Search Results (77,014)

The use of ketamine for the management of neuropsychiatric conditions outside clinical settings has rapidly expanded, creating a critical need to understand diverse individual experiences. We conducted a qualitative content analysis of posts from the r/TherapeuticKetamine subreddit. From 3302 threads, the 500 highest-engagement threads (12,852 comments) were analyzed by independent coders across six domains: perceived positive effects, adverse effects, reasons for use, route of administration, polydrug use, and dose amounts. Mood-related concerns were the primary reason for use (53%). Users reported positive effects, most often improvements in emotional well-being (65%). Adverse effects were predominantly psychological or mood-related (56%). A total of 70% of reported doses exceeded 149 mg, suggesting a trend toward higher dose use. Intravenous administration (40%) and sublingual troches (23%) were the most frequently reported routes. Concurrent use of prescribed psychotropics, cannabis, and psychedelics was also reported. This analysis identified substantial heterogeneity in individual-reported experiences. Frequent high-dose use, dose escalation, and polydrug exposure underscores the importance of clinical monitoring and attention to addiction potential and drug–drug interactions. The findings should be interpreted with caution, as follow-up and clinical verification were not possible; however, the data provide an unfiltered view of individual experiences in relation to ketamine use outside the clinical setting. Full article

Aims: To conduct an exploratory analysis the psychometric properties of the Spanish version of the Soft Skills Questionnaire for Nurses (SSQN) and examine its conceptual coherence and its preliminary empirical behavior among nursing professionals and students. The aim is to critically assess the instrument’s suitability as a tool for exploring perceptions and self-reported soft skills rather than to establish its psychometric validity. Design: Exploratory methodological study focused on analyzing the empirical performance and conceptual adequacy of the SSQN within a Spanish sample, with particular attention to the internal patterns of responses and the coherence between the instrument’s items and its proposed dimensions. Methods: The process included the translation of the questionnaire and an empirical application in a sample of nursing professionals and students. Exploratory analyses were performed, including exploratory factor analysis and reliability assessment (Cronbach’s alpha and McDonald’s omega), using Jeffreys’s Amazing Statistics Program (JASP) (version 1.18.3), in order to examine the structural performance of the instrument and detect possible conceptual and methodological limitations. Results: The SSQN showed notable inconsistencies in its empirical structure, with dimensions that did not display clear or theoretically coherent patterns. Factor inconsistencies and low internal consistency suggest that the instrument does not adequately capture the multidimensionality of interpersonal skills, reflecting weaknesses inherent in its original formulation rather than in the adaptation process. Conclusions: The Spanish version of the SSQN cannot be considered valid or reliable in its current form. The results underscore the need for a thorough revision of the questionnaire and a conceptual rethinking to develop more robust tools for assessing soft skills. Impact: This study highlights the need for a solid methodological evaluation before introducing instruments designed to measure complex and subjective competencies in the healthcare field. Full article

