Cause Of Anomalies Research Articles

Accurate Detection of Road Network Anomaly by Understanding Crowd's Driving Strategies from Human Mobility

There are thousands of road closures and changed traffic rules that impact vehicle routing every day. Detecting the road closures and traffic rule changes is essential for dynamic route planning and navigation serving. In this article, we propose a driving-behavior modeling-based method for accurately and effectively detecting the road anomalies. In the first step, we detect the areas of anomalies by using the deviation between drivers’ actual and expected behaviors. To discover the cause of anomalies, we explore the drivers’ short-term destination and find the crucial link pairs in anomalous areas through a novel optimized link entanglement search algorithm, namely, the Select Link Entanglements (SELES) algorithm. Finally, we analyze the crowd's driving patterns to explain the road network anomalies further. Experiments on a very large GPS dataset demonstrate that the proposed approach outperforms the existing methods in terms of both accuracy and effectiveness.

Detection of anomalous nitrogen dioxide (NO2) concentration in urban air of India using proximity and clustering methods

ABSTRACTOwing to accurate future air quality estimates, need for detecting the anomalously high increase in concentration of pollutants cannot be adjourned. Plentiful approaches were proposed in the past to substantially determine the abnormal conditions, but most of the statistical approaches were computationally expensive and ignored the false alarm ratios. Thus, a hybrid of proximity- and clustering-based anomaly detection approaches to identify anomalies in the air quality data is suggested in this work. The Gaussian distribution property of the real-world data set is utilized further to segregate out anomalies. The results depicted twofold advantages of our approach, by efficient extraction of anomalies and with increased accuracy by reducing the number of false alarms. Specifically, the presence of NO2 concentration in air is investigated in this work, considering its constant increase over decades as well as its inevitable health risks. Furthermore, spatiotemporal segments with anomalously high NO2 concentrations for 14 residential, industrial, and commercial areas of five cities in India are extracted. To validate the results, a comparative analysis with existing approaches of anomaly detection and with two benchmark data sets is performed. Results showed that our method outperformed the existing methods of anomaly detection, when evaluated over metrics such as sensitivity, miss rate, and false alarms. Further, a detailed analysis of extracted anomalies and a detailed discussion about the factors responsible for such anomalies are presented in this work. This study is helpful in educating government and people about spatiotemporal, geographical, and economic conditions responsible for anomalously high NO2 concentrations in air.Implications: Using our methodology, days with extremely high concentration of any pollutant in air, at any particular location, can be extracted. The reasons for such extremely high pollutant concentration on particular days of a year can be studied and preventive measures can be taken by the government. Thus, by identification of causes of anomalies, future similar events can be avoided. This would also help in people’s decision making in case such events occur in the future.

Launching of a cochlear implant program at the Cracow Otolaryngology Clinic

Introduction: It is estimated that hearing impediment affects approximately 12% of the population. Some forms of hearing disorders can be successfully treated conservatively, while in others surgery plays a key role. Often, therapeutic methods are exhausted and the patient faces the decision about the need for acoustic organ prosthesis. Depending on the causes of anomalies, apparatus for air conduction, bone conduction or cochlear implants are used. A cochlear implant replaces the damaged sensory cells of the inner ear. It constitutes an electronic prosthesis of the acoustic organ, composed of two parts. The internal part (implanted in the inner ear) consists of an implant and electrodes, which conduct electrical signals to the cochlea. The external part consists of a microphone, a speech processor, and a transmitter. Care report: A 70-year-old patient with deep hearing loss in the right ear and deafness of the left ear. During the interview, the patient denied mechanical injuries of the head and neck, acoustic trauma, inflammation or otorrhea. Based on the conducted tests, qualification otolaryngology, audiology, and otoneurology, radiology, surdologopedy and psychology were performed for cochlear implant insertion. The patient was qualified for cochlear implantation in the left ear under general anesthesia. The surgical team was composed of dr hab. Maciej Wiatr, prof. dr hab. Kazimierz Niemczyk, prof. dr hab. Jacek Składzień, MD Agnieszka Wiatr with anesthetic monitoring under the supervision of MD Renata Witkowska. The patient was discharged home on day 7 after surgery in good general and local condition. Simultaneous rehabilitation was started at the Center for Daily Rehabilitation of Hearing and Speech at the Specialist Center of Diagnosis and Rehabilitation for Children and Adolescents with Hearing Disabilities of the Polish Association of the Deaf in Cracow.

