Correlation Values Research Articles

Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study sites in Queensland (Emu Park and Townsville), Australia. Assessment and prediction of Hs is extremely important for reliable planning, cost management and implementation of wave energy projects. The study utilized oceanic datasets based on wave measurements obtained from buoys along coastal regions of Queensland that are transmitted to nearby receiver stations. The parameters of the datasets include maximum wave height, zero up crossing wave period, peak energy wave period and sea surface temperature to accurately predict Hs. A new hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short Term (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) is developed which is benchmarked by Multi-Layer Perceptron (MLP), Random Forest (RF) and Categorical Boosting (CatBoost) to compare the performance. All models attain relatively high-performance results. The MVMD-CNN-BiLSTM attains slightly better performance values for both study sites among all developed models with highest correlation values of 0.9957 and 0.9986 for Emu Park and Townsville, respectively. Other performance evaluation metrics were also higher for MVMD-CNN-BiLSTM with lowest error values in comparison to the benchmark models. The annual mean of Hs is also computed to compare and obtain an insight with a linear projection. There is a greater ocean wave energy potential for Emu Park for a 10-year period with a projected mean Hs of 0.865 m in comparison to Townsville where the projected mean was of 0.665 m.

CONTRIBUTION TO PRACTICAL ANALYSIS OF SURFACE ROUGHNESS MODELING DURING DRY HARD TURNING OF COLD WORK TOOL STEEL AISI D2

Quality of products is a major concern for hard machining industry aspects. However, the prediction of surface roughness in the function of the machining parameters is a targeted objective by the majority of researchers due to important industrial interest. This work is a contribution for developing comprehensive analyses of surface roughness models during dry hard turning of cold work steel AISI D2. It is focused on the treatment of predicted surface roughness values in correlation with cutting conditions such as cutting parameters (cutting speed, feed rate, and depth of cut), tools material, tool geometry, workpieces hardness, and machining times in order to establish of orientations for use which applied successfully in the industry from optimal selection of process variables. The approach consists in first conducting a literature survey through collecting and validating surface roughness models together with the corresponding material and also the cutting conditions. Then, results have been tabulated as to permit data mining treatment under data processing software which was developed. As a significant result, a strategic dashboard generating roughness data mining was made available to researchers and industrials. The optimums of parameters which condition the achievement of best machined surface qualities have been selected.

Flood Prediction Using Artificial Neural Networks: A Case Study in Temerloh, Pahang

Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development.

Accuracy of a markerless system to estimate the position of taekwondo athletes in an official combat area

ABSTRACT Obtaining kinematic analysis through a gold-standard method on sports during official competitions is challenging. Markerless tracking system can be an alternative to provide physical and technical-tactical performance data about the athletes. Therefore, we aimed to verify whether the markerless system (OpenPose) accurately tracks taekwondo athletes’ positions on the official combat area. Six taekwondo athletes performed steps and kicks in a self-selected way in two conditions: with and without an opponent. To capture the movement, seven optoelectronic (marker-based system) and two digital cameras (OpenPose markerless system) were placed around the combat area at an acquisition frequency of 120 Hz and 30 Hz, respectively. Positions of the body centre of mass and midpoint of the feet were calculated and analysed through two and three-dimensional reconstruction both. The root mean square (RMS) error comparing both methods ranged from 0.13 to 0.32 m and 0.05 to 0.16 m for two and three-dimensional analysis, respectively. Bland-Altman analysis accepted the agreement between the capture systems, and the intraclass coefficient correlation (ICC) values were classified as excellent (ICC > 0.90) in all analysis. In conclusion, the OpenPose markerless system presents promising results for its use in tracking taekwondo athletes’ position on the competition area.

