We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 minutes allows us to form a multivariable timeseries that serves two-ways: a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and b) based on historical data predict future levels that are correlated with hive’s health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

The choice of holiday destinations is highly depended on climate considerations. Nowadays, since the effects of climate crisis are being increasingly felt, the need of accurate weather and climate services for hotels is crucial. Such a service could be beneficial for both the future planning of tourists’ activities and destinations and for hotel managers as it could help in decision making about the planning and expansion of the touristic season, due to a prediction of higher temperatures for a longer time span, thus causing increased revenue for companies in the local touristic sector. The aim of this work is to calculate predictions on climatic variables using statistical techniques as well as Artificial Intelligence (AI) for a specific area of interest utilising data from in situ meteorological station, and produce valuable and reliable localised predictions with the most cost-effective method possible. This investigation will answer the question of the most suitable prediction method for time series data from a single meteorological station that is deployed in a specific location. As a result, an accurate representation of the microclimate in a specific are is achieved. To achieve this high accuracy in situ measurements and prediction techniques are used. As prediction techniques, Seasonal Auto Regressive Integrated Moving Average (SARIMA), AI techniques like the Long-Short-Term-Memory (LSTM) Neural Network and hybrid combinations of the two are used. Variables of interest are divided in the easier to predict temperature and humidity that are more periodic and less chaotic, and the wind speed as an example of a more stochastic variable with no known seasonality and patterns. Our results show that the examined Hybrid methodology performs the best at temperature and wind speed forecasts, closely followed by the SARIMA whereas LSTM perform better overall at the humidity forecast, even after the correction of the Hybrid to the SARIMA model.

Background: The Sequential Organ Failure Assessment (SOFA) is a scoring system used for the evaluation of disease severity and prognosis of critically ill patients. The impedance ratio (Imp-R) is a novel mortality predictor. Aims: This study aimed to evaluate the combination of SOFA + Imp-R in the prediction of mortality in critically ill patients admitted to the emergency department (ED). Methods: A retrospective cohort study was performed in adult patients with acute illness admitted to the ED of a tertiary-care referral center. Baseline SOFA score and bioelectrical impedance analysis to obtain the Imp-R were performed within the first 24 hours after admission to the ED. A Cox regression analysis was performed to evaluate mortality risk of initial SOFA score plus Imp-R. Harrell's C-statistic and decision curve analyses (DCA) were performed. Results: Out of 325 patients, 240 were included for analysis. Overall mortality was 31.3%. Only 21.3% of non-surviving patients died after hospital discharge, and 78.4% died during hospital stay. Of the latter, 40.6% died in the ED. SOFA and Imp-R values were higher in non-survivors and were significantly associated with mortality in all models. The combination of SOFA + Imp-R significantly predicted 30-day mortality, in-hospital mortality, and ED mortality with area under the curve (AUC) of 0.80 (95% CI: 74-0.86), 0.79 (95% CI: 0.74-0.86) and 0.75 (95% CI: 0.66-0.84) respectively. The DCA showed that combining SOFA + Imp-R improved the prediction of mortality through the lower risk thresholds. Conclusion: The addition of Imp-R to baseline SOFA score at admission to the ED improves mortality prediction in severely acutely ill patients admitted to the ED.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building an effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS.

Due to the high effectiveness of cancer screening and therapies, the diagnosis of second primary cancers (SPCs) has increased in women with endometrial cancer (EC). However, there’s no previous literature mentioned about adequate evidence to support screening for SPCs in endometrial cancer. This study was aimed to develop effective risk prediction models of second primary endometrial cancer in women with obesity (Body-mass index; BMI > 25) and this study includes datasets of the incidence of SPCs and the other risks of SPCs in 4480 primary cancer survivors by a hospital-based cancer registry database. In our study, we found the obesity played a key role in SPCs. There’re 10 independent variables used as predicting variables, which corelated to obesity should be monitored for the early detection of SPCs in endometrial cancer. In conclusion, it is a promising SPCs prediction. The proposed scheme can support the important influence of obesity and clinical data representations in all cases after primary treatments. Our results suggested that obesity is still a crucial risk factor to SPCs in endometrial cancer.

Understanding the relationships between land cover/urban structure patterns and air pollutants is key to sustainable urban planning and development. In this study, we employ a mobile monitoring method to collect PM_2.5 and BC data in the city of Philadelphia, PA during the summer of 2019 and apply the Structure of Urban Landscapes (STURLA) methodology to examine relationships between urban structure and atmospheric pollution. We find that, while PM_2.5 and BC vary by STURLA class, many of the differences in pollutant concentrations between classes are not significant. However, we also find that the proportions in which STURLA components are present throughout the urban landscape can be used to predict urban air pollution. Among frequently sampled STURLA classes, gpl hosted the highest PM_2.5 concentrations on average (16.60 _± 4.29 µg/m³), while tgbwp hosted the highest BC concentrations (2.31 _± 1.94 µg/m³). Furthermore, STURLA combined with machine learning modeling was able to correlate PM_2.5 (R²= 0.68, RMSE 2.82 µg/m³) and BC (R² = 0.64, RMSE 0.75 µg/m³) concentrations with the urban landscape and spatially interpolate concentrations where sampling did not take place. These results demonstrate the efficacy of the STURLA methodology in modeling relationships between air pollution and land cover/urban structure patterns.

Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

The COVID-19 pandemic caused by SARS-CoV-2 poses a devastating threat to human society in terms of health, economy and lifestyle. Establishing accurate and real-time models to predict and assess the impact of the epidemic on the economy is instructive. We have designed a new model to quantitatively assess the impact of the COVID-19 on the economy of China’s mainland. The nominal GDP in the Q1 of 2020 that we predicted for China’s mainland with the Baidu Mi-gration Data is RMB 20,785.7 billion, which is less by 3.59% than that in 2019. The estimated val-ue is confirmed roughly by the official report released in April 17, 2020 (RMB 20,650 billion, 6.8% year-on-year declined). Strict control measures during the epidemic have greatly reduced Chi-na's economic activity and had a serious impact on the country's economy. Orderly promotion of population mobility plays a decisive role in economic recovery.

In automated manufacturing systems, most of the manufacturing processes including machining are automated. Automatic tool change is one of the important parameters for reducing manufacturing lead time. Ceramic cutting tools are used to machine hard materials. Ti[C,N] mixed alumina ceramic cutting tools are widely used to machine hardened steel and Stainless Steel due to its superior mechanical properties. Martensitic stainless steel has wide applications in screws, bolts, nuts and other engineering applications. Machining studies on Martensitic Stainless Steel was conducted using Ti[C,N] mixed alumina ceramic cutting tool. Tool life was evaluated using flank wear criterion. The tool life obtained from experimental machining process was taken as training dataset and test dataset for machine learning. Using the dataset obtained from experimental machining tool life model has been developed using Gradient Descent algorithm. The model was validated using co-efficient of determination. The accuracy of the machine learning model was tested using the test data and 99.83% accuracy was obtained. Tool life model based on Gradient Descent Algorithm was successfully implemented for the tool life of Ti[C,N] mixed alumina ceramic cutting tool.Keywords: keyword 1; keyword 2; keyword 3 (List three to ten pertinent keywords specific to the article; yet reasonably common within the subject discipline.)

In the process of metal cutting, the anti-wear performance of the tool determines the life of the tool and affects the surface quality of the workpiece. The finite element simulation method can directly show the tool wear state and morphology, but due to the limitations of the simulation time and complex boundary conditions, it has not been commonly used in tool life prediction. Based on this, a tool wear model was established on the platform of a finite element simulation software for the cutting process of titanium alloy TC4 by end milling. The key technique is to embed different types of tool wear models into the finite element model in combination with the consequent development technology. The effectiveness of the tool wear model is validated by comparing the experimental results with the simulation results. At the same time, in order to quickly predict the tool life, an empirical prediction formula of tool wear was established, and the change course of tool wear under time change was obtained.

Over the past 150 million years, the Chilean Atacama Desert has been transformed into one of the most inhospitable landscapes by geophysical changes, which makes it an ideal Mars analog that has been explored for decades. However, two heavy rainfalls that occurred in the Atacama in 2015 and 2017 provide a unique opportunity to study the response of resident extremophiles to rapid environmental change associated with excessive water and salt shock. Here we combine mineral/ salt composition measurements, amendment cell culture experiments, and next-generation sequencing analyses to study the variations in salts and microbial communities along a latitudinal aridity gradient of the Atacama Desert. In addition, we examine the reshuffling of Atacama microbiomes after the two rainfall events by comparing with previous researches. Analysis of microbial community composition revealed that soils within the southern arid desert were consistently dominated by Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Chloroflexi, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia. Intriguingly, the hyperarid microbial consortia exhibited a similar pattern to the more southern desert. Salts at the shallow subsurface were dissolved and leached down to a deeper layer, challenging indigenous microorganisms with the increasing osmotic stress. Microbial viability was found to change with aridity and rainfall events. This study sheds light on the structure of xerophilic, halophilic, and radioresistant microbiomes from the hyperarid northern desert to the less arid southern transition region, as well as their response to changes in water availability. Our findings may infer similar events that happened on the wetter early Mars.