Time course and expression pattern of the neuronal markers in the developing human spinal cord

The aim of this study was to examine the spatio-temporal appearance of different neuronal cell subtypes by analyzing expression patterns of several neuronal markers (calretinin, neurofilament 200 (NF200), vanilloid receptor 1(VR1) and calcitonin gene-related peptide (CGRP)) of the embryonic human spinal cord (SC). Developing human SCs from 11 human conceptuses beetwen 5–10 developmental weeks (DW) were examined by light and electron microscopy and immunofluorescence. Light and electron microscopy revealed different embryonic stages of recognizable structure of the SC. NF200, CGRP and VR1 positive cells were observed in SCs during 5th–6th DW. NF200 was predominantly expressed in the ventral part, indicating presence of motoneurons. As development advanced, NF200 was mainly expressed in the marginal zone. Expression of CGRP was intense during all of the investigated periods, predominantly during the 5th–6th DW pointing to neural sensory differentiation, as opposed to the last DW when reduced expression of CGRP in the marginal layer indicated the terminations of the sensory afferents. Expression of VR1 was highest in the intermediate zone, at the beginning and at the end of the investigated periods, pointing to VR1 spatial pattern in the visceral afferents in the grey matter, while the first signs of calretinin were found in the 9th–10th DW ventrally. Delineating the relationships between factors involved in processes of neuronal differentiation as well as spatial and temporal arrangement of SC interrelated neurons can provide a useful information about normal SC development as well as the insight in possible causes of anomalies and disorders during embryonic life.

A 30‐Year Simulation of the Outer Electron Radiation Belt

AbstractAs society becomes more reliant on satellite technology, it is becoming increasingly important to understand the radiation environment throughout the Van Allen radiation belts. Historically most satellites have operated in low Earth orbit or geostationary orbit (GEO), but there are now over 100 satellites in medium Earth orbit (MEO). Additionally, satellites using electric orbit raising to reach GEO may spend hundreds of days on orbits that pass through the heart of the radiation belts. There is little long‐term data on the high‐energy electron flux, responsible for internal charging in satellites, available for MEO. Here we simulate the electron flux between the outer edge of the inner belt and GEO for 30 years. We present a method that converts the >2‐MeV flux measured at GEO by the Geostationary Operational Environmental Satellites spacecraft into a differential flux spectrum to provide an outer boundary condition. The resulting simulation is validated using independent measurements made by the Galileo In‐Orbit Validation Element‐B spacecraft; correlation coefficients are in the range 0.72 to 0.88, and skill scores are between 0.6 and 0.8 for a range of L∗ and energies. The results show a clear solar cycle variation and filling of the slot region during active conditions and that the worst case spectrum overlaps that derived independently for the limiting extreme event. The simulation provides a resource that can be used by satellite designers to understand the MEO environment, by space insurers to help resolve the cause of anomalies and by satellite operators to plan for the environmental extremes.

Anomaly detection in high-dimensional data with the Mahalanobis–Taguchi system

The Mahalanobis–Taguchi (MT) system is a typical Taguchi method and plays an important role in several fields. This study aims at improving the statistical procedure employed for anomaly detection in high-dimensional data with the MT system. The proposed study focuses on estimating the eigenvalues and eigenvectors of the covariance matrix and introduces an estimation procedure based on sparse principal component analysis (SPCA) in the MT system. By incorporating SPCA, eigenvalues and eigenvectors can be accurately estimated for high-dimensional data. In addition, the interpretation of the principal components can become simplified with decreasing number of nonzero elements in the estimated eigenvectors. Numerical experiments have confirmed that the proposed procedure is beneficial for both anomaly detection performance and investigating the cause of anomalies in high-dimensional data. Furthermore, a limitation of the proposed study is its emphasis on improving anomaly detection procedures founded on the first principal component and its residual component. However, the scope of such an anomaly detection procedure can be easily expanded for further improvement.

A probable example of cranial dysraphism from New Orleans, Louisiana

AbstractCranial dysraphism, a pathological condition resulting from a neural tube defect, is a rarely reported condition in archaeological and clinical literature. A defect at bregma was identified on human remains recovered from New Orleans, Louisiana, when exhumation of several commingled bodies occurred in a paupers' cemetery in 2015. Initial speculation regarding the cause of the condition consisted of trauma, pathological condition, a natural variant, or the result of a congenital defect. A differential diagnosis was utilised to approach the breadth of potential causative factors, incorporating clinical information on the bony response of soft tissue defects common in modern reports. However, during a review of the bioarchaeological literature, an alternative explanation for the feature, one that did not result from typically reported causes, revealed that this observed defect was likely an example of the rarely reported condition known as cranial dysraphism. Through review of both clinical and bioarchaeological data, the resulting diagnosis observed here is supported through the unique characteristics that several other authors have identified as associated with cranial dysraphism. Unique features of this bony defect include the smooth walls of the depression, a saucer‐like shape with an anterior rim built up of cortical bone, lack of diploë exposure, a perforation at the base of the saucer, and the retention of cranial sutures. Although the defect also closely resembles the bony response to a cyst, the perforation and anterior rim are supportive of its diagnosis as a cranial dysraphism. This case study of a single occurrence of cranial dysraphism is reported to assist practitioners in differentiating between this condition and other potential causes of anomalies of similar appearance.

An Analysis of Cytogenetic and Clinical Phenotype of Klinefelter Syndrome Over 17 Years

Background: Clinical phenotype in Klinefelter syndrome (KS) shows utmost contrariety according to the genetic presentation. The karyotype 47, XXY is one of the commonest types of sex chromosomal abnormality in males presenting with infertility, hypogonadism, small penis, gynaecomastia and tall stature. Cytogenetic study is the only way to differentiate between chromosomal abnormality and other androgen deficiency disorders. The aim of this study was to investigate cytogenetic and phenotypic profile of Klinefelter syndrome in a group of referred patients with suspected genetic disorders.Methods: This observational study was carried out at the Cytogenetic Laboratory of the Department of Immunology BIRDEM General Hospital for a period of seventeen years from 2000 to 2016. A total of 9,216 patients suspected for different chromosomal abnormalities (e.g. numerical chromosomal disorders, primary amenorrhoea, ambiguous genitalia etc.) were included in this study referred by physicians of various discipline from different areas of Bangladesh. From the patients referred for cytogenetic study, detailed family history and physical findings were noted. Complete genetic examination and pedigree construction was done to exclude non-chromosomal causes of anomalies. For cytogenetic analysis, peripheral lymphocyte culture by the standard method using the G-banding technique was employed.Results: In this study 1.67% (154) of referred patients were diagnosed as Klinefelter syndrome in cytogenetic study and most of them were diagnosed in their adulthood between 20-29 years of age. Classical cytogenetic form of KS-47, XXY (87%) were most common followed by other mosaic and supernumerary X chromosome aneuploidy. Most of the patients presented with tall stature (61.7%) followed by other features such as gynaecomastia (45.5%), eunuchoid skeleton (29.8%), sexual dysfunction (34.41%), small penis (22.7%), and delayed development of secondary sex characteristics (22.7%).Conclusion: Diagnosis of Klinefelter syndrome in early age before puberty is needed to be differentiated from other related disorders and thereby improving the quality of life by providing appropriate and timely treatment. Therefore, we have to focus to improve our overall capacity to diagnose genetic disorders for proper interventionBirdem Med J 2018; 8(2): 126-131

Evaluation of the causes and impact of outliers on residential building energy use prediction using inverse modeling

Inverse modeling techniques are often used to predict the performance and energy use of buildings. Residential energy use is generally highly dependent on occupant behavior; this can limit a model's accuracy due to the presence of outliers. There has been limited data available to determine the cause of and evaluate the impact of such outliers on model performance, and thus limited guidance on how best to address this in model development. Thus the main objective of this work is to link the use of outlier detection methods to the causes of anomalies in energy use data, and to the determination of whether or not to remove an identified outlier to improve an inverse model's performance. A dataset of 128 U.S. residential buildings with highly-granular, disaggregated energy data is investigated. Using monthly data, change-point modeling was determined to be the best method to predict consumption. Three methods then are used to identify outliers in the data, and the cause and impact of these outliers is evaluated. Approximately 19% of the homes had an outlier. Using the disaggregate data, the causes were found to mostly be due to variations in occupant-dependent use of large appliances, lighting, and electronics. In 20% of homes with outliers, the removal of the outlier improved model performance, in particular all outliers identified with both the standard deviation and quartile methods, or all three methods. These two combinations of outlier detection methods are thus recommended for use in improving the prediction capabilities of inverse change point models.

