1,634 research outputs found
Sectors May Use Multiple Technologies Simultaneously: The Rectangular Choice-of-Technology Model with Binding Factor Constraints (Revised)
We develop the rectangular choice-of-technology model with factor constraints, or RCOT, a linear programming input-output model for analysis of the economy of a single region. It allows for one or more sectors to operate more than one technology simultaneously, with the relatively lowest-cost one supplemented by others if it encounters a binding factor constraint. The RCOT model solves for sector outputs, goods prices that are set by the highest-cost technologies in use, and scarcity rents that correspond to binding factor constraints experienced by the lower-cost technologies. The model is motivated by the fact that mineral deposits of different qualities may be exploited simultaneously, as may primary and recycled sources for the same materials or irrigated and rainfed techniques for producing the same crop. RCOT generalizes Carter’s square choice-of-technology model, in particular adding the factor constraints that allow several alternatives to operate simultaneously. The Appendix gives a numerical example.
Sectors May Use Multiple Technologies Simultaneously - The Rectangular Choice-of-Technology Model with Binding Factor Constraints
We develop the rectangular choice-of-technology model with factor constraints, or RCOT, for analysis of the economy of a single region, or of multiple regions in the context of a model of the world economy. RCOT allows for one or more sectors to operate more than one technology simultaneously, using the relatively lowest-cost one first and adding another if and when the preceding one encounters a binding factor constraint. The model is motivated by the evident fact that oil wells and mineral deposits of different qualities may be exploited simultaneously, as may the use of both primary and recycled sources for the same materials. RCOT generalizes Carter’s choice-of-technology model, which allowed one of two choices to all sectors, for up to q choices and adds the factor constraints that allow several alternatives to operate simultaneously. The Appendix gives a numerical example.
Human Ecology: Industrial Ecology
Industrial Ecology aims to inform decision making about the environmental impacts of industrial production processes by tracking and analyzing resource use and flows of industrial products, consumer products and wastes. Quantifying the patterns of use of materials and energy in different societies is one area of research in Industrial Ecology. An extensive literature is devoted in particular to Material Flow Analysis (MFA), the collection of data describing the flows of specific materials from sources to sinks within some portion of the global industrial system. Industrial Ecologists are also concerned with the system-wide environmental impacts associated with products. Design for the Environment involves the design or redesign of specific products so as to reduce their impacts, while Life Cycle Analysis (LCA) quantifies resource use and emissions per unit of product from material extraction to the eventual disposal of the product. The LCA community has created a significant body of best-practice methods and shared data and increasingly incorporates their analyses within input-output models of entire economies to capture that portion of the impact that would otherwise be overlooked. Input-output models, often incorporating both MFA and LCA data, analyze the effects on the environment of alternative consumption and production decisions. Industrial Ecology makes use of this array of top-down and bottom-up approaches, all of which are grounded in its origins in the ecology of the industrial system.
Embodied Resource Flows and Product Flows: Combining the Absorbing Markov Chain with the Input-Output Model
We develop the absorbing Markov chain (AMC) for describing in detail the network of paths through an industrial system taken by an embodied resource from extraction through intermediate products and finally consumer products. We refer to this as a resource-specific network. This work builds on a recent literature in industrial ecology that uses an AMC to quantify the number of times a resource passes through a recycling sector before ending up in a landfill. Our objective is to incorporate into that analysis an input-output (IO) table so that the resource paths explicitly take account of the interdependence of sectors through their reliance on intermediate products. This feature makes it possible to track multiple resources simultaneously and consistently and to represent both resources and products in mixed units. Hypothetical scenarios about technological changes and changes in consumer demand are analyzed using an IO model, and model solutions generate the AMC database. A numerical example is provided. AMC analysis describes the resource-specific networks using matrices that are derived not from the Leontief inverse but from a generalized variant of the Ghosh inverse matrix. The Leontief inverse and especially the Ghosh inverse (although often not identified as such) have been used extensively to analyze ecological systems, and this paper extends these approaches for use in studying material cycles in industrial systems. Constructing the AMC formalizes the resource-specific network analysis and generalizes the content and interpretation of the Ghosh matrix. Path-based analyses derived from AMC theory are discussed in relation to the set of techniques called Structural Path Analysis (SPA). The paper concludes by identifying the three most critical enhancements to the IO model needed for analyzing material cycles: the simultaneous incorporation of waste-processing sectors, stock and flow relationships, and international trade. The idea is to implement an AMC after each model extension. The modeling framework is intended for analyses such as: tracking a resource extracted in one region to landfills in other regions, evaluating ways to intensify secondary recovery at key junctures in-between. There are other ways, of course, to approach such an analysis, but the combination of an extended IO model and an AMC, representing both resources and products in mixed units, provides a comprehensive, systematic and standardized approach that includes many features that are valued in industrial ecology and builds directly on a number of active research programs.
