Contemporary strategies and approaches for characterizing composition and enhancing biofilm penetration targeting bacterial extracellular polymeric substances

Extracellular polymeric substances(EPS)constitutes crucial elements within bacterial biofilms,facili-tating accelerated antimicrobial resistance and conferring defense against the host's immune cells.Developing precise and effective antibiofilm approaches and strategies,tailored to the specific charac-teristics of EPS composition,can offer valuable insights for the creation of novel antimicrobial drugs.This,in turn,holds the potential to mitigate the alarming issue of bacterial drug resistance.Current analysis of EPS compositions relies heavily on colorimetric approaches with a significant bias,which is likely due to the selection of a standard compound and the cross-interference of various EPS compounds.Considering the pivotal role of EPS in biofilm functionality,it is imperative for EPS research to delve deeper into the analysis of intricate compositions,moving beyond the current focus on polymeric materials.This ne-cessitates a shift from heavy reliance on colorimetric analytic methods to more comprehensive and nuanced analytical approaches.In this study,we have provided a comprehensive summary of existing analytical methods utilized in the characterization of EPS compositions.Additionally,novel strategies aimed at targeting EPS to enhance biofilm penetration were explored,with a specific focus on high-lighting the limitations associated with colorimetric methods.Furthermore,we have outlined the challenges faced in identifying additional components of EPS and propose a prospective research plan to address these challenges.This review has the potential to guide future researchers in the search for novel compounds capable of suppressing EPS,thereby inhibiting biofilm formation.This insight opens up a new avenue for exploration within this research domain.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

A new method of characterizing equivalent strain for equal channel angular processing

In order to establish the quantitative relationship between equivalent strain and the performance index of the deformed material within the range of certain passes for equal channel angular processing （ECAP）, a new approach to characterize the equivalent strain was proposed. The results show that there exists better accordance between mechanical property （such as hardness or strength） and equivalent strain after rolling and ECAP in a certain range of deformation amount, and Gauss equation can be satisfied among the equivalent strain and the mechanical properties for ECAP. Through regression analysis on the data of hardness and strength after the deformation, a more generalized expression of equivalent strain for ECAP is proposed as：ε=k0exp[-（k1M-k2）^2], where M is the strength or hardness of the material, k1 is the modified coefficient （k1∈ （0, 1））, ko and k2 are two parameters dependent on the critical strain and mechanical property that reaches saturation state for the material, respectively. In this expression the equivalent strain for ECAP is characterized novelly through the mechanical parameter relating to material property rather than the classical geometry equation.

Experimental and Simulation Analysis of Two-Tone and Three-Tone Photodetector Linearity Characterizing Systems

Two measurement techniques are investigated to characterize photodetector linearity. A model for the two-tone and three-tone photodetector systems is developed to thoroughly investigate the influences of setup components on the measurement results. We demonstrate that small bias shifts from the quadrature point of the modulator will induce deviation into measurement results of the two-tone system, and the simulation results correspond well to experimental and calculation results.

An optimized fitting function with least square approximation in InAs/AlSb HFET small-signal model for characterizing the frequency dependency of impact ionization effect

An enhanced small-signal model is introduced to model the influence of the impact ionization effect on the performance of In As/Al Sb HFET, in which an optimized fitting function D（ωτi） in the form of least square approximation is proposed in order to further enhance the accuracy in modeling the frequency dependency of the impact ionization effect.The enhanced model with D（ωτi） can accurately characterize the key S parameters of In As/Al Sb HFET in a wide frequency range with a very low error function EF. It is demonstrated that the new fitting function D（ωτi） is helpful in further improving the modeling accuracy degree.

APPLICATION OF SURFACE-ENHANCED RAMAN SPECTORSCOPY FOR CHARACTERIZING HUMIC SUBSTANCES

Surface-enhanced Raman Spectroscopy(SERS)has been used to characterize hurnic substances.By using HNO_3 -roughened silver foils as the enhancing surface,both fluorescence quenching and strong SERS signals have been observed at the same time.The results obtained herein show that SERS is a particularly sensitive and highly selective technique for the characterization of humic substances in the environment. Humic substances axe receiving ever-inereasing attention because of their importance in the environment. Very recently,we have reported the first successful application of Raman spectroscopy for characterizing humic substances.In this present paper,the application of surface-enhanced Raman spectroscopy(SERS)for humic substances has further been investigated.To our knowledge,no such studies have previously been reported.The results obtained herein dearly demonstrate that SERS has a great deal of promise as an analytical method for“In Situ”characterizing humic substances in the environment.

Characterizing Internet Backbone Traffic Based on Deep Packets Inpection and Deep Flows Inspection

Based on the massive data collected with a passive network monitoring equipment placed in China＇s backbone, we present a deep insight into the network backbone traffic and evaluate various ways for inproving traffic classifying efficiency in this pa- per. In particular, the study has scrutinized the net- work traffic in terms of protocol types and signatures, flow length, and port distffoution, from which mean- ingful and interesting insights on the current Intemet of China from the perspective of both the packet and flow levels are derived. We show that the classifica- tion efficiency can be greatly irrproved by using the information of preferred ports of the network applica- tions. Quantitatively, we find two traffic duration thresholds, with which 40% of TCP flows and 70% of UDP flows can be excluded from classification pro- cessing while the in^act on classification accuracy is trivial, i.e., the classification accuracy can still reach a high level by saving 85% of the resources.

Modeling and Characterizing Internet Backbone Traffic

With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.

FORWARD GATED-DIODE R-G CURRENT METHOD: A SIMPLE NOVEL TECHNIQUE FOR CHARACTERIZING LATERAL LIGHTLY DOPING REGION OF LDD MOSFET's

This paper presents a simple novel technique-forward gated-diode R-G current method-to determine the lateral lightly-doped source/drain (S/D) region interface state density and effective surface doping concentration of the lightly-doped drain (LDD) N- MOSFET's simultaneously. One interesting result of the numerical analysis is the direct characterization of the interface state density and characteristic gate voltage values corresponding to LDD effective surface doping concentration. It is observed that the S/D N- surface doping concentration and corresponding region's interface state density are R-G current peak position and amplitude dependent, respectively. It is convincible that the proposed method is well suitable for the characterization of deep sub-micron MOSFET's in the current ULSI technology.

Adaptive sampling strategy for characterizing spatial distribution of soil liquefaction potential using cone penetration test

Characterizing spatial distribution of soil liquefaction potential is critical for assessing liquefactionrelated hazards(e.g.building damages caused by liquefaction-induced differential settlement).However,in engineering practice,soil liquefaction potential is usually measured at limited locations in a specific site using in situ tests,e.g.cone penetration tests(CPTs),due to the restrictions of time,cost and access to subsurface space.In these cases,liquefaction potential of soil at untested locations requires to be interpreted from limited measured data points using proper interpolation method,leading to remarkable statistical uncertainty in liquefaction assessment.This underlines an important question of how to optimize the locations of CPT soundings and determine the minimum number of CPTs for achieving a target reliability level of liquefaction assessment.To tackle this issue,this study proposes a smart sampling strategy for determining the minimum number of CPTs and their optimal locations in a selfadaptive and data-driven manner.The proposed sampling strategy leverages on information entropy and Bayesian compressive sampling(BCS).Both simulated and real CPT data are used to demonstrate the proposed method.Illustrative examples indicate that the proposed method can adaptively and sequentially select the required number and optimal locations of CPTs.

Three-dimensional analysis technology for accurately characterizing non-metallic inclusion particles in steel

In this paper, a kind of three-dimensional analysis technology for characterizing non-metallic inclusions in steel was clearly elaborated. It is an electron microscopy observation, namely the non-aqueous electrolysis extraction method with a settled coulometer. In the research,the extraction effects of non-metallic inclusions in different electrolysis systems were studied, and it was concluded that alkalescent 2% TEA non-aqueous electrolyte was applicable for extracting most of non-metallic inclusion particles in steel. And then, in order to ensure the microscopic characterization and statistical calculation of inclusion particles, some electrolysis parameters should be confirmed, such as the size of the sample, control of the electrolysis mass, electric current, etc. Furthermore, for preventing the disturbance of carbides and presenting clear three-dimensional appearance by microscopic characterization, magnetic separation was utilized to separate the inclusion particles from carbides, which was useful for getting more veracious types, particle sizes and chemical composition of inclusions. Moreover, through calculation of quantity and particle size of inclusions in continuous determinate fields, the total quantity per unit volume or area and the particle size distribution of inclusions could be acquired by conversion with electrolysis loss. Besides, the comparison between this method and traditional quantitative metallography was also discussed, and finally, a conclusion was drawn that both of them have respectively applications in characterizing inclusions.

Characterizing Poroelasticity of Biological Tissues by Spherical Indentation: An Improved Theory for Large Relaxation

Flow of fluids within biological tissues often meets with resistance that causes a rate-and size-dependent material behavior known as poroelasticity.Characterizing poroelasticity can provide insight into a broad range of physiological functions,and is done qualitatively in the clinic by palpation.Indentation has been widely used for characterizing poroelasticity of soft materials,where quantitative interpretation of indentation requires a model of the underlying physics,and such existingmodels are well established for cases of small strain and modest force relaxationWe showed here that existing models are inadequate for large relaxation,where the force on the indenter at a prescribed depth at long-time scale drops to below half of the initially peak force.We developed an indentation theory for such cases of large relaxation,based upon Biot theory and a generalized Hertz contact model.We demonstrated that proposed theory is suitable for biological tissues(e.g.,spleen,kidney,skin and human cirrhosis liver)with both small and large relaxations.The proposed method would be a powerful tool to characterize poroelastic properties of biological materials for various applications such as pathological study and disease diagnosis.

Characterizing Constituents of Sediment Phosphorus Fractionation in a Freshwater Shallow Lake System

This study aimed to characterize constituents of phosphorus （P） fractionation as wel as reciprocities among factors in sediments of a freshwater shal ow lake. [Method] Surface sediment was discretely sampled at 24 sites through Van Veen grabs. Based on a modified sequential extraction scheme, P fractionation was determined as Fe/Al bound P （Fe/Al-P）, Ca bound P （Ca-P）, solute and re-ductive P （S/R-P） and organic P （OP）. Curve estimation and Pearson product-mo-ment correlation were employed for statistical analysis. [Result] Total P （TP） content ranged from 443 to 774 mg/kg. Inorganic P （IP） was the major component of TP, of which Ca-P was dominated with an average of 51%±9.7%. Average contents of P fractionation were in the fol owing order： Ca-P（51%） 〉 OP（29%） 〉 S/R-P（8%） 〉Fe/Al-P（7%）. The molar ratio of Fe to P was 11 - 20, close to the threshold value of P leaching. [Conclusion] In freshwater shal ow lakes, IP and Ca-P were prone to be relatively high, whereas Fe/Al-P was low compared with deep lakes. Occurrence of spatial y monotonic gradient indicated the primary causation of anthropogenic sources. Imminent sediment P liberation was also expected. Close associations a-mong TP, Fe/Al-P and Ca-P, implying that anthropogenic P was mainly bound to metals in particulates. Significant correlations of TOC and P fractionation highlighted endogenous mechanism and authigenic origin in sediments.

PhiBench 2.0: characterizing data analytics workloads on Intel Knights Landing

With high computational capacity, e.g. many-core and wide floating point SIMD units, Intel Xeon Phi shows promising prospect to accelerate high-performance computing(HPC) applications. But the application of Intel Xeon Phi on data analytics workloads in data center is still an open question. Phibench 2.0 is built for the latest generation of Intel Xeon Phi(KNL, Knights Landing), based on the prior work PhiBench(also named BigDataBench-Phi), which is designed for the former generation of Intel Xeon Phi(KNC, Knights Corner). Workloads of PhiBench 2.0 are delicately chosen based on BigdataBench 4.0 and PhiBench 1.0. Other than that, these workloads are well optimized on KNL, and run on real-world datasets to evaluate their performance and scalability. Further, the microarchitecture-level characteristics including CPI, cache behavior, vectorization intensity, and branch prediction efficiency are analyzed and the impact of affinity and scheduling policy on performance are investigated. It is believed that the observations would help other researchers working on Intel Xeon Phi and data analytics workloads.

Characterizing Lake Bottom Sediments Using Marine Geophysical Tools

Missouri is a state with rich karst terrain. Geotechnical evaluation of foundation design for bridges and dams requires an understanding of the characteristics of subsurface geological environment, including sediments, bedrock and benthic habitat. It is crucial that the community empowers itself with the knowledge of the karst system’s characteristics in order to potentially use it as a source of water and drainage, but also to avoid the disaster of building constructions too close to vulnerable land on top of massive karst caverns. Electrical resistivity tomography profiling (underwater cables), and continuous resistivity profiling (towed cable) surveys were conducted to characterize the lake sediments (rock and soil) beneath the man-made Little Prairie Lake, in Central of Missouri State, United States. Electrical resistivity (with marine cables and towed cable) was used to determine variability in the lithology and thickness of sediments (soil and rock) beneath the lake with conjunction of echo sounder in order to calculate water depth. Side scan sonar was used to map the variations in the lithology/nature of exposed lakebed sediments and to locate the potential hazard of trees. On land, electrical resistivity tomography was used with multi-channel analysis of surface wave method to determine sediments, joints, and the depth of bedrock. Analyses of the acquired data revealed the location and orientation of the original stream channels (prior to the construction of the earth fill dam). Underwater electrical resistivity tomography and continuous resistivity profiling determined joints, sediments, and bedrock underneath water bodies. Integrated marine geophysical tools help to evaluate the subsurface prior to any construction project (dam or bridge), are useful in determining the characteristics of lithology (fractured rock, intact rock and soil), and make it possible to map benthic habitat and the submerged potential hazards of trees on the lakebed as well as accurately measuring water depth.

