Simultaneous analysis of shape and internal structure of a curved Hibiscus cannabinus pulvinus: X-ray microtomography and semi-automated quantification.

In the Malvaceae family, dynamic solar tracking by leaves is actuated by the deformation of the pulvinus, a thickened region at the leaf blade-petiole junction. While the internal structure is believed to play a crucial role in this process, experimental verification has been challenging due to technical limitations. To address this gap, we developed a semi-automated workflow, which integrates data analysis and image processing to simultaneously analyze the shape and internal structure of a Malvaceae pulvinus using X-ray microtomography. Firstly, we found that kenaf (Hibiscus cannabinus L.), a Malvaceae species with curved pulvini, exhibited solar-tracking leaf movement and selected it as a model system. We employed diffusible iodine-based contrast-enhanced computed tomography to visualize the internal structure of the kenaf pulvinus. Analysis of the pulvini's shape revealed variations in pulvinus morphology, yet plausible prediction of the centerline was accomplished using polar polynomial regression. Upon slicing the pulvini perpendicular to the centerline, we observed distinct gray value gradients along the proximo-distal and adaxial-abaxial axes, challenging threshold-based tissue segmentation. This workflow successfully generated three modified 3D images and derived quantitative parameters. Using these quantitative parameters, we conducted network analysis and found the linkage between the size-normalized cortex cross-sectional area and curvature. Polynomial least absolute shrinkage and selection operator (LASSO) regression revealed the relationship between the size-normalized cortex cross-sectional area and curvature commonly in all three tested samples. This workflow enables simultaneous analysis of the shape and internal structure, significantly improving the reproducibility of Malvaceae leaf pulvinus characterization.

Trade-offs of praise and criticism: Parents' responses to children's performance and children's resilience and depression.

In contrast to cognitive outcomes, parental success-oriented responses to children's performance enhanced the emotional well-being of children. Conversely, parental failure-oriented responses had the opposite impact. Thus, it remains unclear which response or combination of responses parents should employ to maximize their children's development. This research aimed to examine the combined effect of children's perceptions of parental success- and failure-oriented responses on children's depression, with a focus on the mediating role of resilience. A total of 651 pupils (44.7% female, Mage = 10.31, range = 8-12) were investigated in China using polynomial regression and response surface analyses. Our findings suggest that when success- and failure-oriented responses are congruent, failure-oriented responses counteract the protective effect of success-oriented responses against children's depression. The two equally matched responses demonstrated a curvilinear main effect on resilience, indicating that higher resilience was associated with the upper-middle range of the two responses. Moreover, children who reported more success-oriented responses than failure-oriented responses showed greater resilience and decreased depression. Resilience acted as a mediator for the combined effects of parental success and failure-oriented responses on children's depression. The study addressed the parenting dilemma, specifically the trade-off between success- and failure-oriented responses in promoting children's optimal development.

Theory of Mind and Social Informant Discrepancy in Autism.

When autistic youth are asked to assess their own social skills, they frequently rate themselves more favorably than their parents rate them. The magnitude of this informant discrepancy has been shown to relate to key clinical outcomes such as treatment response. It has been proposed that this discrepancy arises from difficulties with Theory of Mind. Participants were 167 youth 11 to 17 years old; 72% male, and their parents. Youth completed self-report measures of social skills and social cognitive tasks, while their parents completed questionnaires regarding social skills. A repeated-measures ANOVA indicated both non-autistic and autistic youth rated themselves more favorably than their parents rated them across all measures. Zero-order correlations revealed that raw differences between parent- and participant-report were negatively correlated with scores on parent-reported Theory of Mind measures. However, polynomial analysis did not indicate interaction effects between parent- and participant-report on any of the measures used. Polynomial regression revealed that increases in parent-reported social skill predicted larger increases in parent-report Theory of Mind at low levels of parent-reported social skill compared to high levels of parent-reported social skill. Participant-report social skills predicted performance on a behavioral Theory of Mind test in a curvilinear fashion, such that the relationship was positive at low levels of participant-reported social skills, but negative at high levels. This study replicates the finding that raw difference score analyses may result in illusory effects that are not supported when using more contemporary analysis methods, and that more complex and subtle relationships between social insight and perspective-taking exist within autistic youth.

Trust (in)congruence, open innovation, and circular economy performance: Polynomial regression and response surface analyses.

Engaging partner firms in open innovation is critical to overcoming internal resource/capability constraints to achieve firm and supply chain circular economy (CE) performance, yet scholars have not examined this link empirically. Further, while researchers have repeatedly emphasized trust as a key driver of open innovation performance, little is known from a dyadic trust perspective (e.g., congruence vs. incongruence, high-high congruence vs. low-low congruence). To fill these gaps, we propose a theoretical model based on the social exchange theory (SET) and test it using a dyadic dataset of listed Chinese manufacturing firms. The results suggest that 1) rather than unilateral trust from the focal firm or its partners, trust congruence is more predictive of successful open process and product innovations, 2) regarding congruence types (low-low vs. high-high), congruence at higher levels of trust facilitates open product innovation more than low-low trust congruence; interestingly, such an effect is not significant for open process innovation, 3) open process innovation has a positive influence on the focal firm's CE performance, but its impact on supply chain CE performance is not statistically significant, and 4) open product innovation has a significantly positive impact on the focal firm and supply chain CE performance. Our findings still hold after analyzing time-lagged models and alternative measurements as robustness checks. Our study provides meaningful theoretical contributions to the literature and useful, practical insights for managing inter-organizational relationships, open innovation, and CE performance.

Team Efficacy Profiles: Congruence Predicts Objective Performance of Athlete Pairs.

Efficacy beliefs targeting personal (self-efficacy), teammates' (other-efficacy), and conjoint (collective efficacy) abilities are each associated with performance of athlete pairs. The purpose of this study was to examine (a) congruence/incongruence of efficacy beliefs between athletes in a pair as a predictor of quality of individual and team performance and (b) quality of performance relative to efficacy congruence at high, moderate, and low levels of efficacy. Eighty-two cheerleading pairs, composed of one base and one flyer, completed questionnaires assessing self-, other, and collective efficacy prior to a national collegiate competition. Individual and team performances were assessed using objective criteria. Polynomial regression analyses indicated that team performance was predicted by congruence of (a) both athletes' collective efficacy beliefs and (b) base self-efficacy and flyer other efficacy. Findings supported that congruence at moderate to high levels of efficacy was associated with better performance relative to incongruent efficacy beliefs across the two athletes.

Parent-adolescent Congruence and Discrepancy in Perceived Parental Emotion Socialization to Anger and Sadness: Using Response Surface Analysis to Examine the Links with Adolescent Depressive Symptoms.

Parents and adolescents often hold discrepant perceptions of parental emotion socialization, which reflect misunderstandings in parent-adolescent communication on emotions and have potential detrimental effects on mental health of adolescents. The present study investigated the associations between parent-adolescent congruence and discrepancy in parental emotion socialization perception to two specific negative emotions (anger and sadness) and depressive symptoms in Chinese adolescents. A total of 372 adolescents (48.4% female, Mage = 13.43, SDage = 0.49) and their parents (79.6% mother, Mage = 41.15, SDage = 5.46) participated in this study. Both parents and adolescents reported perceived parental emotion socialization to anger and sadness, and adolescents reported depressive symptoms. Data were analyzed using polynomial regression and response surface analyses. Both congruence and discrepancy in parent and adolescent's reports were associated with adolescent depressive symptoms. A higher level of adolescent depressive symptoms was associated with higher parent-adolescent congruence in supportive responses to anger, sadness, and nonsupportive responses to anger. A higher level of depressive symptoms was associated with inconsistent reporting of supportive responses to sadness and nonsupportive responses to anger (only when parents had a more positive view than adolescents). This study highlights the significance of evaluating parent-child communication process by assessing perceived emotion socialization from both parents and adolescents and analyzing the reporting congruence and discrepancy. It also suggests that enhancing effective communication regarding parental emotion socialization could be a promising target for adolescent mental health promotion programs.

The relationship between patient-family caregiver congruence/incongruence in acceptance of illness and family caregivers' anticipatory grief.

OBJECTIVE: Cancer is a family affair that negatively impacts the lives of patients and their family caregivers. This study takes a dyadic perspective to investigate the effect of patient-family caregiver illness acceptance congruence/incongruence on family caregivers' anticipatory grief (AG) and examine whether caregivers' resilience moderates the abovementioned relationship. METHODS: For the study, 304 dyads of advanced lung cancer patients and their family caregivers from three tertiary hospitals in Jinan, Shandong Province, China were recruited. The data were analyzed using polynomial regressions and response surface analyses. RESULTS: Family caregivers' AG was lower when patient-family caregiver illness acceptance was congruent rather than incongruent. Lower patient-caregiver illness acceptance congruence was associated with higher AG in family caregivers compared with higher illness acceptance congruence. Family caregivers reported significantly higher AG only if their illness acceptance was lower than that of their patients. In addition, caregivers' resilience moderated the effects of patient-caregiver illness acceptance congruence/incongruence on family caregivers' AG. CONCLUSIONS: Patient-family caregiver illness acceptance congruence was beneficial to family caregivers' AG; resilience can be a protective factor for buffering the impact of illness acceptance incongruence on family caregivers' AG.

Exploring the Multidimensional Relationship Between Medication Beliefs and Adherence to Medications Among Older Adults Living With Multimorbidity Using Polynomial Regression: An Observational Cohort Study.

BACKGROUND: People living with multimorbidity may hold complex beliefs about medicines, potentially influencing adherence. Polynomial regression offers a novel approach to examining the multidimensional relationship between medication beliefs and adherence, overcoming limitations associated with difference scores. PURPOSE: To explore the multidimensional relationship between medication beliefs and adherence among people living with multimorbidity. METHODS: Secondary analysis was conducted using observational data from a cohort of older adults living with ≥2 chronic conditions, recruited from 15 family practices in Ireland in 2010 (n = 812) and followed up in 2012 (n = 515). Medication beliefs were measured with the Beliefs about Medicines Questionnaire-Specific. Adherence was assessed with the medication possession ratio using prescription data from the national primary care reimbursement service. Polynomial regression was used to explore the best-fitting multidimensional models for the relationship between (i) beliefs and adherence at baseline, and (ii) beliefs at baseline and adherence at follow-up. RESULTS: Confirmatory polynomial regression rejected the difference-score model, and exploratory polynomial regression indicated quadratic models for both analyses. Reciprocal effects were present in both analyses (slope [Analysis 1]: ß = 0.08, p = .007; slope [Analysis 2]: ß = 0.07, p = .044), indicating that adherence was higher when necessity beliefs were high and concern beliefs were low. Nonreciprocal effects were also present in both analyses (slope [Analysis 1]: ß = 0.05, p = .006; slope [Analysis 2]: ß = 0.04, p = .043), indicating that adherence was higher when both necessity and concern beliefs were high. CONCLUSIONS: Among people living with multimorbidity, there is evidence that the relationship between medication beliefs and adherence is multidimensional. Attempts to support adherence should consider the combined role of necessity and concern beliefs.

