Thermal effects of mobile phones on human auricle region

Fungal bioleaching of e-waste utilizing molasses as the carbon source in a bubble column bioreactor

Mobile phones are known as the most widely used electronic instruments, and an enormous number of discarded mobile phones are generated. The present work used a pure culture of Penicillium simplicissimum in a bubble column bioreactor to extract Cu and Ni from mobile phone printed circuit boards (MPPCBs) waste. Molasses was used as an efficient carbon source to enhance bioleaching efficiency and increase the cost benefits. The adaptation phase was done at Erlenmeyer flasks to reach 40 g/L of MPPCBs powder. The most significant parameters, including the mass of MPPCBs powder, aeration, molasses concentration, and their interaction, were optimized in order to leach the maximum possible Cu and Ni using central composite design in response surface methodology (RSM). The model p-values for Cu and Ni recovery were 0.0030 and 0.0348, respectively, emphasizing the model's accuracy. 96.94% of Cu was recovered under 8.8% (v/v) of molasses, aeration rate of 0.29 (l/min), and MPPCBs powder of 10 g/L. The optimized condition of Ni leaching was 1.9% (v/v) of molasses, aeration rate of 0.37 (l/min), and MPPCBs powder of 10 g/L, resulting in 71.51% recovery. The present article demonstrated the great potential of P. simplicissimum to improve metal recovery from e-waste utilizing molasses and bubble column bioreactors.

Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite

Background

Cellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach.

Objectives

The purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy.

Materials and methods

In this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22.

Results

A combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%.

Conclusions

The implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM).

Electronic supplementary material

The online version of this article (10.1007/s13167-020-00199-x) contains supplementary material, which is available to authorized users.

Ni and Cu recovery by bioleaching from the printed circuit boards of mobile phones in non-conventional medium

There is a substantial volume of mobile phone waste every year. Due to the disadvantages of traditional methods, it is necessary to look for biological processes that are more eco-friendly and economical to recover metals from e-waste. Fungi provide large amounts of organic acids and dissolve metals but using sucrose in the medium is not economical. In this paper, the main objective is to find a suitable alternative carbon substrate instead of sucrose for fungi bioleaching of Ni and Cu in printed circuit boards (PCBs) of mobile phones using Penicillium simplicissimum. Four kinds of carbon sources (including sucrose, cheese whey, sugar, and sugar cane molasses) were selected. Also, pH and number of spores in inoculum were optimized by response surface methodology (RSM) for all carbon sources. The results showed the simultaneous maximum recovery of Cu and Ni is not possible. For Cu recovery, sugar is the best economical and simplistic medium instead of sucrose. Maximum recovery of Cu (90%) gained at the pH of 7, 3.3 × 10⁷ spores, and in sugar. The amount of Ni recovery (89%) was highest in molasses, at the pH of 2, and 10⁶ spores. The results proved non-conventional carbon sources improve bioleaching efficiency and the possibility of industrialization.