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The analysis of large volumes of data collected from heterogeneous sources is increasingly important for the development of megacities, the advancement of smart city technologies, and ensuring a high quality of life for citizens. This study aimed to develop algorithms for analyzing and interpreting social media data to assess citizens' opinions in real time and for verifying and examining data to analyze social tension and predict the development of situations during the implementation of urban projects. The developed algorithms were tested using an urban project in the field of transportation system development. The study's material included data from social networks, messenger channels and chats, video hosting platforms, blogs, microblogs, forums, and review sites. An interdisciplinary approach was utilized to analyze the data, employing tools such as Brand Analytics, TextAnalyst 2.32, GPT-3.5, GPT-4, GPT-4o, and Tableau. The results of the data analysis showed identical outcomes, indicating a neutral perception among users and the absence of social tension surrounding the project's implementation, allowing for the prediction of a calm development of the situation. Additionally, recommendations were developed to avert potential conflicts and eliminate sources of social tension for decision-making purposes. [ABSTRACT FROM AUTHOR]

Predicting breakthrough papers holds great significance; however, prior studies encountered challenges in this task, indicating a need for substantial improvement. We propose that the failure to capture the dynamic structural-evolutionary features of citation networks is one of the major reasons. To overcome this limitation, this paper introduces a new method for constructing citation cascades of focus papers, allowing the creation of a time-series-like set of citation cascades. Then, through a thorough review, three types of structural indicators in these citation networks that could reflect breakthroughs are identified, including certain basic topological metrics, PageRank values, and the von Neumann graph entropy. Based on the time-series-like set of citation cascades, the dynamic trajectories of these indicators are calculated and employed as predictors. Using the Nobel Prize-winning papers as a landmark dataset, our prediction method yields approximately a 7% improvement in the ROC-AUC score compared to static-based prior methods. Additionally, our method advances in achieving earlier predictions than other previous methods. The main contribution of this paper is proposing a novel method for creating citation cascades in chronological order and confirming the significance of predicting breakthroughs from a dynamic structural perspective. [ABSTRACT FROM AUTHOR]

Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids. [ABSTRACT FROM AUTHOR]

Dementia poses a substantial global health challenge, warranting an exploration of its intricate pathophysiological mechanisms and potential intervention targets. Leveraging multi-omic technology, this study utilizes data from 2251 participants to construct classification models using lipidomic, gut metabolomic, and cerebrospinal fluid (CSF) proteomic markers to distinguish between the states of cognitive decline, namely, the cognitively unimpaired state, mild cognitive impairment, and dementia. The analysis identifies three CSF proteins (apolipoprotein E, neuronal pentraxin-2, and fatty-acid-binding protein), four lipids (DEDE.18.2, DEDE.20.4, LPC.O.20.1, and LPC.P.18.1), and five serum gut metabolites (Hyodeoxycholic acid, Glycohyodeoxycholic acid, Hippuric acid, Glyceric acid, and Glycodeoxycholic acid) capable of predicting dementia prevalence from cognitively unimpaired participants, achieving Area Under the Curve (AUC) values of 0.879 (95% CI: 0.802–0.956), 0.766 (95% CI: 0.700–0.835), and 0.717 (95% CI: 0.657–0.777), respectively. Furthermore, exclusively three CSF proteins exhibit the potential to predict mild cognitive impairment prevalence from cognitively unimpaired subjects, with an AUC of 0.760 (95% CI: 0.691–0.828). In conclusion, we present novel combinations of lipids, gut metabolites, and CSF proteins that showed discriminative abilities between the states of cognitive decline and underscore the potential of these molecules in elucidating the mechanisms of cognitive decline. [ABSTRACT FROM AUTHOR]

The metallurgical sector is one of the important sectors of the Slovak economy. Its integral part is the storage of metallurgical waste, which is accompanied by noise that bothers the inhabitants of the surrounding urban areas. This paper focuses on the analysis of the problem of noise propagation into protected areas located in the vicinity of the metallurgical plant. The paper describes a number of measurements that have been carried out at the slag landfill. Based on these measurements, simulations were performed using a mathematical model, and predictions of noise propagation in the exterior were made. Subsequently, noise reduction measures were proposed. The results obtained by the authors form a methodological basis for addressing such situations, since, during the solution, it was often necessary to deal with non-standard situations that were specific to the area of the technology addressed. This solution was then applied in real practice. [ABSTRACT FROM AUTHOR]

Indian diet has changed over the years as a result of geographic, cultural, and traditional influences. However, Western influences have recently had a substantial impact, including changes in eating behaviors and an increase in the consumption of fast food. Adoption of these foods has also led to a shift from traditional Indian meals, which mostly consists of a variety of grains, vegetables, lentils, and spices. In contrast to fast food, which is frequently highly processed and deficient in important nutrients, these traditional diets are frequently more wholesome, balanced, and nutritious. Obesity rates in India have risen rapidly, with a significant increase in cases of diabetes, heart disease, and other diet-related illnesses. According to the World Health Organization (WHO), the occurrence of obesity tripled between 1975 and 2016 and is continually rising. This research work seeks to give customers a meal plan that complies with their calorie needs based on their BMI and TDEE. The meal plan will only include foods that are available in India. A dish may also be substituted if the user does not have access to it. The meal plan is generated using the random forest classifier and the alternatives are provided by using the KNN algorithm. The classifier has an accuracy of 91%. [ABSTRACT FROM AUTHOR]

Urban green spaces improve quality of life by mitigating urban temperatures. However, there are challenges in obtaining urban data to analyze and understand their influence. With the aim of developing innovative methodologies for this type of research, Artificial Neural Networks (ANNs) were developed to predict daily and hourly temperatures in urban green spaces from sensors placed in situ for 41 days. The study areas were four urban allotment gardens (with dynamic and productive vegetation) and a forested urban park in the city of Valladolid, Spain. ANNs were built and evaluated from various combinations of inputs (X), hidden neurons (Y), and outputs (Z) under the practical rule of "making networks simple, to obtain better results". Seven ANNs architectures were tested: 7-Y-5 (Y = 6, 7, ..., 14), 6-Y-5 (Y = 6, 7, ..., 14), 7-Y-1 (Y = 2, 3, ..., 8), 6-Y-1 (Y = 2, 3, ..., 8), 4-Y-1 (Y = 1, 2, ..., 7), 3-Y-1 (Y = 1, 2, ..., 7), and 2-Y-1 (Y = 2, 3, ..., 8). The best-performing model was the 6-Y-1 ANN architecture with a Root Mean Square Error (RMSE) of 0.42 °C for the urban garden called Valle de Arán. The results demonstrated that from shorter data points obtained in situ, ANNs predictions achieve acceptable results and reflect the usefulness of the methodology. These predictions were more accurate in urban gardens than in urban parks, where the type of existing vegetation can be a decisive factor. This study can contribute to the development of a sustainable and smart city, and has the potential to be replicated in cities where the influence of urban green spaces on urban temperatures is studied with traditional methodologies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a profound impact on people's lives, making accurate prediction of epidemic trends a central focus in COVID-19 research. This study innovatively utilizes a spatiotemporal heterogeneity analysis (GTNNWR) model to predict COVID-19 deaths, simulate pandemic prevention scenarios, and quantitatively assess their preventive effects. The results show that the GTNNWR model exhibits superior predictive capacity to the conventional infectious disease dynamics model (SEIR model), which is approximately 9% higher, and reflects the spatial and temporal heterogeneity well. In scenario simulations, this study established five scenarios for epidemic prevention measures, and the results indicate that masks are the most influential single preventive measure, reducing deaths by 5.38%, followed by vaccination at 3.59%, and social distancing mandates at 2.69%. However, implementing single stringent preventive measures does not guarantee effectiveness across all states and months, such as California in January 2025, Florida in August 2024, and March–April 2024 in the continental U.S. On the other hand, the combined implementation of preventive measures proves 5 to-10-fold more effective than any single stringent measure, reducing deaths by 27.2%. The deaths under combined implementation measures never exceed that of standard preventive measures in any month. The research found that the combined implementation of measures in mask wearing, vaccination, and social distancing during winter can reduce the deaths by approximately 45%, which is approximately 1.5–3-fold higher than in the other seasons. This study provides valuable insights for COVID-19 epidemic prevention and control in America. [ABSTRACT FROM AUTHOR]

A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network's performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%. [ABSTRACT FROM AUTHOR]

When asked to predict how they will perform on an upcoming exam, students are often poorly calibrated, typically in the direction of overpredicting their performance. Research shows that low-performing students' calibration tends to remain poor across multiple tests over the course of a semester. We tested whether these students remain confident in these erroneously high grade predictions across the semester or whether their confidence wanes, suggesting some degree of metacognitive awareness. In two studies, students made grade predictions prior to taking four in-class exams and then rated their level of confidence in their predictions. Results from both studies showed that miscalibration and confidence remained stable across tests, suggesting that low-performing students continued to believe that they would perform well on upcoming exams despite prior evidence to the contrary. [ABSTRACT FROM AUTHOR]

This paper investigated the importance of explainability in artificial intelligence models and its application in the context of prediction in Formula (1). A step-by-step analysis was carried out, including collecting and preparing data from previous races, training an AI model to make predictions, and applying explainability techniques in the said model. Two approaches were used: the attention technique, which allowed visualizing the most relevant parts of the input data using heat maps, and the permutation importance technique, which evaluated the relative importance of features. The results revealed that feature length and qualifying performance are crucial variables for position predictions in Formula (1). These findings highlight the relevance of explainability in AI models, not only in Formula (1) but also in other fields and sectors, by ensuring fairness, transparency, and accountability in AI-based decision making. The results highlight the importance of considering explainability in AI models and provide a practical methodology for its implementation in Formula (1) and other domains. [ABSTRACT FROM AUTHOR]