When people live with multiple ongoing health conditions, they might have complex beliefs about their prescribed medicines. These beliefs could relate to the perceived necessity of medicines (necessity beliefs) and perceived concerns about medicines (concern beliefs). This study aimed to explore how necessity and concern beliefs, in combination, relate to the extent to which people living with multiple ongoing conditions take their medicines as prescribed. The study analyzed an existing dataset that included 812 older adults recruited via family practice settings in Ireland in 2010. Of these, 515 people were followed up again in 2012. All participants were living with at least two ongoing health conditions. Participants self-reported their medication-related necessity and concern beliefs by completing a questionnaire. Their level of medication taking was calculated using pharmacy records. The results showed that having a combination of high necessity beliefs and low concern beliefs was related to higher levels of medication taking than having a combination of low necessity beliefs and high concern beliefs. Having a combination of high necessity beliefs and high concern beliefs was related to higher levels of medication taking than having a combination of low necessity beliefs and low concern beliefs. Attempts to support patients to take their medicines should consider the combined role of their necessity and concern beliefs on behavior.

Exploring flexible polynomial regression as a method to align routine clinical outcomes with daily data capture through remote technologies.

BACKGROUND: Clinical outcomes are normally captured less frequently than data from remote technologies, leaving a disparity in volumes of data from these different sources. To align these data, flexible polynomial regression was investigated to estimate personalised trends for a continuous outcome over time. METHODS: Using electronic health records, flexible polynomial regression models inclusive of a 1st up to a 4th order were calculated to predict forced expiratory volume in 1 s (FEV1) over time in children with cystic fibrosis. The model with the lowest AIC for each individual was selected as the best fit. The optimal parameters for using flexible polynomials were investigated by comparing the measured FEV1 values to the values given by the individualised polynomial. RESULTS: There were 8,549 FEV1 measurements from 267 individuals. For individuals with > 15 measurements (n = 178), the polynomial predictions worked well; however, with < 15 measurements (n = 89), the polynomial models were conditional on the number of measurements and time between measurements. The method was validated using BMI in the same population of children. CONCLUSION: Flexible polynomials can be used to extrapolate clinical outcome measures at frequent time intervals to align with daily data captured through remote technologies.

Reference blood pressure values obtained using the auscultation method for 2-year-old Japanese children: from the Japan Environment and Children's Study.

BACKGROUND: Reference blood pressure (BP) values for Japanese children based on a large number of measurements by auscultation have not yet been established. METHODS: This was a cross-sectional analysis of data from a birth-cohort study. The data from the sub-cohort study conducted for children at the age of 2 years in the Japan Environment and Children's Study from April 2015 to January 2017 were analyzed. BP was measured via auscultation using an aneroid sphygmomanometer. Each participant was measured in triplicate, and the average value of two consecutive measurements with a difference of less than 5 mmHg was recorded. The reference BP values were estimated using the lambda-mu-sigma (LMS) method and compared with those obtained via the polynomial regression model. RESULTS: Data from 3361 participants were analyzed. Although the difference between the estimated BP values by the LMS and the polynomial regression model was small, the LMS model was more valid based on the results of the fit curve of the observed values and regression models for each model. For 2-year-old children with heights in the 50th percentile, the 50th, 90th, 95th, and 99th percentile reference values of systolic BP (mmHg) for boys were 91, 102, 106, and 112, and that for girls were 90, 101, 103, and 109, respectively, and those of diastolic BP for boys were 52, 62, 65, and 71, and that for girls were 52, 62, 65, and 71, respectively. CONCLUSION: The reference BP values for 2-year-old Japanese children were determined based on auscultation and were made available.

Visualization with Prediction Scheme for Early DDoS Detection in Ethereum.

Blockchain technologies have gained widespread use in security-sensitive applications due to their robust data protection. However, as blockchains are increasingly integrated into critical data management systems, they have become attractive targets for attackers. Among the various attacks on blockchain systems, distributed denial of service (DDoS) attacks are one of the most significant and potentially devastating. These attacks render the systems incapable of processing transactions, causing the blockchain to come to a halt. To address the challenge of detecting DDoS attacks on blockchains, existing visualization schemes have been developed. However, these schemes often fail to provide early DDoS detection since they typically display only past and current system status. In this paper, we present a novel visualization scheme that not only portrays past and current values but also forecasts future expected system statuses. We achieve these future predictions by utilizing polynomial regression with blockchain data. Additionally, we offer an alternative DDoS detection method employing statistical analysis, specifically the coefficient of determination, to enhance accuracy. Through our experiments, we demonstrate that our proposed scheme excels at predicting future blockchain statuses and anticipating DDoS attacks with minimal error. Our work empowers system managers of blockchain-based applications to identify and mitigate DDoS attacks at an earlier stage.

