Search Results (464)

This study investigates the influence of driver-related factors on on-street parking choice by integrating stated preference (SP) and revealed preference (RP) survey methods. A hybrid SP–RP survey was designed to simulate realistic parking scenarios, and 423 valid questionnaires were collected online and offline. Key factors affecting parking choice were identified through descriptive analysis, including user acceptance of differentiated pricing and satisfaction with existing policies. The Kaiser–Meyer–Olkin (KMO = 0.904) and Bartlett’s test (p < 0.001) confirmed data suitability for factor analysis. A binary logistic regression model was developed to quantify variable effects under different travel purposes. Key findings include the following: monthly parking fee had the strongest effect (OR = 6.691, p = 0.010) on parking choice for shopping/entertainment trips; model prediction accuracy ranged from 80.87% to 83.56% across travel purposes; and goodness-of-fit metrics were strong (McFadden R² = 0.630, Nagelkerke R² = 0.772). The results provide empirical evidence on parking choice determinants and support the design of demand-responsive parking policies through dynamic and differentiated pricing strategies. Full article

Stated preference surveys have been utilized in the field of sustainable multimodal transport planning for some time. Stated preference, which relies on hypothetical scenarios to determine user preferences, offers critical insights into travelers’ choices between different transport modes. The stated preference method is used in a wide range of transport studies, such as the mode choice, route choice, service attribute analysis, pricing and fare policies, and technical innovations. On the basis of the collected data in stated preference studies, it is possible to optimize current services, forecast future demand, or analyze the possibilities of nonexistent services. A literature review reveals that there are certain gaps regarding the calibration of utility functions in multimodal and new services. Full article

Background/Objectives: Multi-cancer detection (MCD) tests have emerged as a promising tool to redefine the landscape of early cancer detection. Implementation of this novel technology will likely fall to primary care clinicians (PCC). The purpose of this study is to characterize and explore differences in PCCs perceptions and preferences towards MCD testing. Methods: Between March and May of 2023, this cross-sectional survey was administered to 281 PCCs, including physicians and advanced care providers practicing within an integrated healthcare system spanning five states. The survey collected data on self-reported characteristics, perceptions of MCD testing, and preferences for learning about MCD testing. Analysis was limited to those with no prior experience with MCD testing (N = 181, response rate 22.8%). Descriptive statistics summarized key variables and chi-square tests assessed differences in perceptions and preferences by key characteristics. Results: Most PCCs were interested in MCD testing (66.3%), but limited knowledge/awareness of MCD testing and confidence to manage patients with a positive test were observed, along with concerns around cost (76.7%) and misuse/poor implementation. The primary preferences for learning about MCD testing were online courses or classroom instruction (64.5%). Significant differences in perceptions and preferences for learning were observed by location, degree, and years in practice. Conclusions: PCCs in our study held positive views towards MCD testing, but gaps and variation in knowledge and confidence towards MCD testing and concerns around the cost and misuse/poor implementation were observed. While efforts to train and educate all PCCs on MCD testing is a critical first step, more research is needed to understand how best to support implementation tailored to individual and system-level needs and characteristics. Full article

Seasonal variation not only influences the ecological functioning of forest ecosystems but also alters the benefits people derive from them, ranging from physical to psychological restoration. Ignoring such variation in valuation risks, thereby producing seasonally biased and unreliable estimates, constitutes an issue that previous valuation studies have largely overlooked. This study investigates the extent of seasonal bias in willingness to pay (WTP) for forest ecosystem services using a meta-dataset of 476 observations from Korea. Applying pooled, weighted, and robust mixed-effects models, we uncovered substantial seasonal heterogeneity through our analysis: WTP increases by 67% in autumn but declines by 18% and 65% in the summer and winter, respectively. The robust mixed-effects model provided the best empirical fit, highlighting the methodological value of explicitly modeling temporal effects in meta-regression. These results reflect seasonally differentiated engagement with forests and suggest that individuals implicitly recognize the temporal value of ecosystem services. Nevertheless, limitations remain, particularly the heterogeneity of research designs, survey methods, and elicitation formats, which may introduce variability and potential bias. Therefore, while seasonal differences are statistically significant, the results should be interpreted with caution. Extrapolating annual values from single-season data risks systematic distortion, especially when stated preference methods are used. Beyond methodological implications, the findings also underscore a broader point: seasonal rhythms shape not only ecosystem dynamics but also human well-being. Accurately valuing these shifting benefits is essential for making credible economic assessments and sustaining long-term reciprocity between people and nature. Full article

Honey has a long cultural tradition in Italy, valued for its sensory properties and health benefits. However, in recent years, the beekeeping sector has faced various challenges due to climate change, biodiversity loss, and economic pressures. Therefore, growing consumer awareness of sustainability, traceability, and ethical aspects is influencing food choices and putting niche-market products, such as honey, in the spotlight. This research analysed data from an online survey of Italian consumers to examine their attitudes toward honey. The analysis focused on the primary drivers of consumer behaviour, the state of sustainability efforts, and the importance of certifications and traceability in influencing preferences. The results showed that, beyond taste and health considerations, Italian consumers expressed a strong sensitivity and awareness of the beekeeping sector’s needs and their high engagement in ethical issues, food quality, safety and certification standards, and environmental protection. These findings provide useful insights for producers and policymakers to promote sustainable beekeeping and enhance consumer trust by implementing targeted communication strategies and certification schemes. Full article

In recent years, pet owners have been making significant efforts to ensure the well-being of their animals. One straightforward approach to enhance their welfare involves greater attention to nutrition. Consequently, pet owners have become increasingly sensitive regarding the food they provide for their pets. The aim of this study is to examine this sensitivity by investigating the extent to which cat and dog owners prioritize quality attributes and ingredients when selecting pet food. For this purpose, a survey was conducted with pet owners (n = 519) who visited veterinary clinics, using a random sampling method. Of the participants, 51.25% were male and 48.75% were female, with the highest participation observed in the 18–34 age group (60.50%). Among the respondents, 64.93% owned cats, while 35.07% owned dogs. Additionally, 66.67% of pet owners reported using both wet and dry food. When selecting pet food, 42.58% of participants stated that they relied on recommendations from veterinarians. “Pet preference (palatability)” clearly emerged as the most important selection criterion, receiving the highest average score (3.90) and highlighting its decisive influence on purchasing decisions as owners appeared to prioritize what their pets liked most. In contrast, “visual appeal of the food” received the lowest score (2.47). A significant difference (p < 0.001) was found regarding whose recommendation influenced the purchase decision. Moreover, a negative correlation was identified between price sensitivity and other selection criteria, suggesting that price was not a primary driver in most participants’ decisions and became less important as quality-related factors gained priority. Overall, pet preference (palatability) stood out as the dominant driver of purchasing decisions. Full article

Undergraduate engineering programs are associated with high stress and heavy workloads that impact the wellness of students. One university offers a unique undergraduate engineering education program structure offering two degree plan lengths, four or five years, with both involving the same number of courses. These options offer an opportunity for students to select the degree plan length they prefer. The purpose of this research is to explore the motivations for selecting degree plan length and how plan length may be associated with student wellness. An ethics-approved survey of 189 undergraduate students was conducted. Participants responded to a variety of questions that asked about their motivations for selecting their degree plan length and their state of wellness. Mann–Whitney U tests, thematic analyses, and chi-squared tests were used to analyze quantitative and qualitative responses. Results highlighted that there were statistically significant differences (p ≤ 0.001) between the motivators (4 of 8 factors) and perceived wellness (3 of 5 indicators) of students on the four- vs. five-year plans. Overall, it appears that each degree plan length may serve different student needs. Additionally, results suggest that the five-year plan offers the opportunity to reduce the workload and correlates with a better state of perceived wellness. Full article

Fossils are portals to the past, providing researchers with vital information about the evolution of life on Earth throughout the geological eras. The present study synthesizes the recent trends in fossil research, emphasizing the most common techniques found in the specialized literature over the past 20 years. The bibliographic survey revealed that destructive methods continue to play a significant role in scientific production related to this topic, particularly in studies on 3D morphologies, diagenesis, nutritional ecology, dating, elucidating dietary or habitat preferences, or understanding the physiology of extinct species. However, noninvasive tools, such as Raman spectroscopy, are rapidly rising, particularly when integrated with imaging techniques. As such, fossil research continues to advance even beyond the borders of our planet, exploring extraterrestrial samples in a quest to unlock the universal mystery of life. At the same time, the advent of advanced AI methods—particularly model chatbots that rival the capabilities of experienced scientists—has facilitated and enhanced data interpretation and classification. As fossil research evolves, upcoming technological advancements in spatial resolution, penetration depth, and detection sensitivity will integrate state-of-the-art spectroscopic tools. This will undoubtedly take fossil research to new heights, generating breakthroughs that optimize analysis while preserving invaluable specimens. Overall, the present study offers a holistic overview of analytical techniques through meta-analysis and bibliometric mapping, including a critical assessment of commonly used methods and offering a glimpse into the integration of machine learning and AI tools in fossil research. Full article

This research examines road users’ willingness to pay for enhanced active mobility infrastructure at King Mongkut’s Institute of Technology Ladkrabang (KMITL), a suburban university campus in Bangkok, Thailand. The study addresses the need for sustainable transportation solutions in middle-income urban environments by analyzing factors that influence walking and cycling adoption among university community members. The research employed a comprehensive mixed-methods framework combining qualitative SWOT analysis, a stated preference survey of 400 participants, and regularized logistic regression modeling with cross-validation. The analysis revealed that specific infrastructure improvements significantly increase the likelihood of active mobility adoption. Rest areas demonstrated the strongest positive association (OR = 2.15, 95% CI: 1.08–4.27, p = 0.029), followed by CCTV security systems (OR = 1.89, 95% CI: 0.98–3.65, p = 0.047), and improved public transport connectivity (OR = 2.84, 95% CI: 1.42–5.68, p = 0.003). Demographic analysis uncovered notable resistance patterns, with male participants (OR = 0.48, 95% CI: 0.26–0.89, p = 0.020) and higher-income individuals showing reduced willingness to transition from motorized transportation. Using the Contingent Valuation Method with proper bias mitigation strategies, the study quantified potential behavioral changes, projecting a 12–18 min daily increase in active mobility engagement. This enhancement would generate measurable health benefits valued at 2840–4260 THB per person annually using WHO-HEAT methodology. The research contributes valuable insights to the limited body of active mobility literature from Southeast Asian suburban contexts, providing a replicable framework for similar investigations. Full article

Agricultural workers—individuals employed for labor in agriculture—are at high risk of various negative health outcomes, with many impacted by both the existence of health disparities and stress. While the issue of farm stress and associated psychosocial health outcomes has been studied in the general agricultural population, research investigating these issues specifically within the agricultural worker population is sparse. This study presents data from the United States Western Region Agricultural Worker Stress Survey (N = 354), which gauged workers’ perceived stress levels, sources of stress, desired stress management topics, and preferred methods of receiving information and support services. Long working hours, working in extreme temperatures, and a lack of time emerged as the top three stressors. On average, workers across the Western region of the U.S. are experiencing a moderate level of stress, with younger workers reporting greater stressor pileup than their older counterparts. Retirement planning was cited as the most preferred stress management topic, regardless of demographic. Lastly, workers chose in-person counseling as the support modality that they would most likely utilize. This research provides a variety of stress management recommendations such as working with farm owners to increase the safety of their operation, investing in face-to-face counseling services, and utilizing community health workers as sources of support. Full article

Since its introduction into the USA, the spotted lanternfly (SLF), Lycorma delicatula, (White) (Hemiptera: Fulgoridae) has spread across the landscape relatively unchecked. With a wide host range, it is considered a serious pest of native forest species, as well as agricultural crops. Circle traps placed on Ailanthus altissima (Miller) Swingle (Sapindales: Simaroubaceae) are passive traps collecting SLF as they walk up and down the tree trunk. These traps are successful at detecting new populations of SLF, but this can be challenging to implement at a large scale due to costs and host availability. To improve and facilitate SLF trapping practices, we investigated three key trapping components: improved collection containers, placement on alternative hosts, and lure (methyl salicylate) impact. In initial trials comparing collection jars to removable plastic bags, the adult SLF catch was four times higher using the bag design. In a multi-state survey at varying population densities, the bag traps were comparable to the jar traps but were significantly more effective than BugBarrier^® tree bands, especially during the adult stage. Catch and detection in circle traps placed on alternative hosts, Acer spp. L. (Sapindales: Sapindalaceae) and Juglans nigra L. (Fagales: Juglandaceae), were comparable to those placed on the preferred host A. altissima, especially in the earlier life stages. Additionally, detection rates of methyl salicylate-baited traps on all three hosts were comparable to those on non-baited traps. These results suggest that circle traps fitted with bags provide higher trap catch and an improvement in sample quality. In addition, circle traps were equally effective when placed on maple and black walnut, while methyl salicylate lures do not enhance trap catch or detection. Full article

Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the inability to ensure patient data protection. In this work, we present a secure, custom-designed retrieval-augmented generation (RAG) LLM system deployed entirely within our institution and tailored for radiology research. Radiology researchers at our institution evaluated the system against GPT-4-Consensus through a blinded survey assessing factual accuracy (FA), citation relevance (CR), and perceived performance (PP) using 5-point Likert scales. Our system achieved mean ± SD scores of 4.15 ± 0.99 for FA, 3.70 ± 1.17 for CR, and 3.55 ± 1.39 for PP. In comparison, GPT-4-Consensus obtained 4.25 ± 0.72, 3.85 ± 1.23, and 3.90 ± 1.12 for the same metrics, respectively. No statistically significant differences were observed (p = 0.97, 0.65, 0.42), and 50% of participants preferred our system’s output. These results validate that secure, local RAG-based LLMs can match state-of-the-art performance while preserving privacy and adaptability, offering a scalable tool for medical research environments. Full article

