Robust Dynamic Pricing Over Infinite Horizon in The Presence of Model Uncertainty( 2* Journal)

This paper studies a problem of dynamic pricing faced by a retailer with limited inventory, uncertain about the demand rate model, aiming to maximize expected discounted revenue over an infinite time horizon. The retailer doubts his demand model which is generated by historical data and views it as an approximation. Uncertainty in the demand rate model is represented by a notion of generalized relative entropy process, and the robust pricing problem is formulated as a two-player zero-sum stochastic differential game. The pricing policy is obtained through the Hamilton-Jacobi-Isaacs (HJI) equation. The existence and uniqueness of the solution of the HJI equation is shown and a verification theorem is proved to show that the solution of the HJI equation is indeed the value function of the pricing problem. The results are illustrated by an example with exponential nominal demand rate.

Dynamic pricing model and algorithm for perishable products with fuzzy demand (ABS 2* Journal)

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, a-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm.

A hierarchical physical model-based approach to predictive control of a thermal power plant for efficient plant-wide disturbance rejection

Extending the work presented in Prasad et al. (IEEE Proceedings on Control Theory and Applications, 147, 523-37, 2000), this paper reports a hierarchical nonlinear physical model-based control strategy to account for the problems arising due to complex dynamics of drum level and governor valve, and demonstrates its effectiveness in plant-wide disturbance handling. The strategy incorporates a two-level control structure consisting of lower-level conventional PI regulators and a higher-level nonlinear physical model predictive controller (NPMPC) for mainly set-point manoeuvring. The lower-level PI loops help stabilise the unstable drum-boiler dynamics and allow faster governor valve action for power and grid-frequency regulation. The higher-level NPMPC provides an optimal load demand (or set-point) transition by effective handling of plant-wide interactions and system disturbances. The strategy has been tested in a simulation of a 200-MW oil-fired power plant at Ballylumford in Northern Ireland. A novel approach is devized to test the disturbance rejection capability in severe operating conditions. Low frequency disturbances were created by making random changes in radiation heat flow on the boiler-side, while condenser vacuum was fluctuating in a random fashion on the turbine side. In order to simulate high-frequency disturbances, pulse-type load disturbances were made to strike at instants which are not an integral multiple of the NPMPC sampling period. Impressive results have been obtained during both types of system disturbances and extremely high rates of load changes, right across the operating range, These results compared favourably with those from a conventional state-space generalized predictive control (GPC) method designed under similar conditions.

Why do people non-demand reveal in hypothetical double referenda for public goods?

Hypothetical contingent valuation surveys used to elicit values for environmental and other public goods often employ variants of the referendum mechanism due to the cognitive simplicity and familiarity of respondents with this voting format. One variant, the double referendum mechanism, requires respondents to state twice how they would vote for a given policy proposal given their cost of the good. Data from these surveys often exhibit anomalies inconsistent with standard economic models of consumer preferences. There are a number of published explanations for these anomalies, mostly focusing on problems with the second vote. This article investigates which aspects of the hypothetical task affect the degree of nondemand revelation and takes an individual-based approach to identifying people most likely to non-demand reveal. A clear profile emerges from our model of a person who faces a negative surplus i.e. a net loss in the second vote and invokes non self-interested, non financial motivations during the decision process.

Site choices in recreational demand: a matter of utility maximization or regret minimization?

This paper compares the Random Regret Minimization and the Random Utility Maximization models for determining recreational choice. The Random Regret approach is based on the idea that, when choosing, individuals aim to minimize their regret – regret being defined as what one experiences when a non-chosen alternative in a choice set performs better than a chosen one in relation to one or more attributes. The Random Regret paradigm, recently developed in transport economics, presents a tractable, regret-based alternative to the dominant choice paradigm based on Random Utility. Using data from a travel cost study exploring factors that influence kayakers’ site-choice decisions in the Republic of Ireland, we estimate both the traditional Random Utility multinomial logit model (RU-MNL) and the Random Regret multinomial logit model (RR-MNL) to gain more insights into site choice decisions. We further explore whether choices are driven by a utility maximization or a regret minimization paradigm by running a binary logit model to examine the likelihood of the two decision choice paradigms using site visits and respondents characteristics as explanatory variables. In addition to being one of the first studies to apply the RR-MNL to an environmental good, this paper also represents the first application of the RR-MNL to compute the Logsum to test and strengthen conclusions on welfare impacts of potential alternative policy scenarios.

A constraints-based resource discovery model for multi-provider cloud environments

Increasingly infrastructure providers are supplying the cloud marketplace with storage and on-demand compute resources to host cloud applications. From an application user's point of view, it is desirable to identify the most appropriate set of available resources on which to execute an application. Resource choice can be complex and may involve comparing available hardware specifications, operating systems, value-added services, such as network configuration or data replication, and operating costs, such as hosting cost and data throughput. Providers' cost models often change and new commodity cost models, such as spot pricing, have been introduced to offer significant savings. In this paper, a software abstraction layer is used to discover infrastructure resources for a particular application, across multiple providers, by using a two-phase constraints-based approach. In the first phase, a set of possible infrastructure resources are identified for a given application. In the second phase, a heuristic is used to select the most appropriate resources from the initial set. For some applications a cost-based heuristic is most appropriate; for others a performance-based heuristic may be used. A financial services application and a high performance computing application are used to illustrate the execution of the proposed resource discovery mechanism. The experimental result shows the proposed model could dynamically select an appropriate set of resouces that match the application's requirements.

Random Regret Minimization: Exploration of a New Choice Model for Environmental and Resource Economics

This paper introduces the discrete choice model-paradigm of Random Regret Minimization (RRM) to the field of environmental and resource economics. The RRM-approach has been very recently developed in the context of travel demand modelling and presents a tractable, regret-based alternative to the dominant choice-modelling paradigm based on Random Utility Maximization-theory (RUM-theory). We highlight how RRM-based models provide closed form, logit-type formulations for choice probabilities that allow for capturing semi-compensatory behaviour and choice set-composition effects while being equally parsimonious as their utilitarian counterparts. Using data from a Stated Choice-experiment aimed at identifying valuations of characteristics of nature parks, we compare RRM-based models and RUM-based models in terms of parameter estimates, goodness of fit, elasticities and consequential policy implications.

In Demand: Therapeutic Services for Children and Young People Who Have Experienced Sexual Abuse

This paper describes the key findings of an NSPCC study estimating need, in the UK, for therapeutic services for children who have experienced sexual abuse. This is based upon current estimates of the prevalence and impact of sexual abuse towards children and young people against the availability of therapeutic services in the UK. Data were collected on service location, availability, scope and coverage across England, Wales, Northern Ireland and Scotland. Researchers: (1) mapped 508 services; (2) collected data from 195 services via a structured questionnaire; (3) followed up 21 service managers and 11 service commissioners with a semi-structured interview; and (4) carried out two focus groups with young people. Data were collected on service location, availability, scope and coverage The overall level of specialist provision is low, with less than one service available per 10 000 children and young people in the UK. Calculations of need indicate that 57 156 children across the UK in the last year may have been unable to access a service. Findings from services support the view that need outstrips availability; that referral routes are limited, leaving few options for young people who have been raped or seriously sexually assaulted to directly access support; that significant waiting lists mean services must focus on reactive, rather than preventive, work; and that services are less accessible for certain groups, especially sexually abused teenagers, children with disabilities and those from Black, Asian, Minority Ethnic and Refugee backgrounds. Copyright (c) 2012 John Wiley & Sons, Ltd. Key Practitioner Messages Relevant professionals must be adequately trained to talk to children about sexual abuse and to identify those vulnerable in order to identify need. Expert specialist services are well placed to share learning on early help and identification with broader children's service providers. Active steps need to be taken by commissioners in consultation with young people, voluntary sector and adult sexual violence service providers to meet the shortfall at the level of local authorities.

Demand response to improved walking infrastructure: A study into the economics of walking and health behaviour change

Walking is the most common form of moderate‐intensity physical activity among adults, is widely accessible and especially appealing to obese people. Most often policy makers are interested in valuing the effect on walking of changes in some characteristics of a neighbourhood, the demand response for walking, of infrastructure changes. A positive demand response to improvements in the walking environment could help meet the public health target of 150 minutes of at least moderate‐intensity physical activity per week. We model walking in an individual’s local neighbourhood as a ‘weak complement’ to the characteristics of the neighbourhood itself. Walking is affected by neighbourhood
characteristics, substitutes, and individual’s characteristics, including their opportunity cost of time.  Using compensating variation, we assess the economic benefits of walking and how walking behaviour is affected by improvements to the neighbourhood.  Using a sample of 1,209 respondents surveyed over a 12 month period (Feb 2010‐Jan 2011) in East Belfast, United Kingdom, we find that a policy that increased walkability and people’s perception of access to shops and facilities  would lead to an increase in walking of about 36 minutes/person/week, valued at £13.65/person/week. When focusing on inactive residents, a policy that improved the walkability of the area would lead to guidelines for physical activity being reached by only 12.8% of the population who are currently inactive. Additional interventions would therefore be needed to encourage inactive residents to
achieve the recommended levels of physical activity, as it appears that interventions that improve the walkability of an area are particularly effective in increasing walking among already active citizens, and, among the inactive ones, the best response is found among healthier, younger and wealthier citizens.

Using the hidden Markov model to capture quality of care in Lombardy geriatric wards

Quality of care is an important aspect of healthcare monitoring, which is used to ensure that the healthcare system is delivering care of the highest standard. With populations growing older there is an increased urgency in making sure that the healthcare delivered is of the highest standard. Healthcare providers are under increased pressure to ensure that this is the case with public and government demand expecting a healthcare system of the highest quality. Modelling quality of care is difficult to measure due to the many ways of defining it. This paper introduces a potential model which could be used to take quality of care into account when modelling length of stay. The Coxian phase-type distribution is used to model length of stay and the associated quality of care incorporated into the Coxian using a Hidden Markov model. Covariates are also introduced to determine their impact on the hidden level to find out what potentially can affect quality of care. This model is applied to geriatic patient data from the Lombardy region of Italy. The results obtained highlighted that bed numbers and the type of hospital (public or private) can have an effect on the quality of care delivered.

A Scheme Design of Cloud + End Technology in Demand Side Management

Demand Side Management (DSM) plays an important role in Smart Grid. It has large scale access points, massive users, heterogeneous infrastructure and dispersive participants. Moreover, cloud computing which is a service model is characterized by resource on-demand, high reliability and large scale integration and so on and the game theory is a useful tool to the dynamic economic phenomena. In this study, a scheme design of cloud + end technology is proposed to solve technical and economic problems of the DSM. The architecture of cloud + end is designed to solve technical problems in the DSM. In particular, a construct model of cloud + end is presented to solve economic problems in the DSM based on game theories. The proposed method is tested on a DSM cloud + end public service system construction in a city of southern China. The results demonstrate the feasibility of these integrated solutions which can provide a reference for the popularization and application of the DSM in china.

Hydrogel-forming microneedle arrays made from light-responsive materials for on-demand transdermal drug delivery

We describe, for the first time, stimuli-responsive hydrogel-forming microneedle (MN) arrays that enable delivery of a clinically-relevant model drug (ibuprofen) upon application of light. MN arrays were prepared using a polymer prepared from 2-hydroxyethyl methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) by micromolding. The obtained MN arrays showed good mechanical properties. The system was loaded with up to 5% (w/w) ibuprofen included in a light-responsive 3,5-dimethoxybenzoin conjugate. Raman spectroscopy confirmed the presence of the conjugate inside the polymeric MN matrix. In vitro, this system was able to deliver up to three doses of 50 mg of ibuprofen upon application of an optical trigger over a prolonged period of time (up to 160 hours). This makes the system appealing as a controlled release device for prolonged periods of time. We believe that this technology has potential for use in ?on-demand? delivery of a wide range of drugs in a variety of applications relevant to enhanced patient care.

Real-time estimation of battery internal temperature based on a simplified thermoelectric model

Li-ion batteries have been widely used in the EVs, and the battery thermal management is a key but challenging part of the battery management system. For EV batteries, only the battery surface temperature can be measured in real-time. However, it is the battery internal temperature that directly affects the battery performance, and large temperature difference may exist between surface and internal temperatures, especially in high power demand applications. In this paper, an online battery internal temperature estimation method is proposed based on a novel simplified thermoelectric model. The battery thermal behaviour is first described by a simplified thermal model, and battery electrical behaviour by an electric model. Then, these two models are interrelated to capture the interactions between battery thermal and electrical behaviours, thus offer a comprehensive description of the battery behaviour that is useful for battery management. Finally, based on the developed model, the battery internal temperature is estimated using an extended Kalman filter. The experimental results confirm the efficacy of the proposed method, and it can be used for online internal temperature estimation which is a key indicator for better real-time battery thermal management.

Stimuli-Responsive Microneedle Arrays For On-Demand Drug Delivery

Pulsatile, or “on-demand”, delivery systems have the capability to deliver a therapeutic molecule at the right time/site of action and in the right amount (1). Pulsatile delivery systems present multiple benefits over conventional dosage forms and provide higher patient compliance. The combination of stimuli-responsive materials with the drug delivery capabilities of hydrogel-forming MN arrays (2) opens an interesting area of research. In the present work we describe, a stimuli-responsive hydrogel-forming microneedle (MN) array that enable delivery of a clinically-relevant model drug (ibuprofen) upon application of UV radiation (Figure 1A). MN arrays were prepared using a micromolding technique using a polymer prepared from 2-hydroxyethyl methacrylate (HEMA) and ethylene glycol dimethacrylate (EGDMA) (Figure 1B). The arrays were loaded with up to 5% (w/w) ibuprofen included in a light-responsible conjugate (3,5-dimethoxybenzoin conjugate) (2). The presence of the conjugate inside the MN arrays was confirmed by Raman spectroscopy measurements. MN arrays were tested in vitro showing that they were able to deliver up to three doses of 50 mg of ibuprofen after application of an optical trigger (wavelength of 365 nm) over a long period of time (up to 160 hours) (Figure 1C and 1D). The work presented here is a probe of concept and a modified version of the system should be used as UV radiation is shown to be the major etiologic agent in the development of skin cancers. Consequently, for future applications of this technology an alternative design should be developed. Based on the previous research dealing with hydrogel forming MN arrays a suitable strategy will be to use hydrogel-forming MN arrays containing a backing layer made with the material described in this work as the drug reservoir (2). Finally, a porous layer of a material that blocks UV radiation should be included between the MN array and the drug reservoir. Therefore radiation can be applied to the system without reaching the skin surface. Therefore after modification, the system described here interesting properties as “on-demand” release system for prolonged periods of time. This technology has potential for use in “on-demand” delivery of a wide range of drugs in a variety of applications relevant to enhanced patient care.