While Electronic Prescription Systems (EPS) adoption varies across EU Member States, there's a lack of comprehensive comparative analysis. Existing studies focus on single EPSs, employ diverse methodologies, and lack up-to-date data. This study fills this gap by providing a comprehensive overview of EPS development, functionalities, and usage statistics in each EU Member State. Most EU Member States widely adopted EPS by 2022, with exceptions including Germany, France, and Luxembourg, where pilot projects or just plans existed at that time. Out of the 27 EPSs, 25 employ a similar design featuring a central server and end-user software or web-based applications. Among these, 22 are structured as single national systems. The fundamental technical solution is remarkably similar across the EU. Despite these similarities, functionalities, authentication methods, prescription validity, and medication coverage differ significantly among EPSs. A multinational team, including co-authors from each EU Member State, collected data using a structured questionnaire. The study underscores the need for standardized methodologies in EPS research and emphasizes the importance of comprehensive comparative analysis to inform healthcare policies and digitalization efforts.

Gait evaluation is important for apprehension and management of different neurocognitive disorders (NCD). The gait events are changing with the age factor and this variability is being incorrectly linked with people with NCD. So, there is a high need to analyze gait events correctly. The gait analysis is mostly performed on temporal and spectral feature extraction in which there is a high rate of missing important features. Apart from this, monitoring and quantification of Parkinson's disease patients raise many therapeutic challenges in terms of severity analysis of motor symptoms i.e. freezing of gait (FOG), bradykinesia and continuous remote monitoring of patients. The objective of this study is to use a smart insole dataset for the assessment of computational techniques focusing on gait evaluation. The objective of this research study is to use continuous wavelet transform to convert time series signals into an images instead of using more traditional techniques for dealing with time series based on e.g. recurrent architectures. The results evidence that the proposed system works efficiently with the accuracy of 96.5% in gait variability analyzing three cohorts i.e. adults, elderly, and patients with Parkinson's disease (PwPD) and 91% for analyzing the gait symptoms in different severity stages of PD patients.

The biomechanical system of the soft exoskeleton glove is studied in this letter. In this context, several aspects have been treated. The delay time model with fractional-order exponent${\boldsymbol{\beta }} = 1.5$for a soft exoskeleton of a patient that suffered a cerebrovascular accident is proposed. The model contains two major elements of uncertainty: the first element is represented by the time delay that corresponds to the reaction time of the human factor and the second element is given by the disturbing muscular torque of the patient's hand. A robust control system is studied to ensure the desired performances of the tracking error relative to a reference position. The stability of the control system is demonstrated by the Lyapunov techniques. The control method and the resulting algorithms are implemented in the research stage of the soft EXOSKELETON-GLOVE (EX-GLOVE) model. The numerical simulations and experimental tests demonstrated the correctness of the proposed solutions.

Classifying visual information is an apparently simple and effortless task in our everyday routine, but can we automatically predict what we see from signals emitted by the brain? While other researchers have already attempted to answer this question, we are the first to show that a commercially available BCI could be effectively used for visual image classification in real-world scenarios – when testing takes place at a completely different time than training data collection. The task is difficult, as it requires relating the noisy and low-level EEG signals to complex and highly semantic visual categories. In this paper, we propose different learning approaches and show that simpler classifiers such as Ridge Regression with Gabor filtering of the input EEG signal could be more effective than the powerful Long Short Term Memory Networks and Convolutional Neural Networks in this case of limited and noisy training data. We analyzed the importance of each electrode for the visual classification task and noticed that the sensors with the highest accuracy were the ones that recorded brain activity from regions known to be correlated more with higher level recognition and cognitive processes and less to lower-level visual signal processing. The result is also in accordance with research in computer vision with deep neural networks, which shows that semantic visual features are learned only at higher levels of neural depth. While EEG signals are weaker by themselves for the task of visual classification, we demonstrate that they could be powerful when combined with deep visual features extracted from the image, improving performance from 91% to over 97% in a multi-class recognition setting. Our tests show that EEG input brings additional information that is not learned by artificial deep networks on the given image training set. Thus, a commercially available BCI could be effectively used in conjunction with a deep learning based vision system to form together a stronger visual recognition system that is suitable for real-world applications.

Web security issues are and will remain one of the biggest threats on the Internet in nowadays. In order to prevent some of these vulnerabilities, you can use web honeypot programs that collect data about attacks. Having information about those attacks tendencies, an important amount of the vulnerabilities of web applications in an organization can be prevented using web honeypots. In this paper we investigate design considerations for a Wordpress honeypot module, which are capable to preserve the high interaction capability of the honeypot. The experimental results show that the proposed honeypot module helps to find new vulnerabilities in plugins and themes if an attacker tries to exploit one of these.

Containerized applications have become more and more popular, as the container platforms developed to be more stable. In e-health, having a containerized backend infrastructure helps not only deploying to the cloud, but also to on-premise servers, regardless of their operating system. In this context, this paper presents a scalable architecture based on containerized applications for e-health projects. It shows the advantages of using containers not only for scalability, but performance and easy deployment.

The paper treats the control problem of a class of hyper-redundant robot constituted by a chain of continuum segments. The technological model basis is a central, long and thin, highly flexible and elastic backbone. The driving system is a decoupled one. The main parameters of the arm control are determined by the curvature and curvature gradient. The dynamic model is inferred. A sliding mode control system is used in order to achieve a desired shape of the arm. The stability of the closed loop control system is proven. Numerical simulations are also provided to verify the effectiveness of the presented approach.

This paper treats the control problem of a class of hyper-redundant robots. The dynamic model of the arm is described by hyperbolic partial differential equations with uncertain components. By using a spatial weighted error control, the infinite dimensional system control becomes a finite-dimensional control problem. The stability analysis and the resulting controllers are obtained using the concept of boundary geometric control and a spatial weighted error control technique. A robust algorithm that is based on weighted error sliding mode control is discussed. The boundary tendon control determines the system evolution toward a prescribed switching surface, and in order to avoid the oscillations around the switching surface, a damping control determines a direct evolution, along the switching surface, toward the origin. Numerical simulations and experimental results are also provided to verify the effectiveness of the presented approach.

The paper focuses on hyper-redundant arms with continuum elements that perform the grasping function by coiling. First, there is concern with the dynamic model of the continuum arm for the position control during non-contact operations with the environment. A frequency stability criterion based on the Kahman - Yakubovich - Popov Lemma and P and PD control algorithms is proposed. Then, the grasping control of the arm in contact with a load is analyzed. The dynamics of the system are discussed and an extension of the Popov criterion is endorsed. The control algorithms based on SMA actuators are introduced. Numerical simulations and experimental results of the arm motion toward an imposed target are presented.

The grasping control problem for a hyperredundant manipulator is presented. The dynamic model is derived by using Lagrange equations developed for infinite dimensional systems. The algorithms for the position and force control are proposed. The arm fluid pressure control is inferred and the conditions that ensure the stability of the motion are discussed. Numerical simulations are presented.

The control problem of a class of tentacle arm, with variable length, that can achieve any position and orientation in 3D space and can increase the length in order to get a better control in the constraint operator space is presented. First, the dynamic model of the system is inferred. In order to avoid the difficulties generated by the complexity of the nonlinear integral - differential model, the control problem is based on the artificial potential method. Then, the method is used for constrained motion in an environment with obstacles. Numerical simulations for spatial and planar tentacle models are presented in order to illustrate the efficiency of the method.