Search Results (791)

This study is concerned with the formal and functional development of the pragmatic marker not gonna lie. It comprises a detailed investigation into the usage and development of not gonna lie in American English. This study shows that not gonna lie develops from the clause NP BE not going to lie to NP. From its earliest attestations onward, the marker occurs in contexts carrying face threats, which points towards face-threat mitigation as its main function. This discourse function can only be observed for variants with first-person subjects and you in the prepositional phrase (if present). The later omission of elements through the course of the development indicates an increase in syntactic autonomy. The remaining chunk, not gonna lie, leaves little room for variability and is dominated by its discursive function. The findings are interpreted through the lens of usualization as described in the Entrenchment-and-Conventionalization Model. This dynamic, usage-based and cognitive model of language use and change lends itself to providing a fine-grained description and explanation of the grammaticalization-like processes observed in this case study. Full article

Recent advances in precision manufacturing and high-end equipment technologies have imposed ever more stringent requirements on the accuracy, real-time performance, and lightweight design of online steel strip surface defect detection systems. To reconcile the persistent trade-off between detection precision and inference efficiency in complex industrial environments, this study proposes StripSurface–YOLO, a novel real-time defect detection framework built upon YOLOv8n. The core architecture integrates an Efficient Cross-Stage Local Perception module (ResGSCSP), which synergistically combines GSConv lightweight convolutions with a one-shot aggregation strategy, thereby markedly reducing both model parameters and computational complexity. To further enhance multi-scale feature representation, this study introduces an Efficient Multi-Scale Attention (EMA) mechanism at the feature-fusion stage, enabling the network to more effectively attend to critical defect regions. Moreover, conventional nearest-neighbor upsampling is replaced by DySample, which produces deeper, high-resolution feature maps enriched with semantic content, improving both inference speed and fusion quality. To heighten sensitivity to small-scale and low-contrast defects, the model adopts Focal Loss, dynamically adjusting to sample difficulty. Extensive evaluations on the NEU-DET dataset demonstrate that StripSurface–YOLO reduces FLOPs by 11.6% and parameter count by 7.4% relative to the baseline YOLOv8n, while achieving respective improvements of 1.4%, 3.1%, 4.1%, and 3.0% in precision, recall, mAP₅₀, and mAP_50:95. Under adverse conditions—including contrast variations, brightness fluctuations, and Gaussian noise—SteelSurface-YOLO outperforms the baseline model, delivering improvements of 5.0% in mAP₅₀ and 4.7% in mAP_50:95, attesting to the model’s robust interference resistance. These findings underscore the potential of StripSurface–YOLO to meet the rigorous performance demands of real-time surface defect detection in the metal forging industry. Full article

The search for substances that increase the immunity of bees is becoming a necessity in the era of various environmental threats and the declining immunocompetence of these insects. Therefore, we tested the biological and physicochemical properties of 7-diethylamino-4-hydroxycoumarin (7DOC). In a cage test, two groups of bees were created: a control group fed with sugar syrup and an experimental group fed with sugar syrup with the addition of 7DOC. In each group, the longevity of the bees was determined and the protein concentrations and antioxidant activities in the bees’ hemolymph were determined. The bees fed with 7DOC lived 2.7 times longer than those in the control group. The protein concentrations and activities of SOD, CAT, GPx and GST, as well as the TAC levels, were significantly higher in the hemolymph of the supplemented workers. To confirm these potent biological properties of 7DOC, the UV-Vis spectra, emission and excitation of fluorescence, synchronous spectra and finally the fluorescence lifetimes of this compound were measured using the time-correlated single photon counting method, in various environments differing in polarity and in the environment applied in bee research. This compound was shown to be sensitive to changes in solvent polarity. The spectroscopic assays were complemented with crystallographic tests of the obtained monocrystals of the aforementioned compounds, which attested to the aggregation effects observed in the spectra measurements for the selected coumarin. The research results confirm that this compound has the potential to be implemented in apiary management, which will be our application goal, but further research into apiary conditions is required. Full article

The proliferation of Internet of Things (IoT) swarms—comprising billions of low-end interconnected embedded devices—has transformed industrial automation, smart homes, and agriculture. However, these swarms are highly susceptible to firmware anomalies that can propagate across nodes, posing serious security threats. To address this, we propose a novel Remote Attestation (RA) framework for real-time firmware verification, leveraging Relational Graph Neural Networks (RGNNs) to model the graph-like structure of IoT swarms and capture complex inter-node dependencies. Unlike conventional Graph Neural Networks (GNNs), RGNNs incorporate edge types (e.g., Prompt, Sensor Data, Processed Signal), enabling finer-grained detection of propagation dynamics. The proposed method uses runtime Static Random Access Memory (SRAM) data to detect malicious firmware and its effects without requiring access to firmware binaries. Experimental results demonstrate that the framework achieves 99.94% accuracy and a 99.85% anomaly detection rate in a 4-node swarm (Swarm-1), and 100.00% accuracy with complete anomaly detection in a 6-node swarm (Swarm-2). Moreover, the method proves resilient against noise, dropped responses, and trace replay attacks, offering a robust and scalable solution for securing IoT swarms. Full article

The use of Verifiable Credentials under the new eIDAS Regulation introduces privacy concerns, particularly during revocation status checks. This paper proposes a privacy-preserving revocation mechanism tailored to the European Digital Identity Wallet and its Architecture and Reference Framework. Our method publishes a daily randomized revocation list as a cascaded Bloom filter, enhancing unlinkability by randomizing revocation indexes derived from ARF guidelines. The implementation extends open-source components developed by the European Committee. This work demonstrates a practical, privacy-centric approach to revocation in digital identity systems, supporting the advancement of privacy-preserving technologies. Full article

Medical imaging is crucial for diagnosing, monitoring, and treating medical conditions. The medical reports of radiology images are the primary medium through which medical professionals can attest to their findings, but their writing is time-consuming and requires specialized clinical expertise. Therefore, the automated generation of radiography reports has the potential to improve and standardize patient care and significantly reduce the workload of clinicians. Through our work, we have designed and evaluated an end-to-end transformer-based method to generate accurate and factually complete radiology reports for X-ray images. Additionally, we are the first to introduce curriculum learning for end-to-end transformers in medical imaging and demonstrate its impact in obtaining improved performance. The experiments were conducted using the MIMIC-CXR-JPG database, the largest available chest X-ray dataset. The results obtained are comparable with the current state of the art on the natural language generation (NLG) metrics BLEU and ROUGE-L, while setting new state-of-the-art results on F1 examples-averaged F1-macro and F1-micro metrics for clinical accuracy and on the METEOR metric widely used for NLG. Full article

A preservationist Christian sexual ethic affirms heterosexuality as the only normal and natural expression of wholeness in human intimacy, relationships, and lifestyle. However, revisionist critics would maintain that the central problem of the preservationist perspective is the perceived lack of a compelling verifier. A revisionist Christian ethic embraces homosexuality as an alternative form of wholeness in human relationships and lifestyle. Preservationist critics would maintain that the central problem of the revisionist perspective is the perceived lack of a compelling verifier. They would also identify an additional problem for the revisionist position, namely, the perceived problem of self-contradiction. It may seem to some that problems alleged for a particular side cannot be leveraged to the advantage of the opposing side in this debate. Moreover, even the external judgment that a problem exists for a perspective is disputed within that perspective. This may seem to lead to stalemate between the opposing perspectives. However, it may be that a verifier or vindicator exists for one of these perspectives that would commend that perspective as more acceptable than the other. A vindicator for a perspective need only to reinforce that perspective, while a verifier must be an empirically attested ground for the perspective. In this article, I will compare verifiers and vindicators on each side of the debate and inquire whether there is an ace to be found in any of these arenas. I find that a preservationist Christian sexual ethic speaks for itself when its vehicles of verification and vindication are addressed in dialogue with a revisionist perspective. My aim is to increase the possibility of moving the discussion forward in the debate over normative human sexuality. Full article

Embedded system networks are widely deployed across various domains and often perform mission-critical tasks, making it essential for all nodes within the system to be trustworthy. Traditionally, each node is equipped with a discrete Trusted Platform Module (dTPM) to ensure network-wide trustworthiness. However, this study proposes a cost-effective system architecture that deploys software-based TPMs (SWTPMs) on the majority of nodes, while reserving dTPMs for a few central nodes to maintain overall system integrity. The proposed architecture employs IBMACS for system integrity reporting. In addition, a database-based anomaly detection (AD) agent is developed to identify and isolate untrusted nodes. A traffic anomaly detection agent is also introduced to monitor communication between servers and clients, ensuring that traffic patterns remain normal. Finally, a custom measurement kernel is implemented, along with an activation agent, to enforce a measured boot process for custom applications during startup. This architecture is designed to safeguard mission-critical embedded systems from malicious threats while reducing deployment costs. Full article

Unprotected exposures to infected poultry or wild birds, and/or to the related avian influenza virus (AIV)-contaminated environments, could account for AIV infection in workers. This study was aimed at highlighting the ecological interfaces related to domestic poultry and wild birds posing an occupational risk regarding AIV. A search of all the articles investigating the possible presence of AIV in workers attested through virological and serological techniques and published up to August 2019 was performed on PubMed and Scopus electronic databases. Ninety-four articles consisting of 11 virological, 67 serological, and 16 mixed (both virological and serological) studies were obtained. Both virological and serological evidences of AIV infection were mainly related to H5, H7, and H9 subtypes. In addition, one piece of virological evidence for H10 subtype was reported, whereas seropositivity to all hemagglutinin subtypes from H4 to H11 was detected by serological studies. The number of AIV subtype exposures inferred from serological results showed that workers from large-scale industrial poultry farms and markets were the most represented, whereas workers from small-scale and backyard poultry farms showed seropositivity to a greater number of AIV subtypes. Workers exposed to wild bird habitats tested seropositive to H5, H9, and H11. In the occupational settings, direct contact with infected poultry or wild birds could account for AIV infection in workers. This AIV spillover can result in severe health complications for the workers, also posing a potential pandemic risk to the general population. From a public health perspective, the surveillance and early detection of AIV in workplaces should be a priority faced by a one-health approach. Full article

WSNs play a critical role in many applications that require network reliability, such as environmental monitoring, healthcare, and industrial automation. Thus, fault detection and self-healing are two effective mechanisms for addressing the challenges of node failure, communication disruption, a energy constraints faced by WSNs. This paper presents an intelligent framework based on Light Gradient Boosting Machine integration for fault detection and a Flying Fox Optimization Algorithm in dynamic self-healing. The LGBM model provides very accurate and scalable performance related to effective fault identification, whereas FFOA optimizes the recovery strategies to minimize downtown and maximize network resilience. Extensive performance evaluation of the developed system using a large dataset was presented and compared with the state-of-the-art heuristic-based traditional methods and machine learning models. The results showed that the proposed framework could achieve 94.6% fault detection accuracy, with a minimum of 120 milliseconds of recovery time and network resilience of 98.5%. These results hence attest to the efficiency of the proposed approach in ensuring robust and adaptive WSN operations toward the quest for enhanced reliability within dynamic and resource-constrained environments. Full article

Mohandas Gandhi (1869–1948) and Aurobindo Ghose (1872–1950) hold distinct, yet overlapping, positions on the topic of war and peace, violence and nonviolence, and how evil ought to best be confronted. To some extent, the overlaps in their views can be seen as an effect of them basing their respective ideals on a shared foundation of Hindu teaching. More specifically, at least some portion of this overlap can potentially be seen as a function of the influence exerted upon both of these thinkers by the teachings of Swami Vivekananda, who was an inspiration to many modern Hindu thinkers, including both Gandhi and Ghose, as both figures attest. This paper will argue, apart from any historical influence he may or may not have had upon them, that Gandhi’s and Ghose’s views both, in different senses, comport well with the teaching of Swami Vivekananda. Specifically, the argument will be what could be called the utopian and realist orientations of Gandhi and Ghose, respectively, regarding the topic of violence, and we can find a logical reconciliation in Vivekananda’s philosophy of karma yoga: the path to liberation through service to the suffering beings of the world. Full article

The elevational patterns of plant β diversity and their underlying drivers are known to be scale-dependent, but pinpointing the spatial scales at which different ecological processes occur remains challenging using traditional field inventory methods. Remote sensing has emerged as a promising alternative, providing continuous spatial data for monitoring plant diversity. In this study, we used field inventory data and corresponding Sentinel-2 images from a subtropical mountain to simulate pooled assemblages and assess the potential of using multispectral satellite images in predicting tree β diversity. We further examined the scale-dependent elevational gradient of the spectral β diversity and identified primary topographic variables across different spatial extents (0.16–64 ha). The spectral β diversity showed a consistently positive relationship with the inventory β diversity calculated using various indices (average pairwise Sørensen, Jaccard, and Bray–Curtis dissimilarities, as well as multi-community differentiation measures based on Hill numbers), with the strongest correlation observed for abundance-weighted indices and images from early spring and late autumn (R²_max = 0.63). However, a null model-derived β deviation showed only a weak correlation between remote sensing and field-based measures. A declining trend in the spectral β diversity with an increasing elevation was observed and became more pronounced at larger extents. The topographic heterogeneity, represented by the slope and northness, explained the elevational gradients at spatial extents >4 ha, attesting the significant role of environmental filtering in shaping plant diversity patterns, even at fine scales. While the northness was more influential at smaller spatial extents (<4 ha), the slope had a stronger impact at broader spatial extents (>4 ha). This study showcases the potential of using readily available remote sensing data to address difficult questions in plant diversity research. Full article

