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This technical note describes in detail a method for associating individual sheets of blank A4 white paper from the same ream by the physical fit of machine‐cut edges. A large‐scale laboratory trial involving ~700 sheets of paper from 24 different reams (plus one spoiled sample), and more than 20,000 potential physical fits, correctly associated and sequenced 219 pairs of sheets together with a 100% empirical success rate and no false associations. The edge profile of each short machine‐cut end of a sheet of A4 paper allows us to physically fit sheets of paper from the same ream to each other and use this to predict the sequence of sheets in a set of documents. In a real‐life scenario, it may now be possible to detect the substitution or addition of a sheet in a multipage document, link documents from different sources to each other or to a common source of paper (e.g. to paper from a seized printer or from an accused's address) or to date documents. The study provides data for the application of this method in forensic casework and supports the practitioner when forming conclusions in this type of case. [ABSTRACT FROM AUTHOR]

Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems. [ABSTRACT FROM AUTHOR]

This study proposes a novel method to forecast the emerging patenting area with Main Path Analysis and Word Cloud Analysis. To test the methods, we used Wind Power Energy as an example to illustrate the method's usefulness. Firstly, we used "wind power" and "wind energy" to collect 40,827 related journal papers in Scopus and 72,979 related patents in Derwent Innovation databases. Main Path Analysis was conducted to explore the development trajectory. The results of the Main Path Analysis for the papers and patents were visualized with Pajek software. Secondly, we used VOSviewer to extract the technological areas (i.e., keywords) of the collected academic papers and patents. Then, we calculated the average time lag between the first paper published and the first patent filed for each technological area (keyword). Finally, we forecasted the trend of patenting for wind power energy based on the average lag time and academic research themes in recent years. [ABSTRACT FROM AUTHOR]

Paper, a porous-fibrous network, is made up of hydrophilic fibres, which are notably susceptible to deformations due to variations in moisture content. The response of paper sheets to full or partial wetting, based on the water-induced fibre swelling, has mostly been predicted in the past using highly idealised two-dimensional network geometries. The purpose of the present paper is to establish how precisely the two-stage process of determining effective hygro-expansivity under uniform wetting by a two-dimensional model followed by bending analysis using a continuum model predicts the curl of a fully three-dimensional network. In the present multi-scale modelling work, the moisture-induced homogenised sheet scale curl, derived from hygro-elastic deformations using non-uniform moisture profiles, is analysed based on an idealised non-woven three-dimensional fibrous network finite element model. First, the effective paper sheet swelling properties, derived computationally for the uniform wetting condition, are compared with analytically homogenised hygro-elastic properties. Whereas the analytically predicted elastic modulus has an inaccuracy of only ∼ 5%, the hygro-expansivity coefficient is over-predicted by roughly a third for a network with straight fibres. The difference is smaller if waviness of the fibres and wrap-around in the fibre bonds are taken into account. Fibre alignment turns out to have little influence. Subsequently, paper sheet curl predictions made by the analytical model are compared with that computed from the three-dimensional network model. The inaccuracy of the fully analytical model, based on the two-dimensional network description, relative to the fully detailed three-dimensional computational model with straight fibres is on the order of 40%–50%. The fidelity of the simple model is primarily constrained by the inaccuracy of the effective hygro-expansion coefficient. If the expansivity extracted from the three-dimensional simulations under uniform wetting is inserted, the inaccuracy drops to less than 10%. The continuum bending description hence performs adequately, provided it is based on a reliable estimation of the uniform expansion behaviour of the material. • Hygroscopic curl of paper sheets may be predicted using homogenised models. • We assess the accuracy of such predictions. • As a reference simulations of an idealised three-dimensional fibre network are used. • The predictions are accurate provided an accurate hygro-expansivity is used. • Two-dimensional network models may not be sufficient for this. [ABSTRACT FROM AUTHOR]

The biochemical activity inside a cell has recently been suggested to act as a source of hydrodynamic fluctuations that can speed up or slow down enzyme catalysis [Tripathi et al., Commun. Phys. 5, 101 (2022).] The idea has been tested against and largely corroborated by simulations of activated barrier crossing in a simple fluid in the presence of thermal and athermal noise. The present paper attempts a wholly analytic solution to the same noise-driven barrier crossing problem but generalizes it to include viscoelastic memory effects of the kind likely to be present in cellular interiors. A calculation of the model's barrier crossing rate, using Kramers' flux-over-population formalism, reveals that in relation to the case where athermal noise is absent, athermal noise always accelerates barrier crossing, though the extent of enhancement depends on the duration τ0 over which the noise acts. More importantly, there exists a critical τ0—determined by the properties of the medium—at which Kramers' theory breaks down and, on approach to which, the rate grows significantly. The possibility of such a giant enhancement is potentially open to experimental validation using optically trapped nanoparticles in viscoelastic media that are acted on by externally imposed colored noise. [ABSTRACT FROM AUTHOR]

A core aspiration of the ecological sciences is to determine how systems work, which implies the challenge of developing a causal understanding. Causal inference has long been approached from a statistical perspective, which can be limited and restrictive for a variety of reasons. Ecologists and other natural scientists have historically pursued mechanistic knowledge as an alternative approach to causal understanding, though without explicit reference to the requirements of causal statistics. In this paper, I describe the premises of an expanded paradigm for causal studies, the Integrative Causal Investigation Paradigm, that subsumes causal statistics and mechanistic investigation into a multi‐evidence approach. This paradigm is distinct from the one articulated by causal statistics in that it (1) focuses its attention on the long‐term goal of building causal knowledge across multiple studies and (2) recognizes the essential role of mechanistic investigations in establishing a causal understanding. The Integrative Paradigm, consequentially, proposes that there are multiple methodological routes to building causal knowledge and thus represents a pluralistic perspective. This paper begins by describing the crux of the problem faced by causal statistics. To understand this problem, it should be recognized that the word causal has multiple meanings and a variety of evidential standards. An expanded vocabulary is developed so as to reduce ambiguities and clarify critical issues. I further show by example that there is an important ingredient typically omitted from consideration in causal statistics, which is the known information related to the mechanisms underlying relationships being evaluated. To address this issue, I describe a procedure, Causal Knowledge Analysis, that involves an evaluation and compilation of existing evidence indicative of causal content and the features of mechanisms. Causal Knowledge Analysis is applied to three example situations to illustrate the process and its potential for contributing to the development of causal knowledge. The implications of adopting the proposed paradigm and associated procedures are discussed and include the potential for advancing ecology, the potential for clarifying causal methodology, and the potential for contributing to predictive forecasting. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain. [ABSTRACT FROM AUTHOR]

Traditional time series analysis focuses on modeling and predicting time series data obtained from a single observation source. However, in a confrontational environment, one party often creates biased data to interfere with the other party's predictions. To mitigate the observation error inherent in single-source data, there is a growing emphasis on the importance of utilizing and studying multi-source observation data, which motivates this paper to propose methods for analyzing and predicting this kind of data. First, this paper proves that the accuracy of the estimation can be improved by introducing multiple sets of time-invariant data with bias. Subsequently, when this approach is extended to include time-varying data, these findings continue to hold. For time-varying data with bias, this paper proposes a comprehensive prediction procedure to provide the uncertainty distribution for the forthcoming moment. Finally, three examples are proposed to illustrate the effectiveness and efficiency of the methods. [ABSTRACT FROM AUTHOR]

This paper describes how elevating your airport's ability to be relevant for the forecast period and beyond requires more focus than master planning may suggest. The paper stresses the importance of stakeholder identification and communications as a key pathway to relevant innovation, which is desperately needed to ensure support for an industry the world needs. Based on successful and iterative practices used by the leadership team at Pittsburgh International Airport (PIT) over the last nine years, this paper includes practical techniques for creating a more secure future for airports, the aviation industry and any business looking to create impact and ensure future relevance. [ABSTRACT FROM AUTHOR]

The existing branch prediction models can't predict the behaviors of various instructions in the processor accurately, which leads to the limitation of processing efficiency. Therefore, this paper proposed two hybrid prediction solutions, which aimed to combine multiple branch prediction models to improve prediction accuracy and processor execution efficiency. This paper transferred the prediction results of TAGE branch prediction model and BATAGE branch prediction model to the Hybrid model. In the prediction phase, the Hybrid model selected the prediction results of the best-performing branch prediction model based on the historical performance of TAGE and BATAGE. In the update phase, the Hybrid model updated the saturation counter of the entries that needed to be updated according to the designed hybrid prediction strategy. Experiments on 440 test programs provided by the CBP software simulation platform show that the prediction error rates of both hybrid prediction solutions are lower than those of many of the latest mainstream branch prediction models. This study provides an effective solution to the problem of predicting all command mode behaviors. In the branch instruction prediction of real CPU, this research provides some practical value. [ABSTRACT FROM AUTHOR]

Rainfall is one of the remarkable hydrologic variables that is directly connected to the sustainable environment for any region over the globe. The present study aims to review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment of the most popular AI models and a comparison of the results based on the accuracy parameters. 39 journal papers, published in renowned international journals from 2000 to 2023, were studied extensively to categorize modeling techniques, best models, characteristics of input data, the period for the input variables, data division, and so forth. Although certain drawbacks still exist, the results of reviewed studies suggest that AI models may help simulate rainfall in various geographic locations. In some cases, the data splitting mechanism was delivered to the model itself so that the model accuracy gets improved. The recommendations from the reviewed papers will help future researchers fill the research gaps, especially tuning the hyperparameters while building the training models. Hybrid models were advised in some cases to minimize the gap between the simulated and the observed data. All recommendations from reviewed papers aimed to achieve a resilient rainfall forecasting model in the era of climate change. [ABSTRACT FROM AUTHOR]

Recognition of spatio-temporal traffic patterns at the network-wide level plays an important role in data-driven intelligent transport systems (ITS) and is a basis for applications such as short-term prediction and scenario-based traffic management. Common practice in the transport literature is to rely on well-known general unsupervised machine-learning methods (e.g., k-means, hierarchical, spectral, DBSCAN) to select the most representative structure and number of day-types based solely on internal evaluation indices. These are easy to calculate but are limited since they only use information in the clustered dataset itself. In addition, the quality of clustering should ideally be demonstrated by external validation criteria, by expert assessment or the performance in its intended application. The main contribution of this paper is to test and compare the common practice of internal validation with external validation criteria represented by the application to short-term prediction, which also serves as a proxy for more general traffic management applications. When compared to external evaluation using short-term prediction, internal evaluation methods have a tendency to underestimate the number of representative day-types needed for the application. Additionally, the paper investigates the impact of using dimensionality reduction. By using just 0.1% of the original dataset dimensions, very similar clustering and prediction performance can be achieved, with up to 20 times lower computational costs, depending on the clustering method. K-means and agglomerative clustering may be the most scalable methods, using up to 60 times fewer computational resources for very similar prediction performance to the p-median clustering. [ABSTRACT FROM AUTHOR]

