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Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare different state-of-the-art forecasting methods in terms of forecasting performance. Differently from most existing research in logistics, we do not perform this in a case-dependent way but consider a broad set of simulated time series to give more general recommendations. We therefore simulate various linear and nonlinear time series that reflect different situations. Our simulation results showed that the machine learning methods, especially Random Forests, performed particularly well in complex scenarios, with the differentiated time series training significantly improving the robustness of the model. In addition, the time series approaches proved to be competitive in low noise scenarios. [ABSTRACT FROM AUTHOR]

The energy performance of buildings has become a main concern globally in response to increased energy demand, the environmental impacts of energy production, and the reality of energy poverty. To improve energy efficiency, proper building design should be secured at the early design phase. Digital tools are currently available for performing energy assessment analyses and can efficiently handle complex and technically demanding buildings. However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. In this paper, typical residential buildings are considered along with eleven factors that highly affect energy performance. A dataset of 337 instances of such parameters is developed. For each dataset, the building energy performance is estimated based on BIM analysis. Next, statistical and machine learning techniques are implemented to provide artificial models of energy performance. They include statistical regression modeling (SRM), decision trees (DTs), random forests (RFs), and artificial neural networks (ANNs). The analysis reveals the contribution of each factor and highlights the ANN as the best performing model. An easy-to-use interface tool has been developed for the instantaneous calculation of the energy performance based on the independent parameter values. [ABSTRACT FROM AUTHOR]

To mitigate future educational challenges, the early childhood period is critical for cognitive development, so understanding the factors influencing child learning abilities is essential. This study investigates the impact of parenting techniques, sociodemographic characteristics, and health conditions on the learning abilities of children under five years old. Our primary goal is to explore the key factors that influence children's learning abilities. For our study, we utilized the 2019 Multiple Indicator Cluster Surveys (MICS) dataset in Bangladesh. Using statistical analysis, we identified the key factors that affect children's learning capability. To ensure proper analysis, we used extensive data preprocessing, feature manipulation, and model evaluation. Furthermore, we explored robust machine learning (ML) models to analyze and predict the learning challenges faced by children. These include logistic regression (LRC), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), and bagging classification models. Out of these, GB and XGB, with 10-fold cross-validation, achieved an impressive accuracy of 95%, F1-score of 95%, and receiver operating characteristic area under the curve (ROC AUC) of 95%. Additionally, to interpret the model outputs and explore influencing factors, we used explainable AI (XAI) techniques like SHAP and LIME. Both statistical analysis and XAI interpretation revealed key factors that influence children's learning difficulties. These include harsh disciplinary practices, low socioeconomic status, limited maternal education, and health-related issues. These findings offer valuable insights to guide policy measures to improve educational outcomes and promote holistic child development in Bangladesh and similar contexts. [ABSTRACT FROM AUTHOR]

The document "Special Issue 'Algorithms for Feature Selection (2nd Edition)" focuses on research advancements in algorithms, particularly in feature selection techniques. It covers topics such as evolutionary search techniques, ensemble methods, high-dimensional data, time-series data, textual data, and deep feature selection. The document highlights various research papers that address feature selection in fields like bioinformatics, scheduling, and time-series analysis, showcasing the importance of feature selection in different domains. The accepted papers present innovative methods and models for feature extraction, classification, and optimization, demonstrating the diverse applications of algorithms in solving complex data challenges. [Extracted from the article]

The kernel method is a tool that converts data to a kernel space where operation can be performed. When converted to a high-dimensional feature space by using kernel functions, the data samples are more likely to be linearly separable. Traditional machine learning methods can be extended to the kernel space, such as the radial basis function (RBF) network. As a kernel-based method, support vector machine (SVM) is one of the most popular nonparametric classification methods, and is optimal in terms of computational learning theory. Based on statistical learning theory and the maximum margin principle, SVM attempts to determine an optimal hyperplane by addressing a quadratic programming (QP) problem. Using Vapnik–Chervonenkis dimension theory, SVM maximizes generalization performance by finding the widest classification margin within the feature space. In this paper, kernel machines and SVMs are systematically introduced. We first describe how to turn classical methods into kernel machines, and then give a literature review of existing kernel machines. We then introduce the SVM model, its principles, and various SVM training methods for classification, clustering, and regression. Related topics, including optimizing model architecture, are also discussed. We conclude by outlining future directions for kernel machines and SVMs. This article functions both as a state-of-the-art survey and a tutorial. [ABSTRACT FROM AUTHOR]

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test–retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials. We show that the diagnostic accuracy for distinguishing PD from controls decreases by a mean of 1.8% between the first and the second trial, suggesting that task repetition may not be necessary for accurate diagnosis. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), greater variability is seen in subtask duration and sensor-derived measures, reflected in machine learning performance and statistical differences. Our findings also show that this variability differs not only between controls and PD participants but also among groups with varying levels of PD severity, indicating the need to consider population characteristics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks may fail to reveal nuanced variations in movement. [ABSTRACT FROM AUTHOR]

