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This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

Studies have shown positive housing retention and quality of life outcomes in moving on initiatives (MOIs). However, less is known about how movers’ health service use changes post-move. This paper aims to explore physical and mental health service use over time for participants in New York City’s MOI program.

This paper uses data collected at baseline, 12-months post move and 24-months post move to explore patterns in mental and physical health service use and their association with mental and physical health status for participants (N = 41). Health status was measured with the Medical Outcomes Study Short Form Survey Instrument.

Three mental health service use patterns emerged: service use at all time points, inconsistent service use across time points and no service use at any time point. Significant group differences in mental health were found at baseline and 12 months. Two physical health service use patterns emerged: service use at all three time points and inconsistent service use across time points. Significant group differences were found in mental health at 12  and 24 months.

This study showed that physical and mental health service use varied slightly over time for participants, with the majority of service use being for outpatient/non-acute care. The findings also point to possible relationships between service use and mental health status. Positive and negative implications of these findings are framed within the broader context of PSH and MOI goals.

Williams and Williams (2012, 2017) find multiple entrepreneurial motivations are experienced by entrepreneurs in deprived areas at different points in time. Drawing on this prior work this study aims to explore how and why the shifted motivations evolve, as well as, what factors cause this change in deprived areas. The work draws upon temporal motivational theory (TMT) that considers the influence of individuals' needs in determining their time-sensitive motivation.

Six semi-structured interviews with actual entrepreneurs are used to collect qualitative data from deprived areas of Nottingham, which is one of the most deprived cities in the UK. The study employs Interpretative Phenomenological Analysis (IPA) to consider each entrepreneurial endeavour as a unique journey to investigate the shifting of motivations.

A polarization is found in terms of how entrepreneurial motivations evolve in deprived areas. In considering the first task-specific entrepreneurial motivation, time plays a role either in accumulating job dissatisfaction and increasing confidence led by accumulated experience, or in creating random chances that enable individuals to realize that they are able to use existing skills and experiences to start a business. Regarding the second task-specific entrepreneurial motivation when the business becomes more established, it is usually stimulated by increased confidence based on perceived progress. The use of self-help methods and downward comparison found in this study should be noted as they help to re-consider individuals' needs in deprived areas.

This study produces a more comprehensive understanding of the phenomenon of the time effect on shifted motivation at different entrepreneurial phases in a deprived context, which contributes to enrich theoretical knowledge and raise policymakers' awareness of entrepreneurial motivations from these marginalized groups.

Adolescent pregnancy stands as a societal challenge, compelling young individuals to prematurely discontinue their education. Conversely, an expansion of high school education can potentially diminish rates of adolescent pregnancy, given that educational attainment stands as the foremost risk factor influencing sexual initiation, the use of contraceptive methods during initial sexual encounters and fertility. The aim of this paper is to analyze the impact of the implementation of the public educational policy introducing full-time schools (FTS) for high schools in the state of Ceará, Brazil, on early pregnancy rates.

Using the difference-in-differences method with multiple time periods, we measured the average effect of this staggered treatment on the treated municipalities.

The main result indicates a reduction of 0.849 percentage points in the teenage pregnancy rate. Concerning dynamic effects, the establishment of FTS in treated municipalities results in a 1.183–1.953 percentage point decrease in teenage pregnancy rates, depending on the timing of exposure. We explored heterogeneous effects within socioeconomically vulnerable municipalities, yet discerned no impact on this group. Rigorous tests confirm the robustness of the results.

This paper aims to contribute to: (1) the consolidation of research on the subject, given the absence of such research in Brazil to the best of our knowledge; (2) the advancement and analysis of evidence-based public policy and (3) the utilization of novel longitudinal data and methodology to evaluate adolescent pregnancy rates.

Behavioral economics poses a challenge for the welfare evaluation of choices, particularly those that involve risk. It demands that we recognize that the descriptive account of behavior toward those choices might not be the ones we were all taught, and still teach, and that subjective risk perceptions might not accord with expert assessments of probabilities. In addition to these challenges, we are faced with the need to jettison naive notions of revealed preferences, according to which every choice by a subject expresses her objective function, as behavioral evidence forces us to confront pervasive inconsistencies and noise in a typical individual’s choice data. A principled account of errant choice must be built into models used for identification and estimation. These challenges demand close attention to the methodological claims often used to justify policy interventions. They also require, we argue, closer attention by economists to relevant contributions from cognitive science. We propose that a quantitative application of the “intentional stance” of Dennett provides a coherent, attractive and general approach to behavioral welfare economics.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

The COVID-19 pandemic has disrupted the ‘real’ world and substantially impacted the virtual world and thus the Internet ecosystem. It has caused a significant exogenous shock that offers a wealth of natural experiments and produced new data about broadband, clouds, and the Internet in times of crisis. In this chapter, we characterise and evaluate the evolving impact of the global COVID-19 crisis on traffic patterns and loads and the impact of those on Internet performance from multiple perspectives. While we place a particular focus on deriving insights into how we can better respond to crises and better plan for the post-COVID-19 ‘new normal’, we analyse the impact on and the responses by different actors of the Internet ecosystem across different jurisdictions. With a focus on the USA and Europe, we examine the responses of both public and private actors, with the latter including content and cloud providers, content delivery networks, and Internet service providers (ISPs). This chapter makes two contributions: first, we derive lessons learned for a future post-COVID-19 world to inform non-networking spheres and policy-making; second, the insights gained assist the networking community in better planning for the future.