Jakub Możaryn

Background: Virtual reality (VR) offers immersive, interactive environments, with growing evidence supporting its use in mental health diagnostics and therapy. Despite the increasing availability of VR equipment in clinical settings, integration into routine mental health care remains limited. Understanding health care workers’ attitudes, knowledge, and perceived barriers is essential for bridging the gap between technological availability and clinical implementation. Objective: This study aimed to investigate health care workers’ attitudes toward VR in mental health treatment and identify factors influencing its adoption in settings where VR equipment is already available. Methods: A qualitative study design was used, combining structured one-to-one interviews with a supporting narrative literature review. The narrative review informed the interview design and contextualized findings within the existing evidence base. Structured interviews were conducted with 16 mental health professionals (n=12, 75% women; n=4, 25% men) working in psychiatric and mental health settings in one city in southern Poland where VR equipment was available for patient care. Descriptive statistics summarized closed-ended question responses, and thematic analysis was applied to open-ended questions. Results: Of the 16 participants, 10 (62.5%) had heard of VR technology before and held a favourable view of it, but only 6 (37.5%) were aware of its applications in mental health care. Participants aged under 35 years (6/16) rated the value of VR in therapy more positively than those aged over 50 years (3/16). Among those with prior VR exposure, 85.7% (6/7) expressed confidence in its effectiveness compared with 44.4% (4/9) of those without prior exposure. The most frequently mentioned benefits included interactivity, the ability to individualize therapeutic activities to patient needs, isolated visual and auditory interactions, and an increased sense of patient involvement and control. High costs were identified as the primary barrier by 75% (12/16), and 68.8% (11/16) expressed a need for comprehensive training. Women rated their subjective VR knowledge lower than men despite demonstrating comparable objective knowledge during interviews. VR equipment was described as being restricted within certain service areas. Legal and insurance constraints limited the use of home-based VR. Conclusions: Access to VR equipment alone is insufficient to drive adoption in mental health services. Effective integration requires targeted education, comprehensive training, clear institutional guidelines on equipment responsibility and insurance, and supportive infrastructure. Addressing barriers and knowledge gaps identified in this study is essential for realizing VR’s therapeutic potential in routine clinical practice.

Electro‐hydraulic servo drives are fundamental to industrial applications that require high power density and precise positioning. However, significant nonlinearities, parameter uncertainties and friction complicate their control. Traditional controllers have difficulty accommodating these complex behaviors, while advanced methods are too computationally intensive for typical industrial PLCs. Furthermore, many existing neural network controllers lack formal stability guarantees, which poses risks in safety‐critical environments. It requires an advanced control architecture that can robustly adapt online to system nonlinearities, operate efficiently on industrial hardware with fast cycle times, and guarantee closed‐loop stability without requiring offline training or a pre‐existing plant model. This paper presents a hybrid deep learning controller (HDLC) designed to address these challenges. The HDLC features a novel architecture that integrates three learning paradigms: Kohonen maps (SOM), Hebbian learning and a globally stable adaptive law for a multi‐layer perceptron (MLP) controller. The HDLC is coupled with a diagonal recurrent neural network (Model‐DRNN) that performs online system identification, enabling the entire system to adapt in real time. At the same time, a Lyapunov‐based proof ensures stable operation. The HDLC has been implemented and experimentally validated on an industrial PLC‐controlled electro‐hydraulic servodrive with a cycle time of 2 ms. The HDLC demonstrated superior performance in comparative tests against a PID controller and a pre‐trained NN‐based direct inverse control (DIC). In a complex, repetitive tracking task, the HDLC achieved a 70% reduction in integral absolute error compared to the DIC. The HDLC demonstrated the fastest recovery time in a pressure disturbance test and maintained the lowest tracking error. Thus, confirming its robustness and effective online adaptation capabilities. Our research shows that the HDLC offers a practical and reliable solution for advanced control of nonlinear electro‐hydraulic systems.

The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive–capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg–Marquardt algorithm. The model’s accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.

This article proposes a linear−quadratic regulator-based optimal second-order sliding mode controller for trajectory tracking control of robot manipulators under lumped disturbance. The coupled dynamics of the manipulator with a geared motor and the desired joint space trajectory are designed and discussed. The linear−quadratic regulator controller is developed by penalizing corresponding weighting matrices for the nominal input−output linearized robot dynamics. An integral sliding mode control law is incorporated to address external disturbances, ensuring the linear−quadratic regulator's optimized performance remains unaffected. A non-singular terminal sliding mode control law is then cascaded with the optimal integral switching manifold to alleviate chattering effects. The proposed strategy, validated using SimMechanicsTM/Simulink in MATLAB®, demonstrates superior performance compared to known control algorithms by achieving joint trajectories with lower torque and smoother control. The validation of the proposed controller is based solely on simulation and has not been implemented practically. Specifically, the energy consumption and tracking errors of the proposed controller are 27.63 J and 0.00167, respectively.

The three-point controller is one of the simplest controllers used in industry. Most environments with programmable logic controllers and distributed control systems allow such blocks in their design. The three-point controller consists of three switching states, i.e., one input and two outputs, depending on the input signal's positive or negative deviation from the desired value. It allows for an upper and lower limit of deviation where no switching takes place. The three-point step control system extends the step controller with a corrective feedback loop. This paper presents the implementation and parameter evaluation of the three-point step control system. Based on theoretical analysis and simulations, general comments are made on the impact of the controller parameters on the control system's performance. Finally, recommendations for tuning the three-point step controller are formulated.