Search results

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.

The purpose of this paper is to examine the blockchain as a trusted computing platform. Understanding the strengths and limitations of this platform is essential to execute large-scale real-world applications in blockchains.

This paper proposes several modifications to conventional blockchain networks to improve the scale and scope of applications.

Simple modifications to cryptographic protocols for constructing blockchain ledgers, and digital signatures for authentication of transactions, are sufficient to realize a scalable blockchain platform.

The original contributions of this paper are concrete steps to overcome limitations of current blockchain networks.

Currently, there is a conflict in developing countries between the requirements for the self-development of forestry and the insufficient investment in the forestry sector, and the forest ticket system is an innovative forestry management method to solve this contradiction. In the research on the forest ticket system, the study of its price formation mechanism is relatively important. The key issues of the forest ticket system are how to form the forest ticket price and whether the forest ticket pricing methods are reasonable. Solving these problems is the purpose of this study.

This study will use three methods, namely the forest ecosystem service value evaluation index method, the ecosystem service value based on per unit area evaluation method and the contingent valuation method, to study the forest ticket price formation mechanism, filling the gap in the current research on forest ticket pricing methods. It will analyze how these three pricing methods specifically price the forest ticket and evaluate whether these pricing methods are reasonable. This study will then summarize and comprehensively study the forest ticket price formation mechanism and provide policy recommendations for decision-making departments.

The contingent valuation method and the forest ecosystem service value evaluation index method should be mainly used and given priority in the forest ticket pricing process. When the forest ticket is mainly issued for local residents' willingness to compensate for the forestry ecological value, the contingent valuation method should be mainly considered; when the forest ticket is mainly issued for compensating for the ecological value of local used forest land, the forest ecosystem service value evaluation index method should be mainly considered. The ecosystem service value based on per unit area evaluation method does not need to be the focus.

Compared with existing research studies, which focus more on the forest ticket system itself and the definition of forest ticket, this study mainly focuses on the forest ticket price formation mechanism, emphasizing how to form the forest ticket price and whether the forest ticket pricing methods are reasonable, which has a certain degree of innovation and research value and can partially fill the gap in related fields. At the same time, this study has certain help for the enrichment of the forest ticket system and the extension of related research studies.

This study aimed to estimate the economic benefits of PM2.5 emission abatement by Red Pine, Pinus Koraiensis and Quercus, using a metering model analyzing the amount of PM2.5 absorption in Korea.

To estimate the economic effects of PM2.5 adsorptions by trees, the frequency of hospital visits resulting from respiratory and circulatory diseases was estimated using a Probit model based on the data from National Health and Nutrition Survey.

The results show that Quercus and Pinus Koraiensis absorb and eliminate the largest amount of PM2.5. Reducing 1 ton of PM2.5 emission through the planting of trees leads to lower incidences of respiratory and circulatory diseases equivalent to the amount of 95 million won. When the trees planted are 2-year-old Red Pine, Pinus Koraiensis and Quercus, the resulting economic benefits of the PM2.5 abatement would amount to 481 million won, 173 million won and 1,027 million won, respectively. If the trees are 80 years old, the economic benefits are estimated to be 73 billion won for Red Pine, 103 billion won for Pinus Koraiensis and 38 billion won for Quercus.

One limitation of this study is that the weight of PM2.5 adsorbed by each leaf area entirely depended on the experimental results from a prior study and the values are likely to be different from those actually absorbed in natural surroundings. In addition, because of the lack of data from a domestic survey on the surface of leaf area or the reload flow rate of PM2.5, this study referred to data from foreign research. Unfortunately, this specific data may not reflect climatic and terrain characteristics specific to the target country. We used the annual wind speed to calculate the reload flow rate and elimination volume; however, the figures could be more accurate with hourly or daily climate variations. When estimating the health benefits of changes in PM2.5 emissions on respiratory and circulatory diseases, more segmented access to patients' hospital visits and hospital admissions are desirable. Finally, the study focused on the three major tree species of Korea, however, a more detailed study of PM2.5 reduction by various tree types is needed in the future.

This paper quantitatively assessed the amount of PM2.5 adsorption by each of the three tree species. Then, the economic benefits were calculated in terms of how much money would be saved on hospital visits thanks to the reduced PM2.5 levels and lower incidences of respiratory and circulatory system diseases. The net contribution of this study was to prove the trees' function of reducing PM2.5 as it relates to human health. We focused on the most common trees in Korea and compared them to provide new information on the species.

The purpose of this article is to propose a detailed methodology to estimate, model and incorporate the non-constant volatility onto a numerical tree scheme, to evaluate a real option, using a quadrinomial multiplicative recombination.

This article uses the multiplicative quadrinomial tree numerical method with non-constant volatility, based on stochastic differential equations of the GARCH-diffusion type to value real options when the volatility is stochastic.

Findings showed that in the proposed method with volatility tends to zero, the multiplicative binomial traditional method is a particular case, and results are comparable between these methodologies, as well as to the exact solution offered by the Black–Scholes model.

The originality of this paper lies in try to model the implicit (conditional) market volatility to assess, based on that, a real option using a quadrinomial tree, including into this valuation the stochastic volatility of the underlying asset. The main contribution is the formal derivation of a risk-neutral valuation as well as the market risk premium associated with volatility, verifying this condition via numerical test on simulated and real data, showing that our proposal is consistent with Black and Scholes formula and multiplicative binomial trees method.

This study evaluates “potentials for using tourism in promoting indigenous resources for community development at Musina Municipality, Limpopo Province, South Africa.”

The study used a questionnaire survey, focus group discussions, and field observations to gather data. Microsoft Excel, Spreadsheet, cross-tabulation analysis, and manual sorting contributed to quantitative and qualitative data analyses.

The study uncovered vast significant indigenous species, resources, and tourism potentials with low impacts of indigenous species and resource benefits to the local communities. The details pointing to the actual and potential indigenous resources situations around tourism activities in Musina municipality emerged prominently. Thus, the study concluded such significant indigenous species, resources, and better tourism potentials need a well-combined strategy to channel the benefits to the local community's livelihoods.

The issue of indigenous resources, forests, trees, and tourism concerning rural community livelihoods has become of curiosity in the past few years. Nonetheless, few such studies have investigated the synergies between tourism and significant indigenous species and resources to improve their livelihoods.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

Mangrove forests are one of the most bio-diverse and productive wetland environments on earth. However, these unique tropical forest environments that occupy coastal areas are among the most threatened habitats globally. These threats include logging, conversion of land for agriculture and mariculture and degradation due to pollution over the past 50 years. The large population of resilient mangroves occupying the Turneffe Atoll area in Belize faces growing anthropogenic threats such as permanent clearing of land for housing, infrastructural development and pollution and natural factors (climate change). Given the few formal studies done to evaluate mangrove resilience at Turneffe Atoll, the purpose of this study was to evaluate mangrove resilience and nursery functions in the Turneffe Atoll Marine Reserve (TAMR).

Mangrove fish abundance and forest structure was assessed by means of a visual census and the point-centred quarter method (PCQM) for 11 sites that span across conservation and general use zones.

This study found that the more resilient mangroves (lower vulnerability ranks, higher standing biomass and higher fish biomass and abundance) exist in general use zones and warrant the need for improved mangrove conservation measures for these areas by Turneffe Atoll Sustainability Association (TASA).

Limitations of the methods for data collection included accessibility within mangrove forests stands when establishing PCQM, observer bias among data collectors, sites without surrounding mangroves were not captured to serve as a true control group and poor visibility underwater affected the estimation of fish species and size. The timeline for this research was only three months based on available funding, and no follow-up study was done to make a true comparison.

The findings of this research have a guiding role in the formulation of conservation measures such as better waste management, a robust framework for mangrove management, a communication strategy to guide public awareness and long-term monitoring surveys.

The authors propose a dairy bundle, integrating strategies to jointly maximise producer revenue and consumer utility according to the latter's preferences.

An algorithm based on a nested logit model identifies the bundle maximising producer revenue based on factors affecting consumer purchase behaviour. The data are drawn from a mall-intercept survey administered in Iran, with consumers stating a hypothetical choice among a comprehensive set of dairy products.

Demographic characteristics and marketing mix elements significantly affect consumers' preferences. An algorithm based on the estimated dissimilarity parameter determines the best bundle of dairy products, simultaneously obtaining the highest utility and the highest expected revenue.

Consumer preference and maximum producer or retail seller income are considered simultaneously. The bundling promotion strategy is widely used for food offerings and fresh foods and can be extended to other products.

我們擬根據消費者偏好，提出一個整合了多個策略的捆綁包，以使生產製作者得到最高的收入和最佳的消費者效用。

研究人員根據巢式Logit 模型的演算法確認了一個捆綁包，以使生產製作者能得到最高的收入，而這均建基於會影響消費者購買行為的各個因素。有關的數據取自於伊朗的商場內進行的攔截調查，而回應的消費者須假想他們從一整套乳製品中選擇他們會購買的產品。

研究結果顯示，人口特徵和市場營銷組合元素均會顯著地影響消費者的偏好，一個基於估算的相異性參數而建立的演算法可確認最佳的乳製品捆綁包，這演算法同時也可取得最佳的裨益和最高的預期收入。

於本研究中，研究人員同時考慮消費者的偏好和生產製作者或零售賣家的最高收入。捆綁式的促銷策略在食物供品和新鮮食品方面被廣泛使用，這策略可擴展至其他產品。

乳製品捆綁包、消費者偏好、最佳化演算法、巢式Logit 模型.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.