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Bactrocera bryoniae and Bactrocera neohumeralis are highly destructive and major biosecurity/quarantine pests of fruit and vegetable in the tropical and subtropical regions in the South Pacific and Australia. Although these pests have not established in China, precautions must be taken due to their highly destructive nature. Thus, we predicted the potential geographic distribution of B. bryoniae and B. neohumeralis across the world and in particular China by ecological niche modeling of the Maximum Entropy (MaxEnt) model with the occurrence records of these two species. Bactrocera bryoniae and B. neohumeralis exhibit similar potential geographic distribution ranges across the world and in China, and each species was predicted to be able to distribute to over 20% of the globe. Globally, the potential geographic distribution ranges for these two fruit fly species included southern Asia, the central and the southeast coast of Africa, southern North America, northern and central South America, and Australia. While within China, most of the southern Yangtze River area was found suitable for these species. Notably, southern China was considered to have the highest risk of B. bryoniae and B. neohumeralis invasions. Our study identifies the regions at high risk for potential establishment of B. bryoniae and B. neohumeralis in the world and in particular China, and informs the development of inspection and biosecurity/quarantine measures to prevent and control their invasions.

Many researches have brought progress in learning a good generative model by combing the advantages of GAN and VAE, where latent space learning is always important for generating high-quality images. But these existing works mainly seek to impose the latent space a given distribution in advance or to obey a Gaussian distribution with KL divergence penalty, which leads to the difficulties of deciding a suitable prior distribution corresponding to different datasets. Thus in this paper we develop a two-stage combining method of AE and GAN under unsupervised and supervised conditions respectively, each stage performed to improve the effect of modeling latent distribution. In the first stage, an adversarial procedure achieves to match the latent distribution with real data distribution determined by arbitrary dataset without having access to a pre-set prior distribution. In the second stage, besides one adversarial procedure trained for outputting images, the other adversarial procedure is designed to attain the goal of optimizing the latent distribution of the first stage via back-propagation. Therefore, loop optimization of the network parameters during training will at last allow the framework to map an input noise to a high quality image. Extensive experiments are conducted to verify the performance of representing latent space and generating images on different datasets including MNIST, fashion-MNIST, CIFAR-10, and CelebA. The code and tutorials already release at https://github.com/TwistedW/CAE-CGAN .

Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. The objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

Both energy harvesting and storage are similarly constrained by materials availability and economics. Herein, we explore the dual use of earth-abundant and low cost Cu2ZnSnS4 (CZTS) inks not only for solar cells but also energy storage devices. In this report, we only focus on less addressed but important novel issues. One is the effect of CZTS layer thickness on solar cell performance. The other is the properties of CZTS as a supercapacitor electrode. For solar cells, a study of the effect of CZTS thickness on device performance of 55 solar cells revealed that the highest efficiencies are achieved at a CZTS thickness of approximately 0.37 µm. For energy storage, we achieved a highest reported areal (and specific) capacitance of 2.67 F/cm2 (1709 F/g) for CZTS. The dual use of CZTS for both energy harvesting and storage opens opportunities such as cheaper devices arising from economics of scale accompanying increased CZTS inks production.

In the present investigation, an anodic oxide layer was developed on AA2024 specimens by sulfuric acid anodization process and subsequently sealed with chromate free Mn–Mo oxyanions (PMMO) by simple dip method. X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy studies showed the presence of multiple oxidation states of Mn and Mo and the polymolybdate in the sealed anodic oxide layer respectively. Various electrochemical techniques were used to evaluate the corrosion performance of the developed coatings. Cyclic polarization test results showed a wide trans-passive potential region (> 2 V) for PMMO. Electrochemical noise measurement (ENM) results revealed pit growth of PMMO is highly controlled due to the presence of inhibitors in the sealed oxide layer. Scanning vibrating electrode technique (SVET) results revealed a minimal localized distribution of anodic and cathodic corrosion sites on PMMO compared to plain oxide layer (PO) and HTO after 168 h of immersion in 0.6 M NaCl solution. The results obtained from electrochemical impedance spectroscopy (EIS) showed one to two orders magnitude higher impedance (│Z│0.1 Hz) value for PMMO compared to PO and HTO. Neutral salt spray test result of PMMO after 1000 h showed comparable corrosion performance with conventional chromic acid anodized (CAA) specimen.

Economic viability, carbon emission profile and waste management associated with nickel recovery from spent hydrogenation catalysts are studied from sustainability perspectives. The purpose is to determine and compare the economic, environmental and social implications of different nickel reclamation techniques towards clean, safe and sustainable recovery of nickel from spent catalysts. Sustainability evaluation models are formulated to understand and improve the cost, carbon footprint and resource efficiency of a closed-loop nickel recovery process. The economic viability of the process highly depends on market values of recovered nickel and the production batch size. At a selling price higher than $12.60/kg, an operation with a batch size as small as 50 kg/batch would be profitable. The current rising nickel market, at ∼$18–24/kg, favors recovery operations although it also casts a dual effect on production costs. About 73–82% of carbon emission of the process is from the use of energy in the recovery operation. Energy efficiency is therefore identified as the most critical factor to improve the carbon footprint. The closed-loop process also improves resource use efficiency and minimizes toxic waste generation.

As a major family of semi-supervised learning (SSL), graph-based SSL has recently attracted considerable interest in the machine learning community along with application areas such as video semantic analysis. In this paper, we analyze the connections between graph-based SSL and partial differential equation- (PDE) based diffusion. From the viewpoint of PDE-based diffusion, the label propagation in normal graph-based SSL is isotropic accompanied with distance. However, according to the structural assumption, which is one of the two basic assumptions in graph-based SSL, we need to enhance the label propagation between the samples in the same structure while weakening the counterpart between the samples in different structures. Accordingly, we deduce a novel graph-based SSL framework, named structure-sensitive anisotropic manifold ranking (SSAniMR), from PDE-based anisotropic diffusion. Instead of using Euclidean distance only, SSAniMR takes local structural difference into account to make the label propagation anisotropic, which is intrinsically different from the isotropic label propagation process in general graph-based SSL methods. Experiments conducted on the TREC Video Retrieval Evaluation (TRECVID) dataset show that this approach significantly outperforms existing graph-based SSL methods and is effective for video semantic annotation.

This paper reports a novel structural design of integrated shadow mask and its fabrication technique by thin film processes. The proposed Γ-shaped mask structure provides passive matrix OLED devices with a higher aperture ratio than the conventional T-shaped profiled masks. The Γ-shaped mask structure can be realized with a low cost patternable sol–gel and photolithography processes. Full angle deposition can be realized with the new mask structure, which is important for mass production of devices with automatic systems.

Insufficiency of labeled training data is a major obstacle for automatic video annotation. Semi-supervised learning is an effective approach to this problem by leveraging a large amount of unlabeled data. However, existing semi-supervised learning algorithms have not demonstrated promising results in large-scale video annotation due to several difficulties, such as large variation of video content and intractable computational cost. In this paper, we propose a novel semi-supervised learning algorithm named semi-supervised kernel density estimation (SSKDE) which is developed based on kernel density estimation (KDE) approach. While only labeled data are utilized in classical KDE, in SSKDE both labeled and unlabeled data are leveraged to estimate class conditional probability densities based on an extended form of KDE. It is a non-parametric method, and it thus naturally avoids the model assumption problem that exists in many parametric semi-supervised methods. Meanwhile, it can be implemented with an efficient iterative solution process. So, this method is appropriate for video annotation. Furthermore, motivated by existing adaptive KDE approach, we propose an improved algorithm named semi-supervised adaptive kernel density estimation (SSAKDE). It employs local adaptive kernels rather than a fixed kernel, such that broader kernels can be applied in the regions with low density. In this way, more accurate density estimates can be obtained. Extensive experiments have demonstrated the effectiveness of the proposed methods.

This paper reports a method for making integrated shadow masks used in fabrication of passive matrix organic light emitting diode (OLED) displays. Common positive and negative photoresist were employed to produce retrograded strip pillars with large overhang and undercut by a special photolithography procedure. The pillar strips serve as an effective shadow mask for patterning the organic layers and the metal cathodes in passive OLED display devices. Such an integrated shadow mask is viable for large deposition angle up to 70°. The method is also expected to be usable for fabricating more complicated structures for other applications.

This work was an experimental study of the aluminum-induced crystallization (AIC) of amorphous silicon (a-Si) for the fabrication of polycrystalline silicon film. The a-Si film was deposited on silicon wafer by low pressure chemical vapor deposition (LPCVD) technique. Aluminum was sputtered on to the a-Si film at different thicknesses. The samples were annealed for 3 h at different temperatures from 250 to 550 °C. The annealed silicon films were analyzed with emphasis on their crystallinity and morphology. Results showed that in the presence of aluminum, a-Si film started crystallization at a temperature as low as 250 °C. However, high crystallization rate would be achieved only when the annealing was done at temperatures higher than 350 °C. For practical applications, this temperature might well be the lower limit in AIC method for crystallization of silicon. The thickness of aluminum film was found to play a critical role that dictated the extent of crystallization and the preferred orientation of the resulting polycrystalline thin film.

