This research paper investigates ultra-wideband (UWB) localization systems by focusing on the use of average filter (AVG), Kalman filter (KF), and extended Kalman filter (EKF) algorithms, as well as a novel integrated filtering method that incorporates low-pass filter (LPF) into AVG, KF, and EKF. The study aims to improve localization loss in indoor environments using a TurtleBot robot equipped with a camera to observe ground truth positions. To evaluate the effectiveness of the proposed algorithms, a comprehensive comparison of the raw and filtered data with the camera-based ground truth observations is performed. Quantitative analyses of the results, including max, min, max-min, and mean error, are performed to evaluate the localization performance of the algorithms and the integrated filtering method. The results reveal that the integrated filtering method has performed better accuracy in comparison with existing methods.

Drug resistance in tumor cells is a significant roadblock in the clinical management of advanced or recurrent diseases in endometrial cancers. As a part of the tumor-stromal ecosystem, tumor cells are ecologically connected to the cancer-associated fibroblasts (CAFs), which form contributory elements of the tumor microenvironment (TME). We recently reported a novel model of patient-derived CAF-based 2-cell Hybrid Co-Culture (HyCC) to evaluate CAFs' role in developing drug resistance and understanding personalized tumor cell-CAF dialogue. Using our 2-cell HyCC model of patient-derived endometrial CAFs, we present data to demonstrate the direct counter-inhibitory effect of CAFs to combinations of cytotoxic and targeted drugs in developing drug resistance. CAFs derived from resected endometrial tumor samples were first passage-wise characterized before and after freeze-thaw for their positive and negative CAF markers expression pattern. Paclitaxel and its combination with copanlisib, TAK228, lenvatinib, and trametinib were used to test the 3D clonogenic growth of endometrial AN3CA cells on endometrial CAFs. We demonstrate that CAF-mediated resistance to antitumor drugs occurs via direct and indirect contact with CAFs in HyCC. Our data established the strength of the 2-cell HyCC model of patient-derived CAFs in solid tumors and provided a model of resistance to antitumor drugs tailored on a patient-to-patient basis.

Antibody-dependent cell-mediated cytotoxicity (ADCC) by natural killer (NK) lymphocytes eliminates cells infected with viruses. Antiviral ADCC requires three components: 1) antibody; 2) effector lymphocytes with the Fc-IgG receptor CD16A; and 3) viral proteins in infected cell membranes. Fc-afucosylated antibodies bind with greater affinity to CD16A than fucosylated antibodies; individuals’ variation in afucosylation contributes to differences in ADCC. Current assays for afucosylated antibodies involve expensive methods. We report an improved bioassay for antibodies that support ADCC which encompasses afucosylation. This assay utilizes externalization of CD107a by NK-92-CD16A cells after antibody recognition. We used anti-CD20 monoclonal antibodies, GA101 WT or glycoengineered (GE), 10% or ~50% afucosylated, and CD20-positive Raji target cells. CD107a increased detection 7-fold compared to flow cytometry to detect Raji-bound antibodies. WT and GE antibody effective concentrations (EC50s) for CD107a externalization differed by 20-fold, with afucosylated GA101-GE more detectable. The EC50s for CD107a-externalization vs. 51Cr cell death were similar for NK-92 CD16A and blood NK cells. Notably, the %CD107a-positive cells negatively correlated with dead Raji cells and was nearly undetectable at high NK:Raji ratios required for cytotoxicity. This bioassay is very sensitive and adaptable to assess anti-viral antibodies but unsuitable as a surrogate assay to monitor cell death after ADCC.

UPF3B is a constituent of the classical nonsense-mediated mRNA decay (NMD) pathway that degrades both the aberrant transcripts and a set of physiological transcripts. In higher eukaryotes, UPF3B have significant biochemical functions in diverse cellular processes including NMD and translation. UPF3B plays a crucial role in neuronal development and differentiation. Next-generation sequencing technologies identified several loss-of-function mutations in the UPF3B gene that results in neuro-developmental disorders in humans. To uncover the mechanistic role of UPF3B in neuronal functions, we have generated the UPF3B-knockout mammalian cell line model system using CRISPR-Cas9 gene editing method. RNA-Sequencing Analysis of cellular transcriptome from UPF3B-KO cells identified specific genes involved in cell growth and neuronal functions. Altered expression of genes related to the axon guidance pathway delineated the UPF3B function to regulate the neuron-specific genes. Functional enrichment analysis identified the genes involved in the disorders related to mental health and intellectual disability. Our study has the potential to identify the direct players of intellectual disability and will have broader implications.

Military medical service is an important component of the Armed Forces of any country [1]. In wartime, healthcare performs critical functions of medical support for military operations, providing effective and qualified care to the wounded and sick. Consequently, it is imperative to equip military hospitals with state-of-the-art materials and technical resources commensurate with the predefined requirements. In this study, we examine a series of enhancements applied to the TurtleBot 3 Waffle Pi-based medical assistant robot, tailored for deployment in military hospital settings.

Automation is the key aspect of science and technology field and refers to controlling the systems for efficient output requirements. The development of robotics and similar, technologies in 21st century is a vast topic of research and significant progress has been made on this field. This research study focuses on building a ribbon-cutting robot with a Bluetooth interface for event inaugurations. The entire system hinges on constructing a prototype that is mounted on a robotic car frame with four wheels and an Arduino controller. In this case, ribbons are cut using a nickel wire, and it may be paired with an Android device via Bluetooth or an application. The robot is one of the first steps toward the development of autonomous, cognitive robots and can be used in a variety of events. Efficient temperature monitoring is done with help of LM35 sensor and Bluetooth based communication in order to monitor the conditions and effectively control the actions for which robot is designed. The robot has significantly allowed authors for desired tests on ribbon cutting and final results obtained were very exciting for its desired application target. Ribbon-cutting robots find applications in various events, including inaugurations, exhibitions, and ceremonies. Their precision and novelty add value to such occasions.

