Search Results (1,505)

The belief that the moon disturbs sleep is widespread, but the factors associated with it remain poorly understood. I therefore examined how frequently poor sleep is attributed to moon phases, whether this varied across the lunar cycle, and which personal and environmental factors were associated with “moon blaming”. Data were derived from an ongoing online survey. At the time of analysis, 1815 participants had completed a 16-item questionnaire assessing sleep quality, sleep duration, sleep timing on workdays and free days, alarm clock use, environmental and personal sleep-disturbing factors, residential setting, age, gender, attention to lunar phases, and whether the moon was perceived as a cause of poor sleep. The primary outcome was endorsement of the moon as a sleep-disturbing factor. Logistic regression with stepwise Akaike information criterion selection was used to identify the strongest predictors of attributing the moon for poor sleep. Questionnaire timing was also examined across the lunar cycle. Among environmental factors, the moon was the most frequently endorsed cause of poor sleep (36%), followed by outdoor temperature (31%), indoor noise (26%), and bad weather (22%). Rumination was the most commonly reported personal factor (73%), but it did not predict moon attribution. Instead, the strongest correlates were weather-related sleep complaints, tracking lunar phases, age, and gender, with endorsement increasing with age and being more common among women. Moon-related complaints also peaked during the week after the full moon. These findings suggest that perceived lunar effects on sleep are shaped, at least in part, by attributional and expectation-related processes. Full article

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

The advancing fluency of large language models (LLMs) raises important questions about their ability to emulate complex human traits, including emotional expression and personality, across diverse linguistic and cultural contexts. This study investigates whether state-of-the-art LLMs can convincingly mimic emotional nuance in English and personality markers in Arabic, a critical under-resourced language with unique linguistic and cultural characteristics. We conduct two tasks across six models: Jais, Mistral, LLaMA, GPT-4o, Gemini, and DeepSeek. First, we evaluate whether machine classifiers can reliably distinguish between human-authored and AI-generated texts. Second, we assess the extent to which LLM-generated texts exhibit emotional or personality traits comparable to those of humans. Our results demonstrate that AI-generated texts are distinguishable from human-authored ones (F1 > 0.95), though classification performance deteriorates on paraphrased samples, indicating reliance on superficial stylistic cues. Emotion and personality classification experiments reveal significant generalization gaps: classifiers trained on human data perform poorly on AI-generated texts and vice versa, suggesting LLMs encode affective signals differently from humans. Importantly, augmenting training with AI-generated data enhances performance in the Arabic personality classification task, highlighting the potential of synthetic data to address challenges in under-resourced languages. Model-specific analyses show that GPT-4o and Gemini exhibit superior affective coherence, while LLaMA performs worse. Linguistic and psycholinguistic analyses reveal measurable divergences in tone, authenticity, and textual complexity between human and AI texts. These findings have significant implications for affective computing, authorship attribution, and responsible AI deployment, particularly within under-resourced language contexts where generative AI detection and alignment pose unique challenges. Full article

Background/Objectives: With the increasing incidence of drug counterfeiting and the emergence of personalized medicine, the need for unique marking of solid dosage forms, e.g., tablets, has attracted considerable interest in the current research and development landscape. Besides traditional printing methods, laser marking offers several advantages, as it eliminates the need for organic solvents and enables the generation of precise patterns. However, laser exposure may raise safety concerns regarding the stability of photosensitive drugs in the irradiated dosage forms. Therefore, the aim of the present study was to test the photoprotective effect of titanium dioxide (TiO₂) and its various alternatives, e.g., talc, calcium carbonate (CaCO₃), zinc oxide (ZnO), and black iron oxide (Fe₃O₄), alongside a ready-to-use reference formulation, Opadry^® Brown, which contains TiO₂ (titanium-containing, TC) on nifedipine, a light-sensitive model drug. Methods: Laser marking or short-term laser ablation at different wavelengths (193 nm, 248 nm, 532 nm, and 781 nm) was applied to different coating formulations. As a positive control, prolonged exposure to daylight was applied. The properties and photostability of these formulations were evaluated using several analytical methods (i.e., surface profilometry, Raman spectroscopy, and high-performance liquid chromatography (HPLC)). Results: The TiO₂, ZnO, Fe₃O₄, and Opadry^® TC Brown coatings maintained their color during the long-term study under all conditions. Furthermore, the prepared formulations exhibited different ablation depths and morphological changes depending on the coating and laser type. HPLC measurements confirmed significant differences in the protective ability of various pigments against sunlight and different types of lasers. Nevertheless, the obtained Raman spectra were not in complete agreement with HPLC results, which can be attributed to spectral overlap between key nifedipine degradation markers and excipient signals in the tablet core. Conclusions: Overall, laser treatment of tablets containing photosensitive drugs may induce API decomposition; however, this effect can be minimized or avoided by careful selection of the appropriate combination of laser type and photoprotective pigment. Under the applied experimental conditions, Ti:Sa laser treatment was associated with the lowest degree of nifedipine degradation among all formulations, while ZnO-containing coatings demonstrated the most consistent photoprotective performance against the majority of the tested laser types, while Fe₃O₄-containing coatings provided superior protection during prolonged sunlight exposure and Nd:YAG laser irradiation. Full article

