Industrial Control Systems (ICS) are critical to the efficient operation of essential sectors such as manufacturing, energy, and water management. However, their increasing integration with IT systems exposes them to sophisticated cyberattacks, particularly lateral attacks targeting Programmable Logic Controllers (PLCs). Advanced preventive measures are necessary because, despite their significance, many ICS continue to rely on outdated technologies with few security features. This paper proposes a machine learning (ML)-based approach to anomaly detection in ICS communication networks, focusing on techniques such as 1D Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVMs), and Isolation Forest (iForest) algorithms. We generated a dataset by capturing both normal and manipulated ICS communication patterns, including TCP/IP traffic. Simulated lateral attacks provided realistic data for training and testing the ML models. The results demonstrate that the 1D CNN model achieved the highest accuracy (0.92) and F1 score (0.91) with minimal processing time, making it ideal for real-time intrusion detection. This research highlights the potential of ML techniques to fortify ICS cybersecurity and lays the groundwork for future advancements in critical infrastructure resilience.

Millions of individuals worldwide suffer from a motor disability, impeding their ability to perform daily tasks independently. Thanks to modern technologies, such as electrooculography (EOG), it is possible to provide them with a tool enhancing their quality of life. EOG, an electrophysiological method, detects DC potentials influenced by eye movements, making it particularly advantageous for individuals with motor impairments who retain ocular mobility. The article describes pilot experiments that includes the basic design of electrodes configuration for peripheral solutions, their optimization and the addition of sensors to measure other vital physiological parameters such as photoplethysmography (PPG) and electrocardiography (ECG) on demand. We tested all the EOG methodologies and connections that our device should contain with the help of Mindmedia Nexus-10 MKII, PPG signals with the Analog Devices MAXM86146 optical biosensor module and ECG with the Texas Instruments ADS1299 biopotential circuit. The evaluation and filtering of the collected data was carried out in the MATLAB environment. In the final, we present a hardware concept based on a new approach that, unlike the standard EOG, incorporates 3 large-area conductive fabric electrodes in a headband for practical and comfort long-term non-manual human-machine interface (HMI). Our measurements are crucial for monitoring the patient’s health status, mood variations, and can help prevent complications or speed up treatment. The primary goal of this research was present our innovative concept and confirm its feasibility, bringing us closer to the realization of a fully developed and functional device.

This study explores the influence of blade design on the low-pressure extrusion process, which is relevant to techniques like spheronization. We investigate how blade geometry affects the extruded paste and final product properties. A model paste was extruded through a basket extruder with varying blade lengths to create distinct wedge gaps (20°, 26° and 32° contact angles). The theoretical analysis explored paste behavior within the gap and extrudate. A model material enabled objective comparison across blade shapes. Our findings reveal a significant impact of blade design on the pressure profile, directly influencing liquid distribution in the paste and extrudate. It also affects the required torque relative to extruder output. The findings of this study hold significant implications for continuous granulation, a technique employed in the pharmaceutical industry for producing granules with uniform size and properties. Understanding the influence of blade geometry on the extrusion process can lead to the development of optimized blade designs that enhance granulation efficiency, improve product quality, and reduce energy consumption. By tailoring blade geometry, manufacturers can achieve more consistent granule characteristics, minimize process variability, and ultimately produce pharmaceuticals with enhanced efficacy.

Rutherford backscattering spectrometry (RBS) in channeling regimes (RBS/C), as an ion beam analysis method performed on a Tandetron 6MV accelerator, generally gives precise information about the structure of crystalline samples by combining RBS signals in the random and aligned configurations. This paper presents details about the design and implementation of tailored RBS/C measurements (coarse and fine) and data evaluation application developed in MATLAB for in situ accelerator control system ARGUS, delivered by High Voltage Engineering Europa BV (HVEE). Additionally, we examined two different ways of stepping during the measurement to reduce the possible inaccuracies related with goniometer’s backslash affecting the evaluation of spectra. Verification experiment was carried out using a 2-MeV ⁴He+-beam directed on a Si (100) substrate. The channeling effect is seen as channeling dips of a lower signal in an otherwise rather homogeneous plane. Implemented application significantly facilitates the RBS/C measurement and analysis of the experiments, and also extends the ion beam analysis portfolio of Advanced Technologies Research Institute. Finally, software is ready-to-use for any Tandetron based ion beam facility with the ARGUS software for accelerator control.

This paper presents the development and validation of a custom MATLAB library, designed to facilitate seamless and efficient real‐time communication between the Siemens S7‐PLCSIM Advanced simulator and MATLAB/Simulink. The library uses Siemens.Simulation.Runtime.API to enable this integration and its architecture is structured into three classes—PLCSimAdv, PLCSR, and PLCSW. To demonstrate the library’s capabilities, we have chosen the validation parameters and conducted functional experiments including real‐time communication at the millisecond level, integration and control of a simulated industrial process, and simultaneous operation with multiple virtual controllers. One of the library’s preliminary conditions was to avoid the requests for regenerating of any version of control program or Simulink model as it is presently by S‐functions in TIA portal. The results suggest that the proposed library provides a robust tool for various applications in industrial automation, from the digital twin modelling or simulation of advanced technologies to the operation of traditional control systems. This work has the potential to significantly streamline the simulation, control, and validation of automated processes and opens new ways for research and development in automation and control systems.

Wearable electroencephalography (EEG) is rapidly evolving toward lightweight, user-friendly systems that enable brain monitoring in naturalistic settings. Traditional multi-channel, gel-based systems provide broad scalp coverage and high signal fidelity but are impractical for unsupervised or long-term use. This review focuses on the emerging generation of smart wearable EEG devices that are easy to wear, require minimal setup, and typically integrate additional physiological sensors such as photoplethysmography (PPG), temperature, or motion sensors. We review wearable EEG systems across four main form factors: head-worn EEG devices, smart EEG patches and tattoos, in-ear and headphone-based EEG, and glasses-integrated EEG. Head-worn systems offer broader signal coverage and support more complex applications such as sleep staging, human–machine interaction, and epilepsy monitoring. Patch-based systems are well suited to comfortable long-term monitoring, particularly in sleep-related applications. Ear-center systems provide high user comfort and stable signal acquisition from non-traditional electrode locations. Glasses-integrated devices represent an emerging option for unobtrusive daytime neurophysiology. Each category is examined in terms of sensor fusion, technical parameters, and embedded algorithms, with particular emphasis on automated signal analysis. We conclude with a discussion on current limitations, regulatory and usability challenges, and future directions toward unobtrusive, AI-powered neurotechnology for home and clinical use.