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Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), emerged as a global outbreak in 2019, profoundly affecting both human health and the global economy. Various vaccine modalities were developed and commercialized to overcome this challenge, including inactivated vaccines, mRNA vaccines, adenovirus vector-based vaccines, and subunit vaccines. While intramuscular vaccines induce high IgG levels, they often fail to stimulate significant mucosal immunity in the respiratory system. We employed the Newcastle disease virus (NDV) vector expressing the spike protein of the SARS-CoV-2 Beta variant (rK148/beta-S), and evaluated the efficacy of intranasal vaccination with rK148/beta-S in K18-hACE2 transgenic mice. Intranasal vaccination with a low dose (106.0 EID50) resulted in an 86% survival rate after challenge with the SARS-CoV-2 Beta variant. Administration at a high dose (107.0 EID50) led to a reduction in lung viral load and 100% survival against the SARS-CoV-2 Beta and Delta variants. A high level of the SARS-CoV-2 spike-specific IgA was also induced in vaccinated mice lungs following the SARS-CoV-2 challenge. Our findings suggest that rK148/beta-S holds promise as an intranasal vaccine candidate that effectively induces mucosal immunity against SARS-CoV-2. [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]

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]

As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning or partial offloading. However, these methods require a lot of training data and critical deadlines and are weakly adaptive to complex and dynamically changing environments. To overcome this weakness, in this paper, we propose a novel task offloading algorithm based on Lyapunov optimization to maintain the system stability and minimize task processing delay. First, a real-time monitoring system is built to utilize distributed computing resources in an autonomous driving environment efficiently. Second, the computational complexity and memory access rate are analyzed to reflect the characteristics of the deep learning applications to the task offloading algorithm. Third, Lyapunov and Lagrange optimization solves the trade-off issues between system stability and user requirements. The experimental results show that the system queue backlog remains stable, and the tasks are completed within an average of 0.4231 s, 0.7095 s, and 0.9017 s for object detection, driver profiling, and image recognition, respectively. Therefore, we ensure that the proposed task offloading algorithm enables the deep learning application to be processed within the deadline and keeps the system stable. [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]

Discusses the collection fusion problem of image databases, concerned with retrieving relevant images by content based retrieval from image databases distributed on the Web. Focuses on a metaserver which selects image databases supporting similarity measures and proposes a new algorithm which exploits a probabilistic technique using Bayesian estimation for a linear regression model. (Author/LRW)

IOTA Tangle offers a promising approach for distributed ledger technology with the capability to compete with the traditional blockchain. To enable microtransactions the Internet of things (IoT) environment, IOTA employs a direct acrylic graph that ensures the integrity and immutability of the transactions. However, IoT data exhibit time sensitivity, wherein the value is lost after a period. Storing these temporary data for immutable storage would not be affordable in the distributed ledger. This study proposes a novel approach—referred to as D-Tangle—that enables data deletions in the Tangle architecture. To achieve this goal, D-Tangle divides transactions into three categories based on their expiration features and employs the climb-up writing technique. Extensive evaluations prove that D-Tangle enables instant deletions in finite lifetime data. Moreover, immutability and deletion upon request are guaranteed for unknown lifetime data. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2009.10.008 Byline: Deok-Hwan Kim (a), Kwang-Jae Kim (b), K. Sam Park (c) Keywords: Prioritization; Type I and II errors; Pairwise comparisons; Product design and development Abstract: We explore an important problem in prioritizing product design alternatives, using a real-world case. Despite the importance of prioritization in the area of new product development, the development of systematic schemes has been limited and the concepts and methods developed in the decision analysis area do not seem to be used actively. Therefore, we propose a new method, referred to as the compromising prioritization technique, to prioritize the product design alternatives based on paired comparisons. It introduces type I and type II errors and compromises these two errors to arrive at a desirable order of alternatives. To accomplish this, the two indices of homogeneity and separation are developed together with a heuristic algorithm. A comparative study is also conducted to support our method for use in product development and analogous areas. We then demonstrate how to use the developed compromising prioritization technique using a case study on the asymmetric digital subscriber line (ADSL)-based high-speed internet service product. Author Affiliation: (a) Defense Agency for Technology and Quality, Cheongryang P.O. Box 276, Dongdaemun-Gu, Seoul 130-650, Republic of Korea (b) Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-Dong, Nam-Gu Pohang, Kyungbuk 790-784, Republic of Korea (c) Korea University, Business School, Anam-5Ga, Seongbuk, Seoul 136-701, Republic of Korea Article History: Received 3 March 2008; Accepted 14 October 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2005.02.005 Byline: Deok-Hwan Kim (a), Chin-Wan Chung (b), Kobus Barnard (c) Abstract: Research has been devoted in recent years to relevance feedback as an effective solution to improve performance of image similarity search. However, few methods using the relevance feedback are currently available to perform relatively complex queries on large image databases. In the case of complex image queries, images with relevant concepts are often scattered across several visual regions in the feature space. This leads to adapting multiple regions to represent a query in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, we propose a new adaptive classification and cluster-merging method to find multiple regions and their arbitrary shapes of a complex image query. Our method achieves the same high retrieval quality regardless of the shapes of query regions since the measures used in our method are invariant under linear transformations. Extensive experiments show that the result of our method converges to the user's true information need fast, and the retrieval quality of our method is about 22% in recall and 20% in precision better than that of the query expansion approach, and about 35% in recall and about 31% in precision better than that of the query point movement approach, in MARS. Author Affiliation: (a) Department of Mobile Internet, Dongyang Technical College, 62-160 Kochuk-dong, Kuro-gu, Seoul 152-714, South Korea (b) Division of Computer Science, Korea Advanced Institute of Science and Technology, 373-1, Kusong-dong, Yusong-gu, Taejon 305-701, South Korea (c) Computer Science Department, University of Arizona, Gould-Simpson 730, Tucson, AZ 85721, USA

