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Generally, the computational grid consists of a large number of computing nodes, some of them are idle due to the uneven geographical distribution of computing requirements. This may cause workload unbalancing problems, which affect the performance of large-scale computational grids. In order to balance the computing requirements and computing nodes, we propose a job scheduling algorithm based on the workload prediction of computing nodes. We first analyze the causes of workload imbalance and the feasibility of reallocating computing resources. Secondly, we design an application and workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction model. To reduce the complexity of the AWAS algorithm, we propose a parallel job scheduling method based on computing node workload prediction. The experiments show that the AWAS algorithm can balance the workload among different computing nodes on the real-world dataset. In addition, we propose the parallelism of workload prediction model from the perspective of internal structure and data set to make AWAS apply to more computing nodes of the large-scale computing grids. Experimental results show that the combination of the two can achieve satisfactory acceleration efficiency. • In this paper, we propose an application and grid computing nodes workload aware scheduling algorithm (AWAS). • AWAS optimizes grid loads using greedy and heuristic methods. • This paper parallelizes the scheduling process of AWAS to improve its efficiency. • The experiment results show that the AWAS can achieve workload balancing in China National Grid. [ABSTRACT FROM AUTHOR]

In this paper, the crystal structure, physical properties, photoelectric conversion efficiency and large-area development of CsPbI 3 all-inorganic perovskite solar cells are comprehensively analyzed. The research results of the material in photoelectric conversion efficiency, large-area development and preparation technology are summarized. In addition, through theoretical prediction and simulation, this study simulated a CsPbI 3 all-inorganic perovskite solar cell with a photoelectric conversion efficiency of 28.29 %, which provided a theoretical basis for the preparation of high-performance devices. In this paper, the physical properties, photoelectric conversion efficiency, large area and preparation methods of CsPbI 3 all-inorganic perovskite solar cells are summarized in detail. In addition, the theoretical efficiency of the device was predicted, and the CsPbI 3 all-inorganic perovskite solar cell with a theoretical photoelectric conversion efficiency of 28.29 % was successfully obtained. [Display omitted] • CsPbI 3 all-inorganic perovskite solar cells. • Physical Properties of CsPbI 3 Materials. • Research status and preparation methods. • The theoretical efficiency of simulation prediction reaches 28.29 %. • Challenge and strategies. [ABSTRACT FROM AUTHOR]

Estuaries are dynamic environments which are driven by various natural processes like river discharge, tides, waves, influx of saline water and sediments, etc. These ecosystems are the most sensitive to sea level rise and fluctuations in river discharge associated with climate change. A direct response of sea level rise and river discharge can be observed in the water level of estuaries. However, existing models have not considered these parameters for forecasting water level. This paper focuses on developing a water level forecast model for the Chikugo River estuary in Japan using Nonlinear Autoregressive with Exogenous inputs (NARX Model). NARX neural network was used to do the one-step-ahead prediction of water level considering the various parameters that can very well be influenced by climate change: previous water level, river discharge, and salinity. Accordingly, three models were developed: (i) Model I considering previous water level; (ii) Model II additionally considering river discharge; and (iii) Model III additionally considering salinity. All the models showed appreciable performance in forecasting the water level. Model III had the best correlation with the water level with a cross-correlation value of 0.6030, while the river discharge had only a cross-correlation of 0.1113 indicating that the Chikugo River estuary is tide-dominated. The model was trained using different combinations of available data - previous water level, river discharge, and salinity. Cross-correlation results showed a better correlation between water level and salinity than various other combinations trained. Therefore, tidal intrusion influences the water level in the estuary, thereby depicting that sea level rise can affect the water level, and its influence can be well predicted by the developed model. The water level significantly affects the flora and fauna and the predictability of future estuarine floods can help in taking necessary mitigation strategies. • Water Level of Chikugo estuary forecasted using NARX model. • Analyzed the influence of previous water level, discharge and salinity. • Water level has more cross-correlation with salinity than discharge. • Forecasted the water level for various river discharge and salinity. • Model capable to analyse the influence of climate change on water level. [ABSTRACT FROM AUTHOR]

• We abstract a multipath parallel heterogeneous network node mapping model. • We propose a fast convergent network characteristics prediction algorithm. • We design a packet granularity scheduling scheme for multipath communication. • We apply the new results to some known network simulation models. Multipath parallel transmission has become an important research direction to improve big data transmission efficiency of connected vehicles. However, due to the heterogeneity and time-varying characteristics of parallel transmission paths, packets transmitted in parallel are usually out-of-order delivered to the destination, which greatly limits the throughput. To Lift the restriction of out-of-order delivery on the efficiency of big data transmission, this paper proposes a packet-granular real-time shortest delay scheduling scheme for multipath transmission based on path characteristics prediction. The scheme first clusters and models the heterogeneous network, which greatly reduces the complexity of the network. Subsequently, a prediction algorithm that can quickly converge to real-time delay is proposed. Then the details of the scheduling scheme are introduced by modules, and the bandwidth aggregation efficiency close to the theoretical upper limit is proved through simulation. Finally, we summarize the applicable scenarios and future work of the scheme. [ABSTRACT FROM AUTHOR]

Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time-evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient SSSP algorithm for time-evolving graphs, called V-Grouper. The main idea of V-Grouper is to avoid the redundant computations of the same vertex in different snapshots. Our experimental results over real-world time-evolving graphs show that, due to the high similarity of consecutive snapshots, the computation results of one vertex in neighboring snapshots are equal with a high probability. At the beginning of computation, V-Grouper first divides all the versions of a given vertex in different snapshots into vertex groups, where the computation result of each version is predicted based on the aforementioned insight of neighboring snapshots having equal results. The versions of the vertex in each group have the same predicted computation result. During the computation process for each vertex group, only one version needs to participate in computation, avoiding a large number of redundant computations. Experimental results show that V-Grouper is up to 64.31× faster than the state-of-the-art SSSP algorithm. • An efficient algorithm to compute SSSPs of the snapshots of time-evolving graph. • A compact data structure of time-evolving graph to save the memory space and improve the snapshot access speed. • An effective design to speed up the interactions between vertex groups.An effective design to speed up the interactions between vertex groups. [ABSTRACT FROM AUTHOR]

In wastewater treatment plants (WWTPs), the stochastic nature of influent wastewater and operational and weather conditions cause fluctuations in effluent quality. Data-driven models can forecast effluent quality a few hours ahead as a response to the influent characteristics, providing enough time to adjust system operations and avoid undesired consequences. However, existing data for training models are often incomplete and contain missing values. On the other hand, collecting additional data by installing new sensors is costly. The trade-off between using existing incomplete data and collecting costly new data results in three data challenges faced when developing data-driven WWTP effluent forecasters. These challenges are to determine important variables to be measured, the minimum number of required data instances, and the maximum percentage of tolerable missing values that do not impede the development of an accurate model. As these issues are not discussed in previous studies, in this research, for the first time, a comprehensive analysis is done to provide answers to these challenges. Another issue that arises in all data-driven modeling is how to select an appropriate forecasting model. This paper addresses these issues by first testing nine machine learning models on data collected from three wastewater treatment plants located in Iran, Australia, and Spain. The most accurate forecaster, Bayesian network, was then used to address the articulated challenges. Key variables in forecasting effluent characteristics were flow rate, total suspended solids, electrical conductivity, phosphorus compounds, wastewater temperature, and air temperature. A minimum of 250 samples was needed during the model training to achieve a great reduction in the forecasting error. Moreover, a steep increase in the error was observed should the portion of missing values exceed 10%. The results assist plant managers in estimating the necessary data collection effort to obtain an accurate forecaster, contributing to the quality of the effluent. [Display omitted] • Bayesian network is benchmarked against eight AI models to predict wastewater quality. • Bayesian networks can handle missing value in input data due to probabilistic nature. • Key variables in WWTP quality and quantity were specified. • Input data quality has a huge role in successful development of data-driven models. • The prediction error sharply increases when the portion of missing data exceeds 10%. [ABSTRACT FROM AUTHOR]

• An intelligent taxi dispatch system for big cities validated by real-world dataset. • Taxi demand and destination prediction based on historical data. • Realtime taxi assignment and reallocation strategy to optimize multiple goals. • Performance comparison with several popular prediction and optimization strategies. In this paper we describe an intelligent taxi dispatch system that has the goal of reducing the waiting time of the passengers and the idle driving distance of the taxis. The system relies on two separate models that predict the probability distributions of the taxi demand and destinations respectively. The models are learned from historical data and use a combination of long short term memory cells and mixture density networks. Using these predictors, taxi dispatch is formulated as a mixed integer programming problem. We validate the performance of the predictors and the overall system on a real world dataset of taxi trips in New York City. [ABSTRACT FROM AUTHOR]

Network congestion is one of the critical reasons for degradation of data throughput performance in Networks-on-Chip (NoCs), with delays caused by data-buffer queuing in routers. Local buffer or router congestion impacts on network performance as it gradually spreads to neighbouring routers and beyond. In this paper, we propose a novel approach to NoC traffic prediction using Spiking Neural Networks (SNNs) and focus on predicting local router congestion so as to minimize its impact on the overall NoCs throughput. The key novelty is utilizing SNNs to recognize temporal patterns from NoC router buffers and predicting traffic hotspots. We investigate two neural models, Leaky Integrate and Fire (LIF) and Spike Response Model (SRM) to check performance in terms of prediction coverage. Results on prediction accuracy and precision are reported using a synthetic and real-time multimedia applications with simulation results of the LIF based predictor providing an average accuracy of 88.28%–96.25% and precision of 82.09%–96.73% as compared to 85.25%–95.69% accuracy and 73% and 98.48% precision performance of SRM based model when looking at congestion formations 30 clock cycles in advance of the actual hotspot occurrence. • A novel use of SNN to predict router-level congestion in NoC architectures. • We investigate two SNN models (LIF and SRM) to evaluate prediction coverage. • Results are formulated by using synthetic and real-time multimedia applications. • Proposed models provide better accuracy than existing NoC congestion predictors. [ABSTRACT FROM AUTHOR]

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. While analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average. • PARIS presents a machine-learning method to predict HPC application resilience. • PARIS captures the relationship between application characteristics and resilience. • PARIS avoids random fault injection and provides high prediction accuracy. • PARIS provides a foundation for resilience modeling using machine learning. [ABSTRACT FROM AUTHOR]

Water, energy and carbon are three basic environmental factors that affect countries. An in-depth study of the water-energy-carbon (WEC) nexus is of great significance for realizing regional sustainable development. However, at present, research on the evaluation and prediction of large-scale WEC nexus based on multiple perspectives is not sufficiently mature, especially the prediction of WEC nexus efficiency. This study evaluates and predicts the WEC nexus efficiency in 30 regions of China based on a new comprehensive perspective. The WEC efficiency and the slack variables of 30 regions from 2006 to 2020 were calculated by using the slack-based measure model. The 30 regions were divided into 4 efficiency groups using hierarchical cluster analysis. Efficiency trends in 2006-2020 were analyzed for specific regions. The coupling interaction between the WEC nexus is studied based on the perspective of the coupling degree and coupling coordination degree. More importantly, this study is the first to quantitatively predict the WEC efficiency of 30 regions in China from 2021 to 2030, using the rank set pair analysis model. The following results were obtained in this paper. The WEC efficiency has a slow decreasing trend in 2006-2020. A total of 16.7% and 33.3% of the regions are in the extreme and high coupling coordination stages, respectively, and are mainly concentrated in the northern and southeastern parts of China. Fifty percent of the regions have moderate coupling coordination, mainly concentrated in the central and southern regions. From 2021 to 2030, the WEC efficiency of Beijing, Tianjin and Qinghai will remain at a high level; the WEC efficiency of Shandong and other regions will remain at a low level; and 70% of the regions' water efficiency will remain low. This paper has important guiding significance for promoting the regional WEC nexus balance and sustainable development of the economy, society and environment. According to the characteristics of the four efficiency groups, some valuable suggestions on regional sustainable development are proposed. [Display omitted] • The establishment of multiregional input-output index system is proposed from a new perspective. • This research divides 30 regions in China into four feature groups according to different WEC nexus characteristics. • The water-energy-carbon nexus efficiency of 30 regions in China in the next decade is quantitatively predicted. • Improving the water-energy-carbon nexus efficiency and promoting the balance of water-energy-carbon subsystems. [ABSTRACT FROM AUTHOR]

Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) brought light on the need for explainability in multiple domains (e.g., healthcare, finance, justice, and recruiting). Explainability or Explainable AI (XAI) can be defined as everything that makes AI more understandable to human beings. However, XAI features may vary according to the AI algorithm used. Beyond XAI features, different AI algorithms vary in terms of speed, performance, and costs associated with training/running models. Knowing when to choose the right algorithm for the task at hand, therefore, is fundamental in multiple AI systems, for instance, AutoML and AutoAI. In this paper, we propose a method to analyze patterns of high-granularity user interface (UI) events (i.e., mouse, keyboard, and additional custom events triggered on the millisecond scale) to predict when users will interact with UI elements that provide explainability for the AI in place. In this context, this paper presents: (1) a user study involving 37 participants (7 in the pilot phase and 30 in the main experiment phase) in which people performed a task of reporting a bug using a text form associated with an AI data quality meter and its XAI UI element and (2) an approach to model micro behavior using n o d e 2 v e c to predict when the interaction with XAI UI element will occur. The proposed approach uses a rich dataset (approximately 129k events) and combines n o d e 2 v e c and a Logistic Regression classifier. Results obtained show we have obtained an event-by-event prediction of the interaction with XAI with an average F-score of 0.90 (σ = 0. 06). From the presented results, one expects to support researchers in the realm of UI personalization to consider high-granularity interaction data when predicting the need for XAI while users are interacting with AI model outputs. • A study detailing different behaviors while users interact with explainability. • A user model approach to predict interaction with explainability. • The proposed approach obtained an average F-score of 0.90. • Groups do not differ in terms of task time nor event stream length. • An extension for XAI taxonomy encompassing interaction with XAI. [ABSTRACT FROM AUTHOR]

Spark is a more efficient distributed big data processing framework following Hadoop. It provides users with more than 180 adjustable configuration parameters, and how to choose the optimal configuration automatically to make the Spark application run effectively is challenging. The key to address the above challenge is having the ability to predict the performance of Spark applications in different configurations. This paper proposes a new approach based on Adaboost, which can efficiently and accurately predict the performance of a given application with a given Spark configuration. In our approach, Adaboost is used to build a set of performance models at the stage-level for Spark. To minimize the overhead of the modeling, we use the classic projective sampling, a data mining technique that allows us to collect as few training samples as possible while meeting the accuracy requirements. We evaluate the proposed approach on six typical Spark benchmarks with five input datasets. The experimental results show that our approach is less than the previously proposed approach in prediction error and cost. • We use Adaboost algorithm to build a set of performance models at the stage level for Spark prediction. • We use projective sampling to minimize the time and resources spent in modeling. • The experiment results show that our approach is lower than the previously proposed approach in prediction error and cost. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed. • Deep learning by combined CNN-LSTM networks for carbon price forecasting. • Hyperparameter tuning of deep learning models for improving forecast accuracy. • CNN-LSTM models outperform single CNN models and single LSTM models. • Performance metric of deep learning for abrupt changes in carbon price variation. [ABSTRACT FROM AUTHOR]

A significant milestone in China's carbon market was reached with the official launch and operation of the National Carbon Emission Trading Market. The accurate prediction of the carbon price in this market is crucial for the government to formulate scientific policies regarding the carbon market and for companies to participate effectively. Nevertheless, it remains challenging to accurately predict price fluctuations in the carbon market because of the volatility and instability caused by several complex factors. This paper proposes a new carbon price forecasting framework that considers the potential factors influencing national carbon prices, including data decomposition and reconstruction techniques, feature selection techniques, machine learning forecasting techniques for intelligent optimisation, and research on model interpretability. This comprehensive framework aims to improve the accuracy and understandability of carbon price projections to respond better to the complexity and uncertainty of carbon markets. The results indicate that (1) the hybrid forecasting framework is highly accurate in forecasting national carbon market prices and far superior to other comparative models; (2) the factors driving national carbon prices vary according to the time scale. High-frequency series are sensitive to short-term economic and energy market indicators. Medium- and low-frequency series are more susceptible to financial markets and long-term economic conditions than high-frequency series. This study provides insights into the factors affecting China's national carbon market price and serves as a reference for companies and governments to develop carbon price forecasting tools. • A hybrid carbon price prediction model named FS-CEEMDAN-VMD-GWO-LightGBM is proposed. • Accurately predicted the national carbon price in China using multi-influence factor time series data. • Using a data decomposition and reconstruction strategy, the model's accuracy was improved. • The SHAP algorithm is used to assess the impact of various influencing factors on the fluctuations of carbon prices. [ABSTRACT FROM AUTHOR]

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. [ABSTRACT FROM AUTHOR]

