Abstract: A robotic surgical system includes a robotic instrument coupled to a controller and configured to interact with biological tissue. A physiologic sensing subsystem has one or more sensors configured to detect real-time physiological parameters. The sensors include at least one of: tissue oxygenation sensors, perfusion sensors, bioimpedance sensors, or neural conductivity sensors.

Abstract: The invention relates to a batch process for preparing a polyether alcohol P having a hydroxyl number of equal to or lower than 115 mg KOH/g by reacting starter compound S1 and starter compound S2, which starter compounds have one active hydrogen atom or a plurality of active hydrogen atoms, with one or more alkylene oxides in the presence of a composite metal cyanide complex catalyst, comprising a) forming a starter mixture comprising starter compound S1 and the catalyst and activating the catalyst by adding an alkylene oxide, followed by b) continuously adding an alkylene oxide; and c) continuously adding starter compound S2; wherein no alkylene oxide is added between steps a) and b), and the continuous addition of alkylene oxide in step b) is not interrupted before the total weight of alkylene oxide needed to prepare polyether alcohol P has been added; and step c) starts before step b).

Abstract: The invention relates to a batch process for preparing a polyether alcohol P having a hydroxyl number of equal to or lower than 115 mg KOH/g by reacting starter compound S1 and starter compound S2, which starter compounds have one active hydrogen atom or a plurality of active hydrogen atoms, with one or more alkylene oxides in the presence of a composite metal cyanide complex catalyst, comprising a) forming a starter mixture comprising starter compound S1 and the catalyst, followed by b) continuously adding an alkylene oxide; and c) continuously adding starter compound S2; wherein no alkylene oxide is added in step a) or between steps a) and b), and the continuous addition of alkylene oxide in step b) is not interrupted before the total weight of alkylene oxide needed to prepare polyether alcohol P has been added; and step c) starts before step b).

Abstract: Anti-parasitic compounds and uses thereof. Compounds comprising a C-terminal peptide adjuvant conjugated to an N-terminal peptide antigen via a protease-cleavable linker, said peptide adjuvant comprising a peptide analog of C5a, wherein said peptide antigen comprises an antigenic epitope of a parasitic organism, such as T. gondii. Methods of therapeutic or prophylactic treatment of a parasitic infections.

Abstract: A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for improving efficiency and performance of a modeling system. An example method for automatically adjusting a machine learning model in response to a decline in performance of the machine learning model caused by a change in exogenous context includes determining a set of context-relevant variables from a silent variable set that are relevant to the change in exogenous context. The example method also includes training the machine learning model by activating a fewest number of context-relevant variables from the set of context-relevant variables that exhibit a mitigation of the decline in performance of the machine learning model. The example method also includes generating, after training the machine learning model, a first predictive output of the machine learning model that indicates an outcome projected to occur as a result of the change in exogenous context.

Abstract: A robotic surgical system integrates artificial intelligence (AI) to enable dynamic inference arbitration and risk-driven autonomy. The system includes a surgeon console, robotic arms, and a control system with memory and processors configured to execute real-time surgical workflows. AI modules analyze intraoperative data, such as imaging, sensor input, and instrument telemetry, and compute context alignment scores to guide module selection, forecasting, and fallback execution. Confidence metrics are monitored, with thresholds triggering surgeon alerts, handoff, or autonomous continuation. The system supports intraoperative adaptation, surgeon fatigue detection, and real-time annotation of AI outputs for traceability. It enables improved tissue recognition, predictive planning, and context-aware adjustments through training on historical surgical data. AI-assisted decision support, deviation handling, and performance monitoring enhance safety and personalization across diverse procedures.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for improving efficiency and performance of a modeling system. An example method includes receiving, by communications circuitry, a silent variable set. The example method also includes detecting, by context analysis circuitry, occurrence of a triggering condition. The example method also includes adjusting, by model adjustment circuitry, the modeling system based on the silent variable set.

Abstract: A robotic surgical system includes one or more robotic actuators configured to interact with biological tissue during a surgical procedure. A plurality of sensors include at least one of fiber Bragg grating sensors, piezoelectric strain sensors, or magnetostrictive sensors to capture real-time mechanical, elasticity, or deformation data from biological tissues. deep learning engine trained on a dataset comprising tissue mechanical responses across multiple tissue types, pathological states, and patient demographics. Pre-contact predictive adjustment profiles are generated for anticipated tissue interactions using preoperative imaging data registered to intraoperative coordinates. Intraoperative deviations are detected from predicted mechanical behavior and autonomously recalibrate actuator forces. Upcoming surgical maneuvers are anticipated based on prior task sequences and adjust actuator stiffness or damping properties in preparation for anticipated contact.

Abstract: A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures.

Abstract: A robotic surgical system network includes a plurality of robotic surgical systems. Each system includes robotic arms, sensors, a surgeon console, and a control system with an integrated AI module. A network interface is associated with robotic surgical system and provides secure data communication. A central or distributed data repository securely stores surgical data aggregated from the robotic surgical systems. The surgical data includes at least one of procedural data, sensor readings, imaging data, AI decision logs, surgical outcomes, or user interaction data. A training module utilizes aggregated surgical data to train or update AI models for the robotic surgical systems using unsupervised learning, transfer learning, or federated learning techniques. A cybersecurity module implements security measures for data transmission and system access, the measures comprising at least one of encryption, multi-factor authentication, or real-time threat detection.

Abstract: A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy.

Abstract: A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels/Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

Abstract: A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

Abstract: A robotic surgical system. a surgeon console operatively coupled to a patient console and one or more surgical instruments. A surgeon computer is coupled to or integrated with the surgeon console, the surgeon computer further operatively connected to the one or more surgical instruments; A surgical robot is coupled to a robotic surgery control system and a feedback loop. The robotic surgery control system includes or is coupled to an artificial intelligence (AI) system. A feedback loop is further configured to receive performance-related data from the one or more sensors, the data analyzed by the robotic surgery control system or the AI system to dynamically adjust the robotic system's operation as needed. A data extraction module retrieves, from the robotic surgery control system or the AI system. one or more programmed steps executed by the surgeon for positioning at least one of the surgical instruments during the surgical procedure.

Abstract: Systems, apparatuses, and methods are described for a dynamic network identification to facilitate a selection of a desired audio. A premises (e.g., a public bar) may have a plurality of display devices (e.g., television screens) outputting videos associated with a plurality of content items (e.g., television programs). A computing device may assign an audio data of each of the content items to be transmitted over a separate wireless network. A user may be able to listen to the audio of a desired content item by causing a user device to join a wireless network assigned to transmit an audio data of the desired content item. The wireless network may be reused to transmit a different audio data. A network identifier of the wireless network may be renamed to indicate the different audio data. The network identifier may be based on metadata associated with a content item.

Abstract: A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures.

Abstract: A robotic surgical system includes a surgeon consol coupled to a patient console and coupled to one or more surgical instruments. The surgeon consol is used by a surgeon to perform a surgical procedure. A surgeon computer is coupled to or at the surgeon console. The surgeon consol is coupled to the one or more surgical instruments (manipulators). A surgical robot is coupled to a robotic surgery control system and a feedback loop. The feedback loop monitors and collects data from one or more sensors used to provide feedback to the robotic surgical system. An AI system has an AI architecture that uses input data for producing an AI model. A surgical robot is coupled to a robotic surgical control system that is coupled to or includes: the feedback loop, and the artificial intelligence AI system.

Abstract: A surgical robot is coupled to the surgeon console. The surgical robot performs a robotic surgical procedure. The surgical robot includes one or more robotic surgical arms. A control system is coupled to the one or more robotic surgical arms. An artificial intelligence (“AI”) system includes a plurality of machine learning algorithms. The robotic surgical arms are at least partially controlled by the AI system and the control device to process intraoperative data including images captured by cameras and sensor inputs. The machine learning algorithms analyze the intraoperative data in real time, comparing it with stored images and procedural information in image recognition and procedure databases. The one or more machine algorithms enable at least partial identification of anatomical structures.

Abstract: A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels/Deep learning algorithms of the AI system distinguish different anatomical objects from the images.