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Abstract Distributed denial of service (DDoS) attack is one of the most hazardous assaults in cloud computing or networking. By depleting resources, this attack renders the services unavailable to end users and leads to significant financial and reputational damage. Hence, identifying such threats is crucial to minimize revenue loss, market share, and productivity loss and enhance the brand reputation. In this study, we implemented an effective intrusion detection system using deep learning approach. The suggested framework includes three phases: Data pre-processing, Data balancing, and Classification. First, we prepare the valid data, which is helpful for further processing. Then, we balance the given pre-processed data by Conditional generative adversarial network (CGAN), and as a result, we can minimize the bias towards the majority classes. Finally, we distinguish whether the traffic is attack or benign using a stacked sparse denoising autoencoder (SSDAE) with a firefly-black widow (FA-BW) hybrid optimization algorithm. All these experiments are validated through the CICDDoS2019 dataset and compared with well-received techniques. From these findings, we observed that the proposed strategy detects DDoS attacks significantly more accurately than other approaches. Based on our findings, this study highlights the crucial role played by advanced deep learning techniques and hybrid optimization algorithms in strengthening cybersecurity against DDoS attacks.

Cloud adoption in industrial sectors, such as process, manufacturing, health care, and finance, is steadily rising, but as it grows, the risk of targeted cyberattacks has increased. Hence, effectively defending against such attacks necessitates skilled cybersecurity professionals. Traditional human-based cyber-physical education is resource intensive and faces challenges in keeping pace with rapidly evolving technologies. This research focuses on the main advantages of incorporating large language models into cyber-physical education. The ChatGPT platform serves as an online tool to educate students on fundamentals, cyberattacks, and defense concepts, fostering the development of a new generation cybersecurity experts. The proposed learning approach adheres to the ChatGPT-assisted learn-apply-create model. Responding to prompts provided by the learners, the learning phase engages in conceptual learning, the applying phase involves mathematical modeling of various cyberattacks, and the creating phase develops MATLAB program to incorporate attacks into sensor measurements for the experiment and entails developing the necessary attack detection approaches. The effectiveness of the detection method developed by ChatGPT is assessed in both the simulation and real-time scenarios using a J-type thermocouple. The impact of the proposed learning platform over traditional learning methods is evaluated through an extensive comparative feedback analysis on the learner's foundational concepts, computational thinking, programming efficacy, and motivation. The study proved that integrating ChatGPT into engineering education enables students to swiftly learn cyber-physical fundamentals, comprehend and model cyberattacks, create new attack signatures, and contribute to developing detection algorithms. Such integration provides the learners with essential industrial skills crucial in modern industries.

The MATRIX protocol presents a novel miniaturized 24-well plate approach for microbial cultivation profiling, enabling the efficient and parallel assessment of microbial metabolite production. This method integrates a media MATRIX within a microbioreactor to facilitate the creation of diverse growth conditions for microbial cultivation. The 24-well format supports the simultaneous study of multiple microbial strains and conditions, allowing the discovery of novel bioactive compounds. By employing a variety of media compositions and cultivations, MATRIX enhances the understanding of microbial metabolic pathways and interactions. This protocol provides a robust platform for natural product discovery, microbial ecology studies, and drug development, showcasing its potential to uncover new natural products and other valuable bioactives. The MATRIX approach offers a resource-efficient platform for microbial biodiscovery, contributing significantly to natural product research and drug development.

A compact ultra-wide band antenna for breast cancer detection in bio-medical applications is presented in this study. The proposed antenna is printed on 1.6 mm thick FR4 substrate with a dielectric constant of 4.6 and tangential loss of 0.02. The uniform ground plane on the substrate (PCB) has been altered by shortening its length and making minimal changes to its side edges in order to improve overall bandwidth. The suggested antenna is functioning from 3 to 10.2 GHz which covers the ultra-wide band recommended by Federal Communication Commission (FCC) over the 10 dB impedance bandwidth. Additionally, the antenna has a significant overall gain of 3.2 dBi and an overall radiation efficiency of between 89 % and 92 %. Furthermore, the antenna maintains a uniform radiation pattern across its operating band. Parametric research has been conducted on the different parameters to tune the antenna into the required operating band, and placement analysis of the antenna is then investigated by assessing specific absorption rate (SAR).

