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Background: The modern credit card system is critical, but it has not been fully examined to meet the unique financial needs of a constantly changing number of manufacturers and importers. Methods: An intelligent credit card system integrates the features of artificial intelligence and blockchain technology. The decentralized and unchangeable ledger of the Blockchain technology significantly reduces the risk of fraud while maintaining real-time transaction recording. On the other hand, the capabilities of AI-driven credit assessment algorithms enable more precise, effective, and customized credit choices that are specifically tailored to meet the unique financial profiles of manufacturers and importers. Results: Several metrics, including predictive credit risk, fraud detection, credit assessment accuracy, default rate comparison, loan approval rate comparison, and other important metrics affecting the credit card system, have been investigated to determine the effectiveness of modern credit card systems when using Blockchain technology and AI. Conclusion: The study of developing an intelligent scoring system for crediting manufacturers and importers of goods in Industry 4.0 can be enhanced by incorporating user adoption. The changing legislation and increasing security threats necessitate ongoing monitoring. Scalability difficulties can be handled by detailed planning that focuses on integration, data migration, and change management. The research may potentially increase operational efficiency in the manufacturing and importing industries.

OBJECTIVE: To determine the influence of managerial psychology on healthcare workers drawing from the construct of managerial psychology. METHODOLOGY: A quantitative research approach based on survey design was used. The data was gathered from respondents between February - November 2020 who were chosen randomly from five tertiary-level hospitals in Sindh and 37 rural health centres. Data analysis was analyzed using SPSS Version 22 and AMOS version 22. Employing the psychological capital (PsyCap) scale and Deci and Ryan's self-determination questionnaire, this study gathered data from 385 public sector employees working in the healthcare sector, examining the association between the study variables. The inclusion criteria comprised participants who were employees in both clinical and non-clinical capacities, male and female, working in 5 esignated public sector hospitals or 37 Rural Health Centres in Sindh province. RESULTS: The findings demonstrate a favourable association between optimism and job autonomy. Hope had a significant impact on healthcare employees" job autonomy. CONCLUSION: The findings highlight a correlation between psychological capital and job autonomy. KEYWORDS: Psychological capital; job autonomy; managerial psychology, self-efficacy, hope, optimism, resilience

Low-speed internet can negatively impact incident response by causing delayed detection, ineffective response, poor collaboration, inaccurate analysis, and increased risk. Slow internet speeds can delay the receipt and analysis of data, making it difficult for security teams to access the relevant information and take action, leading to a fragmented and inadequate response. All of these factors can increase the risk of data breaches and other security incidents and their impact on IoT-enabled communication. This study combines virtual network function (VNF) technology with software -defined networking (SDN) called virtual network function software-defined networking (VNFSDN). The adoption of the VNFSDN approach has the potential to enhance network security and efficiency while reducing the risk of cyberattacks. This approach supports IoT devices that can analyze large volumes of data in real time. The proposed VNFSDN can dynamically adapt to changing security requirements and network conditions for IoT devices. VNFSDN uses threat filtration and threat-capturing and decision-driven algorithms to minimize cyber risks for IoT devices and enhance network performance. Additionally, the integrity of IoT devices is safeguarded by addressing the three risk categories of data manipulation, insertion, and deletion. Furthermore, the prioritized delegated proof of stake (PDPoS) consensus variant is integrated with VNFSDN to combat attacks. This variant addresses the scalability issue of blockchain technology by providing a safe and adaptable environment for IoT devices that can quickly be scaled up and down to pull together the changing demands of the organization, allowing IoT devices to efficiently utilize resources. The PDPoS variant provides flexibility to IoT devices to proactively respond to potential security threats, preventing or mitigating the impact of cyberattacks. The proposed VNFSDN dynamically adapts to the changing security requirements and network conditions, improving network resiliency and enabling proactive threat detection. Finally, we compare the proposed VNFSDN to existing state-of-the-art approaches. According to the results, the proposed VNFSDN has a 0.08 ms minimum response time, a 2% packet loss rate, 99.5% network availability, a 99.36% threat detection rate, and a 99.77% detection accuracy with 1% malicious nodes.

Robotic process automation (RPA) is a popular process automation technology that leverages software to play the function of humans when employing graphical user interfaces. RPA’s scope is limited, and various requirements must be met for it to be applied efficiently. Business process management (BPM), on the other hand, is a well-established area of research that may provide favorable conditions for RPA to thrive. We provide an efficient technique for merging RPA with BPM (RPABPM) to synchronize the technology for efficient automated business processes. The problem formulation process is carried out to cut management-related expenditures. The proposed RPABPM strategy includes the five stages (design, modeling, execution, monitoring, and optimization) for optimal business automation and energy savings. Effective business process management is proved by employing an end-to-end process. Furthermore, findings have been obtained employing three empirical investigations that are performed to assess the practicality and precision of the proposed RPABPM approach. The first objective of the initial study is to confirm the practicality and precision of the approach employed to evaluate the acceptance, possibility, significance, and integration of RPA with BPM. The second study attempts to verify the method’s high-quality characteristics. The third study attempts to assess the approach’s effectiveness in analyzing and identifying BPM that are best suited for RPA. The proposed RPABPM is validated on the industrial robot manufactured by ABB with six-axis IRB140 and supported with a Windows CE-based Flex Pendant (teach pendant). An IRC5 controller is used to run RobotWare 5.13.10371. A pre-installed .NET Compact Framework 3.5 is used. Finally, the proposed method is compared with state-of-the-art methods from an efficiency and power consumption perspective.

Heavy metal pollution generally occurs due to socio-economic, industrial, and anthropogenic activities, which may cause an environmentally hazardous and serious severe threat to the survival of the organisms (genotoxic, carcinogenic, and clastogenic effects on it). Many physical and chemical remediation approaches have been proposed to deal with this pollution, but these are very time-consuming and costly. While bioremediation stands out as an inexpensive and efficient approach, the use of bacteria is thought to be a potential and productive organism to prevent this pollution. This review has evaluated the bacterial potential to clean up heavy metals from the environment and elucidated the mechanisms responsible for bioremediation.

With the development of information society and network technology, people increasingly depend on information found on the Internet. At the same time, the models of information diffusion on the Internet are changing as well. However, these models experience the problem due to the fast development of network technologies. There is no thorough research in regards to the latest models and their applications and advantages. As a result, it is essential to have a comprehensive study of information diffusion models.The primary goal of this research is to provide a comparative study on the existing models such as the Ising model, Sznajd model, SIR model, SICR model, Game theory and social networking services models. We discuss several of their applications with the existing limitations and further categorizations. Vulnerabilities and privacy challenges of information diffusion models are extensively explored. Furthermore, categorization including strengths and weaknesses are discussed. Finally, limitations and recommendations are suggested with diverse solutions for the improvement of the information diffusion models and envisioned future research directions.

When people use social networks, they often fall prey to a clickbait scam. The scammer attempts to create a striking headline that attracts the majority of users and attaches a link. The user follows the link and can be redirected to a fraudulent resource where the user easily loses personal data. To solve this problem, a Blockchain-enabled deep recurrent neural network (BDRNN) is proposed to detect the nature safe and malicious clickbait from the contents. The proposed BDRNN consists of three phases: analysis of clickbait and source rating, clickbait search process and multi-layered clickbait detection. The analysis of clickbait and source rating phase helps to analyze different sources to detect the clickbait and also rating the content-sources. To achieve the clickbait analysis and source rating, the detection of blocklisted/allowlisted source and source rating check algorithms are introduced. The clickbait search process is accomplished by incorporating the binary search features for a faster and more efficient search process for malicious content-detection. The multi-layered clickbait detection is main phase of the proposed BDRNN that consists of three models: content-to-vector model (layer-1), deep neural network model(layer-2), and Blockchain-enabled malicious content detection model (layer-3). These models collectively detect the malicious and safe clickbait from the contents. The extensive experiments are conducted to determine the effectiveness of the proposed BDRNN model and compared with the existing state-of-the-art neural network models designed for clickbait detection, and the result demonstrates that the proposed BDRNN model outperforms the counterparts from the, accuracy, link detection, memory usage, analogous perspectives, and attacker’s successful content capturing rate.

Deep learning algorithms have a wide range of applications, including cancer diagnosis, face and speech recognition, object recognition, etc. It is critical to protect these models since any changes to them can result in serious losses in a variety of ways. This article proposes the consortium blockchain-enabled conventional neural network (CBCNN), a four-layered paradigm for detecting malicious vehicles. Layer-1 is a convolutional neural network-enabled Internet-of-Things (IoT) model for the vehicle; Layer-2 is a spatial pyramid polling layer for the vehicle; Layer-3 is a fully connected layer for the vehicle; and Layer-4 is a consortium blockchain for the vehicle. The first three layers accurately identify the vehicles, while the final layer prevents any malicious attempts. The primary goal of the four-layered paradigm is to successfully identify malicious vehicles and mitigate the potential risks they pose using multi-label classification. Furthermore, the proposed CBCNN approach is employed to ensure tamper-proof protection against a parameter manipulation attack. The consortium blockchain employs a proof-of-luck mechanism, allowing vehicles to save energy while delivering accurate information about the vehicle’s nature to the “vehicle management system.” C++ coding is employed to implement the approach, and the ns-3.34 platform is used for simulation. The ns3-ai module is specifically utilized to detect anomalies in the Internet of Vehicles (IoVs). Finally, a comparative analysis is conducted between the proposed CBCNN approach and state-of-the-art methods. The results confirm that the proposed CBCNN approach outperforms competing methods in terms of malicious label detection, average accuracy, loss ratio, and cost reduction.

Finding reliable and efficient routes is a persistent problem in megacities. To address this problem, several algorithms have been proposed. However, there are still areas of research that require attention. Many traffic-related problems can be resolved with the help of smart cities that incorporate the Internet of Vehicles (IoV). On the other hand, due to rapid increases in the population and automobiles, traffic congestion has become a serious concern. This paper presents a heterogeneous algorithm called ant-colony optimization with pheromone termite (ACO-PT), which combines two state-of-the-art algorithms, pheromone termite (PT) and ant-colony optimization (ACO), to address efficient routing to improve energy efficiency, increase throughput, and shorten end-to-end latency. The ACO-PT algorithm seeks to provide an effective shortest path from a source to a destination for drivers in urban areas. Vehicle congestion is a severe issue in urban areas. To address this issue, a congestion-avoidance module is added to handle potential overcrowding. Automatic vehicle detection has also been a challenging issue in vehicle management. To address this issue, an automatic-vehicle-detection (AVD) module is employed with ACO-PT. The effectiveness of the proposed ACO-PT algorithm is demonstrated experimentally using network simulator-3 (NS-3) and Simulation of Urban Mobility (SUMO). Our proposed algorithm is compared with three cutting-edge algorithms. The results demonstrate that the proposed ACO-PT algorithm is superior to earlier algorithms in terms of energy usage, end-to-end delay, and throughput.

Air pollution has been notoriously held accountable for a substantial number of deaths in several countries. Moreover, its negative impact on people's health and well-being has also been witnessed in countries where air pollution is below the recommended national levels. The urban cities of Pakistan are among the worst South Asian areas in terms of air pollution. Because of this problem, the health and well-being of citizens are affected. The present study investigates the impact of air pollution on urban residents' happiness and health. It analyzes their willingness to pay for pollution prevention and its determinants by employing the data obtained through a primary survey. Pakistanis are unaware of air pollution's effect on health and quality of life, therefore only 12.5% consider this problem very serious. The results confirm the significantly negative effect of air pollution on happiness. Concerning the willingness to pay, it is differentiated in the form of tax and social contribution. Pakistanis are willing to pay more in social contribution in return for different environmental attributes. The results show that only 13% of respondents are not willing to pay for income contribution to improve air quality reporting indifferent attitude and insufficient knowledge of the environment. Our findings suggest that their apprehension concerning the environment influences people's willingness to pay. The study concludes that despite Pakistan's underdeveloped economic stature and its poor and flexible budgetary allocation for the betterment of air quality, most Pakistanis showed their willingness to pay for environmental protection. The government and environmental organizations ought to generate consensus among the general population about environmental importance, individual responsibility, and social duties thereby lessening the free-rider problem and reducing air pollution for better social welfare.

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems.

Lightweight physical unclonable functions (LPUFs) exploit manufacturing process variations of semiconductor integrated circuits (ICs) to protect IoT-based electronic and smart devices from new cyberattacks. This paper proposes two novel security techniques to enhance the robustness of LPUFs using configurable-based ring oscillator PUFs (CF-ROPUFs). These techniques are the intra-die frequency aware (IFA) approach to improve PUF reliability and the logarithmic gamma function ( $Ln_{\gamma }$ ) technique to enhance PUF randomness. The lightweight CF-ROPUF design is realized on hardware, and data samples are collected under varying temperatures and supply voltages over a population of 30 Spartan-3E FPGAs. Experimental results of the IFA technique in terms of average Hamming Weight (HM) demonstrate that the percentage of the reliable RO sample frequencies PUF output is 98.5%. For the analysis, PUF reliability is evaluated in terms of accuracy, repeatability, and reproducibility, the international organization for Standardization (ISO) standards. The results indicate that the RO samples are accurately measured from the CF-ROPUFs mapped in all the chips. After using the proposed 1-out-of-r coding algorithm, the results demonstrate high average repeatability of 98.2% and a magnified average reproducibility of 99.63%. It is also shown that our CF-ROPUF design is immune from accelerated aging impacts reliability issues. Statistical results show that Ln( $Ln_{\gamma }$ ) enhances the normality and mitigate the negative impacts of the systematic process variations on RO sample frequencies. Randomness results show that CF-ROPUF binary response bits can successfully pass the 15 NIST test suites for true randomness with an enhanced percentage, 93.3%, with the application of the 1-out-of-r coding.

Objective: To assess the impact of laparoscopic simulation training on surgical skills of trainee surgeons. Study Design: Comparative prospective study. Place and Duration of Study: Jinnah Sindh Medical University Karachi, for one month October 2019. Methodology: Total thirty surgical trainees of FCPS year four (R4) were studied. The cases group (fifteen trainees) were given one month laparoscopy training on a LAP Simulator (LAPSim), surgical science Sweden) and the control group (fifteen trainees) were not trained. Their laparoscopic skills were then compared. Objective Structured Assessment of Technical Skills (OSATS) specific and general was used to assess their performance. Data was entered and analyzed using SPSS-23. Results: On LAP Sim Scoring, post-test scores were significantly better than pre-test for all skills in the trained residents (p

Abstract Cloud computing is a model for on-demand delivery of IT resources (e.g., servers, storage, databases, etc.) over the Internet with pay-as-you-go pricing. Although it provides numerous benefits to cloud service users (CSUs) such as flexibility, elasticity, scalability, and economies of scale, there is a large trust deficit between CSUs and cloud service providers (CSPs) that prevents the widespread adoption of this computing paradigm. While some businesses have slowly started adopting cloud computing with careful considerations, others are still reluctant to migrate toward it due to several data security and privacy issues. Therefore, the creation of a trust model that can evolve to reflect the true assessment of CSPs in terms of either a positive or a negative reputation as well as quantify trust level is of utmost importance to establish trust between CSUs and CSPs. In this paper, we propose a fuzzy-logic based approach that allows the CSUs to determine the most trustworthy CSPs. Specifically, we develop inference rules that will be applied in the fuzzy inference system (FIS) to provide a quantitative security index to the CSUs. One of the main advantages of the FIS is that it considers the uncertainties and ambiguities associated with measuring trust. Moreover, our proposed fuzzy-logic based trust model is not limited to the CSUs as it can be used by the CSPs to promote their services through self-evaluation. To demonstrate the effectiveness of our proposed fuzzy-based trust model, we present case studies where several CSPs are evaluated and ranked based on the security index.

The cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law risks, and technology risks. Additionally, this paper presents a risk assessment model that is based on grey system theory (GST), defines indicators for assessment, and fully utilizes the analytic hierarchy process (AHP). Furthermore, we use the GST to predict the risk values by using the MATLAB platform. The GST determines the bottom evaluation sequence, while the AHP calculates the index weights. Based on the GST and the AHP, layer-based assessment values are determined for the bottom evaluation sequence and the index weights. The combination of AHP and GST aims to obtain systematic and structured well-defined procedures that are based on step-by-step processes. The AHP and GST methods are applied successfully to handle any risk assessment problem of the CSP. Furthermore, substantial challenges are encountered in determining the CSP's response time and identifying the most suitable solution out of a specified series of solutions. This issue has been handled using two additive features: the response time and the grey incidence. The final risk values are calculated and can be used for prediction by utilizing the enhanced grey model (EGM) (1,1), which reduces the prediction error by providing direct forecast to avoid the iterative prediction shortcoming of standard GM (1,1). Thus, EGM (1,1) helps maintain the reliability on a larger scale despite utilizing more prediction periods. Based on the experimental results, we evaluate the validity, accuracy, and response time of the proposed approach. The simulation experiments were conducted to validate the suitability of the proposed model. The simulation results demonstrate that our risk assessment model contributes to reducing deviation to support CSPs with the three adopted models.

In the domain of globalization, the global value chain (GVC) is formed across diverse economies. Owing to the expansion of economic globalization and the resultant global trade liberalization, the geographical isolation of production and consumption has resulted in the transfer of polluting emissions from advanced economies to developing states. Developing economies, falling in the middle and low end of the GVC, have to strive hard to expand their production in the GVC, while simultaneously addressing the consequent environmental damage and attending to the core environmental concerns of sustainable growth, energy preservation and drastic cuts in CO2. This paper addresses how manufacturing subsidiaries can improve their low-carbon innovation ability and help to achieve a sustainable competitive advantage through the embedded GVC. Data were gathered employing interview and survey methods, 31 manufacturing companies and 56 enterprise groups across 16 Chinese provinces were selected as the case study. A partial least square structural equation modelling was adopted to analyse the responses. The results indicate that the embedded GVC significantly enhances low-carbon innovation capability and promotes a sustainable competitive advantage. This study proposes that companies should continuously identify high-quality resources from the GVC and discover ways of integrating internal and external carbon innovation resources to form innovation capabilities.

Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of fraud attempts has recently grown significantly, making fraud detection critical when it comes to protecting your personal information or sensitive data. There are several forms of fraud issues, such as stolen credit cards, forged checks, deceptive accounting practices, card-not-present fraud (CNP), and so on. This article introduces the credit card-not-present fraud detection and prevention (CCFDP) method for dealing with CNP fraud utilizing big data analytics. In order to deal with suspicious behavior, the proposed CCFDP includes two steps: the fraud detection Process (FDP) and the fraud prevention process (FPP). The FDP examines the system to detect harmful behavior, after which the FPP assists in preventing malicious activity. Five cutting-edge methods are used in the FDP step: random undersampling (RU), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), singular value decomposition (SVD), and logistic regression learning (LRL). For conducting experiments, the FDP needs to balance the dataset. In order to overcome this issue, Random Undersampling is used. Furthermore, in order to better data presentation, FDP must lower the dimensionality characteristics. This procedure employs the t-SNE, PCA, and SVD algorithms, resulting in a speedier data training process and improved accuracy. The logistic regression learning (LRL) model is used by the FPP to evaluate the success and failure probability of CNP fraud. Python is used to implement the suggested CCFDP mechanism. We validate the efficacy of the hypothesized CCFDP mechanism based on the testing results.

Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce an anomaly detection paradigm called novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data in the healthcare. The proposed paradigm inherits the features from two state-of-the-art algorithms: Scalable Time series Anytime Matrix Profile (STAMP) and Scalable Time-series Ordered-search Matrix Profile (STOMP). The proposed NMP caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP can be used on large multivariate data sets and generates approximate solutions of high quality in a reasonable time. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms, i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

DSW (Distillery Spent Wash) is superfluous residual liquid waste produced during the process of alcohol production. It is one of the most critical environmental issues which cause pollution. Despite strictness of standards imposed on effluent quality, partially or untreated effluent very often finds access to surface and groundwater. The distillery wastewater becomes a serious risk to the water quality in several regions around the globe. The ever-increasing generation of DSW lead to take an attempt to investigate a few aspects of anaerobic digestion of spent wash collected from a distillery. In the present study CSTR (Continuous Stirred Tank Reactor) was used at laboratory scale. The study was carried out to evaluate the performance of CSTR after the effective startup and the gradual increment in the OLR (Organic Loading Rate). The maximum methane gas (CH4) of 71.68 ml at the OLR of 1.0g of COD (Chemical Oxygen Demand)/L was observed. The optimum COD removal efficiency of the CSTR was 91% corresponding to OLR of 1g of COD/L. The pH ranges from 7.1-7.3 gave better performance and maximum stability of the process. By increasing the ORL, the VFA (Volatile Fatty Acids) content was also increased and reached to the 1.5g COD/L. However, the removal efficiency of TS (Total Solids) increased at an accelerated rate of OLR.

Recently, an increasing number of cyber-attacks in the medical field has resulted in great losses in the health care industry, since medical information plays an essential role in human health. To introduce a comprehensive survey about possible cyber-attacks and solutions for these attacks, our paper first presents a brief overview of the necessary background of the dataflow in the medical domain and then identifies the vulnerabilities in each stage of the dataflow. Then, according to the weaknesses identified in the medical system, a classification of cyber-attacks is presented. Additionally, the paper presents research on previous work that focuses on solving these cyber-attacks and identifies the strengths and limitations of the solutions for each attack. More importantly, for data storage assurance, our paper discusses several cybersecurity architectures for the medical domain from the existing literature. The countermeasures from previous papers and architectures that are still weak in terms of resource depletion, attack reduction, applicability, etc. are addressed. Finally, the paper discusses and recommends solutions for future work to decrease cyber-attacks in the medical field so that human health can be guaranteed.

In today’s world, the use of computer networks is everywhere, and to access the home network we use the Internet. IoT networks are the new range of these networks in which we try to connect different home appliances and try to give commands from a remote place. Access to any device over an insecure network invites various types of attacks. User authentication can be performed using some password or biometric technique. However, when it comes to authenticating a device, it becomes challenging to maintain data security over a secure network such as the Internet. Many encryptions and decryption algorithms assert confidentiality, and hash code or message authentication code MAC is used for authentication. Traditional cryptographic security methods are expensive in terms of computational resources such as memory, processing capacity, and power consumption. They are incompatible with the Internet of Things devices that have limited resources. Although automatic Device-to-Device communication enables new potential applications, the limited resources of the networks’ machines and devices impose various constraints. This paper proposes a home device authentication scheme when these are accessed from a remote place. An authentication device is used for the home network and controller device to control home appliances. Our scheme can prevent various attacks such as replay attacks, server spoofing, and man-in-the-middle attack. The proposed scheme maintains the confidentiality and authenticity of the user and devices in the network. At the same time, we check the system in a simulated environment, and the results show that the network’s performance does not degrade much in terms of delay, throughput, and energy consumed.

People who use social networks often fall prey to clickbait, which is commonly exploited by scammers. The scammer attempts to create a striking headline that attracts the majority of users to click an attached link. Users who follow the link can be redirected to a fraudulent resource, where their personal data are easily extracted. To solve this problem, a novel browser extension named ClickBaitSecurity is proposed, which helps to evaluate the security of a link. The novel extension is based on the legitimate and illegitimate list search (LILS) algorithm and the domain rating check (DRC) algorithm. Both of these algorithms incorporate binary search features to detect malicious content more quickly and more efficiently. Furthermore, ClickBaitSecurity leverages the features of a deep recurrent neural network (RNN). The proposed ClickBaitSecurity solution has greater accuracy in detecting malicious and safe links compared to existing solutions.

The steady–state analytical and numerical solutions are obtained for the mixing of effluent with Newtonian fluid in subsurface. The problem presented in this research work is flow through a channel packed by homogeneous porous media, associated to environmental sciences. Simulations are performed employing a time–marching scheme. Numerical method used in the study is a finite element method, while, the frame of reference is Cartesian coordinate system. Adopted algorithm follow semi-implicit technique through Taylor-Galerkin/Pressure-correction scheme. Analytical solution is Obtained and compared with the numerical predictions. Impact of Darcy’s number, influence of change in diffusion coefficient of effluent concentration and time dependent effects of velocity profile is investigated. Numerical predictions are compared against both steady–state and time–dependent analytical solutions obtained in the present study, and observed very good agreement. Various interesting features of the flow are reported. The numerical results exhibit that with increasing fluid inertia, the effluent disperses immediately in flow field when entering from same direction with the flow of fluid. While, increasing dispersion coefficient of effluent at fixed rate of inertia the effluent disperses faster and occupy large area of upstream.

Machine learning (ML) has shown enormous potential in various domains with the wide variations of underlying data types. Because of the miscellany in the data sets and the features, ML classifiers often suffer from challenges, such as feature miss-classification, unfit algorithms, low accuracy, overfitting, underfitting, extreme bias, and high predictive errors. Through the lens of related study and latest progress in the field, this paper presents a novel scheme to construct logical table (LT) unit with two internal sub-modules for algorithm blend and feature engineering. The LT unit works in the deepest layer of an enhanced ML engine engineering (eMLEE) process. eMLEE consists of several low-level modules to enhance the ML classifier progression. A unique engineering approach is adopted in eMLEE to blend various algorithms, enhance the feature engineering, construct a weighted performance metric, and augment the validation process. The LT is an in-memory logical component, that governs the progress of eMLEE, regulates the model metrics, improves the parallelism, and keep tracks of each module of eMLEE as the classifier learns. Optimum fitness of the model with parallel “check, validate, insert, delete, and update”mechanism in 3-D logical space via structured schemas in the LT is obtained. The LT unit is developed in Python, C#, and R libraries and tested using miscellaneous data sets. Results are created using GraphPad Prism, SigmaPlot, Plotly, and MS Excel software. To support the built and implementation of the proposed scheme, complete mathematical models along with the algorithms, and necessary illustrations are provided in this paper. To show the practicality of the proposed scheme, several simulation results are presented with a comprehensive analysis of the outcomes for the metrics of the model that the LT regulates with improved outcomes.

Cyber-physical systems (CPSs) have greatly contributed to many applications. A CPS is capable of integrating physical and computational capabilities to interact with individuals through various new modalities. However, there is a need for such a paradigm to focus on the human central nervous system to provide faster data access. This paper introduces the CPS paradigm that consists of CPS enabled human brain monitoring (CPS-HBM) and efficient data-balancing for CPS (EDB-CPS). The CPS-HBM provides architectural support to make an efficient and secure transfer and storage of the sensed data over fog cloud computing. The CPS-HBM consists of four components: physical domain and data processing (PDDP), brain sensor network (BSN), Service-oriented architecture (SOA), and data management domain (DMD). The EDB-CPS module aims to balance data flow for obtaining better throughput and lower hop-to-hop delay. The EDB-CPS accomplishes the goal by employing three processes: A node advertisement (NA), A node selection and recruitment (NSR), and optimal distance determination with mid-point (ODDMP). The processes of the EDB-CPS are performed on the PDDP of the CPS-HBM module. Thus, to determine the validity of EDB-CPS, the paradigm was programmed with C++ and implemented on a network simulator-3 (NS3). Finally, the performance of the proposed EDB-CPS was compared with state-of-the-art methods in terms of hop-to-hop delay and throughput. The proposed EDB-CPS produced better throughput between 443.2–445.2 KB/s and 0.05–0.078 ms hop-to-hop delay.

Modern information technology (IT) is well developed, and almost everyone uses the features of IT and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures to largely mitigate these threats. The ignorance of or lack of cybersecurity knowledge also causes a critical problem regarding confidentiality and privacy. It is not possible to fully avoid cybercrimes that often lead to sufficient business losses and spread forbidden themes (disgust, extremism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed WBCA trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses Blockchain technology to protect the program from potential threats. The proposed program is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users. A CentOS-based virtual private server is deployed for testing the proposed WBCA to determine its effectiveness. Finally, WBCA is also compared with other state-of-the-art web-based programs designed for cybersecurity awareness.

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

This study was conducted at lab scale to determine the performance of the canal-bed for the removal of turbidity and microorganisms TC (Total Coliforms) from surface water. The canal-bed sediments were collected and analyzed for the characteristics of sediments for grain size distribution, hydraulic conductivity and the POM (Particulate Organic Matter) percent. Canal-bed sediments were containing fine particles less than 0.075mm in the range of 40-58%, with hydraulic conductivity averaged 7ft/day, and the POM 2.75%. The water samples collected from the canal-water have shown average POM 3.6%. Theremoval-reduction in turbidity and TC were determined through the column experiments on the canal-bed sediments. Three columns were prepared at lab-scale by using prepared canal-bed sediment as a filter-bed in the columns for the filtration of raw water samples. Fine particles of the canal-bed grain size D10 0.2 and D10 0.1mm were selected for the filter-bed formation. The prepared concentrated and diluted influent water samples containing turbidity and TC were passed through the washed filter-bed into the columns for 8-weeks filter run. The frequency of sampling and analysis were followedafter the interval of one-week run, the influent (raw water) and effluent (filtered) water samples were collected and analyzed for the turbidity and TC concentrations. The performance of the grain size D10 0.1mm have shown 95-99.95% reduction in turbidity and TC compared to the larger grain size having D10 0.2mm particles.

Pakistan is facing energy scarcity. The biogas is one of the renewable substitutes. It does not only overcome the energy scarcity but also harness the energy from animal dung which causes the CO2 emissions. The present study was done on the appraisal of potential of biogas from the dung of animals (Buffaloes, Cows, Goats and Sheep) within the villages surrounded by Taluka Saeedabad. The purpose of the study was the energy potential of the biogas and the selection of the most suitable design and size of the biogas plant for the villagers. The present study also includes the domestic biogas plant economics. As per the estimation, total quantity of animal dung generated was about 129 tons/day, which can produce 3859 m3 of biogas per day. On the contrary, for cooking villagers require only 2748 m3 of biogas per day. Moreover, the surplus biogas of 1111 m3 per day can be used to produce electricity of 6666 kWh per day, which can fulfill the demand of about half of the population of villages under study. People are using firewood, cotton stalks, kerosene oil and LPG (Liquefied Petroleum Gas) for cooking. Converting animal dung into the biogas not only reduces the consumption of the traditional fuels used (Firewood, Cotton Stalks, Kerosene Oil and LPG), but also prevents about 900 thousand tons of CO2 emissions into the environment. Additionally, a fixed dome biogas plant of 8-10 m3 size was recommended for each of the houses under study.

Abstract The Cloud computing paradigm provides numerous attractive services to customers such as the provision of the on-demand self-service, usage-based pricing, ubiquitous network access, transference of risk, and location independent resource sharing. However, the security of cloud computing, especially its data privacy, is a highly challengeable task. To address the data privacy issues, several mechanisms have been proposed that use the third party auditor (TPA) to ensure the integrity of outsourced data for the satisfaction of cloud users (CUs). However, the role of the TPA could be the potential security threat itself and can create new security vulnerabilities for the customer’s data. Moreover, the cloud service providers (CSPs) and the CUs could also be the adversaries while deteriorating the stored private data. As a result, the objective of this research is twofold. Our first research goal is to analyze the data privacy-preserving issues by identifying unique privacy requirements and presenting a supportable solution that eliminates the possible threats towards data privacy. Our second research goal is to develop the privacy-preserving model (PPM) to audit all the stakeholders in order to provide a relatively secure cloud computing environment. Specifically, the proposed model ensures the quality of service (QoS) of cloud services and detects potential malicious insiders in CSPs and TPAs. Furthermore, our proposed model provides a methodology to audit a TPA for minimizing any potential insider threats. In addition, CUs can use the proposed model to periodically audit the CSPs using the TPA to ensure the integrity of the outsourced data. For demonstrating and validating the performance, the proposed PPM is programmed in C++ and tested on GreenCloud with NS2 by applying merging processes. The experimental results help to identify the effectiveness, operational efficiency, and reliability of the CSPs. In addition, the results demonstrate the successful rate of handling the negative role of the TPA and determining the TPA’s malicious insider detection capabilities.

In this study an effect of acidic pre-treatment on the CS (Canola Straw) and BD (Buffalo Dung) by anaerobic co-digestion was investigated. H2O2 (Hydrogen Peroxide) is a mainly accustomed reagent, used as a bleaching agent in the different industries such as paper and wood. In the present study, it was used as a pre-treatment chemical at varying concentrations in batch reactors. The co-digestion of CS and BD was carried out in SAMPTS (Semi-Automatic Methane Potential Test System) at mesophilic (37±1oC) conditions. The CS was pretreated in glass bottles with different concentrations of the H2O2 for seven days. The inoculum used in the present study was an effluent of the CSTR (Continuous Stirred Tank Reactor), which was treating BD at mesophilic conditions. The specific methane production from the codigestion of canola straw and BD, by the pre-treatment of H2O2 at concentrations of 0.5, 1.0, and 1.5% were 530.8, 544.5, and 510.3 NmL CH4 g/VS, respectively. The significant reduction in the volatile solids of CS was observed at the optimum pre-treatment of 1.0% H2O2.

The focus of the present study was to examine the effect of canal bank filtration on the quality of water and the geological settings along the banks of canals at the shallow depth aquifers. The four Model wells were drilled at different locations of the Line channel, Pinyari and phulali canals in the study area. The samples of soil were collected throughout drilling of the model wells for the analysis of grain size distribution .In addition to this, canal water and model well water samples were collected and analyzed for the water quality characteristics during winter and summer seasons. The analysis of soil and water samples reveals that the ground water is influenced by the grain size distribution, hydraulic conductivity and the location of the model Wells. The model well that has higher percentage of 0.075 mm of grain size distribution(hydraulic conductivity between 10-25 ft/day) was more suitable for the filtration of the canal water through its banks, followed by 0.15 mm of grain size distribution (hydraulic conductivity > 25ft/ day). Moreover, the present study also shows that the canal water filtration is suitable in terms of total alkalinity, nitrate-nitrogen, total iron and pH to get the potable water at the location near upstream of the canal, especially in the summer season.

The SRRMs (Steel Re-Rolling Mills) are being releasing air pollutants in the environment. In order to evaluate their effect on the health of the workers, health and safety issues were analyzed by first measuring the concentrations of SO x (OIxides of Sulphur), NO x (Oxides of Nitrogen), CO (Carbon Monoxide) and O2 (Oxygen) produced in the three SRRMs located in SITE area Hyderabad. The mean concentration of SO x , NO x and CO were in the order of 0.35, 0.280, 6.333 ppm, respectively, whereas the mean concentration of O 2 was 203.53 thousand ppm. As per results, the concentration ofair pollutants, including SOx and NO x were significantly higher than to the NEQS (National Environmental Quality Standards) and NAAQS (National Ambient Air Quality Standards). The concentration ofCO was lower than to the NAAQS, but higher than to the NEQs, while the concentration of O2 was slightly lower than to the standard value. The workers who were exposed to these air pollutants are being suffering from chronic diseases related to breathing and allergies. Moreover, labour staff was lifting heavy loads manually, which causes them to muscular and joint problems. In all the SRRMs under study, the electrical and mechanical equipments were used without any safety. The MSDS were not displayed on the workstations, the housekeeping was inadequate and most of the workers were performing their jobs without personal protective equipment. In addition to these, the other serious issues related to the occupational health and safety were an unhygienic supply of water, higher noise level, placement of explosive cylinders in the open atmosphere and unavailability of the first aid facilities in the Mill premises.

In this paper, FW (Food Waste) and SS (Sewage Sludge) were co-digested for biohydrogen production. After characterization both FW and SS were found as better option forbiohydrogen production. FW was rich in carbohydrate containing specially rice, which was added as more than 50% and easily hydrolyzable waste. FW is considered as an auxiliary substrate for biohydrogen production and high availability of carbohydrate in FW makes it an important substrate for the production of biohydrogen. On the contrary, SS was rich in protein and has a high pH buffering capacity, which makes it appropriate for codigestion. Adequate supplementation of inorganic salts, the addition of hydrogen producing inoculums, protein enrichment and pH buffering capacity of SS and carbohydrate content in FW increases the hydrogen production potential. Various experiments were performed by considering different mixing ratios like 90:10, 80:20, 70:30, 60:40 and 50:50 of FW and SS. The 50:50 and 90:10 mixing ratio of FW and SS were found as best among all other co-digested ratios. The maximum specific hydrogenyield 106.7 mL/gVS added was obtained at a waste composition of 50:50 followed by 92.35 mL/gVSadded from 90:10 of FW to SS. The optimum pH and temperature for operating this process werein the range of 5.5-6.5 and 35°C. The production of clean energy and waste utilization in anaerobic co-digestion process makes biohydrogen generation a promising and novel approach to fulfilling the increasing energy needs as a substitute for fossil fuels.

In the present study, a newly developed VMMI (Volumetric Methane-Measuring Instrument) for laboratory scale anaerobic reactors is presented. The VMMI is a reliable, inexpensive, easy to construct, easy to use, corrosion resistant device that does not need maintenance, can measure a wide flow range of gas at varying pressure and temperature. As per the results of the error analysis, the accuracy of the VMMI is unilateral, i.e. -6.91%. The calibration of VMMI was investigated and a linear variation was found; hence, in situ calibration is recommended for this type of instrument. As per chromatographic analysis, it absorbs almost 100% of the carbon dioxide present in the biogas, results only the methane, and thus eliminates the need of cost intensive composition analysis of biogas through gas chromatograph

Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.

