Robust estimation in simultaneous equations models

In this paper we review existing work on robust estimation for simultaneous equations models. Then we discuss three strategies for obtaining estimators with a high breakdown point, a controllable efficiency, and a reasonable computational cost: (a) robustifying Three-Stages Least Squares, (b) robustifying the Full Information Maximum Likelihood method by minimizing the determinant of a robust covariance matrix of residuals, and (c) generalizing multivariate tauestimators (Lopuhaa 1991) to these models. The latter seems the most promising approach

Study of a regenerative pump using numerical and experimental techniques

Regenerative pumps are the subject of increased interest in industry as these pumps are low cost, low specific speed, compact and able to deliver high heads with stable performance characteristics. The complex flow-field within the pump represents a considerable challenge to detailed mathematical modelling as there is significant flow separation in the impeller blading. This paper presents the use of a commercial CFD code to simulate the flow within the regenerative pump and compare the CFD results with new experimental data. The CFD results demonstrate that it is possible to represent the helical flowfield for the pump which has only been witnessed in experimental flow visualisation until now. The CFD performance results also demonstrate reasonable agreement with the experimental tests. The CFD models are currently being used to optimise key geometric features to increase pump efficiency

Analysis And Prediction Of Cost And Time Overrun Of Millennium Development Goals (MDGS) Construction Projects In Nigeria.

The paper focuses on the analysis and forecast of cost and time overrun of MDGs construction projects in Nigeria. Twenty five MDGs construction projects from (2006-2009) were critically investigated and time and cost overrun of the project were studied. The Statistical Package for Social Scientists (SPSS) 19.0 version was used to analyse the variables using Paired t-test and simple regression at 95% confidence limits. The analysis was based on the adaptation of requisition method. The validity test on the efficiency of the model was highlighted using the confidence interval to enhance the application of the models. Mathematical models were developed. The findings shows that there is a significant different between the total contract sum, cost overrun, total contract duration, and time overrun for the MDGS projects. The study suggests acute need for government to engage in proactive strategic planning and approaches to keep construction project cost and time within reasonable limit for the actualization of MDGs policy of development and environmental sustainability.   Keywords: Analysis and Prediction, Cost Overrun, Time Overrun, Millennium Development Goals and Construction Projects

Active Learning for NLP with Large Language Models

Human annotation of training samples is expensive, laborious, and sometimes challenging, especially for Natural Language Processing (NLP) tasks. To reduce the labeling cost and enhance the sample efficiency, Active Learning (AL) technique can be used to label as few samples as possible to reach a reasonable or similar results. To reduce even more costs and with the significant advances of Large Language Models (LLMs), LLMs can be a good candidate to annotate samples. This work investigates the accuracy and cost of using LLMs (GPT-3.5 and GPT-4) to label samples on 3 different datasets. A consistency-based strategy is proposed to select samples that are potentially incorrectly labeled so that human annotations can be used for those samples in AL settings, and we call it mixed annotation strategy. Then we test performance of AL under two different settings: (1) using human annotations only; (2) using the proposed mixed annotation strategy. The accuracy of AL models under 3 AL query strategies are reported on 3 text classification datasets, i.e., AG's News, TREC-6, and Rotten Tomatoes. On AG's News and Rotten Tomatoes, the models trained with the mixed annotation strategy achieves similar or better results compared to that with human annotations. The method reveals great potentials of LLMs as annotators in terms of accuracy and cost efficiency in active learning settings.Comment: init repor

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL

Rehabilitation of a water distribution system using sequential multiobjective optimization models

Identification of the optimal rehabilitation plan for a large water distribution system (WDS) with a substantial number of decision variables is a challenging task, especially when no supercomputer facilities are available. This paper presents an initiative methodology for the rehabilitation of WDS based on three sequential stages of multiobjective optimization models for gradually identifying the best-known Pareto front (PF). A two-objective optimization model is used in the first two stages where the objectives are to minimize rehabilitated infrastructure costs and operational costs. The optimization model in the first stage applies to a skeletonized WDS. The PFs obtained in Stage 1 are further improved in Stage 2 using the same two-objective optimization problem but for the full network. The third stage employs a three-objective optimization model by minimizing the cost of additional pressure reducing valves (PRVs) as the third objective. The suggested methodology was demonstrated through use of a real and large WDS from the literature. Results show the efficiency of the suggested methodology to achieve the optimal solutions for a large WDS in a reasonable computational time. Results also suggest the minimum total costs that will be obtained once maximum leakage reduction is achieved due to maximum possible pipeline rehabilitation without increasing the existing tanks

Imaginary Worlds and Efficiency Frontiers: Should We Abandon the IQWiG Health Technology Assessment Model?

The contribution of cost-effectiveness analysis to the pricing of pharmaceuticals in Germany is at best marginal and in many, if not most cases, absent. While this may reflect a reasonable belief that cost-effectiveness analysis adds little if anything to pricing and formulary placement decisions, its marginalization reflects considerable dissatisfaction, if not frustration, with modeling efforts by the Institut für Qualität und Wirtschaftlichkeit im Gesundheitseesen (IQWiG). In part, this reflects the rejection of quality adjusted life years (QALYs) as the common outcome standard, together with the adoption of the efficiency frontier as the default framework for modeled claims. The purpose of this commentary is to consider the merits, in the German context, of an efficiency frontier framework for cost-effectiveness and pricing decisions. The commentary concludes that the efficiency frontier framework for health technology assessment, in supporting the creation of non-evaluable claims from models or simulations, fails of to meet the standards of normal science: it fails to support claims that are credible, evaluable and replicable. It should be abandoned. If cost-effectiveness modeling is to play a constructive role in pricing negotiations in Germany then manufacturers should be required to submit evaluable claims. The most effective way of ensuring this is to require manufacturers to accompany any submission for a new product with a protocol detailing how their claims, to include those for clinical outcomes, cost-effectiveness and budget impact, are to be evaluated and reported to decision makers in a meaningful time frame.   Type: Commentar

Bus Transit Operational Efficiency Resulting from Passenger Boardings at Park-and-Ride Facilities

In order to save time and money by not driving to an ultimate destination, some urban commuters drive themselves a few miles to specially designated parking lots built for transit customers and located where trains or buses stop. The focus of this paper is the effect Park-and-Ride (P&R) lots have on the efficiency of bus transit as measured in five bus transit systems in the western U.S. This study describes a series of probes with models and data to find objective P&R influence measures that, when combined with other readily-available data, permit a quantitative assessment of the significance of P&R on transit efficiency. The authors developed and describe techniques that examine P&R as an influence on transit boardings at bus stops and on bus boardings along an entire route. The regression results reported are based on the two in-depth case studies for which sufficient data were obtained to examine (using econometric techniques) the effects of park-and-ride availability on bus transit productivity. Both Ordinary Least Square (OLS) regression and Poisson regression are employed. The results from the case studies suggest that availability of parking near bus stops is a stronger influence on transit ridership than residential housing near bus stops. Results also suggest that expanding parking facilities near suburban park-and-ride lots increases the productivity of bus operations as measured by ridership per service hour. The authors also illustrate that reasonable daily parking charges (compared to the cost of driving to much more expensive parking downtown) would provide sufficient capital to build and operate new P&R capacity without subsidy from other revenue sources