Search Results (41)

National parks, such as Domogled-Valea Cernei and Cozia, alongside protected areas like the Mehedinți Plateau Geopark or the Field of Lapiezuri in Ponoare, represent key assets for attracting tourists interested in nature and sustainability. This study investigates how tourists perceive sustainable ecotourism in Romania’s South-West Oltenia Region and identifies the primary determinants of their travel preferences. Specifically, it examines shifts in destination choices, the orientation toward safer and more sustainable locations, and changes in trip frequency and duration. Data were collected between June and December 2025 via a structured questionnaire administered to 500 visitors to these protected areas. To test the research hypotheses, the data were analyzed using multiple statistical methods, including regression analysis, the Chi-square test, an independent-samples t-test, Pearson correlation, and Analysis of Variance (ANOVA). The statistical results largely confirmed the proposed hypotheses. Notably, a significant relationship between tourist age and visit frequency was identified, indicating distinct behavioral patterns across age groups. Furthermore, perceptions of service quality varied significantly by gender, highlighting divergent experiences and expectations between male and female visitors. Full article

Low-rise terraced housing constitutes a major segment of Taiwan’s residential market, yet stakeholder perception differences during residential development planning remain insufficiently understood, particularly regarding sustainability considerations. In this study, we investigate cognitive gaps among developers, homebuyers, and construction professionals across six planning dimensions, including site selection, housing price, capital capacity, construction risk, building planning, and sustainability. A structured questionnaire survey was conducted in Changhua County, Taiwan, yielding 176 valid responses (37 developers, 92 homebuyers, and 47 construction professionals). Data were analyzed using Cronbach’s α reliability analysis, exploratory factor analysis (EFA), chi-square tests, one-way ANOVA, Fisher’s LSD post hoc comparisons, and robustness analyses using ANCOVA and Tukey’s HSD. Significant stakeholder perception differences were identified for 15 of the 19 planning factors (p < 0.05). Supply-side stakeholders consistently prioritized construction cost, financing capacity, and construction risk, whereas homebuyers placed greater emphasis on transportation convenience, living amenities, spatial quality, and sustainability-related attributes, particularly green building certification and energy efficiency. Construction risk exhibited the largest cognitive gaps, with large effect sizes for construction difficulty (η² = 0.450) and government regulation (η² = 0.454). Within the sustainability dimension, governance transparency remained non-significant, suggesting that governance awareness has not yet matured into a differentiated stakeholder concern. Based on these findings, we propose the Stakeholder Cognitive Gap Framework (SCGF) as a conceptual and diagnostic framework for organizing stakeholder perception patterns. The findings contribute to understanding stakeholder cognitive divergence in residential development planning and provide practical implications for sustainable housing policy, developer decision-making, and participatory planning in non-metropolitan housing markets. Full article

Background and Objectives: Generative artificial intelligence (AI) chatbots have entered medical education faster than guidance for their responsible use. Although a rapidly expanding 2024–2026 literature has examined generative AI adoption, attitudes, and AI literacy among healthcare students, comparatively little is known about which specific tools medical students prefer or whether reliance on them carries measurable cognitive risks. We characterized adoption patterns, tool preferences, perceived benefits, and determinants of cognitive overdependence among medical students. Methods: A single-center cross-sectional survey was administered to 141 medical students across preclinical and clinical years at a single institution. A 28-item instrument captured usage patterns, perceived learning benefit, output trust, verification behavior, and AI overdependence risk. Analyses included t-tests, ANOVA, chi-square tests, Pearson correlations, and hierarchical regression. Results: Unless otherwise specified, values are reported as group mean scores on 1–5 Likert agreement scales or as percentages of respondents. ChatGPT was the primary tool for 69.5% of respondents, followed by Claude (12.1%). Daily users reported greater perceived learning benefit than infrequent users (4.14 vs. 3.36; p < 0.001). Clinical students verified AI outputs more often than preclinical students (3.69 vs. 3.21; p < 0.001), while preclinical students showed higher reliance (p = 0.002); verification moderated overdependence risk across academic years (interaction p = 0.041). AI familiarity (β = 0.31) and verification habit (β = −0.22) were the strongest predictors of integration acceptance (R² = 0.34). Conclusions: Reliance and verification habits diverge by training stage; curricula should pair AI literacy with explicit verification training to mitigate overdependence. As a single-center, self-report study, these findings require multi-center confirmation; nonetheless, to our knowledge, this is among the first studies to jointly profile students’ tool-specific reliability perceptions and to identify verification behavior as a moderator that buffers familiarity-driven overdependence. Full article

Background: Sarcomatoid carcinoma of the upper urinary tract (SCUT) is a rare and aggressive malignancy. Due to its rarity, the molecular landscape and the prevalence of potentially targetable alterations are poorly characterized. We aimed to compare the clinical, pathological, and genomic profiles of SCUT and upper tract urothelial carcinoma (UTUC). Methods: We leveraged the Tempus Lens clinically annotated genomic dataset to extract clinicopathologic and somatic genomic alteration data from patients with UTUC and SCUT. Patients with any-stage disease who underwent either blood- or tissue-based next-generation sequencing were included. Baseline clinical and demographic characteristics were summarized using descriptive statistics. Comparisons between groups were performed using Wilcoxon rank-sum test for continuous variables and the Chi-square test/Fisher’s exact test for categorical variables. Mutational frequencies and pairwise comparisons were performed to assess significant differences between groups. Results: In total, 1721 patients were included, of which 1600 (93%) had UTUC and 121 (7%) had SCUT. Patients with SCUT were younger at diagnosis, 61 years (interquartile range (IQR) 54, 70), compared to UTUC, 71 years (IQR 64, 77) (p < 0.001), and were more likely to have node-positive disease at presentation (all p < 0.001). SCUT patients were more likely to show visceral metastases to the lung (44% vs. 21%), bone (31% vs. 17%), and brain (7% vs. 1%), compared to UTUC (all p < 0.05). Among SCUT patients, the most common genomic alterations were TERT (30%), TP53 (29%), NF2 (19%), PTEN (13%), SETD2 (12%), PBRM1 (12%), and BAP1 (8%). Among UTUC patients, the most common were TERT (52%), TP53 (52%), KMT2D (30%), FGFR3 (25%), ARID1A (20%), and KDM6A (18%). Compared to UTUC, SCUT was significantly enriched with NF2, SETD2, PBRM1, PTEN, and BAP1 (all p < 0.05). SCUT was depleted in FGFR3 (0% vs. 25%) and FGF4 (0% vs. 8%) mutations compared to UTUC (both p < 0.05). Targetable alterations were observed in SCUT, including NF2, SETD2, and PTEN. Conclusion: Compared with UTUC, SCUT exhibits a more aggressive clinical and genomic phenotype, characterized by enrichment in NF2, SETD2, PBRM1, and PTEN. These findings underscore the divergent molecular landscape of SCUT and highlight potentially targetable genomic alterations. Full article

Current multi-modal pose estimation methods often suffer from severe localization divergence and prohibitive computational overhead when confronted with extreme scenarios such as sudden illumination variations, geometric degeneracy, and wheel slippage. To address these critical challenges, this paper presents a tightly coupled multi-modal pose estimation algorithm for mobile robots utilizing adaptive robust manifold filtering. First, a pre-integration-driven iterated error-state Kalman filter (iESKF) is formulated on the Lie group manifold to eliminate redundant re-integration workloads. Second, the Mahalanobis distance chi-square test and M-estimation are introduced to adaptively isolate non-Gaussian heavy-tailed noise caused by perception degradation. Finally, a perception health quantification system and a smooth degradation state machine are designed to handle concurrent perceptual blindness and wheel slippage. Experimental results demonstrate that the algorithm takes an average of only 12.8 ms per frame on edge computing platforms. Under severely degraded and composite environments, the algorithm limits the typical end-to-end closed-loop drift to 1.37 m (with a statistical average of 1.24 m) over a 100-m trajectory, translating to a relative translation error (RTE) of approximately 1.2% to 1.4%. This demonstrates an exceptional balance between high real-time efficiency and robust survivability. Full article

