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Background and Objectives: A tracheostomy is a frequently performed surgical intervention in intensive care units (ICUs) for patients requiring prolonged mechanical ventilation. This procedure can offer significant benefits, including reduced sedation requirements, improved patient comfort, and enhanced airway management. However, it is also associated with various risks, and the absence of standardized clinical guidelines complicates its implementation. This study aimed to determine the prevalence of tracheostomy among ICU patients, while also evaluating patient characteristics, complication rates, and overall outcomes related to the procedure. Materials and Methods: We conducted an observational, cross-sectional, point-prevalence survey across eight ICUs in Italy. Data were collected over two 24 h periods in March and April 2024, with a focus on ICU characteristics, patient demographics, the details of tracheostomy procedures, and associated complications. Results: Among the 92 patients surveyed in the ICUs, 31 (33.7%) had undergone tracheostomy. The overall prevalence of tracheostomy was found to be 9.1%, translating to a rate of 1.8 per 1000 admission days. The mean age of patients with a tracheostomy was 59.5 years (SD = 13.8), with a notable predominance of male patients (67.7%). Neurological conditions were identified as the most common reason for ICU admission, accounting for 48.4% of cases. Tracheostomy procedures were typically performed after a mean duration of 12.9 days of mechanical ventilation, primarily due to difficulties related to prolonged weaning (64.5%). Both early and late complications were observed, and 19.35% of tracheostomized patients did not survive beyond one month following the procedure. The average length of stay in the ICU for these patients was significantly extended, averaging 43.0 days (SD = 34.3). Conclusions: These findings highlight the critical role of tracheostomy in the management of critically ill patients within Italian ICUs. The high prevalence and notable complication rates emphasize the urgent need for standardized clinical protocols aimed at optimizing patient outcomes and minimizing adverse events. Further research is essential to refine current practices and develop comprehensive guidelines for the management of tracheostomy in critically ill patients.

Extracorporeal cardiopulmonary resuscitation (ECPR) is a complex, life-saving procedure that uses mechanical support for patients with refractory cardiac arrest, representing the pinnacle of extracorporeal membrane oxygenation (ECMO) applications. Effective ECPR requires precise patient selection, rapid mobilization of a multidisciplinary team, and skilled cannulation techniques. Establishing a program necessitates a cohesive ECMO system that promotes interdisciplinary collaboration, which is essential for managing acute cardiogenic shock and severe pulmonary failure. ECPR is suited for selected patients, emphasizing the need to optimize every step of cardiac arrest management—from public education to advanced post-resuscitation care. The flexibility of ECMO teams allows them to manage various emergencies such as cardiogenic shock, massive pulmonary embolism, and severe asthma, showcasing the program’s adaptability. Launching an ECPR program involves addressing logistical, financial, and organizational challenges. This includes gaining administrative approval, assembling a diverse team, and crafting detailed protocols and training regimens. The development process entails organizing teams, refining protocols, and training extensively to ensure operational readiness. A systematic approach to building an ECPR program involves establishing a team, defining patient selection criteria, and evaluating caseloads. Critical elements like patient transport protocols and anticoagulation management are vital for the program’s success. In conclusion, initiating an ECPR program demands thorough planning, collaborative effort across specialties, and ongoing evaluation to improve outcomes in critical cardiac emergencies. This guide offers practical insights to support institutions in navigating the complexities of ECPR program development and maintenance.

This paper presents the outcomes of investigations conducted on the development procedure of a personalized prosthetic device for an adult patient. The individualization is achieved through 3D scanning, followed by semi-automated design using the AutoMedPrint system, and low-cost fused deposition modelling (FDM) technology for 3D printing. The prosthesis is aimed for use during bicycle riding and other sport activities. During the conducted experiments outlined in this manuscript, the prosthesis is equipped with force and movement sensors. The purpose is to collect data on its functionality across different scenarios and dynamic activities, aiming to assess potential harm, refine the design, and serve as an initial step before activating the prosthesis end effector. This article describes the methodology in detail, including the process of designing, producing, and programming, as well as laboratory and field test results (including testing performed with and without a patient). Overall, the design and prototype are implemented successfully. A discussion about the need for particular improvements in both the mechanical and electrical areas is finally presented.

