Post-earthquake soil chemical analysis is critical for understanding environmental and agricultural impacts as well as public health concerns. Earthquakes often disrupt soil structures, leading to changes in pH levels, nutrient content, and the release of contaminants such as heavy metals and organic pollutants. These chemical alterations have far-reaching consequences for soil fertility, vegetation growth, and water quality. This study provides a comprehensive review of the changes in soil chemical properties caused by seismic events and highlights their implications for sustainable recovery and environmental resilience. Key mechanisms such as liquefaction, erosion, and industrial contamination are discussed, along with the effects on soil's physical and chemical stability. The paper identifies key challenges in post-earthquake soil assessments, including spatial variability of soil conditions, complex contaminant interactions, and technical limitations in testing and monitoring equipment. The absence of baseline soil data in many seismic regions further hinders accurate assessment. The study emphasizes the need for improved soil monitoring networks, international cooperation, and advancements in analytical techniques. Future research priorities are proposed, including the development of standardized methods for soil chemical assessments, exploration of sustainable remediation technologies, and integration of emerging technologies such as remote sensing and geographic information systems (GIS) to accelerate data collection. The findings underscore the importance of interdisciplinary research involving soil science, environmental health, and civil engineering to foster holistic solutions for mitigating earthquake-induced soil changes and improving post-disaster recovery strategies.

From mazes to automation: Modernizing working memory research in animal models.

Purpose This study explores the potential of the metaverse in cultural tourism, explicitly examining whether it offers truly immersive experiences or remains an unrealistic ambition. This research aims to assess the effectiveness of the metaverse in authentically conveying cultural narratives and enhancing tourists’ engagement with virtual environments. Design/methodology/approach Data collection was done using the survey method, which was analyzed using structural equation modeling to evaluate the effects of awareness, cultural sensitivity and immersive experiences in cultural tourism. The sample included individuals familiar with virtual tourism platforms and their potential applications in the metaverse. Findings This study highlights the significant role of awareness and cultural elements in improving immersive experiences within the metaverse. The results indicate that cultural sensitivity and authenticity in virtual environments influence tourists’ behavioral intentions to revisit physical sites and engage in tourist activities. The findings underscore that immersive experiences are a key factor in driving future tourism behavior in both virtual and physical realms. Originality/value This research advances the understanding of the intersection between emerging technologies and cultural tourism, applying the theory of planned behavior to predict tourist engagement with virtual platforms. The study contributes new insights into how the metaverse can transform tourist experiences. It comprehensively evaluates its immersive potential and its ability to enhance cultural tourism through digital innovation.

The Single-Particle, Clusters and Biomolecules and Serial Femtosecond Crystallography (SPB/SFX) scientific instrument at the European X-Ray Free-Electron Laser (EuXFEL) became operational with user experiments in September 2017. The unique properties and capabilities of the EuXFEL, enabling megahertz data collection rates, provide more rapid data collection with improved statistics compared with other XFEL facilities. This improves the feasibility of obtaining multiple data points in time-resolved experiments and hence enables the observation of reactions in greater detail (molecular movies). In collaboration with the SFX User Consortium (SFX UC), the SPB/SFX instrument was designed to further increase user access and research outcomes. Focusing the pulses downstream of the first interaction region [described previously (Mancuso et al., 2019)], a second experiment plane is enabled, which allows for greater optimization and more efficient usage of available beam time. Additionally, the SFX UC provided further instrumentation to provide improved capabilities on SPB/SFX. The aim for additional and extended functionality for the second interaction region was to enable sample-efficient data collection at atmospheric pressure in an environment where the sample temperature and humidity can be controlled. This paper describes the extended capabilities of the downstream interaction region of the SPB/SFX instrument and its major components, in particular its X-ray focusing optics, vacuum to atmospheric pressure out-coupling, available sample delivery methods and 2D detector, and the supporting optical laser systems for pump-probe experiments.

Assisted reproductive technology in Latin America: the Latin American Registry, 2022.

A cross-cultural adaptation study on the validity and test-retest reliability of pain assessment tools in Albanian older adults with low back pain.

Real-time recording: A scoping review of methods to study children's real-time exposure to food and food marketing online.

YOLO-spectra: A generalized framework for rapid simultaneous detection and classification of Raman spectra in images with mobile devices for enhancing on-site applications.

The present study aimed to identify the components of digital leadership in the universities of Mazandaran Province. The research was applied in terms of purpose and qualitative with an exploratory mixed design in terms of data nature. Accordingly, in the qualitative phase, based on grounded theory, a descriptive–survey method was used to identify the components of digital leadership in the universities of Mazandaran Province. The statistical population consisted of all experts in the scientific community as well as academic specialists, managers, and planners of universities in Mazandaran Province. The sampling method was theoretical purposive and aligned with the principle of theoretical saturation; thus, 14 individuals were selected as the sample. Data collection in the qualitative part was performed through semi-structured interviews. The validity and reliability of the research were confirmed. Based on the qualitative findings, six key components of digital leadership within the context of higher education were identified. These components were extracted using three-stage coding (open, axial, and selective) and the analysis of more than 60 initial concepts. They represent the main dimensions of the role of academic leaders in the process of digital transformation. Thematic analysis of the qualitative data revealed eleven main components related to innovative organizational culture in the universities of Mazandaran Province. Each component reflects the internal and external mechanisms that influence the institutionalization of innovation in the academic environment. These findings indicate that an innovative organizational culture is not merely the product of macro-level policymaking or managerial decisions but rather the outcome of the dynamic interaction between structural, human, value-based, and technological factors.

Voluntary disclosure of information is an indirect mechanism through which International Financial Reporting Standards (IFRS) generate benefits for capital markets. Managers typically employ voluntary disclosure as a substitute for mandatory reporting, thereby conveying confidential company performance information to investors. The aim of this study is to present a comprehensive disclosure model for Iranian commercial insurance companies, focusing on IFRS and utilizing the multi-grounded theory approach. The first step involves formulating research questions based on the dimensions of grounded theory. In the second step, the researcher systematically reviews published articles in reputable domestic and international scientific journals to identify credible and valid documents within an appropriate time frame. Initially, related keywords—both individually and in combination—were examined in Persian and English for the period 2013 to 2024, and for English-language articles from 1980 to 2024. As a result, 27 relevant articles were identified. Since data collection in the grounded theory approach is based on theoretical sampling, in this study, data were synthesized through meta-combination, followed by in-depth interviews. Subsequently, using grounded theory and integrating it through the multi-grounded approach, a comprehensive model for identifying the disclosure framework was developed. Reporting and information disclosure are the most critical tools companies use to communicate with shareholders. When information disclosure is mandated by a regulatory or legislative authority, it is referred to as mandatory disclosure. In contrast, if the disclosure is not influenced by specific regulations and is conducted voluntarily by the company, it is considered voluntary disclosure. The voluntary disclosure theory posits that managers will disclose company information under their control if the benefits of such disclosure outweigh the associated costs.

Objectives: To assess current detection and intervention strategies for pediatric asthma, frontline healthcare providers in an urban environment, and to identify provider-level barriers to guideline-concordant care. Methods: Between March and May 2025, a cross-sectional multicenter survey was administered to a stratified random sample of 450 healthcare providers (pediatricians, general practitioners, nurse practitioners, and registered nurses) working in primary care clinics and pediatric hospitals. A 48-item questionnaire (practice subscale Cronbach’s α = 0.84) measured screening practices, diagnostic test availability (spirometry, FeNO), use of personalized asthma action plans (PAAPs), early-intervention strategies, and perceived barriers. Data collection were electronic, analyses were performed using SPSS v.29. Results: Of 450 invited, 338 providers completed the survey (response rate 75.1%). Of those surveyed, 64.8% report use of symptom-based screening for asthma in children (n = 219), 42.6% report office spirometry availability (n = 144), and 9.8% report point-of-care FeNO availability (n = 33). Among the participants, 51.2% reported giving personalized asthma action plans on a routine basis. When adjusted for years in practice and setting, multivariable logistic regression showed that having on-site spirometry (adjusted OR 2.09, 95% CI 1.38-3.17, p < .001) and having taken training for asthma in the last 3 years (adjusted OR 1.84, 95% CI 1.22-2.78, p = .004) were significant predictors of guideline-concordant practice (as defined below). Main perceived barriers included lack of equipment/time (68%), inadequate training (54%), and not enough reimbursement (41%). Conclusions: Across the various sites in this urban sample, objective testing (spirometry, FeNO) and systematic use of PAAPs were inconsistently outfitted. On-site spirometry and recent provider training were linked to a higher probability of early detection and intervention in accordance with guidelines. Interventions to improve access to objective testing and educate primary care providers may lead to better early detection and less preventable exacerbations.

Advancing Future Surgeons: Utilization of Surgical Simulation in Medical Student Surgery Clerkships.

