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We discuss the most popular static and dynamic models of household behavior. Our main objective is to explain which aspects of household decisions different models can account for. Using this insight, we describe testable implications, identification results, and estimation findings obtained in the literature. Particular attention is given to the ability of different models to answer various types of policy questions.

Geophysical observations from the 2011 moment magnitude (M w ) 9.0 Tohoku-Oki, Japan earthquake allow exploration of a rare large event along a subduction megathrust. Models for this event indicate that the distribution of coseismic fault slip exceeded 50 meters in places. Sources of high-frequency seismic waves delineate the edges of the deepest portions of coseismic slip and do not simply correlate with the locations of peak slip. Relative to the M w 8.8 2010 Maule, Chile earthquake, the Tohoku-Oki earthquake was deficient in high-frequency seismic radiation—a difference that we attribute to its relatively shallow depth. Estimates of total fault slip and surface secular strain accumulation on millennial time scales suggest the need to consider the potential for a future large earthquake just south of this event.

Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time-homogeneity conditions that are like \"time is randomly assigned\" or \"time is an instrument.\" Partial-identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial-identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of T, the number of time periods, is shown by deriving shrinkage rates for the identified set as T grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.

We argue that when innovation is \"sequential\" (so that each successive invention builds in an essential way on its predecessors) and \"complementary\" (so that each potential innovator takes a different research line), patent protection is not as useful for encouraging innovation as in a static setting. Indeed, society and even inventors themselves may be better off without such protection. Furthermore, an inventor's prospective profit may actually be enhanced by competition and imitation. Our sequential model of innovation appears to explain evidence from a natural experiment in the software industry.

Motivated by the rising popularity of electronic appointment booking systems, we develop appointment scheduling models that take into account the patient preferences regarding when they would like to be seen. The service provider dynamically decides which appointment days to make available for the patients. Patients arriving with appointment requests may choose one of the days offered to them or leave without an appointment. Patients with scheduled appointments may cancel or not show up for the service. The service provider collects a \"revenue\" from each patient who shows up and incurs a \"service cost\" that depends on the number of scheduled appointments. The objective is to maximize the expected net \"profit\" per day. We begin by developing a static model that does not consider the current state of the scheduled appointments. We give a characterization of the optimal policy under the static model and bound its optimality gap. Building on the static model, we develop a dynamic model that considers the current state of the scheduled appointments, and we propose a heuristic solution procedure. In our computational experiments, we test the performance of our models under the patient preferences estimated through a discrete choice experiment that we conduct in a large community health center. Our computational experiments reveal that the policies we propose perform well under a variety of conditions.

In this paper, we present a method to make personalized recommendations when user preferences change over time. Most of the works in the recommender systems literature have been developed under the assumption that user preference has a static pattern. However, this is a strong assumption especially when the user is observed over a long period of time. With the help of a data set on employees 'blog reading behavior, we show that users' product selection behaviors change over time. We propose a hidden Markov model to correctly interpret the users 'product selection behaviors and make personalized recommendations. The user preference is modeled as a hidden Markov sequence. A variable number of product selections of different types by each user in each time period requires a novel observation model. We propose a negative binomial mixture of multinomial to model such observations. This allows us to identify stable global preferences of users and to track individual users through these preferences. We evaluate our model using three real-world data sets with different characteristics. They include data on employee blog reading behavior inside a firm, users 'movie rating behavior at Netflix, and users' music listening behavior collected through last.fm. We compare the recommendation performance ofthe proposed model with that of a number of collaborative filtering algorithms and a recently proposed temporal link prediction algorithm. We find that the proposed HMM-based collaborative filter performs as well as the best among the alternative algorithms when the data is sparse or static. However, it outperforms the existing algorithms when the data is less sparse and the user preference is changing. We further examine the performances ofthe algorithms using simulated data with different characteristics and highlight the scenarios where it is beneficial to use a dynamic model to generate product recommendation.

In recent years, there has been a growing interest in utilizing hydrogen as an energy carrier across various transportation sectors, including aerospace applications. This interest stems from its unique capability to yield energy without generating direct carbon dioxide emissions. The conversion process is particularly efficient when performed in a fuel cell system. In aerospace applications, two crucial factors come into play: power-to-weight ratio and the simplicity of the powerplant. In fact, the transient behavior and control of the fuel cell are complicated by the continuously changing values of load and altitude during the flight. To meet these criteria, air-cooled open-cathode Proton Exchange Membrane (PEM) fuel cells should be the preferred choice. However, they have limitations regarding the amount of thermal power they can dissipate. Moreover, the performances of fuel cell systems are significantly worsened at high altitude operating conditions because of the lower air density. Consequently, they find suitability primarily in applications such as Unmanned Aerial Vehicles (UAVs) and Urban Air Mobility (UAM). In the case of ultralight and light aviation, liquid-cooled solutions with a separate circuit for compressed air supply are adopted. The goal of this investigation is to identify the correct simulation approach to predict the behavior of such systems under dynamic conditions, typical of their application in aerial vehicles. To this aim, a detailed review of the scientific literature has been performed, with specific reference to semi-empirical and control-oriented models of the whole fuel cell systems including not only the stack but also the complete balance of plant.

Energy efficiency is inherent for autonomous robotic device. Snakes are well known for their ability to low energy consumption when swimming. However, the swimming know-how is poorly understood. Designing a snake robot inspired by snakes as a tool to find out the swimming energy efficiency crucial point will lead to the development of hyper efficient undulating locomotors. In this article, we introduce a four tendons driven continuum robot made of bio-inspired compliant vertebrae to assess the energy consumption of a planar and a spatial snake motion. The tendon-driven continuum robot constitutes the head–neck part of a locomotor snake robot. A static modeling coupled with an optimization method was implemented to generate bio-inspired motions recorded on snake swimming head. A friction model describing the friction between cables and the disks is investigated and compared to a frictionless model. The proposed prototype is equipped with exteroceptive sensors to record motion and proprioceptive sensors to measure cable forces applied at the tip of the robot. Hence, the work of the forces, thus the energy required to execute a trajectory are computed and analyzed. The energy is introduced as a key criterion to assess the swimming motion of a locomotor snake robot.

ObjectiveAn in vitro relative activity factor (RAF) technique combined with mechanistic static modeling was examined to predict drug-drug interaction (DDI) magnitude and analyze contributions of different clearance pathways in complex DDIs involving transporter substrates. Atorvastatin and rifampicin were used as a model substrate and inhibitor pair.MethodsIn vitro studies were conducted with transfected HEK293 cells, hepatocytes and human liver microsomes. Prediction success was defined as predictions being within twofold of observations.ResultsThe RAF method successfully translated atorvastatin uptake from transfected cells to hepatocytes, demonstrating its ability to quantify transporter contributions to uptake. Successful translation of atorvastatin’s in vivo intrinsic hepatic clearance (CLint,h,invivo) from hepatocytes to liver was only achieved through consideration of albumin facilitated uptake or through application of empirical scaling factors to transporter-mediated clearances. Transporter protein expression differences between hepatocytes and liver did not affect CLint,h,invivo predictions. By integrating cis and trans inhibition of OATP1B1/OATP1B3, atorvastatin-rifampicin (single dose) DDI magnitude could be accurately predicted (predictions within 0.77–1.0 fold of observations). Simulations indicated that concurrent inhibition of both OATP1B1 and OATP1B3 caused approximately 80% of atorvastatin exposure increases (AUCR) in the presence of rifampicin. Inhibiting biliary elimination, hepatic metabolism, OATP2B1, NTCP, and basolateral efflux are predicted to have minimal to no effect on AUCR.ConclusionsThis study demonstrates the effective application of a RAF-based translation method combined with mechanistic static modeling for transporter substrate DDI predictions and subsequent mechanistic interpretation.

With the advancement of smart IoT system, equipment modeling technology that adapts to the power Internet of Things becomes more and more important. In the power grid system, various types of equipment access processes are not standardized, the collected data format is not uniform, and the data is difficult to share. There is an urgent need to build a set of equipment modeling methods suitable for the power Internet of Things. By comparing traditional equipment modeling methods, we propose the static modeling structure of equipment in the power Internet of Things, and constructs a description method of terminal equipments on the cloud platform; combined with the construction requirements of the smart IoT system, design the Cloud-side collaborative dynamic modeling technology which based on equipment online status management, resource status management, alarm management and application management. At the same time, integrated equipment management methods for static modeling and dynamic modeling are realized in combination with application cases, providing technologies basis for unified device access, unified data transmission, and open sharing of data.