Future Growth Patterns Research Articles

A Pre-Experimental Study to Assess the Effect of Video-Assisted Teaching on Knowledge Regarding Preventive Measures of AIDS among Adolescents at Selected School of Bhopal (MP)

Adolescence is the second decade of life (13-19 years), and a period of both physical as well as psychological development. This is a phase of experimentation and risk that includes early sexual debut, sexual coercion and violence, trafficking, and substance abuse. Along with these, other factors such as the lack of knowledge about HIV/AIDS, inaccessibility to health care services and commodities, lack of education and life skills, and early marriage have increased their vulnerability to HIV/AIDS. Since adolescents comprise a major part of reproductive group, they are likely to play a significant role in determining the future growth pattern of India’s population and economy. Thus, it is crucial that investment in terms of finances, research and development policies be done to improve their well-being. A pre-experimental one group pre-test, post-test research design was selected for this study to gather data from adolescents regarding prevention of AIDS. A sample of 100 adolescents was selected using purposive sampling technique. Structured questionnaire was used by the investigator for data collection. Video-assisted teaching was administered to adolescents to improve their knowledge. The structured knowledge questionnaire and video film (video-assisted teaching material) were validated by the experts. Further, pilot study was conducted on 10 samples to assess the reliability of the questionnaire where Karl Pearson correlation co-efficient (r=0.86) proved the reliability. After the pilot study, main study was carried out on 100 adolescents. As a first step pre-test knowledge of adolescents was assessed. Thereafter, video-assisted teaching was provided to the same sample. The same teaching materials were also provided in CD form. Finally, post-test knowledge was taken after 7 days with the same sample. Opinion regarding video film was also taken on day 7. This study revealed that there is significant difference between mean pre-interventional and post-interventional knowledge score on preventive measures of HIV / AIDS among adolescents (mean difference=2.0 and t-value=21.51, significance at 0.05 level). On the basis of its hypothesis H1 was accepted. It signifies that video-assisted teaching is effective in increasing the knowledge of adolescents.

Arctic Plant Responses to Summer Climates and Flooding Events: A Study of Carbon and Nitrogen‐Related Larch Growth and Ecosystem Parameters in Northeastern Siberia

AbstractBuilt upon a 5‐year field investigation and a 13‐year satellite data set, this study examines the intricate interrelationships among ecophysiological parameters of Larix gmelinii trees and the prevailing ecosystem, climatic, and environmental factors present in the Indigirka lowlands of northeastern Siberia. It identified spatial‐temporal patterns in July needle nitrogen (N) content as an indicator of N availability from 2009 to 2013. Needle N content (%) revealed distinct yearly values: 2012 (1.31 ± 0.24), 2013 (1.67 ± 0.39), 2009 (1.72 ± 0.15), 2011 (1.84 ± 0.34), and 2010 (2.08 ± 0.25). Positive correlations were found between ecosystem and larch parameters, as well as between September temperature or February/May precipitation and subsequent July ecosystem productivity. Soil moisture (SM) primarily influences N availability across sites, with higher SM levels reducing N availability. However, July air temperature (AT) is the primary driver of interannual N availability changes, with higher temperatures enhancing N availability. Larch photosynthesis is mainly influenced by solar radiation (SR), temperature, N availability, and SM. Annual fluctuations in SR positively impact larch photosynthesis, while high temperatures or wetting events impose limitations on photosynthesis, even if N availability has increased. Consequently, a moderate correlation exists between N availability and photosynthesis across various sites and years (r = 0.422, P = 0.133, n = 14). In summary, this research provides valuable insights into climatic and environmental impacts on larch trees and ecosystems, emphasizing the significance of SM, AT, and SR for predicting future growth patterns of larch.

