Reducing destructive effects of drought stress on cucumber through seed priming with silicic acid, pyridoxine, and ascorbic acid along with foliar spraying with silicic acid

Cucumber is considered as a drought-sensitive plant so that a decrease in the soil moisture causes decreased yield and quality of cucumber. This study investigates the effects of seed priming and foliar application with silicic acid on biochemical traits of cucumber (Cucumis sativus L.) under drought stress through a split-split plot experiment with three replications. The main plot was allocated to different levels of drought stress including moderate drought stress (80-85% Field Capacity (FC)), severe drought stress (60-65% FC), and without stress – control (90-95% FC). The sub-plot was allocated to seed priming treatments at three levels: control (hydro-priming), ascorbic acid 150 mgL-1, and pyridoxine 0.04%. The sub-sub plot was assigned to foliar spraying with silicic acid at three levels: 0, 100, and 200 mgL-1. The results obtained from the evaluation of all traits showed that under free-stress condition, the best seed priming treatment belonged to pyridoxine 0.04% alone or along with foliar spraying of silicic acid at 100 mgL-1. In moderate drought stress, the best seed priming treatment belonged to pyridoxine 0.04% and foliar spraying with silicic acid at 200 mgL-1, and under severe drought stress, the best seed priming treatment belonged to pyridoxine 0.04% or ascorbic acid at 150 mgL-1

Application of Artificial Neural Networks to Assess Student Happiness

The purpose of this study is to develop an analytical assessment approach to identify the main factors that affect graduate students\u27 happiness level. The two methods, multiple linear regression (MLR) and artificial neural networks (ANN), were employed for analytical modelling. A sample of 118 students at a small non-profit private university constituted the survey pool. Various factors including education, school facilities, health, social activities, and family were taken into consideration as a result of literature review in happiness assessment. A total of 32 inputs and one output variables were identified during survey design phase. The following survey conduction, data collection, cleaning, and preparation; MLR and ANNs were built. ANN models provided better classification performance with over 0.7 R-square and a smaller standard error of estimate compared to MLR. Major policy areas to improve student happiness levels were identified as career services, financial aid, parking and dining services

Identifying the Dimensions and Components Affecting the Formation of Desirable Urban Spaces for Women by Using the Meta-Analysis Method

Analyzing the relationship between gender and space elucidates the roles and needs of both men and women, particularly in urban spaces. This analysis aids in understanding how the constructive roles of women contribute to the production and reproduction of desirable social relations in urban spaces. The presence of women in public and urban spaces not only reflects but also narrates the desirable relationships within a society. The primary objective of this research is to identify the dimensions and components that influence the formation of desirable urban spaces for women using the meta-analysis method. The searches were conducted manually, focusing on the keywords "urban spaces, women." For sub-keywords, the terms "park, public space, single-gender spaces" replaced the first keyword. The study period spans from 2016 to 2022 in Persian sources and from 2016 to 2022 in English sources. SPSS software was employed to conduct meta-analysis tests, resulting in the production of forest, bubble, heterogeneity, and funnel (diffusion bias) diagrams. VOSviewer software was used to assess scientific databases for content production in this field. The results indicate that the main dimensions of the research encompass physical and functional dimensions, structural and spatial dimensions, individual and personality dimensions, and cultural and social dimensions in the design of urban spaces. The position of research in the field of urban space design for women holds significance in the global research literature

Inferences from a network to a subnetwork and vice versa under an assumption of symmetry

This note summarizes some mathematical relations between the probability distributions for the states of a network of binary unitsand a subnetwork thereof, under an assumption of symmetry. These relations are standard results of probability theory, but seem to be rarely used in neuroscience. Some of their consequences for inferences between network and subnetwork, especially in connection with the maximum-entropy principle, are briefly discussed. The meanings and applicability of the assumption of symmetry are also discussed

Pairwise maximum-entropy models: bimodality, bistability, non-ergodicityproblems, and their elimination via inhibition

The pairwise maximum-entropy model [1,2], applied to experimental neuronal data of populations of 200 andmore neurons, is very likely to give a bimodal probability distribution for the population-averaged activity. Wehave provided evidence for this claim, starting from an experimental dataset and then looking at summarizeddata from the literature. The first mode is the one observed in the data. The second mode (unobserved)can appear at very high activities (even 90% of the population simultaneously active) and its height increaseswith population size. This bimodality has several undesirable consequences:1.The presence of two modes is unrealistic in view of observed neuronal activity.2.The prediction of a high-activity mode is unrealistic on neurobiological grounds.3.Boltzmann learning becomes non-ergodic, hence the pairwise model found by this method is not themaximum entropy distribution; similarly, solving the inverse problem by common variants of mean-fieldapproximations has the same problem.4.The Glauber dynamics associated with the model is either unrealistically bistable, or does not reflect thedistribution of the pairwise model

Data from: Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models

Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition