Search Results (4,316)

This article illuminates the cosmology of Raa Haqi (often called Dersim Alevism or Kurdish Alevism), a rarely examined strand within Alevi Studies. Existing scholarship’s emphasis on identity politics and sparse ethnography has left Raa Haqi’s mythological and cosmological dimensions underexplored. This paper approaches Raa Haqi through a dual authority framework: (1) Ocak lineages and Ocak–talip relations—sustained by kinship institutions like kirvelik, musahiplik, and communal rites such as the cem—and (2) jiares, non-human agents from the Batın realm that manifest in Zahir as sacred places, objects, and animals. Methodologically, I conduct a close, motif-based reading of folktales compiled by Caner Canerik (2019, Dersim Masalları I), treating them as ethnographic windows into living theology. The analysis shows that tales encode core principles—rızalık (mutual consent), ikrar (vow), sır (the secret knowledge), fasting and calendrical rites, ritual kinship, and moral economies involving humans, animals, and Batın beings. Dreams, metamorphosis, and jiare-centered orientations structure time–space, ethics, and authority beyond the Ocak, including in individual re-sacralizations of objects and sites. I conclude that these narratives do not merely reflect belief; they actively transmit, test, and renew Raa Haqi’s cosmological order, offering Alevi Studies a theory-grounded, source-proximate account of Kurdish Alevi mythic thought. Full article

Artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, has attracted attention for its potential to enhance diagnostic decision-making. This study aims to compare the decision-making tendencies of clinicians from different disciplines regarding implant planning with the approach of ChatGPT and to evaluate the potential integration of AI into clinical decision-making. This cross-sectional, survey-based study compared the implant planning decisions of prosthodontic and surgical residents with those of ChatGPT 4.0 and each other. Fourteen cases were presented to 20 prosthodontics, 20 oral and maxillofacial surgery residents, and ChatGPT. Participants selected the option they found most appropriate; responses were compared case by case using chi-square tests with p < 0.05. The responses of residents showed no statistically significant differences. However, comparisons between the AI and clinicians revealed significant differences in 11 of the scenarios. In clinical scenarios where no differences were found, both human and artificial intelligence tended to make the same choices on less complicated cases. While AI is increasingly advancing in clinical decision-making, its responses to clinical scenarios may show some inconsistencies. AI demonstrated similarity in simple cases but diverged in complex ones. While promising as a support, broader studies with diverse scenarios are necessary to enhance the integration potential of AI in clinical decision-making. Full article

Municipal solid waste (MSW) refers to solid and semi-solid waste generated during human production and daily activities. The process of incinerating such waste, known as municipal solid waste incineration (MSWI), serves as a critical method for reducing waste volume and recovering resources. Automatic online recognition of flame combustion status during MSWI is a key technical approach to ensuring system stability, addressing issues such as high pollution emissions, severe equipment wear, and low operational efficiency. However, when manually selecting optimized features and hyperparameters based on empirical experience, the MSWI flame combustion state recognition model suffers from high time consumption, strong dependency on expertise, and difficulty in adaptively obtaining optimal solutions. To address these challenges, this article proposes a method for constructing a flame combustion state recognition model optimized based on reinforcement learning (RL), long short-term memory (LSTM), and parallel differential evolution (PDE) algorithms, achieving collaborative optimization of deep features and model hyperparameters. First, the feature selection and hyperparameter optimization problem of the ViT-IDFC combustion state recognition model is transformed into an encoding design and optimization problem for the PDE algorithm. Then, the mutation and selection factors of the PDE algorithm are used as modeling inputs for LSTM, which predicts the optimal hyperparameters based on PDE outputs. Next, during the PDE-based optimization of the ViT-IDFC model, a policy gradient reinforcement learning method is applied to determine the parameters of the LSTM model. Finally, the optimized combustion state recognition model is obtained by identifying the feature selection parameters and hyperparameters of the ViT-IDFC model. Test results based on an industrial image dataset demonstrate that the proposed optimization algorithm improves the recognition performance of both left and right grate recognition models, with the left grate achieving a 0.51% increase in recognition accuracy and the right grate a 0.74% increase. Full article

This paper explores the development and implementation of a brain–computer interface (BCI) system that utilizes electroencephalogram (EEG) signals for real-time monitoring of attention levels to control unmanned aerial vehicles (UAVs). We propose an innovative approach that combines spectral power analysis and machine learning classification techniques to translate cognitive states into precise UAV command signals. This method overcomes the limitations of traditional threshold-based approaches by adapting to individual differences and improving classification accuracy. Through comprehensive testing with 20 participants in both controlled laboratory environments and real-world scenarios, our system achieved an 85% accuracy rate in distinguishing between high and low attention states and successfully mapped these cognitive states to vertical UAV movements. Experimental results demonstrate that our machine learning-based classification method significantly enhances system robustness and adaptability in noisy environments. This research not only advances UAV operability through neural interfaces but also broadens the practical applications of BCI technology in aviation. Our findings contribute to the expanding field of neurotechnology and underscore the potential for neural signal processing and machine learning integration to revolutionize human–machine interaction in industries where dynamic relationships between cognitive states and automated systems are beneficial. Full article

Advanced composite materials and manufacturing technologies are critical to sustain human presence in space. Mechanical testing and analysis are needed to elucidate the effect of processing parameters on composites’ material properties. In this study, test specimens are 3D printed via a fused-filament fabrication (FFF) approach from a basalt moon dust-polylactic acid (BMD-PLA) composite filament and from pure PLA filament. Compression and tensile testing were conducted to determine the yield strength, ultimate strength, and Young’s modulus of specimens fabricated under several processing conditions. The maximum compressive yield strength for the BMD-reinforced samples is 27.68 MPa with print parameters of 100% infill, one shell, and 90° print orientation. The maximum compressive yield strength for the PLA samples is 63.05 MPa with print parameters of 100% infill, three shells, and 0° print orientation. The composite samples exhibit an increase in strength when layer lines are aligned with loading axis, whereas the PLA samples decreased in strength. This indicates a fundamental difference in how the composite behaves in comparison to the pure matrix material. In tension, test specimens have unpredictable failure modes and often broke outside the gauge length. A portion of the tension test data is included to help guide future work. Full article

Brucellosis remains an underreported zoonotic disease in Ecuador. Its control program in cattle integrates diagnostic testing, vaccination, and eradication incentives, although participation is largely voluntary. Since 2025, vaccination has become compulsory nationwide. Human surveillance remains largely passive, and strain-level data are very limited. This study applied an integrated approach, combining serology (Rose Bengal and SAT-EDTA), microbiological culture, and molecular diagnostics, to assess the presence and diversity of Brucella spp. in cattle and pigs from six slaughterhouses in the northern Andean highlands. A total of 2054 cattle and 1050 pigs from Carchi, Imbabura, and Pichincha were sampled. Among cattle, 133 (6.5%; 95% CI: 5.5–7.6) were seropositive, and viable B. abortus strains were isolated from 17 (12.8%). Genus identification was confirmed by IS711-PCR, while species- and biovar-level differentiation was achieved with AMOS-PCR; additional assays targeting the ery gene and RB51 marker were used to distinguish field from vaccine strains. Biotyping and molecular analysis revealed a predominance of B. abortus biovar 4 (13/17 isolates) over biovar 1, all confirmed as field strains. In pigs, 10 animals (0.95%) tested seropositive, but no isolates were recovered, highlighting limitations of serology in swine. Most livestock, including the positives, originated locally, reinforcing the representativeness of our findings. The successful isolation and molecular characterization of B. abortus demonstrates the value of combining diagnostic strategies beyond serology. These results underscore the utility of active surveillance when supported by traceability systems; this approach may also contribute to guide interventions to reduce infection risk in livestock and humans. Full article

Background/Objectives: Spheroid cultures in Matrigel are routinely used to study cell behaviour in complex 3D settings, thereby generating preclinical models of disease. Ideally, researchers would like to modulate gene expression ‘in situ’ for testing novel gene therapies while conserving the spheroid architecture. Here, we aim to provide an efficient method to transfect small RNAs (such as microRNAs and small interfering RNAs, i.e., siRNAs) into human induced pluripotent stem cell (iPSC)-derived 3D lung spheroids, specifically alveolar type II epithelial cells (iAT2) and basal cell (iBC) spheroids. Methods: Transfection of iAT2 spheroids within 3D Matrigel ‘in situ’, whole spheroids released from Matrigel or spheroids dissociated to single cells was explored via flow cytometry using a fluorescently labelled siRNA. Validation of the transfection method was performed in iAT2 and iBC spheroids using siRNA and miRNA mimics and measurement of specific target expression post-transfection. Results: Maximal delivery of siRNA was achieved in serum-free conditions in whole spheroids released from the Matrigel, followed by whole spheroids ‘in situ’. ‘In situ’ transfection of SFTPC-siRNA led to a 50% reduction in the SFTPC mRNA levels in iAT2 spheroids. Transfection of miR-29c mimic and miR-21 pre-miR into iAT2 and iBC spheroids, respectively, led to significant miRNA overexpression, together with a significant decrease in protein levels of the miR-29 target FOXO3a. Conclusions: This study demonstrates successful transfection of iPSC-derived lung spheroids without disruption of their 3D structure using a simple and feasible approach. Further development of these methods will facilitate functional studies in iPSC-derived spheroids utilizing small RNAs. Full article

