| 1. |
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| 2. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 3. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 4. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
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| 6. |
- Iqbal, Javed, et al.
(författare)
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A Novel Single-Fed Dual-Band Dual-Circularly Polarized Dielectric Resonator Antenna for 5G Sub-6GHz Applications
- 2022
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 12:10
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Tidskriftsartikel (refereegranskat)abstract
- In this research article, a single-fed dual-band circular polarized (CP) dielectric resonator antenna (DRA) for dual-function communication, such as GPS and WLAN, was made. Initially, the proposed design process was initiated by designing a linearly polarized singly fed-DRA. To attain CP fields, the cross-shape conformal metal strip was optimized to excite the fundamental and the high-order mode in the two frequency bands. The metallic strip (parasitic) was utilized on top of the rectangular DRA to improve and widen the impedance and axial ratio (AR) bandwidth. This step led to a 2.73% improvement on the lower band and an impact of 6.5% on the upper band while on the other side a significant improvement was witnessed in the AR bandwidth in both frequency bands. A prototype was designed and fabricated in order to validate its operations. The measurement outcomes of the proposed antennas authenticated wideband impedance bandwidths of 6.4% and 25.26%, and 3-dB axial ratios (AR) of 21.26% and 27.82% respectively. The prototype is a decent candidate for a global positioning system (GPS) and wireless local area network (WLAN).
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| 7. |
- Javed, Rida, et al.
(författare)
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Enhancement mechanism of P dopant on atomically distributed FeN 4 P-C electrocatalyst over a wide pH range
- 2022
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Ingår i: Electrochimica Acta. - : Elsevier BV. - 0013-4686. ; 436
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Tidskriftsartikel (refereegranskat)abstract
- Heteroatom doping plays an essential role in improving the catalytic performance of electrocatalysts for oxygen reduction reaction (ORR). However, how to regulate heteroatom doping remains a significant challenge. This paper develops an efficient strategy by using a novel versatile chelating ligand to enhance P loading and expose more metal single Fe atom active sites of FeN4P-C catalyst. The electron distribution of active center is considerably changed by P doping, which significantly influences the catalytic ORR performance. The dopant P in the FeN4P-C catalyst induces a small number of d-electrons from t2g-orbitals around the Fermi level, making the interaction between Fe active site and O2 slightly more robust than in the FeN4[sbnd]C catalyst, as studied by DFT calculations. The as-prepared FeN4P-C catalyst exhibits excellent catalytic ORR activity in both acidic (with a half-wave potential of 0.760 V vs. RHE) and basic (with a half-wave potential of 0.885 V vs. RHE) conditions, which are superior to those of the commercial Pt/C (20 wt%) catalyst. Furthermore, this catalyst also demonstrates outstanding stability and good hydrogen peroxide and methanol tolerance. A Zinc-air battery(ZAB) assembled using the cathode catalyst has validated the high performance of this catalyst. This study provides an efficient method for generating well-defined single-atom active sites to improve catalytic performance and paves the way to identify coordinated single metal atom sites for electrocatalysis applications.
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| 8. |
- Javed, Sajid, et al.
(författare)
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Visual Object Tracking With Discriminative Filters and Siamese Networks: A Survey and Outlook
- 2023
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 45:5, s. 6552-6574
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Tidskriftsartikel (refereegranskat)abstract
- Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep Siamese Networks (SNs) have emerged as dominating tracking paradigms, which have led to significant progress. Following the rapid evolution of visual object tracking in the last decade, this survey presents a systematic and thorough review of more than 90 DCFs and Siamese trackers, based on results in nine tracking benchmarks. First, we present the background theory of both the DCF and Siamese tracking core formulations. Then, we distinguish and comprehensively review the shared as well as specific open research challenges in both these tracking paradigms. Furthermore, we thoroughly analyze the performance of DCF and Siamese trackers on nine benchmarks, covering different experimental aspects of visual tracking: datasets, evaluation metrics, performance, and speed comparisons. We finish the survey by presenting recommendations and suggestions for distinguished open challenges based on our analysis.
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| 9. |
- Khan, Muhammad Ahsan Iqbal, et al.
(författare)
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An Experimental and Comparative Performance Evaluation of a Hybrid Photovoltaic-Thermoelectric System
- 2021
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- The majority of incident solar irradiance causes thermalization in photovoltaic (PV) cells, attenuating their efficiency. In order to use solar energy on a large scale and reduce carbon emissions, their efficiency must be enhanced. Effective thermal management can be utilized to generate additional electrical power while simultaneously improving photovoltaic efficiency. In this work, an experimental model of a hybrid photovoltaic-thermoelectric generation (PV-TEG) system is developed. Ten bismuth telluride-based thermoelectric modules are attached to the rear side of a 10 W polycrystalline silicon-based photovoltaic module in order to recover and transform waste thermal energy to usable electrical energy, ultimately cooling the PV cells. The experiment was then carried out for 10 days in Lahore, Pakistan, on both a simple PV module and a hybrid PV-TEG system. The findings revealed that a hybrid system has boosted PV module output power and conversion efficiency. The operating temperature of the PV module in the hybrid system is reduced by 5.5%, from 55 degrees C to 52 degrees C. Due to a drop in temperature and the addition of some recovered energy by thermoelectric modules, the total output power and conversion efficiency of the system increased. The hybrid system's cumulative output power increased by 19% from 8.78 to 10.84 W, compared to the simple PV system. Also, the efficiency of the hybrid PV-TEG system increased from 11.6 to 14%, which is an increase of 17% overall. The results of this research could provide consideration for designing commercial hybrid PV-TEG systems.
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| 10. |
- Khan, Muhammad Shahzeb, et al.
(författare)
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Leveraging electronic health records to streamline the conduct of cardiovascular clinical trials
- 2023
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Ingår i: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 44:21, s. 1890-1909
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Forskningsöversikt (refereegranskat)abstract
- Conventional randomized controlled trials (RCTs) can be expensive, time intensive, and complex to conduct. Trial recruitment, participation, and data collection can burden participants and research personnel. In the past two decades, there have been rapid technological advances and an exponential growth in digitized healthcare data. Embedding RCTs, including cardiovascular outcome trials, into electronic health record systems or registries may streamline screening, consent, randomization, follow-up visits, and outcome adjudication. Moreover, wearable sensors (i.e. health and fitness trackers) provide an opportunity to collect data on cardiovascular health and risk factors in unprecedented detail and scale, while growing internet connectivity supports the collection of patient-reported outcomes. There is a pressing need to develop robust mechanisms that facilitate data capture from diverse databases and guidance to standardize data definitions. Importantly, the data collection infrastructure should be reusable to support multiple cardiovascular RCTs over time. Systems, processes, and policies will need to have sufficient flexibility to allow interoperability between different sources of data acquisition. Clinical research guidelines, ethics oversight, and regulatory requirements also need to evolve. This review highlights recent progress towards the use of routinely generated data to conduct RCTs and discusses potential solutions for ongoing barriers. There is a particular focus on methods to utilize routinely generated data for trials while complying with regional data protection laws. The discussion is supported with examples of cardiovascular outcome trials that have successfully leveraged the electronic health record, web-enabled devices or administrative databases to conduct randomized trials.
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| 11. |
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| 12. |
- Saif-Ul-Allah, Muhammad Waqas, et al.
(författare)
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Computationally Inexpensive 1D-CNN for the Prediction of Noisy Data of NOx Emissions From 500 MW Coal-Fired Power Plant
- 2022
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Ingår i: Frontiers in Energy Research. - : FRONTIERS MEDIA SA. - 2296-598X. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Coal-fired power plants have been used to meet the energy requirements in countries where coal reserves are abundant and are the key source of NOx emissions. Owing to the serious environmental and health concerns associated with NOx emissions, much work has been carried out to reduce NOx emissions. Sophisticated artificial intelligence (AI) techniques have been employed during the past few decades, such as least-squares support vector machine (LSSVM), artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), to develop the NOx prediction model. Several studies have investigated deep neural networks (DNN) models for accurate NOx emission prediction. However, there is a need to investigate a DNN-based NOx prediction model that is accurate and computationally inexpensive. Recently, a new AI technique, convolutional neural network (CNN), has been introduced and proven superior for image class prediction accuracy. According to the best of the author's knowledge, not much work has been done on the utilization of CNN on NOx emissions from coal-fired power plants. Therefore, this study investigated the prediction performance and computational time of one-dimensional CNN (1D-CNN) on NOx emissions data from a 500 MW coal-fired power plant. The variations of hyperparameters of LSTM, GRU, and 1D-CNN were investigated, and the performance metrics such as RMSE and computational time were recorded to obtain optimal hyperparameters. The obtained optimal values of hyperparameters of LSTM, GRU, and 1D-CNN were then employed for models' development, and consequently, the models were tested on test data. The 1D-CNN NOx emission model improved the training efficiency in terms of RMSE by 70.6% and 60.1% compared to LSTM and GRU, respectively. Furthermore, the testing efficiency for 1D-CNN improved by 10.2% and 15.7% compared to LSTM and GRU, respectively. Moreover, 1D-CNN (26 s) reduced the training time by 83.8% and 50% compared to LSTM (160 s) and GRU (52 s), respectively. Results reveal that 1D-CNN is more accurate, more stable, and computationally inexpensive compared to LSTM and GRU on NOx emission data from the 500 MW power plant.
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| 13. |
- Zafar, Waqar Ali, et al.
(författare)
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Time series subsidence evaluation using NSBAS InSAR: a case study of twin megacities (Rawalpindi and Islamabad) in Pakistan
- 2024
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Ingår i: Frontiers in Earth Science. - : Frontiers Media SA. - 2296-6463. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Ground deformation associated with natural and anthropogenic activities can be damaging for infrastructure and can cause enormous economic loss, particularly in developing countries which lack measuring instruments. Remote sensing techniques like interferometric synthetic aperture radar (InSAR) can thus play an important role in investigating deformation and mitigating geohazards. Rawalpindi and Islamabad are twin cities in Pakistan with a population of approximately 5.4 million, along with important government and private entities of national and international interest. In this study, we evaluate rapid paced subsidence in this area using a modified small baseline subset technique with Sentinel-1A imagery acquired between 2015 and 2022. Our results show that approximately 50 mm/year subsidence occurs in the older city of Rawalpindi, the most populated zone. We observed that subsidence in the area is controlled by the buried splays of the Main Boundary Thrust, one of the most destructive active faults in the recent past. We suggest that such rapid subsidence is most probably due to aggressive subsurface water extraction. It has been found that, despite provision of alternate water supplies by the district government, a very alarming number of tube wells are being operated in the area to extract ground water. Over 2017–2021, field data showed that near-surface aquifers up to 50–60 m deep are exhausted, and most of the tube wells are currently extracting water from depths of approximately 150–160 m. The dropping water level is proportional to the increasing number of tube wells. Lying downstream of tributaries originating from the Margalla and Murree hills, this area has a good monsoon season, and its topography supports recharge of the aquifers. However, rapid subsidence indicates a deficit between water extraction and recharge, partly due to the limitations inherent in shale and the low porosity near the surface lithology exposed in the area. Other factors amplifying the impacts are fast urbanization, uncontrolled population growth, and non-cultivation of precipitation in the area.
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| 14. |
- Abbas, Shahrukh, et al.
(författare)
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Impact Analysis of Large-Scale Wind Farms Integration in Weak Transmission Grid from Technical Perspectives
- 2020
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Ingår i: Energies. - : MDPI. - 1996-1073. ; 13:20
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Tidskriftsartikel (refereegranskat)abstract
- The integration of commercial onshore large-scale wind farms into a national grid comes with several technical issues that predominately ensure power quality in accordance with respective grid codes. The resulting impacts are complemented with the absorption of larger amounts of reactive power by wind generators. In addition, seasonal variations and inter-farm wake effects further deteriorate the overall system performance and restrict the optimal use of available wind resources. This paper presented an assessment framework to address the power quality issues that have arisen after integrating large-scale wind farms into weak transmission grids, especially considering inter-farm wake effect, seasonal variations, reactive power depletion, and compensation with a variety of voltage-ampere reactive (Var) devices. Herein, we also proposed a recovery of significant active power deficits caused by the wake effect via increasing hub height of wind turbines. For large-scale wind energy penetration, a real case study was considered for three wind farms with a cumulative capacity of 154.4 MW integrated at a Nooriabad Grid in Pakistan to analyze their overall impacts. An actual test system was modeled in MATLAB Simulink for a composite analysis. Simulations were performed for various scenarios to consider wind intermittency, seasonal variations across four seasons, and wake effect. The capacitor banks and various flexible alternating current transmission systems (FACTS) devices were employed for a comparative analysis with and without considering the inter-farm wake effect. The power system parameters along with active and reactive power deficits were considered for comprehensive analysis. Unified power flow controller (UPFC) was found to be the best compensation device through comparative analysis, as it maintained voltage at nearly 1.002 pu, suppressed frequency transient in a range of 49.88-50.17 Hz, and avoided any resonance while maintaining power factors in an allowable range. Moreover, it also enhanced the power handling capability of the power system. The 20 m increase in hub height assisted the recovery of the active power deficit to 48%, which thus minimized the influence of the wake effect.
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| 15. |
- Ahmed, A., et al.
(författare)
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Highly efficient composite electrolyte for natural gas fed fuel cell
- 2016
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Ingår i: International journal of hydrogen energy. - : Elsevier. - 0360-3199 .- 1879-3487. ; 41:16, s. 6972-6979
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Tidskriftsartikel (refereegranskat)abstract
- Solid oxide fuel cells (SOFCs) have the ability to operate with different variants of hydro carbon fuel such as biogas, natural gas, methane, ethane, syngas, methanol, ethanol, hydrogen and any other hydrogen rich gas. Utilization of these fuels in SOFC, especially the natural gas, would significantly reduce operating cost and would enhance the viability for commercialization of FC technology. In this paper, the performance of two indigenously manufactured nanocomposite electrolytes; barium and samarium doped ceria (BSDC-carbonate); and lanthanum and samarium doped ceria (co-precipitation method LSDC-carbonate) using natural gas as fuel is discussed. The nanocomposite electrolytes were synthesized using co-precipitation and wet chemical methods (here after referred to as nano electrolytes). The structure and morphology of the nano electrolytes were examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The fuel cell performance (OCV) was tested at temperature (300-600 °C). The ionic conductivity of the nano electrolytes were measured by two probe DC method. The detailed composition analysis of nano electrolytes was performed with the help of Raman Spectroscopy. Electrochemical study has shown an ionic conductivity of 0.16 Scm-1 at 600 °C for BSDC-carbonate in hydrogen atmosphere, which is higher than conventional electrolytes SDC and GDC under same conditions. In this article reasonably good ionic conductivity of BSDC-carbonate, at 600 °C, has also been achieved in air atmosphere which is comparatively greater than the conventional SDC and GDC electrolytes.
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| 16. |
- Alyami, Mana, et al.
(författare)
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Application of metaheuristic optimization algorithms in predicting the compressive strength of 3D-printed fiber-reinforced concrete
- 2024
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Ingår i: Developments in the Built Environment. - 2666-1659. ; 17
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the construction industry has been striving to make production faster and handle more complex architectural designs. Waste reduction, geometric freedom, lower construction costs, and speedy construction make the 3D-printed fiber-reinforced concrete (3DPFRC) alternative for future construction. However, achieving the optimum mixture composition for 3DPFRC remains a daunting task, entailing the consideration of multiple variables and necessitating an extensive trial-and-error experimental process. Therefore, this study investigated the application of different metaheuristic optimization algorithms to predict the compressive strength (CS) of 3DPFRC. A database of 299 data samples with 16 different input features was compiled from the experimental studies in the literature. Six metaheuristic algorithms, such as human felicity algorithm (HFA), differential evolution algorithm (DEA), nuclear reaction optimization (NRO), Harris hawks optimization (HHO), lightning search algorithm (LSA), and tunicate swarm algorithm (TSA) were applied to identify the optimal hyperparameter combination for the random forest (RF) model in predicting the CS of 3DPFRC. Different statistical metrics and 10-fold cross-validation were used to evaluate the accuracy of the models. The TSA-RF model exhibited superior performance compared to other models, achieving correlation (R), mean absolute error (MAE), and root mean square error (RMSE) values of 0.99, 2.10 MPa, and 3.59 MPa, respectively. The LSA-RF model also performed well, with R, MAE, and RMSE values of 0.99, 2.93 MPa, and 6.23 MPa, respectively. SHapley Additive exPlanation (SHAP) interpretability elucidates the intricate relationships between features and their effects on the CS, thereby offering invaluable insights for the performance-based mix proportion design of 3DPFRC.
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| 17. |
- Arikumar, K. S., et al.
(författare)
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FL-PMI : Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:4
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Tidskriftsartikel (refereegranskat)abstract
- Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person's movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.
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| 18. |
- Axfors, Cathrine, et al.
(författare)
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Mortality outcomes with hydroxychloroquine and chloroquine in COVID-19 from an international collaborative meta-analysis of randomized trials
- 2021
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Substantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https://osf.io/QESV4/). We systematically identified unpublished RCTs (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, medRxiv and bioRxiv up to October 16, 2020). All-cause mortality has been extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine and chloroquine. Prespecified subgroup analyses include patient setting, diagnostic confirmation, control type, and publication status. Sixty-three trials were potentially eligible. We included 14 unpublished trials (1308 patients) and 14 publications/preprints (9011 patients). Results for hydroxychloroquine are dominated by RECOVERY and WHO SOLIDARITY, two highly pragmatic trials, which employed relatively high doses and included 4716 and 1853 patients, respectively (67% of the total sample size). The combined OR on all-cause mortality for hydroxychloroquine is 1.11 (95% CI: 1.02, 1.20; I-2=0%; 26 trials; 10,012 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I-2=0%; 4 trials; 307 patients). We identified no subgroup effects. We found that treatment with hydroxychloroquine is associated with increased mortality in COVID-19 patients, and there is no benefit of chloroquine. Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities. Hydroxychloroquine and chloroquine have been investigated as a potential treatment for Covid-19 in several clinical trials. Here the authors report a meta-analysis of published and unpublished trials, and show that treatment with hydroxychloroquine for patients with Covid-19 was associated with increased mortality, and there was no benefit from chloroquine.
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| 19. |
- Irfan, Muhammad, et al.
(författare)
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Benchmark study of UV/Visible spectra of coumarin derivatives by computational approach
- 2017
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Ingår i: Journal of Molecular Structure. - : Elsevier BV. - 0022-2860 .- 1872-8014. ; 1130, s. 603-616
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Tidskriftsartikel (refereegranskat)abstract
- A benchmark study of UV/Visible spectra of Simple coumarins and Furanocoumarins derivatives was conducted by employing the Density Functional Theory (DFT) and Time Dependent Density Functional Theory (TD-DFT) approaches. In this study the geometries of ground and excited states, excitation energy and absorption spectra were estimated by using the DFT functional CAM-B3LYP, WB97XD, HSEH1PBE, MPW1PW91 and TD-B3LYP with 6-31 + G (d,p) basis set. CAM-B3LYP functional was found to have close agreement with the experimental values of Furranocoumarin class of coumarins while MPW1PW91 gave close results for simple coumarins. This study provided an insight about the electronic characteristics of the selected compounds and provided an effective tool for developing and designing the better UV absorber compounds.
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| 20. |
- Irfan, Muhammad, et al.
(författare)
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Design of donor-acceptor-donor (D-A-D) type small molecule donor materials with efficient photovoltaic parameters
- 2017
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Ingår i: International Journal of Quantum Chemistry. - : WILEY. - 0020-7608 .- 1097-461X. ; 117:10
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Tidskriftsartikel (refereegranskat)abstract
- Four Donor-Acceptor-Donor (D-A-D) type of donor molecules (M1-M4) with triphenylamine (TPA) as donor moiety, thiophene as bridge, and thiazolothiazole as acceptor unit were designed and its photovoltaic parameters were equated with reference molecule R. DFT functional CAM-B3LYP/6-31G (d,p) was found best for geometry optimization and TD-CAM-B3LYP/6-31G (d,p) was found suitable for excited state calculations. Among designed donor molecules, M4 manifests suitable lowest band gap of 4.73 eV, frontier molecular orbital energy levels as well as distinctive broad absorption of 455.3 nm due to the stronger electron withdrawing group. The electron-withdrawing substituents contribute to red shifts of absorption spectra and better stabilities for designed molecules. The theoretically determined reorganization energies of designed donor molecules suggested excellent charge mobility property. The lower (e) values in comparison with (h) illustrated that these four donor materials would be ideal for electron transfer and M4 would be best amongst the investigated molecules with lowest (e) of 0.0177. Furthermore, the calculated Voc of M4 is 2.04 V with respect to PC60BM (phenyl-C61-butyric acid methyl ester). This study revealed that the designed donor materials are suitable and recommended for high performance organic solar cell devices.
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| 21. |
- Javed, Muhammad Faisal, et al.
(författare)
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Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- The use of waste foundry sand (WFS) in concrete production has gained attention as an eco-friendly approach to waste reduction and enhancing cementitious materials. However, testing the impact of WFS in concrete through experiments is costly and time-consuming. Therefore, this study employs machine learning (ML) models, including support vector regression (SVR), decision tree (DT), and AdaBoost regressor (AR) ensemble model to predict concrete properties accurately. Moreover, SVR was employed in conjunction with three robust optimization algorithms: the firefly algorithm (FFA), particle swarm optimization (PSO), and grey wolf optimization (GWO), to construct hybrid models. Using 397 experimental data points for compressive strength (CS), 146 for elastic modulus (E), and 242 for split tensile strength (STS), the models were evaluated with statistical metrics and interpreted using the SHapley Additive exPlanation (SHAP) technique. The SVR-GWO hybrid model demonstrated exceptional accuracy in predicting waste foundry sand concrete (WFSC) strength characteristics. The SVR-GWO hybrid model exhibited correlation coefficient values (R) of 0.999 for CS and E, and 0.998 for STS. Age was found to be a significant factor influencing WFSC properties. The ensemble model (AR) also exhibited comparable prediction accuracy to the SVR-GWO model. In addition, SHAP analysis revealed an optimal content of input variables in the concrete mix. Overall, the hybrid and ensemble models showed exceptional prediction accuracy compared to individual models. The application of these sophisticated soft computing prediction techniques holds the potential to stimulate the widespread adoption of WFS in sustainable concrete production, thereby fostering waste reduction and bolstering the adoption of environmentally conscious construction practices.
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| 22. |
- Javed, Muhammad Faisal, et al.
(författare)
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Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants
- 2024
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Ingår i: Scientific Reports. - : Nature Research. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.
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| 23. |
- Khan, Javed
(creator_code:cre_t)
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Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states
- 2013
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.
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| 24. |
- Khan, Javed
(creator_code:cre_t)
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Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states
- 2010
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.
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| 25. |
- Khan, Javed
(creator_code:cre_t)
-
Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states
- 2010
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.
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| 26. |
- Khan, Javed
(creator_code:cre_t)
-
Methods for analyzing high dimensional data for classifying, diagnosing, prognosticating, and/or predicting diseases and other biological states
- 2011
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing, predicting, or prognosticating about a disease that includes obtaining experimental data, wherein the experimental data is high dimensional data, filtering the data, reducing the dimensionality of the data through use of one or more methods, training a supervised pattern recognition method, ranking individual data points from the data, wherein the ranking is dependent on the outcome of the supervised pattern recognition method, choosing multiple data points from the data, wherein the choice is based on the relative ranking of the individual data points, and using the multiple data points to determine if an unknown set of experimental data indicates a diseased condition, a predilection for a diseased condition, or a prognosis about a diseased condition.
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| 27. |
- Khan, Javed
(creator_code:cre_t)
-
Selections of genes and methods of using the same for diagnosis and for targeting the therapy of select cancers
- 2010
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing a disease that includes obtaining experimental data on gene selections. The gene selection functions to characterize a cancer when the expression of that gene selection is compared to the identical selection from a noncancerous cell or a different type of cancer cell. The invention also includes a method of targeting at least one product of agene that includes administration ofatherapeutic agent. The invention also includes the use of a gene selection for diagnosing a cancer.
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| 28. |
- Khan, Javed
(creator_code:cre_t)
-
Sets of probes and primers for the diagnosis of select cancers
- 2012
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A method of diagnosing a disease that includes obtaining experimental data on gene selections. The gene selection functions to characterize a cancer when the expression of that gene selection is compared to the identical selection from a noncancerous cell or a different type of cancer cell. The invention also includes a method of targeting at least one product of a gene that includes administration of a therapeutic agent. The invention also includes the use of a gene selection for diagnosing a cancer.
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| 29. |
- Khan, Muhammad Abbas, et al.
(författare)
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mmWave Four-Element MIMO Antenna for Future 5G Systems
- 2022
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 12:9, s. 4280-
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents an S-shape four-port Multiple Input Multiple Output (MIMO) wideband mmWave antenna with bandwidth of 25 GHz to 39 GHz. The antenna is designed on 0.254 mm ultra-thin RO5880 with permittivity of 2.3. The dimensions of proposed S-shape antenna are 10 x 12 mm for single element and 24 x 24 mm for four-port MIMO configuration. A decoupling network is introduced to further compress mutual coupling among MIMO elements. The peak gain achieved is 7.1 dBi and MIMO assembly delivers diversity scheme. The proposed MIMO antenna is fabricated, and simulated results are found to be in excellent agreement with simulations. Through the results obtained, the proposed MIMO antenna system can be considered as a potential candidate for future mmWave devices.
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| 30. |
- Khan, Wali Ullah, et al.
(författare)
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Opportunities for Physical Layer Security in UAV Communication Enhanced with Intelligent Reflective Surfaces
- 2022
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Ingår i: IEEE Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1284. ; 29:6, s. 22-28
-
Tidskriftsartikel (refereegranskat)abstract
- Unmanned aerial vehicles (UAVs) are an important component of next-generation wireless networks that can assist in high data rate communications and provide enhanced coverage.Their high mobility and aerial nature offer deployment flexibility and low-cost infrastructure support to existing cellular networks and provide many applications that rely on mobile wireless communications. However, security is a major challenge in UAV communications, and physical layer security (PLS) is an important technique to improve the reliability and security of data shared with the assistance of UAVs. Recently, the intelligent reflective surface (IRS) has emerged as a novel technology to extend and/or enhance wireless coverage by reconfiguring the propagation environment of communications. This article provides an overview of how the IRS can improve the PLS of UAV networks. We discuss different use cases of PLS for IRS-enhanced UAV communications and briefly review the recent advances in this area. Then, based on the recent advances, we also present a case study that utilizes alternate optimization to maximize the secrecy capacity for an IRS-enhanced UAV scenario in the presence of multiple Eves. Finally, we highlight several open issues and research challenges to realize PLS in IRS-enhanced UAV communications.
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| 31. |
- Khan, Yasar, et al.
(författare)
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Bio-inspired based meta-heuristic approach for predicting the strength of fiber-reinforced based strain hardening cementitious composites
- 2023
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Ingår i: Heliyon. - : Elsevier. - 2405-8440. ; 9:11
-
Tidskriftsartikel (refereegranskat)abstract
- A recently introduced bendable concrete having hundred times greater strain capacity provides promising results in repair of engineering structures, known as strain hardening cementitious composites (SHHCs). The current research creates new empirical prediction models to assess the mechanical properties of strain-hardening cementitious composites (SHCCs) i.e., compressive strength (CS), first crack tensile stress (TS), and first crack flexural stress (FS), using gene expression programming (GEP). Wide-ranging records were considered with twelve variables i.e., cement percentage by weight (C%), fine aggregate percentage by weight (Fagg%), fly-ash percentage by weight (FA%), Water-to-binder ratio (W/B), super-plasticizer percentage by weight (SP%), fiber amount percentage by weight (Fib%), length to diameter ratio (L/D), fiber tensile strength (FTS), fiber elastic modulus (FEM), environment temperature (ET), and curing time (CT). The performance of the models was deduced using correlation coefficient (R) and slope of regression line. The established models were also assessed using relative root mean square error (RRMSE), Mean absolute error (MAE), Root squared error (RSE), root mean square error (RMSE), objective function (OBF), performance index (PI) and Nash-Sutcliffe efficiency (NSE). The resulting mathematical GP-based equations are easy to understand and are consistent disclosing the originality of GEP model with R in the testing phase equals to 0.8623, 0.9269, and 0.8645 for CS, TS and FS respectively. The PI and OBF are both less than 0.2 and are in line with the literature, showing that the models are free from overfitting. Consequently, all proposed models have high generalization with less error measures. The sensitivity analysis showed that C%, Fagg%, and ET are the most significant variables for all three models developed with sensitiveness index higher than 10 %. The result of the research can assist researchers, practitioners, and designers to assess SHCC and will lead to sustainable, faster, and safer construction from environment-friendly waste management point of view.
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| 32. |
- Mushtaq, Irrum, et al.
(författare)
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Ferrocene-Based Terpolyamides and Their PDMS-Containing Block Copolymers: Synthesis and Physical Properties
- 2022
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Ingår i: Polymers. - : MDPI. - 2073-4360. ; 14:23
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Tidskriftsartikel (refereegranskat)abstract
- Aromatic polyamides are well-known as high-performance materials due to their outstanding properties making them useful in a wide range of applications. However, their limited solubility in common organic solvents restricts their processability and becomes a hurdle in their applicability. This study is focused on the synthesis of processable ferrocene-based terpolyamides and their polydimethylsiloxane (PDMS)-containing block copolymers, using low-temperature solution polycondensation methodology. All the synthesized materials were structurally characterized using FTIR and 1H NMR spectroscopic techniques. The ferrocene-based terpolymers and block copolymers were soluble in common organic solvents, while the organic analogs were found only soluble in sulfuric acid. WXRD analysis showed the amorphous nature of the materials, while the SEM analysis exposed the modified surface of the ferrocene-based block copolymers. The structure–property relationship of the materials was further elucidated by their water absorption and thermal behavior. These materials showed low to no water absorption along with their high limiting oxygen index (LOI) values depicting their good flame-retardant behavior. DFT studies also supported the role of various monomers in the polycondensation reaction where the electron pair donation from HOMO of diamine monomer to the LUMO of acyl chloride was predicted, along with the calculation of various other parameters of the representative terpolymers and block copolymers.
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| 33. |
- Orentas, Rimas J, et al.
(författare)
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Bioinformatic description of immunotherapy targets for pediatric T-cell leukemia and the impact of normal gene sets used for comparison
- 2014
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 4
-
Tidskriftsartikel (refereegranskat)abstract
- Pediatric lymphoid leukemia has the highest cure rate of all pediatric malignancies, yet due to its prevalence, still accounts for the majority of childhood cancer deaths and requires long-term highly toxic therapy. The ability to target B-cell ALL with immunoglobulin-like binders, whether anti-CD22 antibody or anti-CD19 CAR-Ts, has impacted treatment options for some patients. The development of new ways to target B-cell antigens continues at rapid pace. T-cell ALL accounts for up to 20% of childhood leukemia but has yet to see a set of high-value immunotherapeutic targets identified. To find new targets for T-ALL immunotherapy, we employed a bioinformatic comparison to broad normal tissue arrays, hematopoietic stem cells (HSC), and mature lymphocytes, then filtered the results for transcripts encoding plasma membrane proteins. T-ALL bears a core T-cell signature and transcripts encoding TCR/CD3 components and canonical markers of T-cell development predominate, especially when comparison was made to normal tissue or HSC. However, when comparison to mature lymphocytes was also undertaken, we identified two antigens that may drive, or be associated with leukemogenesis; TALLA-1 and hedgehog interacting protein. In addition, TCR subfamilies, CD1, activation and adhesion markers, membrane-organizing molecules, and receptors linked to metabolism and inflammation were also identified. Of these, only CD52, CD37, and CD98 are currently being targeted clinically. This work provides a set of targets to be considered for future development of immunotherapies for T-ALL.
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| 34. |
- Rafique, Asia, et al.
(författare)
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Multioxide phase-based nanocomposite electrolyte (M@SDC where M = Zn2+ / Ba2+/ La2+/Zr-2/Al3+) materials
- 2020
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Ingår i: Ceramics International. - : ELSEVIER SCI LTD. - 0272-8842 .- 1873-3956. ; 46:52, s. 6882-6888
-
Tidskriftsartikel (refereegranskat)abstract
- This paper deals with the development of a highly dense and stable electrolyte on the base of nanoionics oxide interface theory. This gives a comparative study of two-phase nanocomposite electrolytes that are developed for low temperature solid oxide fuel cells (LT-SOFCs). These nanocomposites are synthesised with different oxides, which are coated on the doped ceria that showed high oxide ion mobility for LT-SOFCs. These novel two-phase nanocomposite oxide ionic conductors (MCe0.8Sm0.2O2-MO2, where M = Zn2+/Ba2+/La3+/Zr2+/Al3+) were synthesised by a co-precipitation method. The interface study between these two phases was analysed by electrochemical impedance spectroscopy (EIS), while ionic conductivities were measured with DC conductivity (four probe method). The nanocomposite electrolytes exhibited higher conductivities with the increase of concentration of coated oxides but decreased at a certain level. The structural or morphological properties of the nanocomposite electrolytes were examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The thermal stability was investigated using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The maximum performance of 590 mW/cm(2) at 550 degrees C was obtained for the Zn@SDC based cell, and the rest of the coated samples Ba@SDC, La@SDC, Zr@SDC and Al@SDC based cells showed values of 550 mW/cm(2), 540 mW/cm(2), 450 mW/cm(2), 340 mW/cm(2), respectively, with hydrogen as a fuel. Therefore, the coated-SDC based nanocomposite materials are a good approach for lowering the operating temperature to achieve the challenges of the solid oxide fuel cells (SOFC). These two-phase nanocomposite electrolytes satisfy the all requirements which one electrolyte should have, like high ionic conduction, thermodynamic stability and negligible electronic conduction.
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| 35. |
- Raza, Rizwan, et al.
(författare)
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Composite electrolyte with proton conductivity for low-temperature solid oxide fuel cell
- 2015
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Ingår i: Applied Physics Letters. - : American Institute of Physics (AIP). - 0003-6951 .- 1077-3118. ; 107:18
-
Tidskriftsartikel (refereegranskat)abstract
- In the present work, cost-effective nanocomposite electrolyte (Ba-SDC) oxide is developed for efficient low-temperature solid oxide fuel cells (LTSOFCs). Analysis has shown that dual phase conduction of O-2 (oxygen ions) and H+ (protons) plays a significant role in the development of advanced LTSOFCs. Comparatively high proton ion conductivity (0.19 s/cm) for LTSOFCs was achieved at low temperature (460°C). In this article, the ionic conduction behaviour of LTSOFCs is explained by carrying out electrochemical impedance spectroscopy measurements. Further, the phase and structure analysis are investigated by X-ray diffraction and scanning electron microscopy techniques. Finally, we achieved an ionic transport number of the composite electrolyte for LTSOFCs as high as 0.95 and energy and power density of 90% and 550 mW/cm2, respectively, after sintering the composite electrolyte at 800°C for 4 h, which is promising. Our current effort toward the development of an efficient, green, low-temperature solid oxide fuel cell with the incorporation of high proton conductivity composite electrolyte may open frontiers in the fields of energy and fuel cell technology.
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| 36. |
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| 37. |
- Usman, Muhammad Shariq, et al.
(författare)
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The need for increased pragmatism in cardiovascular clinical trials
- 2022
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Ingår i: Nature Reviews Cardiology. - : Springer Nature. - 1759-5002 .- 1759-5010. ; 19:11, s. 737-750
-
Tidskriftsartikel (refereegranskat)abstract
- The majority of cardiovascular randomized controlled trials (RCTs) test interventions in selected patient populations under explicitly protocol-defined settings. Although these 'explanatory' trial designs optimize conditions to test the efficacy and safety of an intervention, they limit the generalizability of trial findings in broader clinical settings. The concept of 'pragmatism' in RCTs addresses this concern by providing counterbalance to the more idealized situation underpinning explanatory RCTs and optimizing effectiveness over efficacy. The central tenets of pragmatism in RCTs are to test interventions in routine clinical settings, with patients who are representative of broad clinical practice, and to reduce the burden on investigators and participants by minimizing the number of trial visits and the intensity of trial-based testing. Pragmatic evaluation of interventions is particularly important in cardiovascular diseases, where the risk of death among patients has remained fairly stable over the past few decades despite the development of new therapeutic interventions. Pragmatic RCTs can help to reveal the 'real-world' effectiveness of therapeutic interventions and elucidate barriers to their implementation. In this Review, we discuss the attributes of pragmatism in RCT design, conduct and interpretation as well as the general need for increased pragmatism in cardiovascular RCTs. We also summarize current challenges and potential solutions to the implementation of pragmatism in RCTs and highlight selected ongoing and completed cardiovascular RCTs with pragmatic trial designs.
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| 38. |
- Zahid, Nida, et al.
(författare)
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Psychosocial factors influencing quality of life in patients with primary brain tumors in Pakistan : an analytical cross-sectional study
- 2023
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Ingår i: BMC Research Notes. - : Springer Nature. - 1756-0500. ; 16:1
-
Tidskriftsartikel (refereegranskat)abstract
- ObjectiveDespite quality of life (QoL) being recognized as an important outcome in neuro-oncology, there is a lack of research from Pakistan where sociocultural differences may influence QoL. This study aimed to measure the QoL in patients with primary brain tumors (PBTs) and assess its association with mental health outcomes and social support.ResultsOur study included a total of 250 patients, with a median age of 42 years (range 33-54 years). The commonest brain tumors were glioma (46.8%) and meningioma (21.2). The mean global QoL of the sample was 75.73 +/- 14.9. The majority of patients had high social support (97.6%) and were not depressed (90%) or anxious (91.6%).On multivariable linear regression, global QoL was inversely associated with no or low income (beta coefficients: -8.75 to -11.84), having hypertension (-5.53), currently using a urine catheter (-13.55), having low social support (-28.16) suffering from mild (-15.31) or symptomatic (-23.84) depression, or mild anxiety (-13.22).
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| 39. |
- Zara, Zeenat, et al.
(författare)
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Designing dibenzosilole and methyl carbazole based donor materials with favourable photovoltaic parameters for bulk heterojunction organic solar cells
- 2018
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Ingår i: Computational and Theoretical Chemistry. - : Elsevier. - 2210-271X .- 2210-2728. ; 1142, s. 45-56
-
Tidskriftsartikel (refereegranskat)abstract
- Five new Acceptor-Donor-Acceptor (A-D-A) type small donor molecules (M1-M5) namely; Dimethyl cyanoacetate terthiophene di(methylthiophene) dibenzosilole (DMCAO3TBS) (M1), Dimelononitrile terthiophene di(methylthiophene) dibenzosilole (DMCNTBS) (M2), Dimethyl rhodanine terthiophene di(methylthiophene) dibenzosilole (DMRTBS) (M3), Dimelanonitrile terthiophene di(methylthiophene) methyl carbazole (DMCNTCz) (M4) and Dimethyl rhodanine terthiophene di(methylthiophene) methyl carbazole (DMRTCz) (M5) were designed and theoretically explored their electronic, photophysical and geometrical properties via DFT best functional MPW1PW91/6-311G (d,p) with respect to reference molecules Dioctyl cyanoacetate terthiophene di(octylthiophene) dioctylfluorene (DCAO3TF) (Ra) and Dioctyl cyanoacetate terthiophene di(octylthiophene) octylcarbazole (DCAO3TCz) (Rb). Among the designed donor molecules (M1-M5), M2 and M4 represented lowest band gap value (2.480 eV and 2.47 eV) with distinctive broad absorption peak at 598 nm and 601 nm in chloroform. Theoretically estimated reorganization energies of these molecules recommended excellent property of charge mobility. The designed donor molecules (M1-M5), demonstrated lower λe value with reference to their λh, showing that these molecules could be ideal candidates for the transfer of electron while M2 and M4 were found to be best molecules having lowest λe (0.006 eV and 0.005 eV respectively). Additionally the Voc of M2 and M4 are 2.01 eV and 1.85 eV respectively with respect to PCBM.
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