مطالب

This article examines digital transformation in the construction industry, which begins with adopting digital technologies and culminates in comprehensive organizational change. The diverse and often conflicting conceptualizations in this field have created ambiguity in the theoretical framework of digi-tal transformation in construction. Using topic modeling and pattern analysis, this study identifies key themes and trends in the domain. The article analyzes 1,308 articles published between 1990 and 2023 to review research areas related to digital transformation in the construction industry. It identifies six main topics: Security & Safety, Organization & Project Management, Digital Simulation & Interaction, Sustainability, Innovative Building Materials, and Dynamic Monitoring Methods. The analysis reveals that Organization & Project Management is the most researched topic, while Sustainability receives the least attention. The article offers recommendations for advancing research in this field and serves as a valuable reference for researchers and practitioners interested in digital transformation in construction. By addressing existing criticisms, it provides a clear map of the field’s structure and trends, comple-menting previous qualitative studies with a broader, more structured, and objective analysis.

Image classification is a significant process in the field of computer science. It has applications in every field, such as spam detection in emails, medical diagnosis, image recognition, sentiment analysis, object detection, weather forecasting, pattern recognition, and security. Image classification deals with the grouping of images based on labels or characteristics. Feature extraction, feature selection, feature reduction, and classification are the main steps used to classify images. A medicinal and non-medicinal flowers data set is prepared by clicking images for the study. Methodology is used to achieve satisfactory classification results on the seeds, Wisconsin Diagnostic Breast Cancer, Heart Failure Clinical Records, and Wisconsin Prognostic Breast Cancer data sets, which are taken from the University of California, Irvine (UCI) repository. The proposed methodology suggests an efficient feature extraction and selection approach for data sets under consideration. An information gain-based genetic algorithm is used for feature reduction. It is performed on the extracted features to retrieve an optimized feature set. Fitness of the features is evaluated to choose the most relevant features. A neural network is used to classify the obtained feature subset. Better classification results are attained with the help of feature extraction and feature reduction.

Vertical handover (VHO) in heterogeneous wireless networks is essential to keep users continuously connected and also to guarantee that the Quality of Service (QoS) of mobile communications is adequate. The focus of this paper is to apply various Multi-Criteria Decision Making (MCDM) tools to model a comprehensive VHO decision-making framework. This proposed methodology brings together more than one parameter of a given networking environment, such as signal intensity, QoS, and energy requirements, into one or more decision models. Using analytical methods such as the Analytical Hierarchy Process (AHP) for criteria weighting, we continuously optimize network conditions, thereby enhancing the efficiency and reliability of Vertical Handover (VHO). Several experiments were made to test the efficiency of the given MCDM-based VHO algorithm. The performance evaluation of the proposed method reveals superior handover performances in terms of success rates, less latency, and better QoS as compared to other VHO techniques. In addition, our research conclusion implies that integrating MCDM into the VHO decision-making will not only facilitate the network resource optimization but also improve the user satisfaction in the heterogeneous networking domain. This paper, in the wireless communications area, makes a significant contribution by presenting a powerful framework for DNS and VHO. The subsequent studies will be directed towards improving this algorithm for real-time applications and experimentation in the new generation networks, such as 5G Networks.

Extensive technological advancements have highlighted the importance of digital transformation in improving business performance. While prior research on this topic has been done in the information systems and business management domains, it has been limited to specific areas. Therefore, it is crucial to evaluate the impact of digital transformation comprehensively. This research aims to systematically identify critical themes, significant topics, main concepts, and trend priorities. The study involved the analysis of 474 research papers from 2015 to 2024 from reputable databases such as SCOPUS, Web of Science, and IEEE Xplore. First, thematic analysis identified the main themes and interpreted their relationships. Identified themes refer to technological changes at the operational and strategic levels through data analytics, digitalization, collaborative learning, and digital interaction. Realizing that digital transformation leads to value creation, improved service quality, customer experience, and long-term communication in digital ecosystems. These findings were related to dynamic capability theory concepts and compared with theory constructs like sensing, seizing, and transforming. Next, text mining techniques were used for deeper investigation, including word cloud, topic modeling (Latent Dirichlet Allocation), and text clustering (K-means). Findings were categorized into three perspectives: business, customer, and systemic, highlighting the influential role of digital technologies, particularly artificial intelligence (AI) capabilities. Moreover, trend analysis presented research priorities using VOSviewer. Finally, research innovation involved designing thematic networks and examining the relevance of significant topics as a research artifact with subtle differences compared to the conducted research. This novel approach provides five targeted propositions to audiences for future research.

Objective: This study aimed to explore how adoptive parents construct their identities and meaning of parenthood following experiences of infertility. Methods: This qualitative research employed a narrative methodology to investigate the lived experiences of 31 adoptive parents residing in Austria, all of whom had previously experienced infertility. Participants were recruited through purposive sampling, ensuring diversity in gender, relationship status, and duration since adoption. Data were collected through semi-structured interviews, each lasting between 60 and 90 minutes. Interviews were transcribed verbatim and analyzed using thematic narrative analysis with the assistance of NVivo software. Theoretical saturation guided the cessation of data collection. Data analysis focused on identifying shared themes and patterns in the way participants narrated their transition from infertility to adoptive parenthood. Findings: Three major themes emerged from the data: Redefining Parenthood, Emotional Journey, and Social and Relational Adjustments. Within these categories, participants described subthemes such as letting go of biological ties, reconstructing identity, symbolic rituals, emotional ambivalence, hope and renewal, navigating social stigma, and coping with institutional processes. Participants emphasized the importance of narrative reconstruction, spiritual or moral reframing, and peer support in legitimizing their parental identity. Despite the fulfillment found in adoption, many expressed that emotional residues from infertility persisted, shaping their experience of parenthood in both subtle and explicit ways. Conclusion: The findings highlight that adoptive parenthood after infertility is a complex, emotionally layered, and socially negotiated process. Narrative identity construction plays a vital role in helping individuals reconcile reproductive loss with the joys and challenges of adoption.

Objective: The present study aimed to compare the effectiveness of Acceptance and Commitment Therapy (ACT) and Solution-Focused Therapy (SFT) on marital quality of life and sexual communication intelligence in couples experiencing marital conflicts. Methods: This study was applied in terms of its objective and employed a quasi-experimental design with a pretest-posttest structure, a two-month follow-up, and a control group for data collection. The statistical population consisted of all couples aged 25 to 40 who had marital conflicts and visited counseling and psychology clinics in District 13 of Tehran in 2023. From this population, 45 couples were selected using purposive sampling. The research instruments included the Sexual Communication Intelligence Questionnaire (Dehkordi et al., 2021) and the Marital Quality of Life Questionnaire (Babci et al., 1995), which were completed by the couples. One group underwent Solution-Focused Therapy in eight 90-minute sessions, while the other group received Acceptance and Commitment Therapy in ten 90-minute sessions. Data analysis was conducted using repeated measures ANOVA in SPSS software. Findings: The results indicated that Acceptance and Commitment Therapy had a positive effect on marital quality of life and sexual communication intelligence. Additionally, Solution-Focused Therapy was also effective in improving these two components (p < .05). Conclusion: The findings of this study demonstrate the effectiveness of both therapeutic approaches—Solution-Focused Therapy and Acceptance and Commitment Therapy—on marital quality of life and sexual communication intelligence. A comparison of the two methods revealed that Acceptance and Commitment Therapy was more effective than Solution-Focused Therapy.