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Generative AI tools such as ChatGPT are reshaping educational practice, yet first-year teacher-education students often lack the prompt-engineering skills and confidence required to use them responsibly. This pilot study examined whether a concise three-session clinic on prompt engineering could simultaneously boost AI literacy and reduce technology anxiety in prospective teachers. Forty-five freshmen in a Peruvian teacher-education program completed validated Spanish versions of a 12-item AI-literacy scale and a 12-item technology-anxiety scale one week before and after the intervention; normality-checked pre–post differences were analysed with paired-samples t-tests, Cohen’s d, and Pearson correlations. AI literacy rose by 0.70 ± 0.46 points (t (44) = −6.10, p < 0.001, d = 0.91), while technology anxiety fell by 0.58 ± 0.52 points (t (44) = −3.82, p = 0.001, d = 0.56); individual gains were inversely correlated (r = −0.46, p = 0.002). These findings suggest that integrating micro-level prompt-engineering clinics in the first semester can help future teachers engage critically and comfortably with generative AI and guide curriculum designers in updating teacher-training programs. Full article

Underwater image enhancement is crucial for fields like marine exploration, underwater photography, and environmental monitoring, as underwater images often suffer from reduced visibility, color distortion, and contrast loss due to light absorption and scattering. Despite recent progress, existing methods struggle to generalize across diverse underwater conditions, such as varying turbidity levels and lighting. This paper proposes a novel hybrid UNet–Transformer architecture based on MaxViT blocks, which effectively combines local feature extraction with global contextual modeling to address challenges including low contrast, color distortion, and detail degradation. Extensive experiments on two benchmark datasets, UIEB and EUVP, demonstrate the superior performance of our method. On UIEB, our model achieves a PSNR of 22.91, SSIM of 0.9020, and CCF of 37.93—surpassing prior methods such as URSCT-SESR and PhISH-Net. On EUVP, it attains a PSNR of 26.12 and PCQI of 1.1203, outperforming several state-of-the-art baselines in both visual fidelity and perceptual quality. These results validate the effectiveness and robustness of our approach under complex underwater degradation, offering a reliable solution for real-world underwater image enhancement tasks. Full article

Waterfowl semen cryopreservation technology is a key link in genetic resource conservation and artificial breeding, but poultry spermatozoa, due to their unique morphology and biochemical properties, are prone to oxidative stress during freezing, resulting in a significant decrease in vitality. In this study, we first used four different freezing procedures (P1–P4) to freeze duck semen and compared their effects on duck sperm quality. Then, the changes in antioxidant indexes in semen were monitored. The results showed that program P4 (initial 7 °C/min slow descent to −35 °C, followed by 60 °C/min rapid descent to −140 °C) was significantly better than the other programs (p < 0.05), and its post-freezing sperm vitality reached 71.41%, and the sperm motility was 51.73%. In the P1 and P3 groups, the sperm vitality was 65.56% and 53.41%, and the sperm motility was 46.99% and 31.76%, respectively. In terms of antioxidant indexes, compared with the fresh semen group (CK), the activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-px) in the P2 group were significantly decreased (p < 0.05), while the activities of SOD and CAT in the P4 group showed no significant changes (p > 0.05) except that the activity of GSH-px was significantly decreased (p < 0.05). And the CAT and GSH-px activities in the P4 group were significantly higher than those in the P2 group (p < 0.05). The content of malondialdehyde (MDA) in the P2 group was significantly higher than that in the fresh semen group (p < 0.05), and there was no significant difference between the P2 group and the P4 group (p > 0.05). The total antioxidant capacity (T-AOC) content of the P2 and P4 groups was significantly lower than that of the fresh semen group (p < 0.05). The staged cooling strategy of P4 was effective in reducing the exposure time to the hypertonic environment by balancing intracellular dehydration and ice crystal inhibition, shortening the reactive oxygen species accumulation and alleviating oxidative stress injury. On the contrary, the multi-stage slow-down strategy of P2 exacerbated mitochondrial dysfunction and the oxidative stress cascade response due to prolonged cryogenic exposure time. The present study confirmed that the freezing procedure directly affects duck sperm quality by modulating the oxidative stress pathway and provides a theoretical basis for the standardization of duck semen cryopreservation technology. Full article

