Journal of Social and Clinical Psychology最新文献

{"title":"Social Anxiety and Depression in Romantic Relationships: A Three-Sample Exploration.","authors":"Christian Hahn, I. Hahn, L. Campbell","doi":"10.1521/JSCP.2021.40.3.175","DOIUrl":"https://doi.org/10.1521/JSCP.2021.40.3.175","url":null,"abstract":"Introduction: Social anxiety contributes to a variety of interpersonal difficulties and dysfunctions. Socially anxious adults are less likely to marry and more likely to divorce than are non-anxious adults. The present pre-registered study investigated incremental variance accounted for by social anxiety in relationship satisfaction, commitment, trust, and social support. Methods: Three independent samples of adults (N = 888; 53.7% female; Mage = 35.09 years) involved in a romantic relationship completed online self-report questionnaires. Both social anxiety and depression were significantly correlated with relationship satisfaction, commitment, dyadic trust, and social support. Hierarchical regression analyses were conducted with each sample to investigate the incremental variance accounted for by each of social anxiety and depression in relationship satisfaction, commitment, dyadic trust, and social support. Subsequent meta-analyses were run to determine the strength and replicability of the hierarchical models. Results: Results suggest that social anxiety is a robust predictor of unique variance in both perceived social support and commitment. Depression was a robust predictor of unique variance in relationship satisfaction, dyadic trust, social support, and commitment. Discussion: These results help to further understanding of social anxiety in romantic relationships and provide direction for future research and clinical intervention.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45130909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Conservatism and Elected Party Representation on Mental Health Outcomes During Major Elections","authors":"Lauryn E Garner, D. McKay, Sandra L. Cepeda, E. Storch","doi":"10.1521/JSCP.2021.40.3.221","DOIUrl":"https://doi.org/10.1521/JSCP.2021.40.3.221","url":null,"abstract":"Introduction: The American Psychological Association's national surveys have revealed high levels of stress surrounding the political climate since the 2016 United States (U.S.) presidential election. The two current studies aimed to further evaluate the impact of political factors, such as social and economic conservatism and political party affiliation mismatch between individuals and their local or federal officials, on emotional experiences. Methods: Data for these studies were collected through Amazon's Mechanical Turk following the 2016 and 2018 U.S. elections. Results: Results from Study 1 revealed that following the 2016 presidential election, higher social and economic conservatism was associated with less political obsessions and lower levels of depression. Results from Study 2 also demonstrated that following the 2018 midterm elections, higher conservatism predicted lower depression, less political obsessions, lower levels of negative affect, and higher positive affect. Additionally, conservatism moderated the relationship between party affiliation mismatch between participants and their official in the U.S. House of Representatives and both anxiety and obsessive-compulsive symptoms. The relationship between mismatch of party affiliation for participants’ Senators and obsessive-compulsive symptoms was also moderated by conservatism. Discussion: These studies suggest that political factors, particularly conservatism, may impact emotional experiences and mental health symptoms during times of increased political polarization. Future studies should further explore the impact of political divisiveness on individual's stress levels and emotional well-being.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45158669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Equity of Dyadic Coping in Patients with Depression and Their Partners","authors":"Fabienne Meier, S. A. Landolt, T. Bradbury, G. Bodenmann","doi":"10.1521/JSCP.2021.40.3.249","DOIUrl":"https://doi.org/10.1521/JSCP.2021.40.3.249","url":null,"abstract":"Introduction: For couples, depression can position diagnosed partners to receive dyadic coping and mates to primarily provide support. We examine whether inequities in dyadic coping covary with depressive symptoms. Methods: Using data from 62 mixed-gender couples with one partner diagnosed with major depression (60% female), we computed differences between provided and received dyadic coping reported by both partners. With Response Surface Analyses we examined the associations with depressive symptoms. Results: In patients, lower equity of dyadic coping was associated with more depressive symptoms, regardless of whether the patient felt underbenefitted or overbenefitted. In partners, dyadic coping was negatively associated with depressive symptoms while equity of dyadic coping showed no significant associations. Patients and partners both reported providing more dyadic coping than they received. Discussion: Inequities in dyadic coping covary with depressive symptoms in patients, beyond main effects of dyadic coping, justifying the inclusion of couples in treatment for depression.