Nickel induced lateral crystallization of amorphous silicon with and without electric field has been studied. Dendritic silicon growth behavior is observed, with crystallites of a few hundred nanometers in width and up to a few microns in length. The behavior can be understood from the preferential epitaxial growth of silicon from the (1 1 1) facets of the NiSi 2 precipitate, which forms during the early stage of the annealing process. The dendritic growth fronts are different with and without electric field in the nickel induced lateral crystallization process. Electric field is found to be beneficial in increasing the lateral crystallization rate and improving the film crystallinity. Joule heating plays an important role as well to enhance the lateral crystallization.

Ni and its alloys possess a lower reaction rate with Sn than Cu and Cu alloys. Ni-based under bump metallization (UBM) therefore receives considerable attention from the microelectronic packaging industry for the popular flipchip applications. In this work, we study the interfacial reaction of electroless Ni–P (EN) alloy and Ni UBMs with Sn–3.5Ag solder. Morphology and growth kinetics of the formed Ni3Sn4 intermetallic compound (IMC) in both systems are investigated under different reflow durations. With the Ni–P alloy as the UBM, needle-type, boomerang-type and chunk-type IMC grains coexist at short reflow time, but only chunk-type grains remain after prolonged reflow. With pure Ni as UBM, only scallop grains with faceted surfaces are found under both short and long reflow durations. The thickness of the intermetallic compound in both UBM systems is measured under different reflow conditions, from which the growth kinetics parameters are obtained. It is found that the IMC growth rate is higher with the Ni–P UBM than with pure Ni UBM. Another difference between the two UBMs is the existence of Kirkendall voids at the interface: the voids are found inside the Ni3P layer in the Ni–P UBM system after long-time reflow. However, such voids are not observed in the pure Ni UBM system. Accepted version

This paper presents a new design of an integral flexure that serves both the functions of a load beam and a gimbal for application in rotary actuators of hard disk drives (HDD). The design has optimized geometry that improves resonant frequency while retaining adequate rotational flexibility. Prototypes of the integral flexure are fabricated from a single piece of stainless-steel foil by photochemical machining techniques without involving welding and extra alignment. Its performance is simulated using finite element method and resonant frequencies of its prototype are measured. The numerical simulation and experimental results are in good agreement.

Field aided lateral crystallization of amorphous silicon has been carried out at 500 °C with an electric field of 200 V/cm. Scanning electron microscopy and optical microscopy show that large crystalline silicon grains with sizes over 100 μm have been formed in the laterally crystallized region. Raman spectroscopy shows that the large grain with size over 100 μm has a crystalline phase. The secondary ion mass spectrometry shows that the nickel concentration in the lateral diffused region is comparable to that of amorphous region beyond the nickel diffusion front. Nickel diffusion facilitated by the electric field and current flow has been found to be distinct in obtaining extremely large grains at a low temperature of 500 °C. This enhanced lateral crystallization process is capable of producing large polycrystalline silicon grains, which is desirable for fabrication of thin film transistors used in large area electronics.

Thermal aging is one of the accelerated tests for IC package reliability during manufacturing processes and under actual usage conditions. During the process of thermal aging, intermetallic compounds (IMC) grow continuously due to element diffusion, resulting in their morphology change and thickness increase. In this work, the solid state reaction between electroless Ni–P and two types of Sn-based solders (Sn–3.5Ag and Sn–37Pb) has been investigated. Three distinctive layers, Ni 3 Sn 4 , NiSnP and Ni 3 P, were found between the Sn-containing solders and Ni–P under bump metallization. The growth rates of Ni 3 Sn 4 IMC at different temperatures were obtained from the aged samples and the activation energy of Ni 3 Sn 4 growth was estimated. The kinetic data obtained show that the Ni 3 Sn 4 in the Sn–3.5Ag/Ni–P joints grows much faster than with the Sn–37Pb solder under the same condition. Kirkendall voids are found inside the Ni 3 P layer after thermal aging. The void formation mechanism is due to net Ni out-flux into the solder area.