Search Results (543)

Sexual harassment (SH) remains widespread in workplaces and academic settings. Traditional compliance-based training has shown limited success in preventing SH or motivating bystander intervention. This study developed and tested a brief online empathy training module that can be completed in under 30 min that aims to help participants better understand and support people who experience SH and increase their willingness to intervene. Two experiments were conducted with U.S. adults recruited from the CloudResearch Connect platform (Study 1: 122 men and 140 women; Study 2: 132 men, 112 women, 4, other gender) who were randomly assigned to complete the SH empathy module, another empathy training module (burglary empathy training), time management training, a standard SH training module, or a waitlist control condition. Measures assessed empathy, perspective taking, and bystander intervention intentions. In Study 1, empathy correlated with bystander intentions, but there were no significant group differences; women reported higher empathy and bystander intentions than men. In Study 2, participants who received SH empathy training demonstrated higher empathy and perspective taking than those in other groups, and empathy improvements were associated with greater willingness to intervene. Gender did not moderate these effects. Overall, findings suggest that integrating a short empathy module into SH prevention programs can enhance readiness to act as supportive bystanders. Future research should assess the longevity of these effects and whether they translate into real-world behavioral change. Full article

VOCs are significant precursors for the formation of O₃ and SOA, directly impacting human health. This study employs multiple approaches to analyzing atmospheric VOCs by focusing on OVOCs including aldehydes, ketones, and phenols, with a case study in Beijing, China. We analyzed the concentration levels and compositions of VOCs and their atmospheric activities, offering a new perspective on VOCs. This analysis was conducted through offline measurements of volatile phenols and carbonyl compounds, complemented by online VOC observations during the summer period of high O₃ levels. The total atmospheric VOCs concentration was found to be 51.29 ± 10.01 ppbv, with phenols contributing the most (38.87 ± 11.57%), followed by carbonyls (34.91 ± 6.85%), and aromatics (2.70 ± 1.03%, each compound is assigned to only one category based on its primary functional group, with no double counting). Carbonyls were the largest contributors to the OFP at 59.03 ± 14.69%, followed by phenols (19.94 ± 4.27%). The contribution of phenols to the SOAFP (43.37 ± 9.53%) and the L_OH (67.74 ± 16.72%) is dominant. Among all quantified VOC species, phenol and formaldehyde exhibited the highest species-level contributions to atmospheric reactivity metrics, including L_OH, OFP and SOAFP, owing to their combination of elevated concentrations and large kinetic or MIR coefficients. Using the PMF model for source analysis, six main sources of volatile organic compounds were identified. Solvent use and organic chemicals production were found to be the primary contributors, accounting for 31.76% of the total VOCs emissions, followed by diesel vehicle exhaust (17.80%) and biogenic sources (15.51%). This study introduces important OVOCs such as phenols, re-evaluates the importance of OVOCs and their role in atmospheric chemical processes, and provides new insights into atmospheric VOCs. These findings are crucial for developing effective air pollution control strategies and improving air quality. This study emphasizes the importance of OVOCs, especially aldehydes and phenols, in the mechanism of summer O₃ generation. Full article

In the context of the BiblIndex project, which is an online index of biblical textual reuses by the Church Fathers, intrabiblical intertextuality must be considered to better understand the underlying basis of the Church Fathers’ thought. This paper examines the reuse of Psalmic texts in the New Testament as a test case for experimenting with a detection tool that uses traditional natural language processing (NLP) methods exclusively. Biblical verses are compared using similarity measures based on various NLP operations, such as tokenization, lemmatization, part-of-speech tagging, stop word filtering and synset assignment. Textometric measures provide a framework for the numerical assessment of grammatical, lexical and semantic similarities between textual units. We demonstrate the efficiency of this reproducible method, which does not involve a ‘black box’ effect, for detecting and characterizing literal quotations and a significant range of echoes. Full article

