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Biochar potentially enhances maize tolerance to arsenic toxicity by improving physiological and biochemical responses to excessive arsenate

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Biochar 2023年第1期5卷 1222-1242页

作者： Md.Mezanur rahman Ashim Kumar Das Sharmin Sultana Protik Kumar Ghosh Md.Robyul Islam Sanjida Sultana Keya Minhaz Ahmed Sheikh Arafat Islam Nihad mdarifur rahman khan Mylea C.Lovell Md.Abiar rahman S.M.Ahsan Touhidur rahman Anik Pallavi Fnu Lam-Son Phan Tran Mohammad Golam Mostofa Department of Plant Soil ScienceInstitute of Genomics for Crop Abiotic Stress ToleranceTexas Tech UniversityLubbockTX 79409USA Department of Agroforestry EnvironmentBangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706Bangladesh Institute of Biotechnology and Genetic Engineering(IBGE) Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706Bangladesh Department of Agronomy Bangabandhu Sheikh Mujibur Rahman Agricultural UniversityGazipur 1706Bangladesh Plant Pathology Division Bangladesh Rice Research InstituteGazipur 1701Bangladesh Department of Agriculture Bangabandhu Sheikh Mujibur Rahman ScienceTechnology UniversityGopalganj 8100Bangladesh Department of Energy Plant Research Laboratory Michigan State UniversityEast LansingMI 48824USA Plant Resilience Institute Michigan State UniversityEast LansingMI 48824USA Department of Biochemistry and Molecular Biology Michigan State UniversityEast LansingMI 48824USA

Metalloid pollution,including arsenic poisoning,is a serious environmental issue,plaguing plant productivity and quality of life ***,a carbon-rich material,has been known to alleviate the negative effects of environmental pollutants on ***,the specific role of biochar in mitigating arsenic stress in maize remains relatively ***,we elucidated the functions of biochar in improving maize growth under the elevated level of sodium arsenate(Na_(2)AsO_(4),AsV).Maize plants were grown in pot-soils amended with two doses of biochar(2.5%(B1)and 5.0%(B2)biochar Kg^(−1) of soil)for 5 days,followed by exposure to Na_(2)AsO_(4)(’B1+AsV’and’B2+AsV’)for 9 *** plants exposed to AsV only accumulated substantial amount of arsenic in both roots and leaves,triggering severe phytotoxic effects,including stunted growth,leaf-yellowing,chlorosis,reduced photosynthesis,and nutritional imbalance,when compared with control ***,biochar addition improved the phenotype and growth of AsV-stressed maize plants by reducing root-to-leaf AsV translocation(by 46.56 and 57.46%in‘B1+AsV’and‘B2+AsV’plants),improving gas-exchange attributes,and elevating chlorophylls and mineral levels beyond AsV-stressed *** pretreatment also substantially counteracted AsV-induced oxidative stress by lowering reactive oxygen species accumulation,lipoxygenase activity,malondialdehyde level,and electrolyte *** oxidative stress in‘B1+AsV’and‘B2+AsV’plants likely supported by a strong antioxidant system powered by biochar-mediated increased activities of superoxide dismutase(by 25.12 and 46.55%),catalase(51.78 and 82.82%),and glutathione S-transferase(61.48 and 153.83%),and improved flavonoid levels(41.48 and 75.37%,respectively).Furthermore,increased levels of soluble sugars and free amino acids also correlated with improved leaf relative water content,suggesting a better osmotic acclimatization mechanism in biochar-pretreated AsV-exposed plants.

关键词： Arsenic contamination Antioxidant defense system Biochar Gas-exchange attributes Nutrient dynamics Oxidative stress

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Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 123-153页

作者： Muhammad Qasim khan Fazal Malik Fahad Alturise Noor rahman Department of Computer Science Iqra National UniversityPeshawar25000Pakistan Department of Cybersecurity College of ComputerQassim UniversityBuraydah52555Saudi Arabia Department of Computer Science and Engineering Al-Fayha CollegeAl Jubayl31961Saudi Arabia

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband ***,they consume important and scarce network resources such as bandwidth and processing *** have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial *** paper draws its motivation from such real network disaster incidents attributed to signaling *** this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and *** provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding *** important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a *** paper presents an update and an extension of our earlier conference *** our knowledge,no similar survey study exists on the subject.

关键词： Signaling storm problems control signaling load analytical modeling 3GPP networks smart devices diameter signaling mobile broadband data access data traffic mobility management signaling network architecture 5G mobile communication

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A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 501-530页

作者： Khuram Ali khan Saba Mubeen Ishfaq Atiqe Ur rahman Salwa El-Morsy Department of Mathematics University of SargodhaSargodha40100Pakistan Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be *** concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such *** study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and *** results related to these operations are also modified for ***,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and ***,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested *** algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical ***,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

关键词： Hypersoft set Pythagorean fuzzy hypersoft set computational complexity multi-attribute decision-making optimization similarity measures uncertainty

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Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

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Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1637-1666页

作者： Md Hasibur rahman Mohammed Arif Uddin Zinnat Fowzia Ria Rashedur M.rahman Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh

The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text ***,BERT’s size and computational demands limit its practicality,especially in resource-constrained *** research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization *** Bengali being the sixth most spoken language globally,NLP research in this area is *** approach addresses this gap by creating an efficient BERT-based model for Bengali *** have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory *** best results demonstrate significant improvements in both speed and *** instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 *** results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.

关键词： Bengali NLP black-box distillation emotion classification model compression post-training quantization unstructured pruning

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Global Piecewise Analysis of HIV Model with Bi-Infectious Categories under Ordinary Derivative and Non-Singular Operator with Neural Network Approach

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 609-633页

作者： Ghaliah Alhamzi Badr Saad TAlkahtani Ravi Shanker Dubey Mati ur rahman Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh13318Saudi Arabia Department of Mathematics College of ScienceKing Saud UniversityRiyadh11989Saudi Arabia Department of Mathematics Amity School of Applied SciencesAmity University RajasthanJaipur302002India School of Mathematical Sciences Jiangsu UniversityZhenjiang212013China Department of Computer Science and Mathematics Lebanese American UniversityBeirut13-5053Lebanon

This study directs the discussion of HIV disease with a novel kind of complex dynamical generalized and piecewise operator in the sense of classical and Atangana Baleanu(AB)derivatives having arbitrary *** HIV infection model has a susceptible class,a recovered class,along with a case of infection divided into three sub-different levels or categories and the recovered *** total time interval is converted into two,which are further investigated for ordinary and fractional order operators of the AB derivative,*** proposed model is tested separately for unique solutions and existence on bi *** numerical solution of the proposed model is treated by the piece-wise numerical iterative scheme of Newtons *** proposed method is established for piece-wise derivatives under natural order and non-singular Mittag-Leffler *** cross-over or bending characteristics in the dynamical system of HIV are easily examined by the aspect of this research having a memory effect for controlling the said *** study uses the neural network(NN)technique to obtain a better set of weights with low residual errors,and the epochs number is considered *** obtained figures represent the approximate solution and absolute error which are tested with NN to train the data accurately.

关键词： HIV infection model qualitative scheme approximate solution piecewise global operator neural network

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Crystal Structures, Optical, Electrical and Magnetic Properties of Ca0.9Sr0.1Mn1−xMoxO3 Perovskites for the Applications of Multifunctional Devices

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Journal of Applied Mathematics and Physics 2025年第1期13卷 61-86页

作者： Sanjana Afrin Tanvir Ahmed Mithum Kumar Das Mohammad Majibur rahman Mohammed Nazrul Islam khan Mohammad J. Miah Department of Physics Comilla University Cumilla Bangladesh Department of EEE Bangladesh University of Business and Technology Dhaka Bangladesh Department of Environmental Sciences Jahangirnagar University Dhaka Bangladesh Materials Science Division Bangladesh Atomic Energy Commission Dhaka Bangladesh

The standard solid-state reaction method was employed to synthesize the polycrystalline Ca0.9Sr0.1Mn1−xMoxO3 (CSMMO with x = 0.00, 0.02, 0.04, 0.06, and 0.08) ceramics and each composition was sintered at 1300˚C for 4 hours. The crystal structure, microstructure, optical properties, electrical properties, complex initial permeability, and DC magnetization of the prepared compositions were carried out. The compound exhibited perovskite structure with the transformation of phase from orthorhombic symmetry for x = 0.00 to x = 0.04 samples into a tetragonal symmetry for the samples x = 0.06 and x = 0.08. The lattice constant was found to be enhanced with the doping of Mo concentration. The mean grain size was estimated in the micrometer, with values from 1.3045 to 3.0124 µm. Energy band gap (Eg) was determined for each composition and the magnitudes of Eg were lying between 1.85 - 4.00 eV. The Eg values firstly decreased with the doping content up to 2% and, then, increased for 4% to 6% Mo contents, where it again very slightly decreased for 8% Mo content. The lowest value of Eg was obtained for the x = 0.02 composition. The FTIR spectra revealed the existence of a metal oxide band in each of the prepared samples and supported the formation of a single-phase compound of synthesized material without any impurity, as confirmed by the XRD analysis. The electrical properties, such as resistivity and AC conductivity, were collected using the Impedance analyzer at room temperature. The resistivity and AC conductivity increased with the addition of dopant concentration. The frequency-dependent conductivity spectra showed three distinct regions. The complex initial permeability (μ′) was found to be enhanced with the addition of Mo and the highest value was noticed the 6% Mo dopant sample. On the other hand, the lowest loss factor was obtained for the samples x ≥ 0.06. The M-H hysteresis loops of various Ca0.9Sr0.1Mn1−xMoxO3 ceramics were measured both at room temperature (300 K

关键词： Perovskite Optical Band Gap Conductivity Permeability DC Magnetization

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Solid Waste Management:A MADM Approach Using Fuzzy Parameterized Possibility Single-Valued Neutrosophic Hypersoft Expert Settings

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 531-553页

作者： Tmader Alballa Muhammad Ihsan Atiqe Ur rahman Noorah Ayed Alsorayea Hamiden Abd El-Wahed Khalifa Department of Mathematics College of SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia 2 Department of Mathematics University of Management and TechnologyLahore54000Pakistan Department of Mathematics College of ScienceQassim UniversityBuraydah51452Saudi Arabia

The dramatic rise in the number of people living in cities has made many environmental and social problems *** search for a productive method for disposing of solid waste is the most notable of these *** scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated *** goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site *** characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these *** tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s *** this,an intelligent algorithm is *** steps of the suggested algorithm are explained with an example that explains *** compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their *** study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,*** uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior *** is simpler for the decisionmakers to look at each option separately because the decision is *** the computed results,it is discovered that they are consistent and dependable because of their preferred properties.

关键词： Hypersoft expert set Sanchez’s method decision making optimization solid waste management possibility grade fuzzy parameterization

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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Computer Modeling in Engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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