Handling of household and item nonresponse in surveys

Für den Zensus 2000 wird das US Census Bureau eine Stichprobe zur Qualitätsprüfung auswählen (auch bekannt als integrated coverage measurement oder ICM), die die Schätzungen des Zensus verbessern soll. Die ICM-Stichprobe wird durch fehlende Daten aufgrund von Antwortverweigerung der befragten Haushalte oder Antwortverweigerung auf einzelne Fragen beeinflusst. Der Verfasser diskutiert alternative Methoden zur Berücksichtigung von Antwortverweigerung in der ICM-Stichprobe. Hierzu zählen folgende Vorgehensweisen: (1) keine Korrektur bei Antwortverweigerung durch Haushalte und keine Ableitung von Items; (2) Korrektur bei Antwortverweigerung durch Haushalte auf der Basis der Zensus-Kurzcharakteristiken; (3) Ersatz fehlender ICM-Items durch Zensusdaten; (4) Hot-Deck-Ableitungsverfahren. (ICEÜbers)"For the 2000 Census, the U.S. Census Bureau will select a quality check, also known as integrated coverage measurement (ICM), sample to improve Census estimates. The ICM sample is subject to missing data due to household and item nonresponse. This paper discusses alternative methods researched to deal with nonresponse in the ICM sample. These methods include no adjustment for household nonresponse and no item imputation, use of Census short form characteristics to perform household nonresponse adjustment, substitution of Census data for ICM missing items, and alternative hot deck imputation procedures." (author's abstract

Analysis of Impact of Transformer Coupled Input Matching on Concurrent Dual-Band Low Noise Amplifier

Emerging advancements in telecommunication system need robust radio devices which can capable of working multiple frequency bands seamlessly. In any Radio Frequency (RF) receiver architecture, Low Noise Amplifier (LNA) is the mandatory front-end part in which takes place in between antenna and mixer. To support multiple frequency bands with single hardware, concurrent LNA is the more preferred topologies among others. As LNA is the very front end level of receiver, Input matching, Noise Figure (NF) and gain are the major performance parameters to be concerned. In this work, the impact of transformer coupled input matching on concurrent dual-band LNA is analyzed and verified. A concurrent LNA with concurrent matching without transformer coupling is used for comparison. A transformer coupled input matching is proposed for tunable concurrent dual-band LNA. All the circuits are implemented in UMC 180nm CMOS technology, and simulated using Cadence SpectreRF simulation tool

Environmental and Economic Analysis of Selected Pavement Preservation Treatments

Pavements are one of the highest assets and represent massive investment. The need to design and provide a sustainable maintenance service is becoming a priority and this comes mutually with the intentions to reduce impacts caused by maintenance treatments to the environment. This paper through a case study presents a Life Cycle Cost and Assessment technique during a 30 year analysis period to measure the cost effectiveness, embodied energy and carbon emissions of selected preservation treatments. These treatments can either be applied separately or in combination during the preventive maintenance of road pavements. This study entails three life cycle phases of material extraction and production, transportation and construction of maintenance activities. Through a literature review, raw materials energy and emission inventory data was averaged followed by the analysis of the equipment involved by using the specific fuel consumption to calculate the energy and emissions spent by the machine and finally the selected treatment energy and emissions was computed. Results show that preservation treatments can have an LCC of 30-40 % and embodied energy and carbon emission of 3-6 times lower than the traditional approach. This study bridges gaps in literature on integrated evaluation of environmental and economic aspects of preservation treatments

Therapeutic effect of hydroethanolic extract of Trianthema portulacastrum L. against N-Nitroso-N-Methylurea-induced mammary tumors in Wistar rats

