Simple Parameters Research Articles

Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies.

Using airborne LiDAR to map forest microclimate temperature buffering or amplification

Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas.To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat.An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor.We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment.Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests.

Quantitative assessment on synchronisation capability of a line‐start permanent magnet synchronous motor with hybrid rotor

AbstractThe authors devote a quantitative assessment on the synchronisation capability of a line‐start permanent magnet synchronous motor (LSPMSM) equipped with a hybrid rotor. The quantitative assessment is realised by determining a load criterion consisting of load torques and inertias under which the motor will just synchronise. During the process of load‐criterion determination, close attention is paid to parameters governing the motor synchronisation behaviour. Following the principle of stator‐current error minimisation, the time‐variant magnetising inductances and permanent magnet (PM) flux linkages of the analysed LSPMSM are simplified into slip‐dependent constants via the simple and robust genetic algorithm (GA). The load criterion is then obtained after the establishment of a dynamic motor model involving the simplified parameters. Prototyping tests focused on motor speed‐time responses under different mechanical loads are carried out to validate the obtained load criterion and the paper analyses.

Effective and clinically relevant optimisation of cushioning stiffness to maximise the offloading capacity of diabetic footwear

IntroductionOptimising the cushioning stiffness of diabetic footwear/orthoses can significantly enhance their offloading capacity. This study explores whether optimum cushioning stiffness can be predicted using simple demographic and anthropometric parameters. MethodsSixty-nine adults with diabetes and loss of protective sensation in their feet were recruited for this cross-sectional observational study. In-shoe plantar pressure was measured using Pedar® for a neutral diabetic shoe (baseline) and after adding cushioning footbeds of varying stiffness. The cushioning stiffness that achieved maximum offloading was identified for each participant. The link between optimum cushioning stiffness and plantar loading or demographic/anthropometric parameters was assessed using multinomial regression. ResultsPeople with higher baseline plantar loading required stiffer cushioning materials for maximum offloading. Using sex, age, weight, height, and shoe-size as covariates correctly predicted the cushioning stiffness that minimised peak pressure across the entire foot, or specifically in the metatarsal heads, midfoot and heel regions in 70%, 72%, 83% and 66% of participants respectively. ConclusionsIncreased plantar loading is associated with the need for stiffer cushioning materials for maximum offloading. Patient-specific optimum cushioning stiffness can be predicted using five simple demographic/anthropometric parameters. These results open the way for methods to optimise cushioning stiffness as part of clinical practice.

Analysis of the performance of a new concept for automatic draping of wide reinforcement fabrics with pre-shear: A virtual prototyping study

The layup process of large composite structures made from dry reinforcement fabrics is considered. One such structure is a wind turbine blade, for which the current draping process is mostly manual. Automating the draping process will, therefore, lower the costs. Based on a literature review, a new concept is synthesized and analyzed using an advanced finite element model with rigid multi-body kinematics and a dedicated material model for the fabric. The material model is calibrated using experimental coupon tests, i.e. the bias-extension test (shear) and Peirce's cantilever test (out-of-plane bending). The concept is analyzed numerically by means of a simple parameter study and draping test cases on a flat mold as well as a general double-curved mold. The simulation results show that the concept is feasible for the draping operation and is thus qualified for the subsequent physical prototyping.

Position control based on Gaussian function applied on magneto-rheological fluid disc brake of a hybrid mechanical device (MR brake - DC motor)

This article presents an angular position control, based on the Gaussian function, of a Magneto-Rheological fluid disc brake (MR brake) driven by a DC motor. Our proposed control strategy is to apply a continuous magnetic flux density to the MR brake, which will be maximum when the proportional controller of the DC motor reaches the desired position to brake the hybrid device. The MR brake controller activates a braking torque that adopts the behavior of the Gaussian function instead of a pulsed braking torque as provided by other commonly used controllers (On-Off controllers). The response of the MR brake controller, which is presented in a closed-loop feedback system, depends on the angular position error of the shaft and a tuning parameter representing the critical angular position at which the magnetic flux density, which is applied to the MR brake, reaches 60.65% of its maximum value. The advantage is to avoid knowing the dynamic parameters, such as the inertia of the mechanical device or its speed, and to reject these perturbations by a simple tuning parameter of the MR brake. To show the effectiveness of the proposed controller, the dynamic model of a slider-crank mechanism is considered. The results showed similar behavior as conventional controllers, where overshoot and oscillations were minimized. This behavior has been obtained in other research articles using controllers that require a greater amount of data processing.

