Total Correlation Research Articles

Unbound bilirubin and risk of severe neurodevelopmental impairment in extremely low birthweight newborns.

Unbound bilirubin (UB) was measured on day 5 ± 1 in 1101 ELBW newborns in the Aggressive vs Conservative Phototherapy randomized controlled trial. We accessed this dataset to quantify the UB-mediated risk of severe neurodevelopmental impairment (sNDI) in extremely low birthweight (ELBW) newborns. UB levels were standardized within laboratories as z-score percentiles. A dichotomous UB exposure variable was based on sharply increased risk of sNDI at the 85th percentile (UBz>85th). Targeted maximum likelihood estimation with ensemble machine learning (TMLE + ML) and logistic regression (LR) were used to estimate sNDI risk attributable to UBz>85th. UB - total bilirubin correlation was poor: R2 0.24, 95% CI: 0.19, 0.30. The UB level corresponding to the UBz>85th cut-point in this cohort was estimated to be between 0.56 and 0.79 µg/dL. In 825 survivors with known sNDI status the crude risk difference (RD) associated with UBz>85th was 19.5%. With TMLE + ML, the RD was 14.4% (95% CI: 5.7%, 23.1%) corresponding to a number need to harm (NNH) of 7 (95% CI: 4, 18). LR results were similar: RD 12.4% (95% CI: 4.7%, 20.1%), NNH 8 (95% CI: 5, 21). Exposure to UBz>85th was associated with a large increase in risk of sNDI in ELBW newborns. Accessing the only large dataset with measures of unbound bilirubin (UB) and neurodevelopmental outcomes in extremely low birthweight (ELBW) newborns we found poor correlation between UB and total bilirubin (TB). The risk of severe neurodevelopmental impairment (sNDI) associated with UB rose non-linearly with an inflection point at UB z-score 85th percentile (UBz>85th), increasing the risk of sNDI by an estimated 14% (number need to harm: 7). The current incidence of exposure to harmful UB levels in very premature newborns is unlikely to be rare. TB levels provide a false sense of security to clinicians caring for very premature newborns.

EGEFACE: A new face memory test with static and dynamic images

Face memory is a crucial cognitive ability necessary for maintaining a healthy social life. Recent studies reveal large individual differences in face recognition ability. Face memory tests are used to evaluate this ability. The main purpose of this study was to develop a new face memory test (EGEFACE) addressing the limitations of existing tests using both static and dynamic stimuli to increase ecological validity; employing face recognition algorithms to adjust test difficulty; measuring face memory accuracy independently of response bias by including both target-absent and target-present trials and using ROC analysis; and developing a test to measure both ends of the face recognition ability spectrum. After building a new database of static and dynamic faces, we created three difficulty levels using a face recognition algorithm. We collected data from 703 participants in two steps and examined the internal consistency, split-half reliability, and item–total score correlations. The reliability analysis confirmed that both target-absent and target-present trials of EGEFACE were reliable. High EGEFACE performers scored near super recognizer levels on CFMT+, while low performers showed limited overlap with prosopagnosic-level performance on CFMT+, suggesting EGEFACE’s sensitivity across different levels of face recognition ability. Overall, results indicated a moderate positive correlation between EGEFACE and CFMT+, showing that both tests assess similar cognitive skills, while a low to moderate correlation with KFMT suggests that EGEFACE measures cognitive ability that is related to yet distinct from face perception. The results suggest that EGEFACE shows promise as an ecologically valid and effective alternative tool for assessing individual differences in face memory.

Discontinuous Structural Transitions in Fluids with Competing Interactions.

This paper explores how competing interactions in the intermolecular potential of fluids affect their structural transitions. This study employs a versatile potential model with a hard core followed by two constant steps, representing wells or shoulders, analyzed in both one-dimensional (1D) and three-dimensional (3D) systems. Comparing these dimensionalities highlights the effect of confinement on structural transitions. Exact results are derived for 1D systems, while the rational function approximation is used for unconfined 3D fluids. Both scenarios confirm that when the steps are repulsive, the wavelength of the oscillatory decay of the total correlation function evolves with temperature either continuously or discontinuously. In the latter case, a discontinuous oscillation crossover line emerges in the temperature-density plane. For an attractive first step and a repulsive second step, a Fisher-Widom line appears. Although the 1D and 3D results share common features, dimensionality introduces differences: these behaviors occur in distinct temperature ranges, require deeper wells, or become attenuated in 3D. Certain features observed in 1D may vanish in 3D. We conclude that fluids with competing interactions exhibit a rich and intricate pattern of structural transitions, demonstrating the significant influence of dimensionality and interaction features.

Power Loss (ENTROPY) Scale in Schools

The research aims to develop a measurement tool with validity and reliability for measuring loss of power (entropy) in schools. The study group of the research consists of a total of 596 teachers working in 15 public schools in Van Province in the 2023-2024 academic year. As a result of the application of Exploratory Factor Analysis (EFA) to 381 teachers to ensure construct validity, a scale with 26 items and four factors, 1. People-oriented work environment and parents, 2. Energy use and environment, 3. Technology, 4. Achievement of goals and supervision—was created. The total variance rate of the scale was obtained as 63.29%. The factor load values of the items in the scale are between 509 and 829. It was also confirmed by confirmatory factor analysis (CFA) that the scale measures a four-factor structure. In addition, a 26-question scale was studied with a study group consisting of 215 teachers in the confirmatory factor analysis. According to the answers of the study group, the Cronbach Alpha internal consistency coefficient and item total correlations were analyzed. While Cronbach Alpha values were found to be 93, 95, 88, and 92 for each factor and 97 for the whole scale, the lowest value among the item total correlations of the items in the factors was 535, and the highest value was 832. The scale created in the study will be applicable and functional as a valid and reliable measurement tool for schools and valid for other educational organizations and as an Entropy Scale with the results of the validity and reliability analysis carried out and applied.

Effects of Different Nitrogen Fertilizer Application Rates on Soil Microbial Structure in Paddy Soil When Combined with Rice Straw Return.

Metagenomic sequencing of the microbial soil community was used to assess the effect of various nitrogen fertilizer treatments in combination with constant rice straw return to the soil in the tiller layer of Northeast China's black paddy soil used for rice production. Here, we investigated changes in the composition, diversity, and structure of soil microbial communities in the soil treated with four amounts of nitrogen fertilizers (53, 93, 133, and 173 kg/ha) applied to the soil under a constant straw return of 7500 kg/ha, with a control not receiving N. The relationships between soil microbial community structure and soil physical and chemical properties were determined. The results showed that the available K content of the soil significantly (p < 0.05) increased in soil receiving the lowest N-fertilizer dose. When applied at high amounts, N-fertilizer changed the Chao1 and ACE indices of the soil microorganisms (p < 0.05), and the treatments resulted in significant differences in the β-diversity of the soil microorganisms. By NMDS analysis it was demonstrated that the treatment significantly affected the structure of the soil microbial communities. Redundancy analysis showed that the main physicochemical drivers behind these differences were total nitrogen, total potassium, ammonium nitrogen, total phosphorus, and available potassium. The soil microbial communities in the control treatment were negatively correlated with nitrate and ammonium nitrogen; the lowest N-fertilizer treatment produced positive correlations with total nitrogen, total potassium, and total phosphorus and negative correlations with ammonium nitrogen; the highest dose negatively correlated with total nitrogen, available potassium, available phosphorus, total phosphorus, and pH. This study showed that moderate N fertilizer application is an effective way to increase soil microbial diversity and improve soil quality. This experiment provides technical support for the application of the alternative fertilizer technology of straw return to the field and provides a theoretical basis for rational fertilization of paddy fields in a cold climate.

Disentangled representational learning for anomaly detection in single-lead electrocardiogram signals using variational autoencoder

Wearable technology enables the unsupervised recording of electrocardiogram (ECG) signals. Analyzing these high-dimensional ECG data poses challenges regarding statistical approaches and explainability. This work investigates the feasibility of medically explainable anomaly detection through disentangled representational learning of ECGs and personalization to mitigate inter-subject variations. Five open-source ECG datasets were converted into a set of denoised one-second traces of lead I signal, each covering individual features such as wave morphologies and pathologies. A beta total correlation variational autoencoder was optimized on four of these datasets for 68 systematic parameterization variants. The best-performing model revealed disentanglement in the 12-dimensional embedding space, specifically between atrial- and ventricular features. Within the embedding space, a k-nearest neighbor classifier was evaluated on a left-out test set tailored for anomaly detection. The result is a F1 score of 0.94 for the binary prediction of sinus rhythm and the pathological classes: Left bundle branch block, right bundle branch block, myocardial infarction, and AV block (1st degree). The 90.94% accuracy in anomaly detection falls within the range of established detectors (89.38%–99.77%) but offers the advantage of being explainable and largely unsupervised. Model fine-tuning for each of 100 randomly sampled individuals of the Icentia11k dataset mitigated inter-subject variations. The associated F1 score for predicting normal, premature atrial contraction, and premature ventricular contraction from the embedding space was 0.93. The distribution plots of pathologies along the explainable axis were reasonably consistent with medical expertise. The results suggest the presented disentangled variational autoencoder as a robust method for explainable ECG representation.

