Segregation by Race in Public Schools Retrospect and Prospect

Solar energy conversion has been intensively studied in past decades and has been shown to be greatly effective for solving the serious environmental pollution and energy shortage problems. Photoelectrocatalysis and photovoltaics have been considered as the two main approaches for solar energy conversion and utilization, which are generally involved with nanostructured materials and/or catalytic processes, greatly affecting the efficiencies for solar energy conversion. Then, it is necessary to understand the relationship between the physical and chemical properties of nanomaterials and their performances for solar energy conversion. It is also important to explore the fundamentals in catalytic processes for solar energy conversion and make breakthrough in design and synthesis of nanomaterials or nanostructures, characterization of material properties, and performance of novel devices and systems. The aim of this special issue is to present some recent progress in the field of advanced catalysis and nanostructure design for solar energy conversion. A brief summary of all accepted papers is provided below

Enhancing Heat Transfer in Internal Combustion Engine by Applying Nanofluids

Nanofluids exhibit novel properties including significant heat transfer properties that make them potentially useful in internal combustion engine cooling. However, although there is a substantial number of mechanisms proposed, modeling works related to their enhanced thermal conductivity, systematic mechanisms, or models that are suitable for nanofluids are still lacked. With molecular dynamics simulations, thermal conductivities of nanofluids with various nanoparticles have been calculated. Influence rule of various factors for thermal conductivity of nanofluids has been studied. Through defining the ratio of thermal conductivity enhancement by nanoparticle volume fraction, Κ, the impacts of nanoparticle properties for thermal conductivity are further evaluated. Furthermore, the ratio of energetic atoms in nanoparticles, E, is proposed to be an effective criterion for judging the impact of nanoparticles for the thermal conductivity of nanofluids. Mechanisms of heat conduction enhancement are investigated by MD simulations. Altered microstructure and movements of nanoparticles in the base fluid are proposed to be the main reasons for thermal conductivity enhancement in nanofluids. Both the static and dynamic mechanisms for heat conduction enhancement in nanofluids have been considered to establish a prediction model for thermal conductivity. The prediction results of the present model are in good agreement with experimental results

Solar light-driven photocatalytic hydrogen evolution over ZnIn2S4 loaded with transition-metal sulfides

A series of Pt-loaded MS/ZnIn2S4 (MS = transition-metal sulfide: Ag2S, SnS, CoS, CuS, NiS, and MnS) photocatalysts was investigated to show various photocatalytic activities depending on different transition-metal sulfides. Thereinto, CoS, NiS, or MnS-loading lowered down the photocatalytic activity of ZnIn2S4, while Ag2S, SnS, or CuS loading enhanced the photocatalytic activity. After loading 1.0 wt.% CuS together with 1.0 wt.% Pt on ZnIn2S4, the activity for H2 evolution was increased by up to 1.6 times, compared to the ZnIn2S4 only loaded with 1.0 wt.% Pt. Here, transition-metal sulfides such as CuS, together with Pt, acted as the dual co-catalysts for the improved photocatalytic performance. This study indicated that the application of transition-metal sulfides as effective co-catalysts opened up a new way to design and prepare high-efficiency and low-cost photocatalysts for solar-hydrogen conversion

TOT, a Fast Multivariate Public Key Cryptosystem with Basic Secure Trapdoor

In this paper, we design a novel one-way trapdoor function, and then propose a new multivariate public key cryptosystem called $\rm TOT$, which can be used for encryption, signature and authentication. Through analysis, we declare that $\rm TOT$ is secure, because it can resist current known algebraic attacks if its parameters are properly chosen. Some practical implementations for $\rm TOT$ are also given, and whose security level is at least $2^{90}$. The comparison shows that $\rm TOT$ is more secure than $\rm HFE$, $\rm HFEv$ and $\rm Quartz$ (when $n \ge 81$ and $D_{HFE} \ge 129$, $\rm HFE$ is still secure), and it can reach almost the same speed of computing the secret map by $\rm C^\ast$ and $\rm Sflash^{v2}$ (even though $\rm C^\ast$ was broken, its high speed has been affirmed)

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms

Knowledge Matters: Radiology Report Generation with General and Specific Knowledge

Automatic radiology report generation is critical in clinics which can relieve experienced radiologists from the heavy workload and remind inexperienced radiologists of misdiagnosis or missed diagnose. Existing approaches mainly formulate radiology report generation as an image captioning task and adopt the encoder-decoder framework. However, in the medical domain, such pure data-driven approaches suffer from the following problems: 1) visual and textual bias problem; 2) lack of expert knowledge. In this paper, we propose a knowledge-enhanced radiology report generation approach introduces two types of medical knowledge: 1) General knowledge, which is input independent and provides the broad knowledge for report generation; 2) Specific knowledge, which is input dependent and provides the fine-grained knowledge for report generation. To fully utilize both the general and specific knowledge, we also propose a knowledge-enhanced multi-head attention mechanism. By merging the visual features of the radiology image with general knowledge and specific knowledge, the proposed model can improve the quality of generated reports. Experimental results on two publicly available datasets IU-Xray and MIMIC-CXR show that the proposed knowledge enhanced approach outperforms state-of-the-art image captioning based methods. Ablation studies also demonstrate that both general and specific knowledge can help to improve the performance of radiology report generation.Comment: Medical Image Analysi

Screening of potential biomarkers in the occurrence and development of type 1 diabetes mellitus based on transcriptome analysis

Introduction: The aim of the study was to reveal the mechanisms for the pathogenesis and progression of type 1 diabetes mellitus (T1DM). Material and methods: Two mRNA expression profiles and two miRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), functional enrichment analyses, pathways, putative targets for DEMs and the miRNA-gene pairs, protein-protein pairs of DEGs, and PPI network were constructed. Results: Based on mRNA expression profiles, 37 and 110 DEGs were identified, and named as DEGs-short and DEGs-long, respectively. Based on miRNA expression profiles, 15 and six DEMs were identified, and named as DEMs-short and DEMs-long, respectively. DEGs-short were enriched in six GO terms and four pathways, and DEGs-long enriched in 40 GO terms and 10 pathways. Seventeen miRNA-gene pairs for DEMs-short were screened out; hisa-miR-181a and hisa-miR-181c were involved in the most pairs. Twenty pairs for DEMs-long were obtained; hsa-miR-338-3p was involved in all the pairs. KLRD1 was involved in more pairs in the network of DEGs-short. ACTA2 and USP9Y were involved in more pairs in the network of DEGs-long. Conclusions: KLRD1, hisa-miR-181a, and hisa-miR-181c might be pathogenic biomarkers for T1DM, ACTA2, USP9Y, and hsa-miR-338-3p progressive biomarkers of T1DM.