Application of power-law committee machine to combine five machine learning algorithms for enhanced oil recovery screening.

One of the main challenges in screening of enhanced oil recovery (EOR) techniques is the class imbalance problem, where the number of different EOR techniques is not equal. This problem hinders the generalization of the data-driven methods used to predict suitable EOR techniques for candidate reservoirs. The main purpose of this paper is to propose a novel approach to overcome the above challenge by taking advantage of the Power-Law Committee Machine (PLCM) technique optimized by Particle Swam Optimization (PSO) to combine the output of five cutting-edge machine learning methods with different types of learning algorithms. The PLCM method has not been used in previous studies for EOR screening. The machine learning models include the Artificial Neural Network (ANN), CatBoost, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The CatBoost is used for the first time in this work for screening of EOR methods. The role of the PSO is to find the optimal values for the coefficients and exponents of the power-law model. In this study, a bigger dataset than those in previous studies, including 2563 successful worldwide EOR experiences, was gathered. A bigger dataset improves the generalization of the data-driven methods and prevents overfitting. The hyperparameters of the individual machine-learning models were tuned using the fivefold cross-validation technique. The results showed that all the individual methods could predict the suitable EOR method for unseen cases with an average score of 0.868. Among the machine learning models, the KNN and SVM had the highest scores with a value of 0.894 and 0.892, respectively. Nonetheless, after combining the output of the models using the PLCM method, the score of the predictions improved to 0.963, which was a substantial increase. Finally, a feature importance analysis was conducted to find out the most influential parameters on the output. The novelty of this work is having shown the ability of the PLCM technique to construct an accurate model to overcome the class-imbalance issue in EOR screening by utilizing different types of data-driven models. According to feature importance analysis, oil gravity and formation porosity were recognized as the most influential parameters on EOR screening.

Architecture and Development Framework for a Web-Based Risk Assessment and Management Platform Developed on WordPress to Address Opioid Overdose.

The number of overdose-related fatalities continues to reach historic levels across Canada, despite ongoing efforts by authorities. To reduce mortality, a clinical trajectory ranging from preventative measures to crisis intervention, skill training to treatment, and risk assessment to risk management needs to be supported. The web-based Risk Assessment and Management Platform (RAMP) was developed to realize this concept and to empower people who use drugs through an integrated tool that allows them to better understand and manage their risk of overdose. This paper outlines the architecture and development of RAMP, which is built on the WordPress platform. WordPress components are mapped onto a 3-tier architecture that consists of presentation, application, and database layers. The architecture facilitates the development of a modular software that includes several features that are independent in functionality but interact with each other in an integrated platform. The relatively low coupling and high coherence of the features may reduce the cost of maintenance and increase flexibility of future developments. RAMP's architecture comprises a user interface, conceptual framework, and backend layers. The RAMP front end effectively uses some of the WordPress' features such as HTML5, CSS, and JavaScript to create a mobile, friendly, and scalable user interface. The RAMP backend uses several standard and custom WordPress plug-ins to support risk assessment and monitoring, with the goal of mitigating the impacts and eliminating risks together. A rule-based decision support system has been hard-coded to suggest relevant modules and goals to complement each user's lifestyle and goals based on their risk assessment. Finally, the backend uses the MySQL database management system and communicates with the RAMP framework layer via the data access layer to facilitate a timely and secure handling of information. Overall, RAMP is a modular system developed to identify and manage the risk of opioid overdose in the population of people who use drugs. Its modular design uses the WordPress architecture to efficiently communicate between layers and provide a base for external plug-ins. There is potential for the current system to adopt and address other related fields such as suicide, anxiety, and trauma. Broader implementation will support this concept and lead to the next level of functionality.

Seeking versus receiving help: How children integrate suggestions in memory decisions.

The current research examined how seeking versus receiving help affected children's memory and confidence decisions. Baseline performance, when no help was available, was compared to performance when help could be sought (Experiment 1: N = 83, 41 females) or was provided (Experiment 2: N = 84, 44 females) in a sample of predominately White 5-, 7-, and 9-year-olds from Northern California. Data collection occurred from 2018 to 2019. In Experiment 1, 5-year-olds agreed most often with sought-help, whereas 9-year-olds were the only age group reporting lower confidence for sought-help relative to baseline trials. In Experiment 2, agreement and confidence after provided help were similar across age groups. Different developmental patterns when help was sought versus provided underscore the importance of active help-seeking for memory decision-making.

Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression.

Background: Substance use disorders affect 36 million people globally, but only a small proportion of them receive the necessary treatment. E-health interventions have been developed to address this issue by improving access to substance use treatment. However, concerns about participant engagement and adherence to these interventions remain. This review aimed to evaluate adherence to e-health interventions targeting substance use and identify hypothesized predictors of adherence. Methods: A systematic review of literature published between 2009 and 2020 was conducted, and data on adherence measures and hypothesized predictors were extracted. Meta-analysis and meta-regression were used to analyze the data. The two adherence measures were (a) the mean proportion of modules completed across the intervention groups and (b) the proportion of participants that completed all modules. Four meta-regression models assessed each covariate including guidance, blended treatment, intervention duration and recruitment strategy. Results: The overall pooled adherence rate was 0.60 (95%-CI: 0.52-0.67) for the mean proportion of modules completed across 30 intervention arms and 0.47 (95%-CI: 0.35-0.59) for the proportion of participants that completed all modules across 9 intervention arms. Guidance, blended treatment, and recruitment were significant predictors of adherence, while treatment duration was not. Conclusion: The study suggests that more research is needed to identify predictors of adherence, in order to determine specific aspects that contribute to better exposure to intervention content. Reporting adherence and predictors in future studies can lead to improved meta-analyses and the development of more engaging interventions. Identifying predictors can aid in designing effective interventions for substance use disorders, with important implications for e-health interventions targeting substance use.

Quality of life of patients treated with opium tincture or methadone: A randomized controlled trial.

OBJECTIVE: Quality of life (QoL) is an increasingly recognized patient-centered treatment outcome in individuals with opioid use disorder. There is a gap in literature on the impact of opium tincture (OT) on patients' QoL compared to standard treatment options such as methadone. This study aimed to compare the QoL of participants with opioid use disorder receiving OAT using OT or methadone and identify the factors associated with their QoL during treatment. METHODS: The opium trial was a multicenter non-inferiority randomized clinical trial in four private OAT outpatient clinics in Iran. The study assigned patients to either OT (10 mg/ml) or methadone sirup (5 mg/ml) for a follow-up of 85 days. QoL was assessed using the brief version of the World Health Organization Quality of Life instrument (WHOQOL- BREF). RESULTS: A total of 83 participants, 35 (42.2%) in the OT arm and 48 (57.8%) in the methadone arm, completed the WHOQOL-BREF in full and were included in the primary analysis. The mean score of patients' QoL showed improvement compared to baseline, but differences were not statistically significant between OT and methadone arms (p = 0.786). Improvements were mainly observed within the first 30 days of receiving treatment. Being married and lower psychological distress were associated with an improved QoL. Within the social relationships domain, male gender showed significantly higher QoL compared to females. CONCLUSION: OT shows promise as an OAT medication, comparable to methadone in improving patients' QoL. There is a need to incorporate psychosocial interventions to further sustain and improve the QoL in this population. Identifying other social determinants of health which affect QoL and the cultural adaptation of assessments for individuals from various ethnocultural backgrounds are critical areas of inquiry.

Realistic three-dimensional avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus).

Despite the complex geometry of songbird's vocal system, it was typically modelled as a tube or with simple mathematical parameters to investigate sound filtering. Here, we developed an adjustable computational acoustic model of a sparrow's upper vocal tract (Passer domesticus), derived from micro-CT scans. We discovered that a 20% tracheal shortening or a 20° beak gape increase caused the vocal tract harmonic resonance to shift toward higher pitch (11.7% or 8.8%, respectively), predominantly in the mid-range frequencies (3-6 kHz). The oropharyngeal-oesophageal cavity (OEC), known for its role in sound filtering, was modelled as an adjustable three-dimensional cylinder. For a constant OEC volume, an elongated cylinder induced a higher frequency shift than a wide cylinder (70% versus 37%). We found that the OEC volume adjustments can modify the OEC first harmonic resonance at low frequencies (1.5-3 kHz) and the OEC third harmonic resonance at higher frequencies (6-8 kHz). This work demonstrates the need to consider the realistic geometry of the vocal system to accurately quantify its effect on sound filtering and show that sparrows can tune the entire range of produced sound frequencies to their vocal system resonances, by controlling the vocal tract shape, especially through complex OEC volume adjustments.

Opium tincture versus methadone for opioid agonist treatment: a randomized controlled trial.

AIM: To test if opium tincture (OT) was non-inferior to methadone in retaining participants in opioid agonist treatment (OAT). DESIGN: A Phase III, multi-centre, parallel-group, non-inferiority, double-blind randomized controlled trial with an allocation ratio of 1:1. Participants were provided treatment and followed for a period of 85 days. SETTING: Four OAT clinics in Iran. PARTICIPANTS: Two hundred and four participants with opioid use disorder [mean age (standard deviation) = 37.4 (9.3); female 11.3%] recruited between July 2017 and January 2018. INTERVENTIONS: Participants were assigned to either OT (102) or methadone (102) using a patient-centred flexible dosing strategy. MEASUREMENTS: Treatment retention over 85 days was the primary outcome. Self-reported opioid use outside treatment and occurrence of adverse events (AEs) were the secondary outcomes. FINDINGS: Remaining in treatment at the end of the follow-up were 68.6% in the methadone arm and 59.8% in the OT arm. The relative retention rate of methadone to OT was 1.15 (0.97, 1.36) in both intent-to-treat and per-protocol analyses; non-inferiority was not supported statistically, as the upper bound of the confidence interval exceeded our pre-specified non-inferiority margin (1.25). Opioid use outside treatment was reported by 30.3% of OT (n = 152) and 49.4% of methadone (n = 168) patients, a difference in proportions of -19%: 90% confidence interval (-28%, -10%). The total count of AEs in the OT arm (22 among nine individuals) was significantly higher (P = 0.04) than that in the methadone arm (three among two individuals). Nausea was the most common side effect. CONCLUSION: While this study could not conclude the non-inferiority of opium tincture (OT) to methadone for retaining patients in opioid agonist treatment, OT retained 60% of participants to end of follow-up (85 days) and was superior to methadone in reducing self-reported opioid use outside treatment.

Apoptin Overexpression Efficiently Amplified Cytotoxic Effects of PI3K Inhibition Using BKM120 in Lymphoblastic Leukemia Cell Lines.

Purpose: Although the complex structure of acute lymphoblastic leukemia (ALL) and involvement of diverse pathways in its pathogenesis have put an obstacle in the way of efficient treatments, identification of strategies to manipulate the genome of neoplastic cells has made the treatment prospective more optimistic. Methods: To evaluate whether the transduction of apoptin __a gene encoding a protein that participates in the induction of apoptosis__ could reduce the survival of leukemic cells, we generated recombinant lentivirus expressing apoptin, and then, MTT assay, flow cytometric analysis of DNA content, western blotting, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were applied. Results: Transduction of apoptin into different leukemic cells was coupled with the reduction in the viability and proliferative capacity of the cells. Among all tested cell lines, Nalm-6 and C8166 were more sensitive to the anti-leukemic property of apoptin. Moreover, we found that the transduction of apoptin in the indicated cell lines not only induced G2/M cell cycle arrest but also induced apoptotic cell death by altering the balance between pro- and anti-apoptotic target genes. The efficacy of apoptin transduction was not limited to these findings, as we reported for the first time that the overexpression of this gene could potentiate the anti-leukemic property of pan PI3K inhibitor BKM120. Conclusion: The results of this study showed that the transduction of apoptin into lymphoblastic leukemia cell lines induced cytotoxic effects and enhanced therapeutic value of PI3K inhibition; however, further investigations are demanded to ascertain the safety and the efficacy of apoptin transduction in patients with ALL.

