AI and Tech in Behavioral and Social Sciences

Optimal resource allocation in the implementation of administrative policies in the Judiciary is one of the key challenges facing managers in this field. The present study was conducted with the aim of designing a multi-objective mathematical optimization model for resource allocation in the implementation of administrative policies in the Judiciary. The study is applied-developmental in nature. After identifying the key factors through field studies, a multi-objective nonlinear optimization model was designed using mathematical programming techniques. The proposed model includes three objective functions: maximizing effectiveness, minimizing cost, and maximizing mutual influence. These objectives are defined under budget, time, human-resource, and balance constraints among different categories of factors. The results of solving the model showed that adaptability (0.9156) and organizational structure (0.8645) received the highest levels of allocation. In addition, the total system effectiveness reached 0.8532, covering 121.89% of the minimum required level. The model provides a practical tool for senior managers of the Judiciary to support strategic decision-making in resource allocation.

This study was conducted with the aim of designing a qualitative model of smart manufacturing in Iran’s automotive industry. This qualitative study was carried out using the grounded theory method based on the systematic approach of Anselm Strauss and Juliet Corbin (2015). Data were collected through in-depth semi-structured interviews with 12 experts, including production managers, information technology specialists, and university faculty members. Sampling was conducted using purposive and theoretical methods with a snowball strategy until theoretical saturation was achieved. Data analysis was performed using MAXQDA 2020 software in three stages: open coding, axial coding, and selective coding. To ensure the quality of the study, the criteria proposed by Yvonna Lincoln and Egon Guba (1985) were employed, and inter-coder reliability was calculated using Cohen’s kappa coefficient, which was found to be 0.86. The findings led to the identification of 313 open codes, 176 concepts, and 36 main categories within the paradigm model framework. The core phenomenon (smart manufacturing) consisted of six components: real-time data integration, predictive analytics and maintenance, autonomous robotics, mass customization, supply chain optimization, and product quality enhancement. Causal conditions (5 categories), contextual conditions (6 categories), intervening conditions (7 categories), strategies (6 categories), and consequences (6 categories) were identified. The proposed model provides an integrated and localized framework for managers and policymakers in Iran’s automotive industry and can serve as a roadmap for digital transformation in this sector. Successful implementation of this model requires special attention to technological infrastructure, the development of specialized human resources, and the alignment of supportive policies. Quantitative validation of the model using structural equation modeling is recommended for future studies.

Chronic pain is a prevalent and debilitating condition that significantly impacts patients' quality of life. Acceptance and Commitment Therapy (ACT) has proven effective in managing chronic pain, but access to face-to-face therapy remains limited. This study investigated the effectiveness of ACT delivered via digital workbooks on pain intensity and pain acceptance in patients with chronic pain disorders. This semi-experimental study employed a pre-test, post-test, and follow-up design with two groups. The sample consisted of 62 participants (30 in the experimental group and 32 in the control group) recruited from mental health clinics in Tehran. Participants were randomly assigned to either the experimental group, which received a 10-week ACT intervention via digital workbooks, or the control group, which received routine medical care. Data were collected using the Visual Analog Scale (VAS) for pain intensity and the Persian version of the Chronic Pain Acceptance Questionnaire (CPAQ). Data were analyzed using Mixed Analysis of Variance (Mixed ANOVA) with SPSS software (version 26). The results indicated a significant reduction in pain intensity and a significant increase in pain acceptance in the experimental group from pre-test to post-test, with these effects maintained at the three-month follow-up. In contrast, no significant changes were observed in the control group. The Mixed ANOVA revealed significant interaction effects between time and group for both pain intensity (F (1.85,112.85) = 38.90, p<0.001, ɳ2 = 0.39) and pain acceptance (F (1.92,117.12) = 48.75, p<0.001, ɳ2 = 0. 45). The findings suggest that ACT delivered via digital workbooks is an effective intervention for reducing pain intensity and enhancing pain acceptance in patients with chronic pain. This digital format offers a scalable, accessible, and cost-effective alternative to traditional therapy, potentially improving outcomes for individuals with chronic pain disorders. Future research should explore long-term effects and compare digital ACT with other therapeutic modalities.

