| 11. |
- Azevedo, Flavio, et al.
(author)
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Social and moral psychology of COVID-19 across 69 countries
- 2023
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In: Scientific Data. - : NATURE PORTFOLIO. - 2052-4463. ; 10:1
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Journal article (peer-reviewed)abstract
- The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.
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| 12. |
- Kazmi, Bilal, et al.
(author)
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Thermodynamic and economic assessment of cyano functionalized anion based ionic liquid for CO2 removal from natural gas integrated with, single mixed refrigerant liquefaction process for clean energy
- 2022
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In: Energy. - : Pergamon Press. - 0360-5442 .- 1873-6785. ; 239
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Journal article (peer-reviewed)abstract
- The study proposes a novel integrated process in which ionic liquid is utilized to control carbon dioxide (CO2) emissions from the natural gas combined with a single mixed refrigerant-based liquefaction process to assist safe transportation over long distances providing a sustainable and cleaner energy. Commercially amines are utilized for CO2 sequestration, but amines entail energy-intensive regeneration with elevated process costs. The present study offers a solvent screening mechanism based on important parameters such as heat of dissolution, viscosity, selectivity, working capacity, vapor pressure, corrosivity, and toxicity. The selected solvents' performance is computed by sensitivity analysis suggesting imidazolium-based cation 1-hexyl-3-methylimidazolium[Hmim] functionalized with tricyanomethanide(tcm) as anion a potential natural gas sweetening solvent in comparison with commercially used solvent monoethanoloamine(MEA), conventional ILs 1-butyl-3-methylimidazolium hexa-fluorophosphate [Bmim][Pf(6)] and 1-butyl-3-methylimidazolium methyl sulfate [Bmim][MeSO4]. The obtained sweet gas is liquefied using a single mixed refrigerant-based process providing 0.99 mol fraction of liquefied CH4 with less overall specific compression power requirement of 0.41 kW/kg of natural gas. Moreover, an exergy analysis demonstrates that the [Hmim][tcm] based process has lower total exergy destruction of 7.49 x 10(3) kW and is found to utilize less overall specific energy consumption 0.49 kWh/kg of NG in contrast to other studied solvents. Furthermore, a detailed economic analysis establishes [Hmim][tcm]-based CO2 integrated with liquefaction technology offers 50.7%, 74.4%, and 85.8% of total annualized cost (TAC) savings compared with the MEA-amim][Pf(6)]-, and [Bmim][MeSO4], respectively. Hence, [Hmim][tcm] for CO2 removal and integration with liquefaction process will incur unit cost based on the total annualized cost to be $2.2 x 10(4)/kmol of purified NG.
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| 13. |
- Sodhro, Ali Hassan, et al.
(author)
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Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes
- 2021
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In: IEEE Internet of Things Journal. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2327-4662. ; 8:12, s. 9664-9671
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Journal article (peer-reviewed)abstract
- The convergence of artificial intelligence (AI) and the Internet of Things (IoT) promotes energy-efficient communication in smart homes. Quality-of-Service (QoS) optimization during video streaming through wireless micro medical devices (WMMDs) in smart healthcare homes is the main purpose of this research. This article contributes in four distinct ways. First, to propose a novel lazy video transmission algorithm (LVTA). Second, a novel video transmission rate control algorithm (VTRCA) is proposed. Third, a novel cloud-based video transmission framework is developed. Fourth, the relationship between buffer size and performance indicators, i.e., peak-to-mean ratio (PMR), energy (i.e., encoding and transmission), and standard deviation, is investigated while comparing LVTA, VTRCA, and baseline approaches. The experimental results demonstrate that the reduction in encoding (32% and 35.4%) and transmission (37% and 39%) energy drains, PMR (5 and 4), and standard deviation (3 and 4 dB) for VTRCA and LVTA, respectively, is greater than that obtained by baseline during video streaming through WMMD.
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| 14. |
- Tariq, Imran, et al.
(author)
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Ameliorative delivery of docetaxel and curcumin using PEG decorated lipomers : A cutting-edge in-vitro/ in-vivo appraisal
- 2024
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In: Journal of Drug Delivery Science and Technology. - : Elsevier. - 1773-2247. ; 97
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Journal article (peer-reviewed)abstract
- The development of a PEG-decorated lipid-polymer hybrid system camouflaged with natural and synthetic chemotherapeutic moieties is an influential approach melding the biomimetic properties of long-circulating vesicles to utilize different mechanisms to dwindle the tumor growth. Therefore, a safe and efficient lipid-coated nano-particulate system (LCNPs) was proposed to investigate the in-vitro, ex-vivo and in-vivo demeanors of such amalgamation.Docetaxel loaded PLGA nanoparticles (DTX-NPs) were prepared by solvent evaporation while curcumin liposomes were mapped out using the film hydration method. Physicochemical characterizations were executed in terms of size, surface morphology, differential scanning calorimetry (DSC) and fourier-transform infrared spectroscopy (FTIR). In-vitro cytotoxicity was effectuated using MCF-7 cell line. Hemolysis, erythrocyte aggregation and acute in-vivo toxicity were carried out to establish the biocompatibility. The hydrodynamic diameters of samples were in the nano-range and corresponded to the findings of scanning electron microscopy (SEM) and atomic force microscopy (AFM). The absence of distinctive peaks of DTX-NPs in FTIR and DSC analysis of LCNPs depicts the shielding of the lipid bilayer over the nanoparticle. Cytotoxicity induced by the LCNPs represented the efficient delivery of cargo to the tumor cells. LCNPs also exhibited the least toxicity under ex-vivo and in-vivo circumstances compared to free drugs. Additionally, histological studies showed no evidence of substantial necrosis. Additionally, histological studies showed no evidence of notable necrosis.
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| 15. |
- Van Bavel, Jay J., et al.
(author)
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National identity predicts public health support during a global pandemic
- 2022
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In: Nature Communications. - : Nature Portfolio. - 2041-1723. ; 13:1
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Journal article (peer-reviewed)abstract
- Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic. Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = -0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.
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| 16. |
- Abdullah, Gamil M. S., et al.
(author)
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Effect of titanium dioxide as nanomaterials on mechanical and durability properties of rubberised concrete by applying RSM modelling and optimizations
- 2024
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In: Frontiers in Materials. - : Frontiers Media SA. - 2296-8016. ; 11
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Journal article (peer-reviewed)abstract
- The use of rubber aggregates derived from discarded rubber tyres in concrete is a pioneering approach to replacing natural aggregate (NA) and promoting sustainable building practices. Recycled aggregate in concrete serves the dual purpose of alleviating the accumulation of discarded rubber tyres on the planet and providing a more sustainable alternative to decreasing natural aggregate. Due to fact that the crumb rubber (CR) decreases the strength when used in concrete, incorporating titanium dioxide (TiO2) as a nanomaterial to counteract the decrease in strength of crumb rubber concrete is a potential solution. Response Surface Methodology was developed to generate sixteen RUNs which contains different mix design by providing two input parameters like TiO2 at 1%, 1.5%, and 2% by cement weight and CR at 10%, 20%, and 30% as substitutions for volume of sand. These mixtures underwent testing for 28 days to evaluate their mechanical, deformation, and durability properties. Moreover, the compressive strength, tensile strength, flexural strength and elastic modulus were recorded by 51.40 MPa, 4.47 MPa, 5.91 MPa, and 40.15 GPa when 1.5% TiO2 and 10% CR were added in rubberised concrete after 28 days respectively. Furthermore, the incorporation of TiO2 led to reduced drying shrinkage and sorptivity in rubberized concrete, especially with increased TiO2 content. The study highlights that TiO2 inclusion refines pore size and densifies the interface between cement matrix and aggregate in hardened rubberized concrete. This transformative effect results in rubberized concrete demonstrating a commendable compressive strength comparable to normal concrete.
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| 17. |
- Abid, Nosheen, 1993-, et al.
(author)
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Burnt Forest Estimation from Sentinel-2 Imagery of Australia using Unsupervised Deep Learning
- 2021
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In: Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA). - : IEEE. ; , s. 74-81
-
Conference paper (peer-reviewed)abstract
- Massive wildfires not only in Australia, but also worldwide are burning millions of hectares of forests and green land affecting the social, ecological, and economical situation. Widely used indices-based threshold methods like Normalized Burned Ratio (NBR) require a huge amount of data preprocessing and are specific to the data capturing source. State-of-the-art deep learning models, on the other hand, are supervised and require domain experts knowledge for labeling the data in huge quantity. These limitations make the existing models difficult to be adaptable to new variations in the data and capturing sources. In this work, we have proposed an unsupervised deep learning based architecture to map the burnt regions of forests by learning features progressively. The model considers small patches of satellite imagery and classifies them into burnt and not burnt. These small patches are concatenated into binary masks to segment out the burnt region of the forests. The proposed system is composed of two modules: 1) a state-of-the-art deep learning architecture for feature extraction and 2) a clustering algorithm for the generation of pseudo labels to train the deep learning architecture. The proposed method is capable of learning the features progressively in an unsupervised fashion from the data with pseudo labels, reducing the exhausting efforts of data labeling that requires expert knowledge. We have used the realtime data of Sentinel-2 for training the model and mapping the burnt regions. The obtained F1-Score of 0.87 demonstrates the effectiveness of the proposed model.
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| 18. |
- Ahmad, Iftikhar, et al.
(author)
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Using algorithmic trading to analyze short term profitability of Bitcoin
- 2021
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In: PeerJ Computer Science. - : PeerJ Publishing. - 2376-5992. ; 7
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Journal article (peer-reviewed)abstract
- Cryptocurrencies such as Bitcoin (BTC) have seen a surge in value in the recent past and appeared as a useful investment opportunity for traders. However, their short term profitability using algorithmic trading strategies remains unanswered. In this work, we focus on the short term profitability of BTC against the euro and the yen for an eight-year period using seven trading algorithms over trading periods of length 15 and 30 days. We use the classical buy and hold (BH) as a benchmark strategy. Rather surprisingly, we found that on average, the yen is more profitable than BTC and the euro; however the answer also depends on the choice of algorithm. Reservation price algorithms result in 7.5% and 10% of average returns over 15 and 30 days respectively which is the highest for all the algorithms for the three assets. For BTC, all algorithms outperform the BH strategy. We also analyze the effect of transaction fee on the profitability of algorithms for BTC and observe that for trading period of length 15 no trading strategy is profitable for BTC. For trading period of length 30, only two strategies are profitable.
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| 19. |
- Ahmed, Ammar, et al.
(author)
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Enhancing wrist abnormality detection with YOLO : Analysis of state-of-the-art single-stage detection models
- 2024
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In: Biomedical Signal Processing and Control. - : Elsevier. - 1746-8094 .- 1746-8108. ; 93
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Journal article (peer-reviewed)abstract
- Diagnosing and treating abnormalities in the wrist, specifically distal radius, and ulna fractures, is a crucial concern among children, adolescents, and young adults, with a higher incidence rate during puberty. However, the scarcity of radiologists and the lack of specialized training among medical professionals pose a significant risk to patient care. This problem is further exacerbated by the rising number of imaging studies and limited access to specialist reporting in certain regions. This highlights the need for innovative solutions to improve the diagnosis and treatment of wrist abnormalities. Automated wrist fracture detection using object detection has shown potential, but current studies mainly use two-stage detection methods with limited evidence for single-stage effectiveness. This study employs state-of-the-art single-stage deep neural network-based detection models YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through extensive experimentation, we found that these YOLO models outperform the commonly used two-stage detection algorithm, Faster R-CNN, in fracture detection. Additionally, compound-scaled variants of each YOLO model were compared, with YOLOv8 m demonstrating a highest fracture detection sensitivity of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6 m achieved the highest sensitivity across all classes at 0.83. Meanwhile, YOLOv8x recorded the highest mAP of 0.77 for all classes on the GRAZPEDWRI-DX pediatric wrist dataset, highlighting the potential of single-stage models for enhancing pediatric wrist imaging.
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| 20. |
- Ahsan, Hajra, et al.
(author)
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Photocatalysis and adsorption kinetics of azo dyes by nanoparticles of nickel oxide and copper oxide and their nanocomposite in an aqueous medium
- 2022
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In: PeerJ. - : PeerJ. - 2167-8359. ; 10
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Journal article (peer-reviewed)abstract
- Azo dyes are recalcitrant organic pollutants present in textile industry effluents. Conventional treatment methods to remove them come with a range of disadvantages. Nanoparticles and their nanocomposites offer more efficient, less expensive and easy to handle wastewater treatment alternative. Methods. In this study, nanoparticles of nickel oxide (NiO-NPs), copper oxide (CuO-NPs) and their nanocomposite (NiO/CuO-NC) were synthesized using co- precipitation method. The functional groups present on the surface of synthesized nanomaterials were verified using Fourier-transform infrared spectroscopy (FTIR). Surface morphology was assessed using scanning electron microscopy (SEM) whereas purity, shape and size of the crystallite were determined using X-ray diffraction (XRD) technique. The potential of these nanomaterials to degrade three dyes i.e., Reactive Red-2 (RR-2), Reactive Black-5 (RB-5) and Orange II sodium salt (OII) azo dyes, was determined in an aqueous medium under visible light (photocatalysis). The photodegradation effectiveness of all nanomaterials was evaluated under different factors like nanomaterial dose (0.02-0.1 g 10 mL-1), concentration of dyes (20-100 mg L-1), and irradiation time (60-120 min). They were also assessed for their potential to adsorb RR-2 and OII dyes. Results. Results revealed that at optimum concentration (60 mgL-1) of RR-2, RB-5, and OII dyes, NiO-NPs degraded 90, 82 and 83%, CuO-NPs degraded 49, 34, and 44%, whereas the nanocomposite NiO/CuO-NC degraded 92, 93, and 96% of the said dyes respectively. The nanomaterials were categorized as the efficient degraders of the dyes in the order: NiO/CuO-NC > NiO-NPs > CuO-NPs. The highest degradation potential shown by the nanocomposite was attributed to its large surface area, small particles size, and quick reactions which were proved by advance analytical techniques. The equilibrium and kinetic adsorption of RR-2 and OII on NiO-NPs, CuO-NPs, and NiO/CuO-NC were well explained with Langmuir and Pseudo second order model, respectively (R2 ≥0.96). The maximum RR-2 adsorption (103 mg/g) was obtained with NiO/CuO-NC. It is concluded that nanocomposites are more efficient and promising for the dyes degradation from industrial wastewater as compared with dyes adsorption onto individual NPs. Thus, the nanocomposite NiO/CuO-NC can be an excellent candidate for photodegradation as well as the adsorption of the dyes in aqueous media.
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