Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Enhancing Task Assignment in Crowdsensing Systems Based on Sensing Intervals and Location

The popularity of mobile devices with sensors is captivating the attention of researchers to modern techniques,such as the internet of things(IoT)and mobile crowdsensing(MCS).The core concept behind MCS is to use the power of mobile sensors to accomplish a difficult task collaboratively,with each mobile user completing much simpler micro-tasks.This paper discusses the task assignment problem in mobile crowdsensing,which is dependent on sensing time and path planning with the constraints of participant travel distance budgets and sensing time intervals.The goal is to minimize aggregate sensing time for mobile users,which reduces energy consumption to encourage more participants to engage in sensing activities and maximize total task quality.This paper introduces a two-phase task assignment framework called location time-based algorithm(LTBA).LTBA is a framework that enhances task assignment in MCS,whereas assigning tasks requires overlapping time intervals between tasks and mobile users’tasks and the location of tasks and mobile users’paths.The process of assigning the nearest task to the mobile user’s current path depends on the ant colony optimization algorithm(ACO)and Euclidean distance.LTBA combines two algorithms:(1)greedy online allocation algorithm and(2)bio-inspired traveldistance-balance-based algorithm(B-DBA).The greedy algorithm was sensing time interval-based and worked on reducing the overall sensing time of the mobile user.B-DBA was location-based and worked on maximizing total task quality.The results demonstrate that the average task quality is 0.8158,0.7093,and 0.7733 for LTBA,B-DBA,and greedy,respectively.The sensing time was reduced to 644,1782,and 685 time units for LTBA,B-DBA,and greedy,respectively.Combining the algorithms improves task assignment in MCS for both total task quality and sensing time.The results demonstrate that combining the two algorithms in LTBA is the best performance for total task quality and total sensing time,and the greedy algorithm follows it then B-DBA.

Brain Tumor:Hybrid Feature Extraction Based on UNet and 3DCNN

Automated segmentation of brain tumors using Magnetic Resonance Imaging(MRI)data is critical in the analysis and monitoring of disease development.As a result,gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods.It is intended to extract characteristics from an image using the Gray Level Co-occurrence(GLC)matrix feature extraction method described in the proposed work.Using Convolutional Neural Networks(CNNs),which are commonly used in biomedical image segmentation,CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor.Using two segmentation networks,a U-Net and a 3D CNN,we present a major yet easy combinative technique that results in improved and more precise estimates.The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on.Using the dataset,two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region.Then,the estimates was made by two separate models that were put together to produce the final prediction.In comparison to current state-of-the-art designs,the precision(percentage)was 98.35,98.5,and 99.4 on the validation set for tumor core,enhanced tumor,and whole tumor,respectively.

Security and Privacy Challenges in Cyber-Physical Systems

Cyber-Physical Systems, or Smart-Embedded Systems, are co-engineered for the integration of physical, computational and networking resources. These resources are used to develop an efficient base for enhancing the quality of services in all areas of life and achieving a classier lifestyle in terms of a required service’s functionality and timing. Cyber-Physical Systems (CPSs) complement the need to have smart products (e.g., homes, hospitals, airports, cities). In other words, regulate the three kinds of resources available: physical, computational, and networking. This regulation supports communication and interaction between the human word and digital word to find the required intelligence in all scopes of life, including Telecommunication, Power Generation and Distribution, and Manufacturing. Data Security is among the most important issues to be considered in recent technologies. Because Cyber-Physical Systems consist of interacting complex components and middle-ware, they face real challenges in being secure against cyber-attacks while functioning efficiently and without affecting or degrading their performance. This study gives a detailed description of CPSs, their challenges (including cyber-security attacks), characteristics, and related technologies. We also focus on the tradeoff between security and performance in CPS, and we present the most common Side Channel Attacks on the implementations of cryptographic algorithms (symmetric: AES and asymmetric: RSA) with the countermeasures against these attacks.

Enhancement of UAV Data Security and Privacy via Ethereum Blockchain Technology

Unmanned aerial vehicles(UAVs),or drones,have revolutionized a wide range of industries,including monitoring,agriculture,surveillance,and supply chain.However,their widespread use also poses significant challenges,such as public safety,privacy,and cybersecurity.Cyberattacks,targetingUAVs have become more frequent,which highlights the need for robust security solutions.Blockchain technology,the foundation of cryptocurrencies has the potential to address these challenges.This study suggests a platform that utilizes blockchain technology tomanage drone operations securely and confidentially.By incorporating blockchain technology,the proposed method aims to increase the security and privacy of drone data.The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions.TheMetaMask wallet for Ethbalance is necessary for BCT transactions.The present research finding shows that the proposed approach’s efficiency and security features are superior to existing methods.This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries.The proposed platform’s security measures could mitigate privacy concerns,minimize cyber security risk,and enhance public safety,ultimately promoting the widespread adoption of UAVs.The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication,privacy preservation,confidentiality,integrity,and access control.

