Calculated Correlation Coefficients Research Articles

Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving loads is assessed. The raw signal has been improved using a random decrement technique. Take measurements from different locations and calculate correlation coefficients between them, then use these as features to evaluate the structure. In order to create a reliable and potential framework for predicting damage efficiently, these features are used as input variables to the machine learning model. The proposed methodology exhibits promising results in accurately discerning and predicting damage in beam structure. It demonstrates a high level of precision to subtle changes in structural integrity when trained by machine learning on the statistical feature extracted from acceleration signals. As a result of this research, methods for detecting structural damage can be made more reliable and efficient by employing machine learning techniques. Additionally, structures operating in dynamic environments can benefit significantly from the proposed methodology.

Network Graph Laplacian-Based Sensor Projection in System-Level Modeling of Liquid-Metal Loop

Accurate modeling of nuclear reactor transients is important for improving operation and maintenance planning strategies in advanced reactors. An adequate understanding of the connectivity between various components and sensor signals can provide the means to project plant information even where sensors are not operational. This work presents a method for projecting temperature signals using network graph Laplacian and support vector regression (SVR). A system model of a scaled liquid-metal-cooled reactor experiment facility is used to generate thermal-hydraulic data simulating cold-shock transients into the reactor plenum. Two different flow rates are investigated, 0.03 kg/s and 0.06 kg/s, describing the different degrees of nonlinear disturbances in two cases. The temperatures at 72 locations within the system are used to test the temperature prediction model. The inter-connectivity of these points is described using the network graph Laplacian, which describes a location in the loop as a node and then comprises a weighted matrix of connections between each of these nodes. Additional nodes, called ghost nodes, are used to model the heat transfer of the loop with the surroundings. The graph Laplacian and corresponding nodal sensing data are then used to train a kernel, which can inform the impact of various phenomena at one location in the plant on phenomena at other locations in the plant. The sensing data and graph network of these nodes are used to construct a surrogate model of the liquid-metal loop. This model is then used to predict the behavior at certain nodes where the training data are not provided to the model. These model predictions are compared against the test data for the two different inlet flow rates. When optimized, the average error of the simulated temperature data remained below 5% when 3 of the 72 nodes are predicted using the graph-based SVR model. As the number of unknown sensors is increased, the root-mean-square error increases slightly but still remains below 2% when 24 of the 72 nodes are unknown or tested. The test or unknown sensor location plays a larger role than the number of unknowns, with sensor locations near ghost nodes and near the outlet pipes in the plenum having the largest error, with a maximum recorded error of 7%. The objective of the graph-based SVR model is to not only capture the temperature or field variable accurately but also to capture the relative connectivity between the simulated sensors. The correlation coefficient matrix provides a scaled reference for the correlation between temperatures at two different node locations. The calculated correlation coefficient for the simulated temperature data and actual temperature data is within 5% for most of the system, with a maximum relative error of 15%.

Network Graph Laplacian-Based Sensor Projection in System-Level Modeling of Liquid-Metal Loop

Accurate modeling of nuclear reactor transients is important for improving operation and maintenance planning strategies in advanced reactors. An adequate understanding of the connectivity between various components and sensor signals can provide the means to project plant information even where sensors are not operational. This work presents a method for projecting temperature signals using network graph Laplacian and support vector regression (SVR). A system model of a scaled liquid-metal-cooled reactor experiment facility is used to generate thermal-hydraulic data simulating cold-shock transients into the reactor plenum. Two different flow rates are investigated, 0.03 kg/s and 0.06 kg/s, describing the different degrees of nonlinear disturbances in two cases. The temperatures at 72 locations within the system are used to test the temperature prediction model. The inter-connectivity of these points is described using the network graph Laplacian, which describes a location in the loop as a node and then comprises a weighted matrix of connections between each of these nodes. Additional nodes, called ghost nodes, are used to model the heat transfer of the loop with the surroundings. The graph Laplacian and corresponding nodal sensing data are then used to train a kernel, which can inform the impact of various phenomena at one location in the plant on phenomena at other locations in the plant. The sensing data and graph network of these nodes are used to construct a surrogate model of the liquid-metal loop. This model is then used to predict the behavior at certain nodes where the training data are not provided to the model. These model predictions are compared against the test data for the two different inlet flow rates. When optimized, the average error of the simulated temperature data remained below 5% when 3 of the 72 nodes are predicted using the graph-based SVR model. As the number of unknown sensors is increased, the root-mean-square error increases slightly but still remains below 2% when 24 of the 72 nodes are unknown or tested. The test or unknown sensor location plays a larger role than the number of unknowns, with sensor locations near ghost nodes and near the outlet pipes in the plenum having the largest error, with a maximum recorded error of 7%. The objective of the graph-based SVR model is to not only capture the temperature or field variable accurately but also to capture the relative connectivity between the simulated sensors. The correlation coefficient matrix provides a scaled reference for the correlation between temperatures at two different node locations. The calculated correlation coefficient for the simulated temperature data and actual temperature data is within 5% for most of the system, with a maximum relative error of 15%.