Spatio-temporal characterization analysis plays a key role in spatio-temporal data mining tasks such as social relationship inference, traffic flow prediction, and spatio-temporal graph node classification. Although traditional numerical simulation methods are effective, they often struggle to accurately portray the complex characteristics of spatio-temporal data due to the intricacy of the modeling processes and the limitations of underlying assumptions. Models based on statistical learning and machine learning, such as ARIMA (AutoRegressive Integrated Moving Average Model) and SVM (Support Vector Machine), are capable of handling spatio-temporal data to a certain extent, but they are limited in their ability to characterize highly nonlinear data and can fail to effectively capture spatio-temporal correlations. To address these challenges, this paper introduces a new deep learning model, the Attention-Guided Spatio-Temporal Convolutional Network (AG-STCN). In the spatial dimension, the model captures spatial dependencies through an attention-guided soft pruning strategy and graph convolution operations. In the temporal dimension, it employs causal convolutions, gated linear units, and a self-attention mechanism to capture temporal dependencies. Experimental results demonstrate that the AG-STCN significantly outperforms existing baseline methods on real-world datasets for multiple spatio-temporal characterization analysis tasks. [ABSTRACT FROM AUTHOR]

This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations where it is impossible to increase the training datasets for various reasons. We introduce connected mixture components as a source of additional information that can be extracted from a mathematical model. These components are formed using probability mixture models and a special algorithm for merging parameters in the sliding window mode. This approach has been proven effective when applied to real-world time series data for short- and medium-term forecasting. In all cases, the models informed by the connected mixture components showed better results than those that did not use them, although different informed models may be effective for various datasets. The fundamental novelty of the research lies both in a new mathematical approach to informing ML models and in the demonstrated increase in forecasting accuracy in various applications. For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to 27.7 % , and the reduction in Mean Absolute Percentage Error (MAPE) was up to 45.7 % compared with ML models without probability informing. The best metrics values were obtained by an informed ensemble architecture that fuses the results of a Long Short-Term Memory (LSTM) network and a transformer. The Mean Squared Error (MSE) for the electricity transformer oil temperature from the ETDataset had improved by up to 10.0 % compared with vanilla methods. The best MSE value was obtained by informed random forest. The introduced probability-informed approach allows us to outperform the results of both transformer NN architectures and classical statistical and machine learning methods. [ABSTRACT FROM AUTHOR]

Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies. [ABSTRACT FROM AUTHOR]

This study proposes a novel framework for determining variables' weights in transportation assets condition indices calculations using statistical and machine learning techniques. The methodology leverages subjective ratings alongside objective measurements to derive data-driven weights. The motivation for this study lies in addressing the limitations of existing expert-based weighting methods for condition indices, which often lack transparency and consistency; this research aims to provide a data-driven framework that enhances accuracy and reliability in infrastructure asset management. A case study was performed as a proof of concept of the proposed framework by applying the framework to obtain data-driven weights for pavement condition index (PCI) calculations using data for the city of West Des Moines, Iowa. Random forest models performed effectively in modeling the relationship between the overall condition index (OCI) and the objective measures and provided feature importance scores that were converted into weights. The data-driven weights showed strong correlation with existing expert-based weights, validating their accuracy while capturing contextual variations between pavement types. The results indicate that the proposed framework achieved high model accuracy, demonstrated by R-squared values of 0.83 and 0.91 for rigid and composite pavements, respectively. Additionally, the data-driven weights showed strong correlations (R-squared values of 0.85 and 0.98) with existing expert-based weights, validating their effectiveness. This advanceIRIment offers transportation agencies an enhanced tool for prioritizing maintenance and resource allocation, ultimately leading to improved infrastructure longevity. Additionally, this approach shows promise for application across various transportation assets based on the yielded results. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into medical disciplines is rapidly transforming healthcare delivery, with audiology being no exception. By synthesizing the existing literature, this review seeks to inform clinicians, researchers, and policymakers about the potential and challenges of integrating AI into audiological practice. The PubMed, Cochrane, and Google Scholar databases were searched for articles published in English from 1990 to 2024 with the following query: "(audiology) AND ("artificial intelligence" OR "machine learning" OR "deep learning")". The PRISMA extension for scoping reviews (PRISMA-ScR) was followed. The database research yielded 1359 results, and the selection process led to the inclusion of 104 manuscripts. The integration of AI in audiology has evolved significantly over the succeeding decades, with 87.5% of manuscripts published in the last 4 years. Most types of AI were consistently used for specific purposes, such as logistic regression and other statistical machine learning tools (e.g., support vector machine, multilayer perceptron, random forest, deep belief network, decision tree, k-nearest neighbor, or LASSO) for automated audiometry and clinical predictions; convolutional neural networks for radiological image analysis; and large language models for automatic generation of diagnostic reports. Despite the advances in AI technologies, different ethical and professional challenges are still present, underscoring the need for larger, more diverse data collection and bioethics studies in the field of audiology. [ABSTRACT FROM AUTHOR]

This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification. [ABSTRACT FROM AUTHOR]