Machine-Learning-Based Calibration of Temperature Sensors.

Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial neural network (ANN) model for calibration and explored the feasibility and effectiveness of using ANNs to calibrate temperature sensors. The experiment collected multiple sets of temperature data from standard temperature sensors in different environments and compared the calibration results of the ANN model, linear regression, and polynomial regression. The experimental results show that calibration using the ANN improved the accuracy of the temperature sensors. Compared with traditional linear regression and polynomial regression, the ANN model produced more accurate calibration. However, overfitting may occur due to a small sample size or a large amount of noise. Therefore, the key to improving calibration using the ANN model is to design reasonable training samples and adjust the model parameters. The results of this study are important for practical applications and provide reliable technical support for industrial production and scientific research.

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space.

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%.

Evidences of localized coastal warming near major urban centres along the Indian coastline: past and future trends.

Large-scale urbanization near the coasts is reported to directly impact physical and biogeochemical characteristics of near shore waters, through hydro-meteorological forcing, developing abnormalities such as coastal warming. This study attempts to understand the impact-magnitude of urban expansion on coastal sea surface temperature (SST) rise in the vicinity of six major cities along the Indian coastline. Different parameters such as air temperature (AT), relative humidity (RH), wind speed (WS), precipitation (P), land surface temperature (LST) and aerosol optical depth (AOD) representing the climate over the cities were analysed and AT was found to have highest correlation with increasing coastal SST values, specifically, along the western coast (R2 > 0.93). Autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were employed to analyse past (1980-2019) and forecast future (2020-2029) SST trends off all urban coasts. ANN provided comparatively better prediction accuracy with RMSE values ranging from 0.40 to 0.76 K compared to the seasonal ARIMA model (RMSE: 0.60-1 K). Prediction accuracy further improved by coupling ANN with discrete wavelet transformation (DWT) which could reduce the data noise (RMSE: 0.37-0.63 K). The entire study period (1980-2029) revealed significant and consistent increase in SST values (0.5-1 K) along the western coastal cities which varied considerably along the east coast (from north to south), indicating the influence of tropical cyclones combined with increased river influx. Such unnatural interferences in the dynamic land-atmosphere-ocean circulation not only render the coastal ecosystems vulnerable to degradation but also potentially develop a feedback effect which impacts the general climatology of the region.

Modeling and forecasting the total number of cases and deaths due to pandemic.

The COVID-19 pandemic has appeared as the predominant disease of the 21st century at the end of 2019 and was a drastic start with thousands of casualties and the COVID-19 victims in 2020. Due to the drastic effect, COVID-19 scientists are trying to work on pandemic diseases and Governments are interested in the development of methodologies that will minimize the losses and speed up the process of cure by providing vaccines and treatment for such pandemics. The development of a new vaccine for any pandemic requires long in vitro and in vivo trials to use. Thus the strategies require understanding how the pandemic is spreading in terms of affected cases and casualties occurring from this disease, here we developed a forecasting model that can predict the no of cases and deaths due to pandemic and that can help the researcher, government, and other stakeholders to devise their strategies so that the damages can be minimized. This model can also be used for the judicial distribution of resources as it provides the estimates of the number of casualties and number of deaths with high accuracy, Government and policymakers on the basis of forecasted value can plan in a better way. The model efficiency is discussed on the basis of the available dataset of John Hopkins University repository in the period when the disease was first reported in the six countries till the mid of May 2020, the model was developed on the basis of this data, and then it is tested by forecasting the no of deaths and cases for next 7 days, where the proposed strategy provided excellent forecasting. The forecast models are developed for six countries including Pakistan, India, Afghanistan, Iran, Italy, and China using polynomial regression of degrees 3-5. But the models are analyzed up to the 6th-degree and the suitable models are selected based on higher adjusted R-square (R2 ) and lower root-mean-square error and the mean absolute percentage error (MAPE). The values of R2 are greater than 99% for all countries other than China whereas for China this R2 was 97%. The high values of R2 and Low value of MAPE statistics increase the validity of proposed models to forecast the total no cases and total no of deaths in all countries. Iran, Italy, and Afghanistan also show a mild decreasing trend but the number of cases is far higher than the decrease percentage. Although India is expected to have a consistent result, more or less it depicts some other biasing factors which should be figured out in separate research.

Comparison between polynomial regression and weighted least squares regression analysis for verification of analytical measurement range.

OBJECTIVES: Recently, the linearity evaluation protocol by the Clinical & Laboratory Standards Institute (CLSI) has been revised from EP6-A to EP6-ED2, with the statistical method of interpreting linearity evaluation data being changed from polynomial regression to weighted least squares linear regression (WLS). We analyzed and compared the analytical measurement range (AMR) verification results according to the present and prior linearity evaluation guidelines. METHODS: The verification of AMR of clinical chemistry tests was performed using five samples with two replicates in three different laboratories. After analyzing the same evaluation data in each laboratory by the polynomial regression analysis and WLS methods, results were compared to determine whether linearity was verified across the five sample concentrations. In addition, whether the 90% confidence interval of deviation from linearity by WLS was included in the allowable deviation from linearity (ADL) was compared. RESULTS: A linearity of 42.3-56.8% of the chemistry items was verified by polynomial regression analysis in three laboratories. For analysis of the same data by WLS, a linearity of 63.5-78.3% of the test items was verified where the deviation from linearity of all five samples was within the ADL criteria, and the cases where the 90% confidence interval of all deviation from linearity overlapped the ADL was 78.8-91.3%. CONCLUSIONS: Interpreting AMR verification data by the WLS method according to the newly revised CLSI document EP6-ED2 could reduce laboratory workload, enabling efficient laboratory practice.

Response Surface Analysis with Missing Data.

Response Surface Analysis (RSA) is gaining popularity in psychological research as a tool for investigating congruence hypotheses (e.g., consequences of self-other agreement, person-job fit, dyadic similarity). RSA involves the estimation of a nonlinear polynomial regression model and the interpretation of the resulting response surface. However, little is known about how best to conduct RSA when the underlying data are incomplete. In this article, we compare different methods for handling missing data in RSA. This includes different strategies for multiple imputation (MI) and maximum-likelihood (ML) estimation. Specifically, we consider the "just another variable" (JAV) approach to MI and ML, an approach that is in regular use in applications of RSA, and the more novel "substantive-model-compatible" (SMC) approach. In a simulation study, we evaluate the impact of these methods on focal outcomes of RSA, including the accuracy of parameter estimates, the shape of the response surface, and the testing of congruence hypotheses. Our findings suggest that the JAV approach can sometimes distort parameter estimates and conclusions about the shape of the response surface, whereas the SMC approach performs well overall. We illustrate applications of the methods in a worked example with real data and provide recommendations for their application in practice.

High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking Systems.

