为评估处置库的安全性,核素化学形态信息是准确预测核素的运移行为研究的前提。鉴于我国核能工业的迅速发展以及建立环境中核素的确认需求,在我国开展处置库周围核素的化学形态研究具有十分重要的意义。针对地下水中元素种态分布的研究...为评估处置库的安全性,核素化学形态信息是准确预测核素的运移行为研究的前提。鉴于我国核能工业的迅速发展以及建立环境中核素的确认需求,在我国开展处置库周围核素的化学形态研究具有十分重要的意义。针对地下水中元素种态分布的研究需求,以JAVA为开发工具、MySQL作为数据库、Tomcat为容器,开发了一套B/S架构的元素种态分布模拟软件。针对模拟计算中遇到的化学反应平衡非线性方程组求解收敛困难的问题,引入了根据化学反应势能求解的方法和反应因子控制迭代步长,实现了对化学反应非线性方程快速地求解,并可拓展多相平衡计算。以塔木素地下水中镎为考察对象,利用所开发化学形态模拟软件(simulation software on chemical species,SSCS)计算环境中镎的形态和量,并对比PHREEQC的计算结果,相对偏差在10%以内,针对环境中痕量元素的分布,提供了理论计算的解决方案。展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories es...Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.展开更多
Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission sc...Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood,the limiting factor for habitats for saproxylic species in European boreal forests.The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood.We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland.We simulated seven forest management regimes and three climate scenarios(reference,RCP4.5 and RCP8.5)over 100 years.Management regimes included set aside,continuous cover forestry,business-as-usual(BAU)and four modifications of BAU.Habitat suitability was assessed using a speciesspecific habitat suitability index,including 21 fungal and invertebrate species groups.“Winner”and“loser”species were identified based on the modelled impacts of forest management and climate change on their habitat suitability.We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change.Habitat suitability index varied by over 250%among management regimes,while overall change in habitat suitability index caused by climate change was on average only 2%.More species groups were identified as winners than losers from impacts of climate change(52%–95%were winners,depending on the climate change scenario and management regime).The largest increase in habitat suitability index was achieved under set aside(254%)and the climate scenario RCP8.5(>2%),while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species(up to+11%)across all climate change scenarios.Our results show that close-to-nature management regimes(e.g.,continuous cover forestry and set aside)can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime.This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices,even as climate change progresses.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
The sustainability of environmental management initiatives,such as watershed management programs,relies on the presence of effective institutions at the watershed level.However,there needs to be more empirical evidenc...The sustainability of environmental management initiatives,such as watershed management programs,relies on the presence of effective institutions at the watershed level.However,there needs to be more empirical evidence from evaluating the effectiveness of watershed-level institutions.Therefore,this study presents a pioneering effort to evaluate the effectiveness of Nepal’s first watershed conservation committee at the watershed scale,focusing on the case of the Khageri Khola watershed in Central Nepal.The study involved conducting a household survey,key informant interviews,focus group discussions,and field observations to collect and analyze the data.Descriptive analysis,index value calculation,and chi-square statistics were then employed to summarize the results regarding local respondents’perceptions of twelve institutional characteristics,their rationalities,and their association with socio-demographic variables.The results reveal that the watershed conservation committee was perceived as performing well in managing the watershed.Specifically,good interaction,appropriate scale,technical,environmental,social,organizational,and government rationality were perceived as highly effective,with an average index value of less than 0.36.In contrast,clarity of objectives and economic rationality showed moderate effectiveness,with an average index value ranging from 0.36 to 0.65.However,the results suggested that adaptiveness,compliance capacity,and financial rationality merit increased attention,intending to improve their performance.Further,the results showed the association of socio-demographics with respondents’perceptions of various indicators of institutional characteristics and their rationalities.Therefore,the study provides valuable insights for policymakers,researchers,and development practitioners charged with designing sustainable and effective programs and institutions.To enhance the effectiveness and sustainability of watershed management programs,we recommend establishing a policy-guided institutional mechanism at the watershed scale.This mechanism should be based on various institutional characteristics and rationalities and should consider the extant variability in the socio-demographic and topographic characteristics of the watershed.展开更多
In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR i...In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.展开更多