Characterizing big data analytics workloads on POWER8 SMT processors

Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.

Editorial

In pursuit of higher energy density,lower cost,longer lifespan and safety,remarkable research efforts have been taken to innovate various types of energy storage materials/devices,especially metal-ion batteries such as Li-ion batteries(LIBs).One of the major challenges is to elucidate the working mechanisms and/or the controlling factors of any new material in a full battery,which requires adequate characterization/diagnosis techniques.Among the numerous electrochemical ex-situ and insitu characterization techniques,magnetic resonance techniques,including nuclear magnetic resonance(NMR),magnetic resonance imaging(MRI)and electron paramagnetic resonance(EPR),are unique in terms of providing structural information at the atomic level and real-time phase and morphology evolution and characterizing ionic motion at various timescales.This special issue is dedicated to an editorial and a selection of papers on the theme of investigating energy storage materials/devices using magnetic resonance techniques.As the guest editors of this special issue,we are honored to introduce the following high-quality research articles and review articles.

Three novel umami peptides derived from the alcohol extract of the Pacific oyster(Crassostrea gigas):identification,characterizations and interactions with T1R1/T1R3 taste receptors

Oyster(Crassostrea gigas),the main ingredient of oyster sauce,has a strong umami taste.In this study,three potential umami peptides,FLNQDEEAR(FR-9),FNKEE(FE-5),and EEFLK(EK-5),were identified and screened from the alcoholic extracts of the oyster using nano-HPLC-MS/MS analysis,i Umami-Scoring Card Method(i Umami-SCM)database and molecular docking(MD).Sensory evaluation and electronic tongue analysis were further used to confirm their tastes.The threshold of the three peptides ranged from 0.38 to 0.55 mg/m L.MD with umami receptors T1R1/T1R3 indicated that the electrostatic interaction and hydrogen bond interaction were the main forces involved.Besides,the Phe592 and Gln853 of T1R3 were the primary docking site for MD and played an important role in umami intensity.Peptides with two Glu residues at the terminus had stronger umami,especially at the C-terminus.These results contribute to the understanding of umami peptides in oysters and the interaction mechanism between umami peptides and umami receptors.

Characterization, preparation, and reuse of metallic powders for laser powder bed fusion: a review

Laser powder bed fusion(L-PBF) has attracted significant attention in both the industry and academic fields since its inception, providing unprecedented advantages to fabricate complex-shaped metallic components. The printing quality and performance of L-PBF alloys are infuenced by numerous variables consisting of feedstock powders, manufacturing process,and post-treatment. As the starting materials, metallic powders play a critical role in infuencing the fabrication cost, printing consistency, and properties. Given their deterministic roles, the present review aims to retrospect the recent progress on metallic powders for L-PBF including characterization, preparation, and reuse. The powder characterization mainly serves for printing consistency while powder preparation and reuse are introduced to reduce the fabrication costs.Various powder characterization and preparation methods are presented in the beginning by analyzing the measurement principles, advantages, and limitations. Subsequently, the effect of powder reuse on the powder characteristics and mechanical performance of L-PBF parts is analyzed, focusing on steels, nickel-based superalloys, titanium and titanium alloys, and aluminum alloys. The evolution trends of powders and L-PBF parts vary depending on specific alloy systems, which makes the proposal of a unified reuse protocol infeasible. Finally,perspectives are presented to cater to the increased applications of L-PBF technologies for future investigations. The present state-of-the-art work can pave the way for the broad industrial applications of L-PBF by enhancing printing consistency and reducing the total costs from the perspective of powders.

Enhancing the resolution of sparse rock property measurements using machine learning and random field theory

The travel time of rock compressional waves is an essential parameter used for estimating important rock properties,such as porosity,permeability,and lithology.Current methods,like wireline logging tests,provide broad measurements but lack finer resolution.Laboratory-based rock core measurements offer higher resolution but are resource-intensive.Conventionally,wireline logging and rock core measurements have been used independently.This study introduces a novel approach that integrates both data sources.The method leverages the detailed features from limited core data to enhance the resolution of wireline logging data.By combining machine learning with random field theory,the method allows for probabilistic predictions in regions with sparse data sampling.In this framework,12 parameters from wireline tests are used to predict trends in rock core data.The residuals are modeled using random field theory.The outcomes are high-resolution predictions that combine both the predicted trend and the probabilistic realizations of the residual.By utilizing unconditional and conditional random field theories,this method enables unconditional and conditional simulations of the underlying high-resolution rock compressional wave travel time profile and provides uncertainty estimates.This integrated approach optimizes the use of existing core and logging data.Its applicability is confirmed in an oil project in West China.