Enhanced urban growth modelling: Incorporating regional development heterogeneity and noise reduction in a cellular automata model - a case study of Zhengzhou, China

Rapid global urban expansion necessitates accurate simulations to comprehend future growth patterns and their implications. Cellular automata, such as the widely used patch-generating land use simulation (PLUS) model, ensure reliable accuracy in urban growth simulations. However, previous studies have overlooked the development heterogeneity amongst sub-regions by treating the study area as the homogenous. Based on the PLUS model, this study introduces a new model coupling regional development heterogeneity and noise reduction (RDHNR-PLUS), which is an enhanced model that incorporates relative development coefficients derived from nighttime light data to capture regional heterogeneity. It also employs noise reduction techniques inspired by image classification's small patch processing method. Results demonstrate that the relative development coefficients effectively reveal the potential of different regions, while the noise reduction process mitigates simulation noise. The combined approach enhances overall accuracy, figure of merit, and structural similarity by 4.56 %, 20.44 %, and 5.06 %, respectively. Additionally, we simulate and analyse the built-up area of Zhengzhou from 2000 to 2030. The findings indicate rapid past growth, particularly in new urban areas, with projected future expansion likely to slow down. However, built-up areas dominated by medium to high and high densities will occupy three-quarters of the region. These results underscore the sustainability challenges faced by Zhengzhou, including disaster risk, traffic congestion, and air and noise pollution.

Global analysis and prediction scenario of infectious outbreaks by recurrent dynamic model and machine learning models: A case study on COVID-19

It is essential to evaluate patient outcomes at an early stage when dealing with a pandemic to provide optimal clinical care and resource management. Many methods have been proposed to provide a roadmap against different pandemics, including the recent pandemic disease COVID-19. Due to recurrent epidemic waves of COVID-19, which have been observed in many countries, mathematical modeling and forecasting of COVID-19 are still necessary as long as the world continues to battle against the pandemic. Modeling may aid in determining which interventions to try or predict future growth patterns. In this article, we design a combined approach for analyzing any pandemic in two separate parts. In the first part of the paper, we develop a recurrent SEIRS compartmental model to predict recurrent outbreak patterns of diseases. Due to its time-varying parameters, our model is able to reflect the dynamics of infectious diseases, and to measure the effectiveness of the restrictive measures. We discuss the stable solutions of the corresponding autonomous system with frozen parameters. We focus on the regime shifts and tipping points; then we investigate tipping phenomena due to parameter drifts in our time-varying parameters model that exhibits a bifurcation in the frozen-in case. Furthermore, we propose an optimal numerical design for estimating the system’s parameters. In the second part, we introduce machine learning models to strengthen the methodology of our paper in data analysis, particularly for prediction scenarios. We use MLP, RBF, LSTM, ANFIS, and GRNN for training and evaluation of COVID-19. Then, we compare the results with the recurrent dynamical system in the fitting process and prediction scenario. We also confirm results by implementing our methods on the released data on COVID-19 by WHO for Italy, Germany, Iran, and South Africa between 1/22/2020 and 7/24/2021, when people were engaged with different variants including Alpha, Beta, Gamma, and Delta. The results of this article show that the dynamic model is adequate for long-term analysis and data fitting, as well as obtaining parameters affecting the epidemic. However, it is ineffective in providing a long-term forecast. In contrast machine learning methods effectively provide disease prediction, although they do not provide analysis such as dynamic models. Finally, some metrics, including RMSE, R-Squared, and accuracy, are used to evaluate the machine learning models. These metrics confirm that ANFIS and RBF perform better than other methods in training and testing zones.

The Natural History of Benign Thyroid Nodule: Early Follow-up Can Predict its Future Growth Pattern

The Natural History of Benign Thyroid Nodule: Early Follow-up Can Predict its Future Growth Pattern

Managing future urbanization growth patterns using genetic algorithm modeling

PurposeDespite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue.Design/methodology/approachThis study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA.FindingsModel parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands.Originality/valueThe research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city.

Gender Parity in Healthcare: A Chronicle of Escalating Success Rate of Female Radiation Oncologists in India

Objectives This editorial describes the growth pattern of female radiation oncologists (FRO) in India and the prediction of gender equality through a mathematical formulation. Materials and Methods Among the countries in South Asia, India has the largest population of radiation oncologists (RO), a total of 3,763: 1,286 female and 2,477 male radiation oncologists (MROs), and they are registered with the Association of Radiation Oncologists of India (AROI). The data were analyzed to find the differential and cumulative growth pattern of the FROs and MROs and predict gender equality in radiation oncology. The cumulative growth rate indicates the total number of FROs and MROs by end of every year. Differential growth rate indicates the differential increase in the number of FROs and MROs for a particular year. Annual cumulative and differential growth patterns were plotted as a function of the time, and an analytical functional form was fitted to predict the future growth pattern and achievement of gender equality. Results AROI registration of FROs and MROs for 2013–2020 were as follows: FRO: MRO 2013-54: 102, 2014-99: 162, 2015-77: 148; 2016-86: 143, 2017-110: 110, 2018-116: 151, 2019-121: 152, 2020 (October)-129: 110. Differential growth pattern between 2013 and 2020 with the average incremental growth rate for FROs and MROs were 12.7 ± 14.8% and 2.1 ± 32.0%. Differential growth rate FRO fits in a power-law exponent 58.6 ×(Power0.3695), where MRO growth pattern showed a saturation [4.7ln(×) + 128.5] . Gender parity among Indian radiation oncologists is likely to be achieved by end of 2027. Conclusions The present density of FRO in India 34.1% is high compared to developed countries such as the United States (≈26%). It is a big leap for the Indian radiation oncology society tending toward gender parity.

How much carbon storage will the ecological space leave in a rapid urbanization area? Scenario analysis from Beijing-Tianjin-Hebei Urban Agglomeration

Ecological carbon storage is vital to carbon neutrality by capturing and storing carbon from the atmosphere. As one of top global agglomerations, Beijing-Tianjin-Hebei Urban Agglomeration is faced with continuous ecological carbon storage loss during rapid urbanization, balance the socioeconomic development and ecological space maintenance to alleviate the gradually shrinking trend of carbon storage is necessary. In this study, regional ecological spatial quality was evaluated, the area of 25915.60km2 recognized as the ecological restricted area. Coupled with socioeconomic factors, future spatial growth patterns of different ecological priority scenarios were simulated with system dynamic model and cellular automata model. The least ecological space loss predicted from 2030 to 2060 is 2899km2, of which scenario the ecological carbon storage decline from 2093.26Tg to 2059.60Tg. From the city level, Tangshan predicted to suffer the highest shrink rate of 6.52% while Baoding prone to loss the most carbon storage of 9.78Tg. The proposed method can be used to rapid urbanization area ecological carbon storage estimation, the results contribute to regional dominant ecological carbon storage recognition and maintenance.

Do political instability, financial instability and environmental degradation undermine growth? Evidence from belt and road initiative countries

This study investigates the extent to which political instability (PI), financial instability (FI), and environmental degradation undermine economic complexity (EC). EC is more predictive of future growth and development patterns and correlates with the increased ability to create and export a wide variety of sophisticated (high productivity) items. Using data from up to 56 BRI nations, I routinely obtain precise estimates of the negative effect of PI and FI on EC. Further, we examined the indirect effect of PI and FI through the channel of human capital (HC). These findings confirm the core premise that institutional stability largely influences economic complexity by encouraging creative entrepreneurship, HC accumulation, and allocating human resources to productive activities. Moreover, we examined the indirect effect of PI and FI through the channel of CO2 emissions. These findings support the idea that countries with stable institutions can reduce pollution and protect the environment. It could increase economic complexity while reducing environmental degradation. The main findings support the creation of a pro-development institutional framework that enhance environmental sustainability and reduces poverty by increasing economic complexity.

Future growth pattern projections under shared socioeconomic pathways: a municipal city bottom-up aggregated study based on a localised scenario and population projections for China

Precise multi-scenario projections of future economic outputs based on localised interpretations of global scenarios and major growth drivers are important for understanding long-term economic changes. However, few studies have focussed on localised interpretations, and many assume regional uniformity or use key parameters that are recursive or extrapolated by mathematical methods. This study provides a more intuitive and robust economic framework for projecting regional economic growth based on a neoclassical economic model and shared socioeconomic pathways (SSPs) scenarios. A non-uniform version of SSP2 (the middle-of-the-road scenario) was developed, and more detailed population projections for China were adopted using municipal-level data for 340 districts and parameter settings based on China’s recent development. The results show that China’s GDP will vary substantially across SSPs by 2050. Per capita GDP ranges from 19,300 USD under SSP3 (fragmentation) to 41,100 USD under SSP5 (conventional development). Per capita GDP under SSP1 (sustainability) is slightly higher than under SSP2, but lower on average than under SSP5. However, SSP1 is a better choice overall because environmental quality and equity are higher. Per capita GDP growth will generally be higher in relatively low-income regions by 2050, and the upper-middle-income provinces will become China’s new engine for economic growth.