Background: The growing complexity of liquid biopsy in precision oncology demands a structured classification framework that can accommodate its expanding multi-omic scope. As the field has matured from early Tumor Microemboli research—focused on multicellular clusters of circulating tumor cells (CTCs) that drive high-efficiency metastasis—to the broader systemic analysis of the “Tumor Microenvironment” (TME) encompassing malignant and non-malignant components, the need for a hierarchical taxonomy has become evident. Objective: To integrate these diverse data streams into a coherent clinical framework, a multi-tiered classification system is needed. This review proposes a foundational roadmap that formally distinguishes the systemic ecosystem from its physical and functional subsets and highlights their clinical utility in therapeutic decision-making. Proposed Taxonomy: We advocate for the adoption of Circulating Tumor Microenvironment (cTME) as the inclusive term for the systemic environment, encompassing non-cellular factors such as ctDNA, extracellular vesicles, and biophysical attributes. Conversely, physical cellular clusters should be strictly classified as Circulating Tumor Emboli (CTE). Crucially, we define Circulating Tumor-Associated Cells (C-TACs) as the functional cellular subset within the cTME, encompassing single CTCs, CTE, and supporting non-malignant cells like CTECs and CAFs. Clinical Applications: Establishing this distinction allows for the seamless integration of molecular profiling (NGS) and functional assays. We highlight emerging evidence that C-TACs may serve as the primary substrate for Chemo-Response Profiling (CRP), with early proof-of-concept studies reporting high concordance with clinical outcomes that still await independent prospective confirmation. Furthermore, preliminary evidence suggests that identifying these functional units, particularly perioperative CTE, may help predict the efficacy of adjuvant chemotherapy in early-stage malignancies, although this remains to be confirmed in prospective studies. Conclusions: Adopting this unified taxonomy may help advance precision oncology. By recognizing the cTME as the superordinate ecosystem and C-TACs as its functional executors, clinicians may be better positioned to interpret multi-modal liquid biopsy data, providing a conceptual roadmap for integrating these technologies into platforms for personalized cancer management. We emphasize that this framework is intended to be hypothesis-generating and that its clinical applications require prospective validation before routine adoption. Full article

Standard diagnostic categories (International Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM)) were developed as a pragmatic compromise between competing theoretical schools in psychiatry and psychotherapy. Focused on recognizable patterns of symptoms, they produce reliable descriptions and facilitate clinical communication, research, and reimbursement. Such a focus, however, necessarily falls short of the etiological complexity of bodily, personal, interpersonal, and cultural processes that shape human suffering. This article argues that beneath the diversity of approaches seeking to address this gap, a fundamental complementarity emerges—one constitutive of human existence itself: the complementarity between two irreducible ways of being in the world. The first is the organismic–biological dimension, elaborated in Jakob von Uexküll’s biosemiotics: sign-governed, evolutionarily pre-formed processes of meaning-attribution that operate prior to and independent of language. The second is the symbolic–cultural dimension, developed in Ernst Cassirer’s philosophy of symbolic forms: the embedding of human beings in socially created, intersubjectively shared symbol systems through which the world is seen and understood. Although both approaches were published nearly a century ago, this article is not primarily a historical contribution. Rather, it argues that psychopathology and therapy can be understood more fully—and clinical practice enriched—when both dimensions are taken into account as genuinely complementary perspectives. Full article