The problem of 3D gaze estimation can be viewed as inferring the visual axes from eye images. It remains a challenge especially for the head-mounted gaze tracker (HMGT) with a simple camera setup due to the complexity of the human visual system. Although the mainstream regression-based methods could establish the mapping relationship between eye image features and the gaze point to calculate the visual axes, it may lead to inadequate fitting performance and appreciable extrapolation errors. Moreover, regression-based methods suffer from a degraded user experience because of the increased burden in recalibration procedures when slippage occurs between HMGT and head. To address these issues, a high-accuracy 3D gaze estimation method along with an efficient recalibration approach is proposed with head pose tracking in this paper. The two key parameters, eyeball center and camera optical center, are estimated in head frame with geometry-based method, so that a mapping relationship between two direction features is proposed to calculate the direction of the visual axis. As the direction features are formulated with the accurately estimated parameters, the complexity of mapping relationship could be reduced and a better fitting performance can be achieved. To prevent the noticeable extrapolation errors, direction features with uniform angular intervals for fitting the mapping are retrieved over human's field of view. Additionally, an efficient single-point recalibration method is proposed with an updated eyeball coordinate system, which reduces the burden of calibration procedures significantly. Our experiment results show that the calibration and recalibration methods could improve the gaze estimation accuracy by 35 percent (from a mean error of 2.00 degrees to 1.31 degrees) and 30 percent (from a mean error of 2.00 degrees to 1.41 degrees), respectively, compared with the state-of-the-art methods.

Parent-Adolescent Vocational Aspiration Congruence and its Relations with Academic Adjustment.

Adolescents' own vocational aspirations and those of parents for their adolescent children play significant roles in adolescents' development. The present study examined how the (in)congruence between adolescents' vocational aspirations and their parents' aspirations for them were associated with adolescents' academic achievement and test anxiety. The study's sample included 662 parent-adolescent pairs (adolescent Mage = 14.09), and the aspiration and adjustment data were collected at intervals 3 years apart. Using polynomial regression analyses and surface graphs, parent-child aspiration congruence was found to be significantly associated with later academic achievement and test anxiety, but incongruence did not show any significant relationship with either outcome. Such patterns were more prominent among boys (n = 306) than girls and among high socioeconomic status (SES) adolescents (n = 324) than among low SES adolescents. The findings suggest that academic adjustment is more predictable when there is parent-child congruence than when there is incongruence in aspirations.

Exploration of marine red seaweed as a dietary fish meal replacement and its potentiality on growth, hematological, biochemical, and enzyme activity in freshwater fish Labeo rohita.

The present study investigated the dietary fishmeal replacement by marine red seaweed (Halymenia dilatata) meal (RSM) on growth performance, feed utilization, chemical body composition, hematological constituents, digestive, antioxidant, and metabolic enzymes in freshwater fish Labeo rohita (Rohu) fingerlings. The fish were fed with RSM-free control diet (RSM0) and four experimental diets, which replaced fish meal (FM) with varying levels of RSM (25%, 50%, 75%, and 100%, represented as RSM25, RSM50, RSM75, and RSM100 respectively). After a 60-day feeding trial, the survival rate (SR), growth performance (length gain, weight gain, and specific growth rate), protein efficiency ratio, chemical body composition (protein, lipid, and ash), and digestive enzymes (amylase and protease) were significantly increased (P < 0.05) in the fish fed with RSM50 diet containing 39% protein level. The sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) revealed a higher staining intensity of muscle proteins in fish fed with the RSM50 diet. However, the hematological constituents (hemoglobin, hematocrit, red blood cell, white blood cell, mean corpuscular volume, mean corpuscular hemoglobin, and mean corpuscular hemoglobin concentration), antioxidant enzyme - superoxide dismutase, and metabolic enzymes (glutamic oxaloacetic transaminase and glutamic pyruvic transaminase) were not significantly altered in RSM50 diet when compared to control. In contrast, hematological constituents were decreased (P < 0.05), and antioxidant and metabolic enzymes were increased in rohu fed with RSM75 and RSM100 (P < 0.05). Furthermore, these findings suggest that RSM might be adopted at a pace of 37% (estimated polynomial second-order regression) and is found to be beneficial for freshwater fish L. rohita diets that enhance growth and immune responses. The current study recommended substituting (50%) of marine red seaweed (Halymenia dilatata) for fish meal significantly improves the growth performance, chemical body composition, and digestive enzymes of L. rohita and this could be a valuable natural replacement for fishmeal to reduce the production cost of aquatic feed.