A high-order nonlinear Schrödinger equation with the weak non-local nonlinearity and its optical solitons

Abstract The present paper explores a high-order nonlinear Schrodinger equation in a non-Kerr law media with the weak non-local nonlinearity describing solitons' propagation through nonlinear optical fibers. To this end, the real and imaginary parts of the model are firstly extracted using a wave variable transformation. The modified Kudryashov method and symbolic computations are then adopted to successfully retrieve optical solitons of the model. The results presented in the current study demonstrate the great performance of the modified Kudryashov method in handling high-order nonlinear Schrodinger equations

How Does Race Moderate the Effect of Religion Dimensions on Attitudes toward the Death Penalty?

We examined the moderating role of race on the relationship between religion and death penalty attitudes in the United States. We operationalized religion by distinguishing four dimensions: religiosity, spirituality, afterlife beliefs, and denomination. Using 2018 General Social Survey data from 1054 adults, collected by the National Opinion Research Center at the University of Chicago, we show that the impact of each dimension of religion varies across racial groups. Logistic Regression results showed that the likelihood of support for the death penalty was associated with religiosity, spirituality, belief in hell, being female, and being liberal. Adding race as an interaction term moderated the associations of religiosity and spirituality

Exploring the Fusion of Knowledge Graphs into Cognitive Modular Production

Modular production has been recognized as a pivotal approach for enhancing productivity and cost reduction within the industrialized building industry. In the pursuit of further optimization of production processes, the concept of cognitive modular production (CMP) has been proposed, aiming to integrate digital twins (DTs), artificial intelligence (AI), and Internet of Things (IoT) technologies into modular production systems. This fusion would imbue these systems with perception and decision-making capabilities, enabling autonomous operations. However, the efficacy of this approach critically hinges upon the ability to comprehend the production process and its variations, as well as the utilization of IoT and cognitive functionalities. Knowledge graphs (KGs) represent a type of graph database that organizes data into interconnected nodes (entities) and edges (relationships), thereby providing a visual and intuitive representation of intricate systems. This study seeks to investigate the potential fusion of KGs into CMP to bolster decision-making processes on the production line. Empirical data were collected through a computerized self-administered questionnaire (CSAQ) survey, with a specific emphasis on exploring the potential benefits of incorporating KGs into CMP. The quantitative analysis findings underscore the effectiveness of integrating KGs into CMP, particularly through the utilization of visual representations that depict the relationships between diverse components and subprocesses within a virtual environment. This fusion facilitates the real-time monitoring and control of the physical production process. By harnessing the power of KGs, CMP can attain a comprehensive understanding of the manufacturing process, thereby supporting interoperability and decision-making capabilities within modular production systems in the industrialized building industry

The Neuroprotective Effect of Cannabinoid Receptor Agonist (WIN55,212-2) in Paraoxon Induced Neurotoxicity in PC12 Cells and N-methyl-D-aspartate Receptor Interaction

Objective: Considering that cannabinoids protect neurons against neurodegeneration, inthis study, the neuroprotective effect of WIN55,212-2 in paraoxon induced neurotoxicity inPC12 cells and the role of the N-methyl-D-aspartate (NMDA) receptor were evaluated.Materials and Methods: In this study PC12 cells were maintained in Dulbecco's modifiedeagle’s medium (DMEM+F12) culture medium supplemented with 10% fetal bovineserum. The cells were treated with paraoxon (200 μM) in the presence or absence ofWIN55,212-2 (0.1 μM), NMDA receptor agonist NMDA (100 μM), cannabinoid receptorantagonist AM251 and NMDA receptor antagonist MK801 (1 μM) at 15 minutes intervals.After 48 hours of exposure, cellular viability and protein expression of the CB1 receptorwere evaluated in PC12 cells.Results: Following the exposure of PC12 cells to paraoxon (200 μM), a reduction in cellsurvival and protein level of the CB1 receptor was observed (p<0.01). Treatment of thecells with WIN55,212-2 (0.1 μM) and NMDA (100 μM) prior to paraoxon exposure significantlyelevated cell survival and protein level of the CB1 receptor (p<0.01). Also, AM251(1μM) did not inhibit the cell survival and protein level of the CB1 receptor increase inducedby WIN55,212-2 (p<0.001). However, MK801 (1 μM) did inhibit cell survival andprotein expression of the CB1 receptor increase induced by NMDA (p<0.001).Conclusion: The results indicate that WIN55,212-2 and NMDA protect PC12 cellsagainst paraoxon induced toxicity. In addition, the neuroprotective effect of WIN55,212-2and NMDA was cannabinoid receptor-independent and NMDA receptor dependent, respectively

Designing a Matrix Collocation Method for Fractional Delay Integro-Differential Equations with Weakly Singular Kernels Based on Vieta–Fibonacci Polynomials

In the present work, the numerical solution of fractional delay integro-differential equations (FDIDEs) with weakly singular kernels is addressed by designing a Vieta–Fibonacci collocation method. These equations play immense roles in scientific fields, such as astrophysics, economy, control, biology, and electro-dynamics. The emerged fractional derivative is in the Caputo sense. By resultant operational matrices related to the Vieta–Fibonacci polynomials (VFPs) for the first time accompanied by the collocation method, the problem taken into consideration is converted into a system of algebraic equations, the solving of which leads to an approximate solution to the main problem. The existence and uniqueness of the solution of this category of fractional delay singular integro-differential equations (FDSIDEs) are investigated and proved using Krasnoselskii’s fixed-point theorem. A new formula for extracting the VFPs and their derivatives is given, and the orthogonality of the derivatives of VFPs is easily proved via it. An error bound of the residual function is estimated in a Vieta–Fibonacci-weighted Sobolev space, which shows that by properly choosing the number of terms of the series solution, the approximation error tends to zero. Ultimately, the designed algorithm is examined on four FDIDEs, whose results display the simple implementation and accuracy of the proposed scheme, compared to ones obtained from previous methods. Furthermore, the orthogonality of the VFPs leads to having sparse operational matrices, which makes the execution of the presented method easy

Designing a Matrix Collocation Method for Fractional Delay Integro-Differential Equations with Weakly Singular Kernels Based on Vieta&ndash;Fibonacci Polynomials

In the present work, the numerical solution of fractional delay integro-differential equations (FDIDEs) with weakly singular kernels is addressed by designing a Vieta&ndash;Fibonacci collocation method. These equations play immense roles in scientific fields, such as astrophysics, economy, control, biology, and electro-dynamics. The emerged fractional derivative is in the Caputo sense. By resultant operational matrices related to the Vieta&ndash;Fibonacci polynomials (VFPs) for the first time accompanied by the collocation method, the problem taken into consideration is converted into a system of algebraic equations, the solving of which leads to an approximate solution to the main problem. The existence and uniqueness of the solution of this category of fractional delay singular integro-differential equations (FDSIDEs) are investigated and proved using Krasnoselskii&rsquo;s fixed-point theorem. A new formula for extracting the VFPs and their derivatives is given, and the orthogonality of the derivatives of VFPs is easily proved via it. An error bound of the residual function is estimated in a Vieta&ndash;Fibonacci-weighted Sobolev space, which shows that by properly choosing the number of terms of the series solution, the approximation error tends to zero. Ultimately, the designed algorithm is examined on four FDIDEs, whose results display the simple implementation and accuracy of the proposed scheme, compared to ones obtained from previous methods. Furthermore, the orthogonality of the VFPs leads to having sparse operational matrices, which makes the execution of the presented method easy

Neuronal differentiation of PC12 and embryonic stem cells in two- and three-dimensional <i style="mso-bidi-font-style:normal">in vitro</i> culture

305-311The quality of neuronal differentiation and reduction in apoptosis that occurred in two-dimensional (2D) and three-dimensional (3D) culture conditions is compared. PC12 and embryonic stem cells are two commonly utilized cell lines for the study of neuronal regeneration. These cells were induced to neuronally differentiate by adding NGF and retinoic acid respectively. Total neurite length and expression of neuronal markers (MAP-2 and β3-tubulin) was assessed by morphometry and immunocytochemistry. Also, TUNEL assay was used to detect apoptosis. Upon exposure to a differentiation media in the 3D fibrin gel, PC12 and embryonic stem cells stopped dividing, had increased adhesion to the substratum, extended neurite processes and expressed neuronal markers. The same results, however, were not observed with the 2D culture. Also, the apoptosis index performed by TUNEL assay demonstrated a reduction in the degree of apoptosis in the 3D culture compared to 2D culture. Fibrin matrix supports growth and neuronal differentiation of PC12 and embryonic stem cells. In addition, the 3D culture enhanced cellular resistance to apoptosis when compared to the 2D culture. It appears as if a 3D culture system may offer a better technique for future neuronal tissue engineering investigations