Agriculture and rural regions in Europe face a number of economic, social and environmental challenges. Rural areas are active players in the EU’s green transitions. Weeding is one of the most important factors of agricultural production. New weeding techniques are being developed to enhance sustainability. Among them laser based weeding seems a promising alternative for use of chemicals. WeLASER technique is a novel technique of weed control. Its successful implementation depends on many factors related to the innovation itself, policy context, farming conditions and users’ attitudes. Survey was carried out to provide the insight on the attitudes toward the innova-tive (laser) weed control tool (device - autonomous robot). The CATI method was selected for the survey of farmers opinion and carried out in three countries: Denmark, Spain and Poland. Statistical methods were applied to analyze the results. The study provided knowledge on how farmers see the barriers and opportunities of implementing the device in practice. Positive attitudes of farmers were observed but with high expectations related to quality of the technique and systemic conditions of its implementation.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

Ureteral stricture (US) following ureteroscopic lithotripsy (URSL) has emerged as a serious issue due to the widespread use of laser technology. The management of complex US is challenging. This prospective study evaluated the efficacy of robot-assisted uretero-ureterostomy (RAUU) in addressing US following URSL, and analyzed the pathology of transected ureteral tissues to identify the risk factors for US during URSL. Fourteen patients, with a mean age of 49.8 years, were included in this study. The mean stricture length on radiography was 22.66 ± 7.38 mm. Nine (64.2%) patients had experienced failure with previous interventions, including eight patients who underwent endoscopic incision plus balloon dilation. At an average follow-up of 12.8 months, the overall success rates were 92.9% both clinically and radiographically, without major complications. Pathological findings revealed microcalcifications in 57.1% patients and loss of ureteral mucosa in 28.6% patients. In conclusion, this technique of RAUU via end-to-end anastomosis exhibits promise as a viable option for US following URSL in appropriately selected patients, despite serious pathological changes of the ureter. Migration to the site of ureteral perforation may be the most serious factor contributing to the development of US.

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and are useful in a wide range of environments from warehouse automation, to agriculture, and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from theoretical bounds; there are many reasons for this, with the most prominent and impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aims to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas which are not well-explored and the scope of applying existing MRPP approaches to real-world settings.

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigations. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy, and to address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. Firstly, the fruit tree trunk is identified based on the improved YOLOv4 model; secondly, the camera coordinates of the tree trunk are calculated from the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined and the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall of the improved YOLOv4 model for fruit tree trunk detection are 97.05% and 95.42%, respectively, which are 5.92 and 7.91 percentage points higher than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows, and can provide a reference for autonomous visual navigation of orchard robots.

This paper introduces an alternative method to design the path following controller of an under-actuated spherical mobile robot, which has a single pendulum structure its inside as a driving mechanism. The mechanism has an under-actuated problem; two inputs for the roll and pitch but no direct input for the yaw. In order to overcome this problem, the proposed method defines a virtual reference input for the heading direction. The virtual reference input works as a control input since it can be arbitrarily designed just like the reference input of a conventional system. The control scheme using the defined virtual reference input achieves the stabilization and path following control of the under-actuated spherical mobile robot without any additional control technique. The stability of the proposed control scheme is guaranteed by Lyapunov stability analysis, and simulation results are given to demonstrate its performance.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

In large industrial plants, the trend is to move the position of the human to the area of service and diagnostics, in medium and smaller plants the trend is different and is mainly directed to the sphere of close cooperation between the human and the robotic system. This paper deals with the implementation of human-cooperating robots in manufacturing processes. The analytical part of the paper deals with theoretical knowledge, taking into account the latest trends in the subject area. To achieve a successful implementation of cobots based on the above-mentioned operating systems in manufacturing systems, it is necessary to pay attention specifically to the interfacing of the robotic operating system with the control systems of the manufacturing systems at the process level of the enterprise. Within the practical part, an algorithm with well-defined steps towards the successful implementation of cobots in holonic manufacturing processes is proposed. By setting up an experimental workstation in the laboratory, the proposed procedures are verified at the end of the paper. In the context of the implementation of collaborative robots in enterprises, the paper also discusses cloud computing. The creation of a design for the implementation of a collaborative robot with a human represents the missing link in the whole chain of commercial applications of the latest trends from the field of robotic systems in the industrial sphere.

Children with Autism Spectrum Disorder have deficits in social interaction and expressing and understanding emotions. Many robots for children with ASD have been proposed. However, few studies were found about how to design a social robot for children with ASD. Studies reviewed employed non-experimental studies to evaluate a social robot, but the methodology to design a social robot is rare. This study proposes a design path for a social robot for emotional communi-cation for children with ASD, following User-Centered Design approach. This design path is ap-plied with a study case, evaluated with an expert group on psychologist, human-robot interaction, human-computer interaction, and parents of children with ASD. Results show than following the design path proposed to design a social robot to communication emotions for children ASD is favorable.

Longer operating time in radical prostatectomy may escalate the risk of perioperative complications. Various factors like cancer extent, procedure’s level of difficulty, habitus and previous surgeries may lengthen robotic assisted radical prostatectomy (RARP) and therefor compromise outcomes. Objective: this study investigates the influence of operating time on outcomes after RARP in real life settings in a monocentric single surgeon study. Methods: 500 consecutive patients who underwent RARP between April 2019 and August 2022 were included. Patients were divided in three groups. Short (n=157; 31.4%) under or equal to 120 minutes, average (n=255; 51%) between 121 and 180 minutes, long (n=88; 17,6%) above 180 minutes console-time. Demographic, baseline and perioperative data were analyzed and compared between groups. Univariate logistic regression was completed to investigate the association between console-time and outcomes and to predict factors which may prolong surgery. Results: hospital stay and catheter days were significantly longer in group 3 with medians 6 and 7 days (p<0.001 and <0.001, respectively). Those findings were confirmed in univariate analysis, p=0.012 for Catheter days and p<0.001 for Hospital stay. Moreover, major complications were higher in patients with longer procedures p=0.008. Prostate volume was the only predictor for prolonged console time (p=0.005). Conclusion: RARP is a safe procedure and most patients will be discharged uneventfully. Yet longer console-time is associated with longer hospital stay, catheter days and major complications. Caution has to be taken in large prostate to avoid longer procedures, which may prevent postoperative advert events.

Various designs of spherical robots using barycenter offset mechanisms were previously reported. This kind of robots offers advantages such as turning in placeor moving in any direction at any time. Yet, the approaches resulting in a balanced robot, which could roll with reduced energy costs once accelerated, are scarce. We introduce anovel robot of this type. The feasibility of the approach is verified on a cylindrical robot. This paper presents an open-source, remotely controlled, robotic cylinder. The robot uses a specifically designed mechanism to displace a mass in its center, which causes the cylinder to roll forwards or backwards. The movement characteristics of the cylinder, as well as the mathematical and physical limitations of the mechanism were studied. An intuitive user interface allows the robot to be controlled through WiFi. This robot was manufactured only with readily available and inexpensive materials, which would make it a suitable platform for education, typically in the fields of physics and engineering.

