NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Models, Psychological.")'

Clinical psychology : an introduction /

By Carr, Alan,. New York : Routledge, 2012 . xv, 395 p. : 25 cm.. 9780415683968 (hardback) | 9780415683975 (paperback)

The heart & soul of change : delivering what works in therapy /

Washington, DC : American Psychological Association, 2010 . xxix, 455 p. : , Rev. ed. of: The heart & soul of change / [edited by] Mark A. Hubble, Barry L. Duncan, and Scott D. Miller. c1999. 27 cm.. 9781433807091 | 1433807092

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Computer-based testing and the internet : issues and advances issues and advances

Chichester, West Sussex, England ; | Hokoken, NJ : John Wiley & Sons Ltd., 2005 . x, 262 p. : 24 cm.. 047001721X

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Handbook of conflict analysis and resolution /

London ; | New York : Routledge, 2009 . xxiv, 543 : 26 cm.. 9780415577359(Paperback) | 9780203893166 (ebook) | 0203893166 (ebook)

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Understanding psychology and crime : perspectives on theory and action /

By McGuire, James.. [S.l.] : Open University Press, 2004 . 296 p. ; , Paperback. 24 cm.. 0335211194 | 9780335211197

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Handbook of conflict analysis and resolution /

London ; | New York : Routledge, 2009 . xxiv, 543p 26 cm.. 9780415433952 (hardback) | 0415433959 (hardback) | 9780203893166 (ebook) | 0203893166 (ebook)

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Conducting Research in Developmental Psychology: A Topical Guide for Research Methods Utilized Across the Lifespan /

New York: Routledge, 2020 . xiii, 213 p. 9780367340223(pbk)

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Characterization of atrial natriuretic peptide (ANP) response in depression-induced mice models /

By Ali, Saman . . 71p. , Depression is a complex psychological disorder that is also often link to the hormonal imbalances. Our research delves into the intricate relationship between depression and atrial Natriuretic Peptide (ANP), considering the multifaceted influences of neurological, genetic, and environmental factors. With a focus on hormonal imbalances as key contributors to depression, our investigation explores the potential therapeutic effects of Levothyroxine (LT4) in the context of ANP. Utilizing an early weaning mouse model involving maternal separation, we conducted a detailed examination of the anxiolytic effects of LT4, aiming to evaluate its efficacy in alleviating symptoms associated with anxiety and depression. Behavioral assessments and histological analyses were employed to comprehensively evaluate the impact of LT4 on ANP. This study also extends to molecular investigations using RT-PCR to analyze the distribution and expression of ANP within the mouse central nervous system, highlighting the cortex region. Our findings reveal significant differences in brain expression levels of ANP between treated mice and those exhibiting depressive symptoms. This insight suggests potential therapeutic applications of ANP for mitigating depression, presenting intriguing avenues for further research, particularly in the context of depression-induced mouse models through parental separation. This research contributes to an enriched understanding of the complex factors influencing depression and proposes interventions that extend beyond conventional approaches. The integration of behavioral assessments, histological analyses, and molecular investigations offers a holistic perspective, laying the groundwork for future exploration in the critical realm of mental health research. 30cm.

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Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

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