İnönü University is a public university in Malatya, Turkey. On 28 January 1975, the Grand National Assembly of Turkey ordered the establishment of İnönü University in Malatya, the hometown of the second president of Turkey, İsmet İnönü.İnönü University is one of the biggest public university of eastern part of Turkey. İnönü University has 6 campuses, 6 institutes, 14 faculties, 19 research centers and an innovative science and technology park (Technopolis) which is called Malatya Technology Development Zone in Malatya. Over 90,000 students have graduated since 1975 from İnönü University. Inside the university, there is a museum commemorating İsmet İnönü, along-with another museum commemorating Turgut Özal. Currently, İnönü University has over 1600 faculty members and research assistants, around 3500 graduate students and over 41000 (approximately 1500 students from different countries) undergraduate students.İnönü University Turgut Özal Medical Center is one of the biggest research and implementation hospital in the world. It is an approximately 1400 bed hospital with 40+ operating rooms. It serves as a district hospital and accepts patients from neighbor countries as well. It is among the top three hospitals in liver transplant in the world and also serves in other areas such as kidney transplants, bone marrow transplants and burn injuries. Turgut Özal Medical Center has Faculty of Medicine, Faculty of Dentistry, Faculty of Pharmacy, Faculty of Health Sciences and also, it has an Adult Hospital and a Liver Transplant Hospital. İnönü University has planned to have Oncology Hospital until 2018.In addition, İnönü University has the biggest Solar Energy Center in Turkey. With this energy center, İnönü University produces its own electricity for Turgut Özal Medical Center. This center is the most innovative investment and İnönü University did it its own endowment. Solar Energy Center fulfills 30 percent of Turgut Özal Medical Center electricity requirement. İnönü University Solar Energy Center started to work May 2015. It serves as a district hospital and accepts patients from neighbor countries as well. It is among the top three hospitals in liver transplant in the world and also serves in other areas such as kidney transplants, bone marrow transplants and burn injuries.
This paper presents an explainable data-driven framework for reliable prediction and multi-objective optimization design of compressive strength (CS) of green and low-carbon geopolymer concrete (GPC). A comprehensive literature-based database containing 2281 instances with 16 defined input features was employed to develop both individual and stacked ensemble learning models. All stacked ensemble models demonstrated strong predictive capability; meanwhile, stacked learning consistently improved generalization performance. The best stacked ensemble model, SCM-17 (ETR + GBM + RF), achieved a testing accuracy of R2 = 0.967, RMSE = 5.83 MPa, MAE = 3.76 MPa, and MAPE = 9.87%, confirming the robustness of the proposed stacked strategy for CS prediction. Further, to enhance interpretability and extract engineering insight, SHAP-based sensitivity analysis was conducted. The SHAP analysis indicates that precursor-related variables, particularly ground granulated blast furnace slag and fly ash contents, together with curing age and sodium silicate dosage, exhibit the strongest influence on the model-predicted CS. Beyond prediction, a target-strength-oriented multi-objective optimization framework was implemented to identify optimal GPC mixture proportions while simultaneously minimizing carbon emissions, cost, and energy consumption. The optimized solutions demonstrated balanced sustainability-performance trade-offs and confirmed that feasible high-strength mixtures can be obtained without extreme material configurations. Furthermore, selected optimized mixtures were experimentally validated, and the measured CSs showed good agreement with the corresponding target and predicted values, thereby providing independent verification of the proposed inverse-design framework. Lastly, a graphical user interface was developed to enable rapid CS prediction together with sustainability assessment within validated parameter limits. All in all, the proposed integrated prediction-interpretation-optimization workflow provides a reliable decision-support framework for data-driven and sustainability-oriented GPC design.
