National University of Sciences and Technology

This research study examines the attitudes of Pakistani higher education students towards compulsory digital and distance learning university courses amid Coronavirus (COVID-19). Undergraduate and postgraduate were surveyed to find their perspectives about online education in Pakistan. The findings of the study highlighted that online learning cannot produce desired results in underdeveloped countries like Pakistan, where a vast majority of students are unable to access the internet due to technical as well as monetary issues. The lack of face-to-face interaction with the instructor, response time and absence of traditional classroom socialization were among some other issues highlighted by higher education students.

Microscopic entities, microorganisms that drastically affect human health need to be thoroughly investigated. A biofilm is an architectural colony of microorganisms, within a matrix of extracellular polymeric substance that they produce. Biofilm contains microbial cells adherent to one-another and to a static surface (living or non-living). Bacterial biofilms are usually pathogenic in nature and can cause nosocomial infections. The National Institutes of Health (NIH) revealed that among all microbial and chronic infections, 65% and 80%, respectively, are associated with biofilm formation. The process of biofilm formation consists of many steps, starting with attachment to a living or non-living surface that will lead to formation of micro-colony, giving rise to three-dimensional structures and ending up, after maturation, with detachment. During formation of biofilm several species of bacteria communicate with one another, employing quorum sensing. In general, bacterial biofilms show resistance against human immune system, as well as against antibiotics. Health related concerns speak loud due to the biofilm potential to cause diseases, utilizing both device-related and non-device-related infections. In summary, the understanding of bacterial biofilm is important to manage and/or to eradicate biofilm-related diseases. The current review is, therefore, an effort to encompass the current concepts in biofilm formation and its implications in human health and disease.

Results of the ICDAR 2015 Robust Reading Competition are presented. A new Challenge 4 on Incidental Scene Text has been added to the Challenges on Born-Digital Images, Focused Scene Images and Video Text. Challenge 4 is run on a newly acquired dataset of 1,670 images evaluating Text Localisation, Word Recognition and End-to-End pipelines. In addition, the dataset for Challenge 3 on Video Text has been substantially updated with more video sequences and more accurate ground truth data. Finally, tasks assessing End-to-End system performance have been introduced to all Challenges. The competition took place in the first quarter of 2015, and received a total of 44 submissions. Only the tasks newly introduced in 2015 are reported on. The datasets, the ground truth specification and the evaluation protocols are presented together with the results and a brief summary of the participating methods.

The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc. have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird's eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

Nosocomial infections or healthcare associated infections occur in patients under medical care. These infections occur worldwide both in developed and developing countries. Nosocomial infections accounts for 7% in developed and 10% in developing countries. As these infections occur during hospital stay, they cause prolonged stay, disability, and economic burden. Frequently prevalent infections include central line-associated bloodstream infections, catheter-associated urinary tract infections, surgical site infections and ventilator-associated pneumonia. Nosocomial pathogens include bacteria, viruses and fungal parasites. According to WHO estimates, approximately 15% of all hospitalized patients suffer from these infections. During hospitalization, patient is exposed to pathogens through different sources environment, healthcare staff, and other infected patients. Transmission of these infections should be restricted for prevention. Hospital waste serves as potential source of pathogens and about 20%–25% of hospital waste is termed as hazardous. Nosocomial infections can be controlled by practicing infection control programs, keep check on antimicrobial use and its resistance, adopting antibiotic control policy. Efficient surveillance system can play its part at national and international level. Efforts are required by all stakeholders to prevent and control nosocomial infections.

Drought stress, which causes a decline in quality and quantity of crop yields, has become more accentuated these days due to climatic change. Serious measures need to be taken to increase the tolerance of crop plants to acute drought conditions likely to occur due to global warming. Drought stress causes many physiological and biochemical changes in plants, rendering the maintenance of osmotic adjustment highly crucial. The degree of plant resistance to drought varies with plant species and cultivars, phenological stages of the plant, and the duration of plant exposure to the stress. Osmoregulation in plants under low water potential relies on synthesis and accumulation of osmoprotectants or osmolytes such as soluble proteins, sugars, and sugar alcohols, quaternary ammonium compounds, and amino acids, like proline. This review highlights the role of osmolytes in water-stressed plants and of enzymes entailed in their metabolism. It will be useful, especially for researchers working on the development of drought-resistant crops by using the metabolic-engineering techniques.

Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.

Circular RNAs have been identified as naturally occurring RNAs that are highly represented in the eukaryotic transcriptome. Although a large number of circRNAs have been reported, circRNA functions remain largely unknown. CircRNAs can function as miRNA sponges, thereby reducing their ability to target mRNAs. We hypothesize that circRNAs may bind, store, sort, and sequester proteins to particular subcellular locations, and act as dynamic scaffolding molecules that modulate protein-protein interactions. Here, we review the biological implication and function of circRNA-protein interaction, and reveal a dynamic model of the interaction in various tissues, development stages and physiological conditions. Improved techniques to identify and characterize the dynamic RNA-protein interactions may elucidate the molecular mechanisms associated with the expression and functional diversity of circRNAs.

Blockchain have been an interesting research area for a long time and the benefits it provides have been used by a number of various industries. Similarly, the healthcare sector stands to benefit immensely from the blockchain technology due to security, privacy, confidentiality and decentralization. Nevertheless, the Electronic Health Record (EHR) systems face problems regarding data security, integrity and management. In this paper, we discuss how the blockchain technology can be used to transform the EHR systems and could be a solution of these issues. We present a framework that could be used for the implementation of blockchain technology in healthcare sector for EHR. The aim of our proposed framework is firstly to implement blockchain technology for EHR and secondly to provide secure storage of electronic records by defining granular access rules for the users of the proposed framework. Moreover, this framework also discusses the scalability problem faced by the blockchain technology in general via use of off-chain storage of the records. This framework provides the EHR system with the benefits of having a scalable, secure and integral blockchain-based solution.

In the contemporary world, natural fibers reinforced polymer composite (NFRPC) materials are of great interest owing to their eco-friendly nature, lightweight, life-cycle superiority, biodegradability, low cost, noble mechanical properties. NFRPCs are widely applied in various engineering applications and this research field is continuously developing. However, the researchers are facing numerous challenges regarding the developments and applications of NFPRCs due to the inherent characteristics of natural fibers (NFs). These challenges include quality of the fiber, thermal stability, water absorption capacity, and incompatibility with the polymer matrices. Ecological and economic concerns are animating new research in the field of NFRPCs. Furthermore, considerable research is carried out to improve the performance of NFRPCs in recent years. This review highlights some of the important breakthroughs associated with the NFRPCs in terms of sustainability, eco-friendliness, and economic perspective. It also includes hybridization of NFs with synthetic fibers which is a highly effective way of improving the mechanical properties of NFRPCs along with some chemical treatment procedures. This review also elucidates the significance of using numerical models for NFRPCs. Finally, conclusions and recommendations are drawn to assist the researchers with future research directions.

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

The elimination of dyes discharged from industrial wastewater into water bodies is crucial due to its detrimental effects on aquatic organisms and potential carcinogenic impact on human health. Various methods are employed for dye removal, but they often fall short in completely degrading the dyes and generating large amounts of suspended solids. Hence, there is a critical need for an efficient process that can achieve complete dye degradation with minimal waste emission. Among traditional water treatment approaches, photocatalysis stands out as a promising method for degrading diverse toxic and organic pollutants present in wastewater. In this review, the heterogeneous photocatalysis process is well explained for dye removal. This comprehensive review not only provides insightful illumination on the classification of dyes but also thoroughly explains various dye removal methods and the underlying mechanisms of photocatalysis. Furthermore, factors which effect the activity of the photocatalysis process are also explained in detail. Likewise, we categorized the heterogeneous photocatalyst in three generations and observed their activity for dye removal. This review also addresses the challenges and effectiveness of this promising field. Its primary aim is to offer a comprehensive overview of the photocatalytic degradation of pollution and to explore its potential for further future applications.

Android is the first mass-produced consumer-market open source mobile platform that allows developers to easily create applications and users to readily install them. However, giving users the ability to install third-party applications poses serious security concerns. While the existing security mechanism in Android allows a mobile phone user to see which resources an application requires, she has no choice but to allow access to all the requested permissions if she wishes to use the applications. There is no way of granting some permissions and denying others. Moreover, there is no way of restricting the usage of resources based on runtime constraints such as the location of the device or the number of times a resource has been previously used. In this paper, we present Apex -- a policy enforcement framework for Android that allows a user to selectively grant permissions to applications as well as impose constraints on the usage of resources. We also describe an extended package installer that allows the user to set these constraints through an easy-to-use interface. Our enforcement framework is implemented through a minimal change to the existing Android code base and is backward compatible with the current security mechanism.

Energy Storage Technology is one of the major components of renewable energy integration and decarbonization of world energy systems. It significantly benefits addressing ancillary power services, power quality stability, and power supply reliability. However, the recent years of the COVID-19 pandemic have given rise to the energy crisis in various industrial and technology sectors. An integrated survey of energy storage technology development, its classification, performance, and safe management is made to resolve these challenges. The development of energy storage technology has been classified into electromechanical, mechanical, electromagnetic, thermodynamics, chemical, and hybrid methods. The current study identifies potential technologies, operational framework, comparison analysis, and practical characteristics. This proposed study also provides useful and practical information to readers, engineers, and practitioners on the global economic effects, global environmental effects, organization resilience, key challenges, and projections of energy storage technologies. An optimal scheduling model is also proposed. Policies for sustainable adaptation are then described. An extensive list of publications to date in the open literature is canvassed to portray various developments in this area.

Wounds are of a variety of types and each category has its own distinctive healing requirements. This realization has spurred the development of a myriad of wound dressings, each with specific characteristics. It is unrealistic to expect a singular dressing to embrace all characteristics that would fulfill generic needs for wound healing. However, each dressing may approach the ideal requirements by deviating from the 'one size fits all approach', if it conforms strictly to the specifications of the wound and the patient. Indeed, a functional wound dressing should achieve healing of the wound with minimal time and cost expenditures. This article offers an insight into several different types of polymeric materials clinically used in wound dressings and the events taking place at cellular level, which aid the process of healing, while the biomaterial dressing interacts with the body tissue. Hence, the significance of using synthetic polymer films, foam dressings, hydrocolloids, alginate dressings, and hydrogels has been reviewed, and the properties of these materials that conform to wound-healing requirements have been explored. A special section on bioactive dressings and bioengineered skin substitutes that play an active part in healing process has been re-examined in this work.

Image registration is the process of matching, aligning and overlaying two or more images of a scene, which are captured from different viewpoints. It is extensively used in numerous vision based applications. Image registration has five main stages: Feature Detection and Description; Feature Matching; Outlier Rejection; Derivation of Transformation Function; and Image Reconstruction. Timing and accuracy of feature-based Image Registration mainly depend on computational efficiency and robustness of the selected feature-detector-descriptor, respectively. Therefore, choice of feature-detector-descriptor is a critical decision in feature-matching applications. This article presents a comprehensive comparison of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK algorithms. It also elucidates a critical dilemma: Which algorithm is more invariant to scale, rotation and viewpoint changes? To investigate this problem, image matching has been performed with these features to match the scaled versions (5% to 500%), rotated versions (0° to 360°), and perspective-transformed versions of standard images with the original ones. Experiments have been conducted on diverse images taken from benchmark datasets: University of OXFORD, MATLAB, VLFeat, and OpenCV. Nearest-Neighbor-Distance-Ratio has been used as the feature-matching strategy while RANSAC has been applied for rejecting outliers and fitting the transformation models. Results are presented in terms of quantitative comparison, feature-detection-description time, feature-matching time, time of outlier-rejection and model fitting, repeatability, and error in recovered results as compared to the ground-truths. SIFT and BRISK are found to be the most accurate algorithms while ORB and BRISK are most efficient. The article comprises rich information that will be very useful for making important decisions in vision based applications and main aim of this work is to set a benchmark for researchers, regardless of any particular area.

Metal nanoparticles are being extensively used in biomedical fields due to their small size-to-volume ratio and extensive thermal stability. Gold nanoparticles (AuNPs) are an obvious choice for biomedical applications due to their amenability of synthesis, stabilization, and functionalization, low toxicity, and ease of detection. In the past few decades, various chemical methods have been used for the synthesis of AuNPs, but recently, newer environment friendly green approaches for the synthesis of AuNPs have gained attention. AuNPs can be conjugated with a number of functionalizing moieties including ligands, therapeutic agents, DNA, amino acids, proteins, peptides, and oligonucleotides. Recently, studies have shown that gold nanoparticles not only infiltrate the blood vessels to reach the site of tumor but also enter inside the organelles, suggesting that they can be employed as effective drug carriers. Moreover, after reaching their target site, gold nanoparticles can release their payload upon an external or internal stimulus. This review focuses on recent advances in various methods of synthesis of AuNPs. In addition, strategies of functionalization and mechanisms of application of AuNPs in drug and bio-macromolecule delivery and release of payloads at target site are comprehensively discussed.

This release aggregates various minor bug fixes and performance improvements since the main v3.0 release and incorporates PyTorch 1.7.0 compatibility updates. v3.1 models share weights with v3.0 models but contain minor module updates (<code>inplace</code> fields for nn.Hardswish() activations) for native PyTorch 1.7.0 compatibility. Breaking Changes 'GIoU' hyperparameter has been renamed to 'box' to better reflect a criteria-agnostic regression loss term (https://github.com/ultralytics/yolov5/pull/1120) Bug Fixes PyTorch 1.7 compatibility update. <code>torch&gt;=1.6.0</code> required, <code>torch&gt;=1.7.0</code> recommended (https://github.com/ultralytics/yolov5/pull/1233) GhostConv module bug fix (https://github.com/ultralytics/yolov5/pull/1176) Rectangular padding min stride bug fix from 64 to 32 (https://github.com/ultralytics/yolov5/pull/1165) Mosaic4 bug fix (https://github.com/ultralytics/yolov5/pull/1021) Logging directory runs/exp bug fix (https://github.com/ultralytics/yolov5/pull/978) Various additional Added Functionality PyTorch Hub functionality with YOLOv5 .autoshape() method added (https://github.com/ultralytics/yolov5/pull/1210) Autolabelling addition and standardization across detect.py and test.py (https://github.com/ultralytics/yolov5/pull/1182) Precision-Recall Curve automatic plotting when testing (https://github.com/ultralytics/yolov5/pull/1107) Self-host VOC dataset for more reliable access and faster downloading (https://github.com/ultralytics/yolov5/pull/1077) Adding option to output autolabel confidence with --save-conf in test.py and detect.py (https://github.com/ultralytics/yolov5/pull/994) Google App Engine deployment option (https://github.com/ultralytics/yolov5/pull/964) Infinite Dataloader for faster training (https://github.com/ultralytics/yolov5/pull/876) Various additional