Deep learning models have achieved significant progress in developing self-driving algorithms. Despite their advantages, these algorithms typically require substantial amounts of data for effective training. Critical driving data, in particular, is essential for enhancing training efficiency and ensuring driving safety. However, existing methods for identifying critical data often rely on human prior knowledge or are disconnected from the training of self-driving algorithms. In this paper, we introduce a novel data distillation technique designed to autonomously identify critical data for training self-driving algorithms. We conducted experiments with both numerical simulations and the NGSIM dataset, which consists of real-world car trajectories on highway US-101, to validate our approach. In the numerical experiments, the distillation method achieved a test root mean squared error of 1.933 using only 200 distilled training data samples, demonstrating a significant improvement in data efficiency compared to the 1.872 test error obtained with 20,000 randomly sampled training samples. The distilled critical data represents only 1% of the original dataset, optimizing data usage and significantly enhancing computational efficiency. For real-world NGSIM data, we demonstrate the performance of the proposed method in scenarios with extremely sparse data availability and show that our proposed data distillation method outperforms other sampling baselines, including Herding and K-centering. These experimental results highlight the capability of the proposed method to autonomously identify critical data without relying on human prior knowledge. Full article

Background/Objectives: Head and neck cancer (HNC) is the sixth most common cancer worldwide, with a high mortality, particularly from head and neck squamous cell carcinoma (HNSCC). Although some therapeutic strategies are available, they might cause severe side effects. For example, surgery may result in disfigurement and functional loss, severely impacting the patient’s quality of life. Thus, minimally invasive and more effective alternatives are needed. Gold nanoparticle (AuNP)-mediated photothermal therapy (PTT) is a promising approach for HNC, which relies on AuNP photothermal efficiency and tumor localization. This study aimed to synthesize and characterize AuNPs, evaluate their safety without laser activation, and assess their efficacy with laser activation. Methods and Results: Their physicochemical and photostability over three months and sterility were confirmed. In vitro safety was tested using human non-cancerous and HNC cell lines, while in vivo biocompatibility was evaluated in the hen’s egg chorioallantoic membrane (CAM) model, with no adverse effects observed. Upon laser activation, AuNPs reduced HNC cell viability by 50–70%, including HNSCC lines. In vivo biodistribution studies showed that AuNPs remained at the injection site for up to one month without toxicity. Conclusions: Overall, the developed AuNP formulation demonstrates stability, biocompatibility, and prolonged local retention, key attributes for effective and targeted PTT. These findings support the potential of AuNP-mediated photothermal therapy as a promising treatment modality for HNC, although further preclinical and clinical studies are needed to optimize treatment parameters. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

The digital economy is significantly transforming the global economic environment and has emerged as the primary driver behind China’s high-quality development. The comprehensive melding of the cultural and tourism sectors (CTI) serves as a strategic approach to boost regional competitiveness and enhance public welfare. This study investigates the mechanisms and boundary conditions through which the growth of the digital economy across China’s 31 provinces from 2011 to 2023 impacts CTI, aiming to address existing research gaps related to micro-level transmission mechanisms and the analysis of contextual variables. Utilizing a two-way fixed-effects moderated mediation model complemented by instrumental variable (IV-2SLS) regression for testing endogeneity, the research uncovers intricate interactions among the digital economy, CTI, and significant influencing factors. The results strongly suggest that advancements in the digital economy substantially facilitate the integration of cultural and tourism sectors. This beneficial effect is partially mediated through two primary channels: the construction of new infrastructure and enhancements in employment quality, underscoring the critical role of both material and human capital in digital empowerment. Significantly, this research uniquely identifies that new quality productive forces (NQP) have a notable negative moderating impact on the link between the digital economy and cultural–tourism integration. This indicates that in provinces exhibiting high levels of NQP, the positive influence of the digital economy on cultural–tourism integration is considerably diminished. This unexpected finding can be interpreted through mechanisms such as resource dilution, varied integration pathways or maturity effects, along with differences in developmental stages and priorities. Furthermore, it resonates well with the resource-based view, innovation ecosystem theory, and dynamic capability theory. Instrumental variable regression further substantiates the notable positive influence of the digital economy on the integration of cultural tourism. This approach effectively tackles potential endogeneity concerns and reveals the upward bias that may exist in fixed-effects models. The findings contribute significantly to theoretical frameworks by enhancing the understanding of the intricate mechanisms facilitating the digital economy and, for the first time, innovatively designating NQP as a surprising key boundary condition. This enriches theories related to industrial advancement and resource allocation in the digital age. On a practical note, the research provides nuanced and differentiated policy guidance aimed at optimizing pathways for integration across various Chinese provinces at different stages of development. Additionally, it underscores significant implications for other developing nations engaged in digital tourism growth, thereby improving its global relevance. Full article