The Hyakuzahōdan Kikigakishō (百座法談聞書抄, hereafter Hyakuza 百座), compiled in the late Heian period, is an important Buddhist document that records a hundred-day lecture series on the Lotus Sutra (法華経). While previous scholarship has recognized the constructed nature of the text as a kikigaki (聞書), it has predominantly focused on content analysis, implicitly treating the text as a transparent window into the actual preaching event. To move beyond this limitation, this study proposes the analytical framework of dual performativity and, drawing on Diana Taylor’s theory of the archive and the repertoire, reexamines the text’s generative logic and political implications. This study argues that the Hyakuza embodies two interrelated forms of performance: first, the performativity of the hōdan (法談) as a live ritual, understood as a repertoire performance that constructs immediate authority through body, voice, and situational dynamics; second, the performativity of the kikigaki as textual construction, understood as an archival performance that transforms the ephemeral oral event into an authoritative, transmissible text through formulaic rhetoric, localized adaptation, and systematic arrangement. Integrating methodologies from textual history, rhetorical analysis, ritual theory, and intellectual history, this study demonstrates that the Hyakuza is not a neutral transcript of sermons but a meticulous, intentional act of writing with two fundamental aims: on a cultural level, to hierarchically integrate shinbutsu shūgō (神仏習合) through narrative appropriation; on a social level, to symbolically bind Buddhist merit with the institutional identities of aristocrats such as naishinnō (内親王), ultimately serving the self-affirmation internal cohesion, and cultural demarcation of the elite community from the masses, while simultaneously contributing to the state’s project of constructing a unified ideology in the late Heian period. By examining both cross-civilizational universal logic and specific historical context, this study reveals how the Hyakuza’s dual performativity produces and categorizes knowledge narratives while embedding political power dynamics, offering a critical path for the study of kikigaki-genre literature from discourse analysis to politics of textuality. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The threat of plant diseases in economically significant crops of the Solanaceae family, especially tomatoes and potatoes, is a significant challenge to global food security, highlighting the necessity of fast and convenient diagnostic methods. This paper introduces an enhanced MobileNetV2 model to perform automated disease classification through the use of a domain-specific self-supervised learning (SSL) pretraining approach. The model was first trained on 54,303 unlabeled plant images to learn basic botanical representations, followed by fine-tuning under six experimental conditions to optimize disease classification performance. Findings show that SSL pretrained weights consistently outperform traditional ImageNet-based transfer learning, achieving 0.9158 overall accuracy and a weighted F1-score of 0.9143 in joint tomato and potato classification. The model demonstrates strong cross-crop generalization, correctly identifying Early Blight and Late Blight with accuracies of 0.9600 and 0.9359, respectively, and effectively separating disease-specific visual symptoms from host morphology. Confusion matrix analysis further indicates a reduction in misclassification of visually similar necrotic lesions, a common challenge in supervised models. Overall, the proposed SSL architecture enhances the performance of lightweight convolutional neural networks (CNNs) to a large extent, providing a strong, computationally efficient solution for field-deployable diagnostics in precision agriculture, particularly for tomato and potato crops. Full article

Sulfur dioxide is a trace constituent of the upper atmosphere of Venus but plays a dominant role in the photochemistry above the cloud tops. Because SO₂ undergoes indirect dissociation to a relatively long-lived excited state, it has a line-type absorption spectrum in the dissociation region (~190–220 nm). This leads to strong isotopic fractionation under optically thick conditions, a process referred to as self-shielding. Here, I use SO₂ cross-sections, shielding functions, and a simple steady-state photochemical model to estimate sulfur isotope ratios in SO₂. The results indicate that large isotope depletion relative to SO₂ in the deep atmosphere is expected in SO₂ below 70 km altitude, with δ³⁴S ~ −100 to −200 permil. This is readily detectable by the VTLS tunable laser spectrometer planned for the NASA DAVINCI mission. Full article

The rapid advancement of artificial intelligence (AI) and the continuous reform of English education in China have greatly reshaped the professional requirements for college English teachers. In contrast to the predominance of general teacher perspectives in adaptive expertise research, college English teaching offers a discipline-specific context in which adaptive expertise becomes particularly salient in response to AI-related pedagogical change. This study adopts a grounded theory approach to explore the qualities that college English teachers should possess in the era of artificial intelligence. A five-dimensional dynamic model of adaptive expertise was developed, consisting of knowledge expertise, competence expertise, vision expertise, emotional expertise, and development expertise. It shows that knowledge expertise embodies the integration of multiple knowledge domains required in the AI era; competence expertise emphasizes the ability to design and implement AI-enhanced instruction; vision expertise reflects global and future awareness in AI-technology integration; emotional expertise sustains resilience and motivation amid technological change; and development expertise promotes lifelong learning and innovation. These dimensions transfer, enhance, and inspire one another, forming a closed and self-reinforcing loop. It enriches the understanding of teacher professionalism in the AI era and offers a framework for cultivating AI-resilient expertise in higher education. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

