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ABSTRACTAfrican swine fever (ASF) is a highly contagious disease of domestic and wild pigs caused by the African swine fever virus (ASFV). The current research on ASF vaccines focuses on the development of naturally attenuated, isolated, or genetically engineered live viruses that have been demonstrated to produce reliable immunity. As a result, a genetically engineered virus containing five genes deletion was synthesized based on ASFV Chinese strain GZ201801, named ASFV-GZΔI177LΔCD2vΔMGF. The five-gene-deleted ASFV was safe and fully attenuated in pigs and provides reliable protection against the parental ASFV strain challenge. This indicates that the five-gene-deleted ASFV is a potential candidate for a live attenuated vaccine that could control the spread of ASFV.

The new generation antenna has high-quality processing requirements on the resin-rich layer of the reflecting surface. A polishing experiment on the resin-rich layer was carried out to study the parameters affecting the surface roughness of the layer, namely polishing time, abrasive particle size, abrasive particle mass fraction, loading pressure and polishing speed. The results show that under the current conditions, the surface roughness first decreases and then tends to be stable with the increase of polishing time. It increases with the increase of the abrasive particle size or the loading pressure, and first decreases and then increases with the increase of the abrasive particle mass fraction or the polishing speed. On this basis, an optimized process parameter combination is formed, which includes an abrasive size of 25 nm, an abrasive particle mass fraction of 20%, a loading pressure of 14.1 kPa, a polishing speed of 50 r/min, a polishing time of 1.0 hours, and a slurry flow rate of 10 mL/min. A high-quality resin rich layer polished surface with a surface roughness Sa of 4.73 nm can be obtained by machining with this parameter combination.

Background Acute kidney injury (AKI) is a common and serious medical condition with high morbidity and mortality. Recent research has highlighted ferroptosis, a novel form of programmed cell death, as a potential therapeutic target in mitigating renal tubular injury in AKI. Ferrostatin-1, a specific ferroptosis inhibitor, has been demonstrated to prevent renal injury through ferroptosis inhibition. Methods Utilizing a murine AKI model, we investigated the effects of Ferrostatin-1 by administering it post-injury. Through high-throughput sequencing and pathological analysis, we focused on the critical role of ferroptosis-related pathways in the treatment. Results Ferrostatin-1 post-conditioning effectively mitigated oxidative damage and reduced iron content associated with AKI. Additionally, critical ferroptosis-related proteins, such as GPX4, SLC7A11, NRF2, and FTH1, exhibited increased expression levels. In vitro, Ferrostatin-1 treatment of HK-2 cells significantly diminished lipid peroxidation and iron accumulation. Furthermore, Ferrostatin-1 was found to downregulate the PI3K signalling pathway. Conclusion Ferrostatin-1 acted as a potential ferroptosis inhibitor with the capacity to enhance antioxidant defences. This study suggests that Ferrostatin-1 could serve as a promising novel strategy for improving the treatment of AKI and promoting recovery from the condition.

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A plethora of information is now readily available for traffic prediction, making an effective use of them enables better traffic planning. With data coming from multiple sources, and their features spanning spatial and temporal dimensions, there is an increasing demand to exploit them for accurate traffic prediction. Existing methods, however, do not provide a solution for this, as they tend to require expertise feature engineering. In this paper, we propose a general architecture for SpatioTemporal Data Fusion (STDF) with parameter efficiency. To make heterogeneous multi-source data fusion effectiveness, we separate all data into traffic directly related data and traffic indirectly related data. With traffic indirectly related data as the input to Spatial Embedding by Temporal convolutiON (SETON) that simultaneously encodes each feature in both space and time dimensions and traffic directly related data as the input to the graph convolutional network(GCN), we designed a fine-grained feature transformer to match the ones generated by GCN. This is then followed by a fusion module to combine all features to make final prediction. Compared to using GCNs training with only traffic directly related data, experimental results show that our model can achieve a 6.1% improvement in prediction accuracy measured by Root Mean Squared Error.

Limited by the bandwidth of IEEE 802.11p protocol, a large-size data block commonly cannot be transmitted within one communication link in a vehicular ad hoc network (VANET). When transmitting large-size data block in VANETs, the traditional routing-layer-based solutions usually employed a large number of routing paths to extend the data link duration through continuously changing routing path, which commonly leads to frequent communication link disconnection and re-connection. Another shortcoming of the routing-layer solutions in transmitting large-size data block is that dense relay node is required to obtain sufficient stable routing paths for maintaining the long data transmission duration. In this paper, we present a new scheme for efficiently transmitting large-size data block in VANETs, which dedicate to improve the data transmission efficiency through shortening the total duration of the data transmission process at the network architecture and physical connection levels. Based on the assistance of the dual-communication mode and edge computing technique, a heterogeneous network architecture is proposed to introduce multiband orthogonal frequency division multiplexing (MB-OFDM) based ultra-wideband (UWB) technique into the VANETs for transmitting large-size data block. The key challenges of the scheme and alternative solutions for the challenges are analyzed, in which several mechanisms for quick parameter pre-configuration, the quick MB-OFDM UWB link establishment and highly efficient multi-user access to the OFDM-UWB network are presented. The key obstacles that limit the effectiveness of the scheme are also evaluated, of which the results show the presented scheme is feasible.

