There is great interest in the development of prosthetic limbs capable of complex activities that are wirelessly connected to the patient's neural system. Although some progress has been achieved in this area, one of the main problems encountered is the selective acquisition of nerve impulses and the closing of the automation loop through the selective stimulation of the sensitive branches of the patient. Large-scale research and development have achieved so-called "cuff electrodes"; however, they present a big disadvantage: they are not selective. In this article, we present the progress made in the development of an implantable system of plug neural microelectrodes that relate to the biological nerve tissue and can be used for the selective acquisition of neuronal signals and for the stimulation of specific nerve fascicles. The developed plug electrodes are also advantageous due to their small thickness, as they do not trigger nerve inflammation. In addition, the results of the conducted tests on a sous scrofa subject are presented.

This paper presents two distinct methods that demonstrate the increased intensity of a specific emotion when the induced emotion is trained daily for 30 days. For this study, four actors participated in a 30-day exercise trial and were recorded each day using high-level audio equipment. The first method supporting our hypothesis is a deep learning approach. A convolutional neural network pre-trained on Mel-frequency cepstral coefficients analyzed the actors' recordings and delivered the intensity of the detected emotion. The CNN tested 3,561 segments of 0.2-second length, and the results showed a higher level of intensity on the final day of training for each participant. The second method is spectral analysis. The spectrograms generated on the first and final days of the experiment showed that the spectral composition on the final day had a wider range of frequencies than on the first day, further supporting our hypothesis.

Acne diagnosis, severity assessment and treatment follow-up rely primarily on clinical examination. In vivo reflectance confocal microscopy (RCM) provides non-invasively, real-time images of skin lesions with a level of detail close to histopathology. This systematic literature review aims to provide an overview of RCM utility in acne and a summary of specific features with clinical application that may increase objectivity in evaluating this condition. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting our results. We systematically searched three databases: PubMed, Clarivate and Google Scholar (January 2022). All included studies used RCM to investigate acne in human patients and reported the investigated skin area and type (acne lesions or clinically uninvolved skin), the substance used in the case of treatment. Our search identified 2184 records in the three databases investigated. After duplicate removal, 1608 records were screened, 35 were selected for full-text assessment, and 14 were included in this review. We used the QUADAS-2 tool to evaluate the risk of bias and applicability concerns. RCM was selected as the index test and clinical examination as the reference standard. The total number of patients from all studies was 291, with 216 acne patients and 60 healthy participants aged between 13 and 45 years. The 14 considered studies analysed 456 follicles from healthy participants, 1445 follicles from uninvolved skin in acne patients and 1472 acne lesions. Consistent RCM findings concerning follicles of acne patients reported across studies were increased follicular infundibulum size, thick, bright border, intrafollicular content and inflammation. Our analysis indicates that RCM is a promising tool for acne evaluation. Nevertheless, standardization, a unified terminology, consistent research methods and unitary reporting of RCM findings are necessary. PROSPERO registration number CRD42021266547.

This paper presents the effects’ analysis produced by the frequent use of swearing from the perspective of irritability. The analysis was carried out with the help of two psychological questionnaires that were completed by the volunteers before and after the inducement of the negative emotions and automatic recognition functions implemented by Convolutional Neural Networks (CNN), applied for the speech signals of two volunteer groups for whom negative emotions were induced. The CNN architecture uses Mel-frequency cepstral coefficients (MFCCs), obtained from the speech signal, and has 87,944 trainable parameters, the outputs of the network being the 8 main classes of emotion detected by the algorithm (1 neutral, 3 positive, and 4 negative). The CNN also gives information about the negative emotion and irritability level. For the volunteers who swore during the experiment, there is an increase of 14% in negative emotion intensity and of 21% for the irritability level than for the volunteers who didn’t swear during the trials. The use of this current research is the understanding that cursing causes a higher level of irritability.

The natural differences between human-made electronics and biological tissues constitute a huge challenge in materials and the manufacturing of next-generation bioelectronics. As such, we performed a series of consecutive experiments for testing the biofunctionality and biocompatibility for device implantation, by changing the exterior chemical and physical properties of electronics coating it with silicone or hydrogels. In this article, we present a comparison of the main characteristics of an electronic device coated with either silicone or hydrogel (GelMa). The coating was performed with a bioprinter for accurate silicone and hydrogel deposition around different electronic chips (Step-Down Voltage Regulator U3V15F5 from Pololu Corporation). The results demonstrate that the hydrogel coating presents an augmented biomechanical and biochemical interface and superior biocompatibility, lowers foreign body response, and considerably extends the capabilities for bioelectronic applications.

