High-Accuracy Relative Biological Effectiveness Values Following Low-Dose Thermal Neutron Exposures Support Bimodal Quality Factor Response with Neutron Energy

Theoretical evaluations indicate the radiation weighting factor for thermal neutrons differs from the current International Commission on Radiological Protection (ICRP) recommended value of 2.5, which has radiation protection implications for high-energy radiotherapy, inside spacecraft, on the lunar or Martian surface, and in nuclear reactor workplaces. We examined the relative biological effectiveness (RBE) of DNA damage generated by thermal neutrons compared to gamma radiation. Whole blood was irradiated by 64 meV thermal neutrons from the National Research Universal reactor. DNA damage and erroneous DNA double-strand break repair was evaluated by dicentric chromosome assay (DCA) and cytokinesis-block micronucleus (CBMN) assay with low doses ranging 6–85 mGy. Linear dose responses were observed. Significant DNA aberration clustering was found indicative of high ionizing density radiation. When the dose contribution of both the 14N(n,p)14C and 1H(n,γ)2H capture reactions were considered, the DCA and the CBMN assays generated similar maximum RBE values of 11.3 ± 1.6 and 9.0 ± 1.1, respectively. Consequently, thermal neutron RBE is approximately four times higher than the current ICRP radiation weighting factor value of 2.5. This lends support to bimodal peaks in the quality factor for RBE neutron energy response, underlining the importance of radiological protection against thermal neutron exposures.

Power Maximization in Standalone Photovoltaic System: An Adaptive PSO Approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilised in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles towards maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

Economic Assessment of Power Grid Development Using Artificial Bee Colony Algorithm

The electricity system is generally rapidly developing for covering various power demands with requiring a reliable and safe supply where the substructures are expanding further in generation systems, transmission systems, and distribution systems. However, the system must be run economically to access energy at a cost-effective level related to existing energy enterprises and energy consumption in the load which is represented periodically in the total costs of operations for all operating units. As a basis for its determination, the transmission of economic power within the technical limits applicable is taken into consideration. Environmental factors, on the other hand, are also an impediment to technical limitations. As a result, the operation's economic measure is expressed in the process of providing and selling energy to customers. These works use the Artificial Bees Colony algorithm to determine the scheduling of generating units using the basic principle of optimization to describe its relationship as an economic function. The IEEE-30 bus system is used as a basic model for system development. The analysis' findings show that the weighting factor scheme has an impact on the minimum total cost and that the combination of the electricity distribution process and environmental factors has implications for the operational financial condition and electricity production. The power output, in particular, is proportional to the cost of each generating unit.

Reliability Importance Measures considering Performance and Costs of Mechanical Hydraulic System for Hydraulic Excavators

Although some reliability importance measures and maintenance policies for mechanical products exist in literature, they are rarely investigated with reference to weakest component identification in the design stage and preventive maintenance interval during the life cycle. This paper is mainly study reliability importance measures considering performance and costs (RIMPC) of maintenance and downtime of the mechanical hydraulic system (MHS) for hydraulic excavators (HE) with energy regeneration and recovery system (ERRS) and suggests the scheduled maintenance interval for key components and the system itself based on the reliability R i t . In the research, the required failure data for reliability analysis is collected from maintenance crews and users over three years of a certain type of hydraulic excavators. Minitab is used for probable distribution estimation of the mechanical hydraulic system failure times, and the model is verified to obey Weibull distribution. RIMPC is calculated by multiplying the reliability R i t and weighting factor W i and then compared with other classical importance measures. The purpose of this paper is to identify the weakest component for MHS in the design stage and to make appropriate maintenance strategies which help to maintain a high reliability level for MHS. The proposed method also provides the scientific maintenance suggestion for improving the MHS reliability of the HE reasonably, which is efficient, profitable, and organized.

An Improved Image Compression Model Enabled by Adaptive Active Contour and Supervised Learning-Based ROI Classification

This paper plans to develop a novel image compression model with four major phases. (i) Segmentation (ii) Feature Extraction (iii) ROI classification (iv) Compression. The image is segmented into two regions by Adaptive ACM. The result of ACM is the production of two regions, this model enables separate ROI classification phase. For performing this, the features corresponding to GLCM are extracted from the segmented parts. Further, they are subjected to classification via NN, in which new training algorithm is adopted. As a main novelty JA and WOA are merged together to form J-WOA with the aim of tuning the ACM (weighting factor and maximum iteration), and training algorithm of NN, where the weights are optimized. This model is referred as J-WOA-NN. This classification model exactly classifies the ROI regions. During the compression process, the ROI regions are handled by JPEG-LS algorithm and the non-ROI region are handled by wavelet-based lossy compression algorithm. Finally, the decompression model is carried out by adopting the same reverse process.

Determinants of Neonatal Mortality in Kenya: Evidence from the Kenya Demographic and Health Survey 2014

Background: Globally, there has been a marked decline in neonatal mortality and overall child mortality indicators from 1990 to date. In Kenya, neonatal deaths remain unacceptably high, contributing to 40% of under-five mortality rates (U5MR) making it an important health priority. The objective of this study was to identify the determinants of neonatal mortality in Kenya. An understanding of the determinants of neonatal mortality will provide evidence for better interventions to reduce these deaths. Methods: Neonatal deaths from singleton live-born infants were extracted from women’s dataset collected for the 5-year period preceding the study published in the Kenya Demographic and Health Survey (KDHS), 2014. Data were obtained from 18,951 births. There were 356 neonatal deaths recorded. Data were weighted using an individual weighting factor to adjust for the study design and reduce sample variability. Data were analyzed using SPSS version 20.0. Logistic regression was conducted to adjust for confounding factors. Results: Neonatal mortality rate was established at 19/1000 (95% CI:16.8-20.7). Mothers with no education had higher odds of experiencing deaths of neonates with adjusted Odds Ratio (aOR)=2.201, 95% CI: 1.43-4.15,p=0.049) compared to mothers with higher education. Low Birth Weight (LBW) neonates were 3.2 times likely to die in the first 28 days (aOR=3.206, 95% CI: 1.85-12.08, p=0.006) compared to neonates with >3.5 kilograms at birth. Mothers who did not attend ANC during pregnancy and those who attended between 1-3 ANC visits had higher odds of losing their infants (aOR=3.348, 95% CI:1.616-8.53, p=0.041, and aOR=2.316, 95% CI: 1.10-4.88, p=0.027) respectively, compared to mothers who attended >4 ANC visits. Conclusion and Global Health Implications: Improving maternal health and nutrition during pregnancy should be enhanced to ensure adequate weight gain and reduce instances of low birth weight. Community referrals and follow-up for expectant women to take up the requisite 4 ANC visits should be encouraged. Girls’ education should be emphasized to reduce the proportion of illiterate mothers. Copyright © 2021 Imbo et al. Published by Global Health and Education Projects, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY 4.0.

Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.

Bunch Expansion as a Cause for Pulsar Radio Emissions

Electromagnetic waves due to electron–positron clouds (bunches), created by cascading processes in pulsar magnetospheres, have been proposed to explain the pulsar radio emission. In order to verify this hypothesis, we utilized for the first time Particle-in-Cell (PIC) code simulations to study the nonlinear evolution of electron–positron bunches dependant on the initial relative drift speeds of electrons and positrons, plasma temperature, and distance between the bunches. For this sake, we utilized the PIC code ACRONYM with a high-order field solver and particle weighting factor, appropriate to describe relativistic pair plasmas. We found that the bunch expansion is mainly determined by the relative electron–positron drift speed. Finite drift speeds were found to cause the generation of strong electrostatic superluminal waves at the bunch density gradients that reach up to E ∼ 7.5 × 105 V cm−1 (E/(m e c ω p e −1) ∼ 4.4) and strong plasma heating. As a result, up to 15% of the initial kinetic energy is transformed into the electric field energy. Assuming the same electron and positron distributions, we found that the fastest (in the bunch reference frame) particles of consecutively emitted bunches eventually overlap in momentum (velocity) space. This overlap causes two-stream instabilities that generate electrostatic subluminal waves with electric field amplitudes reaching up to E ∼ 1.9 × 104 V cm−1 (E/(m e c ω p e −1) ∼ 0.11). We found that in all simulations the evolution of electron–positron bunches may lead to the generation of electrostatic superluminal or subluminal waves, which, in principle, can be behind the observed electromagnetic emissions of pulsars in the radio wave range.

Enhanced Potential Field-Based Collision Avoidance in Cluttered Three-Dimensional Urban Environments

With the various applications of unmanned aerial vehicles (UAVs), the number of UAVs will increase in limited airspace, leading to an increased risk collision. To reduce such potential risk, this work proposes a collision avoidance strategy for UAVs using an enhanced potential field (EPF) approach in cluttered three-dimensional urban environments. Using the EPF formulated in a two-dimensional environment, the avoidance maneuvers for both horizontal and vertical planes are generated by introducing rotation matrices, and these maneuvers are combined by applying a weighting factor. The numerical simulations with various meaningful scenarios are conducted to validate the performance of the proposed approach. To mimic practical situations, UAV dynamics and sensor limitations were considered. The simulation results show that the proposed approach provides an efficient, reliable, and collision-free path without local minima and unreachable goal issues.