Urban Flood Resilience Evaluation Based on Heterogeneous Data and Group Decision-Making

In recent years, urban floods have occurred frequently in China. Therefore, there is an urgent need to strengthen urban flood resilience. This paper proposed a hybrid multi-criteria group decision-making method to assess urban flood resilience based on heterogeneous data, group decision-making methodologies, the pressure-state-response model, and social-economic-natural complex ecosystem theory (PSR-SENCE model). A qualitative and quantitative indicator system is formulated using the PSR-SENCE model. Additionally, a new weighting method for indicators, called the synthesis weighting-group analytic hierarchy process (SW-GAHP), is proposed by considering both intrapersonal consistency and interpersonal consistency of decision-makers. Furthermore, an extensional group decision-making technology (EGDMT) based on heterogeneous data is proposed to evaluate qualitative indicators. The flexible parameterized mapping function (FPMF) is introduced for the evaluation of quantitative indicators. The normal cloud model is employed to handle various uncertainties associated with heterogeneous data. The evaluations for Beijing from 2017 to 2021 reveal a consistent annual improvement in urban flood resilience, with a 14.1% increase. Subsequently, optimization recommendations are presented not only for favorable indicators such as regional economic status, drainability, and public transportation service capacity but also for unfavorable indicators like flood risk and population density. This provides a theoretical foundation and a guide for making decisions about the improvement of urban flood resilience. Finally, our proposed method shows superiority and robustness through comparative and sensitivity analyses.

Outcome measures for Alzheimer's disease: A global inter-societal Delphi consensus

INTRODUCTION

We aim to provide guidance on outcomes and measures for use in patients with Alzheimer's clinical syndrome.

METHODS

A consensus group of 20 voting members nominated by 10 professional societies, and a non-voting chair, used a Delphi approach and modified GRADE criteria.

RESULTS

Consensus was reached on priority outcomes (n = 66), measures (n = 49) and statements (n = 37) across nine domains. A number of outcomes and measurement instruments were ranked for: Cognitive abilities; Functional abilities/dependency; Behavioural and neuropsychiatric symptoms; Patient quality of life (QoL); Caregiver QoL; Healthcare and treatment-related outcomes; Medical investigations; Disease-related life events; and Global outcomes.

DISCUSSION

This work provides indications on the domains and ideal pertinent measurement instruments that clinicians may wish to use to follow patients with cognitive impairment. More work is needed to develop instruments that are more feasible in the context of the constraints of clinical routine.

Maintenance strategy selection for optimum efficiency – application of AHP constant sum

Purpose

There are limited studies addressing how choosing a maintenance strategy can contribute towards maximising outputs from given inputs, thereby minimising costs and improving a company’s competitiveness. The analytic hierarchy constant sum method (AHCSM) is used to access the appropriateness of maintenance strategies for improving the overall efficiency of a structural steel fabrication construction company.

Design/methodology/approach

A semi-structured interview was formulated with the stakeholders of the quality department to understand the company’s maintenance portfolio and its current functional capability. The information from this case study was then dissected to represent the factors that the company deemed appropriate for evaluating their maintenance strategy. The AHCSM approach provided a framework, which ranked the importance of factors that are sensitive to the construction industry and rank the suitability of maintenance strategies.

Findings

Factors affecting the selection of maintenance strategies to improve business efficiency are productivity, quality, reliability, cost, safety and work environment, morale, inventory and flexibility. Total productive maintenance strategy produces the most desirable outcome; however, the predictive or condition-based maintenance strategy provides an optimum solution for the case study company while considering the equipment usage, frequency of production and the current economic climate.

Originality/value

The approach presented allows practitioners to consider ways to increase the level of production and improve the efficiency of construction businesses without a high increase in investment. The findings can inform gaps in existing maintenance approaches in achieving business objectives.

A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.

Smart Technology Impact on Neighborhood Form for a Sustainable Doha

This study focuses on the significance of neighborhoods in a city as the smallest self-sufficient urban units akin to nuclear families in the larger society. Thus, improvements to the neighborhood form can improve livability, maximize walkability, and better the overall life quality of city inhabitants, which, in turn, advance a city’s overall environmental and social sustainability profile. The physical form (arrangement) and density of neighborhoods are primarily shaped by land ownership regulations, transportation, and communication means. The development of smart technology, especially in the fields of transportation and communication, has led to the reconsideration of some of sustainable urban form concepts such as neighborhoods. Low-density cities like Doha, Qatar can become both sustainable and livable, creating the basis for a sustainable city. First, this paper presents a critical review of Doha’s neighborhoods and advanced transport and communication technologies, in addition to the integration of these technologies with the physical form of neighborhoods. Then, the paper discusses the influences of such technologies on the future sustainability of the city of Doha and its neighborhoods. This investigation is based on a Delphi study to address the characteristics of a neighborhood and to identify two paradigms of ‘good’ design practice.

A Comparative Investigation of Type-2 Fuzzy Sets, Nonstationary Fuzzy Sets and Cloud Models

Since Zadeh introduced fuzzy sets, a lot of extensions of this concept have been proposed, such as type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models, to represent higher levels of uncertainty. This paper provides a comparative investigation of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Type-2 fuzzy sets study the fuzziness of the membership function (MF) using primary MF and secondary MF based on analytic mathematical methods; nonstationary fuzzy sets study the randomness of the MF using primary MF and variation function based on type-1 fuzzy sets theory; cloud models study the randomness of the distribution of samples in the universe and generate random membership grades (MGs) using two random variables based on probability and statistic mathematical methods. They all concentrate on dealing with the uncertainty of the MF or the MG which type-1 fuzzy sets do not consider, and thus have many similarities. Moreover, we find out that, the same qualitative concept “moderate amount” can be represented by an interval type-2 fuzzy set, a nonstationary fuzzy set or a normal cloud model, respectively. Then, we propose a unified mathematical expression for the interval type-2 fuzzy set, nonstationary fuzzy set and normal cloud model. On the other hand, we also find out that, the theory fundament and underlying motivations of these models are quite different. Therefore, We summarize detailed comparisons of distinctive properties of type-2 fuzzy sets, nonstationary fuzzy sets, and cloud models. Further, we study their diverse characteristics of distributions of MGs across vertical slices. The comparative investigation shows that these models are complementary to describe the uncertainty from different points of view. Thus, this paper provides a fundamental contribution and makes a basic reference for knowledge representation and other applications with uncertainty.

Enhancing Recommender Diversity Using Gaussian Cloud Transformation

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.