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The purpose of this paper is to determine if artificial neural network (ANN) works better than linear regression in predicting Hong Kong real estate investment trusts’ (REITs) excess return.

Both ANN and the regression were applied in this study to forecast the Hong Kong REITs’ (HK-REITs) return using the capital asset pricing model and Fama and French’s three-factor models. Each result was further split into annual time series as a measure to investigate the consistency of the performance across time.

ANN had produced a better forecasting results than the regression based on their trading performance. However, the forecasting performance varied across individual REITs and time periods.

ANN should be considered for use when one were to attempt forecasting the HK-REITs excess returns. However, the trading performance should be always compared with buy and hold strategy prior to make any investment decisions.

This paper tested the predicting power of ANN on the HK-REITs and the consistency of its predicting power.

Apple is a representative company of technological innovation (TI) and management. It has launched new and innovative products since 1977, and many companies and business schools around the world have attempted to learn about the success story of Apple's innovation. However, most previous research works on Apple's innovation have been based on qualitative approaches such as experts' opinions. Such studies offer a subjective point of view. By contrast, in this paper the authors aim to study the TI and forecasting of Apple by analyzing its patent applications, which is an objective approach to examining the innovation of Apple from a technological perspective.

TI is an important issue concerning technology management for companies and governments. To examine Apple's TI, the authors analyze all applied patents and construct analytical models according to three approaches. First, they build statistical models using the time series regression and multiple linear regression methods to create a technology map. Second, they cluster all Apple's patents to find its vacant technology domain. Lastly, they use social network analysis to search for technologies central to Apple's future.

The authors' study shows the technological trends and relations between Apple's technologies. This research finds vacant technology areas and central technologies for Apple's TI.

Using statistical and machine learning methods, the authors analyze all Apple's patents in order to predict the firm's future technologies. This research contributes to examining the TI of Apple. Therefore, the results of the patent analysis can highlight the technological opportunities for Apple's TI.

Traditional TI models have been based on qualitative methods. Previous investigations of Apple's TI have also relied on traditional analytical approaches. In this paper, however, the authors develop a quantitative and objective approach for examining Apple's TI.

The purpose of this paper is to propose an optimized hybrid model based on artificial intelligence methods, use the method of time series forecasting, to deal with the price prediction issue of China’s table grape.

The approaches follows the framework of “decomposition and ensemble,” using ensemble empirical mode decomposition (EEMD) to optimize the conventional price forecasting methods, and, integrating the multiple linear regression and support vector machine to build a hybrid model which could be applied in solving price series predicting problems.

The proposed EEMD-ADD optimized hybrid model is validated to be considered satisfactory in a case of China’ grape price forecasting in terms of its statistical measures and prediction performance.

This study would resolve the difficulties in grape price forecasting and provides an adaptive strategy for other agricultural economic predicting problems as well.

The paper fills the vacancy of concerning researches, proposes an optimized hybrid model integrating both classical econometric and artificial intelligence models to forecast price using time series method.

This study investigates the effect of Earnings per Share (EPS), Dividends per Share (DPS), and Book Value per Share (BVPS) on stock market prices in Abu Dhabi Securities Market. All listed companies in the Abu Dhabi Securities Market are included in this study except those companies which were listed after the starting of year 2002. In addition, those companies which had been prevented from trading in the market for one year or more during the years from 2002 to 2006 were also excluded because the available data of these companies are not adequate. As a result, among the 60 listed firms in Abu Dhabi Securities Market, 22 were excluded. Four hypotheses are under consideration in this study. The simple linear regression method was used to test the first three hypotheses based on t‐test, while the multiple linear regression method was used to test the last hypothesis based on F‐test. Moreover, the correlation method is used to support the findings. The current study found a significant effect of EPS and BVPS on stock market price in the Abu Dhabi Securities Market, whereas no significant effect of DPS was found.

The assessment of fuel tank flammability exposure time for transport aircraft is one of the indispensable links in the airworthiness certification process. According to published literature, many factors can affect the flammability exposure time, while systematic analysis and calculations addressing these factors are in shortage.

Based on the requirements for airworthiness certification of domestic large aircraft, the fuel tank flammability exposure time of transport aircraft is calculated with the Monte Carlo evaluation model specified by Federal Aviation Administration. Meanwhile, the influence of each input parameter on the flammability exposure time is obtained by taking user input parameters in the model as independent variables and freezing other factors at the same time. The significance degree of the influence of each factor is discussed by the orthogonal test method. Subsequently, the interaction between the input parameters is studied by response surface method, and a multiple linear regression method is used to establish the functional relationship between the flammability exposure time and the influence parameters.

Research studies show that among the many factors that affect the flammability exposure time, the cruising Mach number, the equilibrium temperature difference and the maximum range are more significant and much attention should be paid to in the airworthiness certification; although there are interactions among various factors, they have different influence on the flammability exposure time, among which the interactions between maximum range and equilibrium temperature difference are the most significant compared with others; established by applying multiple linear regression equation and based on the test data of response surface method, the functional relationship between flammability exposure time and influence parameters is of sufficient reliability and can be used for preliminary prediction of fuel tank flammability exposure time for transport aircraft.

The research achievements of this paper can provide much useful reference for the certification of domestic large aircraft.

The purpose of this paper is to explore the impact and mechanism of WeChat public platforms articles (abbreviated as WPP) on blood donation behavior using data of WPPA and donation behavior data.

This paper uses multiple linear regression methods, web crawlers and natural language processing technology. It first quantifies the impact of WPP published articles on donation behavior. On this basis, it then selects data from the day of article publication to further study the impact of article dissemination on donation behavior from the perspective of reading quantity, and analyzes the influencing factors of article reading quantity.

The results show that on the same day that an article is published, there is an increase of 13.8 and 14.3% in blood donation volume and fan registrations, respectively. The mediating effect exists. However, the day after an article is published, there is no longer any effect on blood donations. With a 1% increase in reading quantity, blood donation volume on the day of article publication increases by 0.13%, and this positive impact is promoted by the quality of the articles. A conc ise articles title and body and rich images help drive reading quantity. Moreover, blood donors prefer to read articles about blood dynamics and donation promotion, while articles about news, announcements and administrative affairs make them less inclined to read.

First, it focuses on WPPA, quantifies the impact of articles on blood donation behavior and analyzes the mechanism. Second, the authors study the impact and timeliness of social media article dissemination to address the insufficiency of existing research. Third, the study provides a scientific basis for the editing and publishing of articles, helping blood banks improve the effectiveness of publicity and recruitment.

The objective of this paper is to investigate the effects and relative influence of: grid patterns; variability of foundation sizes; total surface area; and average surface area, on formwork labour productivity of isolated foundations.

To achieve this objective, a sufficiently large volume of productivity data were collected and analyzed at both levels; macro, and micro, using the linear regression method. As a result, the effects and relative influence of the investigated factors on formwork labour productivity are determined and quantified.

The findings show significant impacts of the buildability factors investigated on formwork labour productivity, and substantiate the importance of applying the rationalization and standardization concepts to the design stage of construction projects.

Further research into the effects of buildability factors on formwork, and other related trades of in situ reinforced concrete material, i.e. rebar fixing/installation and concreting, labour productivity, which are common to other structural elements and activities such as, grade/ground beams, columns, walls, beams, and slabs, is recommended, so that the related findings can ultimately be used to develop an automated “Buildability Design Support System” to formalize the buildability knowledge of reinforced concrete construction projects.

The outcomes of this research provide designers with feedback on how well their designs consider the requirements of buildability principles, and the tangible consequences of their decisions on labour productivity. In addition, practical recommendations deduced from the findings are presented, which upon implementation, can improve the buildability level of this activity, hence translate into higher labour efficiency and lower labour costs. On the other hand, the depicted patterns may provide guidance to construction managers for effective activity planning and efficient labour utilization.

The findings fill a gap in buildability knowledge and its influence on formwork labour productivity of an important, labour intensive, activity within the in situ reinforced concrete construction projects.

The purpose of this paper is to assess the perceptions of the grassroots on the impact of combative policy measures, strategies and programs introduced by the government and the civil society to reduce persistent occurrences of bribery practices in Nigeria.

Multi-stage or cluster sampling was used to acquire the data for this paper via survey questionnaire administered to the grassroots in Abuja, Nigeria. The data set is used to assess the impact of the various policy measures, strategies and programs on the persistence of bribery practices in Nigeria. The multiple linear regression method was used to estimate the data generated from 836 responses in Statistical Package for Social Sciences (SPSS) version 26.

The result of the estimations indicates that the respondents perceived that some of the policy measures, strategies and programs introduced have reduced persistence of bribery practices in Nigeria, whereas others have remained ineffective in reducing the persistence of bribery practices in Nigeria.

Previous studies on the impact of anti-bribery policy measures, strategies and programs were largely based on the perceptions of international institutions and business executives; this study appears to be the pioneer to focus on the perceptions of the grassroots in Abuja, Nigeria.

The purpose of this paper is to present simple correlation for calculating nitrated hydroxyl-terminated polybutadiene (NHTPB) enthalpy of formation.

It uses multiple linear regression methods.

The proposed correlation has determination coefficient 0.96. The correlation has root mean square deviation and the average absolute deviations values 53.4 and 46.1 respectively.

The predictive power of correlation is checked by cross-validation method (R²=0.96, Q L O O 2 = 0.96 ).

The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.