Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.

An efficient transient gene expression system for protein subcellular localization assay and genome editing in citrus protoplasts

Protoplast has been widely used in biotechnologies to circumvent the breeding obstacles in citrus, including long juvenility, polyembryony, and male/female sterility. The protoplast-based transient gene expression system is a powerful tool for gene functional characterization and CRISPR/Cas9 genome editing in higher plants, but it has not been widely used in citrus. In this study, the polyethylene glycol(PEG)-mediated method was optimized for citrus callus protoplast transfection, with an improved transfection efficiency of 68.4%. Consequently, the efficiency of protein subcellular localization assay was increased to 65.8%, through transient expression of the target gene in protoplasts that stably express the fluorescent organelle marker protein. The gene editing frequencies in citrus callus protoplasts reached 14.2% after transient expression of CRISPR/Cas9 constructs. We demonstrated that the intronic polycistronic tRNAgRNA(inPTG) genome editing construct was functional in both the protoplast transient expression system and epicotyl stable transformation system in citrus. With this optimized protoplast transient expression system, we improved the efficiency of protein subcellular localization assay and developed the genome editing system in callus protoplasts, which provides an approach for prompt test of CRISPR vectors.

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.

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.

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.

A Novel RFID Localization Approach to Smart Self-Service Borrowing and Returning System

The misreading problem of a passive ultra-high-frequency(UHF)radio frequency identification(RFID)tag is a frequent problem arising in the field of librarianship.Unfortunately,existing solutions are something inefficient,e.g.,extra resource requirement,inaccuracy,and empiricism.To this end,under comprehensive analysis on the passive UHF RFID application in the librarianship scenario,a novel and judicious approach based on RFID localization is proposed to address such a misreading problem.Extensive simulation results show that the proposed approach can outperform the existing ones and can be an attractive candidate in practice.

Dynamical analysis,geometric control and digital hardware implementation of a complex-valued laser system with a locally active memristor

In order to make the peak and offset of the signal meet the requirements of artificial equipment,dynamical analysis and geometric control of the laser system have become indispensable.In this paper,a locally active memristor with non-volatile memory is introduced into a complex-valued Lorenz laser system.By using numerical measures,complex dynamical behaviors of the memristive laser system are uncovered.It appears the alternating appearance of quasi-periodic and chaotic oscillations.The mechanism of transformation from a quasi-periodic pattern to a chaotic one is revealed from the perspective of Hamilton energy.Interestingly,initial-values-oriented extreme multi-stability patterns are found,where the coexisting attractors have the same Lyapunov exponents.In addition,the introduction of a memristor greatly improves the complexity of the laser system.Moreover,to control the amplitude and offset of the chaotic signal,two kinds of geometric control methods including amplitude control and rotation control are designed.The results show that these two geometric control methods have revised the size and position of the chaotic signal without changing the chaotic dynamics.Finally,a digital hardware device is developed and the experiment outputs agree fairly well with those of the numerical simulations.

Combining local regional therapy and systemic therapy:Expected changes in the treatment landscape of recurrent hepatocellular carcinoma

Improvements in early screening,new diagnostic techniques,and surgical treatment have led to continuous downward trends in hepatocellular carcinoma(HCC)morbidity and mortality rates.However,high recurrence and refractory cancer after hepatectomy remain important factors affecting the long-term prognosis of HCC.The clinical characteristics and prognosis of recurrent HCC are heterogeneous,and guidelines on treatment strategies for recurrent HCC are lacking.Therapies such as surgical resection,radiofrequency ablation,and transhepatic arterial chemoembolization are effective for tumors confined to the liver,and targeted therapy is a very important treatment for unresectable recurrent HCC with systemic metastasis.With the deepening of the understanding of the immune microenvironment of HCC,blocking immune checkpoints to enhance the antitumor immune response has become a new direction for the treatment of HCC.In addition,improvements in the tumor immune microenvironment caused by local treatment may provide an opportunity to improve the therapeutic effect of HCC treatment.Ongoing and future clinical trial data of combined therapy may develop the new treatment scheme for recurrent HCC.This paper reviews the pattern of recurrent HCC and the characteristics of the immune microenvironment,demonstrates the basis for combining local treatment and systemic treatment,and reports current evidence to better understand current progress and future approaches in the treatment of recurrent HCC.

A Proposed Architecture for Local-Host and AWS with Multiagent System

Cloud computing has become one of the leading technologies in the world today.The benefits of cloud computing affect end users directly.There are several cloud computing frameworks,and each has ways of monitoring and providing resources.Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability.From the user’s perspective,cloud computing makes it easy to upload multiagents and operate on different web services.In this paper,the authors used a restful web service and an agent system to discuss,deployments,and analysis of load performance parameters like memory use,cen-tral processing unit(CPU)utilization,network latency,etc.,both on localhost and an Amazon Web Service Elastic Cloud Computing(AWS-EC2)server.The Java Agent Development Environment(JADE)tool has been used to propose an archi-tecture and conduct a comparative study on both local and remote servers.JADE is an open-source tool for maintaining applications on AWS infrastructure.The focus of the study should be to reduce the complexity and time of load perfor-mance parameters by using an agent system on a cloud server instead of establish-ing a massive infrastructure on a local system,even for a small application.