The access of new energy improves the flexibility of distribution network operation, but also leads to more complex mechanism of line loss. Therefore, starting from the nonlinear, fluctuating and multi-scale characteristics of line loss data, and based on the idea of decomposition prediction, this paper proposes a new method of line loss frequency division prediction based on wavelet transform and BIGRU-LSTM (Bidirectional Gated Recurrent Unit-Long Short Term Memory Network).Firstly, the grey relation analysis and the improved NARMA (Nonlinear Autoregressive Moving Average) correlation analysis method are used to extract the non-temporal and temporal influencing factors of line loss, and the corresponding feature data set is constructed. Then, the historical line loss data is decomposed into physical signals of different frequency bands by using wavelet transform, and the multi-dimensional input data of the prediction network is formed with the above characteristic data set. Finally, the BIGRU-LSTM prediction network is built to realize the probabilistic prediction of high-frequency and low-frequency components of line loss. The effectiveness and applicability of the method proposed in this paper were verified through numerical simulation. By dividing the line loss data into different frequency bands for frequency prediction, the mapping relationship between different line loss components and influencing factors was accurately matched, thereby improving the prediction accuracy. [ABSTRACT FROM AUTHOR]

This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. Worked examples are included for both cases. This paper describes some statistical tests for comparing the predictive performance of two or more prediction rules. It covers the cases of both quantitative and qualitative predictions, that is, both regression and classification problems. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a critical role in urban traffic management and planning. Accurate prediction of traffic flow can enhance traffic efficiency, improve traffic safety, conserve traffic resources, and promote sustainable urban development. Graph convolutional networks (GCN) have great potential in traffic flow prediction due to their ability to handle complex data with topological structures while considering spatial relationships between nodes in data. However, it is difficult for the traditional GCN-based models to capture dynamic spatiotemporal dependencies between the stations since these models usually use static relationship graph in GCN. This paper presents an enhanced approach called the coupled generative graph convolution model (CGGCN) for capturing dynamic regional traffic relationships. The CGGCN learns the adjacency matrix of graph convolution in a generative manner, thereby improving the ability of previous graph convolution models to effectively capture the dynamic relationships of regional traffic within multi-layer structures. Additionally, the paper optimizes the fusion method of coupling graph convolution in multi-level matrix feature flow information aggregation, leading to the efficient extraction of spatiotemporal information. To validate the effectiveness of the proposed model, extensive experiments were conducted on two real traffic datasets obtained from New York, namely NYCBike and NYCTaxi. The results demonstrate the superior performance of the CGGCN model when compared to other eight baseline models. [ABSTRACT FROM AUTHOR]

With the continuous advancement of sensing technology, applying large amounts of sensor data to practical prediction processes using artificial intelligence methods has become a developmental direction. In sensing images and remote sensing meteorological data, the dynamic changes in the prediction targets relative to their background information often exhibit more significant dynamic characteristics. Previous prediction methods did not specifically analyze and study the dynamic change information of prediction targets at spatiotemporal multi-scale. Therefore, this paper proposes a neural prediction network based on perceptual multi-scale spatiotemporal dynamic changes (PMSTD-Net). By designing Multi-Scale Space Motion Change Attention Unit (MCAU) to perceive the local situation and spatial displacement dynamic features of prediction targets at different scales, attention is ensured on capturing the dynamic information in their spatial dimensions adequately. On this basis, this paper proposes Multi-Scale Spatiotemporal Evolution Attention (MSEA) unit, which further integrates the spatial change features perceived by MCAU units in higher channel dimensions, and learns the spatiotemporal evolution characteristics at different scales, effectively predicting the dynamic characteristics and regularities of targets in sensor information.Through experiments on spatiotemporal prediction standard datasets such as Moving MNIST, video prediction dataset KTH, and Human3.6m, PMSTD-Net demonstrates prediction performance surpassing previous methods. We construct the GPM satellite remote sensing precipitation dataset, demonstrating the network's advantages in perceiving multi-scale spatiotemporal dynamic changes in remote sensing meteorological data. Finally, through extensive ablation experiments, the performance of each module in PMSTD-Net is thoroughly validated. [ABSTRACT FROM AUTHOR]

