Coping with uncertainty in water management: Qualitative system analysis as a vehicle to visualize the plurality of practitioners' uncertainty handling routines.

Accelerated environmental and societal change and its dynamic present a challenge for water management, making it increasingly relevant to integrate uncertainties into the decision-making process. The challenge to science informing practice is how to provide scientific uncertainty information in a way that this information becomes usable for practitioners. We know that practitioners have developed routines in order to cope with uncertainties, but in order to facilitate the transfer of uncertainty information, this study analyses by whom, when and where in the decision-making process uncertainty routines are used. This research contributes to the plurality of practitioners' perspectives on decision-making under uncertainty in water management. Based on expert elicitation we show that, depending on the business unit and on the time horizon of the management object, practitioners are using different uncertainty routines and hence are in need of more tailor-made uncertainty information to inform their decision-making. Our qualitative systems modeling approach highlighting a reservoir management example serves as a boundary object visualizing the intersection of uncertainty routines and fostering cross-communication and acknowledgement of different perspectives among practitioners. It thus provides a platform for learning. Moreover, it provides a clear understanding of the uncertainty information needs which scientists may cover and increases the usability of their research findings, closing the science-practice gap in adaptive management and transformation processes.

An estimation of distribution algorithm with clustering for scenario-based robust financial optimization.

One important problem in financial optimization is to search for robust investment plans that can maximize return while minimizing risk. The market environment, namely the scenario of the problem in optimization, always affects the return and risk of an investment plan. Those financial optimization problems that the performance of the investment plans largely depends on the scenarios are defined as scenario-based optimization problems. This kind of uncertainty is called scenario-based uncertainty. The consideration of scenario-based uncertainty in multi-objective optimization problem is a largely under explored domain. In this paper, a nondominated sorting estimation of distribution algorithm with clustering (NSEDA-C) is proposed to deal with scenario-based robust financial problems. A robust group insurance portfolio problem is taken as an instance to study the features of scenario-based robust financial problems. A simplified simulation method is applied to measure the return while an estimation model is devised to measure the risk. Applications of the NSEDA-C on the group insurance portfolio problem for real-world insurance products have validated the effectiveness of the proposed algorithm.

Uncertain texture features fusion based method for performance condition evaluation of complex electromechanical systems.

Accurate performance condition evaluation has a pivotal role in maintaining the operating reliability and preventing damage to complex electromechanical systems (CESs), which is still a challenging task. The uncertain features fusion inspired method is developed by utilizing the data-graph conversion, texture analysis, and improved evidence fusion. Unlike the conventional continuous time-series analysis-based methods, the 2D color-spectrums related to the performance conditions are constructed without information losing, and texture features of spectrums are extracted and fused to realize evaluation. The effectiveness of the proposed method is verified by actual evaluation applications. Moreover, the proposed method provides a new idea for large-scale high-dimensional data processing, decision making, uncertainty handling, and other engineering applications.

Low back pain expert systems: Clinical resolution through probabilistic considerations and poset.

OBJECTIVE: Proper diagnosis of Low Back Pain (LBP) is quite challenging in especially the developing countries like India. Though some developed countries prepared guidelines for evaluation of LBP with tests to detect psychological overlay, implementation of the recommendations becomes quite difficult in regular clinical practice, and different specialties of medicine offer different modes of management. Aiming at offering an expert-level diagnosis for the patients having LBP, this paper uses Artificial Intelligence (AI) to derive a clinically justified and highly sensitive LBP resolution technique. MATERIALS AND METHODS: The paper considers exhaustive knowledge for different LBP disorders (classified based on different pain generators), which have been represented using lattice structures to ensure completeness, non-redundancy, and optimality in the design of knowledge base. Further the representational enhancement of the knowledge has been done through construction of a hierarchical network, called RuleNet, using the concept of partially-ordered set (poset) with respect to the subset equality (⊆) relation. With implicit incorporation of probability within the knowledge, the RuleNet is used to derive reliable resolution logic along with effective resolution of uncertainties during clinical decision making. RESULTS: The proposed methodology has been validated with clinical records of seventy seven LBP patients accessed from the database of ESI Hospital Sealdah, India over a period of one year from 2018 to 2019. Achieving 83% sensitivity of the proposed technique, the pain experts at the hospital find the design clinically satisfactory. The inferred outcomes have also been found to be homogeneous with the actual or original diagnosis. DISCUSSIONS: The proposed approach achieves the clinical and computational efficiency by limiting the shortcomings of the existing methodologies for AI-based LBP diagnosis. While computational efficiency (with respect to both time and space complexity) is ensured by inferring clinical decisions through optimal processing of the knowledge items using poset, the clinical acceptability has been ascertained reaching to the most-likely diagnostic outcomes through probabilistic resolution of clinical uncertainties. CONCLUSION: The derived resolution technique, when embedded in LBP medical expert systems, would provide a fast, reliable, and affordable healthcare solution for this ailment to a wider range of general population suffering from LBP. The proposed scheme would significantly reduce the controversies and confusion in LBP treatment, and cut down the cost of unnecessary or inappropriate treatment and referral.