In this study, we propose a two stage randomized response model. Improved unbiased estimators of the mean number of persons possessing a rare sensitive attribute under two different situations are proposed. The propos...In this study, we propose a two stage randomized response model. Improved unbiased estimators of the mean number of persons possessing a rare sensitive attribute under two different situations are proposed. The proposed estimators are evaluated using a relative efficiency comparison. It is shown that our estimators are efficient as compared to existing estimators when the parameter of rare unrelated attribute is known and in unknown case, depending on the probability of selecting a question.展开更多
Potential of seed priming treatments in improving the performance of early planted maize was evaluated against timely planting. Seeds of maize hybrid FH-810 were soaked in water (hydropriming), CaCl2 (2.2%, osmoprimin...Potential of seed priming treatments in improving the performance of early planted maize was evaluated against timely planting. Seeds of maize hybrid FH-810 were soaked in water (hydropriming), CaCl2 (2.2%, osmopriming), moringa leaf extracts (MLE 3.3%, osmopriming) and salicylic acid (SA, 50 mg L–1, hormonal priming) each for 18 h. Untreated and hydroprimed seeds were taken as control. Seeds primed with SA took less time in emergence and had high vigor in early planted maize. Amongst treatments, hormonal priming, reduced the electrical conductivity, increased the leaf relative and chlorophyl contents fol owed by osmopriming with CaCl2 at seedling stage. Likewise, plant height, grain rows and 1 000-grain weight, grain and biological yield and harvest index were also improved by seed priming;however hormonal priming and osmopriming with MLE were more effective in this regard. Improved yield performance by hormonal priming or osmopriming with MLE in early planting primarily owed to increased leaf area index, crop growth and net assimilation rates, and maintenance of green leaf area at maturity. In conclusion, osmopriming with MLE and hormonal priming with SA were the most economical treatments in improving productivity of early planted spring maize through stimulation of early seedling growth at low temperature.展开更多
Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet...Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet cultivation. Beet sugar contents are higher than sugarcane sugar contents, which can be further increased by potassium（K） fertilization. Total K concentration is higher in Pakistani soils developed from mica minerals, but it does not represent plant available K for sustainable plant growth. A pot experiment was conducted in the wire-house of Institute of Soil and Environmental Sciences at University of Agriculture Faisalabad, Pakistan. K treatments were the following： no K（K_0）, K application at 148 kg ha^（–1）（K_1） and 296 kg ha^（–1）（K_2）. Irrigation levels were used as water sufficient at 60% water holding capacity and water deficient at 40% water holding capacity. The growth, yield and beet quality data were analyzed statistically using LSD. The results revealed that increase in the level of K fertilization at water sufficient level significantly increased plant growth, beet yield and industrial beet sugar content. The response of K fertilization under water deficient condition was also similar, however overall sugar production was less than that in water sufficient conditions. It is concluded from this study that K application could be used not only to enhance plant growth and beet yield but also enhance beet sugar content both under water-deficient as well as water-sufficient conditions.展开更多
With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the ou...With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.展开更多
Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been pre...Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.展开更多
One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment wi...One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment with an ophthalmologist,which is both time-consuming and expensive process.Other issues that can arise include a lack of appropriate equipment and trained practitioners,especially in rural areas.Centered on a cognitively motivated attribute extraction and speech recognition approach,this paper proposes a novel idea that immediately determines the eyesight deficiency.The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction.The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment.Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth,established by the expert ophthalmologist’s tests.The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%.The proposed method potentially offers a second opinion to ophthalmologists,and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.展开更多
文摘In this study, we propose a two stage randomized response model. Improved unbiased estimators of the mean number of persons possessing a rare sensitive attribute under two different situations are proposed. The proposed estimators are evaluated using a relative efficiency comparison. It is shown that our estimators are efficient as compared to existing estimators when the parameter of rare unrelated attribute is known and in unknown case, depending on the probability of selecting a question.
基金Endowment Fund Secretariat, University of Agriculture Faisalabad, Pakistan in providing financial support for completion of this study
文摘Potential of seed priming treatments in improving the performance of early planted maize was evaluated against timely planting. Seeds of maize hybrid FH-810 were soaked in water (hydropriming), CaCl2 (2.2%, osmopriming), moringa leaf extracts (MLE 3.3%, osmopriming) and salicylic acid (SA, 50 mg L–1, hormonal priming) each for 18 h. Untreated and hydroprimed seeds were taken as control. Seeds primed with SA took less time in emergence and had high vigor in early planted maize. Amongst treatments, hormonal priming, reduced the electrical conductivity, increased the leaf relative and chlorophyl contents fol owed by osmopriming with CaCl2 at seedling stage. Likewise, plant height, grain rows and 1 000-grain weight, grain and biological yield and harvest index were also improved by seed priming;however hormonal priming and osmopriming with MLE were more effective in this regard. Improved yield performance by hormonal priming or osmopriming with MLE in early planting primarily owed to increased leaf area index, crop growth and net assimilation rates, and maintenance of green leaf area at maturity. In conclusion, osmopriming with MLE and hormonal priming with SA were the most economical treatments in improving productivity of early planted spring maize through stimulation of early seedling growth at low temperature.
文摘Sugar beet（Beta vulgaris L.） is an industrial crop, grown worldwide for sugar production. In Pakistan, sugar is mostly extracted from sugarcane, soil and environmental conditions are equally favorable for sugar beet cultivation. Beet sugar contents are higher than sugarcane sugar contents, which can be further increased by potassium（K） fertilization. Total K concentration is higher in Pakistani soils developed from mica minerals, but it does not represent plant available K for sustainable plant growth. A pot experiment was conducted in the wire-house of Institute of Soil and Environmental Sciences at University of Agriculture Faisalabad, Pakistan. K treatments were the following： no K（K_0）, K application at 148 kg ha^（–1）（K_1） and 296 kg ha^（–1）（K_2）. Irrigation levels were used as water sufficient at 60% water holding capacity and water deficient at 40% water holding capacity. The growth, yield and beet quality data were analyzed statistically using LSD. The results revealed that increase in the level of K fertilization at water sufficient level significantly increased plant growth, beet yield and industrial beet sugar content. The response of K fertilization under water deficient condition was also similar, however overall sugar production was less than that in water sufficient conditions. It is concluded from this study that K application could be used not only to enhance plant growth and beet yield but also enhance beet sugar content both under water-deficient as well as water-sufficient conditions.
基金The authors extend their appreciation to National University of Sciences and Technology for funding this work through Researchers Supporting Grant,National University of Sciences and Technology,Islamabad,Pakistan.
文摘With the advent and advancements in the wireless technologies,Wi-Fi ngerprinting-based Indoor Positioning System(IPS)has become one of the most promising solutions for localization in indoor environments.Unlike the outdoor environment,the lack of line-of-sight propagation in an indoor environment keeps the interest of the researchers to develop efcient and precise positioning systems that can later be incorporated in numerous applications involving Internet of Things(IoTs)and green computing.In this paper,we have proposed a technique that combines the capabilities of multiple algorithms to overcome the complexities experienced indoors.Initially,in the database development phase,Motley Kennan propagation model is used with Hough transformation to classify,detect,and assign different attenuation factors related to the types of walls.Furthermore,important parameters for system accuracy,such as,placement and geometry of Access Points(APs)in the coverage area are also considered.New algorithm for deployment of an additional AP to an already existing infrastructure is proposed by using Genetic Algorithm(GA)coupled with Enhanced Dilution of Precision(EDOP).Moreover,classication algorithm based on k-Nearest Neighbors(k-NN)is used to nd the position of a stationary or mobile user inside the given coverage area.For k-NN to provide low localization error and reduced space dimensionality,three APs are required to be selected optimally.In this paper,we have suggested an idea to select APs based on Position Vectors(PV)as an input to the localization algorithm.Deducing from our comprehensive investigations,it is revealed that the accuracy of indoor positioning system using the proposed technique unblemished the existing solutions with signicant improvements.
基金The authors extend their appreciation to the National University of Sciences and Technology for funding this work through the Researchers Supporting Grant,National University of Sciences and Technology,Islamabad,Pakistan.
文摘Due to the inability of the Global Positioning System(GPS)signals to penetrate through surfaces like roofs,walls,and other objects in indoor environments,numerous alternative methods for user positioning have been presented.Amongst those,the Wi-Fi fingerprinting method has gained considerable interest in Indoor Positioning Systems(IPS)as the need for lineof-sight measurements is minimal,and it achieves better efficiency in even complex indoor environments.Offline and online are the two phases of the fingerprinting method.Many researchers have highlighted the problems in the offline phase as it deals with huge datasets and validation of Fingerprints without pre-processing of data becomes a concern.Machine learning is used for the model training in the offline phase while the locations are estimated in the online phase.Many researchers have considered the concerns in the offline phase as it deals with huge datasets and validation of Fingerprints becomes an issue.Machine learning algorithms are a natural solution for winnowing through large datasets and determining the significant fragments of information for localization,creating precise models to predict an indoor location.Large training sets are a key for obtaining better results in machine learning problems.Therefore,an existing WLAN fingerprinting-based multistory building location database has been used with 21049 samples including 19938 training and 1111 testing samples.The proposed model consists of mean and median filtering as pre-processing techniques applied to the database for enhancing the accuracy by mitigating the impact of environmental dispersion and investigated machine learning algorithms(kNN,WkNN,FSkNN,and SVM)for estimating the location.The proposed SVM with median filtering algorithm gives a reduced mean positioning error of 0.7959 m and an improved efficiency of 92.84%as compared to all variants of the proposed method for 108703 m^(2) area.
文摘One of the most commonly reported disabilities is vision loss,which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient.This procedure,however,usually requires an appointment with an ophthalmologist,which is both time-consuming and expensive process.Other issues that can arise include a lack of appropriate equipment and trained practitioners,especially in rural areas.Centered on a cognitively motivated attribute extraction and speech recognition approach,this paper proposes a novel idea that immediately determines the eyesight deficiency.The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction.The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment.Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth,established by the expert ophthalmologist’s tests.The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%.The proposed method potentially offers a second opinion to ophthalmologists,and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.