Validation of a Questionnaire Assessing Pregnant Women's Perspectives on Addressing the Psychological Challenges of Childbirth.

Pregnant women's experiences and concerns regarding childbirth are complex, necessitating a multidimensional and personalized approach in maternal care. This study explores the psychological and emotional factors influencing pregnant women's decisions regarding their mode of delivery. The results will provide valuable insights for the development of educational and counseling strategies designed to support pregnant women in making informed and conscious decisions about their childbirth. This cross-sectional study aimed to develop and validate a questionnaire exploring the psychological dimensions of childbirth. Factor analysis was employed to assess emotional perceptions, perceived medical risks, and the impact of cesarean section on pregnant women. The questionnaire was distributed online via Google Forms, using social networks like Facebook and Instagram to ensure rapid and broad accessibility. The questionnaire was available for seven months, from January to July 2023. McDonald's ω, Cronbach's α, average inter-item correlation, and total item correlations were calculated to assess the consistency of the questionnaire items in measuring the same construct. The three-factor model emerged as the primary structure based on exploratory and confirmatory factor analyses (EFA and CFA). The first profile, centered on the psychological and emotional benefits of vaginal birth, highlights the importance of the natural birth experience for the mother's psychological well-being. The second profile addresses concerns about medical risks and the need for interventions. The third profile focuses on perceptions and concerns related to the intelligence and adaptability of children born by cesarean section and the effects of anesthesia. Each profile reflects different strategies for seeking control and security amid childbirth uncertainties. These include emphasizing the psychological benefits of vaginal birth, addressing medical risks, and focusing on the impact of interventions on child development. Understanding these variables is essential for providing appropriate counseling and psychosocial support, thereby optimizing the birth experience and promoting the health of both mother and child. The integration of multi-factor and single-factor models in the questionnaire analysis serves complementary purposes, providing distinct yet interrelated insights into the instrument's structure and validity.

Reliability and Validity of the Turkish Version of Work-Related Questionnaire for Upper Extremity Disorders (WORQ-UP).

The Work-Related Questionnaire for Upper Extremity Disorders (WORQ-UP) serves as a patient-reported outcome measure to identify work-related limitations in individuals with upper extremity musculoskeletal disorders. This study aims to adapt the WORQ-UP into Turkish and subsequently evaluate its validity and reliability. The Turkish WORQ-UP, along with the previously validated Turkish Quick DASH, were administered to 136 patients. The Turkish WORQ-UP was applied twice within a 7-14 day interval to assess test-retest reliability. Reliability was assessed using measures of internal consistency and intraclass correlation coefficient (ICC). For validity assessment, Spearman correlation coefficients were calculated between the Turkish WORQ-UP and Quick DASH, and Explanatory (EFA) and Confirmatory factor analysis (CFA) were conducted. In the reliability analysis, items 11, 12, 13, 15, and 16, which did not meet the criterion of item total score correlation >30, were excluded, resulting in a 12-item Turkish WORQ-UP with satisfactory validity and reliability outcomes. The Cronbach's alpha and ICC were calculated as 0.895 and 0.879, respectively, while the SEM and MDC were determined to be 0.93 and 1.85. In the EFA, the Kaiser-Meyer-Olkin measure (0.895) and Bartlett's tests were both significant (p < 0.001). Additionally, the CFA indicated an acceptable fit with two factors. The goodness of fit indices, including χ2/df = 2.09, CFI = 0.934, TLI = 0.918, and RMSEA = 0.08, confirmed the adequacy of the model. The 12-item Turkish WORQ-UP showed a significant and strong correlation (r = 0.754; p < 0.001) with Quick DASH. The Turkish version of WORQ-UP with 12 items had proper psychometric properties to identify work-related limitations in individuals with upper extremity musculoskeletal disorders.

Axiomatic Approach to Measures of Total Correlations.