Plastic is extensively used in nursery and greenhouse operations. Concerns are growing about the potential release of plastic byproducts, such as microplastics and per- and poly-fluoroalkyl substances (PFAS), into water resources. The purpose of this study was to (1) compare perceptions of plastic use and water quality impacts between scientists researching water contaminants and nursery/greenhouse growers, (2) identify barriers to growers reducing plastic use, and (3) explore preferred communication channels for scientists to inform growers about emerging research. An online survey was administered to collect data from scientists in a USDA-funded multi-state Hatch project (N = 20) and nursery/greenhouse growers (N = 66) across the United States. The findings indicated both groups were unsure of the impacts of plastic use. While most respondents perceived surface water pollution as a critical issue, neither scientists nor growers strongly agreed on-farm plastic use poses a significant threat. Both groups recognized the importance of regular water testing, but few believed mandatory changes to plastic use should be enacted without further evidence. Growers cited limited equipment, financial constraints, and uncertain availability of viable plastic alternatives as key barriers. Despite these barriers, growers were willing to learn more, primarily through online resources, short courses, and workshops. The findings underscore the need for targeted research that quantifies plastic byproducts in nursery/greenhouse water and identifies cost-effective alternatives. Timely dissemination of scientific findings using trusted sources will be critical to bridge knowledge gaps and support adoption of best practices to safeguard water quality in surface and groundwater. Full article

The rapid evolution of the transport industry requires a deep understanding of user preferences for emerging mobility solutions, particularly carsharing (CS) and autonomous vehicles (AVs). This study employs machine learning techniques to model transport mode choice, with a focus on traffic safety perceptions of people towards CS and privately shared autonomous vehicles (PSAVs). A stated preference (SP) survey is conducted to collect data on travel behavior, incorporating key attributes such as trip time, trip cost, waiting and walking time, privacy, cybersecurity, and surveillance concerns. Sociodemographic factors, such as income, gender, education, employment status, and trip purpose, are also examined. Three gradient boosting models—CatBoost, XGBoost, and LightGBM are applied to classify user choices. The performance of models is evaluated using accuracy, precision, and F1-score. The XGBoost demonstrates the highest accuracy (77.174%) and effectively captures the complexity of mode choice behavior. The results indicate that CS users are easily classified, while PSAV users present greater classification challenges due to variations in safety perceptions and technological acceptance. From a traffic safety perspective, the results emphasize that companionship, comfort, privacy, cybersecurity, safety in using CS and PSAVs, and surveillance significantly influence CS and PSAV acceptance, which leads to the importance of trust in adopting AVs. The findings suggest that ensuring public trust occurs through robust safety regulations and transparent data security policies. Furthermore, the envisaged benefits of shared autonomous mobility are alleviating congestion and promoting sustainability. Full article

Anxiety disorders, as common neurological diseases in clinical practice, often coexist with depression. Epidemiological surveys indicate that approximately 85% of patients with depression exhibit significant anxiety symptoms. This comorbid state not only exacerbates clinical symptoms but also leads to treatment resistance and prolonged disease duration. This study innovatively developed a compound aromatic plant essential oil (EO) formulation with remarkable anxiolytic and antidepressant effects and systematically elucidated its mechanism of action. The study found that the essential oil formulation, administered via inhalation, could significantly improve behavioral abnormalities in animals subjected to the chronic unpredictable mild stress (CUMS) model, specifically manifesting as (1) the reversal of stress-induced weight gain retardation; (2) a significant increase in sucrose preference; (3) an increase in the total distance of spontaneous activity; and (4) the prolongation of exploration time in the open arms of the elevated plus maze. Neuropathological examinations confirmed that the formulation could effectively protect the structural integrity of hippocampal neurons and alleviate CUMS-induced neural damage. In terms of mechanism of action, the study revealed that the formulation regulates the neurotransmitter system through multiple targets: (1) the upregulation of serotonin (5-HT) and γ-aminobutyric acid (GABA) levels; (2) the downregulation of glutamate (GLU) concentration; and (3) key targets identified via network pharmacological analysis, such as ESR1, STAT3, and PPARG. These findings provide molecular-level evidence for understanding the neuromodulatory effects of aromatic essential oils. Pharmaceutical formulation studies showed that the oil-in-water (O/W) type compound essential oil microemulsion, prepared using microemulsification technology, has a uniform particle size and excellent stability, maintaining stable physicochemical properties at room temperature for an extended period, thus laying a foundation for its clinical application. This study not only validates the practical value of traditional medicine but also provides new ideas for the development of novel anxiolytic and antidepressant drugs, achieving an organic integration of traditional experience and modern technology. Full article