Current studies in disaster sociology, which were initiated and developed mostly in the USA upon the request of the army, are far from meeting the needs today. Today, more than ever, new theoretical and methodological approaches that are not human-centered are needed. The research, a part of which is presented here, aims to render invisible the damages and losses suffered by those who are marginalized by the powerful, in disasters in general and earthquakes in particular. The main question of this research is how to address the damages suffered by soil plants and animals, including immigrants in Turkey, due to the disaster on 6February 2023.(Based on this, the main question of the study is how to address the damages of the natural environment, including plants, animals and soils, as well as Syrian immigrants in Turkey, who were affected by the earthquakes centered in Kahramanmaraş on 6February 2023, which we experienced most recently, will be addressed with an antipositivist approach.) For this purpose, unlike classical sociological approaches, based on relational sociology, how immigrants, plants, animals and soil are affected together during the uncertainty and complexity in daily life has been analyzed based on available written and visual documents. The findings were discussed with a holistic view, based on the ‘One World’ terminology suggested by relational sociologist Bruno Latour. It has been revealed that due to the earthquake turning into a major disaster, the resident population has become openly or secretly immigrants, and they have been marginalized like other creatures, especially international immigrants, most of whom are Syrians, have been blamed, excluded and rendered invisible. While the research results reveal the inadequacy of classical essentialist sociological approaches based on the basic duality of nature and society, they also show that ‘differences’ and ‘uncertainties’ come to the fore in daily life instead of linear determinations. In addition, while the importance and contributions of interdisciplinary and transdisciplinary studies with concepts such as ‘liminality’ and ‘turning point’ are exhibited, on the other hand, some suggestions are made based on Bruno Latour’s ‘One World’ approach.

In the highly competitive employment environment, most college students have left their jobs for a short time after employment, and attention should be paid to students’ career adaptation. However, the further influence of skilled goal orientation, social support and career-determined self-efficacy on college students’ career adaptation needs to be confirmed. This study analyzes the effects of these factors on college students’ career adaptation. This study aims to analyze the impact of mastery goal orientation, social support, and vocational decision self-efficacy on career adaptation among 224 university students in East China. The results indicated that university students generally exhibit positive levels of mastery goal orientation, social support, vocational decision self-efficacy, and overall career adaptation. Female students demonstrate higher levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation compared to male students. As students progress in their academic years, their levels of mastery goal orientation, social support, vocational decision self-efficacy, and career adaptation tend to increase. Students majoring in humanities and social sciences have higher level than students majoring in science and engineering in all factors. Students majoring in humanities and social sciences exhibit more optimism in all factors compared to students in science and technology fields. The relationships among these factors show positive correlations. Mastery goal orientation, social support, and vocational decision self-efficacy all have positive effects on career adaptation. Among these, family support stands out as the most influential subordinate factor of social support on career adaptation. The most influential subordinate factor of vocational decision self-efficacy on career adaptation is conscious decision-making. Therefore, male, lower grade, science and engineering college students are the groups that need to be paid attention to in improving career adaptation. Skilled goal orientation, family support and conscious decision making have a better effect on the improvement of career adaptation. These results can provide important reference information for universities, counselors and college students in the training of career planning, and theoretically enrich the relevant research on college students’ career adaptation, and provide certain enlightenment for future researchers.

Malaria is an infectious disease that poses a significant global health threat, particularly to children and pregnant women. Specifically, in 2020, Rampah Village, Kutambaru sub-district, Langkat Regency, North Sumatra Province, Indonesia, reported 22 malaria cases, accounting for 84% of the local cases. This study aims to develop a malaria prevention model by leveraging community capital in Rampah Village. A mixed-method sequential explanatory approach, combining quantitative and qualitative methods, was employed. Quantitative data were collected through questionnaires from a sample of 200 respondents and analyzed using structural equation modeling (SEM) with Smart PLS (Partial Least Squares) software. The qualitative component utilized a phenomenological design, gathering data through interviews. Quantitative findings indicate that natural capital significantly influences malaria prevention principles. There is also a positive and significant relationship between developmental capital and malaria prevention. Cultural capital shows a positive correlation with malaria prevention, as does social capital. The qualitative phase identified cultural capital within the Karo tribe, such as ‘Rakut si Telu,’ which signifies familial bonds fostering mutual aid and respect. The results of this study are crucial for formulating policies and redesigning community-capital-based malaria prevention programs. These programs can be effectively implemented through cross-sectoral collaboration among health departments, local government, and community members. Malaria is a communicable disease threatening global health, particularly affecting children and pregnant women. In 2020, there were 229 million cases of Malaria worldwide, resulting in 409,000 deaths. In Indonesia, specifically in North Sumatra’s Langkat Regency, Kutambaru District, Rampah Village had 22 cases (84%). The purpose of this research is to formulate a Malaria prevention model using community resources in Rampah Village, Kutambaru District, Langkat Regency. The study employed a mixed-methods sequential explanatory approach, combining quantitative and qualitative methods. Quantitative data was collected through questionnaires, with 200 respondents, and structural equation modeling (SEM) analysis using smart PLS (Partial Least Squares) software. Qualitative data was gathered through interviews. The research findings showed a positive relationship between cultural modalities and Malaria prevention (p = 0.000) with a path coefficient T-value of 12.500. The cultural modality and Malaria prevention relationship were significantly positive (p = 0.000) with a path coefficient T-value of 3.603. A positive and significant correlation also exists between development modalities and Malaria prevention (p = 0.011) with a path coefficient T-value of 2.555. Qualitative research revealed the Rakut si Telu cultural modality of the Karo tribe, meaning that family-based social connections create a sense of helping and respecting one another. The Orat si Waluh cultural modality represents daily life practices in the Karo tribe as a form of community-based Malaria prevention.

Nanomaterials are a recently discovered type of material that is gaining importance and receiving a lot of attention from researchers. Due to their numerous advantages, scholars are studying nanoparticles extensively. The articles in this issue that discuss the various applications of nanoparticles are very interesting. The majority of these articles focus on the use of nanoparticles in the medical sector and their contributions to environmental protection.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.