Statistical Parameters Research Articles

SATELLITE INDICES IN VEGETATION DESCRIPTION - COMPARATIVE ANALYSIS AND INTERDEPENDENCES

The study analyzed the variability of some indices, interdependence relationships and differences in the vegetation description. Based on the Rapid Eye satellite system, images were taken for the ATU Padureni area, Timis County, Romania. From the analysis of the images, the spectral bands were obtained and the NDVI, gNDVI, NDVIre, and RTVIcore indices were calculated. The series of values presented a normal distribution, under conditions of r = 0.982 in the case of gNDVI, r = 0.961 in the case of NDVI, r = 0.983 in the case of NDVIre, respectively r = 0.990 in the case of RTVIcore. The descriptive statistical analysis facilitated obtaining the values of the statistical parameters and the description of the data series for each index. Based on the coefficient of variation (CV), the indices considered showed high variability; CV = 54.7535 in the case of the NDVIre index, CV = 57.2578 in the case of the RTVIcore index, CV = 58.0955 in the case of the NDVI index and CV = 64.5481 in the case of the gNDVI index. Linear models described the variation of the gNDVI index and the NDVIre index in relation to the NDVI index (p = 0). Polynomial models of the 2nd degree described the variation of RTVIcore in relation to NDVI (R2 = 0.918), in relation to gNDVI (R2 = 0.954) and in relation to NDVIre (R2 = 0.938), p = 0. The comparative analysis of gNDVI and NDVIre indices in relation to NDVI led to statistically significant differences (p<0.001) in relation to the mean and median value of the NDVI index.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

Evaluation and comparison of infiltration models for estimating infiltration capacity of different textures of irrigated soils

Accurate estimation of infiltration rates is crucial for effective irrigation system design and evaluation by optimizing irrigation scheduling, preventing soil erosion, and enhancing water use efficiency. This study evaluates and compares selected infiltration models for estimating water infiltration rates in the Shillanat-iv irrigation scheme in northern Ethiopia. Soil samples were collected to determine textural classes using hydrometer soil texture analysis and the United States Department of Agriculture (USDA) textural triangle. The soil textural map of the study was created using the inverse distance weight interpolation technique in ArcGIS version 10.4. Infiltration rates were measured using the double-ring infiltrometer for five soil textures: clay loam, loam, sandy clay loam, clay, and sandy loam. Six infiltration models (Kostiakov, Modified Kostiakov, Revised Modified Kostiakov, Philip, Horton, and Novel) were employed and evaluated using statistical parameters. Model calibration and validation were conducted using data from 38 points within the study area. The parameter values of the infiltration models were optimized using SPSS statistical software using least-squares errors. The results showed that, Revised Modified Kostiakov, Modified Kostiakov, and Novel infiltration models demonstrated superior capability in estimating infiltration rates for clay loam, loam, and sandy loam soil textures, respectively. Horton's model outperformed other models in estimating infiltration rates for both sandy clay loam and clay soil textures. The appropriately fitted infiltration models can be utilized in designing the irrigation system to estimate the infiltration rate of soil textures within the selected irrigation scheme and at similar sites with comparable soil textures.

Internet of Healthcare Things Based Detection of EEG Epileptic Seizures: A Smart System

Background: A seizure is a sudden and uncontrolled electrical activity in the brain that can cause a variety of symptoms, depending on the location and severity of the abnormal activity. It can be a symptom of an underlying neurological disorder or can occur without an apparent cause. Epilepsy is one of the most common causes of seizures. Overactive electrical discharges disrupt normal brain electrical activity and interfere with nerve cell communication. Aim and Objective: A comprehensive analysis of the literature revealed that several CAD system designs have shown to be useful to radiologists in routine medical practice as second-opinion aids for epileptic seizure detection in circumstances where a clear differentiation cannot be formed subjectively. CAD systems are made to help radiologists by automating the examination of medical data and images, improving the efficiency and accuracy of diagnosis. These systems examine patterns in medical imaging using machine learning approaches, which can be quite helpful in spotting small abnormalities that the human eye can miss. Moreover, the objective of this study was to design a smart healthcare system using a combination of DWT, Hjorth, and statistical parameters for seizure detection. Method: In this research article, the authors proposed the framework of the Internet of Healthcare Things (IoHT) for performing seizure detection. The authors used different pre-processing techniques and extracted different features like Hjorth, wavelets, and statistics, which were classified using different machine-learning techniques. This novel methodology combines a number of technologies and techniques to improve seizure detection's precision and dependability. Results: DWT + Hjorth + Statistical parameters with bior 1.5 as the pre-processing technique yielding the best outcomes. 86% accuracy was obtained with kNN for k = 5, 93% accuracy was obtained with a linear kernel for an SVM classifier, and 95.5% accuracy was obtained using a decision tree and logistic regression. The authors also considered another dataset for validation and received 96.83% accuracy with decision tree and logistic regression classifiers considering the bior1.5 wavelet filter as a preprocessing technique. Conclusion: The IoHT framework offers a multi-modal, adaptive method of seizure detection that enables the dynamic modification of detection parameters and the incorporation of extra sensor signals. This improves seizure detection's precision and dependability, which has important implications for patient care and monitoring. This work shows how IoHT and machine learning can be combined to build a reliable, real-time seizure detection system. These developments, which make it possible for prompt interventions and individualized treatment plans, can significantly improve the quality of care for individuals with epilepsy.

