The Construction and Related Industries in a Changing Socio-Economic Environment: The Case of Hong Kong

Hong Kong is well known for its “housing market bubble”. Both theoretical and empirical studies point to the supply side being the “root of all evil”. This paper takes a preliminary step in understanding the supply side of the Hong Kong market by investigating the construction and related industries. After taking into consideration of the unusual public expenditure, the construction industry seems to be “normal” in international standard. Its relationship with the aggregate economy is also examined. Directions for future research are also suggested.housing, construction, government policy, employment, investment

Application of Big Data in Decision Making for Emergency Healthcare Management

Application of big data in healthcare has enhanced efficiency and decision making. This is of critical benefit to patients, healthcare professionals and the healthcare institution. Although various research studies have examined the application of big data analytics in healthcare, few studies have explored its application in emergency medicine. This research study explored the application of big data in emergency medicine in facilitating decision making among paramedics and other healthcare practitioners. Appropriate research studies were identified and reviewed systematically to explore the theme of the study. The study found that big data promoted decision making in emergency medicine through the predictor models, which enabled the healthcare practitioners make informed judgments concerning patient care

On the Stability of the Implicit Prices of Housing Attributes: A Dynamic Theory and Some Evidence

Given the dramatic fluctuations in aggregate housing prices, this paper attempts to examine whether the implicit prices of different housing attributes are “stable.” Theoretically, this paper provides perhaps the first dynamic, general equilibrium model in which housing attributes’ implicit prices fluctuate. Empirically, this paper models the time paths of different implicit prices as auto-regressive processes by employing a hedonic pricing model on a large set of housing transaction data over a relatively long period of time. An endogenous structural break test is then performed. Except for a few attributes, structural breaks are not detected. Directions for future research are discussed.hedonic pricing; structural break; evolution of valuation; housing attributes

Quality evaluation of mycelial Antrodia camphorata using high-performance liquid chromatography (HPLC) coupled with diode array detector and mass spectrometry (DAD-MS)

<p>Abstract</p> <p>Background</p> <p><it>Antrodia camphorata </it>(AC) is an important fungus native to Taiwanese forested regions. Scientific studies have demonstrated that extracts of AC possess a variety of pharmacological functions. This study aims to identify the full profile fingerprint of nucleosides and nucleobases in mycelial AC and to assess the quality of two commercial mycelial AC products.</p> <p>Methods</p> <p>High-performance liquid chromatography coupled with diode array detector and mass spectrometry was employed to identify the major components in mycelial AC. The chemical separation was carried out using a gradient program on a reverse phase Alltima C₁₈AQ analytical column (250 × 4.6 mm, 5 μm) with the mobile phase consisting of deionized water and methanol.</p> <p>Results</p> <p>Ten nucleosides and nucleobases, two maleimide derivatives, and a sterol were identified as the major constituents in mycelial AC. These groups of chemical compounds constitute the first chromatographic fingerprint as an index for quality assessment of this medicinal fungus.</p> <p>Conclusions</p> <p>This study provides the first chromatographic fingerprint to assess the quality of mycelial AC.</p

The value of hippocampal and temporal horn volumes and rates of change in predicting future conversion to AD.

Hippocampal pathology occurs early in Alzheimer disease (AD), and atrophy, measured by volumes and volume changes, may predict which subjects will develop AD. Measures of the temporal horn (TH), which is situated adjacent to the hippocampus, may also indicate early changes in AD. Previous studies suggest that these metrics can predict conversion from amnestic mild cognitive impairment (MCI) to AD with conversion and volume change measured concurrently. However, the ability of these metrics to predict future conversion has not been investigated. We compared the abilities of hippocampal, TH, and global measures to predict future conversion from MCI to AD. TH, hippocampi, whole brain, and ventricles were measured using baseline and 12-month scans. Boundary shift integral was used to measure the rate of change. We investigated the prediction of conversion between 12 and 24 months in subjects classified as MCI from baseline to 12 months. All measures were predictive of future conversion. Local and global rates of change were similarly predictive of conversion. There was evidence that the TH expansion rate is more predictive than the hippocampal atrophy rate (P=0.023) and that the TH expansion rate is more predictive than the TH volume (P=0.036). Prodromal atrophy rates may be useful predictors of future conversion to sporadic AD from amnestic MCI

Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation

Reaction rate predictions of dislocation–precipitate interactions with atomistic simulation

The high strength of many modern engineering alloys can be attributed to the presence of precipitates in the microstructure, which inhibit dislocation motion. Thus, a key step in the quest to comprehensively understand and predict the mechanical behavior of engineering alloys is to develop a sound understanding of dislocation–-precipitate interactions. With many features of the dislocation–precipitate interaction being governed by atomic scale processes, atomistic modeling is a valuable tool for improving our understanding. Under this motivation, we will summarize our recent efforts to predict the rate at which a dislocation overcomes a precipitate using atomistic modeling. Specifically, we will focus on the goal of making predictions at timescales and temperatures comparable to typical experiments. Using direct MD simulation as a standard, we first examine the utility of Transition State and Transition Path theories for this problem, using the Finite Temperature String and Transition Interface Sampling (TIS) methods. We find that the TIS approach is the only method that can produce similar predictions to those of direct MD simulations, for the simple reaction coordinate that we have used

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD