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Detecting mislabeling and identifying unique progeny in Acacia mapping population using SNP markers

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Journal of Forestry Research 2017年第6期28卷 1118-1126页

作者： asif javed muhammad Mohd Zaki Abdullah Norwati muhammad Wickneswari Ratnam Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 Bangi Selangor Malaysia Forestry Biotechnology Division Forest Research Institute Malaysia 52110 Kepong Selangor Darul Ehsan Malaysia Department of Forestry and Range Management University of Agriculture Faisalabad Faisalabad Pakistan

Acacia hybrids offer a great potential for paper industry in Southeast Asia due to their fast growth and ability to grow on abandoned or marginal lands. Breeding Acacia hybrids with desirable traits can be achieved through marker assisted selection(MAS) breeding. To develop a MAS program requires development of linkage maps and QTL analysis. Two mapping populations were developed through interspecific hybridization for linkage mapping and QTL analysis. All seeds per pod were cultured initially to improve hybrid yield as quality and density of linkage mapping is affected by the size of the mapping population. Progenies from two mapping populations were field planted for phenotypic and genotypic evaluation at three locations in Malaysia,(1) Forest Research Institute Malaysia field station at Segamat, Johor,(2) Borneo Tree Seeds and Seedlings Supplies Sdn, Bhd.(BTS) field trial site at Bintulu, Sarawak, and(3) Asiaprima RCF field trial site at Lancang, Pahang. During field planting, mislabeling was reported at Segamat, Johor, and a similar problem was suspected for Bintulu, Sarawak. Early screening with two isozymes effectively selected hybrid progenies, and these hybrids were subsequently further confirmed by using species-specific SNPs. During field planting, clonal mislabeling was reported and later confirmed by using a small set of STMS markers. A large set of SNPs were also used to screen all ramets in both populations. A total of 65.36% mislabeled ramets were encountered in the wood density population and 60.34% in the fibre length mapping population. No interpopulation pollen contamination was detected because all ramets found their match within the same population in ***, mislabeling was detected among ramets of the same population. Mislabeled individuals were identified and grouped as they originated from 93 pods for wood density and 53 pods for fibre length mapping *** average 2 meiotically unique seeds per pod(179 seeds/93 pods) for w

关键词： Tree breeding SNP markers Mislabeling Linkage mapping Quantitative trait loci(QTL) mapping

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

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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

Phonon Thermal Transport at Interfaces of a Graphene/Vertically Aligned Carbon Nanotubes/Hexagonal Boron Nitride Sandwiched Heterostructure

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Chinese Physics Letters 2024年第1期41卷 57-62页

作者：李檬璘 muhammad asif Shakoori 王瑞鹏李海鹏 School of Materials Science and Physics China University of Mining and TechnologyXuzhou 221116China

Molecular dynamics simulation is used to calculate the interfacial thermal resistance of a graphene/carbon nanotubes/hexagonal boron nitride(Gr/CNTs/hBN)sandwiched heterostructure,in which vertically aligned carbon nanotube(VACNT)arrays are covalently bonded to graphene and hexagonal boron nitride *** find that the interfacial thermal resistance(ITR)of the Gr/VACNT/hBN sandwiched heterostructure is one to two orders of magnitude smaller than the ITR of a Gr/hBN van der Waals heterostructure with the same plane *** is observed that covalent bonding effectively enhances the phonon coupling between Gr and hBN layers,resulting in an increase in the overlap factor of phonon density of states between Gr and hBN,thus reducing the ITR of Gr and *** addition,the chirality,size(diameter and length),and packing density of sandwich-layer VACNTs have an important influence on the ITR of the *** the same CNT diameter and length,the ITR of the sandwiched heterostructure with armchair-shaped VACNTs is higher than that of the sandwiched heterostructure with zigzag-shaped VACNTs due to the different chemical bonding of chiral CNTs with Gr and *** the armchair-shaped CNT diameter increases or the length decreases,the ITR of the sandwiched heterostructure tends to ***,the increase in the VACNT packing density also leads to a continuous decrease in the ITR of the sandwiched heterostructure,attributed to the extremely high intrinsic thermal conductivity of CNTs and the increase of out-of-plane heat transfer *** work may be helpful for understanding the mechanism for ITR in multilayer vertical heterostructures,and provides theoretical guidance for a new strategy to regulate the interlayer thermal resistance of heterostructures by optimizing the design of sandwich layer thermal interface materials.

关键词： sandwich shaped packing

Progress in electrocatalytic nitrate reduction for green energy:Catalyst engineering,mechanisms,and techno-economic feasibility

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Journal of Energy Chemistry 2024年第8期95卷 380-406,I0009页

作者： Hafiz muhammad Adeel Sharif Hafiz muhammad Farooq Khan Sadeeq Ullah Yuwei Wang muhammad Ahmad Bo Yang Changping Li muhammad Bilal asif Research Center for Eco-Environmental Engineering Dongguan University of TechnologyDongguan 523808GuangdongChina College of Chemistry and Engineering Shenzhen UniversityShenzhen 518060GuangdongChina FMH College of Medicine and Dentistry LahorePakistan School of Environment and Civil Engineering Research Center for Eco-Environment EngineeringDongguan University of TechnologyDongguan 523106GuangdongChina Advanced Membranes and Porous Materials Center(AMPMC) Physical Sciences and Engineering(PSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955Saudi Arabia

Ammonia(NH_(3))is an irreplaceable chemical that has been widely demanded to keep the sustainable development of modern ***,its industrial production consumes a huge amount of energy and releases extraordinary greenhouse gases(GHGs),leading to various environmental *** the green production of ammonia is a great challenge,which has been extensively pursued in the last *** this review,the most promising strategy,electrochemical nitrate reduction reaction(e-NO_(3)RR),is comprehensively investigated to give a complete understanding of its development and mechanism and provide guidance for future ***,owing to the complex reactions and limited selectivity,a comprehensive understanding of the mechanisms is crucial to further development and ***,NO_(3)^(-)RR is a promising strategy for simultaneous water treatment and NH_(3)production.A detailed overview of the recent progress in NO_(3)^(-)RR for NH_(3)production with nontransition and transition metal based electrocatalysts is *** addition,critical advanced techniques,future challenges,and prospects are discussed to guide future research on transition metal-based catalysts for commercial NH_(3)synthesis by NO_(3)^(-)reduction.

关键词： Nitrate reduction Electrolysis Pollution to solution Transition metal catalysts Ammonia synthesis Green energy

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2131-2164页

作者： Madiha Hameed Aneela Zameer muhammad asif Zahoor Raja Department of Computer Sciences Pakistan Institute of Engineering and Applied SciencesNilore50700Islamabad Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002TaiwanChina Directorate of IT Services University of GujratGujrat50700Pakistan

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer *** datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis *** challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human *** cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left *** paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their *** notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion *** review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation *** explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset *** leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model ***,ViTs alleviate augmentation issues by effectively leveraging original *** a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and *** study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological ***,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic

关键词： Medical image skin cancer classification skin cancer segmentation international skin imaging collaboration convolutional neural network deep learning

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan javed Rashid Mubbashar Saddique muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

Application of Synthetic Iron Oxyhydroxide with Influencing Factors for Removal of As(Ⅴ) and As(Ⅲ) from Groundwater

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Journal of Earth Science 2024年第3期35卷 998-1009页

作者： Shakeel Ahmed Talpur muhammad Yousuf Jat Baloch Chunli Su javed Iqbal Aziz Ahmed Hafeez Ahmed Talpur School of Environmental Studies China University of GeosciencesWuhan 430078China DiSPUTer Department of PsychologicalHealth and Territory SciencesUniversity“G.d′Annunzio”Chieti-Pescara66100 ChietiItaly College of New Energy and Environment Jilin UniversityChangchun 130021China School of Plant Environment and Soil SciencesLouisiana State University Agricultural CentreBaton Rouge LA 70803USA Department od Engineering and Geology University“G.d′Annunzio”Chieti-Pescara66100 ChietiItaly

Synthesized iron oxyhydroxide was applied for the adsorptive removal of As(V)and As(III)from the aquas ***,this investigation highlighted the synergistic effect of calcium carbonate in conjunction with iron oxyhydroxide,resulting in enhanced removal *** experiment was conducted under various conditions:concentration,dosage,pH,agitation,and *** characterizations such as Brunauer Emmett Teller,X-ray diffraction,scanning electron microscopy,and Fourier transform infrared spectroscopy were implied to understand adsorption *** Langmuir model revealed optimal concentrations for As(V)=500μg/L at pH-5 and As(III)=200μg/L at pH-7,resulting in 95%and 93%adsorption efficiencies,*** adsorption capacities“qm”were found to be 1266.943μg/g for As(V)and 1080.241μg/g for As(III).Freundlich model demonstrated favorable adsorption by indicating“n>1”such as As(V)=2.542 and As(III)=2.707;similarly,the speciation factor“RL<1”for both species as As(V)=0.1 and As(III)=0.5,*** kinetic study presented a pseudo-second-order model as best fitted,indicating throughout chemisorption processes for removing As(V)and As(III).Furthermore,incorporating calcium carbonate presented a significant leap in the removal efficiency,indicating As(V)from 95%to 98%and As(III)from 93%to 96%,*** findings offer profound motivation for developing effective and sustainable solutions to tackle arsenic contamination,underscoring the exceptional promise of iron oxyhydroxide in conjunction with calcium carbonate to achieve maximum removal efficiency.

关键词： adsorption arsenic As(V) As(III) iron oxyhydroxide isotherms kinetics groundwater.

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： muhammad Ahmad Nawaz Ul Ghani Kun She muhammad Arslan Rauf Shumaila Khan javed Ali Khan Eman Abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems

CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq muhammad Athar javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence