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The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.

The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.

The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.

The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.

The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.

Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions.

High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose.

The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings.

Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches.

However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

Because of the high volatility with unstable data patterns in the real world, the ability of forecasting price indices is notoriously embarrassing and represents a major challenge with traditional time series mechanisms; especially, most of the traditional approaches are weak to forecast future predictions in the high volatile and unbalanced frameworks under the global and local financial depressions. The purpose of this paper is to propose a new statistical approach for portfolio selection and stock market forecasting to assist investors as well as stock brokers to predict the future behaviors.

This study mainly takes an attempt to understand the trends, behavioral patterns and predict the future estimations under the new proposed frame for the Colombo Stock Exchange (CSE), Sri Lanka. The methodology of this study is carried out under the two main phases. In the first phase, constructed a new portfolio mechanism based on k-means clustering. In the second stage, proposed a nonlinear forecasting methodology based on grey mechanism for forecasting stock market indices under the high-volatile fluctuations. The autoregressive integrated moving average (ARIMA) predictions are used as comparison mode.

Initially, the k-mean clustering was applied to pick out the profitable sectors running under the CSE and results indicated that BFI is more significant than other 20 sectors. Second, the MAE, MAPE and MAD model comparison results clearly suggested that, the newly proposed nonlinear grey Bernoulli model (NGBM) is more appropriate than traditional ARIMA methods to forecast stock price indices under the non-stationary market conditions.

Because of the flexible nonlinear modeling capability, proposed novel concepts are more suitable for applying in various areas in the field of financial, economic, military, geological and agricultural systems for pattern recognition, classification, time series forecasting, etc.

For the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies. However, the NGBM is better both in model building and ex post testing stagers under the s-distributed data patterns with limited data forecastings.

This paper aims to investigate the links between accounting values in Chinese listed companies’ balance sheets and the exposure of their fraudulent activities.

Every balance sheet account is proposed to be a potential vehicle to manipulate financial statements.

Other receivables, inventories, prepaid expenses, employee benefits payables and long-term payables are important indicators of fraudulent financial statements. These results confirm that asset account manipulation is frequently carried out and cast doubt on earlier conclusions by researchers that inflation of liabilities is the most common source of financial statement manipulation.

Previous practices of solely scaling balance sheet values by assets are revealed to produce spurious relationships, while scaling by both assets and sales effectively detects fraudulent financial statements and provides a useful fraud prediction tool for Chinese auditors, regulators and investors.

The influence of the number and arrangement of bolts on the tensile properties of bolted composite laminates was studied in the present study.

Based on the finite element model, the stiffness degradation method is used to simulate the damage evolution process for the failure of bolted composite laminates. Using ABAQUS finite element software combined with material failure criteria, the numerical calculation of the connection strength and failure mode of bolted composite laminates was carried out.

The results of the study show that the tensile strength of the composite laminates connected by three bolts is higher than that of the laminates connected by two bolts. And the arrangement of different bolts has a great influence on the failure strength of bolted laminates.

Bolted connection of composite laminates is a common problem in engineering practice. The effect of bolt arrangement and number on the strength of composite laminates is studied in this manuscript.

The purpose of this paper is to investigate the corrosion behavior of X65 steel in the CO₂-saturated oil/water environment using mass loss method, potentiodynamic polarization technique and characterization of the corroded surface techniques.

The weight loss analysis, electrochemical study and surface investigation were carried out on X65 steel that had been immersed in the CO₂/oil/water corrosive medium to understand the corrosion behavior of gathering and transportation pipeline steel. The weight loss tests were carried out in a 3 L autoclave, and effects of water cut and temperature on the CO₂ corrosion rate of X65 steel were studied. Electrochemical studies were carried out in a three-electrode electrochemical cell with the test temperature was 60°C, and the CO₂ partial pressure was 1 atm by recording open circuit potential/time and potentiodynamic polarization characteristics. The surface and cross-sectional morphologies of corrosion product scales were characterized using scanning electron microscopy. The phases of corrosion product scales were investigated using x-ray diffraction.

The results showed that due to the wetting and adsorption of crude oil, the corrosion morphology of X65 steel changed under different water cuts. When the water cut of crude oil was 40-50 per cent, uniform corrosion occurred on the steel surface, accompanied by local pitting. While the water cut was 70-80 per cent, the resulting corrosion product scales were thick, loose and partial shedding caused platform corrosion. When the water cut was 90 per cent, the damaged area of platform corrosion was enlarged. Crude oil can hinder the corrosion scales from being dissolved by the corrosive medium, and change dimension and accumulation pattern of the crystal grain, thickness and structure of the corrosion scales. Under the corrosion inhibition effect of crude oil, the temperature sensitive point of X65 steel corrosion process moved to low temperature, appeared at about 50°C, lower corrosion rate interval was broadened and the corrosion resistance of X65 steel was enhanced.

The results can be helpful in selecting the applicable corrosion inhibitors and targeted anti-corrosion measures for CO₂-saturated oil/water corrosive environment.

The paper takes the air-ground integrated wireless ad hoc network-integrated system as the research object, this paper aims to propose a distributed robust H_∞ adaptive fault-tolerant control algorithm suitable for the system to distribute to solve the problem of control and communication failure at the same time.

In the paper, the authors propose a distributed robust H_∞ adaptive fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network-integrated system.

The results show that the integrated system has good robustness and fault tolerance performance indicators for flight control and wireless signal transmission when confronted with external disturbances, internal actuator failures and wireless network associated failures and the flight control curve of the quadrotor unmanned aerial vehicle (UAV) is generally smooth and stable, even if it encounters external disturbances and actuator failures, its fault tolerance performance is very good. Then in the range of 400–800 m wireless communication distance, the success rate of wireless signal loop transmission is stable at 80%–100% and the performance is at least relatively improved by 158.823%.

This paper takes the air-ground integrated wireless ad hoc network-integrated system as the research object, based on the robust fault-tolerant control algorithm, the authors propose a distributed robust H_∞ adaptive fault-tolerant control algorithm suitable for the system and through the Riccati equation and linear matrix inequation method, the designed distributed robust H_∞ adaptive fault-tolerant controller further optimizes the fault suppression factor γ, so as to break through the limitation of only one Lyapunov matrix for different fault modes to distribute to solve the problem of control and communication failure at the same time.

In recent years, the demand for oil and gas pipelines has increased rapidly. Due to the restrictions of the pipeline routing, pipelines are generally laid in parallel or in the same trench, which results in stray-current interference between the independent cathodic protection (CP) systems. The purpose of this paper is to study the interference between the long-distance parallel pipelines and to obtain the optimized operation for the CP systems.

In this study, first, the numerical model of parallel pipelines was established using the boundary element analysis software (BEASY). Second, the effects of horizontal distance between parallel pipelines, coating damage rate, soil conductivity and anode output current on the interference of parallel pipelines were studied. Finally, by varying the layout or the output currents of CP stations, an optimized operation scheme osf long-distance parallel pipelines was put forward.

Simulation results showed that with a decrease in soil conductivity or coating damage rate, the interference increased. Moreover, the interference decreased with an increase in horizontal distance between two parallel pipelines or a decrease in anode output current. It was found that there are three methods to reduce the interference between long-distance parallel pipelines: to reduce the output currents of CP stations, combined protection and to close part of the CP stations. Among them, to close part of CP stations was the optimized scheme because of the lowest operating and maintenance cost.

The optimized operation scheme proposed in this study can not only solve the interference between parallel pipelines but also provide guidance for the parallel pipelines to be built in the future. Reasonably arranging the cathodic protection stations using numerical simulation can avoid the interference in the cathodic protection systems, and reduce the construction cost.

In the context of exploring and implementing China's new urbanization, green eco-city have become a transformation model for urban development. Sharpening green buildings in the construction industry can significantly influence and determine China's economic growth trends, as well as the growth and overall development of its national economy. However, current green eco-city still lack appropriate standards and scientific theoretical basis to determine the target star program of green buildings. To fully implement the green building standards, establish and improve a sound technical standard system for the construction of green building demonstration areas, this study considers the spatial layout of green buildings as the core, adopts a plot potential evaluation method for evaluating a few green building plots, and utilizes four factors in verifying plots with great star potential. The study also establishes a system to calculate the star proportion of green buildings and applies the system in calculating the green building ratio of GM New District. Results indicate that the system can quantitatively analyze a plot potential, calculate the star proportion of green buildings scientifically and rationally, and provide some references for the construction of eco-city and the preparation of special planning for green buildings. The system construction is conducive to provide technical support for the construction of green eco-city. The improved system can be applied in the green building demonstration areas in China, and will be a reference model of constructing green building demonstration areas in the country.