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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.