Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networ...Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.展开更多
Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection mechanisms.This study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutiona...Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection mechanisms.This study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection capabilities.Addressing 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 accuracy.The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware classification.Tested on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional methods.This approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity applications.The 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.展开更多
Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landsc...Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landscapes,or from catchment watersheds.The intensified global changes in climate,land use and cover,and pollution that occurred over the past century have brought about adverse impacts on forests and their services in water regulation,signifying the importance of forest hydrological research as a re-emerging topic of scientific interest.This article reviews the literature on recent advances in forest hydrological research,intending to identify leading countries,institutions,and researchers actively engaged in this field,as well as highlighting research hotspots for future exploration.Through a systematic analysis using VOSviewer,drawing from 17,006 articles retrieved from the Web of Science Core Collection spanning 2000–2022,we employed scientometric methods to assess research productivity,identify emerging topics,and analyze academic development.The findings reveal a consistent growth in forest hydrological research over the past two decades,with the United States,Charles T.Driscoll,and the Chinese Academy of Sciences emerging as the most productive country,author,and institution,respectively.The Journal of Hydrology emerges as the most co-cited journal.Analysis of keyword co-occurrence and co-cited references highlights key research areas,including climate change,management strategies,runoff-erosion dynamics,vegetation cover changes,paired catchment experiments,water quality,aquatic biodiversity,forest fire dynamics and hydrological modeling.Based on these findings,our study advocates for an integrated approach to future research,emphasizing the collection of data from diverse sources,utilization of varied methodologies,and collaboration across disciplines and institutions.This holistic strategy is essential for developing sustainable approaches to forested watershed planning and management.Ultimately,our study provides valuable insights for researchers,practitioners,and policymakers,guiding future research directions towards forest hydrological research and applications.展开更多
Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-cons...Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.展开更多
Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.Thi...Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.展开更多
Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and ...Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.展开更多
Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a cru...Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.展开更多
Generally wastewater such agricultural runoff is considered a nuisance;however,it could be harnessed as a potential source of nutrients like nitrates and phosphates in integrated biorefinery context.In the current stu...Generally wastewater such agricultural runoff is considered a nuisance;however,it could be harnessed as a potential source of nutrients like nitrates and phosphates in integrated biorefinery context.In the current study,microalgae Chlorella sp.S5 was used for bioremediation of agricultural runoff and the leftover algal biomass was used as a potential source for production of biofuels in an integrated biorefinery context.The microalgae Chlorella sp.S5 was cultivated on Blue Green(BG 11)medium and a comprehensive optimization of different parameters including phosphates,nitrates,and pH was carried out to acquire maximum algal biomass enriched with high lipids content.Dry biomass was quantified using the solvent extraction technique,while the identification of nitrates and phosphates in agricultural runoff was carried out using commercial kits.The algal extracted lipids(oils)were employed in enzymatic trans-esterification for biodiesel production using whole-cell biomass of Bacillus subtilis Q4 MZ841642.The resultant fatty acid methyl esters(FAMEs)were analyzed using Fourier transform infrared(FTIR)spectroscopy and gas chromatography coupled with mass spectrometry(GC-MS).Subsequently,both the intact algal biomass and its lipid-depleted algal biomass were used for biogas production within a batch anaerobic digestion setup.Interestingly,Chlorella sp.S5 demonstrated a substantial reduction of 95%in nitrate and 91%in phosphate from agricultural runoff.The biodiesel derived from algal biomass exhibited a noteworthy total FAME content of 98.2%,meeting the quality standards set by American Society for Testing and Materials(ASTM)and European union(EU)standards.Furthermore,the biomethane yields obtained from whole biomass and lipid-depleted biomass were 330.34 NmL/g VSadded and 364.34 NmL/g VSadded,respectively.In conclusion,the findings underscore the potent utility of Chlorella sp.S5 as a multi-faceted resource,proficiently employed in a sequential cascade for treating agricultural runoff,producing biodiesel,and generating biogas within the integrated biorefinery concept.展开更多
Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis...Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.展开更多
The phytochrome B (PHYB) gene of Arabidopsis thaliana was introduced into cotton through Agrobacterium tumefaciens.Integration and expression of PHYB gene in cotton plants were confirmed by molecular evidence.Messenge...The phytochrome B (PHYB) gene of Arabidopsis thaliana was introduced into cotton through Agrobacterium tumefaciens.Integration and expression of PHYB gene in cotton plants were confirmed by molecular evidence.Messenger RNA (mRNA) expression in one of the transgenic lines,QCC11,was much higher than those of control and other transgenic lines.Transgenic cotton plants showed more than a two-fold increase in photosynthetic rate and more than a four-fold increase in transpiration rate and stomatal conductance.The increase in photosynthetic rate led to a 46% increase in relative growth rate and an 18% increase in net assimilation rate.Data recorded up to two generations,both in the greenhouse and in the field,revealed that overexpression of Arabidopsis thaliana PHYB gene in transgenic cotton plants resulted in an increase in the production of cotton by improving the cotton plant growth,with 35% more yield.Moreover,the presence of the Arabidopsis thaliana PHYB gene caused pleiotropic effects like semi-dwarfism,decrease in apical dominance,and increase in boll size.展开更多
The prevalence of cardiovascular diseases(CVDs)is increasing at a rapid pace in developed countries,and CVDs are the leading cause of morbidity and mortality.Natural products and ethnomedicine have been shown to reduc...The prevalence of cardiovascular diseases(CVDs)is increasing at a rapid pace in developed countries,and CVDs are the leading cause of morbidity and mortality.Natural products and ethnomedicine have been shown to reduce the risk of CVDs.Schizonepeta(S.)tenuifolia is a medicinal plant widely used in China,Korea,and Japan and is known to exhibit anti-inflammatory,antioxidant,and immunomodulatory activities.We hypothesized that given herbal plant exhibit pharmacological activities against CVDs,we specifically explored its effects on platelet function.Platelet aggregation was evaluated using standard light transmission aggregometry.Intracellular calcium mobilization was assessed using Fura-2/AM,and granule secretion(ATP release)was measured in a luminometer.Fibrinogen binding to integrin a_(Ⅱb)β_3,was assessed using flow cytometry.Phosphorylation of mitogen-activated protein kinase(MAPK)signaling molecules and activation of the protein kinase B(Akt)was assessed using Western blot assays.S.tenuifolia,extract potently and significantly inhibited platelet aggregation,calcium mobilization,granule secretion,and fibrinogen binding to integrin a_(Ⅱb)β_3.Moreover,all extracts significantly inhibited MAPK and Akt phosphorylation.S.tenuifolia extract inhibited platelet aggregation and granule secretion,and attenuated collagen mediated GPVI downstream signaling,indicating the potential therapeutic effects of these plant extracts on the cardiovascular system and platelet function.We suggest that S.tenuifolia extract may be a potent candidate to treat platelet-related CVDs and to be used as an antiplatelet and antithrombotic agent.展开更多
This paper reports the purification and characterization of kinetic parameters of cellulase produced from Trichoderma viride under still culture solid state fermentation technique using cheap and an easily available a...This paper reports the purification and characterization of kinetic parameters of cellulase produced from Trichoderma viride under still culture solid state fermentation technique using cheap and an easily available agricultural waste material, wheat straw as growth supported substrate. Trichoderma viride was cultured in fermentation medium of wheat straw under some previously optimized growth conditions and maximum activity of 398±2.43U/mL obtained after stipulated fermentation time period. Cellulase was purified 2.33 fold with specific activity of 105U/mg in comparison to crude enzyme extract using ammonium sulfate precipitation, dialysis and Sephadex-G-100 column chromatography. The enzyme was shown to have a relative low molecular weight of 58kDa by sodium dodecyl sulphate poly-acrylamide gel electrophoresis. The purified enzyme displayed 6.5 and 55oC as an optimum pH and temperature respectively. Using carboxymethyl cellulose as substrate, the enzyme showed maximum activity (Vmax) of 148U/mL with its corresponding KM value of 68μM. Among activators/inhibitors SDS, EDTA, and Hg2+ showed inhibitory effect on purified cellulase whereas, the enzyme activated by Co2+ and Mn2+ at a concentration of 1mM. The purified cellulase was compatible with four local detergent brands with up to 20 days of shelf life at room temperature suggesting its potential as a detergent additive for improved washing therefore, it is concluded that it may be potentially useful for industrial purposes especially for detergent and laundry industry.展开更多
Therapeutic dentin regeneration remains difficult to achieve,and a majority of the attention has been given to anabolic strategies to promote dentinogenesis directly,whereas,the available literature is insufficient to...Therapeutic dentin regeneration remains difficult to achieve,and a majority of the attention has been given to anabolic strategies to promote dentinogenesis directly,whereas,the available literature is insufficient to understand the role of inflammation and inflammatory complement system on dentinogenesis.The aim of this study is to determine the role of complement C5a receptor(C5aR)in regulating dental pulp stem cells(DPSCs)differentiation and in vivo dentin regeneration.Human DPSCs were subjected to odontogenic differentiation in osteogenic media treated with the C5aR agonist and C5aR antagonist.In vivo dentin formation was evaluated using the dentin injury/pulp-capping model of the C5a-deficient and wildtype mice.In vitro results demonstrate that C5aR inhibition caused a substantial reduction in odontogenic DPSCs differentiation markers such as DMP-1 and DSPP,while the C5aR activation increased these key odontogenic genes compared to control.A reparative dentin formation using the C5a-deficient mice shows that dentin regeneration is significantly reduced in the C5a-deficient mice.These data suggest a positive role of C5aR in the odontogenic DPSCs differentiation and tertiary/reparative dentin formation.This study addresses a novel regulatory pathway and a therapeutic approach for improving the efficiency of dentin regeneration in affected teeth.展开更多
In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern....In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.展开更多
One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which make...One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.展开更多
Effects of dilute acid and acid steam pretreatments were inspected for cellulose production of Eucalyptus leaves through Box-Behenken design, a three variable factors for response surface methodology by Bacillus subti...Effects of dilute acid and acid steam pretreatments were inspected for cellulose production of Eucalyptus leaves through Box-Behenken design, a three variable factors for response surface methodology by Bacillus subtilus K-18. Maximum cellulose production performed in 250 mL erlenmeyer flask with submerged fermentation attained at 50＂C, pH 5, 140 r· min-1 for 24 h. Results showed the efficient cellulose production from acid steam pretreatrnent （being autoclaved at 15 Psi for 15 rain） than acid pretreatment. The optimum condition for maximum carboxymethyl cellulas （CMCase） was 1.811 IU·mL-1·min-1 （0.8% acid cone., 10 g biomass loading, 6 h reaction time） and filter paper activity （FPase） was 2.255 IU·mL·-1·min-1 （1% acid conc., 10 g biomass loading, 8 h reaction time）. Whereas, the acid steam maximum CMCase activity recorded was 2.585 IU·mL-1·min-1 （0.8% acid cone., 15 g substrate loading and 8 h reaction time） and the highest FPase activity was 2.055 IU·mL-1·min-1 （0.8% cone., 10 g biomass, 6 h reaction time then autoclaved）. Results revealed that acid pretreated Eucalyptus leaves were better lignocellulosic biomass for cellulose production by submerged fermentation.展开更多
基金the Deanship of Graduate Studies and Scientific Research at Najran University,Saudi Arabia,for their financial support through the Easy Track Research program,grant code(NU/EFP/MRC/13).
文摘Background:Accurate classification of normal blood cells is a critical foundation for automated hematological analysis,including the detection of pathological conditions like leukemia.While convolutional neural networks(CNNs)excel in local feature extraction,their ability to capture global contextual relationships in complex cellular morphologies is limited.This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification,laying the groundwork for future leukemia diagnostics.Methods:The proposed architecture integrates pre-trained CNNs(ResNet50,EfficientNetB3,InceptionV3,CustomCNN)with Vision Transformer(ViT)layers to combine local and global feature modeling.Four hybrid models were evaluated on the publicly available Blood Cell Images dataset from Kaggle,comprising 17,092 annotated normal blood cell images across eight classes.The models were trained using transfer learning,fine-tuning,and computational optimizations,including cross-model parameter sharing to reduce redundancy by reusing weights across CNN backbones and attention-guided layer pruning to eliminate low-contribution layers based on attention scores,improving efficiency without sacrificing accuracy.Results:The InceptionV3-ViT model achieved a weighted accuracy of 97.66%(accounting for class imbalance by weighting each class’s contribution),a macro F1-score of 0.98,and a ROC-AUC of 0.998.The framework excelled in distinguishing morphologically similar cell types demonstrating robustness and reliable calibration(ECE of 0.019).The framework addresses generalization challenges,including class imbalance and morphological similarities,ensuring robust performance across diverse cell types.Conclusion:The hybrid CNN-Transformer framework significantly improves normal blood cell classification by capturing multi-scale features and long-range dependencies.Its high accuracy,efficiency,and generalization position it as a strong baseline for automated hematological analysis,with potential for extension to leukemia subtype classification through future validation on pathological samples.
文摘Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection mechanisms.This study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection capabilities.Addressing 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 accuracy.The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware classification.Tested on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional methods.This approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity applications.The 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.
基金supported by Yibin University,Sichuan,China and Hebei University,Baoding,China(Grant No.521100221033).
文摘Forest hydrology,the study of water dynamics within forested catchments,is crucial for understanding the intricate relationship between forest cover and water balances across different scales,from ecosystems to landscapes,or from catchment watersheds.The intensified global changes in climate,land use and cover,and pollution that occurred over the past century have brought about adverse impacts on forests and their services in water regulation,signifying the importance of forest hydrological research as a re-emerging topic of scientific interest.This article reviews the literature on recent advances in forest hydrological research,intending to identify leading countries,institutions,and researchers actively engaged in this field,as well as highlighting research hotspots for future exploration.Through a systematic analysis using VOSviewer,drawing from 17,006 articles retrieved from the Web of Science Core Collection spanning 2000–2022,we employed scientometric methods to assess research productivity,identify emerging topics,and analyze academic development.The findings reveal a consistent growth in forest hydrological research over the past two decades,with the United States,Charles T.Driscoll,and the Chinese Academy of Sciences emerging as the most productive country,author,and institution,respectively.The Journal of Hydrology emerges as the most co-cited journal.Analysis of keyword co-occurrence and co-cited references highlights key research areas,including climate change,management strategies,runoff-erosion dynamics,vegetation cover changes,paired catchment experiments,water quality,aquatic biodiversity,forest fire dynamics and hydrological modeling.Based on these findings,our study advocates for an integrated approach to future research,emphasizing the collection of data from diverse sources,utilization of varied methodologies,and collaboration across disciplines and institutions.This holistic strategy is essential for developing sustainable approaches to forested watershed planning and management.Ultimately,our study provides valuable insights for researchers,practitioners,and policymakers,guiding future research directions towards forest hydrological research and applications.
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
基金supported by the Deanship of Graduate Studies and Scientific Research at Najran University through funding code NU/GP/MRC/13/771-1.
文摘Global mortality rates are greatly impacted by malignancies of the brain and nervous system.Although,Magnetic Resonance Imaging(MRI)plays a pivotal role in detecting brain tumors;however,manual assessment is time-consuming and susceptible to human error.To address this,we introduce ICA2-SVM,an advanced computational framework integrating Independent Component Analysis Architecture-2(ICA2)and Support Vector Machine(SVM)for automated tumor segmentation and classification.ICA2 is utilized for image preprocessing and optimization,enhancing MRI consistency and contrast.The Fast-MarchingMethod(FMM)is employed to delineate tumor regions,followed by SVM for precise classification.Validation on the Contrast-Enhanced Magnetic Resonance Imaging(CEMRI)dataset demonstrates the superior performance of ICA2-SVM,achieving a Dice Similarity Coefficient(DSC)of 0.974,accuracy of 0.992,specificity of 0.99,and sensitivity of 0.99.Additionally,themodel surpasses existing approaches in computational efficiency,completing analysis within 0.41 s.By integrating state-of-the-art computational techniques,ICA2-SVM advances biomedical imaging,offering a highly accurate and efficient solution for brain tumor detection.Future research aims to incorporate multi-physics modeling and diverse classifiers to further enhance the adaptability and applicability of brain tumor diagnostic systems.
基金supported by King Abdulaziz University,Deanship of Scientific Research,Jeddah,Saudi Arabia under grant no. (GWV-8053-2022).
文摘Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing faults.Manual fault diagnosis poses safety risks and economic instability,necessitating an automated approach.This study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular Learning.The model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label encoding.Its architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss functions.Fine-tuning techniques prevent over-fitting.Evaluations were conducted on 10 fault classes from the CWRU dataset.FTCNNLSTM was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 instances.The FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other methods.This establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.
基金the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Research Groups Funding Program Grant Code Number(NU/RG/SERC/12/43).
文摘Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.
基金supported by the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,for funding this work under the Distinguished Research Funding Program Grant Code Number(NU/DRP/SERC/12/16).
文摘Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.
基金part of NRPU project(20-1482NRPU/R&D/HEC/2021)The financial support to accomplish this research was provided by Higher Education Commission of PakistanMoreover,this work is financially supported by the Researchers Supporting Project number(RSP2024R197),King Saud University,Riyadh,Saudi Arabia.
文摘Generally wastewater such agricultural runoff is considered a nuisance;however,it could be harnessed as a potential source of nutrients like nitrates and phosphates in integrated biorefinery context.In the current study,microalgae Chlorella sp.S5 was used for bioremediation of agricultural runoff and the leftover algal biomass was used as a potential source for production of biofuels in an integrated biorefinery context.The microalgae Chlorella sp.S5 was cultivated on Blue Green(BG 11)medium and a comprehensive optimization of different parameters including phosphates,nitrates,and pH was carried out to acquire maximum algal biomass enriched with high lipids content.Dry biomass was quantified using the solvent extraction technique,while the identification of nitrates and phosphates in agricultural runoff was carried out using commercial kits.The algal extracted lipids(oils)were employed in enzymatic trans-esterification for biodiesel production using whole-cell biomass of Bacillus subtilis Q4 MZ841642.The resultant fatty acid methyl esters(FAMEs)were analyzed using Fourier transform infrared(FTIR)spectroscopy and gas chromatography coupled with mass spectrometry(GC-MS).Subsequently,both the intact algal biomass and its lipid-depleted algal biomass were used for biogas production within a batch anaerobic digestion setup.Interestingly,Chlorella sp.S5 demonstrated a substantial reduction of 95%in nitrate and 91%in phosphate from agricultural runoff.The biodiesel derived from algal biomass exhibited a noteworthy total FAME content of 98.2%,meeting the quality standards set by American Society for Testing and Materials(ASTM)and European union(EU)standards.Furthermore,the biomethane yields obtained from whole biomass and lipid-depleted biomass were 330.34 NmL/g VSadded and 364.34 NmL/g VSadded,respectively.In conclusion,the findings underscore the potent utility of Chlorella sp.S5 as a multi-faceted resource,proficiently employed in a sequential cascade for treating agricultural runoff,producing biodiesel,and generating biogas within the integrated biorefinery concept.
文摘Introduction:Alzheimer's disease(AD)is a progressive brain disorder that impairs cognitive functions,behavior,and memory.Early detection is crucial as it can slow down the progression of AD.However,early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests.Methods:This study introduces a data acquisition technique and a preprocessing pipeline,combined with multivariate long short-term memory(M-LSTM)and AdaBoost models.These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients,using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database.Results:The methodology proposed in this study significantly improved performance metrics.The testing accuracy reached 80%with the AdaBoost model,while the M-LSTM model achieved an accuracy of 82%.This represents a 20%increase in accuracy compared to a recent similar study.Discussion:The findings indicate that the multivariate model,specifically the M-LSTM,is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
文摘The phytochrome B (PHYB) gene of Arabidopsis thaliana was introduced into cotton through Agrobacterium tumefaciens.Integration and expression of PHYB gene in cotton plants were confirmed by molecular evidence.Messenger RNA (mRNA) expression in one of the transgenic lines,QCC11,was much higher than those of control and other transgenic lines.Transgenic cotton plants showed more than a two-fold increase in photosynthetic rate and more than a four-fold increase in transpiration rate and stomatal conductance.The increase in photosynthetic rate led to a 46% increase in relative growth rate and an 18% increase in net assimilation rate.Data recorded up to two generations,both in the greenhouse and in the field,revealed that overexpression of Arabidopsis thaliana PHYB gene in transgenic cotton plants resulted in an increase in the production of cotton by improving the cotton plant growth,with 35% more yield.Moreover,the presence of the Arabidopsis thaliana PHYB gene caused pleiotropic effects like semi-dwarfism,decrease in apical dominance,and increase in boll size.
基金supported by the National Research Foundation of Koreagrant funded by the Korean Government(MSIP,No.2015R1D1-AIA09057204)
文摘The prevalence of cardiovascular diseases(CVDs)is increasing at a rapid pace in developed countries,and CVDs are the leading cause of morbidity and mortality.Natural products and ethnomedicine have been shown to reduce the risk of CVDs.Schizonepeta(S.)tenuifolia is a medicinal plant widely used in China,Korea,and Japan and is known to exhibit anti-inflammatory,antioxidant,and immunomodulatory activities.We hypothesized that given herbal plant exhibit pharmacological activities against CVDs,we specifically explored its effects on platelet function.Platelet aggregation was evaluated using standard light transmission aggregometry.Intracellular calcium mobilization was assessed using Fura-2/AM,and granule secretion(ATP release)was measured in a luminometer.Fibrinogen binding to integrin a_(Ⅱb)β_3,was assessed using flow cytometry.Phosphorylation of mitogen-activated protein kinase(MAPK)signaling molecules and activation of the protein kinase B(Akt)was assessed using Western blot assays.S.tenuifolia,extract potently and significantly inhibited platelet aggregation,calcium mobilization,granule secretion,and fibrinogen binding to integrin a_(Ⅱb)β_3.Moreover,all extracts significantly inhibited MAPK and Akt phosphorylation.S.tenuifolia extract inhibited platelet aggregation and granule secretion,and attenuated collagen mediated GPVI downstream signaling,indicating the potential therapeutic effects of these plant extracts on the cardiovascular system and platelet function.We suggest that S.tenuifolia extract may be a potent candidate to treat platelet-related CVDs and to be used as an antiplatelet and antithrombotic agent.
文摘This paper reports the purification and characterization of kinetic parameters of cellulase produced from Trichoderma viride under still culture solid state fermentation technique using cheap and an easily available agricultural waste material, wheat straw as growth supported substrate. Trichoderma viride was cultured in fermentation medium of wheat straw under some previously optimized growth conditions and maximum activity of 398±2.43U/mL obtained after stipulated fermentation time period. Cellulase was purified 2.33 fold with specific activity of 105U/mg in comparison to crude enzyme extract using ammonium sulfate precipitation, dialysis and Sephadex-G-100 column chromatography. The enzyme was shown to have a relative low molecular weight of 58kDa by sodium dodecyl sulphate poly-acrylamide gel electrophoresis. The purified enzyme displayed 6.5 and 55oC as an optimum pH and temperature respectively. Using carboxymethyl cellulose as substrate, the enzyme showed maximum activity (Vmax) of 148U/mL with its corresponding KM value of 68μM. Among activators/inhibitors SDS, EDTA, and Hg2+ showed inhibitory effect on purified cellulase whereas, the enzyme activated by Co2+ and Mn2+ at a concentration of 1mM. The purified cellulase was compatible with four local detergent brands with up to 20 days of shelf life at room temperature suggesting its potential as a detergent additive for improved washing therefore, it is concluded that it may be potentially useful for industrial purposes especially for detergent and laundry industry.
基金supported by the NIH/NIDCR grants: R03 DE028637 – SC, R56 DE029816 – SC
文摘Therapeutic dentin regeneration remains difficult to achieve,and a majority of the attention has been given to anabolic strategies to promote dentinogenesis directly,whereas,the available literature is insufficient to understand the role of inflammation and inflammatory complement system on dentinogenesis.The aim of this study is to determine the role of complement C5a receptor(C5aR)in regulating dental pulp stem cells(DPSCs)differentiation and in vivo dentin regeneration.Human DPSCs were subjected to odontogenic differentiation in osteogenic media treated with the C5aR agonist and C5aR antagonist.In vivo dentin formation was evaluated using the dentin injury/pulp-capping model of the C5a-deficient and wildtype mice.In vitro results demonstrate that C5aR inhibition caused a substantial reduction in odontogenic DPSCs differentiation markers such as DMP-1 and DSPP,while the C5aR activation increased these key odontogenic genes compared to control.A reparative dentin formation using the C5a-deficient mice shows that dentin regeneration is significantly reduced in the C5a-deficient mice.These data suggest a positive role of C5aR in the odontogenic DPSCs differentiation and tertiary/reparative dentin formation.This study addresses a novel regulatory pathway and a therapeutic approach for improving the efficiency of dentin regeneration in affected teeth.
基金The authors acknowledge the support from the Ministry of Education and the Deanship of Scientific Research,Najran University,Saudi Arabia,under code number NU/-/SERC/10/616.
文摘In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.
文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.
文摘Effects of dilute acid and acid steam pretreatments were inspected for cellulose production of Eucalyptus leaves through Box-Behenken design, a three variable factors for response surface methodology by Bacillus subtilus K-18. Maximum cellulose production performed in 250 mL erlenmeyer flask with submerged fermentation attained at 50＂C, pH 5, 140 r· min-1 for 24 h. Results showed the efficient cellulose production from acid steam pretreatrnent （being autoclaved at 15 Psi for 15 rain） than acid pretreatment. The optimum condition for maximum carboxymethyl cellulas （CMCase） was 1.811 IU·mL-1·min-1 （0.8% acid cone., 10 g biomass loading, 6 h reaction time） and filter paper activity （FPase） was 2.255 IU·mL·-1·min-1 （1% acid conc., 10 g biomass loading, 8 h reaction time）. Whereas, the acid steam maximum CMCase activity recorded was 2.585 IU·mL-1·min-1 （0.8% acid cone., 15 g substrate loading and 8 h reaction time） and the highest FPase activity was 2.055 IU·mL-1·min-1 （0.8% cone., 10 g biomass, 6 h reaction time then autoclaved）. Results revealed that acid pretreated Eucalyptus leaves were better lignocellulosic biomass for cellulose production by submerged fermentation.
