Sikkim Manipal University

ABSTRACT This investigation focuses on the development of dual‐layer coating of AlN and TiAlN by Chemical vapor deposition (CVD) to improve surface protection for advanced engineering applications. The Aluminium nitride (AlN) interlayer was first deposited on p ‐type c‐Si (100) substrates over a temperature range of 200°C to 800°C, followed by the TiAlN top layer. The effect of deposition temperature on the structural, morphological, mechanical, and electrochemical properties of the coatings was systematically investigated. Surface morphology and microstructure were examined using Field emission scanning electron microscope (FESEM), while phase composition was studied by X‐ray diffraction (XRD). Atomic force microscope (AFM) provided insights into nanoscale surface roughness, and Raman spectroscopy confirmed the vibrational modes of the coating phases. The X‐ray photoelectron spectroscopy (XPS) analysis confirmed the formation of strong Titanium‐Nitride (Ti─N), Aluminium nitride (Al─N), and Aluminium Titanium nitride (Al─Ti─N) bonds, indicating successful synthesis of a chemically stable dual‐layer structure. Nanoindentation revealed that the dual‐layer coating deposited at 700°C exhibited superior hardness and Young's modulus values of 29.64 and 209.69 GPa, respectively, indicating improved mechanical integrity. Electrochemical corrosion testing demonstrated excellent corrosion resistance, with the lowest corrosion current density observed for the coating formed at 700°C. Collectively, the results confirm that CVD‐deposited AlN/TiAlN dual‐layer coating offer a promising route for surface engineering applications that requires high hardness, better tribological property, and corrosion resistance in aggressive environments.

ABSTRACT The significant increase in network users has driven the evolution of networking models like virtualization of the network and cloud computing, resulting in a significant increase in heterogeneous network traffic. Ensuring quality of service (QoS) for real‐time and latency‐sensitive traffic, such as audio conferencing, requires intelligent routing mechanisms that manage congestion and resource allocation effectively. Therefore, this paper proposes a multipath congestion‐aware routing based on the Aquila Wild Geese Optimization (AWGO) model for feasible and reliable data delivery for audio conferencing data in Software‐Defined Networking (SDN) environments. The proposed AWGO is a hybrid optimization approach that integrates the strong global exploration and fast exploitation capabilities of the Aquila Optimizer (AO) with the stability, robustness, and scalability of the Wild Geese Algorithm (WGA) for exploring and optimizing multiple routing paths. Here, an audio conferencing application‐based Software‐Defined Networking (SDN) simulation with OpenFlow is implemented, wherein audio conferencing data are considered as sensitive data. The multiobjective fitness function evaluates candidate paths based on residual energy, distance, link quality, delay, traffic bandwidth, congestion level, data priority, and throughput. A fault‐tolerance mechanism is further incorporated to ensure routing robustness under node failure conditions. The performance of the AWGO is evaluated under different traffic conditions including best effort traffic, sensitive traffic, and unwanted traffic. The AWGO demonstrates enhanced performance, achieving a maximum throughput of 430.71 Kbps, computation time of 9.408 s, bandwidth utilization of 96% under best effort traffic, minimum packet loss of 0.101% and delay of 0.087 ms with sensitive traffic, and a jitter of 5.938 ms for unwanted traffic. This demonstrates that the proposed AWGO model is an efficient and scalable solution for reliable data delivery in SDN.

An immediate need exists for a safe, quick, and effective reversal agent in patients who present with anticoagulant-induced major bleed. Dabigatran, a newer oral anticoagulant and a direct thrombin inhibitor (DTI), can also induce bleeding risk. Idarucizumab is a specific dabigatran reversal drug that the United States Food and Drug Administration (US FDA) has approved in the event of a significant bleeding caused by this drug. In this particular drug-related bleeding, it can be challenging to precisely dose alternative reversal agents like prothrombin complex concentrates (PCCs) and activated PCCs (aPCCs) depending on coagulation characteristics. Additionally, they may result in thromboembolic problems. Despite these drawbacks, the inability to get idarucizumab may necessitate the use of these medications in cases of life-threatening bleeding. We describe the case of a 65-year-old male who reported to the hospital with coagulopathy, anemia, and fresh bleeding per rectum (Hb: 5.8 gm/dL, PT 20.02 seconds, INR: 1.55). He was on dabigatran for the past 1 month. Even after stopping dabigatran, injection of vitamin K, 4 units of blood transfusion, and 8 units of fresh frozen plasma (FFP), he was still bleeding, with fall in hemoglobin level. Following the administration of PCC, he significantly improved, and no additional transfusion products were needed. He could be sent home after 4 days. After 1 month, he returned for follow-up with no further complications.

Rapid urbanization has intensified waste management challenges in the fragile Eastern Himalayan region, especially in the rapidly urbanizing state of Sikkim. To assess landfill impacts on soil health, we analyzed physicochemical and microbial characteristics of soils from the Martam landfill, Sikkim, against a control, for comparative analysis. Landfill soils showed neutral-alkaline pH (7.38-8.02), elevated moisture, phosphorus, and potassium, but depleted organic carbon and nitrogen, indicating nutrient imbalance and soil's organic component loss. Microbial profiling revealed 40 isolates, dominated by Bacillus and Pseudomonas spp. bacteria with known plastic-degrading potential alongside halophilic Salinicoccus roseus, indicative of chemical stress from leachates. These findings demonstrate that landfill accumulation alters soil ecology but also harbours microbial candidates for biodegradation. Understanding these parameters is essential for developing sustainable waste management and soil remediation strategies. For land-scarce regions like Sikkim, reducing landfill reliance through integrated waste management and sustainable treatment technologies is critical for ecological resilience.

Abstract This work presents an integrated framework combining optical signal processing and VLSI system design for the performance evaluation and optimization of 5G waveform candidates under different modulation schemes. The analysis focuses on four major waveforms – OFDM, FBMC, NOMA, and OTFS – and examines their performance based on bit error rate (BER), peak-to-average power ratio (PAPR), and power spectral density (PSD) metrics. Simulation results demonstrate that OTFS outperforms the other waveforms, achieving a BER of 10 −3 at an SNR range of 7–10 dB, corresponding to an SNR gain of up to 8 dB over OFDM. Furthermore, OTFS exhibits a PAPR reduction of approximately 3.5 dB and the lowest out-of-band emissions with a PSD of around −310 dBW/MHz, indicating superior spectral efficiency and reduced interference. These characteristics make OTFS a promising waveform for high-mobility, high-capacity 5G and future 6G communication environments. The integration of optical signal processing techniques enhances system bandwidth and energy efficiency, while VLSI-based architectures support low-latency, high-throughput implementation. Future advancements will explore optical-VLSI codesign and machine learning–assisted frameworks for real-time adaptive processing, improving scalability, power optimization, and intelligent signal handling in next-generation wireless and optical communication systems.

ABSTRACT The traffic management in the network, which is able to serve latency‐sensitive applications such as video conferencing in a very efficient manner, is provided by a software‐defined networking (SDN) system architecture that is centralized and programmable. On the other hand, regular SDN methods typically show that the adaptations to dynamically changing network situations are too conservative and slow. In addition, as a result of insufficient management of long‐range traffic dependencies, they frequently suffer from jitter, latency spikes, and packet loss. To address these limitations, this paper proposes an intelligent SDN‐based routing framework employing a hybrid Alex Quantum Dilated Convolutional Neural Network (AQDCNN). The proposed AQDCNN combines the spatial–temporal feature extraction capability of AlexNet with the long‐term dependency modeling and quantum‐enhanced computational efficiency of QDCNN. The framework is implemented within a multicontroller SDN environment using Mininet and OpenFlow, enabling dynamic network endpoint provisioning, QoS‐aware traffic prioritization, and fault‐tolerant failure adaptation. The experimental results show that the model outperforms the tested algorithms in terms of minimum setup and fast recovery time (1.90 and 2.10 ms) with low recovery overhead (2.70 ms), as well as high FTI equal to 8.50 and the lowest path change frequency equal to 4.20, which are considered significant stability and service continuity results for experiments conducted on a real‐life network topology. The proposed AQDCNN framework is against existing CNN, GN‐DQN, DRL‐SDN, IFRA‐GLB, and MTF‐WMSSA–based routing methods, demonstrating superior results in fault tolerance, rerouting time, and flow setup efficiency.