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The mediating role of attitudes in the effect of human and environment-centered value orientation on green cosmetic product purchasing behavior: comparison of different countries
No full textThe growing concern for environmental issues has reached a critical level, demanding immediate attention. With the widespread integration of cosmetic products into daily routines, the escalating environmental impact cannot be ignored. Consumers are increasingly scrutinizing the environmental footprint of their purchases and the practices of manufacturing companies. To maintain their competitive edge, businesses must proactively understand and address the evolving needs and expectations of their consumers. This study aims to reveal the relationships between consumers’ environmental values, attitudes toward green products, and green cosmetics purchasing behaviors to guide decision-makers in the cosmetics industry. In addition, the study also evaluated the impact of consumers’ cultural backgrounds on their attitudes toward green products and their purchasing behaviors in the green cosmetics sector. We conducted a comprehensive online survey by collecting primary data from 883 respondents from 10 countries in Asia and Europe. Our analysis used partial least squares (PLS) with SMART PLS to comprehensively examine the interrelationships between variables. An ecocentric orientation positively influences attitudes and purchasing behavior toward green cosmetic products, while an anthropocentric orientation has a negative effect. Additionally, consumers’ attitudes mediate the impact of environmental attitudes on purchasing behavior and counteract the negative effect of anthropocentric attitudes. Cultural differences significantly impact consumers’ attitudes and purchasing behavior for green cosmetics. The study’s findings offer valuable insights for cosmetic companies dealing with growing demand for cosmetic products and environmental awareness. The empirical support for existing literature is significant, and the study’s structural model enriches the green cosmetics literature. It’s essential to consider both consumers and producers for sustainable green cosmetic production, ensuring profitability and encouragement for producers. © The Author(s) 2025.5917189920
A Comparative Study of 137Cs Dose Factors for Constant and Depth-dependent Soil Densities
No full textAccurate assessment of external radiation dose rates from 137Cs is essential for evaluating radiological risk in environmental and occupational settings. This study refines dose conversion coefficient calculations by incorporating depth-dependent soil density and addressing limitations in conventional methods that assume constant soil density. We calculated dose conversion coefficients for 137Cs in soil, considering both exponential and Gaussian distributions of activity concentration. Using two models, one with constant density and another with variable density as a function of depth, we compared dose rates to quantify the effect of soil density variations. Results indicate that dose rates are consistently higher when depth-dependent density is applied. The effect is more pronounced when 137Cs activity is distributed over larger depths (i.e., greater relaxation lengths) or when broader Gaussian distributions are considered. This suggests that assuming constant soil density may lead to underestimations of dose rates, especially in heterogeneous or compacted soils. Our findings emphasize the importance of accounting for density variability in dose calculations to enhance radiological risk assessments for areas contaminated with 137Cs.Health Phys. 129(0):000-000; 2025.4034127
Nursing students' cybersecurity practices and perceptions and cybersecurity crime awareness: A cross-sectional study
No full textBackground: Cybersecurity has become a critical issue with the increasing use of digital platforms in healthcare. Understanding nursing students' cybersecurity practices, perceptions, and cybercrime awareness is essential for improving healthcare security and developing strategies to mitigate cyber threats.
Aim: This study aimed to determine nursing students' cybersecurity practices, perceptions, and cybercrime awareness.
Design: A descriptive cross-sectional design was used.
Setting: The study was conducted between April and June 2024 at a School of Nursing within a public university in Türkiye.
Participants: A total of 434 undergraduate nursing students participated in the study.
Methods: Data were collected face-to-face using a paper-and-pencil technique. The data collection tools used included the Personal Information Form, Cyber Security Scale (CSS), and Cyber Crime Awareness Scale (CAS). Data analysis utilised descriptive statistical methods, Pearson correlation analysis, independent samples t-test, ANOVA, and linear regression analysis.
Results: The study revealed that 92.9 % of the students had not received any prior cybersecurity education. The mean CSS score was 87.50 ± 11.40, and the mean CAS score was 174.75 ± 36.75. A moderate positive correlation was found between the CSS and CAS scores (r = 0.576, p < 0.01). A positive relationship was found between computer usage skills and CSS scores (r = 0.190, p < 0.01), while a weak negative correlation was observed between internet usage duration and CSS scores (r = -0.095, p < 0.05). No relationships were identified between the CSS score and age, gender, or cybersecurity education. Linear regression analysis showed that higher computer usage skill levels were significantly associated with increased CSS scores (B = 1.129, p < 0.001).
Conclusions: The findings highlight the importance of integrating cybersecurity education into the nursing curriculum. Enhancing cybersecurity awareness and practices may help protect patient data and support safer healthcare by better preparing nursing students for cyber threats.4055507
A strategy for the one-pot direct production of 5-hydroxymethylfurfural (HMF) from sucrose using organic weak acid in situ as a catalyst
No full text5-Hydroxymethylfurfural (HMF), which can be produced in high yields by acid-catalyzed dehydration of carbohydrates, is used as a feedstock in biofuel and biopolymer production. However, these acidic catalysts are usually added externally, which increases HMF production costs. Furthermore, other important parameters for high yields of HMF production are raw material selection, reaction medium, and separation-purification processes. The aim of this study is to produce HMF in one-pot without the addition of any exogenous catalyst by evaluating the fructose remaining in the production medium in which the glucose from sucrose hydrolysis is microbially fermented into gluconic acid (GA). HMF production from fructose in a biphasic system consisting of a fermentation liquid containing GA and fructose and 2-Methyltetrahydrofuran (2-MeTHF) was optimized using Central Composite Design (CCD). The variables examined included reaction time, reaction temperature, solvent type, salt type, and organic/aqueous phase ratio. In the study, by chelating GA and calcium ions, the low degree of ionization of GA was increased, and HMF was produced in high yields without the addition of a catalyst. Under optimum conditions, using 2-MeTHF/ fermentation liquid (6:1) and 7.56 wt% CaCl2, the yield of HMF produced in pressurized (hydrothermal) vessels at about 147 °C for 158 min reached 77.6 % of the theoretical yield. The amount of GA in the production medium was 92.64 % preserved from degradation. GA and HMF in different phases of the biphasic system were purified from the production medium in high yields. Furthermore, 2-MeTHF, which constitutes the organic phase of the biphasic system, was recovered at approximately 95 % by vacuum evaporation. As a result, HMF production from sucrose, which is a more economical and abundant raw material compared to glucose and fructose, was realized in one-pot without the addition of any catalyst. © 2025 Elsevier Lt
The effect of disaster resilience and trauma exposure on PTSD, depression, and sleep disorder among healthcare workers involved in the Kahramanmaraş Earthquakes (2023): a structural equation model
No full textHealthcare workers (HCWs) serve as the cornerstone of health services, which are among the primary needs during disasters. The chaotic environment caused by disasters can lead to mental health disorders in HCWs, similar to those experienced by disaster victims. Experiencing mental health disorders can hinder HCW's professional approach to intervention. HCWs should not be overlooked for the possibility of experiencing mental health disorders while providing healthcare services during disasters. Therefore, this study aims to examine the impact of psychological resilience and trauma exposure on PTSD, depression, and sleep disorders among HCWs involved in the Kahramanmaraş Earthquakes, which were Turkey's most devastating earthquakes. In this quantitative research, a survey technique was employed, reaching 642 hCWs involved in the Kahramanmaraş Earthquakes. Structural Equation Modeling (SEM) was used to test the impact of variables on each other. According to the SEM results, trauma exposure in the HCWs had a significant and positive effect on PTSD (β=+0.899, p = 0.000), depression (β=+0.685, p = 0.000), and sleep disorders (β=+0.603, p = 0.000). Psychological resilience had a significant and negative effect on PTSD (β=-0.278, p = 0.004) and depression (β=-0.322, p = 0.008). Surprisingly, psychological resilience had a significant and positive effect on sleep disorders (β=+0.692, p = 0.000). In conclusion, while trauma exposure led to PTSD, depression, and sleep disorders in the HCWs, psychological resilience mitigated PTSD and depression. Unexpectedly, psychological resilience increased sleep disorders. For this reason, it is recommended that future studies investigate in detail the reasons why HCWs experience sleep disorders and examine them in depth.3992167
A hybrid decision support system for transport policy selection: A case study on Russia's Northern Sea route in Artic region
No full textThe selection of transport policies by countries is critical both for international relations and for the implementation of effective and sustainable transport practices. Countries consider numerous parameters when deciding on transport policies. This study aims to develop a Decision Support System (DSS) to assist countries in selecting transport policies and to demonstrate its applicability. The primary motivation of the research is to identify transport policies for Russia's Arctic region and to determine the best alternative policy based on expert evaluations. For this purpose, the IF-SIWEC-ARLON (Intuitionistic Fuzzy Sets - Simple Weight Calculation - Alternative Ranking Using Two-Step Logarithmic Normalization) hybrid method was developed as a DSS in this study. To implement this hybrid method, a decision model incorporating experts, criteria, and transport policies was first established. The contribution levels of experts to the decision-making process were calculated using IF sets. Next, the criteria weights were determined using the IF-SIWEC approach. Finally, the ranking of transport policies was carried out using the IF-ARLON method. An algorithm representing the application of this hybrid method was developed and applied to a case study focusing on Russia's transport policy selection process of Northern Sea Route (NSR) in the Arctic region. The results supported the successful application of the IF-SIWEC-ARLON hybrid method. Its robustness was tested through sensitivity analysis scenarios, while the consistency of the findings was verified via comparative analyses. The study concluded that the best transport policy for Russia's Arctic region is "Improved regulatory control with strategic international partnerships", with the most influential criterion in the decision process identified as "State interests, security, and sovereignty.". © 2025 Elsevier Lt
Regorafenib Treatment for Recurrent Glioblastoma Beyond Bevacizumab-Based Therapy: A Large, Multicenter, Real-Life Study
No full textBackground/Objectives: In the REGOMA trial, regorafenib demonstrated an overall survival advantage over lomustine, and it has become a recommended treatment for recurrent glioblastoma in guidelines. This study aimed to evaluate the effectiveness and safety of regorafenib as a third-line treatment for patients with recurrent glioblastoma who progressed while taking bevacizumab-based therapy. Methods: This retrospective, multicenter study in Turkey included 65 patients treated between 2021 and 2023 across 19 oncology centers. The main inclusion criteria were histologically confirmed isocitrate dehydrogenase (IDH)-wildtype glioblastoma, progression after second-line bevacizumab-based treatment, and an Eastern Cooperative Oncology Group (ECOG) performance status score of <= 2. Patients received regorafenib 160 mg once daily for the first 3 weeks of each 4-week cycle. Results: The median age of the patients was 53 years (18-67 years), with a median progression-free survival of 2.5 months (95% Confidence Interval: 2.23-2.75) and a median overall survival of 4.1 months (95% CI: 3.52-4.68). The median overall survival was improved in patients who received subsequent therapy after regorafenib treatment compared with those who did not (p = 0.022). Progression-free survival was longer in patients with ECOG 0-1 than in those with ECOG 2 (p = 0.042). The safety profile was consistent with that of the REGOMA trial, with no drug-related deaths observed. Conclusions: Regorafenib shows good efficacy and safety as a third-line treatment for recurrent glioblastoma after bevacizumab-based therapy. This study supports the use of regorafenib and emphasizes the need for further randomized studies to validate its role and optimize treatment strategies
Incorporation of ultrasound-assisted treatment in cheese to accelerate ripening of Kaşar cheese: Changes in cheese microbiota, proteolysis, enzyme activities and volatile profiles
No full textThis study aimed to assess the impact of using LAB strains (Lactobacillus helveticus DPC 4571 and Lactobacillus casei ATCC 334) directly or after sonication on the quality and ripening of Kaşar cheese. Experimental cheeses were produced with a combination of cheese starters (Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus), adjuncts and sonicated cultures. The cheeses showed remarkable variations in titratable acidity and pH. The bacterial counts showed that sonication was effective on adjunct cultures, causing faster and stronger autolysis of L. helveticus DPC 4571 and an increase in L. casei ATCC 334 during ripening. Cheeses containing adjunct cultures exhibited higher proteolysis, with increased hydrolysis of αs1- and β-caseins. Aminopeptidases activities increased during ripening by adjuncts and sonicated-adjunct cultures. The use of adjunct culture resulted in a greater increase in the free amino acid content. Adjuncts and sonicated-adjunct cultures increased both the quantity and diversity of some volatiles. In conclusion, the inclusion of sonicated culture in the starter system offered a potential approach to accelerate cheese ripening and improve cheese quality. © 2025 Elsevier Ltd4017949
Advancing sea level anomaly modeling in the black sea with LSTM Auto-Encoders: A novel approach
No full textRising sea levels pose significant risks to coastal communities and ecosystems. Accurate modeling of sea level changes is crucial for effective environmental management and disaster mitigation. Machine learning methods are emerging as an important asset in improving sea level predictions and understanding the impacts of climate change. Especially, Long Short-Term Memory (LSTM) models have emerged as a powerful tool for sea level anomaly modeling, but there is an increasing need for more advanced models in this area. This study enhances existing methodologies by introducing a novel approach using an LSTM Auto-Encoder model, designed to compress input data into a lower-dimensional latent space before reconstructing it, thereby capturing complex temporal dependencies and anomalies effectively. We compared LSTM Auto-Encoder model performance with that of a Stacked LSTM network, which learns complex temporal patterns through multiple layers, and a traditional damped-persistence statistical model. Our results demonstrate that the LSTM Auto-Encoder model not only outperformed these models in predicting sea level anomalies across various lead times but also exhibited superior generalization capabilities across both satellite altimeter and in-situ data. These findings highlight the potential of the LSTM Auto-Encoder model as a powerful tool in coastal management and climate change studies, underscoring the critical role of advanced machine learning techniques in enhancing our predictive abilities and informing disaster preparedness strategies. © 2024 Elsevier Lt
[Türkiye'deki Son Sınıf Hemşirelik Öğrencilerinin Beyin Göçüyle İlgili Görüşleri: Nitel Bir Çalışma]
No full textObjective: This study was aimed at investigating the brain drain-related perspectives of nursing students who wanted to emigrate in depth. Methods: The study has the descriptive qualitative research design. The population of the study comprised 13 senior nursing students studying at the Faculty of Nursing of a university and intending to emigrate abroad. Data were collected through face-to-face individual in-depth interviews. The interviews were audio-recorded after the participants’ permission was obtained. This study adopted a qualitative research design based on inductive content analysis to explore the participants’ perspectives in depth. Results: In this study, three themes emerged from the students' opinions: "Perception of brain drain", "Motivation behind brain drain" and "Challenges". The first theme reflected students’ varied views on migration, including both positive and negative perceptions, a growing social trend towards emigration, and feelings of hopelessness and dissatisfaction with nursing in Türkiye, leading to the formation of personal migration plans. The second theme captured motivations such as the influence of relatives and role models abroad, favourable personal conditions, attractive opportunities overseas, language efforts, and openness to new experiences. The final theme focused on challenges, including push factors like poor working conditions and low wages, barriers such as language and time constraints, and emotional concerns such as loneliness and cultural adjustment. Conclusion: The study showed that nursing students’ intention to emigrate stems from mixed perceptions of brain drain, strong personal and social motivations, and various emotional and structural challenges. Addressing these issues may help mitigate future nurse migration. © 2025, Ataturk Universitesi. All rights reserved.2-s2.0-10501877413