Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation,which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted.Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

Selective CO_(2)Electroreduction to Multi-Carbon Products on Organic-Functionalized CuO Nanoparticles by Local Micro-Environment Modulation

Surface functionalization of Cu-based catalysts has demonstrated promising potential for enhancing the electrochemical CO_(2)reduction reaction(CO_(2)RR)toward multi-carbon(C2+)products,primarily by suppressing the parasitic hydrogen evolution reaction and facilitating a localized CO_(2)/CO concentration at the electrode.Building upon this approach,we developed surface-functionalized catalysts with exceptional activity and selectivity for electrocatalytic CO_(2)RR to C_(2+)in a neutral electrolyte.Employing CuO nanoparticles coated with hexaethynylbenzene organic molecules(HEB-CuO NPs),a remarkable C_(2+)Faradaic efficiency of nearly 90%was achieved at an unprecedented current density of 300 mA cm^(-2),and a high FE(>80%)was maintained at a wide range of current densities(100-600 mA cm^(-2))in neutral environments using a flow cell.Furthermore,in a membrane electrode assembly(MEA)electrolyzer,86.14%FEC2+was achieved at a partial current density of 387.6 mA cm^(-2)while maintaining continuous operation for over 50 h at a current density of 200 mA cm^(-2).In-situ spectroscopy studies and molecular dynamics simulations reveal that reducing the coverage of coordinated K⋅H2O water increased the probability of intermediate reactants(CO)interacting with the surface,thereby promoting efficient C-C coupling and enhancing the yield of C_(2+)products.This advancement offers significant potential for optimizing local micro-environments for sustainable and highly efficient C_(2+)production.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Genome-wide investigation to assess copy number variants in the Italian local chicken population

Background Copy number variants(CNV)hold significant functional and evolutionary importance.Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock.High-density chips have enabled the detection of CNV with increased resolution,leading to the identification of even small CNV.This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome.Results Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV.The majority of CNV in the investigated breeds were of types 0 and 1,and the minimum length of CNV was significantly larger than that reported in previous studies.Interestingly,a high proportion of the length of chromosome 16 was covered by copy number variation regions(CNVR),with the major histocompatibility complex being the likely cause.Among the genes identified within CNVR,only those present in at least five animals across breeds(n=95)were discussed to reduce the focus on redundant CNV.Some of these genes have been associated to functional traits in chickens.Notably,several CNVR on different chromosomes harbor genes related to muscle development,tissue-specific biological processes,heat stress resistance,and immune response.Quantitative trait loci(QTL)were also analyzed to investigate potential overlapping with the identified CNVR:54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size,carcass characteristics,egg production,egg components,fat deposition,and feed intake.Conclusions The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

A study of the effect of local scour on the flow field near the spur dike

The flow field near a spur dike such as down flow and horseshoe vortex system(HVS)are susceptible to the topographic changes in the local scouring process,resulting in variation of the sediment transport with time.In this study,large eddy simulations with fixed-bed at different scouring stages were conducted to investigate the changes in flow field.The results imply that the bed deformation leads to an increase in flow rate per unit area,which represent the capability of sediment transportation by water,in the scour hole.Moreover,the intensity of turbulent kinetic energy and bimodal motion near the sand bed induced by the HVS were also varied.However,the peak moments between the two sediment transport mechanisms were different.Hence,understanding the complex feedback mechanism between topography and flow field is essential for the local scour problem.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Present situation and prospect of immunotherapy for unresectable locally advanced esophageal cancer during peri-radiotherapy

Four major studies(Checkmate577,Keynote-590,Checkmate649 and Attraction-4)of locally advanced esophageal cancer published in 2020 have established the importance of immunotherapy,represented by anti-programmed death protein(PD)-1 in postoperative adjuvant treatment and advanced first-line treatment of locally advanced or advanced esophageal cancer and esophagogastric junction cancer,from the aspects of proof of concept,long-term survival,overall survival rate and progression-free survival.For unresectable or inoperable nonmetastatic esophageal cancer,concurrent radiotherapy and chemotherapy is the standard treatment recommended by various guidelines.Because its curative effect is still not ideal,it is necessary to explore radical radiotherapy and chemotherapy in the future,and it is considered to be promising to combine them with immunotherapeutic drugs such as anti-PD-1.This paper mainly discusses how to combine radical concurrent radiotherapy and chemotherapy with immunotherapy for unresectable local advanced esophageal cancer.

Local dose-dense chemotherapy for triple-negative breast cancer via minimally invasive implantation of 3D printed devices

Dose-dense chemotherapy is the preferred first-line therapy for triple-negative breast cancer(TNBC),a highly aggressive disease with a poor prognosis.This treatment uses the same drug doses as conventional chemotherapy but with shorter dosing intervals,allowing for promising clinical outcomes with intensive treatment.However,the frequent systemic administration used for this treatment results in systemic toxicity and low patient compliance,limiting therapeutic efficacy and clinical benefit.Here,we report local dose-dense chemotherapy to treat TNBC by implanting 3D printed devices with timeprogrammed pulsatile release profiles.The implantable device can control the time between drug releases based on its internal microstructure design,which can be used to control dose density.The device is made of biodegradable materials for clinical convenience and designed for minimally invasive implantation via a trocar.Dose density variation of local chemotherapy using programmable release enhances anti-cancer effects in vitro and in vivo.Under the same dose density conditions,device-based chemotherapy shows a higher anticancer effect and less toxic response than intratumoral injection.We demonstrate local chemotherapy utilizing the implantable device that simulates the drug dose,number of releases,and treatment duration of the dose-dense AC(doxorubicin and cyclophosphamide)regimen preferred for TNBC treatment.Dose density modulation inhibits tumor growth,metastasis,and the expression of drug resistance-related proteins,including p-glycoprotein and breast cancer resistance protein.To the best of our knowledge,local dose-dense chemotherapy has not been reported,and our strategy can be expected to be utilized as a novel alternative to conventional therapies and improve anti-cancer efficiency.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

Numerical study on local scour characteristics around submarine pipelines in the Yellow River Delta silty sandy soil under waves and currents

Due to their high reliability and cost-efficiency,submarine pipelines are widely used in offshore oil and gas resource engineering.Due to the interaction of waves,currents,seabed,and pipeline structures,the soil around submarine pipelines is prone to local scour,severely affecting their operational safety.With the Yellow River Delta as the research area and based on the renormalized group(RNG)k-εturbulence model and Stokes fifth-order wave theory,this study solves the Navier-Stokes(N-S)equation using the finite difference method.The volume of fluid(VOF)method is used to describe the fluid-free surface,and a threedimensional numerical model of currents and waves-submarine pipeline-silty sandy seabed is established.The rationality of the numerical model is verified using a self-built waveflow flume.On this basis,in this study,the local scour development and characteristics of submarine pipelines in the Yellow River Delta silty sandy seabed in the prototype environment are explored and the influence of the presence of pipelines on hydrodynamic features such as surrounding flow field,shear stress,and turbulence intensity is analyzed.The results indicate that(1)local scour around submarine pipelines can be divided into three stages:rapid scour,slow scour,and stable scour.The maximum scour depth occurs directly below the pipeline,and the shape of the scour pits is asymmetric.(2)As the water depth decreases and the pipeline suspension height increases,the scour becomes more intense.(3)When currents go through a pipeline,a clear stagnation point is formed in front of the pipeline,and the flow velocity is positively correlated with the depth of scour.This study can provide a valuable reference for the protection of submarine pipelines in this area.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Assessment of earthquake location uncertainties for the design of local seismic networks

The ability to estimate earthquake source locations,along with the appraisal of relevant uncertainties,is paramount in monitoring both natural and human-induced micro-seismicity.For this purpose,a monitoring network must be designed to minimize the location errors introduced by geometrically unbalanced networks.In this study,we first review different sources of errors relevant to the localization of seismic events,how they propagate through localization algorithms,and their impact on outcomes.We then propose a quantitative method,based on a Monte Carlo approach,to estimate the uncertainty in earthquake locations that is suited to the design,optimization,and assessment of the performance of a local seismic monitoring network.To illustrate the performance of the proposed approach,we analyzed the distribution of the localization uncertainties and their related dispersion for a highly dense grid of theoretical hypocenters in both the horizontal and vertical directions using an actual monitoring network layout.The results expand,quantitatively,the qualitative indications derived from purely geometrical parameters(azimuthal gap(AG))and classical detectability maps.The proposed method enables the systematic design,optimization,and evaluation of local seismic monitoring networks,enhancing monitoring accuracy in areas proximal to hydrocarbon production,geothermal fields,underground natural gas storage,and other subsurface activities.This approach aids in the accurate estimation of earthquake source locations and their associated uncertainties,which are crucial for assessing and mitigating seismic risks,thereby enabling the implementation of proactive measures to minimize potential hazards.From an operational perspective,reliably estimating location accuracy is crucial for evaluating the position of seismogenic sources and assessing possible links between well activities and the onset of seismicity.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

基于Local Cascade Ensemble方法的胎儿健康自动分类

为更好地自动评估胎儿宫内状态,提出一种基于local cascade ensemble(LCE)方法的胎儿健康状态分类模型。选用UCI数据集,使用ADASYN方法对不平衡数据集进行填充平衡,接着结合随机森林算法对数据特征进行选择,最后使用LCE方法对胎儿状态进行自动分类。实验结果表明,所提出模型使用的方法平均准确率、精确率、召回率和F1分数分别达到了0.9554、0.9054、0.9557和0.9290,对比传统的机器学习算法能得到更好的分类效果,有效降低了误判率。

Phase induced localization transition

Localization phenomenon is an important research field in condensed matter physics.However,due to the complexity and subtlety of disordered systems,new localization phenomena always emerge unexpectedly.For example,it is generally believed that the phase of the hopping term does not affect the localization properties of the system,so the calculation of the phase is often ignored in the study of localization.Here,we introduce a quasiperiodic model and demonstrate that the phase change of the hopping term can significantly alter the localization properties of the system through detailed numerical simulations,such as the inverse participation ratio and multifractal analysis.This phase-induced localization transition provides valuable information for the study of localization physics.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

In-situ AFM and quasi-in-situ studies for localized corrosion in Mg-9Al-1Fe-(Gd) alloys under 3.5 wt.% NaCl environment

Revealing the localized corrosion process of Mg alloy is considered as one of the most significant ways for improving its corrosion resistance.The reliable monitor should be high distinguishability and real-time in liquid environment.Herein,Mg-9Al-1Fe and Mg-9Al-1Fe-1Gd alloys were designed to highlight the impact of intermetallic on the corrosion behaviour.In-situ AFM with a special electrolyte circulation system and quasi-in-situ SEM observation were used to monitor the corrosion process of the designed alloys.SEM-EDS and TEM-SAED were applied to identify the intermetallic in the designed alloys,and their volta potentials were measured by SKPFM.According to the real-time and real-space in-situ AFM monitor,the corrosion process consisted of dissolution of anodicα-Mg phase,accumulation of corrosion products around cathodic phase and shedding of some fine cathodic phase.Then,the localized corrosion process of Mg alloy was revealed combined with the results of the monitor of corrosion process and Volta potential difference.

Experimental Study of Local Scour Around Four Piles Under Different Attack Angles and Gap Ratios

In an effort to investigate and quantify the patterns of local scour,researchers embarked on an in-depth study using a systematic experimental approach.The research focused on the effects of local scour around a set of four piles,each subjected to different hydromechanical conditions.In particular,this study aimed to determine how different attack angles—the angles at which the water flow impinges on the piles,and gap ratios—the ratios of the spacing between the piles to their diameters,influence the extent and nature of scour.A comprehensive series of 35 carefully designed experiments were orchestrated,each designed to dissect the nuances in how the gap ratio and attack angle might contribute to changes in the local scour observed at the base of pile groups.During these experimental trials,a wealth of local scour data were collected to support the analysis.These data included precise topographic profiles of the sediment bed around the pile groups,as well as detailed scour time histories showing the evolution of scour at strategic feature points throughout the test procedure.The analysis of the experimental data provided interesting insights.The study revealed that the interplay between the gap ratio and the attack angle had a pronounced influence on the scouring dynamics of the pile groups.One of the key observations was that the initial phases of scour,particularly within the first hour of water flow exposure,were characterized by a sharp increase in the scour depth occurring immediately in front of the piles.After this initial rapid development,the scour depth transitioned to a more gradual change rate.In contrast,the scour topography around the piles continuously evolved.This suggests that sediment displacement and the associated sculpting of the seabed around pile foundations are sustained and progressive processes,altering the underwater landscape over time.The results of this empirical investigation have significant implications for the design and construction of offshore multi-pile foundations,providing a critical reference for engineers and designers to estimate the expected scour depth around such structures,which is an integral part of decisions regarding foundation design,selection of structural materials,and implementation of scour protection measures.

Local tetragonal distortion of Pt alloy catalysts for enhanced oxygen reduction reaction efficiency

Platinum-based alloy nanoparticles are the most attractive catalysts for the oxygen reduction reaction at present,but an in-depth understanding of the relationship between their short-range structural information and catalytic performance is still lacking.Herein,we present a synthetic strategy that uses transition-metal oxide-assisted thermal diffusion.PtCo/C catalysts with localized tetragonal distortion were obtained by controlling the thermal diffusion process of transition-metal elements.This localized structural distortion induced a significant strain effect on the nanoparticle surface,which further shortened the length of the Pt-Pt bond,improved the electronic state of the Pt surface,and enhanced the performance of the catalyst.PtCo/C catalysts with special short-range structures achieved excellent mass activity(2.27 Amg_(Pt)^(-1))and specific activity(3.34 A cm^(-2)).In addition,the localized tetragonal distortion-induced surface compression of the Pt skin improved the stability of the catalyst.The mass activity decreased by only 13% after 30,000 cycles.Enhanced catalyst activity and excellent durability have also been demonstrated in the proton exchange membrane fuel cell configuration.This study provides valuable insights into the development of advanced Pt-based nanocatalysts and paves the way for reducing noble-metal loading and increasing the catalytic activity and catalyst stability.