Natural products (NPs) are crucial for the development of novel antibiotics, anticancer agents, and immunosuppressants. To highlight the ability of fungi to produce structurally diverse NPs, this article focuses on the impact of genome mining and CRISPR-Cas9 technology in uncovering and manipulating the biosynthetic gene clusters (BGCs) responsible for NP synthesis. The CRISPR-Cas9 system, originally identified as a bacterial adaptive immune mechanism, has been adapted for precise genome editing in fungi, enabling targeted modifications, such as gene deletions, insertions, and transcription modulation, without altering the genomic sequence. This review elaborates on various CRISPR-Cas9 systems used in fungi, notably the Streptococcus pyogenes type II Cas9 system, and explores advancements in different Cas proteins for fungal genome editing. This review discusses the methodologies employed in CRISPR-Cas9 genome editing of fungi, including guide RNA design, delivery methods, and verification of edited strains. The application of CRISPR-Cas9 has led to enhanced production of secondary metabolites in filamentous fungi, showcasing the potential of this system in biotechnology, medical mycology, and plant pathology. Moreover, this article emphasizes the integration of multi-omics data (genomics, transcriptomics, proteomics, and metabolomics) to validate CRISPR-Cas9 editing effects in fungi. This comprehensive approach aids in understanding molecular changes, identifying off-target effects, and optimizing the editing protocols. Statistical and machine learning techniques are also crucial for analyzing multi-omics data, enabling the development of predictive models and identification of key molecular pathways affected by CRISPR-Cas9 editing. In conclusion, CRISPR-Cas9 technology is a powerful tool for exploring fungal NPs with the potential to accelerate the discovery of novel bioactive compounds. The integration of CRISPR-Cas9 with multi-omics approaches significantly enhances our ability to understand and manipulate fungal genomes for the production of valuable secondary metabolites and for promising new applications in medicine and industry. [ABSTRACT FROM AUTHOR]

Classroom EEG recordings classification has the capacity to significantly enhance comprehension and learning by revealing complex neural patterns linked to various cognitive processes. Electroencephalography (EEG) in academic settings allows researchers to study brain activity while students are in class, revealing learning preferences. The purpose of this study was to develop a machine learning framework to automatically classify different learning-style EEG patterns in real classroom environments. Method: In this study, a set of EEG features was investigated, including statistical features, fractal dimension, higher-order spectra, entropy, and a combination of all sets. Three different machine learning classifiers, random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), were used to evaluate the performance. The proposed framework was evaluated on the real classroom EEG dataset, involving EEG recordings featuring different teaching blocks: reading, discussion, lecture, and video. Results: The findings revealed that statistical features are the most sensitive feature metric in distinguishing learning patterns from EEG. The statistical features and RF classifier method tested in this study achieved an overall best average accuracy of 78.45% when estimated by fivefold cross-validation. Conclusions: Our results suggest that EEG time domain statistics have a substantial role and are more reliable for internal state classification. This study might be used to highlight the importance of using EEG signals in the education context, opening the path for educational automation research and development. [ABSTRACT FROM AUTHOR]

The book Quantum Machine Learning: What Quantum Computing Means to Data Mining, by Peter Wittek, made quantum machine learning popular to a wider audience. The promise of quantum machine learning for big data is that it will lead to new applications due to the exponential speed-up and the possibility of compressed data representation. However, can we really apply quantum machine learning for real-world applications? What are the advantages of quantum machine learning algorithms in addition to some proposed artificial problems? Is the promised exponential or quadratic speed-up realistic, assuming that real quantum computers exist? Quantum machine learning is based on statistical machine learning. We cannot port the classical algorithms directly into quantum algorithms due to quantum physical constraints, like the input–output problem or the normalized representation of vectors. Theoretical speed-ups of quantum machine learning are usually analyzed in the literature by ignoring the input destruction problem, which is the main bottleneck for data encoding. The dilemma results from the following question: should we ignore or marginalize those constraints or not? [ABSTRACT FROM AUTHOR]

Increasing wastewater treatment efficiency is a primary aim in the circular economy. Wastewater physicochemical and biochemical processes are quite complex, often requiring a combination of statistical and machine learning tools to empirically model them. Since wastewater treatment plants are large-scale operations, the limited opportunities for extensive experimentation may be offset by miniaturizing experimental schemes through the use of fractional factorial designs (FFDs). A recycling quality improvement study that relies on non-linear multi-objective multi-parameter FFD (NMMFFD) datasets was reanalyzed. A published NMMFFD ultrafiltration screening/optimization case study was re-examined regarding how four controlling factors affected three paper mill recycling characteristic responses using a combination of statistical and machine learning methods. Comparative machine learning screening predictions were provided by (1) quadratic support vector regression and (2) optimizable support vector regression, in contrast to quadratic linear regression. NMMFFD optimization was performed by employing Pareto fronts. Pseudo-screening was applied by decomposing the replicated NMMFFD dataset to single replicates and then testing their replicate repeatability by introducing belief functions that sought to maximize credibility and plausibility estimates. Various versions of belief functions were considered, since the novel role of the three process characteristics, as independent sources, created a high level of conflict during the information fusion phase, due to the inherent divergent belief structures. Correlations between two characteristics, but with opposite goals, may also have contributed to the source conflict. The active effects for the NMMFFD dataset were found to be the transmembrane pressure and the molecular weight cut-off. The modified adjustment was pinpointed to the molecular weight cut-off at 50 kDa, while the optimal transmembrane pressure setting persisted at 2.0 bar. This mixed-methods approach may provide additional confidence in determining improved recycling process adjustments. It would be interesting to implement this approach in polyfactorial wastewater screenings with a greater number of process characteristics. [ABSTRACT FROM AUTHOR]

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90 % for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent. [ABSTRACT FROM AUTHOR]

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach. [ABSTRACT FROM AUTHOR]

Soil consolidation, particularly in fine-grained soils like clay, is crucial in predicting settlement and ensuring the stability of structures. Additionally, the compressibility of fine-grained soils is of critical importance not only in civil engineering but also in various other fields of study. The compression index (Cc), derived from soil properties such as the liquid limit (LL), plastic limit (PL), plasticity index (PI), water content (w), initial void ratio (e0), and specific gravity (Gs), plays a vital role in understanding soil behavior. This study employs machine learning algorithms—the random forest regressor (RFR), gradient boosting regressor (GBR), and AdaBoost regressor (ABR)—to predict the Cc values based on a dataset comprising 915 samples. The dataset includes LL, PL, W, PI, Gs, and e0 as the inputs, with Cc as the output parameter. The algorithms are trained and evaluated using metrics such as the coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Hyperparameter optimization is performed to enhance the model performance. The best-performing model, the GBR model, achieves a training R2 of 0.925 and a testing R2 of 0.930 with the input combination [w, PL, LL, PI, e0, Gs]. The RFR model follows closely, with a training R2 of 0.970 and a testing R2 of 0.926 using the same input combination. The ABR model records a training R2 of 0.847 and a testing R2 of 0.921 under similar conditions. These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the Cc value for fine-grained soils based on multiple soil parameters. This study involves leveraging the efficiency and effectiveness of these algorithms in geotechnical engineering applications, offering a promising alternative to traditional oedometer testing methods. Accurately predicting the compression index can significantly aid in the assessment of soil settlement and the design of stable foundations, thereby reducing the time and costs associated with laboratory testing. [ABSTRACT FROM AUTHOR]

Statistical education at university level faces significant challenges, particularly with the rapid advancements in technology and evolving teaching methods. Updating teaching methodologies for statistics in higher education is essential. Information technologies can greatly enhance the learning of statistical content, but many existing platforms are not well-suited for university contexts, either due to language barriers or how the content is presented (often confusing, with limited explanations or lacking context). It is crucial to have a student-centered platform that addresses these issues, ensuring an effective and efficient learning experience. This study introduces "EstApp", an innovative mobile application prototype designed for teaching and learning descriptive statistics in university courses. The application was developed using the Design Thinking methodology, which emphasizes user experience and focuses on the needs of the end users. The design process involved stages of empathizing, defining, ideating, prototyping, and testing, culminating in validation through user tests with university students and professors. This study concludes that EstApp's functionalities enhance understanding of (1) statistical models through interactive graphs and data visualizers; (2) probability concepts via a probability calculator; and (3) descriptive statistics through real-time data generation and visualizers. [ABSTRACT FROM AUTHOR]

Selection history is widely accepted as a vital source in attention control. Reward history indicates that a learned association captures attention even when the reward is no longer presented, while statistical learning indicates that a learned probability exerts its influence on attentional control (facilitation or inhibition). Existing research has shown that the effects of the reward history and statistical learning are additive, suggesting that these two components influence attention priority through different pathways. In the current study, leveraging the temporal resolution advantages of EEG, we explored whether these two components represent independent sources of attentional bias. The results revealed faster responses to the target at the high-probability location compared to low-probability locations. Both the target and distractor at high-probability locations elicited larger early Pd (50–150 ms) and Pd (150–250 ms) components. The reward distractor slowed the target search and elicited a larger N2pc (180–350 ms). Further, no interaction between statistical learning and the reward history was observed in RTs or N2pc. The different types of temporal progression in attention control indicate that statistical learning and the reward history independently modulate the attention priority map. [ABSTRACT FROM AUTHOR]

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants. We validate this new approach with data collected from the March 2023 Silicon Valley Bank Failure. The models produced using this novel method successfully identified the instability trend using only 14 input variables out of a possible 3160. The use of parsimonious linear models developed by this method has the potential to identify key financial stability indicators while also increasing the transparency of this complex system. [ABSTRACT FROM AUTHOR]

Building Performance Simulation extensively uses statistical learning techniques for quicker insights and improved accessibility. These techniques help understand the relationship between input variables and the desired outputs, and they can predict unknown observations. Prediction becomes more informative with uncertainty quantification, which involves computing prediction intervals. Conformal prediction has emerged over the past 25 years as a flexible and rigorous method for estimating uncertainty. This approach can be applied to any pre-trained model, creating statistically rigorous uncertainty sets or intervals for model predictions. This study uses data from simulated buildings to demonstrate the powerful applications of conformal prediction in Building Performance Simulation (BPS) and, consequently, to the broader energy sector. Results show that conformal prediction can be applied when any assumptions about input and output variables are made, enhancing understanding and facilitating informed decision-making in energy system design and operation. [ABSTRACT FROM AUTHOR]

Patent keyword analysis is used to analyze the technology keywords extracted from collected patent documents for specific technological fields. Thus, various methods related to this type of analysis have been researched in the industrial engineering fields, such as technology management and new product development. To analyze the patent document data, we have to search for patents related to the target technology and preprocess them to construct the patent–keyword matrix for statistical and machine learning algorithms. In general, a patent–keyword matrix has an extreme zero-inflated problem. This is because each keyword occupies one column even if it is included in only one document among all patent documents. General zero-inflated models have a limit at which the performance of the model deteriorates when the proportion of zeros becomes extremely large. To solve this problem, we applied a Bayesian inference to a general zero-inflated model. In this paper, we propose a patent keyword analysis using a Bayesian zero-inflated model to overcome the extreme zero-inflated problem in the patent–keyword matrix. In our experiments, we collected practical patents related to digital therapeutics technology and used the patent–keyword matrix preprocessed from them. We compared the performance of our proposed method with other comparative methods. Finally, we showed the validity and improved performance of our patent keyword analysis. We expect that our research can contribute to solving the extreme zero-inflated problem that occurs not only in patent keyword analysis, but also in various text big data analyses. [ABSTRACT FROM AUTHOR]