Energy harvesting has emerged as a promising solution for powering aircraft structural health monitoring (SHM) systems by exploiting ambient vibration energy. This work presents a novel clustered piezoelectric energy harvester (CPEH) designed to enable autonomous sensing and wireless data transmission in aircraft structures. Aircraft sections experience complex, multiple vibration modes during flight; however, the proposed harvester is specifically designed to exploit the oscillatory motion of the vertical tail unit (VTU) of a VUT-100 Cobra aircraft during the cruise phase. The energy harvester employs a clustered piezoelectric cantilever configuration incorporating magnetic stiffness nonlinearity, which enhances vibration-induced strain and enables effective frequency tuning. The nonlinear magnetic interaction broadens the operational bandwidth and improves energy conversion performance under low excitation amplitudes. The system is tuned to operate over a broadband frequency range of 110–130 Hz, with optimal performance achieved at acceleration amplitudes of less than 0.5 g, corresponding to the measured VTU vibration levels during the cruise phase of the flight. An experimental prototype was tested in the laboratory under aircraft cruise-phase vibration conditions, successfully achieving maximum power of 0.041 mW at optimum resistance of 390 KΩ and 5.45 mJ of stored energy in a 1000 µF capacitor within 10 min, confirming the feasibility of the proposed harvester for aircraft SHM applications. Full article

Background: The combination of photodynamic therapy (PDT) and immunotherapy has been explored as an innovative approach to enhance efficacy against tumors. However, PDT shows limited effectiveness in treating deep-seated tumors, as light and lasers do not sufficiently penetrate tissue. Methods: Herein, we introduced a nanocarrier (NP_VR) via self-assembly, using an amphiphilic copolymer to co-deliver the hydrophobic photosensitizer verteporfin (VP) and the immunoadjuvant imiquimod (R837). Results: Our X-ray-induced photodynamic therapy (X-PDT) mechanism induced NP_VR to generate a large amount of cytotoxic reactive oxygen species (ROS), which directly killed cancer cells. Moreover, the released R837 facilitated immunogenic cell death following the X-PDT process and promoted the maturation of dendritic cells (DCs), thereby eliciting immune responses against malignant triple-negative breast cancer (TNBC). In animal experiments, the combined therapy using NP_VR showed a tumor growth inhibition rate of ~70%. Conclusions: This novel strategy opens new avenues to designing next-generation nanomedicines for use in immunotherapy and other combination therapies. Full article

Background/Objectives: Disorders of Gut–Brain Interaction (DGBIs), particularly irritable bowel syndrome (IBS), are frequently underdiagnosed in clinical practice, contributing to a substantial hidden burden of disease. This study aimed to quantify this “symptomatic iceberg” by comparing the prevalence of formal IBS diagnoses with a broader symptom-based case definition in a clinical cohort. Methods: We conducted a cross-sectional analysis of 194 adult subjects from a gastroenterology clinic in Western Romania. Data on demographics, clinical diagnoses, self-reported symptoms, and eating behaviors were collected. For the case–control analysis, patients with confirmed organic gastrointestinal pathology or incomplete data were excluded. The final analytical sample consisted of 52 patients classified as having a functional DGBI phenotype and 84 asymptomatic controls without organic disease, while 58 were excluded from the analysis. Results: While only 4.4% (95% CI: 2.0–9.3%) of the cohort (N = 136) had a formal IBS diagnosis, 47.8% (95% CI: 39.6–56.1%) met criteria for an IBS-compatible symptom cluster, yielding an underdiagnosis ratio of 10.8. Neuro-vegetative symptoms such as sweating (19.1%) and dizziness (11.8%) were highly prevalent. In the case–control analysis, patients with a functional DGBI phenotype had a significantly higher mean BMI compared to controls (28.15 ± 6.49 vs. 24.47 ± 4.60 kg/m²; p = 0.001). DGBI cases were less likely to report regular snacking behavior (OR = 0.36; 95% CI: 0.18–0.74; p = 0.009), suggesting behavioral adaptation. A sensitivity analysis excluding participants with CRP > 10 mg/L (n = 98) confirmed the robustness of these associations, indicating that minor systemic inflammation did not bias the primary findings. Full article