The fundamental motor skills and motor coordination performance of children in West Sumatera Province, Indonesia

Background and Study Aims. Good motor coordination is needed to achieve good fundamental motor skills. This study aimed to analyze and describe the relationship between fundamental motor skills and motor coordination performance of elementary school students aged 7 to 9 years old. Material and Methods. The subjects were elementary school students in grades 1, 2, and 3 with a total number of students was 478 (248 male students, and 230 female students) in 8 cities and regencies in West Sumatera, Indonesia. This study used a cross-sectional research design. The fundamental motor skills were examined using the Test of Gross Motor Development-Second Edition. This included assessing locomotor skills (running, galloping, sliding, leaping, hopping, and jumping) and object control skills (striking, catching, throwing, dribbling, rolling, and kicking). Motor coordination performance was assessed by using the Körperkoordinations Test für Kinder including balance beam, moving sideways, jumping sideways, and eye-hand coordination. The data were analyzed by using IBM SPSS statistic version 25. Results. Students in urban areas demonstrated certain levels in Gross Motor Quotients scores, while their counterparts in rural areas achieved marginally higher scores with slightly less variation. In schools where Physical Education teachers were present, students generally scored higher compared to schools without Physical Education teachers, albeit with a bit less variation in the latter. When comparing motor coordination performance, students in urban areas typically outperformed those in rural areas, exhibiting slightly more consistent scores. Similarly, students with access to Physical Education teachers showed better motor coordination performance than those without, though with a somewhat greater range in their scores. A noticeable trend was observed in the Gross Motor Quotients scores of Fundamental Motor Skills, which tended to decrease as age increased. Conversely, students' motor coordination performance generally improved with age. Supporting these observations, the result of the one-sample Kolmogorov-Smirnov test, obtained from Asymp. Sig. (2-tailed), was 0.200 > 0.05. Additionally, the Pearson correlation value between motor coordination performance and fundamental motor skills was 0.633 with a sig value of 0.000, indicating a significant relationship. Conclusions. Based on the study's findings, it is recommended to focus on enhancing motor skill programs for children in rural areas. Schools should invest in Physical Education teachers, especially where they are currently absent, to benefit children. Age-specific programs are needed to address the decline in Gross Motor Quotients with age in children. Regular monitoring and evaluation of these initiatives for children are essential.

An analysis of multiple comparisons is conducted to account for correlations among various investigated variables across four categories of antipsychotics

Background: First-generation antipsychotics are dopamine receptor antagonists, while second-generation atypical antipsychotics are serotonin-dopamine antagonists. Aim: The study aims to compare the effectiveness of current anti-psychotic drugs in treating schizophrenia, focusing on their correlation with responsiveness rates in positive, negative, and psychopathological symptoms. Methods: The study focuses on schizophrenia-spectrum disorders in adults and elderly psychiatric patients at the Princess Aisha Bint Al Hussein Medical Centre. The study will assess the initial severity of patients and evaluate the therapeutic effectiveness of four categorical-based oral and LA-APDs: older oral drugs, older oral and depot drugs, newer oral drugs, and newer oral and depot drugs. The reduction percentages in each component will be monitored, and a Chi Square test will be performed to analyze other variables. Pearson or spearman correlations will be used to explore correlation values. Results: Upon aggregating the various symptom categories into a unified score using the Positive and Negative Scoring Scale (PANSS), this study revealed that 63.7% of patients exhibited a positive response, whereas 36.7% of patients did not respond favourably. This equated to 110 out of 303 patients responding, compared to 193 patients who did not respond. The Pearson and Spearman correlations exhibited a statistically significant positive relationship, with values of +0.581±0.038 and +0.574±0.040, respectively (χ 2=131.885, p-value=0.000). Conclusion: Our study concluded that Psychopathological symptom therapy and patient adherence improved significantly. The efficiency of pleasant symptoms decreased significantly, whereas negative symptoms increased slightly.

Pearson Correlation and Multiple Correlation Analyses of the Animal Fat S-Parameter

In specialised food products, the detection and identification of various sources of oils and fats are essential. Consumers are concerned about the adulteration of vegetable oils to avoid health problems and the risk of developing heart disease. There were a few cases where less expensive animal fat, in particular pork fat, was found in halal meals. The limitations of conventional methods for detecting pork in other meats can be overcome with the use of non-destructive methods. This study reveals the feasibility of using the microwave non-destructive testing method to identify animal fats. In the frequency between 8 GHz and 12 GHz, the microwave non-destructive testing method was used to obtain the S-parameters of the chicken, beef, and pork fats in the form of raw, baked, and oil. Single and multiple correlation analyses were then performed to identify the relationship between the animal fats and to differentiate them. The results of the Pearson correlation analysis show that for all animal fats, both raw and baked fat had a higher correlation value, which indicated they have the same or similar features. The different correlation values obtained from the multiple correlation analysis demonstrated that animal fats have distinct features, regardless of whether they are raw, baked, or liquid forms. The multiple correlation analysis is a more suitable technique to detect animal fats compared to the Pearson correlation analysis.