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 20May 2020. Applying Holt’s second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generated 10-day ahead forecasts of the likely number of infected cases and deaths in India until 29 May2020. Our results show that the number of cumulative cases in India will rise to169109 [PI 95% (14426, 19455)], concurrently the number of deaths may increase to 4863 [PI 95% (4221, 5551)] by 29 May 2020.Further, we have marked the states (e.g. Delhi, Uttar Pradesh, Rajasthan, Madhya Pradesh, Maharashtra, Gujarat, and Tamil Nadu) where outburst is expected by considering the cases above three standard deviations. Under the worst-case scenario, Maharashtra is likely to be the most affected state with around 62628 [PI 95% (52840, 73555)] cumulative cases by 29 May 2020. However, Kerala and Karnataka are likely to remain in the lesser affected region. The presented results mark the states where lockdown by 1 June2020, can be loosened.

BackgroundThe novel virus COVID-19, also known as SARS-CoV‑2, is currently rapidly spreading around the globe and pushing healthcare systems to the limits of their capacity. One of the functions of predictive models is to timely act for epidemic preparedness including hospital preparedness. In Egypt, like many other countries in the world, the epidemic situation and forecasting have not yet sufficiently studied. ObjectiveThe study was carried out to develop a short-term forecast scenario for the COVID-19 epidemic situation in Egypt and predict the hospital needs to accommodate the growing number of cases.MethodsSecondary data from the COVID-2019 daily reports and the report issued 8^th of April by the Egyptian Ministry of Health and Population were used. Due to the daily changing level of knowledge and data, the article reflects the status up to 18 April 2020. The prediction was based on the exponential growth rate model. For the depiction of the situation, the full length of the epidemic timeline was analyzed (from February 14^th till April 18^th). The growth rates and their rates of decline during the period from the 22^nd of March till the 18^th of April were calculated and extrapolated in the coming 7 weeks. The predicted hospital needs were assessed against the announced allocated resources.ResultsThe epidemic curve in Egypt is on the ascending arm as of April, 18. The active cases showed exponential growth from the start of the epidemic till April, 18. At the end of this period time, the recovery rate was 23.12% and the case fatality rate (CFR) was7.39. The case fatality rate median level during the last four weeks was 6.64. The active cases are expected to reach more than 20,000 by late May then starts to decline. The allocated regular hospital beds are predicted to show shortage by the time of the release of the paper. The intensive care units (ICU) beds and ventilators are predicted to show insufficiency on May 6.Conclusions: The COVID-19 epidemic in Egypt is expected to continue on the rise for the next few weeks and expected to start to decline late in May, 2020. Our estimates should be useful in preparedness planning. Serious actions should be taken to provide ICU beds and ventilators enough for the predicted number of cases that would need them, not later than the end of April. Mitigation actions have to continue for the coming 6 weeks or until the epidemic situation is more clearly seen.

In crystal periodic structure prediction, a general equation is needed to determine the period vectors (cell edge vectors), especially when crystals are under arbitrary external stress. It has been derived in Newtonian dynamics years ago, which can be combined with quantum mechanics by further modeling. Here we derived such an equation in statistical physics, applicable to both classical physics and quantum physics by itself.

Decision-makers are concerned with the inherent complexity of the modern world's markets. However, price fluctuations, environmental concerns, technological development, emerging markets, political challenges, and social expectations made the 21^st century's more dynamic and complex. From a policy-making perspective, it is vital to uncover future trends. This paper proposed that artificial intelligence can improve interpretations in complex markets, such as financial and energy markets. In a complex environment, it is critical to investigate maximum available input features to ensure no valuable informative feature is neglected. Some AI-based models are investigated and presented that AI-based models can successfully uncover future trends. From a scenario development perspective purified input features subset refer to driving forces which shape alternative futures. Results showed that using AI can improve our understanding of how input features influence future behaviors and simultaneously improves prediction accuracy and reliability.

To gain insight into riser motions and associated fatigue damage due to vortex-induced vibration (VIV), data loggers such as strain sensors and/or accelerometers are sometimes deployed on risers to monitor their motion in different current velocity conditions. Accurate reconstruction of the riser response and empirical estimation of fatigue damage rates over the entire riser length using measurements from a limited number of sensors can help in efficient utilization of the costly measurements recorded. Several different empirical procedures are described here for analysis of the VIV response of a long flexible cylinder subjected to uniform and sheared current profiles. The methods include weighted waveform analysis (WWA), proper orthogonal decomposition (POD), modal phase reconstruction (MPR), a modified WWA procedure, and a hybrid method which combines MPR and the modified WWA method. Fatigue damage rates estimated using these different empirical methods are compared and cross-validated against measurements. Detailed formulations for each method are presented and discussed with examples. Results suggest that all the empirical methods, despite different underlying assumptions in each of them, can be employed to estimate fatigue damage rates quite well from limited strain measurements.

Oil and gas pipeline vandalism is a recurrent problem in oil rich zones of Nigeria and its West African neighbors and remains a challenge for multinationals to set ahead control measures to avert possible damages to operations both in infrastructure and business profit margins. In this paper, an integrative systems model comprising of a machine intelligence technique called Hierarchical Temporal Memory (HTM) and a sequence learning neural network called the Online-Sequential Extreme Learning Machine (OS-ELM) is proposed for monitoring and prediction of pipeline pressure data. The system models the continual prediction of pipeline oil/gas pressure signals useful for secure monitoring and control to avert acts of vandalism in oil and gas installations. The HTM uses a spatial pooler operated in temporal aggregated fashion and is defined as HTM-SP. The OS-ELM technique uses an explicit hierarchical training scheme so that the best cost estimates may be found after a stipulated number of trial runs. We study the performance of three OS-ELM neural activations: the sigmoid (sig), sinusoidal (sin) and radial basis function (rbf) activations. The results indicate improvement factors of 1.297, 1.297 and 1.300 of the HTM-SP over the OS-ELM sigmoid, sinusoidal and radial basis activations respectively.

In this paper, we tackle air quality forecasting by using machine learning approaches to predict the hourly concentration of air pollutants (e.g., Ozone, PM_2.5 and Sulfur Dioxide). Machine learning, as one of the most popular techniques, is able to efficiently train a model on big data by using large-scale optimization algorithms. Although there exists some works applying machine learning to air quality prediction, most of the prior studies are restricted to small scale data and simply train standard regression models (linear or non-linear) to predict the hourly air pollution concentration. In this work, we propose refined models to predict the hourly air pollution concentration based on meteorological data of previous days by formulating the prediction of 24 hours as a multi-task learning problem. It enables us to select a good model with different regularization techniques. We propose a useful regularization by enforcing the prediction models of consecutive hours to be close to each other, and compare with several typical regularizations for multi-task learning including standard Frobenius norm regularization, nuclear norm regularization, ℓ_2,1 norm regularization. Our experiments show the proposed formulations and regularization achieve better performance than existing standard regression models and existing regularizations.

Modern companies accumulate a vast amount of customer data that can be used for creating a personalized experience. Analyzing this data is difficult and most business intelligence tools cannot cope with the volume of the data. One example is churn prediction, where the cost of retaining existing customers is less than acquiring new ones. Several data mining and machine learning approaches can be used, but there is still little information about the different algorithm settings to be used when the dataset doesn't fit into a single computer memory. Because of the difficulties of applying feature selection techniques at a large scale, Incremental Probabilistic Component Analysis (IPCA) is proposed as a data preprocessing technique. Also, we present a new approach to large scale churn prediction problems based on the mini-batch Stochastic Gradient Decent (SGD) algorithm. Compared to other techniques, the new method facilitates training with large data volumes using a small memory footprint while achieving good prediction results.

This work simulates the performance of lithium batteries for electric vehicles under different charge and discharge rates. The simulation is based on similarity factors for power, voltage and current, reproducing the real operation conditions of an electric vehicle at the model scale. Most current driving modes have been analysed corresponding to discharge rates from 0.1C to 0.37C. The simulation has also been applied to determine charging time using charge power in real conditions, from 6.1 kW to 18.3 kW (0.1 C to 0.3C). Driving conditions are obtained using equations for vehicle motion including all forces. Tests have been run under two configurations, continuous and alternate current circuits, to reproduce the two types of engines that electric vehicles use. The simulation shows very good agreement in charge and discharge processes, average deviation of 3% related to real conditions, and 1.6% between them, which proves the validity of the simulation process.