Comparison between upfront surgery and neoadjuvant chemotherapy in patients with locally advanced gastric cancer:A systematic review

BACKGROUND Gastric cancer(GC)is a major health concern worldwide.Surgical resection and chemotherapy is the mainstay treatment for gastric carcinoma,however,the optimal approach remains unclear and should be different in each individual.Chemotherapy can be administered both pre-and postoperatively,but a multidisciplinary approach is preferred when possible.This is particularly relevant for locally advanced GC(LAGC),as neoadjuvant chemotherapy(NAT)could potentially lead to tumor downsizing thus allowing for a complete resection with curative intent.Even though the recent progress has been impressive,European and International guidelines are still controversial,thus attenuating the need for a more standardized approach in the management of locally advanced cancer.AIM To investigate the effects of NAT on the overall survival(OS),the disease-free survival(DFS),the morbidity and the mortality of patients with LAGC in comparison to upfront surgery(US).METHODS For this systematic review,a literature search was conducted between November and February 2023 in PubMed,Cochrane Library and clinicaltrials.gov for studies including patients with LAGC.Two independent reviewers conducted the research and extracted the data according to predetermined inclusion and exclusion criteria.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was used to form the search strategy and the study protocol has been registered in the International Prospective Register of Systematic Reviews.RESULTS Eighteen studies with 4839 patients with LAGC in total were included in our systematic review.Patients were separated into two groups;one receiving NAT before the gastrectomy(NAT group)and the other undergoing upfront surgery(US group).The OS ranged from 41.6%to 74.2%in the NAT group and from 30.9%to 74%in the US group.The DFS was also longer in the NAT group and reached up to 80%in certain patients.The complications related to the chemotherapy or the surgery ranged from 6.4%to 38.1%in the NAT group and from 5%to 40.5%in the US group.Even though in most of the studies the morbidity was lower in the NAT group,a general conclusion could not be drawn as it seems to depend on multiple factors.Finally,regarding the mortality,the reported rate was higher and up to 5.3%in the US group.CONCLUSION NAT could be beneficial for patients with LAGC as it leads to better OS and DFS than the US approach with the same or even lower complication rates.However,patients with different clinicopathological features respond differently to chemotherapy,therefore currently the treatment plan should be individualized in order to achieve optimal results.

Clinical evidence of neoadjuvant immunotherapy for resectable locally advanced esophageal carcinoma:A systematic review

Background:Immune checkpoint inhibitors(ICIs)as the neoadjuvant therapy for resectable locally advanced esophageal carcinoma(rlaEC)remains challenging given the poor reports of efficacy and safety.This study aimed to summarize reliable evidence for the preoperative neoadjuvant immunotherapy of rlaEc by analyzing all the published clinical trials on the ICIs as the neoadjuvant therapy for rlaEC.Methods:PubMed,Cochrane Library,Embase and ClinicalTrials.gov were searched from inception until June 1st,2023,for available reports to perform a meta-analysis.The primary endpoints were RO resection,objective response rate(ORR),pathological complete response(pCR)and major pathological response(MPR),as well as treatment-related adverse events(AEs)and postoperative complications.The Stata 14.0 software was employed to estimate pooled effect size.Results:A total of 18 single-arm clinical trials involving 625 patients met the inclusion criteria.Meta-analysis showed that,among these patients with rlaEC,the pooled R0 resection rate was 97.0%(95%CI:94.0%-99.0%),the p0oled ORR was 70.0%(95%CI:64.0%-76.0%),the p0oled pCR and MPR rate were 34.0%(95%CI:29.0%-39.0%)and 56.0%(95%CI:47.0%-65.0%)respectively.The incidence of main treatment-related AEs and postoperative complications was about 6%-45% and 8%-19% respectively.Conclusions:Patients with rlaEC were tolerated to neoadjuvant immunotherapy and it might be beneficial to improve efficacy.But this meta-analysis had limitations and the conclusions still needed to be validated by more rigorous phase II randomized controlled clinical trials.

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。

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.

Interplay between topology and localization on superconducting circuits

Topological insulators occupy a prominent position in the realm of condensed matter physics. Nevertheless, the presence of strong disorder has the potential to disrupt the integrity of topological states, leading to the localization of all states.This study delves into the intricate interplay between topology and localization within the one-dimensional Su–Schrieffer–Heeger(SSH) model, which incorporates controllable off-diagonal quasi-periodic modulations on superconducting circuits.Through the application of external alternating current(ac) magnetic fluxes, each transmon undergoes controlled driving,enabling independent tuning of all coupling strengths. Within a framework of this model, we construct comprehensive phase diagrams delineating regions characterized by extended topologically nontrivial states, critical localization, and coexisting topological and critical localization phases. The paper also addresses the dynamics of qubit excitations, elucidating distinct quantum state transfers resulting from the intricate interplay between topology and localization. Additionally, we propose a method for detecting diverse quantum phases utilizing existing experimental setups.