Generalization ability of a CNNγ-ray localization model for radiation imaging

Inγ-ray imaging,localization of theγ-ray interaction in the scintillator is critical.Convolutional neural network(CNN)techniques are highly promising for improvingγ-ray localization.Our study evaluated the generalization capabilities of a CNN localization model with respect to theγ-ray energy and thickness of the crystal.The model maintained a high positional linearity(PL)and spatial resolution for ray energies between 59 and 1460 keV.The PL at the incident surface of the detector was 0.99,and the resolution of the central incident point source ranged between 0.52 and 1.19 mm.In modified uniform redundant array(MURA)imaging systems using a thick crystal,the CNNγ-ray localization model significantly improved the useful field-of-view(UFOV)from 60.32 to 93.44%compared to the classical centroid localization methods.Additionally,the signal-to-noise ratio of the reconstructed images increased from 0.95 to 5.63.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Spatial modeling of the carbon stock of forest trees in Heilongjiang Province, China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.

FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION

In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested.This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.

Local icariin application enhanced periodontal tissue regeneration and relieved local inflammation in a minipig model of periodontitis

Periodontitis is an inflammatory autoimmune disease. Treatment should alleviate inflammation, regulate the immune reaction and promote periodontal tissue regeneration. Icariin is the main active ingredient of Epimedii Folium, and it is a promising compound for the enhancement of mesenchymal stem cell function, promotion of bone formation, inhibition of bone resorption, alleviation of inflammation and regulation of immunity. The study investigated the effect of icariin on periodontal tissue regeneration in a minipig model of periodontitis. The minipig model of periodontitis was established. Icariin was injected locally. The periodontal clinical assessment index, a computed tomography(CT) scan, histopathology and enzyme-linked immune sorbent assay(ELISA)were used to evaluate the effects of icariin. Quantitative analysis results 12 weeks post-injection demonstrated that probing depth,gingival recession, attachment loss and alveolar bone regeneration values were(3.72 ± 1.18) mm vs.(6.56 ± 1.47) mm,(1.67 ± 0.59)mm vs.(2.38 ± 0.61) mm,(5.56 ± 1.29) mm vs.(8.61 ± 1.72) mm, and(25.65 ± 5.13) mm3 vs.(9.48 ± 1.78) mm3 in the icariin group and0.9% NaCl group, respectively. The clinical assessment, CT scan, and histopathology results demonstrated significant enhancement of periodontal tissue regeneration in the icariin group compared to the 0.9% NaCl group. The ELISA results suggested that the concentration of interleukin-1 beta(IL-1β) in the icariin group was downregulated compared to the 0.9% NaCl group, which indicates that local injection of icariin relieved local inflammation in a minipig model of periodontitis. Local injection of icariin promoted periodontal tissue regeneration and exerted anti-inflammatory and immunomodulatory function. These results support the application of icariin for the clinical treatment of periodontitis.

INFLUENCE ANALYSIS IN NONLINEAR MODELS WITH RANDOM EFFECTS

In this paper,a unified diagnostic method for the nonlinear models with random effects based upon the joint likelihood given by Robinson in 1991 is presented.It is shown that the case deletion model is equivalent to the mean shift outlier model.From this point of view,several diagnostic measures,such as Cook distance,score statistics are derived.The local influence measure of Cook is also presented. A numerical example illustrates that the method is available.

APPROXIMATE POWER OF HETEROSCEDASTICITY TEST IN NONLINEAR MODELS WITH ARIMA（0,1,0） ERRORS

This paper presents an approach for estimating power of the score test, based on an asymptotic approximation to the power of the score test under contiguous alternatives. The method is applied to the problem of power calculations for the score test of heteroscedasticity in European rabbit data （Ratkowsky, 1983）. Simulation studies are presented which indicate that the asymptotic approximation to the finite-sample situation is good over a wide range of parameter configurations.

IAP General Circulation Models: A First Step Towards Developing a Local Area Model for Weather Prediction in Nigeria

In an earlier study, the Atmospheric Models Intercomparison Program (AMIP) simulations of African climate using the nine-layer gridpoint atmospheric general circulation model were found to be closely related to the observed European Centre for Medium Range Weather Forecast (ECMWF) temperature data at 500 and 850 hPa. This paper presents the analysis of the simulation of African climate using the Global Ocean-Atmosphere-Land System Model (IAP/LASG GOALS) and the nine-layer spectral general circulation model rhomboidally truncated at zonal wave number 15 (L9R15) developed at the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, Beijing. Both model simulations were not significantly different from the National Center for Environmental Prediction (NCEP) Reanalysis monthly mean data for 1980-1995 in the case of surface air temperature, sea level pressure and precipitation, with the GOALS reproducing the seasonal mean climate over Africa better. The implications of the encouraging results in developing a local area model for Nigeria have been discussed. The great role of topography in the developing of general circulation models for numerical modelling of weather and climate has been stressed.