Generation of Human Micro-Doppler Signature Based on Layer-Reduced Deep Convolutional Generative Adversarial Network.

Human activity recognition (HAR) using radar micro-Doppler has attracted the attention of researchers in the last decade. Using radar for human activity recognition has been very practical because of its unique advantages. There are several classifiers for the recognition of these activities, all of which require a rich database to produce fine output. Due to the limitations of providing and building a large database, radar micro-Doppler databases are usually limited in number. In this paper, a new method for the generation of radar micro-Doppler of the human body based on the deep convolutional generating adversarial network (DCGAN) is proposed. To generate the database, the required input is also generated by converting the existing motion database to simulated model-based radar data. The simulation results show the success of this method, even on a small amount of data.

A Clinical Decision Support System for Increasing Compliance with Protocols in Chemotherapy of Children with Acute Lymphoblastic Leukemia.

Objective: In this survey, a protocol-based Chemotherapy Prescription Decision Support System (CPDSS) was designed and evaluated to reduce medication errors in the chemotherapy process of children with ALL. Methods: The CPDSS algorithm was extracted by the software development team based on the protocol used by doctors to treat children with ALL. The ASP.Net MVC and SQL Server 2016 programming languages were used to develop the system. A 3-step evaluation (technical, retrospective, and user satisfaction) was performed on CPDSS designed at 2 children's hospitals in Tehran. The data were analyzed using descriptive statistics. At the technical evaluation step, users provided recommendations included in the system. Results: In the retrospective CPDSS evaluation step, 1281 prescribed doses of the drugs related to 30 patients were entered into the system. CPDSS detected 735 cases of protocol deviations and 57 (95%, CI = 1.25-2.55) errors in prescribed chemotherapy for children with ALL. In the user satisfaction evaluation, the users approved two dimensions of the user interface and functionality of the system. Conclusions: With the provision of alerts, the CPDSS can help increase compliance with chemotherapy protocols and decrease the chemotherapy prescribing errors that can improve patient safety.

Substance of choice, impact of heroin or opium on treatment retention in a multicentre randomised controlled trial in Iran.

INTRODUCTION: In the Middle East and Asia, illicit opioid use exists across a spectrum between heroin and opium. The impact of primary opioid of choice on opioid agonist treatment retention has not been well evaluated previously, especially for opium tincture, an increasingly popular form of opioid agonist treatment in Iran. This study investigates the relationship between primary opioid of choice, namely heroin or opium, and retention in opium tincture and methadone treatment. METHODS: Participants with opioid use disorder (n = 204) were randomised to receive opium tincture or methadone. All participants were categorised as mainly using opium or heroin. Bivariate analyses between treatment retention and primary opioid of choice (P < 0.05) and logistic regression were conducted. RESULTS: Among the 191 participants included in this analysis, heroin was the primary substance of choice for 135 participants (70.7%) and opium for 56 (29.3%). Bivariate analysis showed that the opium group was more likely to be satisfied with family situation, employed and retained in treatment than the heroin group while less likely to experience incarceration and use multiple substances. When adjusting for covariates, primary opioid of choice was not significantly associated with retention in either methadone or opium tincture treatment arm. DISCUSSION AND CONCLUSIONS: Positive factors, such as employment, housing and family support, seem to collectively explain the higher retention in treatment among those who primarily use opium compared to those who use heroin. To optimise retention in opioid agonist treatment, biopsychosocial care models should be further evaluated to improve psychosocial functioning.

Contextual features in the developing hippocampus: A representational similarity analysis.

