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Plasmodium falciparum malaria contributes to a significant global disease burden. Circumsporozoite protein (CSP), the most abundant sporozoite stage antigen, is a prime vaccine candidate. Inhibitory monoclonal antibodies (mAbs) against CSP map to either a short junctional sequence or the central (NPNA) n repeat region. We compared in vitro and in vivo activities of six CSP-specific mAbs derived from human recipients of a recombinant CSP vaccine RTS,S/AS01 (mAbs 317 and 311); an irradiated whole sporozoite vaccine PfSPZ (mAbs CIS43 and MGG4); or individuals exposed to malaria (mAbs 580 and 663). RTS,S mAb 317 that specifically binds the (NPNA) n epitope, had the highest affinity and it elicited the best sterile protection in mice. The most potent inhibitor of sporozoite invasion in vitro was mAb CIS43 which shows dual-specific binding to the junctional sequence and (NPNA) n . In vivo mouse protection was associated with the mAb reactivity to the NANPx6 peptide, the in vitro inhibition of sporozoite invasion activity, and kinetic parameters measured using intact mAbs or their Fab fragments. Buried surface area between mAb and its target epitope was also associated with in vivo protection. Association and disconnects between in vitro and in vivo readouts has important implications for the design and down-selection of the next generation of CSP based interventions.

Using five waves of panel data from 8,008 children in the ECLS-K, the current study compared children's academic performance growth curves from kindergarten through fifth grade among three types of nondisrupted and three types of disrupted families. The analyses found that children in nondisrupted two-biological-parent and nondisrupted stepparent households consistently made greater progress in their math and reading performances over time than their peers in nondisrupted single-parent, disrupted two-biological-parent, and disrupted alternative families with multiple transitions. These trajectory differences were either partially or completely accounted for by family resources in the kindergarten year (Time 1). Overall, our findings provided strong support for the resource-deprivation perspective and partial support for the instability-stress perspective.

The rapid development of artificial intelligence (AI) has revolutionized numerous scientific and technological fields, including unmanned systems, pattern recognition, natural language processing, and expert systems [...]

With the development of service-oriented architecture, Web service composition has become more important for mitigating potential security vulnerabilities. When the scale of services increases, it will lead to the problem of state explosion. Symbolic model checking is a common method used to alleviate the problem of state-space explosion. However, for a large Web service composition system, the number of services is large, and the corresponding state space may exceed the magnitude of the symbolic model checking that can verify it. As it alleviates the state-space explosion problem, bounded model checking was utilized in the present study to verify the properties of the service composition system. Bounded model checking searches for bounded counterexamples in a limited local space and reduces the state space. This study verifies the composition of semantic Web services described by OWL-S and proposes a timed service model (TSM) to formally model the service composition system. Furthermore, the auto-mapping relationship between the OWL-S service description and the model is established. For more efficient verification, this study uses SMT-based (SMT: satisfiability modulo theory) encoding in the TSM. Finally, a public emergency service composition system was built to verify the proposed model and the efficiency of the proposed algorithm.

With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size.

The advent of big data age has brought about a growing performance and scale of the storage system, as well as huge energy consumption. Based on the sequential data storage featured workload as video surveillance, etc., we proposed EPS-RAID, that is to add a solid state disk(SSD) and a parity disk on S-RAID, and to optimize the random reads and writes in storage system by means of random write logs and Reed-Solomon(RS) code. Preserving the grouping strategy and the local parallel strategy of S-RAID, EPS-RAID has achieved good effect for random reads and writes on standby grouping, especially for the repeated reads and writes on the same logical address. The experiments show that, in the 3 file systems of EXT4, NTFS, NILFS, the energy consumption of EPS-RAID which consists of 2 disks of each grouping has effectively improved under the premise of ensuring performance and reliability.

Information leaks can occur through many Android applications, including unauthorized access to sensors data. Hooking is an important technique for protecting Android applications and add security features to them even without its source code. Various hooking frameworks are developed to intercept events and process their own specific events. The hooking tools for Java methods are varied, however, the native hook has few methods. Besides, the commonly used Android hook frameworks cannot meet the requirement of hooking the native methods in shared libraries on non-root devices. Even though some approaches are able to hook these methods, they have limitations or are complicated to implement. In the paper, a feasible hooking approach for Android native methods is proposed and implemented, which does not need any modifications to both the Android framework and app’s code. In this approach, the method’s reference address is modified and control flow is redirected. Beyond that, this study combines this approach with VirtualXposed which aims to run it without root privileges. This hooking framework can be used to enforce security policies and monitor sensitive methods in shared objects. The evaluation of the scheme demonstrates its capability to perform hook operation without a significant runtime performance overhead on real devices and it is compatible and functional for the native hook.

Grain processing products constitute an essential component of the human diet and are among the main sources of heavy metal intake. Therefore, a systematic assessment of risk factors and early-warning systems are vital to control heavy metal hazards in grain processing products. In this study, we established a risk assessment model to systematically analyze heavy metal hazards and combined the model with the K-means++ algorithm to perform risk level classification. We then employed deep learning models to conduct a multi-step prediction of risk levels, providing an early warning of food safety risks. By introducing a voting-ensemble technique, the accuracy of the prediction model was improved. The results indicated that the proposed model was superior to other models, exhibiting the overall accuracy of 90.47% in the 7-day prediction and thus satisfying the basic requirement of the food supervision department. This study provides a novel early-warning model for the systematic assessment of the risk level and further allows the development of targeted regulatory strategies to improve supervision efficiency.

Covert channels are widely used for secret message transmission on networks, and they are constantly changing and updating to adapt to the new network and communication environment. There are a large number of video packets during VoLTE (Voice over LTE), which can be a suitable carrier for covert channels. In this paper, we design a secure covert storage channel for VoLTE via regulating timestamp of VoLTE packets. First, we analyze the data captured in the real environment and find out two statistical patterns for the timestamp of the video packets. Then, we build the covert channel by modifying timestamp to carry the covert message in the case of maintaining these two patterns. Finally, we simulated the covert channel using the data captured in the real environment. Experimental results show that the covertchannel can communication over VoLTE secretly and robustly with bit-rate up to 120 bit/s, and it is against statistical analysis such as the Kolmogorov-Smirnov(KS) test and linear regression.

Traditional genetic algorithms overemphasize the struggle for survival and neglect all other aspects of biology. In addition, binary encoding is widely used in individual coding. Since the individual chromosomes produced are longer in length, it is difficult to ensure the efficiency of the algorithm. In this study, a coevolutionary genetic algorithm is proposed for web service composition based on quality of service (QoS), which fully considers the individual relationships among populations. The real coding method is adopted to solve the service selection problem based on QoS, so that the negative effect of the long length of chromosomes in the algorithm is avoided. Moreover, in view of the difficulty of determining the weight of each QoS attribute in web services, we propose to use the entropy method to determine the weights of each one. Compared with the traditional genetic algorithm, the experimental results show that the proposed algorithm converges faster in the service composition, and the fitness of the optimal solution is higher.