An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.

A deformation measurement method based on surface texture information of rocks and its application

Users of the digital image correlation method are faced with the problem of poor operability,low repeatability,and lack of standardized specifications for spraying speckles.To solve the problem,the research proposed a rock deformation measurement method that obviates the need to spray speckles.A local binary model was established by using the local binary pattern(LBP)operator based on deep texture features on rock surfaces.The resulting LBP digital speckle pattern can substitute artificial speckle patterns and demonstrates high quality and strong applicability.Based on the LBP digital speckle pattern,the target tracking algorithm was employed to achieve non-contact measurement of the dynamic displacements of rocks.The feasibility and effectiveness of the algorithm in practical application were verified by conducting shear tests on granite and siltstone.Test results show that the deformation characteristics in the displacement nephograms are in line with the measured data pertaining to rock fracturing and conform to the basic characteristics of the shear failure of rocks.The deformation measurement method based on surface texture information can realize non-contact displacement measurement of rocks under conditions without speckles:this obviates the influence of the quality of sprayed speckles on the accuracy of the measurement of deformation.

Development of Algorithm for Person Re-Identification Using Extended Openface Method

Deep learning has risen in popularity as a face recognition technology in recent years.Facenet,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.

Automatic image segmentation method for cotton leaves with disease under natural environment

In order to improve the image segmentation performance of cotton leaves in natural environment, an automatic segmentation model of diseased leaf with active gradient and local information is proposed. Firstly, a segmented monotone decreasing edge composite function is proposed to accelerate the evolution of the level set curve in the gradient smooth region. Secondly, canny edge detection operator gradient is introduced into the model as the global information. In the process of the evolution of the level set function, the guidance information of the energy function is used to guide the curve evolution according to the local information of the image, and the smooth contour curve is obtained. And the main direction of the evolution of the level set curve is controlled according to the global gradient information, which effectively overcomes the local minima in the process of the evolution of the level set function. Finally, the Heaviside function is introduced into the energy function to smooth the contours of the motion and to increase the penalty function Φ(x) to calibrate the deviation of the level set function so that the level set is smooth and closed. The results showed that the model of cotton leaf edge profile curve could be obtained in the model of cotton leaf covered by bare soil, straw mulching and plastic film mulching, and the ideal edge of the ROI could be realized when the light was not uniform. In the complex background, the model can segment the leaves of the cotton with uneven illumination, shadow and weed background, and it is better to realize the ideal extraction of the edge of the blade. Compared with the Geodesic Active Contour(GAC) algorithm, Chan-Vese(C-V) algorithm and Local Binary Fitting(LBF) algorithm, it is found that the model has the advantages of segmentation accuracy and running time when processing seven kinds of cotton disease leaves images, including uneven lighting, leaf disease spot blur, adhesive diseased leaf, shadow, complex background, unclear diseased leaf edges, and staggered condition. This model can not only conduct image segmentation of cotton leaves under natural conditions, but also provide technical support for the accurate identification and diagnosis of cotton diseases.

Chaotic time series multi-step direct prediction with partial least squares regression

Considering chaotic time series multi-step prediction, multi-step direct prediction model based on partial least squares （PLS） is proposed in this article, where PLS, the method for predicting a set of dependent variables forming a large set of predictors, is used to model the dynamic evolution between the space points and the corresponding future points. The model can eliminate error accumulation with the common single-step local model algorithm~ and refrain from the high multi-collinearity problem in the reconstructed state space with the increase of embedding dimension. Simulation predictions are done on the Mackey-Glass chaotic time series with the model. The satisfying prediction accuracy is obtained and the model efficiency verified. In the experiments, the number of extracted components in PLS is set with cross-validation procedure.

Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method（BEF） versus estimates obtained from a local biomass model,based on large-scale empirical field inventory sampling data.The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method.Relative to the local model,BEF overestimated accumulative biomass by 22.12%.The predominant sources of the total deviation （70.94%） were stand-structure variables.Stand age and diameter at breast height are the major factors.Compared with biotic variables,abiotic variables had a smaller overall contribution （29.06%）,with elevation and soil depth being the most important among the examined abiotic factors.Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data.To minimize deviations,stand age and elevation should be included in regional forest-biomass estimation.