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)models.Due to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model failures.Due to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and businesses.This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the network.The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent.The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware attacks.The data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)classifiers.Based on the classification output,the decision for the detection of attackers and authorized users will be identified.The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

Blockchain and IIoT Enabled Solution for Social Distancing and Isolation Management to Prevent Pandemics

Pandemics have always been a nightmare for humanity,especially in developing countries.Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics.Still,developing countries cannot afford such solutions because these may severely damage the country’s econ-omy.Therefore,this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly.The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things(IIoT)and blockchain-enabled technologies.Compared to existing studies,the immutable and tamper-proof contact tracing and quarantine management solution is proposed.The use of advanced technologies and information security is a critical area for practitioners in the internet of things(IoT)and corresponding solutions.Therefore,this study also emphasizes information security,end-to-end solution,and experimental results.Firstly,a wearable wristband is proposed,incorporating 4G-enabled ultra-wideband(UWB)technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization(WHO).Secondly,distributed ledger technology(DLT)omits the centralized dependency for transmitting contact tracing data.Thirdly,a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism.Lastly,based on geofencing techniques,blockchain-enabled machine-to-machine(M2M)technology is proposed for quarantine management.The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management.Unlike existing research studies,the security aspect is also considered in the realm of blockchain.The practical implementation of the proposed solution also obtains the results.The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques,which could help battle pandemic situations.Researchers can also consider the 5G-enabled narrowband internet of things(NB-IoT)technologies to implement contact tracing solutions.

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Software needs modifications and requires revisions regularly.Owing to these revisions,retesting software becomes essential to ensure that the enhancements made,have not affected its bug-free functioning.The time and cost incurred in this process,need to be reduced by the method of test case selection and prioritization.It is observed that many nature-inspired techniques are applied in this area.African Buffalo Optimization is one such approach,applied to regression test selection and prioritization.In this paper,the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization.The proposed algorithm converges in polynomial time(O(n^(2))).In this paper,the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations.An astounding 62.5%drop in size and a 48.57%drop in the runtime of the original test suite were recorded.The obtained results are compared with Ant Colony Optimization.The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities(80%),and a reduction in the overall execution time and size of the resultant test suite.The results and analysis,hence,advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an attacker.Cloud cryptography is the best way to transmit data in a secure and reliable format.Various researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data security.Each algorithm has some data security issues.If some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original data.In this paper,various data security techniques are developed,which can be used to protect the data from attackers completely.First,a customized American Standard Code for Information Interchange(ASCII)table is developed.The value of each Index is defined in a customized ASCII table.When an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the data.After that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original data.When the number of cipher values is double the original data,the attacker tries to decrypt each value.Instead of getting the original data,the attacker gets such data that has no relation to the original data.After that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain text.The boundaries of each Hill text work up to that text.The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms.Then,the Kasiski test is used to verify the validity of the proposed algorithm and found that,if the proposed algorithm is used for data encryption,so an attacker cannot break the proposed algorithm security using any technique or algorithm.

Multi-head attention graph convolutional network model:End-to-end entity and relation joint extraction based on multi-head attention graph convolutional network

At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information.The adjacency matrix constructed by the dependency tree can convey syntactic information.Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description.At the same time,a large amount of irrelevant information will cause redundancy.This paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external tools.MAGCN generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping relations.The authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and CoNLL04.The results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.

Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study

Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data.

An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

Atrocious Impinging of COVID-19 Pandemic on Software Development Industries

COVID-19 is the contagious disease transmitted by Coronavirus.The majority of people diagnosed with COVID-19 may suffer from moderate-tosevere respiratory illnesses and stabilize without preferential treatment.Those who are most likely to experience significant infections include the elderly as well as people with a history of significant medical issues including heart disease,diabetes,or chronic breathing problems.The novel Coronavirus has affected not only the physical and mental health of the people but also had adverse impact on their emotional well-being.For months on end now,due to constant monitoring and containment measures to combat COVID-19,people have been forced to live in isolation and maintain the norms of social distancing with no community interactions.Social ties,experiences,and partnerships are not only integral part of work life but also form the basis of human evolvement.However,COVID-19 brought all such communication to a grinding halt.Digital interactions have failed to support the fervor that one enjoys in face-to-face meets.The COVID-19 disease outbreak has triggered dramatic changes in many sectors,and the main among them is the software industry.This paper aims at assessing COVID-19’s impact on Software Industries.The impact of the COVID-19 disease outbreak has been measured on the basis of some predefined criteria for the demand of different software applications in the software industry.For the stated analysis,we used an approach that involves the application of the integrated Fuzzy ANP and TOPSIS strategies for the assessment of the impact of COVID-19 on the software industry.Findings of this research study indicate that Government administration based software applications were severely affected,and these applications have been the major apprehensions in the wake of the pandemic’s outbreak.Undoubtedly,COVID-19 has had a considerable impact on software industry,yet the damage is not irretrievable and the world’s societies can emerge out of this setback through concerted efforts in all facets of life.

Fuzzy Based Decision Making Approach for Evaluating the Severity of COVID-19 Pandemic in Cities of Kingdom of Saudi Arabia

The World Health Organization declared COVID-19 a pandemic on March 11,2020 stating that it is a worldwide danger and requires imminent preventive strategies to minimise the loss of lives.COVID-19 has now affected millions across 211 countries in the world and the numbers continue to rise.The information discharged by the WHO till June 15,2020 reports 8,063,990 cases of COVID-19.As the world thinks about the lethal malady for which there is yet no immunization or a predefined course of drug,the nations are relentlessly working at the most ideal preventive systems to contain the infection.The Kingdom of Saudi Arabia(KSA)is additionally combating with the COVID-19 danger as the cases announced till June 15,2020 reached the count of 132,048 with 1,011 deaths.According to the report released by the KSA on June 14,2020,more than 4,000 cases of COVID-19 pandemic had been registered in the country.Tending to the impending requirement for successful preventive instruments to stem the fatalities caused by the disease,our examination expects to assess the severity of COVID-19 pandemic in cities of KSA.In addition,computational model for evaluating the severity of COVID-19 with the perspective of social influence factor is necessary for controlling the disease.Furthermore,a quantitative evaluation of severity associated with specific regions and cities of KSA would be a more effective reference for the healthcare sector in Saudi Arabia.Further,this paper has taken the Fuzzy Analytic Hierarchy Process(AHP)technique for quantitatively assessing the severity of COVID-19 pandemic in cities of KSA.The discoveries and the proposed structure would be a practical,expeditious and exceptionally precise evaluation system for assessing the severity of the pandemic in the cities of KSA.Hence these urban zones clearly emerge as the COVID-19 hotspots.The cities require suggestive measures of health organizations that must be introduced on a war footing basis to counter the pandemic.The analysis tabulated in our study will assist in mapping the rules and building a systematic structure that is immediate need in the cities with high severity levels due to the pandemic.

Crowdsourced Requirements Engineering Challenges and Solutions:A Software Industry Perspective

Software crowdsourcing(SW CS)is an evolving software development paradigm,in which crowds of people are asked to solve various problems through an open call(with the encouragement of prizes for the top solutions).Because of its dynamic nature,SW CS has been progressively accepted and adopted in the software industry.However,issues pertinent to the understanding of requirements among crowds of people and requirements engineers are yet to be clarified and explained.If the requirements are not clear to the development team,it has a significant effect on the quality of the software product.This study aims to identify the potential challenges faced by requirements engineers when conducting the SW–CS based requirements engineering(RE)process.Moreover,solutions to overcome these challenges are also identified.Qualitative data analysis is performed on the interview data collected from software industry professionals.Consequently,20 SW–CS based RE challenges and their subsequent proposed solutions are devised,which are further grouped under seven categories.This study is beneficial for academicians,researchers and practitioners by providing detailed SW–CS based RE challenges and subsequent solutions that could eventually guide them to understand and effectively implement RE in SW CS.

Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health system.The paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare mechanisms.In view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher efficacy.At the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be realized.At the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug processing.The paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic scenarios.This paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational factors.This paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

Intelligent Dynamic Inversion Controller Design for Ball and Beam System

The Ball and beam system(BBS)is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties.Designing an effective ball and beam system controller is a real challenge for researchers and engineers.In this paper,the control design technique is investigated by using Intelligent Dynamic Inversion(IDI)method for this nonlinear and unstable system.The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it.The Moore-PenroseGeneralized Inverse(MPGI)is used to invert the prescribed constraint dynamics to realize the baseline control law.A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties,nonlinearities,and external disturbances.The semi-global asymptotic stability of IDI control is guaranteed in the sense of Lyapunov.Numerical simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the controller.In addition to that,comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.

Influence of Modifiers on Supercritical Fluid Chromatography (SFC) and Supercritical Fluid Extraction (SFE), Part I

It is important to understand the mechanism and implications of different modifiers on analytical and preparative processes under chromatography with supercritical fluids (SFs) and under extraction with SFs. Supercritical fluid chromatography (SFC) and supercritical fluid extraction are generally carried out with neat supercritical carbon dioxide (SCCO2) or with SCCO2 containing modifiers (or cosolvents), especially for strongly polar compounds. For example, methanol is added as a cosolvent/modifier to SCCO2 for the extraction/separation of polar compounds. This paper discusses the influence of the modifier on the colligative properties of the principal mobile phase, which may define the situation in the total mobile phase in a chromatography column or in parts of a column under SFC. No colligative behavior of solutions reflects individual properties of the solutes. Their cross-interactions with solvents are discussed.