High-performance computing (HPC) in data centers increases energy use and operational costs. Therefore, it is necessary to efficiently manage resources for the sustainability of and reduction in the carbon footprint. This research analyzes and optimizes ENEA HPC data centers, particularly the CRESCO6 cluster. The study starts by gathering and cleaning extensive datasets consisting of job schedules, environmental conditions, cooling systems, and sensors. Descriptive statistics accompanied with visualizations provide deep insight into collated data. Inferential statistics are then used to investigate relationships between various operational variables. Finally, machine learning models predict the average hot-aisle temperature based on cooling parameters, which can be used to determine optimal cooling settings. Furthermore, idle periods for computing nodes are analyzed to estimate wasted energy, as well as for evaluating the effect that idle node shutdown will have on the thermal characteristics of the data center under consideration. It closes with a discussion on how statistical and machine learning techniques can improve operations in a data center by focusing on important variables that determine consumption patterns. [ABSTRACT FROM AUTHOR]

The aim of this research is to shed light on the complex interactions between player workload, traits, match-related factors, football performance, and injuries in the English Premier League. Using a range of statistical and machine learning techniques, this study analyzed a comprehensive dataset that included variables such as player workload, personal traits, and match statistics. The dataset comprises information on 532 players across 20 football clubs for the 2020–2021 English Premier League season. Key findings suggest that data, age, average minutes played per game, and club affiliations are significant indicators of both performance and injury incidence. The most effective model for predicting performance was Ridge Regression, whereas Extreme Gradient Boosting (XGBoost) was superior for predicting injuries. These insights are invaluable for data-driven decision-making in sports science and football teams, aiding in injury prevention and performance enhancement. The study's methodology and results have broad applications, extending beyond football to impact other areas of sports analytics and contributing to a flexible framework designed to enhance individual performance and fitness. [ABSTRACT FROM AUTHOR]

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model's performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. [ABSTRACT FROM AUTHOR]

Aimed at mitigating the limitations of the existing document entity relation extraction methods, especially the complex information interaction between different entities in the document and the poor effect of entity relation classification, according to the semi-structured characteristics of patent document data, a patent document ontology model construction method based on hierarchical clustering and association rules was proposed to describe the entities and their relations in the patent document, dubbed as MPreA. Combined with statistical learning and deep learning algorithms, the pre-trained model of the attention mechanism was fused to realize the effective extraction of entity relations. The results of the numerical simulation show that, compared with the traditional methods, our proposed method has achieved significant improvement in solving the problem of insufficient contextual information, and provides a more effective solution for patent document entity relation extraction. [ABSTRACT FROM AUTHOR]

Individuals with developmental prosopagnosia (DP) experience severe face memory deficits that are often accompanied by impairments in face perception. Images of human facial features are better discriminated between when they are presented in the locations on the visual field that they typically appear in while viewing human faces in daily life, than in locations which they do not typically appear (i.e., better performance for eyes in the upper visual field, and better performance for mouths in the lower visual field). These feature-to-location tuning effects (FLEs) can be explained by a retinotopically organised visual statistical learning mechanism. We had a large group of DP participants (N = 64), a control group (N = 74) and a group of individuals with a mild form of DP (N = 58) complete a single-feature discrimination task to determine whether face perception deficits in DP can be accounted for by an impairment in face feature-to-location tuning. The results showed that individuals with DP did not have significant FLEs, suggesting a marked impairment in the underlying visual statistical learning mechanism. In contrast, the mild DP group showed normal FLE effects which did not differ from the control group. Both DP groups had impaired single-feature processing (SFP) as compared to the control group. We also examined the effects of age on FLEs and SFP. [ABSTRACT FROM AUTHOR]

Simple Summary: Our study demonstrated neural encoding of incidental sensory regularities leading to modulation of cortical responses to both predictive and predicted sensory stimuli. As in the case of goal-directed behavior, such task-irrelevant predictive mechanisms might result from the brain's intrinsic drive of reducing uncertainty about state transition dynamics of the environment. Statistical learning of sensory patterns can lead to predictive neural processes enhancing stimulus perception and enabling fast deviancy detection. Predictive processes have been extensively demonstrated when environmental statistical regularities are relevant to task execution. Preliminary evidence indicates that statistical learning can even occur independently of task relevance and top-down attention, although the temporal profile and neural mechanisms underlying sensory predictions and error signals induced by statistical learning of incidental sensory regularities remain unclear. In our study, we adopted an implicit sensory conditioning paradigm that elicited the generation of specific perceptual priors in relation to task-irrelevant audio–visual associations, while recording Electroencephalography (EEG). Our results showed that learning task-irrelevant associations between audio–visual stimuli resulted in anticipatory neural responses to predictive auditory stimuli conveying anticipatory signals of expected visual stimulus presence or absence. Moreover, we observed specific modulation of cortical responses to probabilistic visual stimulus presentation or omission. Pattern similarity analysis indicated that predictive auditory stimuli tended to resemble the response to expected visual stimulus presence or absence. Remarkably, Hierarchical Gaussian filter modeling estimating dynamic changes of prediction error signals in relation to differential probabilistic occurrences of audio–visual stimuli further demonstrated instantiation of predictive neural signals by showing distinct neural processing of prediction error in relation to violation of expected visual stimulus presence or absence. Overall, our findings indicated that statistical learning of non-salient and task-irrelevant perceptual regularities could induce the generation of neural priors at the time of predictive stimulus presentation, possibly conveying sensory-specific information about the predicted consecutive stimulus. [ABSTRACT FROM AUTHOR]

Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters. [ABSTRACT FROM AUTHOR]