Breakage critical state of gravels with different gradings. Part II: Constitutive modelling

A unified critical state surface for gravels considering the effect of initial grading and particle breakage was proposed in Part I based on the test results of large-scale triaxial compression tests. In this study, we proposed a state-dependent elastoplastic constitutive model to predict the shear behaviors of gravels in the framework of bounding surface plasticity and critical state theory. A simple state parameter with a reference to the unified critical state surface was applied to the dilatancy equation and the bounding stress ratio in the proposed model. The proposed constitutive model can well capture the grading state index, the relative breakage index, the deviatoric stress and the void ratio at the end of tests, and the stress–strain relationships during the shearing process. The model can still be improved by considering the influence of the confining pressure on the critical state strength.

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Abstract. Knowledge of the spatial and temporal characteristics of solar surface irradiance (SSI) is critical in many domains. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. Inferring SSI from satellite images is, however, not straightforward, and it has been the focus of many researchers in the past 30 to 40 years. For long, the emphasis has been on models grounded in physical laws with, in some cases, simple statistical parametrizations. Recently, new satellite SSI retrieval methods have been emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Météo-France. The performance of the models is evaluated on a test dataset independent from the training set in both space and time and compared to that of a state-of-the-art physical retrieval model from the Copernicus Atmosphere Monitoring Service (CAMS). We found that the data-driven model's performance is very dependent on the training set. Provided the training set is sufficiently large and similar enough to the test set, even a simple MLP has a root mean square error (RMSE) that is 19 % lower than CAMS and outperforms the physical retrieval model at 96 % of the test stations. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set: when geographical separation was enforced between the training and test set, the MLP-based model exhibited an RMSE that was 50 % to 100 % higher than that of CAMS in several locations.

Causal viscous Universe in Sáez–Ballester theory

In this work, the authors examine the dynamics of viscous Universe in the Sáez–Ballester theory of gravity. The solutions of the Friedmann–Robertson–Walker field equations have been derived within the framework of full causal theories presented by Israel and Stewart using simple parametrization of scale factor a( t), i.e., power law relation a( t) = a0 tα and exponential law a( t) = b0eβ t. The power law cosmological model shows that the Universe transitioned from early cosmic deceleration to the present cosmic acceleration, and the exponential cosmological model shows the accelerated expansion of the Universe. It has been discovered that as time passes, energy density and viscosity decrease while temperature rises. Energy conditions are also investigated in which the strong energy condition is violated. The behaviour of the cosmological parameters such as the Hubble parameter, energy density, pressure, coefficient of viscosity, and state-finder parameter has been presented graphically.

Einstein Model of a Graph to Characterize Protein Folded/Unfolded States.

The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states.

Nonlinear shallow water investigation of atmospheric disturbances generated by strong seismic events.

The upper portions of the Earth's atmospheric layer, e.g., the ionospheric plasma layer, can be significantly affected by perturbations generated in the lower layers. In fact, all perturbations formed within the troposphere can easily propagate, not only horizontally within the layer but also vertically reaching the highest regions of the atmosphere far from the Earth's surface, as depicted by the Wentzel-Kramers-Brillouin (WKB) approximation of atmospheric waves. Because all perturbations generated in the atmospheric boundary layer must take into account the effects of the medium's nonlinearity and thus the effects of atmospheric turbulence, in this work the impact of a strong seismic event and the disturbances generated in the flow are analyzed by means of a fully nonlinear model which incorporates a simple parametrization of the seismic event and is based on the classical shallow water. A strict dependence was observed between the model control parameters and the vertical nonvanishing modes from the WKB approximation, and only few specific bands of excited modes are nonvanishing and can eventually propagate to the ionosphere. Moreover, the flow disturbance, generated by a seismic event, presents a multiscale nature characterized by two fixed wavelengths, and the excited modes are harmonics of such distinctive scales.

Data- driven and knowledge- guided prediction model of milling tool life grade

ABSTRACT Models that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.

The influence of pulse repetition frequency on reactive oxygen species production in pulsed He+H2O plasmas at atmospheric pressure

Atmospheric pressure plasmas generated from a helium gas with admixtures of water vapor have numerous applications in biomedicine. It is important that the chemistry of such plasmas can be tightly controlled so that they may be tailored for their intended use. In this study, computational modeling is used to vary the pulse repetition frequency of a nanosecond-pulsed, pin-to-pin He + 0.25% H2O discharge in the range of 1–100 kHz to determine the influence of the pulse repetition frequency on the resulting densities of reactive oxygen species and the rates of dominant reaction pathways involving them. The plasma is simulated using the 0D plasma-chemical kinetics model GlobalKin. The pulse shape is kept constant. The afterglow duration is, therefore, dependent on the repetition frequency. Analysis of the bulk plasma chemistry after the plasma has reached equilibrium shows that the peak electron density is only weakly dependent on the pulse repetition frequency. Increasing the pulse repetition frequency is shown to increase the density of H, O, and OH radicals, while the relationship between the repetition frequency and the densities of species with longer lifetimes, namely, H2O2 and O3, is found to be more complex. These are formed throughout the afterglow, and their density depends on the availability of reactant species, the afterglow duration, and the background gas temperature. This work concludes that the pulse repetition frequency is not a simple control parameter, especially for species that are predominantly produced in the afterglow. Detailed modeling is required for accurate control of species densities using the pulse repetition frequency.

Development of the Taiwan Generic Rock Seismic Velocity Profile

Abstract Reference velocity profiles for ground-motion models (GMMs) provide more information about the site condition represented by the GMM than the simple site parameters used in the GMMs, such as VS30 (time-averaged S-wave velocity of top 30 m strata from the surface). A reference velocity profile for generic soft-rock site conditions is developed for Taiwan using multiple data sets to span the depth range from 0 to 16 km. The measured VS profiles from PS-logging at strong-motion stations with VS30 between 610 and 930 m/s (i.e., 760 m/s ± 20%) were selected to define the top 30 m of the profile. The velocity profiles obtained from multiple existing geophysical studies were then used to extend the VS profile to seismic bedrock (VS of 3.5 km/s). A corresponding generic rock P-wave velocity (VP) profile was developed using VP from PS-logging measurements and an empirical relationship between VP and VS for the deeper part of the profile. The proposed Taiwan Generic Rock (TWGR) model has VS30 of 754 m/s, Z1.0 (thickness of sediments with VS&amp;lt;1.0 km/s) of 29 m, Z2.5 (thickness of sediments with VS&amp;lt;2.5 km/s) of 2.1 km, and κ0 (spectral decay slope) of 0.052 s. The VS values of the TWGR profile at depths of 50 m–8 km are smaller than from the generic rock profile with VS30=760 m/s for California, which lead to different site amplifications between soft-rock sites in Taiwan and California. The TWGR provides information on the applicability of the large empirical data set of strong-motion recordings from Taiwan to other regions.

Three Simple Parameters on Admission to the Emergency Department are Predictors for Endoscopic Intervention in Patients with Suspected Nonvariceal Upper Gastrointestinal Bleeding

