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Modulation of oxidative damage and genotoxicity of biosynthesized zinc oxide nanoparticles via supplementation with modified cinnamaldehyde-loaded chitosan nanoparticles in mice

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Food science and Human Wellness 2024年

作者： Mosaad A.Abdel-Wahhab Aziza A.El-Nekeety asmaa s.salman Zeinab K.Hamza Engy M.Akl soher E.Aly Wenyi Kang Department of Food Toxicology & Contaminants， National Research Centre Dokki， Cairo， Egypt Genetic and Cytology Department， National Research Centre Dokki， Cairo， Egypt Department of Fats and Oils， National Research Centre Dokki， Cairo， Egypt National R & D Center for Edible Fungus Processing Technology， Henan University Joint International Research Laboratory of Food & Medicine Resource Function Functional Food Engineering Technology Research Center Henan

synthesis of zinc oxide nanoparticles (Zn-NPs) via green method is an outstanding alternative to conventional/regular methods; however， the safety or toxicity of the biosynthesized ZnO-NPs in vivo is not fully explored. This study was conducted to evaluate the protective efficiency of cinnamaldehyde-loaded chitosan nanoparticles (Cin@CsNPs) against oxidative damage and genotoxicity of zinc oxide nanoparticles (ZnO-NPs) in mice. ZnO-NPs were biosynthesized using the extract of fresh leaves of Mentha pulegium L. Cin was extracted from cinnamon essential oil， and was loaded into chitosan nanoparticle (Cin@CsNPs). Both ZnO-NPs， Cin@CsNPs and CsNPs were characterized. The in vitro release of Cin@CsNPs was determined. In the biological study， 6 groups of male Balb/c mice were treated by gavage for 3 weeks as follows， control group， the group received ZnO-NPs (25 mg/kg b.w)， the groups received Cin@CsNPs at low dose (50 mg/kg b.w) or high dose (100 mg/kg b.w)， and the groups received ZnO-NPs plus Cin@CsNPs at the two tested doses. Blood and tissue samples were collected for different biochemical， genetical and histological studies. The particle size of ZnO-NPs， CsNPs， and Cin@CsNPs were (20.78 ± 2.6)， (170.0 ± 3.7)， and (218.23 ± 2.9) nm， and ξ-potential were (32.7 ± 4.6)， (8.32 ± 0.27) and (4.8 ± 0.21)， respectively. ZnO-NPs disturbed the biochemical and oxidative stress indices， AFP， CEA， TNF-α， chromosomal aberrations in somatic and germ cells， and sperm abnormality along with severe pathological changes in the hepatic， renal， and testicular tissues. Cin@CsNPs improved significantly all the parameters tested and the histological picture in a dose-dependent. Therefore， the biosynthesized ZnO-NPs exhibit oxidative damage and genotoxicity， and Cin@CsNPs have potential protective effects against the risks of ZnO-NPs and may be a promising tool to overcome the challenges of using Cin in food and pharmaceuticals applications.

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Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

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Computer Modeling in Engineering & sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan salman A.AlQahtani Mabrook s.AlRakhami Ahmed Zohier Elhendi Department of Information systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the sARs-COV-2 CT-scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

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A Novel Approach to Energy Optimization:Efficient Path selection in Wireless sensor Networks with Hybrid ANN

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Computers, Materials & Continua 2024年第5期79卷 2945-2970页

作者： Muhammad salman Qamar Ihsan ulHaq Amil Daraz Atif MAlamri salman A.AlQahtani Muhammad Fahad Munir Department of Electrical and Computer Engineering International Islamic UniversityIslamabad44000Pakistan school of Information science and Engineering NingboTech UniversityNingbo315100China software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia

In pursuit of enhancing the Wireless sensor Networks(WsNs)energy efficiency and operational lifespan,this paper delves into the domain of energy-efficient routing ***,the limited energy resources of sensor Nodes(sNs)are a big challenge for ensuring their efficient and reliable *** data gathering involves the utilization of a mobile sink(Ms)to mitigate the energy consumption problem through periodic network *** mobile sink(Ms)strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points(RPs)instead of all cluster heads(CHs).CHs subsequently transmit packets to neighboring *** unique determination of this study is the shortest path to reach *** the mobile sink(Ms)concept has emerged as a promising solution to the energy consumption problem in WsNs,caused by multi-hop data collection with static *** this study,we proposed two novel hybrid algorithms,namely“ Reduced k-means based on Artificial Neural Network”(RkM-ANN)and“Delay Bound Reduced kmeans with ANN”(DBRkM-ANN)for designing a fast,efficient,and most proficient Ms path depending upon rendezvous points(RPs).The first algorithm optimizes the Ms’s latency,while the second considers the designing of delay-bound paths,also defined as the number of paths with delay over bound for the *** methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide *** addition,a method of using Ms scheduling for efficient data collection is *** simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators.

关键词： Wireless sensor Networks(WsNs) mobile sink(Ms) rendezvous point(RP) machine learning Artificial Neural Networks(ANNs)

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Enhancing skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： syeda shamaila Zareen Guangmin sun Mahwish Kundi syed Furqan Qadri salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and security Zhejiang LabHangzhou311121China Computer science Department MNS University of AgricultureMultan59220Pakistan

skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long short-TermMemory (LsTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

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Enhanced Topic-Aware summarization Using statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq salman Afsar Awan Fahad Ahmad Muhammad Azam Zia Muhammad Zafar Iqbal Department of Computer science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan school of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text summarization(ETs)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document summarization(MDs)from single-Document summarization,offering significant improvements over existing models such as THGs-GMM and Topic-Graphsum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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Computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia school of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, sugeno, and Kang (TsK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Understanding the Novel Approach of Nanoferroptosis for Cancer Therapy

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Nano-Micro Letters 2024年第9期16卷 501-556页

作者： Afsana sheikh Prashant Kesharwani Waleed H.Almalki salem salman Almujri Linxin Dai Zhe‑sheng Chen Amirhossein sahebkar Fei Gao Department of Pharmaceutics School of Pharmaceutical Education and ResearchJamia HamdardNew Delhi 110062India Department of Pharmacology and Toxicology Faculty of PharmacyUmm Al-Qura UniversityMakkahSaudi Arabia Department of Pharmacology College of PharmacyKing Khalid University61421 Asir‑AbhaSaudi Arabia state Key Laboratory of southwestern Chinese Medicine Resources Pharmacy SchoolChengdu University of Traditional Chinese MedicineChengdu 611130People’s Republic of China Department of Pharmaceutical sciences College of Pharmacy and Health SciencesSt.John’s UniversityNew York 11439USA Biotechnology Research Center Pharmaceutical Technology InstituteMashhad University of Medical SciencesMashhadIran Applied Biomedical Research Center Mashhad University of Medical SciencesMashhadIran

As a new form of regulated cell death,ferroptosis has unraveled the unsolicited theory of intrinsic apoptosis resistance by cancer *** molecular mechanism of ferroptosis depends on the induction of oxidative stress through excessive reactive oxygen species accumulation and glutathione depletion to damage the structural integrity of *** to their high loading and structural tunability,nanocarriers can escort the delivery of ferro-therapeutics to the desired site through enhanced permeation or retention effect or by active *** review shed light on the necessity of iron in cancer cell growth and the fascinating features of ferroptosis in regulating the cell cycle and ***,we discussed the effect of ferroptosis-mediated therapy using nanoplatforms and their chemical basis in overcoming the barriers to cancer therapy.

关键词： Ferroptosis Nanoparticles Ferrotherapy Cancer Genes Nanotechnology Toxicity Reactive oxygen species

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AFBNet: A Lightweight Adaptive Feature Fusion Module for super-Resolution Algorithms

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Computer Modeling in Engineering & sciences 2024年第9期140卷 2315-2347页

作者： Lirong Yin Lei Wang siyu Lu Ruiyang Wang Haitao Ren Ahmed Alsanad salman A.AlQahtani Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA school of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China school of Geographical sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： super-resolution feature extraction dynamic convolution attention mechanism gate control

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Biochar-compost-based controlled-release nitrogen fertilizer intended for an active microbial community

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Frontiers of Agricultural science and Engineering 2024年第2期11卷 326-343页

作者： Robiul Islam RUBEL Lin WEI salman ALANAZI Abdulkarim ALDEKHAIL Anne CMCIDREIRA Xufei YANG sanjita WAsTI samarthya BHAGIA Xianhui ZHAO Department of Agricultural and Biosystem Engineering South Dakota State UniversityBrookingsSD 57007USA Tickle College of Engineering University of Tennessee KnoxvilleKnoxvilleTN 37996USA Oak Ridge National Laboratory Oak RidgeTN 37830USA

Nitrogen(N) fertilizers in agriculture suffer losses by volatilization of N to the air, surface runoff and leaching into the soil, resulting in low N use efficiency(NUE)( 50%) and raising severe environmental pollutions. Controlledrelease nitrogen fertilizers(CRNFs) can control the release of N nutrients to NUE in crop production. Different methods were used to develop new ***, different CRNF technologies are still underdeveloped due to inadequate controlling on N releasing time and/or unsustainable diffusion. The study on the influences of CRNF processing parameters on microbial conditions are lacking when the CRNFs composed of various bio-ingredients such as biochar, composts, and biowaste. The complexity of processing methods, material biodegradability, and other physical properties make current CRNFs of questionable value in agricultural production. This research aims to develop a novel biochar-compost-based controlled-release urea fertilizer(BCRUF) to preserve microbial properties carried by the compost. The BCRUF was synthesized by pelletizing the 50:50(dry, wt/wt) mixture of biochar and compost. BCRUF was loaded with urea and then spray-coated with polylactic acid(PLA). The releasing time of two types of BCRUFs, coated and uncoated with PLA, for 80% of N release in water was up to 6 h at three different temperatures(4, 23, and 40 °C), compared to conventional urea fertilizer and commercial environmentally smart N(EsN) fertilizer. The releasing time of coated BCRUF for 80% N release in soil was up to 192 h(8 days). Fourier-transform infrared spectroscopy(FTIR) analysis revealed that no new functional groups were found in the release solution, indicating no new chemical hazards generated. The differential scanning calorimetry(DsC)tests also verified that its thermal stability could be up to 160 °C. The microbe populations in the BCRUF pellets were reduced after the pelleting and drying processes in BCRUF fabrication, but a few bacteria can endure in t

关键词： soil microbial community biochar compost controlled-release nitrogen fertilizer polylactic acid spray coating

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New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

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Computer Modeling in Engineering & sciences 2024年第3期138卷 2749-2767页

作者： shimaa M.Amer Ashraf A.M.Khalaf Amr H.Hussein salman A.Alqahtani Mostafa H.Dahshan Hossam M.Kassem Electronics and Communications Engineering Department Higher Institute of Engineering and TechnologyKafr ElsheikhEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringMinia UniversityMiniaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringTanta UniversityTantaEgypt Electronics and Electrical Communications Engineering Department Faculty of EngineeringHorus University EgyptNew DamiettaEgypt Department of Computer Engineering College of computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia school of Computing Mathematics and EngineeringCharles Sturt UniversityBathurstNSW2795Australia

side lobe level reduction(sLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOs)in recent and future wireless communication systems starting from 5G up to ***,it improves the array gain and directivity,increasing the detection range and angular resolution of radar *** study proposes two highly efficient sLL reduction *** techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,*** convolution process determines the element’s excitations while the GA optimizes the element *** M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,*** the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower sLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher *** the increased HPBWof the odd and even excitations,the element spacing is optimized using the ***,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the ***,for extreme sLL reduction,the DConv/GA is *** this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation *** provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the sLL.

关键词： Array synthesis convolution process genetic algorithm(GA) half power beamwidth(HPBW) linear antenna array(LAA) side lobe level(sLL) quality of service(QOs)

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