Prediction of Vapor-liquid Equilibrium Databy Using Radial Basis Neural Networks

Most of the Chemical Engineering processes are nonlinear and complex in nature. They often require conventional modeling and simulation techniques based on certain simplified transport, kinetic and thermodynamic assumptions. These assumptions may, however, alter the exact nature of the system and would provide misleading information about the complex behavior of the system. An artificial neural network has the ability to overcome these limitations of the conventional approach by extracting the desired information directly from the data. In this paper radial basis network, a new generation of artificial neural network, has been successfully incorporated for the prediction of vapor liquid equilibrium data for binary systems including two azeotropes and a ternary system. Radial basis networks require lesser neurons than standard feed forward backpropagation and they can be trained in a fraction of time. From this work it is been proved that radial basis neural network has been successfully used for the prediction of vapor liquid equilibrium (VLE) data

Modelling the Influential Factors Embedded in the Proportionality Assessment in Military Operations

The ongoing decade was believed to be a peaceful one. However, contemporary conflicts, and in particular, ongoing wars prove the opposite as they show the increase in context complexity when defining their goals as well as execution strategies for building means and methods for achieving them by gaining advantage against their adversaries through the engagement of well-established targets. At the core of the engagement decision relies the principle of proportionality which brings in a direct relation the expected unintended effects on civilian side with the anticipated intended effects on military side. While the clusters of effects involved in the proportionality assessment are clear, the process itself is subjective, governed by different dimensions of uncertainty, and represents the responsibility of military Commanders. Thus, a complex socio-technical process where different clusters of influential factors (e.g., military, technical, socio-ethical) play a role in the decisions made. Having said that, the objective of this research is to capture and cluster these factors, and further to model their influence in the proportionality decision-making process. This decision support system produces military targeting awareness to the agents involved in the processes of building, executing, and assessing military operations. To accomplish the aim of this research, a Design Science Research methodological approach is taken for capturing and modelling the influential factors as a socio-technical artefact in the form of a Bayesian Belief Network (BBN) model. The model proposed is further evaluated through demonstration on three different cases in respect to real military operations incidents and scenarios existing in the scientific literature in this research field. Hence, through this demonstration, it is illustrated and interpreted how the factors identified influence proportionality decisions when assessing target engagement as being proportional or disproportional. In these cases, corresponding measures for strengthening proportionality and reducing disproportionality in military operations are considered.Modelling the Influential Factors Embedded in the Proportionality Assessment in Military OperationspublishedVersio

Prediction of Vapor-liquid Equilibrium Databy Using Radial Basis Neural Networks

Most of the Chemical Engineering processes are nonlinear and complex in nature. They often require conventional modeling and simulation techniques based on certain simplified transport, kinetic and thermodynamic assumptions. These assumptions may, however, alter the exact nature of the system and would provide misleading information about the complex behavior of the system. An artificial neural network has the ability to overcome these limitations of the conventional approach by extracting the desired information directly from the data. In this paper radial basis network, a new generation of artificial neural network, has been successfully incorporated for the prediction of vapor liquid equilibrium data for binary systems including two azeotropes and a ternary system. Radial basis networks require lesser neurons than standard feed forward backpropagation and they can be trained in a fraction of time. From this work it is been proved that radial basis neural network has been successfully used for the prediction of vapor liquid equilibrium (VLE) data

Integrated Safety and Security Risk Assessment Methods: A Survey of Key Characteristics and Applications

Over the last years, we have seen several security incidents that compromised system safety, of which some caused physical harm to people. Meanwhile, various risk assessment methods have been developed that integrate safety and security, and these could help to address the corresponding threats by implementing suitable risk treatment plans. However, an overarching overview of these methods, systematizing the characteristics of such methods, is missing. In this paper, we conduct a systematic literature review, and identify 7 integrated safety and security risk assessment methods. We analyze these methods based on 5 different criteria, and identify key characteristics and applications. A key outcome is the distinction between sequential and non-sequential integration of safety and security, related to the order in which safety and security risks are assessed. This study provides a basis for developing more effective integrated safety and security risk assessment methods in the future

COMPARISON OF HERBAL MOUTHWASH WITH COMMERCIALLY AVAILABLE 0.2% CHLORHEXIDINE AND 2% BETADINE MOUTHWASHES IN PATIENTS AFTER STAGE-1 IMPLANT SURGERY

  Objective: The objective of the current study is to assess the antibacterial efficiency of an herbal mouthwash (clove and neem) against 0.2% chlorhexidine and 2% betadone mouthwash in patients who have undergone Stage-1 implant surgery.Methods: 30 patients undergoing implant surgery (Stage-1) were divided into 3 groups and were given 3 different mouthwashes. The patient was recalled after 15 days. Swab samples from the site of implant were taken after 15 days and cultured. The results were tabulated.Results: 0.2% chlorhexidine and 2% bernadine were found to have better antibacterial efficiency than herbal mouthwash (p>0.05).Conclusion: The herbal mouthwash consisting of neem and clove was not efficient in killing microbes immediately after Stage-1 implant surgery when compared to 0.2% chlorhexidine and 2% bernadine

