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AbstractThe COVID-19 pandemic’s psychological impact prompted this study to investigate the inner child healing course’s effectiveness in reducing fear of COVID-19 and improving emotional family relationships. This quasi-experimental research adopted a pretest-posttest design with a control group. Convenience sampling was employed to select 42 women randomly from five psychotherapy centers in Isfahan (Iran) in 2020. They were then randomly assigned to a control group (n = 21) and an experimental group (n = 21). The experimental group received the inner child healing course in six sessions, whereas the control group received no intervention. The Fear of COVID-19 Scale and the Emotional Family Relationships Questionnaire were utilized for data collection. Analysis of covariance was employed for data analysis using SPSS 25. Participants in the inner child healing group reported significantly higher mean scores on happiness (12.40 ± 1.11) compared to the control group (5.90 ± 2.24). This pattern of higher means in the inner child healing group persisted for freedom and intimacy in family relationships (11.50 ± 1.90 vs. 5.12 ± 1.60), trust in the family (13.61 ± 1.90 vs. 6.21 ± 2.04), and collaborative decision-making with family (12.34 ± 2.05 vs. 6.31 ± 1.80). Statistical analysis revealed significant effects of the inner child healing course on happiness (p<.01), freedom and intimacy with family members (p<.01), trust in family members (p<.01), and collaborative decision-making with family members (p<.01) in women. Notably, the intervention did not yield statistically significant effects on commitment and responsibility or fear of COVID-19 in this sample of married women. This suggests the inner child healing course may improve emotional well-being within families during COVID-19. [ABSTRACT FROM AUTHOR]

Wind is one of the important environmental factors influencing marine target detection as it is the source of sea clutter and also affects target motion and drift. The accurate estimation of wind speed is crucial for developing an efficient machine learning (ML) model for target detection. For example, high wind speeds make it more likely to mistakenly detect clutter as a marine target. This paper presents a novel approach for the estimation of sea surface wind speed (SSWS) and direction utilizing satellite imagery through innovative ML algorithms. Unlike existing methods, our proposed technique does not require wind direction information and normalized radar cross-section (NRCS) values and therefore can be used for a wide range of satellite images when the initial calibrated data are not available. In the proposed method, we extract features from co-polarized (HH) and cross-polarized (HV) satellite images and then fuse advanced regression techniques with SSWS estimation. The comparison between the proposed model and three well-known C-band models (CMODs)—CMOD-IFR2, CMOD5N, and CMOD7—further indicates the superior performance of the proposed model. The proposed model achieved the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), with values of 0.97 m/s and 0.62 m/s for calibrated images, and 1.37 and 0.97 for uncalibrated images, respectively, on the RCM dataset. [ABSTRACT FROM AUTHOR]

