Plasma Nitriding of 90CrMoV8 Tool Steel for the Enhancement of Hardness and Corrosion Resistance

The aim of the study is to apply a plasma nitriding process to the 90CrMoV8 steel commonly employed in wood machining, and to determine its efficiency to improve both mechanical and electrochemical properties of the surface. Treatments were performed at a constant N2:H2 gas mixture and by varying the temperature and process duration. The structural and morphological properties of nitrided layers were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM) coupled with EDS microanalyses. Surface hardening and hardness profiles were evaluated by micro hardness measurements. To simulate the woodmachining conditions, electrochemical tests were carried out with an oak wood electrolyte with the purpose of understanding the effects of the nitriding treatment on the corrosion resistance of the tool in operation. X-ray diffraction analyses revealed the presence of both γ′ (Fe4N) and ε (Fe2–3N) nitrides with a predominance of the ε phase. Moreover, α-Fe (110), γ′ and ε diffraction peaks were shifted to lower angles suggesting the development of compressive stresses in the post nitrided steel. As a result, it was shown that nitriding allowed a significant hardening of steel with hardness values higher than 1200 HV. The diffusion layers were always composed of an outer compound layer and a hardened bulk layer which thickness was half of the total diffusion layer one.No white layer was observed. Similarly, no traces of chromium nitrides were detected. The temperature seemed to be a parameter more influent than the process duration on the morphological properties of the nitrided layer, while it had no real influence on their crystallinity. Finally, the optimal nitriding conditions to obtain a thick and hard diffusion layer are 500 °C for 10 h. On the other hand, to verify the effect of these parameters on the corrosion resistance, potentiodynamic polarization tests were carried out in an original “wood juice” electrolyte. After corrosion, surface was then observed at the SEM scale. Electrochemical study indicated that the untreated steel behaved as a passive material. Although the very noble character of steel was somewhat mitigated and the corrosion propensity increased for nitrided steels, the passive-like nature of themodified surfacewas preserved. For the same optimized parameters as those deduced from the mechanical characterization (500 °C, 10 h), surface presented, in addition to a huge surface hardening, a high corrosion resistance.Regional Council of Burgundy and EGID

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated on NSL-KDD dataset and classified multi attack classification. The model has outperformed with adamax optimizer in terms of accuracy, detection rate and low false alarm rate. The results of LSTM-RNN with adamax optimizer is compared with existing shallow machine and deep learning models in terms of accuracy, detection rate and low false alarm rate. The multi model methodology consisting of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are evaluated on bench mark datasets such as KDD99, NSL-KDD, and UNSWNB15 datasets. The models self-learnt the features and classifies the attack classes as multi-attack classification. The models RNN, and LSTM-RNN provide considerable performance compared to other existing methods on KDD99 and NSL-KDD datase

Impact of COVID 19 on Steel Industry – A case Study of RINL, Visakhapatnam Steel Plant

Steel is by far, the most widely used and is environment friendly as it can be recycled 100%. Steel has wide variety of applications in day to day life. Indeed, steel is the backbone and support of the global economy and infrastructure. Steel has got many forward linkages with manufacturing industries. Status of global steel industry before the onset of COVID 19 and the effect of pandemic on industry in both global and Indian contexts were discussed. The status of Indian economy was briefly explained and projected outlook for steel market after breakout of the pandemic. The effect of COVID on RINL was discussed in detail and various strategies adopted by the industry during the period were explained. Handling of such future eventualities were addressed. After the outbreak, the recovery and outlook were explained in detail. Interventions and initiatives sought from the Government were presented in this paper

Characterisation of Materials used in Flex Bearings of Large Solid Rocket Motors

Solid rocket motors are propulsion devices for both satellite launchers and missiles, which require guidance and steering to fly along a programmed trajectory and to compensate for flight disturbances. A typical solid rocket motor consists of motor case, solid propellant grain, motor insulation, igniter and nozzle. In most solid rocket motors, thrust vector control (TVC) is required. One of the most efficient methods of TVC is by flex nozzle system. The flex nozzle consists of a flexible bearing made of an elastomeric material alternating with reinforcement rings of metallic or composite material. The material characterisation of AFNOR 15CDV6 steel and the natural rubber-based elastomer developed for use in flex nozzle are discussed. This includes testing, modelling of the material, selection of a material model suitable for analysis, and the validation of material model.Defence Science Journal, 2011, 61(3), pp.264-269, DOI:http://dx.doi.org/10.14429/dsj.61.5

An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection

In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high-level features for attack detection. Therefore, an effective deep learning-based intrusion detection system is developed in this paper for DoS and DDoS attack classification. This model includes various phases and starts with the Deep Convolutional Generative Adversarial Networks (DCGAN) based technique to address the class imbalance issue in the dataset. Then a deep learning algorithm based on ResNet-50 extracts the critical features for each class in the dataset. After that, an optimized AlexNet-based classifier is implemented for detecting the attacks separately, and the essential parameters of the classifier are optimized using the Atom search optimization algorithm. The proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15, using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15 dataset and 99.33% for the CICIDS2019 dataset. The investigational results demonstrate that the suggested approach performs superior to other competitive techniques in identifying DoS and DDoS attacks

CONTAINERIZED DEPLOYMENT OF WEBRTC-SIP INTERWORKING FUNCTION TO INTEROPERATE WITH LEGACY SIP LINE-SIDE EDGES

Conventional Session Initiation Protocol (SIP) line-side edges are not always distributed, and they require that registrations and calls be handled by the same entity. Servers that support Web Real-Time Communication (WebRTC) clients do not require a hard state and with cloud deployments they are increasingly being deployed as containerized workloads. Containerized deployments (such as Kubernetes) are typically stateless, and even with stateful implementations special handling is required to ensure that registrations and calls are consistently sent to the same SIP edge node, with high availability, in the face of frequent pod failures. To address such challenges, techniques are presented herein that enable a browser (that is stateless) to register via a Kubernetes cluster of pods (which are, again, stateless) but still connect as a SIP line side to a legacy SIP system that requires stickiness in terms of using the same Transmission Control Protocol (TCP) or Transport Layer Security (TLS) connection for SIP registrations and calls

DYNAMIC TELEMETRY PROFILE ENFORCEMENT IN A CONTROLLER NETWORK

Because telemetry processing can involve high resource usage, such processing is typically provided via a cloud infrastructure. However, there are drawbacks to current implementations involving such cloud infrastructure processing. For example, such processing typically follows standard processing patterns. Yet, with the increasing complexity of different network use cases, there are scenarios that would benefit from dynamic telemetry processing. Presented herein are techniques through which multiple device telemetry profiles can allow a cloud controller to dynamically match a telemetry profile to specific conditions for a tenant network. Each telemetry profile may include selections for data processing through priority and secured queues. Additionally, the cloud controller may have reverse telemetry policies to push reverse telemetry to the customer edge when original usage telemetry data is retrieved, processed, and/or transferred

A Comprehensive Review on Analytical Method Development using RP-HPLC and Recent Advances in Pharmaceutical Applications

The analytical technique of choice for separating, identifying, and quantifying complex mixtures is high-performance liquid chromatography (HPLC). Reverse-phase liquid chromatography (RP-HPLC) is the preferred separation mode for high-performance liquid chromatography (HPLC) due to its adaptability and higher selectivity for hydrophobic compounds. This review article discusses the fundamentals of reversed-phase high-performance liquid chromatography (RP-HPLC). This covers the separation principle, various stationary and mobile phase types, and separation-affecting variables. This article highlights the need of developing and testing such methods in addition to outlining the advantages of using RP-HPLC in industries like pharmaceutical, food, and environmental analysis. As examples of more recent advancements in RP-HPLC, new stationary and mobile phases, RP-HPLC downsizing, and hyphenated methods are also discussed. This review article provides a comprehensive tool for designing, refining, and validating RP-HPLC processes

Multiple Risk Factors of Alcoholic and Non-Alcoholic Myocardial Infarction Patients

Background: Myocardial infarction (MI) is one of the most critical medical emergency and contributor to morbidity and mortality worldwide. Myocardial infarction is the most common form of coronary heart disease and leading cause of premature death. Past century has seen substantial advancement in the field of medical sciences but still mortality trends due to myocardial infarction is increasing in developing countries including India. We have conducted this study to compare the Sociodemographic characteristics of alcoholic and non alcoholic MI patients admitted in coronary care unit of Saveetha Medical College, Chennai, India. Methods: An exploratory cross sectional study was performed by enrolling a convenient sample of 100 Myocardial Infarction patients. Information about Sociodemographic characteristics, past medical history, alcohol and tobacco intake, physical activity, psychological stress and biochemical measurements was gathered. Results: The mean age of the respondents was 46 (SD=6) years and majority of them were male i.e. 82%. 100% married and 89% literate, there were 24% past and 22% present alcoholics. Consumption of alcohol on a monthly, weekly and daily basis was 8%, 11% and 5% respectively. Preference to brandy was 67%, rum was 21% and that the beer was 12%. Current smoker were 20% and former were 11%. 93% and 52% respondents were under medication of beta blocker and angiotensin-converting-enzyme (ACE) inhibitors respectively. Conclusion: Worldwide, MI is the most common cause of mortality and morbidity and hence early diagnosis and management is most essential. Results from our study revealed that, participants had sedentary lifestyles where risk factors of MI such as alcohol consumption, and smoking does existed