BackgroundA considerable number of patients with nonvariceal upper gastrointestinal bleeding (UGIB) need endoscopic intervention. ObjectiveThe aim of this study was to determine factors that predict the need for endoscopic intervention at the time of admission to the emergency department. MethodsConsecutive patients with International Classification of Diseases, Tenth Revision diagnosis code K92.2 (gastrointestinal hemorrhage) who underwent upper endoscopy between February 2019 and February 2022, including patients diagnosed with nonvariceal UGIB in the emergency department in the study were reviewed retrospectively. The patients were divided into two groups: those treated endoscopically and those not treated endoscopically. These two groups were compared according to clinical and laboratory findings at admission and independent predictors for endoscopic intervention were determined using multivariate regression analysis. ResultsAlthough 123 patients (30.3%) were treated endoscopically, endoscopic treatment was not required in 283 (69.7%) patients. Syncope, mean arterial pressure (MAP), and blood urea nitrogen (BUN) at admission were independent predictors for endoscopic intervention in the multivariate analysis, after adjusting for endoscopy time. The area under the curve of the syncope+MAP+BUN combination for endoscopic intervention was 0.648 (95% CI 0.588–0.708). Although the syncope+MAP+BUN combination predicted the need for intervention significantly better than pre-endoscopy Rockall and AIMS65 scores (p = 0.010 and p < 0.001, respectively), there was no significant difference in its comparison with the Glasgow-Blatchford score (p = 0.103). ConclusionsSyncope, MAP, and BUN at admission were independent predictors for endoscopic therapy in patients with nonvariceal UGIB. Rather than using complicated scores, it would be more practical and easier to predict the need for endoscopic intervention with these three simple parameters, which are included in the Glasgow-Blatchford score.

Prediksi Harga pada Trading Forex Pair USDCHF Menggunakan Regresi Linear

In the era of globalization, free trade grows rapidly and technology develops, affecting economic competition. Forex, foreign exchange trading, is one of the investments used to face this challenge. Technical and fundamental analysis is used to predict price movements in forex trading. Previous studies have used linear regression algorithms and other techniques for price prediction in forex. In this study, the linear regression algorithm is used to predict closing prices in forex trading because the linear regression algorithm is an algorithm that has been widely used in predictions, its strengths are in estimating simple model parameters and data based on time series. In addition, the linear regression algorithm can perform analysis using several independent variables so that the prediction results can be more accurate. The purpose of this study is to create a forex price prediction model, to make it easier for traders to make price predictions. A dataset of 2066 data was obtained through the metatrader software and processed through the preprocessing stage. The linear regression model was created using 5 scenarios, and the evaluation was carried out using the Mean Squared Error (MSE) and Root Mean Square Error (RMSE) values to select the best model. The results show that linear regression is able to predict the closing price of the USDCHF pair. The best linear regression model is obtained using the independent variable in scenario 1, namely the Open variable, with a linear regression equation of y=0.0145+0.9849x, the best MSE is 0.0000328509 and the best RMSE is 0.0057315705.

Contactless deformation of fluid interfaces by acoustic radiation pressure

Reversible and programmable shaping of surfaces promises wide-ranging applications in tunable optics and acoustic metasurfaces. Based on acoustic radiation pressure, contactless and real-time deformation of fluid interface can be achieved. This paper presents an experimental and numerical study to characterize the spatiotemporal properties of the deformation induced by acoustic radiation pressure. Using localized ultrasonic excitation, we report the possibility of on-demand tailoring of the induced protrusion at water–air interface in space and time, depending on the shape of the input pressure field. The experimental method used to measure the deformation of the water surface in space and time shows close agreement with simulations. We demonstrate that acoustic radiation pressure allows shaping protrusion at fluid interfaces, which could be changed into a various set of spatiotemporal distributions, considering simple parameters of the ultrasonic excitation. This paves the way for novel approach to design programmable space and time-dependent gratings at fluid interfaces.

