The University of Mazandaran, currently the largest state higher education center in the north of Iran, and had formerly consisted of a number of tertiary education In 1979 the centers were officially merged to form what is now known as University of Mazandaran.The university has about 12,000 students who are currently studying at undergraduate, graduate, and post-graduate levels and over 350 faculty members teaching and researching at different faculties of the university..
Urban disadvantaged areas are highly vulnerable to natural hazards due to physical, social, economic, and institutional deficiencies. These areas-such as historic residential area, decayed residential areas, informal settlements, and social housing-experience distinct challenges that necessitate tailored resilience strategies. Considering Iran's high hazard proneness, strengthening resilience in such areas is an urgent priority. Existing resilience literature often adopts a generalized, city-wide perspective, lacking the granularity for effective local-level resource allocation in diverse disadvantaged areas. This study aims to identify and prioritize resilience criteria and indicators across the four types of disadvantaged areas. A comprehensive literature review extracted potential resilience criteria, refined and validated using the Delphi method with 16 experts engaged via snowball sampling. The Best-Worst Method (BWM), an MCDM approach, weighted and ranked criteria for each area type. Results show economic capacity is most critical in informal settlements. Physical-environmental aspects (building quality, infrastructure) dominate historical and decayed areas. Institutional dimensions (governance, accountability, policy support) are most influential in social housing resilience. Cross-area comparison reveals shared and context-specific determinants, underscoring the need for integrated, area-sensitive resilience planning. Findings offer a framework for urban planners to prioritize targeted interventions, balancing physical upgrades with social empowerment, economic capacity building, and institutional strengthening for sustainable and inclusive resilience.
A significant challenge in identifying epileptic seizures lies in the effective analysis of non-stationary and chaotic electroencephalogram (EEG) signals. This study introduces an innovative approach that combines time series-to-network transformation techniques with deep learning for seizure detection from EEG data. Single-channel EEG signals, along with delta, theta, alpha, beta, and gamma sub-bands, are transformed into complex network representations, from which 16 topological features are extracted based on adjacency relationships and degree distributions. These features are then fed into a novel hybrid deep learning classifier, which leverages an Autoencoder architecture with Long Short-Term Memory (LSTM) units, to robustly identify epileptic seizures through multiple experimental scenarios. The proposed method achieves a remarkable accuracy of 100% in distinguishing between ictal (seizure periods) and non-ictal (seizure-free periods) signals, as well as normal versus epileptic signals from the Bonn dataset. Furthermore, this approach also reaches an accuracy of 100% when differentiating among three categories ictal, interictal (between seizure periods), and normal EEGs. When evaluating five sets from the Bonn dataset, the proposed technique demonstrates an accuracy of 99.90%, sensitivity of 99.90%, and specificity of 99.98%, outperforming existing methods in the literature. Moreover, our method can detect seizures within a time window of less than five seconds.
The aim of this research is to introduce a numerical technique relying on the shifted Jacobi polynomials (SJPs) and the Laplace transform (LT) for anatomizing a class of the fractional variational problems (FVPs). The derivative in this problem is in the sense of the Caputo. To anatomizing FVP, first, we make an approximation for the Caputo operator ((c) D( t)(& varsigma;)m(t)) using the SJPs with unknown parameters. Then, applying LT, we access m(t) and put it in the study problem. The process of this method and the Lagrange multiplier method reduces the study problem to a system of nonlinear algebraic equations. In this system, unknown parameters are calculated by solving the obtained system, and it gives m(t). Finally, some illustrative examples are presented to demonstrate the accuracy and validity of the proposed scheme.
This paper examines the Lagrange synchronization of nonidentical higher-dimensional neural networks, particularly for the Cohen Grossberg octonion-valued neural networks (CGOVNNs) with interaction terms, time-varying, and distributed delays. Achieving synchronization in such systems is extremely difficult since the octonion algebra is non-commutative and non-associative. To address this, the Octonion valued neural network (OVNN) is first split into eight real-valued subsystems, which makes an organized analytical approach possible. A nonlinear controller is developed to guarantee synchronization, and an appropriate Lyapunov function is constructed. The sufficient requirements for Lagrange synchronization using the Lyapunov stability theory are proved. The efficacy of the suggested method is confirmed by numerical simulations. These results shed light on the synchronization of hypercomplex systems and aid in the stability study of high-dimensional neural networks.
BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia.ObjectiveThe aim of this study was to investigate the effect of aerobic training on the expression changes of Notch1, Rbpjk, Hes1, and Hey1genes in the hippocampus of Alzheimer rats.MethodsForty, 8-week-old male Wistar rats were divided into four groups: control (n = 10), exercise (n = 10), AD (n = 10), AD + exercise (n = 10). Endurance training was implemented with increasing intensity starting at 15 m/min in the first week and progressing to speeds of 16-20 m/min over the next five weeks with increased durations each week. After 6 weeks, animals were euthanized and hippocampus was collected, frozen and RNA was isolated to quantify Notch1, Rbpjk, Hes1, and Hey1 expression. All statistical analyses and graphs were conducted using SPSS and visualized using GraphPad Prism, with significance set at p < 0.05.ResultsOne-way analysis of variance with Tukey's post-hoc analysis found that the Alz group had significantly lower expression of Notch1 with increased expression of Rbpjk and Hes1. Conversely, the AD + Ex group was observed to have significantly higher Notch1 and significantly lower Rbpjk compared to the AD group.ConclusionsThese findings suggest that exercise may serve as a complementary neuroprotective intervention via manipulation of Notch1 signaling. Overall, the study highlights the need for further research on the relationship between physical activity and gene expression in the context of AD.
