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This paper developed a model for predicting the temporal failure probability of prestressed concrete (PC) highway bridges. The model updates predictions based on data from nondestructive testing and visual inspections. Chloride-induced corrosion is taken as the main cause for deterioration, and a gamma process describes the reduction in structural capacity. A nonhomogeneous Poisson process models vehicle arrival, in which the vehicle load variability follows a two-peak lognormal mixture distribution. After each inspection, Bayesian inference updates select model parameters and, consequently, the associated temporal structural resistance. Bridge managers can use the updated failure probability predictions to evaluate the remaining useful life of the bridge and determine the maintenance scheme and budget accordingly. A real bridge example illustrated the methodology and justified the probability distributions used for deterioration and vehicle load. Through comparisons with three existing methods, we argued that the proposed model provides more-conservative recommendations, yet existing methods tend to underestimate failure probabilities. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning. [ABSTRACT FROM AUTHOR]

Highlights • The linear attenuation of PT strength yields a logarithmic function. • Oven-dry concrete has much lower PT strength than partially saturated concrete. • Different failure modes can be explained in three ways by various proposed mechanisms. Abstract This article illustrates new research regarding long-term deterioration in the tensile strength of plain concrete samples due to sustained stress loading. Two different water-cement ratio mixtures were subjected to sustained load using a Pressure Tension (PT) apparatus. Both saturated and oven dry samples were tested. Long-term PT strength under different load levels was found to decrease linearly as a function of the logarithm of time, corroborating existing creep studies. Pressure tension testing was used to determine the original strength of samples, and the effects of sustained load on tensile strength were analyzed. These results were compared with previous data obtained by direct tension tests to demonstrate efficacy of pressure tension testing for long-term creep studies. In addition, based on the analysis of failed samples, an assumption for the mechanism of the damage process is proposed. [ABSTRACT FROM AUTHOR]

In the last decade, we have witnessed the dramatic development of the smart home industry. Smart home systems are currently facing an explosive growth of data. Making good use of this vast amount of data has become an attractive research topic in recent years. In order to develop smart home systems' abilities for learning users' behaviors autonomously and offering services spontaneously, a user behavior prediction model based on parallel back propagation neural network (BPNN) and k-nearest neighbor (KNN) algorithms is introduced in this paper. Based on MapReduce, a parallel BPNN algorithm is proposed to improve the prediction accuracy and speed, and a parallel KNN algorithm is developed for user decision-making rule selection. The experimental results indicate that the proposed model is significantly better than traditional user behavior prediction models in term of prediction accuracy and speed. A case study on smart home also illustrates the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Suspended inductors and 2.45 GHz BPF with patterned ground shields on the lossy silicon substrate by using Cu/BCB based wafer level packaging and bulk Si etching technologies were fabricated. Thick BCB interlayer is used as the supporting dielectric and the backside cavity is easily removed by using a two-step back-etching process. The fabricated 2.7 nH inductor has a maximum Q factor 49 at 8.2 GHz and high Q factors more than 22 in the broadband frequency range from 1 to 10 GHz. And the realized 2.45 GHz BPF has the insertion loss of 3.0 dB and the return loss of more than 14 dB at the pass band. It is also featured by more than 48 and 25 dB attenuation at 0.9 and 1.8 GHz respectively, with the second harmonic rejection being 33 dB. [ABSTRACT FROM AUTHOR]

This paper reports on the design, fabrication and electrical characterization of high-density SIS trench capacitors by using a two-step deposition process for fast-filling the deep trenches. LPCVD silicon nitride is employed as the dielectric material to provide high efficiency deposition in the high aspect ratio trenches. The capacitance density in trench capacitors with 25 nm thick SiN is characterized as high as 57.8 nF/mm, while the breakdown voltage in trench capacitors with 35 nm thick SiN is recorded to be as high as 14.5 V. Furthermore, the capacitances are measured over an applied voltage range from −5 to 5 V, showing a small voltage-dependence of 1.2 and 0.6 % V for the 25 and 35 nm thick SiN trench capacitor, respectively. The leakage currents are measured and the current transport mechanisms are analyzed. The ESR and ESL of the capacitors with 25 and 35 nm thick SiN are very small, as low as 35-65 mΩ and 0.2-0.28 pH for 0.04 mm electrode surface. [ABSTRACT FROM AUTHOR]

The article presents an element increment method that is developed by current time increment method of train traction calculation. A railway route was divided, breaking it down into elements of different lengths. A whole train movement simulation curve ( v- t curve and v- S curve) was formed by splitting the joints of each of the elements' individual simulation curves. During this process, the train velocity variance was calculated by time increment method with assistance of polynomial fitting technology. Additionally, a step-by-step method with iteration was used to combine each element and makes the whole simulation curve continuous. Meanwhile, the energy-saving issue was also taken into account to optimize the simulation curve. This article gives more details about the modeling by providing an example of a railway route based on moving block control. The element increment method is a more effective way to calculate train traction of high-speed railway, and it is an alternative method to train movement simulation for aiding macroscopic railway transportation planning. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Typical inspection intervals may be dangerously long for unhealthy bridges. • WPHM associates bridge health states with their failure probabilities. • SMDP determines optimal bridge inspection and repair policies. • Inspection/repair policies depend on pre-specified inspection intervals. • Proposed policy found to save almost 30% in total costs for example bridge. Routine bridge inspections enable timely maintenance plans based on potential risks. These inspections are performed at fixed time intervals, which may be unnecessarily short for bridges in excellent conditions and precariously long for those in poor conditions. Also, deterioration models used in current bridge management approaches fail to include the effect of age, causing inaccurate predictions. This paper develops a hazard-based bridge management approach where inspection frequencies and repair suggestions are optimized based on hazard levels. A two-dimensional Markov model describes the age-dependent bridge deterioration, and a semi-Markov decision process optimizes inspection intervals and repair decisions. The objective is to minimize the expected long-term total annual costs, including expenses associated with maintenance and traffic detours. The modeling process and maintenance management framework was demonstrated on an example bridge superstructure in New York. The results showed lower annual and total costs than the conventional management method. As expected, sensitivity analyses indicated that the pre-specified choices of inspection intervals for low and high hazard levels have a significant effect on the calculated hazard threshold levels. The proposed maintenance management method is adaptable and practical; bridge authorities can identify appropriate repair actions and make inspection decisions based on simple threshold comparisons. [ABSTRACT FROM AUTHOR]

In this paper, the warpage of 3-D multichip module (3D-MCM) on an embedded substrate with flip-chip and wire-bonding interconnection method was studied by finite element (FE) simulation and moiré fringes measurement. Simulation results showed that the warpage of 3D-MCM without underfill presents double-bow-shaped (W-shaped) mode, which is because of the cavity in substrate and the viscoplasticity behavior of solder balls. For the same reason, the warpage of substrate with temperature change has an inflexion around 75^\circC, which indicated that the cavity in the substrate center may decrease the warpage of the substrate. The FE model and simulation results were validated through moiré fringes measurement. Based on this model, the effect of underfill on warpage was discussed. It turned out that using suitable underfill could strengthen the interconnection between component and substrate, and then protect the solder balls and decrease the warpage of module. However, underfill with high coefficient of thermal expansion (CTE) may increase the stress, strain and plastic work density of solder ball, therefore result in the solder ball failure and lower the reliability of the module. The optimal CTE of the underfill, which is the mean magnitude of CTEs of the materials at both sides of unferfill layer, was determined with multiobjective method. [ABSTRACT FROM PUBLISHER]

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods. [ABSTRACT FROM AUTHOR]

In this paper, a copper/benzocyclotene thin film based microstrip bandpass filter featured by a thick dielectric layer is presented. For thickening dielectric layer, a 30 μm‐deep micromachined groove with embedded benzocyclotene polymer is fabricated on the surface of silicon substrate. Mechanical polishing is utilized to planarize the benzocyclotene polymer embedded in the groove. Atomic Force Microscopy is used to characterize the surface roughness of benzocyclobutene (BCB) polymer film before and after polishing. With two additional BCB layer, a total thickness of 75.4 μm dielectric layer is realized, and the insertion loss of the filter can be reduced by 1.34 dB theoretically. The measured insertion loss of fabricated filter is −2.2 dB, which can be further reduced via tighter control of device parameters. [ABSTRACT FROM AUTHOR]

A novel silicon-based suspended MIM capacitor fabrication technique combining thin-film and bulk silicon etching technologies with high-quality factor is presented. The influence of low resistive silicon on the parasitics of integrated capacitors is analyzed by EM simulation. The suspended structure is achieved and optimized by a two-step back-etching process. The Q factor of the 3.3 pF suspended MIM capacitor at 2 GHz is about 79 % larger than the non-suspended one and the Q is increased from 46.8 to 61.9, respectively. And a ten-element π equivalent model including electrode and substrate parasitics as well as dielectric loss is used to fit the suspended MIM capacitors well up to 10 GHz, demonstrating that the suspended MIM capacitors exhibit both lower substrate loss and lower parasitic capacitance of the substrate. [ABSTRACT FROM AUTHOR]

Composite thick films consisting of multi-layered polymers and metals are widely used in integrated circuits(IC) and its packaging, and it arises intricate stress and warpage problems due to complex inner stress distribution and evolution. The wafer warpage origination and evolution of multi-layered polyimide (PI)/Cu composite film is measured in-situ by a Multi-beam Laser Optical Sensor (MOS) system. It's found that PI has an intricate influence on wafer warpage evolution and Cu plastic deformation due to viscoelasticity and glass-transition, and the influence differs in different structures and at different temperatures. Nonlinearity of the curvature–temperature curve of the composite occurs at much lower temperature than in single PI or Cu film, showing mutual effect of PI and Cu. Unlike bare or capping PI film that totally stress relaxed at high temperature, bottom PI coated by Cu film sustains a medium compressive stress, indicating that Cu coating film has restrained stress relaxation of PI. The warpage evolution during heating is different from that during cooling, perhaps due to different deformation mechanism. [ABSTRACT FROM AUTHOR]

The wafer warpage problem, mainly originated from coefficient of thermal expansion mismatch between the materials, becomes serious in wafer level packaging as large diameter wafer is adopted currently. The warpage poses threats to wafer handling, process qualities, and can also lead to serious reliability problems. In this paper, a novel mechanical diced trench structure was proposed to reduce the final wafer warpage. Deep patterned trenches with a depth about 100 μm were fabricated in the Si substrate by mechanical dicing method. Both experiment and simulation approaches were used to investigate the effect of the trenches on the wafer warpage and the influence of the geometry of the trenches was also studied. The results indicate that, by forming deep trenches, the stress on the individual die is decoupled and the total wafer warpage could be reduced. The final wafer warpage is closely related to the trench depth and die width. Trenched sample with a depth of 100 μm can decrease the wafer warpage by 51.4%. [ABSTRACT FROM AUTHOR]

Polyimide (PI) and electro-chemically deposited (ECD) Cu film structures are widely adopted as redistribution layer (RDL) in wafer level packaging (WLP) and one potential problem with these multi-layered film structures is the stresses and wafer warpage generated in the thermal process. To understand the thermo-mechanical properties of these layered structures, experimental studies on four different multi-layered film structures during the thermal cycling were carried out in this paper, including ECD Cu/Si substrate, ECD Cu/PI/Si substrate, PI/ECD Cu/Si substrate, and PI/ECD Cu/PI/Si substrate. It is observed that the ECD Cu film on Si substrate did not exhibit significant stress relaxation at high temperature, which is very different from the result of previous research on sputtered Cu film. Furthermore, a distinct strain hardening of ECD Cu film during the course of thermal loading was noted. PI film in ECD Cu/PI/Si structure served as an effective stress relief layer at high temperature and this phenomenon was elucidated by employing dynamic mechanical analysis to characterize the viscoelastic properties of PI. However, after cooling to room temperature, the curvature of ECD Cu/PI/Si substrate is bigger than that of ECD Cu/Si substrate as the stresses in the film structures were typically biaxial and uniform at wafer level. It is also found that the PI passivation layer had little influence on the thermal behavior of Cu film due to its low modulus. [ABSTRACT FROM AUTHOR]

Through-silicon-via (TSV) fabrication using deep reactive-ion etching has many constraints, such as complicated process and high cost. This paper presents a novel fabrication method of TSVs based on double-sided anisotropic wet etching of (100)-oriented silicon wafer. To increase the I/O interconnection density, a metallization process of TSVs with multiinterconnection is first proposed and realized by spray coating and semiadditive plating process. Both TSVs with one wire and four wires are designed and fabricated. The open/short test validates the feasibility of the process, and the electrical resistances are about 1.2 and \(0.2~\Omega \) , respectively. The proposed TSV with multiinterconnection is applied to the interposer fabrication, which is designed for the 3-D hetero-integration of GaAs photodetector and readout circuit, and demonstrates its application. This fabrication method is featured by both low cost and relatively high interconnection density. In addition, it has the advantages of simple process and reliable electrical interconnection, with potential applications to low or middle interconnection density cases. [ABSTRACT FROM AUTHOR]

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent manufacturing systems. Recently, deep learning based fault diagnosis methods have achieved remarkable advancements with monitored data from a single sensor. However, obtaining satisfactory diagnostic results based on a single sensor is often difficult because the complementary information between different sensors is ignored. Extracting comprehensive fault features from multi-modal data is a problem that remains to be solved. To address these challenges, a time-segment-wise feature fusion Transformer (FFTR) is proposed in this paper. First, the signals from various modalities as multiple channels are normalized channel-by-channel and form a multi-modal sample. Second, a time-segment-wise feature learning network is designed to transform a multi-modal sample into several fusion features through the sequential processes of sample segmentation, segment-level feature extraction and time-aligned feature fusion. Finally, a Transformer network is employed for comprehensive multi-modal feature analysis and fault classification. In addition, a joint loss function is designed to comprehensively train the end-to-end FFTR. The comparison experiment with other baseline methods is conducted on two multi-modal datasets. The experimental results show that FFTR achieves 3.69% and 3.93% higher diagnostic accuracy than baseline on two datasets respectively and can address real-world problems effectively. [ABSTRACT FROM AUTHOR]

A wafer-level packaging design for GaAs-based image sensor is presented. Key processes, such as GaAs/glass wafer bonding, GaAs substrate thinning, through substrate grooves (TSGs) fabrication, redistribution layer formation, polymer passivation, and laser jet bumping, are examined and characterized. GaAs image sensor package with 64 leads is successfully fabricated on 4-in thinned GaAs/glass test vehicle wafer. In the package, two long TSGs are wet etched as the interconnection path. Process parameters are systematically studied and given. Then, fabrication results of these processes were discussed. Finally, electrical tests show that ohmic contact is obtained with a resistance of around 30 \Omega between two nearby interconnections. [ABSTRACT FROM PUBLISHER]

The process development of a novel wafer level packaging with TSV applied in high-frequency range transmission is presented. A specially designed TSV structure (a core TSV and six shielding TSVs) is adopted to connect the components on different sides of the high-resistivity silicon wafer. And the microstrip line in the microwave monolithic integrated circuit is used to transmit high-frequency signal in packaging structure together with the low permittivity intermediate dielectric polymer, benzocyclobutene. The TSV fabrication process and the multi-layer interconnection is illustrated in details. The electrical measurement result of the microstrip lines connected by TSVs reveals the resistances within 0.719 Ω, a return loss better than 23.8 dB and an insertion loss better than 2.60 dB from 14 to 40 GHz. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Vehicles (IoV) technology, IoV applications have been developed from their infant stage to intermediate stage, i.e. from primarily providing entertainment and navigation services to travel guidance and energy saving driving. As the communication technology determines the overall performance of IoV, we first elaborate the architecture of IoV and algorithms of communication schedule. To improve the efficiency and accuracy of information reception and processing, we then propose a new dynamic priority strategy towards key data. The strategy includes a framework of five parts which are the application layer, libraries, the scheduling layer, operation system and hardware. The five parts above have completed the processes of data sending, data transmission and data receiving. In terms of data transmission, a new formula for updating the priorities of data has been put forward along with a procedure for taking feedback into account. A series of experiments have been conducted to validate the performance of the newly proposed strategy. The results have showed that the priority strategy and the updating formula for the priorities are effective in IoV environment. [ABSTRACT FROM AUTHOR]

Passive interposer has become an attractive integrator for 2.5D/3D integration. TSV is the key technology for passive interposer. Coaxial-like transmission structure provides a low-cost solution of shielding TSV for high frequency application. Impedance matching needs coaxial-like TSVs to usually have different diameters. This paper presents an optimized through-via bottom-up filling method, which enables simultaneously copper filling TSVs with different aspect ratios. The demonstrated coaxial-like TSV structure have one signal-TSV and six grounded-TSVs. A potential voids problem of the filling method is identified and solved by adding suppressor. Simultaneously filling of signal-TSV and grounded-TSVs was realized by adjusting electroplating parameters, e.g. current density and stirring rate. The measured result of fabricated coaxial-like TSVs is comparable to the simulation. The result verifies that with this optimized filling method, the design of passive interposer equipped with coaxial-like vias can be more flexible and the impedance matching between TSVs and other components can be easily realized for high frequency applications. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming. Therefore, automatic detection of seizure is of great importance. But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods. We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detection, based on VGG16, VGG19, and ResNet50, respectively. The original dataset is the CHB-MIT scalp EEG dataset. We use short time Fourier transform to generate time-frequency spectrum images as the input dataset, while positive samples are augmented due to the infrequent nature of seizure. The model parameters pretrained on ImageNet are transferred to our models. And the fine-tuned top layers, with an output layer of two neurons for binary classification (seizure or nonseizure), are trained from scratch. Then, the input dataset are randomly shuffled and divided into three partitions for training, validating, and testing the deep transfer CNNs, respectively. The average accuracies achieved by the deep transfer CNNs based on VGG16, VGG19, and ResNet50 are 97.75%, 98.26%, and 96.17% correspondingly. On those results of experiments, our method could prove to be an effective method for cross-subject seizure detection. [ABSTRACT FROM AUTHOR]

Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them. First, we present the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories. Based on this distance, we design an anomaly score function to quantify the differences between different types of anomalous trajectories and normal trajectories. We further propose an anomalous trajectory detection and classification (ATDC) method to find anomalies in different anomalous patterns. Finally, we evaluate the proposed ATDC method through extensive experiments on real cab trajectory data. The results show that the proposed approach outperforms existing methods by a significant margin. [ABSTRACT FROM AUTHOR]

In many complex manufacturing environments, the running equipment must be monitored by Wireless Sensor Networks (WSNs), which not only requires WSNs to have long service lifetimes, but also to achieve rapid and high-quality transmission of equipment monitoring data to monitoring centers. Traditional routing algorithms in WSNs, such as Basic Ant-Based Routing (BABR) only require the single shortest path, and the BABR algorithm converges slowly, easily falling into a local optimum and leading to premature stagnation of the algorithm. A new WSN routing algorithm, named the Quantum Ant Colony Multi-Objective Routing (QACMOR) can be used for monitoring in such manufacturing environments by introducing quantum computation and a multi-objective fitness function into the routing research algorithm. Concretely, quantum bits are used to represent the node pheromone, and quantum gates are rotated to update the pheromone of the search path. The factors of energy consumption, transmission delay, and network load-balancing degree of the nodes in the search path act as fitness functions to determine the optimal path. Here, a simulation analysis and actual manufacturing environment verify the QACMOR's improvement in performance. [ABSTRACT FROM AUTHOR]

Small-scale patchiness intensified the spatial heterogeneous distribution of soil and vegetation organic carbon (C) and total nitrogen (N), thereby inducing large uncertainty in estimating ecosystem C and N stocks. However, patchiness at this scale was difficult to identify by satellite remote sensing and measure in situ. Aerial photographing can provide high-precision images and can potentially improve estimating accuracy in ecosystem C and N stocks. In this study, using combined methods of field sampling, aerial photographing and satellite images, we investigated the characteristics of patchiness, quantified ecosystem C and N stocks in different patches at plot scale (~ 1000 m 2 ) and eventually estimated the uncertainty in ecosystem C and N stocks caused by small-scale patchiness at basin scale. Results showed that: 1) mosaic of vegetation and bald patches coexisted for swamp meadow and alpine meadow, whereas vegetation presented as spot for steppe meadow and alpine steppe; 2) average ecosystem respiration, C and N stocks in intact grassland patches were 2.05 and 1.65, 1.66 and 1.65, 2.01 and 1.69 times higher than those of large and medium bare patches ( P < 0.05); and 3) neglecting the bare patches caused approximate 30%, 15% and 10% overestimation in estimations of ecosystem C emission, C and N stocks. We concluded that small-scale patchiness would result in ecosystem C loss due to decreasing in C input from vegetation and increasing in C output from soil erosion. Our study suggested that integration of field sampling, aerial photographing and satellite images could improve precision in estimation of ecosystem C emission, C and N stocks at basin scale. [ABSTRACT FROM AUTHOR]

This paper presents a novel combined through-wafer-groove fabrication approach, which is applied to the wafer level packaging (WLP) of GaAs charge coupled devices (CCD) for electrical interconnection. The combined methodology includes mechanical dicing of the groove and wet chemical etching for polishing. The parameters of the mechanical dicing are researched, including feed speed, dicing directions of the wafer, and cutting depth, to minimize the chipping. Two kinds of chemical solution are tried, and the results are discussed. Besides, the etch rate is measured, which provides a guideline for the process design. Finally, GaAs-CCD WLP sample is achieved and the electrical properties are tested to validate the feasibility of this fabrication approach. This methodology is featured by low cost, low process temperature, and good process uniformity. [ABSTRACT FROM AUTHOR]

The complete mitochondrial genome sequence of Accipiter virgatus was determined. This mt-genome was 17,952 bp in length and consisted of 22 tRNA genes, 13 protein-coding genes, 2 rRNA genes, one control region (CR) and one pseudo-control region (CCR). Phylogenetic analyses of 14,644 bp of mitochondrial DNA (12 protein-coding genes, 2 rRNAs and 22 tRNAs) revealed the phylogenetic position of Cathartidae ( Cathartes aura ) was more closer to Ciconiidae (storks) than Accipitridae. To investigate the divergence times of the CCRs in Falconiformes, detailed analyses of the noncoding regions (CR and CCR) were performed. We found the recently reported novel gene order in Falconiformes had multiple independent origins and hence cannot be used to infer phylogenetic lineages. Indeed, the molecular clock suggested the CCR in Falconidae emerged about 65.4 million years (Mya), while that in Pandionidae–Accipitridae clade emerged about 19.16 Mya. The intra-genomic homology between the noncoding regions was detected in Spilornis cheela , which supporting the duplication hypothesis. Furthermore, the structure of CCR should be featured by a region containing tandem repeats as two definitely separated clusters of tandem repeats were found. The findings presented here should be considered in future phylogenetic and evolutionary studies targeting the pseudo-control regions of all Falconiformes species. [ABSTRACT FROM AUTHOR]

Objective To study the inhibition effect of non custodial terpenes -3 β - alcohol to experimentally induced autoimmune encephalomyelitis in guinea pigs. Methods Different doses (25 mg/kg, 50 mg/kg and 100 mg/kg) of non custodial terpenes -3 β - alcohol were given to the experimentally induced autoimmune encephalomyelitis model of guinea pigs by gavage for 8 weeks. Plasma levels of CD4+/CD8+, IL-1, IL-2, IL-6, IL-10, neuropeptide Y (NPY), beta endorphin (β - EP), transforming growth factor - β (TGF- β), matrix metalloproteinase (MMP-2), nitric oxide synthase (NOS) and leukocyte differentiation antigen CD3 were assessed. The brain neuron morphology changes was observed under light microscopy while its ultrastructure changes was observed under electron microscope. NOS expression in neurons was observed through immunofluoresce technology. Results Non custodialterpenes -3β- alcohol inhibited the increase of plasma CD4+/CD8+, IL-1, IL-2, IL-6, IL-10, MMP-2, CD³ and NPY while decrease of plasma β -EP, brain TGF- β. It also increase NOS expression in neuronal cytoplasm and maintained neuron morphology. Conclusion Non custodial terpenes -3 β - alcohol inhibited the experimental autoimmune encephalomyelitis in guinea pig. [ABSTRACT FROM AUTHOR]

Highlights: [•] Void free through-mask electroplated Cu interconnect was achieved. [•] Wet etching was used to enhance the wettability of the seed layer. [•] TaN passivation layer can prevent void-formation in the annealing process. [•] The reported Cu interconnect shows good electric performance. [ABSTRACT FROM AUTHOR]

Abstract: Thermomechanical reliability of polyimide layers in a flip-chip-on-lead-frame dual flat no-leads package subjected to thermal cycling test condition was studied by the finite element method and the Taguchi method. Different control factors were considered for optimal design toward enhancement of the thermomechanical reliability of polyimide layers, including diameter of the Cu pillar bumps, polyimide opening, and size of the Al pad. Conforming to design rules, the largest Al pad diameter, the smallest size of bump diameter, and the largest polyimide opening were found to be beneficial to enhance the thermomechanical reliability of polyimide layers. And the optimal design was experimentally verified. [Copyright &y& Elsevier]

Abstract: A new technology in wafer-level packaging by glass frit bonding is developed for wafer level encapsulation of MEMS devices. To improve the uniformity of the dimension of glass frit, a new barrier trench technology (BTT) is developed during bonding process. Glass frit expanding caused by bonding force is well resisted by the BTT. An improved uniformity of bonded glass frit dimension is achieved. Meanwhile, the residual stress of glass frit bonded MEMS wafer with BTT is analyzed by using Finite Element Analysis method. Shear test and water test are also carried out, the gross leak and fine leak test demonstrate that the results fulfill the corresponding MIL-STD. A hermetic sealing and a high process yield are realized. [Copyright &y& Elsevier]