Raj Kumar Goel Institute of Technology

Traumatic brain injury (TBI) and Alzheimer’s disease (AD), are two prominent neurological disorders. Both present a large number of clinical and socio-economic issues, but they are increasingly seen as having common pathways through which they cause disease. For both TBI and AD, oxidative stress and neuro-inflammatory responses lead to neuronal damages, impairment in synaptic function, and continued neuro-degeneration. After TBI, excessive production of oxygen and nitrogen free radicals damages mitochondrial function, while also activating microglial and astroglial cells chronically to cause inflammation. In patients with AD, continued oxidative damage leads to an accumulation of amyloid‑β protein and also leads to an increase in tau phosphorylation. There is an increasing body of literature demonstrating that the pathways by which TBI and AD occur may functionally connect short-term brain injuries to long-term neuro-degenerative processes. This article reviews the available data related to what currently drives the neuronal damage associated with TBI and AD through oxidative stress and inflammation and the available and generating treatment options—particularly antioxidants, anti-inflammatory agents, compounds protecting mitochondria, nanoparticles designed to deliver drugs, and phytochemicals with multi-targeted activity. The focus of this paper is on the scientific data supporting both the mechanistic pathways underlying the damage and the pharmacological/biological therapies that might treat the damage from a single point of view. The innovative aspect of this article lies in its demonstration of the relationship between oxidative stress and inflammation and its impact on the continuum of neuronal disease causing TBI leading to AD, and the potential of multi-targeted therapies to modify the ongoing disease processes.

The increasing demand for machine-to-machine communication has established Internet of Things (IoT)-enabled wireless sensor networks (WSNs) as a fundamental component of contemporary wireless systems. Numerous IoT-driven applications necessitate WSNs to function with optimal energy efficiency and dependable communication performance. Effective cooperation between devices deployed across numerous network layers is required to achieve these goals. Clustering has shown to be effective in improving key performance metrics of WSNs. However, there are major challenges with existing methods, including limited cluster head (CH) lifetime and inadequate cluster quality. These constraints highlight the need for an advanced routing method that ensures efficient CH selection while concurrently improving cluster quality. The optimal CH selection problem in WSNs is addressed in this work using a recently developed adaptive hybrid optimization technique which is a hybrid algorithm of the whale optimization algorithm (WOA), the INFO algorithm, the fussion–fission optimization (FuFiO) and naked mole rat algorithm (NMRA), known as the WIFN algorithm. In comparison to the existing solutions like LEACH, SEP-E, HCR, ERP, SAERP, DRESEP, SEECP, DESTERP, HSSTERP and FESTERP, the suggested WIFN-based clustering protocol performs better, achieving a longer network lifetime in terms of stability period (time period till the death of the first node from the initialization of the network) and consuming less energy. These results validate the suitability of WIFN methodology for creating effective IoT-supported WSNs.

The effective integration of renewable energy sources into the electrical grid is essential to the shift to a sustainable energy system. However, smooth grid integration is hampered by issues like power transfer inefficiencies, harmonic distortion, and variability. This paper proposes a novel dual input Z-source indirect matrix converter (DIZIMC) coupled with an improved dynamic group cooperative search-based artificial neural network (IDGC-ANN) to address these limitations. The proposed DIZIMC with IDGC-ANN enhances energy conversion efficiency by minimizing switching losses, reducing harmonic distortion, and simplifying component design. The proposed system utilizes an ultra-sparse Z-source matrix converter (USZMC) to enhance energy conversion efficiency by reducing switching losses, harmonic distortion, and component complexity. The Z-source network plays a pivotal role in stabilizing the DC link voltage under fluctuating input conditions, enabling reliable operation across a wide range of RE scenarios. Simultaneously, the IDGC-ANN controller enhances overall system performance by dynamically adjusting control parameters in real time, ensuring optimal power conversion efficiency and grid compliance. This intelligent coordination is particularly valuable in real-world applications such as smart microgrids, off-grid hybrid energy systems, and grid-connected solar-wind farms, where variable generation and load demands require adaptive and resilient power management. The integration of an LCL filter minimizes grid harmonics, ensuring the delivery of clean power. Simulation results in MATLAB demonstrate significant improvements in total harmonic distortion (THD) with 0.5

This paper explores the optimization of flow within transport networks, focusing on the efficient transfer of a commodity between two points in the National Capital Region of India. According to a survey, the NCR’s outer ring roads network accommodates an average of 5 lakh vehicles daily, with National Highways accounting for approximately 80

A series of rare-earth (Re2O3 = Pr2O3, Sm2O3, Eu2O3, Dy2O3) containing P2O5-MgO-Na2O-Li2O-TiO2 glasses were synthesised via the melt-quenching technique to investigate changes in structural and optical properties with ionic radii and electronegativity variations. X-ray diffraction confirmed the amorphous nature of the glass matrix, while Fourier Transform Infrared (FTIR) spectroscopy revealed the influence of different rare-earth oxides on the phosphate network structure. Optical absorption analysis showed that the optical band gap energy increases up to Eu2O3 content, correlating with changes in the localised state tails. Photoluminescence studies demonstrated tunable emission characteristics; the CIE 1931 coordinates shifted from the greenish-blue towards the white region as the ionic radii decreased. The Dy2O3 doped glass achieves near-white light emission (colour purity similar to 2%). The correlated colour temperature (CCT) was highly dependent on the chemical nature of the dopant, ranging from warm white light (1687 K for Sm2O3) to cool daylight (5721-6465 K for Pr2O3 and Dy2O3). These results highlight the potential of these glasses for compositionally tuned solid-state lighting applications.