Time series analysis is pivotal for business and financial decision making, especially with the increasing integration of the Internet of Things (IoT). However, leveraging time series data for forecasting requires extensive preprocessing to address challenges such as missing values, heteroscedasticity, seasonality, outliers, and noise. Different approaches are necessary for univariate and multivariate time series, Gaussian and non-Gaussian time series, and stationary versus non-stationary time series. Handling missing data alone is complex, demanding unique solutions for each type. Extracting statistical features, identifying data quality issues, and selecting appropriate cleaning and forecasting techniques require significant effort, time, and expertise. To streamline this process, we propose an automated strategy called Preptimize, which integrates statistical and machine learning techniques and recommends prediction model blueprints, suggesting the most suitable approaches for a given dataset as an initial step towards further analysis. Preptimize reads a sample from a large dataset and recommends the blueprint model based on optimization, making it easy to use even for non-experts. The results of various experiments indicated that Preptimize either outperformed or had comparable performance to benchmark models across multiple sectors, including stock prices, cryptocurrency, and power consumption prediction. This demonstrates the framework's effectiveness in recommending suitable prediction models for various time series datasets, highlighting its broad applicability across different domains in time series forecasting. [ABSTRACT FROM AUTHOR]

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction. [ABSTRACT FROM AUTHOR]

The health monitoring of vegetable and fruit plants, especially during the critical seedling growth stage, is essential to protect them from various environmental stresses and prevent yield loss. Different environmental stresses may cause similar symptoms, making visual inspection alone unreliable and potentially leading to an incorrect diagnosis and delayed corrective actions. This study aimed to address these challenges by proposing a segmented regression model and a Mask R-CNN model for detecting the initiation time and symptoms of nutrient stress in cucumber seedlings within a controlled environment. Nutrient stress was induced by applying two different treatments: an indicative nutrient deficiency with an electrical conductivity (EC) of 0 dSm−1, and excess nutrients with a high-concentration nutrient solution and an EC of 6 dSm−1. Images of the seedlings were collected using an automatic image acquisition system two weeks after germination. The early initiation of nutrient stress was detected using a segmented regression analysis, which analyzed morphological and textural features extracted from the images. For the Mask R-CNN model, 800 seedling images were annotated based on the segmented regression analysis results. Nutrient-stressed seedlings were identified from the initiation day to 4.2 days after treatment application. The Mask R-CNN model, implemented using ResNet-101 for feature extraction, leveraged transfer learning to train the network with a smaller dataset, thereby reducing the processing time. This study identifies the top projected canopy area (TPCA), energy, entropy, and homogeneity as prospective indicators of nutritional deficits in cucumber seedlings. The results from the Mask R-CNN model are promising, with the best-fit image achieving an F1 score of 93.4%, a precision of 93%, and a recall of 94%. These findings demonstrate the effectiveness of the integrated statistical and machine learning (ML) methods for the early and accurate diagnosis of nutrient stress. The use of segmented regression for initial detection, followed by the Mask R-CNN for precise identification, emphasizes the potential of this approach to enhance agricultural practices. By facilitating the early detection and accurate diagnosis of nutrient stress, this approach allows for quicker and more precise treatments, which improve crop health and productivity. Future research could expand this methodology to other crop types and field conditions to enhance image processing techniques, and researchers may also integrate real-time monitoring systems. [ABSTRACT FROM AUTHOR]

In contemporary agriculture, enhancing the efficient production of crops and optimizing resource utilization have become paramount objectives. Garlic growth and quality are influenced by various factors, with fertilizers playing a pivotal role in shaping both aspects. This study aimed to develop classification models for distinguishing garlic fertilizer application differences by employing statistical and machine learning techniques, such as partial least squares (PLS), based on data acquired from a ground-based hyperspectral imaging system in the agricultural sector. The garlic variety chosen for this study was Hongsan, and the fertilizer application plots were segmented into three distinct sections. Data were acquired within the VIS/NIR wavelength range using hyperspectral imaging. Following data acquisition, the standard normal variate (SNV) pre-processing technique was applied to enhance the dataset. To identify the optimal wavelengths, various techniques such as sequential forward selection (SFS), successive projections algorithm (SPA), variable importance in projection (VIP), and interval partial least squares (iPLS) were employed, resulting in the selection of 12 optimal wavelengths. For the fertilizer application difference model, six integrated vegetation indices were chosen for comparison with existing growth indicators. Using the same methodology, the model construction showed accuracies of 90.7% for PLS. Thus, the proposed model suggests that efficient regulation of garlic fertilizer application can be achieved by utilizing statistical and machine learning techniques. [ABSTRACT FROM AUTHOR]