MERCURY POISONING AND MANAGEMENT: A SYSTEMATIC REVIEW

ABSTRACTA great number of literatures have been documented the destructive effects of the heavy metal toxin on the brain, kidneys, and nervous systems.Mercury is one of such chemicals of the environmental toxin which produces adverse effects causing mercury poisoning a leads to accumulation in thetissues of the body. Mercury occurs in various forms even though they are interchangeable which is the deadly feature of this chemical which is thecause for this mercury poisoning. That is, there are three types, namely, elemental, organic, and inorganic mercury. So, mercury has different effectsin the body according to its chemical forms. Exposure to this chemical occurs in two ways through environmental exposure or due to occupationalexposure. Different forms of mercury target different vital organs of the body, on failure of these organs are crucial for the body which might lead tothe death of the individual. Diagnosis of different types of mercury poisoning can be done by analyzing the samples of hair, urine, whole blood, andcertain tissues of the body. This analysis also gives us the results of recent exposure to the chemical. According to the lab diagnostic report and throughpreliminary clinic examination, the patient is treated according to the duration and dosage of mercury exposed to.Keywords: Mercury poisoning, Environmental toxin, Mercury, Occupational hazards

A Prototype Tool for Distinguishing Attacks and Technical Failures in Industrial Control Systems

Critical Infrastructures (CIs) are governed by Industrial Control Systems (ICSs). Modern ICSs do not operate in isolation anymore, but they are connected to the Internet. This transformation introduced numerous advantages, however, there are a few drawbacks as well. Integration with the Internet has left ICS exposed to potential cyber-attacks. Additionally, ICSs could also encounter technical failures during operation. Consequently, it is crucial to distinguish between attacks and technical failures to initiate an appropriate response. There is a deficiency of robust technology to assist operators in distinguishing attacks and technical failures in an ICS environment. However, a framework is proposed to construct Bayesian Network (BN) models that would help to distinguish between attacks and technical failures for different observable problems in our previous work. There are tools available to implement such BN models, but these tools are not appropriate to use in an ICS environment. In order to address this limitation, this paper develops and demonstrates a prototype tool for swift identification of the major cause (Intentional Attack/Accidental Technical Failure) in case of an abnormal behaviour in a component of ICS.The proposed tool enables BN models to automatically update prior probabilities based on the historical data and/or expert knowledge corresponding to the application. The developed tool can be further evaluated and used to distinguish between attacks and technical failures during operation in CIs where ICSs are employed

Holding on to Compliance While Adopting DevSecOps: An SLR

The software industry has witnessed a growing interest in DevSecOps due to the premises of integrating security in the software development lifecycle. However, security compliance cannot be disregarded, given the importance of adherence to regulations, laws, industry standards, and frameworks. This study aims to provide an overview of compliance aspects in the context of DevSecOps and explore how compliance is ensured. Furthermore, this study reveals the trends of compliance according to the extant literature and identifies potential directions for further research in this context. Therefore, we carried out a systematic literature review on the integration of compliance aspects in DevSecOps, which rigorously followed the guidelines proposed by Kitchenham and Charters. We found 934 articles related to the topic by searching five bibliographic databases (163) and Google Scholar (771). Through a rigorous selection process, we selected 15 papers as primary studies. Then, we identified the compliance aspects of DevSecOps and grouped them into three main categories: compliance initiation, compliance management, and compliance technicalities. We observed a low number of studies; therefore, we encourage further efforts into the exploration of compliance aspects, their automated integration, and the development of metrics to evaluate such a process in the context of DevSecOps.publishedVersio

Efficient Lignin Fractionation from Scots Pine (Pinus sylvestris) Using Ammonium-Based Protic Ionic Liquid : Process Optimization and Characterization of Recovered Lignin

Lignin-based chemicals and biomaterials will be feasible alternatives to their fossil-fuelbased counterparts once their breakdown into constituents is economically viable. The existing commercial market for lignin remains limited due to its complex heterogenous structure and lack of extraction/depolymerization techniques. Hence, in the present study, a novel low-cost ammoniumbased protic ionic liquid (PIL), 2-hydroxyethyl ammonium lactate [N11H(2OH)][LAC], is used for the selective fractionation and improved extraction of lignin from Scots pine (Pinus sylvestris) softwood biomass (PWB). The optimization of three process parameters, viz., the incubation time, temperature, and biomass:PIL (BM:PIL) ratio, was performed to determine the best pretreatment conditions for lignin extraction. Under the optimal pretreatment conditions (180 ◦C, 3 h, and 1:3 BM:PIL ratio), [N11H(2OH)][LAC] yielded 61% delignification with a lignin recovery of 56%; the cellulose content of the recovered pulp was approximately 45%. Further, the biochemical composition of the recovered lignin and pulp was determined and the recovered lignin was characterized using 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy, quantitative 31P NMR, gel permeation chromatography (GPC), attenuated total reflectance (ATF)–Fourier transform infrared spectroscopy (ATR-FTIR), and thermal gravimetric analysis (TGA) analysis. Our results reveal that [N11H(2OH)][LAC] is significantly involved in the cleavage of predominant β–O–4’ linkages for the generation of aromatic monomers followed by the in situ depolymerization of PWB lignin. The simultaneous extraction and depolymerization of PWB lignin favors the utilization of recalcitrant pine biomass as feedstock for biorefinery schemes