The quantification of surface abrasion on flint stone tools

Lithic artifacts are some of the most common and numerous remains recovered from paleolithic archaeological sites. However, these materials can undergo multiple post-depositional alterations after their introduction into the archaeological record. Due to the high quantity of lithic remains recovered, a quick, flexible, and effective method for identifying degrees of alteration on the surface of lithic implements is highly desirable. The present study examines the use of gray level images to obtain quantitative data from the surface of flint artifacts and determine whether these images can detect the presence of post-depositional alterations. An experimental collection of flints was subjected to sequential episodes of rounding in a tumbling machine. After each episode, photographs were taken with a microscope, resulting in quantitative surface values using gray level values. The quantitative surface values were used as variables in machine learning models to determine time of exposure and the most salient variables for discrimination. Our results indicate that the extraction of metrics from gray level images successfully capture changes in the surface of flint artifacts caused by post-depositional processes. Additional results provide insight into which areas to sample when seeking post-depositional alterations and underscore the importance of particle size in the generation of alterationsProgram for the Requalification of the University System Margarita Salas, Grant/ Award Number: CA1/RSUE/2021-00743; Ministry of Universities (Ministerio de Universidades); Autonomous University of Madrid (Universidad Autonoma de Madrid); Generalitat de Catalunya, AGAUR agency, Grant/Award Number: 2021SGR01239; Universitat Rovira i Virgili, Grant/Award Number: 2022PFR-URV-64; Spanish Ministry of Science and Innovation, Grant/Award Numbers: CEX2019-000945-M, PID2021-122355NB-C32; Agencia Estatal de Investigacion, Grant/Award Number: HAR2016-76760-C3-2-P; Spanish National Plan for Scientific and Technical Research and Innovation, Grant/Award Number: ID2019-103987 GB-C3

RISK TOO MUCH TO GAIN TOO LITTLE: ASTROTURFING STRATEGY, ITS PRESUMED EFFECTS AND LIMITATIONS

Astroturfing strategies are deceptive mechanisms that hide the source of the information from the publics. By not disclosing the persuasive intent and identity of the sources behind these communicative efforts, organizations expect to get more benefits from their crafted messages. However, the discovery of astroturfing and the real source of the messages could produce negative effects for the organization, often triggering the anger of publics.  Effects of astroturfing differ depending on the situation: successful astroturfing, failed one, and disclosure of the identity of patron and its persuasive or promotional intent. This study creates three possible astroturfing situations and compares their relative effects on credibility, purchase intention, attitude towards the brand and megaphoning produced across the situations, using two different brands scenarios. Based on the findings, the potential costs for the communicators, organizations, and public relations as a profession generated by astroturfing strategies are also discussed. Keywords: astroturfing, deception, effects, public relations, transparency

EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition

Myoelectric control is an area of electromyography of increasing interest nowadays, particularly in applications such as Hand Gesture Recognition (HGR) for bionic prostheses. Today's focus is on pattern recognition using Machine Learning and, more recently, Deep Learning methods. Despite achieving good results on sparse sEMG signals, the latter models typically require large datasets and training times. Furthermore, due to the nature of stochastic sEMG signals, traditional models fail to generalize samples for atypical or noisy values. In this paper, we propose the design of a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition from surface electromyography (sEMG) signals. The proposed EMGTFNet architecture can accurately classify a variety of hand gestures without any need for data augmentation techniques, transfer learning or a significant increase in the number of parameters in the network. The accuracy of the proposed model is tested using the publicly available NinaPro database consisting of 49 different hand gestures. Experiments yield an average test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793 trainable parameters. Our results outperform the ViT without FNB, thus demonstrating that including FNB improves its performance. Our proposal framework EMGTFNet reported the significant potential for its practical application for prosthetic control

From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

A Self-Adaptive Online Brain Machine Interface of a Humanoid Robot through a General Type-2 Fuzzy Inference System

This paper presents a self-adaptive general type-2 fuzzy inference system (GT2 FIS) for online motor imagery (MI) decoding to build a brain-machine interface (BMI) and navigate a bi-pedal humanoid robot in a real experiment, using EEG brain recordings only. GT2 FISs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) maximum number of electroencephalography (EEG) channels is limited and fixed, 2) no possibility of performing repeated user training sessions, and 3) desirable use of unsupervised and low complexity features extraction methods. The novel learning method presented in this paper consists of a self-adaptive GT2 FIS that can both incrementally update its parameters and evolve (a.k.a. self-adapt) its structure via creation, fusion and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structure identification is based on an online GT2 Gath-Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models). The effectiveness of the proposed method is demonstrated in a detailed BMI experiment where 15 untrained users were able to accurately interface with a humanoid robot, in a single thirty-minute experiment, using signals from six EEG electrodes only

Constructive Roles of Organizational Two-Way Symmetrical Communication: Workplace Pseudo-Information Gatekeeping

Misinformation, misunderstanding, and rumors are not foreign to organizations. The cost of pseudo-information can be critical for the organization in terms of profit, stakeholder relationships, and reputation. For those reasons, organizations should make efforts to detect and prevent the spread of pseudo-information. This piece of research proposes and finds support in a model to gatekeep pseudo-information in the workplace, in which two-way symmetrical communication is an essential element for the model, predicting employees’ gatekeeping behaviors, and mediating the relationship between quality of the employee–organization relationship and gatekeeping behaviors. Then, the cultivation of relationships with the employees and the adherence to two-way symmetrical communication are cost-effective methods for the organization. Loyal and satisfied employees voluntarily debunk and combat pseudo-information

Personalised and Adjustable Interval Type-2 Fuzzy-Based PPG Quality Assessment for the Edge

Most of today's wearable technology provides seamless cardiac activity monitoring. Specifically, the vast majority employ Photoplethysmography (PPG) sensors to acquire blood volume pulse information, which is further analysed to extract useful and physiologically related features. Nevertheless, PPG-based signal reliability presents different challenges that strongly affect such data processing. This is mainly related to the fact of PPG morphological wave distortion due to motion artefacts, which can lead to erroneous interpretation of the extracted cardiac-related features. On this basis, in this paper, we propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System (IT2FLS) for assessing the quality of PPG signals. The proposed system employs a personalised approach to adapt the IT2FLS parameters to the unique characteristics of each individual's PPG signals.Additionally, the system provides adjustable levels of personalisation, allowing healthcare providers to adjust the system to meet specific requirements for different applications. The proposed system obtained up to 93.72\% for average accuracy during validation. The presented system has the potential to enable ultra-low complexity and real-time PPG quality assessment, improving the accuracy and reliability of PPG-based health monitoring systems at the edge