An Intelligent and Secure Communication of AIoT enabled Devices empowered with IPK Algorithm

Artificial intelligence Internet of Things (AIoT) will be a necessary part of our lives in the near future. It will be found as quick cooperation in our surroundings through the related sensor-based system. To be sure, even in an indirect method, it will serve us in a couple of structures as esteem included organizations over the cell stages. With the AIoT structures that make usage of data, actually, the data collection from contraptions can in like manner be a goal of cyberattacks. Device to Device (D-2-D) interchanges in AIoT was planned alongside various shows, for instance, Constrained Access Protocol (CoAP). Its huge stresses in the course of action of AIoT are to ensure the security of mechanisms and D-2-D one place to another. Furthermore, present correspondence shows for AIoT arewithout reliability features. It is a result of this that countermeasures in perspective on encryption are starting at now getting importance. There is a requirement for a solid cryptosystem for D-2-D in AIoT. In this investigation paper, we present an encryption technique which is indicated as EPEB as a security answer for AIoT. The proposed methodology works with the message which shows special characters, numbers, and bits for data encryption and decryption. In authority, the end key isn't known so we would encryption to able have the option to gadgets data using particular keys and scramble packet per specialkey

Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

Financial Liberalization And Demand For Money: A Case of Pakistan

Literature in economics has identified many channels through which the financial liberalization may affect demand for money. There are evidences of stability as well as instability of demand for money due to financial development for developing economies. The objective of the current study is to examine the effect of financial liberalization on demand for money in Pakistan, i.e. whether financial liberalization has affected the demand for money or not. The issue is important as stable demand for money function is a prerequisite for formulating and operating monetary policy. To achieve the objective JJ cointegration and auto regressive distributed lag (ARDL) to the cointegration is employed to estimate the long-run equilibrium relationship between broad money M2 and composite financial liberalization index along with other determinants of demand for money like gross domestic product, real deposit rate and exchange rate. In order to assess the stability of the model, the parameter constancy tests, i.e. recursive residuals, CUSUM and CUSUMSQ tests have been applied. The empirical results indicated that for broad money, there exists long-run money demand function. The financial liberalization, gross domestic product and real deposit rate positively affect the demand for money in the long as well as short-run.Demand for money, Financial liberalization, Real deposit rate, Financial reforms, Pakistan, ARDL

Software Testing for cyber Security

Software testing plays a vital role in software security because hackers attack on a system through back channels which they can easily find if there is any error or bug exists in the software. The software security failure can cause the unbearable loss for IT companies and other organizations. Cyber security is another big issue for computer users' personal data as all their information is vulnerable because of easy excess, visibility and availability. Therefore, software testing is also useful to secure the personal information. In this article, cyber security testing based on particle swam optimization algorithm (CST) is proposed for testing of software cyber security testing. CSTPSOA is a PSO base technique which is used to solve the complex multi-level problems and is also used for optimization. In the CST method PSO is used for the optimization of test cases for cyber security testing

COMPARISON OF ARGON PLASMA COAGULATION AND FORMALIN INSTILLATION IN THE TREATMENT OF RADIATION INDUCED HAEMORRHAGIC PROCTOCOLITIS

Background: Radiotherapy is routinely used in the treatment of pelvic malignancies and about 2–5% of these patients develop radiation-induced proctitis or proctocolitis. This complication of radiotherapy is treated in different ways. Two of these treatments, argon plasma coagulation (APC) and formalin instillation, have both been reported as to be successful modalities, but data comparing them are scarce. We conducted this study to compare these two treatment options. Methods: We reviewed the charts of patients who had radiation-induced proctocolitis and who were treated endoscopically at our tertiary care cancer centre with either APC or formalin instillation. Outcomes of the two treatments were compared in terms of bleeding control after the first session of treatment, the number of sessions required and the nal response to therapy. Results: Out of a total of 26 patients presenting with haemorrhagic radiation proctocolitis, 11 were treated with APC and 15 with formalin instillation. Success after the rst session was 53% in the formalin instillation group compared to 18% in the APC-treated group. On repeated sessions, the final response to both treatment modalities was comparable. Conclusion: Efficacy of APC and formalin instillation in the treatment of haemorrhagic radiation proctocolitis is comparable although formalin showed a better outcome after the first session. Key words: Argon plasma coagulation, formalin instillation, proctitis, radiation-induced colitis

A Spatial 3 X 3 Average Filter for De-Noising in Digital Images with the Help of Median Filter

Digital image processing includes many factors like Image enhancement, segmentation, object recognition, removal of noise and many more. Noise removal is the one of the hot area of image processing. Noise can be minimized but cannot removed completely. Scientist and researchers has established many filters, which can minimize the noise in the image and enhance its quality. There are many types of noise and many types of filter for the removal of noise. Many types of noise are used. To remove these types of noise, 3 x 3 average filter is used in this paper and its efficiency is measured. The simulations are performed on the MATLAB

Molecular genetic analysis of leucine tRNA in relevance to type 2 diabetes mellitus

Background. Several point mutations in the mitochondrial DNA cause maternally inherited metabolic disorders. The most common type of mutation A3243G in the gene of transfer RNA leucine (tRNALeu(UUR)) is thought to be responsible for the prevalence of type 2 diabetes mellitus. This study was designed to analyze the tRNALeu(UUR) gene of mtDNA of the diabetic individuals with familial history of diabetes to identify the point mutations A3243G. Material and methods. Saliva samples were preferred as a source of DNA to minimize the risk of infection. DNA was successfully extracted from their saliva. Samples of high-quality DNA was amplified with PCR and sequenced in Macrogen Inc. Korea. Results. The m.3243A>G mutation in mitochondrial tRNALeu(UUR) gene was not observed. Conclusion. The result shows that the m.3243A>G mutation in mitochondrial tRNALeu(UUR) gene is not frequent cause of type 2 and some other factors may be possible i.e. genetic, behavioral or environmental. It is recommended that the sample size for diabetic individuals need to be increased for a future study and screened for the mitochondrial as well as other mutations of nuclear origin