The effect of ionizing radiation on cells is a complex process dependent on several parameters. Cancer treatment commonly involves the use of radiotherapy. In addition to the effective killing of cancer cells, another key aspect of radiotherapy is the protection of healthy cells. An interesting position is occupied by heavy ion radiation in the field of radiotherapy due to its high relative biological effectiveness, making it an effective method of treatment. The high biological efficiency of heavy ion radiation can also pose a danger to healthy cells. The extent of cell death induced by heavy ion radiation in cells was investigated using statistical learning methods in this study. The objective was to predict the healthy cell survival rate based on the physical parameters of the available ionizing radiation. This paper is based on secondary research utilizing the PIDE database. Throughout this study, a local regression and a random forest model were generated. Their predictions were compared to the results of a linear-quadratic model commonly utilized in the field of ionizing radiation using various metrics. The relationship between dose and cell survival rate was examined using the linear-quadratic (LQM) model and local regression (LocReg). An R2 value of 88.43% was achieved for LQM and 89.86% for LocReg. Upon incorporating linear energy transfer, the random forest model attained an R2 value of 96.85%. In terms of RMSE, the linear-quadratic model yielded 9.5910−2, the local regression 9.2110−2, and the random forest 1.96 × 10−2 (lower values indicate better performance). All of these methods were also applied to a log-transformed dataset to decrease the right skewedness of the distribution of the datapoints. This significantly reduced the estimates made with LQM and LocReg (28% decrease in the case of R2), while the random forest retained nearly the same level of estimation as the untransformed data. In conclusion, it can be inferred that dose alone provides a somewhat satisfactory explanatory power for cell survival rate, but the inclusion of linear energy transfer can significantly enhance prediction accuracy in terms of variance and explanatory power. [ABSTRACT FROM AUTHOR]

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. [ABSTRACT FROM AUTHOR]

Crowdfunding platforms are important for startups, since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company information, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are important in analyzing large data sets, detecting anomalies and fraud, and enhancing decision-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandemic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups considering equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically significant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlantic and Cornell Universities, Springer and John Wiley & Sons Ltd. publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level. [ABSTRACT FROM AUTHOR]

Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial–temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial–temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset. [ABSTRACT FROM AUTHOR]

Cognitive radio networks enable the detection and opportunistic access to an idle spectrum through spectrum-sensing technologies, thus providing services to secondary users. However, at a low signal-to-noise ratio (SNR), existing spectrum-sensing methods, such as energy statistics and cyclostationary detection, tend to fail or become overly complex, limiting their sensing accuracy in complex application scenarios. In recent years, the integration of deep learning with wireless communications has shown significant potential. Utilizing neural networks to learn the statistical characteristics of signals can effectively adapt to the changing communication environment. To enhance spectrum-sensing performance under low-SNR conditions, this paper proposes a deep-learning-based spectrum-sensing method that combines multiple signal features, including energy statistics, power spectrum, cyclostationarity, and I/Q components. The proposed method used these combined features to form a specific matrix, which was then efficiently learned and detected through the designed 'SenseNet' network. Experimental results showed that at an SNR of −20 dB, the SenseNet model achieved a 58.8% spectrum-sensing accuracy, which is a 3.3% improvement over the existing convolutional neural network model. [ABSTRACT FROM AUTHOR]

Simple Summary: The listening brain must resolve the mix of sounds that reaches our ears into events, sources, and meanings. In this process, noise—sound that interferes with our ability to detect or understand sounds we need or wish to—is the primary challenge when listening. Importantly, noise to one person, or in one moment, might be an important sound to another, or in the next. Despite the many challenges posed by noise, however, human listeners generally outperform even the most sophisticated listening technologies in noisy listening environments. Because extracting noise from the sound stream is a fundamental process in listening, understanding how the brain deals with noise, in its many facets, is essential to understanding listening itself. Here, we explore what it is that the brain treats as noise and how it is processed. We conclude that the brain has multiple mechanisms for detecting and filtering out noise, and that incorporating cortico-subcortical 'listening loops' into our studies is essential to understanding this early segregation between noise and signal streams. What is noise? When does a sound form part of the acoustic background and when might it come to our attention as part of the foreground? Our brain seems to filter out irrelevant sounds in a seemingly effortless process, but how this is achieved remains opaque and, to date, unparalleled by any algorithm. In this review, we discuss how noise can be both background and foreground, depending on what a listener/brain is trying to achieve. We do so by addressing questions concerning the brain's potential bias to interpret certain sounds as part of the background, the extent to which the interpretation of sounds depends on the context in which they are heard, as well as their ethological relevance, task-dependence, and a listener's overall mental state. We explore these questions with specific regard to the implicit, or statistical, learning of sounds and the role of feedback loops between cortical and subcortical auditory structures. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Processes titled "Process Monitoring and Fault Diagnosis." The issue explores the application of data analytic techniques to enhance stable operation and safety in chemical processes and related industries. The collection of research papers covers various topics, including process fault detection, bearing fault diagnosis, remaining useful life prediction, and more. The papers introduce cutting-edge methodologies and demonstrate their reliability through validation. The issue aims to foster communication and the development of advanced process monitoring techniques. [Extracted from the article]

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts. [ABSTRACT FROM AUTHOR]

One of the main goals of human genetics is to understand the connections between genomic variation and the predisposition to develop a complex disorder. These disease–variant associations are usually studied in a single independent manner, disregarding the possible effect derived from the interaction between genomic variants. In particular, in a background of complex diseases, these interactions can be directly linked to the disorder and may play an important role in disease development. Although their study has been suggested to help complete the understanding of the genetic bases of complex diseases, this still represents a big challenge due to large computing demands. Here, we take advantage of high-performance computing technologies to tackle this problem by using a combination of machine learning methods and statistical approaches. As a result, we created a containerized framework that uses multifactor dimensionality reduction (MDR) to detect pairs of variants associated with type 2 diabetes (T2D). This methodology was tested on the Northwestern University NUgene project cohort using a dataset of 1,883,192 variant pairs with a certain degree of association with T2D. Out of the pairs studied, we identified 104 significant pairs: two of which exhibit a potential functional relationship with T2D. These results place the proposed MDR method as a valid, efficient, and portable solution to study variant interaction in real reduced genomic datasets. [ABSTRACT FROM AUTHOR]