The dynamic evolution and variation of electrical loads is now a priority for optimum management and, above all, forecasting. Indeed, these dynamic load variations require computer tools able to implement optimal load forecasting models. Scientific research into automated models for forecasting electrical loads therefore represents a challenge for scientific researchers. Several research studies have therefore been carried out. These include machine learning approaches such as: LSTM (Long Short-Term Memory), Support Vector Machine (SVM) and others. This article proposes a comparison study between a hybrid model based on the wavelet transform coupled with the adaboost method, and LSTM, for forecasting electricity consumption loads. Python 3.10 was used for the studies. The first results obtained consisted in the study of electricity load forecasts over four (04) data sets of the year (January, April, August and December). These results are very satisfactory, showing a good correlation between the real results of the electrical load consumption data, and the results of the implemented model. The electrical load consumption data are collected from a renewable energy production source: photovoltaic solar energy. The model test results (R2) obtained range from 0.919 to 0.958. These results are representative of real data and reflect the model's performance. The second set of results concerns a comparative study between the methods used by Y. Xie et al (CNN, MLP, LSTM-AM and LSTM-AM-MLP), and our approach. The prediction results obtained between the approaches show a prediction gap ranging from 0.15 to 0.78 for RMSE and 0.83 for MAPE. These results show significant differences between the methods. The minimization of errors by our model, therefore, reflects the model's performance in terms of accuracy, which is necessary for the optimal management of consumer electricity load forecasts in order to ensure balance between supply and demand.

In the context of artificial intelligence's pervasive integration across sectors, the rigorous examination of methodologies used in constructing mathematical models is imperative. This study delves into facial recognition models, particularly in their ability to extract features such as age. We address the prevalent practice of training models with unbalanced datasets and its implications for model performance and bias introduction. Utilizing 27,305 facial images, we investigate the influence of dataset imbalance on age prediction based on ethnicity. Employing preprocessing and oversampling techniques, we equalize sample sizes across ethnicities. A Convolutional Neural Network trained on the balanced dataset serves as our baseline, while three additional models undergo training with a 50% sample size reduction for each group to assess accuracy degradation. Our findings reveal a notable decline in accuracy for ethnic groups with reduced representation. The Asian group experiences a 67.94% accuracy deterioration, followed by a 51.60% drop for the Black group, and a 38.46% decline for the White group. These results underscore the sensitivity of certain ethnic groups to underrepresentation, highlighting the nuanced impact on age prediction accuracy. While a 50% reduction in representation does not uniformly result in a 50% accuracy decline, it unquestionably influences predictive accuracy for affected groups.

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data is generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using the predictive relationship indicates that for simplistic river scenes, the SNR over the river is predictive of the river width to within +/-5m. The presence of clutter (surrounding water bodies) within ~500m of the river causes perturbations in the SNR measured over the river that can render the river width retrievals unreliable. The results of this study indicate that under idealized conditions, GNSS-R data is able to measure river widths as narrow as 160m with high accuracy.

Inland waters pose a unique challenge for water quality monitoring by remote sensing techniques due to their complicated spectral features and small-scale variability. At the same time, collecting the high-quality reference data needed to calibrate remote sensing data products is both time consuming and expensive. In this study, we present the further development of a robotic team composed of an uncrewed surface vessel (USV) providing in situ reference measurements and an unmanned aerial vehicle (UAV) equipped with a hyperspectral imager. Together, this team is able to address the limitations of existing approaches by enabling the simultaneous collection of comprehensive in situ data together with high-resolution hyperspectral imagery. We showcase the capabilities of this team using data collected in a north Texas pond during three collections in the fall of 2020. Machine learning models for 13 variables are trained using the data set of paired in situ measurements and coincident reflectance spectra. These models can successfully estimate physical variables, including temperature, conductivity, pH, and turbidity, as well as the concentrations of blue-green algae, colored dissolved organic matter (CDOM), chlorophyll-a, crude oil, optical brighteners and the ions Ca2+, Cl−, and Na+. We extend the training procedure to utilize conformal prediction to estimate 90% confidence intervals for the output of each trained model. The maps generated by applying each trained model to the collected images reveal the small-scale spatial variability within the pond. Additionally, the permutation importance computed for each feature of the trained models highlights the spectral features relevant to each water quality variable.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.

Despite being considered as natural components of many ecosystems, forest fires pose significant threats to the environment and human health. In order to ensure public safety and effective fire suppression planning, it is necessary to develop reliable prediction models to mitigate forest fire danger. These models should account for specific environmental conditions. The advent of big data in recent years has opened new avenues for improving forest fire predictions. Machine learning techniques, surpassing traditional forecasting methods, have shown significant promise in this area. By applying the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to a real-world dataset, this paper explores the application of machine learning approaches to understand forest fire patterns and predict fire danger. We consider six distinct time stages and incorporate feature selection to refine our predictions. It is important to note that forest fire behavior models are not universally effective due to geographical variations in data. Nevertheless, advanced decision-making techniques are vital in forest fire management. Our research presents a systematic exploration of the topic, comparing various machine learning models, thereby providing a comprehensive baseline for future investigations in this crucial environmental arena.

For a fiber optic gyroscope, thermal deformation of the fiber coil can introduce additional ther-mal-induced phase errors, commonly referred to as thermal errors. Thermal error compensation techniques are effective means of addressing this issue. The principle behind these techniques involves real-time sensing of thermal errors and correcting them within the output signal. Since it is challenging to directly separate thermal errors from the output signal of the fiber optic gyro-scope, it is necessary to predict thermal errors based on temperature. To establish a mathematical model between temperature and thermal errors, this paper measured synchronized data of phase errors and angular velocity for the fiber coil under different temperature conditions and aimed to model it using data-driven methods. Due to the difficulty of conducting tests and the limited number of data samples, an algorithm called TD-model modeling is proposed to address the issue of overfitting, which can reduce the model's generalization ability. First, a theoretical analysis of the phase errors caused by thermal deformation of the fiber coil is performed. Subsequently, the critical parameters, such as the thermal expansion coefficient, are determined, and a theoretical model is established. Finally, the theoretical analysis model is incorporated as a regularization term and combined with the test data to jointly participate in the regression of model coefficients. Through experimental comparative analysis, it is shown that, relative to ordinary regression models, the TD-model effectively mitigates overfitting caused by the limited number of samples, leading to a 58% improvement in predictive accuracy.

Background: Patients with chronic obstructive pulmonary disease (COPD) often suffer from acute exacerbations. Our objective was to describe recurrent exacerbations in a GP based Swiss COPD cohort, and develop a statistical model for predicting exacerbation. Methods: In a questionnaire-based COPD cohort, demographic and medical data were recorded for 24 months. Data set was split into training (75%) and validation (25%) datasets. A negative binomial regression model was developed using the training dataset to predict the exacerbation rate within 1 year. An exacerbation prediction model was developed, and the overall performance was validated. A nomogram was created to facilitate the clinical use of the model. Results: Of the 229 COPD patients analyzed, 77% of patients had no exacerbation during the follow-up. The best subset in the training dataset found that lower forced expiratory volume, high scores on the MRC dyspnoea scale, exacerbation history, being on combination therapy of LABA+ICS or LAMA+LABA at baseline were associated with a higher rate of exacerbation. When validated, the area-under-curve (AUC) was 0.75 for one or more exacerbations. Calibration was accurate (predicted 0.34 exacerbations vs observed 0.28 exacerbations). Conclusion: Nomograms built from these models can assist clinicians in the decision-making process of their COPD care.