This paper introduces Saudi Dialects Cyber Violence Detection (SD-CVD) corpus, a large-scale, class-balanced Saudi-dialect corpus for fine-grained cyber violence detection on online platforms. The dataset contains 88,687 Saudi Arabic tweets annotated using a three-level hierarchical scheme that assigns each tweet to one of 11 mutually exclusive classes, covering benign sentiment (positive, neutral, negative), cyberbullying, and seven hate-speech subtypes (incitement to violence, gender, national, social class, tribal, religious, and regional discrimination). To mitigate the class imbalance common in Arabic cyber violence datasets, data augmentation was applied to achieve a near-uniform class distribution. Annotation quality was ensured through multi-stage review, yielding excellent inter-annotator agreement (Fleiss’ κ > 0.89). We evaluate three modeling paradigms: traditional machine learning with TF–IDF and n-gram features (SVM, logistic regression, random forest), deep learning models trained on fixed sentence embeddings (LSTM, RNN, MLP, CNN), and fine-tuned transformer models (AraBERTv02-Twitter, CAMeLBERT-MSA). Experimental results show that transformers perform best, with AraBERTv02-Twitter achieving the highest weighted F1-score (0.882) followed by CAMeLBERT-MSA (0.869). Among non-transformer baselines, SVM is most competitive (0.853), while CNN performs worst (0.561). Overall, SD-CVD provides a high-quality benchmark and strong baselines to support future research on robust and interpretable Arabic cyber-violence detection. Full article

This paper addresses the Multi-Target Reconnaissance (MTR) problem for heterogeneous Fixed-Wing Unmanned Aerial Vehicles (FW-UAVs), focusing on synchronized and time-optimal mission execution under stringent constraints. A two-stage coordinated assignment and planning framework is proposed. First, a time-balanced clustering algorithm is designed to minimize the overall mission duration while balancing individual UAV workloads by jointly employing a target reallocation strategy and an improved Genetic Algorithm (GA). Subsequently, an online trajectory planning method based on differential flatness is developed, integrating a robust replanning and flight-time synchronization strategy to ensure coordinated execution. Simulation results unequivocally demonstrate that the proposed approach enhances time optimality and temporal coordination in complex scenarios. Full article

Objectives: Exposure to appearance-focused social media often leads to body image disturbance among young women. One promising intervention to lessen this negative impact is mindfulness meditation. This study aimed to determine whether a brief mindfulness meditation intervention could mitigate the adverse effects of exposure to appearance-focused social media content on body image and mood in young Chinese women. Methods: In an online randomized controlled trial, 168 women aged 18–35 years were randomly assigned to either an intervention group (n = 86) that listened to a ten-minute mindfulness meditation audio or to a control group (n = 82) that listened to a ten-minute recorded natural history text. After listening to the audio, participants viewed idealized body images on Xiaohongshu and compared themselves to these images. Outcome measures included state body dissatisfaction and negative mood. Data were collected at baseline (T0), post-intervention (T1), and post-exposure to images (T2). Results: At T0, groups did not differ in age, BMI, education, body dissatisfaction, or negative mood (all p > 0.05). From T0 to T1, both groups showed significant improvements in body dissatisfaction and mood. The intervention group’s scores decreased significantly (p = 0.008; p < 0.01), and the control group also showed significant improvements on both outcome measures (both p < 0.001). However, when exposed to the idealized images, only the intervention group maintained its improvements, with no significant change in body dissatisfaction or mood (p = 0.178 and p = 0.310, respectively) from T1 to T2, whereas the control group’s scores worsened significantly on both outcome measures (p < 0.001 for both). Conclusions: These findings suggest that even a brief mindfulness meditation intervention may buffer against the negative effects of idealized social media content on body image and mood. Full article

Imagery rescripting (IR) has been shown to be effective for emotion regulation in both patients with mental disorders and in experimental settings. However, the effects of rumination on emotion regulation remain ambiguous, with some studies classifying rumination as an adaptive emotion regulation strategy and others as maladaptive. In this pilot study, we aimed to compare the effect of IR with rumination on emotion regulation following sad autobiographical recall. We expected IR to be more effective in recovering positive affect compared to rumination and a control group. In an online experiment, 70 healthy students were randomly assigned to IR, rumination, or a passive control group after recalling a sad autobiographical memory. Mood was assessed using the Positive and Negative Affect Schedule (PANAS) before and after the intervention. In this study, IR was superior to rumination (p = 0.033) in recovering PA after sad autobiographical recall. Therefore, IR affects mood recovery positively after recalling a sad autobiographical memory. Rumination resulted in prolonged impairment in mood and therefore should be regarded as a maladaptive emotion regulation strategy. Full article

