A note on the optimal pricing and production decisions with price‐driven substitution

Recently, Kim and Bell ( 2011 ) developed a revenue managemnent pricing model with price‐driven substitution. The authors considered production decisions under unlimited production capacity and investigated the impact of price‐driven substitution on a firm's pricing and production decisions. The authors modeled the consumer demands for each market segment as linear additive demand function based on exogenous variables, where demand substitution occurred as a function of price differences between the two products. In this article, we extend this work to examine the impact of a production capacity constraint on the firm's joint pricing and inventory decisions. Based on this extended model, we investigate the impact of price‐driven substitution on a firm's pricing and production decisions where there is a limit on total capacity. We show how revenue managers should adjust prices and production levels to take into account price‐driven substitution under a capacity constraint setting. Both deterministic and stochastic models are developed, and the impact of price‐driven substitution and a capacity constraint on the optimal prices, production levels, and revenues is illustrated.     

Virtual reality in Africa – for Africa

The paper describes a number of virtual reality projects taking place in Africa. The availability of VR on PCs, it is argued, has made access possible for a large number of users. Applications for education and training and applications that preserve and promote African culture are particularly highlighted.     

Bithalamic hyperintensity on T2-weighted MR: vascular causes and evaluation with MR angiography

PURPOSE: To determine whether MR angiography can be used to differentiate between the two vascular causes of bithalamic hyperintensity on T2-weighted MR images: "top of the basilar" artery occlusion and deep cerebral vein thrombosis. METHODS: A retrospective review identified six patients with bithalamic T2 hyperintensity of vascular causes. MR angiography was performed in four patients, MR angiography and conventional angiography in one patient, and conventional angiography in one patient. Data pertaining to clinical presentation and hospital course were collected. MR angiographic techniques were multislab overlapping three-dimensional time-of-flight, 2-D time-of-flight, and 2-D phase-contrast. RESULTS: Three cases of top of the basilar artery occlusion and three cases of deep cerebral vein thrombosis were recognized. In all cases, T2 hyperintensity in a vascular distribution suggested cerebral occlusive disease. Infarction involving the thalami and basal ganglia was present in two cases of deep cerebral vein thrombosis. Infarction of the thalami, mesodiencephalic region, and cerebellar hemispheres was present in two cases of basilar artery occlusion. Bithalamic infarction alone was seen in one case of deep cerebral vein thrombosis and one case of basilar artery occlusion. In the five cases in which MR angiography was used, this technique accurately distinguished the vessels involved (arterial or venous). CONCLUSION: MR angiography is a useful adjunct to MR imaging in the evaluation of bithalamic T2 hyperintensity. It does help distinguish between the two vascular causes: top of basilar artery occlusion and deep cerebral vein thrombosis.     

Occupational factors and renal disease

The male-to-female ratio of patients requiring dialysis treatment commonly approaches 2:1. It is proposed that environmental factors, particularly occupational exposure to hydrocarbons, may account for the excess number of male patients. The term "hydrocarbon" refers to the aliphatic, alicyclic, aromatic, and halogenated hydrocarbons (carbon tetrachloride, chloroform); glycols (ethylene glycol, diethylene glycol, dioxane, glycerol); and organic solvents. Hydrocarbons commonly find use as solvents in industrial manufacturing practices because of their lipid solubility. Hydrocarbons have long been known to be neurotoxicants, affecting both peripheral and central nervous systems. Although benzene and its derivative have a known association with uroepithelial tumors, there is now a considerable body of evidence suggesting a possible role for hydrocarbon exposure in the development of non-neoplastic renal diseases. This article presents an epidemiological case for such an association and critically reviews the literature.     

Numerical simulation of 2D granular flow entrainment using DEM

To understand the entrainment process in granular flow, numerical experiments have been conducted using a Discrete Element Method model. A flow channel of 8 m long with \(15^\circ \) slope is setup with monitoring points located in an erodible bed. Particles, ranging from 3 to 4 mm in diameters, are used in the simulations. In the simulations, translational, rotational and average velocities, total volume, shear stresses are calculated in the measurement circles. The sizes of the measurement circles have been varied to see their effects on the results. It is found the minimum size of the measurement circles should include 20–30 particles. An new analytical model has been developed to calculate entrainment in granular flow. Results of the numerical experiment are compared with analytical model. Shear stresses at the interface between flowing particles in motion and the immobile particles in the channel bed, change of depth of erosion and entrainment rate are used to verify the analytical model. It is found that the calculated shear stresses in the PFC model agree well with the shear stresses calculated using Mohr–Coulomb frictional relationship in the analytical model. The calculated depth of erosion using the new analytical model is also compared with that from dynamic and static entrainment model. The results indicates that the analytical model is able to capture the mechanism of erosion and it can be used in granular flow analysis.     

Single point incremental forming: state-of-the-art and prospects

Incremental sheet metal forming in general and Single Point Incremental Forming (SPIF) specifically have gone through a period of intensive development with growing attention from research institutes worldwide. The result of these efforts is significant progress in the understanding of the underlying forming mechanisms and opportunities as well as limitations associated with this category of flexible forming processes. Furthermore, creative process design efforts have enhanced the process capabilities and process planning methods. Also, simulation capabilities have evolved substantially. This review paper aims to provide an overview of the body of knowledge with respect to Single Point Incremental Forming. Without claiming to be exhaustive, each section aims for an up-to-date state-of-the-art review with corresponding conclusions on scientific progress and outlook on expected further developments.     

Learning the higher-order structure of a natural sound

Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as 'features'. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.     

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Reinforcement Learning Rules in a Repeated Game

This paper examines the performance of simple reinforcement learningalgorithms in a stationary environment and in a repeated game where theenvironment evolves endogenously based on the actions of other agents. Sometypes of reinforcement learning rules can be extremely sensitive to smallchanges in the initial conditions, consequently, events early in a simulationcan affect the performance of the rule over a relatively long time horizon.However, when multiple adaptive agents interact, algorithms that performedpoorly in a stationary environment often converge rapidly to a stableaggregate behaviors despite the slow and erratic behavior of individuallearners. Algorithms that are robust in stationary environments can exhibitslow convergence in an evolving environment.