Background/Objectives: Adequate micronutrient intake during pregnancy is critical for fetal development, yet whether pregnant women and healthcare professionals share consistent knowledge, beliefs, and supplementation practices remains poorly characterized. Methods: Two parallel cross-sectional surveys using identical core items were conducted at a German tertiary care center between April and November 2024. Pregnant women (n = 132) and healthcare professionals who initiated the survey (n = 105) completed anonymous QR-code-based questionnaires assessing micronutrient-related knowledge, perceived dietary adequacy, and supplementation practices or recommendation patterns. Comparative analyses were restricted to fully completed healthcare professional questionnaires (n = 80). Group differences were analyzed using chi-square or Fisher’s exact tests. Results: Healthcare professionals demonstrated higher knowledge levels across most micronutrients. Knowledge gaps were most pronounced for vitamin B12, with 53.0% of pregnant women unable to identify any fetal effect compared with 20.0% of providers (p < 0.001). Beliefs about dietary sufficiency were broadly aligned for folic acid (p = 0.452) and vitamin D (p > 0.999), but diverged markedly for vitamin B12, where 79.2% of providers considered dietary intake alone adequate compared with 47.3% of pregnant women (p < 0.001). Substantial differences were observed between patient-reported supplementation practices and provider-reported recommendation patterns: Vitamin B12 (70.0% vs. 3.8%), vitamin D (76.2% vs. 41.3%), omega-3 fatty acids (76.2% vs. 47.5%), and folic acid (98.5% vs. 81.3%; all p < 0.001). The internet was the most frequently cited information source among pregnant women (89.4%), while healthcare professionals reported using both scientific literature (75.0%) and internet-based resources (76.3%), the latter primarily for accessing professional and scientific information. Conclusions: Substantial patient–provider differences in micronutrient knowledge, beliefs, and supplementation practices persist even within a highly educated population at a tertiary care center. These findings suggest potential differences between patient-reported supplementation behavior and provider-reported recommendation practices, particularly for vitamin B12 and vitamin D. These findings suggest that more structured communication regarding micronutrient supplementation during pregnancy is needed. Full article

This paper proposes a robust navigation method based on a robust square-root cubature Kalman filter (RSRCKF) to address the accuracy divergence of integrated navigation systems caused by drilling-induced slippage and the mismatch between the tail-cable encoder and the robot motion during operations of a seafloor drilling robot in deep-sea soft sedimentary layers. Considering the large-deformation mechanical characteristics of the seabed under drilling conditions, a unified state-space model incorporating a time-varying odometer scale-factor error is first established. To alleviate the numerical instability of the nonlinear system in the presence of non-Gaussian noise, a square-root cubature Kalman filter (SRCKF) framework is employed, in which the positive definiteness of the error covariance matrix is dynamically preserved via QR decomposition. Subsequently, an online fault detection mechanism based on a modified chi-square test is developed. By introducing a two-segment IGG (a classical robust weighting scheme) weighting function, an adaptive variance inflation factor is constructed to enable real-time identification and down-weighting of abnormal observations induced by slippage. Field experiments, including drilling and turning tests conducted on tidal mudflats off the coast of Zhoushan, demonstrate that the proposed method can effectively mitigate the impact of “false displacement” disturbances caused by typical soft clay slippage conditions through enhanced statistical robustness. Taking the conventional SINS/OD integration scheme as the baseline, the proposed method achieves an approximate 82.4% reduction in positioning error. These results verify the robustness and engineering applicability of the proposed algorithm in complex seabed environments. Full article

Aiming at the divergence problem of standalone strapdown inertial navigation system (SINS) affected by initial errors, sensor drift, and cumulative errors in complex marine environments, this paper proposes a long-endurance autonomous navigation scheme without external measurement to suppress Schuler oscillations and improve dynamic navigation performance. First, based on the dynamic error model of SINS, the characteristics of Schuler oscillation are analyzed, and a multi-path delayed-solution strategy is developed. By sequentially delaying the SINS calculation loop and performing arithmetic averaging, periodic oscillation errors are automatically canceled. Second, a chi-square test is constructed to assess sea-state complexity in real time, and a robust adaptive Kalman filter is designed with adaptive filter selection to further improve estimation accuracy under dynamic conditions. Finally, the proposed method is systematically validated through static simulations, dynamic simulations, and full-scale ship experiments. Results show that the delayed-solution strategy significantly mitigates Schuler oscillation in attitude and velocity under static conditions. In dynamic simulations and ship trials, compared with pure SINS, single delayed-calculation, and conventional Kalman filter, the proposed approach achieves superior suppression of attitude, velocity, and position errors, with core navigation error indices reduced by at least one order of magnitude. These findings demonstrate that the Schuler period characteristic of inertial navigation errors can be effectively exploited in dynamic conditions, and the coupling of multi-path delayed calculation with robust adaptive filtering enables substantial improvements in autonomous navigation accuracy without external measurement. The proposed method expands the theoretical and engineering framework of autonomous navigation at no additional hardware cost, providing a new technical route for the practical deployment of long-duration SINS. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

To address the issue of reduced accuracy or even divergence in micro-electro-mechanical inertial navigation systems’/global navigation satellite systems’ (MINSs’/GNSSs’) integrated navigation systems caused by small amplitude fault in GNSS measurement information, this paper proposes a robust extended Kalman filter algorithm based on a sliding window fractional-order grey prediction model (SWFGM(1,1)-REKF). When GNSS signals are disrupted, this algorithm first detects system faults through a weighted index sequential probability ratio test (SPRT) detection. Then, it uses GNSS measurements predicted by a sliding window fractional-order grey prediction model (FGM(1,1)) to replace the faulty GNSS data and integrates them with MINSs. Finally, it combines robust estimation to construct a robust extended Kalman filter to correct the integrated information. Simulation and vehicle experiment results show the advancement of SWFGM(1,1)-REKF. When GNSS measurements experience small amplitude abrupt faults, compared with traditional robust extended Kalman filter algorithm based on a chi-square test, the proposed algorithm improves filtering accuracy of velocity and position. In the vehicle small amplitude mutation fault experiment, the velocity and position accuracy are increased by more than 50% and 80% respectively. Full article

