Search Results (310)

Soils are critical natural resources, yet their abiotic contributions to ecosystem services remain largely unexplored in valuation studies. This pilot study represents, to the best of our knowledge, the first attempt to assess the perceived value of geosystem services (GSs) from a consumer perspective. Using a discrete choice experiment with 200 respondents, we evaluated preferences for Rotella apples and Moscatello wine through mixed multinomial logit and latent class models. Results show that attributes related to soil use and soil control were consistently significant drivers of consumer utility (e.g., odds ratios of 9.38 and 5.78 for Moscatello wine and 8.46 and 5.56 for Rotella apples, respectively; p < 0.01). These attributes align more closely with the concept of a “geological fingerprint” than with existing geographical labeling schemes such as the Protected Designation of Origin. Price effects were statistically insignificant, indicating virtually no influence on choices. Both estimated models revealed preference heterogeneity and a substantial number of no-buy responses. This suggests both limited consumer familiarity with GS concepts and a limitation of our attribute descriptions, which likely failed to convey information needed for effective purchasing decisions. This study is exploratory and limited by its convenience sample, imperfect price specification, and inability to estimate willingness-to-pay measures. Nevertheless, it provides empirical support for introducing geological footprint labeling and highlights the need for improved consumer information, policy tools, and public campaigns to promote recognition and sustainable management of geodiversity in agriculture. Full article

The accelerating pace of urbanization and population growth has increasingly disrupted the relationship between the built environment and nature, contributing to a decline in individuals’ psychological and spiritual well-being. Religious buildings—particularly mosques—have the potential to serve as restorative environments that support spiritual reflection and emotional balance. Within this context, the integration of biophilic design principles into sacred architecture has emerged as a significant area of contemporary research. This study investigates how biophilic elements in mosque interiors influence individuals’ perceptions of spirituality and the restorative quality of the space. Mosque interior images were generated using artificial intelligence tools (Midjourney V6.1 and DALL·E 3), informed by biophilic design theory. Grounded in Attention Restoration Theory (ART), the study utilized a quantitative research framework to explore how biophilic elements influence spiritual and restorative perceptions in sacred architectural spaces. Data were collected from 390 individuals in Türkiye with prior worship experience in Republican-era mosques—structures marked by ideological and spatial transformations. Two instruments were used, the Perceived Restorativeness Scale (PRS) and the newly developed Biophilic Spiritual Perception Scale (BSPS), which demonstrated high internal consistency (Cronbach’s α = 0.981). Analyses included exploratory and confirmatory factor analyses, as well as parametric and non-parametric statistical tests. Findings suggest that biophilic design in mosque interiors positively influences both spiritual experience and perceived environmental restorativeness. These results support the view that biophilic design should not be seen merely as an esthetic or ecological approach but as a multidimensional strategy that enhances the emotional and spiritual quality of sacred spaces. Implications for future mosque design in Türkiye are discussed. Full article

Objective: To evaluate patient perceptions regarding ultrasound-guided embryo transfer, visualisation of embryos prior to transfer, and continuity of care with the same physician during in vitro fertilisation (IVF) treatments. Setting: Between January and September 2023, this study was conducted at the IVF MOMO’ FertiLIFE centre in Bisceglie, Italy. Design: Descriptive and observational study based on an anonymous survey administered to IVF patients at the time of embryo transfer. The goal was to assess the subjective emotional and psychological response to selected procedural elements of the embryo transfer process. Participants: Out of 284 distributed questionnaires, 200 were included in the final analysis. Inclusion required fully completed responses. Questionnaires with incomplete, unclear answers or patient refusal were excluded. The study group was compared with the general IVF patient population treated at the centre over the past 5 years to ensure representativeness. Methods: Patients completed a structured questionnaire using a five-point Likert scale. Statistical analysis included descriptive statistics, Spearman’s rank correlation, Friedman test, and exploratory factor analysis. Ethical approval was obtained (CELFer no. 07/2021), and all participants provided written informed consent. Results: The majority of patients reported a heightened sense of calm and reassurance during ultrasound-guided embryo transfer. Viewing embryos on a monitor before transfer was also positively perceived. A strong preference emerged for continuity of care with the same physician throughout the IVF process. While this study did not assess objective stress levels or clinical outcomes, the findings highlight the psychological comfort associated with these patient-centred practices. Limitations: This single-centre study is based on self-reported data and lacks objective assessments of psychological well-being. Therefore, results reflect personal perceptions rather than measurable clinical outcomes. Broader, multicentre research using validated psychological tools is needed to confirm and expand these findings. Furthermore, the questionnaire used in this study was developed internally and not validated externally with standardised psychometric instruments. Conclusions: This study provides insight into IVF patients’ subjective experiences, emphasising the perceived emotional benefits of specific procedural and relational aspects of care. These findings support the integration of patient-centred strategies—such as visual engagement and physician continuity—into routine IVF practice to enhance overall patient well-being. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

This study analyses the effectiveness of a contextualized teaching and learning sequence (TLS) based on forensic entomology (FE) to disprove the idea of spontaneous generation (SG) among students enrolled in the Higher Vocational Education and Training (VET) Cycle in Pathological Anatomy and Cytodiagnosis. Through an inquiry- and project-based learning approach, students replicate a version of Francesco Redi’s historical experiments, enabling them to engage with core scientific concepts such as the metamorphic cycle of insects and the role of entomology in forensic science. The research adopts a semiquantitative and exploratory design. It investigates: (1) whether students’ prior knowledge about FE and related biological processes is sufficient to refute SG; (2) to what extent this knowledge is influenced by their previous academic background and gender; and (3) whether a contextualized TLS can significantly enhance their conceptual understanding. The results reveal that most students begin with limited initial knowledge of FE and multiple misconceptions related to SG, irrespective of their previous study. Gender differences were observed at baseline, with women showing lower prior knowledge, but these differences disappeared after the intervention. The post-intervention data demonstrate a significant improvement in student’s ability to reject SG and explain biological processes coherently. The study highlights the importance of integrating entomology into health-related VET programs, both as a means to promote scientific literacy and correct misconceptions and as a pedagogical tool to foster critical thinking. It also highlights the potential and historically grounded methodologies to equalize learning outcomes and strengthen the scientific preparation of future healthcare professionals. Full article

Teacher education faces persistent challenges, including limited access to authentic field experiences and a disconnect between theoretical instruction and classroom practice. While virtual reality (VR) simulations offer an alternative, most are constrained by inflexible design and lack scalability, failing to mirror the complexity of real teaching environments. This study introduces TeacherGen@i, a generative AI (GenAI)-enhanced VR simulation designed to provide pre-service teachers with immersive, adaptive teaching practice through realistic GenAI agents. Using an explanatory case study with a mixed-methods approach, the study examines the simulation’s usability, design challenges, and instructional utility within a university-based teacher preparation course. Data sources included usability surveys and reflective journals, analyzed through thematic coding and computational linguistic analysis using LIWC. Findings suggest that TeacherGen@i facilitates meaningful development of teaching competencies such as instructional decision-making, classroom communication, and student engagement, while also identifying notable design limitations related to cognitive load, user interface design, and instructional scaffolding. This exploratory research offers preliminary insights into the integration of generative AI in teacher simulations and its potential to support responsive and scalable simulation-based learning environments. Full article

Family empowerment is a key component of effective family-centered practices in healthcare, mental health, and educational services. The Family Empowerment Scale (FES) is the most commonly used instrument to evaluate empowerment in families raising children with emotional, behavioral, or developmental disorders. Despite its importance, the FES for diverse populations, especially Latinx parents, has rarely been evaluated using innovative psychometric approaches. In this study, we evaluated key dimensions and psychometric evidence of the Family Empowerment Scale (FES) for 96 Latinx parents of children with intellectual and developmental disabilities (IDD) in the United States using an exploratory graph analysis (EGA). The EGA identified a five-dimensional structure, and EGA models outperformed the original CFA 3-factor models for both parents of children with autism and other disabilities. This study identified distinct, meaningful dimensions of empowerment that reflect both shared and unique empowerment experiences across two Latinx parent groups. These insights can inform the design of culturally responsive interventions, instruments, and policies that more precisely capture and boost empowerment in Latinx families. This study contributes to closing a gap in the literature by elevating the voices and experiences of Latinx families by laying the groundwork for more equitable support systems in special education and disability services. Full article

This exploratory case study investigates the implementation and educational impact of a Virtual Reality (VR)- and Augmented Reality (AR)-based assistive technology developed to support learners with dyslexia. The intervention, delivered via mobile devices and VR headsets, incorporated gamified and interactive content aimed at enhancing cognitive skills such as attention, inhibition, narrative memory, and phonological awareness. Two in-depth case studies were conducted with primary school students formally diagnosed with dyslexia. Cognitive performance was assessed using the NEPSY-II neuropsychological battery, and user experience was evaluated using the Technology Acceptance Model (TAM). The results showed positive trends in executive function and language-related skills, as well as high motivation and satisfaction. While these findings suggest promising benefits of immersive educational technologies in dyslexia intervention, conclusions regarding efficacy cannot be drawn due to the limited sample size. Further research with larger and controlled designs is needed to validate these initial observations. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

In the context of algorithm selection, the careful design of benchmark functions and problem instances plays a pivotal role in evaluating the performance of optimization methods. Traditional benchmark functions have been criticized for their limited resemblance to real-world problems and insufficient coverage of the problem space. Exploratory landscape analysis (ELA) offers a systematic framework for characterizing objective functions, based on quantitative landscape features. This study proposes a method for generating benchmark functions tailored to single-objective continuous optimization problems with boundary constraints using predefined ELA feature vectors to guide their construction. The process begins with the creation of random decision variables and corresponding objective values, which are iteratively adjusted using the covariance matrix adaptation evolution strategy (CMA-ES) to ensure alignment with a target ELA feature vector within a specified tolerance. Once the feature criteria are met, the resulting topological map point is used to train a neural network to produce a surrogate function that retains the desired landscape characteristics. To validate the proposed approach, functions from the well-known Black Box Optimization Benchmark (BBOB) suite are replicated, and novel functions are generated with unique ELA feature combinations not found in the original suite. The experiment results demonstrate that the synthesized landscapes closely resemble their BBOB counterparts and preserve the consistency of the algorithm rankings, thereby supporting the effectiveness of the proposed approach. Full article

Background/Objectives: End-of-life care is a vital part of nursing education that has been overlooked until recent years. Advance care planning should be incorporated into the prelicensure nursing curriculum to build student nurses’ confidence in aiding patients and families with their preferred future care plans. Advance care planning tools, such as the Advance Health Care Directive (AHCD) and Five Wishes, provide experiential learning opportunities that bridge theoretical knowledge with real-world patient advocacy. In this study, students were asked to complete either the AHCD or Five Wishes document as though planning for their own end-of-life care, encouraging personal reflection and professional insight. Embedding these assignments into nursing education strengthens students’ confidence in facilitating end-of-life discussions. This study applied Kolb’s experiential learning theory, including concrete experience, reflective observation, abstract conceptualization, and active experimentation, to explore student nurses’ perspectives on the Advance Health Care Directive and Five Wishes assignments, as well as their understanding of end-of-life care. Methods: This study used an exploratory–descriptive qualitative design featuring one open-ended question to collect students’ views on the assignments. Results: The final sample comprised 67 prelicensure student nurses from Bachelor of Science and Entry-Level Master’s programs. The Advance Health Care Directive and/or Five Wishes assignment enhanced students’ understanding of end-of-life decision-making. Conclusions: It is essential to complete the assignment and immerse oneself in an end-of-life situation to grasp patients’ perspectives and concerns regarding when to engage in difficult conversations with their patients. Full article

In recent years, the region of Valparaíso has faced devastating fires, notably the Viña del Mar fire on 2 February 2024, which affected 9252 hectares. This study analyzes fire behavior in informal settlements and assesses the effectiveness of different construction materials through scaled prototypes of dwellings made from MDF, OSB, TetraPak, and flame-retardant resin composites. Controlled fire experiments were conducted, recording fire spread times and atmospheric conditions. Results confirm significant differences in fire spread rates and structural survival times between materials, highlighting the practical benefit of fire-resistant alternatives. The Kaplan–Meier survival analysis indicates critical time thresholds for rapid flame escalation and structural collapse under semi-open conditions, supporting the need for improved safety measures. Burn pattern observations further revealed the role of wind, thermal radiation, and material properties in fire dynamics. Overall, this study provides experimental evidence aligned with real fire scenarios, offering quantified insights to enhance fire prevention and response strategies in vulnerable settlements. These findings provide an exploratory basis for understanding fire dynamics in informal settlements but do not constitute definitive design prescriptions. Full article

Climate change disrupts lives globally and poses significant challenges to mental health. Although several scales assess climate anxiety, many either conflate symptoms with coping responses or fail to adequately capture the core symptomatology of anxiety. Hence, this study aimed to develop and validate the Brief Anxiety Scale for Climate Change (BACC), a self-report measure designed to assess symptoms of climate anxiety. A preliminary pool of 21 items was generated based on the diagnostic criteria for generalized anxiety disorder and climate-related stress. Study 1 (n = 300) explored the factor structure via an exploratory factor analysis while Study 2 (n = 400) independently validated the structure via a confirmatory factor analysis (CFA). Analyses of the internal consistency, content validity, and discriminant validity helped refine the scale to a final 13-item version with two factors: cognitive and functional impairment. The CFA results indicated that all the fit indices met the recommended thresholds, and the final version demonstrated excellent internal consistency (Cronbach’s α = 0.92). Additionally, latent correlations revealed that climate anxiety was moderately associated with generalized anxiety and depression. The BACC was developed to identify individuals in the community who experience climate anxiety beyond an adaptive level, thereby promoting sustainable mental health in the context of climate change. These findings suggest that the BACC is a promising tool for assessing climate anxiety. With better identification, mental health professionals, community practitioners, and policymakers can utilize the scale to develop climate-sensitive public health programs and tailored intervention strategies. Full article

Background/Objectives: This study aimed to explore how stressors and facilitators within the work–family interface (WFI) influence mental health outcomes among farming women in rural Chile. The research sought to identify key relational patterns and contextual determinants shaping psychological well-being in this population. Methods: An exploratory mixed-methods design was employed, involving 41 semi-structured interviews analyzed using grounded theory. Qualitative themes were quantified by calculating the percentage of occurrence per interview, allowing for comparative analysis. Pearson correlation and principal component analysis (PCA) were used to examine associations among WFI dimensions and mental health-related variables. Results: Strong and statistically significant correlations emerged between institutional and community facilitators (r = 0.664, p < 0.01) and between gender facilitators and family workload stressors (r = 0.609, p < 0.01). PCA revealed two distinct patterns: women who rely on institutional support often resist traditional family roles, while others find balance through familial support systems. The gendered distribution of caregiving and productive tasks was a key factor in psychological well-being, with some women reporting physical discomfort linked to triple workloads. Conclusions: The dynamics of the WFI in rural contexts are shaped by both sociocultural and institutional factors. The findings highlight the need for culturally sensitive mental health policies that acknowledge and respond to the lived experiences of farming women. Full article