Prevalence and Patterns of Congenital Limb Defects in the North of Iran (2007-2011)

Background: Congenital limb defects (CLDs) are the leading cause of disability across the world.CLDsvary depending on the anatomical location, type, and cause of anomaly. The present study aimed to evaluate the prevalence and patterns of CLDs in the North of Iran. Methods: This descriptive-analytical, hospital-based study was conducted in three hospitals in Gorgan, the capital of Golestan Province in the north of Iran. Samples included 32,895 newborns with CLDs, and stillborn neonates were excluded from the study. Data analysis was performed in SPSS version 16 using Chi-square at the significance level of ≤0.05. Results: Overall prevalence ofCLDs was three cases per 1,000 live births, and the rate was estimated at 3.17 and 2.82 cases per 1,000 live births in male and female infants, respectively. In addition, the prevalence of upper- and lower-limb anomalies was 1.03 and 1.91 cases per 1,000 live births, respectively. In terms ofethnicity, the prevalence of CLDs among the native Fars, Turkmen, and Sistani populations was 3.86, 2.02, and 3.85 cases per 1,000 live births, respectively. Clubfoot was the most common type of CLDs, and the most prevalent type of the associated malformationswas gastrointestinal anomalies. Conclusion: According to the results, the prevalence of CLDs in the north of Iran was similar to European countries (2.11-3.18 cases per 1,000 live births), while it was lower compared to the other regions in Iran (5.8 cases per 1,000 live births).

Anomaly identification of foundation uplift pressures of gravity dams based on DTW and LOF

Anomaly can provide valuable information for dam safety monitoring. In this paper, a methodology integrating dynamic time warping and local outlier factor to identify anomalies of time series on various time scales is proposed. The main steps of the methodology are introduced in detail. First, measured time series are preprocessed using moving average and normalization, respectively, to eliminate influence of random and amplitude variations. Following this, time series of independent variables (predictors) are selected as templates to calculate dynamic time warping distances using the global constraint to measure similarities with the dependent variable (a predicant) on a large time scale. Then, traditional regression models are built to find out contributions of independent variables to structural behavior, as well as to highlight unwanted behaviors at relatively on a small time scale. Furthermore, local outlier factor values of multivariate are computed, which helps finding anomalies on a small time scale. Finally, causes of anomalies are analyzed comprehensively. A case study is presented focus on the measured foundation uplift beneath the local riverbed blocks of Xixi reservoir dam. Results show that point anomalies occur with sudden changes of independent variables, but contextual anomalies boil down to coupled effect of high reservoir level and low ambient temperature. The natural condition of the rock foundation underlying blocks, which is characterized as intensively open tension joints, is responsible for uplift anomalies.

A Minimal-Sensing Framework for Monitoring Multistage Manufacturing Processes Using Product Quality Measurements

For implementing data analytic tools in real-world applications, researchers face major challenges such as the complexity of machines or processes, their dynamic operating regimes and the limitations on the availability, sufficiency and quality of the data measured by sensors. The limits on using sensors are often related to the costs associated with them and the inaccessibility of critical locations within machines or processes. Manufacturing processes, as a large group of applications in which data analytics can bring significant value to, are the focus of this study. As the cost of instrumenting the machines in a manufacturing process is significant, an alternative solution which relies solely on product quality measurements is greatly desirable in the manufacturing industry. In this paper, a minimal-sensing framework for machine anomaly detection in multistage manufacturing processes based on product quality measurements is introduced. This framework, which relies on product quality data along with products’ manufacturing routes, allows the detection of variations in the quality of the products and is able to pinpoint the machine which is the cause of anomaly. A moving window is applied to the data, and a statistical metric is extracted by comparing the performance of a machine to its peers. This approach is expanded to work for multistage processes. The proposed method is validated using a dataset from a real-world manufacturing process and additional simulated datasets. Moreover, an alternative approach based on Bayesian Networks is provided and the performance of the two proposed methods is evaluated from an industrial implementation perspective. The results showed that the proposed similarity-based approach was able to successfully identify the root cause of the quality variations and pinpoint the machine that adversely impacted the product quality.

