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Impact of numerous larval diets on the biology of pink bollworm Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)under laboratory conditions

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Journal of Cotton Research 2024年第1期7卷 69-78页

作者： AKHTAR Shamim ARIF Muhammad Jalal GOGI Muhammad Dildar HAQ imran-ul Department of Entomology University of Agriculture FaisalabadPunjabPakistan Ayub Agricultural Research Institute(Entomological Research Institute) FaisalabadPunjabPakistan Department of Plant Pathology University of AgricultureFaisalabadPunjabPakistan

Background Pink bollworm,Pectinophora gossypiella(Saunders)(Lepidoptera:Gelechiidae)has become a poten-tial pest of cotton by causing substantial yield losses around the world including *** in view the facts like limited research investigations,unavailability,and high cost of artificial diet’s constituents and their premixes,the present research investigations on the dietary aspect of *** were *** larvae of *** were reared on different diets that were prepared using indigenous *** standard/laboratory diet com-prised of wheat germ meal 34.5 g,casein 30.0 g,agar–agar 20.0 g,sucrose 10.0 g,brewer’s yeast 5.0 g,α-cellulose 1.0 g,potassium-sorbate1.5 g,niplagin 0.5 g,decavitamin 0.01 g,choline-chloride 0.06 g,maize-oil 3.30 g,honey 2.0 g,and water 730.0 *** to cotton bolls and wheat germ meal were okra seed sprouts,okra fruit,cottonseed meal,and okra seed meals,which were included in the study to introduce an efficient and economic mass-rearing *** The larval development completed in 19.68d±0.05 d with a weight of 20.18mg±0.20 mg at the fourth instar fed on the cottonseed meal-based diet instead of wheat germ meal based *** the same diet,84.00%±4.00%,17.24 mg±0.03 mg,and 7.76d±0.06 d were recorded as pupae formation,pupal weight,and pupal duration,*** emergence,76.00%±1.00%was recorded from pupae collected from larvae raised on cottonseed meal-based *** male and female moths lived for 40.25d±0.10 d,and 44.34d±0.11 d,*** deposited 21.28±0.04 eggs per day with the viability of 65.78%±0.14%.The larval mortal-ity at the fourth instar was 37.20%±1.36%and malformed pupation of 12.00%±1.41%was *** of wheat germ meal with that of local meals(cottonseed and okra seed)in the standard laboratory diet has saved 463.80 to 467.10 PKR with 1.62 to 1.63 cost economic returns,*** This research is of novel nature as it provides a concis

关键词： Artificial diets Cottonseed meal Laboratory rearing Pectinophora gossypiella Wheat germ meal

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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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

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Towards wafer-scale growth of two-dimensional cerium dioxide single crystal with high dielectric performance

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Nano Research 2024年第9期17卷 8592-8599页

作者： Zhuofeng Shi Muhammad imran Xiaohui Chen Xin Liu Yaqi Zhu Zhaoning Hu Saiyu Bu Jialin Zhang Chunhu Li Xiaodong Zhang Li Lin College of Chemistry and Chemical Engineering Qingdao UniversityQingdao 266000China School of Materials Science and Engineering Peking UniversityBeijing 100871China Center for Nanochemistry Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular ScienceCollege of Chemistry and Molecular EngineeringPeking UniversityBeijing 100871China Key Laboratory of Marine Chemistry Theory and Technology Ministry of EducationCollege of Chemistry and Chemical EngineeringOcean University of ChinaQingdao 266100China

Owing to the atomically thin nature,two-dimensional(2D)oxide materials have been widely reported to exhibit exciting transport and dielectric properties,such as fine gate controllability and ultrahigh carrier mobility,that outperform their bulk ***,unlike the successful synthesis of bulk oxide single crystals,reliable methods for synthesizing large-area single crystal of 2D oxide,that would suppress the negative influence from defective grain boundaries,remain unavailable,especially for nonlayered ***,we report that the lattice symmetry between the substrate and cerium dioxide(CeO_(2))would allow for the aligned nucleation and epitaxial growth of CeO_(2)on sapphire substrates,enabling the wafer-sized growth of CeO_(2)single *** careful tuning of the growth temperature and oxygen flow rate contributed to the harvesting of CeO_(2)wafer with reduced thickness and enhanced growth *** removal of grain boundaries improved the dielectric performance in terms of high dielectric strength(Ebd≈8.8 MV·cm^(-1)),suppressed leakage current,along with high dielectric constants(εr≈24).Our work demonstrates that with fine dielectric performance and ease of synthesizing wafer-sized single crystals,CeO_(2)can function as potential candidate as gate insulator for 2D-materials based nanoelectronics,and we believe the reported protocol of aligned nucleation can be extended to other 2D oxides.

关键词： dielectric materials two-dimensional materials chemical vapor deposition cerium dioxide

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Genome-wide identification and expression profiling of photosystem II(PsbX)gene family in upland cotton(Gossypium hirsutum L.)

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Journal of Cotton Research 2024年第1期7卷 1-14页

作者： RAZA Irum PARVEEN Abida AHMAD Adeel HU Daowu PAN Zhaoe ALI imran DU Xiongming Institute of Cotton Research Chinese Academy of Agricultural SciencesNational Key Laboratory of Cotton Bio-Breeding and Integrated UtilizationAnyangHenan 455000China Department of Biotechnology and Genetic Engineering Kohat University of Science and TechnologyKohatPakistan Department of Botany Kohat University of Science and TechnologyKohatPakistan.

Background Photosystem II(PSII)constitutes an intricate assembly of protein pigments,featuring extrinsic and intrinsic polypeptides within the photosynthetic *** low-molecular-weight transmembrane protein PsbX has been identified in PSII,which is associated with the oxygen-evolving *** expression of PsbX gene protein is regulated by ***’s central role involves the regulation of PSII,facilitating the binding of quinone molecules to the Qb(PsbA)site,and it additionally plays a crucial role in optimizing the efficiency of *** these insights,a comprehensive understanding of the PsbX gene’s functions has remained *** In this study,we identified ten PsbX genes in Gossypium hirsutum *** phylogenetic analysis results showed that 40 genes from nine species were classified into one *** resulting sequence logos exhibited substantial conservation across the N and C terminals at multiple sites among all Gossypium ***,the ortholo-gous/paralogous,Ka/Ks ratio revealed that cotton PsbX genes subjected to positive as well as purifying selection pressure might lead to limited divergence,which resulted in the whole genome and segmental *** expression patterns of GhPsbX genes exhibited variations across specific tissues,as indicated by the ***,the expression of GhPsbX genes could potentially be regulated in response to salt,intense light,and drought ***,GhPsbX genes may play a significant role in the modulation of photosynthesis under adverse abiotic *** We examined the structure and function of PsbX gene family very first by using comparative genom-ics and systems biology approaches in *** seems that PsbX gene family plays a vital role during the growth and development of cotton under stress ***,the results of this study provide basic information to unveil the molecular and physiological function of PsbX genes of cotton plan

关键词： Photosystem Phylogenetic Synteny RNA seq Gene expression Orthologous

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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Computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： Muhammad Rizwan Muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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Computer Systems Science & Engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar imran Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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Regulatory potential of soil available carbon,nitrogen,and functional genes on N_(2)O emissions in two upland plantation systems

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Journal of Integrative Agriculture 2024年第8期23卷 2792-2806页

作者： Peng Xu Mengdie Jiang imran Khan Muhammad Shaaban Hongtao Wu Barthelemy Harerimana Ronggui Hu Key Laboratory of Mountain Surface Processes and Ecological Regulation Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China College of Resources and Environment Huazhong Agricultural UniversityWuhan 430070China Hubei Collaborative Innovation Centre for Grain Industry College of AgricultureYangtze UniversityJingzhou 434025China College of Agriculture Henan University of Science and TechnologyLuoyang 471000China College of Urban and Environmental Sciences Hubei Normal UniversityHuangshi 435002China University of Chinese Academy of Sciences Beijing 100049China