The integration of artificial intelligence (AI) tools, such as the chat generative pre-trained transformer (ChatGPT), into health professions education is rapidly accelerating, creating new opportunities for personalized learning and clinical preparation. These tools have demonstrated the potential to enhance learning efficiency and critical thinking. However, concerns regarding reliability, academic integrity, and potential overreliance highlight the need to better understand how healthcare students adopt and perceive these technologies in order to guide their effective and responsible integration into educational frameworks. This nationwide, cross-sectional, survey-based study was conducted between February and April 2024 among undergraduate students enrolled in medical, pharmacy, nursing, dental, and allied health programs in Saudi Arabia. An online questionnaire collected data on ChatGPT usage patterns, satisfaction, perceived benefits and risks, and attitudes toward integrating them into the curricula. Among 1044 participants, the prevalence of ChatGPT use was 69.25% (n = 723). Students primarily utilized the tool for content summarization, assignment preparation, and exam-related study. Key motivators included time efficiency and convenience, with improved learning efficiency and reduced study stress identified as major benefits. Conversely, major challenges included subscription costs and difficulties in formulating effective prompts. Furthermore, concerns regarding overreliance and academic misconduct were frequently reported. In conclusion, the adoption of generative AI tools such as ChatGPT among healthcare students in Saudi Arabia was high, driven by its perceived ability to enhance learning efficiency and personalization. To maximize its benefits and minimize risks, institutions should establish clear policies, provide faculty oversight, and integrate AI literacy into the education of health professionals. Full article

Background and Objectives: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that affects postural alignment, function, and quality of life. Telerehabilitation has emerged as a promising approach to enhance accessibility and continuity of exercise-based treatment in AIS. This study aimed to compare the effects of telerehabilitation-supported home exercise programs with standard home exercises on posture, pain, body image, and quality of life in adolescents with mild AIS. Materials and Methods: Forty adolescents aged 10–18 years with mild AIS (Cobb angle 10–25°, Risser 0–3) were randomly assigned to two groups: study (n = 20) and control (n = 20). Both groups performed an 8-week home-based exercise program. The study group additionally received weekly online supervision by a physiotherapist. Outcomes included pain severity (VAS), posture (New York Posture Assessment Scale), body image (Walter Reed Visual Assessment Scale), and quality of life (SRS-22 questionnaire). Statistical analyses were performed using non-parametric tests, with a significance level of p < 0.05. Results: Twenty-nine participants completed the study (15 in the study group, 14 in the control group). Significant improvements were observed in the study group in SRS-22 total, pain, and function subscores, as well as posture scores (p < 0.05). In the control group, only the satisfaction subscore improved significantly (p < 0.05). No significant changes were detected in body image (WRVAS) in either group. Between-group comparisons showed greater overall clinical gains in the study group despite similar exercise adherence rates. Conclusions: Supervised telerehabilitation enhances the effectiveness of home-based exercise programs in adolescents with mild AIS by improving postural alignment, reducing pain, and increasing functional capacity and quality of life. Telerehabilitation represents an accessible and efficient complementary strategy for managing AIS when in-person supervision is limited. Full article

Background and Objectives: To evaluate whether regional climatic factors, particularly relative humidity (RH) and temperature, are associated with geographic variation in dry eye disease (DED) prevalence in Argentina. Materials and Methods: A secondary analysis of a nationwide voluntary online cross-sectional survey of DED in Argentina (April 2022; non-probability sample; n = 10,812) was performed. Participants were assigned to one of five standard geopolitical regions. DED prevalence (%) was estimated at regional levels. Monthly mean, maximum, and minimum temperature and RH for April 2022 were obtained from the National Meteorological Service; for each province/region, data corresponded to the principal synoptic station in the provincial capital. Because the survey and climate data were restricted to April, seasonal effects could not be evaluated. Region-level analyses of climate–DED patterns were prespecified as exploratory and descriptive given the small number of regional units (n = 5). Weighted descriptive models were used to compare RH–DED patterns while accounting for regional age and sex distributions. Results: Regional DED prevalence ranged from 37.7% (Northwest) to 49.8% (Cuyo). RH showed a clear inverse pattern with regional DED prevalence, whereas temperature showed no consistent relationship. Weighted descriptive models showed a similar qualitative RH–DED pattern, although residual confounding cannot be excluded. Conclusions: DED prevalence in Argentina shows geographic variability and a descriptive inverse pattern with ambient RH. These ecological findings are hypothesis-generating and must be interpreted cautiously due to sampling bias. Future seasonal, longitudinal, or individual-level studies are needed to confirm and quantify these preliminary signals. Full article

The proliferation of user-generated content in today’s digital landscape has further increased dependence on online reviews as a source for decision-making in the hospitality industry. There has been an increasing interest in automating this decision-support mechanism through recommender systems. However, this process often requires a large amount of labelled corpus to train an effective algorithm, necessitating the use of human annotators for developing training data, where this is lacking. Although the manual annotation can be helpful in enriching the training corpus, it can, on the one hand, introduce errors and annotator bias, including subjectivity and cultural bias, which can affect the quality of the data and fairness in the model. This paper examines the alignment of ratings derived from different annotation sources and the original ratings provided by customers, which are treated as the ground truth. The paper compares the predictions from Generative Pre-trained Transformer (GPT) models against ratings assigned by Amazon Mechanical Turk (MTurk) workers. The GPT 4o annotation outputs closely mirror the original ratings, given its strong positive correlation (0.703) with the latter. The GPT-3.5 Turbo and MTurk showed weaker correlations (0.663 and 0.15, respectively) than GPT 4o. The potential cause of the large difference between original ratings and MTurk (largely driven by human perception) lies in the inherent challenges of subjectivity, quantitative bias, and variability in context comprehension. These findings suggest that the use of advanced models such as GPT-4o can significantly reduce the potential bias and variability introduced by Amazon MTurk annotators, thus improving the prediction accuracy of ratings with actual user sentiment as expressed in textual reviews. Moreover, with the per-annotation cost of an LLM shown to be thirty times cheaper than MTurk, our proposed LLM-based textual review annotation approach will be cost-effective for the hospitality industry. Full article

Background/Objectives: Menu calorie labeling policies aim to promote healthier eating habits, yet their effectiveness remains debated. The present study aimed to evaluate the effectiveness of two alternative qualitative labeling strategies—Physical Activity Calorie Equivalent (PACE) labels and Percent Daily Intake (PDI) pie charts—compared to the standard numeric calorie count mandated in several countries, since they have been proposed to enhance consumer comprehension and decision-making. Methods: A nationwide online survey elicited responses from N = 885 individuals living in Italy. Survey participants were randomly assigned to one of three menu conditions: (1) numeric calorie labeling only, (2) numeric calories plus PACE labels, or (3) numeric calories plus a PDI pie chart. Participants selected a three-course meal from their respective menus. Data on sociodemographic factors, dietary habits, BMI, self-assessed nutritional knowledge, and psychological traits—restrained eating (DEBQ-R) and impulsivity (SUPPS-P)—were collected. Ordinal logistic regressions assessed the impact of labeling format, gender, and nutritional knowledge on total calories ordered. Results: Neither PACE labels nor PDI pie charts significantly influenced total calorie selection compared to numeric calorie labels alone. No significant interactions emerged between labeling format and gender or nutritional knowledge. However, age, BMI, and dietary habits were strongly associated with calorie choices: older adults, individuals with restrictive diets, and those with higher restrained eating scores selected lower-calorie meals, while participants with higher BMI or frequent meat intake opted for more calorie-dense options. Conclusions: Alternative calorie labeling formats alone may be insufficient to alter food choices in online settings. Future interventions should integrate motivational and educational strategies tailored to individual traits and dietary habits, rather than relying solely on calorie presentation format. Full article

Organic chlorine (Org-Cl) in crude oil poses continuous operational and environmental risks during production, trading, and refining processes. This article reviews the management of Org-Cl from its origin assumptions to analysis and mitigation measures and proposes a practical closed-loop framework. Quantitative merit value indicators (typical detection limit/quantitative limit, accuracy, and repeatability) and greenness indicators are used to compare standard methods and advanced methods, and to guide the selection of applicable methods. Corresponding technical maturity levels (TRLs) are assigned to mitigation measures (protective beds/adsorption, HDC, and emerging electrochemical/photochemical routes). Technical economic indicators with reference values (relative capital expenditure/operating expenditure levels) are summarized to assist decision-making. The main findings are as follows: (i) Evidence of secondary formation of organic chlorine under distillation-related conditions still relies on the matrix and requires independent verification; (ii) MWDXRF can achieve rapid screening (usually only 5 to 10 min), while CIC/D5808 supports quality balance arbitration; (iii) adsorption can remove a considerable portion of organic chlorine in light fractions under laboratory conditions, while the survival ability of HDC related to crude oil depends on the durability of the catalyst and the tail gas treatment capacity; and (iv) minimum viable implementation (MVI) combined with online total-chlorine monitoring and a physical principle-based digital twin technology can provide auditable closed-loop control. The limitations of this review include partial reliance on laboratory-scale data, inconsistent reports among studies, and the lack of standardized public datasets for model benchmarking. Prioritization should be given to analysis quality control, process durability indicators, and data governance to achieve reliable digital deployment. Full article

Romantic chemistry is an important indicator of compatibility between prospective romantic partners, but, despite theoretical work, lay understandings of romantic chemistry that could inform theory are still unclear. We used an online survey question to collect romantic chemistry conceptualisations from 571 Australian adults who were currently looking for a romantic partner, of whom 53.06% identified with minority gender and/or sexual identities. We analysed responses using inductive content analysis, which resulted in the construction of categories and sub-categories concerning the multifaceted nature of romantic chemistry, the importance of mutual feelings, and central concepts of interactivity, connection, and attraction. We performed a deductive content analysis using these categories and sub-categories to re-code responses and observed little evidence of differences between groups based on assigned sex, gender, sexual, and minority identities. Our findings suggest that romantic chemistry is only perceivable when multiple facets are experienced and that experiences of facets vary individually, which provides ample grounds for future investigation and measurement of romantic chemistry. Full article

Background/Objective: Nurse educators are central to consolidating nursing as a discipline and shaping professional identity, yet their preparation is heterogeneous. This study aimed to identify profiles of nurse educators based on the value they assign to teaching competencies and to analyze factors influencing these profiles. Methods: A cross-sectional, descriptive research design was applied, using convenience sampling to recruit 326 nurse educators from Spanish universities. Data were collected through a self-administered online questionnaire distributed to nursing faculty from public, private, and affiliated (semi-private) universities across Spain. The instrument included sociodemographic and academic variables, along with nine teaching competencies. Descriptive statistics, cluster analysis, ANOVA, chi-square tests, and multinomial logistic regression were conducted using SPSS. Results: Three distinct profiles of nursing faculty were identified. The academic–pedagogical profile assigned the highest importance to all competencies (means 4.78–4.91), the clinical–pragmatic profile assigned the lowest (3.61–4.04), and the intermediate–researcher profile showed moderate values (4.26–4.50). Doctoral degree (χ² = 65.36, p < 0.001) and pedagogical training (χ² = 33.89, p < 0.001) were the strongest predictors of membership in the academic–pedagogical group, confirmed in multivariate regression (OR for doctorate = 0.07; OR for pedagogical training = 0.13, both p < 0.001). Conclusion: This study delineates three coherent and statistically robust profiles of nursing faculty based on their appraisal of teaching competencies. Academic qualifications and pedagogical training emerged as key determinants of these profiles. Tailored faculty development strategies that reinforce doctoral-level preparation and pedagogical expertise are critical to advancing the quality and consistency of nursing education. Full article