University of Engineering and Technology Lahore

Objective: During COVID-19 pandemic, the institutions in Pakistan have started online learning. This study explores the perception of teachers and students regarding its advantages, limitations and recommendations.Methods: This qualitative case study was conducted from March to April 2020. Using maximum variation sampling, 12 faculty members and 12 students from University College of Medicine and University College of Dentistry, Lahore were invited to participate. Four focus group interviews, two each with the faculty and students of medicine and dentistry were carried out. Data were transcribed verbatim and thematically analyzed using Atlas Ti.Results: The advantages included remote learning, comfort, accessibility, while the limitations involved inefficiency and difficulty in maintaining academic integrity. The recommendations were to train faculty on using online modalities and developing lesson plan with reduced cognitive load and increased interactivities.Conclusion: The current study supports the use of online learning in medical and dental institutes, considering its various advantages. Online learning modalities encourage student-centered learning and they are easily manageable during this lockdown situation. doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2785 How to cite this:Mukhtar K, Javed K, Arooj M, Sethi A. Advantages, Limitations and Recommendations for online learning during COVID-19 pandemic era. 2020;36(COVID19-S4):COVID19-S27-S31. doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2785 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Development of reliable and eco-accommodating methods for the synthesis of nanoparticles is a vital step in the field of nanotechnology. Silver nanoparticles are important because of their exceptional chemical, physical, and biological properties, and hence applications. In the last decade, numerous efforts were made to develop green methods of synthesis to avoid the hazardous byproducts. This review describes the methods of green synthesis for Ag-NPs and their numerous applications. It also describes the comparison of efficient synthesis methods via green routes over physical and chemical methods, which provide strong evidence for the selection of suitable method for the synthesis of Ag-NPs.

In array processors it is important to map problem modules onto processors such that modules that communicate with each other lie, as far as possible, on adjacent processors. This mapping problem is formulated in graph theoretic terms and shown to be equivalent, in its most general form, to the graph isomorphism problem. The problem is also very similar to the bandwidth reduction problem for sparse matrices and to the quadratic assignment problem.

We consider the partitioning of a problem on a domain with unequal work estimates in different subdomains in a way that balances the workload across multiple processors. Such a problem arises for example in solving partial differential equations using an adaptive method that places extra grid points in certain subregions of the domain. We use a binary decomposition of the domain to partition it into rectangles requiring equal computational effort. We then study the communication costs of mapping this partitioning onto different multiprocessors: a mesh- connected array, a tree machine, and a hypercube. The communication cost expressions can be used to determine the optimal depth of the above partitioning.

Sun is an inexhaustible source of energy capable of fulfilling all the energy needs of humankind. The energy from the sun can be converted into electricity or used directly. Electricity can be generated from solar energy either directly using photovoltaic (PV) cells or indirectly using concentrated solar power (CSP) technology. Progress has been made to raise the efficiency of the PV solar cells that can now reach up to approximately 34.1% in multi-junction PV cells. Electricity generation from concentrated solar technologies has a promising future as well, especially the CSP, because of its high capacity, efficiency, and energy storage capability. Solar energy also has direct application in agriculture primarily for water treatment and irrigation. Solar energy is being used to power the vehicles and for domestic purposes such as space heating and cooking. The most exciting possibility for solar energy is satellite power station that will be transmitting electrical energy from the solar panels in space to Earth via microwave beams. Solar energy has a bright future because of the technological advancement in this field and its environment-friendly nature. The biggest challenge however facing the solar energy future is its unavailability all-round the year, coupled with its high capital cost and scarcity of the materials for PV cells. These challenges can be met by developing an efficient energy storage system and developing cheap, efficient, and abundant PV solar cells. This article discusses the solar energy system as a whole and provides a comprehensive review on the direct and the indirect ways to produce electricity from solar energy and the direct uses of solar energy. The state-of-the-art procedures being employed for PV characterization and performance rating have been summarized. Moreover, the technical, economic, environmental, and storage-related challenges are discussed with possible solutions. Furthermore, a comprehensive list of future potential research directions in the field of direct and indirect electricity generation from solar energy is proposed.

Due to the sheer global energy crisis, concerns about fuel exhaustion, electricity shortages, and global warming are becoming increasingly severe. Solar and wind energy, which are clean and renewable, provide solutions to these problems through distributed generators. Microgrids, as an essential interface to connect the power produced by renewable energy resources-based distributed generators to the power system, have become a research hotspot. Modern research in the field of microgrids has focused on the integration of microgrid technology at the load level. Due to the complexity of protection and control of multiple interconnected distributed generators, the traditional power grids are now outmoded. Microgrids are feasible alternatives to the conventional grid since they provide an integrating platform for micro-resources-based distributed generators, storage equipment, loads, and voltage source converters at the user end, all within a compact footprint. A microgrid can be architected to function either in grid-connected or standalone mode, depending upon the generation, integration potential to the main grid, and consumers’ requirements. The amalgamation of distributed energy resources-based microgrids to the conventional power system is giving rise to a new power framework. Nevertheless, the grids’ control, protection, operational stability, and reliability are major concerns. There has yet to be an effective real-time implementation and commercialization of micro-grids. This review article summarizes various concerns associated with microgrids’ technical and economic aspects and challenges, power flow controllers, microgrids’ role in smart grid development, main flaws, and future perspectives.

Due to the monumental growth of Internet applications in the last decade, the need for security of information network has increased manifolds. As a primary defense of network infrastructure, an intrusion detection system is expected to adapt to dynamically changing threat landscape. Many supervised and unsupervised techniques have been devised by researchers from the discipline of machine learning and data mining to achieve reliable detection of anomalies. Deep learning is an area of machine learning which applies neuron-like structure for learning tasks. Deep learning has profoundly changed the way we approach learning tasks by delivering monumental progress in different disciplines like speech processing, computer vision, and natural language processing to name a few. It is only relevant that this new technology must be investigated for information security applications. The aim of this paper is to investigate the suitability of deep learning approaches for anomaly-based intrusion detection system. For this research, we developed anomaly detection models based on different deep neural network structures, including convolutional neural networks, autoencoders, and recurrent neural networks. These deep models were trained on NSLKDD training data set and evaluated on both test data sets provided by NSLKDD, namely NSLKDDTest+ and NSLKDDTest21. All experiments in this paper are performed by authors on a GPU-based test bed. Conventional machine learning-based intrusion detection models were implemented using well-known classification techniques, including extreme learning machine, nearest neighbor, decision-tree, random-forest, support vector machine, naive-bays, and quadratic discriminant analysis. Both deep and conventional machine learning models were evaluated using well-known classification metrics, including receiver operating characteristics, area under curve, precision-recall curve, mean average precision and accuracy of classification. Experimental results of deep IDS models showed promising results for real-world application in anomaly detection systems.

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond. This success of LLMs has led to a large influx of research contributions in this direction. These works encompass diverse topics such as architectural innovations, better training strategies, context length improvements, fine-tuning, multimodal LLMs, robotics, datasets, benchmarking, efficiency, and more. With the rapid development of techniques and regular breakthroughs in LLM research, it has become considerably challenging to perceive the bigger picture of the advances in this direction. Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field. This article provides an overview of the literature on a broad range of LLM-related concepts. Our self-contained comprehensive overview of LLMs discusses relevant background concepts along with covering the advanced topics at the frontier of research in LLMs. This review article is intended to provide not only a systematic survey but also a quick, comprehensive reference for the researchers and practitioners to draw insights from extensive, informative summaries of the existing works to advance the LLM research.

