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
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
The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is consider...The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.展开更多
A 360°video stream provide users a choice of viewing one's own point of interest inside the immersive contents.Performing head or hand manipulations to view the interesting scene in a 360°video is very t...A 360°video stream provide users a choice of viewing one's own point of interest inside the immersive contents.Performing head or hand manipulations to view the interesting scene in a 360°video is very tedious and the user may view the interested frame during his head/hand movement or even lose it.While automatically extracting user's point of interest(UPI)in a 360°video is very challenging because of subjectivity and difference of comforts.To handle these challenges and provide user's the best and visually pleasant view,we propose an automatic approach by utilizing two CNN models:object detector and aesthetic score of the scene.The proposed framework is three folded:pre-processing,Deepdive architecture,and view selection pipeline.In first fold,an input 360°video-frame is divided into three sub frames,each one with 120°view.In second fold,each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score.Finally,decision pipeline selects the sub frame with salient object based on the detected object and calculated aesthetic score.As compared to other state-of-the-art techniques which are domain specific approaches i.e.,support sports 360°video,our syste m support most of the 360°videos genre.Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360°videos.展开更多
Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection o...Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.展开更多
Geoelectric and hydrochemical approaches are employed to delineate the groundwater potential zones in District Okara,a part of Bari Doab,Punjab,Pakistan.Sixty-seven VES surveys are conducted with the Electrical Resist...Geoelectric and hydrochemical approaches are employed to delineate the groundwater potential zones in District Okara,a part of Bari Doab,Punjab,Pakistan.Sixty-seven VES surveys are conducted with the Electrical Resistivity Meter.The resultant resistivity verses depth model for each site is estimated using computer-based software IX1D.Aquifer thickness maps and interpreted resistivity maps were generated from interpreted VES results.Dar-Zarrouk parameters,transverse resistance(TR),longitudinal conductance(SL)and anisotropy(λ)were also calculated from resistivity data to delineate the potential zones of aquifer.70%of SL value is≤3S,30%of SL value is>3S.According to SL and TR values,the whole area is divided into three potential zones,high,medium and low potential zones.The spatial distribution maps show that north,south and central parts of study area are marked as good potential aquifer zones.Longitudinal conductance values are further utilized to determine aquifer protective capacity of area.The whole area is characterized by moderate to good and up to some extent very good aquifer protective area on the basis of SL values.The groundwater samples from sixty-seven installed tube wells are collected for hydro-chemical analysis.The electrical conductivity values are determined.Correlation is then developed between the EC(μS/cm)of groundwater samples vs.interpreted aquifer resistivity showing R2 value 0.90.展开更多
Ionosphereic foF2 variations are very sensitive to the seismic effect and results of ionospheric perturbations associated with earthquakes seem to very hopeful for short-term earthquake prediction. On January 18,2011 ...Ionosphereic foF2 variations are very sensitive to the seismic effect and results of ionospheric perturbations associated with earthquakes seem to very hopeful for short-term earthquake prediction. On January 18,2011 at 20: 23 UT a great earthquake( M = 7. 2)occurred in Dalbandin( 28. 73° N,63. 92° E),Pakistan. In this study,we have tried to find out the features of pre-earthquake ionospheric anomalies by using the hourly day time( 08. 00 a. m.- 05. 00 p. m.) data of critical frequency( foF2) obtained by three vertical sounding stations installed in Islamabad( 33. 78°N,73. 06°E),Multan( 32. 26°N,71. 51°E) and Karachi( 24. 89° N,67. 02° E), Pakistan. The results show the significant anomalies of foF2 in the earthquake preparation zone several days prior to the Dalbandin earthquake. It is also observed that the amplitude and frequency of foF2 anomalies are more prominent at the nearest station to the epicenter as compared to those stations near the outer margin of the earthquake preparation zone. The confidence level for ionospheric anomalies regarding the seismic signatures can be enhanced by adding the analysis of some other ionospheic parameters along with critical frequency of the layer F2.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.
