Global Implications of U.S. Biofuels Policies in an Integrated Partial and General Equilibrium Framework

With the increasing research interests in biofuels, global implications of biofuels production have been generally examined either in a partial equilibrium (PE) or general equilibrium (GE) frameworks. Though both of these approaches have unique strengths, they also suffer from many limitations due to complexity of addressing all the relevant aspects of biofuels. In this paper we have exploited the strengths of both PE and GE approaches for analyzing the economic and environmental implications of the U.S. policies on corn-ethanol and biodiesel production. In this study, we utilize the Forest and Agricultural Sector Optimization Model (FASOMGHG: Adams et al. 1996, 2005; Beach et al. 2009), a non-linear programming, PE model for the United States. We also use the GTAP-BIO model (Birur et al. 2008), a multi-region, multi-sector CGE model for global-scale assessment of biofuels policies. Following Britz and Hertel (2009), we link the GTAP-BIO model through a static, quadratic restricted revenue function obtained from perturbing crop prices from the FASOMGHG model. With this linkage we implement the U.S. Corn ethanol and biodiesel scenarios in the GTAP-BIO model and obtain the FASOMGHG-consistent, global land use changes. The resulting crop price changes from the GE model are fed back into the FASOMGHG model to obtain the disaggregated impacts in the U.S.Biofuels, Indirect land use change, Land use emissions, Partial Equilibrium, Computable General Equilibrium, Land Economics/Use, Resource /Energy Economics and Policy,

Efficient least angle regression for identification of linear-in-the-parameters models

Least angle regression, as a promising model selection method, differentiates itself from conventional stepwise and stagewise methods, in that it is neither too greedy nor too slow. It is closely related to L1 norm optimization, which has the advantage of low prediction variance through sacrificing part of model bias property in order to enhance model generalization capability. In this paper, we propose an efficient least angle regression algorithm for model selection for a large class of linear-in-the-parameters models with the purpose of accelerating the model selection process. The entire algorithm works completely in a recursive manner, where the correlations between model terms and residuals, the evolving directions and other pertinent variables are derived explicitly and updated successively at every subset selection step. The model coefficients are only computed when the algorithm finishes. The direct involvement of matrix inversions is thereby relieved. A detailed computational complexity analysis indicates that the proposed algorithm possesses significant computational efficiency, compared with the original approach where the well-known efficient Cholesky decomposition is involved in solving least angle regression. Three artificial and real-world examples are employed to demonstrate the effectiveness, efficiency and numerical stability of the proposed algorithm

Automated model construction for combined sewer overflow prediction based on efficient LASSO algorithm

The prediction of combined sewer overflow (CSO) operation in urban environments presents a challenging task for water utilities. The operation of CSOs (most often in heavy rainfall conditions) prevents houses and businesses from flooding. However, sometimes, CSOs do not operate as they should, potentially bringing environmental pollution risks. Therefore, CSOs should be appropriately managed by water utilities, highlighting the need for adapted decision support systems. This paper proposes an automated CSO predictive model construction methodology using field monitoring data, as a substitute for the commonly established hydrological-hydraulic modeling approach for time-series prediction of CSO statuses. It is a systematic methodology factoring in all monitored field variables to construct time-series dependencies for CSO statuses. The model construction process is largely automated with little human intervention, and the pertinent variables together with their associated time lags for every CSO are holistically and automatically generated. A fast least absolute shrinkage and selection operator solution generating scheme is proposed to expedite the model construction process, where matrix inversions are effectively eliminated. The whole algorithm works in a stepwise manner, invoking either an incremental or decremental movement for including or excluding one model regressor into, or from, the predictive model at every step. The computational complexity is thereby analyzed with the pseudo code provided. Actual experimental results from both single-step ahead (i.e., 15 min) and multistep ahead predictions are finally produced and analyzed on a U.K. pilot area with various types of monitoring data made available, demonstrating the efficiency and effectiveness of the proposed approach

Cloud computing for the architecture, engineering & construction sector: requirements, prototype & experience

