Types Of Histograms Research Articles

Features of behavior and morphological stability of the N-doped PtCu/C catalyst

A high-performance electrocatalyst based on bimetallic platinum-containing nanoparticles for PEMFCs has been presented. The combination of bimetallic nanoparticles and an N-doped carbon support ensures high ORR activity and stability of the catalyst. The single-reactor method to obtain the material and its higher functional characteristics allow considering the resulting PtCu/C-N electrocatalyst as a promising commercial product. An extended study of morphological characteristics has been conducted to understand the processes of evolution for this material. The in-situ IL-TEM research has allowed studying in detail the degradation process for the material during high-temperature treatment by analyzing changes in the type of histograms of the nanoparticles size distribution and testing the method for calculating the number of their intersections to assess the dynamics of the material degradation.

Texture Analysis in [18F]-Fluciclovine PET/CT Aids to Detect Prostate Cancer Biochemical Relapse: Report of a Preliminary Experience

Background. As artificial intelligence is expanding its applications in medicine, metabolic imaging is gaining the ability to retrieve data otherwise missed by even an experienced naked eye. Also, new radiopharmaceuticals and peptides aim to increase the specificity of positron emission tomography (PET) scans. Herein, a preliminary experience is reported regarding searching for a texture signature in routinely performed [F18]Fluciclovine imaging in prostate cancer. Materials and methods. Twenty-nine patients who underwent a PET/computed tomography (CT) scan with [18F]Fluciclovine because of biochemical prostate cancer relapse were retrospectively enrolled. First- and second-order radiomic features were manually extracted in lesions visually considered pathologic from the Local Image Features Extraction (LIFEx) platform. Statistical analysis was performed on a database of 29 lesions, one1 per patient. The dataset was split to have 20 lesions for the model training set and 9 lesions for the validation set. The Wilcoxon–Mann–Whitney test was used on the training set to select the most significant features (p-value < 0.05) predicting the dichotomous outcome in a univariate analysis. Results. The best model for predicting the outcome was found to be a multiple logistic linear regression model with two features as variables: an intensity histogram type and a gray-level size zone-based type. Conclusions. Texture analysis of [F18]Fluciclovine PET scans helps in defining prostate cancer relapse in a daily clinical setting.

Medical image edge detection in the framework of quantum representations

The utilization of automated techniques for processing and analyzing medical images can offer significant support to doctors in their diagnostic and therapeutic practices. This study focuses on the complex task of detecting edges in medical images. To this end, we propose novel multilevel approaches based on quantum image representations of the one-dimensional and two-dimensional histograms of the gray-level distributions. The quantum Rényi entropy is employed to quantify the quantum information present in the two histogram types. The particle swarm optimization algorithm is utilized to identify the optimal threshold values. The performance of the proposed methods is evaluated by comparing them to benchmark methods using a set of medical images. The numerical results substantiate the efficacy of the introduced approaches.

Oxygenation Instability during Bolus versus Continuous Feeding among Very Low Birth Weight Premature Infants, Supported by Noninvasive Ventilation: A Randomized Prospective Study.

This study aimed to compare oxygenation instability, as documented by the oxygen saturation (SpO2) histograms, during bolus (over 30 minutes) versus continuous (over 2 hours) feeding among very low birth weight (VLBW) premature infants, supported with noninvasive ventilation (NIV). This was a randomized prospective study. VLBW infants supported with NIV received three consecutive feeds in a random order of bolus-continuous-bolus or continuous-bolus-continuous. During each feed, 30 minutes and 2 hours histograms were documented. Twenty-four infants (birth weight [mean ± standard deviation, SD] 820 ± 168 g, gestational age [mean ± SD] 27.0 ± 1.6 weeks) were included in our study (12 infants started with bolus feeding and 12 with continuous feeding) and 72 histograms were obtained (36 during bolus feeding and 36 during continuous feeding). No differences in mean fraction of inspired oxygen (FiO2), and number of apnea events were observed between the two feeding modes. Oxygenation instability as assessed by time spent in different SpO2 ranges and histogram types (stable or unstable) was comparable during bolus and continuous feedings. Changing feeding mode from bolus to continuous or vice versa did not significantly change the oxygenation instability of the group, though individual infants did show a consistence response to feeding length changes. Among VLBW infants supported with NIV, oxygenation instability, as documented by SpO2 histograms, was comparable between bolus and continuous feedings. Individual infants may benefit from specific feeding length, and this can be easily demonstrated by the SpO2 histograms. · Feeding length did not affect oxygenation instability of preterm infants on noninvasive respiratory Support.. · Oxygen saturation histograms allow objective quantification of oxygenation instability at the bedside.. · Individual infants benefit from specific feeding length, as demonstrated by SpO2 histograms..