In relay-assisted Industrial Internet of Things (IIoT) systems with ultra-reliable low-latency communication (uRLLC) requirements, finite blocklength coding imposes stringent resource constraints. In this work, the packet error probability (PEP) and blocklength allocation across two-hop links are jointly optimized to minimize total blocklength (resource consumption) while satisfying reliability, latency, and throughput requirements. The original multi-variable problem is decomposed into two tractable subproblems. In the first subproblem, for a fixed total blocklength, the achievable rate is maximized. A near-optimal PEP is first derived via theoretical analysis. Subsequently, theoretical analysis proves that blocklength must be optimized to equalize the achievable rates between the two hops to maximize system performance. Consequently, the closed-form solution to optimal blocklength allocation is derived. In the second subproblem, the total blocklength is minimized via a bisection search method. Simulation results show that by adopting near-optimal PEPs, our approach reduces computation time by two orders of magnitude while limiting the achievable rate loss to within 1% compared to the exhaustive search method. At peak rates, the hop with superior channel conditions requires fewer resources. Compared with three baseline algorithms, the proposed algorithm achieves average resource savings of 21.40%, 14.03%, and 17.18%, respectively. Full article

Proxy re-signature enables transitive authentication of digital identities across different domains and has significant application value in areas such as digital rights management, cross-domain certificate validation, and distributed system access control. However, most existing proxy re-signature schemes, which are predominantly based on traditional public-key cryptosystems, face security vulnerabilities and certificate management bottlenecks. While identity-based schemes alleviate some issues, they introduce key escrow concerns. Certificateless schemes effectively resolve both certificate management and key escrow problems but remain vulnerable to quantum computing threats. To address these limitations, this paper constructs an efficient post-quantum certificateless proxy re-signature scheme based on algebraic lattices. Building upon algebraic lattice theory and leveraging the Dilithium algorithm, our scheme innovatively employs a lattice basis reduction-assisted parameter selection strategy to mitigate the potential algebraic attack vectors inherent in the NTRU lattice structure. This ensures the security and integrity of multi-party communication in quantum-threat environments. Furthermore, the scheme significantly reduces computational overhead and optimizes signature storage complexity through structured compression techniques, facilitating deployment on resource-constrained devices like Internet of Things (IoT) terminals. We formally prove the unforgeability of the scheme under the adaptive chosen-message attack model, with its security reducible to the hardness of the corresponding underlying lattice problems. Full article

The rising demand for energy resources and industrial goods presents significant challenges to sustainable development. Sunflower oil, commonly utilized in the food sector, biofuels, and various industrial applications, is notably affected by this demand. In Bulgaria, it serves as a primary source of vegetable fats, ranking second to butter in daily consumption. The aim of this study is to evaluate and propose methods to improve energy and resource efficiency in sunflower oil production in Bulgaria. The analysis is based on data from an energy audit conducted in 2023 at an industrial sunflower oil production facility. Reconstruction and modernization initiatives, which included the installation of high-performance, energy-efficient equipment, led to a 34% increase in energy efficiency. The findings highlight the importance of adjusting the technological parameters such as temperature, pressure, grinding level, and pressing time to reduce energy use and operational costs. Additionally, resource efficiency is improved through more effective raw material utilization and waste reduction. These strategies not only enhance the economic and environmental performance of sunflower oil production but also support sustainable development and competitiveness within the industry. The improvement reduces hexane use by approximately 2%, resulting in energy savings of 12–15 kWh/t of processed seeds and a reduction in CO₂ emissions by 3–4 kg/t, thereby improving the environmental profile of sunflower oil production. Full article

Combining products of natural origin with different mechanisms of action on insect herbivores may provide an alternative among methods of plant protection against pests that are less risky for the environment. The aim of the study was to evaluate the effectiveness of mixtures of Thuja occidentalis L. essential oil and diatomite (EO + DE) compared to each substance separately in reducing economically important pests such as black bean aphid (BBA) Aphis fabae Scop., Colorado potato beetle (CPB) Leptinotarsa decemlineata Say., and pea leaf weevil (PLW) Sitona lineatus L. The effects on mortality (all pests) and foraging intensity (CPB and PLW) were tested. The improvement in effectiveness using a mixture of EO + DE versus single components against BBA was dose- and the developmental stage-dependent. The effect of enhancing CPB foraging inhibition through DE addition was obtained at a concentration of 0.2% EO (both females and males of CPB) and 0.5% EO (males) in no-choice experiments. In choice experiments, mixtures EO + DE with both 0.2% and 0.5% EO concentrations resulted in a significant reduction in CPB foraging. A significant strengthening effect of EO 0.5% through the addition of DE at a dose of 10% against PLW males was observed in the no-choice experiment, while, when the beetles had a choice, the synergistic effect of a mixture of EO 0.5% and DE 10% was also apparent in females. In conclusion, the use of DE mixtures with EO from T. occidentalis appears to be a promising strategy. The results support the idea of not using doses of EO higher than 0.5%. Full article