The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated ...The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated by using Fourier transform infrared spectroscopy,thermogravimetric analysis and UV-Vis spectroscopy.It was observed that incorporating natural melanin on TiO_(2) nanoparticles(TiO_(2)-Mel)occurred at 2.01 eV with a low value of Urbach energy around 100 meV indicating improvement in the crystalline structure.Magnetic measurement at room temperature showed diamagnetic behavior.Furthermore,thermal results showed that TiO_(2)-Mel is stable even at temperatures up to 400℃.According to the results obtained by the thermal stability of melanin with titanium dioxide,it can be a good candidate in many applications such as solar cells and optoelectronics.展开更多
Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the med...Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.Optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.展开更多
Improving the energy conversion efficiency in metallic fuel(e.g.,Al)combustion is always desirable but challenging,which often involves redox reactions of aluminum(Al)with various mixed oxidizing environments.For inst...Improving the energy conversion efficiency in metallic fuel(e.g.,Al)combustion is always desirable but challenging,which often involves redox reactions of aluminum(Al)with various mixed oxidizing environments.For instance,Al-O reaction is the most common pathway to release limited energy while Al-F reaction has received much attentions to enhance Al combustion efficiency.However,microscopic understanding of the Al-O/Al-F reaction dynamics remains unsolved,which is fundamentally necessary to further improve Al combustion efficiency.In this work,for the first time,Al-O/Al-F reaction dynamic effects on the combustion of aluminum nanoparticles(n-Al)in oxygen/fluorine containing environments have been revealed via reactive molecular dynamics(RMD)simulations meshing together combustion experiments.Three RMD simulation systems of Al core/O_(2)/HF,n-Al/O_(2)/HF,and n-Al/O_(2)/CF4 with oxygen percentage ranging from 0%to 100%have been performed.The n-Al combustion in mixed O_(2)/CF_4 environments have been conducted by constant volume combustion experiments.RMD results show that Al-O reaction exhibits kinetic benefits while Al-F reaction owns thermodynamic benefits for n-Al combustion.In n-Al/O_(2)/HF,Al-O reaction gives faster energy release rate than Al-F reaction(1.1 times).The optimal energy release efficiency can be achieved with suitable oxygen percentage of 10%and 50%for n-Al/O_(2)/HF and n-Al/O_(2)/CF_4,respectively.In combustion experiments,90%of oxygen percentage can optimally enhance the peak pressure,pressurization rate and combustion heat.Importantly,Al-O reaction prefers to occur on the surface regions while Al-F reaction prefers to proceed in the interior regions of n-Al,confirming the kinetic/thermodynamic benefits of Al-O/Al-F reactions.The synergistic effect of Al-O/Al-F reaction for greatly enhancing n-Al combustion efficiency is demonstrated at atomicscale,which is beneficial for optimizing the combustion performance of metallic fuel.展开更多
Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ...Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.展开更多
This research investigates the hydrothermal synthesis and annealing duration effects on nickel sulfide(NiS_(2)quantum dots(QDs)for catalytic decolorization of methylene blue(MB)dye and antimicrobial efficacy.QD size i...This research investigates the hydrothermal synthesis and annealing duration effects on nickel sulfide(NiS_(2)quantum dots(QDs)for catalytic decolorization of methylene blue(MB)dye and antimicrobial efficacy.QD size increased with longer annealing,reducing catalytic activity.UV–vis,XRD,TEM,and FTIR analyses probed optical structural,morphological,and vibrational features.XRD confirmed NiS2's anorthic structure,with crystallite size growing from 6.53 to 7.81 nm during extended annealing.UV–Vis exhibited a bathochromic shift,reflecting reduced band gap energy(Eg)in NiS_(2).TEM revealed NiS_(2)QD formation,with agglomerated QD average size increasing from 7.13 to 9.65 nm with prolonged annealing.Pure NiS_(2)showed significant MB decolorization(89.85%)in acidic conditions.Annealed NiS_(2) QDs demonstrated notable antibacterial activity,yielding a 6.15mm inhibition zone against Escherichia coli(E.coli)compared to Ciprofloxacin.First-principles computation supported a robust interaction between MB and NiS_(2),evidenced by obtained adsorption energies.This study highlights the nuanced relationship between annealing duration,structural changes,and functional properties in NiS_(2)QDs,emphasizing their potential applications in catalysis and antibacterial interventions.展开更多
This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols w...This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.展开更多
文摘为评估处置库的安全性,核素化学形态信息是准确预测核素的运移行为研究的前提。鉴于我国核能工业的迅速发展以及建立环境中核素的确认需求,在我国开展处置库周围核素的化学形态研究具有十分重要的意义。针对地下水中元素种态分布的研究需求,以JAVA为开发工具、MySQL作为数据库、Tomcat为容器,开发了一套B/S架构的元素种态分布模拟软件。针对模拟计算中遇到的化学反应平衡非线性方程组求解收敛困难的问题,引入了根据化学反应势能求解的方法和反应因子控制迭代步长,实现了对化学反应非线性方程快速地求解,并可拓展多相平衡计算。以塔木素地下水中镎为考察对象,利用所开发化学形态模拟软件(simulation software on chemical species,SSCS)计算环境中镎的形态和量,并对比PHREEQC的计算结果,相对偏差在10%以内,针对环境中痕量元素的分布,提供了理论计算的解决方案。
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
文摘Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.
基金Open access funding provided by Norwegian University of Life Sciences。
文摘Forest degradation induced by intensive forest management and temperature increase by climate change are resulting in biodiversity decline in boreal forests.Intensive forest management and high-end climate emission scenarios can further reduce the amount and diversity of deadwood,the limiting factor for habitats for saproxylic species in European boreal forests.The magnitude of their combined effects and how changes in forest management can affect deadwood diversity under a range of climate change scenarios are poorly understood.We used forest growth simulations to evaluate how forest management and climate change will individually and jointly affect habitats of red-listed saproxylic species in Finland.We simulated seven forest management regimes and three climate scenarios(reference,RCP4.5 and RCP8.5)over 100 years.Management regimes included set aside,continuous cover forestry,business-as-usual(BAU)and four modifications of BAU.Habitat suitability was assessed using a speciesspecific habitat suitability index,including 21 fungal and invertebrate species groups.“Winner”and“loser”species were identified based on the modelled impacts of forest management and climate change on their habitat suitability.We found that forest management had a major impact on habitat suitability of saproxylic species compared to climate change.Habitat suitability index varied by over 250%among management regimes,while overall change in habitat suitability index caused by climate change was on average only 2%.More species groups were identified as winners than losers from impacts of climate change(52%–95%were winners,depending on the climate change scenario and management regime).The largest increase in habitat suitability index was achieved under set aside(254%)and the climate scenario RCP8.5(>2%),while continuous cover forestry was the most suitable regime to increase habitat suitability of saproxylic species(up to+11%)across all climate change scenarios.Our results show that close-to-nature management regimes(e.g.,continuous cover forestry and set aside)can increase the habitat suitability of many saproxylic boreal species more than the basic business-as-usual regime.This suggests that biodiversity loss of many saproxylic species in boreal forests can be mitigated through improved forest management practices,even as climate change progresses.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
文摘The sustainability of environmental management initiatives,such as watershed management programs,relies on the presence of effective institutions at the watershed level.However,there needs to be more empirical evidence from evaluating the effectiveness of watershed-level institutions.Therefore,this study presents a pioneering effort to evaluate the effectiveness of Nepal’s first watershed conservation committee at the watershed scale,focusing on the case of the Khageri Khola watershed in Central Nepal.The study involved conducting a household survey,key informant interviews,focus group discussions,and field observations to collect and analyze the data.Descriptive analysis,index value calculation,and chi-square statistics were then employed to summarize the results regarding local respondents’perceptions of twelve institutional characteristics,their rationalities,and their association with socio-demographic variables.The results reveal that the watershed conservation committee was perceived as performing well in managing the watershed.Specifically,good interaction,appropriate scale,technical,environmental,social,organizational,and government rationality were perceived as highly effective,with an average index value of less than 0.36.In contrast,clarity of objectives and economic rationality showed moderate effectiveness,with an average index value ranging from 0.36 to 0.65.However,the results suggested that adaptiveness,compliance capacity,and financial rationality merit increased attention,intending to improve their performance.Further,the results showed the association of socio-demographics with respondents’perceptions of various indicators of institutional characteristics and their rationalities.Therefore,the study provides valuable insights for policymakers,researchers,and development practitioners charged with designing sustainable and effective programs and institutions.To enhance the effectiveness and sustainability of watershed management programs,we recommend establishing a policy-guided institutional mechanism at the watershed scale.This mechanism should be based on various institutional characteristics and rationalities and should consider the extant variability in the socio-demographic and topographic characteristics of the watershed.
基金funded byResearchers Supporting Project Number(RSPD2024R 553),King Saud University,Riyadh,Saudi Arabia.
文摘In this work,we aim to introduce some modifications to the Anam-Net deep neural network(DNN)model for segmenting optic cup(OC)and optic disc(OD)in retinal fundus images to estimate the cup-to-disc ratio(CDR).The CDR is a reliable measure for the early diagnosis of Glaucoma.In this study,we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images.Our DNN model is based on modifications to Anam-Net,incorporating an anamorphic depth embedding block.To reduce computational complexity,we employ a fixed filter size for all convolution layers in the encoder and decoder stages as the network deepens.This modification significantly reduces the number of trainable parameters,making the model lightweight and suitable for resource-constrained applications.We evaluate the performance of the developed model using two publicly available retinal image databases,namely RIM-ONE and Drishti-GS.The results demonstrate promising OC segmentation performance across most standard evaluation metrics while achieving analogous results for OD segmentation.We used two retinal fundus image databases named RIM-ONE and Drishti-GS that contained 159 images and 101 retinal images,respectively.For OD segmentation using the RIM-ONE we obtain an f1-score(F1),Jaccard coefficient(JC),and overlapping error(OE)of 0.950,0.9219,and 0.0781,respectively.Similarly,for OC segmentation using the same databases,we achieve scores of 0.8481(F1),0.7428(JC),and 0.2572(OE).Based on these experimental results and the significantly lower number of trainable parameters,we conclude that the developed model is highly suitable for the early diagnosis of glaucoma by accurately estimating the CDR.
基金Funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(No.RG-21-09-53)。
文摘The natural Melanin/TiO_(2) was synthesized by the use of ultrasonication under UV radiation.The influence of natural melanin on the structural,optical and thermal properties of TiO_(2) nanoparticles was investigated by using Fourier transform infrared spectroscopy,thermogravimetric analysis and UV-Vis spectroscopy.It was observed that incorporating natural melanin on TiO_(2) nanoparticles(TiO_(2)-Mel)occurred at 2.01 eV with a low value of Urbach energy around 100 meV indicating improvement in the crystalline structure.Magnetic measurement at room temperature showed diamagnetic behavior.Furthermore,thermal results showed that TiO_(2)-Mel is stable even at temperatures up to 400℃.According to the results obtained by the thermal stability of melanin with titanium dioxide,it can be a good candidate in many applications such as solar cells and optoelectronics.
文摘Magnesium(Mg)and its alloys are emerging as a structural material for the aerospace,automobile,and electronics industries,driven by the imperative of weight reduction.They are also drawing notable attention in the medical industries owing to their biodegradability and a lower elastic modulus comparable to bone.The ability to manufacture near-net shape products featuring intricate geometries has sparked huge interest in additive manufacturing(AM)of Mg alloys,reflecting a transformation in the manufacturing sectors.However,AM of Mg alloys presents more formidable challenges due to inherent properties,particularly susceptibility to oxidation,gas trapping,high thermal expansion coefficient,and low solidification temperature.This leads to defects such as porosity,lack of fusion,cracking,delamination,residual stresses,and inhomogeneity,ultimately influencing the mechanical,corrosion,and surface properties of AM Mg alloys.To address these issues,post-processing of AM Mg alloys are often needed to make them suitable for application.The present article reviews all post-processing techniques adapted for AM Mg alloys to date,including heat treatment,hot isostatic pressing,friction stir processing,and surface peening.The utilization of these methods within the hybrid AM process,employing interlayer post-processing,is also discussed.Optimal post-processing conditions are reported,and their influence on the microstructure,mechanical,and corrosion properties are detailed.Additionally,future prospects and research directions are proposed.
基金support by the National Natural Science Foundation of China(NSFC,Grant Nos.12002324,12372341,12172342)。
文摘Improving the energy conversion efficiency in metallic fuel(e.g.,Al)combustion is always desirable but challenging,which often involves redox reactions of aluminum(Al)with various mixed oxidizing environments.For instance,Al-O reaction is the most common pathway to release limited energy while Al-F reaction has received much attentions to enhance Al combustion efficiency.However,microscopic understanding of the Al-O/Al-F reaction dynamics remains unsolved,which is fundamentally necessary to further improve Al combustion efficiency.In this work,for the first time,Al-O/Al-F reaction dynamic effects on the combustion of aluminum nanoparticles(n-Al)in oxygen/fluorine containing environments have been revealed via reactive molecular dynamics(RMD)simulations meshing together combustion experiments.Three RMD simulation systems of Al core/O_(2)/HF,n-Al/O_(2)/HF,and n-Al/O_(2)/CF4 with oxygen percentage ranging from 0%to 100%have been performed.The n-Al combustion in mixed O_(2)/CF_4 environments have been conducted by constant volume combustion experiments.RMD results show that Al-O reaction exhibits kinetic benefits while Al-F reaction owns thermodynamic benefits for n-Al combustion.In n-Al/O_(2)/HF,Al-O reaction gives faster energy release rate than Al-F reaction(1.1 times).The optimal energy release efficiency can be achieved with suitable oxygen percentage of 10%and 50%for n-Al/O_(2)/HF and n-Al/O_(2)/CF_4,respectively.In combustion experiments,90%of oxygen percentage can optimally enhance the peak pressure,pressurization rate and combustion heat.Importantly,Al-O reaction prefers to occur on the surface regions while Al-F reaction prefers to proceed in the interior regions of n-Al,confirming the kinetic/thermodynamic benefits of Al-O/Al-F reactions.The synergistic effect of Al-O/Al-F reaction for greatly enhancing n-Al combustion efficiency is demonstrated at atomicscale,which is beneficial for optimizing the combustion performance of metallic fuel.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number PNURSP2024R333,Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.
文摘This research investigates the hydrothermal synthesis and annealing duration effects on nickel sulfide(NiS_(2)quantum dots(QDs)for catalytic decolorization of methylene blue(MB)dye and antimicrobial efficacy.QD size increased with longer annealing,reducing catalytic activity.UV–vis,XRD,TEM,and FTIR analyses probed optical structural,morphological,and vibrational features.XRD confirmed NiS2's anorthic structure,with crystallite size growing from 6.53 to 7.81 nm during extended annealing.UV–Vis exhibited a bathochromic shift,reflecting reduced band gap energy(Eg)in NiS_(2).TEM revealed NiS_(2)QD formation,with agglomerated QD average size increasing from 7.13 to 9.65 nm with prolonged annealing.Pure NiS_(2)showed significant MB decolorization(89.85%)in acidic conditions.Annealed NiS_(2) QDs demonstrated notable antibacterial activity,yielding a 6.15mm inhibition zone against Escherichia coli(E.coli)compared to Ciprofloxacin.First-principles computation supported a robust interaction between MB and NiS_(2),evidenced by obtained adsorption energies.This study highlights the nuanced relationship between annealing duration,structural changes,and functional properties in NiS_(2)QDs,emphasizing their potential applications in catalysis and antibacterial interventions.
基金This research project was funded by the Deanship of Scientific Research,Princess Nourah bint Abdulrahman University,through the Program of Research Project Funding After Publication,grant No(43-PRFA-P-58).
文摘This study presents a layered generalization ensemble model for next generation radio mobiles,focusing on supervised channel estimation approaches.Channel estimation typically involves the insertion of pilot symbols with a well-balanced rhythm and suitable layout.The model,called Stacked Generalization for Channel Estimation(SGCE),aims to enhance channel estimation performance by eliminating pilot insertion and improving throughput.The SGCE model incorporates six machine learning methods:random forest(RF),gradient boosting machine(GB),light gradient boosting machine(LGBM),support vector regression(SVR),extremely randomized tree(ERT),and extreme gradient boosting(XGB).By generating meta-data from five models(RF,GB,LGBM,SVR,and ERT),we ensure accurate channel coefficient predictions using the XGB model.To validate themodeling performance,we employ the leave-one-out cross-validation(LOOCV)approach,where each observation serves as the validation set while the remaining observations act as the training set.SGCE performances’results demonstrate higher mean andmedian accuracy compared to the separatedmodel.SGCE achieves an average accuracy of 98.4%,precision of 98.1%,and the highest F1-score of 98.5%,accurately predicting channel coefficients.Furthermore,our proposedmethod outperforms prior traditional and intelligent techniques in terms of throughput and bit error rate.SGCE’s superior performance highlights its efficacy in optimizing channel estimation.It can effectively predict channel coefficients and contribute to enhancing the overall efficiency of radio mobile systems.Through extensive experimentation and evaluation,we demonstrate that SGCE improved performance in channel estimation,surpassing previous techniques.Accordingly,SGCE’s capabilities have significant implications for optimizing channel estimation in modern communication systems.