Comparative analysis of surface roughness level for bearing coating using VMD of vibration signal

Degradation in surface finish of the material is an important concern in engineering applications. However, most of the conventional techniques available to monitor the surface roughness requires dismantling of the machine element. Though in rotary components like bearing, it is practically not feasible. Therefore, in this work, an on-board technique is proposed to compare the level of surface roughness based on vibration signature. To demonstrate, five different industrial standard coatings (Nickel, Copper, Zinc phosphate (ZnP), Silver, and Black oxide) were carried out on raw five ball bearings (NBC6205). Two sets of coated bearing were prepared, where first set was utilised for experimentation and second for the purpose of measuring surface roughness of bearing surface. These coated bearings were tested at five different RPMs ranging from 300 RPM to 1500 RPM and their vibration signals were recorded. The recorded vibration signals must be having characteristics originated from ball rolling on different level of surface roughness and hence distributed in nature. Further, to target distributed characteristics present in the signal, commonly used statistical parameters for vibration signature analysis (RMS, Crest factor, Variance, Skewness, Kurtosis, Shannon entropy and Log energy) were calculated. Then, correlation of the parameters was checked in relation to the different levels of surface roughness but no relation was found. The signals were then decomposed into six intrinsic mode functions (IMFs) using Variational Mode Decomposition (VMD) method. Again, same statistical parameters were calculated for these decomposed levels, it has been noted that Shannon entropy have shown correlation to surface roughness in at least one decomposed level between 900 to 1500 RPM with minimum value of chain index as 176.65. Moreover, in this RPM range, responsive frequency bands found to be shifted towards higher side i.e. from 567 to 4590 Hz.

Design of Novel TRPA1 Agonists Based on Structure of Natural Vasodilator Carvacrol-In Vitro and In Silico Studies.

Considering the escalating global prevalence and the huge therapeutic demand for the treatment of hypertension, there is a persistent need to identify novel target sites for vasodilator action. This study aimed to investigate the role of TRPA1 channels in carvacrol-induced vasodilation and to design novel compounds based on carvacrol structure with improved activities. In an isolated tissue bath experiment, it was shown that 1 µM of the selective TRPA1 antagonist A967079 significantly (p < 0.001) reduced vasodilation induced by 3 mM of carvacrol. A reliable 3D-QSAR model with good statistical parameters was created (R2 = 0.83; Q2 = 0.59 and Rpred2 = 0.84) using 29 TRPA1 agonists. Obtained results from this model were used for the design of novel TRPA1 activators, and to predict their activity against TRPA1. Predicted pEC50 activities of these molecules range between 4.996 to 5.235 compared to experimental pEC50 of 4.77 for carvacrol. Molecular docking studies showed that designed molecules interact with similar amino acid residues of the TRPA1 channel as carvacrol, with eight compounds showing lower binding energies. In conclusion, carvacrol-induced vasodilation is partly mediated by the activation of TRPA1 channels. Combining different in silico approaches pointed out that the molecule D27 (2-[2-(hydroxymethyl)-4-methylphenyl]acetamide) is the best candidate for further synthesis and experimental evaluation in in vitro conditions.