A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias.

We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.

CORRELATIVE STUDY OF SOLAR TRANSIENT PARAMETERS ASSOCIATED WITH DST INDEX FOR SOLAR CYCLE 23 AND 24

Geomagnetic storms and solar coronal mass ejections (CMEs) have been shown to be associated since the discovery of CMEs in solar activity (Yurchyshyn et al., 2004). An intense geomagnetic storm can either be stemmed from a CME, or from a coronal hole. However, a great geomagnetic storm can only be sourced from a CME (Le et al., 2012). The aim of this paper is to study the relation between the coronal mass ejections (CMEs), and their associated solar flares and correlate this with the Disturbance storm time (Dst) index. We have studied the count rate of soft X-rays class of X, M and C type through a histogram type plot for studying the number of X-rays of different class observed during solar cycle 23 and 24. We have also studied the angular width, speed of coronal mass ejections (CMEs) and disturbance storm time (Dst) index of solar cycle 23 and 24 for the whole cycle (Pant et al., 2021).

Bell Shape Embodying Zhongyong: The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs

As an essential subset of Chinese music, traditional Chinese folk songs frequently apply the anhemitonic pentatonic scale. In music education and demonstration, the Chinese anhemitonic pentatonic mode is usually introduced theoretically, supplemented by music appreciation, and a non-Chinese-speaking audience often lacks a perceptual understanding. We discovered that traditional Chinese anhemitonic pentatonic folk songs could be identified intuitively according to their distinctive bell-shaped pitch distribution in different types of pitch histograms, reflecting the Chinese characteristics of Zhongyong (the doctrine of the mean). Applying pitch distribution to the demonstration of the Chinese anhemitonic pentatonic folk songs, exemplified by a considerable number of instances, allows the audience to understand the culture behind the music from a new perspective by creating an auditory and visual association. We have also made preliminary attempts to feature and model the observations and implemented pilot classifiers to provide references for machine learning in music information retrieval (MIR). To the best of our knowledge, this article is the first MIR study to use various pitch histograms on traditional Chinese anhemitonic pentatonic folk songs, demonstrating that, based on cultural understanding, lightweight statistical approaches can progress cultural diversity in music education, computational musicology, and MIR.

An Algorithm for Image Segmentation in HSI Color Space

This paper presents a new segmentation algorithm that uses priority components in HSI (Hue, Saturation, Intensity) color space. The algorithm (named SegPC) is designed for HSI color space to support image understanding in different areas of human activities because this color space is modeled to be closer to humans’ color perception. The innovative idea for the usage of all color components with respect to their importance is exploited in algorithm development. Two parameters are used for controlling the segmentation process, the first parameter is the number of colors used in the segmentation process and the second is the order of applying calculated thresholds for color components. The main goal of the research is the analysis of the proposed new segmentation algorithm. A set of images with different types of histograms is used for practical examination of the developed algorithm. The peak signal-to-noise ratio (PSNR) is used as a measure of the effect of image segmentation. The highest PSNR values (≈22 for images with histogram near to bi-modal type and ≈23 for images with histogram with several peak values) are calculated for the priority of color components in which the first analyzed component is color intensity and the number of colors in the segmented image is a quarter of the number of colors in the input image. This result could be explained by the meaning of the intensity component which describes the brightness of the color and because of this, it carries a significant part of color information. The time complexity of SegPC is evaluated through theoretical analysis and experimentally. It is observed that performance time depends on three variables: first, the size of the image strongly influences time complexity (when the size of the image increases then the time for processing increases too), second, the number of colors also is related to time for processing, and third, the selected color components chosen as per the priority has a weak influence on time complexity because it defines the number of pixels which have to be additionally analyzed. The developed algorithm presents promising results. Thus, it could be applied in image analysis and natural sciences.

Oxygen saturation histogram classification system to evaluate response to doxapram treatment in preterm infants.

An oxygen saturation (SpO2) histogram classification system has been shown to enable quantification of SpO2 instability into five types, based on histogram distribution and time spent at SpO2 ≤ 80%. We aimed to investigate this classification system as a tool to describe response to doxapram treatment in infants with severe apnea of prematurity. This retrospective study included 61 very-low-birth-weight infants who received doxapram. SpO2 histograms were generated over the 24-h before and after doxapram start. Therapy response was defined as a decrease of ≥1 histogram types after therapy start. The median (IQR) histogram type decreased from 4 (3-4) before to 3 (2-3) after therapy start (p < 0.001). The median (IQR) FiO2 remained constant before (27% [24-35%]) and after (26% [22-35%]) therapy. Thirty-six infants (59%) responded to therapy within 24 h. In 34/36 (94%) of the responders, invasive mechanical ventilation (IMV) was not required during the first 72 h of therapy, compared to 15/25 (60%) of non-responders (p = 0.002). Positive and negative predictive values of the 24-h response for no IMV requirement within 72 h were 0.46 and 0.94, respectively. Classification of SpO2 histograms provides an objective bedside measure to assess response to doxapram therapy and can serve as a tool to detect changes in oxygenation status around respiratory interventions. The SpO2 histogram classification system provides a tool for quantifying response to doxapram therapy. The classification system allowed estimation of the probability of invasive mechanical ventilation requirement, already within a few hours of treatment. The SpO2 histogram classification system allows an objective bedside assessment of the oxygenation status of the preterm infant, making it possible to assess the changes in oxygenation status in response to respiratory interventions.

The Cuts Selection Method Based on Histogram Segmentation and Impact on Discretization Algorithms.

The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm.

Research of methods of distribution of graphic objects on websites for assessment of interface quality

Formulation of the problem. An actual problem when creating web resources is the test-ing of the designed design, which is the direction of testing the user interface. This paper con-siders ways to automate the analysis of the interface of web pages in terms of visual perception of man. The aim of the work. The aim of the work is to improve the quality of automated testing of the graphical user interface based on the use of methods of analysis of color digital images and detection of graphic objects. Methods of selecting objects on digital images. The traditional method of segmentation is described – threshold binarization, which results in a binary image. The JSEG algorithm is more advanced. According to this method, instead of estimating the parameters of the texture model, the homogeneity of each image fragment is checked, which leads to a reduction in the amount of computation. Website interface evaluation methods. In order to qualitatively evaluate the user interface, it is necessary to identify the basic principles on which designers rely when designing interfaces. Basic principles of interface construction: shape, size, brightness, color, direction, location. Determining the brightness characteristics of the image. To estimate the brightness pa-rameters, you can use the estimation of the image histogram, which displays the brightness value. The optimal type of brightness histogram is the normal distribution. Determining the number of primary colors in the image. Performed by constructing a histogram for an indexed image on a given color map. It is optimal to use no more than 3 primary colors. Selectionobjects in the image. The methods of image segmentation described above are implemented and the imperfection of these methods is shown. The application of the JSEG algorithm gave a more positive result, but requires additional adaptation to our tasks. Conclusions. The study and comparative analysis of digital image processing methods to automate the process of assessing the quality of the graphical user interface. It is concluded that the application of the classical approach to image segmentation did not give the desired result for image analysis of web pages, namely there are problems of inability to separate adjacent graphics at the threshold binarization, and combine text characters into one block. The JSEG algorithm includes color quantization and spatial segmentation operations, due to which the result of object selection is better, but still requires further settings.

Contact orientation distributions for visualisation of granular fabric

Contact orientation distributions are widely used to describe the fabric of granular assemblies and its evolution under load. Different types of visualisations, mainly histograms, are used in the literature to plot these distributions. While there are different ways to create such histograms, however, there is generally no discussion of how the chosen way affects the visualisation of the distribution and its interpretation. We develop in this paper a novel, rigorous framework for discussing contact orientations and their visualisation through histograms. This allows presenting, for the first time, in a unified way several existing visualisations and explaining how they are computed. We identify first some issues in how existing visualisations represent the main features of the contact distribution. We then exploit the framework to introduce new histogram types that avoid these issues and provide a better insight into the granular fabric.

An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.

