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
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
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 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
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
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
Continuously changing climate and availability of different rice genotypes make it necessary to find optimum time of sowing as well as suitable variety for cultivation to get maximum productivity under a specific set ...Continuously changing climate and availability of different rice genotypes make it necessary to find optimum time of sowing as well as suitable variety for cultivation to get maximum productivity under a specific set of climatic conditions. A field study was carried out to search out the suitable rice transplanting time for four different coarse genotypes under the semi-arid environment of Faisalabad. The experiment was conducted at Agronomic Research Area, University of Agriculture, Faisalabad and was laid out in randomized complete block design (RCBD) with split plot arrangement keeping transplanting time in main plots while rice genotypes in subplots. Variability among treatments was measured by Fisher’s ANOVA (P ≤ 5%) and LSD test was applied to compare the differences among treatments’ means. The ANOVA indicated statistically significant differences among genotypes as well as transplanting dates irrespective of all studied traits while interactive effects of both were found to be non-significant. NIBGE-1 performed best with maximum paddy yield of 6.05 t/ha while KSK-434 performed poor with paddy yield of 2.78 t/ha. Increased paddy yield and yield related parameters of all rice genotypes were recorded where transplantation was done on 25th of June. Generally, paddy yield decreased with delaying the transplanting time. The results suggested that NIBGE-1 can perform better under the semi-arid conditions of Faisalabad and last week of June might be the optimum time for nursery transplantation. It can also be further elucidated that late transplanting causes yield reduction which could not be recommended among farmers.展开更多
The confluence of cheap wireless communication, sensing and computation has produced a new group of smart devices and by using thousands of these kind of devices in self-organizing networks has formed a new technology...The confluence of cheap wireless communication, sensing and computation has produced a new group of smart devices and by using thousands of these kind of devices in self-organizing networks has formed a new technology that is called wireless sensor networks (WSNs). WSNs use sensor nodes that placed in open areas or in public places and with a huge number that creates many problems for the researchers and network designer, for giving an appropriate design for the wireless network. The problems are security, routing of data and processing of large amount of data etc. This paper describes the types of WSNs and the possible solutions for tackling the listed problems and solution of many other problems. This paper will deliver the knowledge about the WSN and types with literature review so that a person can get more knowledge about this emerging field.展开更多
BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases t...BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases threatened,the health and lives of millions of people across the globe and within the first year,global prevalence of anxiety and depression increased by 25%with the greatest influx in places highly affected by COVID-19.AIM To explore the psychological impact of the pandemic and resultant restrictions in different countries using an opportunistic sample and online questionnaire in different phases of the pandemic.METHODS A repeated,cross-sectional online international survey of adults,16 years and above,was carried out in 10 countries(United Kingdom,India,Canada,Bangladesh,Ukraine,Hong Kong,Pakistan,Egypt,Bahrain,Saudi Arabia).The online questionnaire was based on published approaches to understand the psychological impact of COVID-19 and the resultant restrictions.Five standardised measures were included to explore levels of depression[patient health questionnaire(PHQ-9)],anxiety[generalized anxiety disorder(GAD)assessment],impact of trauma[the impact of events scale-revised(IES-R)],loneliness(a brief loneliness scale),and social support(The Multidimensional Scale of Perceived Social support).RESULTS There were two rounds of the online survey in 10 countries with 42866 participants in Round 1 and 92260 in Round 2.The largest number of participants recruited from the United Kingdom(112985 overall).The majority of participants reported receiving no support from mental health services throughout the pandemic.This study found that the daily cumulative COVID-19 cases had a statistically significant effect on PHQ-9,GAD-7,and IES-R scores.These scores significantly increased in the second round of surveys with the ordinary least squares regression results with regression discontinuity design specification(to control lockdown effects)confirming these results.The study findings imply that participants’mental health worsened with high cumulative COVID-19 cases.CONCLUSION Whist we are still living through the impact of COVID-19,this paper focuses on its impact on mental health,discusses the possible consequences and future implications.This study revealed that daily cumulative COVID-19 cases have a significant impact on depression,anxiety,and trauma.Increasing cumulative cases influenced and impacted education,employment,socialization and finances,to name but a few.Building a database of global evidence will allow for future planning of pandemics,particularly the impact on mental health of populations considering the cultural differences.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
Cloud computing is high technology, which fulfills needs of common as well as enterprise level to meet their information and communication technology requirements and so on. Cloud computing extends existing informatio...Cloud computing is high technology, which fulfills needs of common as well as enterprise level to meet their information and communication technology requirements and so on. Cloud computing extends existing information technology capabilities and requirements. Many technologies are being merged with cloud computing, same as that orchestrations can boost cloud service provisioning process. The usage of orchestrations can play vital role to provision cloud services. Cloud service providers can create scalable cloud services at low cost by organizing cloud infrastructure by using cloud orchestrations. Dynamic orchestration flows can generate required cloud computing services to meet service level agreements and quality of services. There is a need to understand issues and barriers involved to integrate cloud orchestrations with cloud service provisioning process. There is also need to understand business related problems bordering cloud computing technology. There is much capacity to do targeted research work for cloud orchestrations and its integration with service level agreements as well as with SLI (service level integration) layer. In this article we have elaborated detailed analysis and identified a number of issues that will affect the cloud service users as well as cloud service providers and cloud service provisioning system. We are defining an approach to orchestrate cloud infrastructure by using orchestration flows, to generate cloud services in order to meet service level agreements and quality of standard.展开更多
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.
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.
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.
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.
文摘Continuously changing climate and availability of different rice genotypes make it necessary to find optimum time of sowing as well as suitable variety for cultivation to get maximum productivity under a specific set of climatic conditions. A field study was carried out to search out the suitable rice transplanting time for four different coarse genotypes under the semi-arid environment of Faisalabad. The experiment was conducted at Agronomic Research Area, University of Agriculture, Faisalabad and was laid out in randomized complete block design (RCBD) with split plot arrangement keeping transplanting time in main plots while rice genotypes in subplots. Variability among treatments was measured by Fisher’s ANOVA (P ≤ 5%) and LSD test was applied to compare the differences among treatments’ means. The ANOVA indicated statistically significant differences among genotypes as well as transplanting dates irrespective of all studied traits while interactive effects of both were found to be non-significant. NIBGE-1 performed best with maximum paddy yield of 6.05 t/ha while KSK-434 performed poor with paddy yield of 2.78 t/ha. Increased paddy yield and yield related parameters of all rice genotypes were recorded where transplantation was done on 25th of June. Generally, paddy yield decreased with delaying the transplanting time. The results suggested that NIBGE-1 can perform better under the semi-arid conditions of Faisalabad and last week of June might be the optimum time for nursery transplantation. It can also be further elucidated that late transplanting causes yield reduction which could not be recommended among farmers.
文摘The confluence of cheap wireless communication, sensing and computation has produced a new group of smart devices and by using thousands of these kind of devices in self-organizing networks has formed a new technology that is called wireless sensor networks (WSNs). WSNs use sensor nodes that placed in open areas or in public places and with a huge number that creates many problems for the researchers and network designer, for giving an appropriate design for the wireless network. The problems are security, routing of data and processing of large amount of data etc. This paper describes the types of WSNs and the possible solutions for tackling the listed problems and solution of many other problems. This paper will deliver the knowledge about the WSN and types with literature review so that a person can get more knowledge about this emerging field.
基金Supported by MRC Global Health Research Program,No.MR.N006267/1.
文摘BACKGROUND Depression and anxiety were both ranked among the top 25 leading causes of global burden of diseases in 2019 prior to the coronavirus disease 2019(COVID-19)pandemic.The pandemic affected,and in many cases threatened,the health and lives of millions of people across the globe and within the first year,global prevalence of anxiety and depression increased by 25%with the greatest influx in places highly affected by COVID-19.AIM To explore the psychological impact of the pandemic and resultant restrictions in different countries using an opportunistic sample and online questionnaire in different phases of the pandemic.METHODS A repeated,cross-sectional online international survey of adults,16 years and above,was carried out in 10 countries(United Kingdom,India,Canada,Bangladesh,Ukraine,Hong Kong,Pakistan,Egypt,Bahrain,Saudi Arabia).The online questionnaire was based on published approaches to understand the psychological impact of COVID-19 and the resultant restrictions.Five standardised measures were included to explore levels of depression[patient health questionnaire(PHQ-9)],anxiety[generalized anxiety disorder(GAD)assessment],impact of trauma[the impact of events scale-revised(IES-R)],loneliness(a brief loneliness scale),and social support(The Multidimensional Scale of Perceived Social support).RESULTS There were two rounds of the online survey in 10 countries with 42866 participants in Round 1 and 92260 in Round 2.The largest number of participants recruited from the United Kingdom(112985 overall).The majority of participants reported receiving no support from mental health services throughout the pandemic.This study found that the daily cumulative COVID-19 cases had a statistically significant effect on PHQ-9,GAD-7,and IES-R scores.These scores significantly increased in the second round of surveys with the ordinary least squares regression results with regression discontinuity design specification(to control lockdown effects)confirming these results.The study findings imply that participants’mental health worsened with high cumulative COVID-19 cases.CONCLUSION Whist we are still living through the impact of COVID-19,this paper focuses on its impact on mental health,discusses the possible consequences and future implications.This study revealed that daily cumulative COVID-19 cases have a significant impact on depression,anxiety,and trauma.Increasing cumulative cases influenced and impacted education,employment,socialization and finances,to name but a few.Building a database of global evidence will allow for future planning of pandemics,particularly the impact on mental health of populations considering the cultural differences.
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Cloud computing is high technology, which fulfills needs of common as well as enterprise level to meet their information and communication technology requirements and so on. Cloud computing extends existing information technology capabilities and requirements. Many technologies are being merged with cloud computing, same as that orchestrations can boost cloud service provisioning process. The usage of orchestrations can play vital role to provision cloud services. Cloud service providers can create scalable cloud services at low cost by organizing cloud infrastructure by using cloud orchestrations. Dynamic orchestration flows can generate required cloud computing services to meet service level agreements and quality of services. There is a need to understand issues and barriers involved to integrate cloud orchestrations with cloud service provisioning process. There is also need to understand business related problems bordering cloud computing technology. There is much capacity to do targeted research work for cloud orchestrations and its integration with service level agreements as well as with SLI (service level integration) layer. In this article we have elaborated detailed analysis and identified a number of issues that will affect the cloud service users as well as cloud service providers and cloud service provisioning system. We are defining an approach to orchestrate cloud infrastructure by using orchestration flows, to generate cloud services in order to meet service level agreements and quality of standard.
