Dispersion-Assisted Tunable Fluorescence from Carbon Dots

In this study, carbon dots (CDs) synthesized by hydrothermal method with amino-rich surface exhibit tunable fluorescence across entire visible range by simply controlling the concentration. A comprehensive comparison has been performed for the first time between concentration-induced aggregation of the single-type CDs and electrostatic-induced agglomeration of opposite-charged CDs in terms of their fluorescence properties. Experimental results show that both the aggregation of CDs and internal absorption filtration are possible causes of the concentration-dependent fluorescence emission. Subsequently, the inter distance of adjacent CDs in their aggregates was enlarged by forming rigid double-stranded DNA (dsDNA) between adjacent CDs through base pairing. It is clear that the contact of CDs induces the changes of fluorescence emission and light absorption. Through a better understanding of the mechanisms behind concentration-induced multicolor emission, this work can provide a novel strategy to develop the advanced applications of CDs.

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

An Experimental Study on Clay and Sand Mixes to Develop a Non-Linear Homogenized Model for Earth Construction Materials

Due to its ecological interest and large availability, a renewed attention is paid to earth as building material. Indeed, raw earth consumes CO2 only during its processing and transportation, and it provides a natural hygrothermal comfort. However, its mechanical properties are highly linked to its composition, which causes an important variability of performances. That is why any soil has to be characterized before being used as a building material. The aim of this study is to propose a model able to predict the hydromechanical behavior of a reconstituted soil according to its composition. As earth is a heterogeneous material, the model is based on homogenization procedures. The sand is considered as spherical inclusions inside a clay matrix. The particularity of the model stands to consider both positive and negative effects of volume variation and mechanical properties of clay under hydric variations. The model parameters are determined according to an original experimental campaign, which is conducted on various mixes of a single type of clay (kaolinite) and of sand, and water. The experimental study provides some mechanical properties of the mixes versus water content and sand content to test the ability of the homogenization model to assess the main properties of this material.

Predicting the Redox Potentials of Phenazine Derivatives using DFT Assisted Machine Learning

Here, four machine-learning models were employed to predict the redox potentials of phenazine derivatives in DME using DFT. A small dataset of 189 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Models were validated on the external test-set containing new functional groups and diverse molecular structures and achieved reasonable accuracies (R2 > 0.57). Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with reasonable accuracy (R2 > 0.6). This type of performance for predicting redox potential from such a small and simple dataset of phenazine derivatives has never been reported before. Redox Flow Batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives saving computational and experimental resources. This approach could potentially identify novel molecules for green energy storage systems such as RBF.

Tiling a tubule: How increasing complexity improves the yield of self-limited assembly

The ability to design and synthesize ever more complicated colloidal particles opens the possibility of self-assembling a zoo of complex structures, including those with one or more self-limited length scales. An undesirable feature of systems with self-limited length scales is that thermal fluctuations can lead to the assembly of nearby, off-target states. We investigate strategies for limiting off-target assembly by using multiple types of subunits. Using simulations and energetics calculations, we explore this concept by considering the assembly of tubules built from triangular subunits that bind edge to edge. While in principle, a single type of triangle can assemble into tubules with a monodisperse width distribution, in practice, the finite bending rigidity of the binding sites leads to the formation of off-target structures. To increase the assembly specificity, we introduce tiling rules for assembling tubules from multiple species of triangles. We show that the selectivity of the target structure can be dramatically improved by using multiple species of subunits, and provide a prescription for choosing the minimum number of subunit species required for near-perfect yield. Our approach of increasing the system’s complexity to reduce the accessibility of neighboring structures should be generalizable to other systems beyond the self-assembly of tubules.

Genotypic and Phenotypic Characteristics of Hereditary Colorectal Cancer

The genomic causes and clinical manifestations of hereditary colorectal cancer (HCRC) might be stratified into 2 groups, namely, familial (FCRC) and a limited sense of HCRC, respectively. Otherwise, FCRC is canonically classified into 2 major categories; Lynch syndrome (LS) or associated spectra and inherited polyposis syndrome. By contrast, despite an increasing body of genotypic and phenotypic traits, some FCRC cannot be clearly differentiated as definitively single type, and the situation has become more complex as additional causative genes have been discovered. This review provides an overview of HCRC, including 6 LS or associated spectra and 8 inherited polyposis syndromes, according to molecular pathogenesis. Variants and newly-identified FCRC are particularly emphasized, including MUTYH (or MYH)-associated polyposis, Muir-Torre syndrome, constitutional mismatch repair deficiency, EPCAM-associated LS, polymerase proofreading-associated polyposis, RNF43- or NTHL1-associated serrated polyposis syndrome, PTEN hamartoma tumor syndrome, and hereditary mixed polyposis syndrome. We also comment on the clinical utility of multigene panel tests, focusing on comprehensive cancer panels that include HCRC. Finally, HCRC surveillance strategies are recommended, based on revised or notable concepts underpinned by competent validation and clinical implications, and favoring major guidelines. As hereditary syndromes are mainly attributable to genomic constitutions of distinctive ancestral groups, an integrative national HCRC registry and guideline is an urgent priority.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal

<div>Synchronous Rain streaks and Raindrops Removal (SR3) is a very hard and challenging task, since rain streaks and raindrops are two wildly divergent real-scenario phenomena with different optical properties and mathematical distributions. As such, most of existing deep learning-based Singe Image Deraining (SID) methods only focus on one of them or the other. To solve this issue, we propose a new, robust and hybrid SID model, termed Robust Attention Deraining Network (RadNet) with strong robustenss and generalztion ability. The robustness of RadNet has two implications :(1) it can restore different degenerations, including raindrops, rain streaks, or both; (2) it can adapt to different data strategies, including single-type, superimposed-type and blended-type. Specifically, we first design a lightweight robust attention module (RAM) with a universal attention mechanism for coarse rain removal, and then present a new deep refining module (DRM) with multi-scales blocks for precise rain removal. The whole process is unified in a network to ensure sufficient robustness and strong generalization ability. We measure the performance of several SID methods on the SR3 task under a variety of data strategies, and extensive experiments demonstrate that our RadNet can outperform other state-of-the-art SID methods.</div>

Robust Attention Deraining Network for Synchronous Rain Streaks and Raindrops Removal

<div>Synchronous Rain streaks and Raindrops Removal (SR3) is a very hard and challenging task, since rain streaks and raindrops are two wildly divergent real-scenario phenomena with different optical properties and mathematical distributions. As such, most of existing deep learning-based Singe Image Deraining (SID) methods only focus on one of them or the other. To solve this issue, we propose a new, robust and hybrid SID model, termed Robust Attention Deraining Network (RadNet) with strong robustenss and generalztion ability. The robustness of RadNet has two implications :(1) it can restore different degenerations, including raindrops, rain streaks, or both; (2) it can adapt to different data strategies, including single-type, superimposed-type and blended-type. Specifically, we first design a lightweight robust attention module (RAM) with a universal attention mechanism for coarse rain removal, and then present a new deep refining module (DRM) with multi-scales blocks for precise rain removal. The whole process is unified in a network to ensure sufficient robustness and strong generalization ability. We measure the performance of several SID methods on the SR3 task under a variety of data strategies, and extensive experiments demonstrate that our RadNet can outperform other state-of-the-art SID methods.</div>

Evolution of volatility and composition in sesquiterpene-mixed and <i>α</i>-pinene secondary organic aerosol particles during isothermal evaporation

Efforts have been spent on investigating the isothermal evaporation of α-pinene secondary organic aerosol (SOA) particles at ranges of conditions and decoupling the impacts of viscosity and volatility on evaporation. However, little is known about the evaporation behavior of SOA particles from biogenic organic compounds other than α-pinene. In this study, we investigated the isothermal evaporation behavior of the α-pinene and sesquiterpene mixture (SQTmix) SOA particles under a series of relative humidity (RH) conditions. With a set of in situ instruments, we monitored the evolution of particle size, volatility, and composition during evaporation. Our finding demonstrates that the SQTmix SOA particles evaporated slower than the α-pinene ones at any set of RH (expressed with the volume fraction remaining, VFR), which is primarily due to their lower volatility and possibly aided by higher viscosity under dry conditions. We further applied positive matrix factorization (PMF) to the thermal desorption data containing volatility and composition information. Analyzing the net change ratios (NCRs) of each PMF-resolved factor, we can quantitatively compare how each sample factor evolves with increasing evaporation time or RH. When sufficient particulate water content was present in either SOA system, the most volatile sample factor was primarily lost via evaporation, and changes in the other sample factors were mainly governed by aqueous-phase processes. The evolution of each sample factor of the SQTmix SOA particles was controlled by a single type of process, whereas for the α-pinene SOA particles it was regulated by multiple processes. As indicated by the coevolution of VFR and NCR, the effect of aqueous-phase processes could vary from one to another according to particle type, sample factors, and evaporation timescale.