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Background: Leading urology guidelines recommend intravesical chemotherapy and immunotherapy for non-muscle invasive bladder cancer (NMIBC) to reduce the risk of recurrence and progression. This study aims to evaluate the self-reported practices regarding intravesical therapy use for patients with non-muscle-invasive bladder cancer among urologists in Karachi. Methodology: In this cross-sectional study, a proforma-based survey was conducted between January and March 2018, in which printed questionnaires regarding adjuvant treatment options for NMIBC were administered and collected by primary investigators among urologists in leading institutions of Karachi. Results: Overall, 80% of respondents reported routine administration of single instillation of intravesical chemotherapy (SICA) after transurethral resection of bladder tumor (TURBT), and almost one-third of them (37.3%) give SICA within 6 hours inward. A quarter of respondents practice induction therapy routinely in low-risk BC using Mitomycin, whereas 76.5% in high-risk cases use Bacillus Calmette Geurin (BCG) as immunotherapy. Regarding the time duration of intravesical therapy installation, most of the urologists reported for 45 mins (49%) followed by 30 mins (29.4%), 2 hrs (17.6%), and 3 hrs (3.9%). Only 39% reported routinely using maintenance therapy with BCG for high-risk BC. As for BCG failure, 76.5% of participants reported radical cystectomy. Conclusion: Our survey results provide evidence of variation in practices among urologists and poor guideline adherence with the risk of under-treatment of patients with NMIBC. This requires joint efforts of all those involved in the treatment of non-muscle invasive bladder cancer to improve the quality of care.

End-to-end learning for autonomous driving uses a convolutional neural network (CNN) to predict the steering angle from a raw image input. Most of the solutions available for end-to-end autonomous driving are computationally too expensive, which increases the inference of autonomous driving in real time. Therefore, in this paper, CNN architecture has been trained which is lightweight and achieves comparable results to Nvidia’s PilotNet. The data used to train and evaluate the network is collected from the Car Learning to Act (CARLA) simulator. To evaluate the proposed architecture, the MSE (mean squared error) is used as the performance metric. Results of the experiment shows that the proposed model is 4x lighter than Nvidia’s PilotNet in term of parameters but still attains comparable results to PilotNet. The proposed model has achieved 5.1×10−4 MSE on testing data while PilotNet MSE was 4.7×10−4.

Background and Objective: COVID-19 has emerged as a serious threat to the public health. It has been declared as Public Health Emergency of International Concern (PHEIC) because of morbidity and mortality associated with it all over the world. The objective of the present study was to analyze COVID-19 related fatalities in terms of comorbidity, length of hospital stays and critical illness in Allied hospitals of Rawalpindi Medical University. Methods: A retrospective hospital data-based research was carried out on n = 25 fatalities registered in three Allied hospitals (Rawalpindi Institute of Urology & Transplantation, Benazir Bhutto Hospital and Holy Family Hospital) of Rawalpindi Medical University. The data included age, gender, date of admission and death, severity of illness, comorbidity, oxygen administration or ventilator support and was gathered through consecutive sampling. The data was analyzed by using SPSS version 25.0. Fisher’s Exact test was applied to determine statistical significance of association between comorbidity and need for ventilation. Statistical significance of association between length of hospital stay and comorbidity was verified by independent sample t-test. Results: Of the total n = 25 COVID-19 related fatalities in Allied hospitals, 76% were males. The mean age of study subjects was 55.9 ± 15.28 years. The greatest number of overall deaths was among 51 – 70 years old patients. About 44% fatalities had comorbid states with hypertension and diabetes constituting the highest (45.4%) proportion. Comorbidity had statistically significant association (P < 0.02) with need of ventilators in critically ill patients while length of hospital stays depicted insignificant association with comorbidity (P > 0.80). Conclusion: People above the age of 50 years are more likely to die due to COVID-19. Comorbid states of hypertension and diabetes should be carefully managed to avoid grave consequences.

Abstract Miscible CO2-based enhanced oil recovery (CO2-EOR) flooding has been used, on many occasions, to maximize the recovery by mobilizing the residual oil. The success of this method, however, depends highly on the minimum miscibility pressure (MMP), the compatibility of CO2 with the reservoir rock and fluids. Although there have been several studies on the application of CO2-EOR, the impact of oil compositions associated with MMP on the feasibility of this technique has not been addressed elsewhere. In this study, a simulation study was carried out to investigate the effect of three different oil compositions on the miscibility and ultimate oil recovery of miscible CO2-EOR method. The results obtained indicated the oil type does not pose a significant impact on the success of miscible CO2-EOR. It was also found that the ultimate recovery of oil increases in a short period of time by injecting CO2 at the miscible conditions at the early stage of natural production. Comparatively, the reservoir with the fluid sample S2 seems to be a suitable choice for the miscible CO2 flooding once the recovery factor was considered together with the total amount of the fluid injected and produced.

In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.

