The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human re...The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.展开更多
Salinity is a major abiotic stress that hinders plant development and productivity and influences agricultural yield.Seed priming is a technique used to boost germination and seedling growth under abiotic stress.A pot...Salinity is a major abiotic stress that hinders plant development and productivity and influences agricultural yield.Seed priming is a technique used to boost germination and seedling growth under abiotic stress.A pot experiment was conducted to evaluate the impact of seed priming with potassium nitrate(KNO_(3))at various levels(0%,0.50%,1.00%and 1.50%)under salt stress(0,75,100 mM NaCl)on two maize verities(MNH360 and 30T60)for the growth,development and metabolic attributes results revealed that in maize variety MNH360,KNO_(3)priming’s significantly enhanced growth parameters than in maize variety 30T60 under control and salt-stressed conditions.Priming with KNO_(3)enhanced carotenoids and total chlorophyll in the 30T60 variety that protected the maize plants from salt stress.Nevertheless,it was shown that priming with 1.00%KNO_(3)acts as safeguarded to protect them from oxidative damage by salt stress minimizing reactive oxygen species(ROS)formation through increased levels of malondialdehyde(MDA),catalase(CAT),peroxidase(POD),ascorbate peroxidase(APX),and total soluble protein.The findings of the present study confirm that the use of the KNO_(3)seed priming technique is a lowcost,environmentally friendly technique for mitigating adverse impacts of salt stress in maize crops by activating the antioxidant defense system and improving chlorophyll and osmolyte contents.展开更多
Drilling muds with less environmental impact are highly desired over conventional diesel-based mud systems,especially in light of the emerging strict environmental laws.In this article,a novel oil-in-water(O/W)emulsio...Drilling muds with less environmental impact are highly desired over conventional diesel-based mud systems,especially in light of the emerging strict environmental laws.In this article,a novel oil-in-water(O/W)emulsion drilling fluid formulated with a methyl ester extracted from Indian mango seed oil was evaluated.The effect of the weight percent of different constituents of the emulsion/suspension including the oil phase,bentonite,and polyanionic cellulose polymer on the rheology and the fluid loss was examined.The methyl ester oil phase/mud system displayed superior physical,chemical,rheological and filtration properties relative to the diesel and the mango seed oil.Eco-toxicity of the methyl ester and diesel(O/W)emulsion mud systems was assessed using the acute lethal concentration test.The Indian mango methyl ester(O/W)emulsion mud displayed much less impact on fish population.Flow characteristics collected from the flow model at 85°C suggested excellent shear thinning behavior of the Indian mango methyl ester(IMME)(O/W)emulsion mud.Moreover,the IMME(O/W)emulsion displayed strong pseudoplastic behavior,an attractive feature in a drilling mud,with increasing clay content and polymer concentration.The methyl ester mud was thermally stable over a wide range of the constituent concentrations.Furthermore,a particle size analysis revealed that engineered drilling muds targeting suspension of particles with certain size range can be formulated by changing the volume fraction of the methyl ester in the mud system.展开更多
基金This work is supported by EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab,Prince Sultan University,Kingdom of Saudi Arabia,by paying the APC.
文摘The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.
文摘Salinity is a major abiotic stress that hinders plant development and productivity and influences agricultural yield.Seed priming is a technique used to boost germination and seedling growth under abiotic stress.A pot experiment was conducted to evaluate the impact of seed priming with potassium nitrate(KNO_(3))at various levels(0%,0.50%,1.00%and 1.50%)under salt stress(0,75,100 mM NaCl)on two maize verities(MNH360 and 30T60)for the growth,development and metabolic attributes results revealed that in maize variety MNH360,KNO_(3)priming’s significantly enhanced growth parameters than in maize variety 30T60 under control and salt-stressed conditions.Priming with KNO_(3)enhanced carotenoids and total chlorophyll in the 30T60 variety that protected the maize plants from salt stress.Nevertheless,it was shown that priming with 1.00%KNO_(3)acts as safeguarded to protect them from oxidative damage by salt stress minimizing reactive oxygen species(ROS)formation through increased levels of malondialdehyde(MDA),catalase(CAT),peroxidase(POD),ascorbate peroxidase(APX),and total soluble protein.The findings of the present study confirm that the use of the KNO_(3)seed priming technique is a lowcost,environmentally friendly technique for mitigating adverse impacts of salt stress in maize crops by activating the antioxidant defense system and improving chlorophyll and osmolyte contents.
基金acknowledge Schulich School of Engineering,The University of Calgary,for their support.
文摘Drilling muds with less environmental impact are highly desired over conventional diesel-based mud systems,especially in light of the emerging strict environmental laws.In this article,a novel oil-in-water(O/W)emulsion drilling fluid formulated with a methyl ester extracted from Indian mango seed oil was evaluated.The effect of the weight percent of different constituents of the emulsion/suspension including the oil phase,bentonite,and polyanionic cellulose polymer on the rheology and the fluid loss was examined.The methyl ester oil phase/mud system displayed superior physical,chemical,rheological and filtration properties relative to the diesel and the mango seed oil.Eco-toxicity of the methyl ester and diesel(O/W)emulsion mud systems was assessed using the acute lethal concentration test.The Indian mango methyl ester(O/W)emulsion mud displayed much less impact on fish population.Flow characteristics collected from the flow model at 85°C suggested excellent shear thinning behavior of the Indian mango methyl ester(IMME)(O/W)emulsion mud.Moreover,the IMME(O/W)emulsion displayed strong pseudoplastic behavior,an attractive feature in a drilling mud,with increasing clay content and polymer concentration.The methyl ester mud was thermally stable over a wide range of the constituent concentrations.Furthermore,a particle size analysis revealed that engineered drilling muds targeting suspension of particles with certain size range can be formulated by changing the volume fraction of the methyl ester in the mud system.