Accurate forecast of tail water level (TWL) is of great importance for the safe and economic operation and management of hydropower stations. The predictive performance is significantly influenced by the backwater effect of downstream hydropower stations and tributaries, but the explicit quantification method of the backwater effect is lacked. In this study, a deep learning model based forecasting framework for TWL predictions is established and applied to forecast TWL of Xiangjiaba (XJB) hydropower stations, which is influenced by the backwater effect of downstream tributaries including Hengjiang River (HJR) and Minjiang River (MJR). Firstly, the lag time of the backwater effect of HJR and MJR is analyzed based on the permutation importance. The results demonstrate that the lag time of backwater effect on the TWL of XJB is 5-7 hours for the HJR and 1-2 hours for the MJR. Then, the runoff thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained by scenario comparison, and the results show that the thresholds of HJR and MJR are 700 m3/s and 7000 m3/s respectively. Finally, the deep learning methods based TWL forecasting model is established based on the lag time and threshold analysis. The model is used to forecast the TWL in future 48 hours. The results show that the forecasting model has a good predictive performance with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm and the maximum absolute error is 63.35 cm.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

With the rise of the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), an abundant amount of trajectory data has made research on location prediction more popular. The check-in data shared through LBSN hides information related to life patterns, obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices is different from check-in data that has semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose A Self-Attention for Next Location Prediction based on Semantic Mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k nearest POI, then use the reciprocal of the distance from the stay point to the k nearest POI and the number of categories as weights. Finally, we use the probability to express semantic without losing other important semantic information. Furthermore, this research combined with sequential pattern mining can bring richer semantic features. In order to better perceive the trajectory, temporal features learn periodicity of time series by sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

Taste determination in small molecules is critical in food chemistry, but traditional experimental methods can be time-consuming. Consequently, computational techniques have emerged as val-uable tools for this task. In this study, we explore taste prediction using various molecular feature representations and assess the performance of different machine learning algorithms on a dataset comprising 2,601 molecules. The results reveal that GNN-based models outperform other ap-proaches in taste prediction. Moreover, consensus models that combine diverse molecular repre-sentations demonstrate improved performance. Among these, molecular fingerprints + GNN con-sensus model emerges as the top performer, highlighting the complementary strengths of GNNs and molecular fingerprints. These findings have significant implications for food chemistry research and related fields. By leveraging these computational approaches, taste prediction can be expedited, leading to advancements in understanding the relationship between molecular structure and taste perception in various food components and related compounds.

Machine learning methods have been a standard approach to select features that are associated with an outcome and build a prediction model when the number of candidate features is large. LASSO has been one of the most popular approaches to this end. LASSO approach selects features with large regression estimates, rather than based on statistical significance, associating the outcome, by imposing L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model loses the prediction accuracy. In this paper, we propose to use the standard regression methods (without any penalizing approach) with stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation based method selects very small number of features while maintaining a high prediction power, while LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high dimensional data even without advanced bioinformatics knowledge.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

Liver cirrhosis development is a multifactorial process resulting by a combination of environmental and genetic factors. The aim of the study was to develop accurate non-invasive diagnostic and prognostic models for alcoholic cirrhosis. Consecutive subjects with at-risk alcohol intake were retrospectively enrolled (110 cirrhotic patients and 411 non-cirrhotics). At enrollment, the data about the lifetime drinking history were collected and all patients were tested for PNPLA3 rs738409, TM6SF2 rs58542926 and HSD17B13 rs72613567 variants. In cross-sectional analyses, models for the diagnosis of cirrhosis were developed using multivariate logistic regression. A predictive score for cirrhosis development over 24 years was built by evaluating time-dependent AUC curves. The best diagnostic accuracy was demonstrated by the model which also includes daily alcohol consumption, duration of hazardous alcohol use and genetic variants, with AUCs of 0.951 (95% CI 0.925-0.977) and 0.887 (95% CI 0.925-0.977) for cirrhosis and compensated cirrhosis, respectively. The predictive model for cirrhosis future development (AUC of 0.836 95% CI: 0.769 - 0.904) accounted for age at onset of at-risk alcohol consumption and the number of PNPLA3 and HSD17B13 variant alleles. We have developed accurate genetic and alcohol consumption models for the diagnosis of alcoholic cirrhosis and the prediction of its future risk.

Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

Grain yield prediction affects policy making in various aspects such as agricultural production planning, food security assurance, and adjustment of foreign trade. Accurately predicting grain yield is of great significance in ensuring global food security. This paper is based on the MODIS remote sensing image data products from 2010 to 2020, and adds band information such as vegetation index and temperature to form composite remote sensing data as a data set. Aiming at the lack of models for large-scale forecasting and the need for human intervention in traditional models, this paper proposes a grain production estimation model based on deep learning. First, image cropping and yield mapping techniques are used to process the data to generate training samples. Then the channel and spatial attention mechanism (Convolutional Block Attention Module, CBAM) is added for extracting spatial information in different remote sensing bands to improve the efficiency of the model. Long Short-Term Memory (LSTM) neural networks is also added to obtain feature information in the time dimension. Finally, a national-scale grain yield prediction model is constructed. The proposed model was tested on data from 2018 to 2020 showing an average R2 of 0.940 and an average RMSE of 80,020 tons, indicating that it can predict Chinese grain yield better. The model proposed in this paper extracts grain yield information directly from the composite remote sensing data, and solves the problem of small-scale research and imprecise yield prediction in an end-to-end manner.

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

Falls are the third-leading cause of disabilities among the elderly population worldwide. In Pakistan, the prevalence is 44%; out of them, 8% develop injuries, placing them at high risk for hospitalization or even premature death. Interestingly, fall is multifactorial, and fall risk depends on individual characteristics (intrinsic factors) and environmental features (extrinsic factors), which can be different from context to context. Therefore, regular assessment of fall risk factors is required to develop a strategy for fall prevention. This study aimed to identify intrinsic risk factors for falls in Pakistani elders living in the communities, and provide evidence for preventive strategies of falls in elderlies. Data were collected from 140 Muslim elders from two residential areas of Peshawar, Khyber Pakhtunkhwa, from July 2022 to August 25, 2022, after taking permission from Zhengzhou University Ethical Review Board (ZUIRB #202254), and District Health Department Office (DHO #14207).Participants were informed and concern was taken before collect data. Data were collected using the Time Up and Go Test (TUGT), the Mini-Mental Status Examination, and interviews regarding the prayer practice. Factors associated with falls were; age, gender, education, cognitive status, TUGT level, incorrect prayer postures, poor vision, and history of falls with a significance of (P.<0.05). Poor cognition, low vision, poor walking speed, and prayers without body movements have good membership with falls risk prediction as (P<0.005) in Omnibus, Lemeshow score (0.77). Hence, our study provides a road map for future risk assessment for falls by adding the four mentioned risk factors as offered in the proposed model to facilitate taking timely action to prevent fall-related ailments in Pakistani elders.

: Border management serves as a crucial control checkpoint for governments to regulate the quality and safety of imported food. In 2020, the ensemble learning prediction model EL V.1 was introduced to Taiwan's border food management. This model primarily assesses the risk of imported food by combining five algorithms to determine whether quality sampling should be performed on imported food at the border. In this study, a second-generation ensemble learning prediction model, EL V.2, was developed based on seven algorithms to enhance the "detection rate of unqualified cases" and improve the robustness of the model. The chi-square test was employed to compare the efficacy of "pre-launch (2019) random sampling inspection" and "post-launch (2020–2022) model prediction sampling inspection". For cases recommended for inspection by the ensemble learning model and subsequently inspected, the unqualified rates were 5.10%, 6.36%, and 4.39% in 2020, 2021, and 2022, respectively, which were significantly higher (p=0.000***) compared with the random sampling rate of 2.09% in 2019. The prediction indices established by the confusion matrix were used to further evaluate the prediction effects of EL V.1 and EL V.2, and the EL V.2 model exhibited superior predictive performance compared with EL V.1, and both models outperformed random sampling.

Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of public preparedness. Traditionally, government agencies have employed nationwide random telephone surveys to gauge the public’s attitudes and actions towards disaster preparedness. However, these surveys may lack generalizability in certain affected locations due to low response rates or areas not covered by the survey. To address this issue and enhance the comprehensiveness of disaster preparedness assessments, we develop a framework that seamlessly integrates machine learning and simulation. Our approach leverages machine learning algorithms to establish relationships between public attitudes towards disaster preparedness and demographic characteristics. Using Monte Carlo simulation, we generate datasets that incorporate demographic information of the affected location based on government-provided demographic distribution data. The generated dataset is then input into the machine learning model to predict the disaster preparedness attitudes of the affected population. We demonstrate the effectiveness of our framework by applying it to Miami-Dade County, where it accurately predicts the level of disaster preparedness. With this innovative approach, relief agencies can have a clearer and more comprehensive understanding of public disaster preparedness.