Time-series (TS) predictions use historical data to forecast future values. Various industries, including stock market trading, power load forecasting, medical monitoring, and intrusion detection, frequently rely on this method. The prediction of stock-market prices is significantly influenced by multiple variables, such as the performance of other markets and the economic situation of a country. This study focuses on predicting the indices of the stock market of the Kingdom of Saudi Arabia (KSA) using various variables, including opening, lowest, highest, and closing prices. Successfully achieving investment goals depends on selecting the right stocks to buy, sell, or hold. The output of this project is the projected closing prices over the next seven days, which aids investors in making informed decisions. Exponential smoothing (ES) was employed in this study to eliminate noise from the input data. This study utilized exponential smoothing (ES) to eliminate noise from data obtained from the Saudi Stock Exchange, also known as Tadawul. Subsequently, a sliding-window method with five steps was applied to transform the task of time series forecasting into a supervised learning problem. Finally, a multivariate long short-term memory (LSTM) deep-learning (DL) algorithm was employed to predict stock market prices. The proposed multivariate LSTMDL model achieved prediction rates of 97.49% and 92.19% for the univariate model, demonstrating its effectiveness in stock market price forecasting. These results also highlight the accuracy of DL and the utilization of multiple information sources in stock-market prediction. [ABSTRACT FROM AUTHOR]

We aim to develop a theoretical methodology for the accurate aqueous pKa prediction of structurally complex phenolic antioxidants and cannabinoids. In this study, five functionals (M06-2X, B3LYP, BHandHLYP, PBE0, and TPSS) and two solvent models (SMD and PCM) were combined with the 6-311++G(d,p) basis set to predict pKa values for twenty structurally simple phenols. None of the direct calculations produced good results. However, the correlations between the calculated Gibbs energy difference of each acid and its conjugate base, Δ G a q (B A) ° = Δ G a q A − ° − Δ G a q (H A) ° , and the experimental aqueous pKa values had superior predictive accuracy, which was also tested relative to an independent set of ten molecules of which six were structurally complex phenols. New correlations were built with twenty-seven phenols (including the phenols with experimental pKa values from the test set), which were used to make predictions. The best correlation equations used the PCM method and produced mean absolute errors of 0.26–0.27 pKa units and R2 values of 0.957–0.960. The average range of predictions for the potential antioxidants (cannabinoids) was 0.15 (0.25) pKa units, which indicates good agreement between our methodologies. The new correlation equations could be used to make pKa predictions for other phenols in water and potentially in other solvents where they might be more soluble. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate. [ABSTRACT FROM AUTHOR]

A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes. [ABSTRACT FROM AUTHOR]

Rural tourism in Serbia had its chance to shine with the advent of the COVID-19 pandemic. The aim of this study was to determine to what extent the quality of rural service can contribute to improving rural tourism, and predictions for the continuation of the trend in terms of increasing the number of overnight stays in rural households. The obtained results show a small number of services in the sector could be improved, but that all elements except price value can influence the future development of rural tourism and that the number of overnight stays is expected to continue to grow. The importance and innovativeness of the research is reflected in the specific methodology that was applied, and the results complement those of previous research. It has been shown that villages in Serbia can create a barrier against COVID-19 through tourism. [ABSTRACT FROM AUTHOR]

Hydropower plays an essential role in Europe's energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower's contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works. [ABSTRACT FROM AUTHOR]

The interrelationship of the minerals calcium (Ca2+), magnesium (Mg2+) and silicon (Si) in the sediments and in the body walls of four tropical sea cucumber species was explored by modeling the concentrations of these minerals. The elemental concentrations of Ca2+, Mg2+ and Si were measured in the body walls and in the ambient sediments occupied by the sea cucumbers Holothuria scabra, H. leucospilota, H. atra and Bohadschia marmorata. The results indicate that the concentrations of Ca2+ and Mg2+ in the body walls of the four sea cucumber species are significantly different from each other, indicating a varying degree of biomineralization across sea cucumber taxa. In contrast, only B. marmorata showed a significant difference in the concentration of Si when compared to the rest of the species tested. Further analysis using linear mixed models revealed that the Ca2+, Mg2+ and Si concentrations in the body walls of the tested sea cucumber species are associated with the sediment concentrations of the same elements. The relatively high concentrations of Ca2+ and Mg2+ in the sediments indicate that these minerals are sufficiently high in sea cucumbers to support their biomineralization. The relationship between the Mg/Ca ratio in the body walls of the sea cucumbers and minerals in the sediments revealed that Si was the only mineral that was not correlated with the Mg/Ca ratio. Predicting the relationship of the elements Ca2+, Mg2+ and Si between the sediments and the body walls of sea cucumbers may be complex due to the various factors that influence the metabolism and biomineralization in sea cucumbers. [ABSTRACT FROM AUTHOR]

In the modern world, chronic kidney disease is one of the most severe diseases that negatively affects human life. It is becoming a growing problem in both developed and underdeveloped countries. An accurate and timely diagnosis of chronic kidney disease is vital in preventing and treating kidney failure. The diagnosis of chronic kidney disease through history has been considered unreliable in many respects. To classify healthy people and people with chronic kidney disease, non-invasive methods like machine learning models are reliable and efficient. In our current work, we predict chronic kidney disease using different machine learning models, including logistic, probit, random forest, decision tree, k-nearest neighbor, and support vector machine with four kernel functions (linear, Laplacian, Bessel, and radial basis kernels). The dataset is a record taken as a case–control study containing chronic kidney disease patients from district Buner, Khyber Pakhtunkhwa, Pakistan. To compare the models in terms of classification and accuracy, we calculated different performance measures, including accuracy, Brier score, sensitivity, Youdent, specificity, and F1 score. The Diebold and Mariano test of comparable prediction accuracy was also conducted to determine whether there is a substantial difference in the accuracy measures of different predictive models. As confirmed by the results, the support vector machine with the Laplace kernel function outperforms all other models, while the random forest is competitive. [ABSTRACT FROM AUTHOR]

Linear transformations such as min–max normalization and z-score standardization are commonly used in logistic regression for the purpose of scaling. However, the work in the literature on linear transformations in logistic regression has two major limitations. First, most work focuses on improving the fit of the regression model. Second, the effects of transformations are rarely discussed. In this paper, we first generalized a linear transformation for a single variable to multiple variables by matrix multiplication. We then studied various effects of a generalized linear transformation in logistic regression. We showed that an invertible generalized linear transformation has no effects on predictions, multicollinearity, pseudo-complete separation and complete separation. We also showed that multiple linear transformations do not have effects on the variance inflation factor (VIF). Numeric examples with a real data were presented to validate our results. Our results of no effects justify the rationality of linear transformations in logistic regression. [ABSTRACT FROM AUTHOR]

Kenya's vulnerability to climate variability and change has been compounded by dependence on rain-fed agriculture with constrained capacity to adapt, a rapidly growing population, low-mechanized and low-input smallholder agricultural systems, and compromised soil fertility. The Ukraine war, COVID-19 and the desert locust invasion have only amplified the prevailing sensitivity to shocks in the agriculture sector, creating an emphasis on the need to strengthen local agricultural production to reduce reliance on imports. This paper seeks to assess the opportunities for improving agriculture adaptation and resilience based on future expected changes in climate, length of the growing period and agro-ecologies. The study uses 2020 as the baseline year and explores changes in agro-ecological zones (AEZs) in "near future" 2040 through two representative concentration pathways, 4.5 and 8.5, representing a medium carbon emissions and a dire emissions future, respectively. Google Earth Engine and R Statistics are used in data-processing. Down-scaled climate projections from CIMP5 are used for future analyses combined with static soil suitability and drainage data. Fuzzy logic is used to normalize inputs and compute the agro-ecological zones (AEZ). Interesting results emerge from the study that validate the hypothesis that the seasons and production potential are shifting. Lowland drylands will experience an increasingly long growing period, creating the potential for diversifying production systems from rangelands to agro-pastoral systems, with the capacity to grow more drought-resistant crops and the potential to take advantage of increased runoff for water harvesting. Midland highland areas, which form part of the food basket areas, have already started experiencing a reduction in the length of the growing period and agricultural potential. In these areas, resilience mechanisms will need to consider the expected future reduction in rain-fed agricultural potential, gendered preferences, convergence of technology and indigenous coping mechanisms, and drought-resilience-focused diversification. [ABSTRACT FROM AUTHOR]

Electronic word-of-mouth data on social media influences stock trading and the confidence of stock markets. Thus, sentiment analysis of comments related to stock markets becomes crucial in forecasting stock markets. However, current sentiment analysis is mainly in English. Therefore, this study performs multilingual sentiment analysis by translating non-native English-speaking countries' texts into English. This study used unstructured data from social media and structured data, including trading data and technical indicators, to forecast stock markets. Deep learning techniques and machine learning models have emerged as powerful ways of coping with forecasting problems, and parameter determination greatly influences forecasting models' performance. This study used Long Short-Term Memory (LSTM) models employing the genetic algorithm (GA) to select parameters for predicting stock market indices and prices of company stocks by hybrid data in non-native English-speaking regions. Numerical results revealed that the developed LSTMGA model with hybrid multilingual sentiment data generates more accurate forecasting than the other machine learning models with various data types. Thus, the proposed LSTMGA model with hybrid multilingual sentiment analysis is a feasible and promising way of forecasting the stock market. [ABSTRACT FROM AUTHOR]

Hydrostatic bearings for liquid rocket engine turbopumps provide distinctive advantages, including high load capacity even with low viscosity cryogenic fluid and extending life span by minimizing friction and wear between rotor and bearing surfaces. Application of hydrostatic bearings into turbopumps demands a reliable test database with well-quantified operating parameters and experimentally validated accurate performance predictive tools. The present paper shows the comprehensive experimental data and validation of predicted static load characteristics of hydrostatic journal bearings lubricated with air, water, and liquid nitrogen. Extensive experiments for static load characteristics of hydrostatic bearings are conducted using a turbopump-rotor-bearing system simulator while increasing supply pressure (Ps) into the test bearings. The test results demonstrate notable effects of the test fluids and their temperatures, as well as Ps, on the bearing performance. In general, the measured bearing flow rate, rotor displacement, and stiffness of the test bearings steadily increase with Ps. The static load bearing characteristics predictions considering flow turbulence and compressibility matched well with the experimental results. The work with independent test data and engineering computational programs will further the implementation of hydrostatic bearings in high-performance turbopump shaft systems with improved efficiency and enhanced reusability of liquid rocket engine sub-systems. [ABSTRACT FROM AUTHOR]

During on-site verification activities conducted by the Technical Secretariat of Organization for the Prohibition of Chemical Weapons, identification by gas chromatography retention indices (RI) data, in addition to mass spectrometry data, increase the reliability of factual findings. However, reference RIs do not cover all the possible chemical structures. That is why it is important to have models to predict RIs. Applicable only for narrow data sets of chemicals with a fixed scaffold (G- and V-series gases as example), the non-learning incremental method demonstrated predictive median absolute and percentage errors of 2–4 units and 0.1–0.2%; these are comparable with the experimental bias in RI measurements in the same laboratory with the same GC conditions. It outperforms the accuracy of two reported machine learning methods–median absolute and percentage errors of 11–52 units and 0.5–2.8%. However, for the whole Chemical Weapons Convention (CWC) data set of chemicals, when a fixed scaffold is absent, the incremental method is not applicable; essential machine learning methods achieved accuracy: median absolute and percentage errors of 29–33 units and 0.5–2.2%, depending on the machine learning method. In addition, we have developed a homology tree approach as a convenient method for the visualization of the CWC chemical space. We conclude that non-learning incremental methods may be more accurate than the state-of-the-art machine learning techniques in particular cases, such as predicting the RIs of homologues and isomers of chemicals related to CWC. [ABSTRACT FROM AUTHOR]

In this paper, various machine learning algorithms were used in order to predict the evolution of open-angle glaucoma (POAG). The datasets were built containing clinical observations and objective measurements made at the Countess of Chester Hospital in the UK and at the "St. Spiridon" Hospital of Iași, Romania. Using these datasets, different classification problems were proposed. The evaluation of glaucoma progression was conducted based on parameters such as VFI (Visual field index), MD (Mean Deviation), PSD (Pattern standard deviation), and RNFL (Retinal Nerve Fiber Layer). As classification tools, the following algorithms were used: Multilayer Perceptron, Random Forest, Random Tree, C4.5, k-Nearest Neighbors, Support Vector Machine, and Non-Nested Generalized Exemplars. The best results, with an accuracy of over 90%, were obtained with Multilayer Perceptron and Random Forest algorithms. The NNGE algorithm also proved very useful in creating a hierarchy of the input values according to their influence (weight) on the considered outputs. On the other hand, the decision tree algorithms gave us insight into the logic used in their classification, which is of practical importance in obtaining additional information regarding the rationale behind a certain rule or decision. [ABSTRACT FROM AUTHOR]

This essay describes the author's lifetime experiences with predictability theory and weather predictions. [ABSTRACT FROM AUTHOR]

The depletion of natural resources and greenhouse gas emissions related to the manufacture and use of ordinary Portland cement (OPC) pose serious concerns to the environment and human life. The present research focuses on using alternative binders to replace OPC. Geopolymer might be the best option because it requires waste materials enriched in aluminosilicate for its production. The research on geopolymer concrete (GPC) is growing rapidly. However, substantial effort and expenses are required to cast specimens, cures, and tests. Applying novel techniques for the said purpose is the key requirement for rapid and cost-effective research. In this research, supervised machine learning (SML) techniques, including two individual (decision tree (DT) and gene expression programming (GEP)) and two ensembled (bagging regressor (BR) and random forest (RF)) algorithms were employed to estimate the compressive strength (CS) of GPC. The validity and comparison of all the models were made using the coefficient of determination (R2), k-fold, and statistical assessments. It was noticed that the ensembled SML techniques performed better than the individual SML techniques in forecasting the CS of GPC. However, individual SML model results were also in the reasonable range. The R2 value for BR, RF, GEP, and DT models was 0.96, 0.95, 0.93, and 0.88, respectively. The models' lower error values such as mean absolute error (MAE) and root mean square errors (RMSE) also verified the higher precision of ensemble SML methods. The RF (MAE = 2.585 MPa, RMSE = 3.702 MPa) and BR (MAE = 2.044 MPa, RMSE = 3.180) results are better than the DT (MAE = 4.136 MPa, RMSE = 6.256 MPa) and GEP (MAE = 3.102 MPa, RMSE = 4.049 MPa). The application of SML techniques will benefit the construction sector with fast and cost-effective methods for estimating the properties of materials. [ABSTRACT FROM AUTHOR]

Humans and animals are both susceptible to highly pathogenic avian influenza (HPAI) viruses. In the future, HPAI has the potential to be a source of zoonoses and pandemic disease drivers. It is necessary to identify areas of high risk that are more vulnerable to HPAI infections. In this study, we applied unbiased predictions based on known information to find points of localities with a high probability of point prevalence rate. To carry out such predictions, we utilized the inverse distance weighting (IDW) and kriging method, with the help of the R statistical computing program. The provinces of Jeollanam-do, Gyeonggi-do, Chungcheongbuk-do and Ulsan have high anticipated risk. This research might aid in the management of avian influenza threats associated with various potential risks. [ABSTRACT FROM AUTHOR]

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian ( n = 28) and Asian Chinese ( n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids., Competing Interests: Aidan Joblin-Mills, Zhanxuan E. Wu and Karl Fraser are/were employees of AgResearch Limited The paper reflects the views of the scientists and not the company.

People in shared building space have an important role in energy consumption because they can turn on/off equipment and heat/cooling systems. This behaviour can be influenced by giving then locally tailored context information (energy consumption, temperature, luminosity) and information about the cost of their actions. This paper presents an approach to create personalized local energy consumption predictions in a building using past sensor data, correlated with external conditions to create local context predictions. This prediction is sent in real-time to people's mobile devices in order to influence their behaviour when increasing or decreasing temperature using heating or cooling systems. This information is essential for sustainability actions in shared spaces, where this information can have an important role. Also, the data (temperature) representation in the building information model (BIM) module can help the user understand environment conditions and, together with the user sharing their thermal feelings, can be used to change behaviour. This approach using BIM's representation models allows Things2People interaction to improve energy savings in these shared spaces. [ABSTRACT FROM AUTHOR]

Future stock prices depend on many internal and external factors that are not easy to evaluate. In this paper, we use the Hidden Markov Model, (HMM), to predict a daily stock price of three active trading stocks: Apple, Google, and Facebook, based on their historical data. We first use the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to choose the numbers of states from HMM.We then use the models to predict close prices of these three stocks using both single observation data and multiple observation data. Finally, we use the predictions as signals for trading these stocks. The criteria tests' results showed that HMM with two states worked the best among two, three and four states for the three stocks. Our results also demonstrate that the HMM outperformed the naïve method in forecasting stock prices. The results also showed that active traders using HMM got a higher return than using the naïve forecast for Facebook and Google stocks. The stock price prediction method has a significant impact on stock trading and derivative hedging. [ABSTRACT FROM AUTHOR]

The integrity of the Blesbok Spruit catchment has been significantly compromised over the past decades, mainly due to the discharge of mining effluent and sewage. This research investigated the hydrological responses, in terms of water quality, in the event of land cover change within the catchment to make predictions on the future sustainability of the region's water resources with the application of Partial Least Squares (PLS) regression analysis. The quantification of hydrological responses in terms of water quality towards land cover changes has not been completed by previous research studies within the catchment. This research established the catchment's present state of water quality and formulated PLS model equations to enable the prediction of future concentrations of specific water quality parameters in association with future land cover change. A change in land cover was found to have various negative influences. The retransformation of land cover into natural areas is accompanied with unintended and undesirable effects due to the degradation of the catchment's buffering capabilities and the absence of the enforcement of the decommissioning of mining operations. For the Blesbok Spruit catchment to avoid a future water predicament, systematic and interdisciplinary measures need to be implemented according to these and other related findings, to ensure the future sustainability of the catchment and the region as a whole. [ABSTRACT FROM AUTHOR]

Health literacy measurement is important to improve equity, health and well-being as part of health system transformation. However, health literacy data of good quality are often lacking or difficult to access for decision-makers. To better inform policy, research and practice, this paper discusses how to move from project-based health literacy data and measurement to an integrated system of analytics and insights enhancing data-driven value creation in health-literate organizations. There is a need for the development of health literacy data pipelines, data dashboards, and data governance mechanisms which are timely and trustworthy. Investing in health literacy data analytics and data governance can pave the way for the integration of health literacy as an acknowledged global health indicator in large-scale surveys, ventures, and daily business. Leadership and management buy-in are needed to steer the process. Lessons learned from decades of measurement research combined with strategic implementation of systematic use of health literacy monitoring may accelerate the progress.

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data. [ABSTRACT FROM AUTHOR]

The innovation of geopolymer concrete (GPC) plays a vital role not only in reducing the environmental threat but also as an exceptional material for sustainable development. The application of supervised machine learning (ML) algorithms to forecast the mechanical properties of concrete also has a significant role in developing the innovative environment in the field of civil engineering. This study was based on the use of the artificial neural network (ANN), boosting, and AdaBoost ML approaches, based on the python coding to predict the compressive strength (CS) of high calcium fly-ash-based GPC. The performance comparison of both the employed techniques in terms of prediction reveals that the ensemble ML approaches, AdaBoost, and boosting were more effective than the individual ML technique (ANN). The boosting indicates the highest value of R2 equals 0.96, and AdaBoost gives 0.93, while the ANN model was less accurate, indicating the coefficient of determination value equals 0.87. The lesser values of the errors, MAE, MSE, and RMSE of the boosting technique give 1.69 MPa, 4.16 MPa, and 2.04 MPa, respectively, indicating the high accuracy of the boosting algorithm. However, the statistical check of the errors (MAE, MSE, RMSE) and k-fold cross-validation method confirms the high precision of the boosting technique. In addition, the sensitivity analysis was also introduced to evaluate the contribution level of the input parameters towards the prediction of CS of GPC. The better accuracy can be achieved by incorporating other ensemble ML techniques such as AdaBoost, bagging, and gradient boosting. [ABSTRACT FROM AUTHOR]