Stars in the USNO-B1 Catalog with Proper Motions Between 1.0 and 5.0 arcseconds per year
This paper examines a subset of objects from the USNO-B1 catalogue with
listed proper motions between 1.0 and 5.0 arcseconds per year. We look at the
degree of contamination within this range of proper motions, and point out the
major sources of spurious high proper motion objects. Roughly 0.1% of the
objects in the USNO-B1 catalogue with listed motions between 1.0 and 5.0
arcseconds per year are real. Comparison with the revised version of Luyten's
Half Second catalogue indicates that USNO-B1 is only about 47% complete for
stars in this range. Preliminary studies indicate that there may be a dip in
completeness in USNO-B1 for objects with motions near 0.1 arcseconds per year.
We also present two new stars with motions between 1.0 and 5.0 arcseconds per
year, 36 new stars with confirmed motions between 0.1 and 1.0 arcseconds per
year, several new common proper motion pairs, and the recovery of LHS237a
(VBs3).Comment: 42 pages, 16 figures, uses AASTeX v5.2, accepted by A
The rectangular sector-by-technology model: not every economy produces every product and some products may rely on several technologies simultaneously
This paper identifies a fundamental challenge in the development of input-output databases of the world economy intended for analysis of alternative scenarios with a model of the world economy. Primary data sources for individual economies generally do not use the same sectoral classification schemes, in part for lack of coordination and also because not all economies produce all goods. In addition, some economies produce a given good simultaneously using several technologies, sometimes quite distinct in terms of factor requirements and cost structures, information that can and should be retained for scenario analysis. To accommodate the relevant information in a way that is both precise and parsimonious, we introduce rectangular input-output matrices and a modeling framework for analyzing them. This framework extends an existing input-output/linear programming model, the rectangular choice-of-technology (RCOT) model, and integrates it into an existing input-output/linear programming model of the world economy, the World Trade Model (WTM). The desirable properties of the resulting WTM/RCOT model are illustrated through a numerical example. This formulation requires that a criterion be specified to choose among available options, and we discuss some alternative criteria. Square input-output matrices and their inverses are only a special case of this more general formulation, and we show how moving to the rectangular formulation expands the types of questions that input-output analysts are able to address
Deferiprone modulates in vitro responses by peripheral blood T cells from control and relapsing remitting multiple sclerosis subjects
T cells are important mediators of autoimmune inflammation in relapsing remitting multiple sclerosis (RRMS). Previous studies found that deferiprone, an iron chelator, suppressed disease activity in a mouse model of multiple sclerosis, and inhibition of T cell proliferation was implicated as a putative mechanism. The objective of the present study was to examine the effects of deferiprone on suppressing in vitro responses of T cells from control and RRMS subjects. Peripheral blood T cells were co-stimulated with anti-CD3 + anti-CD28 and cultured with or without interleukin 2 (IL-2). Proliferating CD4+ T cells from control and RRMS subjects, cultured with or without IL-2, decreased in response to 75 ÎĽM deferiprone, although the extent of decreased proliferation of CD4+ T cells from RRMS subjects was less than for control subjects. Proliferating CD8+ T cells from control subjects, cultured with or without IL-2, also decreased in response to 75 ÎĽM deferiprone, and this decrease was seen in proliferating CD8+ T cells from RRMS cultured with IL-2. CD4+CD25+ and CD8+CD25+ cells from control subjects, cultured with or without IL-2, declined in 75 M deferiprone, but the decrease was smaller than for the CD4+ and CD8+ proliferative responses. CD4+CD25+ and CD8+CD25+ cells from RRMS subjects showed more variability than for control subjects, but CD4+CD25+ cultured with IL-2 and CD8+CD25+ cells cultured without IL-2 significantly declined in 75 ÎĽM deferiprone. CD4+FoxP3+ and CD4+CD25+FoxP3+ cells tended to remain constant or increase. In summary, deferiprone induced declines in proliferative responses at a dosage that is within peak serum pharmacological concentrations
Prognostic value of lymphocyte vascular density and e-cadherin in inflammatory breast cancer
Background: We recently evaluated four laboratory assays, vascular endothelial growth factor D (VEGF-D), E-cadherin, lymphatic vessel density (LVD) measured by podoplanin, and intra-lymphatic tumor emboli (ILTE), which showed notable differences between inflammatory breast cancer (IBC) and non-inflammatory locally advanced breast cancer (LABC). In this study we investigated the potential of the three most quantitatively measured markers, E-cadherin, LVD and VEGF-D, to predict survival in the IBC patients.
Materials and Methods: This study involved the 100 cases identified in the Inflammatory Breast Cancer Registry (IBCR) whose tumors were previously evaluated for the four assays noted above. Living patients were recontacted and survival data were available for up to 17 years. Overall survival (OS) was analyzed through the Kaplan-Meier method stratified by E-cadherin, LVD, VEGF-D, and response to chemotherapy. The differences in OS curves were compared using the log-rank test.
Results: The median OS for patients with high LVD was 6.63 years (95% CI: 4.06 to 10.14), compared to median at 10 years not reached in those with low LVD (p = 0.03). There was a trend towards a longer median OS in patients with high E-cadherin (10.14, 95% CI: 6.63 to 11.67), compared with those with low E-cadherin (6.26, 95% CI: 3.42 to undeterminable). VEGF-D levels showed no correlation with survival.
Conclusion: Low LVD significantly predicts better survival. High E-cadherin expression, as with non-IBC breast cancer and several other malignancies, tends to be associated with a better prognosis
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