMs) framework specifically designed for time series modeling and the prediction of wind shear intensity. The framework utilizes the ability of TCNs to capture intricate temporal patterns and integrates it with the predictive strengths of TBMs, such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Categorical Boosting (CatBoost), resulting in robust forecast. To ensure optimal performance, hyperparameter tuning was performed using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), enhancing predictive accuracy. The effectiveness of the framework is validated through comparative analyses with standalone machine learning models such as XGBoost, RF, and CatBoost. The proposed TCN-XGBoost model outperformed these alternatives, achieving a lower Root Mean Squared Error (RMSE: 1.95 for training, 1.97 for testing), Mean Absolute Error (MAE: 1.41 for training, 1.39 for testing), and Mean Absolute Percentage Error (MAPE: 7.90% for training, 7.89% for testing). Furthermore, the uncertainty analysis demonstrated the model’s reliability, with a lower mean uncertainty (7.14 × 10−8) and standard deviation of uncertainty (6.48 × 10−8) compared to other models. These results highlight the potential of the TCNs-TBMs framework to significantly enhance the accuracy of wind shear intensity predictions, emphasizing the value of advanced time series modeling techniques for risk management and decision-making in the aviation industry. This study highlights the framework’s broader applicability to other meteorological forecasting tasks, contributing to aviation safety worldwide.

Design of judicial public opinion supervision and intelligent decision-making model based on Bi-LSTM

Fuzzy preference modeling in intelligent decision support systems aims to improve the efficiency and accuracy of decision-making processes by incorporating fuzzy logic and preference modeling techniques. While network public opinion (NPO) has the potential to drive judicial reform and progress, it also poses challenges to the independence of the judiciary due to the negative impact of malicious public opinion. To tackle this issue within the context of intelligent decision support systems, this study provides an insightful overview of current NPO monitoring technologies. Recognizing the complexities associated with handling large-scale NPO data and mitigating significant interference, a novel judicial domain NPO monitoring model is proposed, which centers around semantic feature analysis. This model takes into account time series characteristics, binary semantic fitting, and public sentiment intensity. Notably, it leverages a bidirectional long short-term memory (Bi-LSTM) network (S-Bi-LSTM) to construct a judicial domain semantic similarity calculation model. The semantic similarity values between sentences are obtained through the utilization of a fully connected layer. Empirical evaluations demonstrate the remarkable performance of the proposed model, achieving an accuracy rate of 85.9% and an F1 value of 87.1 on the test set, surpassing existing sentence semantic similarity models. Ultimately, the proposed model significantly enhances the monitoring capabilities of judicial authorities over NPO, thereby alleviating the burden on public relations faced by judicial institutions and fostering a more equitable execution of judicial power.

Empirical Evidence of Sustainable Development Goals (SDGs) on Talent Management Practices and Employee Engagement in Government Linked Companies

Objective: The study analyzed the relationship between Sustainable Development Goals (SDGs) on talent management practices and employee engagement at Government Linked Companies (GLCs). Method: Questionnaires were distributed and gathered from 164 employees at 47 GLCs and Partial Least Square Structural Equation Modeling (PLS-SEM) technique was used to test the research hypotheses. Results and Discussion: Questionnaires were distributed and gathered from 164 employees at 47 GLCs and Partial Least Square Structural Equation Modeling (PLS-SEM) technique was used to test the research hypotheses. Among all Malaysian GLCs from various sectors available for the study, the researcher had narrowed down the selection to the 47 companies which are listed on Bursa Malaysia stock exchange (PCG, 2015). The findings discovered that four hypotheses are not supported with the finding of this study. There were significant and positive relationships between talent development and employee engagement (vigor, dedication) and talent identification and employee engagement (dedication, absorption). Originality/Value: The results of the study revealed that talent development and talent identification are pivotal in fostering employee engagement. Specifically, talent development positively correlates with vigor and dedication, underscoring the necessity for GLCs to invest in robust developmental programs that enhance employees' energy and commitment to their roles.