Irritable Bowel Syndrome as a Stress Prediction Model in Mexican Dentistry Students

The Irritable Bowel Syndrome is a functional gastrointestinal disorder associated with stress that may occur in the academic context, given that students are exposed to high demands during their education process which can lead to the development of diseases. The purpose of this study was to examine the correlation and predictive value between the Irritable Bowel Syndrome (IBS) with stress in dentistry students at a public university in Mexico, taking into account the gender variable. The design is cross-sectional with 740 dental students. Nowack’s Stress Profile was used as well as the ROMA III criteria to identify IBS. Multiple lineal regression models were built, in which the dependent variable was IBS and the independent ones were the stress profile variables and group data. Such models were built independently for each sex and their results were compared. The IBS model in males was significant in the following variables: IBS = – 1.454 (problem concentration) + .315 (negative valuation) – .390 (social support network) + .513 (type A behavior) – .464 (rest/sleep), with a strength of R2 = .685; F = 75.72; p < .01 which explains 68% of the variance; and for females it was: ISB = – .041 (positive valuation) – .082 (eating habits/nutrition) with a strength of R2 = .643; F = 42.69; p < .01, which explains 64% of the variance. The comparative análisis determined that male dental students are at a greater risk of developing IBS due to stress. Therefore, programs aimed at a timely identification of the stress risk variables would be recommended to prevent consequences to the health, inadequate academic performance and wellbeing of dental students in Mexico.

Modeling surface quality, cost and energy consumption during milling of alloy 2017A: a comparative study integrating GA-ANN and RSM models

ABSTRACT The use of simple machining features to determine cutting parameters and the machining process is limited, as parts may contain complex features interacting with each other. This study, therefore, focuses on pocket/groove features and proposes an approach integrating hybrid GA-ANN and RSM algorithms to predict surface quality, cost, and energy consumption (QCE). A parametric study was carried out, taking into account the swarm population size (pop) and the number of neurons (n) in the hidden layer, to find the best prediction using the hybrid GA-ANN algorithm. The results showed the highest correlation values (R2) for all output variables (above 0.97%). The study also revealed that the allocation of machining strategies and sequences can have a significant impact on energy consumption, with a 99.25% variation between minimum and maximum values. Mean square error (MSE) data confirmed the effectiveness of the GA-ANN model. Compared with RSM model predictions, energy consumption (Etot), cost (Ctot), and surface quality (Ra) values all showed statistically significant increases of 90.9%, 96.55%, and 40.18%, respectively. This study highlights the potential of the GA-ANN hybrid approach for multi-criteria prediction (quality, cost, and energy: QCE) in comparison with the RSM method, offering potential improvements for machining 2017A alloy.

Shared input and recurrency in neural networks for metabolically efficient information transmission.

Shared input to a population of neurons induces noise correlations, which can decrease the information carried by a population activity. Inhibitory feedback in recurrent neural networks can reduce the noise correlations and thus increase the information carried by the population activity. However, the activity of inhibitory neurons is costly. This inhibitory feedback decreases the gain of the population. Thus, depolarization of its neurons requires stronger excitatory synaptic input, which is associated with higher ATP consumption. Given that the goal of neural populations is to transmit as much information as possible at minimal metabolic costs, it is unclear whether the increased information transmission reliability provided by inhibitory feedback compensates for the additional costs. We analyze this problem in a network of leaky integrate-and-fire neurons receiving correlated input. By maximizing mutual information with metabolic cost constraints, we show that there is an optimal strength of recurrent connections in the network, which maximizes the value of mutual information-per-cost. For higher values of input correlation, the mutual information-per-cost is higher for recurrent networks with inhibitory feedback compared to feedforward networks without any inhibitory neurons. Our results, therefore, show that the optimal synaptic strength of a recurrent network can be inferred from metabolically efficient coding arguments and that decorrelation of the input by inhibitory feedback compensates for the associated increased metabolic costs.

Pengaruh Pembelajaran Akidah Akhlak terhadap Akhlak Karimah siswa kelas IX MTs Raudlatul Jannah Cijujung Kecamatan Sukaraja Kabupaten Bogor

The development of science and technology is currently very rapid, marked by the emergence of AI (Artificial Intelligence) and IoT (Internet of Things), apart from being able to improve human welfare, it will also have a negative impact. As a bulwark for students' self-defense in facing various challenges of the times, students need to receive religious education so that they do not fall into bad things. Learning moral beliefs is one part of the field of Islamic religious education that can provide knowledge, appreciation and a strong will to practice good morals and avoid bad morals. The aim of this research is to determine the effect of learning moral beliefs on the morals of class IX students at MTs Raudlatul Jannah Cijujung, Sukaraja District, Bogor Regency. This research uses quantitative research methods to determine the effect of variable X on variable Y. The population in this research is class IX students at MTs Raudlatul Jannah Cijujung. This research sample was taken using non-probability sampling techniques with total sampling or saturated sampling. Samples were taken from the entire population, namely 25 respondents. The data collection techniques used in this research are library and field methods. The instrument in this research uses a Likert scale in checklist form. The research results show that calculating the person coefficient using the Product Moment formula obtained a correlation value of 0.824 at a significance level of 5% from the r table of 0.413. Meanwhile, based on the t test, the tcount value is 12.274 > ttable is 1.717, meaning tcount is greater than ttable, so it can be concluded that in this study Ha is accepted and H0 is rejected. This means that there is an influence of learning moral beliefs on students' morals.

THE RELATIONSHIP BETWEEN MINDFULNESS AND FEAR OF FAILURE IN STUDENTS WHO ARE WORKING ON A THESIS AT PADANG STATE UNIVERSITY

Fear of failure is an emotional reaction that causes fear when in a situation that might cause failure. The phenomenon of fear of failure can be related to mindfulness, where mindfulness can reduce fear of failure. This study aims to determine the relationship between mindfulness and fear of failure in students who are working on a thesis at Padang State University. This study uses a correlational quantitative research design. The targets of this study were students who were working on a thesis at Padang State University using proportional sampling technique totalling 236 people. The mindfulness measuring instrument in this study used The Kentucky Inventory of Mindfulness Skills (KIMS) and the fear of failure scale using the Performance Failure Appraisal Inventory (PFAI). Based on the data analysis that has been done, the correlation value is obtained r = -0.298 and p = 0.000 (p < 0.05), which indicates that there is a significant negative relationship between mindfulness and fear of failure of students who are working on their thesis at Padang State University.

Pyruvate is modified by tea/coffee metabolites and reversely correlated with multiple system atrophy and Parkinson's disease

IntroductionMultiple system atrophy (MSA) is a rapidly progressing neurodegenerative disorder. Although diverse biomarkers have been established for Parkinson's disease (PD), no widely accepted markers have been identified in MSA. Pyruvate and lactate are the end-product of glycolysis and crucial for brain metabolism. However, their correlation with MSA remains unclear. Moreover, it is elusive how lifestyles modify these metabolites. MethodsTo investigate the correlation and diagnostic value of plasma pyruvate and lactate levels in MSA and PD. Moreover, we explored how lifestyle-related metabolites interact with these metabolites in determining the disease risk. We assayed the 3 metabolites in pyruvate/lactate and 6 in the tea/coffee metabolic pathways by targeted mass spectrometry and evaluate their interactions and performance in diagnosis and differentiation between MSA and PD. ResultsWe found that 7 metabolites were significantly different between MSA, PD and healthy controls (HCs). Particularly, pyruvate was increased in PD while significantly decreased in MSA patients. Moreover, the tea/coffee metabolites were negatively associated with the pyruvate level in HCs, but not in MSA and PD patients. Using machine-learning models, we showed that the combination of pyruvate and tea/coffee metabolites diagnosed MSA (AUC = 0.878) and PD (AUC = 0.833) with good performance. Additionally, pyruvate had good performance in distinguishing MSA from PD (AUC = 0.860), and the differentiation increased (AUC = 0.922) when combined with theanine and 1,3-dimethyluric acid. ConclusionsThis study demonstrates that pyruvate correlates reversely with MSA and PD, and may play distinct roles in their pathogenesis, which can be modified by lifestyle-related tea/coffee metabolites.

Modified DNA-based Cryptography System in the Cloud: Deep Maxout-based Fined Tuned Key Generation

Cloud security is a set of practices and tools created to address both internal and external security threats to businesses. Organizations must have cloud security as they implement their digital transformation schemes and include cloud-based tools and services in their infrastructure. Cryptography is a mechanism for preventing illegal access to data. In this paper, modified DNA-based cryptography (MDNAC), which is defined as data hiding with respect to DNA sequence is used. The steps involved in the proposed MDNAC is: encryption and decryption with optimal key generation. A way of converting plain text into cipher text is known as encryption. Two components make up the encryption process: a key and an encryption algorithm. For the encryption algorithm, we employed a modified DNA algorithm. In the decryption phase, the reverse operation is performed to get the plain text from the cipher text. Moreover, a deep learning model is used for generating the keys; the model used is deep maxout. To ensure the appropriate key generation process, the weights of the deep maxout are optimally tuned by the new feedback assisted Archimedes optimization (FAAO) algorithm. Based on the generated keys, the encryption process takes place. Finally, the performance of MDNAC is evaluated using conventional methods with respect to different measures. Additionally, the MDNAC obtained a correlation value of 0.20297 for the mean case scenario, despite the fact that the corresponding values are 0.02%, 0.17%, 0.2%, 0.7%, 0.12%,0.41%, 0.86%, and 0.46% as compared to the other models such as FAT, AOA, BMO, COOT, BOA, SSO, WOA, and LES respectively.

Advanced Farming Strategies Using NASA POWER Data in Peanut-Producing Regions without Surface Meteorological Stations

Understanding the impact of climate on peanut growth is crucial, given the importance of temperature in peanut to accumulate Growing Degree Days (GDD). Therefore, our study aimed to compare data sourced from the NASA POWER platform with information from surface weather stations to identify underlying climate variables associated with peanut maturity (PMI). Second, we sought to devise alternative methods for calculating GDD in peanut fields without nearby weather stations. We utilized four peanut production fields in the state of Georgia, USA, using the cultivar Georgia-06G. Weather data from surface stations located near peanut fields were obtained from the University of Georgia’s weather stations. Corresponding data from the NASA POWER platform were downloaded by inputting the geographic coordinates of the weather stations. The climate variables included maximum and minimum temperatures, average temperature, solar radiation, surface pressure, relative humidity, and wind speed. We evaluated the platforms using Pearson correlation (r) analysis (p < 0.05), linear regression analysis, assessing coefficient of determination (R2), root mean square error (RMSE), and Willmott index (d), as well as principal component analysis. Among the climate variables, maximum and minimum temperatures, average temperature, and solar radiation showed the highest R2 values, along with low RMSE values. Conversely, wind speed and relative humidity exhibited lower correlation values with errors higher than those of the other variables. The grid size from the NASA POWER platform contributed to low model adjustments since the grid’s extension is kilometric and can overlap areas. Despite this limitation, NASA POWER proves to be a potential tool for PMI monitoring. It should be especially helpful for growers who do not have surface weather stations near their farms.

Development of a Simple Game-Based PJOK Learning Model to Improve Locomotor Basic Movements and Cooperation of Lower Grade Elementary School Students

This study aims to determine the correlation between the Problem-Based Learning learning model and the Discovery Learning model with students' creativity and learning motivation. This research uses a quantitative approach of correlation type. The sample in this study used purposive sampling, namely grade 6 with a total of 40 students, consisting of 19 students at SD Negeri Awu Awu and 21 students at SD Negeri Wingkoharjo. Data collection using observation sheets and questionnaire statements. Data analysis in this study used Product moment Correlation Test and t-Independent Test. The results of this study indicate that there is a significant correlation between the application of the Problem-Based Learning learning model and the learning creativity of students at SD Negeri Awu Awu with a pearson correlation value = 0.660 and a p-value (sig-2tailed) of 0.002. There is a significant correlation between the application of the Problem-Based Learning learning model and student learning motivation at SD Negeri Awu Awu with a pearson correlation value = 0.834 and a p-value (sig-2tailed) of 0.000. There is a significant correlation between the application of the Discovery Learning learning model and students' learning creativity at SD Negeri Wingkoharjo with a pearson correlation value = 0.660 and a p-value (sig-2tailed) of 0.001. There is a significant correlation between the application of the Discovery Learning learning model and student learning motivation at SD Negeri Wingkoharjo with a pearson correlation value = 0.501 and a p-value (sig-2tailed) of 0.021. There is no significant difference between creativity and motivation of students in the application of both learning models with a significance value of 0.064 for learning creativity and with a significance value of 0.477 for learning motivation.