Correlations play a pivotal role in various fields of science, particularly in quantum mechanics, yet their proper quantification remains a subject of debate. In this work, we aimed to discuss the challenge of defining a reliable measure of total correlations. We first outlined the essential properties that an effective correlation measure should satisfy and reviewed existing measures, including quantum mutual information, the p-norm of the correlation matrix, and the recently defined quantum Pearson correlation coefficient. Additionally, we introduced new measures based on Rényi and Tsallis relative entropies, as well as the Kullback-Leibler divergence. Our analysis revealed that while quantum mutual information, the p-norm, and the Pearson measure exhibit equivalence for two-qubit systems, they all suffer from an ordering problem. Despite criticisms regarding its reliability, we argued that QMI remains a valid measure of total correlations.

FMIF: facial multi-feature information fusion for driver fatigue detection

FMIF: facial multi-feature information fusion for driver fatigue detection

STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition

IntroductionEmotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.MethodsWe propose the Spatiotemporal Adaptive Fusion Network (STAFNet), a novel framework combining adaptive graph convolution and temporal transformers to enhance the accuracy and robustness of EEG-based emotion recognition. The model includes an adaptive graph convolutional module to capture brain connectivity patterns through spatial dynamic evolution and a multi-structured transformer fusion module to integrate latent correlations between spatial and temporal features for emotion classification.ResultsExtensive experiments were conducted on the SEED and SEED-IV datasets to evaluate the performance of STAFNet. The model achieved accuracies of 97.89% and 93.64%, respectively, outperforming state-of-the-art methods. Interpretability analyses, including confusion matrices and t-SNE visualizations, were employed to examine the influence of different emotions on the model's recognition performance. Furthermore, an investigation of varying GCN layer depths demonstrated that STAFNet effectively mitigates the over-smoothing issue in deeper GCN architectures.DiscussionIn summary, the findings validate the effectiveness of STAFNet in EEG-based emotion recognition. The results emphasize the critical role of spatiotemporal feature extraction and introduce an innovative framework for feature fusion, advancing the state of the art in emotion recognition.

Pengaruh Penerapan Iso 9001 dan Kualitas Produk Pada Keputusan Pembelian

This study examines the effect of ISO 9001 implementation and product quality on Purchasing Decisions. This study uses a quantitative method with primary data through a survey to collect data. Respondents in this study were employees of PT Epson Indonesia, with the number of employees who were sampled was 30 employees. The sampling method used in the study was purposive sampling, while the data processing method used the Corrected Item to Total Correlation method for validity testing and the Cronbach's Alpha method for reliability testing. The results showed that from 15 question instruments related to the ISO 9001 variable, product quality, and Purchasing Decisions were declared valid with a significant percentage of 0.05 or 5% and were declared consistent/reliable with Cronbach's alpha of 0.938&gt; 0.60.

Validity and Reliability of the Turkish Version of the Low Back Activity Confidence Scale (LoBACS).

The Low Back Activity Confidence Scale (LoBACS) is a 15-item scale designed to assess low back pain (LBP) through self-efficacy, a key predictor of functional recovery. This study aimed to culturally adapt and evaluate the validity and reliability of the Turkish version of LoBACS in patients with LBP. The translation and adaptation followed Beaton et al.'s protocol. Content and face validity were assessed with a pre-patient group. Both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to evaluate construct validity. Internal consistency, as well as test-retest reliability, were evaluated in a sample of 150 patients aged 18-70years. Concurrent validity was measured alongside the Oswestry Back Pain Disability Questionnaire (ODQ) and Quebec Back Pain Disability Scale (QBPDS). Two factors emerged from factor analysis, with item loadings for Functional Self-efficacy (FnSE) ranging from 0.745 to 0.896 and for Self-Regulatory and Exercise Self-efficacy (Self-Reg&ExSE) from 0.817 to 0.940. Cronbach's alpha was high for FnSE, Self-Reg&ExSE, and the total scale (α = 0.941). Total correlation for each item ranged between 0.770 and 0.925. Test-retest reliability was also high (r = 0.941, p < .01). LoBACS showed moderate agreement with ODQ and QBPDS, demonstrating concurrent validity. In conclusion, the Turkish version of LoBACS is a valid and reliable tool for measuring LBP-related self-efficacy.

Reliability and Validity Study of the Turkish Version of the Motivation and Attitude to Change Health (MATCH) Scale.

