Bottleneck Stage Research Articles

Semantic Segmentation of the Prostate Based on Onefold and Joint Multimodal Medical Images Using YOLOv4 and U-Net

Magnetic Resonance Imaging is increasing in importance in prostate cancer diagnosis due to the high accuracy and quality of the examination procedure. However, this process requires a time-consuming analysis of the results. Currently, machine vision is widely used in many areas. It enables automation and support in radiological studies. Successful detection of primary prostate tumors depends on the effective segmentation of the prostate itself. At times, a CT scan may be performed; alternatively, MRI may be the selected option. The data always reach a bottleneck stage. This paper presents the effective training of deep learning models to segment the prostate based on onefold and multimodal medical images. This approach supports the computer-aided diagnosis (CAD) system for radiologists as the first step in cancer exams. A comparison of two approaches designed for prostate segmentation is described. The first combines YOLOv4, the object detection neural network, and U-Net for a semantic segmentation based on onefold modality MRI images. The second presents the same method trained on multimodal images—a CT and MRI mixed dataset. The learning process was carried out in a cloud environment using GPU cards. The experiments are based on data from 120 patients who have undergone MRI and CT examinations. Several metrics evaluated the trained models. In the prostate semantic segmentation process, better results were achieved by mixed MRI with CT datasets. The best model achieved the value of 0.9685 for the Sørensen–Dice coefficient for the threshold value of 0.6.

ERP neural correlates of the interpreter advantage in coordination for interpreting students

Abstract The task of interpreting requires managing multiple tasks simultaneously, but how this practice of multitasking may contribute to the nonverbal domain of executive functioning has been explored in only a few studies, and little is known about the Event-related Potentials (ERP) neural correlates of this potential advantage. To fill this gap, we conducted an ERP study asking consecutive interpreting students and bilingual controls to perform a psychological refractory period (PRP) dual-task, comprising an auditory (Task 1) and a visual (Task 2) discrimination task. They were performed separately or together, the performance differences between which, i.e., dual-task costs, served as indices of coordination. Smaller costs for interpreting students in either or both tasks are considered an interpreter advantage in coordination, and those confined to Task 2 further indicate an advantage in bottleneck switching. The latter was exactly observed in stimulus-locked lateralized readiness potential (LRP) onset latency, suggesting such an advantage was probably due to efficient switching from the response selection of one task to that of the other at the bottleneck stage of dual-task processing. Smaller dual-task costs of stimulus-locked LRP onset latency thus constitute the neural correlates of the interpreter advantage in coordination.

Optimizing cutting speeds in a machining bottleneck stage in order to reach the takt time with the minimum machining cost

Optimizing cutting speeds in a machining bottleneck stage in order to reach the takt time with the minimum machining cost

Material Removal Mechanism of SiC Ceramic by Porous Diamond Grinding Wheel Using Discrete Element Simulation.

SiC ceramics are typically hard and brittle materials. Serious surface/subsurface damage occurs during the grinding process due to the poor self-sharpening ability of monocrystalline diamond grits. Nevertheless, recent findings have demonstrated that porous diamond grits can achieve high-efficiency and low-damage machining. However, research on the removal mechanism of porous diamond grit while grinding SiC ceramic materials is still in the bottleneck stage. A discrete element simulation model of the porous diamond grit while grinding SiC ceramics was established to optimize the grinding parameters (e.g., grinding wheel speed, undeformed chip thickness) and pore parameters (e.g., cutting edge density) of the porous diamond grit. The influence of these above parameters on the removal and damage of SiC ceramics was explored from a microscopic perspective, comparing with monocrystalline diamond grit. The results show that porous diamond grits cause less damage to SiC ceramics and have better grinding performance than monocrystalline diamond grits. In addition, the optimal cutting edge density and undeformed chip thickness should be controlled at 1-3 and 1-2 um, respectively, and the grinding wheel speed should be greater than 80 m/s. The research results lay a scientific foundation for the efficient and low-damage grinding of hard and brittle materials represented by SiC ceramics, exhibiting theoretical significance and practical value.

A Review on Sample Preparation for Bio analytical Method Development by HPLC

This article compares the benefits and drawbacks of each approach while reviewing recent advancements in bioanalysis sample preparation methods. It also provides an update on fundamental concepts, theories, applications, and automation opportunities. Because every biological matrix has its own special difficulties and complexity, sample preparation is thought to be the bottleneck stage in bioanalysis. In the last ten years, new sample preparation methods for bioanalysis have developed quickly. In the preclinical and clinical phases of drug development, it is crucial to create reliable bioanalytical method(s). Consequently, it is widely acknowledged that sample preparation A number of excellent review papers have recently been published in the literature that address various scientific and technical facets of bioanalysis. The use of bioanalysis in the pharmacokinetic/pharmacodynamic characterization of novel chemical entities, beginning with their finding and continuing through their market authorization, is now generally acknowledged. Physico-chemical and biological technologies used in bioanalysis, including mass spectrometry, immunoassay, and chromatography. Liquid chromatography-mass spectrometry is a method used in labs for the qualitative and quantitative study of drug substances and metabolites. It makes use of liquid chromatography/RP HPLC. The current review concentrated on different extraction methods, including protein precipitation, solid phase extraction, and liquid-liquid extraction, all of which are crucial for sample preparation and RP HPLC sample detection.

Biomass Carbon Anchored Co‐N4 Single‐Atom Catalyst for Efficient Hydrogen Evolution, Oxygen Evolution and Alcohol Oxidation Reaction

AbstractGreat efforts have been made to boost the performance of electrochemical catalysts by regulating the electronic and geometric structures. However, the electrochemical catalytic properties have entered the bottleneck stage only depend on these methods, especially in alkaline or neutral conditions. It is highly desired to develop new strategy to make robust electrocatalysts. In this study, an atomic Co‐N4 catalyst embedded on nitrogen‐doped 3D hierarchically porous carbon has been prepared via a facile method of calcining the coordination compound. Benefiting from the unique properties of Co single‐atoms, they are coordinated with the heteroatom nitrogen of o‐phenylenediamine and melamine and anchored on the nitrogen‐doped porous carbon carriers. The as‐synthesized Co‐N‐LPC with a maximum Co single‐atom loading of 3.82 wt % features a high specific surface area of 196.6 m2 g−1 and exhibits decently high hydrogen evolution, oxygen evolution and alcohol oxidation catalytic activities in 1.0 mol L−1 KOH electrolyte with competitive overpotentials of 131, 318 and 124 mV at 10 mA cm−2 current density respectively. This study offers a cost‐effective electrocatalyst for water splitting or alcohol oxidation that can be used instead of noble metals in various renewable energy storage and conversion applications.

Developmental hourglass: Verification by numerical evolution and elucidation by dynamical-systems theory.

Determining the general laws between evolution and development is a fundamental biological challenge. Developmental hourglasses have attracted increased attention as candidates for such laws, but the necessity of their emergence remains elusive. We conducted evolutionary simulations of developmental processes to confirm the emergence of the developmental hourglass and unveiled its establishment. We considered organisms consisting of cells containing identical gene networks that control morphogenesis and evolved them under selection pressure to induce more cell types. By computing the similarity between the spatial patterns of gene expression of two species that evolved from a common ancestor, a developmental hourglass was observed, that is, there was a correlation peak in the intermediate stage of development. The fraction of pleiotropic genes increased, whereas the variance in individuals decreased, consistent with previous experimental reports. Reduction of the unavoidable variance by initial or developmental noise, essential for survival, was achieved up to the hourglass bottleneck stage, followed by diversification in developmental processes, whose timing is controlled by the slow expression dynamics conserved among organisms sharing the hourglass. This study suggests why developmental hourglasses are observed within a certain phylogenetic range of species.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Temporal and spatial changes of water quality in intensively developed urban rivers and water environment improvement: a case study of the Longgang River in Shenzhen, China.

