Peec-Based On-Chip Pdn Impedance Modeling using Layered Green\u27s Function

This paper presents an impedance model of on-chip power distribution network (PDN), which is an efficient criterion for estimating simultaneous switching noises (SSNs) on 3-D integrated circuit (IC). The impedance of on-chip PDN, including the effect of silicon substrate, is accurately modeled based on partial element equivalent circuit (PEEC) and layered Green\u27s function (LGF). The equivalent circuit model of PDN is extracted based on the physical dimensions and electrical material characteristic of PDN at first. And then the LGF is used to consider the effect of silicon substrate for improving the accuracy of on-chip PDN impedance model. The effectiveness of proposed model has been validated by full wave simulation. The high order resonance of PDN impedance can also be accurately predicted

Joint relay selection and resource allocation for energy-efficient D2D cooperative communications using matching theory

Device-to-device (D2D) cooperative relay can improve network coverage and throughput by assisting users with inferior channel conditions to implement multi-hop transmissions. Due to the limited battery capacity of handheld equipment, energy efficiency is an important issue to be optimized. Considering the two-hop D2D relay communication scenario, this paper focuses on how to maximize the energy efficiency while guaranteeing the quality of service (QoS) requirements of both cellular and D2D links by jointly optimizing relay selection, spectrum allocation and power control. Since the four-dimensional matching involved in the joint optimization problem is NP-hard, a pricing-based two-stage matching algorithm is proposed to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three-dimensional matching is conducted to match users, relays and the spectrum resources reused by transmitter-to-relay links. In the process of preference establishment of the second stage, the optimal transmit power is solved to guarantee that the D2D link has the maximized energy efficiency. Simulation results show that the proposed algorithm not only has a good performance on energy efficiency, but also enhances the average number of served users compared to the case without any relay

Functional divergence and adaptive selection of KNOX gene family in plants

KNOTTED-like homeodomain (KNOX) genes are transcriptional regulators that play an important role in morphogenesis. In the present study, a comparative analysis was performed to investigate the molecular evolution of the characteristics of the KNOX gene family in 10 different plant species. We identified 129 KNOX gene family members, which were categorized into two subfamilies based on multiple sequence alignment and phylogenetic tree reconstruction. Several segmental duplication pairs were found, indicating that different species share a common expansion model. Functional divergence analysis identified the 15 and 52 amino acid sites with significant changes in evolutionary rates and amino acid physicochemical properties as functional divergence sites. Additional selection analysis showed that 14 amino acid sites underwent positive selection during evolution, and two groups of co-evolutionary amino acid sites were identified by Coevolution Analysis using Protein Sequences software. These sites could play critical roles in the molecular evolution of the KNOX gene family in these species. In addition, the expression profiles of KNOX duplicated genes demonstrated functional divergence. Taken together, these results provide novel insights into the structural and functional evolution of the KNOX gene family

PEEK in Fixed Dental Prostheses: Application and Adhesion Improvement

Polyetheretherketone (PEEK) has been widely applied in fixed dental prostheses, comprising crowns, fixed partial dentures, and post-and-core. PEEK’s excellent mechanical properties facilitate better stress distribution than conventional materials, protecting the abutment teeth. However, the stiffness of PEEK is not sufficient, which can be improved via fiber reinforcement. PEEK is biocompatible. It is nonmutagenic, noncytotoxic, and nonallergenic. However, the chemical stability of PEEK is a double-edged sword. On the one hand, PEEK is nondegradable and intraoral corrosion is minimized. On the other hand, the inert surface makes adhesive bonding difficult. Numerous strategies for improving the adhesive properties of PEEK have been explored, including acid etching, plasma treatment, airborne particle abrasion, laser treatment, and adhesive systems

Semi-quantitative risk assessment on Listeria monocytogenes in retail Chinese salads for Shanghai residents

Objective To elucidate the prevalence of Listeria monocytogenes in Chinese salads in Shanghai, and to explore measures that can avoid cross-contamination and reduce the risk of infection caused by L. monocytogenes in salads. Methods Health risk and the influencing factors were estimated using a swift quantitative microbiological risk assessment (sQMRA) method with the prevalence of L. monocytogenes in Chinese salads collected from retail market in Shanghai, dietary consumption data and experimental data. Results The detection rate of L. monocytogenes in retail salads in Shanghai was 3.97% (6/151), the average concentration in contaminated samples was 60.53 CFU/g. For sensitive and non-sensitive people, the estimated incidence of nine different scenarios were 2.36×10-4-3.49×10-4 and 2.36×10-6-3.49×10-6, respectively, and the estimated cases were 162-431 and 6-17. Containers of different materials had different influence on the cross-contamination parameters of L. monocytogenes. The cross-contamination of the glass container was relatively high, and the estimated cases were the lowest. The cross-contamination parameters and ingestion of the ceramic containers were both low, and the estimated cases were the highest. After washing with sterile water or cleanser essence of the glass container and ceramic container, the estimated cases of L. monocytogenes infection were significantly reduced. Conclusion There was a certain risk of the contamination of L. monocytogenes in Chinese salads in Shanghai. Using glass containers and adopting cleaning measures could significantly reduce the number of cases of L. monocytogenes infection caused by cross-contamination during the preparation of Chinese salads in the kitchen

Improved Ferroelectric Properties in Hf0.5Zr0.5O2 Thin Films by Microwave Annealing

In the doped hafnia(HfO2)-based films, crystallization annealing is indispensable in forming ferroelectric phases. In this paper, we investigate the annealing effects of TiN/Hf0.5Zr0.5O2/TiN metal-ferroelectric-metal (MFM) capacitors by comparing microwave annealing (MWA) and rapid thermal annealing (RTA) at the same wafer temperature of 500 °C. The twofold remanent polarization (2Pr) of the MWA device is 63 µC/cm2, surpassing that of the RTA device (40 µC/cm2). Furthermore, the wake-up effect is substantially inhibited in the MWA device. The orthorhombic crystalline phase is observed in the annealed HZO films in the MWA and RTA devices, with a reduced TiN and HZO interdiffusion in MWA devices. Moreover, the MFM capacitors subjected to MWA treatment exhibit a lower leakage current, indicating a decreased defect density. This investigation shows the potential of MWA for application in ferroelectric technology due to the improvement in remanent polarization, wake-up effect, and leakage current

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini

The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E’jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5–95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination

Nondestructive Identification of Salmon Adulteration with Water Based on Hyperspectral Data

For the identification of salmon adulteration with water injection, a nondestructive identification method based on hyperspectral images was proposed. The hyperspectral images of salmon fillets in visible and near-infrared ranges (390–1050 nm) were obtained with a system. The original hyperspectral data were processed through the principal-component analysis (PCA). According to the image quality and PCA parameters, a second principal-component (PC2) image was selected as the feature image, and the wavelengths corresponding to the local extremum values of feature image weighting coefficients were extracted as feature wavelengths, which were 454.9, 512.3, and 569.1 nm. On this basis, the color combined with spectra at feature wavelengths, texture combined with spectra at feature wavelengths, and color-texture combined with spectra at feature wavelengths were independently set as the input, for the modeling of salmon adulteration identification based on the self-organizing feature map (SOM) network. The distances between neighboring neurons and feature weights of the models were analyzed to realize the visualization of identification results. The results showed that the SOM-based model, with texture-color combined with fusion features of spectra at feature wavelengths as the input, was evaluated to possess the best performance and identification accuracy is as high as 96.7%

Integration of Artificial Neural Network Modeling and Hyperspectral Data Preprocessing for Discrimination of Colla Corii Asini Adulteration

The study of hyperspectral imaging in tandem with spectral preprocessing and neural network techniques was conducted to realize Colla Corii Asini (CCA, E’jiao) adulteration discrimination. CCA was adulterated with pig skin gelatin (PSG) in the range of 5–95% (w/w) at 5% increments. Three methods were used to pretreat the original spectra, which are multiplicative scatter correction (MSC), Savitzky-Golay (SG) smoothing, and the combination of MSC and SG (MSC-SG). SPA was employed to select the characteristic wavelengths (CWs) to reduce the high dimension. Colour and texture features of CWs were extracted as input of prediction model. Two kinds of artificial neural network (ANN) with three spectral preprocessing methods were applied to establish the prediction models. The prediction model of generalized regression neural network (GRNN) in tandem with the MSC-SG preprocessed method presented satisfactory performance with the correct classification rate value of 92.5%. The results illustrated that the integration of preprocessing methods, hyperspectral imaging features, and ANN modeling had a great potential and feasibility for CCA adulteration discrimination

Application of lecture-and-team-based learning in stomatology: in-class and online

Abstract Background This study aimed to evaluate stomatological students’ learning efficacy and their attitude towards Lecture-Team-Based Learning (LTBL) on topics regarding the design of removable partial dentures via in-class, online, and both in combination. Methods Students from seven distinct grades participated in the course in their fourth academic year (Years 2015, 2016, 2017, 2018, 2019, 2020, and 2021). Students of Years 2015–2019 attended in-class LTBL, students of Year 2020 attended online LTBL, and students of Year 2021 attended the combination mode. The scores of three examinations were compared, namely, individual readiness assessment test, team readiness assurance test, and individual application test. Visual Analog Scales (VAS) were used for students to self-assess their mastery of prosthodontics knowledge before and after the course. Anonymous questionnaires were delivered to evaluate their satisfaction with LTBL via a Likert scale. Results In each academic year, the three exam scores were significantly improved as the course progressed and VAS-post scores were significantly higher than VAS-pre scores. The three examination and VAS scores of students in Year 2020 were significantly lower than those in Years 2019 and 2021. Students were highly satisfied with the LTBL course based on the three parameters of knowledge acquisition, teamwork, and classroom atmosphere. Conclusion Students were highly satisfied with the LTBL course and their learning performance was improved as the course progressed both in-class and online. Online LTBL could be adopted when students have to study online, while in-class LTBL could perform better when combined with video records of an online LTBL course