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Institution

De La Salle University

EducationManila, Philippines
About: De La Salle University is a education organization based out in Manila, Philippines. It is known for research contribution in the topics: Population & Computer science. The organization has 2951 authors who have published 4374 publications receiving 49567 citations. The organization is also known as: Pamantasang De La Salle.


Papers
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Journal ArticleDOI
TL;DR: Using quantitative and qualitative data, the authors present selected characteristics of 626 women who reported complications of induced abortion in five hospitals: one in Nairobi, two in Lima, and two in Manila, showing some marked differences in demographic characteristics.
Abstract: Using quantitative and qualitative data, the authors present selected characteristics of 626 women who reported complications of induced abortion in five hospitals: one in Nairobi, two in Lima, and two in Manila. Although there are some similarities, the findings show some marked differences in demographic characteristics. In Nairobi nearly all respondents were single, nulliparous, and 25 years or younger; in Lima and Manila most were either married or in union, usually aged 25 years or older and had at least 1 child. There was evidence of repeat abortions, especially in Nairobi where 26% had had at least one previous abortion. Access to safe abortions is severely restricted and is obtained through a secret referral system. A list of potentially hazardous local abortifacients range from the drinking of strong Kenyan tea to dangerous practices such as insertion of sharp objects into the uterus or drinking chemicals and toxic substances.

35 citations

Journal ArticleDOI
TL;DR: In this paper, the potential energy surfaces corresponding to dissociative adsorption at different symmetric sites on the Pt(111) surface and for parallel and perpendicular orientations of H2 with respect to the surface, on the basis of the density functional theory, were calculated.
Abstract: We investigate the interaction of molecular hydrogen with the Pt surface. In particular, we calculate the potential energy surfaces (PESs) corresponding to the dissociative adsorption of H2 at different symmetric sites on the Pt(111) surface and for parallel and perpendicular orientations of H2 with respect to the surface, on the basis of the density functional theory. The PES results show the dependence of the activation barriers on the H2 adsorption site and orientation relative to the surface. For parallel orientations, the barrier is lowest (almost zero) when the H2 center of mass (CM) is directly above the top site while the H atoms are directed towards the hcp and fcc hollow sites. The activation barriers for the perpendicular orientation are always much higher than those for the parallel orientation, which indicates that the parallel orientation is favored for H2 adsorption. This result also suggests that an incident H2 initially in a nonparallel (e.g., perpendicular) orientation tends to reorient itself towards a parallel orientation where it is easier for H2 to be adsorbed.

35 citations

Journal ArticleDOI
TL;DR: This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method, which functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders.
Abstract: Biomass polygeneration system is one of the most attractive biomass technologies due to its technicality, feasibility and high associated investment returns. The synthesis, design and economic aspects of constructing a processing system using this technology are well-developed and have recently reached the stage of industrial implementation. Nonetheless, the early stage of technology development focuses on process and product safety and tends to ignore other risk aspects that are closely associated with the biomass value chain. Due to the complex nature of the biomass value chain, conventional risk mitigation strategies are ineffective in mitigating risks at the management level. More recent approaches, particularly stochastic programming methods, have yielded robust results in addressing technological risks and design uncertainties. However, such approaches are still unable to effectively consider non-quantitative risks such as business risks and regulatory risks. Hence, this study proposes a combined method of an analytical model and stochastic programming approach to prioritize risks and risk mitigation strategies for decision-making purposes. This work presents a novel multiple-criteria decision-making expert system based on fuzzy set theory, which is the Decision and Evaluation-based Fuzzy Analytic Network Process (DEFANP) method. The novel method functions to prioritize risk mitigation strategies within a network relationship of project goals, key components of the biomass industry and industrial stakeholders. As the stochastic risk mitigation counterpart, the fluctuations and uncertainties in operations, transportation, market supply-demand and price are modeled using the Monte Carlo simulation method. From this, risks of implementing biomass polygeneration systems can be mitigated by selecting a strategy that yields the highest analytical indicator while reconciling with the corresponding probabilities of achieving management goals. A palm biomass polygeneration system in Malaysia is presented as case study where the key implementation risks are regulatory risks, financing risks, technology risks, supply chain and feedstock risks, business risks, social and environmental risks.

35 citations

Journal ArticleDOI
TL;DR: In this paper, a fuzzy mixed integer linear program (FMILP) is developed for multi-period CCS systems, accounting for the technical risk arising from uncertainties in estimates of sink parameters, while still attaining satisfactory CO2 emissions reduction.

35 citations

Journal ArticleDOI
TL;DR: A needed paradigm shift in disaster risk analysis is discussed to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems and a review of recent studies that underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies.
Abstract: The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems. Language: en

35 citations


Authors

Showing all 2995 results

NameH-indexPapersCitations
Shin-ichi Ohkoshi6748015208
Raymond R. Tan514469869
Ming-Lang Tseng503079968
Dominic C. Y. Foo462857007
Masahiko Tani433616446
Denny K. S. Ng412275089
Rudy Setiono391158361
Michael Y. Roleda381034156
Arvin C. Diesmos361126528
Hideaki Kasai335716033
Anthony S.F. Chiu331144732
Joris De Schutter322754524
Maricar S. Prudente291004693
Kathleen B. Aviso291952802
Carlo Magno271512449
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
202276
2021600
2020523
2019463
2018372