To study the operational behaviour of λ-terms, we will use the denotational (mathematical) approach. A denotational semantics for a language is based on the choice of a space of semantics values, or denotations, where terms are to be interpreted. Choosing a space with nice mathematical properties can help in proving the semantic properties of terms, since to this aim standard mathematical techniques can be used.

λ Δ -Models

http://rc.rcjournal.com/content/respcare/53/1/104.full.pdf

Handbook of Disaster Research.

The field of supply chain management has been extensively studied, while its role in disaster relief operations has received limited contributions. Recent dedicated special issues on Humanitarian Operations and Supply Chain Management (HOSCM) and a dedicated Journal focusing on humanitarian logistics and supply chain management clearly indicate the growing popularity of the HOSCM literature. The purpose of our current study is to undertake an extensive review of extant literature published in operations and supply chain management journals as well as popular interdisciplinary journals. A review of 362 papers published between 2011 and 2017 provides a thematic outline of the study. The study pivots around nine key themes, which have gained prominent attention from HOSCM scholars, and draws a roadmap from reviewing earlier review papers to performance evaluation of HOSCM related studies. Some of the key themes include humanitarian logistics, theory focused research, case studies, mathematical models, humanitarian supply chain properties and resources needed for efficient and effective management of humanitarian operations. Finally, our study offers several research directions which may take the existing debates to a next level.

Humanitarian supply chain management: a thematic literature review and future directions of research

With the emergence of big data and new data sources, a challenge posed to today's organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN+, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organization's decision-making. An extended model-based framework connects decision-making to big data sources.A better understanding of decision-making is achieved with the framework.A modeling process is outlined for systematically using the framework in practice.Decision-making can be improved through the use of standard models and notations.Lessons were learned from a case study on a Brazilian disaster early-warning center.

Bridging the gap between decision-making and emerging big data sources

An estimated 100,000 people work today as commercial content moderators. These moderators are often exposed to disturbing content, which can lead to lasting psychological and emotional distress. This literature review investigates moderators’ psychological symptomatology, drawing on other occupations involving trauma exposure to further guide understanding of both symptoms and support mechanisms. We then introduce wellness interventions and review both programmatic and technological approaches to improving wellness. Additionally, we review methods for evaluating intervention efficacy. Finally, we recommend best practices and important directions for future research. Content Warning: we discuss the intense labor and psychological effects of CCM, including graphic descriptions of mental distress and illness.

The Psychological Well-Being of Content Moderators: The Emotional Labor of Commercial Moderation and Avenues for Improving Support

A turbine air sealing and condenser air removal system for use in steam plant equipment is arranged to increase steam plant efficiency, reduce oxygen concentration in condensate being returned to the steam generators, and simplify system arrangement and maintenance. This system incorporates dry running shaft seals at the high and low pressure turbine shaft glands. The turbine shaft glands are exhausted to a vacuum header which is exhausted by vacuum pumps. Air from the condenser is also exhausted to the common vacuum header. Non-rotating air seals on the turbine such as valve stem seals, which must only accommodate linear movement, can incorporate metallic bellows or conventional packings to prevent air leakage into the steam path or steam leakage out into the surrounding environment. The bellows seals may also incorporate stem glands which are exhausted to the turbine exhaust trunk to minimize the internal pressure of the bellows and prevent catastrophic failure which might occur if the bellows were to be pressurized with high pressure steam.

Steam seal air removal system

Disaster managers depend on timely and accurate information for task-related decision-making in highly complex and dynamic environments. New data sources, like online social media provide an increasing volume of data that promises improvements in situation awareness. But it remains difficult to focus data collection on information needs and integrate relevant information back into decision-making. In this paper, we present the observation-aware Decision Model and Notation (oDMN), which connects tasks, decisions, information and data sources based on standardized models and notations as well as on domain-specific information models. The integrated model allows for deriving information requirements and determining the impact of incoming observations on relevant tasks and decisions. To demonstrate its usefulness, we apply the model to a case centered on logistics operations during the 2015 Nepal earthquake response. The results show that the model is indeed able to formally connect tasks, decisions, information and data sources, and thus support better decision-making.

oDMN: An Integrated Model to Connect Decision-Making Needs to Emerging Data Sources in Disaster Management

Serious games are an effective tool for giving players a hands-on, immersive experience of crisis situations. To simplify the design of such games while ensuring their relevance, we propose a design method that is based on reference tasks. The feasibility of this approach is demonstrated by the improved design of the serious game “Disaster in my Backyard” that has been played during ISCRAM Summer school 2013. The design incorporates humanitarian logistics, search-and-rescue and coordination tasks. We also present the lessons learned from this instantiation of the game and give an outlook towards future research, such as the evaluation of tools for crisis response and management through the use of serious games and reference tasks.

Reference Task-based Design of Crisis Management Games

Online social media has been recognized as a valuable information source for disaster management whose volume, velocity and variety exceed manual processing capacity. Current machine learning systems that support the processing of such data generally follow a human-in-the-loop approach, which has several inherent limitations. This work applies the human-is-the-loop concept from visual analytics to semi-automate a manual content moderation workflow, wherein human moderators take the dominant role. The workflow is instantiated with a supervised machine learning system that supports moderators with suggestions regarding the relevance and categorization of content. The instantiated workflow has been evaluated using in-depth interviews with practitioners and serious games. which suggest that it offers good compatibility with work practices in humanitarian assessment as well as improved moderation quality and higher flexibility than common approaches.

A Human-is-the-Loop Approach for Semi-Automated Content Moderation

Social media platforms have come into the focus of research as sources of information about the unfolding situation in disaster contexts. Incorporating information from social media into decision-making is still difficult though. One reason may be that the prevalent approach to data analysis works bottom-up, which has several limitations. In this paper, we adopt a top-down approach by means of a novel keyword-based method for identifying potentially relevant information in social media data based on structured knowledge of activities undertaken in a domain. The application of the method to the context of humanitarian logistics using four social media datasets shows its capability to identify potentially relevant information via reference tasks and to match identified information with decision-makers’ activities. In addition, we offer a first set of domain-specific keywords to identify information related to infrastructure and resources in humanitarian logistics.

Daniel Link

Papers

Steam seal air removal system

oDMN: An Integrated Model to Connect Decision-Making Needs to Emerging Data Sources in Disaster Management

Reference Task-based Design of Crisis Management Games

A Human-is-the-Loop Approach for Semi-Automated Content Moderation

A Method for Extracting Task-related Information from Social Media based on Structured Domain Knowledge