Purpose: – The purpose of this paper is to present a literature review of humanitarian logistics (HL) that aims to identify trends and suggest some directions for future research. Design/methodology/approach: – This conceptual paper develops a research framework for literature review through qualitative and quantitative content analysis. First, previous literature reviews in HL are updated and detailed. Then, seven classification criteria are added to earlier ones in order to advance the literature analysis. Findings: – The conclusions identify some literature gaps and research opportunities. The main conclusions are the need for more studies into the disaster recovery phase and the need for closer relationships between academia and humanitarian organizations to increase the number of applied research. Research limitations/implications: – The literature is limited to academic peer-reviewed journals because of their academic relevance, accessibility, and ease of searching. Practical implications: – Help potential researchers to set up a research agenda for future work. Social implications: – Reinforce earlier calls to increase truly applied research and improve social impact of the field. Originality/value: – In total, 228 papers that were published in the HL area are reviewed, giving rise to the most extensive literature review in this area. New dimensions for literature review in HL are proposed, which give some new insights into potential research directions.

Mental health benefits have been linked to human interaction with nature. However, most studies have been carried out in developed countries, limiting the generalizability of empirical findings to other parts of the world. To address this gap, this study was conducted in Brazil. The objective of the study was to assess whether the frequency of contact with nature affects the occurrence of anxiety, stress, and depression. Data were collected between June and July 2022 through an online survey (n = 1186, 1 − α = 0.95, p = 0.05, 1 − β = 0.85, rho = 0.1). Thus, the public online survey made it possible to carry out voluntary response sampling suitable for an exploratory study, with the purpose of an initial understanding of an under-researched population. First, a logistic regression was performed for quantifying the association between contact with nature and mental symptoms. In addition, three groups of people having different frequencies (low, medium, and high) of contact with nature and a reference group, comprised of those who reported no contact, were compared using Kruskal–Wallis and Dwass–Steel–Chritchlow–Fligner tests. This study employs a cross-sectional design and relies on retrospective recall. As a result, the research hypothesis was confirmed. People who very rarely have contact with nature had a 97.95% probability of moderate occurrence of stress, which decreases to 20.98% for people who have contact with nature frequently. Furthermore, in the same comparison, the probability of occurrence was 3.6 times lower for anxiety and 4.8 times lower for depression. In conclusion, the evidence indicates that the greater the frequency of contact with nature, the lower the occurrence of stress, anxiety, and depression symptoms. Nevertheless, the benefits of this contact were significant only when its frequency was moderate (about once or twice a week) or higher.

Panic buying and hoarding express common human behavior in times of crisis. Early in COVID-19, as the pandemic crisis intensified, toilet paper was one of the emblematic cases of panic buying. Using a Geographic Information System (GIS) to cross official per capita income data and real toilet paper transactions obtained from groceries spread around the city of São Paulo (Brazil), this study compares sales levels during the period in which panic purchases took place to the sales levels off that period. As expected, that data disclose noticeable panic buying. Regression analysis reveals that there is a significant positive correlation between average income per capita and panic buying. The results also indicate that panic buying happens in every income class, including low-income ones and contribute to enhancing the understanding of demand behavior during periods of crisis.

Background: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions.