Traditional Methods Research Articles

Carbon emissions trading price forecasts by multi-perspective fusion

<p>The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.</p>

Synthesis, Characterization, and Optimization of Novel Furan-ring Fused Chalcones via Radical Cyclization of α,β-Unsaturated Ketones and Cyclic Ketone

Abstract: Novel Furan-ring Fused Chalcones (FFC) were synthesized using a radical cyclization reaction of α,β-unsaturated ketones with cyclic ketone as the model reaction to attain this goal. In this study, traditional and microwave-assisted methods for the efficient and cost-effective synthesis of furan-ring fused chalcones in mild reaction conditions are compared and optimized. The goal is to develop a reliable and adaptable synthetic technique that may be used to produce these useful chalcone derivatives quickly and effectively. The optimal experimental conditions for these reactions were carefully determined using two independent methodologies: conventional (Method A) and microwave (Method B). The results indicated that the proposed method B could be used effectively in the future to synthesize novel furans with short reaction times and acceptable yields (87-94 %), and products were purified by column chromatography and preparative thin layer chromatography (PTLC). All new compounds were characterized by 1H-NMR, 13C-NMR, LC-MS, and elemental analyses.

Empowering English as a Foreign Language Education With the Production-Oriented Approach: A Systematic Review

<p style="text-align:justify">The field of English as a Foreign Language (EFL) instruction has gained recognition worldwide, particularly in non-native English-speaking countries. However, traditional teaching methods fall short of meeting the needs of today's EFL learners. In response, the Production-Oriented Approach (POA) has emerged as a new pedagogical approach to address these challenges. Despite its significant impact on EFL education, there is a lack of reviews on the POA. This study conducted a systematic review to explore the POA's effectiveness, challenges, and future trends. Using the PRISMA framework, 31 articles from reputable databases (Web of Science, Education Resources Information Center, Scopus, and Google Scholar) were analyzed, covering the years 2018 to 2023. The study's comprehensive analysis, employing Nvivo, revealed that the POA has immense potential for enhancing English learners' language skills, particularly in reading, speaking, writing, and translating. Moreover, the study highlighted the POA's positive influence on learners' affective factors, including motivation, confidence, critical thinking, and self-directed learning abilities. Nonetheless, the POA faces challenges for both teachers and learners, particularly during the transition from traditional methods and the associated increase in workload. Despite these challenges, this study unequivocally establishes the POA as an effective pedagogical approach for teaching EFL. Additionally, it identifies research gaps that must be addressed to sustain and improve EFL education in the future through further development of the POA.</p>

Success factors of real estate crowdfunding projects: Evidence from Spain

AbstractReal estate crowdfunding is a relatively new alternative financing and investing method. This research aims to identify factors which might increase investors' willingness to participate in real estate crowdfunding campaigns. We analyse 195 lending-based real estate crowdfunding campaigns from four Spanish platforms. Project success is measured by duration, i.e. the time required to reach the funding target. We assess the impact of the funding target, the annual return, the loan duration, several risk-related metrics and the minimum investment amount. We find that the higher the funding target and the minimum investment amount, the longer it takes to reach the target. We document that investors prefer projects where the maturity of the loan is shorter. We also find that construction-type projects reach the funding target faster than other type of fundraising goals. At the same time, we do not find any association between the annual return or risk-related metrics and project success. To assure successful fundraising, real estate crowdfunding platforms should prioritize those real estate projects which are highly popular among investors (i.e. construction-type projects with short maturity). Real estate developers, in turn, should crowdfund projects which are demanded by the crowdinvestors and use their traditional financing methods for the remaining projects.

Multiscale and multidisciplinary data-driven reservoir characterization of a fractured carbonate field in Kurdistan

Abstract The combination of traditional subsurface interpretation techniques with advanced data analytics is a key stepping stone for better predicting reservoir quality, especially in heterogeneous and complex geological systems. The Peshkabir oil and gas field, located in the north of the Kurdistan Region of Iraq and within the Tawke Production Sharing Contract, is one such heterogeneous system. Well oil rates vary significantly across the field and cannot be simply correlated to fracture densities measured at the wells. Understanding which fractures matter and what influences reservoir deliverability is a question of major importance for maximizing oil production. The carbonate reservoirs include karstified vuggy zones and hydrothermal dolostones, in addition to an extensively developed fractured network. This paper presents a geological conceptual model for the Peshkabir field, and an application of Python based data science techniques to identify key predictors of reservoir deliverability from drilling, logging and production data. We demonstrate that the major advantage of the application of advanced data analytics is that it can enable the recognition of patterns and associations in a complex, high-dimensional parameter environment whereas traditional interpretation methods typically only allow for the comparison of two or three parameters at a time. This method allows the integration of dynamic and static data effectively and empowers the interpreter to incorporate all the available insights which, coupled with domain knowledge, allows for data-driven decision-making.