With the high yield of many wells represented by Well JT1 in the Maokou Formation, has catalyzed a surge in exploration activities along the platform margin facies of the Maokou Formation in central Sichuan and further showed the significant exploration potential of the Maokou Formation in the northern slope. However, the fracture cave body of the Maokou Formation exhibits a high degree of development, strong longitudinal and horizontal heterogeneity, large formation pressure differences, and drilling events such as gas kicks and lost circulation occur frequently, which seriously affects the efficient implementation of drilling. Understanding the spatial distribution of the three-pressure in the formation can help better deal with and solve the above problems. Therefore, in order to help the safe, high-quality and rapid drilling of the Maokou Formation in the study area, and enhance the efficiency of oil and gas development, this paper explores the research on the prediction method of the three-pressure of jointing well-seismic data based on the geomechanical experimental data and the actual drilling data. In the process of prediction of pore pressure, this study found that the pore pressure and formation velocity in the study area have an exponential relationship. In order to enhance the applicability of the Filippone's method in the study area and improve the prediction accuracy of pore pressure, the linear relationship between pore pressure and formation velocity in the Filippone's method is modified to an exponential relationship, and a pore pressure prediction model suitable for the work area was established. Based on the Mohr–Coulomb criterion and Huang's model, the prediction models of collapse pressure and fracture pressure applicable to the study area were established, respectively. Then, the elastic parameters were obtained through pre-stack inversion, and the three-pressure bodies were calculated based on the elastic parameter bodies. The results indicate that: (1) The three-pressure prediction method of the jointing well-seismic data in this paper can predict the formation's longitudinal and transverse pressure anomaly zones in advance. (2) The Maokou Formation in the study area is characterized by abnormally high pressure, to balance the pressure of the high-ground formation, high-density drilling fluid is necessary. (3) The prediction results of three-pressure in this paper are highly consistent with the actual drilling engineering events, which verifies the reliability of the three-pressure prediction results presented in this study. The results of the study can provide a basis for decision-making in drilling geological design, such as the determination of drilling fluid density, the evaluation of borehole stability and other engineering problems that require support from three-pressure data. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data. [ABSTRACT FROM AUTHOR]

This paper proposes an interpretation of Aristotle's understanding of tyche (τύχη), a Greek term that can be alternatively translated as luck, fortune, or fate. The paper disentangles various threads of argument in the primary sources to argue for a realist understanding of what we moderns call "luck." In short, it contends that Aristotle's account of these issues is mostly correct and merits close attention when canvassing recent philosophical debates about luckology. Aristotle argues that science pertains to the general rule; it is not about the particular. Particular events have contingent content that exceeds the scope of science. Even if we could predict all future events with ultimate accuracy, we would still be left wondering why good or bad things happen to specific people. Although luck is not a scientific category, it has an existential reality that leaves momentous events open to metaphysical and even religious interpretation. [ABSTRACT FROM AUTHOR]

This study investigates dynamic interactive patterns of the dry bulk shipping markets through the lens of structural vector autoregression (VAR) models. There are two distinct hypotheses in the extant literature. One is that asymmetry between spot and forward freight agreement (FFA) markets causes FFA prices to respond more rapidly to new information than spot prices, termed the Physical-Paper Hypothesis in this paper. The other hypothesis is that there are some diverse shocks with different propagation mechanisms, termed the Shock-Diversity Hypothesis. This paper combines these two seemingly separate hypotheses at one time in two illustrative structural VAR models, based on a dataset of weekly spot, FFA, and time charter (TC) rates in the dry bulk shipping markets. The two structural VAR models show that spot, FFA, and TC rates have different, particularly dynamic responses to identified permanent shocks. Furthermore, they show that the reduced-form tri-variate FFA-Spot-TC VAR model produces more accurate forecasts of future spot rates than the bi-variate FFA-Spot and TC-Spot VAR models. Therefore, market participants can use VAR models as an information-processing tool for understanding of (e.g. data descriptions and structural inferences), and forecasting in, dry bulk shipping markets. [ABSTRACT FROM AUTHOR]

The issue of pensions has become increasingly topical. This paper presents the ordered weighted averaging real average pension (OWARAP) operator. The OWARAP operator is based on the ordered weighted averaging (OWA) operator and calculates the future average retirement benefit taking into account price changes. Moreover, this work extends the OWARAP operator by using order-inducing variables, generalized means, and probabilities. This paper ends by analyzing the applicability of the OWARAP operator and its extensions in forecasting the real average Social Security benefits for retired workers in each state of the United States (U.S.). The results demonstrate the usefulness of the proposed approach in retirement decision making. [ABSTRACT FROM AUTHOR]

The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO's COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model's ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model's capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance. [ABSTRACT FROM AUTHOR]

Nowadays, accurate and efficient short-term traffic flow forecasting plays a critical role in intelligent transportation systems (ITS). However, due to the fact that traffic flow is susceptible to factors such as weather and road conditions, traffic flow data tend to exhibit dynamic uncertainty and nonlinearity, making the construction of a robust and reliable forecasting model still a challenging task. Aiming at this nonlinear and complex traffic flow forecasting problem, this paper constructs a short-term traffic flow forecasting hybrid optimization model, SSA-ELM, based on extreme learning machine by embedding the sparrow search algorithm in order to solve the above problem. Extreme learning machine has been widely used in short-term traffic flow forecasting due to its characteristics such as low computational complexity and fast learning speed. By using the sparrow search algorithm to optimize the input weight values and hidden layer deviations in the extreme learning machine, the sparrow search algorithm is utilized to search for the global optimal solution while taking into account the original characteristics of the extreme learning machine, so that the model improves stability while increasing prediction accuracy. Experimental results on the Amsterdam A10 road traffic flow dataset show that the traffic flow forecasting model proposed in this paper has higher forecasting accuracy and stability, revealing the potential of hybrid optimization models in the field of short-term traffic flow forecasting. [ABSTRACT FROM AUTHOR]