This study aims to culturally adapt the 'Motivation and Attitudes Towards Changing Health (MATCH)' scale into Turkish and assess its reliability and validity within the Turkish population. The Turkish version of the MATCH questionnaire, designed to gauge patient motivation for initiating or sustaining behaviour changes, was created through translation and adaptation. A total of 305 patients diagnosed with chronic illnesses completed the questionnaire and scale to evaluate its validity and reliability. The Turkish version of MATCH demonstrated strong internal consistency and reliability. The Cronbach's alpha coefficient for the entire scale was 0.737, with subdimensions ranging between 0.69 and 0.75. Notably, no item displayed a total correlation value below 0.40. Furthermore, test-retest analysis indicated high intraclass correlation coefficients. Consequently, no items were removed due to their consistently high reliability values among all nine items. The Turkish-adapted MATCH scale proves to be a valid and reliable instrument for assessing motivational status within the Turkish population.

Semantic Segmentation of Aerial Laser Point Clouds Based on Deep-Residual Enhanced Coding of Multi-Feature Information

The semantic segmentation of laser point clouds is critical for many applications of aerial point clouds. However, most of the existing deep learning networks do not make full use of point cloud data information. PointNet++ was chosen as the baseline network, and a deep-residual enhanced encoding method of multi-feature information is proposed in this work. Firstly, a more efficient network structure to enhance geometric information encoding is constructed, called the GEO–PointNet layer. Then, a novel structure for feature aggregation, named SEP–PointNet, is introduced to encode the auxiliary and geometric features of points separately. Additionally, the segmentation network is deepened in the way of residual structures, which can effectively restrain network degradation. Meanwhile, ‘Dropout’ operations are applied to the fully connected layer to cope with the problem that the model is prone to overfitting due to many network parameters. Finally, a novel segmentation network, named SGDD–PointNet++, is built, and its effectiveness was evaluated by using four airborne benchmark datasets. The experimental results performed on the DALES dataset indicate that the overall accuracy and average intersection-over-union (mIoU) value of the modified PointNet++ networks are better than the original baseline and the other two state-of-the-art segmentation methods. The overall accuracy of the improved SGDD–PointNet++ network reached 87.88%. For the category IoU, it also outperforms other networks, and it has a maximum accuracy increment of 11.43%. Meanwhile, in terms of the generalization capabilities of the trained models, the proposed network can provide better discrimination effects for three public aerial datasets than other methods.

Multi-Feature Fusion for Estimating Above-Ground Biomass of Potato by UAV Remote Sensing.

Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R2 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period.

Electron Correlation in 2D Periodic Systems from Periodic Bootstrap Embedding.

Given the growing significance of 2D materials in various optoelectronic applications, it is imperative to have simulation tools that can accurately and efficiently describe electron correlation effects in these systems. Here, we show that the recently developed bootstrap embedding (BE) accurately predicts electron correlation energies and structural properties for 2D systems. Without explicit dependence on the reciprocal space sum (k-points) in the correlation calculation, our proof-of-concept calculations shows that BE can typically recover ∼99.5% of the total minimal basis electron correlation energy in 2D semimetal, insulator, and semiconductors. We demonstrate that BE can predict lattice constants and bulk moduli for 2D systems with high precision. Furthermore, we highlight the capability of BE to treat electron correlation in twisted bilayer graphene superlattices with large unit cells containing hundreds of carbon atoms. We find that as the twist angle decreases toward the magic angle, the correlation energy initially decreases in magnitude, followed by a subsequent increase. We conclude that BE is a promising electronic structure method for future applications to 2D materials.

Four New Patient-Reported Outcome Measures Examining Health-Seeking Behavior in Persons With Type 2 Diabetes Mellitus (REDD-CAT): Instrument Development Study.