Background and Objectives: Redistribution hypothermia occurs during anesthesia despite active intraoperative warming. Prewarming increases the heat absorption by peripheral tissue, reducing the central to peripheral heat gradient. Therefore, the addition of prewarming may offer a greater preservation of intraoperative normothermia as compared to intraoperative warming only. Materials and Methods: A single-center clinical trial of adults scheduled for non-cardiac surgery. Patients were randomized to receive or not a prewarming period (at least 10 min) with convective air devices. Intraoperative temperature management was identical in both groups and performed according to a local protocol. The primary endpoint was the incidence, the magnitude and the duration of hypothermia (according to surgical time) between anesthetic induction and arrival at the recovery room. Secondary outcomes were core temperature on arrival in operating room, surgical site infections, blood losses, transfusions, patient discomfort (i.e., shivering), reintervention and hospital stay. Results: In total, 197 patients were analyzed: 104 in the control group and 93 in the prewarming group. Core temperature during the intra-operative period was similar between groups (p = 0.45). Median prewarming lasted 27 (17–38) min. Regarding hypothermia, we found no differences in incidence (controls: 33.7%, prewarming: 39.8%; p = 0.37), duration (controls: 41.6% (17.8–78.1), prewarming: 45.2% (20.6–71.1); p = 0.83) and magnitude (controls: 0.19 °C · h−1 (0.09–0.54), prewarming: 0.20 °C · h−1 (0.05–0.70); p = 0.91). Preoperative thermal discomfort was more frequent in the prewarming group (15.1% vs. 0%; p < 0.01). The interruption of intraoperative warming was more common in the prewarming group (16.1% vs. 6.7%; p = 0.03), but no differences were seen in other secondary endpoints. Conclusions: A preoperative prewarming period does not reduce the incidence, duration and magnitude of intraoperative hypothermia. These results should be interpreted considering a strict protocol for perioperative temperature management and the low incidence of hypothermia in controls.

This paper presents results from experimental studies that assess the utilization of virtual, augmented, and mixed reality (VR, AR, MR) at different stages of developing personalized 3D printed upper limb prostheses for adult patients. The prostheses are designed automatically using the AutoMedPrint system, leveraging 3D scans as described in various prior publications. Various stages of development of the prosthesis are made as applications of different extended reality technologies. An assembly instruction is implemented as an immersive VR application, a configurator is designed as AR application and a configurator and try-on application is prepared and deployed in MR. The applications are tested by an international group of experts during a scheduled experiment. The experts then participate to surveys and comparatively evaluate the potential of all the XR technologies. The paper presents the development of these applications, provides a detailed account of the experimental process, including the rankings of XR technologies for different applications throughout the lifecycle of a prosthetic device.

Every year, especially in urban areas, the population density rises quickly. The effects of catastrophes (i.e., war, earthquake, fire, tsunami) on people are therefore significant and grave. Assisting the impacted people will soon involve human-robot Search and Rescue (SAR) operations. Therefore, it is crucial to connect contemporary technology (i.e., robots and cognitive approaches) to SAR to save human lives. However, these operations also call for careful consideration of several factors, including safety, severity, and resources. Hence, ethical issues with technologies in SAR must be taken into consideration at the development stage. In this study, the most relevant ethical and design issues that arise when using robotic and cognitive technology in SAR are discussed with a focus on the response phase. Among the vast variety of SAR robots that are available nowadays, snake robots have shown huge potential; as they could be fitted with sensors and used for transporting tools to hazardous or confined areas that other robots and humans are unable to access. With this perspective, particular emphasis has been put on snake robotics in this study by considering ethical and design issues. This endeavour will contribute to providing a broader knowledge of ethical and technological factors that must be taken into account throughout the design and development of snake robots.