This paper proposed a triangular inequality-based rewiring method for the Rapidly exploring Random Tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm’s performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

Roaming complexity in terrains and unexpected environments poses significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration namely walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates webbed duck feet design to walk on different terrains, swim in water and tackle obstructions on its way. The designed robot is compact and easy-to-use and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Due to the design, the webbed feet are able to open and close with the help of water pressure. The Klann linkage has been used to convert rotational motion to walking and swimming as an animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring the tight caves, closed spaces and moving on uneven challenging terrains such as sand, mud or water. The presented model and design of the amphibious robot has been inspired by the working principle of duck feet. The propulsion generated through this feet system has been observed to be better and more controlled then the initial design and prototype of designed duck feet robot reported in [1]. The direction of movement can be managed, and the speed can be controlled using a larger contact area with water. Since duck is able to roam on both land and water, their feet movement during walking and swimming is adopted in designing the robot’s four legs. Klann linkage is used to mimic such duck feet motion and the control mechanism of the feet is driven by DC motors and DC servo motors which are governed by an Arduino microcontroller. The robot is capable of sensing the presence of water through conductive sensors and detects obstacles using ultrasonic sensor while walking. Due to its amphibian nature and other features in movement, the robot is capable of traversing diversified terrains. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in near future.

In this paper, we propose two novel methods for robot-world/hand-eye calibration and provide a comparative analysis against six state-of-the-art methods. We examine the calibration problem from two alternative geometrical interpretations, called hand-eye and robot-world-hand-eye, respectively. The study analyses the effects of specifying the objective function as pose error or reprojection error minimization problem. We provide three real and three simulated datasets with rendered images as part of the study. In addition, we propose a robotic arm error modeling approach to be used along with the simulated datasets for generating a realistic response. The tests on simulated data are performed in both ideal cases and with pseudo-realistic robotic arm pose and visual noise. Our methods show significant improvement and robustness on many metrics in various scenarios compared to state-of-the-art methods.

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the application of deep learning methods in task generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. A number of the most promising approaches are evaluated and the most successful for grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic-dysfunction associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The efficacy of the model was assessed through the evaluation of the Out-of-Bag (OOB) error estimate. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using 3 models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest), the AUCs were 0.882, 0.972 and 0.96 respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Rice false smut caused by Ustilaginoidea virens is one of the most devastating diseases on rice worldwide, which results in serious reduction of rice quality and yield. As an airborne fungal disease, early diagnosis of rice false smut, monitoring the epidemics and distribution of its pathogens, is particularly important to management the infection. In this study, a quantitative loop-mediated isothermal amplification (q-LAMP) method for the U. virens detection and quantifying was developed. This method has higher sensitivity and efficiency compared to quantitative real-time PCR (q-PCR) method. The species-specific primer sets UV-2 used was design based on the unique sequence of U. virens ustiloxins biosynthetic gene (NCBI accession number: BR001221.1). The q-LAMP assay was able to detect a concentration of 6.4 spores/mL at an optimal reaction temperature of 63.4℃ within 60 min. Moreover, the q-LAMP assay can even achieve accurate quantitative detection when there were only 9 spores on the tape. A linearized equation for the standard curve, y =-0.2866x + 13.829 (x is the amplification time, the spore number = 100.65y), was established for detection and quantifying of U. virens. In field detection applications, this q-LAMP method is more accurate and faster than traditional observation method. Collectively, this study has established a powerful and simple monitoring tool for U. virens, which provide valuable technical supports for forecast and management of rice false smut, and theoretical basis for precise fungicide application.

Initial attempts to develop monoclonal antibodies as therapeutics to resolve influenza infections focused mainly on searching for antibodies with the potential to neutralise the virus in vitro with classical haemagglutination inhibition and micro-neutralisation assays. This led to the identification of many antibodies that bind to the head domain of haemagglutinin (HA) which generally have potent neutralisation capabilities that block viral entry or viral membrane fusion. However, this class of antibodies has a narrow breadth of protection in that they are usually strain specific. This led to the emphasis on stalk targeting antibodies which are able to bind a broad range of viral targets that span across different influenza subtypes. Recently, a third class of antibodies targeting the vestigial esterase (VE) domain have been characterised. In this review, we describe the key features of neutralising VE targeting antibodies and compare them with head and stalk class antibodies.

Autonomous mobile robots are essential to the industry, and human-robot interactions are becoming more common nowadays. These interactions require that the robots navigate scenarios with static and dynamic obstacles in a safely manner, avoiding collisions. This paper presents a physical implementation of a method for dynamic obstacle avoidance using a Long Short-Term Memory (LSTM) neural network that obtains information from the mobile robot’s LiDAR for it to be capable of navigating through scenarios with static and dynamic obstacles while avoiding collisions and reaching its goal. The model is implemented using a TurtleBot3 mobile robot within an OptiTrack Motion Capture (MoCap) system for obtaining its position at any given time. The user operates the robot through these scenarios, recording its LiDAR readings, target point, position inside the MoCap system, and its linear and angular velocities, all of which serve as the input for the LSTM network. The model is trained on data from multiple user-operated trajectories across five different scenarios, outputting the linear and angular velocities for the mobile robot. Physical experiments prove that the model is successful in allowing the mobile robot to reach the target point in each scenario while avoiding the dynamic obstacle, with a validation accuracy of 98.02%.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

In an agricultural system, finding suitable watering, pesticide, and soil content to provide the right nutrients for the right plant is a mystery so far. Plants cannot speak and cannot ask which food they require. These problems can be addressed by applying intelligent (fuzzy logic) controllers to IoT devices in order to enhance communication between crops, ground mobile robots, aerial robots, and the entire farm system. The application of fuzzy logic in agriculture is a promising technology that can be used to optimize crop yields and reduce water usage. It was developed based on language and the air properties in the agricultural fields were formulated. The entire system was simulated in the MATLAB/SIMULINK environment with Cisco Packet Tracer integration. The inputs for the system were soil moisture sensors, temperature sensors, humidity sensors, and outputs were pump flow, valve opening, water level, and moisture in sounding. The obtained results were the output of opening valve, moisture in sound, pump flow rate, outflows, water level, and ADH values, which are 10.00000013 rad/sec, 34.72%, 4.494%, 0.025 m^3/sec, 73.31 cm^3, and 750 values respectively. The outflow rate increase indicates that the water is released from the tanks, and the control signal fluctuates, indicating that the valve is opening.