The rapid growth of digital imaging technologies has made high-quality visual data increasingly accessible; however, the storage, transmission, and restoration of high-resolution images remain challenging in bandwidth-limited and resource-constrained environments. Although compression methods reduce file size, they may remove critical details required for scientific, medical, remote-sensing, and security applications. To address this limitation, this study proposes an attention-guided dynamic ensemble framework for Single Image Super-Resolution (SISR). The proposed method integrates several representative super-resolution models, including LapSRN, SRResNet, ResNeXt-based SR, SRCNN/FSRCNN, and ESPCN, and uses an attention-guided selection module to assign the most suitable model to different image regions based on local characteristics such as edges, textures, and smooth areas. The selected outputs are then fused by a convolutional integration network to generate the final high-resolution image. Experiments on DIV2K and BSDS300 show that the proposed method improves reconstruction quality, particularly in terms of structural similarity and texture preservation. On DIV2K, the proposed method achieved 33.40 dB PSNR and 0.9172 SSIM; on BSDS300, it achieved 28.13 dB PSNR and 0.8497 SSIM. These findings indicate that dynamic model selection can reduce the limitations of individual super-resolution models and improve detail recovery in feature-diverse images.

This quasi-experimental study examined the effectiveness of computerized cognitive rehabilitation in improving academic engagement among lower-secondary students experiencing academic underachievement. The study used a pre-test/post-test design with an experimental group and a control group. The statistical population consisted of lower-secondary students with academic underachievement in Tehran during the first semester of the 2023-2024 academic year. Thirty students were selected through convenience sampling and randomly assigned to an experimental group (n = 15) and a control group (n = 15). Academic engagement was measured using the Zarang Academic Engagement Questionnaire, which assesses cognitive, motivational, and behavioral engagement. The experimental group completed a 12-session computerized cognitive rehabilitation program using Captain's Log version 14, delivered twice weekly and designed to train 22 foundational and higher-order cognitive skills, including focused and sustained attention, divided and selective attention, working memory, auditory and visual processing speed, response inhibition, problem solving, and logical reasoning. The control group did not receive the computerized intervention during the study period. Data were analyzed with analysis of covariance after checking the main statistical assumptions. The experimental group showed a marked increase in academic engagement from pre-test (M = 80.86, SD = 9.87) to post-test (M = 103.26, SD = 11.10), whereas the control group showed only a small descriptive increase from pre-test (M = 86.46, SD = 11.36) to post-test (M = 88.80, SD = 10.00). ANCOVA indicated a statistically significant group effect after controlling for pre-test scores, F(1, 27) = 7.11, p = .013, partial eta squared = .208. These findings suggest that computerized cognitive rehabilitation may be a useful school-based adjunct intervention for strengthening academic engagement among students with academic underachievement, although future studies with larger samples, active control conditions, and follow-up assessments are needed.

Aggression during the early school years interferes with peer relationships, classroom adjustment, and academic functioning. School-based social-emotional learning (SEL) programs can reduce behavioral problems, yet children may benefit most from formats that are developmentally engaging and expressive. This study examined whether a digital art-based SEL program could reduce aggression in second-grade students.A quasi-experimental pretest-posttest design with a control group was used. Fifty-two second-grade students from public elementary schools in Mazandaran Province, Iran, were selected through cluster sampling and assigned to an experimental group (n = 28) or a control group (n = 24). Aggression was assessed at baseline and post-intervention using the Persian version of the Buss-Perry Aggression Questionnaire. The experimental group participated in an eight-week intervention comprising 16 sessions (two 60-minute sessions per week) integrating CASEL-based SEL competencies with digital art activities, whereas the control group continued routine instruction. Descriptive statistics and mixed-design ANOVA were used for analysis. The groups were comparable at baseline on demographic characteristics and aggression scores. From pretest to posttest, the experimental group showed marked reductions in total aggression (47.85 ± 5.20 to 31.80 ± 4.50), physical aggression (12.45 ± 2.10 to 8.30 ± 1.85), verbal aggression (11.80 ± 2.40 to 7.50 ± 1.90), anger (13.10 ± 2.60 to 9.20 ± 2.10), and hostility (10.50 ± 2.30 to 6.80 ± 1.70), whereas changes in the control group were small. Mixed ANOVA showed a significant group × time interaction for total aggression, F(1, 50) = 26.07, p = .001, η² = .34, as well as for physical aggression, F(1, 50) = 22.15, p = .001, η² = .31; verbal aggression, F(1, 50) = 18.92, p = .001, η² = .27; anger, F(1, 50) = 24.56, p = .001, η² = .33; and hostility, F(1, 50) = 19.45, p = .001, η² = .28. A digital art-based SEL program appears to be an effective and engaging school-based approach for reducing aggression in elementary school students. Larger multi-site studies with follow-up assessments are recommended.