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.

Universities and other educational institutions may find it difficult to afford the cost of obtaining cutting-edge teaching resources. This study introduces the adoption of a novel open prototyping framework in the context of mechatronics education, employing low-cost commercial off-the-shelf (COTS) components and tools for the motion control module. The goal of this study is to propose a novel structure for the motion control module in the engineering mechatronics curriculum. The objective is to foster a new teaching method. From a methodology perspective, students are involved in a series of well-organised theoretical lectures as well as practical, very engaging group projects in the lab. To help students understand, draw connections, and broaden their knowledge, the methods of surface learning and deep learning are frequently mixed thoroughly. The structure of the course as well as the key topics are discussed. The proposed open framework, which consists of an elevator model, is presented in details. Students’ early evaluation indicates that the course organisation and subjects are successful and beneficial.

Cerebrovascular ischemia is a common clinical disease encompassing a series of complex pathophysiological processes in which oxidative stress plays a major role. The present study aimed to evaluate the effects of Dexmedetomidine, Clonidine, and Propofol in a model of hypoxia/reoxygenation injury. Microglial cells were exposed to 1%hypoxia for 3 h and reoxygenated for 3 h, and oxidative stress was measured by ROS formation and the expression of inflammatory process genes. Mitochondrial dysfunction was assessed by membrane potential maintenance and the levels of various metabolites involved in energetic metabolism. The results showed that Propofol and α2-agonists attenuate the formation of ROS during hypoxia and after reoxygenation. Furthermore, the α2-agonists treatment restored membrane potential to values comparable to the normoxic control and were both more effective than Propofol. At the same time, Propofol, but not α2-agonists, reduces proliferation (Untreated Hypoxia = 1.16 ± 0.2, Untreated 3 h Reoxygenation = 1.28 ± 0.01 vs. Propofol hypoxia = 1.01 ± 0.01 vs. Propofol 3 h Reoxygenation = 1.12 ± 0.03) and microglial migration. Interestingly, all of the treatments reduced inflammatory gene and protein expressions and restored energy metabolism following hypoxia/reoxygenation (ATP content in hypoxia/reoxygenation 3 h: Untreated = 3.11 ± 0.8 vs. Propofol = 7.03 ± 0.4 vs. Dexmedetomidine = 5.44 ± 0.8 vs. Clonidine = 7.70 ± 0.1), showing that the drugs resulted in a different neuroprotective profile. In conclusion, our results may provide clinically relevant insights for neuroprotective strategies in intensive care units.

The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees of Freedom (DOF) manipulator on the Robot Operating System (ROS). The dataset created from the simulation is divided 65–35 for training–testing of the proposed model. The metrics used for model validation include spread value, cost and runtime for the training dataset, and Mean Relative Error, Normal Mean Square Error, and Mean Absolute Error for the testing dataset. A comparative analysis of the CSOA-RBFNN model is performed with an artificial neural network, support vector regression model, and with with other meta-heuristic RBFNN models, i.e., PSO-RBFNN and GWO-RBFNN, that show the effectiveness and superiority of the proposed technique.

The term perception-driven obstacle-aided locomotion (POAL) was proposed to describe locomotion in which a snake robot leverages a sensory-perceptual system to exploit the surrounding operational environment and to identify walls, obstacles, or other structures as a means of propulsion. To attain POAL from a control standpoint, the accurate identification of push-points and reliable determination of feasible contact reaction forces are required. This is difficult to achieve with rigidly actuated robots because of the lack of compliance. As a possible solution to this challenge, our research group recently presented Serpens, a low-cost, open-source, and highly compliant multi-purpose modular snake robot with a series elastic actuator (SEA). In this paper, we propose a new prototyping iteration for our snake robot to achieve a more dependable design. The following three contributions are outlined in this work as a whole: the remodelling of the elastic joint with the addition of a damper element; a refreshed design for the screw-less assembly mechanism that can now withstand higher transverse forces; the re-design of the joint module with an improved reorganisation of the internal hardware components to facilitate heat dissipation and to accommodate a larger battery with easier access. The Robot Operating System (ROS) serves as the foundation for the software architecture. The possibility of applying machine learning approaches is considered. The results of preliminary simulations are provided.

Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).

As a result of several governments closing educational facilities in reaction to the COVID-19 pandemic in 2020, almost 80% of the world’s students were not in school for several weeks. Schools and universities are thus increasing their efforts to leverage educational resources and provide possibilities for remote learning. A variety of educational programs, platforms, and technologies are now accessible to support student learning; while these tools are important for society, they are primarily concerned with the dissemination of theoretical material. There is a lack of support for hands-on laboratory work and practical experience. This is particularly important for all disciplines related to science, technology, engineering, and mathematics (STEM), where labs and pedagogical assets must be continuously enhanced in order to provide effective study programs. In this study, we describe a unique perspective to achieving multi-sensory learning through the integration of virtual and augmented reality (VR/AR) with haptic wearables in STEM education. We address the implications of a novel viewpoint on established pedagogical notions. We want to encourage worldwide efforts to make fully immersive, open, and remote laboratory learning a reality.

Snake robot locomotion in a cluttered environment where the snake robot utilises a sensory-perceptual system to perceive the surrounding operational environment for means of propulsion is defined as perception-driven obstacle-aided locomotion (POAL). From a control point of view, achieving POAL with traditional rigidly-actuated robots is challenging because of the complex interaction between the snake robot and the immediate environment. To simplify the control complexity, compliant motion and fine torque control on each joint is essential. Accordingly, intrinsically elastic joints have become progressively prominent over the last years for a variety robotic applications. Commonly, elastic joints are considered to outperform rigid actuation in terms of peak dynamics, robustness, and energy efficiency. Even though a few examples of elastic snake robots exist, they are generally expensive to manufacture and tailored to custom-made hardware/software components that are not openly available off-the-shelf. In this work, Serpens, a newly-designed low-cost, open-source and highly-compliant multi-purpose modular snake robot with series elastic actuator (SEA) is presented. Serpens features precision torque control and stereoscopic vision. Only low-cost commercial-off-the-shelf (COTS) components are adopted. The robot modules can be 3D-printed by using Fused Deposition Modelling (FDM) manufacturing technology, thus making the rapid-prototyping process very economical and fast. A screw-less assembly mechanism allows for connecting the modules and reconfigure the robot in a very reliable and robust manner. The concept of modularity is also applied to the system architecture on both the software and hardware sides. Each module is independent, being controlled by a self-reliant controller board. The software architecture is based on the Robot Operating System (ROS). This paper describes the design of Serpens and presents preliminary simulation and experimental results, which illustrate its performance.

In nature, snakes can gracefully traverse a wide range of different and complex environments. Snake robots that can mimic this behaviour could be fitted with sensors and transport tools to hazardous or confined areas that other robots and humans are unable to access. In order to carry out such tasks, snake robots must have a high degree of awareness of their surroundings (i.e., perception-driven locomotion) and be capable of efficient obstacle exploitation (i.e., obstacle-aided locomotion) to gain propulsion. These aspects are pivotal in order to realise the large variety of possible snake robot applications in real-life operations such as fire-fighting, industrial inspection, search-and-rescue, and more. In this paper, we survey and discuss the state of the art, challenges, and possibilities of perception-driven obstacle-aided locomotion for snake robots. To this end, different levels of autonomy are identified for snake robots and categorised into environmental complexity, mission complexity, and external system independence. From this perspective, we present a step-wise approach on how to increment snake robot abilities within guidance, navigation, and control in order to target the different levels of autonomy. Pertinent to snake robots, we focus on current strategies for snake robot locomotion in the presence of obstacles. Moreover, we put obstacle-aided locomotion into the context of perception and mapping. Finally, we present an overview of relevant key technologies and methods within environment perception, mapping, and representation that constitute important aspects of perception-driven obstacle-aided locomotion.