Assessment of spatio-temporal characteristics of groundwater quality at Khordha District, India

Groundwater is the major basis for drinking in various parts of our country. However, it gets contaminated by the toxic materials found in rocks, because of which it becomes unfit for various domestic and irrigation purposes. In present study, the groundwater quality and its availability for various purposes were examined by the parameters such as pH, DO, EC, TDS, temperature, salinity, Alkalinity, Total hardness, calcium hardness, magnesium hardness, chloride, fluoride, nitrate and phosphate during the post monsoonseason of 2022 and pre monsoon season of 2023 from 49 sampling locations. The above mentioned parameters were used for assessing appropriateness of groundwater for irrigation and domestic purposes by comparing them with the Bureau of Indian standards (BIS). The results revealed that groundwater shows wide variations among various parameters between two seasons during 2022–23. Obtained results show that water used for potable purposes by people in various parts of study area doesn’t meet necessary standards.In some of the sampling locations, it is found that the water exhibits very poor quality or unfit maybe for one season or both seasons. Analysis of parameters is done for calculating correlation coefficient of specified parameters. Strong linear relationships, both positive and negative, were found between several pairs of water quality measures according to their research.

D-Learning: An Experimental Approach to Determining Student Learning Outcomes Using Augmented Reality (AR) Technology

This study investigates the impact of applying digital learning through the use of augmented reality technology in university education on student learning outcomes. The research strategy and design were developed with the aim of answering the following research questions: Do different forms of active learning using augmented reality (AR) influence student knowledge?; Is it possible to assess the impact of AR implementation in teaching on the quality of learning outcomes? The main goal of his research is to identify the information objects Situated, Games, and Research within the Active Learning information package and, based on them, examine and determine the existence, relationship, and intensity of connections of the created information construct Knowledge. The research was conducted through the application of an experimental technique with incorporated survey and knowledge test methods on a sample of 270 participants, in 3 groups of 90 students each. Knowledge was examined through initial tests and final tests for each form of the conducted experiment: Situated, Games, and Research. The obtained results were processed using statistical methods of calculating correlation coefficients and factor analysis. The results indicate that students taught through all forms of AR active learning achieve a statistically significantly higher level of knowledge, thus confirming the hypothesis. In conclusion, the obtained research results provide a basis for further research using the described methodology and the further development of educational application solutions based on new AR technologies.

Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models

AbstractIn situ wave observations are essential for implementing coastal management strategies, designing coastal and offshore structures and understanding many processes related to sedimentology and marine biology. Despite their importance, these observations are rather scarce in the coastal areas of southeastern South America. Moreover, the existing observations are discontinuous, with several gaps and no simultaneity. Today, global wave models have improved their performance by providing reliable wave parameters with high resolution and coverage even at near‐shore areas. In this work, wave parameters from five global models were compared with significant wave heights (HS), peak periods (T) and propagation direction at nine sites located at the Southwestern South Atlantic Ocean. The global products used were WAVERYS (Global Reanalysis Wav 001 032), IOWAGA (WAVEWATCH III Hindcast – WW3 – Global grid – CFSR), ERA5 (ECMWF reanalysis), NOAA WW3 – 30‐year Hindcast Phase 2 and NCEP/EMC Operational global wave model GFS – Wave. Mean arithmetic error (Bias), root mean square deviation, scatter index and the Pearson linear correlation coefficient (r) were assessed at each location where wave observations were available. The best agreements for HS were obtained at the deepest sites located farther from the coast. Lower statistical metrics were achieved at coastal sites where the bathymetry is roughly represented by the global models. The calculated correlation coefficients were found to be lower for T than for HS. Overall, T was slightly underestimated by global models. Wave propagation directions were excellently represented by global models at offshore sites. The WAVERYS model is the global product that best represents the wave parameters in the study area, probably due to its higher spatial resolution (0.2°) and more realistic atmospheric forcing (ERA5). In addition, it satisfactorily represents the temporal variability of HS in open water as well as swell characteristics at the outer the Río de la Plata.

Efficacy and Concurrent Validity of Computerized Brain Training Based on Everyday Living (BTEL) Based on Instrumental Activities of Living for Cognitively Healthy Old Individuals: A Preliminary Study

Background: Interventions to prevent or attenuate cognitive decline and dementia in older adults are becoming increasingly important. Recently, cognitive training exercise can be via computer or mobile technology for independent or home use. Recent meta-analysis has reported that Computerized Cognitive Training (CCT) is effective at enhancing cognitive function in healthy older and Alzheimer’s disease adults, although little is known about individual characteristics of each computerized program. Objective: We developed a new CCT named Brain Training Based on Everyday Living (BTEL) to enhance cognitive capacity for Instrumental Activities of Daily Living (IADL). We aim to evaluate the efficacy of the BTEL among cognitively healthy old individuals and to explore its concurrent validity and construct concept. Methods: We conducted a double-blind study where 106 individuals aged 65 years and older (intervened = 53, control = 53) worked on the active and placebo tasks three times a week over three months (clinical trial: UMIN000048730). The main results were examined using ANCOVA and calculating correlation coefficients. Results: We found no effect on total score of the three tests; however, there was significant effect for the BTEL on: recognition in MMSE, and immediate recall in HDSR. The tasks are associated with prefrontal cortex. In addition, correlations indicated that each BTEL domain had some validity as a cognitive assessment tool. Different from previous CCT, we determined the neuropsychological characteristics of specific cognitive tasks of the BTEL to a certain degree. Conclusions: We found modest efficacy of the BTEL in cognitively healthy old individuals and confirmed its concurrent validity and the conceptual construct.

Deep learning tool: reconstruction of long missing climate data based on spatio-temporal multilayer perceptron

AbstractLong-term monitoring of climate data is significant for grasping the law and development trend of climate change and guaranteeing food security. However, some weather stations lack monitoring data for even decades. In this study, 62 years of historical monitoring data from 105 weather stations in Xinjiang were used for missing sequence prediction, validating proposed data reconstruction tool. First of all, study area was divided into three parts according to the climatic characteristics and geographical locations. A spatio-temporal multilayer perceptron (MLP) was established to reconstruct meteorological data with three time scales (Short term, cycle and long term) and one spatio dimension as inputing (rolling predictions, one step predicts one day), filling in long sequence blank data. By designing an end-to-end model to autonomously detect the locations of missing data and make rolling predictions,we obtained complete meteorological monitoring data of Xinjiang from 1961 to 2022. Seven kinds of parameter reconstructed include maximum temperature (Max_T), minimum temperature (Min_T), mean temperature (Ave _ T), average water vapor pressure (Ave _ WVP), relative humidity (Ave _ RH), average wind speed (10 m Ave _ WS), and sunshine duration (Sun_H). Contrasted the prediction accuracy of the model with general MLP and LSTM, results shows that, in the seven types of parameters, designed spatio-temporal MLP decreases MAE and MSE by 7.61% and 4.80% respectively. The quality of reconstructed data was evaluated by calculating correlation coefficient with the monitored sequences of nearest station,determining the applicable meteorological parameters of the model according to the results. Results show that,proposed model reached satisfied average correlation coefficient for Max_T, Min_T, Ave _ T and Ave _ WVP parameters are 0.969, 0.961, 0.971 and 0.942 respectively. The average correlation coefficient of Sun_H and Ave _ RH are 0.720 and 0.789. Although it is difficult to predict extreme values, it can still capture the period and trend; the reconstruction effect of 10 m Ave _ WS is poor, with the average similarity of 0.488. Proposed method is applicable to reconstruct Max_T, Min_T, Ave _ T and Ave _ WVP, but not recommended to reconstruct Sun_H, Ave _ RH and Ave _ WS.