The Architecture, Engineering \& Construction (AEC) sector is a highly fragmented, data intensive, project based industry, involving a number of very different professions and organisations. Projects carried out within this sector involve collaboration between various people, using a variety of different systems. This, along with the industry's strong data sharing and processing requirements, means that the management of building data is complex and challenging. This paper presents a solution to data sharing requirements of the AEC sector by utilising Cloud Computing. Our solution presents two key contributions, first a governance model for building data, based on extensive research and industry consultation. Second, a prototype implementation of this governance model, utilising the CometCloud autonomic cloud computing engine based on the Master/Work paradigm. we have integrated our prototype with the 3D modelling software Google Sketchup. The approach and prototype presented has applicability in a number of other eScience related applications involving multi-disciplinary, collaborative working using Cloud computing infrastructure

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension, and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. First, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. This paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners

Towards the adoption of automated regulatory compliance checking in the built environment

Automated compliance checking brings advantages to the built environment but, currently, there has been no meaningful adoption, despite the increasing maturity of asset information models. This paper addresses this by ascertaining the blockers/obstacles to adoption and develops a road-map to overcome them. This work has been conducted in the UK and a road-map has been produced to drive forward adoption. More specifically this paper has assessed the current state of the art in the field and engaged with industry to examine the attitudes to the digitisation of regulatory compliance processes. The results showed that industry believes that adoption of automation was both feasible and desirable, with the caveat that human oversight be maintained. Our road-map’s methodical list of steps was judged to have the potential to bring the construction industry to the verge of mass industrialisation of auto mated compliance checking by 2025

Dopaminergic Retinal Cell Loss and Visual Dysfunction in Parkinson Disease

Objective: Considering the demonstrated implication of the retina in Parkinson disease (PD) pathology and the importance of dopaminergic cells in this tissue, we aimed to analyze the state of the dopaminergic amacrine cells and some of their main postsynaptic neurons in the retina of PD. Methods: Using immunohistochemistry and confocal microscopy, we evaluated morphology, number, and synaptic connections of dopaminergic cells and their postsynaptic cells, AII amacrine and melanopsin‐containing retinal ganglion cells, in control and PD eyes from human donors. Results: In PD, dopaminergic amacrine cell number was reduced between 58% and 26% in different retinal regions, involving a decline in the number of synaptic contacts with AII amacrine cells (by 60%) and melanopsin cells (by 35%). Despite losing their main synaptic input, AII cells were not reduced in number, but they showed cellular alterations compromising their adequate function: (1) a loss of mitochondria inside their lobular appendages, which may indicate an energetic failure; and (2) a loss of connexin 36, suggesting alterations in the AII coupling and in visual signal transmission from the rod pathway. Interpretation: The dopaminergic system impairment and the affection of the rod pathway through the AII cells may explain and be partially responsible for the reduced contrast sensitivity or electroretinographic response described in PD. Also, dopamine reduction and the loss of synaptic contacts with melanopsin cells may contribute to the melanopsin retinal ganglion cell loss previously described and to the disturbances in circadian rhythm and sleep reported in PD patients. These data support the idea that the retina reproduces brain neurodegeneration and is highly involved in PD pathology.This work was supported by the Michael J. Fox Foundation for Parkinson’s Research. I.O.-L. and X.S.-S. acknowledge financial support from the Ministry of Education, Spain (FPU 14/03166; FPU 16/04114). N.C. acknowledges financial support from the Ministry of Economy and Competitiveness, Spain (MINECO-FEDER-BFU2015-67139-R), Carlos III Institute of Health (RETICS-FEDER RD16/0008/0016), Retina Asturias Association, and Generalitat Valenciana-European Regional Development Fund (IDIFEDER/2017/064). The Brain and Body Donation Program has been supported by the NIH National Institute of Neurological Disorders and Stroke (U24 NS072026), the NIH National Institute on Aging (P30 AG19610), the Arizona Department of Health Services, the Arizona Biomedical Research Commission, and the Michael J. Fox Foundation for Parkinson’s Research

Millimeter Wave Channel Measurements in a Railway Depot

Millimeter wave (mmWave) communication is a key enabling technology with the potential to deliver high capacity, high peak data rate communications for future railway services. Knowledge of the radio characteristics is of paramount importance for the successful deployment of such systems. In this paper mmWave channel measurements are reported for a railway environment using a wideband channel sounder operating at 60GHz. Highly directional antennas are deployed at both ends of the link. Data is reported for path loss, root mean square (RMS) delay spread and K-factor. Static and mobile measurements are considered. Analysis shows that the signal strength is strongly dependent (up to 25dB) on the azimuth orientation of the directional transmit and receive antennas. A path loss exponent of n=2.04 was extracted from the Line-of-Sight measurements with optimally aligned antennas. RMS delay spreads ranged from 1ns to 22ns depending on antenna alignment. 50% of the measured K-factors were found to be less than 6dB. We conclude this is the result of ground reflections in the vertical Tx-Rx plane

A Retrospective Database Analysis of Neonatal Morbidities to Evaluate a Composite Endpoint for Use in Preterm Labor Clinical Trials

Objective To propose and assess a composite endpoint (CE) of neonatal benefit based on neonatal mortality and morbidities by gestational age (GA) for use in preterm labor clinical trials. Study Design A descriptive, retrospective analysis of the Medical University of South Carolina Perinatal Information System database was conducted. Neonatal morbidities were assessed for inclusion in the CE based on clinical significance/risk of childhood neurodevelopmental impairment, frequency, and association with GA in a mother– neonate linked cohort, comprising women with uncomplicated singleton pregnancies delivered at !24 weeks’ GA. Results Among 17,912 mother–neonate pairs, neonates were at a risk of numerous severe but infrequent morbidities. Clinically important, predominantly rare events were combined into a CE comprising neonatal mortality and morbidities, which decreased in frequency with increasing GA. The highest CE frequency occurred at \u3c31 weeks. High frequency of respiratory distress syndrome, bronchopulmonary dysplasia, and sepsis drove the CE. Median length of hospital stay was longer at all GAs in those with the CE compared with those without. Conclusions Descriptive epidemiological assessment and clinical input were used to develop a CE to measure neonatal benefit, comprising clinically meaningful outcomes. These empirical data and CE allowed trials investigating tocolytics to be sized appropriately

Phosphorylated α‐synuclein in the retina is a biomarker of Parkinson's disease pathology severity

Background: PD patients often have visual alterations, for example, loss of visual acuity, contrast sensitivity or motion perception, and diminished electroretinogram responses. PD pathology is mainly characterized by the accumulation of pathological α‐synuclein deposits in the brain, but little is known about how synucleinopathy affects the retina. Objective: To study the correlation between α‐synuclein deposits in the retina and brain of autopsied subjects with PD and incidental Lewy body disease. Methods: We evaluated the presence of phosphorylated α‐synuclein in the retina of autopsied subjects with PD (9 subjects), incidental Lewy body disease (4 subjects), and controls (6 subjects) by immunohistochemistry and compared the retinal synucleinopathy with brain disease severity indicators. Results: Whereas controls did not show any phosphorylated α‐synuclein immunoreactivity in their retina, all PD subjects and 3 of 4 incidental Lewy body disease subjects had phosphorylated α‐synuclein deposits in ganglion cell perikarya, dendrites, and axons, some of them resembling brain Lewy bodies and Lewy neurites. The Lewy‐type synucleinopathy density in the retina significantly correlated with Lewy‐type synucleinopathy density in the brain, with the Unified Parkinson's disease pathology stage and with the motor UPDRS. Conclusion: These data suggest that phosphorylated α‐synuclein accumulates in the retina in parallel with that in the brain, including in early stages preceding development of clinical signs of parkinsonism or dementia. Therefore, the retina may provide an in vivo indicator of brain pathology severity, and its detection could help in the diagnosis and monitoring of disease progression.This work was supported by the Michael J. Fox Foundation for Parkinson's Research. I.O.L. acknowledges financial support from the Ministerio de Educación, Spain (FPU 14/03166). N.C. acknowledges financial support from the Ministerio de Economía y Competitividad, Spain (MINECO‐FEDER‐BFU2015‐67139‐R), Generalitat Valenciana (Prometeo 2016/158), and Instituto Carlos III (ISCIII RETICS‐FEDER RD12/0034/0010). The Brain and Body Donation Program has been supported by the National Institute of Neurological Disorders and Stroke (U24 NS072026), the National Institute on Aging (P30 AG19610), the Arizona Department of Health Services, the Arizona Biomedical Research Commission, and the Michael J. Fox Foundation for Parkinson's Research