High resolution and stealthy attacks and their variants such as Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown have completely exposed the vulnerabilities in Intel's computing architecture over the past few years. Mitigation techniques against such attacks are not very effective for two reasons: 1) Most mitigation techniques protect against a specific vulnerability and do not take a system-wide approach, and 2) they either completely remove or greatly reduce the performance benefits of resource sharing. In this work, we argue in favor of detection-based protection, which would help apply mitigation only after successful detection of the attack at runtime. As such, detection would serve as the first line of defense against such attacks. However, for a detection based protection strategy to be effective, detection needs to be highly accurate, to incur minimum system overhead at runtime, should cover a large set of attacks and be capable of early stage detection, i.e., at the very least before the attack is completed. We propose a machine learning based side-channel attack (SCA) detection tool, called WHISPER that satisfies the above mentioned design constraints. WHISPER uses multiple machine learning models in an Ensemble fashion to detect SCAs at runtime using behavioral data of concurrent processes, that are collected through hardware performance counters (HPCs). Through extensive experiments with different variants of state-of-the-art attacks, we demonstrate that the proposed tool is capable of detecting a large set of known attacks that target both computational and storage parts in computing systems. We present experimental evaluation of WHISPER against Flush+Reload, Flush+Flush, Prime+Probe, Spectre and Meltdown attacks. The results are provided under variable system load conditions and stringent evaluation metrics comprising detection accuracy, speed, system-wide performance overhead and distribution of error (i.e., False Positives & False Negatives). Our experiments show that WHISPER can detect a large and diverse attack vector with more than 99% accuracy at a reasonably low performance overhead.

Cognitive radio (CR) provides a way to utilize the radio resources in a smart manner. In order to exploit the benefits of CR technology either in CR systems or in CR enabled wireless sensor networks, CR based smart grids, CR based wireless body area networks (WBANs) and CR based Internet of Things (IOTs), it is necessary to characterize the radio traffic in a realistic way. In perspective of CR operating models, the radio traffic can be modeled either using conditional or unconditional modeling approaches. In unconditional modeling approach, observed data traffic is solely considered as interference while in conditional modeling approach the observed data traffic is first classified as signal and noise. Prior knowledge about the statistics of interference in unconditional modeling approach while signal (primary user) and noise (secondary user) characterization in conditional modeling framework will help in efficient utilization of radio resources within CR-enabled radio environment. Furthermore, performance analysis is carried out for both the unconditional and conditional models based on devised information theoretic criterion. Multivariate Gaussian mixture (MGM) as a special case of hidden markov random field (HMRF) and Gaussian mixture (GM) are suitable candidate models for the observed data traffic in ISM (Industrial, Scientific and Medical) band based on devised criterion in unconditional and conditional models, respectively.

Evidently, criminal network activities have shown an increasing trend in terms of complexity and frequency, particularly with the advent of social media and modern telecommunication systems. In these circumstances, law enforcement agencies have to be armed with advance criminal network analysis (CNA) tools capable of uncovering with speed, probable key hidden relationships (links/edges) and players (nodes) in order to anticipate, undermine and cripple organised crime syndicates and activities. The development of link prediction models for network orientated domains is based on Social Network Analysis (SNA) methods and models. The key objective of this research is to develop a link prediction model that incorporates a fusion of metadata (i.e. environment data sources such as arrest warrants, judicial judgement, wiretap records and police station proximity) with a time-evolving criminal dataset in order to be aware of real-world situations to improve the quality of link prediction. Based on the review of related work, most of the models are constructed by leveraging on classical machine learning (ML) techniques such as support vector machine (SVM) without metadata fusion. The problem with the use of classical ML techniques is the lack of available domain dataset which is sufficiently large for training purpose. Compared to sociaI network, criminal network dataset by nature tends to relatively much smaller. In view of this, deep reinforcement learning (DRL) technique which could improve the training of models with the self-generated dataset is leveraged upon to construct the model. In this research, a purely time-evolving DRL model (TDRL-CNA) without metadata fusion is designed as a baseline for comparison with the metadata fusion model (FDRL-CNA). The experimental results show that the predictive accuracy of new and recurrent links by the FDRL-CNA model is higher than the baseline TDRL-CNA model that does not factor data fusion from different data sources.

Cache Side Channel Attacks (SCAs) have gained a lot of attention in the recent past. Since, these attacks exploit the caching hardware vulnerabilities, they are fast and dangerous. Detection of cache side channel attacks is an important step towards mitigating against such hostile entities. Researchers have already proposed different techniques to detect cache side channel attacks. This paper provides a detailed survey of literature related to the state-of-the-art detection techniques for cache based side channel attacks. We identify a set of important characteristics that can be used to characterize a CSCA (cache side channel attack) detection technique. We use the identified features to compare and contrast the most important detection techniques and provide the important observations. We also identify some of the challenges that the research community will have to resolve in future to improve the efficiency of cache side channel detection techniques. To the best of our knowledge, this is the first work to do such a study. We believe that this paper will prove useful to researchers in the area of systems security.