The correlation between CT perfusion deficits and immediate post-endovascular treatment contrast extravasation on dual energy CT in acute ischemic stroke patients

PurposeAfter endovascular therapy (EVT) for ischemic stroke, post-EVT CT imaging often shows areas of contrast extravasation (CE) caused by blood brain barrier disruption (BBBD). Before EVT, CT-perfusion (CTP) can be used to estimate salvageable tissue (penumbra) and irrevocably damaged infarction (core). In this study, we aimed to correlate CTP deficits to CE, as a surrogate marker for BBBD, after EVT for ischemic stroke. MethodsIn this single center study, EVT patients between 2010 and 2020 in whom both CTP at baseline and DECT post-EVT was performed were included. The presence of core and penumbra on CTP was assessed per ASPECTS region, resulting in a CTP-ASPECTScore and a CTP-ASPECTScore+penumbra. Likewise, CE on DECT was scored per ASPECTS region, resulting in a CE-ASPECTS. Correlation was assessed using Kendall’s tau correlation and positive predictive values (PPV) were calculated per ASPECTS region. Bland-Altman plots were created to visualize the agreement between the two scores. Results194 patients met our inclusion criteria. The median core and penumbra were 8 cc (IQR 1–25) and 103 cc (IQR 68–141), respectively. The median CTP-ASPECTScore, CTP-ASPECTScore+penumbra, and CE-ASPECTS were 7 (IQR 4–9), 3 (IQR 1–4), and 6 (IQR 4–9), respectively. The correlation between CTP-ASPECTScore and CE-ASPECTS was τ = 0.21, P <.001, and τ = 0.13, P =.02 between CTP-ASPECTScore+penumbra and CE-ASPECTS. Bland-Altman plots showed a mean difference (CTP-ASPECTS minus CE-ASPECTS) of 0.27 (95 %CI −6.7–7.2) for CTP-ASPECTScore and −3.2 (95 %CI −9.7–3.2) for CTP-ASPECTScore+penumbra. The PPVs of the CTP-ASPECTScore and CTP-ASPECTScore+penumbra were highest for the basal ganglia. ConclusionThere is a weak although significant correlation between pre-EVT CTP-ASPECTS and post-EVT CE-ASPECTS. The weak correlation may be attributed to various imaging limitations as well as patient related factors.

High-temperature effect on the mechanical behavior of recycled fiber-reinforced concrete containing volcanic pumice powder: An experimental assessment combined with machine learning (ML)-based prediction

The increasing volume of waste generated by various activities has increased interest in using waste to create sustainable construction materials to achieve possible benefits. In addition, using recycled materials to produce fresh concrete is a desirable option because of its low cost, lower landfill space requirement, and the completed concrete quality. Therefore, an experimental inquiry is undertaken to ascertain the impacts of up to 20 wt% cement displaced by Volcanic Pumice Powder (VPP) with the incorporation of 1% and 2% Recycled Nylon Fiber (RNF) on the mechanical properties of concrete composites following room temperature to high-temperature (600 °C) exposure. Fresh concrete characteristics tests were performed, including slump, compacting factor, Kelly ball penetration, and density. The heat resistance of the concrete was then measured by calculating the percentage decrease in weight, the splitting tensile strength, and the compressive strength of the specimens. Heating mainly raised VPP's pozzolanic reactivity and lowered high vapor pressure through melting RNF. Therefore, VPP and RNF-treated concrete had superior mechanical performance than control concrete even when exposed to elevated temperatures. Further, the microstructural modifications brought on by RNF and VPP additions were also explored by deploying Scanning Electron Microscopy (SEM). The use of VPP in concrete led to an improvement in fresh properties, while RNF demonstrated deterioration in the same qualities. Despite this, supervised machine learning techniques are a central focus of this investigation because of their potential to predict concrete characteristics accurately. To predict the fresh and mechanical characteristics of concrete, both the Random Forest (RF) and the K-Nearest Neighbors (KNN) algorithm, along with their ensemble model counterparts, were explored. The outcomes revealed that RNF and VPP considerably improved the concrete's heat resilience and mechanical characteristics and halted the concrete composites' explosive spalling behavior at 600 °C temperatures. To prevent strength loss at high temperatures, it was discovered that adding 1% RNF content to concrete with 10% VPP was the best combination. In addition, the high coefficient correlation (R2) value of the RF and the ensemble model indicates great accuracy in outcome prediction, while the low R2 value of the KNN model indicates that the KNN model is less accurate.