Multi‐band millimetre wave indoor directional channel measurements and analysis for future wireless communication systems

AbstractSingle‐input single‐output (SISO) three‐dimensional (3D) wideband indoor directional measurements collected in a factory environment and an office environment at 38 and 70 GHz are presented. 3D single‐input multiple‐output (SIMO) dual polarised measurements with 1 × 2 antenna configurations were also carried out in a meeting room, a conference room, and an office room at the 60 GHz band. The measurements cover both azimuth and elevation by rotating the directional antenna (RDA) at the receiver side. Different statistical channel parameters such as power delay profile, power angle profile, root‐mean‐square delay spread, angular spread, and path loss were estimated for different possible antenna orientations between the transmitter and the receiver, which include line‐of‐sight, obstructed line‐of‐sight, and non‐line‐of‐sight. The polarisation effects on path loss models and the delay and angular spread models based on the surface area of the environment are studied. The results will be valuable for the design of indoor millimetre wave cellular networks.

Methods and algorithms of optimization in computer engineering: review and comparative analysis

Introduction: The main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Methods: One of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered. Results: Through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal.Conclusions: A comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented

Performance Comparison of Empirical Models for Estimating Global Solar Irradiation in the Soudano-Sahelian Zone of Cameroon: The Case of the City of Maroua and Garoua

The main objective of this study is to compare thirty-five (35) solar radiation models available in the open literature in order to predict monthly solar radiation in two main cities of Cameroon. This estimation and comparison are based on selected statistical comparison parameters named, root mean square error (RMSE), mean bias error (MBE), mean percentage error (MPE) and determination coefficient (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;). These different models are implemented using regression analysis tools named Exel and MATLAB. Estimated values were compared with measured values according to normalized values of statistical parameters, using measured meteorological data of more than 19 years, from 1984 to 2015. All the models have been classified with their associated ranking according to their statistical parameter accuracy. From this study it appears that the models of Ertekin and Yaldiz (MOD20), Togrul and Onat (MOD28), are more accurate than other models. Indeed, for the city of Maroua (MBE%=-2.82E-14; RMSE%=0.862; MPE=-0.00845; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.985), while for Garoua (MBE%=-9.21E-15; RMSE%=0.806; MPE=-0.00631; R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.959). according to their accuracy these models can be therefore be used to predict monthly solar radiation for soudano-sahelian regions of Cameroon. Correlation equations found in this paper will help solar energy researcher to estimate data with trust because of its fine agreement with the observed one. hence the models presented in this study could be used to evaluate accurately the solar radiation at any locations with similar climate.

Leukemia Cancer Cells Segmentation and Classification using Machine Learning

Determining the aim of the project is to detect the leukemia at earlier stage with the help of image processing techniques. Leukemia means blood cancer which is featured by the uncontrolled and abnormal production of white blood cells (leukocytes) by the bone marrow in the blood. Acute Lymphoblastic Leukemia (ALL) is a type of leukemia which is more common in children. The term Acute‟ means that leukemia can progress quickly and if not treated may lead to fatal death within few months. Due to its non specific nature of the symptoms and signs of ALL leads wrong diagnosis. Even hematologist finds it difficult to classify the leukemia cells, there manual classification of blood cells is not only time consuming but also inaccurate. Therefore, early identification of leukemia yields in providing the appropriate treatment to the patient. Detection through images is fast and cheap method as there is no special need of equipment for lab testing. We have focused on the changes in the geometry of cells like area, perimeter and statistical parameters like mean and standard deviation which separates white blood cells from other blood components using processing tools like MATLAB. After recognizing its statistical properties, types of leukemia will be identified based on the irregularities in the shape.

Distinguishing Iron Deficiency Anemia From Beta-Thalassemia Trait: Comparative Analysis of CRUISE Index and Other Traditional Diagnostic Indices.

Introduction Iron deficiency anemia and beta-thalassemia trait are two common and important differentials of microcytic hypochromic anemia. Various discrimination indices using two or more common complete blood cell count (CBC) parameters have been used to distinguish between the two since1973. Recently, a new discriminant index, the CRUISE index, was proposed in the year 2019. The efficacy of various older indices along with the CRUISE index was evaluated for patients in our geographical area. Materials and method Ours was a laboratory-based, cross-sectional study where 100 patients, based on inclusion and exclusion criteria, with microcytic hypochromic anemia were evaluated for CBC parameters along with serum ferritin and hemoglobin-high performance liquid chromatography (Hb HPLC). A total of eight discrimination indices namely, Mentzer, Srivastava, Shine & Lal, Green & King, RDWI, England & Fraser, Kerman I and CRUISE index were used and evaluated for their diagnostic efficacy using different statistical parameters. ROC curves were obtained and a new cut-off value was proposed for our population. Data was analysed using Microsoft Excel (Microsoft® Corp., Redmond, WA, USA) and SPSS v29.0.2.0 (20) (IBM Corp., Armonk, NY, USA). Results Out of the total 100 cases, 39 were beta-thalassemia trait and 61 were iron deficiency anemia cases. The average age was 36.7 (±12.7 SD) years. Among the 73 females, 43 were diagnosed as iron deficiency anemia (IDA) and 30 as beta-thalassemia trait (BTT) cases. Among the 27 males, 18 were diagnosed as IDA and nine as BTT cases. The mean values were significantly lower in IDA patients for mean corpuscular volume (MCV) (p=.008), mean corpuscular haemoglobin (MCH) (p=.003), and mean corpuscular haemoglobin concentration (MCHC) (p=.003) and significantly higher for red cell distribution width (RDW) (p=.020). The mean ferritin levels in cases of IDA were 7.61 (±3.75) mcg/L and in BTT were 87.09 (±66.77 SD) mcg/L. The mean HbA2 levels in IDA cases were 2.75% (±0.41% SD) and BTT cases were 5.57% (±0.73% SD). CRUISEindex revealed the highest AUC (0.934), YI (76.21) and accuracy (90%) followed by the Mentzer index with a diagnostic accuracy of 81%. Shine & Lal index revealed the lowest AUC (0.710), YI (3.28) and accuracy (41%). Conclusion CRUISE index, which was recently proposed, was ranked 1st in terms of AUC, YI, and accuracy and was considered 2nd best in terms of sensitivity for differentially diagnosing the two conditions. Mentzer index, a commonly used index, also revealed a high diagnostic accuracy in our study for differentiating BTT from IDA. CRUISE index being a novel index, more research work needs to be carried out in various other geographical setups to evaluate the efficacy of this index.

Modeling of scour hole characteristics under turbulent wall jets using machine learning

The novelty of the present study is to investigate the parameters that depict the scour hole characteristics caused by turbulent wall jets and develop new mathematical relationships for them. Four significant parameters i.e., depth of scouring, location of scour depth, height of the dune and location of dune crest are identified to represent a complete phenomenon of scour hole formation. From the gamma test, densimetric Froude number, apron length, tailwater level, and median sediment size are found to be the key parameters that affect these four dependent parameters. Utilizing the previous data sets, Multi Regression Analysis (linear and non-linear) has been performed to establish the relationships between the dependent parameters and influencing independent parameters. Further, artificial neural network-particle swarm optimisation (ANN-PSO) and gene expression programming (GEP) based models are developed using the available data. In addition, results obtained from these models are compared with proposed regression equations and the best models are identified employing statistical performance parameters. The performance of the ANN-PSO model (RMSE = 1.512, R2 = 0.605), (RMSE = 6.644, R2 = 0.681), (RMSE = 6.386, R2 = 0.727) and (RMSE = 1.754, R2 = 0.636) for predicting four significant parameters are more satisfactory than that of regression and other soft computing techniques. Overall, by analysing all the statistical parameters, uncertainty analysis and reliability index, ANN-PSO model shows good accuracy and predicts well as compared to other presented models.

Evaluation of the Goss-modified solvation parameter model for the characterization of biphasic systems and descriptor assignments