This study evaluated the therapeutic action of hydroethanolic extract of Trianthema portulacastrum L. (TPE) on N-nitroso-N-methylurea (NMU)-induced mammary tumors in Wistar rats. A hydroethanolic was prepared and subjected to qualitative and quantitative phytochemical screening. After acclimatization, Wistar rats were divided into 4 groups of 6 rats each: Group A (vehicle control), Group B (TPE control), Group C (TPE treatment) and group D (NMU control). NMU (50 mg/kg body weight) was injected intraperitoneally at 50, 80 and 110 days of age. After the induction of palpable tumors,the rats were administered 200 mg/kg bw of TPE by oral gavage for 2 months. The treatment with TPE significantly (p<0.05) decreased tumor incidence, frequency, size and malignancy in comparison to the tumor-bearing rats that were not administered TPE. Immunohistochemical analysis revealed that TPE treatment significantly reduced the expression of PCNA, VEGF, ER-α and ER-β, and caused non-significant reductions in matrix metallopeptidase-9 (MMP-9). Caspase-3 expression significantly increased in TPE-treated rats in comparison with NMU-treated controls. The qRT-PCR resultsshowed PCNA and ER-β expression was down regulated and caspase-3 expression was up regulated in the TPE-treated group. The present study showed the in vivo therapeutic action of TPE extract on NMU-induced mammary tumors. TPE exhibited antitumor activity through its antiproliferative, antiangiogenic, pro-apoptotic, and estrogen receptor-modulatory properties

Analysis of Genetic Diversity in Twelve Cultivars of Pea Based on Morphological and Simple Sequence Repeat Markers

Pea(Pisum sativum L.)is the second most important legume crop worldwide after chickpea (Cicer arietinum L.) and valuable resources for their genetic improvement. This study aimed to analyze genetic diversity of pea cultivars through morphological and molecular markers. The present investigation was carried out with 12 pea cultivars using 28 simple sequence repeat markers. A total of 60 polymorphic bands with an average of 2.31 bands per primer were obtained. The polymorphic information content, diversity index and resolving power were ranged from 0.50 to 0.33, 0.61 to 0.86 and 0.44 to 1.0 with an average of 0.46, 0.73 and 0.76, respectively. The 12 pea cultivars were grouped into 3 clusters obtained from cluster analysis with a Jaccardd's similarity coefficient range of 0.47-0.78, indicating the sufficient genetic divergence among these cultivars of pea. The principal component analysis showed that first three principal components explained 86.97% of the total variation, suggesting the contribution of quantitative traits in genetic variability. The contribution of 32.59% for number of seeds per plant, stem circumference, number of pods per plant and number of seeds per pod in the PC1 leads to the conclusion that these traits contribute more to the total variation observed in the 12 pea cultivars and would make a good parental stock material. Overall, this SSR analysis complements morphological characters of initial selection of these pea germplasms for future breeding program

Metal Oxide-based Gas Sensor Array for the VOCs Analysis in Complex Mixtures using Machine Learning

Detection of Volatile Organic Compounds (VOCs) from the breath is becoming a viable route for the early detection of diseases non-invasively. This paper presents a sensor array with three metal oxide electrodes that can use machine learning methods to identify four distinct VOCs in a mixture. The metal oxide sensor array was subjected to various VOC concentrations, including ethanol, acetone, toluene and chloroform. The dataset obtained from individual gases and their mixtures were analyzed using multiple machine learning algorithms, such as Random Forest (RF), K-Nearest Neighbor (KNN), Decision Tree, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminant Analysis, Artificial Neural Network, and Support Vector Machine. KNN and RF have shown more than 99% accuracy in classifying different varying chemicals in the gas mixtures. In regression analysis, KNN has delivered the best results with R2 value of more than 0.99 and LOD of 0.012, 0.015, 0.014 and 0.025 PPM for predicting the concentrations of varying chemicals Acetone, Toluene, Ethanol, and Chloroform, respectively in complex mixtures. Therefore, it is demonstrated that the array utilizing the provided algorithms can classify and predict the concentrations of the four gases simultaneously for disease diagnosis and treatment monitoring

Determination and expression of genes for resistance to blast (Magnaporthe oryza) in Basmati and non-Basmati indica rices (Oryza sativa L.)

One hundred and twenty two (122) genotypes of Basmati and non-Basmati Indica rice genotypes were evaluated for expression of resistance against blast disease under induced epiphytotic conditions. Disease severity (%) and area under disease progress curve (AUDPC) parameters were used for screening the blast resistance. Only 13 genotypes expressed resistance against the blast disease. Nine genotypes carried blast resistance genes but, were susceptible under induced epiphytotic conditions. The rice genotype VLD-61 had no resistance genes; however, it expressed strong resistance against blast. An empirical breeding strategy for development of blast resistant improved varieties of rice was also discussed.Keywords: Magnaporthe oryzae, restriction digestion, molecular breeding, Basmati riceAfrican Journal of Biotechnology Vol. 12(26), pp. 4098-410