Orbital angular momentum (OAM) mode free-space optical (FSO) communication provides a high-capacity solution for wireless urban networks in the future. Yet, its use in actual scenarios is assailed by four main challenges: (1) divergence and structural aberration of OAM beams over different link lengths, (2) distance-dependent beam spread-receiver aperture mismatch, (3) absence of power-efficient adaptive hardware for conditioning beams over link lengths, and (4) decoding failure due to atmospheric turbulence-induced mode crosstalk and ring deformation. To address these challenges, we present ADAPT OAM, A Passive Optical Architecture for Distance-Adaptive OAM-FSO Communication. ADAPT-OAM introduces a distance-aware passive optical pre-conditioning framework that reshapes OAM beams prior to propagation, eliminating the need for adaptive optics or machine learning–based correction. In the transmitter, a new rotatable Holographic Phase Plate (HPP) with several lens regions is employed to pre-curve OAM beams for transmission distances of 200 m to 1700 m. These lens regions regulate beam divergence and control the radial intensity distribution of OAM modes during propagation and a special Achromatic Doublet Lens provides accurate collimation with very low chromatic and spherical aberration. The Graded Index (GRIN) lens at the receiver carries out spatial refocusing of arriving beams that have possible residual deformation during atmospheric propagation, thus realigning the beam for correct mode separation. It is preceded by a fork diffraction grating, which space-resolves OAM modes, and a photodetector array that passively demultiplexes them into digital symbols without moving parts, feedback control, or machine learning. Simulation-based evaluation demonstrates that ADAPT OAM attains a Bit Error Rate (BER) less than 10⁻⁶, Mode Purity Index (MPI) greater than 94
The information sharing on behalf of the delivery of the packet in the network router and the original information that is share to the correct person is enabled for the analysis and then the analysis is done. The traffic related problem based on the sharing the information is occur when two or more sis sent in the same routing path. So the packet or the information has been sent using the single router or an execution time delivery for the analysis. The method of novel QoS Routing, fault tolerance is used in this study for the analysis of the system to produce the formation and the analysis of the packet routing. Fault tolerance is used in the deliver the packets without fail and to deliver it in the fast manner. The QoS is used for managing the information or the packets which makes and send the data which is the high priority of the data and the analysis of the dependency is analysed. The finding of the CRAFT related protocol and the efficient routing has been found. Routing system the effective method for the analysis and the protocol transfer has been enabled using the CRAFT method. The suggested system explains the algorithms for enhancing fault detection, fault isolation, data redundancy, robustness against coalescing innovative QoS routing, and fault tolerance improving QoS parameters in the ad-hoc wireless network utilising CRAFT protocol. Compared to the early existing system, fault detection is now three times better. The execution time is 96% faster, and the decreased latency is less than 2 ms. The data redundancy rate is likely about 90% when restricted to the current services, which have improved in terms of resilience by 93%, jitter by 7%, and packet arrival time by over 50 ms per transmission.
The Metal Additive Manufacturing (AM) has become a revolutionary manufacturing paradigm which facilitates the production of complex, lightweight and high-performance parts which are more design flexible compared to the traditional subtractive manufacturing. The review discusses the leading metal 3D printing technologies and they include Selective Laser Melting (SLM), Direct Metal Laser Sintering (DMLS), Electron Beam Melting (EBM), Directed Energy Deposition (DED), and Binder Jetting. The experiment examines how the feedstock properties, especially powder morphology and wire based materials affect the process stability and quality of the part. Moreover, the most important process parameters, including laser power, scanning speed, and thermal conditions are addressed in connection with microstructure development, defect formation and residual stresses. Mechanical performance such as tensile strength, hardness, fatigue resistance, and anisotropy are measured and methods of post-processing are measured through heat treatment, hot isostatic pressing, and surface finishing. The review also shows new industrial uses and explains the existing issues in terms of cost, reproducibility, certification, and sustainability, as well as describes the future research opportunities in multi-material printing, in-situ monitoring, and AI-based optimization of processes.
Bismuth vanadate and Cobalt-doped bismuth vanadate nanomaterials are synthesized via a hydrothermal approach, and the electrochemical performance of cobalt-doped (25 %) bismuth vanadate (Co-BiVO4) is investigated for supercapacitor applications. Structural and surface analysis confirm Cobalt integration and development of the required structure. Electrochemical studies are conducted with 0.1 M Potassium Hydroxide (KOH), showing that Co-doping enhances performance, reveals a higher specific capacitance of 2997.13 F g- 1 at 0.5 A g- 1, appreciably greater than BiVO4 (889.46 F g- 1), and retains 82.7 % capacitance after 10,000 cycles with 98.7 % Coulombic efficiency. Electrochemical impedance spectroscopy shows reduced charge transfer resistance (2.167 Omega) in confirming better conductivity. A Symmetric two-electrode device provides a specific capacitance of 2144 F g-1 at 0.5 A g-1, its practical applicability is demonstrated using a Light Emitting Diode (LED). Theo-retical intuitions from Density Functional Theory (DFT) simulations, performed using Quantum Atomistix Toolkit (ATK), support the experimental results. Density of states and Band structure analysis reveal bandgap narrowing and the existence of Cobalt-3d states close to the Fermi level. Co-BiVO4 determines a superior quantum capac-itance of 960.94 F g-1 at-3 V than BiVO4 (479.09 F g-1), validating its excellent charge storage potential for energy applications.
Accurate and early prediction of lane-change maneuvers is a critical requirement for advanced driver-assistance systems and autonomous driving applications. Lane-change behavior is influenced by complex visual cues, temporal driving patterns, and dynamic interactions among surrounding traffic participants. This paper presents an explainable deep neural architecture for lane-change prediction that integrates vision-based perception with vehicle interaction modeling in a unified engineering framework. The proposed system employs a Vision Transformer to extract high-level spatial representations from camera inputs, while a recurrent temporal model captures sequential driving behavior over time. In parallel, a graph neural network models interactions between the ego vehicle and surrounding vehicles, enabling the system to reason about traffic dynamics and relative motion. These multimodal representations are fused to predict lane-change intentions in a probabilistic manner. To enhance transparency and trustworthiness, multiple explainable artificial intelligence techniques are incorporated to interpret both visual and behavioral decision factors. Experimental evaluation on publicly available traffic datasets demonstrates that the proposed architecture achieves improved prediction accuracy and early detection of lane-change maneuvers under varying traffic conditions. The explainability analysis further provides human-understandable insights into model decisions, supporting safer deployment in real-world transportation systems. The proposed approach contributes to intelligent transportation engineering by combining perception, interaction modeling, and explainability within a single predictive framework, facilitating reliable decision-making for next-generation vehicular systems.
