Showing papers by "Ghulam Ishaq Khan Institute of Engineering Sciences and Technology published in 2021"

Nexus between agro-ecological efficiency and carbon emission transfer: evidence from China

Abstract: The economy of China is growing rapidly With this overwhelming growth, the country is experiencing a higher level of carbon emissions Amid this backdrop, China is under immense pressure to reduce carbon emissions up to a sustainable level This study adapted 31 provincial panel data from 2007 to 2017 using factor analysis system SBM-undesirable model to calculate the agro-ecological output of each province respectively and used a carbon transfer network impact analysis panel to calculate ecological performance impacts Results show that (1) overall agro-ecological efficiency in China shows an upward trend but regional differences are evident The efficiency in the eastern region is higher than that in the central and western regions but the extent of informatization in the central region is higher than that in the western region (2) Informatization will significantly promote agro-ecological efficiency (3) Changes in agricultural planting structure, agricultural value-added per capita, employment of human capital in the agricultural sector, and agricultural scale management are also important factors affecting agro-ecological growth (4) China's amount of carbon transfer is growing year by year, and energy-intensive areas and heavy industry bases are undertaking carbon transfer from the eastern coastal regions; (5) Jiangsu, Henan, and Hebei (Hubei) have the highest centers between 2007 and 2012; (6) inter-provincial carbon transmission is concentrated mainly in the metal smelting and rolling processing industries as well as in the coal, heat, and supply industries

Air electrodes and related degradation mechanisms in solid oxide electrolysis and reversible solid oxide cells

Abstract: This paper reviews the existing literature on the degradation behaviour of different air electrodes used for solid oxide electrolysis cells (SOECs) and reversible solid oxide cells (RSOCs). It begins with a brief introduction to solid oxide cells (SOCs). An overview and degradation behaviour of different fuel electrodes and electrolyte materials during the SOEC operation are then provided briefly. The major focus of the current review is to understand air electrode degradation in detail. Therefore, the existing proposed mechanisms for air electrode delamination, and various studies reporting the delamination issue during SOEC operation are intensively reviewed. An introduction to RSOCs and the degradation issues for different air electrodes during RSOC tests are then discussed. Finally, mitigation strategies for delamination; recommendations for future degradation studies and some suggestions to develop more active and stable air electrodes for future SOEC and RSOC applications are presented.

Reinforcement Learning Assisted Impersonation Attack Detection in Device-to-Device Communications

Abstract: In device-to-device (D2D) communications, the channel gain between a transmitter and a receiver is difficult to predict due to channel variations. Hence, an attacker can easily perform an impersonation attack between two authentic D2D users. As a countermeasure, we propose a reinforcement learning-based technique that guarantees identification of the impersonator based on channel gains. To show the merit of our technique, we report its performance in terms of false alarm rate, miss-detection rate, and average error rate. The secret key generation rate is also determined under the impersonation attack based on physical layer security.

Photocatalytic degradation of crystal violet dye under sunlight by chitosan-encapsulated ternary metal selenide microspheres.

Abstract: Organic dyes that are extensively released in wastewater from various industries remain the priority concern in the modern world. Therefore, a novel catalyst, bismuth–iron selenide, was prepared through the solvothermal process for photocatalytic degradation of a carcinogenic crystal violet dye. The catalyst was supported with chitosan to form iron–bismuth selenide–chitosan microspheres (BISe-CM). The synthesized catalyst was composed of iron, bismuth, and selenium in a definite proportion based on EDX analysis. FTIR analysis confirmed the synthesis of BISe-CM from characteristic bands of metal selenium bond as well as the typical bands of chitosan. SEM analysis illustrated the average diameter of the barren catalyst to be 54.8 nm, while the average size of the microspheres was 982.5 um. The BISe-CM has the surface of a pore with an average size of 0.5 um. XRD analysis revealed that the synthesized catalyst was composed of Fe3Se4 and Bi2Se3. The prepared catalyst showed better degradation efficiency for crystal violet dye at optimized conditions under solar irradiation. Employing 0.2 g of BISe-CM resulted in complete degradation for 30 ppm of crystal violet dye in 150 min at pH 8.0. The reusability of the catalyst up to four consecutive times makes it a more attractive and practical candidate. Moreover, the catalyst followed pseudo-first-order kinetics in the decontamination of crystal violet. Conclusively, the novel photocatalyst showed the best decolorizing property of crystal violet under sunlight irradiation and could be a suitable alternative for dye decontamination from wastewater.

