Search Results (1,433)

Tea cultivation faces significant threats from red spider mite (RSM: Oligonychus coffeae) infestations, which reduce yields and economic viability in major tea-producing regions. Current automated detection methods rely on supervised deep learning models requiring extensive labeled data, limiting scalability for smallholder farmers. This article proposes AIM-Net (AI-based Infestation Mapping Network) by evaluating SwAV (Swapping Assignments between Views), a self-supervised learning framework, for classifying RSM infestation severity (Mild, Moderate, Severe) using a geo-referenced, field-acquired dataset of RSM infested tea-leaves, Cam-RSM. The methodology combines SwAV pre-training on unlabeled data with fine-tuning on labeled subsets, employing multi-crop augmentation and online clustering to learn discriminative features without full supervision. Comparative analysis against a fully supervised ResNet-50 baseline utilized 5-fold cross-validation, assessing accuracy, F1-scores, and computational efficiency. Results demonstrate SwAV’s superiority, achieving 98.7% overall accuracy (vs. 92.1% for ResNet-50) and macro-average F1-scores of 98.3% across classes, with a 62% reduction in labeled data requirements. The model showed particular strength in Mild_RSM-class detection (F1-score: 98.5%) and computational efficiency, enabling deployment on edge devices. Statistical validation confirmed significant improvements (p < 0.001) over baseline approaches. These findings establish self-supervised learning as a transformative tool for precision pest management, offering resource-efficient solutions for early infestation detection while maintaining high accuracy. Full article

The accuracy of classification and localization of plants on images obtained from the board of an unmanned aerial vehicle (UAV) is of great importance when implementing precision farming technologies. It allows for the effective application of variable rate technologies, which not only saves chemicals but also reduces the environmental load on cultivated fields. Machine learning algorithms are widely used for plant classification. Research on the application of the YOLO algorithm is conducted for simultaneous identification, localization, and classification of plants. However, the quality of the algorithm significantly depends on the training set. The aim of this study is not only the detection of a cultivated plant (soybean) but also weeds growing in the field. The dataset developed in the course of the research allows for solving this issue by detecting not only soybean but also seven weed species common in the fields of Kazakhstan. The article describes an approach to the preparation of a training set of images for soybean fields using preliminary thresholding and bound box (Bbox) segmentation of marked images, which allows for improving the quality of plant classification and localization. The conducted research and computational experiments determined that Bbox segmentation shows the best results. The quality of classification and localization with the application of Bbox segmentation significantly increased (f1 score increased from 0.64 to 0.959, mAP50 from 0.72 to 0.979); for a cultivated plant (soybean), the best classification results known to date were achieved with the application of YOLOv8x on images obtained from the UAV, with an f1 score = 0.984. At the same time, the plant detection rate increased by 13 times compared to the model proposed earlier in the literature. Full article

Fetal tachyarrhythmias, particularly supraventricular tachycardia (SVT) and atrial flutter (AFL), pose significant clinical challenges, especially when complicated by hydrops fetalis. This article provides a comprehensive review of the tachyarrhythmia types, the diagnostic modalities applied, and the therapeutic strategies followed in fetal tachyarrhythmias. Diagnostic techniques such as M-mode echocardiography and fetal magnetocardiography (fMCG) are highlighted for their capacity to provide real-time, high-quality assessments of fetal cardiac rhythms. The review, also, focuses on pharmacologic management via transplacental therapy, discussing the safety and efficacy of the key agents including digoxin, flecainide, and sotalol, under different clinical scenarios, such as hydropic fetus and renal impairment. In addition to transplacental administration, alternative approaches such as direct fetal intramuscular or intravascular injections are examined. These direct methods, while potentially more effective in refractory cases, carry risks that necessitate specialized expertise and careful consideration of maternal and fetal safety. The limitations of current evidence, largely based on small case studies and retrospective analyses, underscore the need for larger, prospective multicenter observational studies and randomized control trials to establish standardized protocols for fetal tachyarrhythmia management. Overall, this review advocates for a personalized, multidisciplinary approach, emphasizing early fetal tachyarrhythmias diagnosis, tailored treatment regimens that balances efficacy with safety, and rigorous monitoring to optimize outcomes for both the fetus and the mother. Full article

Mitomycin C (MMC) is a widely employed chemotherapeutic agent, particularly in non-muscle invasive bladder cancer (NMIBC), where it functions by inducing DNA cross-linking and promoting tumor cell apoptosis. However, the tumor microenvironment (TME) significantly influences the therapeutic efficacy of MMC. Among the key regulators within the TME, the complement system and the coagulation pathway play a crucial role in modulating immune responses to cancer therapies, including MMC. This article explores the interaction between platinum nanoparticles (PtNPs) with human serum (HS) of NMIBC patients (T1 and Ta subtypes) at three different points: before the chemotherapy instillation of MMC (t₀) and three (t₃) and six months (t₆) after the treatment with MMC. This novel nanoproteomic strategy allowed the identification of a TME proteomic signature associated with the response to MMC treatment. Importantly, two proteins involved in the immune response were found to be deregulated across all patients (T1 and Ta subtypes) during MMC treatment: prothrombin (F2) downregulated and complement component C7 (C7) upregulated. By understanding how these biomarker proteins interact with MMC treatment, novel therapeutic strategies can be developed to enhance treatment outcomes and overcome resistance in NMIBC. Full article

Objective: This study aims to critically review and synthesize the existing literature on the use of voice analysis in assessing nasal obstruction, with a particular focus on acoustic parameters. Data sources: PubMed, Scopus, Web of Science, Ovid Medline, and Science Direct. Review methods: A comprehensive literature search was conducted without any restrictions on publication year, employing Boolean search techniques. The selection and review process of the studies followed PRISMA guidelines. The inclusion criteria comprised studies with participants aged 18 years and older who had nasal obstruction evaluated using acoustic voice analysis parameters, along with objective and/or subjective methods for assessing nasal obstruction. Results: Of the 174 abstracts identified, 118 were screened after the removal of duplicates. The full texts of 37 articles were reviewed. Only 10 studies met inclusion criteria. The majority of these studies found no significant correlations between voice parameters and nasal obstruction. Among the various acoustic parameters examined, shimmer was the most consistently affected, with statistically significant changes identified in three independent studies. A smaller number of studies reported notable findings for fundamental frequency (F0) and noise-related measures such as NHR/HNR. Conclusion: This systematic review critically evaluates existing studies on the use of voice analysis for assessing and monitoring nasal obstruction and hyponasality. The current evidence remains limited, as most investigations predominantly focus on glottic sound and dysphonia, with insufficient attention to the influence of the vocal tract, particularly the nasal cavities, on voice production. A notable gap exists in the integration of advanced analytical approaches, such as machine learning, in this field. Future research should focus on the use of advanced analytical approaches to specifically extrapolate the contribution of nasal resonance to voice thus defining the specific parameters in the voice spectrogram that can give precise information on nasal obstruction. Full article

The aim of the article is to analyze the possibilities of using a lightweight dynamic cone probe DCP to determine the quality of compaction of surface layers of embankments (from 0.10 m to approx. 0.80 m below ground level). For this purpose, comparative tests of non-cohesive soil used for the construction of embankments were carried out using the DCP test and direct tests of the degree of compaction I_S in a calibration chamber with the following dimensions: height 1.10 m and diameter 0.75 m. The subsoil was prepared from difficult-to-compact sand (Sa) with a uniformity coefficient of C_U = 3.10 and curvature coefficient of C_C = 0.99. The soil in the laboratory in the calibration chamber was compacted in layers using a vibratory plate compactor. A database for statistical analysis was obtained, n = 68 cases described by seven variables: z, ρ, w, ρ_d, I_S, PI, N₁₀(DCP). It was found that the DCP probe can be used to assess the degree of compaction of embankments made of non-cohesive soil, using the developed relationship I_S = f(z, N₁₀(DCP)). Full article

In this article, we investigate the fractional soliton solutions as well as the chaotic analysis of the fourth-order nonlinear Ablowitz–Kaup–Newell–Segur wave equation. This model is considered an intriguing high-order nonlinear partial differential equation that integrates additional spatial and dispersive effects to extend the concepts to more intricate wave dynamics, relevant in engineering and science for understanding complex phenomena. To examine the solitary wave solutions of the proposed model, we employ sophisticated analytical techniques, including the generalized projective Riccati equation method, the new improved generalized exponential rational function method, and the modified F-expansion method, along with mathematical simulations, to obtain a deeper insight into wave propagation. To explore desirable soliton solutions, the nonlinear partial differential equation is converted into its respective ordinary differential equations by wave transforms utilizing $\beta$-fractional derivatives. Further, the solutions in the forms of bright, dark, singular, combined, and complex solitons are secured. Various physical parameter values and arrangements are employed to investigate the soliton solutions of the system. Variations in parameter values result in specific behaviors of the solutions, which we illustrate via various types of visualizations. Additionally, a key aspect of this research involves analyzing the chaotic behavior of the governing model. A perturbed version of the system is derived and then analyzed using chaos detection techniques such as power spectrum analysis, Poincaré return maps, and basin attractor visualization. The study of nonlinear dynamics reveals the system’s sensitivity to initial conditions and its dependence on time-decay effects. This indicates that the system exhibits chaotic behavior under perturbations, where even minor variations in the starting conditions can lead to drastically different outcomes as time progresses. Such behavior underscores the complexity and unpredictability inherent in the system, highlighting the importance of understanding its chaotic dynamics. This study evaluates the effectiveness of currently employed methodologies and elucidates the specific behaviors of the system’s nonlinear dynamics, thus providing new insights into the field of high-dimensional nonlinear scientific wave phenomena. The results demonstrate the effectiveness and versatility of the approach used to address complex nonlinear partial differential equations. Full article

As a type of distinctive pit on Mars, skylights are entrances to subsurface lava caves. They are very important for studying volcanic activity and potential preserved water ice, and are also considered as potential sites for human extraterrestrial bases in the future. Most skylights are manually identified, which has low efficiency and is highly subjective. Although deep learning methods have recently been used to identify skylights, they face challenges of few effective samples and low identification accuracy. In this article, 151 positive samples and 920 negative samples based on the MRO-HiRISE image data was used to create an initial skylight dataset, which contained few positive samples. To augment the initial dataset, StyleGAN2-ADA was selected to synthesize some positive samples and generated an augmented dataset with 896 samples. On the basis of the augmented skylight dataset, we proposed YOLOv9-Skylight for skylight identification by incorporating Inner-EIoU loss and DySample to enhance localization accuracy and feature extracting ability. Compared with YOLOv9, the P, R, and the F1 of YOLOv9-Skylight were improved by about 9.1%, 2.8%, and 5.6%, respectively. Compared with other mainstream models such as YOLOv5, YOLOv10, Faster R-CNN, Mask R-CNN, and DETR, YOLOv9-Skylight achieved the highest accuracy (F1 = 92.5%), which shows a strong performance in skylight identification. Full article

In this article, we investigate the influence of viscosity on the evolution of the cosmos within the framework of the newly proposed $f(Q,L_m)$ gravity. We have considered a linear functional form $f(Q,L_m)=\alpha Q+\beta L_m$ with a bulk viscous coefficient $\zeta ={\zeta }_0+{\zeta }_{1}H$ for our analysis and obtained exact solutions to the field equations associated with a flat FLRW metric. In addition, we utilized Cosmic Chronometers (CC), CC + BAO, CC + BAO + GRB, and GRB data samples to determine the constrained values of independent parameters in the derived exact solution. The likelihood function and the Markov Chain Monte Carlo (MCMC) sampling technique are combined to yield the posterior probability using Bayesian statistical methods. Furthermore, by comparing our results with the standard cosmological model, we found that our considered model supports the acceleration of the universe in late time. Full article