Assessment of kinetic of AD (Anaerobic Digestion) is a beneficial practice to forecast the performance of the process. It is helpful in the design of AD vessels, substrate feeding and digestate exit systems. The aim of this work was to assess the kinetics of anaerobically digested buffalo dung at different quantities of water added. It comprises the assessment of the specific methane production on the basis of VS (Volatile Solids) added in each reactor by using three first order models, i.e. the modified Gompertz model, the Cone model and the Exponential Curve Factor model. The analysis was tested by using the three statistical parameters, i.e. the coefficient of multiple determinations, the standard deviation of residuals and the Akaike?s Information Criteria. The result reveals that the Exponential Curve Factor model was the best model that described the experimental data well. Moreover, there was not a direct or indirect relation between the kinetic coefficients of the AD process with the varying total or volatile solid content

The scholarship of language education, particularly with reference to learning and use of English, is marked by varieties of English. One may note two broad models: (1) ENL, ESL, and EFL; (2) EIL, ELF, and WEs. Although the scholarship is replete with debates, the debates seem to only construct and maintain that learning English and its use are neutral activities that earn and equip a learner with certain capital rather than make him/her as such. This paper draws upon Foucault’s theories of discourse and disciplinary power. This paper takes Pakistani state-mandated English textbooks of Grades 1–5 as official documents that contain qualitative data. The qualitative data are analyzed thematically. Two major themes of male body and female body are found which are analyzed and discussed through the Foucauldian lens. The paper holds that learning English and its use perpetuate and produce docility. The paper contends that neither is language a neutral tool nor are its use and learning value-free activities in any of its varieties.

This study was conducted to assess the MSW (Municipal Solid Waste) generated in Hyderabad city for its suitability to make compost product through AB (Aerobic Biological) treatment. Assessment of MSW regarding its generation rate, quantification and characterization decides its suitability for composting process. Three AB treatment reactors R1 (natural air circulation and manually mixed reactor), R2 (compressed air circulation and manually mixed reactor) and R3 (compressed air circulation and mechanically mixed reactor) were designed and fabricated. AB treatment of the segregated food and yard waste reveals that there is no any significant change occurs in the moisture content of the compost product in all the reactors but, significant loss of VS (Volatile Solids) and gain of ash content was observed for reactor R2. Thus, the reactor R2 is the most efficient reactor in comparison to other reactors. Moreover, the mechanical mixing in AB treatment does not significantly increase VS loss. Further the reactor R1 does not consumes electricity and thus can be employed as the solution for converting segregated food and yard waste from MSW into a compost product

Generation of methane from animal dung through AD (Anaerobic Digestion) is the most feasible way to get energy from it. Pakistan has about 70 million heads of cattle and buffalos, and about 90 million heads of sheep and goats. The dung from these animals can overcome the energy crisis and can fulfill the future energy demands of Pakistan. In present study, buffalo dung is used as the substrate for anaerobic digestion process, whereas the production of methane was analyzed as the function of buffalo dung to water ratio. Six batch reactors with different buffalo dung to water ratios were incubated in the AMPTS (Automatic Methane Potential Test Setup) for 51 days. The highest methane production was observed from the buffalo dung to water ratio of 2.0 i.e. 226.4 NmL/gVS loss , followed by 198.6 NmL/ gVS loss from the buffalo dung to the water ratio of 1.0. The suitable hydraulic retention time of the anaerobic digester treating buffalo dung was observed as 20 days

This paper introduces the design, implementation, and performance analysis of the scalable and mobility-aware hybrid protocol named boarder node medium access control (BN-MAC) for wireless sensor networks (WSNs), which leverages the characteristics of scheduled and contention-based MAC protocols. Like contention-based MAC protocols, BN-MAC achieves high channel utilization, network adaptability under heavy traffic and mobility, and low latency and overhead. Like schedule-based MAC protocols, BN-MAC reduces idle listening time, emissions, and collision handling at low cost at one-hop neighbor nodes and achieves high channel utilization under heavy network loads. BN-MAC is particularly designed for region-wise WSNs. Each region is controlled by a boarder node (BN), which is of paramount importance. The BN coordinates with the remaining nodes within and beyond the region. Unlike other hybrid MAC protocols, BN-MAC incorporates three promising models that further reduce the energy consumption, idle listening time, overhearing, and congestion to improve the throughput and reduce the latency. One of the models used with BN-MAC is automatic active and sleep (AAS), which reduces the ideal listening time. When nodes finish their monitoring process, AAS lets them automatically go into the sleep state to avoid the idle listening state. Another model used in BN-MAC is the intelligent decision-making (IDM) model, which helps the nodes sense the nature of the environment. Based on the nature of the environment, the nodes decide whether to use the active or passive mode. This decision power of the nodes further reduces energy consumption because the nodes turn off the radio of the transceiver in the passive mode. The third model is the least-distance smart neighboring search (LDSNS), which determines the shortest efficient path to the one-hop neighbor and also provides cross-layering support to handle the mobility of the nodes. The BN-MAC also incorporates a semi-synchronous feature with a low duty cycle, which is advantageous for reducing the latency and energy consumption for several WSN application areas to improve the throughput. BN-MAC uses a unique window slot size to enhance the contention resolution issue for improved throughput. BN-MAC also prefers to communicate within a one-hop destination using Anycast, which maintains load balancing to maintain network reliability. BN-MAC is introduced with the goal of supporting four major application areas: monitoring and behavioral areas, controlling natural disasters, human-centric applications, and tracking mobility and static home automation devices from remote places. These application areas require a congestion-free mobility-supported MAC protocol to guarantee reliable data delivery. BN-MAC was evaluated using network simulator-2 (ns2) and compared with other hybrid MAC protocols, such as Zebra medium access control (Z-MAC), advertisement-based MAC (A-MAC), Speck-MAC, adaptive duty cycle SMAC (ADC-SMAC), and low-power real-time medium access control (LPR-MAC). The simulation results indicate that BN-MAC is a robust and energy-efficient protocol that outperforms other hybrid MAC protocols in the context of quality of service (QoS) parameters, such as energy consumption, latency, throughput, channel access time, successful delivery rate, coverage efficiency, and average duty cycle.