With the exploding growth of smart devices and application of 5G communications, there are more and more computation-intensive tasks which are delay-sensitive. Mobile edge computing (MEC) is a new paradigm that provides rich computing resources in close proximity to mobile users. However, how to make the decision of whether offloading or not as well as control the transmission power jointly remains a challenge. In this paper, we first study the joint multiuser offloading and transmission power control optimization problem in a multi-channel wireless interference scenario. We formulate the joint optimization problem into a system-wide computation overhead minimization problem as a mixed integer non-linear program. We then propose an efficient semi-distributed algorithm consisting of two subalgorithms of offloading scheme and transmission power control. The numerical simulation results demonstrate that the proposed algorithm can effectively reduce system-wide computation overhead and increase the number of beneficial edge computing users, compared with all local or edge computing and fixed transmission power schemes.

Vehicular Edge Computing (VEC) has been studied as an important application of mobile edge computing in vehicular networks. Usually, the generalization of VEC involves large-scale deployment of dedicated servers, which will cause tremendous economic expense. We also observe that the Parked Vehicles (PVs) in addition to mobile vehicles have rich and underutilized resources for task execution in vehicular networks. Thus, we consider scheduling PVs as available edge computing nodes to execute tasks, and this leads to a new computing paradigm, called by parked vehicle edge computing (PVEC). In this paper, we investigate PVEC and explore opportunistic resources from PVs to run distributed mobile applications. PVs coordinate with VEC servers for collective task execution. First, a system architecture with primary network entities is proposed for enabling PVEC. We also elaborately design an interactive protocol to support mutual communications among them with security guarantee. Moreover, we measure the availability of opportunistic resources and formulate a resource scheduling optimization problem by using Stackelberg game approach. A subgradient-based iterative algorithm is presented to determine workload allocation among PVs and minimize the overall cost of users. Numerical results indicate that compared with existing schemes, PVEC serves more vehicles and reduces service fee for users. We also demonstrate that the Stackelberg game approach is effective and efficient.

The user operates the smart home devices year in year out, have produced mass operation data, but these data do not be utilized well in past. Nowadays, these data can be used to predict user’s behavior custom with the development of big data and machine learning technologies, and then the prediction results can be employed to enhance the intelligence of a smart home system. In view of this, this paper proposes a novel unsupervised user behavior prediction (UUBP) algorithm, which employs an artificial neural network and proposes a forgetting factor to overcome the shortcomings of the previous prediction algorithm. This algorithm has a high-level of autonomous and self-organizing learning ability while does not require too much human intervention. Furthermore, the algorithm can better avoid the influence of user’s infrequent and out-of-date operation records, because of the forgetting factor. Finally, the use of real end user’s operation records to demonstrate that UUBP algorithm has a better level of performance than other algorithms from effectiveness.

Background/purpose: Periodontitis is an inflammatory, destructive disease caused by periodontal bacteria, and its molecular mechanism remains unclear. The aims of this study are to evaluate the expressions of Wnt3a and Dkk1 in experimental periodontitis (EP) and preliminarily explore their roles in periodontal diseases. Materials and methods: A total of 64 six-week-old male Sprague–Dawley rats were randomly divided into a normal group and an EP group. The EP group was prepared by using silk ligature combined with intraoral bacteria inoculation. To assess the periodontal inflammation and bone destruction extent, hematoxylin and eosin staining and tartrate-resistant acid phosphatase staining was performed 2 weeks, 4 weeks, and 6 weeks after the modeling, respectively, and immunohistochemistry and enzyme-linked immunosorbent assay were also performed to detect the changes of Wnt3a and Dkk1 in periodontal tissue and plasma. Results: Wnt3a expression was significantly decreased in the EP group when compared with the normal group (P

The high implementation complexity of multiband orthogonal frequency-division multiplexing (MB-OFDM) ultra-wideband (UWB) technology is the major hurdle for applying it to vehicular communications. This paper presents a comprehensive synchronizer with low complexity and high performance for the MB-OFDM UWB. A low-complex overall architecture is proposed, in which the sub-functions are divided into amplitude-detection-based functions and phase-detection-based functions. All of the amplitude-detection-based functions are implemented based on a simplified cross correlation-based matched filter, and a serial structure-based auto-correlation block is designed exclusively for carrier frequency offset estimation. Several effective methods for the sub-functions are proposed based on the proposed overall architecture. Evaluation results show that the proposed synchronizer has high performance with low complexity.

With an increasing number of aircraft systems, a fully manual developmental approach is impractical for finding optimal hardware and software mapping from overwhelming configurations for Distributed Integrated Modular Avionics (DIMA) systems. The automation of finding such optimized mapping should be available and thoroughly understood. This paper is an investigation on the foundations of optimal hardware and software mapping for DIMA. We begin by reviewing the DIMA system architecture. Following that, we present the problem statement of hardware and software mapping and its ensuring mathematical optimization models. A set of primary architectural quality metrics (e.g., reliability and scalability) and aircraft constraints (e.g., segregation and resource constraints) are identified, which can be used to compose an objective function or compare and trade alternatives. Based on the quality metrics and aircraft constraints, we synthesize an encompassing formulation by means of multi-objective optimization. Various optimization approaches for hardware and software mapping are then reviewed and compared. Case studies of DIMA optimization are presented for avionics systems, in which running time is reported for different optimization problems with different objectives and constraints. In addition, we present and discuss open issues and future trends, from which future developments may draw upon.

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy of cloud-assisted IoV from the viewpoint of the service relationship between cloud computing and IoV. Then, we review the traditional traffic prediction approached used by both Vehicle to Infrastructure (V2I) and Vehicle to Vehicle (V2V) communications. On this basis, we propose a mobile crowd sensing technology to support the creation of dynamic route choices for drivers wishing to avoid congestion. Experiments were carried out to verify the proposed approaches. Finally, we discuss the outlook of reliable traffic prediction.