Nowadays, environmental electromagnetic fields exist everywhere and the whole population is exposed. The most widespread technologies engendering exposure to electromagnetic fields for the public and workers are radiofrequency wireless communication systems. Such exposure can have direct effects on living tissue involving biological problems or personal symptoms, as well as indirect effects restricting healthcare appliances. This assessment aims to analyze, examine and categorize the consequences of non-ionizing electromagnetic fields emitted by radiofrequency devices. This concerns biological effects in living tissues and atypical personal symptoms for short and long-term exposures. The evaluation methodology in this field of investigation is also discussed. In the article, these consequences as well as their evaluation techniques, in the case of wireless digital communication tools, are analyzed and reviewed. The categorization of exposure sources, the corresponding effects and their assessment methodologies are analyzed. The concept of systematic reviews and meta-analyses and their applications in various assessments of these effects and symptoms are reviewed and discussed.

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Assessment and Categorization of Biological Effects and Atypical Symptoms Owing to Exposure to RF Fields from Wireless Energy Devices

Comparison of personal exposure to Radiofrequency Electromagnetic Fields from Wi-Fi in a Spanish university over three years.

The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM’s vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM’s Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.

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NextGEM: Next-Generation Integrated Sensing and Analytical System for Monitoring and Assessing Radiofrequency Electromagnetic Field Exposure and Health

This study compares the performance of graph convolutional neural network (GCN) models with conventional natural language processing (NLP) models for classifying scientific literature related to radio frequency electromagnetic field (RF-EMF). Specifically, the study examines two GCN models: BertGCN and the citation-based GCN. The study concludes that the model achieves consistently good performance when the input text is long enough, based on the attention mechanism of BERT. When the input sequence is short, the composition parameter λ, which combines output values of the two subnetworks of BertGCN, plays a crucial role in achieving high classification accuracy. As the value of λ increases, the classification accuracy also increases. The study also proposes and tests a simplified variant of BertGCN, revealing performance differences among the models under two different data conditions by the existence of keywords. This study has two main contributions: (1) the implementation and testing of a variant of BertGCN and citation-based GCN for document classification tasks related to radio frequency electromagnetic fields publications, and (2) the confirmation of the impact of model conditions, such as the existence of keywords and input sequence length, in the original BertGCN. Although this study focused on a specific domain, our approaches have broader implications that extend beyond scientific publications to general text classification.

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Classification of Research Papers on Radio Frequency Electromagnetic Field (RF-EMF) Using Graph Neural Networks (GNN)

This paper 1 presents an overview of a data augmented classification and multi-span (multiple) answer system for extracting key information from academic publications. This study consists of two sections: (a) implementing a new fine-tuned model to solve the multiple answer extraction issue (b) reporting results of sub-classification in various RF-EMF topics. In our previous study, it has been found that the essential cause of the low performance of the extractive question answering (EQA) system for certain types of questions was the multiple answer issue. To solve this problem, this study applies the TASE (TAg-based Span Extraction) technique and introduces the results. Our approach can retrieve multiple answers spreading over a given text by referring to the pre-trained TASE model with fine accuracy. In addition, this work adopts 'PEO (Population, Exposure, Outcome)' from the 'PECO' of the WHO-funded study on RF-EMF safety, as our holistic research framework. Based on the PEO perspective, the results of three sub-topic (RF, SAR, Causal Relationship) classifications are presented. For both models of multi-span answer and classification tasks, the data-augmenting method plays an important role. In particular, it is found that our proposed system outperforms the pre-trained BERT model in multi-span answer tasks with our RF-EMF dataset.

Deep learning models for multiple answers extraction and classification of scientific publications

The effect of radiofrequency electromagnetic fields (RF-EMF) on biomarkers of oxidative stress in vivo and in vitro: A protocol for a systematic review

Gernot Schmid

Papers

The effect of radiofrequency electromagnetic fields (RF-EMF) on biomarkers of oxidative stress in vivo and in vitro: A protocol for a systematic review