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47245437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anxiety- and Depression-Related Individual Differences in Affective and Cognitive Judgments of Self-Referential Praise and Criticism","authors":"Wanyu Zhang, Yunxiao Zhou, Jiehui Hu, Zhao Gao, Shan Gao","doi":"10.1521/JSCP.2021.40.3.201","DOIUrl":"https://doi.org/10.1521/JSCP.2021.40.3.201","url":null,"abstract":"Introduction: While praise is generally pleasant and criticism unpleasant, individual differences in response to social evaluations arise from distinct personal traits and states. Here, we investigate how processing of self-referential praise and criticism varies with personal attributes related to anxiety and depression, two highly prevalent and often chronic affective conditions. Methods: Ninety-three healthy participants first completed questionnaires for anxiety- and depression-related traits and states, and then they were scheduled to perform an evaluation task to rate praise and criticism for pleasantness and truthfulness. Results: Fear of negative evaluation positively correlated with unpleasantness of criticism. Trait- and state-anxiety and depression were positively associated with the truthfulness of criticism but negatively associated with that of praise. We further divided participants into high- and low-scoring groups based on the medians of their scores of each scale that displayed significant correlations with comment ratings and found group differences in their responses to praise and criticism. Discussion: The findings suggest that more highly anxious and depressed individuals may be subject to negatively-distorted self-representations in response to self-referential evaluations, thus exhibiting attenuated rejection for criticism or reduced acceptance for praise, which may have important implications not only for facilitating daily social interactions but also for subclinical and clinical diagnosis and treatment given that affective and cognitive processing of self-referential evaluations serves as a critical process exhibiting the sense of the self.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42085768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of Improved Version of Sigmoidal Function with Biases for Classification Task in ELM Domain","authors":"S. Mugunthan, T. Vijayakumar","doi":"10.36548/JSCP.2021.2.002","DOIUrl":"https://doi.org/10.36548/JSCP.2021.2.002","url":null,"abstract":"Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 70-82 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.002 71 ISSN: 2582-2640 (online) Submitted: 26.03.2021 Revised: 15.04.2021 Accepted: 4.05.2021 Published: 25.05.2021 column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44932439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Efficient Dimension Reduction based Fusion of CNN and SVM Model for Detection of Abnormal Incident in Video Surveillance","authors":"R. Sharma, Akey Sungheetha","doi":"10.36548/JSCP.2021.2.001","DOIUrl":"https://doi.org/10.36548/JSCP.2021.2.001","url":null,"abstract":"Performing dimensionality reduction in the camera captured images without any loss is remaining as a big challenge in image processing domain. Generally, camera surveillance system is consuming more volume to store video files in the memory. The normally used video stream will not be sufficient for all the sectors. The abnormal conditions should be analyzed carefully for identifying any crime or mistakes in any type of industries, companies, shops, etc. In order to make it comfortable to analyze the video surveillance within a short time period, the storage of abnormal conditions of the video pictures plays a very significant role. Searching unusual events in a day can be incorporated into the existing model, which will be considered as a supreme benefit of the proposed model. The massive video stream is compressed in preprocessing the proposed learning method is the key of our proposed algorithm. The proposed efficient deep learning framework is based on intelligent anomaly detection in video surveillance in a continuous manner and it is used to reduce the time complexity. The dimensionality reduction of the video captured images has been done by preprocessing the learning process. The proposed pre-trained model is used to reduce the dimension of the extracted image features in a sequence of video frames that remain as the valuable and anomalous events in the frame. The selection of special features from each frame of the video and background subtraction process can reduce the dimension in the framework. The proposed method is a combination of CNN and SVM architecture for the detection of abnormal conditions at video surveillance with the help of an image classification procedure. This research article compares various methods such as Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.02 Pages: 55-69 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.2.001 56 ISSN: 2582-2640 (online) Submitted:6.03.2021 Revised: 30.03.2021 Accepted: 21.04.2021 Published: 10.05.2021 background subtraction (BS), temporal feature extraction (TFE), and single classifier classification methods.