In many scientific fields (e.g., statistics, data science, machine learning, and image processing), effectively quantifying the statistical difference between two probability distributions is an important task. Although a wide variety of measures have been proposed in the literature, some of them (such as the chi-square divergence and the Kullback–Leibler divergence) do not satisfy one or both of two key axioms: normalization and symmetry. This paper proposes a new index for quantifying the statistical difference between two probability distributions, called the distribution discrepancy index (DDI). The proposed DDI is based on the recently developed concepts of informity and cross-informity in informity theory. Its value ranges from 0 to 1, with values close to 1 indicating a large discrepancy and values close to 0 indicating minimal discrepancy. The DDI satisfies the two key axioms and is applicable to both discrete and continuous distributions. This paper also proposes the distribution similarity index (DSI) as a complement to the DDI. Three examples are presented to compare the DDI with three existing discrepancy measures (the Hellinger distance, total variation distance, and Jensen–Shannon divergence) and the DSI with two existing similarity measures (the Bhattacharyya coefficient and overlapping index). Full article

Purpose: To evaluate demographic representation in AI-generated and search-engine-sourced images of North American ophthalmologists, overall and stratified by subspecialty, and compare these with actual demographic data. Methods: This cross-sectional analysis examined 2000 images (1000 AI-generated and 1000 search-engine-sourced) across ten North American ophthalmology subspecialties. Images were sourced from four AI platforms (DALL·E 3, Firefly, Midjourney, Grok-2) and four search engines (Google, Bing, DuckDuckGo, Yahoo!). Using a standardized framework, reviewers assessed gender, race, age group, and professional attire. Pearson chi-squared tests were used to compare image sets with actual demographic data from the Association of American Medical Colleges and Canadian Institute for Health Information. Results: AI-generated images depicted 69% men compared to 64% in search-engine-sourced images (p = 0.047), though both were lower than the actual proportion of male ophthalmologists in North America (71–73%, p < 0.001). White individuals were overrepresented in AI-generated images (81%) relative to both search-engine-sourced images (74%, p = 0.001) and actual demographic data (69%, p < 0.001). Younger individuals (under 50 years) were significantly overrepresented in both image sets, with 82% in AI-generated images and 73% in search-engine-sourced images, compared to only 45–46% in actual demographic data (p < 0.001 for both). AI-generated images also depicted ophthalmologists with significantly more stereotypical medical accessories, including stethoscopes (17% vs. 2%, p < 0.001), glasses (45% vs. 30%, p < 0.001), and white coats (68% vs. 53%, p < 0.001), compared to search-engine-sourced images. Conclusions: AI-generated images diverge from actual demographics, presenting a younger, more stereotypical workforce that paradoxically aligns closer to gender parity than reality. Full article

Background: Ischemic stroke remains the most feared complication of atrial fibrillation (AF), and thromboembolic risk is commonly estimated using clinical scores that may not fully capture the cardiometabolic dimension of cerebrovascular vulnerability. The aim of this research was to assess whether additional parameters can be used, to predict ischemic stroke risk in patients with AF, in order to explore whether TG/HDL-C may complement conventional clinical risk scores for ischemic stroke risk stratification in PAF, and to better characterize a metabolically high-risk phenotype beyond the recommendations provided by the CHA₂DS₂-VA score, which is useful but still far from perfect in predicting AF-associated ischemic stroke risk. Methods: In this retrospective, single-center observational study, we evaluated whether the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDLc), a simple surrogate of atherogenic dyslipidemia and insulin resistance, is associated with ischemic stroke risk in patients with paroxysmal atrial fibrillation (PAF). We screened 1111 consecutive AF admissions between 1 January 2015 and 31 December 2016 and, from these 1111 AF cases, we extracted only the patients with PAF for analysis. Patients were stratified based on TG/HDLc values into two groups, Group 1 (TG/HDLc > 2.5; n = 155) and Group 2 (TG/HDLc < 2.5; n = 194). Statistical analysis was performed with MedCalc v23.4.0 using Chi-square and unpaired/Welch’s t-tests as appropriate, Pearson correlations, Kaplan–Meier analysis with log-rank testing, Cox regression for first ischemic stroke, and multivariable logistic regression to identify independent correlates of TG/HDLc > 2.5. Results: Patients with TG/HDLc > 2.5 had a significantly higher prevalence of ischemic stroke after AF onset compared with those with TG/HDLc < 2.5 (37.4% vs. 21.1%, p = 0.0008), despite similar CHA₂DS₂-VA and HAS-BLED scores, and also exhibited a higher burden of cerebrovascular and neurodegenerative findings, including cortical atrophy and cerebral lacunarism. Ischemic stroke-free survival curves diverged significantly over time (log-rank p = 0.0186), and an elevated TG/HDLc ratio was associated with a 68% higher hazard of first ischemic stroke (HR 1.68; 95% CI 1.09–2.60). In multivariable analysis, type 2 diabetes mellitus (OR 4.53), hyperuricemia (OR 3.83), dyslipidemia (OR 1.94), stroke (OR 1.77), and cortical atrophy (OR 4.48) were independently associated with TG/HDLc > 2.5. Conclusions: These findings suggest that TG/HDLc identifies a metabolically high-risk PAF phenotype associated with greater cerebrovascular burden and reduced ischemic stroke-free survival, providing an inexpensive and broadly available marker that may complement conventional clinical risk scores. Full article

Aside from languages having no form of written expression, it is usually the case with every language on this planet that texts are written in a single character. But every rule has its exceptions. A very rare exception is Japanese, the texts of which are written in the three kinds of characters. In European languages, no one can find a text written in a mixture of the Latin, Cyrillic, and Greek alphabets. For several Japanese texts currently available, we conduct a quantitative analysis of how the three characters are mixed using a methodology based on a binary pattern approach to the sequence that has been generated by a procedure. Specifically, we consider two different texts in the former and present constitutions as well as a famous American story that has been translated at least 13 times into Japanese. For the latter, a comparison is made among the human translations and four machine translations by DeepL and Google Translate. As metrics of divergence and diversity, the Hellinger distance, chi-square value, normalized Shannon entropy, and Simpson’s diversity index are employed. Numerical results suggest that in terms of the entropy, the 17 translations consist of three clusters, and that overall, the machine-translated texts exhibit entropy higher than the human translations. The finding suggests that the present method can provide a tool useful for stylometry and author attribution. Finally, through comparison with the diversity index, capabilities of the entropic measure are confirmed. Lastly, in addition to the abovementioned texts, applicability to the Japanese version of the periodic table of elements is investigated. Full article

Background and Objectives: Clinical ethical judgments are often elicited through scenario-based (vignette-based) dilemmas that guide interpretation, reasoning, and moral judgment. Despite its importance, little is known about how healthcare professionals and students respond to such scenario-based dilemmas in Eastern European settings. This study explored differences in ethical decision-making between senior medical/dental students and practicing clinicians in Romania, focusing on how scenarios-based dilemmas influence conditional versus categorical responses. Materials and Methods: A cross-sectional survey was conducted with 244 participants (51 senior students; 193 practitioners). Respondents completed a validated 35-item questionnaire presenting hypothetical ethical scenarios across seven domains: informed consent, confidentiality, medical errors, public health duties, end-of-life decisions, professional boundaries, and crisis ethics. Each scenario used a Yes/No/It depends response structure. Group comparisons were analyzed using chi-square and non-parametric tests (α = 0.05). Results: Scenario-based dilemmas elicited frequent conditional reasoning, with “It depends” emerging as the most common response (47.8%). Strong consensus appeared in rejecting concealment of harmful errors and in treating unvaccinated families, reflecting robust professional norms. Divergences arose in areas where scenario-based dilemmas emphasized system-level duties: students more often supported annual influenza vaccination (52.9% vs. 32.6%, p = 0.028) and organ purchase authorization (76.47% vs. 62. 18%, p = 0.043), while practitioners more frequently endorsed higher insurance contributions for unhealthy lifestyles (48.7% vs. 23.5%, p = 0.003). Conclusions: Scenario-based dilemmas strongly shape moral decision-making in healthcare. While students tended toward principle-driven transparency, practitioners showed pragmatic orientations linked to experience and system stewardship. To promote high-quality clinical work and align decision-making with best practice and health policy, our findings support institutional protocols for transparent error disclosure, continuing professional development in ethical communication, the possible adoption of annual influenza vaccination policies for healthcare personnel as policy options rather than categorical imperatives, and structured triage frameworks during crisis situations. These proposals highlight how scenario-based ethics training can strengthen both individual reasoning and systemic resilience. Full article