Urban sprawl is a major spatial transformation process characterized by the unplanned expansion of urban areas into surrounding rural and natural landscapes. As a result, rural areas at the urban fringe are increasingly exposed to pressures driven by population growth, housing demand, and expanding transportation infrastructure, giving rise to hybrid “rurban” landscapes. Managing these areas requires not only monitoring land use change but also identifying zones at risk of transformation before irreversible change occurs.Since the 1980s, Antalya has experienced rapid population growth, tourism-driven economic expansion, and largely uncoordinated development, leading to significant changes in surrounding agricultural and rural landscapes. This study develops a Geographic Information System (GIS) and remote sensing–based spatial analysis framework to systematically identify rural areas under urban sprawl pressure. The approach integrates the direction and intensity of urban expansion with existing rural land use patterns to examine their spatial interactions.Using multi-temporal satellite imagery within a GIS environment, the spatio-temporal dynamics of urban growth were analyzed and rural areas with high susceptibility to urban encroachment were delineated. The results show that the impacts of urban sprawl extend beyond existing built-up areas and are increasingly concentrated in transitional rural zones that remain unurbanized yet are critical for planning.The identified spatial patterns provide a basis for an early warning framework to support the protection of agricultural land and the development of preventive land use policies. Although tested in Antalya, the proposed framework is transferable to regions experiencing similar urbanization dynamics.
Completely representing the properties of a building in a seismic assessment through data collection is a demanding endeavor. Current approaches rely on manual, time-consuming methods that are not integrated with digital systems which may result in incomplete or improper data collection processes, leading to incorrect earthquake performance analysis. In this paper, an expert system that integrates Building Information Modeling (BIM) and Digital Twin (DT) technologies is developed to assist, optimize, manage, and digitalize the data collection process and to take data-based decisions to increase the accuracy, transparency and efficiency of the assessment process. The developed system effectively optimizes data collection with genetic algorithm. Moreover the system contains an integrated database which can manage collected data from multi-resource through seismic performance analysis process and also visualizes the collected data through the embedded game engine. Seismic performance analyses of two existing buildings managed by the developed system demonstrated that the developed DT approach provides complete and efficient data collection and evaluation for seismic assessment.
ObjectivesThis study aims to examine the perspectives of mothers of children with autism spectrum disorder (ASD) regarding their children's care and educational needs.MethodsA semi-structured interview form was used in the study. The study group consisted of 15 mothers of children under the age of 18 diagnosed with ASD. Data were analyzed using descriptive analysis. During this process, the interviews were audio-recorded and note-taking, and the interview transcripts were then transcribed verbatim. The data were carefully read to create meaningful units and codes, and similar codes were combined into themes. The resulting themes were interpreted to holistically reflect the mothers' experiences and perceptions of their children's education and care needs. To ensure the accuracy and reliability of the data during the analysis, discussions were held among the researchers regarding coding consistency and theme consistency.ResultsThe data obtained in the study were presented under two themes: educational needs and care needs of children with ASD. Categories related to the themes and codes belonging to the categories were created.ConclusionsThe results of the study revealed that mothers of children diagnosed with autism in the preschool period most needed access to accurate and reliable information about autism and appropriate referral support during the diagnosis process. In addition, it was found that mothers also needed to be informed and receive psychological support.
This paper presents an innovative and explainable AI framework for predicting the mechanical performance of three-dimensionally printed strain-hardening cementitious composites (3DP-SHCC), focusing on compressive strength (CS) and flexural strength (FS). A rigorously curated database of 202 mechanical performance records collected from state-of-the-art literature was used to develop AI models. To enhance robustness and overcome the limitations of conventional tuning methods, GBM was integrated with four metaheuristic optimization algorithms-GWO, WOA, HHO, and SSA-combined with five-fold cross-validation. The outcomes show that HHO-GBM achieved the highest accuracy for CS prediction (R2 = 0.951) during the test phase, while SSA-GBM performed best for FS prediction (R2 = 0.897). SHAP and ICE analyses identified binder composition, loading direction, and fiber parameters as key drivers. Additionally, a user-friendly, real-time decision-support interface was developed and validated using independent unseen mixtures. Overall, the proposed framework offers a reliable and engineering-ready tool for 3DP-SHCC design.
