Demographic changes in the 21st century, increased population of the elderly and high prevalence of related diseases call for new healthcare strategies that can change the behavior and lifestyle of elderly individuals. Innovative information and communication technology, such as mobile health (mHealth), can play a significant role. The present study was conducted aiming to assess the effectiveness of mHealth in improving health behaviors among an elderly population. This paper presents a systematic review involving a search of PubMed, Web of Science (ISI), Scopus, Science Direct and Embase databases from [2012–2016]. Our search resulted after initial evaluations 12 articles. Inclusion criteria mostly revolved around interventional studies, other studies were excluded because of their methodology, non-elderly target groups and irrelevant to the subject. Findings showed that mHealth can improve care, self-management, self-efficacy, behavior promotion (quality of sleep, diet, physical activity mental health) and medication adherence. The mHealth technology has proven effective for disease prevention, lifestyle changes, management of cardiovascular disease and diabetes, and is a suitable tool for elderly people. In conclusion, it seems that mHealth can facilitate behavioral changes; although, further research is necessary in this regard.

This study aimed to predict the drying kinetics, energy utilization (E_u), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (D_eff), E_u, EUR, exergy efficiency, and exergy loss. The value of the D_eff was varied from 4.19 × 10^–10 to 1.18 × 10^–9 m²/s. The highest value E_u, EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R²) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict E_u, EUR, exergy efficiency, and exergy loss, with R² of .9989, .9988, .9986, and .9978, respectively.

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications.