Figure 1 shows the statistical data of the total cargo handling (fwt) in Johor Port Berhad and four different market stages. Stage 1: Trough describes in 2000. However, in the early 2000, more investment from foreign countries and big companies has been used to set up factories and development especially in the area of Johor Port. For example, industrial area Pasir Gudang, Tebrau, Ulu Tiram, Tampoi, Senai, Batu Pahat and Pontian. As a result, the development of the manufacturing industry, manufacturing and more advanced. Among which there are factories that have gained international fame such as Shimano, Sony, Philips, Panasonic, Brother, Fujitsu, Hitachi and more. This led to the demand and supply of drybulkcargo and general cargo were increasing from year to year. In addition, advances in liquid demand, such as chemical, oil, gas and more are increased respectively because Johor is the largest contributor of Malaysian palm oil. Besides, there are a number of larger chemical companies operated in Pasir Gudang, Malaysia namely Felda Johor Bunker, Oil Depo Felda, Sime Darby, Akzo Nobel Oleochemical, Mox Gases and others. This is also contributed to the increase in total liquid bulkcargo volume handled by Johor Port.
Concerning estimating in maritime economics aspects, sufficient research has been done in attempting to conjecture crude oil or drybulk indices, prices and freight rates. Barely any of these studies use AIS information. They basically focus around customs information, shipping indices, and other information. Han et al. (2014) presents an improved Support Vector Machine (SVM) model to forecast drybulkcargo freight index (BDI). This work looks at determining consequences of three other forecasting techniques and concludes that the proposed method has higher precision in estimating the short term pattern of the BDI. Yu et al. (2008) proposes an empirical mode decomposition (EMD) based neural network to forecast world crude oil spot price. The outcomes from the estimation of West Texas Intermediate (WTI) crude oil spot price show the attractiveness of the proposed method. The study done by Li and Parsons (1997) demonstrates that neural networks altogether beats customary time series models, similar to the Autoregressive Integrated Moving Average (ARIMA) or Autoregressive Conditional Heteroskedasticity (GARCH) models, in estimating oil tanker freight rates.
A simple model, C = mGNP + b, was used. Projections were carried out for two scenarios, the worst and best possible cases. The basis for the GNP growth rate in worst case scenario, are elaborated in section 3.1 and the general assumptions are given in this section. It is assumed that the port should be planned for catering to any situation falling with in this range of demand. Table 3.3 shows the growth percentages assumed for both optimistic and pessimistic scenarios. The results of the analysis are given in Appendix - 1A. The analysis shows that, ‘the least squares fit’ is very good. Figure 3.1 shows the regression curve. The statistic, coefficient of determination r 2 , is 0.97 for the least squares fit between Total cargo and GNP meaning whereby that the fitted equation C = 0.00000403GNP – 4.75 explains 97% of the total variation in the data about the average ‘C’. The standard error values for the dependent variable ‘C’, the coefficient ‘m’ and the constant ‘b’ are respectively 0.29, 0.000000289, and 0.72 respectively. This shows that there is a high degree of correlation between the variables. Other statistics for this relationships and equations for Break Bulk, Containerised cargo, and drybulkcargo with GNP are given in Appendix -1A. The traffic projections in the port up to 2015 were carried out, year by year for both scenarios as explained below.
and urbanization will continue to stimulate the growth of the two major cargoes. Besides, other emerging countries like India, Brazil, and Russia will also maintain the continuous growth with the same demands, which will support the long-haul drybulkcargo transport. The major carriers are Panamax, Capesize, and VLOC vessels. Second, grain is mostly affected by the weather. However, other factors are also give pressure upon the volume, structure and patterns of grain shipments. There are 4 major influences (a) the shift in demand and usage (e.g. industrial purposes vs. feed); (b) environmental and energy policies that promote the use of alternative energy sources such as biofuels; (c) the evolution in consumption and demand patterns (e.g. higher meat consumption in emerging developing countries leads to more grain shipments for feedstock); and (d) trade measures aimed at promoting or restricting trade flows 6 . The major exporters are Argentina, Australia, Canada, the European Union and the United States, but the major importers are the European Union, Russia, Asia, and Africa, which are mostly developing countries. The long distance between those countries gives the bulk carrier employment. The trade of grain will have an impact on the handymax and handy vessels.
In this research, we utilize a logit model in order to assess the probability of a drybulk carrier being scrapped depending on vessels’ main characteristics, such as age and size, and market specific factors including freight rate level, bunker prices, interest rates, scrap prices and market volatility. As expected, the results confirm the existence of a positive relation between age and probability of scrapping a vessel across all drybulk sub-sectors. However, the results reveal that the relation between vessel size and scrapping probability can vary across different drybulk segments. In particular, while the state of freight market is inversely related to the probability of scrapping, higher bunker prices seem to increase the probability of scrapping smaller tonnage. Moreover, market variables such as level of interest rates, scrap values and market volatility seem to have a positive effect on probability of scrapping drybulk carriers.
For the moment, overwhelmingly dominant products and lower-occupied proportion of secondary products poses a current situation of railway freight transport products structure in China.Development of fast railway freight product proceedingslow, which share a lower proportion of freight product structure.Thus, it leads to a small share in the freight transport market of small batch but high-value.Railway freight product structure lags behind, cannot effectively to adapt to market changes, to meet the fluctuation of the transport demand, and limit the improvement of transport efficiency.Although the bulkcargo is still the main object of railway transportation, due to the production efficiency of original freight products (such as district train, train detaching and attaching, exchange train) is low.Resource usage is larger and accounts for a large proportion in the freight product structure anddirectly affect the overall ability of the railway transportation. As a result of unconducive to improvement of transport efficiency. 4.Transport efficiency optimization modelling of railway freight product based on AHP
To simulate seasonal flow variation, three types of flooding scenarios are considered. These are 1) average flood year 2) wet year and 3) dry year. For each of the flood years the monsoon (May-October) and the dry season (November-April) are analyzed separately. Average flood year is defined as the flood year when 20% - 24% of the entire country is flooded . For any year if the country is flooded less than this amount is considered as dry year and for any year if the country is flooded more than this amount is considered as wet year . According to these criteria, the year 2001 is considered as dry year (2.71% flooded), the year 2000 is considered as the average year (24.19% flooded) and the year 1998 is considered as the wet year (67.93% flooded). To compute the total flow volume in the GBM estuarine systems, the flow volumes of the estuaries of the EES, CES and WES are computed by integrating the discharge hydrographs using the following equation:
Cargo Public Autotransport (DCPAT) in the Metropolitan Zone of the State of Hidalgo (ZMEH), since today they are managed through old habits, emanated empirically from the experience of managers or recommendations generated from parents to children. Therefore, it is necessary to establish a knowledge con- struct, under a methodological process, that links the contextual, referential and empirical framework of the DCPAT in relation to the theoretical basis of man- agement competencies and their performance in the referred sector.
The drybulk shipping market entered a deep and lasting depression after the 2008 financial crises with extremely low freight rates for the whole of the drybulk sector. Historically low earnings for different subsectors within the drybulk market made it difficult for drybulk shipping companies and ship owners to keep vessels in operation and some resorted to retiring ships through scrapping. The recurring relapse into successive depression periods over the last few years, interrupted only by modest or short-lived recoveries, relates the extent of the post-2008 shipping crisis to the crises triggered by the 1929 financial crash and later by the oil shock in the 1970s. The scrapping volume in the 1980s marked the second significant capacity retirement in the course of the last century; the first was seen in the early 1930s with lay-up climbing in both instances to a significant percentage of the fleet (Thanopoulou, 1995). However, the current shipping crisis lacks a number of typical characteristics of the previous major shipping depressions. For instance, there has not been significant lay-up activity (Alizadeh et al, 2014) or fleet reduction through scrapping, while, on the contrary, the drybulk fleet continued to grow after 2008 (cf. Figure 1).
All the data collected were subjected to statistical analysis of variance using the statistical software, Statistics Version 8.0.They were separated using Duncan's Multiple Range Test (DMRT). Regression analysis was also carried out using the same package. It was meant to formulate different statistical models in order to relate soil bulk density, penetration resistance, soil moisture content, plant height and traffic intensity to yield of soybean.
A study of variation ranges of the free swell index and the differential free swell of the soils indicates that these soils exhibit high degree of expansiveness. The range of values of unconfined compressive strength hints at low to moderate shear strength of these soils. The optimum moisture content and maximum dry density value ranges highlights the fact that these soils are less suitable for the construction of earthen embankments, earthen dams and other similar structures. The low range of California bearing ratio values indicate the low suitability of these soils for the construction of roads.
While investor sentiment may refer to as the propensity to trade on noise rather information, it may also refer to investor optimism or pessimism (Antoniou et al., 2013). It is evidenced that individuals with positive (negative) sentiment make optimistic (pessimistic) judgments and selections (Bower, 1981; Wright and Bower, 1992). Measuring sentiment is subjective since there is no consensus on what the appropriate proxies are (Schmeling, 2009). We combine five proxies which in our view reflect the sentiment of participants in the shipping market, in addition to a component of non-sentiment related idiosyncratic variation. We classify our proxies into three main categories: market expectations (net contracting and money committed), valuation (price-to-earnings and second-hand-to-newbuilding vessel price ratios) and liquidity (turnover ratio). Our choice of sentiment proxies is based on the notion that optimism/pessimism about the overall state of the dry-bulk shipping market can affect the decision of investors about the sale and purchase of second-hand or the order of newbuilding vessels. Following Baker and Wurgler (2006) and Baker et al. (2012), we orthogonalize the raw sentiment proxies and then construct total, market and sector-specific sentiment indices using the first principal component method.
Third, three alternative distributions (SGED, HYP and SST) generally show better accuracy than commonly used distributions. With HYP distribution, the backtesting results show that for 14 times the P values are rejected at the 1% sig- nificance and 6 times at the 5% significance level among the 96 cases, accounting for about 20%. HYP, GHST, NIG distributions are all in the GH family, but GHYP outperforms the other two distinctively. With SGED distribution, the backtesting results show that for 9 times the P values are rejected at the 1% sig- nificance level, and 11 times at the 5% significance level, accounting for 17%. With SST distribution, the backtesting results show that for 11 times the P values are rejected at the 1% significance level, and 6 times at the 5% significance level, accounting for 17%, which displays the best accuracy on four samples. Risk pre- diction models based on these three distributions perform relatively well in the drybulk shipping market, which provides empirical evidence for risk managers that they can consider SGED, HYP or SST distribution to model and forecast risks.
porosity; and (d) bulk density (dry weight basis) between secondary peat swamp forests, drained peat swamp forests, cleared peat swamp forests, and mature oil palm plantations at three different depths within the peat profiles: surface peat (black bar); below the water table (dark grey bar); and deep peat (light grey bar). Average values for land conversion classes and standard error bars are shown.
C3 is the route transporting Iron Ore from Turabao, Brasil to the range of Beilun- Baoshan, China. In practice, few vessels are open next to Turabao, Brasil but more tends to be open either in European seas or in Chinese seas. Therefore, charterers need to fix vessels coming under ballast condition to load the cargo. On the one hand, vessels ballasting from Europe are closer to Puerto Bolivar (route C7). One the other hand, vessels ballasting from the Pacific Basin are closer to Richard’s Bay (route C4). In these conditions, it is clear that C4 and C7 rates could spill over route C3.
Stability strategy: When the Baltic Dry Index is relatively stable at about 900 points, the supply is stable, the freight rate is stable, and the shipping capacity meets the market demand for goods, so maintaining the existing fleet number and ship tonnage is the best way to make money. It is good time to choose shipbuilding, reduce shipbuilding costs and risks; work with large fuel suppliers to lock down oil prices to reduce fuel costs; reduce operating costs and management costs through the use of advanced science and technology, communications technology, and management concepts. Strive to improve the quality and level of service, and strive to establish a large, advanced, high-quality brand image different from other shipping companies.
Then what are the possible explanations for total losses arising out of foundering, collision, fire/explosion, contact, grounding and other causes? Is the high loss rate for a particular ship type attributable to ship age, ship size, ship design, trade pattern or area of operation like domestic or international trade? It is true that these factors do contribute to the accidents although it has not been established that there is a close relationship between the rate of total losses and each one of these factors. For instance, older ships tend to have higher loss rates than newer ones. At the same time, however, a well maintained old ship under a quality conscious management might not have accidents. The tanker pollution accident in 2002 involved a year 2000 built double hulled chemical tanker mt. Eastern Fortitude that grounded on a rock and spilled bunkers on to nearby beaches (Intertanko, 2002, pp.16-17). Even if compared between different ship types of the same age, the loss rate of general cargo ships is still higher than for tankers and bulkcarriers. Hence this is not simply a problem of ageing ships.