Scientific research in the study of Human-Robot Interactions (HRI) has advanced significantly in recent years. Social humanoid robots have undergone severe testing and have been implemented in a variety of settings, including educational institutions, healthcare facilities and senior care centers. Humanoid robots have also been evaluated across diverse population groups. However, the research on different children-groups is still scarce, especially deaf children. This feasibility study includes both hearing children and deaf children, and aim to assess the interaction between children and a humanoid robot without the use of sounds and voices. In addition, an experiment was conducted where the children were asked to watch a video and then assess whether the NAO humanoid robot responded to the emotions shown in the video in a congruent or incongruent way. The findings indicated that there was no difference between the children who were present-ed with congruent emotions in the NAO as opposed to the children who were presented with in-congruent emotions in the robot. Only the ability to predict emotion in videos and gender were significant predictors of guessing the correct emotion in robot. Although no significantly differ-ence was found between hearing and deaf children, this feasibility study is intended to lay the groundwork for future research in this unexplored field of study.

Recognizing trust as a pivotal element for success within Human-Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified 'Inspector game' while we monitor their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human-robot interaction in collaborative environments.

The COVID-19 outbreak demonstrated the viability of various remote working solutions, telepresence robots (TPRs) being one of them. High-quality video transmission is one of the cornerstones of using such solutions, as most of the information about the environment is acquired through vision. This study aims to compare the camera capabilities of four models of popular telepresence robots using compact reduced LogMAR and Snellen optometry charts as well as text displayed on the projector screen. The symbols from the images are extracted using OCR (Optical Character Recognition) software and the results of the recognition are compared with the symbols on the charts. Double 3 TPR provides the best quality images of optometric charts, but the OCR results of measurements of the image on the projector do not show the clear advantage of one single model over the others. The results demonstrated by Temi 2 and Double 3 TPRs are generally better than the others, suggesting that these TPRs are more feasible to be used in teaching and learning scenarios.

China is rich in coal resources, but the complex hydrogeological conditions lead to difficulties in coal mining, such as coal mine collapse, roof and water damage, and other accidents that occur frequently, resulting in many casualties and property losses. The use of coal mine hole detection technology to detect and analyze the internal environment of the coal mine in advance helps to reduce safety hazards and prevent coal mine accidents, for the existing coal mine hole detection technology operation is cumbersome, not easy to control, and insufficient depth of jacking prob-lems, this paper designs a new type of mine hole detection small robot. Firstly, this paper ana-lyzes the function and structural design of the mine hole detection robot, designs the variable diameter function according to the characteristics of narrow and uneven mine holes in coal mines, and analyzes the mechanics of the critical parts through theoretical calculations; secondly, based on the three-dimensional modeling software, established the structural model of the small robot for mine hole detection; after that, designed the robot's hardware circuits and control pro-grams for the presence of water and gas inside the mine hole, highlighting the need to carry out a new type of mine hole detection small robot. After that, we designed the robot's hardware circuit and control program, giving particular emphasis to the safety design of the circuit for the pres-ence of water and gas inside the mine hole of the coal mine. Finally, to verify the feasibility of the design program, the basic parameters and function tests of the mine hole detection small robot are carried out. The experimental results show that the mine hole inspection small robot realized by this design can adapt to the working hole diameter from 65 mm to 100 mm, the maximum working power is only 12 W, and the maximum crawling speed is 3.96 m/min. The maximum crawling slope reaches 90°, which can meet the actual mine hole inspection needs. The research can provide theoretical and design guidance for developing the mine hole detection robot with substantial engineering practical reference value.

Cochlear implantation (CI) allows rehabilitation for patients with severe to profound hearing impairment. Although the use of a robotic assistant provides technical assistance to the surgeon, the assessment of the impact of its use on auditory outcomes remains uncertain. We aim to com-pare the hearing results of patients who underwent bilateral cochlear implantation, one side was performed with manual insertion and the other side with robot-assisted insertion. The electrode array intrascalar positioning and the surgery duration were also studied. This retrospective intra-individual study involved 10 patients who underwent bilateral cochlear implantation. The study included 2 infants and 8 adults. The unique composition of this cohort enabled us to utilize each patient as their own control. Regarding speech disyllabic recognition, pure tone average, ECAP, ratio of array translocation, basilar membrane rupture, percentage of translocated electrodes there was no difference between manual and robot-assisted CI groups. This study is the first to compare intra-individual hearing performance after cochlear implantation either manually or robot-assisted. The number of patients and the time delay between manual and robotic implantation may have led to a lack of power, but there was no apparent difference in hearing performance between manual and robotic implantation.

Aiming at the problem that soft robots are challenging to take into account both flexibility and bearing capacity, Using the software Pneu-Net driver as the deformation mode, magnetorheological fluid material phase transformation to achieve variable stiffness, A pneumatic modular soft joint structure with dynamically adjustable deformation and stiffness in all directions is proposed. In this paper, mathematical models of deformation and variable stiffness are established. The correspondence between gas pressure and deformation and applied current and joint stiffness is obtained. The finite element analysis method was used to simulate the deformation characteristics and variable stiffness characteristics of modular joints based on the Yeoh model. Finally, the theoretical and simulation results are verified by deformation and variable stiffness tests. It is proved that the modular joint has good rigid-flexible coupling characteristics.

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural robot, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting and other agricultural robot operations, as well as reducing the operation energy consumption. The traditional boustrophedon or heuristic search algorithm based CCPP methods, when coping with the field with irregular boundaries, obstacles and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, which introduces the idea of chromosome pairs and multi-points mutation to improve its global optimization ability in complex environments, reduce the repeated work areas and the number of turns and U-turns, and thereby improve the operation efficiency. The simulation and experimental results on simple regular fields showed that the proposed improved genetic algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, although the proposed CCPP method increased the number of turns by 13.76%, the area of repeated operation and the number of U-turns were decreased by 38.54% and 35.00% respectively, and saves the battery voltage by 7.82% on average compared to the boustrophedon-based CCPP method. This proved that the proposed CCPP method has strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. As attempts to provide standardized and personalized treatment, a robot-ic-assisted device combined with EMG biofeedback specifically dedicated to shoulder MSDs has been developed. The aim of this study was to determine the efficacy of an 8-week rehabilitation program (≈3 sessions a week) using a ro-botic-assisted device combined with EMG biofeedback (RA-EMG group) in comparison with a conventional program (CONV group) in patients presenting with shoulder MSDs. This study is a retrospective cohort study including data from 2010 to 2013 on patients initially involved in a physical rehabilitation program in a private clinic of Chicoutimi (Canada) for shoulder MSDs. Shoul-der flexion strength and range of motion were collected before and after the rehabilitation program. Forty-four patients participated in a conventional pro-gram using dumbbell (CONV group) while 72 of them completed a program on robot-assisted device with EMG and visual biofeedback (RA-EMG group), whereby both programs consisted in 2 sets of 20 repetitions at 60% of maximal capacity. Results showed that the RA-EMG had significantly greater benefits than the Conv group for shoulder flexion strength (+103.1% vs 67%, p = 0.016) and range of motion (+14.4% vs 6.1%, p = 0.046). The current retrospective co-hort study showed that a specific and tailored rehabilitation program with constant effort by automatic adjustment of the level of resistance was able to potentiate strength and range of motion shoulder flexion after an 8-week reha-bilitation period in comparison with a conventional approach in patients with shoulder MSDs. This study provides new insight on shoulder MSD rehabilita-tion and future research should be pursued to determine the added potential of this approach for abduction and external rotation with a randomized controlled design.

The magnetic navigation system (MNS) with gradient and uniform saddle coils is one of the effective systems to manipulate various medical magnetic robot due to its compact structure and uniformity of the magnetic field and field gradient. However, it has limited operating time and large inductance effect due to the high current density and numerous turns of coils, respectively. In this paper, we propose an optimization methodology of the MNS considering the current density and inductance of the coils. We introduce objective functions, constraints, and design variables of the MNS. Then, the MNS was designed by an optimization algorithm. The constructed MNS was compared with the conventional MNS, and it could generate 22 % stronger magnetic field or field gradient while the maximum three-dimensional rotating magnetic field was improved by 42 %. We also demonstrated an unclogging performance of a helical robot was improved by 54 % with the constructed MNS.

To solve the problem that sampling-based Rapidly-exploring Random Tree (RRT) method is difficult to guarantee optimality. This paper proposed the Post Triangular Processing of Midpoint Interpolation method minimized the planning time and shorter path length of the sampling-based algorithm. The proposed Post Triangular Processing of Midpoint Interpolation method makes a closer to the optimal path and somewhat solves the sharp path problem through the interpolation process. The experiments were conducted to verify the performance of the proposed method. Applying the method proposed in this paper to the RRT algorithm increases the efficiency of optimization compared to the planning time.

The active power-assist function greatly expands the potential applications of exoskeleton robots, yet the motion intention estimation (MIE) for active power-assist strategy is quite problematic. Through the analysis of the conduction path and the different stage manifestations of motion intention in human body, we confirmed that the joint torque of human body meets the basic requirements of MIE for the active power-assist that we suggest, namely: (i) it reflects the direction and intensity of the wearer’s efforts; (ii) it precedes the human limb motion; (iii) it generates real-time and continuous output. Thus, an online calculation method of human joint torque was proposed. The sensing system integrated in exoskeleton robots was designed to perceive motion data and foot contact force of a human body. A special inverse dynamics with a parameterized model of the human body was proposed. Contrast experiments were carried out with the motion capture system, which results’ accuracy and similarity were evaluated via the root mean square error and correlation coefficient. The comparative analysis of two synchronous results shows good accuracy of the proposed MIE method, which lays the foundation for the realization of active power-assist.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

Accurate determination of protein localization, levels, or protein-protein interactions is pivotal for the study of their function, and in situ protein labeling via homologous recombination has emerged as a critical tool in many organisms. While this approach has been refined in various model fungi, the study of protein function in most plant pathogens has predominantly relied on ex-situ or overexpression manipulations. To dissect the molecular mechanisms of development and infection for Verticillium dahliae, a formidable plant pathogen responsible for vascular wilt diseases, we have established a robust, homologous recombination-based in situ protein labeling strategy in this organism. Utilizing Agrobacterium tumefaciens-mediated transformation (ATMT), this methodology facilitates the precise tagging of specific proteins at their C-termini with epitopes, such as GFP and Flag, within the native context of V. dahliae. We demonstrate the efficacy of our approach through the in situ labeling of VdCf2 and VdDMM2, followed by subsequent confirmation via subcellular localization and protein-level analyses. Our findings confirm the applicability of homologous recombination for in situ protein labeling in V. dahliae and suggest its potential utility across a broad spectrum of filamentous fungi. This labeling method stands to significantly advance the field of functional genomics in plant pathogenic fungi, offering a versatile and powerful tool for the elucidation of protein function.

Steroid-responsive meningitis-arteritis (SRMA) is a predominantly Th-2 immune-mediated dis-ease, but exact pathomechanism remains still unclear. Interleukin-31 (IL-31) is predominantly produced by T cells with a Th-2 phenotype during proinflammatory conditions. We hypothesize, IL-31 might be involved in the pathogenesis of SRMA. IL-31 was measured in archived samples (49 serum and 52 CSF samples) of dogs with SRMA, Meningoencephalitis of unknown origin (MUO), infectious meningoencephalitis, atopic dermatitis and of healthy control dogs using a competitive canine IL-31 ELISA. Mean serum IL-31 level in dogs with SRMA (n=18) was mildly higher compared to dogs with atopic dermatitis (n=3) and MUO (n=15) and markedly higher than in healthy controls (n=10) and dogs with infectious meningoencephalitis (n=3). Dogs with SRMA in the acute stage of the disease and without any pre-treatment had the highest IL-31 levels. The mean CSF IL-31 value of dogs with SRMA (n=23) was quite similar to healthy controls (n=8) and did not differ significantly from dogs with MUO (n=19) and infectious meningoencephalitis (n=2). Based on this study, an involvement of IL-31 in the pathogenesis of the systemic Th-2 im-mune-mediated immune response in SRMA can be assumed as a further component leading to an aberrant immune reaction.