Detection and interpretation of anomalous water use for non-residential customers

Smart water meters can help businesses save water. But achieving this goal requires trusted algorithms for processing the data and intuitive, interactive software systems to support end users in decision making. This paper presents an algorithm and a web-based software system to detect and visualise anomalous water use. The algorithm calculates an anomaly score for each day, together with a rationale describing the symptoms of unusual water use. The score for each day is based on ten features of daily demand and its historical context. The score and its rationale are posted to users to help them track down the underlying physical causes of anomalies. Using data from two aquatic leisure centres, we demonstrate that anomaly scores give better coverage than traditional threshold-based systems, that end users are able to utilise the timely feedback to save water, and that the algorithm is reasonably robust to parameter settings.

Applying complexity theory: A primer for identifying and modeling firm anomalies

This essay elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., non-innovative versus highly innovative, small versus large size in sales or number of employees, or serving local versus international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability)—even if directional analyses (e.g., regression analysis, including structural equation modeling) indicates that the independent (main) effects of the individual antecedents relate to outcomes directionally—because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of non-innovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing—null hypotheses statistic testing (NHST)—to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies—associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advances strategic management beyond the current dominant logic of directional modeling of single outcomes.

Root Cause Failure Analysis of Corrosion in Wet Gas Piping

This paper discusses the root causes and operational mitigations of corrosion anomalies reported for the wet gas system of a floating production storage and offloading (FPSO) vessel. This system is classified in general into the flowing and dead leg sections and operated below 62 °C, where a protective FeCO3 film will not form. Corrosion which is temperature dependent is general. The root cause of the anomalies in the flowing wet gas system was found to be due to CaN and solid deposition in the LP separator, increasing levels of corrosive condensate, which drips down the pipe walls, resulting in wall losses of less than 50%. Evacuation of deposits in the LP separator and subsequent control of its formation rate would mitigate anomalies attributed to increasing levels of corrosive condensates. The corrosion pattern at elbows, modified by turbulent flow of emergent fluid streams through the complex-geometry elbows, is mitigated by velocity control. The root cause of the inspection anomalies at dead leg locations was due to the lifting of the PSV and/or infrequent flow through a bypass valve and directly to flare. Infrequent transportation of condensed, corrosive water thereby resulted in dead leg corrosion. Proposed mitigation entails replacement of defective PSVs and modifying flow frequencies. This paper summarizes all the identified root causes and proposed mitigations and provides a greater understanding of the internal degradation mechanisms operating in wet gas systems in general.

A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction

In the operation of artificial satellites, it is very important to monitor the health status of the systems and detect any symptoms of anomalies in the housekeeping data as soon as possible. Recently, the data-driven approach to the system monitoring problem, in which statistical machine learning techniques are applied to the large amount of measurement data collected in the past, has attracted considerable attention. In this paper, we propose a new data-driven health monitoring and anomaly detection method for artificial satellites based on probabilistic dimensionality reduction and clustering, taking into consideration the miscellaneous characteristics of the spacecraft housekeeping data. We applied our method to the telemetry data of the small demonstration satellite 4 (SDS-4) of the Japan Aerospace Exploration Agency (JAXA) and evaluated its effectiveness. The results show that the proposed system provides satellite operators with valuable information for understanding the health status of the system and inferring the causes of anomalies.

