Statistical Variability Research Articles

Spatial variations and determinants of iron containing foods consumption among 6–23 months old children in Ethiopia: spatial, and multilevel analysis

Consuming foods high in iron benefits metabolic processes as well as the development of the neonatal and fetal brain. Despite the significance of eating foods high in iron for public health, Ethiopian practices are still limited when compared to the World Health Organization's (WHO) assessment of its consumption of such foods. This study used the Ethiopia Demographic and Health Survey (EDHS) to evaluate the consumption of iron-rich foods, regional clustering, and related characteristics among children aged 6–23 months. The information was taken from the typical EDHS 2019 dataset, which included a weighted sample of 1572 young children aged 6–23 months old in total. Utilizing Kuldorff's SaTScan version 9.6 software, spatial scan statistics were produced. Software from ArcGIS 10.8 was used to display the regional distribution of inadequate consumption of foods high in iron. Utilizing multilevel or mixed effects logistic regression analysis, the associated determinants for a healthy diet rich in foods containing iron were found. In the final model, a P-value of < 0.05 was announced as a statistical significance variable. Overall, in Ethiopia, children aged 6–23 months consumed iron-rich foods at a rate of 27.14% (95% CI 24.99–29.39). Poor intake of foods heavy in iron is concentrated in Ethiopia's regional states of Afar, a sizable portion of Amhara, Oromia, Tigray, Somali, Gambela, and SNNPS. Primary and secondary education (AOR = 1.73, CI 95%: 1.23, 2.41), and (AOR = 1.97,CI 95%: 1.25, 3.10), having ≥ 2 under five children, and current status of breastfeeding (AOR = 0.62 (CI 95%: 0.45, 0.84), and (AOR = 0.32, CI 95%: 0.23, 0.44), giving birth at health facilities (AOR = 1.51, CI 95%: 1.06, 2.13),being from Afar and Somali regions (AOR = 0.39, 95%: 0.17, 0.93), and (AOR = 0.26, CI 95%: 0.10, 0.69) have shown statistically significant association with the outcome variable respectively. In Ethiopia, providing high-iron meals and supplements to under-2-year-old children represents minimal, but persistent, public health expenses. Based on the identified determinants, the Ethiopian federal ministry of health and other stakeholders should pay special attention to the locations designated as hot spots for maternal and child health service enhancement to promote the consumption of iron-rich meals among children aged 6–23 months.

Blockchain technology concerns and corporate financial risk prevention—A quasi-natural experiment for Chinese listed A-share companies

The rapid development of blockchain technology (BT) has gained widespread attention from all sectors of society. In order to remain competitive, enterprises have responded positively to this research and development, which is conducive to the enhancement of their own digital construction. BT can provide technical support for companies to strengthen their internal management and reduce business risks caused by information asymmetry. However, the ability to reduce enterprise financial risk effectively depends on the breadth and depth of BT concerns. This study constructs word-frequency statistical variables of BT concerns based on the annual report data of listed companies. It conducts an empirical study on the issue of BT concerns affecting corporate financial risk prevention and regulation based on a propensity score matching difference-in-difference model. The results show that: (1) Attention to BT significantly reduces corporate financial risk (2) In terms of the mechanism effect, Attention to BT can significantly reduce corporate financial risk by optimizing accounts receivable management, enhancing debt servicing ability, and improving corporate performance. (3) The heterogeneity analysis shows that the concern of BT in reducing financial risk is even more significant for enterprises whose social responsibility is not effectively regulated and for hybrid enterprises. This study enriches the literature on the mechanism of BT to enterprise financial risk and provides a reference for both enterprise-level blockchain concerns and strengthening enterprise financial risk management.

Wait and watch: A trachoma surveillance strategy from Amhara region, Ethiopia.

Trachoma recrudescence after elimination as a public health problem has been reached is a concern for control programs globally. Programs typically conduct district-level trachoma surveillance surveys (TSS) ≥ 2 years after the elimination threshold is achieved to determine whether the prevalence of trachomatous inflammation-follicular (TF) among children ages 1 to 9 years remains <5%. Many TSS are resulting in a TF prevalence ≥5%. Once a district returns to TF ≥5%, a program typically restarts costly mass drug administration (MDA) campaigns and surveys at least twice, for impact and another TSS. In Amhara, Ethiopia, most TSS which result in a TF ≥5% have a prevalence close to 5%, making it difficult to determine whether the result is due to true recrudescence or to statistical variability. This study's aim was to monitor recrudescence within Amhara by waiting to restart MDA within 2 districts with a TF prevalence ≥5% at TSS, Metema = 5.2% and Woreta Town = 5.1%. The districts were resurveyed 1 year later using traditional and alternative indicators, such as measures of infection and serology, a "wait and watch" approach. These post-surveillance surveys, conducted in 2021, were multi-stage cluster surveys whereby certified graders assessed trachoma signs. Children ages 1 to 9 years provided a dried blood spot and children ages 1 to 5 years provided a conjunctival swab. TF prevalence in Metema and Woreta Town were 3.6% (95% Confidence Interval [CI]:1.4-6.4) and 2.5% (95% CI:0.8-4.5) respectively. Infection prevalence was 1.2% in Woreta Town and 0% in Metema. Seroconversion rates to Pgp3 in Metema and Woreta Town were 0.4 (95% CI:0.2-0.7) seroconversions per 100 child-years and 0.9 (95% CI:0.6-1.5) respectively. Both study districts had a TF prevalence <5% with low levels of Chlamydia trachomatis infection and transmission, and thus MDA interventions are no longer warranted. The wait and watch approach represents a surveillance strategy which could lead to fewer MDA campaigns and surveys and thus cost savings with reduced antibiotic usage.

Statistical Characterization of Full-Scale Thermophilic Biological Systems to Inform Process Optimization

This paper focuses on using a novel approach to assess the statistical variability of management data from an aerobic thermophilic biological plant (AWTP) utilizing a fluidized bed biological reactor. A proper statistical characterization of full-scale thermophilic biological systems, in fact, may inform process optimization in the light of a future automation of treatment plants. We present a case study that spans the period from 2018 to 2023 and encompasses various high-strength aqueous waste (AW) in continuous mode. Key aspects of the proposed analytical approach include: (i) utilizing advanced descriptive statistics, such as violin graphs, to depict the variability of monitored parameters over five years; (ii) conducting correlation analyses (Spearman and Pearson correlation matrices) specifically focusing on nitrogenous forms within the AW; (iii) applying multivariate statistical analysis to assess the correlation between pollutants released and the plant’s energy and oxygen consumption; and (iv) reconstructing parameter trends by considering periodic and random components, thus enhancing the understanding of the system’s behavior over time. The findings presented in this paper offer valuable insights into the performance and optimization of AWTPs, potentially leading to a proper planning of the loads and consequent feeding of the plants. If properly enacted, our approach may provide a significant contribution to the field of aqueous waste management.

Seismic landslide susceptibility assessment using principal component analysis and support vector machine

Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects. However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA–SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the “Very high” landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides. This study provides a new approach for seismic landslide susceptibility assessment in areas lacking in landslide inventory data.

Economic Performance of the Economy of Kosovo and Metohija

The problem of analysing the factors of the dynamics of the economic performance of every economy, which means the economy of the southern provinces of Serbia, the economy of Kosovo and Metohija, is continuously very current, challenging, significant, and complex. Adequate control of key factors can significantly influence the achievement of the target economic performance of any economy. The application of multi-criteria decision-making methods enables adequate control of the key factors of the economic performance of each economy. Taking this into account, this paper analyses the dynamics of the economic performance of the economy of Kosovo and Metohija in the period 2013 – 2022 based on the LMAW-DNMA method. In the top five years according to the economic performance of the economy of Kosovo and Metohija according to the LMAW-DNMA methods, it falls in the following order: 2022, 2021, 2019, 2017 and 2018. The worst economic performance of the economy of Kosovo and Metohija was achieved in 2020, partly due to the corona virus pandemic. Recently, the economic performance of the economy of Kosovo has improved significantly and Metohija. Adequate management of the analysed statistical variables (gross domestic product, inflation, agriculture, industry, export, import, capital, income, and taxes) influenced this. Similarly, the economic climate, foreign direct investments, the energy crisis, the digitalisation of the company’s entire operation, and other factors. Their adequate control can greatly influence the achievement of the target economic performance of the economy of Kosovo and Metohija.

Statistical Copolymerization‐Induced Self‐Assembly (stat‐PISA) for Colloidal Hydrogels

AbstractDespite recent advances in colloidal hydrogels, designing general and robust strategies that enable the controlled fabrication of colloidal hydrogels with strong mechanical properties still proves challenging. Herein, the statistical copolymerization‐induced self‐assembly (stat‐PISA) of monomers with hydrogen bonding ability is developed as a general strategy for colloidal hydrogels with high stretchability, anti‐swelling, and self‐healing abilities. This concept is first verified by the statistical dispersion copolymerization of acrylic acid and diacetone acrylamide, yielding statistical copolymer colloidal particles that further coalesced into a 3D colloidal network due to the hydrogen bonding interactions and hydrophobic microdomains on the colloidal surface. Both the microstructure and the mechanical properties of the colloidal hydrogels can be readily regulated by varying the stat‐PISA formulation. Besides, the colloidal hydrogel is developed as a selective dye adsorbent, with excellent selectivity for positively charged dyes. Due to its anti‐swelling property, the colloidal hydrogel is evaluated as a flexible strain sensor for motion detection underwater. The broad feasibility of this strategy is demonstrated by three additional stat‐PISA formulations, which all yielded colloidal hydrogels with good mechanical properties. This strategy is anticipated to inspire the design of colloidal hydrogels with robust mechanics and tailor‐made functionality, and broaden the potential applications of colloidal hydrogels.

An Optimum Load Forecasting Strategy (OLFS) for Smart Grids Based on Artificial Intelligence

Recently, the application of Artificial Intelligence (AI) in many areas of life has allowed raising the efficiency of systems and converting them into smart ones, especially in the field of energy. Integrating AI with power systems allows electrical grids to be smart enough to predict the future load, which is known as Intelligent Load Forecasting (ILF). Hence, suitable decisions for power system planning and operation procedures can be taken accordingly. Moreover, ILF can play a vital role in electrical demand response, which guarantees a reliable transitioning of power systems. This paper introduces an Optimum Load Forecasting Strategy (OLFS) for predicting future load in smart electrical grids based on AI techniques. The proposed OLFS consists of two sequential phases, which are: Data Preprocessing Phase (DPP) and Load Forecasting Phase (LFP). In the former phase, an input electrical load dataset is prepared before the actual forecasting takes place through two essential tasks, namely feature selection and outlier rejection. Feature selection is carried out using Advanced Leopard Seal Optimization (ALSO) as a new nature-inspired optimization technique, while outlier rejection is accomplished through the Interquartile Range (IQR) as a measure of statistical dispersion. On the other hand, actual load forecasting takes place in LFP using a new predictor called the Weighted K-Nearest Neighbor (WKNN) algorithm. The proposed OLFS has been tested through extensive experiments. Results have shown that OLFS outperforms recent load forecasting techniques as it introduces the maximum prediction accuracy with the minimum root mean square error.

Effects of gamma irradiation on morphological changes in Porang (Amorphophallus muelleri Blume)

Porang is agricultural plant that become important in the recent years. However, harnessing them through traditional genetic breeding is time-consuming and expensive. Inducing mutagenesis may be a short-time option for its genetic improvement. Therefore, the aim of this reserach is to determine the effect of gamma irradiation in the morphological changes of Porang. This research used completely randomized design (CRD) consisted of 6 levels of radiation including 0 Gray, 2 Gray, 4 Gray, 6 Gray, 8 Gray and 10 Gray. All treatments were replicated 4 times to account for statistical variability. The 30 days after planting of explants were then exposed to varying doses of gamma irradiation using a gamma cobalt-60 at Badan Tenaga Nuklir Nasional (BATAN) Jakarta Indonesia. Porang height, total shoot and leaves, width and length of leaves were characterized after irradiation. Quantitative data were analyzed by using Anova one way and duncan on 5% significance level. Level irradiation of 2 Gray affected the number of shoots and leaves, plant height, width and length of leaves and leaf morphology and colour. Gamma radiation has succeeded in increasing the agricultural value of porang and producing porang with higher genetic diversity than wildtypes.

Heart Rate Variability-Based Prediction of Autonomic Dysreflexia After Spinal Cord Injury.

Autonomic dysreflexia (AD) is a common autonomic complication of spinal cord injury (SCI) characterized by a sudden increase is blood pressure triggered by peripheral stimulation, such as bladder distention. Iatrogenic AD events often occur during various medical procedures including urodynamic assessments (UDSs) used to evaluate lower urinary tract (LUT) function in individuals with SCI. To date, there are no established clinical practices that would allow early detection of the development of episodes of AD. Heart rate variability (HRV) is a reliable and non-invasive metric for evaluating autonomic regulation of the cardiovascular system, with demonstrated utility in people with SCI during UDSs. We aim to provide a comprehensive evaluation of cardiovascular function during UDS-induced AD using ultra-short-term HRV analysis and identify changes in cardiovascular dynamics to predict the onset of AD. We assessed cardiovascular data in a total of 24 participants with sensorimotor complete SCI above T6 (17 males, 7 females, median age = 43 [36-50] years) who experienced AD during UDS. We used continuous electrocardiographic recordings to evaluate HRV in 60 sec overlapping windows during filling cystometry. The mean of "normal-to-normal" heartbeats (meanNN), its standard deviation (SDNN), and the root mean square of successive differences (RMSSD) were calculated and used in all subsequent analyses. We found that SDNN and RMSSD diminished during the early phase of bladder filling and sharply increased during AD. Using the lowest point of statistical variability in heart rate (i.e., SDNN), we were able to predict AD events within 240 sec (percentile 25-percentile 75: 172-339 sec) before the first systolic blood pressure peak after AD onset (sensitivity = 0.667; specificity = 0.875). Our results indicated a temporary increase in sympathetic activity during the early phase of bladder filling, which is followed by an increase in parasympathetic outflow to the heart when AD occurs. These findings have significant clinical implications that extend beyond the context of UDS and demonstrate the importance of identifying early changes in HRV in order to accurately predict AD episodes in people living with SCI.

Comparing Two Bootstrapped Regions in Images: The D-Test

ObjectivesMany molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available. Material and methodsAfter presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance. ResultsThe results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test. ConclusionThis paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.

Trends and impacts of temperature and fire regimes in South Africa’s coastal national parks: implications for tourism

Climate change poses significant risks on coastal national park-based tourism through its effect on accessibility, comfort levels, and spatiotemporal changes of attractions. Wildfires and extreme temperatures have become issues of concern. Due to the widespread occurrence of fires in national parks along coastal areas and the noticeable changes in temperature regimes, more needs to be done to deepen understanding of their trends and impacts and devise appropriate management regimes. This study analyses the trends and impacts of fires and temperature-related variables in six coastal South African national parks, namely Agulhas, Garden Route, West Coast, Table Mountain, Namaqua, and Addo Elephant National Park. The triangulation and convergence model of the mixed-methods research design was adopted as the inquiry strategy. Data on statistical weather variables from the South African Weather Services and the National Aeronautics and Space Administration were used to calculate temperature trends using Mann–Kendall trend tests and homogeneity analysis. Data collection included questionnaire surveys, key informant interviews, field observations, and document analysis. The study observed statistically significant (p < 0.05) changes in temperature-related variables in all coastal national parks. There is also an increase in the intensity and spatial spread of fires, resulting in increased fire damage over time. Despite the current efforts of the coastal parks to manage wildfires and the changing climatic patterns, the fire and temperature regimes are threatening the biophysical environment with resultant effects on tourism and the economic viability of the national parks. The need to reengineer the fire suppression mechanisms and temperature-resilient tourism facilities has been observed.

