Case-carburized low-alloy steels such as EN353, SAE8620 and 20MnCr5 are widely used in gears, shafts and transmission components because carburization combines a hard, wear-resistant surface with a tough core. Physical vapour deposition of chromium nitride (CrN) improves wear and corrosion resistance of such components, but its effect on fatigue behaviour is inconsistently reported: some studies find CrN delays crack initiation and improves fatigue life, while others report reduced endurance limits from coating defects, residual stresses or interfacial debonding. This paper presents a systematic review, following a pre-defined search and screening protocol, of fatigue fundamentals case-carburization metallurgy, PVD/CrN coating technology, and comparative fatigue and fractographic behaviour reported for carburized and CrN-coated steels. A quantitative data-extraction table supplements the qualitative synthesis, recording case depth, hardness, retained austenite, coating parameters and reported fatigue outcome for every study, noting unreported variables. The synthesis shows that almost no study evaluates more than one carburizing steel under identical conditions, that rotating-bending fatigue is under-studied relative to axial and rolling-contact loading, and that fractographic observations are rarely correlated quantitatively with fatigue-life data. These gaps motivate a systematic comparison of the three steels under identical CrN coating and rotating-bending conditions, combined with correlated SEM fractography.
Bare-metal deployments of Kubernetes-based container platforms such as Red Hat OpenShift lack the native, cloud-provider-integrated load-balancing services that are taken for granted in public cloud environments. MetalLB fills this gap by exposing Kubernetes Services of type Load Balancer on bare-metal or on-premises infrastructure, using either Layer 2 (ARP/NDP) or Border Gateway Protocol (BGP) advertisement modes. This paper presents a design and evaluation study of the BGP advertisement mode of MetalLB as deployed on OpenShift, augmented with Bidirectional Forwarding Detection (BFD) for rapid link and peer failure detection. We describe the underlying protocol mechanics of BGP and BFD, the MetalLB controller/speaker architecture, and the integration points with OpenShift's OVN-Kubernetes network stack. A reference architecture is proposed in which each cluster node peers with top-of-rack switches over eBGP and advertises Virtual IP (VIP) service routes, while BFD sessions running beneath the BGP peering’s shrink failure-detection time from tens of seconds to the sub-second range. A testbed methodology and representative performance results are presented, comparing convergence and failover behavior with and without BFD. The study concludes that combining MetalLB, BGP, and BFD provides a practical, vendor-neutral, and highly available load-balancing fabric for on-premises Kubernetes and OpenShift clusters, at the cost of additional operational complexity in router and cluster network configuration.
In database management systems (DBMSs), query optimization is a frequent practice. Its primary goal is to maximize query execution efficiency among many possibilities. In recent years, the world of data-driven applications has grown rapidly, and effective query processing is an essential element of high-performance systems and efficient resource use in cloud computing environments. In this paper, the authors review key query optimization strategies used in state-of-the-art DBMSs. These include heuristic-based optimization, cost-based optimization, semantic-based optimization for both join and indexing strategies and sub-query rewriting methods. The study contextually explores new AI/ML-based optimization techniques that contribute towards enhancing adaptive query execution and automated tuning in the dynamic environment. Apart from this, the paper also discusses the role of query execution plans, cost models and optimization frameworks and how they can improve the performance of databases in enterprise systems, databases in a distributed environment of the cloud and big data analytics platforms. Using a systematic literature review of recent relevant studies, researchers identify current progress and challenges in query optimization alongside possible future research directions. The paper shows that in the context of many complex (possibly correlated) workloads, modern database systems need some degree of intelligent/elastic behaviour to operate with bounded latency and resource consumption.
Real-time video surveillance is an essential component of the modern security system, particularly in the context of monitoring public areas, particularly in densely populated areas such as airports, train stations, and urban junctions. Occlusion, quick movement, and high item density are some of the issues that traditional surveillance systems face when accurately detecting and effectively monitoring objects. YOLO, which stands for "You Only Look Once," is a framework that includes DeepSORT, which stands for "Simple Online and Realtime Tracking with a Deep Association Metric," for effective object tracking. This framework is proposed in this study as a robust multi-object identification and tracking system. By utilizing appearance traits and Kalman filtering, YOLO guarantees that objects are localized and classified quickly and accurately, whereas DeepSORT ensures that identification is maintained consistently between frames. Under various lighting and crowd density situations, the combined model was implemented and tested on video feeds captured from the real world. Given that the system can handle occlusions and re-identify lost targets with high accuracy, low latency, and resilience, as demonstrated by the results of the experiments, it is a feasible option for applications that include intelligent surveillance. When applied in dynamic and complex public contexts, this technique dramatically improves situational awareness and assists proactive security management.
With the increasing number of vehicles on the road, parking management has become a significant challenge in urban areas. The Smart Parking Lot Helper aims to optimize parking space utilization using IoT and AI technologies. This paper explores sensor-based detection, mobile application integration, automated fee deduction systems, and the overall benefits of a smart parking system. We analyse existing research in smart parking solutions, present a feasible implementation plan, and discuss the potential impacts on smart cities.