Functional divisions of labor in support of memory have been reported along the anterior-posterior axis of the hippocampus. However, little is known about how the developing hippocampus represents associative memories along this axis. The present research employed representational similarity analysis to ask whether developmental differences exist in the extent to which the anterior versus the posterior hippocampus represent features of the context and associative memories. Functional magnetic resonance imaging data were collected during the retrieval phase of an associative recognition task from 8-year-olds, 10-year-olds, and adults (N = 58). Participants were asked to retrieve pairs of items, which were presented either in the same location as during encoding or in a flipped location. In the anterior hippocampus and only for adults, pattern similarity between the two studied pair conditions was greater than pattern similarity between studied pairs presented in the same location and novel pairs. In contrast, this difference was not significant in the posterior hippocampus. Older, but not younger, children showed a similar, albeit attenuated, similarity pattern to that of adults, but measures of patterns similarity predicted associative recognition across ages. In addition, exploratory analyses showed that similarity patterns in the adult posterior, but not anterior, hippocampus tracked the order of the runs. Overall, the results suggest functional and developmental dissociations in processing different contextual features, with the anterior hippocampus responding to salient and rapid-changing features and the posterior hippocampus responding to slower-changing features of the context.

Shedding Light on Targeting Chronic Myeloid Leukemia Stem Cells.

Chronic myeloid leukemia stem cells (CML LSCs) are a rare and quiescent population that are resistant to tyrosine kinase inhibitors (TKI). When TKI therapy is discontinued in CML patients in deep, sustained and apparently stable molecular remission, these cells in approximately half of the cases restart to grow, resuming the leukemic process. The elimination of these TKI resistant leukemic stem cells is therefore an essential step in increasing the percentage of those patients who can reach a successful long-term treatment free remission (TFR). The understanding of the biology of the LSCs and the identification of the differences, phenotypic and/or metabolic, that could eventually allow them to be distinguished from the normal hematopoietic stem cells (HSCs) are therefore important steps in designing strategies to target LSCs in a rather selective way, sparing the normal counterparts.

Galvanic Vestibular Stimulation Effects on EEG Biomarkers of Motor Vigor in Parkinson's Disease.

Background: Impaired motor vigor (MV) is a critical aspect of Parkinson's disease (PD) pathophysiology. While MV is predominantly encoded in the basal ganglia, deriving (cortical) EEG measures of MV may provide valuable targets for modulation via galvanic vestibular stimulation (GVS). Objective: To find EEG features predictive of MV and examine the effects of high-frequency GVS. Methods: Data were collected from 20 healthy control (HC) and 18 PD adults performing 30 trials total of a squeeze bulb task with sham or multi-sine (50-100 Hz "GVS1" or 100-150 Hz "GVS2") stimuli. For each trial, we determined the time to reach maximum force after a "Go" signal, defined MV as the inverse of this time, and used the EEG data 1-sec prior to this time for prediction. We utilized 53 standard EEG features, including relative spectral power, harmonic parameters, and amplitude and phase of bispectrum corresponding to standard EEG bands from each of 27 EEG channels. We then used LASSO regression to select a sparse set of features to predict MV. The regression weights were examined, and separate band-specific models were developed by including only band-specific features (Delta, Theta, Alpha-low, Alpha-high, Beta, Gamma). The correlation between MV prediction and measured MV was used to assess model performance. Results: Models utilizing broadband EEG features were capable of accurately predicting MV (controls: 75%, PD: 81% of the variance). In controls, all EEG bands performed roughly equally in predicting MV, while in the PD group, the model using only beta band features did not predict MV well compared to other bands. Despite having minimal effects on the EEG feature values themselves, both GVS stimuli had significant effects on MV and profound effects on MV predictability via the EEG. With the GVS1 stimulus, beta-band activity in PD subjects became more closely associated with MV compared to the sham condition. With GVS2 stimulus, MV could no longer be accurately predicted from the EEG. Conclusions: EEG features can be a proxy for MV. However, GVS stimuli have profound effects on the relationship between EEG and MV, possibly via direct vestibulo-basal ganglia connections not measurable by the EEG.

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.

An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation.

BACKGROUND: Acute graft-versus-host disease (aGvHD) is a complex and often multisystem disease that causes morbidity and mortality in 35% of patients receiving allogeneic hematopoietic stem cell transplantation (AHSCT). OBJECTIVE: This study aimed to implement a Clinical Decision Support System (CDSS) for predicting aGvHD following AHSCT on the transplantation day. MATERIAL AND METHODS: In this developmental study, the data of 182 patients with 31 attributes, which referred to Taleghani Hospital Tehran, Iran during 2009-2017, were analyzed by machine learning (ML) algorithms which included XGBClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, and RandomForestClassifier. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, and specificity. Using the machine learning developed model, a CDSS was implemented. The performance of the CDSS was evaluated by Cohen's Kappa coefficient. RESULTS: Of the 31 included variables, albumin, uric acid, C-reactive protein, donor age, platelet, lactate Dehydrogenase, and Hemoglobin were identified as the most important predictors. The two algorithms XGBClassifier and HistGradientBoostingClassifier with an average accuracy of 90.70%, sensitivity of 92.5%, and specificity of 89.13% were selected as the most appropriate ML models for predicting aGvHD. The agreement between CDSS prediction and patient outcome was 92%. CONCLUSION: ML methods can reliably predict the likelihood of aGvHD at the time of transplantation. These methods can help us to limit the number of risk factors to those that have significant effects on the outcome. However, their performance is heavily dependent on selecting the appropriate methods and algorithms. The next generations of CDSS may use more and more machine learning approaches.

"Association between dietary inflammatory index (DII) and risk of irritable bowel syndrome: a case-control study".

BACKGROUND: Pathophysiology of IBS is not well recognized; however, several studies have shown the possible relationship between diet and risk of IBS. We assessed the ability of the dietary inflammatory index (DII) to predict the risk of IBS. METHODS: The subjects were 155 IBS cases and 310 age- and sex-matched healthy controls (aged ≥18 years). The participants were recruited from June, 2019 to March, 2020. IBS was recognized using the Rome IV criteria. DII score was computed based on dietary intake using a 168-item FFQ. The DII score was calculated based on energy-adjusted amounts of nutrients using residual method. Logistic regression models were used to estimate multivariable odds ratios (ORs). RESULTS: The mean DII score was significantly higher among IBS patients in comparison to healthy controls (0.78 ± 2.22 vs. - 0.39 ± 2.27). In crude model, increase in DII as continuous variable was associated with a significant increase in the risk of IBS (OR (95% CI): 1.26 (1.1-15.38)). Furthermore, the association remained significant even after adjusting for age and sex (OR (95% CI): 1.28 (1.1-17.41)) and after multivariate adjustment (OR (95% CI): 1.38 (1.2-1.56)). In crude, age and sex adjusted and multivariate-adjusted models subjects in fourth quartile of DII had higher OR in comparison to subjects in first quartile. CONCLUSIONS: This study showed a possible positive association between a pro-inflammatory diet and the risk of IBS. Thus, encouraging intake of more anti-inflammatory dietary factors and reducing intake of pro-inflammatory factors may be a strategy for reducing risk of IBS.

Developmental Differences in Subjective Recollection and Its Role in Decision Making.

We examined how subjective assessments of recollection guide decision making. Subjective recollection was dissociated from accuracy during a forced-choice recognition task. Distracters were either similar to targets (match condition) or to other studied, but untested items (nonmatch condition). We assessed 223 participants (112 males) across three experiments (137 White, 37 Asian-American, 7 African-American, 4 American-Indian, 32 mixed race, 6 undisclosed). In Experiment 1, 6- to 10-year-olds and adults (N = 119) were less accurate (d = 0.70), but more likely to claim subjective recollection and make memory selections in anticipation of a reward in the nonmatch condition (ds = 0.64-0.70). This pattern was eliminated in 6- to 7-year-olds when we limited the number of selections (Experiment 2, N = 52), but was replicated when we required the selections to be counted (Experiment 3, N = 52), underscoring the effects of decision complexity on children's self-reflections.