The management of type 2 diabetes mellitus (T2DM) includes mastery of complex care activities, self-management skills, and routine health care encounters to optimize glucose control and achieve good health. Given the lifelong course of T2DM, patients are faced with navigating complex medical and disease-specific information. This health-seeking behavior is a driver of health disparities and is associated with hospitalization and readmission. Given that health-seeking behavior is a potentially intervenable social determinant of health, a better understanding of how people navigate these complex systems is warranted. To address this need, we aimed to develop new patient-reported outcome (PRO) measures that evaluate health-seeking behavior in persons with T2DM. These new PROs were designed to be included in the Re-Engineered Discharge for Diabetes-Computer Adaptive Test (REDD-CAT) measurement system, which includes several other PROs that capture the importance of social determinants of health. Overall, 225 participants with T2DM completed 56 self-report items that examined health-seeking behaviors. Classical Test Theory and Item Response Theory were used for measurement development. Exploratory factor analysis (EFA; criterion ratio of eigenvalue 1 to eigenvalue 2 being >4; variance for eigenvalue 1 ≥40%) and confirmatory factor analysis (CFA; criterion 1-factor CFA loading <.50; 1-factor CFA residual correlation >.20; comparative fit index ≥0.90; Tucker-Lewis index ≥0.90; root mean square error of approximation <0.15) were used to determine unidimensional sets of items. Items with sparse responses, low-adjusted total score correlations, nonmonotonicity, low factor loading, and high residual correlations of high error modification indices were candidates for exclusion. A constrained graded response model was used to examine item misfit, and differential item functioning was examined to identify item bias. Cronbach α was used to examine internal consistency reliability for the new PROs (criterion ≥0.70), and floor and ceiling effects were examined (criterion ≤20%). Four unidimensional sets of items were supported by EFA (all EFA eigenvalue ratios >4; variance for eigenvalue 1=41.4%-67.3%) and CFA (fit statistics all exceeded criterion values). This included (1) "Health-Seeking Behavior: PCP-Specific" (6 items); (2) "Health-Seeking Behavior: General Beliefs" (13 items); (3) "Health-Seeking Behavior: Family or Friends-Specific" (5 items); and (4) "Health-Seeking Behavior: Internet-Specific" (4 items). All items were devoid of differential item functioning for age, sex, education, or socioeconomic status factors. "Health-Seeking Behavior: General Beliefs" was developed to include both a computer adaptive test and a 6-item short form version; all other PROs were developed as static short forms. The psychometric reliability of these new PROs was supported; internal consistency ranged from acceptable to excellent (Cronbach α=.78-.91), and measures were free of significant floor or ceiling effects (floor effects range: 0%-8.9%; ceiling effects range: 0%-8.4%). The new REDD-CAT Health-Seeking Behavior PROs provide reliable assessments of health-seeking behaviors among those with T2DM.

ULOTrack: Underwater Long-Term Object Tracker for Marine Organism Capture

Underwater object tracking holds considerable significance in the field of ocean engineering. Additionally, it serves as a crucial component in the operations of autonomous underwater vehicles (AUVs), particularly during tasks associated with capturing marine organisms. However, the attenuation and scattering of light result in shortcomings such as poor contrast in underwater images. Additionally, the motion deformation of marine organisms poses a significant challenge. Therefore, existing tracking algorithms face difficulty in direct application to underwater object tracking. To overcome this challenge, we propose a novel tracking architecture for the marine organism capturing of AUVs called ULOTrack. ULOTrack is based on a performance discrimination and re-detection framework and constitutes three modules: (1) an object tracker, which can extract multi-feature information of the underwater target; (2) a multi-layer tracking performance discriminator, which serves the purpose of evaluating the stability of the current tracking state, thereby reducing potential model drift; and (3) lightweight detection, which can predict the candidate boxes to relocate the lost tracked underwater object. We conduct comprehensive experiments to validate the efficacy of the designed modules. Finally, the results of the experimentation demonstrate that ULOTrack significantly outperforms existing approaches. In the future, we aim to carefully scrutinize and select more suitable features to enhance tracking accuracy and speed.

Finding Critical Factors for Developing Dynamic Models to Optimize Greenhouse Solar Dryer’s Environmental Conditions

This paper is focused on the formulation of a mathematical model for greenhouse solar dryer systems in the context of determining the principal drivers of efficiency of the solar dryer. The model was built by evaluating the correlation coefficients of each term and only those terms which had high levels of significance were retained. To test this model, experiments were replicated with measurements being taken in the different sections of the greenhouse to provide a large spectrum of the environmental conditions involved. The values of the technical coefficients are predicated on internal air temperature, humidity ratio and weight of the product with fixed scales, avoiding sophisticated complex models prone to overfitting. By using terms with high correlation, the model is less likely to be affected by outliers and total correlation or cross correlation is low, thereby making the model practical. The outcomes of this research, therefore, offer a scientifically viable and a practical approach to enhancing drying processes in greenhouse solar dryer systems.