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

The glucose-insulin regulatory system and its glucose oscillations is a recurring theme in the literature because of its impact on human lives, mostly the ones affected by diabetes mellitus. Several approaches were proposed, from mathematical to data-based models, with the aim of modeling the glucose oscillation curve. Having such a curve, it is possible to predict, when injecting insulin in type 1 diabetes (T1D) individuals. However, the literature presents prediction horizons no longer than 6 hours, which could be a problem considering their sleeping time. This work presents Tesseratus, a model that adopts a multi-agent approach to combine machine learning and mathematical modeling to predict the glucose oscillation up to 8 hours. Tesseratus uses the pharmacokinetics of insulins and data collected from T1D individuals. Its outcome can support endocrinologists while prescripting daily treatment for T1D individuals, and provide personalized recommendations for such individuals, to keep their glucose concentration in the ideal range. Tesseratus brings pioneering results for prediction horizons of 8 hours for nighttime, in an experiment with seven real T1D individuals. It is our claim that Tesseratus will be a reference for classification of glucose prediction model, supporting the mitigation of short- and long-term complications in the T1D individuals.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSD^TM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years is posing challenges related to resource management, safety, and security. In order to ensure safe mobility and security in the smart city environment, this paper proposes a novel Artificial Intelligence (AI) based approach empowering the authorities to better visualize the threats and to help them identify the highly-reported crime zones yielding greater predictability of crime hot-spots in a smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crimes to be committed. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) exploited in each dense crime region to predict the number of crimes in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time period scenarios i.e. (a) year-wise, i.e., for each year and (b) for the whole period of ten years, using an 80:20 ratio where 80\% data was used for training and 20\% data was used for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47.

Learning systems have been very focused on creating models that are capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in order to interpret and explain their results. The need for interpretation is greater when these models are used to support decision making. In some areas this becomes an indispensable requirement, such as in medicine. This paper focuses on the prediction of cardiovascular disease by analyzing the well-known Statlog (Heart) Data Set from the UCI’s Automated Learning Repository. This study will analyze the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. It will be analyzed on a large set of classification techniques and performance metrics. Demonstrating that it is possible to make explainable and reliable models that have a good commitment to predictive performance.

Human civilizations are under enormous threats due to the outbreak of novel coronavirus (COVID-19) originated from Wuhan, China. The asymptomatic carriers are the potential spreads of this novel virus. Since, guaranteed antiviral treatments have not been available in the market so far, it is really challenging to fight against this contagious disease. To save the living mankind, it is urgent to know more about how the virus transmits itself from one to another quite rapidly and how we can predict future infections. Scientists and Researchers are working hard in investigating to understand its high infection rate and transmission process. One possible way to know is to use our existing COVID-19 infection data and prepare a useful model to predict the future trend. Mathematical modelling is very useful to understand the basic principle of COVID-19 transmission and provide necessary guidelines for future prediction. Here, we have reviewed 9 distinct commonly used models based on Mathematical implementations for COVID-19 transmission and dig into the deep head to head comparison of each model. Finally, we have discussed interesting key behaviour of each model, relevant upcoming important issues, challenges and future directions.

Life evolved in an euxinic world with subsequent oxic 'invasion' leading to two parallel but interconnected biospheres, hydrogen sulphide (H₂S) and hydrogen peroxide (H₂O₂) exemplify these worlds respectively. Their concentration gradients have informational value in meromictic lakes. Similarly, it is posited, there exists a whole body chemocline in humans in which the two molecules form an inversely coupled oxic/sulphidic oscillator (OSO). The OSO is hormetic and characterised by a range of amplitudes and frequencies in health. Deviations from its baseline profile heralds the onset of SIRS before the appearance of clinical signs. Loss of oscillator status and transition to a steady state causes widespread intercellular and inter-organ communication failure presaging multi-organ dysfunction. The salient clinico-pathophysiological features of SIRS of any aetiology are emergent phenomena related to the OSO profile. Extent of recovery of organ function will mirror the recovery of the OSO profile thereby providing a tool to predict outcomes in SIRS.

A patient will visit physicians when he/she feels ill. This illness is not for COVID-19 but it is a general tendency of human being to visit doctor probably it can not be controlled by general drug. When a patient comes to a doctor, the doctor examines him/her after knowing his/her problem. The physician always asks him/her about some questions related to him/her daily life. For example, if a young male patient comes to a doctor with a symptom of fever and cough, the first question doctor asked him that he has a habit of smoking. Then doctor asks him whether this type of symptom appeared often to him previously or not. If the answers of both questions are yes, then the first one is habit and the second one is that he may suffering from some serious disease or a disease due to the weather. The aim of this paper is to consider habit of the patient as well as he/she has been affected by a critical disease. This information is used to build a model that will predict whether there is any possibility of his/her being affected by COVID-19. This research work contributes to tackle the pandemic situation occurred due to Corona Virus Infectious Disease, 2019 (Covid-19). Outbreak of this disease happens based on numerous factors such as past health records and habits of patients. Health records include diabetes tendency, cardiovascular disease existence, pregnancy, asthma, hypertension, pneumonia; chronic renal disease may contribute to this disease occurrence. Past lifestyles such as tobacco, alcohol consumption may be analyzed. A deep learning based framework is investigated to verify the relationship between past health records, habits of patients and covid-19 occurrence. A stacked Gated Recurrent Unit (GRU) based model is proposed in this paper that identifies whether a patient can be infected by this disease or not. The proposed predictive system is compared against existing benchmark Machine Learning classifiers such as Support Vector Machine (SVM) and Decision Tree (DT).

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24^th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25^th May to 11^th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

SARS-CoV-2 has been the talk of the town ever since the beginning of 2020. The pandemic has brought the complete world on a halt. Every country is trying all possible steps to combat the disease ranging from shutting the complete economy of the country to repurposing of drugs and vaccine development. The rapid data analysis and widespread tools, software and databases have made bioinformatics capable of giving new insights to the researchers to deal with the current scenario more efficiently. Vaccinomics, the new emerging field of bioinformatics uses concepts of immunogenetics and immunogenomics with in silico tools to give promising results for wet lab experiments. This approach is highly validated for the designing and development of potent vaccines. The present in-silico study was attempted to identify peptide fragments from spike surface glycoprotein that can be efficiently used for the designing and development of epitope-based vaccine designing approach. Both B-cell and T-cell epitopes are predicted using integrated computational tools. VaxiJen server was used for prediction of protective antigenicity of the protein. NetCTL was studied for analyzing most potent T cell epitopes and its subsequent MHC-I interaction through tools provided by IEDB. 3D structure prediction of peptides and MHC-I alleles (HLA-C*03:03) was further done to carry out docking studies using AutoDock4.0. Various tools from IEDB were used to predict B-cell epitopes on the basis of different essential parameters like surface accessibility, beta turns and many more. Based on results interpretation, the peptide sequence from 1138-1145 amino acid and sequence WTAGAAAYY and YDPLQPEL were obtained as a potential B-cell epitope and T-cell epitope respectively. This in-silico study will help us to identify novel epitope-based peptide vaccine target in spike protein of SARS-CoV-2. Further, in-vitro and in-vivo study needed to validate the findings.

This study presents a prediction model based on Logistic Growth Curve to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depended on partial data from the COVID-19 cases in Malaysia. The model is then studied together with the effectiveness of the three phases of MCO implemented in Malaysia. Evidence from this study suggests that results of the LGC prediction model match with the progress and effectiveness of the MCO to flatten the curve, thus helped to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

Wind power as a renewable source of energy, has numerous economic, environmental and social benefits. In order to enhance and control the renewable wind power, it is vital to utilize models that predict wind speed with high accuracy. Due to neglecting of requirement and significance of data preprocessing and disregarding the inadequacy of using a single predicting model, many traditional models have poor performance in wind speed prediction. In the current study, for predicting wind speed at target stations in the north of Iran, the combination of a multi-layer perceptron model (MLP) with the Whale Optimization Algorithm (WOA) used to build new method (MLP-WOA) with a limited set of data (2004-2014). Then, the MLP-WOA model was utilized at each of the ten target stations, with the nine stations for training and tenth station for testing (namely: Astara, Bandar-E-Anzali, Rasht, Manjil, Jirandeh, Talesh, Kiyashahr, Lahijan, Masuleh and Deylaman) to increase the accuracy of the subsequent hybrid model. Capability of the hybrid model in wind speed forecasting at each target station was compared with the MLP model without the WOA optimizer. To determine definite results, numerous statistical performances were utilized. For all ten target stations, the MLP-WOA model had precise outcomes than the standalone MLP model. The hybrid model had acceptable performances with lower amounts of the RMSE, SI and RE parameters and higher values of NSE, WI and KGE parameters. It was concluded that WOA optimization algorithm can improve prediction accuracy of MLP model and may be recommended for accurate wind speed prediction.

In this paper an integrated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early-stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive Diesel engine, considering different working points including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable to properly assess pollutant formation, specifically to estimate NOx concentrations. Soot formation trend was also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new Diesel engines, thanks to its ability to provide reliable engine performance and emissions estimations from the early-stage of a new engine development.

Accurate prediction of defects in software components plays a vital role in administrating the quality of the quality and efficiency of the system to be developed. So we have written a systematic literature review in order to evaluate the four main defect prediction techniques. Defect prediction paves way for the testers to find bugs and modify them in order to achieve input to output conformance. In this paper we have discussed the open issues in predicting software defects and have provided with a detailed analyzation of different methods including Machine Learning, Integrated Approach, Cross-Project and Deep Forest algorithm in order to prevent these flaws. However, it is almost impossible to rule which method is better than the other so every technique can be analyzed separately and the best technique according to the problem at hand can be used or can be altered to create hybrid technique suitable for the cause.

The future of the Colorado River water supply (WS) affects millions of people and the U.S. economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year-averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020. Further research to update similar multi-year prediction of the Colorado River WS is needed. Such information could aid in management decision making in the face of future water shortages.

A combination of physical-chemical analysis has been used to monitor the aging of red wines from D.O. Toro (Spain). The changes in the chemical composition of wines that occur along aging time can be permitted to discriminate wine samples collected after one, four, seven and ten months of aging. Different computational models were used to develop a good authenticity tool to certificate wines. In this research different models have developed: Artificial Neural Network models (ANNs), Support Vector Machine (SVM) and Random Forest (RF) models. The results obtained for the ANN model developed with sigmoidal function in the output neuron and the RF model permit to determine the aging time, with an average absolute percentage deviation below 1% and it can conclude that these two models have demonstrated its capacity as a valid tool to predict the wine age.

The propeller jet from a ship has a significant component directed upwards towards the free surface of the water, which can be used for ice management. This paper describes a comprehensive laboratory experiment where the influences of operational factors affecting a propeller wake velocity field were investigated. The experiment was done on a steady wake field to investigate the characteristics of the axial velocity of the fluid in the wake and the corresponding variability downstream of the propeller. The axial velocities and the variability recorded were time-averaged. Propeller rotational speed was found to be the most significant factor, followed by propeller inclination. The experimental results also provide some idea about the change of the patterns of the mean axial velocity distribution against the factors considered for the test throughout the effective wake field, as well as the relationships to predict the axial velocity for known factors.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

This study presents the interaction with human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a Protein-Protein-Interaction Networks analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2,753 proven interactions and 1,708 interactors, specific to ORF7b. We used these proteins as functional seeds and we got a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. Seven high ranked proteins as hub and seven as bottleneck following a hub-and-spoke network-architectural-model were found. Through this interaction model, we identified significant GO-processes (5,057 terms in 15 categories) induced in human metabolism by ORF7b. High statistical significance processes of dysregulated molecular cell mechanisms by the action of ORF7b were discovered. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems and the molecular mechanisms that supervise to programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant-Connected-Components core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b mainly acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation, transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes, within the limits of the poor knowledge that these sectors still impose.

Satellite altimetry is successfully developing during the past three decades for the sea level, ocean dynamics, coastal oceanography, planetary waves, ocean tides, wind and wave, ice cover, Earth’s gravity field, and climatology research. We propose a new essential add-on of satellite altimetry related to the peculiarities of the orbits of the Topex/Poseidon and Jasons’ satellite missions which were not mentioned before in the scientific publications. Derived subsets of “self-crossover” and “inter-crossover” points in sub-polar latitudes are discussed in detail in the context of water exchange, and wind-wave dynamics, and potential challenges to be solved. The relatively short time lags between measurements at these crossovers provide additional information on anomalies of magnitudes and directions of ocean currents, and characteristics of wind-driven waves. Resulting data snapshots with constant space and time intervals can be regarded as time series of virtual buoys, an analog of continuous buoy measurements of the sea level, wind speed, and wave height. Areas of the World Ocean where these specific crossovers occur are described in the context of water exchange, wind wave studies, and potential challenges to be solved. The value of these special crossovers for studies and monitoring of the sub-polar seas is illustrated by a case study.

Traffic accidents are among the most common causes of death worldwide. According to statistics from the World Health Organization (WHO), 50 million people are involved in traffic accidents every year. Canada, particularly Montreal, is not immune to this problem. Data from the Société de l’Assurance Automobile du Québec (SAAQ) shows that there were 392 deaths on Québec roads in 2022, 38 of them related to the city of Montreal. This value represents an increase of 29.3% for the city of Montreal compared to the average for the years 2017 to 2021. In this context, it is important to take concrete measures to improve traffic safety in the city of Montreal. In this article, we present a web-based solution based on machine learning that predicts the severity of traffic accidents in the city of Montreal. This solution uses dataset of traffic accidents that occurred in Montreal between 2012 and 2021. Classification algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Random Forest (RF) and Gradient Boosting (GB) were used to develop the prediction model. When evaluating the prediction model, performance metrics such as precision, recall, F1 score, and accuracy are taken into account. The performance analysis shows an excellent accuracy of 96% for the prediction model based on the XGBoost classifier. The other models (CatBoost, RF, GB) achieved 95%, 93% and 89% accuracy, respectively. The prediction model based on the XGBoost classifier was deployed using a client-server web application managed by Swagger-UI and the Flask Python framework.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF-target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application, utilizing the R programming language, which predicts TF-target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF-target gene networks. Despite its current limitation to human data, it provides a platform for exploring gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to obtain the execution status, jump directions, and jump address of conditional branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of updates to the branch target buffer (BTB). When it exceeds the threshold, BTB is locked to prevent attackers from analyzing the execution status of branches based on the time difference of whether BTB is updated. Moreover, the hybrid physical unclonable function (PUF) circuit is designed to provide confidentiality protection for the jump directions, jump addresses, and their indexes, preventing attackers from stealing these critical data. If these mechanism fails and attackers successfully tamper with conditional branches, this paper proposes a control flow integrity (CFI) protection mechanism based on branch labels to timely detect tampering with instruction codes, jump addresses, and jump directions. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries which are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.

This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

Evolutionary Machine Learning (EML) has demonstrated its effectiveness in modeling various real-world application domains, including the dynamics of diseases and the creation of predictive models across diverse fields. However, the challenge of data imbalance adversely impacts the performance of EML models. While strategies involving misclassification costs or cost-sensitive learning exist to address this issue, their integration into EML has yet to be thoroughly explored. This paper aims to fill this gap by investigating the application of cost-sensitive EML for intelligent health risk prediction. More specifically, this paper delves into the utilization of cost-sensitive genetic programming (CSGP) and cost-sensitive genetic algorithms (CSGA), which are among the most widely used algorithms in the EML category. The study entails training CSGP and CSGA to predict the early onset of three chronic conditions (hypertension, diabetes, and fatty acid-related disorders) through the development of an EML model. Subsequently, the model's performance is evaluated through standard fitness functions such as sensitivity, specificity, and F1-score. From the experimental results, EML models demonstrate promising results in identifying the occurrence of chronic diseases. Further optimization of the cost-sensitive EML model holds the potential for enhanced outcomes in modeling medical problems.

To study the oil and gas distribution law in the slope area of the hydrocarbon-bearing basin, the distribution of the effective dominant path of sand bodies transporting oil and gas is a fundamental subject. In this paper, we introduced an effective way to predict such transport paths and applied the method to study the sand bodies of the lower first member of the Shahejie Formation (E3s1L) in the southern area of the Qikou Depression in the Bohai Bay Basin (Qinan). We first define the dominant hydrocarbon transport paths of the sand body as the superimposed area of the contiguously distributed area of the sand body and the paleotectonic ridges at the top of the formation where the sand body is located. Then we define the oil and gas supply area of the oil source fault as the overlapped area of the favorable transport path of oil source faults and the width of the associated fracture zone. Stacking the two areas, we can delineate the distribution of the effective dominant transport paths of the sand bodies. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the E3s1L Formation in the Qinan area are distributed throughout the region with better development in the west than in the east. The paths in the eastern part extend from north to south, while the paths in the western part extend from northeast to southwest, which is favorable for the convergence of oil and gas generated from the source rocks of the third member of Shahejie Formation (Es3) in the Northeastern main depression of Qikou and the formation of oil and gas accumulations in the l E3s1L Formation. The results are consistent with the fact that oil and gas discovered in the E3s1L Formation in the Qinan region are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible to predict the effective dominant path of sand bodies transporting oil and gas.

Commercial buildings situated in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This research paper aims to address this gap by investigating the application of innovative deep learning models in day-ahead hourly cooling load prediction for commercial buildings situated in tropical climates. A range of deep learning techniques, including Deep Neural Network, Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. An extensive comparative analysis is conducted to identify the most effective hybrid model. Sequence-to-sequence model provided better performance compared to the other single and hybrid models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. Further, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies.

Tight gas, a category of Deep Green Energy Resources, rely on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually added rules. Therefore, it is possible to make reservoir evaluation via using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical-tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7,000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR logging based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR logging based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Lymphoma diagnoses in the U.S. are substantial, with an estimated 89,380 new cases in 2023, necessitating innovative treatment approaches. Phase 1 clinical trials play a pivotal role in this context. We developed a binary predictive model to assess trial adherence to expected average durations, analyzing 1,089 completed Phase 1 lymphoma trials from clinicaltrials.gov. Using machine learning, the Random Forest model demonstrated high efficacy with an accuracy of 0.7248 and ROC-AUC of 0.7701 for lymphoma trials. Importantly, this model maintained an accuracy of 0.7405 when applied to lung cancer trials, showcasing its versatility. A key insight is the correlation between higher predicted probabilities and extended trial durations, offering nuanced insights beyond binary predictions. Our research contributes to enhanced clinical research planning and potential improvements in patient outcomes in oncology.

Changes in sea level exhibit nonlinearity, nonstationarity, and multivariable characteristics, making traditional time series forecasting methods less effective in producing satisfactory results. To enhance the accuracy of the predictions of changes in sea level, this study introduced an improved VMD–EEMD–LSTM hybrid model. This model decomposes satellite altimetry data from near the Dutch coast using VMD, resulting in components of the Intrinsic Mode Function (IMF) with various frequencies and a residual sequence. EEMD further dissects the residual sequence obtained from VMD into second-order components. These IMFs decomposed by VMD and EEMD are utilized as features in the LSTM model for making predictions, culminating in the final forecasted results. The experimental results demonstrated significant improvements in the predictive performance compared with the VMD–LSTM model. The RMSE (root mean square error) decreased by an average of 58.68%, the MAE (mean absolute error) reduced by an average of 59.96%, and the R2 (coefficient of determination) increased by an average of 49.85% compared with the VMD–LSTM model. In comparison with the EEMD–LSTM model, the RMSE decreased by an average of 26.95%, the MAE decreased by an average of 28.00%, and the R2 increased by an average of 6.53%. The VMD–EEMD–LSTM model exhibited significantly improved predictive performance.

In a super-aged society, maintaining healthy aging, preventing death, and enabling a continua-tion of economic activities are crucial. This study sought to develop a model for predicting the survival time in community-dwelling older individuals by using a deep learning method, and to identify the level of influence of various risk factors on the survival period, so that older in-dividuals can manage their own health. This study used the Korean National Health Insurance Service claims data. We observed community-dwelling older people, aged 66 years, for 11 years and developed a survival time prediction model. Of the 189,697 individuals enrolled at baseline, 180,235 (95.0%) survived from 2009 to 2019, while 9,462 (5.0%) died. Using deep learning based models (C statistics = 0.7011), we identified Charlson’s comorbidity index; the frailty index; long-term care benefit grade; disability grade; income level; a combination of diabetes mellitus, hypertension, and dyslipidemia; sex; smoking status; and alcohol consumption habit as factors impacting survival. In particular, Charlson’s comorbidity index (SHAP value: 0.0445) and frailty index (SHAP value: 0.0443) were strong predictors of survival time. Older individuals should rec-ognize modifiable risk factors that affect survival period in order to manage their own health.

Recently, NOAA has included GNSS (Global Navigation Satellite System) Radio Occultation (RO) data as one of the crucial long-term observables for weather and climate applications. To include more GNSS RO data in the numerical weather prediction system, the NOAA Commercial Weather Data Pilot program (CWDP) started to explore the commercial RO data available on the market. After two rounds of pilot studies, the CDWP decided to award the first Indefinite Delivery Indefinite Quantity (IDIQ) contract to GeoOptics and Spire Incs. in 2020. This study examines the quality of Spire data products for weather and climate applications. Spire RO data are collected from commercial CubeSats through careful comparison with the data from Formosa Satellite Mission 7–Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), ERA-5, and high-quality radiosonde data. The results demonstrated that although with lower Signal-Noise-Ratio (SNR) in general, the pattern of the lowest penetration height for Spire is similar to those for COSMIC-2. The Spire and COSMIC-2 penetrate heights are between 0.6 and 0.8 km altitude at the tropical oceans. Although using different GNSS RO receivers, the precision of Spire STRASP receivers is of the same quality as those of COSMIC-2 Global Positioning System - GPS, GALILEO, and GLObal NAvigation Satellite System – GLONASS (TGRS) receivers. The retrieval accuracy from Spire is very compatible with those from COSMIC-2. We validated Spire temperature and water vapor profiles by comparing them with collocated radiosonde data. Generally, over the height region between 8 km and 16.5 km, the Spire temperature profiles match those from RS41 RAOB very well with temperature biases &lt; 0.02 K. Over the height range from 17.8 to 26.4 km, the temperature differences are ~-0.034 K with RS41 RAOB being warmer. We also estimated the error covariance matrix for Spire, COSMIC-2, and KOMPSAT-5. Results showed that the COSMIC-2 estimated error covariance values are slightly more significant over the oceans at the mid-latitudes (45oN-30oN and 30oS-45oS), which may also be owing to COSMIC-2 SNR being lower at those latitudinal zones.

This study describes the AMD impacted environments and critically discusses the effects of AMD, current prediction and prevention methods and treatment technologies. The study further, crit-ically analyses the case studies, gaps in AMD research and the challenges and opportunities of-fered by AMD. The study outlined future technological intervention aiming at a paradigm shift towards reducing the volume of sludge generated as well as the operating cost and improving AMD treatment efficiency. The integrated AMD treatment technologies that result in a holistic approach toward sustainable AMD treatment are a current need. As a result, a sustainable AMD treatment strategy has been made possible due to water reuse and rich resource recoveries such sulphuric acid, rare earth elements, and metals. The cost of AMD treatment can be decreased with the use of recovered water and resources, which is essential for developing a sustainable AMD treatment process.

Asphalt pavement temperature has several applications, including pavement structural design and evaluation, asphalt mixture design, asphalt binder grade determination and material aging characterization. However, available asphalt pavement temperature prediction models were mostly developed for temperate climatic conditions, outside of the tropics. Before such models are adopted for use in a tropical climate, their applicability must be evaluated. This study evaluated five empirical asphalt pavement temperature prediction models developed by Lukanen et al. [1], Park et al. [2], Diefenderfer et al. [3], Taamneh [4] all in the United States and Asefzadeh et al. [5] in Canada to ascertain their prediction accuracy in tropical climate, using Ghana as a case study. In-situ asphalt pavement temperature data were measured at two sites for eight months: Kumasi and Tamale in the Forest and Savannah climatic zones, respectively. The measured pavement temperature data were statistically compared with predicted temperatures, and it was found that the Park et al. [2] model provided a more accurate pavement temperature prediction in both climatic zones. The other models either over-predicted or under-predicted asphalt pavement temperature with significant error margins. However, there is a need to improve the prediction accuracy of the Park et al. [2] model or a local model developed.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

Comparison with conventional reservoirs, the prediction of pressure in ultra-deep tight sandstone formations is difficult. The prediction of seismic pressure is more challenging than well logging pressure prediction. The main methods for seismic pressure prediction include equivalent depth method, Eaton method, Fillippone formula, and its modified versions. Among them, the Eaton method is widely used and has good effectiveness. However, this method relies on difficult-to-obtain normal compaction trend line, which leads to low prediction accuracy in space. To address this issue, a method combining the Eaton method and Collocated cokriging is proposed. The Eaton formula is used to predict formation pressure at the well, with compressional wave velocity as the covariate for predicting the main variable, formation pressure. By simulating the shear wave velocity based on seismic compressional wave velocity, the influence of various parameters on the prediction results is analyzed, and the accuracy of this method is verified by comparing it with other methods. The proposed method is then applied to predict formation pressure in the ultra-deep formations of the Junggar Basin. The simulation results show that the Collocated cokriging method achieves higher planar accuracy and better matches the experimental expectations in terms of prediction results. The application results also demonstrate the scientific effectiveness of the combined method, which has achieved good results in practical production applications.

Nickel base superalloy GH4169 is a material with strong mechanical properties and is difficult to process. In order to reduce tool wear during material processing and improve workpiece surface processing quality, based on the finite element simulation software DEFORM, the influence of n, ap, and fz parameters on tool wear during carbide tool milling GH4169 was studied, and a simulation orthogonal experimental model was established, The prediction model of tool wear is obtained; The ultrasonic vibration milling is compared with ordinary milling, and the improvement degree of different coating materials on carbide tool wear is explored. The results show that the ultrasonic vibration signal is helpful to reduce tool wear, improve the surface quality of the workpiece, and improve the stability of the milling process. TIALN/TIN (WC) composite coated tools have good cutting performance, help to reduce tool temperature, reduce tool wear, and improve tool life.