Cybercrimes are cases of indictable offences and misdemeanors that involve computers or communication tools as targets and commission instruments or are associated with the prevalence of computer technology. Common forms of cybercrimes are child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing, and other forms of cyber hacking. They mostly lead to a privacy breach, security violation, business loss, financial fraud, or damage in public and government properties. Thus, this study intensively reviews cybercrime detection and prevention techniques. It first explores the different types of cybercrimes and discusses their threats against privacy and security in computer systems. Then, it describes the strategies that cybercriminals may utilize in committing these crimes against individuals, organizations, and societies. It also reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyzes the vulnerabilities of each technique. Finally, it provides recommendations for the development of a cybercrime detection model that can detect cybercrimes effectively compared with the existing techniques.

Mobile applications are advancing toward higher network and compute requirements, which are similar to the requirements of server applications. Users prefer to perform their tasks on the go from mobile devices instead of stationary desktop and server systems. Modern mobile applications limit mobile battery times as charging is lost quickly due to high processing and data demands. Resources other than energy are also limited in mobile devices (memory, CPU millions of instructions per second, etc.).Mobile edge computing (MEC) technologies facilitate resource-scarcemobile devices to enhance their capabilities and execute data/computeintensive applications while collaborating with resource-rich network servers to enable ubiquitous computing. On the other hand, mobile cloud computing provides higher resources for applications that have low response requirements (noninteractive applications). Bothmobile edge computing andmobile cloud computing enable technologies for Internet of Things (IoT), smart grids, and e-health applications.

Published in May-2021

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

The management of the abundant amount of used plastic bottle waste is a major concern nowadays, because it is a major contributor to landfills and overburdens waste processing facilities. Once disposed of, plastic can take centuries to break down, hence, recycling not only manages the waste efficiently, but it reduces the environmental impact and creates economic opportunities, as well. An incentive-based Reverse Vending machine (RVM) is an effective way to involve the general public in the management of plastic waste. The existing solutions are either very expensive, from a computation and cost perspective, or they lack the robustness and durability necessary for deployment. In this research, we have developed an incentive-based low-cost RVM for the collection, identification, classification, and sorting of used plastic bottles with the addition of a reward-based user application. The developed RVM includes a low-cost computing device running a lightweight image processing algorithm, sensors, camera, and a self-designed mechanical arm. To support the low computing device in the RVM, a lightweight MobileNet model has been trained using transfer learning. A dataset of 10,983 pet bottle images was collected using a camera installed inside the machine for the classification model. The results of the study show that MobileNet achieved 99.2% testing accuracy, which is better than the existing bottle-classification approaches. Moreover, the weight of the developed model was only 12 Mb, which is fourteen and six times less than inceptionV3 and Res-Net (Residual Neural Networks), respectively. Furthermore, the developed RVM costs a fraction of the price, compared to the existing solutions. Two versions of the machine have been deployed at a University for more than 6 months, collecting over 650 kg of plastic waste.

The assessment of food quality is of significant importance as it allows control over important features, such as ensuring adherence to food standards, longer shelf life, and consistency and quality of taste. Rice is the predominant dietary source of half the world’s population, and Pakistan contributes around 80% of the rice trade worldwide and is among the top three of the largest exporters. Hitherto, the rice industry has depended on antiquated methods of rice quality assessment through manual inspection, which is time consuming and prone to errors. In this study, an efficient desktop-application-based rice quality evaluation system, ‘National Grain Tech’, based on computer vision and machine learning, is presented. The analysis is based on seven main features, including grain length, width, weight, yellowness, broken, chalky, and/or damaged kernels for six different types of rice: IRRI-6, PK386, 1121 white and Selah, Super kernel basmati brown, and white rice. The system was tested in rice factories for 3 months and demonstrated 99% accuracy in determining the size, weight, color, and chalkiness of rice kernels. An accuracy of 98.8% was achieved for the classification of damaged and undamaged kernels, 98% for determining broken kernels, and 100% for paddy kernels. The results are significant because the developed system improves the local rice quality testing capacity through a faster, more accurate, and less expensive mechanism in comparison to previous research studies, which only evaluated four features of the singular rice type, rather than the seven features achieved in this study for six rice types.

Plant bioactive compounds, particularly apigenin, have therapeutic potential and functional activities that aid in the prevention of infectious diseases in many mammalian bodies and promote tumor growth inhibition. Apigenin is a flavonoid with low toxicities and numerous bioactive properties due to which it has been considered as a traditional medicine for decades. Apigenin shows synergistic effects in combined treatment with sorafenib in the HepG2 human cell line (HCC) in less time and statistically reduces the viability of tumor cells, migration, gene expression and apoptosis. The combination of anti-cancerous drugs with apigenin has shown health promoting potential against various cancers. It can prevent cell mobility, maintain the cell cycle and stimulate the immune system. Apigenin also suppresses mTOR activity and raises the UVB-induced phagocytosis and reduces the cancerous cell proliferation and growth. It also has a high safety threshold, and active (anti-cancer) doses can be gained by consuming a vegetable and apigenin rich diet. Apigenin also boosted autophagosome formation, decreased cell proliferation and activated autophagy by preventing the activity of the PI3K pathway, specifically in HepG2 cells. This paper provides an updated overview of apigenin’s beneficial anti-inflammatory, antibacterial, antiviral, and anticancer effects, making it a step in the right direction for therapeutics. This study also critically analyzed the effect of apigenin on cancer cell signaling pathways including the PI3K/AKT/MTOR, JAK/STAT, NF-κB and ERK/MAPK pathways.