Electromagnetic Source Imaging via a Data-Synthesis-Based Convolutional Encoder-Decoder Network.

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

JITL-MBN: A Real-Time Causality Representation Learning for Sensor Fault Diagnosis of Traction Drive System in High-Speed Trains.

A traction drive system (TDS) in high-speed trains is composed of various modules including rectifier, intermediate dc link, inverter, and others; the sensor fault of one module will lead to abnormal measurement of sensor in other modules. At the same time, the fault diagnosis methods based on single-operating condition are unsuitable to the TDS under multi-operating conditions, because a fault appears various in different conditions. To this end, a real-time causality representation learning based on just-in-time learning (JITL) and modular Bayesian network (MBN) is proposed to diagnose its sensor faults. In specific, the proposed method tracks the change of operating conditions and learns potential features in real time by JITL. Then, the MBN learns causality representation between faults and features to diagnose sensor faults. Due to the reduction of the nodes number, the MBN alleviates the problem of slow real-time modeling speed. To verity the effectiveness of the proposed method, experiments are carried out. The results show that the proposed method has the best performance than several traditional methods in the term of fault diagnosis accuracy.

Factors affecting students’ concept retention in learning science online using instructional videos

Effective science instruction in a blended learning approach is synonymous with the strategic use of instructional videos (IVs) to fill the gap in teacher support. This study aims to determine the IVs’ effectiveness in improving students' concept retention and overall learning experiences. The experimental group was exposed to instruction integrating IVs via embedded mixed-method design, whereas the control group was exposed to traditional lecture methods. The results showed that students' post-test scores and concept retention improved significantly in the experimental group, where students reported better learning experiences than in the control group. This beneficial effect of a technology-integrated approach can be attributed to various elements of IVs, such as engaging content, motion graphics, video length, the language used, and the speaker's perspective. This study recommends that IVs be used to enhance learning opportunities and results in the teaching and learning process.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

Nondestructive assessment of ZnO surge arrester operating condition based on time-domain dielectric characteristics

Given the expansion of the economy and the rise in electricity demand, it is imperative to ensure the safe operation of the power system. Zinc oxide (ZnO) surge arresters play a crucial role in preventing overvoltage that can potentially harm the grid. The study presents an innovative non-destructive assessment approach for evaluating the working condition of ZnO surge arresters based on time-domain dielectric characteristics, aiming to guarantee their safe functioning within the power grid. The proposed technique provides a platform for conducting aging experiments updates the procedure for preparing piezoresistors in ZnO surge arresters and examines their performance. Furthermore, a test bed was utilized to investigate polarization/depolarization currents. The findings revealed that the pressure-sensitive voltage exhibited a minimum value of 693.09 U1mA ·cm2 using the traditional solid-phase approach while employing the chemical precipitation method with a 2:1 alcohol-to-water volume ratio resulted in a maximum value of 1028.41 U1mA ·cm2. Under the traditional solid-phase method, the voltage gradient varied from 411.04 V·mm−1 to 585.12 V·mm−1 when using the chemical precipitation method with an alcohol-to-water volume ratio of 2:1 V ·mm−1. The condition of ZnO surge arresters can be evaluated non-destructively by the polarization/depolarisation charge differential, which increases with the average service life. In conclusion, this study provides methodological criteria for assessing ZnO surge arrester conditions to ensure safe operation of power grids.

Multitask-Guided Deep Clustering With Boundary Adaptation.

