The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising.
In the realm of targeted advertising, the demand for precision is paramount, and the traditional centralized machine learning paradigm fails to address this necessity effectively. Two critical challenges persist in the current advertising ecosystem: the data privacy concerns leading to isolated data islands and the complexity in handling non-Independent and Identically Distributed (non-IID) data and concept drift due to the specificity and diversity in user behavior data. Current federated learning frameworks struggle to overcome these hurdles satisfactorily. This paper introduces Fed-GANCC, an innovative federated learning framework that synergizes Generative Adversarial Networks (GANs) and Group Clustering. The framework incorporates a user data augmentation algorithm predicated on adversarial generative networks to enrich user behavior data, curtail the impact of non-uniform data distribution, and enhance the applicability of the global machine learning model. Unlike traditional approaches, our framework offers user data augmentation algorithms based on adversarial generative networks, which not only enriches user behavior data but also reduces the challenges posed by non-uniform data distribution, thereby enhancing the applicability of the global machine learning (ML) model. The effectiveness of Fed-GANCC is distinctly showcased through experimental results, outperforming contemporary methods like FED-AVG and FED-SGD in terms of accuracy, loss value, and receiver operating characteristic (ROC) indicators within the same computing time. Experimental results vindicate the effectiveness of Fed-GANCC, revealing substantial enhancements in accuracy, loss value, and receiver operating characteristic (ROC) metrics compared to FED-AVG and FED-SGD given the same computational time. These outcomes underline Fed-GANCC's exceptional prowess in mitigating issues such as isolated data islands, non-IID data, and concept drift. With its novel approach to addressing the prevailing challenges in targeted advertising such as isolated data islands, non-IID data, and concept drift, the Fed-GANCC framework stands as a benchmark, paving the way for future advancements in federated learning solutions tailored for the advertising domain. The Fed-GANCC framework promises to offer pivotal insights for the future development of efficient and advanced federated learning solutions for targeted advertising.
Human papillomavirus (HPV) is a double-standard DNA virus, as well as the source of infection to the mucous membrane. It is a sexually transmitted disease that brings the changes in the cervix cells. Oncogenes, E6 and E7 play a pivotal role in the HPV infection. Identifying these genes to detect HPV strains, especially a prevalent HPV16 strain, will bring a great impact. Among different sensing strategies for pathogens, the dielectric electrochemical biosensor shows the potential due to its higher sensitivity. In this research, HPV16-E7 DNA sequence was detected on the carbodiimidazole-modified interdigitated electrode (IDE) surface with the detection limit of 1 fM. To enhance the sensitivity, the target sequence was conjugated on gold nanoparticle (GNP) and attained detection to the level of 10 aM. This produced ~100 folds improvement in detecting HPV16-E7 gene and 4 folds increment in the current flow. The stability of HPV16-E7 DNA sequences on GNP was verified by the salt-induced GNP aggregation. The current system has shown the higher specificity by comparing against non-complementary and triple-mismatched DNA sequences of HPV16-E7. This demonstration in detecting HPV16-E7 using dielectric IDE sensing system with a higher sensitivity can be recommended for detecting a wide range of disease-causing DNA-markers.
Perovskite-like oxides SrCo1-xTixO3-δ (SCTx, x = 0.1, 0.2, 0.4, 0.6) were used as heterogeneous catalysts to activate peroxymonosulfate (PMS) for phenol degradation under a wide pH range, exhibiting more rapid phenol oxidation than Co3O4 and TiO2. The SCT0.4/PMS system produced a high activity at increased initial pH, achieving optimized performance at pH ≥ 7 in terms of total organic carbon removal, the minimum Co leaching and good catalytic stability. Kinetic studies showed that the phenol oxidation kinetics on SCT0.4/PMS system followed the pseudo-zero order kinetics and the rate on SCT0.4/PMS system decreased with increasing initial phenol concentration, decreased PMS amount, catalyst loading and solution temperature. Quenching tests using ethanol and tert-butyl alcohol demonstrated sulfate and hydroxyl radicals for phenol oxidation. This investigation suggested promising heterogeneous catalysts for organic oxidation with PMS, showing a breakthrough in the barriers of metal leaching, acidic pH, and low efficiency of heterogeneous catalysis.
Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node's reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.
Bacterial Extracellular Vesicles (BEVs) possess the capability of intracellular interactions with other cells, and, hence, can be utilized as an efficient cargo for worldwide delivery of therapeutic substances such as monoclonal antibodies, proteins, plasmids, siRNA, and small molecules for the treatment of neurodegenerative diseases (NDs). BEVs additionally possess a remarkable capacity for delivering these therapeutics across the blood-brain barrier to treat Alzheimer's disease (AD). This review summarizes the role and advancement of BEVs for NDs, AD, and their treatment. Additionally, it investigates the critical BEV networks in the microbiome-gut-brain axis, their defensive and offensive roles in NDs, and their interaction with NDs. Furthermore, the part of BEVs in the neuroimmune system and their interference with ND, as well as the risk factors made by BEVs in the autophagy-lysosomal pathway and their potential outcomes on ND, are all discussed. To conclude, this review aims to gain a better understanding of the credentials of BEVs in NDs and possibly discover new therapeutic strategies.
Background: Though many literatures documented burnout and occupational hazard among healthcare workers and frontliners during pandemic, not many adopted a systemic approach to look at the resilience among this population. Another under-studied population was the large numbers of global healthcare workers who have been deployed to tackle the crisis of COVID-19 pandemic in the less resourceful regions. We investigated both the mental wellbeing risk and protective factors of a deployed healthcare workers (DHWs) team in Wuhan, the epicenter of the virus outbreak during 2020. Method: A consensual qualitative research approach was adopted with 25 DHWs from H province through semi-structured interviews after 3 months of deployment period. Results: Inductive-Deductive thematic coding with self-reflexivity revealed multi-layered risk and protective factors for DHWs at the COVID-19 frontline. Intensive working schedule and high-risk environment, compounded by unfamiliar work setting and colleagues; local culture adaptation; isolation from usual social circle, strained the DHWs. Meanwhile, reciprocal relationships and "familial relatedness" with patients and colleagues; organizational support to the DHWs and their immediate families back home, formed crucial wellbeing resources in sustaining the DHWs. The dynamic and dialectical relationships between risk and protective factors embedded in multiple layers of relational contexts could be mapped into a socio-ecological framework. Conclusion: Our multidisciplinary study highlights the unique social connectedness between patient-DHWs; within DHWs team; between deploying hospital and DHWs; and between DHWs and the local partners. We recommend five organizational strategies as mental health promotion and capacity building for DHWs to build a resilient network and prevent burnout at the disaster frontline.