This research investigates the integration of quantum hardware-assisted security into critical applications, including the Industrial Internet-of-Things (IIoT), Smart Grid, and Smart Transportation. The Quantum Physical Unclonable Functions (QPUF) architecture has emerged as a robust security paradigm, harnessing the inherent randomness of quantum hardware to generate unique and tamper-resistant cryptographic fingerprints. This work explores the potential of Quantum Computing for Security-by-Design (SbD) in the Industrial Internet-of-Things (IIoT), aiming to establish security as a fundamental and inherent feature. SbD in Quantum Computing focuses on ensuring the security and privacy of Quantum computing applications by leveraging the fundamental principles of quantum mechanics, which underpin the quantum computing infrastructure. This research presents a scalable and sustainable security framework for the trusted attestation of smart industrial entities in Quantum Industrial Internet-of-Things (QIoT) applications within Industry 4.0. Central to this approach is the QPUF, which leverages quantum mechanical principles to generate unique, tamper-resistant fingerprints. The proposed QPUF circuit logic has been deployed on IBM quantum systems and simulators for validation. The experimental results demonstrate the enhanced randomness and an intra-hamming distance of approximately 50% on the IBM quantum hardware, along with improved reliability despite varying error rates, coherence, and decoherence times. Furthermore, the circuit achieved 100% reliability on Google’s Cirq simulator and 95% reliability on IBM’s quantum simulator, highlighting the QPUF’s potential in advancing quantum-centric security solutions. Full article

This study explored whether sleep duration, insomnia, social jetlag, and circadian preference predicted adolescents’ risk of anxiety and depression two years later. High school students initially aged 16–17 years were, in 2019 and 2021, invited to a web-based survey assessing sleep patterns, insomnia, circadian preference, anxiety, and depression. Sleep duration, insomnia, circadian preference, depression, and anxiety were assessed using the Munich ChronoType Questionnaire, the Bergen Insomnia Scale, the reduced Morningness–Eveningness Questionnaire, the Patient Health Questionnaire-9, and the Generalized Anxiety-Disorder 7, respectively. Analyses were conducted using logistic regression analyses. The analytic longitudinal sample comprised 1456 students (initial mean age 16.4 years; 61.4% girls). Short school night sleep duration, chronic insomnia, and more severe insomnia symptoms at baseline predicted greater risk of anxiety and depression at follow-up when controlled for anxiety and depression at baseline. Neither free night sleep duration nor social jetlag at baseline were related to the risk of anxiety and depression at follow-up. When circadian preference was investigated continuously, greater morningness at baseline predicted lower risk of anxiety and depression at follow-up. When circadian preference was investigated categorically, evening preference type was associated with higher risk of depression at follow-up than intermediate preference type, while the prospective risk of anxiety and depression otherwise did not differ in relation to circadian preference. The results attest to prospective associations between adolescent sleep problems at baseline and later risk of anxiety and depression. Full article

The effectiveness and uses of chitosan nanoparticles (CNPs) in drug delivery systems are examined in this work. Important results include the improved drug encapsulating efficiency: CNPs showed up to 90% encapsulation for different therapeutic agents. Furthermore, the research shows that CNPs provide extended-release patterns, greatly enhancing medication bioavailability especially for hydrophobic compounds. One interesting outcome was the greater drug stability in acidic surroundings, which are common in the stomach, where CNPs turn into a gel and later inflate in the intestine where the drug is released. Moreover, CNPs showed a 2–3-fold improvement in the absorption of encapsulated pharmaceuticals relative to traditional formulations, therefore indicating their capacity to overcome the problems of low oral bioavailability. These nanoparticles’ pH-sensitive character produced a 50–70% increase in drug release at certain pH values, hence maximizing therapeutic results. Significantly less systemic toxicity was seen in the in vivo tests, and at therapeutic dosages there were no noted side effects. Histological study confirmed the biocompatibility and non-toxicity of CNPs, therefore attesting their fit for long-term usage. These results highlight the great potential of CNPs in providing effective, focused, continuous drug release, hence improving therapeutic effectiveness and patient compliance. Full article