The solvation parameter model uses six descriptors identified as excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, McGowan's characteristic volume, V, and the gas-liquid partition constant on hexadecane at 25 °C, L to model the distribution of neutral compounds in biphasic systems. Abraham's version of this model uses all six descriptors with two separate linear free energy relationship models for the transfer of compounds from a gas phase to a condensed phase and between condensed phases. Goss proposed a modification to this model that uses a single calibration model regardless of the physical state for each phase and five of the descriptors employed in Abraham's model (E descriptor is eliminated). The capability of Abraham's model and the Goss-modified model to characterize the contribution of intermolecular interaction to retention for gas and reversed-phase liquid chromatographic systems and distribution in liquid-liquid partition systems is evaluated using the WSU compound descriptor database. These more accurate values for the Abraham descriptors have not been utilized previously for the evaluation of the Goss-modified model and should be more capable of discerning subtle differences in model performance. It is shown that model quality defined by statistical parameters favors Abraham's model over the Goss-modified model with differences in model quality greater for systems in which Abraham's model indicates a significant contribution from electron lone pair interactions and for systems in which one phase is a solvent containing perfluoroalkyl substituents. There is a small systematic difference for the terms describing the combined contributions of cavity formation and dispersion interactions and for interactions of a dipole-type. The contribution of hydrogen-bonding interactions is virtually identical for the two models. The model intercepts are generally different and potentially assigned to a larger contribution from lack-of-fit for the Goss-modified model. Although the Abraham model descriptors have been routinely employed for applications using the Goss-modified model the possibility that Goss-model specific descriptors should be employed was evaluated. Using the Solver method and Goss-model specific calibration models for chromatographic and liquid-liquid partition systems a new set of Goss-specific descriptors was calculated for 28 varied compounds. These descriptors show good statistical agreement with the Abraham descriptor values with an average deviation of 0.009, -0.003, -0.004, and -0.023, respectively, for the S, A, B, and L descriptors, corresponding to a relative absolute deviation in percent of 2.2 %, 3.9 %, 4.3 %, and 1.2 %, respectively.

Statistical modeling and optimization of heavy metals (Pb and Cd) adsorption from aqueous solution by synthesis of Fe3O4/SiO2/PAM: isotherm, kinetics, and thermodynamic

In this study, magnetic material was synthesized using iron salts, then silicon-specific material was used to gain porosity, straight-chain polyacrylamide (PAM) was modified to give the surface functional properties, and the final product synthesized Fe3O4/SiO2/PAM nanocomposite material. Heavy metal (Pb and Cd) removal studies were carried out with the synthesized composite material, considering the central composite design and response surface methodology (CCD-RSM) optimization model. The effects of various parameters, for example, the initial concentration, pH, adsorbent dose, temperature and contact time, were investigated as a part of this study. To optimize these parameters, the CCD-RSM model was applied to design the experiments. Analysis of variance (ANOVA) was applied to evaluate statistical parameters and investigate interactions of variables. In the designed experimental set, the amount of adsorbent (30 mg), pH 7.0 value, temperature (40 °C), initial concentration of Pb (80 mg/L) and Cd (20 mg/L) and 90 min contact time were determined as the optimum conditions. The high coefficient of determination of both metals showed good agreement between experimental results and predicted values (R2 0.99; 0.95). TEM, SEM, XRD, FTIR, BET and Zeta potential analyses were performed to characterize the structure and morphology of the adsorbent. In Pb2+ and Cd2+ heavy metal removal studies, maximum adsorption capacities were determined as 66.54 and 13.22 mg/g, respectively. Additionally, adsorption isotherms, adsorption kinetics and thermodynamic modeling studies were conducted. Features such as large surface area and high adsorption capacity of the synthesized nanoparticles were observed. In this study, Fe3O4/SiO2/PAM demonstrated its potential as an effective adsorbent for the removal of heavy metal ions present in simulated wastewater samples. In particular, we can say that the material has a strong selectivity, as well as a high affinity for Pb(II) ions.

Forensic age estimation of adolescents using computed tomography of the clavicles.

In forensic age estimation, CT imaging of the clavicles is used to determine an age over completed 21years. If ossification of the medial clavicular epiphysis is complete, young men are assumed to be over 21years of age. The aim of this study is to check the statistical parameters (specificity, predictive probability) for the characteristic "completed ossification of the medial clavicles". 285 male patients who, for various reasons, received a chest CT at the Medical Center of the University of Freiburg between 1st December 2019 and 6th December 2022 were screened for the study, of whom 203 patients were included in the study. The stage of clavicular ossification was classified as stage 1 - 5 according to Schmeling. While 70 out of 71 patients under 21years of age were correctly estimated to be under 21years of age, there was one patient whose ossification on one side was classified as stage 4 and who would therefore have been estimated to be over 21years of age. If only subjects whose ossification stage was the same on both sides are included, the specificity of the test method is 100% and the positive predictive probability is 100%. If patients for whom only one side is stage 4 are also included, the specificity is 98.6%. Thus, only the complete and symmetrical ossification of both clavicles (stage 4 according to the Schmeling classification) in a standardised thin-layer CT can be classified as a reliable indicator of an age over 21years in young men. In the case of asymmetric ossification of the medial clavicles (stage 4 is not reached on one side), false positive evaluations and the incorrect assumption of an age over 21years can occur.