This study set out to investigate the influence of stemflow of Anabasis aphylla L. and Pteropyrum aucheri Jaub. & Spach on soil and concomitant plants in Central Iran. To this end, the stemflow was measured on ten mature plants with five replicates per species from March 21, 2014, to May 2, 2014. Moreover, the volume of collected flows was measured after each rainfall using measuring cylinders. Then, the volume was divided by the crown area to find the flow depth for each stem. On the other hand, to investigate the influence of stemflow on nursing understory plants, the plants were treated with and without stemflow at different depths. In this regard, the Lepidium sativum Linn was selected as the intended species to investigate the stemflow nursing effect of A. aphylla and P. aucheri. The results of the study indicated that the average rate of stemflow in P.aucheri and A.aphylla was 18.5% and 13.4% of gross rainfall, respectively. Furthermore, the average funneling ratio was found to be 29 and 39.9 for A.aphylla and P.aucheri, respectively. The moisture, OM, and MWD obtained for the soil under A.aphylla and P.aucheri were significantly different when treated with stemflow in depths of 80 and 100 cm from the case when the underlying soil of the species was treated without streamflow. However, while the variations in soil’s pH were not significant at different depths in both treatments and bare soil, the variations showed a slight increase in A.aphylla compared to P.aucheri and bare soil. On the other hand, the investigation of the two treatments in terms of EC values revealed that salinity was higher in the treatment without stemflow. It was also found that the efficiency of stemflow nursing was up to 10 cm and 7 cm in radius in P.aucheri and A.aphyll, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Magnetic resonance imaging (MRI), electric compound action potential (eCAP), and electric auditory brainstem response (eABR) are among the routine assessments performed before and/or after cochlear implantation. The objective of this review was to systematically summarize and critically appraise existing evidence of the prognostic value of eCAP, eABR, and MRI for predicting post--cochlear implant (CI) speech perception outcomes in children, with a particular focus on the lesion site. Method: The present systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement. Three electronic databases (ProQuest, PubMed, and Scopus) were searched with no restrictions on language, publication status, or year of publication. Studies on children identified with sensorineural hearing loss, auditory neuropathy spectrum disorder, cochlear nerve deficiency, or cochleovestibular nerve abnormalities reporting the relevance of eCAP, eABR, and/or MRI results to CI speech perception outcomes were included. The literature search yielded 1,887 publications. Methodological quality and strength of evidence were assessed by the Crowe Critical Appraisal Tool and the Grading of Recommendations Assessment, Development, and Evaluation tool, respectively. Results: Of the 25 included studies, the relevance of eCAP, eABR, and/or MRI findings to post-CI speech perception outcomes was reported in 10, 11, and 11 studies, respectively. The studies were strongly in support of the prognostic value of eABR and MRI for CI outcomes. However, the relevance of eCAP findings to speech perception outcomes was uncertain. Conclusion: Despite the promising findings, caution is warranted in interpreting them due to the observational and retrospective design of the included studies, as well as the heterogeneity of the population and the limited control of confounding factors within these studies. Supplemental Material: https://doi.org/10.23641/asha.26169859 [ABSTRACT FROM AUTHOR]

Background: This study examined the proportion of Iranian children who met the World Health Organization (WHO) Guidelines for physical activity, sedentary behaviour and sleep for children under 5 years. Additionally, it investigated the feasibility and acceptability of the methods to be used in the SUNRISE study. Methods: This pilot study was conducted among 83 children aged 3 and 4 years in preschools and health care centres in Iran, in 2022. Physical activity, sedentary behaviour and sleep (ActiGraph wGT3x‐BT); fine and gross motor skills (validated activities); and executive functions (the Early Years Toolbox) were assessed. Results: Only four (4.8%) children met all recommendations of the WHO guidelines. The proportion of children who met MVPA, TPA, screen time, restrained sitting and sleep were 44.6%, 38.6%, 19.3%, 38.6% and 65.1%, respectively. Fifty‐two (62.6%) children wore the ActiGraph for at least three full days. A total of 97.6%, 95.1% and 91.5% of children completed anthropometric, EF and motor skill assessments, respectively. Conclusion: This pilot study was feasible and acceptable among Iranian children. Regarding the low proportion of children who met the WHO guidelines, it is recommended that long‐term and practical strategies be developed to promote healthier lifestyles among preschool children in Iran. [ABSTRACT FROM AUTHOR]

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Recognizing the emotions from speech signals is very important in different applications of human-computer-interaction (HCI). In this paper, we present a novel model for speech emotion recognition (SER) based on new multi-task parallel convolutional autoencoder (PCAE) and transformer networks. The PCAEs have been proposed to generate high-level informative harmonic sparse features from the input. With the aid of the proposed parallel CAE, we can extract nonlinear sparse features in an ensemble manner improving the accuracy and the generalization of the model. These PCAEs also address the problem of the loss of initial sequential information during convolution operations for SER tasks. We have also proposed using a transformer in parallel with PCAEs to gather long-term dependencies between speech samples and make use of its self-attention mechanism. Finally, we have proposed a multi-task loss function made up of two terms of classification and AE mapper losses. This multi-task loss tries not only to reduce the classification error but also the regression error caused by the PCAEs which also work as mappers between the input and output Mel-frequency-cepstral-coefficients (MFCCs). Thus, we can both focus on finding accurate features with PCAEs and improving the classification results. We have evaluated our proposed method on the RAVDESS SER dataset in different terms of accuracy, precision, recall, and f1-score. The average accuracy of the proposed model on eight emotions outperforms all the recent baselines. [ABSTRACT FROM AUTHOR]