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This paper aims to obtain the evolution law of dynamic performance of CR400BF electric multiple unit (EMU).

Using the dynamic simulation based on field test, stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers were tested. Stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to research the evolution law with running mileage of dynamic index of CR400BF EMU.

The results showed that stiffness and damping coefficient subjected to normal distribution, the mean and variance were computed and the evolution law of stiffness and damping coefficient with running mileage was obtained.

Firstly, based on the field test we found that stiffness of rotary arm nodes and damping coefficient of anti-hunting dampers subjected to normal distribution, and the evolution law of stiffness and damping coefficient with running mileage was proposed. Secondly stiffness, damping coefficient, friction coefficient, track gauge were taken as random variables, the stochastic dynamics simulation method was constructed and applied to the research to the evolution law with running mileage of dynamic index of CR400BF EMU.

This research analyzes the cycle of the dry bulk shipping market (DBSM) as a representative of spot and period charter rates in dry bulk shipping to develop strategies for investment timing (i.e. asset play) and fleet trading (chartering strategy).

Spectral analysis is a numerical approach to extract significant cyclicality, which may be utilized to develop trading strategies. Instead of working with a single dataset (univariate), a system approach can be utilized to observe a significant shipping market cycle in its multi-variate circumstance. In this paper, a system dynamics design is employed to extract cyclicality in the DBSM in its particular industrial environment. The system dynamic design has competitive forecasting accuracy relative to univariate time series models and artificial neural networks (ANNs) in terms of forecasting outcomes.

The results show that the system dynamic design has a better forecasting performance according to three evaluation metrics, mean absolute scale error (MASE), root mean square error (RMSE) and mean absolute percentage error (MAPE).

Cyclical analysis is a significantly useful instrument for shipping asset management, particularly in market entry–exit operations. This paper investigated the cyclical nature of the dry bulk shipping business and estimated significant business cycle periodicity at around 4.5-year frequency (i.e. the Kitchin cycle).

This study aims to explain the facts about behavioral biases that cannot be explained by rational patterns in ship investments of small-size shipping companies.

A qualitative approach was adopted in this study. The systematic approach of Wolcott (1994) and the action flows proposed by Miles and Huberman (1994) were taken into consideration.

Factors affecting ship investments are classified as ship finance, market timing, ship specifications and profile and business models of ship investors. In addition, behavioral biases that stand out under each theme are explained in the light of behavioral finance literature.

The originality of this study rests on the lack of studies on behavioral aspects of ship investments.

This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

This paper aimed a fractional-order sliding mode-based lateral lane-change control method that was proposed to improve the path-tracking accuracy of vehicle lateral motion.

In this paper the vehicle presighting and kinematic models were established, and a new sliding mode control isokinetic convergence law was devised based on the fractional order calculus to make the front wheel turning angle approach the desired value quickly. On this basis, a fractional gradient descent algorithm was proposed to adjust the radial basis function (RBF) neuron parameter update rules to improve the compensation speed of the neural network.

The simulation results revealed that, compared to the traditional sliding mode control strategy, the designed controller eliminated the jitter of the sliding mode control, sped up the response of the controller, reduced the overshoot of the system parameters and facilitated accurate and fast tracking of the desired path when the vehicle changed lanes at low speeds.

This paper combines the idea of fractional order calculus with gradient descent algorithm, proposed a fractional-order gradient descent method applied to RBF neural network and fast adjustment the position and width of neurons.

While previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.

Data of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.

This study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.

Major industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.

This study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.

The aim of this study is to depict the spatial pattern of the development level of China's state-owned forest farms, thereby providing theoretical reference and empirical evidence for the improvement of the corresponding development policies.

A development evaluation index system was established in this paper to comprehensively measure the development level of China's state-owned forest farms based on the Pressure-State-Response (PSR) model analysis framework and the actual situation of state-owned forest farms by using the entropy weight - technique for order preference by similarity to an ideal solution (entropy weight TOPSIS) evaluation method and exploratory spatial analysis method.

Studies show that the state-owned forest farms in China are generally not well developed. The pressure system that represents the input level displays an apparent restrictive effect on provinces whose comprehensive score <0.15. The response system, which represents development dynamism, has an apparent restrictive function on the provinces whose comprehensive score is 0.35. In terms of the specific spatial characteristics, the V-shape displayed by southwest–northwest and southeast–northwest has an inward trend of gradual reduction, with high-low agglomeration and low-low agglomeration correlation effects as well as apparent basin characteristics.

In this paper, the development level and spatial pattern of state-owned forest farms in China were accurately depicted, and the development path support and decision-making basis were provided for improving the overall development level of state-owned forest farms in China.

This paper recommends new criteria for selecting seaports of embarkation during military deployments. Most importantly, this research compares the current port selection criterion, which is to select the seaport with the shortest inland transport time from the deploying installation, to the proposed port selection criteria, which are to select the seaport based on the shortest combined inland and oceanic transit time to the destination theater.

The authors construct an original integer program to select seaports that minimize the expected delivery timeline for a set of notional, but realistic, deployment requirements. The integer program is solved considering the current as well as the proposed port selection criteria. The solutions are then compared using paired-samples t-tests to assess the statistical significance of the port selection criteria.

This work suggests that the current port selection criterion results in a 10–13% slower delivery of deploying forces as compared to the proposed port selection criteria.

This work assumes deterministic inland transit times, oceanic transit times, and seaport processing rates. Operational fluctuations in transit times and processing rates are not expected to change the findings from this research.

This research provides evidence that the current port selection criterion for selecting seaports for military units deploying from the Continental United States is suboptimal. More importantly, logistics planners could use these recommended port selection criteria to reduce the expected delivery timelines during military deployments.

Several military doctrinal references suggest that planners select seaports based on habitual installation-to-port pairings, especially for early deployers. This work recommends a change to the military's current port selection process based on empirical analyses that show improvements to deployment timelines.

The purpose of this study is to fill the gap and apply a fault tree analysis (FTA) in detention lists of Black Sea Region published port state reports from 2005 to 2016. The study analyzes valid records of 2,653 detained ships with 6,374 deficiencies based on a strategic management approach. This paper sets up FTA technique to assess the detention probability of a random ship which calls the Black Sea Region with the help of detention lists published within subject years.

This paper is not published elsewhere, and it is based on an original work, which figures out detention probability of a regular ship at Black Sea Region port state control from published lists of Black Sea Memorandum of Understanding (MoU). By utilizing these detention lists, a generic fault tree diagram is drawn. Those probabilities could be used strategically with the most seen deficiencies in the region which all could guide the users, rule makers and the controllers of the maritime system.

FTA has conducted based on the data which was collected from website of BS MoU detention lists that published from 2005 to 2016. Those lists have been published on monthly basis from 2011 to 2016 and on quarterly basis from 2005 to 2010. Proper detention records have been included into the research, whereas some missing records were excluded. Subject lists have been harmonized and rearranged according to Black Sea MoU Detention Codes which was published on October 2017 at Black Sea MoU’s website. According to BS MoU Annual Reports, 58,620 ships were inspected from 2005 to 2016 as seen in Table 1. Those ships were inspected by each member country’s PSOs in the light and guidance of predefined selection criteria of the region. Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply a safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

In the light of the above analysis, the future research on this subject could be studied on other regions which might enable a benchmark opportunity to users. Also, insurance underwriters have their own reports and publications that could clarify different points of view for merchant mariners and regulators. In this research, FTA is used as a main method to figure out the root causes of the detentions. For future researches, different qualitative and quantitative methods could be used under the direction of subjects.

Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

With the nature of carriage, shipping business carry out its essential economic attendance in world trade system via inclusion in national and international transportation. As a catalyst in international trade, shipping itself enables time, place and economic benefits to users (Bosneagu, Coca and Sorescu, 2015). Social and institutional pressures generate shipping industry as one of the most regulated global industries which creates high complexity. Industry evolved to multi-directional structure ranges from international conventions (IMO and ILO) to “supra-national interferences” (EU directives), to regional guidance (MoUs) to national laws (flag states). Ship operators endeavor to adopt/fit its industry environment where rules are obvious. With adaptation of industrial environment, ship operators are able to create an important core competency.

This study enlightens the most recorded deficiencies and analyzed them with the help of fault three method. These calculated frequencies/probabilities show the most seen nonconformities and the root causes of detentions in the Black Sea Region in which those results will be benefited strategically that enables a holistic point of view that guide the owners/managers, charterers/sellers/shippers, classification societies, marine insurance underwriters, ship investors, third parties, rule makers and the controllers of the system to apply safety culture.