Comparison of Diurnal,Seasonal and Solar Cycle Variations of High-latitude,Mid-latitude and Low-latitude Ionosphere

Comparison of regular(diurnal,seasonal and solar cycle)variations of high-latitude,mid-latitude and low-latitude ionospheric characteristics has been provided on basis of local empirical models of the peak electron density and the peak height.The local empirical models were derived from the hand-scaled ionogram data recorded by DPS-4 digisondes located at Norilsk(69°N,88°E),Irkutsk(52°N,104°E)and Hainan(19°N,109°E)for a 6-year period from December,2002 to December,2008.The technique used to build the local empirical model is described.The primary focus is diurnal-seasonal behavior under low solar activity and its change with increasing solar activity.Both common and specific features of the high-latitude(Norilsk),mid-latitude(Irkutsk)and low-latitude(Hainan)regular variations were revealed using their local empirical models.

Mesh Smoothing Method Based on Local Wave Analysis

As the mesh models usually contain noise data,it is necessary to eliminate the noises and smooth the mesh.But existed methods always lose geometric features during the smoothing process.Hence,the noise is considered as a kind of random signal with high frequency,and then the mesh model smoothing is operated with signal processing theory.Local wave analysis is used to deal with geometric signal,and then a novel mesh smoothing method based on the local wave is proposed.The proposed method includes following steps：Firstly,analyze the principle of local wave decomposition for 1D signal,and expand it to 2D signal and 3D spherical surface signal processing;Secondly,map the mesh to the spherical surface with parameterization,resample the spherical mesh and decompose the spherical signals by local wave analysis;Thirdly,propose the coordinate smoothing and radical radius smoothing methods,the former filters the mesh points＇ coordinates by local wave,and the latter filters the radical radius from their geometric center to mesh points by local wave;Finally,remove the high-frequency component of spherical signal,and obtain the smooth mesh model with inversely mapping from the spherical signal.Several mesh models with Gaussian noise are processed by local wave based method and other compared methods.The results show that local wave based method can obtain better smoothing performance,and reserve more original geometric features at the same time.

Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios:a nonlinear VAR approach

The subprime crisis was quite damaging for hedge funds.Using the local projection method(Jordà2004,2005,2009),we forecast the dynamic responses of the betas of hedge fund strategies to macroeconomic and financial shocks—especially volatility and illiquidity shocks—over the subprime crisis in order to investigate their market timing activities.In a robustness check,using TVAR(Balke 2000),we simulate the reaction of hedge fund strategies’betas in extreme scenarios allowing moderate and strong adverse shocks.Our results show that the behavior of hedge fund strategies regarding the monitoring of systematic risk is highly nonlinear in extreme scenarios—especially during the subprime crisis.We find that countercyclical strategies have an investment technology which differs from procyclical ones.During crises,the former seek to capture non-traditional risk premia by deliberately increasing their systematic risk while the later focus more on minimizing risk.Our results suggest that the hedge fund strategies’betas respond more to illiquidity uncertainty than to illiquidity risk during crises.We find that illiquidity and VIX shocks are the major drivers of systemic risk in the hedge fund industry.

A Novel Unsupervised Change Detection Method with Structure Consistency and GFLICM Based on UAV Images

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

Evaluation of the geopotential value W_0^(LVD) of China

Estimation of the zero-height geopotential value W_0^（LVD） for the CVD（China Vertical Datum） plays a fundamental role in the connection of traditional height reference systems into a global height system.Estimation the W_0^（LVD） of China is based on the computation of the mean geopotential offset between the value W_0= 62636856.0 m^2 s^（-2）, selected as reference in this study, and the unknown geopotential value W_0^（LVD）. This estimation is based on the combination of ellipsoidal heights, levelled heights（referring to the CVD）, and some physical parameters, such as geopotential values, gravity values, and geoid undulations.The geoid undulations derived from the GGM（Global Geopotential Models）. This combination is performed through three approaches： The first one is based on the theory of Molodensky, and the second one compares levelled heights and geopotential values derived from the GGMs, while the third one analyses the differences between GPS/Levelling and GGMs geoid undulations. The approaches are evaluated at 65 benchmarks（BMs） covered around Qingdao where the tide gauge is used to observe the local mean sea level of China. The results from three approaches are very similar. Furthermore, the W_0^（LVD）determined for the China local vertical datum was 62636852.9462 m^2 s^（-2）, indicates a bias of about3.0538 m^2/s^（-2） compared to the conventional value of 62636856.0 m^2 s^（-2）.

Theory of passive localization for underwater sources based on acoustic ray modeling

The theory of passive localization for underwater sources based on acoustic ray channel modeling is discussed. The principles of channel modeling in Ray-theory, determination of eigenrays which connect source and receiver, analysis of DOA arriving structure and time delay spectrum arriving structure, their relationship to source location are given in the paper. Source location is estimated by matching measured DOA and TDS to their calculated counterparts. The method of Ray-theory based passive localization features its simplicity, less calculation, short array aperture and robust performance to environment parameters, as compared with those methods based on Normal Mode theory.