Multitask learning uses external knowledge to improve internal clustering and single-task learning. Existing multitask learning algorithms mostly use shallow-level correlation to aid judgment, and the boundary factors on high-dimensional datasets often lead algorithms to poor performance. The initial parameters of these algorithms cause the border samples to fall into a local optimal solution. In this study, a multitask-guided deep clustering (DC) with boundary adaptation (MTDC-BA) based on a convolutional neural network autoencoder (CNN-AE) is proposed. In the first stage, dubbed multitask pretraining (M-train), we construct an autoencoder (AE) named CNN-AE using the DenseNet-like structure, which performs deep feature extraction and stores captured multitask knowledge into model parameters. In the second phase, the parameters of the M-train are shared for CNN-AE, and clustering results are obtained by deep features, which is termed as single-task fitting (S-fit). To eliminate the boundary effect, we use data augmentation and improved self-paced learning to construct the boundary adaptation. We integrate boundary adaptors into the M-train and S-fit stages appropriately. The interpretability of MTDC-BA is accomplished by data transformation. The model relies on the principle that features become important as the reconfiguration loss decreases. Experiments on a series of typical datasets confirm the performance of the proposed MTDC-BA. Compared with other traditional clustering methods, including single-task DC algorithms and the latest multitask clustering algorithms, our MTDC-BA achieves better clustering performance with higher computational efficiency. Deep features clustering results demonstrate the stability of MTDC-BA by visualization and convergence verification. Through the visualization experiment, we explain and analyze the whole model data input and the middle characteristic layer. Further understanding of the principle of MTDC-BA. Through additional experiments, we know that the proposed MTDC-BA is efficient in the use of multitask knowledge. Finally, we carry out sensitivity experiments on the hyper-parameters to verify their optimal performance.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Exploring Bimanual Haptic Feedback for Spatial Search in Virtual Reality.

Spatial search tasks are common and crucial in many Virtual Reality (VR) applications. Traditional methods to enhance the performance of spatial search often employ sensory cues such as visual, auditory, or haptic feedback. However, the design and use of bimanual haptic feedback with two VR controllers for spatial search in VR remains largely unexplored. In this work, we explored bimanual haptic feedback with various combinations of haptic properties, where four types of bimanual haptic feedback were designed, for spatial search tasks in VR. Two experiments were designed to evaluate the effectiveness of bimanual haptic feedback on spatial direction guidance and search in VR. The results from the first experiment reveal that our proposed bimanual haptic schemes significantly enhanced the recognition of spatial directions in terms of accuracy and speed compared to spatial audio feedback. The second experiment's findings suggest that the performance of bimanual haptic feedback was comparable to or even better than the visual arrow, especially in reducing the angle of head movement and enhancing searching targets behind the participants, which was supported by subjective feedback as well. Based on these findings, we have derived a set of design recommendations for spatial search using bimanual haptic feedback in VR.

Technology's impact on language learning: Meta-analysis on variables and effectiveness

This study explores the correlation between technology utilization and language acquisition while analyzing the impact of moderating variables on this relation. Our meta-analysis approach analyzes data from 43 extracts out of 19 primary studies published between 2012 and 2021. Our data analysis employs a random-effect model utilizing a significance level of α = 0.05. Additionally, the authors examine four moderating variables: level of education, location of research, proficiency in language, and year of publication. Technology-based language acquisition outperforms traditional methods, indicating a significant and moderate impact on the learning process. This study enhances comprehension of the efficacy of technology in language acquisition by identifying various factors, such as the geographical location of research, methods of assessing language proficiency, and technology type employed. However, there is insufficient evidence to support the notion that educational level or sample size significantly impact technology-based language acquisition. This meta-analysis highlights the importance of considering nuanced factors when integrating technology into language learning. The findings emphasize the possibility of technology to transform methods of acquiring language and urge additional investigation into customized strategies that optimize its advantages.

GRU combined model based on multi-objective optimization for short-term residential load forecasting

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting.

Adaptive solar power generation forecasting using enhanced neural network with weather modulation

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices.

Optimal resource allocation in hierarchical organizations under uncertainty: An interval-valued n-person cooperative game approach

In many real production scenarios, departmental organizations often exhibit a hierarchical structure, where departments cooperate with subordinate departments to optimize resource allocation and maximize their respective benefits. However, due to a lack of information or data, many model parameters in the allocation process cannot be precisely defined. In response to this challenge, an interval n-person hierarchical resource allocation model is proposed to achieve maximum economic benefit in uncertain environments. Based on the concepts of satisfactory degrees of comparing intervals and interval-valued cores of interval-valued n-person cooperative games, an auxiliary nonlinear programming model and method are developed to solve the interval-valued cores of such cooperative games. The approach explicitly considers the inclusion and/or overlap relations between intervals, whereas the traditional interval ranking method may not guarantee the existence of interval-valued cores. The proposed method offers cooperative opportunities under uncertain conditions. Finally, the feasibility and applicability of the models and methods are demonstrated through a numerical example and comparison with other methods.

Personalized multi-attribute recommendation method based on partial order set

With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.

Bearing fault detection technology for automated machinery based on acoustic analysis

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency.