Probable Scenarios Research Articles

Multi-objective optimization design of sustainable biofuel network with integrated fuzzy analytic hierarchy process

Biofuel is one of the emerging sources of renewable energy, which is produced from biomass materials called feedstocks. Wood biomass, in particular, can improve air quality by reducing the smoke created by burning woody biomass. In this research, our key objective is to model the wood biomass supply chain to promote the effective operation of biofuel. Initially, we propose the mixed integer linear programming (MILP) model with multiple objectives for assessing the biofuel supply chain. Next, the fuzzy analytic hierarchy process (FAHP) is applied to assess uncertain data from decision makers and integrated with the proposed mathematical model. The developed model evaluates the perspective of sustainability by considering total cost – the surrogate criterion for the economic aspect, unsatisfied demand – the surrogate criterion for the social aspect, and CO2 emission – the surrogate criterion for the environmental aspect, into account. Next, both the supply of wood biomass from each farm node and the demand for produced electricity are evaluated with probabilistic scenarios and stochastic analysis in our analysis. Then, the sensitivity analysis and Pareto analysis are performed by varying parameters related to objective weights to verify and validate the model functionalities and objective tradeoffs. Finally, the regional case study in Thailand based on the wood-biomass data is applied with a real-world context to validate the model functionalities. Given that farmers’ locations in the case study are distant with diverse areas, the results of tradeoff analysis in our study can provide a strategic choice for enhancing competitive advantages for key decision makers in the biofuel network.

The cemetery of the Trzciniec Cultural Circle community in Łubna in light of radiocarbon chronology (AMS)

The barrow cemetery in Łubna (Łubna-Jakusy), Poland, excavated shortly after World War II , is one of the best-known necropolises in the western area of the Trzciniec Cultural Circle. During the archaeological research conducted by K. Jażdżewski, up to 27 mounds wereexcavated. In this article, the authors present and specify the absolute chronology of this site. Thanks to a search in the museum storerooms, it was possible to obtain osteological material, which was subsequently dated in the Poznań Radiocarbon Laboratory using the AM S method. These absolute dates combined with the results of a study of the archaeological material allowed the chronology of almost half of the excavated barrows to be defined. The dates obtained thanks to modelling functions answer the question about the timeframe the necropolis was in use. Additionally, the authors present a probable scenario for the development of the cemetery in Łubna, outlining the direction of expansion and characterizing its spatial arrangement.

Coordination of power system stabilizers using scenario based optimization for enhancement of small‐signal stability considering uncertainties

AbstractThis paper proposes a method based on scenario optimization and meta‐heuristic algorithm to optimize the power system stabilizers (PSSs) for enhancement of small‐signal stability (SSS) considering power system uncertainties such as photovoltaic generation, wind turbine generation, and load. The proposed method consists of two stages. In the first stage, the uncertainties are modeled using probability functions and scenarios are created based on them. These scenarios are then used to obtain a joint probability where the most likely probabilities are chosen for the second stage. The second stage utilizes these critical scenarios to formulate an optimization problem. The objective function of the optimization problem is to enhance system's small signal stability margin with PSS parameters as constraints. The proposed method is then solved using a new meta‐heuristic optimization algorithm known as Jelly Search Optimizer to obtain the optimized parameter of PSSs. The proposed method is tested on a two‐area and five‐area system, and the results show that the system SSS margin is improved for the majority of uncertainty situations. Moreover, the proposed method is around 1.6 times faster compared to the conventional probability method when tested in the modified two‐area system while 5.7 times faster when tested in the modified five‐area system.

Navigating Geopolitical Change in Northeast Asia: A Realist Approach to Analyze the Matrix Scenario of US‐China Conflict and US–North Korea Relations

This article examines the intricate dynamics of US‐China and US–North Korea relations, focusing on the strategic competition between the United States and China. Drawing upon the realist theories of Kissinger and Mearsheimer, the study delves into how various intensities of US‐China conflict might influence North Korea's strategic value and the prospects for improved US–North Korea relations. The research underscores that escalating US‐China competition could reshape the geopolitical landscape in Northeast Asia, paving the way for new strategic alignments and partnerships. Four distinct scenarios are presented, each reflecting a different intensity of US‐China conflict and the ensuing reactions from both North Korea and the USA. The analysis suggests that the most probable scenario entails an intensifying US‐China rivalry, which correspondingly elevates North Korea's strategic significance. Such a turn of events would necessitate a shift in US strategy – from isolating North Korea to forging more favorable geopolitical conditions. Potential strategies might include reducing North Korea's reliance on China, disrupting the alignment between China, Russia, and North Korea, or even contemplating the integration of Pyongyang into the US security framework. These strategic adjustments could reshape the power dynamics and address regional challenges in Northeast Asia, promoting peace and stability and safeguarding the nation's core interests.

A Multi-Scenario analysis method for collapse risks due to initial local failure of building structures

Traditional progressive failure analysis is usually computationally expensive and can only be used to analyze a limited number of cases. The resulting mechanical data on structural components are not suitable for displaying collapses or providing an intuitive reference for emergency management. In addition, numerical models do not incorporate uncertain factors in failure development, which makes it difficult to deduce multiple scenarios of future. This paper proposes a multi-scenario progressive failure analysis method based on a sub-region model for emergency management. Firstly, the material model is augmented with a possibility factor to evaluate the failure of components based on internal forces, and thereby deduce different progressive collapse scenarios. Secondly, the building structure is divided into several sub-regions based on the damage correlation of components, so that the degree of regional collapse can be demonstrated at a more macroscopic scale, which is more suitable for emergency management. By analyzing typical failure scenarios, the collapse evolution rules at the sub-region level can be summarized, which can be used for deducing the results of multiple collapse scenarios rapidly. The proposed method can identify the weak positions of buildings, deduce highly probable progressive damage scenarios, and exhibit macro collapse processes. The multi-scenario analysis results can serve as a reference for collapse safety management in risk pre-assessment and help to reconstruct the collapse process in post-disaster analysis.

Cyber-physical system planning for VPPs supporting frequency regulation considering hierarchical control and multidimensional uncertainties

Renewable energy resources with uncertain output are being connected to the power grid, which puts great pressure on the grid frequency regulation (FR). Virtual power plants (VPPs), which aggregate numerous flexible resources, have significant potential for participation in FR services. However, the FR capability of VPPs is often unreliable due to uncertain resource potential and unreliable communications, which limits their FR applications. Therefore, a cyber-physical system (CPS) planning method considering multidimensional uncertainties for hierarchical VPPs to provide reliable FR capacity is proposed in this paper. Firstly, a fast and reliable hierarchical architecture for VPPs is presented. Then, the reliable capacity model of VPPs for service planning is established within this architecture. The minimum probability scenario method based on the Pauta criterion and the failure mode and effects analysis (FMEA) method are used to quantify the impact of information-physical uncertainty in the model. Based on this, a CPS planning model for VPPs with the objective of minimizing investment and the constraints of normal capacity, reliable capacity and delay under various scenarios is constructed. By linearizing the model, the optimal solution for coordinating flexible resource pool composition, edge terminal allocation and communication can be solved. Numerical simulations on the improved IEEE 33-node and 123-node systems demonstrate the effectiveness of the proposed method.

Scenario-Based Robust Optimization for Two-Stage Decision Making Under Binary Uncertainty

This paper addresses problems of two-stage optimization under binary uncertainty. We define a scenario-based robust optimization (ScRO) formulation that combines principles of stochastic optimization (by constructing probabilistic scenarios) and robust optimization (by protecting against adversarial perturbations within discrete uncertainty sets). To solve it, we develop a sparse row generation algorithm that iterates between a master problem (which provides a lower bound based on minimal uncertainty sets) and a history-based subproblem (which generates an upper bound and updates minimal uncertainty sets). We generate scenarios and uncertainty sets from element-wise probabilities using a deviation likelihood method or from historical samples using a sample clustering approach. Using public data sets, results suggest that (i) our ScRO formulation outperforms benchmarks based on deterministic, stochastic, and robust optimization; (ii) our deviation likelihood and sample clustering approaches outperform scenario generation baselines; and (iii) our sparse row generation algorithm outperforms off-the-shelf implementation and state-of-the-art cutting plane benchmarks. An application to a real-world ambulance dispatch case study suggests that the proposed modeling and algorithmic approach can reduce the number of late responses by more than 25%. Funding: K. Wang’s research was supported by the National Natural Science Foundation of China [Grants 72322002, 52221005, and 52220105001]