The human inner perception is the core technology for human‐robot interactions. Emotion recognition has been established as a representative study for recognising the internal state of human beings in addition to research regarding intention recognition. In the study of emotion recognition using EEG signals, emotion evaluation is categorised into two methods: discrete emotion and continuous emotion. This study proposes an ant colony optimisation‐bidirectional LSTM network model. Unlike other LSTM network models, this model improves performance by applying more weight values that are valid for emotion recognition in the current LSTM cell state using past and future biosignal information and combining ACO to find the optimal combination of emotion recognition features among many features. Furthermore, it extracts valid features using peripheral nervous system signals PPG, GSR, and EOG as well as the central nervous system signals EEG, simultaneously. The authors reinforce feature performance by adding brain lateralisation for emotion recognition. Emotion label data were recorded by performing annotation labelling, in which the values are between −100 and 100 in the arousal‐valence domain. The experimental result shows that the ACO‐bidirectional LSTM model using brain lateralisation in the MAHNOB‐HCI, DEAP, MERTI‐Apps database yields the best valence performance, RMSE of 0.0442, 0.0523, and 0.0568, respectively. [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]

Long‐term electroencephalography (EEG) monitoring is time‐consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short‐term window size. Therefore, our method can be utilized to interpret long‐term EEG results and detect momentary seizure waveforms in diagnostic systems. [ABSTRACT FROM AUTHOR]

Defect inspection of polarised films is a crucial process in TFT-LCD (thin film transistor-liquid crystal display) production. In this paper we propose a defect inspection system for TFT-LCD film images that detects film defects in a real-time production environment and classifies them based on the type of a defect. The proposed system is designed to locate defects promptly using an adaptive threshold technique and determines the defect type through image analysis using various features, such as the geometric characteristics and the shape descriptor with intensity distribution called the shape+ID descriptor. The experimental results using a set of test images obtained in a real production line are quite promising. Compared with existing methods, our method identifies defects effectively and efficiently enough to be used in a real-time environment. It also achieves a high correctness in classifying the defect type for various types of defects. In addition, it demonstrates robustness with respect to scale and rotational transformation. [ABSTRACT FROM AUTHOR]