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing. [ABSTRACT FROM AUTHOR]

Heme biosynthesis is essential for almost all living organisms. Despite its conserved function, the pathway's enzymes can be located in a remarkable diversity of cellular compartments in different organisms. This location does not always reflect their evolutionary origins, as might be expected from the history of their acquisition through endosymbiosis. Instead, the final subcellular localization of the enzyme reflects multiple factors, including evolutionary origin, demand for the product, availability of the substrate, and mechanism of pathway regulation. The biosynthesis of heme in the apicomonad Chromera velia follows a chimeric pathway combining heme elements from the ancient algal symbiont and the host. Computational analyses using different algorithms predict complex targeting patterns, placing enzymes in the mitochondrion, plastid, endoplasmic reticulum, or the cytoplasm. We employed heterologous reporter gene expression in the apicomplexan parasite Toxoplasma gondii and the diatom Phaeodactylum tricornutum to experimentally test these predictions. 5-aminolevulinate synthase was located in the mitochondria in both transfection systems. In T. gondii, the two 5-aminolevulinate dehydratases were located in the cytosol, uroporphyrinogen synthase in the mitochondrion, and the two ferrochelatases in the plastid. In P. tricornutum, all remaining enzymes, from ALA-dehydratase to ferrochelatase, were placed either in the endoplasmic reticulum or in the periplastidial space. [ABSTRACT FROM AUTHOR]

Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential or non-essential for survival in each species. Recently, a range of features linked to gene essentiality have been inferred using a machine learning (ML)-based approach, allowing essentiality predictions within a species. Nevertheless, predictions between species are still elusive. Here, we undertake a comprehensive study using ML to discover and validate features of essential genes common to both C. elegans and D. melanogaster. We demonstrate that the cross-species prediction of gene essentiality is possible using a subset of features linked to nucleotide/protein sequences, protein orthology and subcellular localisation, single-cell RNA-seq, and histone methylation markers. Complementary analyses showed that essential genes are enriched for transcription and translation functions and are preferentially located away from heterochromatin regions of C. elegans and D. melanogaster chromosomes. The present work should enable the cross-prediction of essential genes between model and non-model metazoans. [ABSTRACT FROM AUTHOR]

Simple Summary: The use of 'in paddock' walk-over-weighing scales (WOW) enables cattle liveweight (LW) data collection, remotely and individually, at a high frequency (e.g., daily). Liveweight data obtained can be used to calculate the liveweight change experienced by a dam at calving (ΔLWC), which is linked with calf birth weight (CBW). This study utilised WOW technology to investigate the degree of association between CBW, LW before and after calving, ΔLWC, and cow non-foetal weight loss at calving (NFW) (ΔLWC–CBW = NFW, e.g., membranes, fluids). Such outcomes could contribute to predicting CBW by assessing ΔLWC without the need of weighing the calf manually, which can be a labour and time-consuming operation in the extensive conditions of beef production. There was no correlation between CBW and the LW of the dam before and after calving; however, positive associations between CBW, ΔLWC, and NFW were found. Particularly, 56% of the variation in ΔLWC was attributed to CBW. These findings suggest that the remote monitoring of ΔLWC has potential to be used for CBW predictions, regardless of the LW of the dam around calving time. The present study aimed to develop predictive models of calf birth weight (CBW) from liveweight (LW) data collected remotely and individually using an automated in-paddock walk-over-weighing scale (WOW). Twenty-eight multiparous Charolais cows were mated with two Brahman bulls. The WOW was installed at the only watering point to capture LW over five months. Calf birth date and weight were manually recorded, and the liveweight change experienced by a dam at calving (ΔLWC) was calculated as pre-LW minus post-LW calving. Cow non-foetal weight loss at calving (NFW) was calculated as ΔLWC minus CBW. Pearson's correlational analysis and simple linear regressions were used to identify associations between all variables measured. No correlations were found between ΔLWC and pre-LW (p = 0.52), or post-LW (p = 0.14). However, positive associations were observed between ΔLWC and CBW (p < 0.001, R2 = 0.56) and NFW (p < 0.001, R2 = 0.90). Thus, the results suggest that 56% of the variation in ΔLWC is attributed to the calf weight, and consequently could be used as an indicator of CBW. Remote, in-paddock weighing systems have the potential to provide timely and accurate LW data of breeding cows to improve calving management and productivity. [ABSTRACT FROM AUTHOR]