Determination of drug-related problems according to PAIR criteria in dialysis patients: a cross-sectional study in tertiary care hospital

BackgroundDialysis patients are at high risk for drug-related problems (DRPs), which have significant consequences for their morbidity, mortality, and quality of life. Improved clinical outcomes can be achieved by preventing, identifying, and resolving these problems.MethodsThis is a retrospective observational study. In this study, the PAIR instrument (Pharmacotherapy Assessment in Chronic Renal Disease) was validated for use in Turkish. Validation consisted of three stages: translation back-translation with expert panel evaluation, reliability analysis using the test-retest method, and conceptual validity with both Pharmaceutical Care Network Europe (PCNE) and PAIR used to determine DRPs prevalence.ResultsIn total, 104 patients (mean ± SD age, 54.1 ± 15.8 years; 53.8% male) were included in the study. An expert panel evaluated the items in the criterion based on their intelligibility, service of purpose, differentiation, and cultural suitability during the translation stage. Content validity index (CVI) score was found to be 0.95. The reliability analysis was performed by applying the test-retest method and calculating correlation coefficient on 30 randomly selected patients one month later. Correlation coefficient (p) was found to be 0.8. To evaluate conceptual validity, 104 patients’ pharmacotherapy plans were assessed using both the PAIR and PCNE criteria. The prevalence of DRPs according to PAIR criteria (100.0%) and PCNE (73.1%) were statistically significantly different (p < 0.001).ConclusionsAs a result, PAIR criteria can identify clinically relevant DRPs in patients with CKD and is a new, validated tool to be used in Turkey, but may not be adequate for patients receiving dialysis. Therefore, it needs to be reviewed and updated for dialysis patients.

Effect of maternal eating behaviors and attitudes towards the feeding process on feeding problems of pre-school age children

Background & AimsFeeding problems in pre-school children are common and negative maternal feeding practices may even worsen the child's problematic eating behaviour. Therefore, investigating the effects of maternal eating behaviors and attitudes towards the feeding process on pre-school children’s feeding problems may be helpful for preventing feeding problems. This study sought to investigate the effects of maternal eating behaviors and attitudes towards the feeding process on feeding problems of pre-school children. MethodsMothers of 373 children aged 3-6 were included in this cross-sectional study and data was collected by an online questionnaire including the scales of three factor eating questionnaire (TFEQ), mother’s attitudes towards the feeding process (MATFPS) and behavioral pediatric feeding assessment (BPFAS), as well as demographics and anthropometric measures (height and weight). Spearman’s rho test was used to calculate correlation coefficients between the, TFEQ, MATFP and BPFA scales. In order to identify independent predictors of child feeding behaviors, a multiple linear regression model was used. ResultsResults showed that uncontrolled eating subscale was positively (r=0.160, p<0.001) and cognitive restriction subscale negatively (r=-0.126, p<0.05) correlated with MATFP. MATFP was also positively correlated with BPFA (r=0.368, p<0.001). Regression analysis indicated that BPFA was significantly predicted by MATFP which was the most important contributor of child feeding problems (β=0.24 t=4.88, p<0.001). ConclusionsThis study indicated that maternal eating behaviors were related to maternal attitudes towards the feeding process and, mothers’ attitudes were associated with feeding problems of their pre-school children.

A Systematic Review and Meta-analysis: Paternal Anxiety and the Emotional and Behavioral Outcomes in Their Offspring

