Enhancement of spatial resolution of ghost imaging via localizing and thresholding

In ghost imaging, an illumination light is split into test and reference beams which pass through two different optical systems respectively and an image is constructed with the second-order correlation between the two light beams. Since both light beams are diffracted when passing through the optical systems, the spatial resolution of ghost imaging is in general lower than that of a corresponding conventional imaging system. When Gaussian-shaped light spots are used to illuminate an object, randomly scanning across the object plane, in the ghost imaging scheme, we show th√at by localizing central positions of the spots of the reference light beam, the resolution can be increased by a factor of 2^(1/2) same as that of the corresponding conventional imaging system. We also find that the resolution can be further enhanced by setting an appropriate threshold to the bucket measurement of ghost imaging.

Arterial stimulation with simultaneous venous sampling for localizing insulinomas

Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.

A wireless magnetic sensor for localizing in-vivo medical micro-devices

In order to measure the position and orientation of in-vivo medical micro-devices without the line-of- sight constraints, a wireless magnetic sensor is developed for an electromagnetic localization method. In the electromagnetic localization system, the wireless magnetic sensor is embedded in the micro-devices to measure alternating magnetic signals. The wireless magnetic sensor is composed of an induction coil, a signal processor, a radio frequency （R.F） transmitter, a power manager and batteries. Based on the principle of electromagnetic induction, the induction coil converts the alternating magnetic signals into electrical signals. Via the RF transmitter, the useful data am wirelessly sent outside the body. According to the relation between the magnetic signals and the location, the position and orientation of the micro-devices can be calculated. The experiments demonstrate the feasibility of localizing in-vivo medical micro-devices with the wireless magnetic sensor. The novel localization system is accurate and robust.

A TDOA-Based Ultrasonic Absolute Localizing System of a Rail Robot in Greenhouse

In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization system comes from the usage of beacon systems each of which is composed of an RF single receiver and an ultrasonic transmitter.The RF single receiver gets the synchronization signal from the mobile robot,and the ultrasonic transmitter sends ultrasonic signal,thus the distance from the beacon to the ultrasonic receiver can be measured.The position of a beacon in coordinate system of robot can be calculated according to distance information from the beacons to two ultrasonic receivers which are mounted on the robot.Based on the coordinate transformation,the position of a mobile robot can be calculated from the beacon＇s absolute position information in the global coordinate system.Experiments demonstrate the effectiveness of the proposed method in real world applications.

Localizing Jammer in an Indoor Environment by Estimating Signal Strength and Kalman Filter

Localizing a jammer in an indoor environment in wireless sensor networks becomes a significant research problem due to the ease of blocking the communication between legitimate nodes. An adversary may emit radio frequency to prevent the transmission between nodes. In this paper, we propose detecting the position of the jammer indoor by using the received signal strength and Kalman filter (KF) to reduce the noise due to the multipath signal caused by obstacles in the indoor environment. We compare our work to the Linear Prediction Algorithm (LP) and Centroid Localization Algorithm (CL). We observed that the Kalman filter has better results when estimating the distance compared to other algorithms.

Identifying and localizing of seismogenic electromagnetic anomalies from data observed by permanent MT stations

In this research, an arbitrarily oriented electric dipole at subsurface is used to simulate Seismogenic Electromagnetic(SEM) radiation emanating from a seismic zone during its gestation phase. Analytical synthesis of responses at the Lijiang magnetotelluric(MT) station has revealed that SEM radiation could induce identifiable anomalies in the electromagnetic(EM)spectrum, apparent resistivity and phase within specific frequency bands. Background variations were extracted from long-term observation data of Dali and Lijiang MT stations, enabling the identification of SEM anomalies related to the Yunlong and Yangbi earthquakes. Multiple parameters of dipole sources at subsurface were obtained by applying the Differential Ant Colony Optimization(DACO) algorithm to anomalous data of two stations with multi-frequencies and various response functions. The spatial distribution of these predicted dipoles is predominantly clustered in or around the seismogenic area, with their azimuthal orientation aligning towards the seismogenic fault in general. This study has demonstrated the potential of using subsurface electric dipole simulations for SEM radiation analysis, offering a feasible approach for the prediction and understanding of seismogenic zones.

Swarm intelligence based clustering and localizing methods for loitering munitions in a satellite denied environment

In the networking of loitering munitions during a battle,clustering and localizing algorithms become a major problem because of their highly dynamic topological structure,incomplete connectivity,and limited energy.This paper proposed swarm intelligence based collaborative localizing,clustering,and routing scheme for an ad hoc network of loitering munitions in a satellite denied environment.A hybrid algorithm was first devised by integrating an improved coyote optimization algorithm with a simplified grey wolf optimizer under the sinusoidal crossover strategy.The performance of this algorithm was considerably improved thanks to integration.On this basis,a swarm intelligence based localizing algorithm was presented.Bounding cubes were created to reduce the initial search space,which effectively lowered the localizing error.Second,an energysaving clustering algorithm based on the hybrid algorithm was put forward to enhance the clustering efficiency by virtue of grey wolf hierarchy.Meanwhile,an analysis model was developed to determine the optimal number of clusters using the lowest possible number of transmissions.Ultimately,a compressed sensing routing scheme based on the hybrid algorithm was proposed to transmit data from a cluster head to a base station.This algorithm constructed an efficient routing tree from the cluster head to the base station,so as to reduce the routing delay and transmission count.As revealed in the results of simulation experiments,the proposed collaborative localizing,clustering and routing algorithms achieved better performance than other popular algorithms employed in various scenarios.

