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A safe and security classification protocol named SSC was proposed for two-tiered sensor networks,which enable storage nodes to process classification correctly without knowing both the value of classifying rules and the data which will be classified.To protect privacy,an oblivious comparison technique was presented.MHash,which enable storage nodes to compare data items from sink and sensors without knowing their values.Based on MHash and prefix membership verification technique,classification target was achieved in protecting the privacy of both sensor collected data and sink issued classification rules.To verify the correctness of classification results,a crossed neighborhood technique was proposed which organize sensors and data items in one sensor in sequences,to allow the sink checking the correctness of sampling classification results.Analysis and experimental results validate the efficacy and efficiency of SSC protocol.

A privacy preserving conditional aggregation protocol was proposed that enabled storage nodes to aggregate the data items satisfied sink issued queries correctly while prevented them from revealing both sensor collected data and Sink issued queries.To protect privacy for sensor col data and sink issued queries,an encoding method based on prefix membership verification and bloom filters was proposed to encode both sensor collected data and sink issued queries,which allowed storage nodes process queries correctly without knowing their actually value.Homomorphism encryption method was adopted to encrypt sensor collected data,which enabled storage nodes to process aggregation on encrypted data items.To reduce corresponding energy consumption between sensors and storage nodes,a code table method was proposed to represent and transmit data items.Analysis and experime validate the efficacy and efficiency of the proposed protocol.

The 5-lipoxygenase (5LO) enzyme is widely distributed within the central nervous system. Previous works showed that this protein is up-regulated in Alzheimer's disease (AD), and plays an active role in the development of brain amyloidosis in the APP transgenic mice. In the present paper, we studied the effect of its pharmacological inhibition on the entire AD-like phenotype of a mouse model with plaques and tangles, the 3 × Tg mice. Compared with mice receiving placebo, the group treated with zileuton, a specific 5LO inhibitor, manifested a significant improvement of their memory impairments. The same animals had a significant reduction in Aβ levels and deposition, which was secondary to a down-regulation of the γ-secretase pathway. Additionally, while total tau levels were unchanged for both groups, zileuton-treated mice had a significant reduction in its phosphorylation state and insoluble forms, secondary to a decreased activation of the cdk5 kinase. These data establish a functional role for 5LO in the pathogenesis of the full spectrum of the AD-like phenotype and represent the successful completion of the initial step for the preclinical development of 5LO inhibitors as viable therapeutic agents for AD.

Cracks have consistently been a significant challenge limiting the development of additive manufactured nickel-based superalloys. It is essential to investigate the location of cracks and their forming mechanism. This study extensively examines the impact of solidification process, microstructural evolution, and stress concentration on crack initiation during direct energy deposition (DED). The results emphasize that the crack formation is significantly related to large-angle grain boundaries, rapid cooling rates. Cracks caused by large-angle grain boundaries and a fast-cooling rate predominantly appear near the edge of the deposited samples. Liquation cracks are more likely to form near the top of the deposited sample, due to the presence of eutectics. The secondary dendritic arm and the carbides in the interdendritic regions can obstruct liquid flow during the final stage of solidification, which results in the formation of solidification cracks and voids. This work paves the way to avoid cracks in nickel-based superalloys fabricated by DED, thereby enhancing the performance of superalloys. [ABSTRACT FROM AUTHOR]

Ceramic cores are the key intermediate components of hollow blades for aero-engine. Conventional processes, such as hot-press molding and gel film casting, face difficulties in fabricating complex-structured ceramic cores due to the complexity of moulds and long process cycles. Stereolithography 3D printing provides a new idea for the fabrication of complex-structured ceramic cores. The effect of sintering temperature on open porosity, bulk density, weight loss rate, shrinkage rate, flexural strength and microstructure of the Al2O3-based ceramic core doped with 10vol.% polysilazane (PSZ) was studied. The sintering mechanism of PSZ-reinforced ceramic cores was analyzed. Results show that the optimum sintering temperature of PSZ-reinforced ceramic cores is 1,450 °C. At this temperature, the open porosity of the ceramic core is 36.60%, bulk density is 2.33 g·cm-3, weight loss rate is 22.11%, shrinkage rate along the X, Y, Z directions is 5.72%, 5.01%, 9.61%, respectively; the flexural strength is 28.794 MPa at 25 °C and 13.649 MPa at 1,500 °C. Properties of 3D printing PSZ-reinforced ceramic cores can meet the casting requirement of superalloy hollow blades, which is expected to promote the industrial application of 3D printing complex structure ceramic cores. [ABSTRACT FROM AUTHOR]

