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A new generation of multivalent antibody-recruiting molecules (ARMs) with dual-targeting tumor-binding termini (TBT), including hyaluronic acid targeting CD44 and nanobody 7D12 or peptide GE11 targeting EGFR, was constructed for cancer immunotherapy. The 7D12 or GE11 were assembled onto β-cyclodextrin-grafted hyaluronic acid (HACD) with multivalent rhamnose via host-guest interaction to form macromolecule complexes. The immunological studies proved that these complexes had dual-targetability on CD44 and EGFR and the rhamnose on HACD could recruit anti-Rha antibodies to mediate cytotoxicity against the targeted tumor cells. This bispecific ARM strategy provides a platform for cancer immunotherapy.

Double auction is one of the most promising solutions to allocate virtual machine (VM) resources in two-sided cloud markets, which can increase the utilization rate of VM resources. However, most cloud auction mechanisms simply assume that the auctioneer is fully trusted while ignoring bid-privacy preservation and trade fairness in the process of auction. Previous studies have indicated that some cryptographic tools can be used to resolve the above issues, but the poor performance makes those techniques difficult to practice. In this paper, we propose a Secure and Fair Double AuCtion framework (named SF-DAC) for cloud virtual machines, which performs cloud auction efficiently while guaranteeing both bid privacy and trade fairness. We design secure 3-party computation protocols that support secure comparison and secure sorting, which enable us to construct a secure double auction scheme that outperforms all prior comparable solutions. Furthermore, we propose a fair trading mechanism based on smart contracts to prevent the bidders from halting the auction without financial penalties. The extensive experiments demonstrate that SF-DAC achieves an order of magnitude reduction in computation and communication costs than prior arts.

As one of the Generation IV nuclear reactors, the SCWR (supercritical water-cooled reactor) has high economy and safety margin, good mechanical properties for its high thermal efficiency, and simplified structure design. As the key component of nuclear reactor, the fuel assembly has always been the main issue for the design of the SCWR. The design of the fuel assembly for CSR1000 proposed by the Nuclear Power Institute of China (NPIC) has been optimized and presented in this study, which is composed of four subassemblies welded by four filler strips and guide thimbles arranged close together in the cross-shaped passage. Aiming at improving the hydraulic buffer performance of the cruciform control rod, the scram time and terminal velocity of control rod assembly were calculated to assess the scram performance based on the computational fluid dynamics and dynamic mesh method, and the mechanical property and neutronic performance of assemblies were also investigated. It has been demonstrated that the optimized fuel assembly had good feasibility and performance, which was a promising design for CSR1000.

Mobile crowdsensing systems use the extraction of valuable information from the data aggregation results of large-scale IoT devices to provide users with personalized services. Mobile crowdsensing combined with edge computing can improve service response speed, security, and reliability. However, previous research on data aggregation paid little attention to data verifiability and time sensitivity. In addition, existing edge-assisted data aggregation schemes do not support access control of large-scale devices. In this study, we propose a time-sensitive and verifiable data aggregation scheme (TSVA-CP-ABE) supporting access control for edge-assisted mobile crowdsensing. Specifically, in our scheme, we use attribute-based encryption for access control, where edge nodes can help IoT devices to calculate keys. Moreover, IoT devices can verify outsourced computing, and edge nodes can verify and filter aggregated data. Finally, the security of the proposed scheme is theoretically proved. The experimental results illustrate that our scheme outperforms traditional ones in both effectiveness and scalability under time-sensitive constraints.

As a typical novel IoT(Internet of Things) architecture, fog-based IoT is promising to decrease the overhead of processing and movement of large-scale data by deploying storage and computing resources to network edges. However, since the edge-deployed fog nodes cannot be fully trustable, fog-based IoT suffers some security and privacy challenges. This paper proposes a novel privacy-preserving scheme that can implement data aggregation from a subset of devices in fog-based IoT. Firstly, our scheme identifies the subset to be aggregated by computing the Jaccard similarity of attribute vectors of the query users and IoT devices, where the local differential privacy is employed to protect the attribute vectors. In addition, we use local differential privacy truth discovery to protect the data of IoT devices and improve the accuracy of the aggregation result. Finally, experiments show that our scheme is efficient and highly available by comparing it with state-of-the-art works. Theoretical analyses demonstrate that our proposed scheme has excellent performance on both computational costs and communication costs.

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders’ private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.

The author-proposed skeletal sand mold, which mainly includes a shell, air cavities and a truss support structure, has been experimentally proven to be very useful in controlling the cooling of casting at local areas and at different periods of the casting process. The modeling and simulation of the casting process using a skeletal sand mold were systemically analyzed. Complicated casting/mold and mold/air boundaries, and the thermal and mechanical behavior of the skeletal sand mold during the casting process were highlighted. A numerical simulation of the casting process of a stress frame specimen using a skeletal sand mold was performed. The temperature, stress and displacement fields of the casting and skeletal sand mold were obtained and compared with those using a traditional sand mold. The simulated results were validated with experiments. Using the skeletal sand mold, the cooling rate of the casting can be greatly improved due to the significant heat release from mold surface to environment. The residual stress and deformation of the casting can be reduced because of the decreased stiffness of this kind of mold. Although the skeletal sand mold is susceptible to cracking, it can be avoided by filleting in the conjunctions and increasing the shell thickness.