Degradation of Congo red dye using ternary metal selenide-chitosan microspheres as robust and reusable catalysts

Abstract: Water pollution is a threatening environmental concern these days, which requires serious attention Water is contaminated by the heavy discharge of industrial effluents containing organic wastes Herein, this research work focused on the demand for overcoming water pollution utilizing clean and renewable energy (solar light irradiations) sources for photocatalytic degradation of Congo red (CR) dye with the help of ternary metal selenide-chitosan microspheres (ZBiSe-CM) First, zinc–bismuth-selenide nanoparticles (ZBiSe-NPs) were successfully synthesized via the solvothermal process and then supported with chitosan to prevent leaching of the catalyst SEM micrographs showed the average size of newly synthesized nanocomposites catalysts was 309 nm The average size of the ZBiSe-CM microspheres was calculated as 812 um with a spherical shape and a porous surface The presence of prominent peaks in the EDX spectrum for bismuth, selenium, and zinc confirmed the synthesis of the nanoparticles XRD analysis demonstrated the calculated crystallite size of ZBiSe-NPs was 2704 nm The photocatalytic behavior of the catalyst was studied at optimized experimental conditions The ZBiSe-NPs showed high photocatalytic degradation efficiency (up to 9963%) for 40 ppm concentration of CR at catalyst dosage 0225 g, pH 80, and temperature 36–38 °C for 2 h of solar light illumination Photodegradation of CR dye in the presence of ZBiSe-CM follows pseudo-first-order kinetics and the ZBiSe-CM degraded CR dye in five consecutive cycles with high decontamination efficiency The newly synthesized ternary metal selenide-chitosan microspheres could be used for the decontamination of dyes from industrial wastewater

Social Media for Knowledge Acquisition and Dissemination: The Impact of the COVID-19 Pandemic on Collaborative Learning Driven Social Media Adoption

Abstract: During the COVID-19 outbreak, educational institutions were closed, and students worldwide were confined to their homes. In an educational environment, students depend on collaborative learning (CL) to improve their learning performance. This study aimed to increase the understanding of social media adoption among students during the COVID-19 pandemic for the purpose of CL. Social media provides a learning platform that enables students to easily communicate with their peers and subject specialists, and is conducive to students' CL. This study addresses the key concept of CL during the COVID-19 pandemic by assessing social media use among students in higher education. The relationship between social media use and students' performance is crucial to understanding the role of social media during a pandemic. This study is based on constructivism theory and the technology acceptance model. Structural equation modeling was used to analyze the conceptual model using SmartPLS. The research findings indicate that social media plays an important role during the pandemic because it provides opportunities for students to enhance CL under the aforementioned situations. This study makes noteworthy theoretical and practical contributions.

Wideband Microwave Absorber Comprising Metallic Split-Ring Resonators Surrounded With E-Shaped Fractal Metamaterial

Abstract: A specially designed metallic E-shaped fractal-based perfect metamaterial absorber (PMA) with fairly wideband absorptivity in the K- and Ka-bands of the microwave regime was investigated. The PMA top surface is comprised of square-shaped split-ring resonators (SRRs) surrounded with the stated fractal design. The absorptivity of PMA was analyzed in the range of 20 – 30 GHz for the normal and oblique incidence of waves. Both the transverse electric (TE) and transverse magnetic (TM) modes were taken up to observe the robustness of the proposed design. It was observed that the fractal resonators exhibit capacitive effect at low frequencies, whereas the SRRs manifest capacitive effect at higher frequencies. The simulation and measured results were found to be in fairly good agreement. It is expected that the proposed design of PMA would be useful for 5G communication applications.

Fabrication of rGO-CuO and/or Ag2O nanoparticles incorporated polyvinyl acetate based mixed matrix membranes for the removal of Cr6+ from anti-corrosive paint industrial wastewater

Abstract: Herein we report the fabrication of ternary system mixed matrix membranes based on polyvinyl acetate (PVAc) incorporated reduced graphene oxide (rGO) and CuO or Ag2O nanoparticles as filler in different weight ratios via non-solvent induced phase inversion separation method. The fabricated membranes were characterized for chemical composition porosity, pore distribution, surface roughness, thickness, water contact angle and mechanical strength. Scanning electron microscopy and atomic force microscopy analyses revealed that pure PVAc exhibited smooth and uniform surface while PVAc/rGO/Ag2O and PVAc/rGO/ACuO membranes possessed rough and uneven topography and higher surface porosity. PVAc/rGO/CuO and PVAc/rGo/Ag2O membranes exhibited total permeate flux of 1.60 × 10–2 g/m2 s and 1.28 × 10–2 g/m2 s corresponding to net removal of 99.9% and 98.0% respectively from model wastewater of Cr6+ under the optimized experimental conditions. Interestingly, under these optimized conditions, PVAc/rGO/CuO membrane achieved Cr6+ removal of 99.9% from anti-corrosive paint industrial wastewater (as real sample) with a flux of 1.90 × 10-2 g/m2 s, which was much higher than many state of the art membranes. The uptake of Cr6+ from real solution was confirmed by EDX and AFM analysis of the post-separation membrane. The newly designed PVAc/rGO/CuO membrane, attributed to cost-effectiveness, environmental greenness, ease of syntheisis and high efficiency, can be envisioned of potential applications for the remediation of Cr6+ and other similar pollutants from real industrial wastewater.

On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets

Abstract: Frequent itemsets mining is an active research problem in the domain of data mining and knowledge discovery. With the advances in database technology and an exponential increase in data to be stored, there is a need for efficient approaches that can quickly extract useful information from such large datasets. Frequent Itemsets (FIs) mining is a data mining task to find itemsets in a transactional database which occur together above a certain frequency. Finding these FIs usually requires multiple passes over the databases; therefore, making efficient algorithms crucial for mining FIs. This work presents a graph-based approach for representing a complete transactional database. The proposed graph-based representation enables the storing of all relevant information (for extracting FIs) of the database in one pass. Later, an algorithm that extracts the FIs from the graph-based structure is presented. Experimental results are reported comparing the proposed approach with 17 related FIs mining methods using six benchmark datasets. Results show that the proposed approach performs better than others in terms of time.

Optimality study of China’s crude oil imports through China Pakistan economic corridor using fuzzy TOPSIS and Cost-Benefit analysis

Abstract: With China's increasing role in the international political arena, it needs to focus on the diversification of its energy import routes China is heavily reliant on importing fossil fuels for its industrial and domestic needs, and its traditional trade routes are vulnerable to political, logistical, and security disadvantages China has been importing from American, African, and Middle Eastern countries through Myanmar's and Eastern China's seaports The China Pakistan Economic Corridor (CPEC), if utilized for the crude oil imports, can become a viable alternative, and may result in the reduction of the vulnerabilities faced by existing routes To assess this proposition, this study has focused on identifying China’s most optimal route for crude oil import through a multi-criteria decision making (MCDM) technique; Fuzzy-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) Furthermore, to confirm the economic viability of the CPEC route, its Cost-Benefit Analysis (CBA) has also been performed Besides, Fuzzy TOPSIS has been used to identify the best transport mechanism from the CPEC’s seaports till Western China MCDM criteria in both cases include time, economic costs, energy consumption, environmental emissions, and security risks Results indicate that the maritime route, passing through Myanmar is the most optimal route, followed by the CPEC route Both these routes are prioritised over the traditional route which passes through Eastern China seaports

Moving toward green mobility: overview and analysis of electric vehicle selection, Pakistan a case in point

Abstract: Environmental concerns are on the rise around the world, as carbon emissions are increasing rapidly and which is one of the leading causes of global warming. These emissions come from different sectors but the biggest contribution comes from the transport sector. As countermeasures, countries are shifting toward electric vehicles, which are much greener modes of transportation. Currently, different types of electric vehicles are available in the market. The selection of a viable type of electric vehicle for a developing country like Pakistan needs proper evaluation. In this study, two techniques are used. Firstly, fuzzy Strength, Weaknesses, Opportunities, and Threats (SWOT) approach is used for the selection of a suitable vehicle for the future market of Pakistan. Secondly, fuzzy linear programming is used to check the viability of electric vehicles in the current market scenario. Using these techniques, hydrogen fuel cell vehicle is selected as the most advantageous vehicle for Pakistan’s future market and the hybrid electric vehicle comes out as the best alternative for the current market. The techniques and the case study of Pakistan are the main novelty of this study, as no literature exists on electric vehicles with these methods on Pakistan’s perspective. Based on the results, different recommendations are given to the policymakers of Pakistan. The recommended policies could make electric vehicles a prime competitor to the combustion vehicles in the current and future markets.

An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment

Abstract: Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

Social Phenomena and Fog Computing Networks: A Novel Perspective for Future Networks

Abstract: Fog computing is an emerging technology that aims at reducing the load on cloud data centers by migrating some computation and storage toward end-users. It leverages the intermediate servers for local processing and storage while making it possible to offload part of the computation and storage to the cloud. Inspired by the benefits of fog computing, we present a novel paradigm that considers the context of social phenomena. Online and off-line human interactions and the mobile social network's relentless growth allowed real-world data and created users' traces. We categorize social phenomena into two main groups to integrate with fog computing from social interactions' continuous development. In this regard, the first contribution addresses the social relationship between the end-users and fog nodes based on personal benefits. The social relationship considers trust, reciprocity, incentives, and selfishness mechanisms. The second contribution describes the group-based social behavior, i.e., centrality, community, and colocation in fog computing networks (FCNs). We also discuss the impact of social phenomena on FCNs in network performance, resource allocations, security, and privacy. We present open challenges and highlight future directions on social perception to encourage follow-up work.

Green gap in GaN-based light-emitting diodes: in perspective

Abstract: Significant progress has been made in the advancement of light-emitting devices in both the blue and the red parts of the emission spectrum. However, the quantum efficiency of green light-emitting ...

Polarization-insensitive dual-wideband fractal meta-absorber for terahertz applications.

Abstract: Terahertz (THz) metamaterial absorbers have realized a prodigious reputation due to the limitation of natural absorbing materials in this range. Getting wideband absorption characteristics is challenging and arduous, especially in the THz band. Self-similar repeated fractal elements offer a promising solution to attain broadband absorption response due to their inherent multiple resonance characteristics. Therefore, by captivating the advantage of fractal geometry, we proposed a dual and wideband meta-absorber operating in the THz regime. The metamaterial absorber design comprises the assembly of self-similar square-shaped blocks arranged in a specific pattern to construct the fractal geometry. The proposed THz absorber demonstrates 90% absorption under normal incident waves for two operating bands from 9.5–10.55 THz and 12.3–13.35 THz. The suggested metamaterial absorber also shows good and stable absorption responses under different oblique incidence angles for transverse electric (TE) and transverse magnetic (TM) wave polarization. Moreover, this absorber manifests over 85% absorptivity in its entire operating range (9–14 THz) under the incidence angle of 60° and 70° for TM mode. Furthermore, it gives a polarization-insensitive behavior under the effect of different polarization angles. This kind of wideband absorber catches fascinating applications in THz detection, imaging, cloaking, and optoelectronic devices.

Suppressing the efficiency droop in AlGaN-based UVB LEDs.

Abstract: The optoelectronic properties of semiconducting aluminum gallium nitride (AlGaN)-based ultraviolet-B (UVB) light-emitting diodes (LEDs) are crucial for real-world medical applications such as cancer therapy and immunotherapy However, the performance of AlGaN-based UVB LED devices is still poor due to the low hole injection efficiency Therefore, we have numerically investigated the performance of AlGaN-based UVB LEDs for the suppression of efficiency droop as well as for the enhancement of hole injection in the multiquantum wells (MQWs) The influence of the undoped (ud)-AlGaN final quantum barrier (FQB), as well as the Mg-doped multiquantum barrier electron blocking layer (p-MQB EBL), on the efficiency droop has been focused on specifically To evaluate the performance of the proposed device, we have compared its internal quantum efficiency (IQE), carrier concentration, energy band diagram, and radiative recombination rate with the conventional device structure Furthermore, the influence of Al composition in the Al-graded p-AlGaN hole source layer (HSL) on the operating voltages of the proposed UVB LEDs was considered The simulation results suggest that our proposed structure has a high peak efficiency and much lower efficiency droop as compared to the reference structure (conventional) Ultimately, the radiative recombination rate in the MQWs of the proposed UVB LED-N structure has increased up to ∼73%, which is attributed to the enhanced level of electron and hole concentrations by ∼64% and 13%, respectively, in the active region Finally, a high efficiency droop of up to ∼42% in RLED has been successfully suppressed, to ∼7%, by using the optimized ud-AlGaN FQB and the p-MQB EBL, as well as introducing Al-graded p-AlGaN HSL in the proposed UVB LED-N structure

Sustainable supplier selection for the cold supply chain (CSC) in the context of a developing country

Abstract: Cold supply chain (CSC) is a process that involves temperature-controlled activities ranging from the acquisition of raw materials and down to the end consumers. A sustainable cold chain supplier is the one that incorporates sustainable practices in its complete cycle of operations. This is to ensure keeping the products from going to waste, especially in the case of a developing country. To identify the driving factors towards the sustainable cold chain supplier, this study utilizes the interpretive structural modelling (ISM) approach in the first phase. Fifteen various sustainability factors were analyzed and the “utilization of renewable resources” factor concluded to be the most important driving factor. By implementing renewable resources, a supplier can be able to convert its manufacturing processes and services to sustainable assets. The second phase of this study conducts the selection of cold chain suppliers in the context of Pakistan. For this purpose, fuzzy VIKOR, a multi-criteria decision-making (MCDM) technique is incorporated to analyze eight suppliers based on fifteen distinct criteria. The results concluded Mitchell foods to be the most economically, environmentally and socially sustainable suppliers in the context of Pakistan. This study recommends providing business-friendly incentives to suppliers like Mitchells and new investors who tend to keep their operations sustainable by adopting renewable resources. Furthermore, the relaxation of taxes and creating job employability by working with sustainable suppliers can contribute positively towards economic growth and the overall society. The study holds novelty in the area of cold chain supplier selection for Pakistan by utilizing a novel approach in the form of ISM and fuzzy VIKOR techniques, thus forming a major application of this research study.