QUALITATIVE ENHANCEMENT OF THE DNA PLOIDY ANALYSIS CRITERIA TO ASSESS MALIGNANT TRANSFORMATION RISK

<h3>Background</h3> DNA ploidy analysis is arguably the most sensitive and reproducible approach to assess risk in oral potentially malignant disorders. Although aneuploidy has been associated with a high risk of malignant transformation (MT), the current diagnostic criteria of aneuploidy are not specific to oral mucosal tissues and thus yield positive predictive values (PPVs) ranging from 30% to 40%. <h3>Objective</h3> The aims of this study were to analyze the ploidy status of archived material diagnosed as oral leukoplakia (OL) with follow-up data on MT and to review the risk classification criteria. <h3>Methods</h3> Nuclei suspensions were enzymatically prepared from formalin-fixed, paraffin-embedded tissue from 121 patients with OL and stained with propidium iodide for flow cytometry. Twenty-three patients had MT. Histograms were initially analyzed for the proportion of nuclei in G₁ phase (2c), S phase, G₂ phase (4c), and 5c-exceeding rate (5cER). Histograms diagnosed as aneuploid were further investigated qualitatively on the basis of nuclei distribution pattern. Log-rank test on Kaplan-Meier curves (LRKM; <i>P</i> < .05) as well as sensitivity (SS) and specificity (SP) were calculated for both traditional aneuploidy-based risk (TAR) and enhanced aneuploidy-based risk (EAR). <h3>Results</h3> Seven aneuploid histogram types were detected, of which only 3 proved truly high risk. EAR was then stratified as low risk (diploid), medium risk (low-risk aneuploid), and high risk, whereas TAR remained as diploid (low risk) and aneuploid (high risk). Aneuploidy in TAR had PPV of 55%, SS of 88%, and SP of 81% (LRKM <i>P</i> < .0001). High risk in EAR showed PPV of 79%, SS of 82%, and SP of 94% (LRKM <i>P</i> < .0001). <h3>Conclusions</h3> Enhancement of TAR based on qualitative histogram classification significantly improves predictive values. Flow cytometry is an accessible approach to assess MT risk in OL.

A NOVEL METHOD FOR PROTECTIVE FACE MASK DETECTION USING CONVOLUTIONAL NEURAL NETWORKS AND IMAGE HISTOGRAMS

Abstract. This paper proposes a new hybrid method for automatic detection and recognition of the presence/absence of a protective mask on human's face. It combines visual features extracted using Convolutional Neural Network (CNN) with image histograms that convey information about pixel intensity. Several pre-trained models for building feature extraction systems using a CNN and several types of image histograms are considered in this paper. We test our approach on the Medical Mask Dataset and perform cross-corpus analysis on two other databases named Masked Faces (MAFA) and Real-World Masked Face Dataset (RMFD). We demonstrate that the proposed hybrid method increases the Unweighted Average Recalls (UARs) of recognition of the presence/absence of a protective mask on human's face in comparison with traditional CNNs on the MAFA and RMFD databases by 0.96% and 1.32%, respectively. The proposed method can be generalized and used for other tasks of biometry, computer vision, machine learning and automatic face recognition.

Cargo Ships’ Heat Demand - Operational Experiment

Abstract The paper presents the results of an experiment conducted on two cargo ships – a 5300 TEU container with a steam heating system and a 7500 dwt general cargo ship with a thermal oil system. On both ships research has been carried out using specially designed measuring equipment. After gathering data about flow velocity and temperatures (steam/ cooling water/ thermal oil/ seawater/ outside air), calculations have been done, resulting in histograms. For both types of histograms (heat demand and service time), the probability density function was fitted, using the K-S statistical test. The last step was comparison of the probability distribution mean to seawater and the outside air temperatures by linear regression and the coefficient of determination. The dependencies between the mentioned temperatures and heat demand were noted.

Perceptual Quality Assessment of Enhanced Images Using a Crowd-Sourcing Framework

In this work, we present a psychophysical study, in which, we analyzed the perceptual quality of images enhanced with several types of enhancement algorithms, including color, sharpness, histogram, and contrast enhancements. To estimate and compare the qualities of enhanced images, we performed a psychophysical experiment with 35 source images, obtained from publicly available databases. More specifically, we used images from the Challenge Database, the CSIQ database, and the TID2013 database. To generate the test sequences, we used 12 different image enhancement algorithms, generating a dataset with a total of 455 images. We used a Double Stimulus Continuous Quality Scale (DSCQS) experimental methodology, with a between-subjects approach where each subject scored a subset of the total database to avoid fatigue. Given the high number of test images, we designed a crowd-sourcing interface to perform an online psychophysical experiment. This type of interface has the advantage of making it possible to collect data from many participants. We also performed an experiment in a controlled laboratory environment and compared its results with the crowd-sourcing results. Since there are very few quality enhancement databases available in the literature, this works represents a contribution to the area of image quality.