Development of single-event-effects analysis system at the IMP microbeam facility

Single-event-effects (SEEs) in integrated circuits (ICs) caused by galactic single ions are the major cause of anomalies for a spacecraft. The main strategies to decrease radiation failures for spacecraft are using SEEs less-sensitive devices and design radiation hardened ICs. High energy ion microbeam is one of the powerful tools to obtain spatial information of SEEs in ICs and to guide the radiation hardening design. The microbeam facility in the Institute of Modern Physics (IMP), Chinese Academy of Science (CAS) can meet both the liner energy transfer (LET) and ion range requirements for SEEs simulation experiments on ground. In order to study SEEs characteristics of ICs at this microbeam platform, a SEEs analysis system was developed. This system can target and irradiate ICs with single ions in micrometer-scale accuracy, meanwhile it acquires multi-channel SEE signals and maps the SEE sensitive regions online. A 4-Mbit NOR Flash memory was tested with this system using 2.2GeVKr ions, the radiation sensitive peripheral circuit regions for SEEs of 1 to 0 and 0 to 1 upset, multi-bit-upset and single event latchup have been obtained.

Depth Estimation Using the Tilt Angle of Gravity Field due to the Semi-Infinite Vertical Cylindrical Source

Tilt angle filter is an interpretation method that is used to determine the source borders locations from potential fields data. Moreover, the tilt angle is applied for estimation of the anomaly source depth, such as contact-depth method and tilt-depth method. In this paper an application of the tilt angle technique obtained from the first vertical and horizontal gradients of the gravity anomaly from semi-infinite vertical cylindrical source is described. The technique is based on the tilt angle and derivatives ratio. In this approach the depth estimates are proportional to the computed tilt angles and their distances from the cross section center of the anomaly cause on the surface. This new method is termed the tilt-distance-depth (TDD). The method is demonstrated using synthetic gravity data, with and without random noise, and real gravity data from Iran. The results are also compared with the solutions from Euler deconvolution technique and inverse modelling using Modelvision software.

Análisis de series temporales de precipitación y vegetación para la detección de anomalías en la producción de alimentos en el Cuerno de África. El caso de Lower Shabelle (Somalia)

<p align="justify">The Horn of Africa is one of the most food-insecure locations around the world due to the continuous increase of its population and the practice of the subsistence agriculture. This causes that much of the population cannot take the minimum nutritional needs for a healthy life. Moreover, this situation of food vulnerability may be seriously affected in the coming years due to the effects of climate change. The aim of this work is combine the information about the state of the vegetation that offers the NDVI with rainfall data to detect negative anomalies in food production. This work has been used the monthly products of NDVI MOD13A3 of MODIS and the rainfall estimation product TAMSAT, both during the period 2001-2015. With these products we have calculated the average of the entire time period selected and we have detected the years whose NDVI values were further away from the average, being these 2010, 2011 and 2014. Once detected the years with major anomalies in NDVI, there has been an exclusive monthly analysis of those years, where we have analysed the relationships between the value of NDVI and monthly rainfall, obtaining a direct relationship between the two values. It also has been used crop calendar to focus the analysis in the months of agricultural production and finding that the main cause of anomalies in vegetation is a decrease in the registration of rainfall during the months of agricultural production. This reason explains the origin of the food shortages that occurred in 2010 and 2011 that generated an enormous humanitarian crisis in this area.</p>

Diameter dependent thermal sensitivity variation trend in Ni/4H-SiC Schottky diode temperature sensors

This report is on the diameter dependent thermal sensitivity variation trend of Ni/4H-nSiC Schottky barrier diode (SBD) temperature sensors. Scaled SBDs of 2, 1.6, and 1.2 mm in diameter were fabricated using standard photolithography process comprising a field plate and a guard ring as edge terminations on the same epitaxial wafer. Taking into consideration the heat loss and power consumption, the thermal sensitivities of the fabricated SBDs were measured in the current range from 1 μA to 50 pA. The temperature was varied from 273 to 473 K in step of 25 K. An authoritative consequence of the present study is the observed increase in thermal sensitivity with the diameter of the fabricated SBDs. An exhaustive investigation confirms that in all diodes, there exist nanosized patches, which assumed to have different barrier heights and hence ascertained to be the main cause of anomalies in thermal sensitivity variation with diode size. Taking into account the effective area of these patches, theoretically fitted I–V curves to experimental data show that the numbers of patches were higher in diode with least size and decrease with the increase in the size of the diode. The corresponding barrier heights of these patches were found to be distributed in a Gaussian like manner at the fabricated Ni/4H-nSiC interface with least standard deviation (σ0) in the diode of maximum size.