Chemical, biochemical and microbiological indicators to assess soil quality in temperate agro-ecosystems

Soil is a critically important component of the earth’s biosphere. Developing agricultural production systems able to conserve soil quality is essential to guarantee the current and future capacity of soil to provide goods and services. This study investigates the potential of microbial and biochemical parameters to be used as early and sensitive soil quality indicators. Their ability to differentiate plots under contrasting fertilization regimes is evaluated based also on their sensitivity to seasonal fluctuations of environmental conditions and on their relationship with soil chemical parameters. Further, the study addresses some of the critical methodological aspects of microplate-based fluorimetric enzyme assays, in order to optimize assay conditions and evaluate their suitability to be used as a toll to asses soil quality. The study was based on a long-term field experiment established in 1966 in the Po valley (Italy). The soil was cropped with maize (Z. mays L.) and winter wheat (T. aestivum L.) and received no organic fertilization, crop residue or manure, in combination with increasing levels of mineral N fertilizer. The soil microbiota responded to manure amendment increasing it biomass and activity and changing its community composition. Crop residue effect was much more limited. Mineral N fertilization stimulated crop residue mineralization, shifted microbial community composition and influenced N and P cycling enzyme activities. Seasonal fluctuations of environmental factors affected the soil microbiota. However microbial and biochemical parameters seasonality did not hamper the identification of fertilization-induced effects. Soil microbial community abundance, function and composition appeared to be strongly related to soil organic matter content and composition, confirming the close link existing between these soil quality indicators. Microplate-based fluorimetric enzyme assays showed potential to be used as fast and throughput toll to asses soil quality, but required proper optimization of the assay conditions for a precise estimation of enzymes maximum potential activity.

Estimation of Perceived Quality in Convergent Services

Triple-Play (3P) and Quadruple-Play (4P) services are being widely offered by telecommunication services providers. Such services must be able to offer equal or higher quality levels than those obtained with traditional systems, especially for the most demanding services such as broadcast IPTV. This paper presents a matrix-based model, defined in terms of service components, user perceptions, agent capabilities, performance indicators and evaluation functions, which allows to estimate the overall quality of a set of convergent services, as perceived by the users, from a set of performance and/or Quality of Service (QoS) parameters of the convergent IP transport network

Automatic Bandwidth Estimation Strategy for High-Quality Non-Parametric Modeling based Moving Object Detection

Here, a novel and efficient moving object detection strategy by non-parametric modeling is presented. Whereas the foreground is modeled by combining color and spatial information, the background model is constructed exclusively with color information, thus resulting in a great reduction of the computational and memory requirements. The estimation of the background and foreground covariance matrices, allows us to obtain compact moving regions while the number of false detections is reduced. Additionally, the application of a tracking strategy provides a priori knowledge about the spatial position of the moving objects, which improves the performance of the Bayesian classifier

Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Sequential estimation of the success probability p in inverse binomial sampling is considered in this paper. For any estimator pˆ , its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters a and b for pˆ

p , respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as p→0, and which guarantee that, for any p∈(0,1), the risk is lower than its asymptotic value. This allows selecting the required number of successes, r, to meet a prescribed quality irrespective of the unknown p. In addition, the proposed estimators are shown to be approximately minimax when a/b does not deviate too much from 1, and asymptotically minimax as r→∞ when a=b.

Uncertainty Estimation for Performance Evaluation of a Confocal Microscope as Metrology Equipment

Both in industry and research, the quality control of micrometric manufactured parts is based on the measurement of parameters whose traceability is sometimes difficult to guarantee. In some of these parts, the confocal microscopy shows great aptitudes to characterize a measurand qualitatively and quantitatively. The confocal microscopy allows the acquisition of 2D and 3D images that are easily manipulated. Nowadays, this equipment is manufactured by many different brands, each of them claiming a resolution probably not in accord to their real performance. The Laser Center (Technical University of Madrid) has a confocal microscope to verify the dimensions of the micro mechanizing in their own research projects. The present study pretends to confirm that the magnitudes obtained are true and reliable. To achieve this, a methodology for confocal microscope calibration is proposed, as well as an experimental phase for dimensionally valuing the equipment by 4 different standard positions, with its seven magnifications and the six objective lenses that the equipment currently has, in the x–y and z axis. From the results the uncertainty will be estimated along with an effect analysis of the different magnifications in each of the objective lenses.

Estimation of an origin-destination trip table based on observed link volumes and turning movements. Executive summary. Final report.

Federal Highway Administration, Washington, D.C.

Estimation of an origin-destination trip table based on observed link volumes and turning movements. Volume 1: technical report. Final report.

Federal Highway Administration, Washington, D.C.

Estimation of an origin-destination trip table based on observed link volumes and turning movments. Volume 2: user's manual. Final report.

Federal Highway Administration, Washington, D.C.

Estimation of an origin-destination trip table based on observed link volumes and turning movements. Volume 3: program manual. Final report.

Federal Highway Administration, Washington, D.C.

Export memory: a preliminary investigation of its quality, its use, and its link to export performance

Exporting is one of the main ways in which organizations internationalize. With the more turbulent, heterogeneous, sophisticated and less familiar export environment, the organizational learning ability of the exporting organization may become its only source of sustainable competitive advantage. However, achieving a competitive level of learning is not easy. Companies must be able to find ways to improve their learning capability by enhancing the different aspects of the learning process. One of these is export memory. Building from an export information processing framework this research work particularly focuses on the quality of export memory, its determinants, its subsequent use in decision-making, and its ultimate relationship with export performance. Within export memory use, four export memory use dimensions have been discovered: instrumental, conceptual, legitimizing and manipulating. Results from the qualitative study based on the data from a mail survey with 354 responses reveal that the development of export memory quality is positively related with quality of export information acquisition, the quality of export information interpretation, export coordination, and integration of the information into the organizational system. Several company and environmental factors have also been examined in terms of their relationship with export memory use. The two factors found to be significantly related to the extent of export memory use are acquisition of export information quality and export memory quality. The results reveal that export memory quality is positively related to the extent of export memory use which in turn was found to be positively related to export performance. Furthermore, results of the study show that there is only one aspect of export memory use that significantly affects export performance – the extent of export memory use. This finding could mean that there is no particular type of export memory use favored since the choice of the type of use is situation specific. Additional results reveal that environmental turbulence and export memory overload have moderating effects on the relationship between export memory use and export performance.

Quality of transmission estimation in WDM and elastic optical networks accounting for space-spectrum dependencies

We develop a framework for estimating the quality of transmission (QoT) of a new lightpath before it is established, as well as for calculating the expected degradation it will cause to existing lightpaths. The framework correlates the QoT metrics of established lightpaths, which are readily available from coherent optical receivers that can be extended to serve as optical performance monitors. Past similar studies used only space (routing) information and thus neglected spectrum, while they focused on oldgeneration noncoherent networks. The proposed framework accounts for correlation in both the space and spectrum domains and can be applied to both fixed-grid wavelength division multiplexing (WDM) and elastic optical networks. It is based on a graph transformation that exposes and models the interference between spectrum-neighboring channels. Our results indicate that our QoT estimates are very close to the actual performance data, that is, to having perfect knowledge of the physical layer. The proposed estimation framework is shown to provide up to 4 × 10-2 lower pre-forward error correction bit error ratio (BER) compared to theworst-case interference scenario,which overestimates the BER. The higher accuracy can be harvested when lightpaths are provisioned with low margins; our results showed up to 47% reduction in required regenerators, a substantial savings in equipment cost.

Prospective Estimation of Radiation Dose and Image Quality for Optimized CT Performance

X-ray computed tomography (CT) is a non-invasive medical imaging technique that generates cross-sectional images by acquiring attenuation-based projection measurements at multiple angles. Since its first introduction in the 1970s, substantial technical improvements have led to the expanding use of CT in clinical examinations. CT has become an indispensable imaging modality for the diagnosis of a wide array of diseases in both pediatric and adult populations [1, 2]. Currently, approximately 272 million CT examinations are performed annually worldwide, with nearly 85 million of these in the United States alone [3]. Although this trend has decelerated in recent years, CT usage is still expected to increase mainly due to advanced technologies such as multi-energy [4], photon counting [5], and cone-beam CT [6].

Despite the significant clinical benefits, concerns have been raised regarding the population-based radiation dose associated with CT examinations [7]. From 1980 to 2006, the effective dose from medical diagnostic procedures rose six-fold, with CT contributing to almost half of the total dose from medical exposure [8]. For each patient, the risk associated with a single CT examination is likely to be minimal. However, the relatively large population-based radiation level has led to enormous efforts among the community to manage and optimize the CT dose.

As promoted by the international campaigns Image Gently and Image Wisely, exposure to CT radiation should be appropriate and safe [9, 10]. It is thus a responsibility to optimize the amount of radiation dose for CT examinations. The key for dose optimization is to determine the minimum amount of radiation dose that achieves the targeted image quality [11]. Based on such principle, dose optimization would significantly benefit from effective metrics to characterize radiation dose and image quality for a CT exam. Moreover, if accurate predictions of the radiation dose and image quality were possible before the initiation of the exam, it would be feasible to personalize it by adjusting the scanning parameters to achieve a desired level of image quality. The purpose of this thesis is to design and validate models to quantify patient-specific radiation dose prospectively and task-based image quality. The dual aim of the study is to implement the theoretical models into clinical practice by developing an organ-based dose monitoring system and an image-based noise addition software for protocol optimization.

More specifically, Chapter 3 aims to develop an organ dose-prediction method for CT examinations of the body under constant tube current condition. The study effectively modeled the anatomical diversity and complexity using a large number of patient models with representative age, size, and gender distribution. The dependence of organ dose coefficients on patient size and scanner models was further evaluated. Distinct from prior work, these studies use the largest number of patient models to date with representative age, weight percentile, and body mass index (BMI) range.

With effective quantification of organ dose under constant tube current condition, Chapter 4 aims to extend the organ dose prediction system to tube current modulated (TCM) CT examinations. The prediction, applied to chest and abdominopelvic exams, was achieved by combining a convolution-based estimation technique that quantifies the radiation field, a TCM scheme that emulates modulation profiles from major CT vendors, and a library of computational phantoms with representative sizes, ages, and genders. The prospective quantification model is validated by comparing the predicted organ dose with the dose estimated based on Monte Carlo simulations with TCM function explicitly modeled.

Chapter 5 aims to implement the organ dose-estimation framework in clinical practice to develop an organ dose-monitoring program based on a commercial software (Dose Watch, GE Healthcare, Waukesha, WI). In the first phase of the study we focused on body CT examinations, and so the patient’s major body landmark information was extracted from the patient scout image in order to match clinical patients against a computational phantom in the library. The organ dose coefficients were estimated based on CT protocol and patient size as reported in Chapter 3. The exam CTDIvol, DLP, and TCM profiles were extracted and used to quantify the radiation field using the convolution technique proposed in Chapter 4.

With effective methods to predict and monitor organ dose, Chapters 6 aims to develop and validate improved measurement techniques for image quality assessment. Chapter 6 outlines the method that was developed to assess and predict quantum noise in clinical body CT images. Compared with previous phantom-based studies, this study accurately assessed the quantum noise in clinical images and further validated the correspondence between phantom-based measurements and the expected clinical image quality as a function of patient size and scanner attributes.

Chapter 7 aims to develop a practical strategy to generate hybrid CT images and assess the impact of dose reduction on diagnostic confidence for the diagnosis of acute pancreatitis. The general strategy is (1) to simulate synthetic CT images at multiple reduced-dose levels from clinical datasets using an image-based noise addition technique; (2) to develop quantitative and observer-based methods to validate the realism of simulated low-dose images; (3) to perform multi-reader observer studies on the low-dose image series to assess the impact of dose reduction on the diagnostic confidence for multiple diagnostic tasks; and (4) to determine the dose operating point for clinical CT examinations based on the minimum diagnostic performance to achieve protocol optimization.

Chapter 8 concludes the thesis with a summary of accomplished work and a discussion about future research.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Non-institutionalized nonagenarians health-related quality of life and nutritional status: is there a link between them?

Introduction: Nonagenarian population, clearly increasing, shows different characteristics from the rest of elderly people. Health-related quality of life is a way to study population health in physical, psychological and social dimensions. Objectives: To examine the relationship between nutritional status and health-related quality of life in a group of free-living nonagenarians. Differences with octogenarians were also studied. Methods: Within Villanueva Older Health Study, 20 non-institutionalised people (92.5±3.5 years; 80% women) make the nonagenarian subsample. Nutritional risk was assessed by Mininutritional Assessment questionnaire, dietary intake by a 24-hour dietary recall and health-related quality of life by EuroQoL-5D questionnaire. SPSS was used for statistical analysis. Results: 40% nonagenarians were at risk of malnutrition. Dietary assessment showed magnesium, zinc, potassium, folic acid, vitamin D and vitamin E deficiencies. Problems in mobility were more frequently reported (80%). EQ-5Dindex was associated with MNA (p<0.05). Self-care dimension was associated with calcium and niacin (p<0.05), retinol and cholesterol (p<0.01) intake. Usual activities dimension was associated with niacin (p<0.01) and cholesterol(p<0.05) intake. Pain/discomfort dimension was associated with protein (p<0.01), energy, selenium and niacin (p<0.05) intake. Anxiety/depression was associated with protein(p<0.01) and selenium (p<0.05) intake. Conclusions: Risk of malnutrition is a factor associated to health-related quality of life. Results suggest that energy and some nutrient intakes could be possibly associated to health-related quality of life but further research on this influence is required.