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A direct correlation exists between waste disposal, disease spread and public health. This article systematically reviewed healthcare waste and its implication for public health. This review identified and described the associations and impact of waste disposal on public health.

This paper systematically reviewed the literature on waste disposal and its implications for public health by searching Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), PubMed, Web of Science, Scopus and ScienceDirect databases. Of a total of 1,583 studies, 59 articles were selected and reviewed.

The review revealed the spread of infectious diseases and environmental degradation as the most typical implications of improper waste disposal to public health. The impact of waste includes infectious diseases such as cholera, Hepatitis B, respiratory problems, food and metal poisoning, skin infections, and bacteremia, and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife.

Infectious diseases such as cholera, hepatitis B, respiratory problems, food and metal poisoning, skin infections, bacteremia and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife are some of the public impacts of improper waste disposal.

Healthcare industry waste is a significant waste that can harm the environment and public health if not properly collected, stored, treated, managed and disposed of. There is a need for knowledge and skills applicable to proper healthcare waste disposal and management. Policies must be developed to implement appropriate waste management to prevent public health threats.

The interconnections between climate change and health are well studied. However, there is a perceived need for studies that examine how responses to health hazards (e.g. cardiovascular diseases, ozone layer effects, allergens, mental health and vector-borne diseases) may assist in reducing their impacts. The purpose of this paper is to review the evidence on health responses to climate hazards and list some measures to address them.

A mixed literature review, bibliometric analysis and an original online survey were undertaken on 140 participants from 55 countries spread across all geographical regions.

The bibliometric analysis identified that most climate-related health hazards are associated with extreme weather events. However, only one-third of the investigated papers specifically analysed the connections between climate change and health hazards, revealing a thematic gap. Also, although Africa is highly affected by climate change, only 5% of the assessed studies focused on this continent. Many respondents to the survey indicated “heat distress” as a significant vulnerability. The survey also identified social determinants relevant to climate-induced health vulnerabilities, such as socioeconomic and environmental factors, infrastructure and pre-existing health conditions. Most respondents agree that policies and regulations are the most effective adaptation tools to address the public health hazards triggered by climate change. This paper presents some suggestions for optimising public health responses to health hazards associated with climate change, such as the inclusion of climate-related components in public health policies, setting up monitoring systems to assess the extent to which specific climate events may pose a health threat, establishing plans to cope with the health implications of heatwaves, increased measures to protect vulnerable groups and education and awareness-raising initiatives to reduce the overall vulnerability of the population to climate-related health hazards. These measures may assist the ongoing global efforts to understand better – and cope with – the impacts of climate change on health.

The combination of a literature review, bibliometric analysis and an original world survey identified and presented a wide range of responses.

Degradable mulch film (DMF) is a potential alternate to polyethylene (PE) mulching. In this regard, the purpose of this paper is to explore the effects and paths of natural disaster shock and risk aversion influencing farmers’ DMF adoption.

This research is conducted by collecting cross-sectional data of corn farmers in Zhangye, China. First, by using the Tobit model, the paper attempts to explore the effects of natural disaster shock and risk aversion influencing farmers’ DMF adoption. Second, IV-Tobit model is applied to deal with endogenous problems between risk aversion and DMF adoption. Additionally, the researchers used a moderating model to analyze feasible paths of natural disaster shock and risk aversion impacting farmers’ DMF adoption.

The outcomes show that natural disaster shock and risk aversion significantly and positively affect farmers’ DMF adoption. Though risk aversion plays a significant moderating effect in influencing farmers’ DMF adoption by natural disaster shock, the moderating effect has a serious disguising effect. By considering the heterogeneity of risk aversion, the paper further confirms that if the intensity of natural disaster shock is increased by one unit, the intensity of MDF adoption by farmers with high-risk aversion also tends to increase by 15.85%.

This study is the pioneer one, which is evaluating the intensity of farmers’ DMF adoption from adoption ratio, investment amount, labor input and adoption time. Additionally, the research provides important guidelines for policymakers to motivate medium and low-risk aversion farmers to adopt DMF.

We study five exogenous shocks: climate, violence, price hikes, spoilage and the COVID-19 lockdown. We analyze the association between these shocks and trader characteristics, reflecting trader vulnerability.

Using primary survey data on 1,100 Nigerian maize traders for 2021 (controlling for shocks in 2017), we use probit models to estimate the probabilities of experiencing climate, violence, disease and cost shocks associated with trader characteristics (gender, size and region) and to estimate the probability of vulnerability (experiencing severe impacts).

Traders are prone to experiencing more than one shock, which increases the intensity of the shocks. Price shocks are often accompanied by violence, climate and COVID-19 shocks. The poorer northern region is disproportionately affected by shocks. Northern traders experience more price shocks while Southern traders are more affected by violence shocks given their dependence on long supply chains from the north for their maize. Female traders are more likely to experience violent events than men who tend to be more exposed to climate shocks.

The data only permit analysis of the general degree of impact of a shock rather than quantifying lost income.

This paper is the first to analyze the incidence of multiple shocks on grain traders and the unequal distribution of negative impacts. It is the first such in Africa based on a large sample of grain traders from a primary survey.

The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

Mental budgeting, as a part of mental accounting theory, is expected to impact a household's budgetary management in terms of expenses. The purpose of this paper is to study whether and how mental budgeting can explain differences in farmers' reactions to different incentives of low-toxicity pesticide use.

Based on data from a survey of 393 vegetable farmers in the Sichuan Province, this analysis, using a Likert Scale approach, first explores whether farmers utilize mental budgeting. Secondly, using a Probit model, this paper analyzes how mental budgeting affects farmers' intentions to switch to low-toxicity pesticide use when faced with different incentives.

The results show that the majority of farmers categorize agricultural inputs into different groups and that 26.46% of the investigated farmers utilize mental budgeting for pest control practices. In addition, farmers who utilizing mental budgeting report a higher willingness to switch to low-toxicity pesticides when they're presented with a specific subsidy. Furthermore, if offered a price premium for quality, the willingness to switch to low-toxicity pesticides for farmers utilizing mentally budget is lower compared to other farmers.

This paper examines the existence of mental budgeting among farmers. It provides a better understanding of how farmers categorize agricultural inputs and their mental mechanisms with respect to agricultural expenses. Finally, this paper is the first to study the effects of mental budgeting on farmers' reactions to different incentives aimed at stimulating the adoption of low-toxicity pesticides.

This study aims to examine the effect of crop diversification (CD), as a climate change adaptation strategy, on farm household’s welfare in terms of farm income and demand for labor. It explores whether adoption of CD is a win-win strategy on household income and demand for on-farm labor. It also examines the determinants of rural household’s net farm income and family labor demand.

A household-plot level data were collected in 2015 from 929 rural farm households and 4,778 plots in the Nile Basin of Ethiopia. The data comprise farm and household characteristics accompanied by geo-referenced climate data such as long-term average temperature and amount and variability of growing season rainfall. The authors estimate an endogenous switching regression model to measure the effect of CD on the farm household’s welfare, using net farm income and household labor demand as a welfare indicator.

The results indicate heterogeneous effects of climate variables on farm income between adopters and non-adopters of CD. The study also confirms the win-win effect of adoption of CD with a positive and significant effect on farm income and a reduction in demand for on-farm labor. The results suggest that adoption of CD helps improve the well-being of farm households and build a resilient agricultural system.

As the study used a cross-sectional data, it is limited to show the time effect of practicing CD on the household’s welfare.

First, the authors investigate, to their knowledge for the first time, the existence of synergy or tradeoff in the effect of CD on two dimensions of rural households’ welfare (net farm income and labor demand). Second, they investigate the heterogeneous effect of climate change adaptation strategies on the farm household’s welfare between adopters and non-adopters. This is unlike previous studies that consider climate change adaptation strategies as having a homogeneous effect. However, this approach is inappropriate since the effect of adaptation strategies is different for adopters and non-adopters.

Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.

In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.

To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.

Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.