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| 1 | Suitable energy platform significantly improves charge separation of g-C3N4 for CO2 reduction and pollutant oxidation under visible-light显示文摘The photocatalytic activities of g-C3N4 can be significantly improved by increasing life time of the photogenerated charges. Here, in this work we introduced TiO2 as proper energy platform to accept the photogenerated electrons from g-C3N4 during photocatalysis. The nanophotocatalysts formed from the combination of a suitable amount of TiO2 nanoparticles and g-C3N4 nanosheets showed 8.75 and 4.22% enhancement in photocatalytic activities for CO2 reduction and 2-chlorophenol(2-CP) degradation under visible light illumination as compared to bare g-C3N4. Based on the surface photovoltage spectra, photoluminescence spectra and examination of formed hydroxyl radicals, it was confirmed that these enhanced photoactivities were attributed to the much-improved charge separation via the electron transfer from g-C3N4 to TiO2. From trapping experiments,it was found that hydroxyl radicals were the major species involved in the photocatalytic degradation of 2-CP.This study is helpful to synthesize efficient photocatalysts to cope with energy and environmental issues. | Amir Zada Nauman Ali Fazle Subhan Natasha Anwar Muhammad Ishaq Ali Shah Muhammad Ateeq Zahid Hussain Khair Zaman Momin Khan | 2019 | Progress in Natural Science:Materials International2019，29，2： | 1 |
| 2 | Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks显示文摘The modern paradigm of the Internet of Things(IoT)has led to a significant increase in demand for latency-sensitive applications in Fog-based cloud computing.However,such applications cannot meet strict quality of service(QoS)requirements.The large-scale deployment of IoT requires more effective use of network infrastructure to ensure QoS when processing big data.Generally,cloud-centric IoT application deployment involves different modules running on terminal devices and cloud servers.Fog devices with different computing capabilities must process the data generated by the end device,so deploying latency-sensitive applications in a heterogeneous fog computing environment is a difficult task.In addition,when there is an inconsistent connection delay between the fog and the terminal device,the deployment of such applications becomes more complicated.In this article,we propose an algorithm that can effectively place application modules on network nodes while considering connection delay,processing power,and sensing data volume.Compared with traditional cloud computing deployment,we conducted simulations in iFogSim to confirm the effectiveness of the algorithm.The simulation results verify the effectiveness of the proposed algorithm in terms of end-to-end delay and network consumption.Therein,latency and execution time is insensitive to the number of sensors. | Syed Rizwan Hassan Ishtiaq Ahmad Jamel Nebhen Ateeq Ur Rehman Muhammad Shafiq Jin-Ghoo Choi | 2022 | Computers, Materials & Continua2022，，3： | 1 |
| 3 | Energy Efficiency Trade-off with Spectral Efficiency in MIMO Systems显示文摘5G technology can greatly improve spectral efficiency(SE)and throughput of wireless communications.In this regard,multiple inputmultiple output(MIMO)technology has become the most influential technology using huge antennas and user equipment(UE).However,the use of MIMO in 5G wireless technology will increase circuit power consumption and reduce energy efficiency(EE).In this regard,this article proposes an optimal solution for weighing SE and throughput tradeoff with energy efficiency.The research work is based on theWyner model of uplink(UL)and downlink(DL)transmission under the multi-cell model scenario.The SE-EE trade-off is carried out by optimizing the choice of antenna and UEs,while the approximation method based on the logarithmic function is used for optimization.In this paper,we analyzed the combination of UL and DL power consumption models and precoding schemes for all actual circuit power consumption models to optimize the trade-off between EE and throughput.The simulation results show that the SE-EE trade-off has been significantly improved by developing UL and DL transmission models with the approximation method based on logarithmic functions.It is also recognized that the throughput-EE trade-off can be improved by knowing the total actual power consumed by the entire network. | Rao Muhammad Asif Mustafa Shakir Jamel Nebhen Ateeq Ur Rehman Muhammad Shafiq Jin-Ghoo Choi | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 4 | Deep Deterministic Policy Gradient to Regulate Feedback Control Systems Using Reinforcement Learning显示文摘Controlling feedback control systems in continuous action spaces has always been a challenging problem.Nevertheless,reinforcement learning is mainly an area of artificial intelligence(AI)because it has been used in process control for more than a decade.However,the existing algorithms are unable to provide satisfactory results.Therefore,this research uses a reinforcement learning(RL)algorithm to manage the control system.We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient(DDPG).The actor-critic scenario using DDPG is implemented to build the RL agent.In addition,a framework has been created for traditional feedback control systems to make RL implementation easier for control systems.The RL algorithms are robust and proficient in using trial and error to search for the best strategy.Our proposed algorithm is a deep deterministic policy gradient,in which a large amount of training data trains the agent.Once the system is trained,the agent can automatically adjust the control parameters.The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment.The performance of the proposed RL algorithm is compared with a proportional integral derivative(PID)controller and a linear quadratic regulator(LQR)controller.The simulation results of the proposed scheme are promising for the feedback control problems. | Jehangir Arshad Ayesha Khan Mariam Aftab Mujtaba Hussain Ateeq Ur Rehman Shafiq Ahmad Adel M.Al-Shayea Muhammad Shafiq | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 5 | Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization显示文摘An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance.In this research,a novel control techniquebased Hybrid-Active Power-Filter(HAPF)is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor(PF)and Total–Hormonic Distortion(THD)and the performance of a system.This work proposed a soft-computing technique based on Particle Swarm-Optimization(PSO)and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods.Moreover,the control algorithms are implemented for an instantaneous reactive and active current(Id-Iq)and power theory(Pq0)in SIMULINK.To prevent the degradation effect of disturbances on the system’s performance,PS0-PI is applied in the inner loop which generate a required dc link-voltage.Additionally,a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions.The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system.Therefore,the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller(FLC)are optimal that will detect reactive power and harmonics much faster and accurately.The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI(Voltage Source Inverter),and thus the simulation has been taken in SIMULINK/MATLAB.The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation.As a result of the comparison,it can be concluded that the PSO-basedAdaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques. | Waleed Rafique Ayesha Khan Ahmad Almogren Jehangir Arshad Adnan Yousaf Mujtaba Hussain Jaffery Ateeq Ur Rehman Muhammad Shafiq | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 6 | Role of Organic Amendments to Mitigate Cd Toxicity and Its Assimilation in Triticum aestivum L.显示文摘In soil biota,higher and enduring concentration of heavy metals like cadmium(Cd)is hazardous and associated with great loss in growth,yield,and quality parameters of most of the crop plants.Recently,in-situ applications of eco-friendly stabilizing agents in the form of organic modifications have been utilized to mitigate the adverse effects of Cd-toxicity.This controlled experiment was laid down to appraise the imprints of various applied organic amendments namely poultry manure(PM),farmyard manure(FYM),and sugarcane press mud(PS)to immobilize Cd in polluted soil.Moreover,phytoavailability of Cd in wheat was also accessed under an alkaline environment.Results revealed that the addition of FYM(5–10 ton ha^(-1))in Cd-contaminated soil significantly increased germination rate,leaf chlorophyll content,plant height,spike length,biological and grain yield amongst all applied organic amendments.Moreover,the addition of FYM(5–10 ton ha^(-1))also reduced the phytoavailability of Cd by 73–85%in the roots,57–83%in the shoots,and 81–90%in grains of wheat crop.Thus,it is affirmed that incorporation of FYM(5–10 ton ha^(-1))performed better to enhance wheat growth and yield by remediating Cd.Thus,the application of FYM(5–10 ton ha^(-1))reduced the toxicity induced by Cd to plants by declining its uptake and translocation as compared to all other applied organic amendments to immobilize Cd under sandy alkaline polluted soil. | Tauqeer Ahmad Yasir Sobia Aslam Muhammad Shahid Rizwan Allah Wasaya Muhammad Ateeq Muhammad Naeem Khan Sikander Khan Tanveer Walid Soufan Basharat Ali Allah Ditta Arpna Kumari Ayman EL Sabagh | 2022 | Phyton-International Journal of Experimental Botany2022，91，11： | 0 |
| 7 | Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography显示文摘Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,respectively.So,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results. | Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 8 | Short-Term Wind Energy Forecasting Using Deep Learning-Based Predictive Analytics显示文摘Wind energy is featured by instability due to a number of factors,such as weather,season,time of the day,climatic area and so on.Furthermore,instability in the generation of wind energy brings new challenges to electric power grids,such as reliability,flexibility,and power quality.This transition requires a plethora of advanced techniques for accurate forecasting of wind energy.In this context,wind energy forecasting is closely tied to machine learning(ML)and deep learning(DL)as emerging technologies to create an intelligent energy management paradigm.This article attempts to address the short-term wind energy forecasting problem in Estonia using a historical wind energy generation data set.Moreover,we taxonomically delve into the state-of-the-art ML and DL algorithms for wind energy forecasting and implement different trending ML and DL algorithms for the day-ahead forecast.For the selection of model parameters,a detailed exploratory data analysis is conducted.All models are trained on a real-time Estonian wind energy generation dataset for the first time with a frequency of 1 h.The main objective of the study is to foster an efficient forecasting technique for Estonia.The comparative analysis of the results indicates that Support Vector Machine(SVM),Non-linear Autoregressive Neural Networks(NAR),and Recurrent Neural Network-Long-Term Short-Term Memory(RNNLSTM)are respectively 10%,25%,and 32%more efficient compared to TSO’s forecasting algorithm.Therefore,RNN-LSTM is the best-suited and computationally effective DL method for wind energy forecasting in Estonia and will serve as a futuristic solution. | Noman Shabbir Lauri Kütt Muhammad Jawad Oleksandr Husev Ateeq Ur Rehman Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 9 | Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum显示文摘The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields. | SVinson Joshua ASelwin Mich Priyadharson Raju Kannadasan Arfat Ahmad Khan Worawat Lawanont Faizan Ahmed Khan Ateeq Ur Rehman Muhammad Junaid Ali | 2022 | Computers, Materials & Continua2022，，9： | 0 |
| 10 | Seed Priming and Foliar Supplementation withβ-aminobutyric Acid Alleviates Drought Stress through Mitigation of Oxidative Stress and Enhancement of Antioxidant Defense in Linseed(Linum usitatissimum L.)显示文摘Drought is one of the critical limitations to agricultural soils and crop plants.Scarcity of water is increasing due to climate change that lead to increasing threats to global food security.Therefore,ecofriendly and cost effective strategies are highly desirable for mitigating drought stress along with sustainable and smart agricultural production.The aim of the study was to mitigate DS using seed priming and exogenous supplementation ofβ-aminobutyric acid(BABA)in linseed(Linum usitatissimum L.).Different doses(0,50,100 and 150μM)of BABA were used for seed priming agent and foliar spraying under three soil moisture levels viz.,25%(SM25),45%(SM45)and 65%(SM65).The response variables of both experiments included different agro-botanical traits and oxidative stress indicators such as melondialdehyde content,free proline accumulation,and antioxidant defense in plants.The linseed plants showed water stress at SM25 that reduced plant height,number of branches per plant,time taken to flower initiation and heading,and root and shoot dry weights.Additionally,the number of capsules and seeds per capsule showed a significant decline at SM25,which led to a drastic reduction in 100-seed weight yield in linseed plants in both experiments.However,seed priming and foliar supplementation with of BABA(50–100μM)significantly improved these morpho-agronomical attributes in linseed plants under DS.The results revealed that the BABA was fully active in linseed plants at SM25.Interestingly,the combination of SM25 with BABA significantly improved the antioxidant enzymes superoxide dismutase(SOD),catalase(CAT),ascorbate peroxidase(APX),and peroxidase(POD)activity,which significantly enhanced DS tolerance in linseed plants.These findings might be useful to oil seed breeders and farmers linseed for breeding program in linseed plants as well as sustainable agricultural production of oil seed crop plants. | Tauqeer Ahmad Yasir Muhammad Ateeq Allah Wasaya Mubshar Hussain Naeem Sarwar Khuram Mubeen Mudassir Aziz Muhammad Aamir Iqbal Chukwuma COgbaga Ibrahim Al-Ashkar Md Atikur Rahman Ayman El Sabagh | 2023 | Phyton-International Journal of Experimental Botany2023，92，11： | 0 |
| 11 | Performance Evaluation of Virtualization Methodologies to Facilitate NFV Deployment显示文摘The development of the Next-Generation Wireless Network(NGWN)is becoming a reality.To conduct specialized processes more,rapid network deployment has become essential.Methodologies like Network Function Virtualization(NFV),Software-Defined Networks(SDN),and cloud computing will be crucial in addressing various challenges that 5G networks will face,particularly adaptability,scalability,and reliability.The motivation behind this work is to confirm the function of virtualization and the capabilities offered by various virtualization platforms,including hypervisors,clouds,and containers,which will serve as a guide to dealing with the stimulating environment of 5G.This is particularly crucial when implementing network operations at the edge of 5G networks,where limited resources and prompt user responses are mandatory.Experimental results prove that containers outperform hypervisor-based virtualized infrastructure and cloud platforms’latency and network throughput at the expense of higher virtualized processor use.In contrast to public clouds,where a set of rules is created to allow only the appropriate traffic,security is still a problem with containers. | Sumbal Zahoor Ishtiaq Ahmad Ateeq Ur Rehman Elsayed Tag Eldin Nivin AGhamry Muhammad Shafiq | 2023 | Computers, Materials & Continua2023，，4： | 0 |
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