Various concentrations of Mg-doped ZnO nanorods (NRs) were prepared using co-precipitation technique. The objective of this study was to improve the photocatalytic properties of ZnO. The effect of Mg ...doping on the structure, phase constitution, functional groups presence, optical properties, elemental composition, surface morphology and microstructure of ZnO was evaluated with XRD, FTIR, UV–Vis spectrophotometer, EDS, and HR-TEM, respectively. Optical absorption spectra obtained from the prepared samples showed evidence of blueshift upon doping. XRD results revealed hexagonal wurtzite phase of nanocomposite with a gradual decrease in crystallite size with Mg addition. PL spectroscopy showed trapping efficiency and migration of charge carriers with electron–hole recombination behavior, while HR-TEM estimated interlayer d-spacing. The presence of chemical bonding, vibration modes and functional groups at the interface of ZnO was revealed by FTIR and Raman spectra. In this study, photocatalytic, sonocatalytic and sonophotocatalytic performance of prepared NRs was systematically investigated by degrading a mixture of methylene blue and ciprofloxacin (MBCF). Experimental results suggested that improved degradation performance was shown by Mg-doped ZnO NRs. We believe that the product synthesized in this study will prove to be a beneficial and promising photocatalyst for wastewater treatment. Conclusively, Mg-doped ZnO exhibited substantial (
p
< 0.05) efficacy against gram-negative (G-ve) as compared to gram-positive (G+ve) bacteria. In silico molecular docking studies of Mg-doped ZnO NRs against DHFR (binding score: − 7.518 kcal/mol), DHPS (binding score: − 6.973 kcal/mol) and FabH (− 6.548 kcal/mol) of
E. coli
predicted inhibition of given enzymes as possible mechanism behind their bactericidal activity.
The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)–based computer ...vision techniques.
Caries, alveolar bone recession, and interradicular radiolucencies were labeled on 206 digital PRs by 3 specialists (2 oral pathologists and 1 endodontist). The PRs were divided into “Training and Validation” and “Test” data sets consisting of 176 and 30 PRs, respectively. Multiple transformations of image data were used as input to deep neural networks during training. Outcomes of existing and purpose-built DL architectures were compared to identify the most suitable architecture for automated analysis.
The U-Net architecture and its variant significantly outperformed Xnet and SegNet in all metrics. The overall best performing architecture on the validation data set was “U-Net+Densenet121” (mean intersection over union mIoU = 0.501; Dice coefficient = 0.569). Performance of all architectures degraded on the “Test” data set; “U-Net” delivered the best performance (mIoU = 0.402; Dice coefficient = 0.453). Interradicular radiolucencies were the most difficult to segment.
DL has potential for automated analysis of PRs but warrants further research. Among existing off-the-shelf architectures, U-Net and its variants delivered the best performance. Further performance gains can be obtained via purpose-built architectures and a larger multicentric cohort.
This study presents geochemical, petrophysical and seismic interpretation and modeling data of source and reservoir rocks of Eocene and Paleocene sequences of Balkassar oilfield, Potwar Basin, ...Pakistan. Integrated approaches were applied to evaluate structure and various rocks i.e., Patala, Lockhart and Chorgali formations. Grid of eleven seismic lines, well logs, well tops and ten fresh rock cutting samples were used. TOC content of Patala Formation ranges between 0.25 − 1.16 wt. %, with S2 values 0.11-0.63 mg HC/g rock, thus indicating poor to fair hydrocarbon potentialwhich issupported by no distinct presence of hydrocarbon peaks in pyrolysis-GC pyrograms. Petrographic analysis showed dominant presence of vitrinitic Type III and inertinitic Type IV kerogen phytoclasts with minor amount of AOM. High abundance of reworked vitrinite and inertinite phytoclasts appear to have influenced the vitrinite reflectance data. Basin modeling predicts Lockhart Formation began oil generation at 10.30Ma age at depth 1557 m and reached peak generation at 8.43Ma age at depth 1802 m. Patala formation began oil generation at 10.56Ma age at depth 1472 m and reached its peak generation at 8.43Ma age at depth of 1768 m. 3D horizon mapping depicts that Chorgali formation is uniformly distributed and faulted from northwest corner.
With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and ...interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.
Display omitted
•CO2 removal from NG using ionic liquid and dual mixed refrigerant–based liquefaction.•Tetracyanoborate functionalized ionic liquid led to ≥99 wt% CH4 recovery and purity.•ANN based ...optimization to remove CO2 from NG lead to specific load 0.129 kWh/kg NG.•5 MRs were found as optimized with energy 0.5 kWh/kgLNG and 47.1% exergy efficiency.•Proposed process shows high exergy stability 0.97 and environmental benign index 6.4.•TAC savings of 52–63% of proposed process compared to base case designs.
In this study, less viscous 1–hexyl–3–methylimidazolium tetracyanoborate is used to capture CO2 from the natural gas. The obtained sweet gas was liquefied through dual mixed refrigerant technology. Energy,exergy,environment, exergo–environment, and economic analysis were performed to estimate the feasibility of the process. Energy of CO2 capture process was optimized through artificial neural network using feed forward model of 2*10*3 structural design. The results revealed that the carbon capture process provides 82.1 % of exergy efficiency while MEA–based process has 58 %. The NG liquefaction process was optimized based on three case studies using 3, 4, and 5 combinations of mixed refrigerants (MRs) to minimize the specific energy consumption (SEC). The results indicated that the combination of 5 MRs provide a least SEC of 9.6 kW/kmol of LNG. Furthermore, proposed process shows less specific CO2 emissions of 0.91 kg of CO2/kmol with a carbon capture rate of ≥99 %. Exergo–environment analysis reveals that the proposed process is an environmentally benign process in terms of environmental effectiveness and exergy stability factors. Economic analysis of this process provides total cost savings of 62.7 %, 55.37 %, and 52.77 % relative to MEA in combination of 3, 4 and 5 MRs, respectively.
Cretaceous and Jurassic deposits in the sub-surface of Ahmedpur East Oilfield, Punjab Platform, Pakistan, have been geochemically analysed. Source rock potential of three formations namely Mughal Kot ...and Goru (Cretaceous) and Samana Suk (Jurassic) located in Middle Indus Basin have been investigated. The formations were investigated using total organic carbon and Rock-Eval pyrolysis analysis measurements. Analysis were performed on fourteen samples from wells, having TOC 0.57–1.65 wt.%, the S2 values of the sample shows poor to fair generative potential and lies between 0.25–2.59 mg HC/g rock, HI values are generally low 37–150 mg HC/g TOC and Tmax values ranges from 417–453 °C. The analysis shows Mughal Kot and Samana Suk formations samples have entered maturity window zone, however Goru Formation samples lies in immature zones, shown by hydrogen index vs Tmax plot. The samples from Mughal Kot and Goru formations have kerogen type III with poor generative potential, however Samana Suk Formation have fair generative potential having kerogen type III indicated by S2 vs TOC plot, hence from the results some minor gas could be expected to have been generated by Samana Suk Formation. Targeting these formations as a source rock to expel hydrocarbons involves high risk and exploration of such reserves may not be beneficial in terms of economic value.
This paper reports numerical modeling of perovskite solar cell which has been knotted with Distributed Bragg Reflector pairs to extract high energy efficiency. The geometry of the proposed cells is ...simulated with three different kinds of perovskite materials including CH3NH3PbI3, CH3NH3PbBr3, and CH3NH3SnI3. The toxic perovskite material based on Lead iodide and lead bromide appears to be more efficient as compared to non-toxic perovskite material. The executed simulated photovoltaic parameters with the highest efficient structure are open circuit voltage = 1.409 (V), short circuit current density = 24.09 mA/cm2, fill factor = 86.18%, and efficiency = 24.38%. Moreover, a comparison of the current study with different kinds of structures has been made and surprisingly our novel geometry holds enhanced performance parameters that are featured with back reflector pairs (Si/SiO2). The applied numerical approach and presented designing effort of geometry are beneficial to obtain results that have the potential to address problems with less efficient thin-film solar cells.
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-methylimidazoliumHmim 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 hexafluorophosphate BmimPf6 and 1-butyl-3-methylimidazolium methyl sulfate BmimMeSO4. 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 Hmimtcm based process has lower total exergy destruction of 7.49 × 103 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 Hmimtcm-based CO2 integrated with liquefaction technology offers 50.7%, 74.4%, and 85.8% of total annualized cost (TAC) savings compared with the MEA-,BmimPf6-, and BmimMeSO4, respectively. Hence, Hmimtcm for CO2 removal and integration with liquefaction process will incur unit cost based on the total annualized cost to be $2.2 × 104/kmol of purified NG.
•CO2 removal from natural gas integrated with single mixed refrigerant.•Tricyanomethanide functionalized ionic liquid led to 99 wt% CH4 recovery.•Hmimtcm-based IL offers Specific thermal load of 0.43 kWh/kg natural gas.•Unit cost for upgrading and liquefaction processes to be $ 2 × 104/kmol of NG.•$ 2.2 × 104/kmol of purified NG rich in CH4.
This study investigates the causal relationship between renewable energy sources and clean environmental economic growth among South Asian economies. This study comprises the panel data sets for ...eight (8) South Asian countries, and data start from 2003 to 2017. This study implies a Hausman test to identify which particular tests are more suitable and selected a fixed effect test and granger causality test for effective analysis perspective. Moreover, this study further relies on the panel vector error correction model (PVECM) test to suggest for long-run relationship existence among variables. Furthermore, the evaluation of the panel and the dynamic ordinary least squares regression shows that the production of renewable energy has compelled an effect on economic growth. While other sources of energy for instance, hydropower, geothermal, wind, and solar, have valuable and considerable influence on the economic growth of South Asian economies. The results reveal with these remarks the existence of positive associations among productions of renewable energies, energy dependence, and gross domestic product per capita. The obtained results reveal that renewable energy sources show a momentous effect on the economic growth of South Asian economies.
Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a breakthrough technology. In a nutshell, IoT is the integration of devices and data such that processes are ...automated and centralized to a certain extent. IoT is revolutionizing the way business is done and is transforming society as a whole. As this technology advances further, the need to exploit detection and weakness awareness increases to prevent unauthorized access to critical resources and business functions, thereby rendering the system unavailable. Denial of Service (DoS) and Distributed DoS attacks are all too common. In this paper, we propose a Protocol Based Deep Intrusion Detection (PB-DID) architecture, in which we created a data-set of packets from IoT traffic by comparing features from the UNSWNB15 and Bot-IoT data-sets based on flow and Transmission Control Protocol (TCP). We classify non-anomalous, DoS, and DDoS traffic uniquely by taking care of the problems like imbalanced and over-fitting. We have achieved a classification accuracy of 96.3% by using deep learning (DL) technique.
PDF
