检索结果-南通市图书馆

Modelling of debris-flow susceptibility and propagation: a case study from Northwest Himalaya

在线全文

学校读者我要写书评

暂无评论

Journal of Mountain Science 2024年第1期21卷 200-217页

作者： Hamza DAUD Javed Iqbal TANOLI Sardar Muhammad ASIF Muhammad QASIM Muhammad ALI junaid khan Zahid Imran BHATTI Ishtiaq Ahmad khan JADOON Badong National Observation and Research Station of Geohazards China University of GeosciencesWuhan 430074China Department of Earth Sciences COMSATS University IslamabadAbbottabad Campus 22010Pakistan Department of Earth Sciences Sultan Qaboos UniversityAl-KhoudMascat 123Oman Faculty of Engineering China University of Geosciences(Wuhan)Wuhan 430074China Department of Earth Sciences Abbottabad University of Science and TechnologyHavelian 22500Pakistan

The geological and geographical position of the Northwest Himalayas makes it a vulnerable area for mass movements particularly landslides and debris flows. Mass movements have had a substantial impact on the study area which is extending along Karakorum Highway(KKH) from Besham to Chilas. Intense seismicity, deep gorges, steep terrain and extreme climatic events trigger multiple mountain hazards along the KKH, among which debris flow is recognized as the most destructive geohazard. This study aims to prepare a field-based debris flow inventory map at a regional scale along a 200 km stretch from Besham to Chilas. A total of 117 debris flows were identified in the field, and subsequently, a point-based debris-flow inventory and catchment delineation were performed through Arc GIS analysis. Regional scale debris flow susceptibility and propagation maps were prepared using Weighted Overlay Method(WOM) and Flow-R technique sequentially. Predisposing factors include slope, slope aspect, elevation, Topographic Roughness Index(TRI), Topographic Wetness Index(TWI), stream buffer, distance to faults, lithology rainfall, curvature, and collapsed material layer. The dataset was randomly divided into training data(75%) and validation data(25%). Results were validated through the Receiver Operator Characteristics(ROC) curve. Results show that Area Under the Curve(AUC) using WOM model is 79.2%. Flow-R propagation of debris flow shows that the 13.15%, 22.94%, and 63.91% areas are very high, high, and low susceptible to debris flow respectively. The propagation predicated by Flow-R validates the naturally occurring debris flow propagation as observed in the field surveys. The output of this research will provide valuable input to the decision makers for the site selection, designing of the prevention system, and for the protection of current infrastructure.

关键词： North Pakistan Debris flow Flow-R Propagation Susceptibility mapping Debris-flow inventory Weighted Overlay Method

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad khan Yuanqing Xia Ahmed khan Muhammad Sadiq Mahmood alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman khan Muhammad Khalid khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor alam Salman A.khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral images.The potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable attention.One encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image classification.As a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent beneficiaries.This paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and vegetables.By meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical use.Additionally,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural context.The findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in agriculture.Nevertheless,we also shed light on the various issues and limitations of working with spectral images.This comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

Symmetries,optimal system,exact and soliton solutions of(3+1)-dimensional Gardner-KP equation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Ocean Engineering and Science 2024年第2期9卷 178-190页

作者： Amjad Hussain Ashreen Anjum M.junaid-U-Rehman Ilyas khan Mariam A.Sameh Amnah S.Al-Johani Department of Mathematics Quaid-I-Azam University 45320Islamabad 44000Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-MajmaahP.O Box 66Majmaah 11952Saudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo 11845Egypt Mathematics Department Faculty of ScienceUniversity of TabukTabukSaudi Arabia

In this research article,the(3+1)-dimensional nonlinear Gardner-Kadomtsov-Petviashvili(Gardner-KP)equation which depicts the nonlinear modulation of periodic waves,is analyzed through the Lie group-theoretic technique.Considering the Lie invariance condition,we find the symmetry generators.The pro-posed model yields eight-dimensional Lie algebra.Moreover,an optimal system of sub-algebras is com-puted,and similarity reductions are made.The considered nonlinear partial differential equation is re-duced into ordinary differential equations(ODEs)by utilizing the similarity transformation method(STM),which has the benefit of yielding a large number of accurate traveling wave solutions.These ODEs are further solved to get closed-form solutions of the Gardner-KP equation in some cases,while in other cases,we use the new auxiliary equation method to get its soliton solutions.The evolution profiles of the obtained solutions are examined graphically under the appropriate selection of arbitrary parameters.

关键词： Gardner-KP equation Lie symmetry analysis Optimal system New auxiliary equation method Solitary wave solutions

A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1124-1139页

作者： junaid khan Eunkyu Lee Kyungsup Kim Department of Environmental&IT Engineering Chungnam National UniversityDaejeonRepublic of Korea Research and Development Department SafeTech ResearchIncDaejeonRepublic of Korea Department of Computer Engineering Chungnam National UniversityDaejeonRepublic of Korea

The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new prediction learning model is proposed in this study.The proposed model has two main components:(1)the alpha–beta filter algorithm is the main prediction module,and(2)the learning module is a feedforward artificial neural network(FF‐ANN).Furthermore,the model uses two inputs,temperature sensor and humidity sensor data,and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings.Using the novel proposed technique,prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network,and also reduces the root mean square error(RMSE)and mean absolute error(MAE).We carried out different experiments with different experimental setups.The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter.A higher prediction accuracy was achieved,and the MAE and RMSE were 35.1%–38.2%respectively.The final proposed model results show increased performance when compared to traditional methods.

关键词： alpha beta filter artificial neural network navigation prediction accuracy target tracking problems

Performance of hot-mix asphalt using polymer-modified bitumen and marble dust as a filler

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Traffic and Transportation Engineering(English Edition) 2023年第3期10卷 385-398页

作者： Diyar khan Rawid khan Muhammad Tariq khan Muhammad alam Tanveer Hassan Department of Civil Engineering University of Engineering and TechnologyPeshawarPakistan Faculty of Transport and Aviation Engineering Silesian University of TechnologyKatowice 40-019Poland Department of Civil Engineering University of Engineering and TechnologyMardanPakistan Department of Built Environment and Architecture London South Bank UniversityLondon SE10AAUK

Marble dust waste powder generated by the marble cutting industry has a high alkalinity.In this research,the use of marble dust(MD)as a mineral filler substitute in hot mixed asphalt(HMA)was evaluated.The Marshall mix design was used to determine the optimum bitumen content(OBC)for all of the mixtures.For each of the four MD contents,i.e.,0,2%,4%,and 6%by weight of the total aggregates,four different bitumen percentages were used.The results of the Marshall stability test showed that the optimum filler content was 4%MD.Samples were prepared with 0 MD in the control mix and varying percentages of MD as an alternate filler.In addition,MD aided in increasing the Marshall stability,rutting resistance,and permanent deformation and reduced the fatigue life of asphalt mixtures.As the percentage of MD increases,the rutting resistance and stiffness at high temperatures both increase.As the percentage of MD increases,the fatigue life reduces.Rut resistance in high-temperature conditions can be improved by using MD in HMA as a partial substitute for stone dust(SD).In areas where extensive MD waste is present,MD can be incorporated into HMA mixtures instead of conventional fillers.

关键词： Asphalt mixture Marble dust Wheel tracker test Dynamic modulus test Four-point fatigue beam test

Static recovery of A5083 aluminum alloy after a small deformation through various measuring approaches

在线全文

学校读者我要写书评

暂无评论

Journal of Materials Science & Technology 2022年第9期104卷 202-213页

作者： Sheng Ding Jingwei Zhang Sabrina alam khan Jun Yanagimoto Department of Mechanical Engineering The University of TokyoHongo 7-3-1Bunkyo CityTokyo 113-8656Japan

Static recovery was confirmed to be the dominant softening mechanism during annealing for the studied A5083 aluminum alloy.The kinetics of static recovery,described based on the activation parameters(activation energy and volume of static recovery)and the static softening fraction,were mainly studied through two thermomechanical tests,namely,double-pass compression and stress relaxation tests.A new approach was proposed to measure the static softening fraction in the stress relaxation test.In general,a higher temperature or strain rate accelerates static recovery,while interestingly,the effect of pre-strain on static recovery is opposite in cases with and without external stress,owing to the enhanced static recovery by external stress during annealing.In addition,microhardness tests and electron backscatter diffraction(EBSD)characterization were also conducted to verify the accuracy of the results.The grain average misorientation approach based on EBSD characterization was confirmed to be effective in distinguishing and quantifying static recovery.It is noteworthy that the special stress hardening phenomenon occurring at 400℃and 0.1/s is caused by strain aging,showing the complexity of the material behaviors after deformation of the studied aluminum alloy.

关键词： Aluminum alloy Static recovery Double-pass compression test Stress relaxation test Electron backscatter diffraction(EBSD) Grain average misorientation

Exploring the nano-fungicidal efficacy of green synthesized magnesium oxide nanoparticles(MgO NPs)on the development,physiology,and infection of carrot(Daucus carota L.)with Alternaria leaf blight(ALB):Molecular docking

在线全文

学校读者我要写书评

暂无评论

Journal of Integrative Agriculture 2023年第10期22卷 3069-3080页

作者： Lukman AHAMAD Azmat ALI khan Masudulla khan Orudzhev FARID Mahboob alam Plant Pathology/Nematology Laboratory Department of BotanyAligarh Muslim UniversityAligarh 202002India Regional Plant Quarantine Station Salt LakeKolkata 700097India Pharmaceutical Biotechnology Laboratory Department of Pharmaceutical ChemistryCollege of PharmacyKing Saud UniversityRiyadh 11451Saudi Arabia Botany Section Women’s CollegeAligarh Muslim UniversityAligarh 202002India Amirkhanov Institute of Physics Dagestan Federal Scientific CenterRussian Academy of SciencesMakhachkala 367003Russia Department of Safety Engineering Dongguk UniversityGyeongju-si 780714Republic of Korea

In this research,green synthesized magnesium oxide nanoparticles(MgO NPs)from lemon fruit extracts and their fungicidal potential was evaluated against Alternaria dauci infection on carrot(Daucus carota L.)under greenhouse conditions.The scanning and transmission electron microscopy(SEM and TEM)and ultra-violet(UV)visible spectroscopy were used to validate and characterize MgO NPs.The crystalline nature of MgONPs was determined using selected area electron diffraction(SAED).MgO NPs triggered substantial antifungal activity against A.dauci when exposed to 50 and 100 mg L^(–1)concentrations but the higher antifungal potential was noticed in 100 mg L^(–1)under invitro conditions.In fungal inoculated plants,a marked decrease in growth,photosynthetic pigments,and an increase in phenol,proline contents,and defense-related enzymes of carrot were seen over control(distilled water).However,foliar application of MgO NPs at 50 and 100 mg L^(–1)resulted in significant improvement of plant growth,photosynthetic pigments,phenol and proline contents,and defense enzymes activity of carrots with and without A.dauci infection.Spraying of MgO NPs at 100 mg L^(–1)had more plant length(17.11%),shoot dry weight(34.38%),plant fresh weight(20.46%),and root dry weight(49.09%)in carrots when challenged with A.dauci over inoculated control.The leaf blight indices and percent disease severity were also reduced in A.dauci inoculated plants when sprayed with MgO NPs.The non-bonding interactions of Alternaria genus protein with nanoparticles were studied using molecular docking.

关键词： green synthesis MgO nanoparticles alternaria leaf blight photosynthetic pigments defense enzymes carrot

Energy and Life Cycle Assessment of Zinc/Water Nanofluid Based Photovoltaic Thermal System

在线全文

学校读者我要写书评

暂无评论

Energy Engineering 2022年第2期119卷 827-846页

作者： junaid khan Rashid Ali Muzammil Zubair Syed Mohd Yahya SEARL Laboratory Department of Mechancial EngineeringAligarh Muslim UniversityAligarh202002India

Cooling the PV surface in a Photovoltaic Thermal system is a pivotal operational aspect to be taken into account to achieve optimized values of performance parameters in a Photovoltaic Thermal System.The experimental design used in this study facilitates the flow of varying concentrations of Zn-water nanofluid in serpentine copper tubing installed at the rear of the PV panel thereby preventing the PV surface temperature from increasing beyond the threshold value at which a decrease in electrical efficiency starts to occur.This fusion of solar thermal with PV devices leads to better electrical and thermal efficiency values resulting in decreased cell degradation over time and maximization of the lifespan of the PV module and the energy output from the PV system.Due to the superior thermal heat properties of nanofluids,their usage in such systems has become increasingly widespread.Life cycle metrics which include Energy Payback period,Energy Production Factor and life cycle conversion efficiency were evaluated for the PVT system by exhaustively chalking fundamental parameters such as embodied energy of the PVT setup and the total energy output from the PVT system.This research aims to be a major milestone in the evolutionary journey of Photovoltaic Thermal modules by guiding the engineers working on the theory,design and implementation of PVT systems towards its economic feasibility,environmental impact and energy sustainability.

关键词： Photovoltaic thermal system nanofluids performance parameters life cycle assessment