Hepatitis C and hepatitis B are major health-related issues all over the globe. Both of these are iral infections that can be disseminated via sexual contact, by blood or the use of blood products, and through methods of intra-familial transmission. The collection of blood samples of factory workers was done aseptically at KBK Electronics. Overall, 272 samples of blood were taken from a variety of individuals. Economically accessible ELISA (Enzyme linked Immunosorbent Assay) was used to screen the individuals for Hepatitis B surface Antigen (HBsAg) and also for anti- HCV antibodies. Screening test was performed as directed by the instructions of the manufacturer. Name, gender and age group of each person was noted. Overall, 271 individuals were inspected to check the presence of HCV and HBV. The age of all the individuals ranged between 19 -50 years. The overall prevalence of both HCV and HBV was determined as 18.4% (50/271). As compare to HBV, HCV has a higher prevalence, that is, 16.17% (44/271). On the other hand, the prevalence of HBV was only 2.2% (6/271). The results of this study and previous literature highlight the pressing need to conduct epidemiological studies in smaller regions of the country. It will help to specify the areas with higher HCV/HBV prevalence. Only then, various methods for treating and even precluding both of these infections can be implemented effectively. Copyright (c) 2021 Muhammad Khurram, Asma Irshad, Muhammad Alamgir,Usman Ayub Awan, Haleema Sadia, Ammna Sayeed

Background: Pseudo-epitheliomatous Hyperplasia (PEH) is a benign proliferation of epithelium occurring in response to various neoplastic lesions, infections and inflammatory processes. It can be mistaken especially in small biopsies for an invasive oral squamous cell carcinoma (OSCC). The objective of this study was to determine the expression and comparison of immunohistochemical marker E-cadherin in OSCC and PEH lesions. Materials & Methods: This cross-sectional study was conducted in Department of Histopathology, Armed Forces Institute of Pathology, Rawalpindi, Pakistan from January 2013 to March 2016. 60 paraffin embedded cases, 30 each of PEH and OSCC were retrieved and stained with haematoxylin and eosin. They were then immune-stained with E-cadherin and expression evaluated and compared in two lesions by histopathologist. Age, sex, site of lesion and E-cadherin expression were variables. Age was described by mean and SD and other variables as frequency and percentages. Results: Mean age of OSCC group was 60.1±17.3 years and PEH group 52.7±16.6 years. OSCC group included 16 (53.33%) men and 14 (46.67%) women. PEH group included 18 (60%) men and 12 (40%) women. In OSCC group, site of lesion was buccal mucosa 12 (40%) cases, gingiva 10 (33.3%), tongue 7 (23.3%) and floor of mouth 1 (3.4%) case. In PEH group, site of lesion was buccal mucosa 12 (40%) cases, tongue 11 (36.67%), gingiva 6 (20%) and palate 1 (3.3%) case. The expression of E-cadherin was negative in all 30 cases of OSCC and positive in 29/30 (96.67%) cases in PEH. Conclusion: E-cadherin can be used as an ancillary marker in the differentiation of oral squamous cell carcinoma and pseudo-epitheliomatous hyperplasia.

The study aims to determine the corporate brand image developed by the green supply chain management affected by the green purchasing, green communications, the customer cooperation, eco-design, internal environment management and investment recovery extent. This research adopts a positivist paradigm using quantitative techniques of data collection through surveys and applying a deductive approach to answer the research problem. The population for this research was based upon the entire list of manufacturing companies that are listed on Pakistan Stock Exchange. Out of these companies a representative sample of 120 was selected. This study confirms the impact of ecofriendly activities, inventory, green supply chain, internal environment and customer cooperation on the image of a corporation through a mediating variable green communications. This study has this limitation of being a cross-sectional study as the data was collected through convenient sampling and Secondly, this study is only conducted in one country Pakistan and is only restricted to one sector.

Abstract Properties of rock, such as effective porosity, permeability and pore size distribution (PSD), are generally referred to as petrophysical properties. These properties are among the most significant for reservoir evaluation. Acid stimulation treatments are usually used in sandstones to mitigate the impact of formation damage, with the aim of restoring or enhancing the natural matrix permeability and consequently boosting the well productivity. Hydrochloric acid (HCl) is commonly used in the preflush stage to remove calcium and other metal ions, preventing the development of calcium fluoride (CaF2) and other silicate precipitates that could block the pore throats, while an acid mixture (HF–HCl combination) is usually preferred as the main stimulation fluid for the removal of quartz and remaining metal ions. However, sometimes the application of these acids can lead to other problems, including fast reactions, corrosion of pipes, environmental hazards, precipitation reactions and formation damage due to the incompatibility of HCl with clay minerals, so chelating agents have been proposed as an alternative for matrix stimulation fluids. In this study, three different chelating agents, ethylenediaminetetraacetic acid (EDTA), N-(2-hydroxyethyl) ethylenediamine-N,N′,N′-triacetic acid (HEDTA) and N-acetyl-l-glutamic acid (GLDA), have been used to stimulate Berea sandstone, Colton tight sandstone and Guelph dolomite samples. Core flood experiments were conducted on 1.5 × 3 (in2) core plugs, at a temperature below 180 °F. A slow injection rate of (1–0.5 cc/min) was chosen for the treatment fluid, promoting the dissolution of ions by increasing the contact time between the fluid and the rock. Furthermore, nuclear magnetic resonance, wettability and micro-computed tomography (CT scan) analyses were employed to evaluate the effect of the acid treatment on formation properties such as porosity, PSD, pore topology, wettability and pore structure. After exposing the samples to HEDTA, large wormholes were detected in their pore network, demonstrating that HEDTA has the highest potential to create new pore spaces when compared to GLDA and EDTA when reacted with both types of samples.