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

As development progresses, when the bottom hole flowing pressure or formation pressure is less than the saturation pressure of crude oil in the reservoir, oil and gas two-phase seepage occurs in the reservoir. Due to the characteristics of oil and gas two-phase seepage, after the oil and gas two-phase seepage occurs in the reservoir, the well production will be reduced, or even greatly reduced. Therefore, how to predict the horizontal well capacity better in this case is an important problem that needs to be solved urgently. In this paper, the method and process of establishing the transient calculation model of two-phase flow in horizontal wells are introduced in detail from three aspects: fluid physical properties, reservoir oil-gas two-phase seepage, and the coupling model of Inflow Performance and Flow in Wellbore. The model is more reliable through the verification of production data from five wells in two oilfields.

The cost to train a basic qualified U.S. Navy fighter aircraft pilot is nearly $10M. The training includes primary, intermediate, and advanced stages, with the advanced stage involving extensive flight training and being very expensive as a result. Despite the screening tests in place and early-stage attrition taking place, 4.5% of aviators undergo attrition in this most expensive stage. Key reasons for aviator attrition include poor flight scores, voluntary withdrawals, and medical causes. Reduction in late-stage attrition offers several financial and operational benefits to the U.S. Navy. To that end, this research leverages feature extraction and machine-learning techniques on the very sparse flight test grades of student aviators to identify those with a high risk of attrition early in training. Using about 10 years of historical U.S. Navy pilot training data, trained models accurately predicted 50% of attrition with a 4% false positive rate. Such models could help the U.S. Navy save nearly $20M a year in attrition costs. In addition, machine-learning models were trained that can recommend a suitable training aircraft type for each student aviator. These capabilities could help better answer the need for pilots and reduce the time and cost to train them.

Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCT) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualized prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: A radiomics-only model, a clinical-only model and a combined radiomics-clinical model. The models' performance was evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.84 (± 0.17; 95% CI ) vs 0.60 (± 0.22; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models and showed good discrimination with an area under the curve of 0.94 ( ± 0.10; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics-clinical model represents an exciting non-invasive prediction tool for individualized prediction of LN metastasis in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application.

Time series data having low signal-to-noise ratio, non-stationarity and non-linearity are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time series data. In this paper, we develop an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future time, we use only the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing involves updating only the current data into the process. We evaluate the performance of O-LGT by analyzing the high-frequency Limit Order Book (LOB) data from the China market. It shows that our model in most cases achieves similar speed with much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 40 times faster than the existing high-accuracy neural network models for the LOB data in China market.

Background. COVID-19 efforts were often ineffective in controlling the spread of the pandemic. Identifying ineffective controls during a pandemic is thus vital. Method. Utilizing publicly available data on COVID deaths in the counties of US states, we create an index to capture and interpret ineffectiveness in the efforts to reduce the spread of the pandemic in US counties. This index is based on the Intervened Poisson Distribution (IPD) introduced originally by Shanmugam. Motivation for the research idea occurred while we noticed the data dispersion of the COVID deaths is smaller than the average only in some counties. Under-dispersed data is common in statistical modeling. A novel approach we adapted in this article includes the estimation of an intervention parameter estimated through iterative non-linear optimization. Results. Twenty-five counties in California, Idaho, Minnesota, Mississippi, Montana, Nebraska, North Carolina, North Dakota, Texas, and Utah were found to be ineffective in controlling for fatalities based on the expected probability distribution. A review of the policies enacted in these areas would provide insight into ineffective prevention efforts, and some of these issues are documented in current literature. Conclusion. The IPD index an innovate way to document efficacy of interventions during pandemics. The IPD may identify ineffective efforts prior to statistical models intended to evaluate efficacy of efforts.

Bayesian models allow us to investigate children’s belief revision alongside physiological states like “surprise”. Recent work finds that pupil dilation (or the “pupillary surprise response”) following expectancy-violations may be predictive of belief revision. How can probabilistic models inform interpretations of “surprise”? Shannon Information considers the likelihood of an observed event, given prior beliefs – suggesting stronger surprise occurs following unlikely events. In contrast, Kullback-Leibler divergence considers the “dissimilarity” between prior beliefs and updated beliefs following observations – with greater surprise indicating more change between belief states to accommodate information. To assess these accounts under different learning contexts, we use Bayesian models that compare these computational measures of “surprise” to contexts where children are asked to either predict or to evaluate the same evidence during a water displacement task. We find correlations between the computed Kullback-Leibler divergence and children’s pupillometry responses only when children actively make predictions, and no correlation between Shannon Information and pupillometry. This suggests that when children attend to their beliefs and make predictions, pupillary responses may signal the degree of divergence between a child’s current beliefs and updated, more accommodating beliefs.

Prediction of protein-ligand binding affinities is crucial for computational drug discovery. A number of deep learning approaches have been developed in recent years to improve the accuracy of such affinity prediction. While the predicting power of these systems have advanced to some degrees depending on the dataset used for model training and testing, the effects of the quality and quantity of the underlying data have not been thoroughly examined. In this study, we employed erroneous datasets and data subsets of different sizes, created from one of the largest databases of experimental binding affinities, to train and evaluate a deep learning system based on convolutional neural networks. Our results show that data quality and quantity do have significant impacts on the prediction performance of trained models. Depending on the variations in data quality and quantity, the performance discrepancies could be comparable to or even larger than those observed among different deep learning approaches. In particular, the presence of proteins during model training leads to a dramatic increase in prediction accuracy. This implies that continued accumulation of high-quality affinity data, especially for new protein targets, is indispensable for improving deep learning models to better predict protein-ligand binding affinities.

MicroRNAs (miRNAs) are a class of small non-coding RNAs, which function as regulators of gene expression and contribute in numerous physiological processes. However, little is known referring to miRNAs function in insect chemosensation. In the current study, nine small RNA libraries were constructed and sequenced from the antennae of nymphs, adult males and females of Apolygus lucorum. In total, 399 miRNAs were identified including 275 known and 124 novel miRNAs. Known miRNAs were classified into 71 families, amongst which, 23 families were insect-specific. Expression profile analysis showed that miR-7-5p_1 was the most abundant miRNAs in the antennae of A. lucorum. Altogether, 69708 target genes related to biogenesis, membrane and binding activities were predicted for 399 miRNAs. Particularly, 15 miRNAs were found to target 16 olfactory genes. These miRNAs could be involved in regulation of olfactory-associated genes ex-pression. Comparing the antennae of nymphs, adult males and females, 94 miRNAs were found to be differentially expressed. The expression levels of some differentially expressed miRNAs measured by qPCR were consistent with sequencing results. This study provides a global miRNAs transcriptome in the antennae of A. lucorum and valuable information for further investigation on miRNA-mRNA interactions, especially the functions of miRNAs in regulating chemosensation.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviours and urban mobility. Here we propose a framework, ActivityNET, using machine learning (ML) algorithms to predict passengers’ trip purpose from smart card data and Points-of-Interest (POIs) data. The feasibility of the framework is demonstrated in two phases. Phase I focuses on extracting activities from individuals’ daily travel patterns from smart card data and combining them with POIs using the proposed ‘activity-POIs consolidation algorithm’. Phase II feeds the extracted features into an artificial neural network (ANN) with multiple scenarios and predicts trip purpose under primary activities (home and work) and secondary activities (entertainment, eating, shopping, child drop-offs/pick-ups and part-time work) with high accuracy. As a case study, the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose. The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.

The new Internet of Things (IoT) applications are enabling the development of projects that help monitoring people with different diseases in their daily lives. Alzheimer’s is a disease that affects neurological functions and needs support to maintain maximum independence and security of patients during this stage of life, as the cure and reversal of symptoms have not yet been discovered. The IoT-based monitoring system provides the caregivers’ support in monitoring people with Alzheimer’s Disease (AD). This paper presents an ontology-based computational model which receives physiological data from external IoT applications, allowing to identify of potentially dangerous behaviors for patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, it is the only one that uses analysis of Context Histories to perform predictions. The research also proposes a simulator to generate activities of the daily life of patients allowing the creation of datasets. These datasets were used to evaluate the contributions of the model and were generated according to the standardization of the ontology. The simulator generated 1025 scenarios applied to guide the predictions, which achieved average accurary of 97.44%. The experiments also allowed the learning of 20 relevant lessons on technological, medical and methodological aspects of DCARE that are recorded in this article.

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.