Exploring the impacts of risk perception and risk management planning on innovation orientation: a PLS-SEM approach

PurposeAlthough risk and innovation are often linked, current studies exploring the correlation between these terms are scarce. This study empirically examines the relationship between construction firms’ risk perception, risk management planning and innovation orientation.Design/methodology/approachA conceptual framework was formed through the literature and validated by the partial least squares structural equation modeling (PLS-SEM) technique, employing the data collected from 182 practitioners working in the Vietnamese construction sector.FindingsThis study highlighted that innovation orientation can be categorized as creation and adoption. The findings indicated that while risk perception can hinder innovation creation, effective risk management planning can mitigate the adverse effects. The findings also highlighted that the focus on innovation adoption will negatively affect innovation creation. Additionally, the findings showed that risk management planning has a positive relationship with innovation creation.Originality/valueThis study fills the research gap by explaining why innovation creation is often prioritized by innovation adoption, as risk perception acts as a deterrent. The results imply that construction firms should balance adopting innovations and fostering an environment conducive to innovation creation. It is recommended that these firms invest organizational resources in forming effective risk management plans to create a more secure environment for innovation creation.

The Impact of Economic Factors on Saudi Arabia’s Foreign Trade with BRICS Countries: A Gravity Model Approach

Our investigation, bolstered by the robust gravity trade model and panel data econometric technique, underscores the pivotal factors that influence trade interactions between Saudi Arabia and the BRICS nations—Brazil, Russia, India, China, and South Africa. The study, spanning from 1998 to 2023, delves into key economic metrics such as the gross domestic product, exchange rate fluctuations, inflationary trends, political conditions, and trade deals. We employ a range of econometric strategies, including pooled Ordinary Least Squares (OLS) and fixed effects models, to reveal that the GDP of BRICS states consistently and significantly impacts trade volumes. Specifically, a 1% increase in the GDP of partner countries correlates with a 0.37% rise in trade volume within the pooled OLS model. This effect amplifies to 1.43% when adjusting for temporal and country-specific factors in the fixed effects, underscoring the importance of accommodating unobserved heterogeneity, which refers to the unmeasured factors that can influence the relationship between GDP and trade volume. The political stability of BRICS nations mitigates transactional risks and promotes more stable trade relationships, thereby enhancing trade flows. Fluctuations in exchange rates exert positive and significant effects. This indicates that a more robust Saudi Riyal, an essential policy instrument, can enhance trade by increasing the competitiveness of Saudi exports. This study demonstrates that economic magnitude, political stability, and exchange rates affect Saudi Arabia’s trade with BRICS nations. These results bolster the Kingdom’s Vision 2030 objectives for economic diversification. This research advocates for stable political climates and strategic trade agreements to enhance trade relations. This study asserts that this approach will guarantee sustainable growth and diminish the Kingdom’s reliance on oil exports, instilling optimism in the Saudi economy.

Deriving comprehensive literature trends on multi-omics analysis studies in autism spectrum disorder using literature mining pipeline

Autism spectrum disorder (ASD) is characterized by highly heterogenous abnormalities in functional brain connectivity affecting social behavior. There is a significant progress in understanding the molecular and genetic basis of ASD in the last decade using multi-omics approach. Mining this large volume of biomedical literature for insights requires considerable amount of manual intervention for curation. Machine learning and artificial intelligence fields are advancing toward simplifying data mining from unstructured text data. Here, we demonstrate our literature mining pipeline to accelerate data to insights. Using topic modeling and generative AI techniques, we present a pipeline that can classify scientific literature into thematic clusters and can help in a wide array of applications such as knowledgebase creation, conversational virtual assistant, and summarization. Employing our pipeline, we explored the ASD literature, specifically around multi-omics studies to understand the molecular interplay underlying autism brain.

Sustainable human resource management and sustainable performance: mediating role of organization citizenship behavior

Purpose This study aims to investigate the influence of sustainable human resource management (SHRM) policies on sustainability performance of higher education institutions (HEIs). Furthermore, this study also examined the mediating role of organizational citizenship behavior toward sustainability (OCBS) between SHRM and HEIs’ sustainability performance. The ability motivation opportunity (AMO) and stakeholder theories are used to conceptualize the model. Design/methodology/approach Data were collected from 162 academic staff of 12 government and private universities in India by using snowball sampling method. The partial least square structured equation modeling technique was used to analyze the data. Findings The overall results suggest that SHRM, such as sustainability motivation, sustainability-building practices, green employee engagement initiatives and social considerations, significantly impact OCBS among academic staff and the sustainability performance of HEIs. These findings provide a better understanding of how to improve the sustainability performance of HEIs through SHRM and OCBS among academic staff. Originality/value This study significantly extends the growing literature on SHRM and sustainability performance by focusing on sustainable HR practices in the HEIs. To the best of the authors’ knowledge, this is one of the first studies to empirically investigate SHRM and sustainability performance in the context of education sector in India.