Optimized siting and sizing of distribution-network-connected battery energy storage system providing flexibility services for system operators

This paper develops a two-stage model to site and size a battery energy storage system in a distribution network. The purpose of the battery energy storage system is to provide local flexibility services for the distribution system operator and frequency containment reserve for normal operation (FCR-N) for the transmission system operator. In the first stage, the priority is to fulfil the flexibility needs of the distribution system operator by managing congestions or interruptions of supply in the local network. Thus, the first stage allocates the battery to ensure reliable electricity supply in the local distribution network. The minimum required size of the battery is also determined in the first stage. The second stage optimally sizes the battery energy storage system to boost the profit by providing frequency containment reserve for normal operation. The first and second stages both solve stochastic optimization problems to design the battery energy storage system. However, the first stage considers worst-case scenarios while the second stage utilizes the most probable scenarios derived from the historical data. To validate the proposed model, real-world data from the years 2021 and 2022 in Finland are employed. The battery placement is conducted for both the IEEE 33-bus system and a Finnish case study. The profitability of the model is compared across different cases for the Finnish case study. Finally, the paper assesses the impacts of cycle aging on the battery's total profit.

A robust evaluation of 49 high-dose-rate prostate brachytherapy treatment plans including all major uncertainties.

Uncertainties in radiotherapy cause deviation from the planned dose distribution and may result in delivering a treatment that fails to meet clinical objectives. The impact of uncertainties is unique to the patient anatomy and the needle locations in HDR prostate brachytherapy. Evaluating this impact during treatment planning is not common practice, relying on margins around the target or organs-at-risk to account for uncertainties. A robust evaluation framework for HDR prostate brachytherapy treatment plans was evaluated on 49 patient plans, measuring the range of possible dosimetric outcomes to the patient due to 14 major uncertainties. Patient plans were evaluated for their robustness to uncertainties by simulating probable uncertainty scenarios. Five-thousand probabilistic and 1943 worst-case scenarios per patient were simulated by changing the position and size of structures and length of dwell times from their nominal values. For each uncertainty scenario, the prostate D90 and maximum doses to the urethra, D0.01cc , and rectum, D0.1cc , were calculated. The D90 was an average 1.16±0.51% (mean±SD) below nominal values for the probabilistic scenarios; the D0.01cc metric was 2.24±0.90% higher; and D0.1cc was greater by 0.48±0.30%. The D0.01cc and D90 metrics were more sensitive to uncertainties than D0.1cc , with a median of 79.0% and 84.9% of probabilistic scenarios passing the constraints, compared to 96.5%. The median pass-rate for scenarios that passed all three metrics simultaneously was 63.4%. Assessing treatment plan robustness improves plan quality assurance, is achievable in less than 1-min, and identifies treatment plans with poor robustness, allowing re-optimization before delivery.

Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids

While established deterministic capacity planning models for single-component energy storage systems exist, little attention has been given to probabilistic sizing of hybrid energy storage systems (ESSs) using swarm-based meta-heuristic algorithms. This highlights two key research opportunities, namely: (1) studying the impact of preserving model-inherent characteristics and optimising daily system dispatch on narrowing reality gaps in hybrid ESS designs, and (2) the optimal integration of hybrid ESSs into grid-connected micro-grids based on their applications, with potentially significant financial implications for model designs. In response, this paper introduces a novel probabilistic hybrid ESS capacity planning optimisation model based on a state-of-the-art meta-heuristic algorithm. To demonstrate the effectiveness of the model within a community micro-grid scheme, a case study of an eco-village in Aotearoa New Zealand is presented. The simulation results indicate a ∼4 % and ∼36 % premium above the deterministic results respectively in the most likely case and worst-case probabilistic scenarios. On the other hand, the best-case stochastic estimate of the life-cycle cost of the hybrid ESS is found to be ∼39 % lower than that of the deterministic modelling. Additionally, the economics of temporal energy arbitrage using the battery bank is investigated, indicating that at the current capital cost of stationary LiFePO4 batteries and the present fixed feed-in-tariff (NZ$0.08/kWh), it is not economically viable to cycle the storage for arbitrage reasons alone. In conclusion, this paper highlights the critical need to incorporate probabilistic optimisation techniques and emphasises the importance of sizing and scheduling co-optimisation when designing hybrid ESSs for integration into grid-connected micro-grids.

