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Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%. [ABSTRACT FROM AUTHOR]

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model. [ABSTRACT FROM AUTHOR]

The ongoing global spread of Newcastle disease underscores the crucial need for continued research on the efficacy of current vaccines against various circulating strains of Newcastle disease virus (NDV). The fusion gene of a representative Egyptian genotype VII.1.1 strain was used to substitute its corresponding gene in the K148/08 vaccinal strain after site directly mutating its cleavage site from 112RRQKRF117 to 112GKQGRL117. Fusion gene exchange between GVII and GI did not affect the thermostability of GI K148/08. Attenuation of the rescued virus was confirmed by mean death time 144 h with an intracerebral pathogenicity index of 0.00. Survival analysis after the challenge experiment confirmed that 107 EID50 was the protective dose of rK148/GVII-F. The haemagglutination inhibition level of antibodies required for full clinical protection was > 3.3 log2 for rK148/GVII-F and > 4.1 log2 for both K148/08 and LaSota. Oropharyngeal viral shedding was reduced on the 5th and 7th days post-challenge in the rK148/GVII-F vaccinated group. Replication and tropism investigations confirmed the respirotropic nature of LaSota, enterotropic nature of K148/08, and further attenuation of rK148/GVII-F. Altogether, rK148/GVII-F is a thermostable, safe, effective, and genetically stable vaccine candidate that could be adequate for use in countries that encounter GVII.1.1 and in those with tropical climate, such as most Middle Eastern countries. A thermostable, safe, and effective NDV GVII recombinant vaccine was generated. Fusion gene replacement with GVII did not affect GI K148/08 virus thermostability. Strain rK148/GVII-F provided adequate protection against a lethal NDV challenge. Oropharyngeal shedding was significantly reduced on post-challenge days 5 and 7. [ABSTRACT FROM AUTHOR]

This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods. [ABSTRACT FROM AUTHOR]

In 2020, the Y280-lineage H9N2 low-pathogenic avian influenza virus (LPAIV) was introduced into South Korea for the first time. Current vaccines are focused on the control of Y439-like viruses; however, there are continuous reports of decrease in egg production and secondary infections caused by Y280-lineage H9N2 LPAI infection in chickens. Therefore, there is an urgent need to develop effective novel vaccines against Y280-lineage H9N2 LPAI. Most commercialized avian influenza vaccines are oil-adjuvanted inactivated vaccines, which are labour-intensive to administer and require higher dosage. In this study, rK148/Y280-HA, a novel recombinant Newcastle disease virus (NDV) vectored vaccine against Y280-lineage H9N2 LPAI, was developed and evaluated using two mass-applicable administration methods, spray vaccination and drinking water vaccination. Regardless of low serum antibody haemagglutination inhibition titres against NDV and Y280-lineage H9N2 LPAI after applying the rK148/Y280-HA vaccine, vaccination with either administration method protected chickens against virulent NDV and Y280-lineage H9N2 LPAIV after the challenge. Taken together, these results indicate that the rK148/Y280 vaccine can be administered using facile mass-application methods to provide protection against the Y280-lineage LPAI. RESEARCH HIGHLIGHTS NDV vectored vaccine harbouring Y280-lineage H9N2 HA protein was successfully generated. NDV vectored vaccine provides protection against NDV. NDV vectored vaccine with H9N2 HA protects against homologous H9N2 LPAIV. [ABSTRACT FROM AUTHOR]

Adaptive services in pervasive environments are based on the correct detection of context. However, sensor malfunctions and inappropriate inference in regards to dynamic environments can lead to incorrect context detection that is unintended by the user. In addition, an appropriate context-aware method needs to be accurate even when environmental conditions change. Feedback from the user is one of the methods used to correctly acquire contextual information and feedback data that can be used to select the adaptive context-aware method. This paper presents a scheme that evaluates context-aware methods based on the feedback data from the user. The evaluation is performed by comparing the feedback data within the context of the currently running context-aware methods. The error rates of all the contextaware methods are calculated and the service provider then selects the appropriate context-aware method which possesses the smallest error rate amongst them. Experiment results show that the proposed method improves the context-aware rate by up to 10.3% compared to the trust-worthiness based method and 16.4% compared to the voting method. [ABSTRACT FROM PUBLISHER]

Abstract: The prioritization of engineering characteristics (ECs) provides an important basis for decision-making in QFD. However, the prioritization results in the conventional QFD may be misleading since it does not consider the uncertainty of input information. This paper develops two robustness indices and proposes the notion of robust prioritization that ensures the EC prioritization to be robust against the uncertainty. The robustness indices consider robustness from two perspectives, namely, the absolute ranking of ECs and the priority relationship among ECs. Based on the two indices, robust prioritization seeks to identify a set of ECs or a priority relationship among ECs in such a way that the result of robust prioritization is stable despite the uncertainty. Finally, the proposed robustness indices and robust prioritization are demonstrated in a case study conducted on the ADSL-based high-speed internet service. [Copyright &y& Elsevier]

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics. [ABSTRACT FROM AUTHOR]