On 21 February 2020, a violent COVID-19 outbreak, which was initially concentrated in Lombardy before infecting some surrounding regions exploded in Italy. Shortly after, on 9 March, the Italian Government imposed severe restrictions on its citizens, including a ban on traveling to other parts of the country. No travel, no virus spread. Many regions, such as those in southern Italy, were spared. Then, in June 2020, under pressure for the economy to reopen, many lockdown measures were relaxed, including the ban on interregional travel. As a result, the virus traveled for hundreds of kilometers, from north to south, with the effect that areas without infections, receiving visitors from infected areas, became infected. This resulted in a sharp increase in the number of infected people; i.e., the daily count of new positive cases, when comparing measurements from the beginning of July to those from at the middle of September, rose significantly in almost all the Italian regions. Upon confirmation of the effect of Italian domestic tourism on the virus spread, three computational models of increasing complexity (linear, negative binomial regression, and cognitive) have been compared in this study, with the aim of identifying the one that better correlates the relationship between Italian tourist flows during the summer of 2020 and the resurgence of COVID-19 cases across the country. Results show that the cognitive model has more potential than the others, yet has relevant limitations. The models should be considered as a relevant starting point for the study of this phenomenon, even if there is still room to further develop them up to a point where they become able to capture all the various and complex spread patterns of this disease. [ABSTRACT FROM AUTHOR]

The Nobel laureate Niels Bohr once said that: "Predictions are very difficult, especially if they are about the future". Nonetheless, models that can forecast future COVID-19 outbreaks are receiving special attention by policymakers and health authorities, with the aim of putting in place control measures before the infections begin to increase. Nonetheless, two main problems emerge. First, there is no a general agreement on which kind of data should be registered for judging on the resurgence of the virus (e.g., infections, deaths, percentage of hospitalizations, reports from clinicians, signals from social media). Not only this, but all these data also suffer from common defects, linked to their reporting delays and to the uncertainties in the collection process. Second, the complex nature of COVID-19 outbreaks makes it difficult to understand if traditional epidemiological models, such as susceptible, infectious, or recovered (SIR), are more effective for a timely prediction of an outbreak than alternative computational models. Well aware of the complexity of this forecasting problem, we propose here an innovative metric for predicting COVID-19 diffusion based on the hypothesis that a relation exists between the spread of the virus and the presence in the air of particulate pollutants, such as PM2.5, PM10, and NO2. Drawing on the recent assumption of 239 experts who claimed that this virus can be airborne, and further considering that particulate matter may favor this airborne route, we developed a machine learning (ML) model that has been instructed with: (i) all the COVID-19 infections that occurred in the Italian region of Emilia-Romagna, one of the most polluted areas in Europe, in the period of February–July 2020, (ii) the daily values of all the particulates taken in the same period and in the same region, and finally (iii) the chronology according to which restrictions were imposed by the Italian Government to human activities. Our ML model was then subjected to a classic ten-fold cross-validation procedure that returned a promising 90% accuracy value. Finally, the model was used to predict a possible resurgence of the virus in all the nine provinces of Emilia-Romagna, in the period of September–December 2020. To make those predictions, input to our ML model were the daily measurements of the aforementioned pollutants registered in the periods of September–December 2017/2018/2019, along with the hypothesis that the mild containment measures taken in Italy in the so-called Phase 3 are obeyed. At the time we write this article, we cannot have a confirmation of the precision of our predictions. Nevertheless, we are projecting a scenario based on an original hypothesis that makes our COVID-19 prediction model unique in the world. Its accuracy will be soon judged by history—and this, too, is science at the service of society. [ABSTRACT FROM AUTHOR]

Policy decision-making for system resilience to a hazard requires the estimation and prediction of the trends of growth and decline of the impacts of the hazard. With focus on the recent worldwide spread of CoVid-19, we take the infection rate as the relevant metric whose trend of evolution to follow for verifying the effectiveness of the countermeasures applied. By comparison with the theories of growth and recovery in coupled socio-medical systems, we find that the data for many countries show infection rate trends that are exponential in form. In particular, the recovery trajectory is universal in trend and consistent with the learning theory, which allows for predictions useful in the assistance of decision-making of emergency recovery actions. The findings are validated by extensive data and comparison to medical pandemic models. [ABSTRACT FROM AUTHOR]

Schizophrenia Spectrum Disorder (SSD) is a chronic psychiatric disorder with a modest treatment outcome. In addition, relapses are commonplace. Here, we sought to identify factors that predict relapse latency and frequency. To this end, we retrospectively analyzed data for individuals with SSD. Medical records of 401 individuals with SSD were analyzed (mean age: 25.51 years; 63.6% males) covering a five-year period. Univariate and multivariate Penalized Likelihood Models with Shared Log-Normal Frailty were used to determine the correlation between discharge time and relapse and to identify risk factors. A total of 683 relapses were observed in males, and 422 relapses in females. The Relapse Hazard Ratio (RHR) decreased with age (RHR = 0.99, CI: (0.98-0.998)) and with participants' adherence to pharmacological treatment (HR = 0.71, CI: 0.58-0.86). In contrast, RHR increased with a history of suicide attempts (HR = 1.32, CI: 1.09-1.60), and a gradual compared to a sudden onset of disease (HR = 1.45, CI: 1.02-2.05). Gender was not predictive. Data indicate that preventive and therapeutic interventions may be particularly important for individuals who are younger at disease onset, have a history of suicide attempts, have experienced a gradual onset of disease, and have difficulties adhering to medication.