Caches are an important component of modern computing systems given their significant impact on performance. In particular, caches play a key role in the cloud due to the nature of large-scale, data-intensive processing. One of the key challenges for the cloud providers is how to share the caching capacity among tenants, under the circumstance that each often requires a different degree of quality of service (QoS) with respect to data access performance. The invariant is that the individual tenants' QoS requirements should be satisfied while the cache usage is optimized in a system-wide manner. In this paper, we introduce a learning-based approach for dynamic cache management in a cloud, which is based on the estimation of data access pattern of a tenant and the prediction of cache performance for the access pattern in question. We consider a variety of probability distributions to estimate the data access pattern, and examine a set of learning-based regression techniques to predict the cache hit rate for the access pattern. The predicted cache hit rate is then used to make a decision whether reallocating cache space is needed to meet the QoS requirement for the tenant. Our experimental results with an extensive set of synthetic traces and the YCSB benchmark show that the proposed method consistently optimizes the cache space while satisfying the QoS requirement. • Formulation of a problem of multi-tenant, QoS-preserved dynamic caching in a cloud • Estimation of data access pattern using KS test with probabilistic density functions • Examination of regression methods to predict cache hit rates from query density • Experiment on Apache Ignite with synthetic traces and Yahoo! Cloud Serving Benchmark [ABSTRACT FROM AUTHOR]

Latent Sector Errors (LSEs) happen at a significant frequency in the field and can impose a huge risk to data reliability. Disk scrubbing is a background process that reads disks periodically to detect LSEs timely, thus shortening the window of vulnerability to data loss. Nowadays, proactive error prediction, using machine learning techniques, has been proposed to improve storage system reliability by increasing the scrubbing rate for disks with higher error rates. Unfortunately, the majority of works incur non-trivial scrubbing costs and overlook the relationship between complete disk failures and LSEs. In this paper, we attempt to maintain or improve data reliability at reduced scrubbing costs. In particular, we design a novel adaptive approach that enforces a lower scrubbing rate for healthy disks and a higher scrubbing rate for disks which are subject to LSEs. Besides LSEs that are specific to partial disk failures, we also adjust scrubbing rates according to complete disk failure rates, because disks typically develop LSEs before they finally fail. Moreover, a voting-based method that exploits the periodic characteristic of scrubbing is proposed to ensure prediction accuracy. Experimental results on a real-world field dataset have demonstrated the effectiveness of our proposed approach. Specifically, the results show that we can achieve the same level of reliability, in terms of Mean-Time-To-Detection (MTTD), as the traditional fixed-rate scrubbing scheme with almost 49% less scrubbing costs or we can improve the reliability by a factor of 2.4X without extra scrubbing costs. Compared with the state-of-the-art approaches, our method can achieve the same level of reliability with nearly 32% less scrubbing costs. [ABSTRACT FROM AUTHOR]

This paper investigates an approach for predicting whirl flutter bifurcations using pre-flutter output data. The approach leverages the critical slowing down phenomenon, which makes aeroelastic systems recover to equilibrium from disturbances more slowly when closer to the flutter boundary. By quantifying this phenomenon based on output data from pre-flutter transient responses, one can predict the flutter onset and the amplitude of limit-cycle oscillations (bifurcation diagram). The approach is demonstrated for a propeller-nacelle system using output data from transient simulations. Bifurcation diagrams are predicted within a few percentages of reference results using output data at two forward speeds, as low as 25% to 30% below the whirl flutter speed. Extensive sensitivity analyses highlight the accuracy and robustness of predictions for various output data choices and levels of nonlinearity, providing insights for future applications to higher-complexity systems. This work opens a new path to predicting whirl flutter bifurcations during the design of aeroelastic systems prone to this instability. • Investigated an output-based whirl flutter bifurcation analysis approach. • Bifurcation diagrams of propeller-nacelle system predicted within a few percentages of reference solutions. • Predictions only required output data at two pre-flutter forward speeds, as low as 25% to 30% below the whirl flutter speed. • Sensitivity analyses highlighted the accuracy and robustness of predictions. • The proposed approach opens a new path to whirl flutter bifurcation analysis in support of aeroelastic design. [ABSTRACT FROM AUTHOR]

The framework density (FD) of zeolites can be defined as the number of tetrahedrally coordinated atoms (T-atoms) per 1000 Å3 and it is used to distinguish zeolites from tectosilicates and is an important index for evaluating the structure-void ratio of a zeolite. While the synthesis of extra-large pore zeolites is well known in the literature, it is extremely challenging to prepare extra-large pore zeolites with a low framework density. Only a few extra-large pore zeolites with low framework density have been synthesized to date, which raises the question of why such zeolites are so difficult to synthesize? To obtain such novel zeolites, experimentalists use qualitative analysis to identify structural patterns in experimental data and fine-tune synthesis descriptors. The aim of this paper is to demonstrate that machine learning can be utilized to predict the framework density of zeolites from synthesis descriptors, thus providing a platform for developing new zeolite structures. A machine learning approach is applied in this study to correlate the molar composition of a gel containing 11 metal oxides, fluoride, water, hydroxide ions, and organic structure agents, as well as operating parameters, using a large collection of synthetic records of 151 zeolite frameworks compiled from the literature, along with the framework density and the type of ring that results. Besides providing excellent accuracy, the decision tree provides a heuristic insight into the chemistry of zeolite synthesis by showing the conditions that result in low framework density. The data as presented does not provide details. However, machine learning algorithms can be used to find hidden patterns in the data, providing a deeper understanding of the chemistry involved in the formation of low framework density zeolites. This approach can, in principle, be applied to the synthesis of any material to rationalize empirical knowledge and fine-tune the synthesis conditions to achieve the desired properties. By using machine learning, low framework density of zeolites with extra-large pores can be predicted very accurately based on their molar composition and process parameters. In addition, conditions for the formation of these zeolites are described. By using simple experimental parameters, machine learning can provide high quality predictions for the synthesis of low framework density zeolites with extra-large pores, thereby accelerating the development of new zeolites. [Display omitted] • ML predicts framework density of zeolites from molar composition of the gel. • The decision tree provides heuristic insights into the chemistry of low FD zeolites. • ML deployment enables researchers to fine-tune conditions for zeolite synthesis. [ABSTRACT FROM AUTHOR]

In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013 to 2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks. • Search engine data is useful for predicting visitation to park units the next year. • Overall, using Google Trends performs better than an autoregressive model. • Using Google Trends to forecast visitation is useful for some, but not all, parks. • Utility of Google Trends is weakly correlated with higher surrounding populations. [ABSTRACT FROM AUTHOR]

Abstract We address the problem of forecasting high-dimensional functional time series through a two-fold dimension reduction procedure. The difficulty of forecasting high-dimensional functional time series lies in the curse of dimensionality. In this paper, we propose a novel method to solve this problem. Dynamic functional principal component analysis is first applied to reduce each functional time series to a vector. We then use the factor model as a further dimension reduction technique so that only a small number of latent factors are preserved. Classic time series models can be used to forecast the factors and conditional forecasts of the functions can be constructed. Asymptotic properties of the approximated functions are established, including both estimation error and forecast error. The proposed method is easy to implement, especially when the dimension of the functional time series is large. We show the superiority of our approach by both simulation studies and an application to Japanese age-specific mortality rates. [ABSTRACT FROM AUTHOR]

Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems neuroscience. Here, we perform a literature survey of the rapidly work on phenotype prediction in healthy subjects or general population to sketch out the current state and ongoing developments in terms of data, analysis methods and reporting. Excluding papers on age-prediction and clinical applications, which form a distinct literature, we identified a total 108 papers published since 2007. In these, memory, fluid intelligence and attention were most common phenotypes to be predicted, which resonates with the observation that roughly a quarter of the papers used data from the Human Connectome Project, even though another half recruited their own cohort. Sample size (in terms of training and external test sets) and prediction accuracy (from internal and external validation respectively) did not show significant temporal trends. Prediction accuracy was negatively correlated with sample size of the training set, but not the external test set. While known to be optimistic, leave-one-out cross-validation (LOO CV) was the prevalent strategy for model validation (n = 48). Meanwhile, 27 studies used external validation with external test set. Both numbers showed no significant temporal trends. The most popular learning algorithm was connectome-based predictive modeling introduced by the Yale team. Other common learning algorithms were linear regression, relevance vector regression (RVR), support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), and elastic net. Meanwhile, the amount of data from self-recruiting studies (but not studies using open, shared dataset) was positively correlated with internal validation prediction accuracy. At the same time, self-recruiting studies also reported a significantly higher internal validation prediction accuracy than those using open, shared datasets. Data type and participant age did not significantly influence prediction accuracy. Confound control also did not influence prediction accuracy after adjusted for other factors. To conclude, most of the current literature is probably quite optimistic with internal validation using LOO CV. More efforts should be made to encourage the use of external validation with external test sets to further improve generalizability of the models. [ABSTRACT FROM AUTHOR]

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs. [Display omitted] • Establish a prediction system based on feature selection, deep learning, and optimization algorithms. • A novel two-stage feature selection method is applied for dimensionality reduction. • The proposed hybrid deep learning model CLT outperforms other commonly used models. • The CLT optimized by GJO improves the prediction performance. [ABSTRACT FROM AUTHOR]

The green growth of Beijing-Tianjin-Hebei (BTH) urban agglomeration plays a leading and exemplary role in overcoming internal resource restrictions, addressing climate change, and supporting China's high-quality growth. From the standpoint of pollution reduction and carbon reduction, this paper first conducts a comprehensive evaluation of the environmental impact based on combined weighting technique. The Logarithmic Mean Divisia Index (LMDI) model is used to decompose the environmental impact drivers in distinct areas. A decoupling effort index is further constructed to measure the effect of various efforts on the decoupling of economic growth and environmental impact, the improved grey Markov model is applied to predict the future trend of regional decoupling efforts. The results of empirical analysis based on data of the BTH region during 2011–2018 show that: 1) the environmental impact index of Beijing is the lowest followed by Tianjin and Hebei; 2) environmental regulation exerts the most significant impact on reducing environmental pressure in Beijing while technology progress and energy intensity have the most significant effect on easing environmental pressure in Tianjin; 3) strong decoupling efforts have been found in Beijing, Tianjin and Hebei, however, such effect is more significant in Beijing; 4) Beijing's decoupling state is mostly driven by regulatory effect, intensity effect, and scale effect, while Tianjin and Hebei's decoupling states are primarily driven by improvements in environmental regulation and energy intensity; 5) according to the forecast outcome of the improved grey Markov technique, a state of strong decoupling effort will be maintained in the BTH area by 2025, and the decoupling effort index in Beijing will remain the highest while the index in Hebei will remain the lowest. • A comprehensive evaluation of the environmental impact of BTH based on combined weighting method is estimated. • Determinants of environmental impact is decomposed as regulatory, technological, intensity, growth and scale effects. • LMDI model is constructed to estimate the five determinants of regional environmental impact. • Decoupling effort approach and improved grey Markov model are used to analyze and predict the status of various decoupling efforts. • Various suggestions are proposed from the perspective of regional heterogeneity. [ABSTRACT FROM AUTHOR]

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but – for this case study – multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods. • Data preprocessing is an essential step in a modelling framework for wastewater-based epidemiology. • Equal spacing of the scatted data is essential but downsampling is sufficient. • Data smoothing is key for regression and forecast. • For long term WBE data series, multivariate regression is essential. • Short term signal prediction (7 days) is reasonable accurate. [ABSTRACT FROM AUTHOR]

Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake. [ABSTRACT FROM AUTHOR]

Independent realization is a commonly used shortcut for deriving forecast properties. It is also an unrealistic assumption in many empirical applications. In this paper we consider the effect of the assumption when deriving the properties of panel data forecasts. To do so, we derive and compare the asymptotic forecast loss of a set of vector autoregressions (VARs) under both independent and same-sample realization. We show that adopting the independent realization assumption can result in an overstatement of forecast loss when common forms of parameterized heterogeneity (such as fixed effects) are included in the VAR. Because these parameters are often used in dynamic panel forecasting applications, our results imply that the independent realization shortcut should only be used with caution when working with panel data. [ABSTRACT FROM AUTHOR]

• To our best knowledge, this is the first paper to investigate pupillary response and blink during a whole conversation under different communication load conditions using low-cost eyewear. • Differently from research using conversation involving manual-visual tasks, we considered the communication load of conversational situations in this study. We applied two conversational tasks with clearly defined turns, i.e. answering yes/no questions and a word-guessing game, to induce different communication load for both listening and speaking conditions. • Through comparing the pupillary response and blink data in conversation-level and in segment-level, we derived findings in eye activity during conversations. With the emergence of low-cost wearable hardware for eye activity analysis comes the opportunity to use pupil and blink behavior during conversations to improve human computer interaction. Conversations in general can be decomposed into four segments, i.e. listening, speaking, thinking (transition from listening to speaking) and waiting (transition from speaking to listening). However, pupil and blink behavior during these segments of conversations is not yet fully understood. In this paper, pupillary response and blink in conversations are analyzed at the conversation-level and segment-level. 24 participants undertook two natural conversational tasks with different levels of communication load, while their eye activity was continuously recorded by eyewear. More intense conversation was found to impose a significant increase in pupil diameter, that is, higher communication load produces larger increases in pupil size, like other mental tasks. During conversations, pupil diameter increased more during speaking than during listening among conversations, while blink showed no difference between listening and speaking. Comparing two the transition segments, pupil size was found to be larger after speaking than before speaking, while more blinks occurred before speaking than after speaking. These findings on eye activity in conversations could be used to infer a user's communication load or predict their conversational state, which can be applied in turn-taking prediction, interruption management systems or computer-mediated conversational systems. [ABSTRACT FROM AUTHOR]

Floating platforms are being deployed and developed further for offshore wind power generation in deep water. Platforms undergo surge and pitch in waves, causing their supported wind turbine to oscillate back and forth. An important input to modelling the platform – and hence hub – motion are the forces, moments and energy dissipation provided by the turbine rotor. Blade-element momentum theory (BEMT) provides a numerically-efficient method of calculating these quantities in steady flow, but its application to an oscillating turbine is largely untested. To test how appropriate a quasi-steady BEMT methodology is for this application, BEMT (with a high-axial-induction-factor correction) is compared with CFD using an actuator-line representation of the NREL 5 MW design, typical of the type of turbine that might be deployed. For simplicity we assume constant rotor speed and pitch angle. The paper demonstrates that, for typical wave periods and onset wind, a quasi-steady BEMT model provides a good description of loads on an oscillating turbine rotor and provides near-identical results to CFD for forces and power dissipation. However, a common time-dependent BEMT variant does not improve predictions in this application, but proves detrimental to power prediction. The paper also derives a simple formula, based on steady-flow load characteristics, to estimate dissipative damping by the rotor. [ABSTRACT FROM AUTHOR]

Carbon emissions play a crucial role in inducing global warming and climate change. Accurate and stable carbon emissions forecasting is beneficial for formulating emissions reduction schemes and achieving carbon neutrality as early as possible. Although previous studies have concentrated on employing one or several methods for carbon emissions forecasting, the improvement in forecasting performance is limited because they ignore the importance of objectively selecting the models and the necessity of interval forecasting. In this paper, a novel ensemble prediction system, composed of data decomposition, model selection, phase space reconstruction, ensemble point prediction, and interval prediction, is proposed to conduct both point and interval predictions, which has been proven to be effective in prompting carbon emissions forecasting accuracy and stability. According to the empirical results, the mean MAPE results of our proposed forecasting strategy in point prediction are 1.1102% (in Dataset A) and 1.1382% (in Dataset B), and the mean CWC values in the interval forecasting are 0.3512 and 0.1572, respectively. Thus, the proposed forecasting system improves the forecasting performance relative to other models considerably, which can provide meaningful references for policymakers. • An ensemble forecasting system is developed for carbon management. • Comprehensive indicator is proposed to determine the best predictor adaptively. • Optimal distribution and reduction coefficient are used for interval forecasting. • Multi-objective tangent search algorithm is proposed for interval forecasting. [ABSTRACT FROM AUTHOR]

Using the data from GCC countries, this paper analyses the co-movement between oil price, EU carbon allowance prices, global clean energy index and equity index from three GCC counties, namely, Kuwait, Saudi Arabia and the United Arab Emirates. Almost no previous research has investigated the dynamic interrelations in the conventional energy markets, like those of the GCC countries, against the dramatic growth in clean energy production and the new emissions trading schemes. Employing three different multivariate GARCH models, we document the existence of volatility spillover effects and co-movement among global clean energy production, crude oil price, CO 2 emission price and each of the three GCC energy stock markets. Furthermore, we found that the conditional variances of all return series are influenced by the shocks coming from the markets themselves. Volatilities in all the markets under consideration are highly persistent, and the long-run persistent volatilities are more pronounced especially for oil and CO 2 emission prices. The forecasting exercise demonstrates the superior performance of the multivariate diagonal-BEKK GARCH models. • We investigate the dynamic interrelations in the GCC energy equity markets. • Conditional variances are influenced by the shocks coming from the markets themselves. • The long-run persistent volatilities are more pronounced especially for oil and CO 2 emission prices. • Forecasting demonstrates the superior performance of the multivariate diagonal-BEKK GARCH. [ABSTRACT FROM AUTHOR]