Randomising spatial patterns supports the integration of intraspecific variation in ecological niche models

Ecological niche models (ENMs) are an essential modelling technique in biodiversity prediction and conservation and are frequently used to forecast species responses to global changes. Classic species‐level models may show limitations as they assume species homogeneity, neglecting intraspecific variation. Composite ENMs allow the integration of intraspecific variation by combining intraspecific‐level ENMs, capturing individual environmental responses over the species' geographic range. While recent studies suggest that accounting for intraspecific variation improves model predictions, we currently lack methods to test the significance of the improvement. Here, we propose a null model approach that randomises observed intraspecific structures as an appropriate baseline for comparison. We illustrate this approach by comparing predictive performance of a species‐level ENM to composite ENMs for European beech Fagus sylvatica. To investigate the influence of spatial lineage structure, we tested all models against the same withheld data to allow comparison across models based on five common performance metrics. We found that the species‐level ENM expressed higher overall performance (i.e. AUC, TSS, and Boyce index) and specificity (ability to predict absences), while the composite ENMs achieved higher sensitivity (ability to predict presences). In line with this, the composite ENMs also showed increased sensitivity and decreased specificity compared to the null models that randomised lineage structure. We showed that the assessment of model performance strongly varies based on the used measures, emphasising a careful investigation of multiple measures for evaluation. The application of null models allowed us to disentangle the effect of observed patterns of intraspecific variation in ENMs. Further, we highlight the validation and use of well‐founded subgroups for modelling. Although intraspecific variation improves the prediction of occurrences of European beech, it did not fully outcompete the classic species‐level model and should be used with care and rather to improve understanding and to supplement, not replace, species‐level models.

Tectonostratigraphic models of an extensional back-arc basin: inferences for the evolution of the Pannonian Basin System

The subsidence history and sedimentary architecture of extensional basins are controlled by the interplay between tectonics, mantle dynamics and climatic variations. Observations from outcrop to regional seismic scales coupled with numerical modelling techniques, including basin-scale stratigraphic, crustal-scale tectonic and mantle-scale subduction models, contribute to the quantification of basin evolution. Here, we review and discuss both tectonic and stratigraphic applications of such models and compare them with observations from the Pannonian Basin system of Central Europe. We show that diachronous localization of back-arc extension, crustal thinning, asthenospheric upwelling and subsequent thermal relaxation are superimposed on an overall slow plate convergence. These processes result in contrasting subsidence and uplift patterns and a variable heat flow evolution in different parts of the basin system. The crustal stress-field of the sub-basins does not always reflect their contrasting vertical motions. Extensional deformation generally migrates from the basin margins towards the basin centre. Structural inversion is localized along hot and weak inherited zones from the Middle Miocene to Present. Tectonic subsidence focuses sedimentation along deep depocentres, sourced by exhumed footwalls and from the neighbouring orogens. The overall post-rift sediment progradation is influenced by inherited bathymetries from the preceding syn-rift stage and by enhanced differential vertical motions.

A Data-Driven Model for Rapid CII Prediction

The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation.

Abstract 4129223: Large Language Modeling-Enabled Analysis of Atrial Fibrillation Topics and Sentiments on Social Media

Purpose: We utilized large language modeling (LLM) and automated topic modeling techniques to describe public perceptions regarding AF on Reddit, a social media platform. Methods: In this qualitative study, we curated all AF-related discussions on Reddit from 2006 to 2023 by searching for all posts and comments containing AF-related terms (“atrial fibrillation”, “afib”, and “atrial flutter”). Discussions were embedded using a state-of-the-art embedding model (BAAI/bge-base-en-v1.5). Embeddings are reduced in dimensionality using Uniform Manifold Approximation and Project and clustered using K-Means Clustering to find topics and groups. Topic and group labels are enhanced using large language models (Llama2 and GPT4, respectively). Results: We identified a total of 86,323 discussions related to AF from 38,183 unique users between April 14, 2006 and November 20, 2023. Discussions around wearables and ablations increased over time, while discussions around anticoagulation had periodic spikes. The topic modeling pipeline automatically identified 64 topics and 9 groups (Figure 1). Groups emphasized the following themes: lifestyle factors and triggers associated with AF (groups 1, 5, 7); patient experiences with management strategies including anticoagulation, rate control, pharmacological rhythm control, and ablation (groups 2, 4, 5); experiences with wearable devices (group 8); psychological burden of living with AF (group 6); and online advertisements for AF-related medications (groups 3, 9). Conclusions: Our AI-enabled topic modeling analysis revealed public perceptions about AF including triggers, experiences with different management strategies, impact of wearables, and psychological burden of the diagnosis, which may help guide potential strategies for improving shared decision making around the AF patient journey.