Pediatric asthma is a multifactorial and heterogeneous disease determined by the dynamic interplay of genetic susceptibility, environmental exposures, and immune dysregulation. Recent advances have highlighted the pivotal role of epigenetic mechanisms, in particular, DNA methylation, histone modifications, and non-coding RNAs, in the regulation of inflammatory pathways contributing to asthma phenotypes and endotypes. This review examines the role of respiratory viruses such as respiratory syncytial virus (RSV), rhinovirus (RV), and other bacterial and fungal infections that are mediators of infection-induced epithelial inflammation that drive epithelial homeostatic imbalance and induce persistent epigenetic alterations. These alterations lead to immune dysregulation, remodeling of the airways, and resistance to corticosteroids. A focused analysis of T2-high and T2-low asthma endotypes highlights unique epigenetic landscapes directing cytokines and cellular recruitment and thereby supports phenotype-specific aspects of disease pathogenesis. Additionally, this review also considers the role of miRNAs in the control of post-transcriptional networks that are pivotal in asthma exacerbation and the severity of the disease. We discuss novel and emerging epigenetic therapies, such as DNA methyltransferase inhibitors, histone deacetylase inhibitors, miRNA-based treatments, and immunomodulatory probiotics, that are in preclinical or early clinical development and may support precision medicine in asthma. Collectively, the current findings highlight the translational relevance of including pathogen-related biomarkers and epigenomic data for stratifying pediatric asthma patients and for the personalization of therapeutic regimens. Epigenetic dysregulation has emerged as a novel and potentially transformative approach for mitigating chronic inflammation and long-term morbidity in children with asthma. Full article

“The Internet of Things” is a relatively new idea that refers to objects that can connect to the Internet and exchange data. The Internet of Things (IoT) enables novel interactions between objects and people by interconnecting billions of devices. While there are many IoT-related products, challenges pertaining to their effective implementation, particularly the lack of knowledge and confidence about security, must be addressed. To provide IoT-based enterprises with a platform for efficiency and sustainability, this study aims to identify the critical elements that influence the growth of a successful company integrated with an IoT system. This study proposes a decision support tool that evaluates the influential features of IoT using the Pythagorean Fuzzy and Interval Grey approaches within the Analytical Hierarchy Process (AHP). This study demonstrates that security, value, and connectivity are more critical than telepresence and intelligence indicators. When both strategies are used, market demand and information privacy become significant indicators. Applying the Pythagorean Fuzzy approach enables the identification of sensor networks, authorization, market demand, and data management in terms of importance. The application of the Interval Grey approach underscores the importance of data management, particularly in sensor networks. The indicators that were finally ranked are compared to obtain a good coefficient of agreement. These findings offer practical insights for promoting sustainability in enterprise operations by optimizing IoT infrastructure and decision-making processes. Full article

In this study, we introduce an enhanced hybrid Autoencoder–Dense–Transformer Neural Network (AE-DTNN) model for developing an effective intrusion detection system (IDS) aimed at improving the performance and robustness of threat detection strategies within a rapidly changing and increasingly complex network landscape. The Autoencoder component restructures network traffic data, while a stack of Dense layers performs feature extraction to generate more meaningful representations. The Transformer network then facilitates highly precise and comprehensive classification. Our strategy incorporates adaptive synthetic sampling (ADASYN) for both binary and multi-class classification tasks, complemented by the edited nearest neighbors (ENN) technique and the use of class weights to mitigate class imbalance issues. In experiments conducted on the NF-BoT-IoT-v2 dataset, the AE-DTNN-based IDS achieved outstanding performance, with 99.98% accuracy in binary classification and 98.30% in multi-class classification. On the NSL-KDD dataset, the model reached 98.57% accuracy for binary classification and 97.50% for multi-class classification. Additionally, the model attained 99.92% and 99.78% accuracy in binary and multi-class classification, respectively, on the CSE-CIC-IDS2018 dataset. These results demonstrate the exceptional effectiveness of the proposed model in contrast to conventional approaches, highlighting its strong potential to detect a broad range of network intrusions with high reliability. Full article

The increasing demand for critical raw materials such as copper and cobalt highlights the need for efficient beneficiation of low-grade ores. This study investigates a copper–cobalt sulfide ore (0.99% Cu, 0.028% Co) using froth flotation to produce high-grade concentrates. Various types of surfactants are applied in different ways, each serving an essential function such as acting as collectors, frothers, froth stabilizers, depressants, activators, pH modifiers, and more. A series of flotation tests employing different collectors (SIPX, PBX, AERO, DF 507B) and process conditions was conducted to optimize recovery and selectivity. Methyl isobutyl carbinol (MIBC) was consistently used as the foaming agent, and 700 g/L was used as the slurry density at 25 °C. Dosages of 30 and 100 g/t¹ were used in all tests. Notably, adjusting the pH to ~4 using HCl significantly improved cobalt concentrate separation. The optimized flotation conditions yielded concentrates with over 15% Cu and metal recoveries exceeding 80%. Mineralogical characterization confirmed the selective enrichment of target metals in the concentrate. The results demonstrate the potential of this beneficiation approach to contribute to the European Union’s supply of critical raw materials. Full article

