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This paper aims to examine the effect of housing wealth on household consumption when there are resale and refinancing constraints that prevent housing assets from being cashed out.

Based on Household Expenditure Survey data in Hong Kong from 1999 to 2010, regression analysis is applied to compare the housing wealth effects of private and subsidized homeowners. Propensity score matching is adopted to ensure that the two groups of homeowners share similar household income. Further regression analysis is conducted to examine private homeowners’ consumption when their recourse mortgages are in negative equity.

Subsidized homeowners, who are not allowed to resell their units before sharing their capital gain with the government, experienced an insignificant housing wealth effect. While private homeowners experienced a significant housing wealth effect, the effect was weakened in the presence of a resale constraint induced by negative equity. The results remain robust after the application of more rigorous sample selection through propensity score matching.

The analyses are subject to two potential data limitations. One is a relatively small sample size. The other is that data on financial assets and mortgages are unavailable and have to be indirectly controlled through household characteristics. Nevertheless, our estimated marginal propensity to consume out of housing wealth is 0.03 of the annual household consumption for private homeowners, which is within the range of estimates reported in previous literature.

This study shows that the housing wealth effect enjoyed in the private sector does not necessarily apply to the subsidized sector where resale and refinancing constraints exist. This is not to suggest that the constraints be removed. Rather, policymakers should be aware of the tradeoff: while the constraints ensure that government subsidies are used to assist home ownership, not capital gain, they also bring about consumption inequality in a society, especially in a booming housing market.

Our findings extend the literature on the housing wealth effect, which has been exclusively focusing on private homeowners, to subsidized homeowners. This study also adds to the literature on housing welfare by highlighting that the resale constraints of subsidized housing can weaken the housing wealth effect.

The purpose of this paper is to introduce an orthogonal frequency‐division multiplexing (OFDM)‐based 2‐phase physical‐layer feedback space (PFS) for distributed relay selection (RS) in virtual multiple‐input multiple‐output (VMIMO) systems. The proposed method is evaluated with numeric and simulation results.

Unlike minislots used in current MAC layer feedback, the authors introduce physical‐layer feedback space (PFS) which hosts one‐bit for each candidate relay (CR), for instance, a 48‐subcarrier OFDM symbol hosts 48 bits. In the two‐phase feedback procedure, each CR firstly hears the invitation and respond between cluster heads (CH) of upper‐/down‐stream cluster and decides whether to be a qualified relay or not. Then if it is qualified, it randomly selects one subcarrier and sends a pre‐equalized one‐bit feedback. The upper‐stream CH evaluate all feedbacks in PFS, then selects all or a subset of relays with successful feedback and broadcasts its decision to all nodes. Although the number of successful feedbacks (NSF) drops as number of CR increases (NCR), 7∼8 CR can be selected with high probability when NCR is triple that of total subcarriers (48).

In current literature, several traditional MAC layer feedback mechanisms are designed for relay selection in virtual MIMO (VMIMO) system most of which are based upon feedback minislots. For such methods, the number of active nodes should be estimated first and known to all participating nodes to choose optimal feedback possibility to obtain best successful feedback possibility. Furthermore, each minislot should include unique identification of candidate relay thus such methods can NOT be referred as one‐bit feedback. In the new method, though OFDM subcarriers play the same role as minislots, they are exactly one‐bit fashioned and occupies only multiple OFDM symbols which is much shorter than that in current methods.

By using physical‐layer feedback space, exactly only one‐bit is required for each candidate relay to send its feedback, and the overall overhead introduced for all of them is K‐bit provided by the required PFS, which spans only fixed duration of the PFS, which is one or more OFDM symbols. Therefore, the proposed scheme can greatly reduce overhead and feedback delay during relay selection.

This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.

The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.

The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.

The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.

The purpose of this paper is to reveal the effect of microstructure on the corrosion behavior of CoCrNi alloy in 3.5 Wt.% NaCl solution.

The as-cast CoCrNi alloy was prepared by arc melting, and the cold-rolled and annealed alloys were prepared by processing the as-cast alloy.

The experimental results showed that a protective passivation film was formed on the surfaces of these CoCrNi MEA, and the stability and compactness of alloys increased in the sequence of cold-rolled, as-cast and annealed CoCrNi alloys. The annealed CoCrNi alloys had the best pitting resistance.

This study proposes the effect of the microstructure of CoCrNi alloy on corrosion resistance.

The purpose of this paper is to introduce a weighted average method to process speed measurements from multiple magnetic sensors, which are installed on road segments. Speeds are weighted‐averaged in a fix duration time (5 minutes) for each sensor across location index of the sensor where it was installed. The proposed method is evaluated with numeric and simulation results.

Unlike traditional vehicle average speed measurements, the authors propose a weighted‐average speed measurement method of road segment, using wireless magnetic sensor nodes, which are installed on the measured road segment. Magnetic sensors offer a non‐contact vehicle detection method, and small sensors with relatively low power consumption. Using magnetic sensors, the local changes in the Earth's magnetic field caused by the presence of a moving vehicle can be measured and the vehicle's speed obtained. Next, using adaptive weighted average algorithm and space weighted algorithm in a fixed period, the weighted average travel speed of road segment can be obtained.

In current literature, there are many methods to measure vehicles' speed on road, such as image‐based, radar‐based, GPS‐based, double‐loop‐based or magnetic sensor‐based, but most of them only provide individual vehicle speed. Using probe vehicles, mean travel speed of road segment can be obtained, but it is costly on hardware and measurement, because many probe‐vehicles need to be used on roads and many measurements need to be done everyday. GPS data can be used to provide valuable travel speed data for Intelligent Transportation System (ITS). However, not every vehicle is equipped with GPS and to access ID numbers for personal cars would entail privacy problems. Mean travel speed of road segment is obtained based on statistical average speed. Generally, statistical average speed is used, which is based upon Gaussian distribution is not true in traffic systems.

By using wireless magnetic sensor nodes, vehicle instantaneous speeds are obtained in a fixed time when vehicles are passing over sensor nodes and then the adaptive weighted average speed on each sensor node location is computed based on the monitoring data from each sensor node in the fixed time. Considering different weights of each lane and road space (in the middle of the road segment or near the intersection), the proposed scheme can obtain the weighted‐average speed of the road segment.

This study aims to investigate tourists’ impressions of the smart destinations model from the socio-technical systems and the technology–organization–environment (TOE) perspectives. Specifically, it aims to explore how information source credibility and cloud infrastructure influence tourists’ use of intelligent technology.

This measurement development is based on prior literature; after being evaluated for face and content validity, the authors used random sampling to collect data and conducted a field survey of tourists through Taoyuan Airport and using the airport MRT between December 2022 and March 2023. After confirming that tourists knew the destination information and had experience using travel-related mobile applications to plan their itinerary, the authors further invited tourists to participate in the survey, and 512 valid questionnaires were analyzed by the structural equation modeling approach.

The finding pointed out that source credibility and intelligent technology were innovative technologies that benefitted tourists, as were mobile travel planning apps, which created a relational context based on interests and activities from the socio-technical and TOE perspectives.

Technological innovation is closely related to the development of smart cities; tourists who used travel itineraries successfully understood travel-related actions and significantly had more positive affective images of the city.

There exists a large volume of literature evaluating the mortgage prepayment risk based on the USA's and other developed countries' experiences. With access to detailed loan‐level data provided by a national mortgage lender in China, the purpose of this paper is to revisit the empirical question of the determinants of mortgage prepayment probability.

The paper utilizes unique loan‐level data from a large national residential mortgage lender in China to provide insights into borrowers' prepayment behavior in China under recent market conditions, focusing on the customers' behavior and the segmentation analysis of the prepayment risk.

The results from a logit model indicate that housing appreciation rate and the stock market performance are important considerations for Chinese borrowers. It was also found that borrowers show the heterogeneous prepayment behavior in different groups. For instance, households who purchase new houses are less sensitive to the LTV ratio. These findings provide empirical support for the risk management in the rapid growing mortgage market in China as well as those in other transitional economies.

Because of the lack of the literature in the immature mortgage markets, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further using their own datasets.

The paper includes implications for policy makers to further strategic decision making using a quantitative framework which has not been fully developed in most of the state‐owned banks in China.

This paper has contributed to the analysis of Chinese mortgage prepayment pattern using a large recent micro‐loan level data set. As mortgage borrowers' behavior can be country specific due to the differences in housing policies and housing market structure, this paper sheds new light on the driver factors determining mortgage prepayment in China's mortgage market.

This investigation applied a hybrid method combining a trained artificial neural network (ANN) and the sequential quadratic programming (SQP) method to determine an optimal parameter setting for a reflow soldering process of ball grid array packages in printed circuit boards.

Nine experiments based on an orthogonal array table with three‐controlled inputs and average shear forces of solder spheres as a quality target were utilized to train the ANN and then the SQP method was implemented to search for an optimal setting of parameters.

The ANN can be utilized successfully to predict the shear force under different reflow soldering conditions after being properly trained and the identified optimal parameter setting are capable of striking the balance between the average shear forces and the manufacturing cycle time.

The reflow time and the peak temperature were found to be the most significant factors for the reflow process via analysis of variance.

This study provided an algorithm integrating a black‐box modeling approach (i.e. the ANN predictive model) with the SQP method to resolve an optimization problem. This algorithm offered an effective and systematic way to identify an optimal setting of the reflow soldering process. Hence, the efficiency of designing the optimal parameters was greatly improved.

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

User engagement is critical for online health Q&A communities. Financial incentives, which vary across different communities and reward schemes, are expected to motivate such contribution behaviors. Even though financial incentives have been extensively examined in prior studies, the impact of newly designed contingent financial incentives of a new pay-for-answer reward scheme has not been empirically examined in any online health Q&A community. Given this research gap, our study aims to perform an exploratory investigation of the effects of contingent financial incentives on user engagement in terms of knowledge contribution and social interactions.

Based on expectancy-value theory and equity theory, a research model was developed to reflect the influences of contingent financial incentives on user engagement. A unique dataset was gathered from a large online health Q&A community utilizing this contingent financial incentive reward structure, and the Heckman selection model was applied using a two-step procedure to test these hypotheses. Possible endogeneity issues were also addressed in the robustness check.

Our results demonstrate that the effect of contingent financial incentives on answer quantity and quality is quadratic. Additionally, our study reveals that this contingent financial incentive enhances both comment and emotional interactions among users.

Our study enriches the literature on financial incentives, knowledge contribution and user engagement by revealing the nuanced effects of financial incentives within a novel pay-for-answer scheme. This study also offers significant implications for practitioners involved in online community incentive design.