Dynamic nitrification and denitrification processes are affected by changes in soil redox conditions,and they play a vital role in regulating soil N_(2)O emissions in rice-based *** is imperative to understand the influences of different upland crop planting systems on soil N_(2)O *** this study,we focused on two representative rotation systems in Central China:rapeseed–rice(RR)and wheat–rice(WR).We examined the biotic and abiotic processes underlying the impacts of these upland plantings on soil N_(2)O *** results revealed that during the rapeseed-cultivated seasons in the RR rotation system,the average N_(2)O emissions were 1.24±0.20 and 0.81±0.11 kg N ha^(–1)for the first and second seasons,*** values were comparable to the N_(2)O emissions observed during the first and second wheat-cultivated seasons in the WR rotation system(0.98±0.25 and 0.70±0.04 kg N ha^(–1),respectively).This suggests that upland cultivation has minimal impacts on soil N_(2)O emissions in the two rotation *** positive correlations were found between N_(2)O fluxes and soil ammonium(NH_(4)^(+)),nitrate(NO_(3)^(–)),microbial biomass nitrogen(MBN),and the ratio of soil dissolved organic carbon(DOC)to NO_(3)^(–)in both RR and WR rotation ***,the presence of the AOA-amoA and nirK genes were positively associated with soil N_(2)O fluxes in the RR and WR systems,*** implies that these genes may have different potential roles in facilitating microbial N_(2)O production in various upland plantation *** using a structural equation model,we found that soil moisture,mineral N,MBN,and the AOA-amoA gene accounted for over 50%of the effects on N_(2)O emissions in the RR rotation *** the WR rotation system,soil moisture,mineral N,MBN,and the AOA-amoA and nirK genes had a combined impact of over 70%on N_(2)O *** findings demonstrate the interactive effects of functional genes and soil factors,including

关键词： upland-rice cultivation N_(2)O emission regulatory factors functional genes

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Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

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Journal of Intelligent Learning Systems and Applications 2024年第1期16卷 12-22页

作者： Shadman Mahmood Khan Pathan Sakan Binte imran Departmentof Electrical and Computer Engineering Old Dominion University Norfolk USA Sir Salimullah Medical College Dhaka Bangladesh

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

关键词： Cardiovascular Disease Machine Learning Deep Learning Predictive Modeling Risk Assessment Comparative Analysis Gradient Boosting LSTM

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Highly responsive broadband Si-based MoSphototransistor on high-k dielectric

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Science China(Information Sciences) 2024年

作者： Ali imran Xin HE Jiwei LIU Qinghai ZHU Muhammad SULAMAN Fei XUE Mingsheng XU Deren YANG Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of Technology ZJU-Hangzhou Global Scientific and Technological Innovation Centre College of Integrated Circuits State Key Laboratory of Silicon and Advanced Semiconductor MaterialsZhejiang University School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor MaterialsZhejiang University

The demand for photodetectors and image sensors has grown exponentially in the past decade in biomedical, security surveillance, robotics, automotive, quality control, image recognition, and military applications, due to their superior quality, broadband detection, lower noise, and economic viability. Here,we report a MoS2channel-based phototransistor over an HfO2/n-Si substrate isolated by an h BN layer. The high photoresponse is achieved through the integration of the photoconduction, photogating, and mobility enhancement process by utilizing excellent features of MoS2, HfO2/Si, and h BN. The capacitive coupling of the photogenerated carriers by high-k dielectric HfO2leads to modulation of MoS2Fermi level due to electrostatic doping. Furthermore, the MoS2also contributes to the photogeneration of carriers due to its semiconducting nature, leading to additional photocurrent. Ultimately, the combination of photogating,photoconduction, and swift carrier extraction with remarkable mobility of 11.65 cm2·V-1·s-1results in high responsivity, external quantum efficiency, and detectivity of 4.5×108A·W-1, 0.72×106, and 6.20×1016Jones at 266 nm illumination, respectively. The device also demonstrates broadband photoresponse from 266–1000 nm wavelengths. The high responsivity distinguishes the potential of our device for the future of optoelectronics and broadband image sensing applications.

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