The present study aims to examine the factors affecting the digital supply chain in the food retail industry. In terms of purpose, the research is applied, and in terms of data collection, it follows a mixed-methods (qualitative-quantitative) approach. The statistical population in the qualitative phase includes professors, experts in technology and supply chain management, and senior managers in production and research and development within the online food retail industry. In the quantitative phase, the statistical population consists of all managers and employees of online supermarkets in Tehran. The sample size in the qualitative phase was determined based on theoretical saturation (10 participants) using purposive sampling, while in the quantitative phase, a sample of 380 participants was selected through stratified random sampling. Data collection tools included interviews in the qualitative phase and a researcher-developed questionnaire in the quantitative phase. The validity of the questionnaire was confirmed through face and content validity by several experts, convergent validity was verified by calculating the average variance extracted (AVE), and discriminant validity was assessed through the square root of AVE. The reliability of the questionnaire was established using Cronbach’s alpha, which yielded a value of 0.898 for the entire instrument. Data analysis was performed using SmartPLS 3 software. Among the 29 available indicators (items), seven main components were identified. The results indicated that economic factors, social factors, technological factors, environmental factors, managerial factors, organizational factors, and industrial factors were recognized as causal conditions influencing the digital supply chain in the food retail industry. Additionally, the model fit was also evaluated.

The research was carried out to examine the impact of consumer perceptions regarding green products, the appeal of advertising, and pricing on the intention to repurchase Sariayu face masks among consumers in Cimahi City. Data were gathered via an online questionnaire during a single data collection phase (cross-sectional study). The research instrument was subjected to a series of validity and reliability assessments, along with classical assumption tests, and was analyzed through multiple regression techniques. This data collection tool targeted respondents aged 17-31 years who had previously purchased Sariayu brand face masks, which were the primary focus of this investigation. A total of 120 respondents took part in this study. Data analysis was performed using SPSS version 27 software. The results reveal that perceptions of eco-friendly products, the appeal of advertising, and pricing exert a positive and significant effect on repurchase intentions. Furthermore, the sampling method, sample size, and analytical techniques employed can be tailored to align with the characteristics of the research subject. These results are anticipated to offer valuable insights for the management of the research subject in formulating relevant strategies and programs. For future studies, it is suggested to broaden the geographical scope, include respondents from a more diverse age range, and incorporate additional variables such as brand image or green trust.

This study investigates the role of mistakes and affective experiences during online pre-lab activities in predicting students’ situational engagement (conceptualized here as a simultaneous experience of interest, skill, and challenge, i.e. optimal learning moments) in subsequent laboratory sessions in an undergraduate chemistry laboratory course ( n = 256). The data collection followed an ecological momentary assessment design. We specified multilevel structural equation models (MSEMs), including two- and three-level structural equation models, to examine how mistakes impacted students’ situational engagement during pre-lab activities and subsequent laboratory sessions. The findings indicate that mistakes in pre-lab tasks were associated with lower perceived skill and higher experience of challenge during that task, but did not predict students’ interest, skill, challenge, or situational engagement in the subsequent laboratory session. Autoregressive effects from pre-lab activities on students’ situational engagement during lab sessions were observed across all elements of engagement, while skill and challenge during pre-lab activities also predicted higher interest in the subsequent laboratory session. Based on our findings, we propose that while mistakes in the pre-lab activities do not play a significant role in predicting students’ engagement upon entering the laboratory, affective experiences during pre-lab activities can play a significant role in predicting students’ engagement in the laboratory, and that laboratory engagement could be enhanced by providing students with sufficiently challenging pre-lab activities.

This study aims to provide a comprehensive model to assist investors in strategic decision-making amid market uncertainty. Global economic uncertainty characterized by cycles of stagflation and recession has recurred in history and is expected to recur until 2025. This condition encourages the importance of investment strategies that can protect asset values from economic pressures. This study uses a quantitative approach with forecasting methods and risk analysis based on time series data. The data used are daily gold and silver prices from the London Bullion Market Association (LBMA) in USD, collected over a two-year period, namely from January 3, 2023 to January 4, 2025. The data is secondary and obtained from the official LBMA website. The research stages begin with a literature study to understand relevant concepts and methods, followed by data collection, and continued with data preprocessing. The preprocessing stages include checking for outliers, handling missing values using the series mean method, and merging data for temporal consistency. For the forecasting process, the Fuzzy Time Series–Markov Chain method is used, which consists of several steps: the formation of universe and interval sets using the Sturges formula, the definition of fuzzy sets, the fuzzification process, the formation of Fuzzy Logical Relationships (FLR) and Fuzzy Logical Relationship Groups (FLRG), and the preparation of transition probability matrices. The forecasting results are obtained through the defuzzification process, which are then evaluated using the Mean Absolute Percentage Error (MAPE) indicator to assess the accuracy of the model. Risk analysis is carried out using the Value at Risk (VaR) approach using the Extreme Value Theory (EVT) method and the Generalized Pareto Distribution (GPD). The entire analysis process is carried out using Microsoft Excel and RStudio software to ensure accuracy and efficiency in data processing and statistical modeling. This study has succeeded in developing a hybrid Fuzzy Time Series–Markov Chain model to forecast precious metal prices, especially gold and silver, with a very high level of accuracy. Based on an evaluation of various training and testing data proportions, the best model was obtained at a 95:5 ratio, with MAPE values of 0.66% for gold and 1.18% for silver in the training data, and 0.55% and 0.94% in the testing data. These results indicate that the model is able to effectively capture historical price patterns and provide predictions close to the actual value.

