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This study aims to analyze the differences of electrocardiograph (ECG) characteristics for female drivers in calm and anxious states during driving.

The authors used various materials (e.g. visual materials, auditory materials and olfactory materials) to induce drivers’ mood states (calm and anxious), and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data. Physiological changes in ECG during the stimulus process were recorded using PSYLAB software. The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.

The results show significant differences in the characteristic parameters of female driver’s ECG signals, including (average heart rate), (atrioventricular interval), (percentage of NN intervals > 50ms), (R wave average peak), (Root mean square of successive), (Q wave average peak) and ( S wave average peak), in time domain, frequency domain and waveform in emotional states of calmness and anxiety.

Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving. It can be used for the development of personalized driver assistance system and driver warning system.

Only a few attempts have been made on the influence of human emotions on physiological signals in the transportation field. Hence, there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states. This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.

Front-of-pack nutritional labels have been extensively studied to support consumers in making healthier and more informed food choices. However, existing research has gathered conflicting evidence about which category of label, nutrient-specific or summary labels, is more effective. As a result, the European Union has postponed its decision on selecting a unified label to collect additional information. This study specifically focusses on individuals with noncommunicable diseases, an overlooked yet relevant segment of consumers who can significantly benefit from the proper use of nutritional labels in their self-care.

In a sequence of three studies grounded in the front-of-pack acceptance model and focussing on customers with specific noncommunicable diseases, the authors examined the different effects of the NutrInform Battery and Nutri-Score on food acceptance and portion selection. This research involved the use of structural equation modelling and ANOVA and was conducted with a cumulative sample of 2,942 EU adults, residing in countries with or without previous exposure to nutritional labels.

The results suggest that among individuals with noncommunicable diseases, nutrient-specific labels are perceived as more useful and easier to use. They also generate a better attitude towards the usage of nutritional labels and are more effective in nudging those consumers towards a proper selection of portions.

The results provide valuable insights into how front-of-pack nutritional labels can impact the food choices of individuals with noncommunicable diseases and have implications for public health policies.

Investigation of the effects of NutrInform Battery and Nutri-Score on consumers with noncommunicable diseases, an area currently under-researched.

The development of models that allows the evaluation and prediction of erosion processes is an important tool for the management and planning of coastal systems. Mangrove forests systems are under threat by the impacts of erosion, which is also intensified by human activity (and aggravated in the scenarios of global warming and climate change). The purpose of this paper is to develop a model of geographic information systems (GIS) that can be used for any estuary area, but it can also be used for mangroves.

This paper uses georeferentiation which is defined as a set of parameters that best characterize the mangrove areas: elevation (m); geomorphology; geology; land cover; anthropogenic activities; distance to the coastline (m) and maximum tidal range (m). Three different methods are used to combine the various vulnerability parameters, namely, DRASTIC index, analytical hierarchy process (AHP) and square root of the geometric mean.

The three approaches presented in this work show different types evaluating vulnerability to erosion, highlighting a stronger overvaluation of the areas presented with a high vulnerability, through the use of DRASTIC index when compared with two other approaches. The use of the AHP shows similarity to the square root of the geometric mean model, but the AHP also presents a higher percentage of vulnerable areas classified as having medium to very high vulnerability. On the other hand, the use of square root of the geometric mean led to a higher percentage of areas classified as having low and very low vulnerability.

These three qualitative models, based on a cognitive approach, using the set of parameters defined in this research, are a good tool for the spatial distribution of erosion in different mangroves in the world.

Global warming and climate change scenarios require adaptation and mitigation options supported by science-based strategies and solutions.