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

The quantitative prediction and control of assembly stress are crucial for the accuracy of precision optical instruments during their manufacturing process. As a typical optical component, the multipoint bonded flat lens is widely used in precision optical instruments, in which the main assembly stress (i.e., radial adhesive stress) and its induced nonuniform stress field in the lens vary with environmental parameters and time. These factors ultimately determine the accuracy and stability of such precision instruments. Effective prediction of the radial adhesive stress is important for bonding parameter optimization and accuracy enhancement but remains difficult to obtain due to the lack of direct measurement devices and effective prediction methods. Here, a novel prediction method is presented to estimate the radial adhesive stress applied on a multipoint bonded flat lenses. First, a quantitative characterization method (QCM) is developed to concisely describe the nonuniform stress field in a lens using quantitative characterization parameters (QCPs). Then, based on the QCM and the theoretical model established in our previous work (Xiong et al., 2022), a prediction method is developed for the radial adhesive stress applied on a lens by analyzing the stress field in the lens. In this method, a backpropagation neural network is constructed to obtain the mapping relationship between the key QCPs of the stress field and the radial adhesive stress applied on the lens. Finally, an experimental verification of the prediction method is carried out. This paper proposes a novel method to predict the radial adhesive stress for multipoint bonded optical lenses, which can be adopted to monitor the variation characteristics of assembly stress in optical instruments during the entire life cycle and then further employed to optimize the bonding parameters during the lens manufacturing process. This paper attempts to construct a direct mapping relationship between assembly quality and assembly parameters for precision instruments, supporting the quantitative optimization of assembly parameters and quantitative control and prediction of assembly quality during the manufacturing and working process. [Display omitted] • A quantitative characterization method to describe the stress field in a lens is proposed. • Quantitative features of the stress field in multipoint bonded lenses are revealed. • The radial adhesive stress is predicted by analyzing the stress field in the lens. • A mapping relationship between assembly quality and assembly parameter is constructed. [ABSTRACT FROM AUTHOR]

Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research. [ABSTRACT FROM AUTHOR]

The peer review process is the main academic resource to ensure that science advances and is disseminated. To contribute to this important process, classification models were created to perform two tasks: the review score prediction (RSP) and the paper decision prediction (PDP). But what challenges prevent us from having a fully efficient system responsible for these tasks? And how far are we from having an automated system to take care of these two tasks? To answer these questions, in this work, we evaluated the general performance of existing state-of-the-art models for RSP and PDP tasks and investigated what types of instances these models tend to have difficulty classifying and how impactful they are. We found, for example, that the performance of a model to predict the final decision of a paper is 23.31% lower when it is exposed to difficult instances and that the classifiers make mistake with a very high confidence. These and other results lead us to conclude that there are groups of instances that can negatively impact the model's performance. That way, the current state-of-the-art models have potential to helping editors to decide whether to approve or reject a paper; however, we are still far from having a system that is fully responsible for scoring a paper and decide if it will be accepted or rejected. [ABSTRACT FROM AUTHOR]

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]

The issues of the evaluation and prediction of the reliability and testability of mining machinery and equipment are becoming particularly relevant, since the safety of technological processes and human life is reaching a new level of realisation due to changes in mining technology. The work is devoted to the development of a logical model for analysing the controllability of mining equipment. The paper presents a model of reliability of the operation of mining equipment on the example of a mine load and passenger hoist. This generalised model is made in the form of a graph of transitions and supplemented with a system of equations. The model allows for the estimation of the reliability of equipment elements and equipment as a whole. A mathematical and logical model for the calculation of the availability and downtime coefficients of various designs of mining equipment systems is proposed. This model became the basis for the methods to calculate the optimal values of diagnostic depth. At these calculated values, the maximum value of availability factor will be obtained. In this paper, an analytical study was carried out and dependences of the readiness factor of parameters of the investigated system such as the intensity of control of technical systems, intensity of failures, etc., were constructed. The paper proposes a mathematical model to assess the reliability of mine hoisting plants through its integration into the method of improving the reliability of mine hoisting plants. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of photovoltaic (PV) power generation has led to an increased focus on accurate forecasting of PV power output. Interval forecasting, which provides uncertainty measurement information for forecasting results, has become a hot research topic in this field. However, the accuracy of single models or traditional multi-model forecasting methods is often insufficient for meeting the forecasting requirements. In addition, interval forecasting places higher demands on the learning and generalization capabilities of forecasting models. To address these issues, this paper proposes an intelligent multi-model forecasting method based on output features clustering and convolutional neural network–long short term memory (CNN–LSTM) for PV power interval forecasting. First, multiple feature indices are constructed to represent the differences in PV output features for different months. The intelligent clustering method is then employed to achieve the multi-model clustering for forecasting model. Finally, CNN–LSTM is utilized to implement the PV power interval forecasting. The combination of CNN and LSTM effectively improves the modeling accuracy of the intelligent forecasting model. Based on actual data from PV power stations, the method described in this paper narrows down the forecasting interval compared with the single model, reducing prediction interval normalized average width by more than 4%. [ABSTRACT FROM AUTHOR]

An ensemble n-sub-epidemic modeling framework that integrates sub-epidemics to capture complex temporal dynamics has demonstrated powerful forecasting capability in previous works. This modeling framework can characterize complex epidemic patterns, including plateaus, epidemic resurgences, and epidemic waves characterized by multiple peaks of different sizes. In this tutorial paper, we introduce and illustrate SubEpiPredict, a user-friendly MATLAB toolbox for fitting and forecasting time series data using an ensemble n-sub-epidemic modeling framework. The toolbox can be used for model fitting, forecasting, and evaluation of model performance of the calibration and forecasting periods using metrics such as the weighted interval score (WIS). We also provide a detailed description of these methods including the concept of the n-sub-epidemic model, constructing ensemble forecasts from the top-ranking models, etc. For the illustration of the toolbox, we utilize publicly available daily COVID-19 death data at the national level for the United States. The MATLAB toolbox introduced in this paper can be very useful for a wider group of audiences, including policymakers, and can be easily utilized by those without extensive coding and modeling backgrounds. [ABSTRACT FROM AUTHOR]

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the "weekly Naïve" benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management. [ABSTRACT FROM AUTHOR]

This paper addresses a notable gap in the field of photovoltaic system forecasting by introducing the Machine Learning-based PV Prediction and Fault Analysis System (ML-PVPFAS). This framework is designed to optimize the decomposition of variational systems automatically, using a multi-objective intelligent optimization method to establish its weight. The paper evaluates a range of Machine Learning (ML) and Ensemble Learning (EL) techniques for diagnosing faults in PV arrays, with a primary focus on identifying and categorizing complex faults that could affect these arrays. These faults encompass multiple anomalies and defects with similar current-voltage curves, which have not been previously examined. The analysis of prediction scores reveals that the ML-PVPFAS approach outperforms other methods, with the lowest Mean Absolute Percentage Error (MAPE) at 4.93, Root Mean Squared Error (RMSE) at 4.64, high Accuracy at 90.31%, Precision at 93.60%, a strong Pearson Correlation Coefficient of 0.90, and a fast Computation Time of 77.64 milliseconds. The results suggest that ML-PVPFAS is a dependable and practical algorithm for predicting the power output of PV solar systems, making it a valuable contribution to the field of predictive modeling. [ABSTRACT FROM AUTHOR]

Most of the deep-learning algorithms on stock price volatility prediction in the existing literature use data such as same-frequency market indicators or technical indicators, and less consider mixed-frequency data, such as macro-data. Compared with the traditional model that only inputs the same-frequency data such as technical indicators and market indicators, this study proposes an improved deep-learning model based on mixed-frequency big data. This paper first introduces the reserve restricted mixed-frequency data sampling (RR-MIDAS) model to deal with the mixed-frequency data and, secondly, extracts the temporal and spatial features of volatility series by using the parallel model of CNN-LSTM and LSTM, and finally utilizes the Optuna framework for hyper-parameter optimization to achieve volatility prediction. For the deep-learning model with mixed-frequency data, its RMSE, MAE, MSLE, MAPE, SMAPE, and QLIKE are reduced by 18.25%, 14.91%, 30.00%, 12.85%, 13.74%, and 23.42%, respectively. This paper provides a more accurate and robust method for forecasting the realized volatility of stock prices under mixed-frequency data. [ABSTRACT FROM AUTHOR]

The layout and configuration of urban infrastructure are essential for the orderly operation and healthy development of cities. With the promotion and popularization of new energy vehicles, the modeling and prediction of charging pile usage and allocation have garnered significant attention from governments and enterprises. Short-term demand forecasting for charging piles is crucial for their efficient operation. However, existing prediction models lack a discussion on the appropriate time window, resulting in limitations in station-level predictions. Recognizing the temporal nature of charging pile occupancy, this paper proposes a novel stacked-LSTM model called attention-SLSTM that integrates an attention mechanism to predict the charging demand of electric vehicles at the station level over the next few hours. To evaluate its performance, this paper compares it with several methods. The experimental results demonstrate that the attention-SLSTM model outperforms both LSTM and stacked-LSTM models. Deep learning methods generally outperform traditional time series forecasting methods. In the test set, MAE is 1.6860, RMSE is 2.5040, and MAPE is 9.7680%. Compared to the stacked-LSTM model, MAE and RMSE are reduced by 4.7%and 5%, respectively; while MAPE value decreases by 1.3%, making it superior to LSTM overall. Furthermore, subsequent experiments compare prediction performance among different charging stations, which confirms that the attention-SLSTM model exhibits excellent predictive capabilities within a six-step (2 h) window. [ABSTRACT FROM AUTHOR]

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance. [ABSTRACT FROM AUTHOR]

Let G = (V,E) be a simple, connected and un-directed graph. We introduce a new notion of rainbow vertex antimagic coloring. This is a natural expansion of rainbow vertex coloring combined with antimagic labeling. For f: E(G) → {1, 2, . . ., |E(G)|}, the weight of a vertex v ∈ V (G) against f is wf (v) = Σe∈E(v)f(e), where E(v) is the set of vertices incident to v. The function f is called vertex antimagic edge labeling if every vertex has distinct weight. A path is considered to be a rainbow path if for each vertex u and v, all internal vertices on the u - v path have different weights. The rainbow vertex antimagic connection number of G, denoted by rvac(G), is the smallest number of colors taken over all rainbow colorings induced by rainbow vertex antimagic labelings of G. In this paper we aim to discover some new lemmas or theorems regarding to rvac(G). Furthermore, to see the robust application of rainbow vertex antimagic coloring, at the end of this paper we will illustrate the implementation of RVAC on spatial temporal graph neural networks (STGNN) multistep time series forecasting on subsidized diesel consumption of some petrol stations. [ABSTRACT FROM AUTHOR]