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles (EVs) globally, the demand for lithium-ion batteries has grown dramatically, resulting in many batteries being retired in the future. Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery ageing level on service and provide regroup strategy of the retied batteries in secondary use. There are still limitations on the current rapid battery capacity estimation methods, such as direct current internal resistance (DCIR) and electrochemical impedance spectroscopy (EIS), in terms of efficiency and robustness. To address the challenges, this paper proposes an improved version of DCIR, named pulse impedance technique (PIT), for rapid battery capacity estimation with more robustness. First, PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency, in which the coherence analysis is used to guide the selection of a reliable frequency band. The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method, which obtains more information on the battery capacity evaluation. Second, various statistical variables are used to extract ageing features and Pearson correlation analysis is applied to determine the highly correlated features. Then a linear regression model is developed to map the relationship between extracted features and battery capacity. To validate the performance of the proposed method, the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series. The results reveal that the proposed PIT can provide comparative indicators to EIS, which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost. Conflict of Interest Statement The authors declare no conflicts of interest.

The thermal comfort of pedestrians in a humid subtropical climate according to different thermal perception ratings.

This study aims to evaluate agreement among subjective thermal comfort, thermal sensation, thermal perception, and thermal tolerance indices, according to pedestrians in downtown Santa Maria, southern Brazil, which has a humid subtropical climate (Cfa). Between August 2015 and July 2016 (three periods), 1728 questionnaires were applied. Evaluation of the dependence of statistical variables was based on gender and age, at three periods of time: August 2015 (864 respondents), January 2016 (432 respondents), and July 2016 (432 respondents). Statistical evaluation was based on Pearson's chi-square test using RStudio software, and a significance level (α) of 5% for thermal comfort, thermal sensation, thermal preference, and thermal tolerance was used. Results indicated that age and gender affect the relationship between the variables. Thermal comfort and thermal tolerance presented the best correlation and coherence, regardless of age or gender. This study contributes to knowledge on the local microclimate and can contribute to urban planning to implement strategies that improve pedestrians' thermal comfort.

The Implementation Path and Improvement Strategy of National Defense Education in Colleges and Universities under the Perspective of “Three-Whole Parenting”

Abstract National defense in colleges and universities is a crucial component of the nation’s overall defense strategy. It significantly influences the enhancement of national defense awareness among college students. This study develops a regression analysis model, applies likelihood estimation for parameter calculation, and uses the coefficient of determination alongside the F-test to evaluate its significance. The model analyzes factors affecting national defense education perceptions among college students and identifies cognitive variations across different statistical variables. It concludes by pinpointing shortcomings in national defense education, proposing a “three-whole-education” teaching model, and exploring innovative implementation strategies. The results show that the national defense awareness β=0.527, sig=0.000, indicating that the national defense awareness of college students plays a very crucial role in the national defense education of college students. It is followed by military training (β=0.435, sig=0.000) and again by military theory class (β=0.293, sig=0.015). This study is of great significance to the research on the construction and innovation of military training systems and the operation modes of national defense education in colleges and universities.

Data analysis and modeling study of the relationship between physical activity and sleep disorder intervention for college students

Abstract Accompanied by the increasing prevalence of sleep disorders among students in colleges and universities, there is an urgent need for colleges and universities to provide a scientific basis and practical guidance for exercise intervention for sleep disorders among students. In this paper, in order to study the relationship between physical exercise and sleep disorder intervention, after analyzing the etiology and pathology of sleep disorders, we first surveyed students of a university in Guangzhou to analyze the differential characteristics of students’ sleep quality under different statistical variables. Then, a logistic linear regression model was established to analyze the influencing factors of college students’ sleep quality by taking the variables with statistically significant results in the one-way analysis as independent variables and college students’ sleep quality as dependent variables. Finally, college students with more serious sleep disorders in the university were subjected to a semester of walking, running, and soccer physical exercise to analyze the role of physical activity in influencing the intervention of sleep disorders in students. It was found that the PSQI scores of the walking group, running group, and soccer group before and after one semester of walking exercise decreased by 2.29, 3.16, and 3.18 points, respectively, and the scores of the rest of the items decreased to different degrees, which reflected the good effect of the physical exercise intervention on the intervention of sleep disorders. Explore the effects of physical exercise intervention on the quality of sleep among college students and provide practical suggestions for improving college students’ sleep quality through exercise.