Background/Objectives: Assistive technologies are commonly used as a compensatory strategy for individuals with neurological conditions. However, several negative factors have been associated with their use, leading to their non-use or interruption. Therefore, the aim of the present study was to examine the potential of the Assistive Technology Device Predisposition Assessment (ATD-PA) as an outcome measure to identify psychosocial and user-perceived factors associated with the non-use or interruption of assistive technology, particularly mobility devices. Methods: A descriptive, cross-sectional and non-experimental design was employed, as no variables were manipulated. The sample was selected using non-probability convenience sampling and consisted of 80 participants, of which 14 participants discontinued or interrupted the use of assistive technology. An ad hoc sociodemographic questionnaire was administered, along with the Assistive Technology Device Predisposition Assessment, based on the Matching Person and Technology (MPT) model. Results: Factors related to non-use or interruption appeared to be associated with higher perceived levels of global health, self-care, and physical well-being. Findings from the ATD-PA, used as an indicator of subjective satisfaction, showed strong associations between the perceived level of loss and the need for assistive technologies in domains such as comfort, self-care, and general health (r = 0.72–0.90). The perceived benefit of the device was closely linked to knowledge of its use, safety, fit with personal habits, and perceived capability and stamina (r = 0.69–0.94). Comfort using the device was mainly reported in familiar environments such as with family and friends. In contrast, comfort in broader community contexts did not demonstrate meaningful associations. Conclusions: Findings are consistent with Lauer’s model of non-use and highlight the importance of psychosocial determinants such as perceived health, safety, support, and contextual comfort in understanding the interruption or non-use of assistive technology, in line with the International Classification of Functioning, Disability and Health framework. The ATD-PA shows potential as an outcome-oriented tool to support follow-up and the early identification of risk factors for non-use. Longitudinal studies are needed to better understand usage patterns over time. In Spain, the lack of standardized outcome evaluation protocols and systematic follow-up processes underscore the need for structured monitoring strategies in assistive technology provision. Full article

Transplanting status is a significant indicator for rice cultivation, and is essential for field management, food security and agricultural production. However, traditional characterization cannot detect the transplanting status in a timely and effective manner; manual seedling replanting is labor-intensive, has a high cost and is inefficient. This study proposed a detection method for floating seedlings and missed transplanting. The method employed a self-built improved YOLO, namely HGV-YOLO. We leverage a HorBlock module to achieve the splitting of the morphological features of rice seedlings in different dimensions of the backbone network of YOLOv8n, which enabled the network to further enhance the classification and recognition ability of rice seedlings. Furthermore, Grouped Spatial Convolution (GSConv) replaces convolution, and the VOV-GSCSP replaces the C2f modules, reducing the number of parameters and improving the model’s inference speed. To improve the model’s bounding box precision, the WIoU loss function was also incorporated. Finally, we use the least squares method to predict the center point of the rice seedlings. The experimental results indicate that HGV-YOLO achieves a precision of 93.7%, a recall of 83.1%, and an mAP@0.5 of 91.1%. Compared to YOLOv8n, HGV-YOLO reduces Params by 3.1% and GFLOPs by 1.2%, respectively, while improving mAP@0.5 by 2.3%. Compared to YOLOv3-tinyYOLOv5 and YOLOv6, HGV-YOLO achieves increases in mAP@0.5 of 4.6 %, 3.1%, and 2.8%, respectively. In summary, the HGV-YOLO model exhibits a strong performance and provides valuable insights for advancing the autonomous navigation of rice transplanting robotics. Full article

