A complete framework of predicting the attributes of sea clutter under different operational conditions, specified by wind speed, wind direction, grazing angle, and polarization, is proposed for the first time. This framework is composed of empirical spectra to characterize sea-surface profiles under different wind speeds, the Monte Carlo method to generate realizations of sea-surface profiles, the physical-optics method to compute the normalized radar cross-sections (NRCSs) from individual sea-surface realizations, and regression of NRCS data (sea clutter) with an empirical probability density function (PDF) characterized by a few statistical parameters. JONSWAP and Hwang ocean-wave spectra are adopted to generate realizations of sea-surface profiles at low and high wind speeds, respectively. The probability density functions of NRCSs are regressed with K and Weibull distributions, each characterized by two parameters. The probability density functions in the outlier regions of weak and strong signals are regressed with a power-law distribution, each characterized by an index. The statistical parameters and power-law indices of the K and Weibull distributions are derived for the first time under different operational conditions. The study reveals succinct information of sea clutter that can be used to improve the radar performance in a wide variety of complicated ocean environments. The proposed framework can be used as a reference or guidelines for designing future measurement tasks to enhance the existing empirical models on ocean-wave spectra, normalized radar cross-sections, and so on.

BACKGROUND: Quantitative structure-activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature.
METHODS: In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed.
RESULTS: The findings revealed that employing the coefficient of determination (r2) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies.
CONCLUSIONS: This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

Neutron induced reactions with different target materials and isotopes are very important for completion of nuclear data base as well as for many applications. Since the experimental determinations of the reaction cross sections for such reactions are not always available, attempts were made to introduce empirical or semi empirical formulae. In this work, new empirical formulation for (n, 2n) nuclear reactions on natural isotopes for neutron energies range 8-20 MeV, based on some published experimental works. The deuced formulae are then used to calculate the excitation functions for the (n,2n) on target isotopes having atomic numbers in the range 21-79. The cross section values for many reactions, having previously measured values, are determined and inter-comparison with the experimental and the TENDL-2019 database values are given using some statistical error parameters. Special attention was made to some Lanthanide isotopes. The results showed satisfied predictions in most cases.

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

The physical properties of electronic devices made by 2,6-diphenyl anthracene (DPA) are influenced by the microtexture of DPA surfaces. This work focused on the experimental investigation of the 3-D surface microtexture of DPA thin films deposited on OTS (octadecyltrichlorosilane), HMDS (Hexamethyldisilasane), OTMS (octadecyltrimethoxysilane), and Si/SiO2 (300 nm SiO2 thickness) substrates with 5 and 50 nm thicknesses and 5 and 10 μm scan size. The thin film surfaces were recorded using atomic force microscopy (AFM) and their images were stereometrically analyzed to obtain statistical parameters, in accordance with ASME B46.1-2009 and ISO 25178-2: 2012. The results showed the effect of different manufacturing parameters on microtexture values where the granular structure is confirmed in all films. In addition, root mean square is increased by increasing the thickness from 5 to 50 nm for all types of substrates.

Leakage detection is a fundamental problem in water management. Its importance is expressed not only in avoiding resource wastage, but also in protecting the environment and the safety of water resources. Therefore, early leak detection is increasingly urged. This paper used an intelligent leak detection method based on a model using statistical parameters extracted from acoustic emission (AE) signals. Since leak signals depend on many operation conditions, the training data in real-life situations usually has a small size. To solve the problem of a small sample size, a data improving method based on enhancing the generalization ability of the data was proposed. To evaluate the effectiveness of the proposed method, this study used the datasets obtained from two artificial leak cases which were generated by pinholes with diameters of 0.3 mm and 0.2 mm. Experimental results show that the employment of the additional data improving block in the leak detection scheme enhances the quality of leak detection in both terms of accuracy and stability.

The cross-sections for proton-induced nuclear reactions on natural nickel were experimentally measured for the natNi(p,x)60,61,64Cu, natNi(p,x)55,57,58Co and natNi(p,x)57Ni reactions from their respective thresholds up to 17 MeV proton energy. The stacked-foil technique in combination with HPGe γ-ray spectroscopy was used. Three codes were used in the theoretical calculations namely ALICE-IPPE, EMPIRE-3.2.2 and TENDL-2017. Comparison of the present results with the earlier reported experimental data and with the code results were carried out.

BACKGROUND: Scoliosis is a spine abnormal deviation, which is an idiopathic disorder among children and adolescents. As a matter of the fact, distribution of loads on the patient\'s spine and load-carrying capacity of the vertebral column are both random variables. Therefore, the probabilistic approach may consider as a sophisticated method to deal with this problem.
METHODS: Reliability analysis is a probabilistic-based approach to consider the uncertainties of load and resistance of the vertebral column. The main contribution of this paper is to compare the reliability level of a normal and scoliosis spinal. To do so, the numerical analyses associated with the inherent random parameters of bones and applied load are performed. Then, the reliability indices for all vertebrae and discs are determined. Accordingly, as the main innovation of this paper, the system reliability indices of the spinal column for both normal and damaged backbone systems are represented.
RESULTS: Based on the required reliability index for normal spinal curvature the target system reliability level for scoliosis disorder is proposed.
CONCLUSIONS: Since the proposed target reliability index is based on the strength limit state of the vertebral column, it can be considered as a reliability level for any proposed treatment approaches.

Screening is a special issue in medical questions concerning disease prevention. Preconditions for screening are clearly defined by the World Health Organization. High prevalence, effectiveness of therapy, availability of accepted test procedure and consensus concerning the economic concerns are necessary for successful implementation of a screening program. Preventive diagnostic studies can only be understood if one is familiar with the statistical terms sensitivity, specificity, prevalence, incidence and bias (especially overdiagnosis and lead time bias). Aspects of radiation protection are especially important in asymptomatic volunteers. The new radiation protection law in Germany also gives the opportunity to define new screening procedures even with use of radiation exposure in individual prevention programs. Potential diseases for radiological secondary prevention with high mortality are malignant tumors (especially breast cancer, lung cancer, colorectal cancer) and cardiovascular diseases (coronary heart disease, stroke or aortic aneurysm).

Deposition of aerosols in the respiratory system depends inter alia on their size and the respiratory tract deposition is appreciable for nanometer-sized particles. This article presents the results of measurements of size distributions of aerosols in the range of several nanometers up to about 20 μm in the underground mine excavations of an active hard coal mine. The study included practically all particles of a respirable fraction. The results showed that a high concentration of fine and ultrafine aerosols occurs in key underground workplaces especially during mining machine operations, although their contribution to total mass concentration is usually negligible.