Stress has negative effects on University students' mental health and academic progress. The aim of this study was to investigate Greek University students' stress. Specifically, the levels of students' stress, the reactions to stressors, the management strategies, and the impact of stress on student well-being were investigated. The study sample was recruited from the student community of Greek public Universities (all levels of study and from different academic fields). Students were invited to participate in online research on social media and were given the hyperlink leading to a Google Form. Data collection was carried out in November 2020, during the COVID-19 lockdown in Greece. Participating students were asked to complete the following five questionnaires: 1. Questionnaire of demographic and other characteristics (gender, age, level/year of study, smoking habits, marital status, if they work, if they live alone), 2. Student-Life Stress Inventory (Revised-SSI-R), 3. Perceived Stress Scale (PSS), 4. World Health Organisation-Five Well-Being Index (WHO-5), and 5. Brief COPE (an abbreviated version of the COPE Inventory).The study involved 610 students (433 women, 71.0%), aged 18-65years (average age 23.29years, SD 6.77years). Specifically, 478 were undergraduate students (78.4%), 101 Master's students (16.5%), and 31 PhD students (5.1%), 94.1% were unmarried, 9.5% worked, 35.2% were living alone, and 18.4% were smokers. Overall, students' stress was assessed as mild for 149 of the students (24.4%), moderate for 313 students (51.3%), and severe for 148 students (24.3%). Results demonstrated that stressors, such as the feeling of pressure, changes in life, and self-imposed factors threaten normal student life. Also, differences were found in the perception of stress, the reaction to it, its management according to students' gender/year/level of study/work/living conditions, and the negative impact of stress on students' well-being. Linear regression analysis showed that well-being, as measured by the WHO-5, could be predicted by the overall scale of the SSI-R, and more specifically, this impact was negative. A statistically significant function was found [F (1,609)=104.263, p<0.001], R2=0.146. These findings highlight the need for competent counseling and psychological support bodies in the Universities, to reduce the negative stressors, and build students' resilience and stress management skills.

Bayesian hypothesis generation: a probabilistic framework for evaluating novel hypotheses before data collection

This study aims to design an online marketing model for international maritime logistics services based on a business-to-business (B2B) approach, focusing on identifying digital adoption barriers and proposing strategic solutions to enhance marketing performance. The study employed a descriptive–analytical mixed-method design integrating both quantitative and qualitative approaches. The research population included marketing managers, digital transformation specialists, and B2B clients from international maritime logistics companies. Data collection was carried out using standardized questionnaires and semi-structured interviews. The quantitative data were analyzed through SPSS using descriptive statistics, correlation tests, and multiple regression analysis to determine the predictive relationships between digital readiness, organizational capability, technological barriers, management commitment, and online marketing performance. The qualitative data, collected through interviews with industry experts, were analyzed using NVivo software and thematic coding to identify key patterns and themes related to digital transformation, organizational behavior, and strategic innovation within maritime marketing contexts. The regression results revealed that digital readiness (β = 0.364, p &lt; 0.001), organizational capability (β = 0.286, p &lt; 0.01), customer trust (β = 0.195, p &lt; 0.01), and management commitment (β = 0.244, p &lt; 0.01) significantly and positively predicted online marketing performance, while technological barriers had a negative effect (β = -0.221, p &lt; 0.01). The overall model explained 50.8% of the variance in marketing performance (R² = 0.508). Qualitative results identified six central themes: technological barriers, organizational barriers, customer challenges, economic and regulatory constraints, strategic enablers, and innovation opportunities. Effective online marketing in maritime logistics depends on strengthening digital readiness, organizational capability, leadership commitment, and customer trust while mitigating technological and structural barriers. The proposed model offers a strategic framework for achieving digital transformation and sustainable competitiveness in B2B maritime marketing.

Recreational cannabis use is the driving factor for participation in medical cannabis trials in inflammatory rheumatic diseases.