Research on deformation and temperature characteristics of micro negative Poisson's ratio anchor rod

This study aims to investigate the global deformation, temperature characteristics, and their correlation during the quasi-static tensile process of micro-NPR anchor rods. The LEW-500 static tensile testing system was used to carry out the uniaxial tensile testing with three deformation rates, and the deformation and temperature of the anchor rods were studied through the DIC system and IRT system. The mechanical results indicate that the micro-NPR anchor rods have the characteristics of high elongation, high constant resistance, no yielding platform, and no obvious necking. The DIC results show that the deformation of anchor rods can be divided into the initial and accelerated differentiation stages. In the initial differentiation stage, the statistical dispersion of deformation is low and increases slowly with time. In the accelerated differentiation stage, a relatively concentrated local deformation appears, shown by the sharp rise and decline of statistical dispersion. The IRT results indicate that the surface temperature of the anchor rods also increases with deformation, and the upward trend exhibits distinct characteristics in two differentiation stages. The faster the deformation rate, the higher the temperature of the anchor rods’ surface. The correlation coefficient of axial strain and temperature difference in the anchor rods is calculated, and the correlation coefficient is maintained at a high level in the accelerated differentiation stage. The linear fitting of the standard deviation of axial strain and the temperature difference is perfect. This study provides a foundation and a new idea for future deformation patterns and monitoring analysis in on-site applications of micro-NPR anchor rods through the joint analysis of two non-contact measurement methods.

Multicriteria fuzzy statistical analysis of biogas energy systems dependability

The work is devoted to the problems of assessing the dependability of autonomous energy systems with biogas type of electrical generation. The purpose of the work is to develop an intelligent tool for multi-criteria dependability assessment, taking into account the statistical uncertainty of individual indicators. A three-level hierarchy (according to the degree of generalization) of dependability indicators, represented by statistical (at the lower level) and fuzzy linguistic (starting from the second level) variables, has been developed. It is proposed to implement the transition from statistical values of lower-level indicators to numerical values of second-level indicators using an artificial neural network. Fuzzification of second-level indicators was carried out using L. Zadeh’s z-number apparatus, which allows taking into account statistical uncertainty. To determine the integral dependability indicator (top of the hierarchy) based on second-level indicators, it is proposed to use the Mamdani fuzzy inference algorithm. The constructed procedure for determining the level of dependability allows us to obtain data for making scientifically based decisions when operating biogas energy systems.

Designing a Hybrid Method of Artificial Neural Network and Particle Swarm Optimization to Diagnosis Polyps from Colorectal CT Images.

Since colorectal cancer is one of the most important types of cancer in the world that often leads to death, computer-aided diagnostic (CAD) systems are a promising solution for early diagnosis of this disease with fewer side effects than conventional colonoscopy. Therefore, the aim of this research is to design a CAD system for processing colorectal Computerized Tomography (CT) images using a combination of an artificial neural network and a particle swarm optimizer. First, the data set of the research was created from the colorectal CT images of the patients of Loghman-e Hakim Hospitals in Tehran and Al-Zahra Hospitals in Isfahan who underwent colorectal CT imaging and had conventional colonoscopy done within a maximum period of one month after that. Then the steps of model implementation, including electronic cleansing of images, segmentation, labeling of samples, extraction of features, and training and optimization of the artificial neural network (ANN) with a particle swarm optimizer, were performed. A binomial statistical test and confusion matrix calculation were used to evaluate the model. The values of accuracy, sensitivity, and specificity of the model with a P value = 0.000 as a result of the McNemar test were 0.9354, 0.9298, and 0.9889, respectively. Also, the result of the P value of the binomial test of the ratio of diagnosis of the model and the radiologist from Loqman Hakim and Al-Zahra Hospitals was 0.044 and 0.021, respectively. The results of statistical tests and research variables show the efficiency of the CTC-CAD system created based on the hybrid of the ANN and particle swarm optimization compared to the opinion of radiologists in diagnosing colorectal polyps from CTC images.

High-Field Transport and Statistical Variability of Nanosheet Oxide Semiconductor FETs With Channel Length Scaling

High-Field Transport and Statistical Variability of Nanosheet Oxide Semiconductor FETs With Channel Length Scaling