THE POTENTIAL OF METHANOL AS AN ALTERNATIVE MARINE FUEL FOR INDONESIAN DOMESTIC SHIPPING

This analysis aims to provide insight and to explore the future usage of methanol as an alternative marine fuel for domestic ships in Indonesia. An overview of potential application, analysis of resources availability, and stakeholder readiness on the topic are provided; related challenges are also identified and further examined. The potential performance of methanol as a fuel is discussed and evaluated via two different perspectives (the ship-owner perspective and the government one) through case studies of two passenger ships owned by the shipping company Pelayaran Indonesia (PELNI): MV. Labobar and MV. Gunung Dempo. As ship-owners tend to look very closely at the economic aspects, a feasibility study is performed by developing a combinatorial scenario approach based on the combination of economic measures of merit (NPV and payback period) along with a technical scenario (main-pilot fuel set-up); the variables included in the calculation are: ship age, ship productivity, and macro-economy conditions. Regarding the government perspective, the main issues are environmental protection and policy compliance. These issues are evaluated by examining six emission types (NOx, SOx, CO2, CH4, N2O, and PM). Additionally, since there is a trade-off situation in government subsidies between the government and ship-owner interests, an optimisation and sensitivity analysis is performed by utilizing a combinatorial scenario model to determine optimum methanol price and external variables influencing the decision to support further use of methanol in the Indonesian market. An important finding was that Indonesia has certain advantages/drives to introduce methanol as a marine fuel. However, methanol competitiveness is mainly dependent on ship productivity and the price difference between methanol and marine diesel oil (MDO). Additionally, policy analysis (through an optimisation approach) could be one of the government options in order to determine the optimum condition in establishing methanol as a marine fuel. Finally, short, medium, and long term recommendations are also provided as the basis for future consideration.

Optimization of corn commodity Warehouse Receipt System (WRS) in South Sulawesi based on system dynamics

The purpose of this research is to study various factors that influence farmers in using the warehouse receipt system (WRS) and to apply the warehouse receipt system policy scenario in South Sulawesi. The method used is a dynamic system. The simulation results of the actual model type are that the income obtained from the actual (existing) model using WRS is higher than the direct income (selling corn directly) without entering the warehouse (WRS), as well as real income (where 100% of the corn that is produced) entered the warehouse immediately sold at the time without any delay in selling). The ideal type of warehouse receipt system shows the result that there is a difference in income (lost profit/benefit loss) if it does not optimize the existence of the warehouse, in other words, the average annual loss of income is 113.5 billion. The model shows that by delaying the sale, the profit difference (difference in income) is obtained from 38.35 billion to 189.77 billion or an average of 113.5 billion per year. The scenario model was developed with the consideration of optimizing the ideal model, namely optimizing the difference in income obtained from selling delays of 30% with a strategy of increasing agricultural productivity through increasing farmer productivity, which can be done in various ways such as training, socialization, education, institutional Warehouse receipt systems must carry out changes to increase productivity and performance in the field, build strong stakeholder support between local and central government

Scenario model to forecast behavior of intrusive plant communities in response to control effects in arid agriculture

The presence of large areas of anthropogenic transformation of plant communities with a potentially negative impact on adjacent territories makes it relevant to develop various methods for automated monitoring and modeling of processes occurring in these ecosystems. Based on the results of previous studies of phytocoenoses, the authors selected four groups of indicators for constructing a scenario model: integral characteristics of intrusive plant communities (IPC), including those obtained by using remote dynamic methods; integral indicators of the negative impact of IPC on the adjacent agro-ecosystem; indicators of the distribution of mobile forms of trace elements in the soil; and indicators of soil microbiota. As the result, a hypothetical formula is obtained that allows, with minimal impact on the biosystem of technogenic IPC, to sufficiently reduce its adverse impact on the adjacent agro-ecosystem. Further refinement and dissemination of the scenario model and its connection to databases on plant communities will automatically change the values of the coefficients in the solving equations, thereby providing the most accurate and reliable forecast of the response of agro-ecosystems to various control actions.

More than meets the eye: use of computer vision algorithms to identify stone tool material through the analysis of cut mark micro-morphology

Artificial intelligence algorithms have recently been applied to taphonomic questions with great success, outperforming previous methods of bone surface modification (BSM) identification. Following these new developments, here we try different deep learning model architectures, optimizers and activation functions to assess if it is possible to identify a stone tool’s raw material simply by looking at the cut marks that it created on bone. The deep learning models correctly discerned between flint, sandstone and quartzite with accuracy rates as high as 78%. Also, single models seem to work better than ensemble ones, and there is no optimal combination of hyperparameters that perform better in every possible scenario. Model fine-tuning is thus advised as a protocol. These results consolidate the potential of deep learning methods to make classifications out of BSM’s microscopic features with a higher degree of confidence and more objectively than alternative taphonomic procedures.

Scenario model of intelligent decision support based user digital life models

Introduction. In the decision support domain, the practice of using information from user digital traces has not been widespread so far. Earlier, the authors of this paper developed a conceptual framework of intelligent decision support based on user digital life models that was aimed at recommending decisions using information from the user digital traces. The present research is aiming at the development of a scenario model that implements this framework. Purpose: the development of a scenario model of intelligent decision support based on user digital life models and an approach to grouping users with similar preferences and decision-making behaviours. Results: A scenario model of intelligent decision support based on user digital life models has been developed. The model is intended to recommend to the user decisions based on the knowledge about the user decision-maker type, decision support problem, and problem domain. The scenario model enables to process incompletely formulated problems due to taking into account the preferences of users who have preferences and decision-making behaviour similar to the active user. An approach to grouping users with similar preferences and decision-making behaviours has been proposed. The approach enables to group users with similar preferences and decision-making behaviours based on the information about user behavioural segments that exist in various domains, behavioural segmentation rules, and user actions represented in their digital life models. Practical relevance: the research results are beneficial for the development of advanced recommendation systems expected to tracking digital traces.

The effect of climate change on habitat suitability and a distribution model of the Iranian fat-tailed gecko, Eublepharis angramainyu Anderson and Leviton, 1966 (Sauria: Eublepharidae) since the Last Interglacial to 2050

Surveying the role of climate changes on the species distributions in the past, present and future, and correlating these with changes in distribution ranges have attracted considerable research interest. The leopard geckos of the genus Eublepharis Gray, 1827 (family Eublepharidae), as a vicariate group, comprises six valid species distributed from Turkey through the Iranian Plateau to India, of which E. angramainyu, E. macularius and E. turcmenicus occur in Iran. In this study, we modelled the potential distribution areas for E. angramainyu and determined the suitable habitats in the past (the last interglacial [LIG] and mid-Holocene [MH]), present (1950–2000), and also predicted four scenarios in the future (2050) by using the maximum entropy approach (MaxEnt). The obtained models indicated very good values of the area under curve (AUC): LIG = 0.996 ± 0.003, MH = 0.996 ± 0.004, contemporary period = 0.995 ± 0.004, and the future = 0.997 ± 0.002. Precipitation of the coldest quarter and precipitation of the warmest quarter were the most important factors shaping the distribution of E. angramainyu. As it seems, climatic changes have been responsible for a southward shift in distribution and suitable habitats of E. angramainyu from the LIG (~150,000–120,000 years ago) to the future. The representative concentration pathway (RCP) 2.6 scenario model of the future predicted a much more restricted distribution and less suitable habitats due to radiation of the forcing level which reaches a value of around 3.1 W/m² by mid-century and returns to 2.6 W/m² by 2100.

Multi-Scenario Model of Plastic Waste Accumulation Potential in Indonesia Using Integrated Remote Sensing, Statistic and Socio-Demographic Data

As a significant contributor of plastic waste to the marine environment, Indonesia is striving to construct a national strategy for reducing plastic debris. Hence, the primary aim of this study is to create a model for plastic waste quantity originating from the mainland, accumulated in estuaries. This was achieved by compiling baseline data of marine plastic disposal from the mainland via comprehensive contextualisation of data generated by remote sensing technology and spatial analysis. The parameters used in this study cover plastic waste generation, land cover, population distribution, and human activity identification. These parameters were then used to generate the plastic waste disposal index; that is, the distribution of waste from the mainland, flowing through the river, and ultimately accumulating in the estuary. The plastic waste distribution is calculated based on the weighting method and overlap analysis between land and coastal areas. The results indicate that 0.6% of Indonesia, including metropolitan cities, account for the highest generation of plastic waste. Indicating of plastic releases to the ocean applied by of developing three different scenarios with the highest estimation 11.94 tonnes on a daily basis in an urban area, intended as the baseline study for setting priority zone for plastic waste management.

Analisis Pengaruh Pertumbuhan Skuter Otomatis Bertenaga Listrik Terhadap Perubahan Teknologi CVT Menggunakan Model Sistem Dinamis

This research shows that we all have realized the need for clean air and globally have regulated exhaust emissions from air pollution, so electric vehicles have emerged. But this phenomenon has an impact on belt companies that supply parts for CVT gasoline-powered automatic scooters because the majority of electric motors no longer need a belt as a drive transfer from the engine to the wheel. Simulated with a dynamic model for the projection of the next 20 years from 2019 to 2039, with two scenarios, namely COVID-19 and if the government's policy target of 20% of the motorbike market switches to electricity in 2025, then gradually increases to 30% in 2035. PT XYZ as a belt supplier with an 85% market share in Indonesia, will get a net profit of 50% if this scenario is achieved until 2039 became around 131 billion per year, previously with the no-scenario model it was projected to get a net profit of around 221 billion. Maybe if the demand for electric motorbikes is more than what the government has targeted, it is likely that the two-wheeled automotive belt business could be threatened in the next 15 - 20 years. Meanwhile, the largest Expense is the cost of imported materials, reaching 65% of the total cost of Expenses consisting of Salary Expenses (Salary Expense), total training costs, cost of sales, total loss costs, material costs, and Operating overhead Recommendations that business can survive, companies can reduce material costs by developing local materials, and help shape the automotive belt use ecosystem by working with automotive manufacturers.