Exploring Modal Choices for Sustainable Urban Mobility: Insights from the Porto Metropolitan Area in Portugal

Efficient and sustainable urban mobility is critical for contemporary cities, and understanding the factors influencing modal choices is essential for addressing transportation challenges in metropolitan areas. This study focuses on the Porto Metropolitan Area (AMP) in Portugal and aims to gain insights into these factors. Using data from the last mobility survey (IMob) conducted in 2017, a multinomial logit (MNL) model is used to analyze individual modal choices amongst private motorized vehicles (PMVs), public transport (PT), and active modes (AMs). The findings unveiled that demographic, socioeconomic, and travel-related characteristics substantially influence individual modal choices within the studied area. Moreover, probability scenarios highlight the importance of financial considerations, environmental consciousness, and accessibility to public transport in promoting sustainable transportation options. These insights have significant implications for policymakers and stakeholders involved in urban planning and transportation management. This study contributes to the literature by providing valuable insights into individuals’ transportation preferences and behaviors, facilitating decision-making based on evidence for infrastructure improvements and targeted interventions. By promoting sustainable transportation alternatives and reducing reliance on PMVs, this study aims to enhance the livability and sustainability of the AMP, aligning with long-term sustainability goals.

Probabilistic scenario analysis of integrated road-power infrastructures with hybrid fleets of EVs and ICVs

Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, …) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service.

Quasi-periodic oscillation detected in γ-rays in blazar PKS 0346−27

Aims. We present a variability study of the blazar PKS 0346−27 conducted between December 2018 and January 2022 using archival γ-ray observations from Fermi-LAT. Methods. We used Lomb–Scargle periodogram and weighted wavelet transform methods to detect the presence of periodicity or quasi-periodicity and localize this feature in time and frequency space. We estimated the significance of the periodicity feature using a Monte Carlo simulation approach. We also determined the global significance of the periodicity to test the robustness of our claim. To explore the most probable scenario, we modeled the light curve with both a straight-jet and a curved-jet model. Results. We detect a periodicity feature of ∼100 days in the entire period of observation with a statistical significance of 3σ, which amounts to a 99.7% confidence level. The global significance of this feature is found to be 96.96%. Based on the Akaike information criterion, the most probable explanation is that the observed emission is enhanced due to the helical motion of a blob within a curved jet. Conclusions. The origin of this quasi-periodic oscillation is very likely a region of enhanced emission moving helically inside a curved jet. This work presents strong evidence for jet curvature in the source and an independent (albeit a little serendipitous) procedure to estimate the curvature in blazar jets.

Determination of dynamic amplification factors for small- and medium-span highway bridges considering the effect of automated truck platooning loads

Automated truck platooning (ATP) has a good potential in energy conservation and carbon emissions reduction, yet it has raised a wide concern to the safety and performance of highway bridges. Accurately estimating ATP-induced load effects on highway bridges is necessary since it plays a crucial role in both bridge safety evaluation and traffic/ATP decision-making. Conventionally, the dynamic load effects induced by ATP on highway bridges are simplified by multiplying the static load effects with the code-specified dynamic amplification factor (DAF) which has not taken the ATP into consideration. This approach may need improvements as the actual DAFs of a bridge can be significantly affected by ATP loads. In this study, the DAFs of highway bridges under ATP loads are investigated via both theoretical analyses and numerical simulations. Several standard highway bridges with different cross-sections and span lengths and probable ATP loading scenarios are considered, and the influences of multiple factors are investigated, including the truck mass, the number of platooned trucks, and the road surface condition. Both theoretical and simulation results show that highway bridges are at risk of two types of resonance due to ATP loads. The resonance types can be roughly identified by the span length of the bridge. Specifically, bridges with span lengths shorter than 15 m are mainly affected by steady resonance while bridges with longer span lengths are at high risk of transient resonance. Both types of resonance can lead to increased DAFs of highway bridges, which may exceed the DAFs specified in bridge design codes. To accommodate ATP loads, proper DAFs are proposed as a supplement to the current bridge design codes.

A futurology of the sustainable development process of Hormozgan Province, Iran

Sustainable development has received unprecedented attention in the 21st century, necessitating meticulous planning and coordination across economic, social, and cultural domains to ensure equitable resource utilization for both current and future generations. This investigation centers on the sustainable development process in Hormozgan Province, utilizing a futurology approach to pinpoint influential factors in sustainability. Employing documentary and survey methods, the study collected data through questionnaires and interviews with development experts in the region. A mixed-methods analysis, integrating qualitative and quantitative techniques, was conducted, employing the MICMAC technique for data analysis and the Scenario wizard software to generate and evaluate scenarios. The research identified eight critical factors shaping the prospects of sustainable development in Hormozgan Province. These factors produced 27 probabilities, subsequently categorized into 12 plausible and 4 highly probable scenarios within the province's development plan. The probability analysis indicated a prevailing inclination towards desirable and ideal states in the realm of sustainable development for Hormozgan Province. This investigation contributes valuable insights into the landscape of sustainable development, underscoring the significance of addressing underlying factors for effective implementation. The identified factors and scenarios provide a roadmap for policymakers to foster sustainability and prosperity in Hormozgan Province. Ultimately, this research emphasizes the pressing need for sustainable development planning in today's world. By capitalizing on the potential of futurology and well-informed decision-making, Hormozgan Province can pave the way towards a sustainable future for its populace and future generations. The outcomes of this study call upon all stakeholders to collaborate responsibly, working towards a more sustainable and flourishing society.

The cost-effectiveness of a real-time seizure detection application for people with epilepsy

ObjectivesAutomated seizure detection modalities can increase safety among people with epilepsy (PWE) and reduce seizure-related anxiety. We evaluated the potential cost-effectiveness of a seizure detection mobile application for PWE in Singapore. MethodsWe used a Markov cohort model to estimate the expected changes to total costs and health outcomes from a decision to adopt the seizure detection application versus the current standard of care from the health provider perspective. The time horizon is ten years and cycle duration is one month. Parameter values were updated from national databases and published literature. As we do not know the application efficacy in reducing seizure-related injuries, a conservative estimate of 1% reduction was used. Probabilistic sensitivity analysis, scenario analyses, and value of information analysis were performed. ResultsAt a willingness-to-pay of $45,000/ quality-adjusted life-years (QALY), the incremental cost-effectiveness ratio was $1,096/QALY, and the incremental net monetary benefit was $13,656. Probabilistic sensitivity analyses reported that the application had a 99.5% chance of being cost-effective. In a scenario analysis in which the reduction in risk of seizure-related injury was 20%, there was a 99.8% chance that the application was cost-effective. Value of information analysis revealed that health utilities was the most important parameter group contributing to model uncertainty. ConclusionsThis early-stage modeling study reveals that the seizure detection application is likely to be cost-effective compared to current standard of care. Future prospective trials will be needed to demonstrate the real-world impact of the application. Changes in health-related quality of life should also be measured in future trials.

Potential Sustainability Scenarios for Gringsing Weaving: How Important is Education?

The Gringsing weaving craft industry is a culture-based creative industry with its values ​​and characteristics as a local cultural heritage with innovation and creativity that are important for developing countries and economic growth. Karangasem, Bali, is full of culture-based weaving craft industries such as gringsing weaving. With the rapid modernization of technology, Bali Aga ethnic weavers must adapt to changing times by improving education, skills, and knowledge to maintain the continuity of local traditions. This research aims to determine the probability scenario for the sustainability of ikat weaving in Tenganan Pegringsingan Village, Karangasem. This research is quantitative, with primary data from the results of FGDs with experts who will later determine probability scenarios for sustainability. Researchers will place more emphasis on educational probability scenarios to improve the skills and knowledge of craftsmen. The analysis technique used is SMIC-Prob analysis. The analysis results show that of the five scenarios formulated, there is the best combination with a probability of 0.104, namely the scenario of craftsmen producing as usual, opening a fashion stall, increasing production, and increasing knowledge. The conclusion of the sensitivity of the scenario shows the highest elasticity in the sustainability of gringsing weaving by realizing an educational scenario to increase skills and knowledge of 1.102. It can be concluded that the sustainability of gringsing weaving is largely determined by the educational scenario to improve the skills and knowledge of craftsmen. Training programs and instilling character values ​​as support for gringsing weaving craftsmen need to be improved to maintain the sustainability and development of gringsing weaving.

Co-optimization of building energy systems with renewable generations combining active and passive energy-saving

Establishing clean and efficient building energy systems (BES) is an efficient path to promote the low-carbon energy transition to achieve the goal of carbon peak and carbon neutrality. To fully tap the energy saving potential of buildings, a co-optimization method combined active and passive energy-saving technologies is proposed for BES. The structures and characteristics of active and passive energy-saving means are modeled and analyzed. On this basis, a double-layer co-optimization model is built to optimize the planning and operation of BES separately. Furthermore, a carbon tax is introduced to establish the connection between active and passive means, and actual uncertainties of source-load are considered through the probabilistic scenario generation and interval linear programming approach. Case studies on the BES in Xiong’an New Area, China show that the proposed co-optimization method saves about 2.2%–3.4% costs than considering only the active energy-saving. Analysis of forecast errors for different scenarios reveals planning options and detailed costs under different levels of uncertainty.

The distributions under two species-tree models of the total number of ancestral configurations for matching gene trees and species trees

Given a gene-tree labeled topology G and a species tree S, the ancestral configurations at an internal node k of S represent the combinatorially different sets of gene lineages that can be present at k when all possible realizations of G in S are considered. Ancestral configurations have been introduced as a data structure for evaluating the conditional probability of a gene-tree labeled topology given a species tree, and their enumeration assists in describing the complexity of this computation. In the case that the gene-tree labeled topology G=t matches that of the species tree S, by techniques of analytic combinatorics, we study distributional properties of the total number of ancestral configurations measured across the different nodes of a random labeled topology t selected under the uniform and the Yule probability models. Under both of these probabilistic scenarios, we show that the total number Tn of ancestral configurations of a random labeled topology of n taxa asymptotically follows a lognormal distribution. Over uniformly distributed labeled topologies, the asymptotic growth of the mean and the variance of Tn are found to satisfy EU[Tn]∼2.449⋅1.333n and VU[Tn]∼5.050⋅1.822n, respectively. Under the Yule model, which assigns higher probabilities to more balanced labeled topologies, we obtain the mean EY[Tn]∼1.425n and the variance VY[Tn]∼2.045n.

Sustainability Analysis Of Gerinsing Weaving Industry: Probability Approach

The gerinsing weaving (tenun gerinsing) craft industry is a culture-based creative industry that has its own values ??and characteristics as a local cultural heritage. It can also link traditional knowledge with innovation and creativity which is considered important for a number of developing countries because it has economic potential and has an impact on economic development. Karangasem as one of the regencies in Bali which is full of culture-based woven crafts industries such as geringsing woven fabrics.There are many stakeholders who care about and have an interest in the sustainability of the gringsing weaving industry. The existence of uncertainty also cannot be ruled out to maintain its sustainability. The objectives to be achieved in this study are: 1) to determine the probability scenario of grinsing weaving sustainability; 2) to determine the sensitivity of scenarios driving the sustainability of grinsing weaving in Tenganan Pegringsingan Village, Karangasem.
 Primary data were obtained from respondents through distributing questionnaires to craftsmen, consumers and stakeholders. The distribution of this questionnaire is a survey to obtain data as a comparison of the results of Focus Group Discussion (FGD) with experts who will later determine the probability scenario of sustainability. The prospective analysis used in this study is the SMIC-Prob analysis.
 ResultsThe analysis shows that of the four (4) scenarios formulated as a result of the FGD with experts, there is a combination of scenarios with the highest conditional opportunity, namely the 1101 combination, which means that the scenario of craftsmen producing as usual, apart from producing, also side comersial, and reducing production is the best combination with probability 0.124. While the sensitivity of the scenario is shown with the highest elasticity results in the increase scenario (increasing production) of 1.422, which is heavily influenced by the scenario of continuing to produce as usual of -0.624, which means that every 100% increase in the chance of producing as usual will reduce the chance of craftsmen increasing production by as much as 62.4 percent.