Advanced therapy medicinal products (ATMPs), especially effective against cancer, remain costly due to their reliance on genetically modified T cells. Contamination during production is a major concern, as traditional quality control methods involve samplings, which can themselves introduce contaminants. It is therefore necessary to develop methods for detecting contamination without sampling and, if possible, in real time. In this article, we present a white light spectroscopy method that makes this possible. It is based on shape analysis of the absorption spectrum, which evolves from an approximately Gaussian shape to a shape modified by the 1/λ component of bacterial absorption spectra when contamination develops. A warning value based on this shape descriptor is proposed. It is demonstrated that a few hours are sufficient to detect contamination and trigger an alarm to quickly stop the production. This time-saving should reduce the cost of these new drugs, making them accessible to as many people as possible. This method can be used regardless of the type of contaminants, provided that the shape of their absorption spectrum is sufficiently different from that of pure T cells so that the shape descriptor is efficient. Full article

Conventional melanoma is exceedingly rare in the pediatric population, particularly among prepubescent children, and its diagnosis and management necessitate a multidisciplinary approach. The objective of this present report is to delineate the diagnostic pathway and therapeutic management of a 4-year-old girl with conventional melanoma, with particular focus on the molecular context. A pigmented lesion located on the auricle was surgically excised, and subsequent histopathological and immunohistochemical analyses confirmed the diagnosis of malignant melanoma (pT3b). Radiologic investigations revealed no evidence of metastatic disease, and comprehensive genetic testing utilizing next-generation sequencing (NGS) identified no pathogenic variants in the germline genes examined, nor in the BRAF, NRAS, KRAS, and TP53 genes within the excised lesion. The patient remains in good general health. This case report adds to the limited body of literature on melanoma in pediatric patients and underscores the importance of thorough diagnostic evaluation in this age group. Full article

The rapid proliferation of Internet of Things (IoT) devices has significantly increased vulnerability to Distributed Denial of Service (DDoS) attacks, which can severely disrupt network operations. DDoS attacks in IoT networks disrupt communication and compromise service availability, causing severe operational and economic losses. In this paper, we present a Deep Learning (DL)-based Intrusion Detection System (IDS) tailored for IoT environments. Our system employs three architectures—Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformer-based models—to perform binary, three-class, and 12-class classification tasks on the CiC IoT 2023 dataset. Data preprocessing includes log normalization to stabilize feature distributions and SMOTE-based oversampling to mitigate class imbalance. Experiments on the CIC-IoT 2023 dataset show that, in the binary classification task, the DNN achieved 99.2% accuracy, the CNN 99.0%, and the Transformer 98.8%. In three-class classification (benign, DDoS, and non-DDoS), all models attained near-perfect performance (approximately 99.9–100%). In the 12-class scenario (benign plus 12 attack types), the DNN, CNN, and Transformer reached 93.0%, 92.7%, and 92.5% accuracy, respectively. The high precision, recall, and ROC-AUC values corroborate the efficacy and generalizability of our approach for IoT DDoS detection. Comparative analysis indicates that our proposed IDS outperforms state-of-the-art methods in terms of detection accuracy and efficiency. These results underscore the potential of integrating advanced DL models into IDS frameworks, thereby providing a scalable and effective solution to secure IoT networks against evolving DDoS threats. Future work will explore further enhancements, including the use of deeper Transformer architectures and cross-dataset validation, to ensure robustness in real-world deployments. Full article

Grapevine (Vitis vinifera L.) cultivation is an important agricultural sector worldwide. Its expansion into new areas, like Kazakhstan, brings significant phytosanitary risks. Viroids, such as grapevine yellow speckle viroid 1 (GYSVd-1) and hop stunt viroid (HSVd), are RNA pathogens that threaten vineyard productivity. They can cause a progressive decline through latent infections. Traditional diagnostic methods are usually targeted and therefore not suitable for thorough surveillance. In contrast, modern high-throughput sequencing (HTS) methods often face challenges due to their high costs and complicated sample preparation, such as ribosomal RNA (rRNA) depletion. This study introduces a simplified diagnostic workflow that overcomes these barriers. We utilized the latest Oxford Nanopore V14 cDNA chemistry, which is designed to prevent internal priming, by substituting a targeted oligo(dT)VN priming strategy to facilitate the sequencing of non-polyadenylated viroids from total RNA extracts, completely bypassing the rRNA depletion step and use of random oligonucleotides for c DNA synthesis. This method effectively detects and identifies both GYSVd-1 and HSVd. This workflow significantly reduces the time, cost, and complexity of HTS-based diagnostics. It provides a powerful and scalable tool for establishing strong genomic surveillance and phytosanitary certification programs, which are essential for supporting the growing viticulture industry in Kazakhstan. Full article