Background: Neisseria meningitidis B is one of the main causative pathogens of meningitis and other forms of severe meningococcal disease. In the past decade, meningococcal B vaccines have been developed to address this infection and its sequelae. Objective: This article aims to present an example of how the EU regulatory framework allowed the early authorisation of two life-saving vaccines initially based on immunogenicity surrogates of clinical evidence. This was subsequently followed by post-marketing surveillance providing real-world evidence to support their safety profile and impact on the paediatric population in the EU. Methods: We review the evidence supporting the initial regulatory approval of the vaccines, the confirmatory data demonstrating vaccine effectiveness post-authorisation, and the real-world impact of these vaccines on the paediatric population. Results: Two vaccines were approved in the EU for active immunisation to prevent IMD caused by MenB (4CMenB in 2013 and MenB-fHBP in 2017). Both marketing authorisations were based on immunogenicity data (efficacy studies were not feasible due to the rarity of the disease) and safety data generated from pre-authorisation studies. Additional pharmacovigilance activities to further investigate the safety profile and effectiveness studies were requested to be conducted after approval. Both the effectiveness and safety profile of the vaccines were confirmed by these data. Conclusions: This paper illustrates that the EU medicines regulatory framework and safety monitoring system are robust. By supplementing the initial evidence with post-authorisation studies, further effectiveness and safety data enabled regulators to confirm the positive benefit–risk of the vaccines without delaying their access to the people who need them. Full article

The vast volume of media content, combined with the costs of manual annotation, challenges scalable codebook analysis and risks reducing decision-making accuracy. This study evaluates the effectiveness of large language models (LLMs) and multi-agent teams in structured media content analysis based on codebook-driven annotation. We construct a dataset of 200 news articles on U.S. tariff policies, manually annotated using a 26-question codebook encompassing 122 distinct codes, to establish a rigorous ground truth. Seven state-of-the-art LLMs, spanning low- to high-capacity tiers, are assessed under a unified zero-shot prompting framework incorporating role-based instructions and schema-constrained outputs. Experimental results show weighted global F1-scores between 0.636 and 0.822, with Claude-3-7-Sonnet achieving the highest direct-prompt performance. To examine the potential of agentic orchestration, we propose and develop a multi-agent system using Meta’s Llama 4 Maverick, incorporating expert role profiling, shared memory, and coordinated planning. This architecture improves the overall F1-score over the direct prompting baseline from 0.757 to 0.805 and demonstrates consistent gains across binary, categorical, and multi-label tasks, approaching commercial-level accuracy while maintaining a favorable cost–performance profile. These findings highlight the viability of LLMs, both in direct and agentic configurations, for automating structured content analysis. Full article

The article provides a comprehensive analysis of the Magal Gebreel granitic suites (MGGs) using petrological (fieldwork, petrography, mineral chemistry, and bulk rock analysis) aspects to infer their petrogenesis and emplacement setting. Our understanding of the development of the northern portion of the Arabian Nubian Shield is significantly improved by the Neoproterozoic granitic rocks of the seldom studied MGGs in Egypt’s south Eastern Desert. According to detailed field, mineralogical, and geochemical assessments, they comprise syn-collision (granodiorites) and post-collision (monzogranites, syenogranites, and alkali feldspar rocks). Granodiorite has strong positive Pb, notable negative P, Ti, and Nb anomalies, and is magnesian in composition. They have high content of LREEs (light rare-earth elements) compared to HREEs (heavy rare-earth elements) and clear elevation of LFSEs (low-field strength elements; K Rb, and Ba) compared to HFSEs (high-field strength elements; Zr and Nb), which are in accord with the contents of I-type granites from the Eastern Desert. In this context, the granodiorites are indicative of an early magmatic phase that probably resulted from the partial melting of high K-mafic sources in the subduction zone. Conversely, the post-collision rocks have low contents of Mg#, CaO, P₂O₅, MgO, Fe₂O₃, Sr, and Ti, and high SiO₂, Fe₂O₃/MgO, Nb, Ce, and Ga/Al, suggesting A-type features with ferroan affinity. Their P, Nb, Sr, Ba, and Ti negative anomalies are in accord with the findings for Eastern Desert granites of the A2-type. Furthermore, they exhibit a prominent negative anomaly in Eu and a small elevation of LREEs in relation to HREEs. The oxygen fugacity (fO₂) for the rocks under investigation can be calculated using the biotite chemistry. The narrow Fe/(Fe + Mg) ratio range (0.6–0.75) indicates that they crystallized under moderately oxidizing conditions between ~QFM +0.1 and QFM +1. The A-type rocks were formed by the partial melting of a tonalite source (underplating rocks) in a post-collisional environment during the late period of extension via slab delamination. The lithosphere became somewhat impregnated with particular elements as a result of the interaction between the deeper crust and the upwelling mantle. Full article

The increase in garbage generated in modern societies demands the implementation of a more sustainable model as well as new methods for efficient waste management. This article describes the development and implementation of a prototype of a smart bin that automatically sorts waste using a multi-agent system and blockchain integration. The proposed system has sensors that identify the type of waste (organic, plastic, paper, etc.) and uses collaborative intelligent agents to make instant sorting decisions. Blockchain has been implemented as a technology for the immutable and transparent control of waste registration, favoring traceability during the classification process, providing sustainability to the process, and making the audit of data in smart urban environments transparent. For the computer vision algorithm, three versions of YOLO (YOLOv8, YOLOv11, and YOLOv12) were used and evaluated with respect to their performance in automatic detection and classification of waste. The YOLOv12 version was selected due to its overall performance, which is superior to others with mAP@50 values of 86.2%, an overall accuracy of 84.6%, and an average F1 score of 80.1%. Latency was kept below 9 ms per image with YOLOv12, ensuring smooth and lag-free processing, even for utilitarian embedded systems. This allows for efficient deployment in near-real-time applications where speed and immediate response are crucial. These results confirm the viability of the system in both accuracy and computational efficiency. This work provides an innovative solution in the field of ambient intelligence, characterized by low equipment cost and high scalability, laying the foundations for the development of smart waste management infrastructures in sustainable cities. Full article

With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study presents a novel web-based platform that automates text data generation and labeling by integrating Llama 3.3, an open-source large language model (LLM), with advanced prompt engineering techniques. A core contribution of this work is the Attributed Prompt Engineering Framework, which enables modular and configurable prompt templates for both data generation and labeling tasks. This framework combines zero-shot, few-shot, role-based, and chain-of-thought prompting strategies within a unified architecture to optimize output quality and control. Users can interactively configure prompt parameters and generate synthetic datasets or annotate raw data with minimal human intervention. We evaluated the platform using both benchmark datasets (AG News, Yelp, Amazon Reviews) and two fully synthetic datasets we generated (restaurant reviews and news articles). The system achieved 99% accuracy and F1-score on generated news article data, 98% accuracy and F1-score on generated restaurant review data, and 92%, 90%, and 89% accuracy and F1-scores on the benchmark labeling tasks for AG News, Yelp Reviews, and Amazon Reviews, respectively, demonstrating high effectiveness and generalizability. A usability study also confirmed the platform’s practicality for non-expert users. This work advances scalable NLP data pipeline design and provides a cost-effective alternative to manual annotation for supervised learning applications. Full article

Many academic introductions suffer from inconsistencies and a lack of comprehensive structure, often failing to effectively outline the core elements of the research. This not only impacts the clarity and readability of the article but also hinders the communication of its significance and objectives to the intended audience. This study aims to automate the CaRS (Creating a Research Space) model using machine learning and natural language processing techniques. We conducted a series of experiments using a custom-developed corpus of 50 biology research article introductions, annotated with rhetorical moves and steps. The dataset was used to evaluate the performance of four classification algorithms: Prototypical Network (PN), Support Vector Machines (SVM), Naïve Bayes (NB), and Random Forest (RF); in combination with six embedding models: Word2Vec, GloVe, BERT, GPT-2, Llama-3.2-3B, and TEv3-small. Multiple experiments were carried out to assess performance at both the move and step levels using 5-fold cross-validation. Evaluation metrics included accuracy and weighted F1-score, with comprehensive results provided. Results show that the SVM classifier, when paired with Llama-3.2-3B embeddings, consistently achieved the highest performance across multiple tasks when trained on preprocessed dataset, with 79% accuracy and weighted F1-score on rhetorical moves and strong results on M2 steps (75% accuracy and weighted F1-score). While other combinations showed promise, particularly NB and RF with newer embeddings, none matched the consistency of the SVM–Llama pairing. Compared to existing benchmarks, our model achieves similar or better performance; however, direct comparison is limited due to differences in datasets and experimental setups. Despite the unavailability of the benchmark dataset, our findings indicate that SVM is an effective choice for rhetorical classification, even in few-shot learning scenarios. Full article