Additive manufacturing provides a possibility to realize high throughput investigation of material preparation and processing. In order to thoroughly investigate the effect of the key parameters on the soundness, microstructure and mechanical properties of the fabricated parts, sixty three combinations of laser power (twelve levels) and scanning speed (sixteen levels) with fifty eight levels of laser energy densities were realized in a single Inconel 718 alloy bar shaped specimen using the laser powder-bed fusion (L-PBF) technique. The combination of parameters varied from bottom to top of the bar, with each combination repeating every eight layers. Its spatial porosity distribution was measured by the Computed tomography (CT) test; its microstructure was observed by optical microscope, scanning electronic microscope and EBSD; the microhardness was measured as well. A comprehensive view of the relationship of these parameters and the porosity fraction, melt pool stacking, primary grain arm spacing and microhardness was obtained. Although high laser power and slow scanning speed are helpful to achieve low porosity, but the laser power has more significant effect on the quality of the specimen than the scanning speed. Only if at high levels the volumetric laser energy density could be used as an index to correlate to the quality of the specimen. The processing chart for the guidance of L-PBF was obtained. As the laser power is higher than 140 W and the scanning speed lower than 1000 mm/s, sound specimens would be realized. This method demonstrated the great potential for high throughput investigations of the L-PBF process.

As the basis of the Internet of Things (IoT), sensor networks have materialized its computation and communication capability into anything in our modern lives. Service composition provides us a promising way to cooperate various sensors to build more powerful IoT applications over sensor networks. However, the limited capability of sensor node poses great challenges not only to trustworthy service composition but also to secure data aggregation. The complex composite structure, computation-intensive evaluation, and massive data transmission become burdens for service composition in sensor networks. To overcome these issues, this paper proposes a distributed approach to enable efficient trustworthy service composition with secure data transmission in sensor networks. By analyzing dependency relationships, the rules for computing service trust and data trust are proposed based a multi-level trust model. Then, each target component service can be evaluated independently through a model checker. Moreover, an identity-based aggregate signature is introduced in the composite evaluation to guarantee the secure data transmission among different components. The experimental results show that our approach not only achieves efficient trustworthy service composition with complex invocation structures, but also reduces the costs in the secure data transmission.

The Internet of Things (IoT), as a emerging strategic industry, is widely used in digital homes, smart manufacturing, smart cities, and other fields. However since service capability of a single IoT device is limited, it is difficult to meet the complex service requirements of users. Multi-domain sharing, dynamic integration, and collaborative development have become new tendencies of IoT service development. This paper focuses on the privacy leakage detection of such composite services. Firstly, due to the heterogeneity of various privacy information, we study the dependencerelationamong sensitivity of privacy information. Secondly, dynamic changes of third-party service-calling rights can cause unauthorized access to privacy information during the sharing of shared services. To avoid illegal access, we propose a dynamic privacy information leakage detection method based on security label. Specifically, our research includes the dependencies between services, services and privacy information, constructing a knowledge and privacy information map, generating a private information security label for the combined service, and determining whether the call will cause privacy information disclosure by dynamically verifying the security label during the service invocation process. Finally, we use extensive experiments to validate our methods efficacy and performance.

Selective laser melting (SLM) is a promising powder bed fusion additive manufacturing technique for metal part fabrication. In this paper, varying scanning speed in the range of 500 mm/s to 1900 mm/s, and laser power in the range of 100 W to 200 W, were realized from layer to layer in a cycle of 56 layers in a single cuboid Inconel 718 alloy specimen through SLM. Layer-wise variation of microstructure and porosity were acquired, showing the layer-wise controlling capability of microstructural soundness. The melt pool size and soundness are closely linked with the energy input. High energy density led to sound regions with larger, orderly stacked melt pools and columnar grains, while low energy density resulted in porous regions with smaller, mismatched melt pools, un-melted powder, and equiaxed grains with finer dendrites. With the increase of laser energy density, the specimen shifts from porous region to sound region within several layers.

A multishell mold structure and water-immersion cooling method (MSMWI) is proposed for the directional solidification of castings. A four-layer-shell sand mold was designed for a bar with diameter of 40 mm. As the aluminum melt was poured, the multishell mold was gradually immersed in water, and the water level drove the advancement of the solidification front from bottom to top. The multishell mold was helpful for the heat insulation of its upper part, and its bottom was chilled by the water. Therefore, directional solidification of the bar was vertically realized. The water-cooled solidification process of the bar was 5.8 times faster than that by air natural cooling (MSMNC), and the temperature gradient was increased by 78 times. The secondary dendrite arm spacing (SDAS) and eutectic silicon were significantly refined. Its tensile strength, elongation, and hardness were increased by 56%, 185%, and 62.6%, respectively.

SLM (selective laser melting) is a very promising manufacturing method for complicated precision parts of metals and alloys. However, deformation of the bridge structure during fabrication process and post treatment is still a typical problem. In this paper, the deformation of aluminum alloy AlSi10Mg flat bridge structure with various span lengths was investigated by numerical simulation and experiment. The longer the span is, the greater arch bending deformation is. A mechanism of deformation of bridge was discussed. During selective laser melting process, the first layer of bridge bends upward because of no constraint. Then the arch bending results less or no powder on it after powder sweeping for next layer. The bridge bends up further during the formation of the following layer. These alternative phenomena cause the evolution of deformation. However, as the bridge becomes thicker and thicker, its stiffness increases, which leads to the difficulty of its further bending. Therefore, after some layers, there is even no further bending during the forming of following layers with the arch shape kept in the initial layers. Decreasing the laser energy density for the bridge area and using supports are helpful for the reduction of the arch bending deformation. Keywords: SLM, Bridge, AlSi10Mg, Deformation, Numerical simulation