In several regions of the Pakistan, crop cultivation is leading to the production crop residues and its disposal problems. It has been suggested that the co-digestion of the crop residues with the buffalo dung might be a disposal way for the wasted portion of the crops' residue. The objective of present study was to optimize the anaerobic co-digestion of canola straw and the buffalo dung through batch experiments in order to obtain maximum methane production. The optimization was carried out in three stages. In first stage, the best canola straw to buffalo dung ratio was evaluated. In second stage, the best concentration of sodium hydrogen carbonate was assessedas the alkaline pretreatment chemical, whereas in the third stage most suitable particle size of the canola strawwas evaluated. The assessment criteria for the optimization of a co-digestion were cumulative methane production and ABD (Anaerobic Biodegradability). The results yield that anaerobic co-digestibility of the canola straw and the buffalo dung is obviously influenced by all the three factors of optimization. The maximum methane production was obtained as 911 NmL from the canola straw to buffalo dung ratio of 40:60, the alkaline doze of 0.6 gNaHCO3 / gVS and canola straw particle size of 2mm. However, because of the higher shredding cost to produce 2mm sized canola straw, particle size 4mm could be the best canola straw particle size.

Significant research has been conducted for maintaining a high standard of communication and good coverage in wireless sensor networks (WSNs), but extra power consumption and mobility issues are not yet fully resolved. This paper introduces a memory-less location mobility-aware Lattice Mobility Model (LMM) for WSNs. LMM is capable of concurrently determining the node and sink mobility. LMM has a lower pause time, fewer control packets, and less node dependency (e.g., the energy consumed by each node in each cycle that is independent of the data traffic). LMM accurately determines a node’s moving location, the distance from its previous location to its current location, and the distance from its existing location to its destination. Many existing mobility models only provide a model how nodes move (e.g., to mimic pedestrian behavior), but do not actually control the next position based on properties of the underlying network topology. To determine the strength of LMM, OMNet++ was used to generate the realistic scenario to safeguard the affected area. The operation in affected area comprises searching for, detecting, and saving survivors. Currently, this process involves a time-consuming, manual search of the disaster area. This contribution aims to identify an energy efficient mobility model for a walking pattern in this particular scenario. LMM outperforms other mobility models, including the geographic-based circular mobility model (CMM), the random waypoint mobility model (RWMM) and the wind mobility model (WMM), The simulation results also demonstrate that the LMM requires the least time to change the location, has a lower drop rate, and has more residual energy savings than do the WMM, RWMM, and CMM.

This study investigates the challenges faced by Pakistani and Chinese English language instructors in developing communicative competence among senior high school students, and highlights the fundamental loopholes. Employing a thematic analysis within a qualitative comparative case study, the findings reveal learners' substandard prior knowledge as the biggest loophole in both Pakistan and China, which causes instructors to overuse local languages. There exists misalignment in curriculum, instruction, and assessment; consequently, the instructional practices are drastically examination-oriented producing rote-learners, and significantly hamper the development of communicative competence. Pakistani syllabi appear to be outdated. There is unavailability of modern linguistic resources in Pakistan while the Chinese instructors evade using them despite their availability. Chinese teacher-student interaction and Pakistani parental involvement are bleak. Findings suggest that respective governments should pay attention to their junior high school systems for developing students' prerequisite knowledge and implement leaner-centered instruction. Pakistani senior high school syllabi should be modified.

ABD (Anaerobic Biodegradability) and BMP (Biochemical Methane Potential) of banana plant waste, canola straw, cotton stalks, rice straw, sugarcane trash and wheat straw co-digested with buffalo dung was evaluated through AMPTS (Automatic Methane Potential Test System). The substrates were analyzed for moisture, TS (Total Solids) and VS (Volatile Solids), ultimate analysis (CHONS), pH and TA (Total Alkalinity). The BMPobserved during incubation of 30 days at the temperature of 37±0.2°C was 322 Nml CH4/g VSadd for wheat straw followed by 260, 170, 149, 142 and 138 Nml CH4/gVSadd for canola straw, rice straw, cotton stalks, banana plant waste and sugarcane trash respectively, whereas the maximum theoretical BMP was 481 Nml CH4/gVSadd for cotton stalks, followed by 473, 473, 446, 432 and 385 Nml CH4/gVSadd for wheat straw, banana plant waste, canola straw, rice straw and sugarcane trash respectively. The percentage ABD values were in the range of 68-30%. In addition to this, the effect of lignin content in the crop residue was evaluated on the ABD. The results of this study indicate that, the co-digestion of the crop residues with buffalo dung is feasible for production of renewable methane

Kinetic studies of AD (Anaerobic Digestion) process are useful to predict the performance of digesters and design appropriate digesters and also helpful in understanding inhibitory mechanisms of biodegradation. The aim of this study was to assess the anaerobic kinetics of crop residues digestion with buffalo dung. Seven crop residues namely, bagasse, banana plant waste, canola straw, cotton stalks, rice straw, sugarcane trash and wheat straw were selected from the field and were analyzed on MC (Moisture Contents), TS (Total Solids) and VS (Volatile Solids) with standard methods. In present study, three first order exponential models namely exponential model, exponential lag phase model and exponential curve factor model were used to assess the kinetics of the AD process of crop residues and the effect of lag phase and curve factor was analyzed based on statistical hypothesis testing and on information theory. Assessment of kinetics of the AD of crop residues and buffalo dung follows the first order kinetics. Out of the three models, the simple exponential model was the poorest model, while the first order exponential curve factor model is the best fit model. In addition to statistical hypothesis testing, the exponential curve factor model has least value of AIC (Akaike's Information Criterion) and can generate methane production data more accurately. Furthermore, there is an inverse linear relationship between the lag phase period and the curve factor.

With breakthrough of technological advancement, the significance of data transmission has been in highly demanding. On the other hand, limited buffering capacity has been great challenge that limits the Quality of Service (QoS) and degrade the performance of the network particularly in Wireless Mesh Networks (WMNs). Thus, it is important to provide an efficient utilization bandwidth and buffer management. Furthermore, the QoS in WMNs depends immensely on intelligent buffer management to avoid unexpected congestion and data loss. Some algorithms have been introduced to improve the buffer capacity and management. However, these suffer from high latency, even the loss of data because of congestion in the buffers, eventually resulting in low throughput. To address these issues we introduce the scheme “Smart Bandwidth Friendly Buffers (SBFB) for Wireless Mesh Networks. The SBFB is inspired by the features of existing schemes like the EZ Flow, WRED (Weighted Random Early Detection, and Back-off mechanism algorithms. The SBFB scheme contributes to prioritize the packets, allowing the down link nodes to perceive the buffer capacity prior to transmission and it also hunts for alternative routes in case of buffer buildup. Our proposed algorithm is validated using Network simulator-3 (NS3). Based on the experimental results, we have determined that SBFB has lesser congestion and packet loss probability when compared to other known contemporary schemes.