This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in recognizing lower limb human motion detection. With respect to time-domain features, their values may be analogous because two different gait subphases and locomotion mode may have similar muscle activity pattern. Therefore, it is not easy to select the proper feature set of sEMG signals. The TAS feature extraction algorithm reflects better timing characteristics of sEMG signals than the existing time-domain feature extraction algorithm. Therefore, it can provide high accuracy in lower limb human motion detection. Experimental results show that the average detection accuracy values of the proposed method in terms of the gait subphase detection, locomotion mode recognition, and mode change detection are increased by 8%, 5%, and 4% better than those of feature values of the Willison amplitude, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a high-performance graphics processing unit (GPU)-based parity computing scheduler, which we call GPU-redundant array of inexpensive disks (RAID), to reduce the encoding and decoding time for storage applications. The proposed GPU-RAID differs from existing RAID in that it performs additional pairwise-parallel XOR operations between data code words in each data stripe by applying divide-and-conquer approach using extra reserved space and it also increases parallelism by processing multiple strips in parallel using multiple GPU threads. And so the proposed GPU-RAID pipelines data blocks into solid-state disks and parity blocks into hard disk drives at the target server. The proposed algorithm decreases the span complexity of the parity computation schedule to O( l o g2 n w) where n is the number of disks and w is the number of code words in a block, and it can be applied to various types of erasure codes. Experimental results show that the proposed storage application (SA1) improves average encoding performance by 63%, and 41%, and average decoding performance by 58%, and 38%, compared with traditional storage applications GPUStore (SA3) and Gibraltar RAID(SA2), respectively. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the recent big-data processing in multimedia devices becoming a popular application, a fast and energy efficient storage area network system for smart classroom is required. Traditional storage management system for smart classroom show low performance when small read and write operations are executed. This paper proposes a smart classroom storage management system (SCSMS) which consists of new adaptive chunking and XOR reference matrix based erasure coding techniques for multimedia devices with higher input/output performance and low energy consumption. The SCSI initiator is installed in multimedia devices such as smart TVs, smart phones and personal computers. The proposed adaptive chunking and exclusive-or (XOR) reference matrix-redundant array of inexpensive disks (XRM-RAID)are provided at a target server based on flash array storage, respectively. Adaptive chunking differs from traditional chunking in that it reduces the number of read and write operations by merging small files into a united chunk. XRM-RAID differs from existing RAID in that it reduces the number of XOR operations to generate parity data in the RAID system. This paper provides web based monitoring application of the proposed SCSMS. Experimental results show that the energy consumption of the proposed SCSMS is improved by 32 %, 42 % and 58 % compared to Huang et al., Kim et al. and Scott et al. with respect to file size and buffer size. In terms of the average write throughput, the proposed SCSMS has higher performance by 22 %, 32 % and 56 % compared to Huang et al., Kim et al. and Scott et al. with respect to file size and buffer size. [ABSTRACT FROM AUTHOR]

Although power conversion efficiency (PCE) of state-of-the-art perovskite solar cells has already exceeded 20%, photo- and/or moisture instability of organolead halide perovskite have prevented further commercialization. In particular, the underlying weak interaction of organic cations with surrounding iodides due to eight equivalent orientations of the organic cation along the body diagonals in unit cell and chemically non-inertness of organic cation result in photo- and moisture instability of organometal halide perovskite. Here, a perovskite light absorber incorporating organic-inorganic hybrid cation in the A-site of 3D APbI3 structure with enhanced photo- and moisture stability is reported. A partial substitution of Cs+ for HC(NH2)2+ in HC(NH2)2PbI3 perovskite is found to substantially improve photo- and moisture stability along with photovoltaic performance. When 10% of HC(NH2)2+ is replaced by Cs+, photo- and moisture stability of perovskite film are significantly improved, which is attributed to the enhanced interaction between HC(NH2)2+ and iodide due to contraction of cubo-octahedral volume. Moreover, trap density is reduced by one order of magnitude upon incorporation of Cs+, which is responsible for the increased open-circuit voltage and fill factor, eventually leading to enhancement of average PCE from 14.9% to 16.5%. [ABSTRACT FROM AUTHOR]

User adaptive broadcasting service is a critical factor to implement ubiquitous computing for many clients and smart devices in the mobile environment. Service providers apply the user's preference to supply the user adaptive broadcasting service. However, in an error-prone environment, such as wireless networks, faults due to diverse conditions weaken the efficiency and fairness of the broadcasting service. It is crucial to broadcast to the user adaptively in the error-prone environment. This paper suggests a mechanism to enhance efficiency and maintain the fairness of the service by rescheduling the faulty item when the fault occurs. The proposed method uses the fault queue to get faulty items and can afford to provide fair broadcasting by supplementing the shortage item and provide adaptive broadcasting to the user groups by selecting the item of largest deviation value of preference among user groups in faulty items and new arrived items. Experimental results show that the proposed method reduces waiting time up to 31% compared to the retransmission scheme, while maintaining fairness. [ABSTRACT FROM AUTHOR]