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Urinary tract infections (UTIs) are a common bacterial infection in women of childbearing age. Streptococcus agalactiae (Group B Streptococcus—GBS), a rare causative pathogen of UTIs in this population, is particularly important due to the potential risk during pregnancy, when it can lead to life-threatening neonatal infections. The current study analyzed 17,273 urine samples collected from consecutive women aged 18–45 years from Arad County, Western Romania. A total of 2772 samples tested positive for UTIs. In 130 cases, GBS was identified as the causative agent. Univariate logistic regression analysis revealed that women aged 25–34 years were more likely to test positive for GBS than those aged 18–24 years (cOR = 1.91, 95% CI: 1.07–3.43, p = 0.03). Antibiotic sensitivity testing revealed that all GBS strains were fully sensitive to penicillin, ampicillin, and vancomycin. High resistance was observed for clindamycin (77.34%) and tetracycline (88.46%). While GBS was found to be a rare pathogen in UTIs, our results underscore the importance of monitoring GBS in women of childbearing age, especially due to its risks during pregnancy, and emphasize the need for appropriate antibiotic management.

The protozoan parasite, Toxoplasma gondii, has been linked to several psychiatric disorders, including schizophrenia. The aim of this study was to assess the prevalence of T. gondii IgG antibodies and risk factors associated with seroprevalence in patients diagnosed with schizophrenia. This seroepidemiological study assessed 196 participants, divided into two groups. The study group consisted of 98 schizophrenic patients and was matched with 98 healthy blood donors. A questionnaire was used to gather information regarding potential risk factors associated with T. gondii seroprevalence. Results revealed a higher seroprevalence of T. gondii IgG antibodies in schizophrenic patients (69.39%, 68/98) when compared to healthy controls (51.02%, 50/98) (OR: 2.18; 95% CI: 1.21–3.9; p = 0.01). Patients with schizophrenia who consumed raw or undercooked meat (80.65%, 25/31) (OR: 3.75; 95% CI: 1.25–11.21, p = 0.02) and those with a lower educational level (77.59%, 45/58) (OR: 3.5; 95% CI: 1.59–7.54, p = 0.002) presented increased T. gondii seropositivity rates versus their control counterparts. Our findings indicate a high T. gondii IgG seroprevalence in patients diagnosed with schizophrenia compared to healthy blood donors. Factors associated with T. gondii seroprevalence were consumption of raw or uncooked meat and a lower educational attainment. This study provided the first data regarding the potential risk factors for toxoplasmosis in Romanian patients diagnosed with schizophrenia and may serve as a foundation for future research and the development of preventive strategies.

Urinary tract infections (UTIs) are a common bacterial infection in women of childbearing age. Streptococcus agalactiae (Group B Streptococcus—GBS), a rare causative pathogen of UTIs in this population, is particularly important due to the potential risk during pregnancy, when it can lead to life-threatening neonatal infections. The current study analyzed 17,273 urine samples collected from consecutive women aged 18–45 years from Arad County, Western Romania. A total of 2772 samples tested positive for UTIs. In 130 cases, GBS was identified as the causative agent. Univariate logistic regression analysis revealed that women aged 25–34 years were more likely to test positive for GBS than those aged 18–24 years (cOR = 1.91, 95% CI: 1.07–3.43, p = 0.03). Antibiotic sensitivity testing revealed that all GBS strains were fully sensitive to penicillin, ampicillin, and vancomycin. High resistance was observed for clindamycin (77.34%) and tetracycline (88.46%). While GBS was found to be a rare pathogen in UTIs, our results underscore the importance of monitoring GBS in women of childbearing age, especially due to its risks during pregnancy, and emphasize the need for appropriate antibiotic management. [ABSTRACT FROM AUTHOR]

Automation Studio, a specialised simulation software, offers virtual commissioning capabilities and robust tools for modelling the behaviour and performance of a pneumatic robot controlled by a PLC. Conversely, Unity is a versatile platform primarily used for creating high-quality 3D games and interactive simulations, providing immersive experiences with DTs through mixed-reality environments. This paper provides a study that compares and contrasts the simulation power of Automation Studio and the immersive capabilities of Unity in the context of developing digital twins for a mechatronic system. This research explores how these complementary approaches enhance the development, validation, understanding, and interaction with digital twins. By examining both platforms in this context, the article provides valuable insights for engineers, developers, and researchers looking to create digital twins for mechatronic systems, but not only. This study demonstrates the potential of leveraging the combined power of simulation and immersive capabilities to improve the interaction between the real robotic arm manipulator cylindrical type and its digital twin in different scenarios, using an OPC approach for mirroring. The combination of Automation Studio and Unity provides a powerful platform for applied science education in the field of the digital twin of mechatronic systems.

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

We show that in plasmas generated in deuterium in the presence of sputtered W surfaces, various molecular tungsten species are formed, whose chemical composition depends on the presence of gaseous impurities, namely, nitrogen, oxygen, and hydrogen. A magnetron discharge was used for plasma sustaining, and the species were investigated by mass spectrometry and optical emission spectroscopy. The identified tungsten-containing molecules are described by the chemical formula WOxNyDzHt, where x = 0–4, y = 0–3, z = 0–3, t = 0–5. Presumptively, even higher mass tungsten molecular species are present in plasma, which were not detected because of the limitation of the spectrometer measurement range to 300 amu. The presence of these molecules will likely impact the W particle balance and dust formation mechanisms in fusion plasmas. [ABSTRACT FROM AUTHOR]

Monitoring and tracking issues related to autonomous mobile robots are currently intensively debated in order to ensure a more fluent functionality in supply chain management. The interest arises from both theoretical and practical concerns about providing accurate information about the current and past position of systems involved in the logistics chain, based on specialized sensors and Global Positioning System (GPS). The localization demands are more challenging as the need to monitor the autonomous robot’s ongoing activities is more stringent indoors and benefit from accurate motion response, which requires calibration. This practical research study proposes an extended calibration approach for improving Omnidirectional Mobile Robot (OMR) motion response in the context of mechanical build imperfections (misalignment). A precise indoor positioning system is required to obtain accurate data for calculating the calibration parameters and validating the implementation response. An ultrasound-based commercial solution was considered for tracking the OMR, but the practical observed errors of the readily available position solutions requires special processing of the raw acquired measurements. The approach uses a multilateration technique based on the point-to-point distances measured between the mobile ultrasound beacon and a current subset of fixed (reference) beacons, in order to obtain an improved position estimation characterized by a confidence coefficient. Therefore, the proposed method managed to reduce the motion error by up to seven-times. Reference trajectories were generated, and robot motion response accuracy was evaluated using a Robot Operating System (ROS) node developed in Matlab-Simulink that was wireless interconnected with the other ROS nodes hosted on the robot navigation controller.

The protozoan parasite, Toxoplasma gondii, has been linked to several psychiatric disorders, including schizophrenia. The aim of this study was to assess the prevalence of T. gondii IgG antibodies and risk factors associated with seroprevalence in patients diagnosed with schizophrenia. This seroepidemiological study assessed 196 participants, divided into two groups. The study group consisted of 98 schizophrenic patients and was matched with 98 healthy blood donors. A questionnaire was used to gather information regarding potential risk factors associated with T. gondii seroprevalence. Results revealed a higher seroprevalence of T. gondii IgG antibodies in schizophrenic patients (69.39%, 68/98) when compared to healthy controls (51.02%, 50/98) (OR: 2.18; 95% CI: 1.21–3.9; p = 0.01). Patients with schizophrenia who consumed raw or undercooked meat (80.65%, 25/31) (OR: 3.75; 95% CI: 1.25–11.21, p = 0.02) and those with a lower educational level (77.59%, 45/58) (OR: 3.5; 95% CI: 1.59–7.54, p = 0.002) presented increased T. gondii seropositivity rates versus their control counterparts. Our findings indicate a high T. gondii IgG seroprevalence in patients diagnosed with schizophrenia compared to healthy blood donors. Factors associated with T. gondii seroprevalence were consumption of raw or uncooked meat and a lower educational attainment. This study provided the first data regarding the potential risk factors for toxoplasmosis in Romanian patients diagnosed with schizophrenia and may serve as a foundation for future research and the development of preventive strategies. [ABSTRACT FROM AUTHOR]

The need for protection of electrical machines comes as a demand of safety regulations in the automotive industry as well as a result of the general desire to obtain a robust and reliable electric powertrain. This paper introduces a hybrid method for estimating the temperature of the rotor of an Induction Machine (IM) based on a Nonlinear Autoregressive Network with Exogenous inputs (NARX) used as a prediction function within a particle filter. The temperature of the stator case is measured, and the information is used as an input to a NARX network and as a variable to a thermal process with first-order dynamics which serves as an observation function. Uncertainties of the NARX and thermal model are determined and used to correct the posterior estimate. Experimental data are used from a real IM test-bench and the results prove the applicability and good performance.