Abstract 4129397: Cardiovascular Risk Models Using Large-Scale Physical Examination Data

Background: Physical examination (PE) data offer more potential key risk factors that can enhance the development of risk models. Additionally, the latest CVD risk model, PREVENT, developed from an American population, necessitates validation within China. Objectives: This study aimed to develop CVD risk models based on large-scale PE records and compare them with PREVENT model in Chinese adults. Methods: Data were collected from a China national database (2016-2024), including 929682 participants aged 30-79 years without previous CVD (Table I). The variables covered demographics, comorbidities, and PE items, resulting in 59 features as input. The endpoint events were defined as total CVD and its subtypes, ASCVD and heart failure (HF). Sex-specific survival analysis was developed based on age-scale, utilizing Cox Proportional Hazards and Random Survival Forests (RSF). Feature importance was assessed using the average minimal depth in RSF. The performance was evaluated by the Harrell C-statistic in the validation set. Results: The results showed that our RSF model achieved the best performance in total CVD, with the highest C-index of 0.82 (Table II). The top 15 predictive risk factors for each gender are presented in Figure 1. Additionally, validation of the PREVENT model in our Chinese population dataset showed its great generalizability, particularly for HF events. Conclusions: Overall, this research underscores the importance of leveraging large-scale PE data and advanced modeling techniques to improve CVD risk prediction. Beyond traditional risk factors, greater emphasis should be placed on more demographic characteristics, comorbidities, urinalysis, and ECG features.

A Model-Based Approach to the Problem of Variable Choice

Abstract This article distinguishes causal modeling, metaphysical, epistemic, and modeling reasons for variable/parameter choice. I argue in favor of justifying variable/parameter choices by appealing to modeling reasons concerning the limitations of the available measurements, experimental data, modeling techniques, and modeling frameworks. I use this “tyranny of availability” to identify normative criteria for variable/parameter choice that apply across most scientific modeling contexts and investigate their metaphysical and epistemological implications.

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

On Efficient Training of Large-Scale Deep Learning Models

The field of deep learning has witnessed significant progress in recent times, particularly in areas such as computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications, enhancing industrial productivity and facilitating social development. However, it suffers extremely from the unstable training process and stringent requirements of computational resources. With the increasing demands on the adaption of computational capacity, though numerous studies have explored the efficient training field to a certain extent, a comprehensive summarization/guideline on those general acceleration techniques of training large-scale deep learning models is still much anticipated. In this survey, we present a detailed review of the general techniques for training acceleration. We consider the fundamental update formulation and split its basic components into five main perspectives: (1) “data-centric,” including dataset regularization, data sampling, and data-centric curriculum learning techniques, which can significantly reduce the computational complexity of the data samples; (2) “model-centric,” including acceleration of basic modules, compression training, model initialization, and model-centric curriculum learning techniques, which focus on accelerating the training via reducing the calculations on parameters and providing better initialization; (3) “optimization-centric,” including the selection of learning rate, the employment of large batch size, the designs of efficient objectives, and model average techniques, which pay attention to the training policy and improving the generality for the large-scale models; (4) “budgeted training,” including some distinctive acceleration methods on source-constrained situations, e.g., for limitation on the total iterations; and (5) “system-centric,” including some efficient distributed frameworks and open source libraries that provide adequate hardware support for the implementation of the above-mentioned acceleration algorithms. By presenting this comprehensive taxonomy, our survey presents a comprehensive review to understand the general mechanisms within each component and their joint interaction. Meanwhile, we further provide a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms. Overall, we hope that this survey will serve as a valuable guideline for general efficient training.