A multi-sensor relation model for recognizing and localizing faults of machines based on network analysis

Recently,advanced sensing techniques ensure a large number of multivariate sensing data for intelligent fault diagnosis of machines.Given the advantage of obtaining accurate diagnosis results,multi-sensor fusion has long been studied in the fault diagnosis field.However,existing studies suffer from two weaknesses.First,the relations of multiple sensors are either neglected or calculated only to improve the diagnostic accuracy of fault types.Second,the localization for multi-source faults is seldom investigated,although locating the anomaly variable over multivariate sensing data for certain types of faults is desirable.This article attempts to overcome the above weaknesses by proposing a global method to recognize fault types and localize fault sources with the help of multi-sensor relations(MSRs).First,an MSR model is developed to learn MSRs automatically and further obtain fault recognition results.Second,centrality measures are employed to analyze the MSR graphs learned by the MSR model,and fault sources are therefore determined.The proposed method is demonstrated by experiments on an induction motor and a centrifugal pump.Results show the proposed method’s validity in diagnosing fault types and sources.

Video Games Localization into Arabic:Gamers’Reactions to Localizing PUBG and Free Fire

The Middle East and North Africa(MENA)region has an active gaming community,with Arab gamers being reliant on games produced in Europe,America,and Japan due to the lack of significant game production companies in the MENA region.This study explores the gamers’reactions to the localization process of two video games,namely PUBG and Free Fire.For data collection purposes,a five-point Likert scale questionnaire that consisted of 18 items and six constructs,namely need for subtitled games,technical aspects,language issues,language preference,attitudes to game localization,and future actions and recommendations,was designed to elicit the reactions of 112 participants.Upon analyzing the responses,the findings showed that the better the technical aspects and language issues of the games’performance,the more positive participants’attitudes to game localization.The study recommends that further research could be conducted on the localization of video games with different themes into Arabic.

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 on the Transmission Dynamics of the Omicron Variant of COVID-19 Using Nonlinear Mathematical Models

This research examines the transmission dynamics of the Omicron variant of COVID-19 using SEIQIcRVW and SQIRV models,considering the delay in converting susceptible individuals into infected ones.The significant delays eventually resulted in the pandemic’s containment.To ensure the safety of the host population,this concept integrates quarantine and the COVID-19 vaccine.We investigate the stability of the proposed models.The fundamental reproduction number influences stability conditions.According to our findings,asymptomatic cases considerably impact the prevalence of Omicron infection in the community.The real data of the Omicron variant from Chennai,Tamil Nadu,India,is used to validate the outputs.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Multi-Material Topology Optimization for Spatial-Varying Porous Structures

This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.

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.

Mechanistic investigation on Ag-Cu_(2)O in electrocatalytic CO_(2) to CH_(4) by in situ/operando spectroscopic and theoretical analysis

Silver-copper electrocatalysts have demonstrated effectively catalytic performance in electroreduction CO_(2) toward CH_(4),yet a revealing insight into the reaction pathway and mechanism has remained elusive.Herein,we construct chemically bonded Ag-Cu_(2)O boundaries,in which the complete reduction of Cu_(2)O to Cu has been strongly impeded owing to the presence of surface Ag shell.The interfacial confinement effect helps to maintain Cu^(+)sites at the Ag-Cu_(2)O boundaries.Using in situ/operando spectroscopy and theoretical simulations,it is revealed that CO_(2) is enriched at the Ag-Cu_(2)O boundaries due to the enhanced physisorption and chemisorption to CO_(2),activating CO_(2) to form the stable intermediate^(*)CO.The boundaries between Ag shell and the Cu_(2)O mediate local^(*)CO coverage and promote^(*)CHO intermediate formation,consequently facilitating CO_(2)-to-CH_(4) conversion.This work not only reveals the structure-activity relationships but also offers insights into the reaction mechanism on Ag-Cu catalysts for efficient electrocatalytic CO_(2) reduction.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

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.

Biodiversity responses to insular fragmentation in Amazonia: Two decades of research in the Balbina Hydroelectric Reservoir

As hydropower development expands across lowland tropical forests,flooding and concomitant insular fragmentation have become important threats to biodiversity.Newly created insular landscapes serve as natural laboratories to investigate biodiversity responses to fragmentation.One of these most iconic landscapes is the Balbina Hydroelectric Reservoir in Brazilian Amazonia,occupying>400000 ha and comprising>3500 forest islands.Here,we synthesise the current knowledge on responses of a wide range of biological groups to insular fragmentation at Balbina.Sampling has largely concentrated on a set of 22 islands and three mainland sites.In total,39 studies were conducted over nearly two decades,covering 17 vertebrate,invertebrate,and plant taxa.Although species responses varied according to taxonomic group,island area was consistently included and played a pivotal role in 66.7%of all studies examining patterns of species diversity.Species persistence was further affected by species traits,mostly related to species capacity to use/traverse the aquatic matrix or tolerate habitat degradation,as noted for species of vertebrates and orchid bees.Further research is needed to improve our understanding of such effects on wider ecosystem functioning.Environmental Impact Assessments must account for changes in both the remaining habitat amount and configuration,and subsequent long-term species losses.

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.