Myoelectric exoprostheses serve to aid in the everyday activities of patients with forearm or hand amputations. While electrical signals are known key factors controlling exoprosthesis, little is known about how we can improve their transmission strength from the forearm muscles as to obtain better sEMG. The purpose of this study is to evaluate the role of the forearm fascial layer in transmitting myoelectrical current. We examined the sEMG signals in three individual muscles, each from six healthy forearms (Group 1) and six amputation stumps (Group 2), along with their complete biometric characteristics. Following the tests, one patient underwent a circumferential osteoneuromuscular stump revision surgery (CONM) that also involved partial removal of fascia and subcutaneous fat in the amputation stump, with re-testing after complete healing. In group 1, we obtained a stronger sEMG signal than in Group 2. In the CONM case, after surgery, the patient’s data suggest that the removal of fascia, alongside the fibrotic and subcutaneous fat tissue, generates a stronger sEMG signal. Therefore, a reduction in the fascial layer, especially if accompanied by a reduction of the subcutaneous fat layer may prove significant for improving the strength of sEMG signals used in the control of modern exoprosthetics.

In this article, we present our research achievements regarding the development of a remote sensing system for motor pulse acquisition, as a first step towards a complete neuroprosthetic arm. We present the fabrication process of an implantable electrode for nerve impulse acquisition, together with an innovative wirelessly controlled system. In our study, these were combined into an implantable device for attachment to peripheral nerves. Mechanical and biocompatibility tests were performed, as well as in vivo testing on pigs using the developed system. This testing and the experimental results are presented in a comprehensive manner, demonstrating that the system is capable of accomplishing the requirements of its designed application. Most significantly, neural electrical signals were acquired and transmitted out of the body during animal experiments, which were conducted according to ethical regulations in the field.

Speech emotion recognition (SER) is a promising ongoing research area with important applications for forensics and law enforcement operations, among others. Approaches have been previously proposed to integrate SER systems to assist in surveillance tasks, emergency services, police investigations, or other operations, especially in the attempt to anticipate and prevent potential criminal acts or even to counter terrorist activities. One of the challenges presented by these tasks consists of discerning patterns in the temporal evolution of the affective content that would indicate suspicious behavior and warrant further inquiry. In this work, we gain insight into these patterns and prove that 1) if a human interaction is emotionally triggering for the subject, then their affective response will not decay instantly, but over a longer time period, and subsequent emotionally neutral interactions will still be accompanied by an aroused negative affective state (emotional remanence); and 2) if an emotionally charged event is forthcoming for the subject, as the event draws closer, the subject will experience higher intensity emotions and will exhibit a correspondingly increased affective response. In order to provide a reasonable partial proxy for the high-stakes conditions and triggers expected in real-life scenarios, we have developed a speech dataset comprising 270 recordings of 18 students behind on their university exams and about to attempt them for the second or third time; thus, the upcoming exams and the potential consequences of failing them represent the emotionally charged event. Human evaluators labeled the recordings in terms of the identified emotional classes (grouped into negative emotional classes and the neutral state) and of arousal-valence affect space values. Analyzing the annotations made by the evaluators, we prove that the subjects' affective response is significantly higher as the emotionally charged event approaches, and emotional remanence can be observed even 15 minutes after the initial interaction, or even after 30 minutes when under the added influence of the event's imminence. We show that the arousal increases (higher intensity affective response) as the event draws closer, while the valence decreases (more negative affective response), again supporting the second hypothesis, and suggesting that such patterns would be relevant for the targeted applications. We propose and implement a SER system using artificial neural networks (ANNs) based on multilayer perceptron (MLP) models, obtaining good performance (up to 72.7% accuracy) when training in a speaker-independent manner, and yielding classification and regression results consistent with those given by human evaluation, supporting the possibility and usefulness of using machine learning (ML) systems to monitor affective responses in order to automatically detect the patterns associated with the behaviors relevant for forensic and law enforcement applications and to facilitate intervention and prevention.

The objective of this extended version of our previous paper [1] is to present some new motion algorithms tested on a neural prosthesis prototype that is equipped with manually fabricated Velostat sensors used for providing tactile feedback to the patient. This neural prosthesis was developed as part of a larger research project that aims to capture biological signals with the help of implants at the level of the median nerve and ulnar nerve. These signals will later be processed with various techniques to be suitable for triggering the movement of the prosthesis. Such a prosthetic device is intended to help the large number of patients who suffer from upper limb amputation. The implemented motion algorithms are based on how the motors, that are part of the mechatronics structure, rotate in one direction or another, with a certain speed, to achieve a specific movement. The implemented algorithms are functional, they have been successfully tested on the experimental assembly containing the mechatronics structure and the command-and-control block. The signals received from the pressure sensors with Velostat can also be used for the stimulation of the implanted electrodes, wrapped around the remaining nerves of the patients' stump, and the user will perceive a tactile sensation. This way the bidirectional communication will be possible between the user and the prosthesis.

The objective of this paper is to find a solution for providing tactile feedback to a neural prosthesis prototype. Several sensors for contact and pressure were developed based on a sandwich-like structure with Velostat for getting a reaction from the prosthesis. An innovative motion algorithm, that uses the sensorial feed-back, was implemented: the "Double Click" command on a laptop touchpad. The signals received from the Velostat sensors presented in this paper can also be used for the stimulation of implanted electrodes, wrapped around the remaining nerves of the patients' stump, and the user will perceive a tactile sensation. This way the bidirectional communication will be possible between the user and the prosthesis.

This paper presents an ongoing development of an arm neuroprosthesis implantable interface consisting of a quadrupole implantable cuff-electrode, a neural analog front-end, a low power Arduino microcontroller, an off the shelf 433MHz transmitter, and a 3.7V 430mAh Li-Po battery. Implantable cuff electrode made of PDMS and Au has been successfully fabricated and tested for biocompatibility. A low power and low noise analog front-end were designed to filter and amplify the electroneurogram signal sensed by the cuff-electrode. The system was tested in the laboratory with input signals similar to the actual biological signals, verifying the correct operation of the implantable device. Finally, the analog front-end module was encapsulated in PDMS and implanted in the hind limb of a swine giving valuable training and knowledge for further development of the neuroprosthesis implantable interface.

Several methods were reported in the scientific literature for the classification of the infant cries, in order to automatically detect the need behind their tears and help the parents and caretakers. In the same scope, this paper has an original approach in which the sounds that precede the cry are used. Such sounds can be considered primitive words and are classified according to the “Dunstan Baby Language”. The paper verifies the universal baby language hypothesis starting from the research reported in a previous article. A CNN architecture trained with recordings of babies from Australia was used for classifying the audio material coming from Romanian babies. It was an attempt to see what happens should the participants belong to a different cultural landscape. The database loaded with the sounds made by Romanian babies was labelled by doctors in the maternity hospitals and two Dunstan experts, separately. Finally, the results of the CNN automatic classification were compared to those obtained by the Dunstan coaches. The conclusions have proved that Dunstan language is universal.

The paper presents a mechatronic structure for an artificial hand that can be used in several types of prostheses, depending on performance/costs requirements. The data presented in the introductory section justify the importance of the research, since limb amputation is quantitatively significant among medical issues at the global level. The mechanical structure was realized through 3D printing, after it was designed with SolidWorks software package. For better operation flexibility and accuracy, haptic feedback was included using both pressure sensors and “artificial skin” made of Velostat. The motors that command the mobile elements are included in the empty space inside the hand, while the electronics (build around an Arduino board) is embedded in the forearm. The mechatronic structure is light, versatile and can be used in both myoelectric and neural prostheses. The main original contribution of the paper is the haptic feedback using both pressure sensors and Velostat. The work is a part of a multidisciplinary project that will use this structure in a neural prosthesis with neural bio-feedback.

The paper presents several experiments realized with an original model, the enhanced cellular automata with autonomous agents, in order to simulate the evolution of disease spreading The simulations presented in the paper contain specific details of the actual Covid-19 infection: the atypicality of evolution of cases, the proximity required for infection, the long gestation time The simulations show that the combination of cellular automata with autonomous agents can be used to model the evolution of a disease, due to its sensitivity to parameters associated to processes of infection and healing The ability of the modeling system to find critical situations is also discussed The details of the model include the topography of the space (contextual cellular automata), the timer associated with each autonomous agent to model the change of state (and the testing strategies), a FIFO memory that models the treatment facilities and the global control loop that introduces the central control of the system associated to the law enforcement and authorities

Novel research results of an ongoing development of a personalized forearm neuroprosthesis are being presented. A complete design process shows how the mechanical structure is developed from 3D scanning to 3D printing. With an individual DC motor for each finger and one for the palm, the neuroprosthesis achieves 15 degrees of freedom. Artificial skin cover fingers and palm, acting as resistive pressure sensors, giving a linear full range readout voltage proportional to the applied pressure. Fabrication steps and first-generation implantable electrodes are presented together with analog front-end electronics and an implantable wireless communication system. Arduino is used to process received neural motor signals, control the mechanical hand and process the received feedback signals from the artificial skin. The feedback signals will be wirelessly transmitted to implantable electrode actuators, for stimulation of sensorial nerves. Thus, the work of a bidirectional neuroprosthesis system integration is presented.

The increasing complexity and dynamics of the financial domain impose appropriate tools to keep transactions efficient and secure. We propose to use a new technology, BlockChain (BC), and a revived technology, Artificial Intelligence (AI), to build IT-based tools for financial confirmation and diagnosis. We describe first the mechanisms for both, confirmation and diagnosis. Then, the type of computation involved is analysed. The two technologies involved in our proposal, BC and AI, are computationally intensive. The time and energy involved cannot be minimized using off-the-shelf solutions. Finally, the architectural environment for an efficient implementation is proposed and evaluated. The paper represents the description of our proposed approach to solve critical problems affecting the current financial systems.

The chapter overviews the methods, algorithms, and architectures for random number generators based on cellular automata, as presented in the scientific literature. The variations in linear and two-dimensional cellular automata model and their features are discussed in relation to their applications as randomizers. Additional memory layers, functional nonuniformity in space or time, and global feedback are examples of such variations. Successful applications of cellular automata random number/signal generators (both software and hardware) reported in the scientific literature are also reviewed. The chapter includes an introductory presentation of the mathematical (ideal) model of cellular automata and its implementation as a computing model, emphasizing some important theoretical debates regarding the complexity and universality of cellular automata.

The objective of this study was to evaluate the effectiveness of different surgical implants for the reconstruction of severe acetabular bone defects in revision arthroplasty of the hip. The current study is a retrospective study on 32 patients with Paprosky type IIIA or IIIB acetabular defects operated between January 2012-December 2015 in a single hospital. The mean follow-up was 21 months (12-43 months). Five different types of reconstruction methods were used: primary uncemented cups with or without screws, cemented acetabular cups, tantalum cups, metal augments and antiprotrusio cages. Bone allograft was available in all cases. Functional outcome after surgery was evaluated using Harris Hip Score. Based on Paprosky classification, the study included 16 type IIIA and 16 type IIIB acetabular defects. Bone graft was used in 71.8% of the cases (23 out of 32 patients). Tantalum cups were used in 15 cases (46.9%), being the preferred implant. Primary uncemented cups were used in 2 cases, cemented acetabular cups were used in 4 cases, trabecular metal augments were used in 5 cases and antiprotrusion cages were used in 6 cases. The mean Harris Hip Score improved from 37.3�7.4 pre-operatively to 82.1�7.2 at final follow-up. In conclusion, the current study demonstrates that various methods of reconstruction are efficient in the short and medium-term.

This paper presents an original method of intention detection that can open a new direction of research in voice-based affective computing. A deep learning approach was used to detect the consistency between real and expressed intentions of a speaker (or the inconsistency, that is related to deceiving - or manipulative - intention), as reflected in their voice. The labeling and triangulation of results imply a qualitative research method, critical discourse analysis, and require expert evaluation. The method was implemented in a software platform integrated with the neural network programming frame. The deep learning architecture selected is based on similar models used by the authors in affective computing applications. The experimental research applied the proposed method for a famous historical case: US President Richard Nixon's audio speeches from the 'Watergate affair'. A labeled data base of 2758 files (2 seconds audio fragments) was generated, based on publicly available voice recordings of President Nixon. These files were used for training and tests and an accuracy of over 94% was obtained.

This paper presents an application of convolutional neural networks (CNN) for the recognition of the so-called “Dunstan baby language” that consists of five “words” or phonemes used by babies of age under 3 months to communicate their needs before they start crying. The model was derived from a CNN architecture which was successfully applied by the authors for voice-based emotion detection. The input of the neural network is the spectrogram obtained from the audio records of babies' voices and is processed as a two-dimensional image. The architecture was trained for a set of 250 small duration recordings and was tested for other 65 recordings with a recognition rate of 89%. The length of all audio files is less than 1 second; the recordings were extracted from certified Dunstan language recordings. The most important original contribution of the paper is the recognition of the actual “baby words” (and not the baby cry as was done before). This architecture offers an efficient tool for the verification of the “universal baby language” hypothesis, according to which the language of infants does not depend on culture, family, etc.