Anxiety disorders are highly prevalent worldwide; however, the literature lacks a meta-analytic quantification of the risk posed by fathers' anxiety for offspring development. This systematic review and meta-analysis aimed to provide a comprehensive estimate of the magnitude of the association between paternal anxiety and emotional and behavioral problems of offspring. In February 2022, Web of Science, Ovid (Embase, MEDLINE, PsycINFO), Trip Database, and ProQuest were searched to identify all quantitative studies that measured anxiety in fathers and emotional and/or behavioral outcomes in offspring. No limits were set for offspring age, publication language, or publication year. Summary estimates were extracted from the primary studies. Meta-analytic random-effects 3-level models were used to calculate correlation coefficients. Quality was assessed using the Newcastle-Ottawa Scale. The study protocol was preregistered with PROSPERO (CRD42022311501) and adhered to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) reporting guidelines. Of 11,746 records identified, 98 were included in the meta-analysis. Small but significant associations were found between paternal anxiety and offspring emotional and behavioral problems overall (r= 0.16, 95% CI [0.13, 0.19]) and behavioral (r= 0.19, 95% CI [0.13, 0.24]), emotional (r= 0.15, 95% CI [0.12, 0.18]), anxiety (r= 0.13, 95% CI [0.11, 0.16]), and depression (r= 0.13, 95% CI [0.03, 0.23]) problems. Some significant moderators were identified. Paternal mental health is associated with offspring development, and the offspring of fathers with anxiety symptoms or disorders are at increased risk of negative emotional and behavioral outcomes, in line with the principles of multifinality and pleiotropy. The substantial heterogeneity among studies and the overrepresentation of White European American groups in this literature highlight the need for further research. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list.

Evaluation of the Oxford cognitive screen Indonesian version (OCS-Ina) for assessing cognitive function disorders: validation and reliability study

Background and objectives. The Oxford Cognitive Screen (OCS) is a valuable tool for assessing cognitive function, particularly in the context of aphasia and neglect. This study aims to assess the validity and reliability of the Indonesian version of OCS (OCS-INA), which has been adapted and translated following WHO guidelines. Materials and methods. The adaptation and translation process of OCS, in accordance with WHO guidelines, preceded the validity and reliability testing of OCS-INA. The study included participants aged 18 years and above with normal cognitive function, as assessed by the Moca-INA. Data collection took place in social institutions and healthcare facilities that met the specified inclusion criteria. Results. Among the 104 participants meeting the inclusion criteria, a majority were male (51.92%), with ages ranging from 20 to 87 years, and the highest age group being above 60 years (60%). Most participants had a high school education level (35.58%) and were not employed (62.5%). Validity testing, using the Spearman correlation coefficient formula, revealed that the majority of domains had calculated correlation coefficients (r values) exceeding the table values (0.1927). In terms of reliability, the kappa p statistic indicated very good agreement for six examination tasks: semantic (0.874), orientation (0.842), verbal memory (0.822), episodic memory (0.870), and visual field test (1.000). Good agreement scores were obtained for the picture naming test (0.774), sentence reading (0.726), and calculation (0.774). Conclusions. In conclusion, OCS-INA demonstrates both validity and reliability as a screening tool for cognitive impairment. It serves as a valuable complement to existing instruments used for similar purposes.

Построение краткосрочного прогноза количества вагонов на станции и путях необщего пользования

Objective: collect raw data for building predictive models. Analyze the initial data, identify data outliers and outliers, divide the data into time intervals, calculate correlation coefficients, partial autocorrelation, cross-correlation, analyze the trend and seasonality of the obtained time series. Using autoregressive models, machine learning models, neuro-fuzzy models to build forecasts of time series and determine the quality of the obtained forecasts. Methods: point density, autocorrelation, partial autocorrelation, cross-correlation, Foster-Stewart test, Dickey-Fuller test, ARMA, MLP, Encoder-Decoder LTSM, TSK, Fuzzy-Partitions, SCRG, Transformers. Results: we obtained estimates of the prediction accuracy of the selected models, compared the results of the predictive models trained on different samples of initial data. Conclusions are made about the efficiency and methods of building predictive models. Practical significance: the &#x0D; significance of building accurate predictive models for the key quantitative indicators of stations and nonpublic routes operation is shown. The factors influencing the accuracy of the obtained forecasts are analyzed.

Co-prevalence of arterial aneurysm location - a correlation analysis based on a retrospective cross-sectional observational study.

Background: The aim of this retrospective single-centre cross-sectional observational study was to investigate co-prevalence of arterial aneurysm location systematically. Patients and methods: Patients with the diagnosis of any arterial aneurysm from January 2006 to January 2016 were investigated in a single centre. Patients with hereditary disorders of connective tissue, systemic inflammatory disease, or arterial pathologies other than true aneurysms were excluded. Aneurysm locations were assessed for every patient included. For patients with at least two co-existing aneurysms, co-prevalence of aneurysm location was investigated by calculating correlation coefficients and applying Fisher's exact test. This study report is prepared according to the STROBE statement. Results: Of 3107 identified patients with arterial aneurysms, 918 were excluded. Of the remaining 2189 patients, 951 patients with at least two aneurysms were included in the study. Bilateral aneurysm combinations of paired iliac, femoral and popliteal arteries showed the highest correlation (ϕ=0.35 to 0.67), followed by bilateral combinations of subclavian (ϕ=0.36) and internal carotid (ϕ=0.38) arteries. Abdominal aortic aneurysms in combination with visceral artery aneurysms (ϕ=-0.24 to -0.12), popliteal arteries (ϕ=-0.22) and the ascending aorta (ϕ=-0.19) showed the lowest correlation, followed by the descending aorta in combination with the common iliac arteries (ϕ=-0.12 to -0.13). Conclusions: In our study sample, aneurysm co-prevalence was highly non-random. This should be considered in the context of aneurysm screening programs.

Multi‐chaotic maps and blockchain based image encryption

SummaryThe vast technological developments make data transmission more frequent over networks. So, the data needs to be secured and for that reason, there is a requirement to develop an effective encryption model. This article proposes a novel image encryption model using multi‐chaotic maps and blockchain (MCBE). Chaotic maps have been used in encryption models as chaotic maps have the properties of randomness, and non‐periodicity that get utilized in improving the efficiency of an encryption model. Logistic and tent maps have been employed as multi‐chaotic maps to make the encryption process effective in this work. A logistic map has a simple structure and is easily implementable but has some drawbacks like small keyspace. Keyspace and randomness are enhanced by using a tent map along with a logistic map. The proposed MCBE methodology applies to grayscale or colored images having different file formats and sizes. Initially, in the confusion phase, the permutation of the original image is performed based on a random permutation that changes the original image pixel's position. The encryption model applies multi‐chaotic maps, row‐wise and column‐wise in the diffusion phase to promote the efficiency of the encryption model. Finally, blockchain has been implemented using the SHA‐256 hash function to obtain the encrypted image that enhances the security by increasing the keyspace, to resist brute force attacks. The evaluation performance of the MCBE has been analyzed against various attacks namely statistical attacks, differential attacks, NIST randomness test, noise, and cropping attacks. The evaluated keyspace is which has high key sensitivity with a correlation of encrypted images close to 0. Maximum achieved entropy value of 7.9998, SSIM value between original and encrypted images nearby 0 confirms the dissimilarity. The Number of Pixels Change Rate (NPCR) value of 99.62%, and the value of Unified Average Changed Intensity (UACI) value of 33.54% are within the specified standard range. The calculated correlation coefficient, NPCR, and UACI values have been compared with the existing algorithms, and the results show that the proposed MCBE methodology has better performance than the other state‐of‐the‐art methods. The experimental results indicate that the chaotic ranges generated by multi‐chaotic maps and the SHA‐256 hash function improve the keyspace and security of the encryption model confirming the efficacy of the proposed model MCBE.

Abstract 6519: Dendritic cell vaccine cell numbers and survival in patients with primary glioblastoma

Abstract Standard glioblastoma (GBM) treatment is associated with poor survival. A promising novel immunotherapy consists of autologous dendritic cells (DC) pulsed with autologous tumor antigens (ATA) from a lysate of irradiated self-renewing autologous cancer cells. An open-label, phase 2 trial was conducted in 57 patients with primary GBM. Here we report correlations between survival and DC-ATA doses. Two autologous intermediate products were manufactured for each patient: a tumor cell line established from resected GBM, and DC differentiated from cryopreserved peripheral blood monocytes (MC). Patients were enrolled prior to standard radiation therapy (RT) and temozolomide (TMZ) chemotherapy. DC-ATA were manufactured during RT/TMZ, divided into individual doses, then cryopreserved in liquid nitrogen. For each patient-specific vaccine batch, cell numbers were calculated using a hemocytometer; viability was determined before cryopreservation (cryo) using trypan blue and post-cryo using 7ADD per flow cytometry. After RT/TMZ, up to 8 doses were injected s.c. concurrent with TMZ over 6 months. Patients were grouped into tertiles based on DC-ATA cell numbers and survival. Prism 10.03 was used to generate survival curves and calculate correlation coefficients and log-rank and T-Test comparisons. As previously reported, cell line success rate was 71/73, sufficient MC numbers were collected by leukapheresis for 63/65; DC-ATA was successfully manufactured for 60/60; 57 patients were treated; median progression free survival (PFS) and overall survival (OS) were 10.4 and 16.0 months respectively. Correlations were strong between total and viable DC-ATA pre-cryo (0.97, p&amp;lt;0.0001) and post-cryo (0.95, p&amp;lt;0.0001), but weaker between DC-ATA pre- and post-cryo (0.30, p=0.02), and between viable DC-ATA pre- and post-cryo (0.37, p=0.004). When patients were grouped by OS tertiles, medians were 9.8, 18.1, and more than 36 months. There were no differences in the average millions (M) of DC-ATA pre-cryo (10.0, 9.7, 9.6), viable DC-ATA pre-cryo (8.5, 7.8, 7.9), DC-ATA post-cryo (9.0, 10.3, 10.8) or viable DC-ATA post-cryo (6.0, 7.3, 7.4). Similarly, when patients were grouped into tertiles by DC-ATA numbers, there were no differences in OS curves associated with DC-ATA pre-cryo, viable DC-ATA pre-cryo, DC-ATA post-cryo, or viable DC-ATA post-cryo. Pre-cryo viable DC-ATA numbers ranged from 0.27 to 27.0 M. An ineffective dose was not identified; pre-cryo viable DC-ATA doses of less than 2M were associated with OS of 10.4, 13.0, 13.3, and 36.4+ months. Also, there was no OS difference by number of MC used to manufacture DC or by number of DC manufactured. In conclusion, there was no evidence for a dose/response relationship between DC-ATA dose and OS for any of the parameters tested. This data supports that pre-cryo cell counts can be used to establish treatment doses. Pre-cryo viable cell counts of 2M DC-ATA per dose seem sufficient. Citation Format: Robert O. Dillman, Gabriel I. Nistor, Krystal Godding, Rockelle Robles, Hans S. Keirstead. Dendritic cell vaccine cell numbers and survival in patients with primary glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6519.

P210 Association between Non-Alcoholic Fatty Liver Disease and Related Factors in Patients with Idiopathic Chronic Ulcerative Colitis

Abstract Background UC is an immunomodulated inflammatory intestinal disease that can involve systemic or extraintestinal manifestations. Few studies have evaluated the possible association between UC and nonalcoholic fatty liver disease (NAFLD). However, the prevalence of NAFLD among patients with UC has been reported to be higher than in the general population. Objetive: To determine the frequency of NAFLD in patients with UC and the factors associated with the simultaneous presence of both diseases. Methods This was an observational, cross-sectional, relational, and analytical study that included 40 patients with UC. Data on demographics, clinical information, biochemical parameters, endoscopic findings, and histological features were collected for each patient. Disease activity was assessed using the Truelove and Witts scales, Mayo endoscopic scale, and Riley histological index. NAFLD was evaluated using transient elastography (FibroScan®), determining the controlled attenuation parameter (CAP) with a cutoff score of 248 dB/m to define steatosis. Hepatic fibrosis was considered when the measurement exceeded 6.5 kPa. The risk of steatohepatitis was estimated using the FAST index. Data were analyzed using the MyFibroScan® app, selecting "multi-etiology" as a parameter. Statistical analysis was performed using SPSS version 26, calculating correlation coefficients using Spearman's Rho. A value of p &amp;lt; 0.05 was considered significant. Results The clinical and demographic characteristics of the patients are described in Table 1. A positive correlation was found between CAP and Body Mass Index (r = 0.36; p = 0.02). Statistically significant differences were observed between CAP and Truelove and Witts scale (p = 0.02). Differences were also noted between kPa value (p = 0.03) and FAST index (p &amp;lt; 0.001) and the medical treatment used to control UC. No associations or correlations were observed between CAP, kPa, FAST index, and endoscopic or histological severity or other clinical characteristics of the patients. Conclusion The presence of NAFLD in patients with UC may be of metabolic origin, predominantly associated with overweight/obesity, and determined by the clinical-biochemical immunomodulated inflammatory activity of the disease and the concurrent treatment used. These findings highlight the importance of a multidisciplinary approach in the care of patients with UC.

Monte Carlo simulation of photopolymerizable ceramic suspension curing profile in vat photopolymerization

Vat photopolymerization holds promise for intricate ceramic structures through additive manufacturing. However, the scattering of ceramic pastes during printing can result in inaccuracies and defects. To address this, we propose utilizing a Monte Carlo method to account for scattering in absorbing media, predicting the curing profile of photopolymerizable ceramic suspensions. This method considers factors like the reflective air-slurry interface and the impact of particle size distributions on the curing profiles, which have been relatively overlooked in prior research. Employing physical equations, we developed a Matlab simulation program, facilitating the study of various process parameters on curing performance. The simulations accurately predict solidification width and depth for slurries with distinct volume fractions and powder particle sizes. The calculated correlation coefficient score was 0.9530 regarding curing depth in all six cases, significantly supporting the proposed method. Overall, this study underscores the potential of the Monte Carlo method in enhancing the precision of vat photopolymerization 3D printing for ceramics.

The Correlation Between Reading Comprehension and Vocabulary Mastery of Class XI Students at Madrasah Aliyah Al-Khairaat Ulatan

This study aims to investigate the relationship between the reading comprehension and vocabulary proficiency of students in the eleventh grade at Madrasah Aliyah Al-khairaat Ulatan. The research employs a quantitative approach with a correlational design, and the sampling method utilized is total sampling. The study includes 26 students from the eleventh-grade class at Madrasah Aliyah Al-khairaat Ulatan. The research instrument consists of a multiple-choice test, and data collection involves two types of tests: a reading comprehension test and a vocabulary mastery test, each comprising 10 questions. Data analysis was conducted manually using SPSS 25. The findings of the research reveal a significant correlation between reading comprehension and vocabulary mastery, with a two-tailed significance (Sig.) value of 0.007. The calculated correlation coefficient (r) is 0.518, indicating a strong correlation (greater than 0.5 but less than 0.75). The Pearson correlation coefficient of 1 signifies a positive correlation between the two variables. Therefore, the research concludes that the alternative hypothesis (Ha): "The value of r count 0.518 &gt; r table 0.388" is accepted, indicating a correlation between reading comprehension and vocabulary mastery among students in class XI at Madrasah Aliyah Al-khairaat Ulatan.

Postconcussion Moderate to Vigorous Physical Activity Predicts Anxiety Severity among Adolescent Athletes.

This study aimed to examine how moderate-to-vigorous physical activity (MVPA) during concussion recovery influences self-reported anxiety symptoms at follow-up assessment. We hypothesized that more MVPA after concussion would be associated with lower anxiety rating at follow-up. We performed a prospective study of participants aged 13-18 yr initially assessed within 14 d of diagnosed concussion. Participants rated concussion symptoms using the Post-Concussion Symptom Inventory and were provided a wrist-worn actigraphy device to track activity for 1 wk after assessment. At follow-up assessment, participants rated anxiety symptoms using the four-question Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety subscale. Each question ranged from 1 (never) to 5 (almost always), with an overall score range of 4-20. For univariable analysis, we calculated correlation coefficients between MVPA and PROMIS anxiety subscale scores. We then created a multiple linear regression model with follow-up PROMIS anxiety subscale score as the outcome and MVPA, sex, initial symptom severity, and preconcussion anxiety as predictors. We enrolled and initially tested 55 participants, and 48 were included in the final analysis (age, 14.6±2.7 yr; 56% female; initial assessment, 7.3± 3.1 d; follow-up assessment, 42.0±29.7 d). We observed an inverse and low correlation between MVPA and follow-up PROMIS anxiety subscale T-scores ( r = -0.30, P = 0.04). Multivariable regression results indicated that MVPA ( β = -5.30; 95% confidence interval (CI), -10.58 to -0.01), initial Post-Concussion Symptom Inventory score ( β = 0.11; 95% CI, 0.03 to 0.19), and preconcussion anxiety ( β = 5.56; 95% CI, 0.12 to 11.0), but not sex ( β = -2.60; 95% CI, -7.14, to 1.94), were associated with follow-up PROMIS anxiety subscale T-scores. After adjusting for covariates, more MVPA early after concussion predicted lower PROMIS anxiety subscale scores at follow-up. Although initial concussion symptom severity and preconcussion anxiety were also associated with follow-up PROMIS anxiety subscale score, MVPA represents a modifiable factor that may contribute to lower anxiety symptoms.