Objective: Chronic depression accompanied by high self-criticism remains difficult to treat and is often associated with persistent functional impairment. This study examined whether Cognitive Analytic Therapy (CAT), delivered with digital support tools, reduced depressive symptoms and self-criticism in adults diagnosed with persistent depressive disorder (PDD). Method: The study used a randomized pretest-posttest-follow-up design. Sixty participants with PDD and elevated self-criticism were allocated to either CAT (16 weekly sessions) or a waitlist treatment-as-usual (TAU) condition. Outcomes were assessed with the Beck Depression Inventory-II (BDI-II) and the Levels of Self-Criticism Scale (LSCS) at baseline, post-treatment, and 2-month follow-up. Data were analyzed using mixed-design analysis of variance. Results: Significant Group x Time interaction effects were observed for depression, F(2, 116) = 45.32, p < .001, eta_p^2 = .438, and self-criticism, F(2, 116) = 52.18, p < .001, eta_p^2 = .473. In the CAT group, BDI-II scores declined from 32.45 (SD = 4.12) at baseline to 18.20 (SD = 3.85) at post-test and 17.10 (SD = 3.90) at follow-up, whereas the TAU group showed only modest change. LSCS scores in the CAT group declined from 85.60 (SD = 8.45) to 58.30 (SD = 7.20) and 55.40 (SD = 6.95), while the TAU group remained comparatively stable. Conclusion: Within the limits of the present design, CAT supplemented with digital monitoring and self-help tools was associated with substantial and sustained reductions in depressive symptoms and self-critical thinking. The findings support CAT as a promising intervention for chronic depressive presentations marked by harsh self-relating.

Eating disorders (EDs) and body image disturbance remain major mental health concerns among adult women, and the persistence of shame, self-criticism, and relapse after standard treatment highlights the need for compassion-based interventions. This study investigated the effectiveness of Compassion-Focused Therapy (CFT) integrated with digital tools in improving body appreciation and self-compassion while reducing eating disorder psychopathology in adult women with disordered eating symptoms. Using a semi-experimental pretest–posttest design with a two-month follow-up, 60 women from Tehran, Iran, were randomly assigned to either an intervention group receiving 12 weekly group CFT sessions with digital support or a waitlist control group; 53 participants completed the study (26 in the intervention group and 27 in the control group). Outcomes were assessed using the Body Appreciation Scale-2 (BAS-2), the Eating Disorder Examination Questionnaire (EDE-Q), and the Self-Compassion Scale–Short Form (SCS-SF), and the data were analyzed using mixed ANOVA. The findings showed significant Time × Group interaction effects for all three outcomes, indicating superior improvement in the intervention group relative to controls. Body appreciation increased from 2.85 ± 0.71 at pretest to 3.92 ± 0.64 at posttest and remained stable at 3.88 ± 0.68 at follow-up, with a significant interaction effect, F(1.68, 85.64) = 42.35, p < 0.001, η² = 0.45. Eating disorder psychopathology declined markedly from 3.65 ± 0.89 to 2.10 ± 0.75 and was maintained at 2.15 ± 0.78, F(1.72, 87.72) = 55.18, p < 0.001, η² = 0.52. Self-compassion improved from 2.45 ± 0.62 to 3.55 ± 0.58 and remained at 3.50 ± 0.60, F(1.85, 94.35) = 38.92, p < 0.001, η² = 0.43. In contrast, the waitlist group showed no meaningful change across time. Overall, digitally supported CFT appears to be an effective and durable intervention for improving body image and reducing eating disorder symptoms in adult women.