The water quality status, spatial and temporal change processes, and water environment improvement process of urban rivers are valuable lessons to be learned under the sustainable development strategy. This study aims to reveal the water environment improvement process of intensively developed urban rivers, elucidate the spatial and temporal distribution characteristics of major pollutants, and provide recommendations for their water environment management. Water quality data from eight monitoring sites (2007-2020) in the Longgang River basin in Shenzhen, China, and comprehensive pollution index method (CPI), modified comprehensive pollution index method (M-CPI), and Pearson correlation analysis method were used for comprehensive analysis. The study shows that TN, TP, NH3-N, and COD have the greatest influence on the water quality of Longgang River, with the average pollution contribution of 53.39%, 14.49%, 11.66%, and 4.92%, in order. In 2015-2020, the water quality of the main stream of the Longgang River in the wet season was worse than that in the dry season, while the water quality of the tributaries Dingshan River and the Huangsha River in the dry season was worse than the wet season. The spatial distribution characteristics based on M-CPI indicate that the water quality of the lower reaches of Longgang River, the tributaries Dingshan River and Huangsha River, is relatively poor. In addition, the water environment improvement process of Longgang River can be divided into 3 stages: engineering stage (2007-2013, rating changed from heavily polluted to basically qualified), bottleneck stage (2013-2017, rating fluctuated slightly above and below basically qualified), and ecological restoration stage (2017-2020, rating reached qualified in 2019).

ShadowFormer: Global Context Helps Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as well as inconsistent illumination between shadow and non-shadow regions. It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions. In this work, we first propose a Retinex-based shadow model, from which we derive a novel transformer-based network, dubbed ShandowFormer, to exploit non-shadow regions to help shadow region restoration. A multi-scale channel attention framework is employed to hierarchically capture the global information. Based on that, we propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to evaluate the proposed method. Our method achieves state-of-the-art performance by using up to 150X fewer model parameters.

Constructing high K+ concentration layer to expedite K+ intercalation in graphite: towards superior rate capability without trading off power density of potassium-ion batteries

Graphite anode for potassium-ion batteries suffers poor rate capability because of the sluggish diffusion kinetics of K+. According to the rate law of chemical reactions, enrichment of K+ on the outer layer of graphite anode is expected to improve the rate capability. However, the surface of graphite is too smooth to adsorb adequate K+ to enhance rate capability. Herein, MoS2 is coated on exfoliated graphite to enrich the concentration of K+ on the surface of EG by forming K2S in high-voltage region, leading to an extremely high diffusion coefficient of up to 1000 times than that of bare graphite at the bottleneck stage, which accelerates the reaction rate of electrochemical intercalation process. Consequently, the EG/MoS2 electrode exhibits superior rate performances (125 mAh/g at 3.2 A/g), which is 11 times higher than that of EG. Moreover, the specific capacity of EG/MoS2 is 169 mAh/g at 1.6 A/g below 0.36 V, which is almost 25 times higher than that of bare EG (7 mAh/g). Our study provides new fundamental insights to boost power density of the anodes for PIBs without trading off energy density.

Magnetic field improvement of hydrogen evolution reaction in MOF-derived NiCo2S4 nanostructure

Great efforts have been made to increase the hydrogen evolution reaction (HER) of electrocatalysts by porous structure, dopants and heterostructure engineering. However, the HER catalytic properties have entered a bottleneck stage only depend on these methods. As a contact-free field to enhance the electrochemical reactions, magnetic field has attracted extensive attention recently. In this study, the Ni-MOF derived NiCo2S4 nanostructures are prepared via a facile hydrothermal preparation. By applying an external magnetic field, the NiCo2S4 delivers much better HER performance than in normal condition (without magnetic field). An overpotentials of 104 mV and a Tafel slope of 54 mV·dec−1 are attained at 10 mA cm−2 in 1 M KOH under a moderated magnetic field of 100 mT, showing that the external magnetic field taking a positive effect to enhance the kinetic process of the NiCo2S4 nanostructure. The presented results can offer an alternative strategy for further improving the HER property of electrocatalysis by utilizing an external magnetic field.

Comprehensive information integration network for left atrium segmentation on LGE CMR images

Automatic and accurate segmentation of the left atrium (LA) is a prerequisite for quantifying the LA, and effective LA segmentation is helpful for the clinical diagnosis of patients with atrial fibrillation. The existing LA segmentation methods are prone to a lack of structural prediction of the pulmonary vein and mitral valve, and the structure of the LA is prone to over- and under-segmentation. To solve these problems, we propose a comprehensive information integration network (CII-Net) to segment the LA from late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) images. The CII-Net integrates multiscale information from the input stage, high-level semantic information from the bottleneck stage, interaction information from the encoding and decoding stages, and information from the output stage, which can more completely capture the comprehensive image characteristics. We conducted experiments on 154 LGE CMR cases with atrial fibrillation proposed by the organizer of the 2018 atrial segmentation challenge. Compared with state-of-the-art methods, the proposed CII-Net obtains an average DICE score of 91.9%, average Jaccard score of 85.1%, average Hausdorff distance of 5.924 mm, and average symmetric surface distance of 0.993 mm without post-processing, demonstrating the potential for clinical application.

Preparation of New Materials for Chitin

As one of the extremely rich polysaccharides in the world, chitin is also a material with excellent biocompatibility, biodegradable, green, non-toxic and harmless properties, so the new materials made of chitin are widely used in the food industry, biomedicine, health and safety and other fields. The development of new chitin materials has inhibited the use of traditional petroleum-based materials to some extent and improved the ecological environment. However, due to the defects of traditional extraction methods, the industrialization scale and commercialization of chitin are still in a bottleneck stage. This is due to the preparation of chitin new materials generally need to dissolve and extract it, and chitin has a high molecular weight, the existence of a large number of strong intermolecular hydrogen bonds makes it crystallinity high. At the same time, the structure of chitin is also relatively special due to its regular intermolecular arrangement, so chitin is difficult to melt and does not dissolve in water, alkaline solvents and most organic solvents.Therefore, in order to avoid the processing problems caused by chitin extraction, direct extraction and dissolved chitin extraction are introduced to prepare new materials, and proposed the development prospect of chitin.

The role of resveratrol on rheumatoid arthritis: From bench to bedside.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by symmetrical polyarthritis as its main clinical manifestation. Uncontrolled RA eventually leads to joint deformities and loss of function. Currently, the pathogenesis of RA remains under discussion, and RA treatment is still at the bottleneck stage. Resveratrol has long been regarded as a potential antioxidant drug for RA treatment. Currently, resveratrol is considered to exert therapeutic effects on RA by activating silent information regulator 1 (SIRT1) and its downstream pathways. There is notable crosstalk between the SIRT1 and NF-κB pathways, and these pathways, which play an essential role in the development of RA, are unexpectedly linked to the influence of resveratrol. Based on recent studies of almost all the pathways that resveratrol can affect, this review summarizes a regulatory chain of core components that cover multiple tracks. We also list the effects of resveratrol on immune cells and other subtle controls, which can help clinicians understand the known mechanism of resveratrol and better treat patients with RA.

Numerical Evaluation of Novel Bio‐Photovoltaic Devices Inspired by Photosynthetic Process and High Poly (3,4‐Ethylenedioxythiophene): Poly (styrenesulfonate) Efficacy as Hole Transport Layer

Organic‐based photosystem I (PSI) protein complex bio‐solar cells have appeared as strong candidates for very low‐cost and environmentally friendly photovoltaic devices. The main challenge is their low power conversion efficiency (PCE). Such bottleneck stage in efficiency could be improved from the aspects of the absorber layer and carrier transporting layers. The proposed efficient, eco‐friendly, and simple procedure by applying a uniform pulsed electric field to align PSI dipoles leads to improved significant efficiency. Herein, the experimentally found functional data to benchmark cells have been employed to validate the first PSI numerical model using solar cell capacitance simulator‐1D for the first time. Simulation results show a reasonable agreement with those of experimental. The performance response of the second indium tin oxide/PEDOT: PSS/PSI/Au model is evaluated by varying the physical parameters of the hole transport layer (HTL). PCE characteristic achieved a fourfold increment in the crucial conduction band offset region owing to the reasonable barrier formation of electrons in the HTL/absorber interface. The optimum doping concentration is authorized at a certain μp > 10−2 cm2 V−1 S−1 with an HTL thickness in the order of 102 nm estimating an efficient numerical model compared to the HTL‐free cell.

DRAM-CAM: General-Purpose Bit-Serial Exact Pattern Matching

Exact pattern matching is a widely used kernel in many applications. A DRAM-based processing-in-memory (PIM) architecture, Sieve, was recently proposed to alleviate the bottleneck stage of sequence matching in genomics. This paper observes that other exact-pattern-matching-intensive workloads can benefit from a similar architecture. We extend Sieve with several cost-effective modifications, such as a population count logic, chip-level parallelism support, and a hardware data transposition unit, making a general-purpose DRAM-CAM and key-value store that outperforms both CPU and various PIM solutions.

Influence of an R&D lot on productivity in semiconductor manufacturing

Both high productivity of finished goods and new product development are important for manufacturing firms to compete in a market successfully. High productivity is closely related to near-term profit, while new product development is imperative for long-term competitiveness. In a semiconductor fabrication facility, the final stage of new product development is testing new R&D lots in a real mass production environment. Because the test shares the same facility of mass production, if the R&D lots are processed to prioritize fast product development, it negatively influences the productivity of the lines. This study develops a stochastic model to analyze how the flow speed of R&D lots affects starvation of a bottleneck stage and the throughput of regular production lots on a semiconductor fabrication line. The model calculates the distributions of the delay time of R&D lots and the starvation time of the bottleneck stage under a given flow control policy of the R&D lots. Numerical examples are provided to illustrate how this model can be used on actual manufacturing lines to determine appropriate speeds of R&D lots.

Queueing-Theoretic Performance Analysis of a Low-Entropy Labeled Network Stack

Theoretical modeling is a popular method for quantitative analysis and performance prediction of computer systems, including cloud systems. Low entropy cloud (i.e., low interference among workloads and low system jitter) is becoming a new trend, where the Labeled Network Stack (LNS) based server is a good case to gain orders of magnitude performance improvement compared to servers based on traditional network stacks. However, it is desirable to figure out 1) where the low tail latency and the low entropy of LNS mainly come from, compared with mTCP, a typical user-space network stack in academia, and Linux network stack, the mainstream network stack in industry, and 2) how much LNS can be further optimized. Therefore, we propose a queueing theory-based analytical method defining a bottleneck stage to simplify the quantitative analysis of tail latency. Facilitated by the analytical method, we establish models characterizing the change of processing speed in different stages for an LNS-based server, an mTCP-based server, and a Linux-based server, with bursty traffic as an example. Under such traffic, each network service stage's processing speed is obtained by non-intrusive basic tests to identify the slowest stage as the bottleneck according to traffic and system characteristics. Our models reveal that the full-datapath prioritized processing and the full-path zero-copy are primary sources of the low tail latency and the low entropy of the LNS-based server, with 0.8%-24.4% error for the 9 9 t h percentile latency. In addition, the model of the LNS-based server can give the best number of worker threads querying a database, improving 2.1 × -3.5 × in concurrency.

Multiscale modeling of plasma–surface interaction—General picture and a case study of Si and SiO2 etching by fluorocarbon-based plasmas

The physics and chemistry of plasma–surface interaction is a broad domain relevant to various applications and several natural processes, including plasma etching for microelectronics fabrication, plasma deposition, surface functionalization, nanomaterial synthesis, fusion reactors, and some astrophysical and meteorological phenomena. Due to their complex nature, each of these processes is generally investigated in separate subdomains, which are considered to have their own theoretical, modeling, and experimental challenges. In this review, however, we want to emphasize the overarching nature of plasma–surface interaction physics and chemistry, by focusing on the general strategy for its computational simulation. In the first half of the review, we provide a menu card with standard and less standardized computational methods to be used for the multiscale modeling of the underlying processes. In the second half, we illustrate the benefits and potential of the multiscale modeling strategy with a case study of Si and SiO2 etching by fluorocarbon plasmas and identify the gaps in knowledge still present on this intensely investigated plasma–material combination, both on a qualitative and quantitative level. Remarkably, the dominant etching mechanisms remain the least understood. The resulting new insights are of general relevance, for all plasmas and materials, including their various applications. We therefore hope to motivate computational and experimental scientists and engineers to collaborate more intensely on filling the existing gaps in knowledge. In this way, we expect that research will overcome a bottleneck stage in the development and optimization of multiscale models, and thus the fundamental understanding of plasma–surface interaction.