Iterative Component Research Articles

Necrotizing Enterocolitis Detection in Premature Infants Using Broadband Optical Spectroscopy.

Necrotizing enterocolitis (NEC) is a devastating disease affecting premature infants. Broadband optical spectroscopy (BOS) is a method of noninvasive optical data collection from intra-abdominal organs in premature infants, offering potential for disease detection. Herein, a novel machine learning approach, iterative principal component analysis (iPCA), is developed to select optimal wavelengths from BOS data collected invivo from neonatal intensive care unit (NICU) patients for NEC classification. Neural network models were trained for classification, with a reduced-feature model distinguishing NEC with an accuracy of 88%, a sensitivity of 89%, and a specificity of 88%. While whole-spectrum models performed the best for accuracy and specificity, a reduced feature model excelled in sensitivity, with minimal cost to other metrics. This research supports the hypothesis that the analysis of human tissue via BOS may permit noninvasive disease detection. Furthermore, a medical device optimized with these models may potentially screen for NEC with as few as seven wavelengths.

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech

Background and purposeDiagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. Materials and methodsMODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. ResultsThe HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. ConclusionsThe findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Directed Lobish-based explainable feature engineering model with TTPat and CWINCA for EEG artifact classification

Background and ObjectiveElectroencephalography (EEG) signals are crucial to decipher various brain activities. However, these EEG signals are subtle and contain various artifacts, which can happen due to various reasons. The main aim of this paper is to develop an explainable novel machine learning model that can identify the cause of these artifacts. Material and methodA new EEG signal dataset was collected to classify various types of artifacts. This dataset contains eight classes: seven are artifacts, and one is the EEG signal without artifacts. A novel feature engineering model has been proposed to classify these artifact classes automatically. This model contains three main steps: (i) feature generation with the proposed transition table pattern (TTPat), (ii) the proposed cumulative weight-based iterative neighborhood component analysis (CWINCA)-based feature selection, and (iii) classification using t algorithm-based k-nearest neighbors (tkNN). The novelty of this work is TTPat feature extractor and CWINCA feature selector. Channel-based transformation is performed using the proposed TTPat, which extracts 392 features from the transformed EEG signal. A novel CWINCA feature selector is proposed. The artifacts are classified using tkNN algorithm. ResultsThe proposed TTPat and CWINCA-based feature engineering model obtained a classification accuracy ranging from 66.39% to 97.69% for 30 cases. We presented the explainable results using a new symbolic language termed Directed Lobish. ConclusionsThe results and findings demonstrated that the proposed explainable feature engineering (EFE) model is good at artifact detection and classification. Directed Lobish has been presented to obtain explainable results and is a new symbolic language.

Lobish: Symbolic Language for Interpreting Electroencephalogram Signals in Language Detection Using Channel-Based Transformation and Pattern.

Electroencephalogram (EEG) signals contain information about the brain's state as they reflect the brain's functioning. However, the manual interpretation of EEG signals is tedious and time-consuming. Therefore, automatic EEG translation models need to be proposed using machine learning methods. In this study, we proposed an innovative method to achieve high classification performance with explainable results. We introduce channel-based transformation, a channel pattern (ChannelPat), the t algorithm, and Lobish (a symbolic language). By using channel-based transformation, EEG signals were encoded using the index of the channels. The proposed ChannelPat feature extractor encoded the transition between two channels and served as a histogram-based feature extractor. An iterative neighborhood component analysis (INCA) feature selector was employed to select the most informative features, and the selected features were fed into a new ensemble k-nearest neighbor (tkNN) classifier. To evaluate the classification capability of the proposed channel-based EEG language detection model, a new EEG language dataset comprising Arabic and Turkish was collected. Additionally, Lobish was introduced to obtain explainable outcomes from the proposed EEG language detection model. The proposed channel-based feature engineering model was applied to the collected EEG language dataset, achieving a classification accuracy of 98.59%. Lobish extracted meaningful information from the cortex of the brain for language detection.

Utilizing language models for advanced electrocardiogram analysis

Electrocardiography (ECG) signals are often referred to as the language of the heart and have been widely utilized for diagnosing various heart ailments, particularly arrhythmias. Consequently, numerous machine learning models have been employed to automatically detect heart disorders using ECG signals. In this research, the primary objective is to detect arrhythmias using a center-symmetric self-organized textual pattern. A novel feature engineering model has been introduced, which includes the following components: (i) multilevel feature extraction using the proposed center-symmetric self-organized textual pattern (CSSOTP), (ii) iterative neighborhood component analysis (INCA), and (iii) classification using k-nearest neighbors (kNN) with 10-fold cross-validation. During the feature extraction phase, a multilevel feature extraction model incorporating maximum absolute pooling (MAP) and the proposed CSSOTP was applied. The CSSOTP was designed to select the most appropriate pattern for the given data block. A large language model (LLM) was leveraged to generate text for creating patterns, and ChatGPT was used to assist in text generation. To identify the most informative features, the INCA feature selector was employed, and the selected features were subsequently classified using the kNN classifier. An impressive 96.20 % classification accuracy was achieved by the CSSOTP-based feature engineering model when tested on an ECG dataset containing 17 classes. Furthermore, comparisons with state-of-the-art feature engineering models were conducted, demonstrating that the proposed model has superior classification capabilities.

Combining reference-star and angular differential imaging for high-contrast imaging of extended sources

Context. High-contrast imaging (HCI) is a technique designed to observe faint signals near bright sources, such as exoplanets and circumstellar disks. The primary challenge in revealing the faint circumstellar signal near a star is the presence of quasi-static speckles, which can produce patterns on the science images that are as bright, or even brighter, than the signal of interest. Strategies such as angular differential imaging (ADI) or reference-star differential imaging (RDI) aim to provide a means of removing the quasi-static speckles in post-processing. Aims. In this paper, we present and discuss the adaptation of state-of-the-art algorithms, initially designed for ADI, to jointly leverage angular and reference-star differential imaging (ARDI) for the direct HCI of circumstellar disks. Methods. Using a collection of HCI datasets, we assessed the performance of ARDI in comparison to ADI and RDI based on iterative principal component analysis (IPCA). These diverse datasets were acquired under various observing conditions and include the injection of synthetic disk models at various contrast levels. We also considered reference stars with different levels of correlation with the science targets. Results. Our results demonstrate that ARDI with IPCA improves the quality of recovered disk images and the sensitivity to planets embedded in disks, compared to ADI or RDI individually. This enhancement is particularly pronounced when dealing with extended sources exhibiting highly ambiguous structures that cannot be accurately retrieved using ADI alone, and when the quality of the reference frames is suboptimal, leading to an underperformance of RDI. We finally applied our method to a sample of real observations of protoplanetary disks taken in star-hopping mode, and propose to revisit the protoplanetary claims associated with these disks. Among eight proposed protoplanets claimed through velocity kinks or direct imaging, none of them were re-detected in our new processed images.

Overlap between individual differences in cognition and symptoms of schizophrenia

BackgroundNeurocognitive impairment is a core feature of schizophrenia spectrum disorders (SSDs), and the relationship between cognition and symptoms in SSDs has been widely researched. Negative symptoms are related to a wide range of cognitive impairments; however, the aspects of negative symptoms that underpin this relationship have yet to be specified. Study designWe used iterative Constrained Principal Component Analysis (iCPCA) to explore the relationship between 18 cognitive measures (including processing speed, attention, working, spatial and verbal memory and executive functions) and 46 symptoms in schizophrenia at the individual item level while minimizing the risk of Type I errors. ICPCA was conducted on a sample of SSD patients in the early stages of psychiatric treatment (n = 121) to determine the components of cognition overlapping with symptoms measured by the Scale for the Assessment of Negative Symptoms (SANS) and the Scale for the Assessment of Positive Symptoms (SAPS). ResultsWe found that a verbal memory component was associated with items from SANS and SAPS related to impoverished and disorganized emotional communication, language, and thought. In contrast, a working memory component was associated with SANS items related to motor system impoverishment. ConclusionsThe iCPCA allowed us to explore the associations between individual items, optimized to understand the overlap between symptoms and cognition. The specific symptoms linked to verbal and working memory impairments imply distinct brain networks, which further investigation may lead to our deeper understanding of the illness and the development of treatment methods.

DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals

Hunger is a physiological state that arises from complex interactions of multiple factors, including higher brain center control. We purposed to develop an accurate and efficient machine-learning model for the automated detection of hunger using EEG signals. We prospectively acquired 14-channel EEG (sampling frequency 128 Hz) from 43 and 48 fasted and post-prandial healthy subjects (hungry vs. control groups, respectively) using the EMOTIV EPOC+ mobile brain cap system. To augment the hunger response, fasted subjects were also shown video images of food during EEG recording. The EEG signals were divided into 15-second segments. 877 and 852 participants/subjects were in the hungry and control groups. We created a novel handcrafted architecture—decomposed shifted window (DSWIN)—that combined swin patch division with tunable Q-factor wavelet transform-based signal decomposition for multilevel feature extraction of EEG signals. Textural and statistical features were extracted from the multiple patches and decomposed signals using a novel penta pattern-based extractor and statistical moments, respectively, and then merged. Iterative neighborhood component analysis (INCA) and iterative ReliefF (IRF) were applied. Twenty-eight selected feature vectors were generated, which were then fed to a shallow k-nearest neighbors (kNN) classifier to calculate channel-wise prediction vectors. From the 28 channel-wise prediction vectors, another 26 modes of function-based voted results were calculated using iterative hard majority voting, and the best overall model result was selected using a greedy algorithm. Our model attained 99.54% and 82.71% binary classification accuracies of hungry status vs. control using 10-fold and leave-one-subject-out cross-validations, respectively.

Deuterium permeation and retention behavior in copper-based brazing filler materials

Copper-based brazing filler materials are commonly employed in vacuum brazing techniques to achieve joining of plasma-facing components for ITER. In this work, gas-driven permeation (GDP) and thermal desorption spectroscopy (TDS) experiments have been performed to study the hydrogen isotopes permeation and retention behavior in two types of brazing filler materials (CuMnNi and CuGeNi metals). Experimental results show that CuMnNi filler exhibits the similar deuterium (D) permeability with CuMnNi filler but lower diffusion coefficients than CuGeNi filler. Three distinct peaks are observed between 380 K and 980 K for CuMnNi filler. However, no evident desorption peak is observed for CuGeNi filler. CuGeNi filler may be considered as a potential brazing filler material for reducing tritium inventory.

Supporting adolescent well-being at school: Integrating transformative social and emotional learning and trauma-informed education

There is an urgent need to support the social and emotional well-being of adolescents with experiences of adversity and trauma. Adolescence is a critical period of development for promoting social and emotional competencies, which can prevent poor mental health and problematic substance use and promote thriving during challenging teenage years and beyond. Both transformative social and emotional learning (TSEL) and trauma-informed programs for schools (TIPS) have been identified as promising practices for supporting social and emotional well-being among young people with experiences of adversity. We propose a pragmatic theory of action for schools for implementing and evaluating initiatives that integrate TSEL and TIPS made up of three iterative components: awareness, assessment, and action. The TSEL + TIPS Theory of Action is illustrated by a case study of a cross-sectoral collaboration of government, schools, researchers, healthcare, and adolescents to implement TSEL and TIPS initiatives in British Columbia, Canada. The case study provides evidence for the feasibility of TSEL + TIPS Theory of Action and may serve as an example for other regions as experiences of adversity and poor mental health continue to rise among adolescents globally.

Fabrication and load test of ITER assembly tools for lifting heavy components

Korea Domestic Agency (ITER Korea, KODA) has procured 44 kind's assembly tools for ITER project. The assembly tools are classified by the steel structure, lifting table and lifting accessory. ‘PF 1, 6 and Central Solenoid Lifting Frame’ (CS Lifting Frame) and ‘Sector Lifting Tool’ (SLT) are the representative assembly tool classified the lifting accessory. The assembly tools handle the heavy component which over 1,000 tones for payload. In this reason, the verification of the strength of the tools is very important and the integrity of lifting accessory tools was verified by not only structural analysis but also load test at factory under 1.5 times the payload for minimum 15 min. It is the requirements of the technical specification. The main role of PF 1, 6 and CS Lifting Frame is to lift and transfer the ITER components (PF1 coil, PF6 coil and CS coil) from the assembly hall to the tokamak fit and their temporary support positions. The CS coil is heaviest component in payloads handled by CS Lifting Frame. The weight is about 1,000 tones and CS Lifting Frame was verified by 1,500 tones load test. Sector Lifting Frame is to lift and transfer the 40° sector module from Sector Sub-Assembly Tool (SSAT) to tokamak fit. The sector module consists of Vacuum Vessel (VV), VV Thermal Shield and Two Toroidal Flied Coils. The total weight of the sector module is about 1,300 tones and Sector Lifting Frame was verified by 2,000 tones load test. The load test was carried out according to test procedure approved by IO. This paper describes the overall test method, customized test JIG and result of the lifting tools for handling heavy components of ITER.

Large vessel occlusion detection by non-contrast CT using artificial ıntelligence

IntroductionComputer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke.MethodsWe use our dataset. which contains 324 patient’s CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis—INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation.ResultsBy using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy.ConclusionThe obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance.

Lattice 123 pattern for automated Alzheimer’s detection using EEG signal

AbstractThis paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech

BackgroundTimely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. Materials and methodsIn this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. ResultsTo validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. ConclusionsOur results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.

Connected Community Classification (C3): Development, Validation, and Geospatial Application for Population Health Promotion and Equity.

Social determinants of health (SDOH) impact population health. Leveraging community-level strengths related to SDOH through a social infrastructure perspective can optimize health behaviors and health outcomes to promote health equity. Our aims were to develop, validate, and apply the Connected Community Classification (C3) as comprehensive community-level measure of protective SDOH and structural factors in the Four Corners states region of the United States. C3 was developed using an iterative principal component analysis of publicly available data mapped to 5 SDOH domains. Regional clustering of C3 by zip code tabulation area (ZCTA) was identified using spatial autocorrelation methods. In adjusted spatial autoregressive models, we analyzed the association of C3 with high-risk health behaviors and chronic disease prevalence using publicly available data for population-level estimates of fruit and vegetable intake, physical activity, obesity, smoking, alcohol use, coronary heart disease (CHD), diabetes, and cancer. C3 was found to be reliable and valid; a C3 value of 10 indicates communities with greater connection (high), while a value of 1 indicates communities with greater separation (low) to social infrastructure. Lower connection, as measured by C3, was significantly inversely associated with lower fruit and vegetable intake, lower physical activity, and higher rates of obesity, smoking, CHD, diabetes, and cancer. C3 was significantly positively associated with heavy alcohol use. These findings demonstrate that communities connected to social infrastructure have better population health outcomes. C3 captures protective community attributes and can be used in future applications to assist health researchers, practitioners, nonprofits, and policymakers to advance social connection and health equity in geographically diverse underserved regions.

Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals

AbstractElectroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.

Can You Understand Why I Am Crying? A Decision-making System for Classifying Infants’ Cry Languages Based on DeepSVM Model

Scientific and therapeutic advances in perinatology and neonatology have improved the survival prospects of preterm and extremely-low-birth-weight infants. Infants’ cries are a valuable noninvasive tool for monitoring their neurologic health, especially if they are premature. Automatic acoustic analysis and data mining are employed in this study to determine the discriminative features of preterm and full-term infant cries. The use of machine learning for recognizing sounds in a newborn's cry language has received less attention than previous methods for analyzing the sounds. Moreover, to extract appropriate features from infant cries, adequate knowledge and appropriate signal descriptors are required. Accordingly, to analyze infant cry language, we propose an approach that uses fractal descriptors to extract discriminant features from spectrograms of windowed signals, followed by iterative neighborhood component analysis (iNCA) to select appropriate features. Additionally, the improved deep support vector machine (DeepSVM) is used to classify the infants’ crying types and their meanings. The proposed method is verified using a newborn sound dataset. According to the classification of five types of crying perception based on various characteristics, 98.34% of all crying perceptions have been recognized. Although there are many classes examined, the feature extraction method based on the fractal method and our optimal classification have a much higher diagnostic accuracy compared with similar methods for analyzing baby crying language. The proposed method can overcome many problems associated with analyzing babies’ crying sounds and understanding their language, such as uncertainty and unusual errors in classification.

The Radiation Impact of Delayed Photoneutron Production in ITER Components

Photoneutrons may be generated in beryllium by energetic gamma rays via the reaction 9Be(γ,n)8Be. In ITER, the beryllium layer of the first wall may be the source of such photoneutrons. During plasma operation, these are of insignificant intensity compared with D-T neutrons from the plasma, but after shutdown, photoneutrons produced by decay gammas from neutron-activated material may be significant enough to impact sensitive electronic components in diagnostic or remote handling equipment that would not otherwise be exposed to neutrons. Studies have been performed to characterize the expected photoneutron source and to evaluate the fluxes arising in detailed three-dimensional models of the ITER tokamak. The results show photoneutron fluxes approaching 105 n/cm2·s within the vessel and up to 103 n/cm2·s elsewhere within the bioshield 14 days after shutdown. When first-wall panels are being transported to the Hot Cell Facility after irradiation, a photoneutron flux exceeding 104 n/cm2·s within the transfer cask is predicted 21 days after shutdown. The peak values in the surrounding building are between 102 and 103 n/cm2·s at the same time.

Development of the Protective Strategies for Psychedelics Scale: A novel inventory to assess safety strategies in the context of psychedelics.

Individuals who use psychedelics take efforts to mitigate unintended consequences. Despite the demonstrated utility of analogous protective behavioral strategies (PBS) assessments for other substances, no standardized scale exists to capture these protective strategies for psychedelic use. The present study addresses a notable gap concerning the assessment of psychedelic use, specifically by developing a scale measuring the protective strategies employed around use, called the Protective Strategies for Psychedelics Scale (PSPS). A sample (Mage = 36.85 years old, standard deviation = 10.3; male = 61.9%; White = 85.2%) of 434 adults with lifetime use of psychedelics reported on initial qualitatively developed items for the PSPS, PBS scales for cannabis and alcohol, and use of alcohol, cannabis, and psychedelics. Iterative principal components analyses began with 37 items and yielded a 32-item two-factor solution demonstrating excellent internal reliability (Cronbach's α = .95) and accounted for 51.3% of the variance. Nineteen items loaded on PSPS factor 1, which focused on long-term preparation, emphasizing strategies focused on mood/intentions, preparing the substance, environment, and scheduling episode of use; 13 items loaded on factor 2, which focused on short-term preparation, highlighting strategies surrounding social context, health, and other substances. The PSPS demonstrated convergent validity with validated PBS scales for cannabis and alcohol (r = 0.71-0.79, p < 0.001), and was moderately associated with lifetime psychedelic use (r = 0.28, p < 0.001). The PSPS demonstrates promising psychometric properties, and future work validating the scale for diverse samples across research and clinical settings is warranted.

Recent DIII-D progress toward validating models of tungsten erosion, re-deposition, and migration for application to next-step fusion devices

Fundamental mechanisms governing the erosion and prompt re-deposition of tungsten impurities in tokamak divertors are identified and analyzed to inform the lifetime of tungsten plasma-facing components in ITER and other future devices. Various experiments conducted at DIII-D to benchmark predictive models are presented, leveraging the DiMES removable sample exposure probe capability and the Metal Rings Campaign, in which toroidally symmetric rows of tungsten-coated tiles were installed in the DIII-D divertor. In tokamak divertors, the width of the electric sheath is of the order of the main ion Larmor radius, and a vast majority of sputtered tungsten impurities are typically ionized within the sheath. Therefore, W prompt redeposition is mainly governed by the ratio of the characteristic ionization mean-free path of neutral tungsten to the width of the sheath. In-situ monitoring of the prompt redeposition of tungsten impurities in divertors is demonstrated via the use of WII/WI line ratios and the ionizations/photon (S/XB) method in L-mode discharges. Even with this relatively limited set of emission measurements, net erosion measurements were found to be a consistent upper bound to an analytic scaling based on the ratio of the W ionization length, and the width of the magnetic sheath rather than the ratio of and the W+ gyro-radius. In the far-scrape-off layer (SOL) of the ITER divertor, however, it is calculated that the measurement of photon emissions associated with the ionization of tungsten impurities up to may be required. Finally, W deposition patterns on DiMES collector probes, interpreted via DIVIMP-WallDYN modelling, reveal the key roles of progressive W erosion/re-deposition staps and E × B drifts in regulating long-range high-Z material migration.