A vision of the future Internet is introduced in such a fashion that various computing devices are connected together to form a network called Internet of Things (IoT). This network will generate massive data that may be leveraged for entertainment, security, and most importantly user trust. Yet, trust is an imperative obstruction that may hinder the IoT growth and even delay the substantial squeeze of a number of applications. In this survey, an extensive analysis of trust management techniques along with their pros and cons is presented in a different context. In comparison with other surveys, the goal is to provide a systematic description of the most relevant trust management techniques to help researchers understand that how various systems fit together to bring preferred functionalities without examining different standards. Besides, the lessons learned are presented, and the views are argued regarding the primary goal trust which is likely to play in the future Internet.

Deployed in harsh or hostile environments, it is usually impossible to replace/recharge the power source of a sensor node in a wireless sensor network. Therefore, the only solution is an energy efficient communication system. This paper presents an energy efficient system based on multipath collaborative communication having noise and fading. The collaborative communication exploits spatial diversity to achieve high gain in received power, low bit error rate (BER), and high energy savings even if the received signals are out-of-phase. The experimental results confirm that the benefits are further enhanced by the use of the multipath environment in combination with collaborative communication. For the trade-off analysis between energy consumption and transmission distances, the multipath collaborative communication is compared with the single-input-single-output (SISO) system. Collaborative communication performs better over the long distance; however, the SISO is suitable for short distances. The proposed collaborative communication system can achieve 99% energy savings in comparison to the SISO system.

The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.

The prediction of hidden or missing links in a criminal network, which represent possible interactions between individuals, is a significant problem. The criminal network prediction models commonly rely on Social Network Analysis (SNA) metrics. These models leverage on machine learning (ML) techniques to enhance the predictive accuracy of the models and processing speed. The problem with the use of classical ML techniques such as support vector machine (SVM), is the dependency on the availability of large dataset for training purpose. However, recent ground breaking advances in the research of deep reinforcement learning (DRL) techniques have developed methods of training ML models through self-generated dataset. In view of this, DRL could be applied to other domains with relatively smaller dataset such as criminal networks. Prior to this research, few, if any, previous works have explored the prediction of links within criminal networks that could appear and/or disappear over time by leveraging on DRL technique. Therefore, in this paper, the primary objective is to construct a time-based link prediction model (TDRL) by leveraging on DRL technique to train using a relatively small real-world criminal dataset that evolves over time. The experimental results indicate that the predictive accuracy of the DRL model trained on the temporal dataset is significantly better than other ML models that are trained only with the dataset at specific snapshot in time.

Identity authentication is a main line of defense for network security, and passwords have long been the mainstream of identity authentication. In the field of password security research, large-scale password datasets have played an important role in the efficiency evaluation of password attack algorithms, the feasibility detection of password strength meters, and the correction of password probability models. However, due to user privacy, timeliness, effectiveness and other factors, it is still very difficult for researchers to obtain real large-scale user plaintext passwords. Based on this, this paper proposes a fast simulative password set generation algorithm based on structure partitioning and string recombination, denoted as SPSR-FSPG. The algorithm uses the probability context-free grammar to model the structure of the password, and constructs a string generation model based on the recurrent neural network to generate different types of strings, so as to learn the character composition of the password in the original dataset. In addition, the model fully considers the user's password reuse and modification behavior. Finally, the method is verified by experiment on six real Chinese and English password sets. The results show that the generation rate of SPSR-FSPG is faster than other algorithms. In terms of true password coverage, the SPSR-FPSG simulative password set is increased by 11.36% and 17.5, respectively, relative to SPPG and PCFG, and is increased by about 122.73% and 130.3%, respectively, compared to OMEN and 4-Markov. And the fit of the Zipf distribution is maintained at a level above 0.95, it is better than 0.9 of SPPG. At the same time, the SPPR-FPSG simulative password set is closer to the real password set in terms of length and character composition.

Recently, Ostad-Sharif et al. pointed out the susceptibility of three different authentication schemes themed for telecare medicine/medical information systems to key compromise impersonation attack (KCIA). To further address this issue, they proposed an ECC-based authentication and key generation scheme for healthcare applications. In this paper, we show that Ostad-Sharif et al.'s scheme is not only affected with key compromise impersonation attack but also suffers from a key compromise password guessing attack. Several papers have been published by the researchers by applying KCIA on existing authentication protocols. Before any further move in research in this direction, researchers must contemplate about KCIA. We conclude this article with a rigorous analysis of KCIA along with two questions to ponderon for the research community working in this field.

The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project).

Oil spillage over a sea or ocean surface is a threat to marine and coastal ecosystems. Spaceborne synthetic aperture radar (SAR) data have been used efficiently for the detection of oil spills due to their operational capability in all-day all-weather conditions. The problem is often modeled as a semantic segmentation task. The images need to be segmented into multiple regions of interest such as sea surface, oil spill, lookalikes, ships, and land. Training of a classifier for this task is particularly challenging since there is an inherent class imbalance. In this work, we train a convolutional neural network (CNN) with multiple feature extractors for pixel-wise classification and introduce a new loss function, namely, “gradient profile” (GP) loss, which is in fact the constituent of the more generic spatial profile loss proposed for image translation problems. For the purpose of training, testing, and performance evaluation, we use a publicly available dataset with selected oil spill events verified by the European Maritime Safety Agency (EMSA). The results obtained show that the proposed CNN trained with a combination of GP, Jaccard, and focal loss functions can detect oil spills with an intersection over union (IoU) value of 63.95%. The IoU value for sea surface, lookalikes, ships, and land class is 96.00%, 60.87%, 74.61%, and 96.80%, respectively. The mean intersection over union (mIoU) value for all the classes is 78.45%, which accounts for a 13% improvement over the state of the art for this dataset. Moreover, we provide extensive ablation on different convolutional neural networks (CNNs) and vision transformers (ViTs)-based hybrid models to demonstrate the effectiveness of adding GP loss as an additional loss function for training. Results show that GP loss significantly improves the mIoU and F1 scores for CNNs as well as ViTs-based hybrid models. GP loss turns out to be a promising loss function in the context of deep learning with SAR images.

Social media can complement organizational communication strategy which is integral to employee engagement. However, successful case studies which can allow replication are limited. The objective of the study is to describe the design, implementation, and evaluation of a social media-based communication strategy in a tertiary care hospital in Pakistan. The leadership of the pediatric service line developed an intervention plan to engage the employees with the newly reframed vision to improve patient and family experience. An online communication platform—Facebook page—was created for all employees of the pediatric service line. The strategy to influence employees was based on Cialdini's six principles of persuasion. Implementation of the strategy between October 2017 and December 2019 was evaluated for reach, discussion themes, and outcomes using the framework by Murdough (2009). Quantitative indicators included total posts, mean comments, and reactions per post. Posts were qualitatively analyzed with an emergent approach for insights into the discussion. The analysis revealed a total of 9,085 posts, with mean reactions per post of 8.4, mean comments of 7.2, and active viewership by 90% members on average. In terms of post types, photos were the highest (4,779), while videos were the lowest (1,163). Qualitative analysis indicated 54% of the posts were of the theme “inspirational and thought provoking,” while the greatest engagement was generated on the theme “challenges and solution.” The authors conclude that the strategy was successfully implemented to maintain active membership, engage employees in meaningful conversations, and have them express intent to execute quality improvement projects.

In this work, the spectral properties of distributed Bragg reflector-based photonic crystal (DBR-PhC) structures were studied for the near-infrared (NIR) range. Different structural properties were varied to study their effect on the quality of the stopband and the appearance of the resonant dips in the reflection spectra of the DBR-PhC structure. The investigated structural features included the depth of PhC holes, hole radius, and number of PhC elements in the DBR structure. The 11-layered DBR structure was designed with a 2.4/1.4 refractive index contrast of alternating layers. The study aimed to achieve optical filtering properties in the DBR-PhC structure, to simplify the structural complexity of Fabry-Pérot filters by eliminating the FP cavity and upper-DBR mirror. The proposed DBR-PhC device can be used in different optical filtering and sensing applications.

The design of an intracavity spectroscopy based two-mode biomedical sensor involves a thorough investigation of the system. For this purpose, the individual components that are present in the system must be examined. This work describes the principle of two very important gadgets, namely the Fibre Bragg Grating (FBG), and the tunable coupler. We adhere to a Petri network scheme to model and analyze the performance of the FBG, and the results mirror strikingly low difference in the values of Bragg Wavelength during its ascending and descending operational principle, thereby maintaining the accuracy of the sensor’s results. Next, a pseudocode is developed and implemented for the investigation of the optical coupler in LabView. The values of its maximum output power are determined, and the coupling ratio for various values of controlling voltage is determined at three different wavelengths. The hysteresis results mirror an extremely low difference between the forward and reverse values in the results. Both the results of the FBG and the coupler are thereby extremely reliable to use them in the laser system, as evident from the respective intensity noise outcomes, as well as the experimentation on substances of interest (Dichloro Methane and Propofol).

An analytical model to predict the surface roughness for the plasma-enhanced chemical vapor deposition (PECVD) process over a large range of temperature values is still nonexistent. By using an existing prediction model, the surface roughness can directly be calculated instead of repeating the experimental processes, which can largely save time and resources. This research work focuses on the investigation and analytical modeling of surface roughness of SiO2 deposition using the PECVD process for almost the whole range of operating temperatures, i.e., 80 to 450 °C. The proposed model is based on experimental data of surface roughness against different temperature conditions in the PECVD process measured using atomic force microscopy (AFM). The quality of these SiO2 layers was studied against an isolation layer in a microelectromechanical system (MEMS) for light steering applications. The analytical model employs different mathematical approaches such as linear and cubic regressions over the measured values to develop a prediction model for the whole operating temperature range of the PECVD process. The proposed prediction model is validated by calculating the percent match of the analytical model with experimental data for different temperature ranges, counting the correlations and error bars.

The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

and Job description) on employees serving presently at the DMHL. The resources and services they offer create opportunities for learning, support literacy and education, shape the new ideas and perspectives that are fundamental to creative and innovative society. In the first place the study focuses on enhancement of skills of human resources. In the second place, it focuses on strengthening the quality of information provided to its beneficiaries. The study is Significant in many counts. It would be beneficial for re-structuring the human resource development policies for educating library authorities of University of Karachi as well as other public sector universities of the country. Mix method of research was employed. A questioner was used to get firsthand information from a universe of 101 respondents (the employees of Library Information Science professionals, Information Technology related and other supporting staff of DMHL). Short interviews were also conducted wherever necessary.

Task of selecting the most suitable industrial steam boiler for a given process industry is a critical decision and becoming increasingly complex. That is why; the present research focuses on the use of Analytical Hierarchy Process (AHP) and TOPSIS in the selection of boiler for a soda ash industry. First the problem is prearranged in the typical AHP hierarchy, and then TOPSIS technique is employed. Soda ash industry experts are consulted to prioritize the parameters influencing in the selection of boilers. Boiler alternatives available in the market are evaluated for these parameters. TOPSIS method has been used on the bases of AHP pairwise comparison weights. The results indicates that experts looks more concerned about factors like steam capacity, working temperature and designed working pressure of boilers as compared to its cost. Final results also indicate that factors like weight and area of heating surfaces of boiler are very low on the priority list of industry experts. Moreover, it is concluded that the first, second and the fifth alternative ranks are the same in both methods. The way in which AHP and TOPSIS have been utilized will significantly help industrial managers involving in the selection process of a steam boiler. Keywords: Multi-criteria decision making (MCDM), Boiler selection, Soda ash industry, AHP, TOPSIS

In recent years, intelligent railway operation and maintenance using cloud technology have received wide consideration for the provision of public transport services. Under the cloud environment, information security is critical to ensure the integrity of data through protection from unauthorized manipulation and the confidentiality through protection against the leakage of sensitive information. This paper proposes a lightweight authenticated encryption scheme with associated data based on a novel discrete chaotic S-box coupled map lattice (SCML), which avoids the dynamic degradation of the digital chaotic system and low efficiency of the chaos-based cryptosystem. Based on the chaotic SCML, an authenticated encryption scheme that protects the confidentiality and integrity in one pass is presented in detail. The security analysis and performance simulations in software and hardware show that the proposed scheme is efficient and provides adequate security through authentication and encryption. Such a scheme could be used for applications in a railway cloud service that requires moderate security and low-cost implementations.

We live in a digital world where every detail of our information is being transferred from one smart device to another via cross-platform, third-party cloud services. Smart technologies, such as, smartphones are playing dynamic roles in order to successfully complete our daily routines and official tasks that require access to all types of critical data. Before the advent of these smart technologies, securing critical information was quite a challenge. However, after the advent and global adoption of such technologies, information security has become one of the primary and most fundamental task for security professionals. The integration of social media has made this task even more challenging to undertake successfully. To this day, there are plentiful studies in which numerous authentication and security techniques were proposed and developed for smartphone and cloud computing technologies. These studies have successfully addressed multiple authentication threats and other related issues in existing the smartphone and cloud computing technologies. However, to the best of our understanding and knowledge, these studies lack many aspects in terms of authentication attacks, logical authentication analysis, and the absence of authentication implementation scenarios. Due to these authentication anomalies and ambiguities, such studies cannot be fully considered for successful implementation. Therefore, in this paper, we have performed a comprehensive security analysis and review of various smartphone and cloud computing authentication frameworks and protocols to outline up-to-date authentication threats and issues in the literature. These authentication challenges are further summarized and presented in the form of different graphs to illustrate where the research is currently heading. Finally, based on those outcomes, we identify the latest and existing authentication uncertainties, threats, and other related issues to address future directions and open research issues in the domain of the smartphone- and cloud-computing authentication.

Blockchain can potentially be deployed in a wide range of applications due to its capability to ensure decentralization, transparency, and immutability. In this paper, we design a cryptographic membership authentication scheme (i.e., authenticating graph data) to support blockchain-based identity management systems (BIMS). Such a system is designed to bind a digital identity object to its real-world entity. Specifically, we introduce a new transitively closed undirected graph authentication (TCUGA) scheme, which only needs to use node signatures (e.g., certificates for identifying nodes). The trapdoor hash function used in our scheme allows the signer to efficiently update the certificates without the need to re-sign the nodes. In other words, our scheme is efficient even though the graph dynamically adds or deletes vertices and edges. Moreover, our proposal can efficiently provide a proof when the edge between two vertices does not exist, thus solving the existing intractability issue in transitive signature (the main tool for authenticating graph data). Finally, we prove the security of our proposed TCUGA in the standard model and evaluate its performance to show its feasibility for BIMS.

Visual saliency models aim to detect important and eye catching portions in a scene by exploiting human visual system characteristics. The effectiveness of visual saliency models is evaluated by comparing saliency maps with a ground truth data set. In recent years, several visual saliency computation algorithms and ground truth data sets have been proposed for images. However, there is lack of ground truth data sets for videos. A new human labeled ground truth is prepared for video sequences that are commonly used in video coding. The selected videos are from different genres including conversational, sports, outdoor, and indoor having low, medium, and high motion. Saliency mask is obtained for each video by nine different subjects, which are asked to label the salient region in each frame in the form of a rectangular bounding box. A majority voting criteria is used to construct a final ground truth saliency mask for each frame. Sixteen different state-of-the-art visual saliency algorithms are selected for comparison and their effectiveness is computed quantitatively on the newly developed ground truth. It is evident from results that multiple kernel learning and spectral residual-based saliency algorithms perform best for different genres and motion-type videos in terms of F-measure and execution time, respectively.

Cloud computing has seen massive growth in this decade. With the rapid development of cloud networks, cloud monitoring has become essential for running cloud systems smoothly. Cloud monitoring collects monitoring metrics from the cloud's physical and virtual infrastructures. In terms of data collection, cloud monitoring can be intrusive or non-intrusive. Monitoring data collection non-intrusively from the host operating system (OS) is a challenging task. The aim of this paper was to collect monitoring data from the host OS non-intrusively and to link those data with the cloud controller for use in monitoring. Monitoring data were collected from Procfs of the host OS and that information was linked with the monitoring dashboard on the cloud controller node. The results show that the proposed solution is an efficient, lightweight, and scalable cloud monitoring framework that produces negligible overhead.

The application of wireless body area network (WBAN) in health sector is crucial. It prolongs the life expectancy and improves the quality of life by enabling remote health monitoring. Despite many advantages, it may result and exacerbates various extant health issues especially in the context of mobile multimedia. Due to high data rate of mobile multimedia applications, heat generated from sensor devices is considerably high and results in thermal dissipation. The thermal impact of sensors on human body has largely been a neglected topic. The paper rigorously analyzes the specific absorption rate (SAR) impact on human body. Moreover, the paper largely contributes by comprehensively evaluating SAR impact under various circumstances and dependency factors. The analysis and evaluation show promising results that can significantly help the successful design and deployment of WBAN strategies. The paper also provides great guidance to numerous practitioners of this domain.

Human cystic echinococcosis (CE) is a zoonotic disease caused by the larval stage of Echinococcus granulosus sensu lato that causes economic losses by affecting livestock and also poses a public health threat worldwide. The present study is the first retrospective report on the seroprevalence of anti-E. granulosus antibodies in humans in Pakistan. The study used data from 93 blood analysis reports of patients suspected of having CE from different medical centers in Lahore, Pakistan. Out of 93 sera samples, 20 (21.5%) were seropositive, and higher seropositivity (17.2%) was recorded with the indirect hemagglutination test (IHA) than with enzyme-linked immunosorbent assay (ELISA). The findings indicated that age, gender, and year had no significant relationship with the seropositivity of CE. The current study provides directions towards the management of the disease in the near future in Pakistan.

Internet of things is widely used in the current era to collect data from sensors and perform specific tasks through processing according to the requirements. The data collected can be sent to a blockchain network to create secure and tamper-resistant records of transactions. The combination of blockchain with IoT has huge potential as it can provide decentralized computation, storage, and exchange for IoT data. However, IoT applications require a low-latency consensus mechanism due to its constraints. In this paper, CBCIoT, a consensus algorithm for blockchain-based IoT applications, is proposed. The primary purpose of this algorithm is to improve scalability in terms of validation and verification rate. The algorithm is developed to be compatible with IoT devices where a slight delay is acceptable. The simulation results show the proposed algorithm’s efficiency in terms of block generation time and transactions per second.

Communication and computing shape up base for explosion of Internet of Things (IoT) era. Humans can efficiently control the devices around their environment as per requirements because of IoT, the communication between different devices brings more flexibility in surrounding. Useful data is also gathered from some of these devices to create Big Data; where, further analysis assist in making life easier by developing good business models corresponding to user needs, enhance scientific research, formulating weather prediction or monitoring systems and contributing in other relative fields as well. Thus, in this research a remotely deployable IoT enabled Wind Sonic Anemometer has been designed and deployed to calculate average wind speed, direction, and gust. The proposed design is remotely deployable, user-friendly, power efficient and cost-effective because of opted modules i.e., ultrasonic sensors, GSM module, and solar panel. The testbed was also deployed at the roof of Computer & Information Systems Engineering (CIS) department, NED UET. Further, its calibration has been carried out by using long short-term memory (LSTM), a deep learning technique; where ground truth data has been gathered from mechanical wind speed sensor (NRG-40 H) deployed at top of Industrial & Manufacturing (IM) department of NED UET. The obtained results are satisfactory and the performance of designed sensor is also good under various weather conditions.