Background: The Mediterranean diet (MedDiet) has been associated with anti-inflammatory effects and potential benefits in several chronic conditions. However, adherence to the MedDiet and its relationship with lifestyle factors and disease severity across different rheumatological diseases remain poorly characterized. Objectives: This study aimed to evaluate differences in MedDiet adherence among patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and fibromyalgia (FM), and to explore its association with cardiovascular comorbidities, bowel habits, and disease-related outcomes. Methods: In this monocentric cross-sectional study, adherence to the MedDiet was assessed using the 14-item PREDIMED questionnaire. Self-reported data on sociodemographic characteristics, cardiovascular comorbidities, bowel habits, and dietary behaviors were collected through questionnaires. Disease activity and severity were assessed using validated disease-specific measures. Differences in MedDiet adherence across diagnostic groups were evaluated using non-parametric tests. Multivariable models were performed to examine associations between MedDiet adherence and cardiovascular comorbidities or disease outcomes, adjusting for potential confounders including age, sex, BMI, smoking status, and educational level. Results: A total of 422 participants were included (RA n = 165, PsA n = 85, FM n = 172). Significant differences in MedDiet adherence were observed across diagnostic groups (p < 0.001), with the highest adherence in RA, intermediate values in PsA, and the lowest in FM. Compared with the other groups, a higher proportion of FM participants reported food intolerances (46.5%) and restrictive diets, including lactose-free (34.9%) and gluten-free (15.1%) diets. In the FM group, high adherence to the MedDiet was significantly associated with lower FIQR scores (β = −16.9; 95% CI −32.1 to −1.7; p = 0.01) and lower PDS scores (β = −4.34; 95% CI −7.81 to −0.86; p = 0.01). Sensitivity analyses using the continuous PREDIMED score confirmed these associations. Conclusions: Adherence to the MedDiet differs across rheumatological diseases, with the lowest adherence observed in FM. Higher adherence was associated with lower disease severity and impact in FM. These findings highlight the potential relevance of nutritional counselling in rheumatological diseases and support the need for longitudinal and interventional studies evaluating the role of the MedDiet within multidisciplinary disease management. Full article

Background: Misperception of body weight has been found to negatively impact both diet and physical activity levels, particularly in youth with overweight and obesity. Objectives: This study assessed consistency between actual and perceived weight status and lifestyle factors in a sample of 455 children and adolescents (55% males, 8–13 years) attending a summer camp in Northern Italy. Methods: Weight status was defined applying Body Mass Index (BMI) cut-offs. Adherence to the Mediterranean Diet (MD), physical activity level, sleep duration, and sleep quality were assessed through validated questionnaires. Self-perception was evaluated through 5-point Likert scales, with graphical representations. Results: Comparison between self-perceived and assessed parameters revealed a poor concordance across all types of variables. Approximately half of participants (43–55%) correctly rated their weight status (κ = 0.12; 95% CI: 0.05–0.19), diet quality (κ = 0.09; 95% CI: 0.02–0.15), physical activity level (κ = 0.18; 95% CI: 0.11–0.26), sleep time (κ = 0.10; 95% CI: 0.03–0.17), and sleep quality (κ = 0.18; 95% CI: 0.12–0.24). Participants 12–13 years old were more likely to have a greater weight status perception compared to younger subjects (OR = 2.13; 95% CI: 1.08–4.21). Being in a condition of overweight or obesity significantly decreased the odds of correct weight perception (OR = 0.13; 95% CI: 0.08–0.21). Similarly, subjects with higher adherence to the MD, adequate sleep time, and low sleep quality were more conscious about their diet and sleep patterns. Conclusions: Overall, these findings highlight a certain degree of misclassification, especially in subjects who need to improve their lifestyles, highlighting the potential relevance of fostering accurate self-perception during developmental age. Full article