This research explores natural fiber composites (NFCs) as alternatives to traditional materials such as fiberglass. By examining fibers like abaca, bamboo, banana, coir, flax, hemp, jute, kenaf, pineapple leaf (PALF), sisal, and others, we emphasize their lower weight, cost-effectiveness, competitive mechanical properties, commendable specific strength, and potential for biodegradability. NFCs offer practical and eco-friendly substitutes for conventional composite fibers in industries like construction, automotive, and packaging. The article provides a brief overview of NFCs, covering their chemical, physical, and mechanical characteristics, and highlights diverse applications. The comparative analysis underscores the potential of natural fibers as sustainable choices in various industries.

Additional cytogenetic abnormalities (ACA) are known to crop up in Ph+ cells of chronic myeloid leukemia (CML) patients due to cytogenetic evolution. But the frequency of molecular evolution and ACA is much less in Ph− cells of CML patients and is poorly understood. We report an interesting and rare case of Ph+ CML, who progressed to B lymphoblastic crisis, achieved remission, and later developed Ph− acute myeloid leukemia (AML) with KMT2A gene rearrangement and no detectable BCR- ABL transcripts.

The rising prevalence of Lyme disease (LD) in North America and Europe has emerged as a pressing public health concern. Despite the availability of veterinary LD vaccines, no vaccine is currently available for human use. Outer surface protein C (OspC) found on the outer membrane of the causative agent, Borrelia burgdorferi, has been identified as a promising target for LD vaccine development due to its sustained expression during mammalian infection. However, the efficacy and immunological mechanisms of LD vaccines solely targeting OspC are not well characterized. In this study, we developed an attenuated Vaccinia virus (VV) vectored vaccine encoding type A OspC (VV-OspC-A). Two doses of the VV-OspC-A vaccine conferred complete protection against homologous B. burgdorferi challenge in mice. Furthermore, the candidate vaccine also prevented the development of carditis and lymph node hyperplasia associated with LD. When investigating the humoral immune response to vaccination, VV-OspC-A was found to induce a robust antibody response predominated by the IgG2a subtype, indicating a Th1-bias. Using a novel quantitative flow cytometry assay, we also determined that elicited antibodies were capable of inducing antibody-dependent cellular phagocytosis in vitro. Finally, we demonstrated that VV-OspC-A vaccination generated a strong antigen-specific CD4+ T-cell response characterized by the secretion of numerous cytokines upon stimulation of splenocytes with OspC peptides. This study suggests a promising avenue for LD vaccine development utilizing viral vectors targeting OspC and provides insights into the immunological mechanisms that confer protection against B. burgdorferi infection.

Automated analysis of the scientific literature using natural language processing (NLP) can accelerate the identification of potentially unexplored formulations that enable innovations in materials engineering with fewer experimentation and testing cycles. This strategy has been successful for specific classes of inorganic materials, but their general application in broader material domains such as bioplastics remains challenging. To begin addressing this gap, we explore correlations between the ingredients and physicochemical properties of seaweed-based biofilms from a corpus of 2000 article abstracts from the scientific literature since 1958, using a supervised word co-occurrence analysis and an unsupervised approach based on the language model MatBERT without fine-tuning. Using known relations between ingredients and properties for test scenarios, we discuss the potential and limitations of these NLP approaches for identifying novel combinations of polysaccharides, plasticizers, and additives that are related to the functionality of seaweed biofilms. The model demonstrates a valuable predictive ability to identify ingredients associated with increased water vapor permeability, suggesting its potential utility in optimizing formulations for future research. Using the model further revealed alternative combinations that are underrepresented in the literature. This automated method facilitates the mapping of relationships between ingredients and properties, guiding the development of seaweed bioplastic formulations. The unstructured and heterogeneous nature of the literature on bioplastics represents a particular challenge that demands ad hoc fine-tuning strategies for state-of-the-art language models for advancing the field of seaweed bioplastics.