Regression problems with multiple functional predictors have been studied previously. In this paper, we investigate functional quantile linear regression with multiple functional predictors within the framework of reproducing kernel Hilbert spaces. The estimation procedure is based on an ℓ 1 -mixed-norm penalty. The learning rate of the estimator in prediction loss is established and a lower bound on the learning rate is also presented that matches the upper bound up to a logarithmic term. [ABSTRACT FROM AUTHOR]

High-performance flutter and post-flutter predictions are critical in the design of a vast range of nonlinear fluid-structural systems. However, these predictions are often unfeasible due to their high computational cost. Moreover, traditional methods for flutter and post-flutter predictions are model intrusive and limited to one varying parameter at a time. To address these shortcomings, this paper introduces a novel output-based method for forecasting flutter bifurcations and post-flutter dynamics in nonlinear fluid-structural systems with multiple varying parameters. The proposed method uses only output data from a handful of transient responses in the pre-flutter regime to predict the flutter boundary and a range of the bifurcation diagram. The method is demonstrated in analytical systems where the predicted bifurcation diagrams match the exact solutions. Next, the method is applied to a geometrically nonlinear wing considering variations in the flow speed and torsion stiffness. The predicted bifurcation diagrams closely agree with time-marching solutions despite the proposed method requires only a handful of transient calculations. These results show that the proposed method enables computationally efficient and accurate output-based flutter bifurcation forecasting in nonlinear fluid-structural systems with multiple varying parameters. [ABSTRACT FROM AUTHOR]

• Brain age is estimated using a 3D CNN from 12,802 full T1-weighted images. • Regions used to drive predictions are different for linearly and nonlinearly registered data. • Linear registrations utilise a greater diversity of biologically meaningful areas. • Correlations with IDPs and non-imaging variables are consistent with other publications. • Excluding subjects with various health conditions had minimal impact on main correlations. Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors Δ B r a i n A g e = A g e P r e d i c t e d − A g e T r u e correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between Δ BrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the Δ BrainAge from models such as this network were predictive of any health outcomes. [ABSTRACT FROM AUTHOR]

Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t -test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification. [ABSTRACT FROM AUTHOR]

High-performance aircraft often suffer from the consequences of tail buffeting at moderate subsonic Mach numbers and medium to high angles of attack. The impact of the aircraft's highly unsteady flow field on the tails can result in significant structural fatigue and degraded handling qualities. Various methods have been developed to predict tail buffeting. Stochastic response methods are among frequently used approaches. For such methods the size of the excitation data set can become an issue, especially when the auto- and cross-spectra of all available excitation signals on the configuration are considered. The present paper demonstrates how to modify stochastic tail buffeting prediction methods using Proper Orthogonal Decomposition (POD). The approach is based on the modal decomposition of the aerodynamic buffet excitation data set. It notably reduces the computational effort for structural response and loads prediction with limited losses in accuracy while using all power- and cross-spectra of the reduced dataset. The method was applied to the computational buffeting prediction for a generic configuration with double-delta wing and horizontal tail plane (HTP) over a wide range of angles of attack. It was shown that the POD-modes of the aerodynamic buffet excitation resembled the characteristics of configuration's complex vortical flow field. The predicted structural response and loads converged well with increasing number of POD-modes. With the presented approach, the computational effort of stochastic tail buffeting prediction has been reduced by orders of magnitude compared to the case with the full aerodynamic buffet excitation data set. [ABSTRACT FROM AUTHOR]

Modeling is an important statistical tool to Forest Science, especially to forest planning by predicting the forest's yield and assortments, for instance. This paper evaluated the accuracy of bivariate and generalized linear mixed modeling in the representation of the Pinus taeda L. trunk taper and the estimation of its assortments. To compose the fitting data, 558 trees from plantations located in the Southern region of Santa Catarina, Brazil, were scaled. Initially, the data's bivariate normality was evaluated, and the bivariate standard normal distribution was fitted. Six generalized linear mixed models were fitted for the bivariate representation of diameter and height in the trunk. Afterwards, some statistical indices were obtained to verify the quality of the fitted models and, in a complementary way, of the bivariate graphs of the residuals. Even with the application of Box-Cox transformation, the results indicate the non-normality of the variables, but the transformation contributed to improve the model fitting in 50%. The ordinal and exponential models obtained the best statistics for height representation, with the Akaike Information Criterion (AIC) value being reduced from 16,430.13 to 5,686.78 when considering normal distribution. When evaluating the assortments prediction, there were high discrepancies in the estimated values (246 logs for sawmill and 120 logs for veneers) versus the observed ones (881 logs for sawmill and 628 logs for veneers), which corresponds to a 75% underestimation of total logs per hectare. Thus, the generalized linear mixed modeling improved the trunk taper representation, and the bivariate modeling was not efficient to predict assortments production. • Wood stock quantification in a forest stand is important to substantiate decisions. • Trunk profile prediction occurs through modeling, mostly by one-dimensional models. • Mixed, bivariate and generalized linear models can bring more precise data. • The cited models did not generate a better precision in the data. • New research is needed to improve their application to model the tree profile. [ABSTRACT FROM AUTHOR]

In this paper we explore the phenomenon of pleiotropy in neurodegenerative diseases, focusing on Alzheimer's disease (AD). We summarize the various techniques developed to investigate pleiotropy among traits, elaborating in the polygenic risk scores (PRS) analysis. PRS was designed to assess a cumulative effect of a large number of SNPs for association with a disease and, later for disease risk prediction. Since genetic predictions rely on heritability, we discuss SNP-based heritability from genome-wide association studies and its contribution to the prediction accuracy of PRS. We review work examining pleiotropy in neurodegenerative diseases and related phenotypes and biomarkers. We conclude that the exploitation of pleiotropy may aid in the identification of novel genes and provide further insights in the disease mechanisms, and along with PRS analysis, may be advantageous for precision medicine. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vector autoregressive (VAR) model of order one for multivariate binary time series. Multivariate binary time series data are used in many fields such as biology and environmental sciences. However, modeling the dynamics in multiple binary time series is not an easy task. Most existing methods model the joint transition probabilities from marginals pairwisely for which the resulting cross-dependency may not be flexible enough. Our proposed model, Bernoulli VAR (BerVAR) model, is constructed using latent multivariate Bernoulli random vectors. The BerVAR model represents the instantaneous dependency between components via latent processes, and the autoregressive structure represents a switch between the hidden vectors depending on the past. We derive the mean and matrix-valued autocovariance functions for the BerVAR model analytically and propose a quasi-likelihood approach to estimate the model parameters. We prove that our estimator is consistent under mild conditions. We perform a simulation study to show the finite sample properties of the proposed estimators and to compare the prediction power with existing methods for binary time series. Finally, we fit our model to time series of drought events from different regions in Mexico to study the temporal dependence, in a given region and across different regions. By using the BerVAR model, we found that the cross-region dependence of drought events is stronger if a rain event preceded it. [ABSTRACT FROM AUTHOR]

The present paper concerns the extreme wave loads acting on an offshore structure; specifically the wave-in-deck loading component that arises when the height of an incident wave crest exceeds the elevation of the topside structure. In this case wave inundation occurs, the resulting loads on the topside structure represent a significant part of the total wave load. A new model for the effective prediction of this important loading component is presented. This is based upon the conservation of momentum, is formulated in a Lagrangian frame of reference, can incorporate any incident wave form, and takes due account of the porosity (or openness) of the topside structure. Comparisons between the model predictions and wide-ranging laboratory observations are shown to be in good agreement; the latter based upon deterministic focused wave events that are known to be representative of the largest waves arising in realistic sea-states. In addition, comparisons are also made with independent cfd calculations. Taken together, the proposed model is shown to accommodate changes in the spectral shape, the spectral peak period, the incident crest elevation (and hence the level of inundation), the directional spread of the incident waves, and the porosity of the topside structure. Importantly, this agreement applies to both non-breaking and breaking waves, involves no empirical calibration, and can be achieved with limited computational resources. As such, the model is ideally suited to design/re-assessment calculations in which the reliability of any offshore structure must be based upon a rigorous assessment of the long-term distribution of the total wave loads, including any wave-in-deck loading component. [ABSTRACT FROM AUTHOR]

The Ultra-Violet Index (UVI) is widespread used to communicate the UV radiation intensity to the general public. The knowledge of the UVI value and of its daily variation is essential for many techniques for monitoring the personal exposure to UV radiation. The UVI values are usually provided by the meteorological services and nowadays it is very common to find UVI forecast tools even in smartphone apps. In this paper, with the aim to evaluate the prediction accuracy of six smartphone apps, a measurement campaign of UVI has been carried out. The measurements have been conducted for the site of Pisa (central Italy), using a portable photoradiometer equipped with a UV erythemal irradiance probe (operating range 250–400 nm). The measured UVI values have been compared with the predicted UVI values (using smartphone apps). Bland-Altman and Passing-Bablok methods have been used to compare the data, the comparison has been conducted on the basis of 90 different UVI measurements, taken at different times, different days and different sky conditions. From the comparison between measured and predicted UVI values it has been possible to observe a general poor accuracy of the apps. The percentage deviations between measured and predicted UVI values were quite high and only one app was able to predict more than 70% of the measured data with an average percentage deviation lower than 30%. • A measurement campaign of UV Index values, carried out for the site of Pisa (Italy), is described. • Measured UV Index values are compared to predicted values, obtained using smartphone apps. • The prediction accuracy of six smartphone apps is assessed. • Significant underestimation of UV Index are obtained in correspondence of the measured UVI peaks. • The results obtained in this study can be useful for promoting a more conscious use of these apps. [ABSTRACT FROM AUTHOR]

Both amplitude modulation and frequency modulation of Vortex-induced Vibration (VIV) are observed in a recent model test of a flexible cylinder under oscillatory flow, but its hydrodynamics has not yet been broached in detail. This paper employs the Forgetting Factor Least Squares (FF-LS) method for identification of time-varying hydrodynamics of a flexible cylinder under modulated VIV. The FF-LS method's applicability to accurately identify time-varying hydrodynamic coefficients is demonstrated through an elastically mounted rigid cylinder under flow with a given modulated motion. Furthermore, we propose a framework to predict instantaneous amplitude (envelope) and frequency using time-varying hydrodynamic coefficients to establish their analytical relationship. This prediction method is further extended to a highly tensioned flexible cylinder through Fourier series expansion in the spatial domain. By performing the identification procedure for all sampled data of a flexible cylinder undergoing oscillatory flow, we obtain the corresponding time-varying hydrodynamics in the cross-flow direction considering the amplitude and frequency modulation. The results show that, under modulated VIV, hydrodynamic coefficients of the flexible cylinder also show time-varying characteristics. We further investigate differences between identified hydrodynamic coefficients and those obtained from the database of a cylinder with modulated motion under flow. Prediction results using these identified time-varying coefficients reveal that the time-varying excitation coefficients mainly influence the amplitude modulation, and the time-varying added-mass coefficients contain the major information of frequency modulation. These results further suggest including the temporal derivative of the instantaneous amplitude as one determining parameter in building databases to improve the prediction of modulated VIV. [ABSTRACT FROM AUTHOR]