In this study, an extensive literature review has been conducted on the material characterization of UHPC and its potential for large-scale field applicability. The successful production of ultra-high performance concrete (UHPC) depends on its material ingredients and mixture proportioning, which leads to denser and relatively more homogenous particle packing. A database was compiled from various research and field studies around the world on the mechanical and durability performance of UHPC. It is shown that UHPC provides a viable and long-term solution for improved sustainable construction owing to its ultra-high strength properties, improved fatigue behavior and very low porosity, leading to excellent resistance against aggressive environments. The literature review revealed that the curing regimes and fiber dosage are the main factors that control the mechanical and durability properties of UHPC. Currently, the applications of UHPC in construction are very limited due to its higher initial cost, lack of contractor experience and the absence of widely accepted design provisions. However, sustained research progress in producing UHPC using locally available materials under normal curing conditions should reduce its material cost. Current challenges regarding the implementation of UHPC in full-scale structures are highlighted. This study strives to assist engineers, consultants, contractors and other construction industry stakeholders to better understand the unique characteristics and capabilities of UHPC, which should demystify this resilient and sustainable construction material.

Methyl ammonium lead halide-based hybrid perovskite solar cells (PSCs) have been intensively studied in recent years because of their high efficiency and low processing costs.

The development of new energy materials that can be utilized to make renewable and clean fuels from abundant and easily accessible resources is among the most challenging and demanding tasks in science today. Solar-powered catalytic water-splitting processes can be exploited as a source of electrons and protons to make clean renewable fuels, such as hydrogen, and in the sequestration of CO2 and its conversion into low-carbon energy carriers. Recently, there have been tremendous efforts to build up a stand-alone solar-to-fuel conversion device, the "artificial leaf", using light and water as raw materials. An overview of the recent progress in electrochemical and photo-electrocatalytic water splitting devices is presented, using both molecular water oxidation complexes (WOCs) and nano-structured assemblies to develop an artificial photosynthetic system.

The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

Internet of Things (IoT) has been a major research topic for almost a decade now, where physical objects would be interconnected as a result of convergence of various existing technologies. IoT is rapidly developing; however there are uncertainties about its security and privacy which could affect its sustainable development. This paper analyzes the security issues and challenges and provides a well defined security architecture as a confidentiality of the user's privacy and security which could result in its wider adoption by masses.

The impending environmental issues and growing concerns for global energy crises are driving the need for new opportunities and technologies that can meet significantly higher demand for cleaner and sustainable energy systems. This necessitates the development of transportation and power generation systems. The electrification of the transportation system is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper inspects the present status, latest deployment, and challenging issues in the implementation of Electric vehicles (EVs) infrastructural and charging systems in conjunction with several international standards and charging codes. It further analyzes EVs impacts and prospects in society. A complete assessment of charging systems for EVs with battery charging techniques is explained. Moreover, the beneficial and harmful impacts of EVs are categorized and thoroughly reviewed. Remedial measures for harmful impacts are presented and benefits obtained therefrom are highlighted. Bidirectional charging offers the fundamental feature of vehicle to grid technology. In this paper, the current challenging issues due to the massive deployment of EVs, and upcoming research trends are also presented. It is envisioned that the researchers interested in such areas can find this paper valuable and an informative one-stop source.

, Escherichia coli, Staphylococcus aureus, Bacillus subtilis and Klebsiella pneumoniae, are the major cause of diarrheal infections in children and adults. Their structure badly affects the human immune system. It is important to explore new antibacterial agents instead of antibiotics for treatment. This project is an attempt to explain how gold nanoparticles affect these bacteria. We investigated the important role of the mean particle size, and the inhibition of a bacterium is dose-dependent. Ultra Violet (UV)-visible spectroscopy revealed the size of chemically synthesized gold nanoparticle as 6-40 nm. Atomic force microscopy (AFM) analysis confirmed the size and X-ray diffractometry (XRD) analysis determined the polycrystalline nature of gold nanoparticles. The present findings explained how gold nanoparticles lyse Gram-negative and Gram-positive bacteria.

Excessive emission of greenhouse gases into the atmosphere has resulted in a progressive climate change and global warming in the past decades. There have been many approaches developed to reduce the emission of Carbon Dioxide (CO2) into the atmosphere, among which Carbon Capture and Storage (CCS) techniques has been recognized as the most promising method. This paper provides a deeper insight about the CCS technology where CO2 is captured and stored in deep geological formations for stabilization of the earth's temperature. Principles of capturing and storage for a long-term sequestration are also discussed together with the processes, mechanisms and interactions induced by supercritical CO2 upon injection into subsurface geological sites. Keywords: CCS technologies, CO2 capture, CO2 transportation, CO2 storage, Leakage, Costs

Gaining an architectural level understanding of a software system is important for many reasons. When the description of a system's architecture does not exist, attempts must be made to recover it. In recent years, researchers have explored the use of clustering for recovering a software system's architecture, given only its source code. The main contributions of this paper are given as follows. First, we review hierarchical clustering research in the context of software architecture recovery and modularization. Second, to employ clustering meaningfully, it is necessary to understand the peculiarities of the software domain, as well as the behavior of clustering measures and algorithms in this domain. To this end, we provide a detailed analysis of the behavior of various similarity and distance measures that may be employed for software clustering. Third, we analyze the clustering process of various well-known clustering algorithms by using multiple criteria, and we show how arbitrary decisions taken by these algorithms during clustering affect the quality of their results. Finally, we present an analysis of two recently proposed clustering algorithms, revealing close similarities in their apparently different clustering approaches. Experiments on four legacy software systems provide insight into the behavior of well-known clustering algorithms and their characteristics in the software domain.

Abstract In the past years, research has been focused on biodegradable materials to replace petroleum based plastics for food packaging application. For this purpose, biopolymers are considered the most promising material because of their biodegradable nature and long shelf life properties like resistance to chemical or enzymatic reactions. Starch is renewable, cheap, and abundantly available biopolymer. However, the intermolecular forces and hydrogen bonds in starch resist it to be processed as a thermoplastic material. To overcome this issue, different plasticizers are used to have deformable thermoplastic material called thermoplastic starches (TPSs). A plasticizer enhances the flexibility, the process stability of starch below the degradation temperature. Plasticizer lowers the glass transition temperature ( T g ). TPS is very promising among the biobased materials available for the production of biodegradable plastic. TPS have some limitations; bad mechanical properties and water sensitivity. Starch absorbs water under higher relative humidity. This work will provide an outline about the research that has been done on TPS during last 15 years as biodegradable food packaging material. Practical Applications The basic role of food packaging material is to make it cost effective that satisfies industry requirements and consumer desires, and provide protection from three major classes of external influences: chemical, biological, and physical, e.g., such as exposure to gases, barrier to microorganisms, or from mechanical damage, respectively. These external influences may damage the quality of the food and shelf life. For this motive, starch has become the most preferred option among the verified classes of synthetic and natural materials. Retrogradation of starch chains in presence of water make it impossible to be use as packaging material. To overcome this issue, Starch has been plasticized with water and low molecular weight additive that can interact with its backbone by hydrogen bonding to produce thermoplastic starch (TPS). The objective of this review is to summarize numerous studies related to interaction of plasticizers and starch for the production of biodegradable TPS food packaging materials.

To minimize the detrimental effects of contaminated water, technology-based smart treatment processes are prerequisite for sustainable supply of drinking water. Nano-sized metal oxides are the best choice futuristic adsorbents for the removal of water toxins as such materials are associated with the characteristics of simplicity, versatility, efficacy and high surface reactivity. In this study, we describe a nanostructured ZnO adsorbent, which displays remarkable efficiency toward the removal of widely used azo dyes, methyl orange (MO) and amaranth (AM), from aqueous systems. The ZnO nanoparticles (ZnO-NPs) were prepared by simple co-precipitation method, and the structural morphology of the as-prepared NPs was revealed by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform IR (FTIR) and Brunauer–Emmett–Teller (BET). After complementary characterization, as-prepared ZnO-NPs were further used as adsorbent for the removal of toxic azo dyes (MO and AM) from water. The results revealed that an amount of 0.3 g ZnO-NPs showed maximum removal efficiency of each dye (40 ppm) at pH 6. It was further confirmed that the adsorption of both dyes on ZnO-NPs strongly followed the Langmuir model whereas the kinetics studies revealed that each adsorption process was pseudo second order. Moreover, the findings suggested that R-SO3– groups were active sites and the electrostatic attraction between the dyes (MO–, AM–) and ZnO-NPs+ may be the prime adsorption mechanism of designated removal systems.