UNDERSTANDING THE DYNAMIC BEHAVIOUR OF THE AUSTRALIAN RETIREMENT VILLAGE INDUSTRY: A CAUSAL LOOP DIAGRAM

The retirement village industry in Australia has been accommodating an increasing number of residents in recent decades. However, a thorough understanding of the dynamic behaviour of the industry remains largely unknown, which prevents a better prediction of its future growth. This study incorporates system dynamics thinking into residents’ relocation decisions and aims to develop a causal loop diagram to have a full understanding of the complex interactions of variables affecting their relocation, which in turn determines the future growth pattern of the industry. Based on thematic analysis using literature review and interview data, five primary causal loops are identified, including the positive reinforcing loops of word-of-mouth effect and new-supply effect, and three negative balancing loops of the move-out effect, price effect and home-village distance effect. Of these five causal loops, the most dominant ones in determining the system behaviour are the word-of-mouth (reinforcing) and home-village distance (balancing) effects. The causal loop diagram provides a better understanding of the underlying structure of the retirement village industry and will help guide the industry and policy makers in formulating strategies to create a more ageing-friendly living environment for seniors in Australia.

Extension of the seaport life cycle (SLC) by utilising existing inland capacity for current and future trade preparation

This paper explores the structure of the seaport life cycle (SLC) of Malaysian container seaports to restructure the seaport development plan. Quadratic regression analysis is proposed to stratify the life cycle stages of seaports through the quantification of throughput volume, and forecast future growth patterns of seaports. The findings show that Port Klang, Penang Port, PTP and Kuantan Port are experiencing the growth stage in their seaport life cycles, while Johor Port is standing at the decline stage of the seaport life cycle. Penang Port and Port Klang are expected to continue growing until 2050. Meanwhile, PTP, Johor Port and Kuantan Port have entered the maturity stage in 2018 and are expected to move into the declining stage by 2050. This paper provides significant evidence about the need to establish a link between seaports and dry ports, and develop a symbiotic strategy to prolong the life cycle of seaports. Academically, this paper provides significant literature on Malaysian seaport cycles, which may be utilised for seaport development plans to cater to the demands of the domestic and global market.

The initial engraftment of tumor cells is critical for the future growth pattern: a mathematical study based on simulations and animal experiments

BackgroundXenograft mouse tumor models are used to study mechanisms of tumor growth and metastasis formation and to investigate the efficacy of different therapeutic interventions. After injection the engrafted cells form a local tumor nodule. Following an initial lag period of several days, the size of the tumor is measured periodically throughout the experiment using calipers. This method of determining tumor size is error prone because the measurement is two-dimensional (calipers do not measure tumor depth). Primary tumor growth can be described mathematically by suitable growth functions, the choice of which is not always obvious. Growth parameters provide information on tumor growth and are determined by applying nonlinear curve fitting.MethodsWe used self-generated synthetic data including random measurement errors to research the accuracy of parameter estimation based on caliper measured tumor data. Fit metrics were investigated to identify the most appropriate growth function for a given synthetic dataset. We studied the effects of measuring tumor size at different frequencies on the accuracy and precision of the estimated parameters. For curve fitting with fixed initial tumor volume, we varied this fixed initial volume during the fitting process to investigate the effect on the resulting estimated parameters. We determined the number of surviving engrafted tumor cells after injection using ex vivo bioluminescence imaging, to demonstrate the effect on experiments of incorrect assumptions about the initial tumor volume.ResultsTo select a suitable growth function, measurement data from at least 15 animals should be considered. Tumor volume should be measured at least every three days to estimate accurate growth parameters. Daily measurement of the tumor volume is the most accurate way to improve long-term predictability of tumor growth. The initial tumor volume needs to have a fixed value in order to achieve meaningful results. An incorrect value for the initial tumor volume leads to large deviations in the resulting growth parameters.ConclusionsThe actual number of cancer cells engrafting directly after subcutaneous injection is critical for future tumor growth and distinctly influences the parameters for tumor growth determined by curve fitting.

Guidelines for a Polycentric Region to Reduce Vehicle Use and Increase Walking and Transit Use

Problem, research strategy, and findings: The monocentric development pattern in the Alonso–Mills–Muth model underpinned theoretical discussions of urban form in the 1960s and 1970s and truly dominated theory up to the point when Joel Garreau published Edge City: Life on the New Frontier in the early 1990s. Monocentric development patterns remain dominant to this day among smaller metropolitan areas in the United States. However, for larger metropolitan areas in the United States, regional transportation plans suggest a paradigm shift to a polycentric structure. We review 126 regional transportation plans in the United States and find that a hierarchy of centers connected by high-quality transit has become the dominant vision for most of them. The plan for Salt Lake City (UT), for example, strives for a multicentered region even though secondary centers are only beginning to emerge beyond a dominant downtown. Generally missing from regional transportation plans are quantitative criteria for designating and guiding centers: In no case are the quantitative criteria empirically based on proven transportation benefits. Here we investigate how the built environment characteristics of centers are associated with people’s travel mode choices and vehicle use. We employ visual and exploratory approaches through a generalized additive model (GAM) to identify nonlinear relationships between travel outcomes and “D” variables (density, diversity, design, destination accessibility, and distance to transit) within centers. The model and plots help us recommend the built environment characteristics of centers.Takeaway for practice: The built environment thresholds and relevant tools provided here can enable planners to make informed decisions about future growth patterns, set realistic—yet visionary—goals, and improve the overall health of its residents and communities. We provide strategies and tools that planning agencies, such as metropolitan planning organizations, transit agencies, and municipalities, can adopt to channel developments into centers.

Market Size Growth Survival in Multi-Generation Technology Environment: A Predictive Review of the Indian Air-Conditioner and Refrigerator Industry

The ensuing paper aims to explore the future growth pattern of the market size of air-conditioner and refrigerator industry in India. Though this industry has witnessed phenomenal growth in the past, with multi-generation technology products driving it, its growth has remained erratic in nature. This paper also ratifies if the industry would survive the existing market size growth trend. In this predictive assessment, univariate time series data of net sales, collected from CMIE, is used. The data, spreading across 14 years, have 56 observations and exhibit both trend and seasonality. Forecast of market size is made using the best model derived from comparative approaches that include SARIMA, triple exponential smoothing and neural network. SARIMA model is found to best fit the historical data for predictive purpose and the study outcome suggests market size to grow till 2020. Finally, Weibull’s function is used to analyze reliability of the forecast results which indicates diminishing trend of the market size growth. Finally, it is concluded that the current erratic nature of market size growth would disappear.

Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran

Urban expansion has caused major changes in land use and urban growth patterns in metropolitan areas. While urban growth in these areas is a well-known phenomenon, the trends, the spatial pattern of growth, and its driving forces are still relatively unknown. This study attempted to analyze these issues in Tabriz Metropolitan Area (TMA). The SLEUTH model was used to analyze urban growth patterns and predict future growth. Given that socio-economic driving forces are overlooked in this model, Logistic Regression (LR) and Multi-Criterion Evaluation (MCE) were used to develop three growth scenarios to provide a better representation of future growth patterns. The results show that the edge growth pattern was dominant over the entire region while the dispersed growth pattern appears to be greater in some sub-regions. Accessibility to main roads, cities, and services had a positive impact while proximity to industrial lands had the most negative effect on growth patterns of TMA. Predicting future growth scenarios indicate that the highest rate of urban growth in 2035 will be, respectively, uncontrolled growth, historical growth, and planned growth. The implications of this study could be useful for policymakers and planners regarding the realities of spatial patterns and future urban growth.

Improvement of feedlot operations through statistical learning and business analytics tools

A decision-support, modeling tool is developed that can project future cattle growth patterns in a feedlot based on a low dimensionality dataset available at the start of the feeding process. This work adapts the predictive performance of two well-known statistical machine modeling tools, gradient boosting and random forest regression, to predict future cattle growth. Time series analysis techniques are then used to create an ensemble method that further improves prediction accuracy from individual modeling outputs. Hierarchical clustering techniques are used to leverage projected growth patterns to increase group homogeneity when assigning cattle to different feeding pens. Finally, a profit maximization method is developed that estimates the optimal time each individual cattle should remain in the system under different revenue and cost estimates.The purpose of this work is to incentivize the implementation of modern statistical learning tools in cattle management operations, especially within low-to-mid scale operations that traditionally rely on the expertise of its workers and have limited cattle and process information. Access to ‘off-the-shelf’ statistical learning tools, requiring minimal user-interaction, not only enhances prediction accuracy but helps automate operational decisions. This results in higher process efficiencies and improved standardization practices, while also helping identify profit opportunities. Finally, integrating these components into a single operating framework allows the tool to adapt to changes in data characteristics, which is especially important within non-standardized processes. We show the application of this tool through a case study implementation on a mid-scale operation in the northwestern state of Sonora, Mexico. From our case-study results, it was found that the modeling tool can satisfactorily predict growth patterns based on a low-dimensional set. It also can also capture historic decision-making when segmenting cattle into homogenous groups during their feeding process. Furthermore, it can help identify profit opportunities when estimating optimal cattle system times under varying market conditions.

Modeling of urban change using remote sensing data and cellular automata technique

The understanding of urban development trends is crucial to the Egyptian government, developers, planners, resource managers, and environmental protection agencies, who are saddled with the responsibilities of conserving valuable resources and maintaining environmental integrity. The Greater Cairo Metropolitan Area is one of the most intensively populated areas in the world and experiences rapid urban expansion due to population growth in residential complexes and work facilities. Urban expansion is being witnessed in both formal and informal settlements. Informal urban expansion leads to poor planning and development. Remote sensing and modeling techniques are effective tools for monitoring and planning such phenomena. This paper utilizes the use of Landsat data and cellular automata techniques to quantify and map urban growth in the Greater Cairo Metropolitan Area. Additionally, these tools are used to estimate urbanization trends with a view of future growth pattern forecast for 2050. The region has experienced extensive conversion to urban land cover over the last 20 years, accounting for approximately 10 % of the former agriculture area (21,113 ha). Over this period, urban areas expanded by 65,460 ha, equivalent to 3273 ha per annum. It is predicted that the study area will continue to experience a rapid increase in the urbanization rate. The Greater Cairo Metropolitan Area is projected to lose approximately 44,544 ha of fertile agriculture land by 2050 should no action be taken.

Relaciones funcionales entre la temperatura del agua de mar y el coeficiente de Brody en cultivos suspendidos de Argopecten purpuratus

Experimentos naturales muestran evidencia que a temperaturas mas altas el crecimiento de Argopecten purpuratus se ve beneficiado. Asi, en condiciones de eventos de El Nino la velocidad de crecimiento se puede hasta duplicar al ser comparada con periodos de su ausencia. Sin embargo, estudios recientes mostraron que aunque las temperaturas promedios de cultivo en Peru (Playa El Carbon) y en Chile (Bahia de Tongoy) pudieran mostrar valores similares, se evidenciaron diferencias en la velocidad de crecimiento. Para explicar esta situacion se relaciono la velocidad de crecimiento mediante regresiones lineales, determinada con el coeficiente de Brody ( k ) de la ecuacion de von Bertalanffy y la temperatura a 10 m de profundidad, utilizandose las unidades termicas acumuladas (UTA) en distintas escalas de tiempo. Los resultados indicaron alta correlacion entre las UTA y k al primer (R 2 = 0,69) y segundo mes de edad (R 2 = 0,80). Los resultados pueden ser utilizados para evaluar el comportamiento futuro de cohortes de ostion del norte o predecir el crecimiento futuro de A. purpuratus en modelos de simulacion bioeconomicos si se dispone de pronosticos oceanograficos de corto, mediano o largo plazo de las condiciones de temperatura en la zona de estudio.

Sovereign Risk Premia and the International Balance Sheet: Lessons from the European Crisis

The external dimension has emerged as an important factor in the euro debt crisis. The crisis has also shown that fluctuations in risk premia can be dramatic. We investigate the relevance of the net international investment position for sovereign risk perception and the role of market uncertainty in this relation. Furthermore, we ask whether the composition of net external assets, in terms of debt and equity instruments, is relevant in explaining sovereign risk premia and their fluctuations in time. We find that both public debt and NIIP are subject to fluctuations in risk premia; the external variable is more sensitive to the uncertainty of future expectations, and net external debt is what drives this result. Net foreign debt liabilities are associated with a lower government bond yield spread when market optimism justifies their presence with high future growth patterns; however, it becomes an important risk factor for sovereigns when global uncertainty increases and the capacity to repay foreign debt becomes a concern. Portfolio equity and FDI are related to sovereign risk in a stable manner, while a given amount of net external debt can be associated with government yield spread spikes as high as 4 %.