Development and validation of a standard area diagram set for assessment of anthracnose severity on cucumber leaves

AbstractAnthracnose, caused by species in the Colletotrichum orbiculare complex, is a major foliar disease in open‐field cucumber farming worldwide. This study aimed to develop and validate a standard area diagram (SAD) set to estimate the severity of anthracnose on cucumber leaves. For this purpose, a SAD set with nine levels of severity (1; 3; 5; 10; 20; 30; 40; 50 and 60%) is proposed. The SAD set was validated by 16 raters with no experience in evaluating plant diseases. Both accuracy and precision improved when the proposed SAD set was employed. The statistical parameters were bias coefficient factor‐Cb (no SAD set = 0.891, with SAD set = 0.982); correlation coefficient‐r (no SAD set = 0.851, with SAD set = 0.941); and Lin's concordance correlation coefficient‐ρc (no SAD set = 0.755, with SAD set = 0.924). In addition, estimates were more reliable: intra‐class correlation coefficient‐ρ (no SAD set = 0.646, with SAD set = 0.887). The SAD set proposed here is a useful tool for improving visual assessments of anthracnose severity on cucumber leaves.

HBIM for Conservation of Built Heritage

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation.

Transformer Fault Diagnosis Based on Intelligent Algorithm and Partial Discharge

Abstract As an important part of power transmission and transformation equipment and power systems, transformers’ operating conditions will have a direct impact on the stability and reliability of the power system. In view of the problems of high diagnosis cost and low accuracy of diagnosis results in existing fault diagnosis technology, this paper uses the pulse current method to detect the pulse signal generated by the discharge model based on the partial discharge experimental platform data, analyzes and studies the discharge characteristics of different discharge defects, and draws For the partial discharge phase distribution (PRPD) spectrum of each defect type, the statistical characteristic parameters characterizing the characteristics of the spectrum were extracted. The partial discharge statistical characteristics were used as classification feature quantities and input into three network models: RBF, GRNN, and PSO-GRNN. The results show that PSO-GRNN has a good recognition effect and is suitable for partial discharge type identification. It has important practical significance and application value for the all-round health management of power transformers.

Inference on spatiotemporal dynamics for coupled biological populations.

Mathematical models in ecology and epidemiology must be consistent with observed data in order to generate reliable knowledge and evidence-based policy. Metapopulation systems, which consist of a network of connected sub-populations, pose technical challenges in statistical inference owing to nonlinear, stochastic interactions. Numerical difficulties encountered in conducting inference can obstruct the core scientific questions concerning the link between the mathematical models and the data. Recently, an algorithm has been proposed that enables computationally tractable likelihood-based inference for high-dimensional partially observed stochastic dynamic models of metapopulation systems. We use this algorithm to build a statistically principled data analysis workflow for metapopulation systems. Via a case study of COVID-19, we show how this workflow addresses the limitations of previous approaches. The COVID-19 pandemic provides a situation where mathematical models and their policy implications are widely visible, and we revisit an influential metapopulation model used to inform basic epidemiological understanding early in the pandemic. Our methods support self-critical data analysis, enabling us to identify and address model weaknesses, leading to a new model with substantially improved statistical fit and parameter identifiability. Our results suggest that the lockdown initiated on 23 January 2020 in China was more effective than previously thought.

Light crude oil rheology under chemical solvents treatment

Abstract This work attempts to study the rheological behavior of Algerian light crude oil from Hassi-Messaoud field with and without chemical solvents in order to improve the flow characteristics. Using the rheometer AR2000, an experimental investigation was conducted to measure the rheological properties via flow test and dynamic mode (oscillation) at various temperatures. Several factors such as temperature (20, 30, and 45 °C), shear rate (between 0.01 and 700 s−1), and solvent concentration (between 2 and 6% of toluene, naphtha, and kerosene) on the rheological parameters have all been studied for this purpose. The statistical parameter standard error (SE) provided justification for the experimental validation of the Herschel-Bulkley model. The results of the flow test showed that these solvents had a significant impact on the flow characteristics of light crude oil at various temperatures, with toluene being the most effective. The viscoelastic properties of crude oil were shown to be considerably influenced by temperature and solvent type, as demonstrated by the dynamic mode study that identified the complex modulus (G*), elastic modulus (G′), and viscous modulus (G″).