Physiological and Psychological Responses to Coding Combined with Horticultural Activity

This study was conducted to determine the physiological and psychological benefits of integrating software coding and horticultural activity. Participants included 30 adults in their 20s. The subjects randomly engaged in activities—namely, connecting Arduino components, coding, planting, and a combined coding and horticultural activities. During the activity, two subjective evaluations were conducted at the end of each activity, and participants’ brain waves were measured. The spectral edge frequency 50% of alpha spectrum band (ASEF50) and ratio of sensorimotor rhythm from mid beta to theta (RSMT) were activated in the prefrontal lobe as participants performed combined coding and horticultural activities. When performing these combined activities, relative beta (RB) increased, and relative theta (RT) decreased in the prefrontal lobe. In addition, ASEF50, relative low beta (RLB), and relative mid beta (RMB) were activated during plant-based activities (planting and a combined coding and horticultural activities). The subjective evaluations revealed that the plant-based activities had a positive effect on participants’ emotions. This study shows that activities combining coding and horticulture had a positive impact on physiological relaxation and increased concentration in adults compared with other activities and was also linked with positive subjectively reported emotions.

NEUTRON DEPTH PROFILE CALCULATIONS USING ARTIFICIAL NEURAL NETWORKS

Neutron depth profiling (NDP) is a non-destructive technique used for identifying the concentration of impurity isotopes below the sample surface. NDP is carried out by detection of the emitted charged particles resulting from bombarding the sample with neutrons. NDP specifies the isotopic concentration versus the sample depth for a few micrometers below the surface. The sample is bombarded inside a research reactor using a thermal neutron beam. Charged particles like alpha particles or protons are produced from the neutron induced reactions in the sample. Each neutron isotopic interaction produces a certain Q, indicating a specific kinetic energy for the emitted charged particle. As the charged particle travels through the sample to eject the surface, it loses energy to atoms (electrons) on its path. The charged particle energy loss holds information regarding the number of atoms by which the emitted particle passed, thus indicating its original depth. The purpose of this work is to check the capability of Artificial Neural Networks (ANNs) in predicting the boron concentration profile across a boro-silicate sample of thickness 3.5 μm divided into 10 layers. Each layer included different boron concentration than the other. Also, the boron concentration had the values {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. Training, validation, and test data were generated synthetically using MCNP6 in which the boron concentrations varied in the layer number from one sample to another. MCNP6 model consisted of a silicon barrier detector, boro-silicate sample, chamber body and an idealized thermal neutron source. The detector, sample, and the source were located in a voided chamber. The samples were irradiated with a 0.025 eV monoenergetic thermal neutron beam from a monodirectional disk source. To cover the whole area of the samples, the thermal neutron beam had a radius of 3 cm. The silicon detector active volume was modelled as a 100 μm thick and 3 cm radius facing the sample directly. The sample, beam, and the detector were placed on the same axis. Ten ANN regression models were developed, one for each layer boron concentration prediction where the input for each model was the alpha spectrum read by the detector, while the output was the boron concentration for each layer. Results showed regression values higher than 0.94 for all of the developed models. ANNs proved its capability of predicting the boron profile form the alpha spectrum read by the detector regarding neutron depth profiling in a boro-silicate samples.

The $A_{\alpha}$- spectrum of graph product

Let $A(G)$ and $D(G)$ denote the adjacency matrix and the diagonal matrix of vertex degrees of $G$, respectively. Define $$ A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G) $$ for any real $\alpha\in [0,1]$. The collection of eigenvalues of $A_{\alpha}(G)$ together with multiplicities is called the $A_{\alpha}$-\emph{spectrum} of $G$. Let $G\square H$, $G[H]$, $G\times H$ and $G\oplus H$ be the Cartesian product, lexicographic product, directed product and strong product of graphs $G$ and $H$, respectively. In this paper, a complete characterization of the $A_{\alpha}$-spectrum of $G\square H$ for arbitrary graphs $G$ and $H$, and $G[H]$ for arbitrary graph $G$ and regular graph $H$ is given. Furthermore, $A_{\alpha}$-spectrum of the generalized lexicographic product $G[H_1,H_2,\ldots,H_n]$ for $n$-vertex graph $G$ and regular graphs $H_i$'s is considered. At last, the spectral radii of $A_{\alpha}(G\times H)$ and $A_{\alpha}(G\oplus H)$ for arbitrary graph $G$ and regular graph $H$ are given.