Connection Incentives in Cost Sharing Mechanisms with Budgets

In a cost sharing problem on a weighted undirected graph, all other nodes want to connect to the source node for some service. Each edge has a cost denoted by a weight and all the connected nodes should share the total cost for the connectivity. The goal of the existing solutions (e.g. folk solution and cycle-complete solution) is to design cost sharing rules with nice properties, e.g. budget balance and cost monotonicity. However, they did not consider the cases that each non-source node has a budget which is the maximum it can pay for its cost share and may cut its adjacent edges to reduce its cost share. In this paper, we design two cost sharing mechanisms taking into account the nodes' budgets and incentivizing all nodes to report all their adjacent edges so that we can minimize the total cost for the connectivity.Comment: arXiv admin note: substantial text overlap with arXiv:2201.0597

Cost Sharing under Private Costs and Connection Control on Directed Acyclic Graphs

We consider a cost sharing problem on a weighted directed acyclic graph (DAG) with a source node to which all the other nodes want to connect. The cost (weight) of each edge is private information reported by multiple contractors, and among them, only one contractor is selected as the builder. All the nodes except for the source need to share the total cost of the used edges. However, they may block others' connections to the source by strategically cutting their outgoing edges to reduce their cost share, which may increase the total cost of connectivity. To minimize the total cost of connectivity, we design a cost sharing mechanism to incentivize each node to offer all its outgoing edges and each contractor to report all the edges' weights truthfully, and show the properties of the proposed mechanism. In addition, our mechanism outperforms the two benchmark mechanisms

A STOCHASTIC PROGRAMMING APPROACH TO ANALYZE DESIGN AND MANAGEMENT OF FLEXIBILITY IN INFRASTRUCTURE SYSTEMS OPERATING UNDER LONG-TERM UNCERTAINTY

Ph.DDOCTOR OF PHILOSOPH

Strategic Real Option and Flexibility Analysis for Nuclear Power Plants Considering Uncertainty in Electricity Demand and Public Acceptance

Nuclear power is an important energy source especially in consideration of CO2 emissions and global warming. Deploying nuclear power plants, however, may be challenging when uncertainty in long-term electricity demand and more importantly public acceptance are considered. This is true especially for emerging economies (e.g., India, China) concerned with reducing their carbon footprint in the context of growing economic development, while accommodating a growing population and significantly changing demographics, as well as recent events that may affect the public's perception of nuclear technology. In the aftermath of the Fukushima Daiichi disaster, public acceptance has come to play a central role in continued operations and deployment of new nuclear power systems worldwide. In countries seeing important long-term demographic changes, it may be difficult to determine the future capacity needed, when and where to deploy it over time, and in the most economic manner. Existing studies on capacity deployment typically do not consider such uncertainty drivers in long-term capacity deployment analyses (e.g., + 40 years). To address these issues, this paper introduces a novel approach to nuclear power systems design and capacity deployment under uncertainty that exploits the idea of strategic flexibility and managerial decision rules. The approach enables dealing more pro-actively with uncertainty and helps identify the most economic deployment paths for new nuclear capacity deployment over multiple sites. One novelty of the study lies in the explicit recognition of public acceptance as an important uncertainty driver affecting economic performance, along with long-term electricity demand. Another novelty is in how the concept of flexibility is exploited to deal with uncertainty and improve expected lifecycle performance (e.g. cost). New design and deployment strategies are developed and analyzed through a multistage stochastic programming framework where decision rules are represented as non-anticipative constraints. This approach provides a new way to devise and analyze adaptation strategies in view of long-term uncertainty fluctuations that is more intuitive and readily usable by system operators than typical solutions obtained from standard real options analysis techniques, which are typically used to analyze flexibility in large-scale, irreversible investment projects. The study considers three flexibility strategies subject to uncertainty in electricity demand and public acceptance: 1) phasing (or staging) capacity deployment over time and space, 2) on-site capacity expansion, and 3) life extension. Numerical analysis shows that flexible designs perform better than rigid optimal design deployment strategies, and the most flexible design combining the above strategies outperforms both more rigid and less flexible design alternatives. It is also demonstrated that a flexible design benefits from the strategies of phasing and capacity expansion most significantly across all three strategies studied. The results provide useful insights for policy and decision-making in countries that are considering new nuclear facility deployment, in light of ongoing challenges surrounding new nuclear builds worldwide

Development of a Waste-to-Energy Decision Support System (WTEDSS)

International audienceRapid increase in urban population has created the need for the development of efficient Decision Support Systems (DSS) guiding municipal planners to mitigate urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. To ensure sustainable urban planning, a DSS must provide not only an optimal planning solution based on input assumptions, but must also help to identify concrete city challenges, determine available resources (e.g., land and energy sources) and highlight any implementation constraints. It must support the creation of flexible interactive scenarios for urban development and their realistic representation in an urban context. This paper presents a Waste-to-Energy Decision Support System (WTEDSS) that identifies the optimal long-term deployment strategy for waste-to-energy infrastructures under future uncertain operational conditions and then directly assesses its feasibility and integration into an urban environment using 3D visualization. The WTEDSS is designed as an interactive and analytical waste management planning tool integrating four modules: data analytics, optimization, simulation and a user-friendly graphical interface. Emphasis is placed on the development and integration of the optimization module and 3D urban simulation, which provides users with decision support based on 3D visualized optimum facilities deployment plans. The optimization module receives calibrated data and solves a model based on inputs obtained from the user interface. The simulation platform developed in Unity 3D provides a friendly real-world environment for studying and understanding the facility deployment process over time and space, while also considering uncertainty

Oral cancer cells may rewire alternative metabolic pathways to survive from siRNA silencing of metabolic enzymes.

BackgroundCancer cells may undergo metabolic adaptations that support their growth as well as drug resistance properties. The purpose of this study is to test if oral cancer cells can overcome the metabolic defects introduced by using small interfering RNA (siRNA) to knock down their expression of important metabolic enzymes.MethodsUM1 and UM2 oral cancer cells were transfected with siRNA to transketolase (TKT) or siRNA to adenylate kinase (AK2), and Western blotting was used to confirm the knockdown. Cellular uptake of glucose and glutamine and production of lactate were compared between the cancer cells with either TKT or AK2 knockdown and those transfected with control siRNA. Statistical analysis was performed with student T-test.ResultsDespite the defect in the pentose phosphate pathway caused by siRNA knockdown of TKT, the survived UM1 or UM2 cells utilized more glucose and glutamine and secreted a significantly higher amount of lactate than the cells transferred with control siRNA. We also demonstrated that siRNA knockdown of AK2 constrained the proliferation of UM1 and UM2 cells but similarly led to an increased uptake of glucose/glutamine and production of lactate by the UM1 or UM2 cells survived from siRNA silencing of AK2.ConclusionsOur results indicate that the metabolic defects introduced by siRNA silencing of metabolic enzymes TKT or AK2 may be compensated by alternative feedback metabolic mechanisms, suggesting that cancer cells may overcome single defective pathways through secondary metabolic network adaptations. The highly robust nature of oral cancer cell metabolism implies that a systematic medical approach targeting multiple metabolic pathways may be needed to accomplish the continued improvement of cancer treatment

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.Comment: conference pape