Rift Valley fever (RVF) is a re-emerging vector-borne zoonotic disease that causes outbreaks in humans and animals across Africa. To better understand RVF at human–animal interfaces, a prospective longitudinal survey of people, livestock, and mosquitoes was conducted from 2016 to 2018, in two regions of Tanzania, with distinct climatic zones (Iringa and Morogoro). Molecular and serological tools for testing (RT-qPCR and IgM/IgG ELISA) for RVF virus (RVFV) were used to assess infection and exposure in people and animals. Mosquitoes were collected quarterly from 10 sentinel locations. In total, 1385 acutely febrile humans, 4449 livestock, and 3463 mosquito pools were tested. In humans, IgM seroprevalence was 3.75% (n = 52/1385), and overall seroprevalence (IgM and/or IgG positive) was 8.30% (n = 115/1385). People from Iringa had a higher exposure risk than those from Morogoro (aOR 2.63), and livestock owners had an increased risk compared to non-owners (aOR 2.51). In livestock, IgM seroprevalence was 1.09%, while overall seroprevalence was 10.11%. A total of 68.4% of herds had at least one seropositive animal. Sentinel animal follow-up revealed that the probability of seroconversion was significantly higher in Morogoro. Low-level RVFV RNA was detected in 8 human and 22 mosquito pools. These findings indicate active transmission among vectors, livestock, and people during the study period, highlighting the need for One Health surveillance approaches for RVFV and other arboviruses. Full article

Parkinson’s disease (PD), a progressive neurodegenerative disorder affecting over 10 million people worldwide, necessitates continuous symptom monitoring to optimize treatment and enhance quality of life. Effective communication between patients and healthcare providers (HCPs) is vital but often hindered by fragmented data and cognitive impairments. PARKA AI, a novel iOS application, leverages Apple Watch HealthKit data (e.g., tremor detection, mobility metrics, heart rate, and sleep patterns) and integrates it with self-reported logs (e.g., mood, medication adherence) to empower PD self-management and improve patient–HCP interactions. Employing a human-centered design approach, we developed a high-fidelity prototype using a large language model (LLM)— Google Gemini 1.5 Flash—to process and analyze self-reports and objective sensor-derived data from Apple Healthkit to generate patient-friendly summaries and concise HCP reports. PARKA AI provides accessible data visualizations, personalized self-management tools, and streamlined HCP reports to foster engagement and communication. This paper outlines the derived design requirements, prototype features, and illustrative use cases to show how LLMs can be used in digital health tools. Future work will focus on real-world usability testing to validate the application’s efficacy and accessibility. Full article

This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic oncology drug combinations with mechanistic insights. Using a retrieval-augmented generation (RAG) approach, over 50,000 in vitro drug pair assay results and 1631 human clinical trial and preclinical test entries were integrated to enhance predictive accuracy and explainability. Validation achieved an F1 score of 0.80, demonstrating the framework’s potential to streamline drug discovery and improve translational strategies in cancer treatment. Full article

The concept of green cities promotes efficient utilisation of resources, with urban rooftop farms (URFs) being a key initiative involving a series of actions and decisions between stakeholders and the state. The new public governance discourse (NPGD) emphasises this interplay between the state, the market, and civil society to strengthen collaboration and network-driven social innovation and requires a comprehensive understanding of human/stakeholder behaviour. In this study, we explore the connection between organisational rational choice in URF policy development and social innovation. Through a review of the existing literature on URF policies and a case study of Dhaka, Bangladesh, we investigate the development of a comprehensive policy via participation and collaboration, considering the popularity of URFs and the absence of governing mechanisms in Dhaka. The results suggest that, despite the rising popularity of URFs in Dhaka, existing policies and strategies lack clarity. The review findings suggest that a participatory and co-productive approach is optimal for URF policy formulation. This would require active engagement from community members, local governments, and non-governmental organisations and gaining an enhanced understanding of stakeholder dynamics by testing stakeholder salience and co-production theories for successful URF governance. Full article