Recently, emerging edge computing technology has been proposed as a new paradigm that compensates for the disadvantages of the current cloud computing. In particular, edge computing is used for service applications with low latency while using local data. For this emerging technology, a neural network approach is required to run large-scale machine learning on edge servers. In this paper, we propose a pod allocation method by adding various graphics processing unit (GPU) resources to increase the efficiency of a Kubernetes-based edge server configuration using a GPU-based embedded board and a TensorFlow-based neural network service application. As a result of experiments performed on the proposed edge server, the following are inferred: 1) The bandwidth, according to the time and data size, changes in local (20.4–42.4 Mbps) and Internet environments (6.31–25.5 Mbps) for service applications. 2) When two neural network applications are run on an edge server consisted with Xavier, TX2 and Nano, the network times of the object detection application are from 112.2 ms (Xavier) to 515.8 ms (Nano); the network times of the driver profiling application are from 321.8 ms (Xavier) to 495.7 ms (Nano). 3) The proposed pod allocation method demonstrates better performance than the default pod allocation method. We observe that the number of allocatable pods on three worker nodes increases from five to seven, and compared to other papers, the proposed offloading shows similar or better response times in environments where multiple deep learning applications are implemented. [ABSTRACT FROM AUTHOR]

• This paper proposes a new DOSbFC algorithm to remove baseline drift of EOG Signals. • This paper presents a new electrode positioning scheme based on eyeglasses. • This paper provides a long-term eye movement detection function with high accuracy. This paper presents a new method to remove baseline drift and noise by using a differential electrooculography (EOG) signal based on a fixation curve (DOSbFC) and a new electrode positioning scheme based on eyeglasses for user convenience. In addition, a desktop application and mobile applications to control the human–computer interface were implemented. Finally, we created experimental EOG eyeglasses and a new detection protocol using the proposed method for long-term step-by-step detection of eye movements and user comfort. The proposed DOSbFC calculates the difference values of accumulated EOG signals between the initial eye movement and fixation time. It allows long-term detection of eye movements with high accuracy and only requires a single calibration. The vertical and ground electrodes of the standard electrode positioning scheme caused discomfort of subjects; the proposed electrode positioning scheme solves these problems and enables the use of existing eyeglasses without design modification. The experimental results demonstrated that the average accuracy of the long-term eye movement detection was 94%, whereas those of the band pass filter and wavelet transform were 61% and 64%, respectively. This was because baseline drift and noise were removed by averaging the signal variations. Further experimental results demonstrated that the average information transfer rate of the proposed method was 6.0, whereas those of the band pass filter and wavelet transform were 1.1 and 0.9, respectively. [ABSTRACT FROM AUTHOR]

This study aims to propose a novel approach for gender recognition using best feature subset based on recursive feature elimination (RFE) in normal walking. This study has focused on the analysis of gait characteristics by distinguishing the gait phases as initial contact (IC), Mid-stance (MS), Pre-swing, and swing (SW), and collected the large number of gait to improve the reliability of quantitative assessment of natural variability associated with muscle activity during free walking. The gait system was designed using pressure and a tri-axis accelerometer sensor, and a 9-channel electromyography sensor for measuring the data. Gender recognition method was proposed using support vector machine (SVM) and random forest (RF) based on RFE to determine best feature subset. Statistical results show that effects of gender-based differences on gait characteristic including temporal, kinematics, and muscle activity were investigated. The temporal parameters of stride time and gait cycle (%) in the gait phases of IC, MS, and SW were significantly different between females and males (p < 0.01). The females exhibited both a lower angle and a root mean square acceleration of the knee joint as compared to the males, and there was a clear gender-based difference with respect to knee angle movement. In addition, most muscle activation measurements in the females were larger than those of the males with respect to the gait phases. Gender classification result shows that SVM-RFE was 99.11% (SVM classifier) and RF-RFE was 98.89% (SVM and RF classifier), having powerful performance. • The paper investigates the statistical effect of gender-based differences on gait. • The paper has focused to analysis gait characteristics in gait sub-phases. • A novel approach for gender classification is proposed using RFE. • The paper has the powerful performance for gender classification using SVMRFE. [ABSTRACT FROM AUTHOR]

Influenza epidemics frequently and unpredictably break out all over the world, and seriously affect the breeding industry and human activity. Inactivated and live attenuated viruses have been used as protective vaccines but exhibit high risks for biosafety. Subunit vaccines enjoy high biosafety and specificity but have a few weak points compared to inactivated virus or live attenuated virus vaccines, especially in low immunogenicity. In this study, we developed a new subunit vaccine platform for a potent, adjuvant‐free, and multivalent vaccination. The ectodomains of hemagglutinins (HAs) of influenza viruses were expressed in plants as trimers (tHAs) to mimic their native forms. tHAs in plant extracts were directly used without purification for binding to inactivated Lactococcus (iLact) to produce iLact‐tHAs, an antigen‐carrying bacteria‐like particle (BLP). tHAs BLP showed strong immune responses in mice and chickens without adjuvants. Moreover, simultaneous injection of two different antigens by two different formulas, tHAH5N6 + H9N2 BLP or a combination of tHAH5N6 BLP and tHAH9N2 BLP, led to strong immune responses to both antigens. Based on these results, we propose combinations of plant‐based antigen production and BLP‐based delivery as a highly potent and cost‐effective platform for multivalent vaccination for subunit vaccines. [ABSTRACT FROM AUTHOR]

This paper proposes new GPU-accelerated high-performance encoding and decoding for hierarchical RAID in a multiple virtual machine environment. Pass-through GPU technology is used to provide dedicated access to GPU cores for each virtual machine, and for a virtual desktop, it also enables higher encoding and decoding performance than traditional vGPU technology. The proposed hierarchical RAID reduces the GPU overhead and resists node failure. Experimental results show that the average encoding performance of the proposed hierarchical RAID 55 improves by 11.03%, compared to another hierarchical RAID 51, with respect to various file sizes. In addition, the average disk-based decoding performance of the proposed hierarchical RAID 55 also improves by 59.61%. [ABSTRACT FROM AUTHOR]