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A noval RUL prediction method for rolling bearing: TcLstmNet-CBAM
Connected map-induced resource allocation scheme for cognitive radio network quality of service maximization
Abstract Quality of Service (QoS) in cognitive radio networks (CRNs) is achieved through fair resource allocation and scheduling for secondary users regardless of channel capacity through multi-channel communications. Fairness index updates are periodic towards multi-user allocations to meet the QoS demands. In this article, a Connected Resource Map-induced Resource Allocation Scheme (CRM-RAS) is introduced. The proposed scheme identifies radio and user resource availability and constructs an allocation map from the primary users. For a periodic allocation interval, the map’s fairness index is updated through maximum resource utilization and QoS factor. This QoS factor is computed based on low latency and high allocation rates that are directly proportional to the fairness index. The fairness index is verified using distributed federated learning that is active between the primary and secondary user terminals. If the fairness index drops below the actual allocation rate, then the scheduling for resource allocation with concurrency is pursued. Based on the improving fairness index through concurrent scheduling the distributed federated learning encourages consecutive radio resource allocation. Thus the process is repeated until the allocation map is confined to a one-to-one connectivity between the primary and secondary users. The proposed CRM-RAS achieves 8.15% high sum rate and 8.88% less error rate for the maximum SNR.
Wild strawberries on Mars
Prevalence of smoking and smoking cessation and associated factors in diabetes population aged 50 years and over in Europe
How to fix a gut microbiome ravaged by antibiotics
Structural roles and gender disparities in corruption networks
Stress-induced phase separation in plastics drives the release of amorphous polymer micropollutants into water
Abstract Residual stress is an intrinsic property of semicrystalline plastics such as polypropylene and polyethylene. However, there is no fundamental understanding of the role intrinsic residual stress plays in the generation of plastic pollutants that threaten the environment and human health. Here, we show that the processing-induced compressive residual stress typically found in polypropylene and polyethylene plastics forces internal nano and microscale segregation of low molecular weight (MW) amorphous polymer droplets onto the plastic’s surface. Squeeze flow simulations reveal this stress-driven volumetric flow is consistent with that of a Bingham plastic material, with a temperature-dependent threshold yield stress. We confirm that flow is thermally activated and stress dependent, with a reduced energy barrier at higher compressive stresses. Transfer of surface segregated droplets into water generates amorphous polymer micropollutants (APMPs) that are denatured, with structure and composition different from that of traditional polycrystalline microplastics. Studies with water-containing plastic bottles show that the highly compressed bottle neck and mouth regions are predominantly responsible for the release of APMPs. Our findings reveal a stress-induced mechanism of plastic degradation and underscore the need to modify current plastic processing technologies to reduce residual stress levels and suppress phase separation of low MW APMPs in plastics.
Levodopa influence on turning dynamics in people with Parkinson’s disease
Formation of a membraneless compartment regulates bacterial virulence
Optimization of pressure swing adsorption in a three-layered bed for hydrogen purification using machine learning model
A brand-new colour created by lasers, a pig-liver transplant trial gets the green light, and a nugget-sized chunk of lab-grown meat
Reaching the pinnacle of high-capacity optical transmission using a standard cladding diameter coupled-core multi-core fiber
Time-course of muscle fatigue development during intense exercise in hypoxia and normoxia
Cellular immunotherapy targeting CLL-1 for juvenile myelomonocytic leukemia
Abstract Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder that predominantly affects infants and young children. Hematopoietic stem cell transplantation (HSCT) is standard of care, but post-HSCT relapse is common, highlighting the need for innovative therapies. While adoptive immunotherapy with chimeric antigen receptor (CAR) T cells has improved outcomes for patients with advanced lymphoid malignancies, it has not been comprehensively evaluated in JMML. In the present study, we use bulk and single-cell RNA sequencing, mass spectrometry, and flow cytometry to identify overexpression of CLL-1 (encoded by CLEC12A) on the cell surface of cells from patients with JMML. We develop immunotherapy with CLL-1 CAR T cells (CLL1CART) for preclinical testing and report in vitro and in vivo anti-leukemia activity. Notably, CLL1CART reduce the number of leukemic stem cells and serial transplantability in vivo. These preclinical data support the development and clinical investigation of CLL-1-targeting immunotherapy in children with relapsed/refractory JMML.
An enhanced ensemble defense framework for boosting adversarial robustness of intrusion detection systems
Abstract Machine learning (ML) and deep neural networks (DNN) have emerged as powerful tools for enhancing intrusion detection systems (IDS) in cybersecurity. However, recent studies have revealed their vulnerability to adversarial attacks, where maliciously perturbed traffic samples can deceive trained DNN-based detectors, leading to incorrect classifications and compromised system integrity. While numerous defense mechanisms have been proposed to mitigate these adversarial threats, many fail to achieve a balance between robustness against adversarial attacks, maintaining high detection accuracy on clean data, and preserving the functional integrity of traffic flow features. To address these limitations, this research investigates and integrates a comprehensive ensemble of adversarial defense strategies, implemented in two key phases. During the training phase, adversarial training, label smoothing, and Gaussian augmentation are employed to enhance the model’s resilience against adversarial perturbations. Additionally, a proactive preprocessing defense strategy is deployed during the testing phase, utilizing a denoising sparse autoencoder to cleanse adversarial input samples before they are fed into the IDS classifier. Comparative evaluations demonstrate that the proposed ensemble defense framework significantly improves the adversarial robustness and classification performance of DNN-based IDS classifiers. Experimental results, validated on the CICIDS2017 and CICIDS2018 datasets, show that the proposed approach achieves aggregated prediction accuracies of 87.34% and 98.78% under majority voting and weighted average schemes, respectively. These findings underscore the effectiveness of the proposed framework in combating adversarial threats while maintaining robust detection capabilities, thereby advancing the state-of-the-art in adversarial defense for intrusion detection systems.