Evaluation of DNA ploidy with intraoperative flow cytometry may predict long-term survival of patients with supratentorial low-grade gliomas: Analysis of 102 cases

ObjectiveThe objective of the present study was evaluation of the prognostic significance of DNA ploidy assessed with intraoperative flow cytometry (iFC) during resection of low-grade gliomas (LGG). Patients and methodsRetrospective analysis of 102 consecutive cases of newly diagnosed WHO grade II supratentorial gliomas, surgical removal of which was accompanied by iFC, was done. There were 46 diffuse astrocytomas (DA) and 56 oligodendrogliomas (OD). According to iFC, 68 tumors (67%) were considered non-aneuploid and 34 (33%) aneuploid. Median extent of resection (EOR) was 95% (mean, 89.0 ± 14.5%). Postoperative FRT with or without adjuvant chemotherapy was administered in 24 cases (24%). ResultsWith median follow-up of 29.9 months (range, 9.4–66.9 months), neither median overall survival (OS) nor median progression-free survival (PFS) were reached. The 3-year actuarial OS and PFS rates were 95.8% and 86.3%, respectively. Non-aneuploid DNA histogram type of the tumor was associated with significantly longer OS both in univariate (P = 0.0094) and multivariate (P = 0.0232) analyses, and with longer PFS in univariate analysis (P = 0.0184). Aneuploidy was encountered more frequently in DA, than in OD (43% vs. 25% of cases; P = 0.0488). Tumor progression was noted in 15 DA and 4 patients succumbed to the disease; in this subgroup the main unfavorable prognostic factors for OS and PFS were presence of aneuploidy (P = 0.0510) and MIB-1 index ≧3.9% (P = 0.0141), respectively. Aneuploid DA more frequently progressed to glioblastoma than to anaplastic astrocytoma, but this difference did not reach statistical significance (P = 0.1362). In contrast, only one OD progressed (non-aneuploid neoplasm), and no one patient with such tumor died of the disease. ConclusionsDNA ploidy assessed with iFC may be effectively used as prognostic indicator in cases of LGG, especially of DA. Aneuploid tumors demonstrate more aggressive clinical course translated into shorter OS of patients. Thus, their detection during surgery may be helpful for decision on the optimal EOR, and for choice of the most appropriate postoperative adjuvant therapy.

Exploring finger vein based personal authentication for secure IoT

Personal authentication is getting harder and harder in the internet of things (IoT). Existing methods used for personal authentication, such as passwords and the two-factor authentication (2FA), are inadequate and ineffective due to human error and other attacks. To support more secure IoT, this paper proposes a finger vein based personal authentication method by exploring competitive orientations and magnitudes from finger vein images. Finger vein recognition has been proven to be a reliable and promising solution for biometric-based personal authentication. The stable and rich piecewise line features in finger vein images can be used to clearly represent finger vein patterns for personal authentication. In this paper, we propose an efficient local descriptor for finger vein feature extraction, namely the histogram of competitive orientations and magnitudes (HCOM). For a finger vein image, two types of local histograms are extracted and fused together to efficiently and adequately represent the competitive information: the histogram of competitive orientations (HCO) and the local binary pattern histogram generated from the image of competitive magnitudes (named as HCMLBP). The extensive experimental results from the application of the proposed method to the public finger vein database MMCBNU_6000, demonstrate that the proposed method outperforms state-of-the-art orientation coding (OC)-based methods and other commonly used local descriptors. Additionally, the proposed method can be used for finger vein image enhancement.

Promising electroplating solution for facile fabrication of Cu quantum point contacts

In this article, we report on the fabrication and transport measurements of Cu quantum point contacts prepared by a novel, electrochemically assisted mechanically controllable break junction (EC-MCBJ) method. By employing photolithography and wet-etching processes, suspended electrode pairs were patterned and fabricated successfully on Si microchips. Rather than adopting an acid Cu electroplating solution, a novel alkaline electroplating solution was developed and utilized to establish Cu nanocontacts between electrode pairs. Typically, the widths of the as-fabricated Cu nanocontacts were found to be smaller than 18 nm. A large number of Cu quantum point contacts were then produced and characterized by a home-built MCBJ setup. In addition to the conventional histogram, where peaks tend to decrease in amplitude with increasing conductance, an anomalous type of conductance histogram, exhibiting different peak amplitudes, was observed. Through statistical analysis of the maximum allowable bending of the Si microchips, and theoretical calculations, we demonstrated that our alkaline Cu electroplating solution affords Cu nanocontacts that are compatible with subsequent MCBJ operations, which is essential for the fabrication of Cu quantum point contacts. As sophisticated e-beam lithography is not required, the EC-MCBJ method is fast, simple, and cost-effective. Moreover, it is likely to be suitable for the fabrication and characterization of quantum point contacts of various metals from their respective electroplating solutions.