In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM's purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222. [ABSTRACT FROM AUTHOR]

Coastal and underwater archaeological sites pose significant challenges in terms of investigation, conservation, valorisation, and management. These sites are often at risk due to climate change and various human-made impacts such as urban expansion, maritime pollution, and natural deterioration. However, advances in remote sensing (RS) and Earth observation (EO) technologies applied to cultural heritage (CH) sites have led to the development of various techniques for underwater cultural heritage (UCH) exploration. The aim of this work was the evaluation of an integrated methodological approach using ultra-high-resolution (UHR) bathymetric data to aid in the identification and interpretation of submerged archaeological contexts. The study focused on a selected area of the submerged Archaeological Park of Baia (Campi Flegrei, south Italy) as a test site. The study highlighted the potential of an approach based on UHR digital bathymetric model (DBM) derivatives and the use of machine learning and statistical techniques to automatically extract and discriminate features of archaeological interest from other components of the seabed substrate. The results achieved accuracy rates of around 90% and created a georeferenced vector map similar to that usually drawn by hand by archaeologists. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, physical activity was significantly decreased in all age groups. The purpose of this study is to investigate children's motor skills by administering an adapted physical activity program for the development of coordination skills. The sample consisted of 28 children aged 8 to 12 years, 20 neurotypical children and 8 with ASD-HF. Data collection employed various methods, including a personal information form, a leisure time exercise questionnaire, a semi-structured interview, and the Harre Dexterity Circuit for assessing physical activity improvements. The results of the two-way analysis of variance (two-way ANOVA) Tukey HSD test showed the differences between the specific groups, ASD-P vs. ASD-O (p < 0.001), ASD-O vs. AB-O (p < 0.001), ASD-O vs. AB-P (p < 0.001), ASD-O vs. AB-P (p = 0.136), ASD-P vs. AB-P (p < 0.0010, and AB-P vs. AB-O (p = 0.003). As result, both groups showed strong interest in practicing physical activity, and no statistical difference was found in doing so at home or in attendance, showing that physical activity practice is a tool for parent–child bonding and mutual enjoyment, emphasizing that the mixed approach yielded positive results in all participants. [ABSTRACT FROM AUTHOR]

Background: The lack of reliable biomarkers in response to anti-TNFα biologicals hinders personalized therapy for Crohn's disease (CD) patients. The motivation behind our study is to shift the paradigm of anti-TNFα biomarker discovery toward specific immune cell sub-populations using single-cell RNA sequencing and an innovative approach designed to uncover PBMCs gene expression signals, which may be masked due to the treatment or ongoing inflammation; Methods: The single-cell RNA sequencing was performed on PBMC samples from CD patients either naïve to biological therapy, in remission while on adalimumab, or while on ustekinumab but previously non-responsive to adalimumab. Sieves for stringent downstream gene selection consisted of gene ontology and independent cohort genomic profiling. Replication and meta-analyses were performed using publicly available raw RNA sequencing files of sorted immune cells and an association analysis summary. Machine learning, Mendelian randomization, and oligogenic risk score methods were deployed to validate DEGs highly relevant to anti-TNFα therapy response; Results: This study found PLCB1 in CD4+ T cells and CRTAM in double-negative T cells, which met the stringent statistical thresholds throughout the analyses. An additional assessment proved causal inference of both genes in response to anti-TNFα therapy; Conclusions: This study, jointly with an innovative design, uncovered novel candidate genes in the anti-TNFα response landscape of CD, potentially obscured by therapy or inflammation. [ABSTRACT FROM AUTHOR]

This study concerns the application of statistical learning (SL) in thermal stress assessment compared to the results accomplished by an international expert group when developing the Universal Thermal Climate Index (UTCI). The performance of diverse SL algorithms in predicting UTCI equivalent temperatures and in thermal stress assessment was assessed by root mean squared errors (RMSE) and Cohen's kappa. A total of 48 predictors formed by 12 variables at four consecutive 30 min intervals were obtained as the output of an advanced human thermoregulation model, calculated for 105,642 conditions from extreme cold to extreme heat. Random forests and k-nearest neighbors closely predicted UTCI equivalent temperatures with an RMSE about 3 °C. However, clustering applied after dimension reduction (principal component analysis and t-distributed stochastic neighbor embedding) was inadequate for thermal stress assessment, showing low to fair agreement with the UTCI stress categories (Cohen's kappa < 0.4). The findings of this study will inform the purposeful application of SL in thermal stress assessment, where they will support the biometeorological expert. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) is a groundbreaking statistical machine learning method for advancing plant and animal breeding. Nonetheless, its practical implementation remains challenging due to numerous factors affecting its predictive performance. This research explores the potential of data augmentation to enhance prediction accuracy across entire datasets and specifically within the top 20% of the testing set. Our findings indicate that, overall, the data augmentation method (method A), when compared to the conventional model (method C) and assessed using Mean Arctangent Absolute Prediction Error (MAAPE) and normalized root mean square error (NRMSE), did not improve the prediction accuracy for the unobserved cultivars. However, significant improvements in prediction accuracy (evidenced by reduced prediction error) were observed when data augmentation was applied exclusively to the top 20% of the testing set. Specifically, reductions in MAAPE_20 and NRMSE_20 by 52.86% and 41.05%, respectively, were noted across various datasets. Further investigation is needed to refine data augmentation techniques for effective use in genomic prediction. [ABSTRACT FROM AUTHOR]