Background and Objective: Heart failure (HF) is a major cause of morbidity in adults with congenital heart disease (ACHD), who may also have limited access to transplant. Intermittent levosimendan administration has shown benefit in advanced HF due to acquired heart disease, but currently, there are no data on ACHD. We aimed to evaluate the effects of this treatment in ACHD patients with advanced heart failure, focusing on both clinical status and objective outcome measures. Materials and Methods: We conducted a single-center retrospective analysis of ACHD patients aged >18 years with advanced HF who received ≥ three intermittent levosimendan infusions (either 12.5 mg once monthly or 6.25 mg every two weeks over a 6 h infusion) between March 2020 and January 2025 at a tertiary ACHD center. Clinical outcomes during follow-up were compared with those in the year preceding treatment. Primary endpoints included safety and HF-related adverse events, particularly HF hospitalizations. Secondary endpoints included changes in New York Heart Association (NYHA) class, nt-pro-B-natriuretic peptide (nt-proBNP) values, and ventricular systolic function assessed by echocardiography. Results: Twelve patients (median age 44.6 years, 25% female) were included, with heterogeneous congenital diagnoses and advanced HF. Five patients had a systemic right ventricle (sRV) and one had a single ventricle with previous Fontan palliation. During a median follow-up of 1.3 years, intermittent levosimendan was well-tolerated, with no treatment-limiting adverse events. Two patients (16%) required hospitalization for HF during follow-up compared with 8 (66%) in the year preceding treatment. The incidence of HF hospitalizations decreased from 0.83 to 0.20 events per person-year during follow-up (p = 0.03), although findings should be interpreted cautiously given the small sample size and retrospective design. NYHA functional class improved significantly (p = 0.005). While no significant changes were observed in NT-proBNP or left ventricular ejection fraction, patients with a systemic right ventricle (sRV) showed an increase in right ventricular fractional area change (27 ± 7.4% to 30.6 ± 7%, p = 0.02); however, this observation should be regarded as exploratory given the limited sample size. Two deaths occurred, consistent with the severity of the underlying disease and not directly attributable to levosimendan and the Fontan patient received a successful heart and liver transplant. Conclusions: In a small, real-world cohort of ACHD and advanced HF, intermittent levosimendan administration was safe and associated with improved symptoms, reduced HF hospitalizations, and signals of enhanced systemic right ventricular function. These hypothesis-generating findings may help inform future multicenter studies in ACHD patients with advanced HF, suggesting a potential role for intermittent levosimendan in selected patients, while highlighting the need for prospective, adequately powered studies to confirm its efficacy and better define optimal patient selection. Full article

Course recommendation systems based on deep learning have demonstrated powerful feature extraction capabilities in dealing with information overload in massive open online courses (MOOCs), and have become an irreplaceable mainstream method. However, the learner–course interactions are usually scarce in reality, which limits the representation power of course recommendation. In addition, the contribution of learner and course attribute information to course recommendation has not been sufficiently explored by most existing methods. To tackle these challenges, a personalized course recommendation model based on attribute-interaction joint encoding and hypergraph reconstruction (AIHR-PCRM) is proposed in this paper. Specifically, a course hypergraph reconstruction (CHR) method is designed to construct higher-order associations for each course to explore more reliable global collaboration signals. Unlike existing hypergraph constructions that directly take learners as hyperedges, CHR explicitly couples three steps, including invalid learner elimination, high-order reachability induction, and similarity-based hyperedge filtering, to substantially raise the signal-to-noise ratio of the resulting hypergraph. Based on this, a hypergraph global collaborative learning module (HGM) can alleviate the issue of data sparsity. Then, a joint encoding module (JEM) is utilized to enhance learner behavior sequence representations by simultaneously fusing hypergraph-level global signals with attribute-level local semantics. Finally, a bidirectional self-attention module (BSM) is introduced to blend the contextual information of the learner behavior sequence, and to further provide a recommendation. Experimental results on three real-world datasets revealed that the proposed model has already achieved the best recall and ndcg scores compared to those of several existing models. Full article

Background/Objectives: General practitioners (GPs) face critical workforce shortages and high turnover globally. While external factors are known to influence turnover intention (TI), the role of individual psychological traits is less well understood. This study examines the association between Type D personality (TDP) and TI among GPs and the co-occurring statistical associations of perceived occupational stress (POS) and job satisfaction (JS). Methods: A cross-sectional survey was conducted from September to October 2024 among 383 GPs in Longhua District, Shenzhen, China. Participants completed a structured questionnaire assessing socio-demographic characteristics, TDP, POS, JS, and TI. After controlling potential confounders, correlation and regression analyses were performed to assess associations between TDP, POS, JS, and TI. Structural equation modeling (SEM) was conducted to examine the specific indirect associative components within the covariance between TDP and TI. Results: After adjusting for confounding factors, TDP was significantly positively associated with TI (B = 0.71) and POS (B = 0.30), and significantly negatively associated with JS (B = −0.24). In the hypothesized structural model, the proportions of total standardized covariance attributable to the indirect associative paths involving POS alone, JS alone, and the serial combination of POS and JS were 17.28%, 9.90%, and 3.50%, respectively, summing to 30.68% of the model-implied association. Conclusions: GPs with TDP reported a higher level of turnover intention, and this association was statistically accompanied by elevated occupational stress and diminished job satisfaction. Healthcare managers may consider implementing targeted interventions aimed at reducing stress and enhancing satisfaction, particularly among GPs with TDP, although the effectiveness of such strategies requires confirmation in future longitudinal or intervention studies. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

The Draw-a-Person (DAP) test has been a widely used practical instrument in psychological and developmental assessments to measure children’s cognitive development via human-figure drawings. Unfortunately, its traditional scoring process relies on manual inspections conducted by professionals, which is highly subjective and difficult to scale. In order to resolve these problems, this paper presents a hybrid approach that leverages deep-learning-based visual recognition and rule-based structural reasoning for automated evaluation of children’s DAP drawings. Specifically, the model assesses drawings based on 40 features, including anatomical parts, appearance-derived attributes, and high-level structural-drawing relations. A multi-label CNN built upon the ResNet-50 model predicts the visibles, and rule-based geometrical reasoning is adopted to infer structures, including attachments, proportions, symmetries, and placements. These two aspects are combined into a single hybrid representation yielding interpretable feature scoring consistent with developmental-evaluation standards. The proposed framework performs very well across multiple feature analyses, achieving a Micro-F1 of 95.32% and Macro-F1 of 91.72% on the test dataset, and demonstrating robust multi-label classification ability even on rare features. It provides a promising method for evaluating Draw-a-Person drawings, while offering reliable capabilities for feature analysis and scoring with accurate anatomical feature detection and reasonable structural and higher-level feature detection despite the challenging diversity of children’s drawing styles. The enforced rule-based structural reasoning improves interpretability and objectivity. Our future work includes extending the framework to cover further detailed DAP features. Full article

As artificial intelligence (AI) is increasingly integrated into the workplace, employee–AI collaboration is evolving from a personal productivity tool to a social cue that coworkers can observe and interpret. Existing research has largely emphasised the performance and well-being effects of employee–AI collaboration; however, few studies have revealed, from the observer’s perspective, its potential negative spillover mechanisms on coworkers’ helping behaviour. Based on attribution theory, this study constructs a theoretical model of ‘employee–AI collaboration–coworker attributions–coworker helping behaviour’, distinguishing two mechanisms—laziness attribution and responsibility-avoidance attribution—and examines the boundary role of human–AI task interdependence. Study 1, based on 375 two-wave coworker survey responses, tested the hypotheses using hierarchical regression and bootstrapping methods. Study 2 employed a 2 × 2 scenario experiment to further test the effects of employee–AI collaboration and human–AI task interdependence on coworker attributions and willingness to help. The results indicate that higher perceived employee–AI collaboration is associated with lower coworker helping behaviour; laziness attribution and responsibility-avoidance attribution play a mediating role between perceived employee–AI collaboration and coworker helping behaviour. The higher the human–AI task interdependence, the more likely coworkers are to interpret employee–AI collaboration as laziness or responsibility-avoidance, thereby reinforcing the aforementioned negative effects. Full article

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), particularly semaglutide, have demonstrated efficacy for weight loss in obesity; however, up to 40% of weight lost may derive from lean body mass. The ketogenic diet independently improves insulin sensitivity and promotes fat oxidation while preserving lean tissue. This study aimed to describe changes in body composition, insulin sensitivity, and cardiometabolic markers in patients who followed a personalized ketogenic dietary protocol while receiving low-dose semaglutide over a 6-month insulin resistance reversal program. Methods: Seven analyzed adults (six female, one male) with overweight or obesity (baseline BMI 25.6–47.2 kg/m²) participated in a clinician-supervised 6-month program combining a whole-food ketogenic diet with semaglutide (≤1.0 mg/week). Body composition and fasting metabolic markers were assessed at 1, 3, and 6 months. Results: Mean total weight loss was 21.9 kg, of which a mean of 92% was attributable to BIA-estimated fat mass. Skeletal muscle mass was largely preserved as measured by BIA (mean loss 1.2 kg), and one patient gained lean tissue. Fasting insulin declined by a mean of 15.6 µIU/mL. Visceral fat decreased by a mean of 37.0%. Six of seven patients showed reductions in high-sensitivity C-reactive protein. Triglycerides decreased in six of seven patients, and HDL cholesterol increased in all seven. LDL cholesterol responses were heterogeneous. Conclusions: In this small, uncontrolled case series, combining a ketogenic diet with low-dose semaglutide was associated with substantial fat loss, apparent preservation of lean mass as measured by BIA, and improvements in insulin sensitivity and cardiometabolic markers. Because the semaglutide dose and dietary protocol were individualized to each patient’s response, the program illustrates a personalized approach to insulin resistance. These preliminary findings are hypothesis-generating and warrant confirmation in controlled prospective studies. Full article

Global food production must reduce environmental impact while meeting rising demand for dietary protein. Plant-based meats aim to preserve the sensory and cultural role of animal meat while lowering greenhouse gas emissions, land use, and health risks. Advances in protein structure and flavor chemistry have improved product quality, yet consumers continue to prioritize taste and texture over sustainability, and systematic large-scale consumer surveys are scarce. It remains unclear how plant-based products rank against animal benchmarks and which product attributes most strongly influence overall liking. Here we show, in a large-scale blinded in-person sensory evaluation across 14 product categories, 2684 consumers, more than 11,000 product evaluations and 800,000 data points, that plant-based products still trail animal benchmarks at the category average level but approach parity in selected formats. Plant-based unbreaded chicken filets, chicken nuggets, and burgers achieved mean overall liking scores of 5.1, 4.9, and 5.2, differing from the animal benchmarks by only $\Delta$ = 0.1, 0.2, and 0.3 points on a seven-point scale. For unbreaded chicken filets and burgers, 48% and 47% of the participants rated the plant-based product the same as or better than the animal benchmark. Categories with higher sensory parity captured 5–14% market share compared with less than 1% for low-parity categories. Penalty analysis identified savoriness, aftertaste, juiciness, and tenderness as the strongest determinants of liking. These findings show that sensory parity is technically achievable but not yet consistent across product types. By publicly sharing all the sensory, preference, and market-linked data, we establish an open benchmark for alternative protein performance to democratize research and accelerate principled data-driven innovation. Full article

In ecosystems of algorithmic visibility, Instagram bios operate as high salience microdiscourses of self-presentation and signaling. We examine whether observable bio attributes are associated with visibility and interaction among Brazilian sports journalists. We analyzed 151 public Instagram profiles (≥100,000 followers) and extracted bios and profile metadata via automated collection. Bio attributes (length, emojis, @mentions, hashtags, location, informational cues, and external links) were related to followers, average likes and comments, and engagement rate (primary outcome) using Spearman rank correlations under conservative interpretation. Emojis and mentions were near universal; links were common; hashtags and locations were rare. Associations were small and exploratory: personal information correlated negatively with followers; hashtags correlated positively with likes and comments but relied on five cases; and references to other platforms correlated negatively with engagement. Overall, bios appear to function mainly as signaling infrastructures, with any performance effects likely indirect and mediated by content practices and platform exposure within this ecosystem. Full article