In this paper, estimation and prediction inference of power Muth distribution, with the progressive censoring data, are described. The maximum likelihood and Bayesian approaches of the unknown parameters are considered. Several Bayesian estimators are obtained against different symmetric and asymmetric loss functions, such as squared error, linex and general entropy. Also, the asymptotic confidence intervals and highest posterior density credible intervals of them are derived. Most focus of this paper is Bayesian prediction of the removed units in multiple stages of the progressively censored sample, so that, the Gibbs and Metropolis samplers are used, to reach this end. To compare the performance of different methods, Mont Carlo simulation is employed. Moreover, one practical data set is analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

In this paper, the stability of the prediction variance of orthogonal composite designs (orthogonal array composite, orthogonal array composite minimax loss, orthogonal uniform composite and orthogonal uniform composite minimax loss designs) was evaluated and compared for 3 ≤ k ≤ 6 using the fraction of design space plot in the hypersphere and hypercube regions. These designs were shown to have stable and consistent scaled prediction variance (SPV) throughout the design region. The orthogonal array composite minimax loss designs performed better with the least SPV than the other orthogonal composite designs for most factors in the hypersphere region. Also, the orthogonal array composite designs performed better in the hypercube region compared to other competing designs. [ABSTRACT FROM AUTHOR]

Recently, numerous studies have demonstrated that the physics-informed neural network (PINN) can effectively and accurately resolve hyperelastic finite deformation problems. In this paper, a PINN framework for tackling hyperelastic-magnetic coupling problems is proposed. Since the solution space consists of two-phase domains, two separate networks are constructed to independently predict the solution for each phase region. In addition, a conscious point allocation strategy is incorporated to enhance the prediction precision of the PINN in regions characterized by sharp gradients. With the developed framework, the magnetic fields and deformation fields of magnetorheological elastomers (MREs) are solved under the control of hyperelastic-magnetic coupling equations. Illustrative examples are provided and contrasted with the reference results to validate the predictive accuracy of the proposed framework. Moreover, the advantages of the proposed framework in solving hyperelastic-magnetic coupling problems are validated, particularly in handling small data sets, as well as its ability in swiftly and precisely forecasting magnetostrictive motion. [ABSTRACT FROM AUTHOR]

Legal Charge Prediction (LCP) is a task of predicting charge labels for which a criminal should be charged based on the factual case descriptions. In recent years, the advancement of deep learning networks has led to the emergence of effective approaches for LCP through the Fine-tuning of pre-trained language models. While existing methods primarily treat it as multiple binary classifications, excelling in the single charge label given factual case descriptions, they still exhibit substantial room for improvement in multi-label LCP. This is because single-label classification methods do not effecitively deal with the relationship between multiple labels, mainly determining the likelihood of a factual description’s association to each individual charge label. Recognizing the need to enhance the relationship among multi-label charges in Fine-tuning-based discriminative charge prediction, this paper introduces a multi-label LCP method based on summarization generation. This method reconstructs the multi-label classification problem as a summarization generation task, capturing the relationship among multiple labels within the summarization content. Empirical findings from the Chinese legal domain’s CAIL2018 dataset demonstrate the method’s capacity to discern the relationship between multiple legal charge labels with Micro-F1 and Macro-F1 enhancements, compared to the fine-tuning-based approaches. [ABSTRACT FROM AUTHOR]

Partial discharges in power cables are an inherent phenomenon during their operation, often leading to failures and significant financial losses. Various prediction methods exist, but they often lack sensitivity in detecting partial discharges and fail to pinpoint the location of potential faults. Conversely, the acoustic emission method offers a more effective solution, enabling precise monitoring of partial discharge levels within equipment and accurate localization of faults in power cables. Enhancing the quality of result analysis requires adhering to specific requirements and implementing various procedures to improve diagnostic effectiveness. This paper provides an overview of fault diagnosis utilizing acoustic emission across different evaluation techniques, presenting results obtained from operating various power cables. [ABSTRACT FROM AUTHOR]

This paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant changes in vibration patterns, indicating an approaching train. [ABSTRACT FROM AUTHOR]

The degradation of bridge structural performance arises from the combined influence of various factors. Performance assessment and reliable prediction of bridge performance degradation through effective utilizing of detection information updates is a challenging problem. In this paper, the concept of performance indicators is redefined, employing to delineate bridge performance degradation. A bridge performance degradation model (the error ≤8%) is formulated, considering the multiple-variable Bayesian dynamic linear method (MBDLM) and revealing the coupling mechanisms among factors influencing bridge performance degradation. On this basis, the prediction performance of the model is quantitatively evaluated by three metrics: mean squared error, predictive mean squared error and mean absolute percentage error. A methodology is presented for the assessment, prediction, and maintenance reinforcement of in-service bridge structural performance degradation. This approach holds promise for future applications in safety assessments and the decision-making process for preventive maintenance of operational bridges. [ABSTRACT FROM AUTHOR]

The deployment of deep learning architectures on low-computational resource devices is challenging due to their high number of parameters and computational complexity. These heavy and complex architectures result in increased latency in real-time applications. However, splitting the deep architecture in a way that parallelizes the forward propagation into different subnets deploying into multiple low-computational resource devices, and then, aggregating the predictions may reduce the latency while preserving the performance. In this paper, we propose a novel deep learning architecture called Ensembled Parallel Networks (EnParaNets) that leverage network dissection, knowledge distillation, and ensemble learning strategies to reduce inference time while maintaining, even in some cases, outperforming the baseline accuracy in real-time applications. The methodology involves splitting the original network into N equal-sized blocks, forming N Sub-ParaNets for each block, and enhancing their representations using (A) contrastive knowledge distillation along with reducing Kullback–Leibler divergence between logits distributions of the teacher and student networks, and (B) L2 loss between intermediate representations of the original network and corresponding Sub-ParaNets. Predictive distributions from each Sub-ParaNet are assembled to form the final EnParaNet. The proposed EnParaNet outperforms the baseline models of seven diverse architectures: ResNet56, VGG_13, WRN_40_2, DenseNet, ResNeXt50, MobileNetv2, and ShuffleNetv2 in terms of accuracy while reducing inference time significantly using training methods A and B, respectively. Our proposed EnParaNet enhances ResNet56, VGG_13, WRN_40_2, MobileNetv2, DenseNet, ResNeXt50, and ShuffleNetv2 by 2.69%, 0.24%, 1.95%, 7.69%, 0.33%, 2.13%, and 3.12%, respectively, using training method A, where the inference time is reduced by 45%, 24%, 47%, 31%, 33%, 32%, and 44%, respectively. With training method B, EnParaNet achieves improvements of 1.75%, 2.90%, 1.09%, 3.91%, and 1.66%, with inference time reductions of 50%, 42%, 49%, 48%, and 49%, respectively. Moreover, a comprehensive ablation study analyzes the performance of the proposed technique and highlights its effectiveness and challenges. Furthermore, we also evaluate the performance of EnParaNet in transferability and adversarial robustness tasks. [ABSTRACT FROM AUTHOR]

This paper proposes an optimized Long Short-Term Memory (LSTM+) model for predicting cumulative confirmed cases of COVID-19 in Germany, the UK, Italy, and Japan. The LSTM+ model incorporates two key optimizations: (1) fine-adjustment of parameters and (2) a 're-prediction' process that utilizes the latest prediction results from the previous iteration. The performance of the LSTM+ model is evaluated and compared with that of Backpropagation (BP) and traditional LSTM models. The results demonstrate that the LSTM+ model significantly outperforms both BP and LSTM models, achieving a Mean Absolute Percentage Error (MAPE) of less than 0.6%. Additionally, two illustrative examples employing the LSTM+ model further validate its general applicability and practical performance for predicting cumulative confirmed COVID-19 cases. [ABSTRACT FROM AUTHOR]

In the past two decades, model averaging has attracted more and more attention and is regarded as a much better tool to solve model uncertainty than model selection. Compared with the conditional mean regression, the quantile regression serves as a robust alternative and shows a lot more information about the conditional distribution of a response variable. In this paper, we propose a jackknife model averaging procedure that chooses the weights by minimizing a leave-one-out cross-validation criterion function for mixed-data kernel-weighted spline quantile regressions that contain both continuous and categorical regressors when all candidate models are potentially misspecified. We demonstrate the JMA estimator is asymptotically optimal in terms of minimizing the out-of-sample final prediction error. Simulation experiments are conducted to assess the relative finite-sample performance of the proposed JMA method with respect to other model selection and averaging methods. Our JMA method is applied to the wage and house datasets. [ABSTRACT FROM AUTHOR]

Approximate computing plays a crucial role in faster operation for large-scale data computation in error-resilient applications. An approximate adder is a digital circuit that performs addition with less accuracy to achieve faster processing time and lower hardware overhead. This approach is well suited for error-tolerant applications where minor errors in the output are acceptable. In this paper, an approximate carry prediction adder (ACPA) is proposed to add the mantissa in a 32-bit single precision floating point adder, termed as approximate floating point precise carry prediction adder (AFPCPA). The proposed ACPA utilizes a carry prediction circuit to generate a precise carry for the precise part leading to an increase in accuracy. The error characteristics and hardware utilization of AFPCPA and other existing approximate adder architectures are compared. The results show that the proposed AFPCPA vide, on average, 50.56%, 59.66%, 56.13%, and 81.40% reduction in standard deviation, mean absolute error, normalized mean error distance, and mean square error, respectively. In addition, the proposed AFPCPA shows on average, 18.97% and 6.68% lesser hardware utilization and delay, respectively compared to existing approximate adder architectures and accurate adder. Finally, a 3-tap FIR Filter is designed using the proposed AFPCPA and compared with existing architectures. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a forecasting method with global interval input and output for interval-valued financial time series by combining the VAR(p) process, the volatility information and neural network, namely VAR-NN. To reflect the volatility information, four types of interval-valued data volatility information from both the relative and absolute perspectives are constructed. Furthermore, the neural network is combined to produce the parameters. The developed forecasting model is finally applied to the highest and lowest hourly prices of the Shanghai Composite Index prediction. Numerical study shows the feasibility and validity of the developed improved VAR model. [ABSTRACT FROM AUTHOR]

Variational weighted fuzzy time series models are known to perform better than traditional fuzzy time series models, due to their ability to handle outliers and noise. However, the performance of variational weighted fuzzy time series models is highly dependent on the partitioning of the universe of discourse. In this paper, we propose a novel variational weighted fuzzy model integrated with particle swarm optimization for choosing the best possible partitioning of the universe of discourse. Our approach combines the strengths of variational weighted fuzzy time series to handle uncertain and imprecise data and particle swarm optimization techniques to find the best model parameters. We evaluate the performance of the proposed approach on standard time series datasets and compare it with other forecasting methods in the literature. Our findings demonstrate that the proposed approach achieves accurate and reliable forecasts, outperforming other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]