Probabilistic inference is a fundamental task in modern machine learning. Recent advances in tensor network (TN) contraction algorithms have enabled the development of better exact inference methods. However, many common inference tasks in probabilistic graphical models (PGMs) still lack corresponding TN-based adaptations. In this paper, we advance the connection between PGMs and TNs by formulating and implementing tensor-based solutions for the following inference tasks: (A) computing the partition function, (B) computing the marginal probability of sets of variables in the model, (C) determining the most likely assignment to a set of variables, (D) the same as (C) but after having marginalized a different set of variables, and (E) generating samples from a learned probability distribution using a generalized method. Our study is motivated by recent technical advances in the fields of quantum circuit simulation, quantum many-body physics, and statistical physics. Through an experimental evaluation, we demonstrate that the integration of these quantum technologies with a series of algorithms introduced in this study significantly improves the performance efficiency of existing methods for solving probabilistic inference tasks.

In this study, a kind of Ni-based superalloy specially designed for additive manufacturing (AM) was investigated. Thermo-Calc simulation and differential scanning calorimetry (DSC) analysis were used to determine phases and their transformation temperature. Experimental specimens were prepared by laser metal deposition (LMD) and traditional casting method. Microstructure, phase constitution and mechanical properties of the alloy were characterized by scanning electron microscopy (SEM), transmission scanning electron microscopy (TEM), X-ray diffraction (XRD) and tensile tests. The results show that this alloy contains two basic phases, γ/γ', in addition to these phases, at least two secondary phases may be present, such as MC carbides and Laves phases. Furthermore, the as-deposited alloy has finer dendrite, its mean primary dendrite arm space (PDAS) is about 30-45 μm, and the average size of γ' particles is 100-150 nm. However, the dendrite size of the as-cast alloy is much larger and its PDAS is 300-500 μm with secondary and even third dendrite arms. Correspondingly, the alloy displays different tensile behavior with different processing methods, and the as-deposited specimen shows better ultimate tensile stress (1,085.7±51.7 MPa), yield stress (697±19.5 MPa) and elongation (25.8%±2.2%) than that of the as-cast specimen. The differences in mechanical properties of the alloy are due to the different morphology and size of dendrites, γ', and Laves phase, and the segregation of elements, etc. Such important information would be helpful for alloy application as well as new alloy development. [ABSTRACT FROM AUTHOR]

Programmable quantum systems based on Rydberg atom arrays have recently been used for hardware-efficient tests of quantum optimization algorithms [Ebadi et al., Science, 376, 1209 (2022)] with hundreds of qubits. In particular, the maximum independent set problem on so-called unit-disk graphs, was shown to be efficiently encodable in such a quantum system. Here, we extend the classes of problems that can be efficiently encoded in Rydberg arrays by constructing explicit mappings from a wide class of problems to maximum-weighted independent set problems on unit-disk graphs, with at most a quadratic overhead in the number of qubits. We analyze several examples, including maximum-weighted independent set on graphs with arbitrary connectivity, quadratic unconstrained binary optimization problems with arbitrary or restricted connectivity, and integer factorization. Numerical simulations on small system sizes indicate that the adiabatic time scale for solving the mapped problems is strongly correlated with that of the original problems. Our work provides a blueprint for using Rydberg atom arrays to solve a wide range of combinatorial optimization problems with arbitrary connectivity, beyond the restrictions imposed by the hardware geometry.

Based on the design of a post-buckling silicone gel column (SGC), a novel type of low-frequency vibration isolator is presented, and the vibration isolation performance of this isolator is studied by combining theoretical analysis and experimental verification. The stiffness characteristics of the post-buckling SGC are derived, and its recovery force curves with different parameters are analyzed using two kinds of elliptic integral functions. Displacement transmissibility is formulated using harmonic balance method (HBM), and the influences of the excitation amplitude, damping ratio, SGC section diameter, and Young’s modulus are discussed in terms of the transmissibility. The performance of the SGC system is verified through a series of experimental studies based on the developed experimental prototype. The result shows that the proposed post-buckling spring vibration isolator has a good vibration isolation effect, especially in the low-frequency domain, which may provide a feasible novel design idea for a low-frequency vibration isolator.

We introduce $\texttt{Yao}$, an extensible, efficient open-source framework for quantum algorithm design. $\texttt{Yao}$ features generic and differentiable programming of quantum circuits. It achieves state-of-the-art performance in simulating small to intermediate-sized quantum circuits that are relevant to near-term applications. We introduce the design principles and critical techniques behind $\texttt{Yao}$. These include the quantum block intermediate representation of quantum circuits, a builtin automatic differentiation engine optimized for reversible computing, and batched quantum registers with GPU acceleration. The extensibility and efficiency of $\texttt{Yao}$ help boost innovation in quantum algorithm design.

Cinnamomum camphora (L.) Presl. is one of the most important hardwood species indigenous to China that possesses significant antifungal activity. The chemical composition of the extracts from the xylem parts of C. camphora was examined by various solvent extractions. Thirty different components accounting for 79.8% of the total methanol extracts from the xylem of C. camphora were identified by gas chromatography-mass (GC/MS) spectrometry. The major chemical components of methanol extracts are camphor (14.3%), α-terpineol (9.9%), and trans-linalool oxide (furanoid) (7.7%). The chemical composition of chloroform extracts are mainly camphor (17.6%), α-terpineol (11.8%), tetradecanal (5.6%), and (-)-g-cadinene (7.4%). The extracts of C. camphora were tested for resistance to two wood-decaying fungus with hyphal growth. All the C. camphora extracts showed some antifungal activity against the test fungus. The 50% effective concentration of chloroform extracts for Coriolus versicolor (C. versicolor) was 7.8 mg/mL, which was highly toxic, followed by acetone extracts. The methanol extracts with 8 mg/mL concentration had the best suppression effect for Gloeophyllum trabeum (G. trabeum) with an EC50 of 0.3 mg/mL. The results indicated that the major components of the extracts had antifungal activities; thus C. camphora could provide a renewable source for wood preservatives.

We propose a qubit efficient scheme to study ground-state properties of quantum many-body systems on near-term noisy intermediate-scale quantum computers. One can obtain a tensor network representation of the ground state using a number of qubits smaller than the physical degrees of freedom. By increasing the number of qubits, one can exponentially increase the bond dimension of the tensor network variational ansatz on a quantum computer. Moreover, we construct circuits blocks which respect U(1) and SU(2) symmetries of the physical system and show that they can significantly speed up the training process and alleviate the gradient vanishing problem. To demonstrate the feasibility of the qubit efficient variational quantum eigensolver in a practical setting, we perform first-principles classical simulation of differentiable programming of the circuits. Using only six qubits, one can obtain the ground state of a 4×4 square lattice frustrated Heisenberg model with fidelity over 97%. Arbitrarily-long-range correlations can also be measured on the same circuit after variational optimization.

Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and optimizes them using gradient search. The concept emerges from deep learning but is not limited to training neural networks. We present the theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher-order derivatives of the program accurately and efficiently using automatic differentiation. We present essential techniques to differentiate through the tensor networks contraction algorithms, including numerical stable differentiation for tensor decompositions and efficient backpropagation through fixed-point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second-order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient-based variational optimization of infinite projected entangled pair states for the quantum antiferromagnetic Heisenberg model and obtain state-of-the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.

Fusarium solani H915 is a fungus derived from mangrove sediments. From its ethyl acetate extract, a new alkenoic acid, fusaridioic acid A (1), three new bis-alkenoic acid esters, namely, fusariumester A1 (2), A2 (3) and B (4), together with three known compounds (5⁻7), were isolated. The structures of the new compounds were comprehensively characterized by high resolution electrospray ionization-mass spectrometry (HR-ESI-MS), 1D and 2D nuclear magnetic resonance (NMR). Additionally, the antifungal activities against tea pathogenic fungi Pestalotiopsis theae and Colletotrichum gloeosporioides were studied. The new compound, 4, containing a β-lactone ring, exhibited moderate inhibitory activity against P. theae, with an MIC of 50 μg/disc. Hymeglusin (6), a typical β-lactone antibiotic and a terpenoid alkaloid, equisetin (7), exhibited potent inhibitory activities against both fungal species. The isolated compounds were evaluated for their effects on zebrafish embryo development. Equisetin clearly imparted toxic effect on zebrafish even at low concentrations. However, none of the alkenoic acid derivatives exhibited significant toxicity to zebrafish eggs, embryos, or larvae. Thus, the β-lactone containing alkenoic acid derivatives from F. solani H915 are low in toxicity and are potent antifungal agents against tea pathogenic fungi.

We propose to use the sub-system fidelity, defined by comparing a pair of reduced density matrices, to identify and locate zero modes relating two degenerate ground states in one-dimensional interacting many-body systems, where the degeneracy arises from either symmetry protected topology (SPT), or from discrete symmetry breaking (DSB). A theorem is provided to construct locally indistinguishable (LI) states by linear recombination of the degenerate states of minimal bulk entanglement entropy. The theorem enables us to construct local operators that swap exactly the LI states and are therefore the zero energy modes in the many-body system. Interestingly, they can be located anywhere in the DSB case, but can only be accommodated near the edges in the SPT case. This can be used to identify or distinguish SPT states against the so-called cat states in the DSB case. We illustrate the results for the anisotropic Haldane chain and the interacting Kitaev model.