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43884567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Fault Diagnostics using Convolutional Neural Network and Adam Stochastic Optimization","authors":"S. Shakya","doi":"10.36548/JSCP.2021.1.005","DOIUrl":"https://doi.org/10.36548/JSCP.2021.1.005","url":null,"abstract":"Navigation, aviation and several other fields of engineering extensively make use of rotating machinery. The stability and safety of the equipment as well as the personnel are affected by this machinery. Use of deep learning as the basis of intelligent fault diagnosis schemes has and investigation of other relevant fault diagnosis schemes has a large scope for development. Thorough exploration needs to be performed in deep neural network (DNN) based schemes as shallow layer network structure based fault diagnosis schemes that are currently available has several considerable limitations. The nonlinear problems may be processed during intelligent fault diagnosis using deep convolutional neural network, which is a special structure DNN. The convolutional neural network (CNN) based scheme is emphasized in this paper. The principle and basic structure of the model are introduced. In rotating machinery, the fault diagnosis schemes using CNN are analyzed and summarized. Various CNN schemes, the potential mechanisms and performance diagnosis are analyzed. A novel smart fault diagnosis strategy is proposed while highlighting the potential aspects of existing schemes and reviewing the challenges.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46161926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Detection using Two-Layered Novel Convolutional Neural Networks","authors":"P. Karuppusamy","doi":"10.36548/JSCP.2021.1.004","DOIUrl":"https://doi.org/10.36548/JSCP.2021.1.004","url":null,"abstract":"In the recent years, there has been a high surge in the use of convolutional neural networks (CNNs) because of the state-of-the art performance in a number of areas like text, audio and video processing. The field of remote sensing applications is however a field that has not fully incorporated the use of CNN. To address this issue, we introduced a novel CNN that can be used to increase the performance of detectors built that use Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Moreover, in this paper, we have also increased the accuracy of the CNN using two improvements. The first improvement involves feature vector transformation with Euler methodology and combining normalized and raw features. Based on the results observed, we have also performed a comparative study using similar methods and it has been identified that the proposed CNN proves to be an improvement over the others.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46993462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big Data Analysis and Perturbation using Data Mining Algorithm","authors":"W. Haoxiang, S. Smys","doi":"10.36548/JSCP.2021.1.003","DOIUrl":"https://doi.org/10.36548/JSCP.2021.1.003","url":null,"abstract":"The advancement and introduction of computing technologies has proven to be highly effective and has resulted in the production of large amount of data that is to be analyzed. However, there is much concern on the privacy protection of the gathered data which suffers from the possibility of being exploited or exposed to the public. Hence, there are many methods of preserving this information they are not completely scalable or efficient and also have issues with privacy or data utility. Hence this proposed work provides a solution for such issues with an effective perturbation algorithm that uses big data by means of optimal geometric transformation. The proposed work has been examined and tested for accuracy, attack resistance, scalability and efficiency with the help of 5 classification algorithms and 9 datasets. Experimental analysis indicates that the proposed work is more successful in terms of attack resistance, scalability, execution speed and accuracy when compared with other algorithms that are used for privacy preservation.","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46927857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Chaos Whale Optimization and BAT Integrated Algorithm for Parameter Estimation in Sewage Treatment","authors":"Akey Sungheetha, R RajeshSharma","doi":"10.36548/JSCP.2021.1.002","DOIUrl":"https://doi.org/10.36548/JSCP.2021.1.002","url":null,"abstract":"Biological and social issues rise with faults that occur in waste water treatment plant (WWTP). Nature as well as humans are negatively impacted by the dangerous effects of poorly treated wastewater. This paper combines the fuzzy logic, chaos theory, whale optimization algorithm (WOA) and BAT algorithm (FCW-BAT) to create a novel model for parameter estimation. The WWTP applications are exposed to FCW-BAT algorithm for identifying nonwell-structured domain, validating decision rules, cost reduction and estimation of several relevant attributes from the complete dataset. The significant data is retained while reducing the complete feature set using FCW-BAT prior to the classification process. Estimation of data uncertainty and fuzzification is performed with the cost function fast fuzzy c-means. The WOA parameters are estimated and tuned with the help of several chaos sequence maps. Complex real-time datasets consisting of missing values and several uncertainty features are tested and experimented. Shorter execution time, higher convergence speed, lower error and improved performance are obtained with the sine chaos map embedded in the proposed algorithm. Additionally, the WWTP sensor process faults may also be detected by the proposed model with great levels of accuracy enabling the system operators to make appropriate control decisions. Journal of Soft Computing Paradigm (JSCP) (2021) Vol.03/ No.01 Pages: 10-18 http://irojournals.com/jscp/ DOI: https://doi.org/10.36548/jscp.2021.1.002 11 ISSN: 2582-2640 (online) Submitted:10.02.2021 Revised: 05.03.2021 Accepted: 28.03.2021 Published: 15.04.2021","PeriodicalId":48202,"journal":{"name":"Journal of Social and Clinical Psychology","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69625492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}