Evaluation of IMERG and GSMaP for Tropical Cyclone Applications

AbstractThis study compares rainfall from NASA Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG V06) and JAXA Global Satellite Mapping of Precipitation (GSMaP V4) for tropical cyclone (TC) applications against satellite microwave‐derived Goddard Profiling Algorithm (GPROF V05) precipitation data retrieved from 2000 to 2012. From a global data set of storms, all three products show consistent patterns in 1‐dimensional azimuthal averages and in 2‐dimensional rainfall distributions (where spatial correlation values are near 1.0). However, both IMERG and GSMaP overestimate precipitation amounts against GPROF, and IMERG overestimations are much higher than GSMaP within 125 km of the storm center. Based on this analysis, IMERG and GSMaP rainfall could be used to analyze TC precipitation patterns at high spatiotemporal resolutions. However, caution is required if high accuracy TC precipitation amplitude is required, particularly for IMERG. This study highlights opportunities to improve future versions of IMERG and GSMaP retrieval processing to reduce the discrepancies with GPROF.

Determining Formaldehyde Phytoremediation Efficacy of Selected Ornamental Plants

In the recent past, deterioration of indoor air quality has become a serious concern due to the increased energy efficiency and reduced air exchange rate inside urban buildings. Volatile organic compounds (VOCs) are a major category of indoor air pollutants that can lead to adverse health outcomes such as headaches, allergies and asthma in those exposed for prolonged periods. Formaldehyde, the VOC used in the present study is a common contaminant of indoor air which originates from particle board, plywood, paper products, certain adhesives, tobacco smoke and other sources. The use of plants for the mitigation of indoor air pollution is viewed as a cost effective and eco-friendly method with untapped potential. The main objective of the present study was to determine the formaldehyde removal efficacy of selected plant species while investigating the mechanisms used by these plants in the removal process. The selected plant species were Zamioculcas zamiifolia, Hedera helix, Dracaena sanderiana and Ficus sp. Plants of the same age were used with three replicates from each species. Phytoremediation potential was assessed by exposing the plants for 24 hours to gaseous formaldehyde (2.0 μL L-1) in air tight chambers made of Plexiglass (an inert material for VOCs) with dimensions of 0.9 m height×0.58 m length×0.55 m width. Formaldehyde removal was measured using GC-MS and the result was expressed as removal percentages. Several leaf anatomical characters (cuticle thickness, epidermal thickness and mesophyll layer thickness), stomatal characters (stomatal density, stomatal index, guard cell length and potential conductance index) and physiological characters (stomatal conductance and photosynthetic rate) were measured to study the possible mechanisms involved in the formaldehyde removal process. The data were subjected to ANOVA and Pearson correlation test. According to the results obtained, Ficus sp. had the highest formaldehyde removal percentage (92.80%) while Hedera helix had the lowest (56.86%). The study further showed that stomatal density and stomatal conductance play a major role in formaldehyde removal by these plants while cuticle and epidermal thickness act as hindrances to this process. This conclusion was further confirmed by Pearson‘s correlation values in correlation analysis. The current study was carried out for 24 hours of exposure and it would be of interest to explore, the long term behavior of the plants through further studies. Overall, the results justify future studies on a cross-section of diverse plant species for formaldehyde removal efficiency to determine species with superior removal efficiencies for a better phytoremediation process. &#x0D; Keywords: Phytoremediation, VOCs, Formaldehyde, GC-MS technology