Real options and demographic decisions:empirical evidence from East and West Germany

Iyer and Velu (2006) have convincingly argued that contemporary analyses of fertility behaviour fail to explain why a woman (or a couple) will choose to postpone childbirth, and in particular to consider the role of uncertainty in this regard. They have addressed this lacuna in the literature by using a real options approach to model fertility decisions by relating uncertainty experienced by individuals to the likelihood of childbirth. However, they did not present empirical evidence. Since the theory implies the existence of two offsetting effects of uncertainty on fertility decisions, a positive insurance effect and a negative option value effect, it is not easy to reject the theory on the basis of empirical analysis, when one of these effects offsets the other. We construct such a test for East (and also West) Germany during that country's reunification, which takes advantage of the fact that because of the country's strong welfare system, the insurance effect should be dominated by the option value effect, thereby suggesting that the net relationship should be negative. The results provide rather strong support for the real options link, especially for Eastern Germany.

The propensity to disrupt and the disruption nucleolus of a characteristic function game

Gately [1974] recently introduced the concept of an individual player's “propensity to disrupt” a payoff vector in a three-person characteristic function game. As a generalisation of this concept we propose the “disruption nucleolus” of ann-person game. The properties and computational possibilities of this concept are analogous to those of the nucleolus itself. Two numerical examples are given.

Harnessing demographic differences in organizations:what moderates the effects of workplace diversity?

To account for the double-edged nature of demographic workplace diversity (i.e,. relational demography, work group diversity, and organizational diversity) effects on social integration, performance, and well-being related variables, research has moved away from simple main effect approaches and started examining variables that moderate these effects. While there is no shortage of primary studies of the conditions under which diversity leads to positive or negative outcomes, it remains unclear which contingency factors make it work. Using the Categorization-Elaboration Model as our theoretical lens, we review variables moderating the effects of workplace diversity on social integration, performance, and well-being outcomes, focusing on factors that organizations and managers have control over (i.e., strategy, unit design, human resource, leadership, climate/culture, and individual differences). We point out avenues for future research and conclude with practical implications.

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Element characteristic tolerance for semi-batch fixed bed biomass pyrolysis

Biomass pyrolysis to bio-oil is one of the promising sustainable fuels. In this work, relation between biomass feedstock element characteristic and crude bio-oil production yield and lower heating value was explored. The element characteristics considered in this study include moisture, ash, fix carbon, volatile matter, C, H, N, O, S, cellulose, hemicellulose, and lignin content. A semi-batch fixed bed reactor was used for biomass pyrolysis with heating rate of 30 °C/min from room temperature to 600 °C and the reactor was held at 600 °C for 1 h before cooling down. Constant nitrogen flow (1bar) was provided for anaerobic condition. Sago and Napier glass were used in the study to create different element characteristic of feedstock by altering mixing ratio. Comparison between each element characteristic to crude bio-oil yield and low heating value was conducted. The result suggested potential key element characteristic for pyrolysis and provide a platform to access the feedstock element acceptance range.

Unveiling temporal correlations characteristic of a phase transition in the output intensity of a fiber laser

We use advanced statistical tools of time-series analysis to characterize the dynamical complexity of the transition to optical wave turbulence in a fiber laser. Ordinal analysis and the horizontal visibility graph applied to the experimentally measured laser output intensity reveal the presence of temporal correlations during the transition from the laminar to the turbulent lasing regimes. Both methods unveil coherent structures with well-defined time scales and strong correlations both, in the timing of the laser pulses and in their peak intensities. Our approach is generic and may be used in other complex systems that undergo similar transitions involving the generation of extreme fluctuations.

Survival in the pre-senile dementia frontotemporal lobar degeneration with TDP-43 proteinopathy:effects of genetic, demographic and neuropathological variables

Factors associated with survival were studied in 84 neuropathologically documented cases of the pre-senile dementia frontotemporal dementia lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) proteinopathy (FTLD-TDP). Kaplan-Meier survival analysis estimated mean survival as 7.9 years (range: 1-19 years, SD = 4.64). Familial and sporadic cases exhibited similar survival, including progranulin (GRN) gene mutation cases. No significant differences in survival were associated with sex, disease onset, Braak disease stage, or disease subtype, but higher survival was associated with lower post-mortem brain weight. Survival was significantly reduced in cases with associated motor neuron disease (FTLD-MND) but increased with Alzheimer's disease (AD) or hippocampal sclerosis (HS) co-morbidity. Cox regression analysis suggested that reduced survival was associated with increased densities of neuronal cytoplasmic inclusions (NCI) while increased survival was associated with greater densities of enlarged neurons (EN) in the frontal and temporal lobes. The data suggest that: (1) survival in FTLD-TDP is more prolonged than typical in pre-senile dementia but shorter than some clinical subtypes such as the semantic variant of primary progressive aphasia (svPPA), (2) MND co-morbidity predicts poor survival, and (3) NCI may develop early and EN later in the disease. The data have implications for both neuropathological characterization and subtyping of FTLD-TDP.

Pyrolysis of Napier grass in a fixed bed reactor:effect of operating conditions on product yields and characteristic

This study presents a report on pyrolysis of Napier grass stem in a fixed bed reactor. The effects of nitrogen flow (20 to 60 mL/min), and reaction temperature (450 to 650 degrees C) were investigated. Increasing the nitrogen flow from 20 to 30 mL/min increased the bio-oil yield and decreased both bio-char and non-condensable gas. 30 mL/min nitrogen flow resulted in optimum bio-oil yield and was used in the subsequent experiments. Reaction temperatures between 450 and 600 degrees C increased the bio-oil yield, with maximum yield of 32.26 wt% at 600 degrees C and a decrease in the corresponding bio-char and non-condensable gas. At 650 degrees C, reductions in the bio-oil and bio-char yields were recorded while the non-condensable gas increased. Water content of the bio-oil decreased with increasing reaction temperature, while density and viscosity increased. The observed pH and higher heating values were between 2.43 to 2.97, and 25.25 to 28.88 MJ/kg, respectively. GC-MS analysis revealed that the oil was made up of highly oxygenated compounds and requires upgrading. The bio-char and non-condensable gas were characterized, and the effect of reaction temperature on the properties was evaluated. Napier grass represents a good source of renewable energy when all pyrolysis products are efficiently utilized.