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.
基金funded by the Deanship of Scientific Research,Najran University,Kingdom of Saudi Arabia,Grant Number NU/MID/18/035.
文摘The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.
文摘A 360°video stream provide users a choice of viewing one's own point of interest inside the immersive contents.Performing head or hand manipulations to view the interesting scene in a 360°video is very tedious and the user may view the interested frame during his head/hand movement or even lose it.While automatically extracting user's point of interest(UPI)in a 360°video is very challenging because of subjectivity and difference of comforts.To handle these challenges and provide user's the best and visually pleasant view,we propose an automatic approach by utilizing two CNN models:object detector and aesthetic score of the scene.The proposed framework is three folded:pre-processing,Deepdive architecture,and view selection pipeline.In first fold,an input 360°video-frame is divided into three sub frames,each one with 120°view.In second fold,each sub-frame is passed through CNN models to extract visual features in the sub-frames and calculate aesthetic score.Finally,decision pipeline selects the sub frame with salient object based on the detected object and calculated aesthetic score.As compared to other state-of-the-art techniques which are domain specific approaches i.e.,support sports 360°video,our syste m support most of the 360°videos genre.Performance evaluation of proposed framework on our own collected data from various websites indicate performance for different categories of 360°videos.
文摘Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of society.The selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic performance.There are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal features.In this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of students.The main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic performance.The key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based fusion.Whereas,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity measures.The proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of instances.The validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and precision.Results reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better than the results of existing approach.
文摘Geoelectric and hydrochemical approaches are employed to delineate the groundwater potential zones in District Okara,a part of Bari Doab,Punjab,Pakistan.Sixty-seven VES surveys are conducted with the Electrical Resistivity Meter.The resultant resistivity verses depth model for each site is estimated using computer-based software IX1D.Aquifer thickness maps and interpreted resistivity maps were generated from interpreted VES results.Dar-Zarrouk parameters,transverse resistance(TR),longitudinal conductance(SL)and anisotropy(λ)were also calculated from resistivity data to delineate the potential zones of aquifer.70%of SL value is≤3S,30%of SL value is>3S.According to SL and TR values,the whole area is divided into three potential zones,high,medium and low potential zones.The spatial distribution maps show that north,south and central parts of study area are marked as good potential aquifer zones.Longitudinal conductance values are further utilized to determine aquifer protective capacity of area.The whole area is characterized by moderate to good and up to some extent very good aquifer protective area on the basis of SL values.The groundwater samples from sixty-seven installed tube wells are collected for hydro-chemical analysis.The electrical conductivity values are determined.Correlation is then developed between the EC(μS/cm)of groundwater samples vs.interpreted aquifer resistivity showing R2 value 0.90.
基金partly supported by the Natural Science Foundation of China,Contract No. 41274061
文摘Ionosphereic foF2 variations are very sensitive to the seismic effect and results of ionospheric perturbations associated with earthquakes seem to very hopeful for short-term earthquake prediction. On January 18,2011 at 20: 23 UT a great earthquake( M = 7. 2)occurred in Dalbandin( 28. 73° N,63. 92° E),Pakistan. In this study,we have tried to find out the features of pre-earthquake ionospheric anomalies by using the hourly day time( 08. 00 a. m.- 05. 00 p. m.) data of critical frequency( foF2) obtained by three vertical sounding stations installed in Islamabad( 33. 78°N,73. 06°E),Multan( 32. 26°N,71. 51°E) and Karachi( 24. 89° N,67. 02° E), Pakistan. The results show the significant anomalies of foF2 in the earthquake preparation zone several days prior to the Dalbandin earthquake. It is also observed that the amplitude and frequency of foF2 anomalies are more prominent at the nearest station to the epicenter as compared to those stations near the outer margin of the earthquake preparation zone. The confidence level for ionospheric anomalies regarding the seismic signatures can be enhanced by adding the analysis of some other ionospheic parameters along with critical frequency of the layer F2.