In the current study, bulk tungsten material surfaces are exposed to hydrogen, deuterium, and helium plasmas in the radiofrequency domain (13.56 MHz) at an input power of 250 W using the hollow-cathode configuration. The ejected material is collected on titanium substrates at various distances (from 6 mm up to 40 mm). Therefore, the exposed tungsten materials are investigated for surface changes (blister occurrence, dust formation, or nano-structuration), along with the crystallinity, depending on the plasma's exposure times (from 30 min up to 120 min for each plasma type). Also, the collected materials are analyzed (morphological, structural, and statistical investigations) for dust and dust film-like appearance. Plasma discharges are analyzed using two methods: optical emission spectroscopy, and single Langmuir probes, to emphasize the nature of the used plasmas (cold discharges, ~2 eV), along with the presence of tungsten emission (e.g., WI 406.31 nm, WI 421.31 nm) during the plasma lifetime. By using a dedicated protocol, a method was established for obtaining fusion-relevant tungsten surfaces in the hydrogen and deuterium plasma discharges. By using the implemented method, the current paper introduces the possibility of obtaining a new tungsten morphology, i.e., the dandelion-like shape, by using helium plasma, in which the W18O49 compound can be found. [ABSTRACT FROM AUTHOR]

Automation Studio, a specialised simulation software, offers virtual commissioning capabilities and robust tools for modelling the behaviour and performance of a pneumatic robot controlled by a PLC. Conversely, Unity is a versatile platform primarily used for creating high-quality 3D games and interactive simulations, providing immersive experiences with DTs through mixed-reality environments. This paper provides a study that compares and contrasts the simulation power of Automation Studio and the immersive capabilities of Unity in the context of developing digital twins for a mechatronic system. This research explores how these complementary approaches enhance the development, validation, understanding, and interaction with digital twins. By examining both platforms in this context, the article provides valuable insights for engineers, developers, and researchers looking to create digital twins for mechatronic systems, but not only. This study demonstrates the potential of leveraging the combined power of simulation and immersive capabilities to improve the interaction between the real robotic arm manipulator cylindrical type and its digital twin in different scenarios, using an OPC approach for mirroring. The combination of Automation Studio and Unity provides a powerful platform for applied science education in the field of the digital twin of mechatronic systems. [ABSTRACT FROM AUTHOR]

Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot. [ABSTRACT FROM AUTHOR]

Nowadays, electroactive materials based on conjugated polymers for energy storage and electrochromic window applications attract large interest due to their low cost, accessible synthetic procedures, and interesting electrochemical properties. Herein, we report on the performance of two propylenedioxythiophene (ProDOT)-based polymers having varying length and functionality side chains, which were explored to assess their potential for these applications. The polymers were obtained by oxidative chemical polymerization and processed from organic solvents into thin coatings with different molecular assemblies. Preliminary studies on their chemical structure and optical and electrochemical characteristics were performed to evidence how these are influenced by the side chain substituent nature. When tested as electrode material in the three-electrode cell configuration, the synthesized ProDOT-based polymers provided the highest specific areal capacitance of 1.059 mF/cm2 at a scan rate of 10 mV/s and 0.538 mF/cm2 at 0.01 mA/cm2 in cyclic voltammetry and galvanostatic charge–discharge measurements, respectively. One of the polymers showed electrochromic response, with ultrafast color change from deep purple to highly transmissive green/blue. A coloration efficiency of 123 cm2/C and a maximum CE decay of 9.9% after 100 cycles was achieved for this material, which is also able to efficiently store electrical charge, thus demonstrating potential for use in energy storage smart window applications where the energy level can be estimated by simple visual observation. [ABSTRACT FROM AUTHOR]