Modeling Wind‐Blown Umbrella Clouds in Lagrangian Dispersion Models

AbstractThe ash and gas released by large explosive volcanic eruptions rises to its neutral buoyancy level in the atmosphere, then spreads laterally to form an umbrella cloud. Density stratification of the atmosphere generates buoyancy forces in the cloud, which drive the outward spread. Although umbrella clouds are often modeled as circular axisymmetric structures, in practice they are usually influenced quite strongly by the meteorological wind, with spread in the upwind direction halted by the oncoming wind, and different rates of spreading in the downwind and crosswind directions. In this work, we derive a simple parametrization of non‐axisymmetric umbrella cloud spreading from a much more complex physically based shallow‐layer intrusion model. The new parametrization is quick to evaluate and so is suitable for use in operational Volcanic Ash Transport and Dispersion Models (VATDMs). In contrast to previous parametrizations, in which there is assumed to be no interaction between a circular umbrella cloud and the meteorological wind, here the umbrella cloud is influenced by the wind and adopts a shape determined by the balance of buoyant spreading and downwind drag forces. We apply the new scheme to four historical case studies of eruptions at Puyehue 2011, Pinatubo 1991, Ulawun 2019, and Calbuco 2015. The results are compared with VATDM simulations using a conventional circular umbrella cloud parametrization. Using the new scheme, good descriptions of cloud spread are recovered and the prediction of horizontal ash distribution is improved relative to the axisymmetric parametrization.

Monocyte-to-lymphocyte ratio correlates with parathyroid hormone concentration in patients with severe symptomatic aortic stenosis

PurposeThe normal healthy valve is devoid of inflammatory cells, however background of aortic stenosis (AS) may include inflammatory processes. Moreover, the link between hyperparathyroidism and heart failure is postulated. Simple whole blood analysis with indices is a beneficial tool in cardiovascular diseases’ assessment. The purpose of the study was to evaluate correlation between parathyroid hormone (PTH) and simple blood parameters in severe AS. Material and methodsThe study included 62 patients with severe AS. Patients with inflammatory or autoimmune co-morbidities were excluded. Blood samples were collected, and clinical and demographic data were analyzed. ResultsThe final study group comprised 55 patients (31 females, 56.4%; mean age 77.13 (SD 6.76)). In 23 patients (41.8%), PTH concentration was markedly increased. The study group was divided into two subgroups according to the PTH concentration. Patients from both groups did not differ significantly in terms of age and co-morbidities. PTH concentration correlated positively with monocyte-lymphocyte ratio (MLR) (p ​= ​0.008, Spearman rho 0.356) and platelet-lymphocyte ratio (PLR) (p ​= ​0.047, Spearman rho 0.269), creatinine level (p ​= ​0.001, Spearman rho 0.425) and glomerular filtration rate (GFR-MDRD) (p ​= ​0.009, Spearman rho −0.349).The multivariable logistic regression with backward analysis revealed MLR (p ​= ​0.029) and GFR (p ​= ​0.028) as independent significant predictors of abnormal PTH values.The receiver operator characteristics (ROC) curve was performed for the model of MLR and GFR-MDRD (AUC ​= ​0.777), yielding the sensitivity of 60.9% and specificity of 90.6%. ConclusionsPTH concentration correlates with monocyte-to-lymphocyte and platelet-to-lymphocyte ratios in calcified AS.

Searching for New Globular Clusters in M31 with Gaia EDR3

We have found 50 new globular cluster (GC) candidates around M31 with Gaia Early Data Release 3 (EDR3), with the help of Pan-STARRS1 DR1 magnitudes and Pan-Andromeda Archaeological Survey (PAndAS) images. Based on the latest Revised Bologna Catalog and simbad, we trained two random forest (RF) classifiers, the first one to distinguish extended sources from point sources and the second one to further select GCs from extended sources. From 1.85 million sources of 16 m < g < 19.5 m and within a large area of ∼392 deg2 around M31, we selected 20,658 extended sources and 1934 initial GC candidates. After visual inspection of the PAndAS images, to eliminate the contamination from noncluster sources, particularly galaxies, we finally got 50 candidates. These candidates are divided into three types (a, b, and c), according to their projected distance D to the center of M31 and their probability of being a true GC, P GC, which is calculated by our second RF classifier. Among these candidates, 14 are found to be associated (in projection) with the large-scale structures within the halo of M31. We also provide several simple parameter criteria for selecting extended sources effectively from Gaia EDR3, which can reach a completeness of 92.1% with a contamination fraction lower than 10%.