Efficient Cyberattack Detection Methods in Industrial Control Systems.

The article deals with the issue of detecting cyberattacks on control algorithms running in a real Programmable Logic Controller (PLC) and controlling a real laboratory control plant. The vulnerability of the widely used Proportional-Integral-Derivative (PID) controller is investigated. Four effective, easy-to-implement, and relatively robust methods for detecting attacks on the control signal, output variable, and parameters of the PID controller are researched.

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach.

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion block that relies on fuzzy logic.

Forgery Cyber-Attack Supported by LSTM Neural Network: An Experimental Case Study.

This work is concerned with the vulnerability of a network industrial control system to cyber-attacks, which is a critical issue nowadays. This is because an attack on a controlled process can damage or destroy it. These attacks use long short-term memory (LSTM) neural networks, which model dynamical processes.

The Effect of Nasogastric Feeding after Surgery in Patients with Head and Neck Cancers - Retrospective Single Center Experience.

This study aimed to assess clinical factors that could predict the need for nasogastric feeding after surgery in patients with head and neck cancer (HNC) and evaluate the effect of tube feeding on selected laboratory parameters.This single-center retrospective study included 153 patients who underwent surgery for HNC. Data on patient and tumor characteristics were collected, along with laboratory measurements.

Digital Twins in the Practice of High-Energy Physics Experiments: A Gas System for the Multipurpose Detector.

The digital twins technology delivers a new degree of freedom into system implementation and maintenance practice. Using this approach, a technological system can be efficiently modeled and simulated. Furthermore, such a twin offline system can be efficiently used to investigate real system issues and improvement opportunities, e.

LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors.

This work thoroughly compares the efficiency of Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical processes used in Model Predictive Control (MPC). Two simulated industrial processes were considered: a polymerisation reactor and a neutralisation (pH) process. First, MPC prediction equations for both types of models were derived.

Fast Real-Time Model Predictive Control for a Ball-on-Plate Process.

This work is concerned with an original ball-on-plate laboratory process. First, a simplified process model based on state-space process description is derived. Next, a fast state-space MPC algorithm is discussed.

Parotid gland tumors. Results of retrospective analysis of 149 patients treated at the Clinical Department of Cranio-Maxillofacial Surgery, Clinic of Otolaryngology and Oncologic Laryngology of Military Institute of Medicine in Warsaw in years 2006-2016.

Wstęp: Nowotwory mające swoją etiologię w gruczołach ślinowych występują rzadko i stanowią ok. 3-10% wszystkich guzów w obrębie głowy i szyi. Materiał i metody: W latach 2006-2016 w Klinicznym Oddziale Chirurgii Czaszkowo-Szczękowo-Twarzowej Wojskowego Instytutu Medycznego w Warszawie z powodu nowotworów ślinianek przyusznych hospitalizowano i leczono 149 pacjentów.