We report the first case of Daratumumab interference of allogeneic crossmatch tests repeatedly causing aberrant false-positive results, which inadvertently delayed transplant for a waitlisted renal patient with multiple myeloma. Daratumumab is an IgG1κ human monoclonal antibody commonly used to treat multiple myeloma, characterized by cancerous plasma cells and often leads to renal failure requiring kidney transplant, by depleting CD38-expressing plasma cells. In this case study, the patient had end-stage renal disease secondary to multiple myeloma and was continuously receiving Daratumumab infusions. The patient did not have any detectable antibodies to human leukocyte antigens but repeatedly had unexpected positive crossmatch by the flow cytometry-based method with 26 of the 27 potential deceased organ donors, implying donor-recipient immunological incompatibility. However, further review and analysis suggested that the positive crossmatches were likely false-positive as a result of interference from Daratumumab binding to donor cell surface CD38 as opposed to the presence of donor-specific antibodies. The observed intensity of the false-positive crossmatches was also highly variable, potentially due to donor- and/or cell-dependent expression of CD38. The variability of CD38 expression was, therefore, for the first time, characterized on the T and B cells isolated from various tissues and peripheral blood of 78 individuals. Overall, T cells were found to have a lower CD38 expression profile than the B cells, and no significant difference was observed between deceased and living individuals. Finally, we show that a simple cell treatment by dithiothreitol can effectively mitigate Daratumumab interference thus preserving the utility of pre-transplant crossmatch in multiple myeloma patients awaiting kidney transplant.

Abstract Successful treatment against infectious agents depends on rapid and accurate detection of the causative organisms. Misdiagnosis can hamper such success while leading to improper advising of antibiotics. In Bangladesh, the majority of the diagnostic centers detect and identify pathogens through culture and biochemical test-based methods and suggest antibiotics based solely on the results of disk-diffusion methods. This pilot study tried to validate the identity of the isolates characterized by diagnostic facilities near Dhaka. One hundred and twenty pre-characterized clinical isolates were analyzed biochemically and genotypically. Random Amplification of Polymorphic DNA-PCR, rcsA, and phoA genes-based PCR, and Loop-Mediated Isothermal Amplification (LAMP)-based identification of Klebsiella pneumoniae and Escherichia coli, respectively, followed by 16S rRNA sequencing confirmed misidentification of some clinical pathogens of other genera as Klebsiella spp. and E. coli. According to the antibiotic susceptibility testing guidelines, antibiotic choice, sensitivity pattern, and breakpoint measurement are different for each group of organisms. The lack of adherence to proper standards results in misdiagnosis and may facilitate the development of antibiotic resistance. The pilot study observers misidentification of clinical pathogens identified by the diagnostic centers. Well-characterized rapid molecular techniques like LAMP are suggested in clinical diagnosis to avoid misdiagnosis and subsequently circumvent antibiotic resistance development.

According to the requirement of 20kg load of casting barrel, we design a three-dimensional model of the robot and analyze the three stages of liquid level change in the casting barrel during the casting process. Through the analysis, it is determined that the time and angle function is an implicit function, and it is impossible to find the analytical expression of time about angle. The kinetic function of the casting barrel is simulated by MATLAB, and it is found that the function image has mutation points, and when the original function is fitted and approximated by fifth-degree polynomial interpolation, the fifth-degree polynomial function image of the time versus the angular acceleration has a large amplitude of oscillation, especially the mutation points. A PID algorithm based on the discretization of the time and acceleration function is introduced for correction, and the results show that this method effectively reduces the oscillation amplitude of the image and improves the coincidence with the original function.

The article explores the concept of 'synthetic man' (SM) in the context of 'synthetic biology' (SB), analysing the potential, challenges, and ethical implications of these achievements. Through dialogue with ChatGPT 4, an in-depth understanding of the evolution, contributions of SM, ChatGPT's contribution, and concerns associated with SM is sought, providing insight into how this technology could affect our society. Our study uses a structured dialogue framework with ChatGPT that addresses essential SM questions regarding SM history, contributions, challenges, and concerns. ChatGPT responses are analysed to extract relevant information and identify trends and insights. The ChatGPT dialogue reveals that MS is still a theoretical and speculative concept with significant advances in gene therapy and genetic engineering. The potential contributions of SM in addressing global challenges such as health, environment and education are identified. Ethical and social concerns such as health risks and psychological impact are also highlighted. Differences in ChatGPT responses were observed between different sessions or even within the same session, which highlighted the limitations in the replicability of the research and sometimes the need for clarification of the questions. The article is distinguished by its unique approach of using an advanced language model to explore a complex and speculative topic. The LLM Dialogue Framework on Technology (LLM-DFonTech) framework proposal for structured dialogue with ChatGPT on various technological topics related to the original contribution provides an adaptable tool for interdisciplinary research in emerging technologies. This methodology opens up new possibilities for analyzing and developing trends in technology and society.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

As a typical visual positioning system, monocular ranging is widely used in various fields. However, when the distance increases, there is a greater error. YOLOv8 network has the advantages of fast recognition speed and high accuracy. This paper proposes a method by combining YOLOv8 network recognition with a monocular ranging method to achieve target localization and grasping for the NAO robots. By establishing a visual distance error compensation model and applying it to correct the estimation results of the monocular distance measurement model, the accuracy of the NAO robot's long-distance monocular visual positioning is improved. Additionally, a grasping control strategy based on pose interpolation is proposed. Throughout, the proposed method's advantage in measurement accuracy was confirmed via experiments, and the grasping strategy has been implemented to accurately grasp the target object.

MultiCal is an affordable, high-precision measuring device designed for the on-site calibration of industrial robots. Its design features a long measuring rod with a spherical tip that is attached to the robot. By restricting the rod’s tip to multiple fixed points under different rod orientations, the relative positions of these points are accurately measured beforehand. A common issue with MultiCal is the gravity deformation of the long measuring rod, which introduces measurement errors into the system. This problem becomes especially serious when calibrating large robots, as the length of the measuring rod needs to be increased to enable the robot to move in a sufficient space. To address this issue, we propose two improvements in this paper. Firstly, we suggest the use of a new design of the measuring rod that is lightweight yet has high rigidity. Secondly, we propose a deformation compensation algorithm. Experimental results have shown that the new measuring rod improves calibration accuracy by 20% to 39%, while by using the deformation compensation algorithm, the accuracy increases by 6% to 16%. In the best configuration, the calibration accuracy is similar to that of a measuring arm with a laser scanner, producing an average positioning error of 0.274 mm and a maximum positioning error of 0.838 mm. The improved design is cost-affordable, robust, and has sufficient accuracy, making MultiCal a more reliable tool for industrial robot calibration.

Robot-assisted gait training (RAGT) is a promising treatment for stroke rehabilitation. Although the coordination between the upper and lower limbs is important for locomotor training, commercially available robotics for gait training mainly focus on the restoration of lower limb function. We aimed to evaluate the feasibility and usability of complex upper and lower limb RAGT in stroke patients using the GTR-A®, end effector-type robotic device. Patients with subacute stroke (N=9) received 30-minute RAGT thrice a week for two weeks (six sessions). Functionally, the hand grip strength (HGS), Functional Ambulatory Categories, modified Barthel Index, muscle strength test sum score, Berg Balance Scale, Timed Up and Go test, and Short Physical Performance Battery were used. The heart rate and a structured questionnaire were used to evaluate cardiorespiratory fitness and the usability of RAGT. Among the nine patients, all functional parameters between the baseline and post-training were significantly improved after RAGT, except for HGS and the muscle strength test. The questionnaire’s mean scores for each domain were as follows: safety 4.40±0.35, effects 4.23±0.31, efficiency 4.22±0.77, and satisfaction 4.41±0.25. The GTR-A® is a feasible and safe robotic device for patients with gait impairment after stroke. It showed functional improvement with endurance training effects.

In-depth research on the rehabilitation mechanism and motion characteristics of a rehabilitation training robot based on 4-UPUS parallel mechanism. The overall design and the working principle of each key component are explained, and the robot rehabilitation scheme is described. The static analysis and modal analysis of the robot bearing structure are carried out to verify whether the static mechanical characteristics of the mechanism meet the application requirements under working conditions.A simplified mathematical model of the six-bar linkage mechanism is established, and the kinematics of the mechanism is solved by the geometric analysis method. The motion characteristic diagram of each key point is obtained through simulation calculation. Establish the motion model of the parallel mechanism, and discuss the working space and motion performance analysis of the mechanism. The forward solution analysis of the mechanism position is carried out by using the numerical analysis method, and the three-dimensional graphics of the attitude angle and linear displacement of the reachable working space are obtained.Taking the UPUS single branch chain as the analysis object, the single open chain analysis method is used to solve the kinematics image of the corresponding surface in the working space of the mechanism, which verifies the correctness of the kinematics theoretical solution and the feasibility of simulation.The research results show that the 4-UPUS parallel mechanism rehabilitation training robot can cooperate with the ankle joint for rehabilitation training. It makes up for the single movement of the current lower limb rehabilitation robot and the unsatisfactory rehabilitation effect, and provides a reference for the practical application of the subsequent ankle joint rehabilitation robot system.

We transformed the existing learning program Language Shower, which is used in some Norwegian day-care centers in the Grorud district of Oslo municipality, into a digital solution using an app for smartphone or tablet with the option for further enhancement of presentation by a NAO robot. The solution was tested in several iterations and multiple day-care centers over several weeks. Measurements of the children’s progress across learning sessions indicate a positive impact of the program using a robot as compared to the program without robot. In-situ observations and interviews with day care center staff confirmed the solution’s many advantages, but also revealed some important areas for improvement. In particular, the speech recognition needs to be more flexible and robust, and special measures have to be in place to handle children speaking simultaneously.

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

What is life, what is the difference between something that is alive and something that is not, are viruses living beings, or what would life be like elsewhere in the universe, are questions that still do not have clear-cut answers fully accepted by the scientific community. Based on the fundamental attributes of all living things, I define life as a process that takes place in very ordered organic structures and is characterized by being automatic, interactive and evolutionary. I also define a living being as an organic, highly ordered, automatic, interacting and evolutionary system, and a robot as an ordered automatic and interacting system. Based on this definition and what we know about the biology of viruses, I maintain that they should be considered as living entities. Finally, I explain why if there were life elsewhere in the universe, it would be very similar to what we know on our planet.

Multi-floor environments are usually ignored while designing an autonomous robot for indoor cleaning applications. However, for efficient operation in such environments, the ability of a robotic platform to traverse staircases is crucial. Staircase detection and localization is highly important for planning the traversal on staircases. This paper describes a deep learning approach using Convolutional Neural Networks (CNNs) based Robot Operation System (ROS) to staircase detection and localization. We use an object detection network to detect staircases in images. We also localize these staircases using a contour detection algorithm to detect the target point, a point close to the center of the first step, and the angle of approach to the target point. Experiments are performed with data obtained from images captured on different types of staircases at different view points/angles. Results show that the approach is very accurate in identifying the presence of the staircase in the working environment and is also able to locate the target point with good accuracy.

The paper presents an innovative hexapod walking robot built with 3-UPU parallel mechanism. In the robot, the parallel mechanism is used as both an actuator to generate walking and also a connecting body to connect two groups of three legs, thus enables the robot to walk with simple gait by very few motors. In the paper, the forward and inverse kinematics solutions are obtained. The workspace of the parallel mechanism is analyzed using limit boundary search method. The walking stability of the robot is analyzed, which yields the robot’s maximum step length. The gait planning of the hexapod walking robot is studied for walking on both flat and uneven terrains. The new robot, combining the advantages of parallel robot and walking robot, has a large carrying capacity, strong passing ability, flexible turning ability, and simple gait control for its deployment for uneven terrain.

Handedness reflects functional body and hemispheric lateralization, proving relevant to contextualize cognitive and behavioral profiles. This study aimed to determine the lateralization of the hands, feet and eyes in patients with various neurological disorders and to test the Edinburgh Handedness Inventory (EHI), a widely used handedness measure. The study design included a literature overview and a clinical study. The overview spanned the literature of the past 50 years since the EHI was developed. Three hundred seventy-four patients with immune-mediated (IM) or noIM neurological diseases and healthy subjects underwent personal and family history of handedness, stressful events and IM diseases, and EHI. The literature overview showed that handedness, which develops under the influence of genetic and acquired factors, is an indirect indicator of hemispheric lateralization; hemispheric lateralization is bidirectionally related to the immune system. In the clinical study, the EHI showed appropriate reliability and structural validity; its scores related to chronological age, onset age, and disease duration and helped distinguish patients with IM disease from other patients and healthy subjects. Therefore, handedness may change over the course of life under the influence of pathology and could predict IM neurological diseases. The EHI is an appropriate measure of handedness.

The normal ageing process significantly affects resistance arteries, leading to various biological and physiological consequences. Systolic hypertension is a common occurrence in ageing individuals, accompanied by increased large artery stiffening, heightened pulsatility, small artery remodeling, and damage to critical microvascular structures. Starting from young adulthood, there is a progressive elevation in mean arterial pressure, supported by clinical and epidemiological evidence as well as findings from animal models. The myogenic response, a protective mechanism for the microcirculation, may face disruptions during ageing. Dysregulation of calcium entry channels (T-type, L-type, TRP channels), dysfunction in intracellular calcium storage and extrusion mechanisms, altered expression of potassium channels, and a change in smooth muscle calcium sensitization may contribute to age-related dysregulation of myogenic tone. Flow-mediated vasodilatation, a hallmark of endothelial function, is compromised in ageing. This result of endothelial dysfunction is related to oxidative stress, lower nitric oxide bioavailability and a low-grade inflammatory response, further exacerbating vascular dysfunction. Resistance artery remodeling in ageing emerges as a hypertrophic response of the vessel wall, often seen in conjunction with outward remodeling (in normotension), but can also present as inward hypertrophic remodeling (in hypertension). The remodeling process involves oxidative stress, inflammation, reorganization of extracellular matrix fiber proteins and actin cytoskeletal components. Reactive oxygen species (ROS) signaling and chronic low-grade inflammation, often referred to as "inflammaging”, play substantial roles in age-related vascular dysfunction. Due to its role in regulation of vascular tone as well as structural proteins, the RhoA/Rho-kinase pathway becomes a new target in age-related vascular dysfunction and associated diseases. Understanding the intricate interplay of these factors is crucial for developing targeted interventions to mitigate the consequences of ageing on resistance arteries and enhance overall vascular health.

The prevalence of allergic diseases, including food allergy, is increasing, especially in developed countries. Implementation of an elimination diet is not a sufficient therapeutic strategy in patients with food allergy, whose quality of life is significantly impaired. In recent years, new effective therapeutic strategies have been developed, such as the application of oral, sublingual, and epicutenous immunotherapy. Oral immunotherapy is the most often applied strategy because of its effectiveness and ease of application, with an acceptable safety profile. The effectiveness of oral immunotherapy in patients with egg, cow's milk and peanut allergy has been proven both in terms of raising of the threshold and the development of tolerance, and in some patients, the development of sustainable unresponsiveness. Although oral immunotherapy is an effective treatment for food allergy, several limitations, including long duration and a significant rate of reported adverse events, reduces its success. Therefore, new therapeutic options, such as treatment with biologicals, either as combinations with food allergen immunotherapy or as monotherapy with the aim of improving the efficacy and safety of treatment are being investigated..

The availability of simple SARS-CoV-2 detection methods is crucial to contain the COVID-19 pandemic. This study examined whether a commercial LAMP assay can reliably detect SARS-CoV-2 genomes directly in respiratory samples without having to extract nucleic acids (NA) beforehand. Nasopharyngeal swabs (NPS, n = 220) were tested by real-time reverse transcription (RT)-PCR and with the LAMP assay. For RT-PCR, NA were investigated. For LAMP, NA from 26 NPS in viral transport medium (VTM) were tested. The other 194 NPS were analyzed directly without prior NA extraction [140 samples in VTM; 54 dry swab samples stirred in phosphate buffered saline]. Ten NPS were tested directly by LAMP using a sous-vide cooking unit. The isothermal assay demonstrated excellent specificity (100%) but moderate sensitivity (68.8%), with a positive predictive value of 1 and a negative predictive value of 0.65 for direct testing of NPS in VTM. The use of dry swabs, even without NA extraction, improved the analytical sensitivity; up to 6% of samples showed signs of inhibition. The LAMP could be performed successfully with a sous-vide cooking unit. This technique is very fast, requires little laboratory resources and can replace rapid antigen tests or verify reactive rapid tests on site.

The presence of premature termination codons (PTCs) in transcripts is dangerous for the cell as they encode potentially deleterious truncated proteins that can act with dominant-negative or gain-of-function effects. To avoid synthesis of these shortened polypeptides, several RNA surveillance systems can be activated to decrease the level of PTC-containing mRNAs. Nonsense-mediated mRNA decay (NMD) ensures an accelerated degradation of mRNAs harboring PTCs by using several key NMD factors such as up-frameshift (UPF) proteins. Another pathway called nonsense-associated altered splicing (NAS) upregulates transcripts that have skipped disturbing PTCs by alternative splicing. Therefore, these RNA quality control processes eliminate abnormal PTC-containing mRNAs from the cells by using positive and negative responses. In this review, we will describe the general mechanisms of NMD and NAS and their respective involvement in the decay of aberrant immunoglobulin and TCR transcripts in lymphoid cells.

The turnover of the RNA molecules is determined by the rates of transcription and RNA degradation. Several methods have been developed to study mRNA turnover since the beginnings of molecular biology. Here we summarize the main methods to measure RNA half-life: transcription inhibition, gene control and metabolic labelling. These methods were used to detect the cellular activity of the mRNAs degradation machinery, including the exo-ribonuclease Xrn1 and the exosome. Less progress has been made in the study of the differential stability of mature RNAs because the different methods have often yielded inconsistent results so that an mRNA considered to be stable can be classified as unstable by another method. Recent advances in the systematic comparison of different method variants in yeast have permitted the identification of the least invasive methodologies that reflect half-lives the most faithfully, which is expected to open the way for a consistent quantitative analysis of the determinants of mRNA stability.

This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. An deep artificial neural network (ANN) with perceptrons classifies EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation patterns generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type, comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. Experimental results encompass EMG pattern recognition, OPG, and coordinated fish locomotion, with additional topics illustrated through numerical simulations.

This paper proposes the application of force-projecting bilateral control to a master-follower teleoperation system with a pneumatic manipulator on the follower side, and evaluates its effectiveness. The proposed method directly projects the operating force on the master side to the driving force on the follower side, eliminating the need for both position control and external force detection on the follower side, thereby solving the problem of low rigidity and response delay of pneumatic manipulators and providing highly stable sensor-less force presentation against variable environments. In this study, dynamic response analyses of a 1-DOF master-follower system were performed by numerical simulation using a linear system model, followed by experimental verification by implementing an actual system with an external force estimator. The results showed that the proposed force-projecting bilateral control has significantly higher positioning rigidity and better force control stability than the conventional force-reflecting bilateral control. A theoretical consideration was also given using equivalent transformation of force transfer functions to provide evidence for the high stability.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today’s workplaces, particularly in manufacturing setups due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker’s performance in a human-robot collaborative environment. In this study, factory workers’ mental workload has been assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals have been collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot’s movement speed, and cobot’s payload capacity on the mental stress of a human worker have been observed, for a task designed in the context of a smart factory. Task complexity and cobot’s speed have proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually, they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) have been utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression has performed better for most of the targets and the best correlation (rsq-adj = 0.654146) has been achieved for predicting missed beeps, behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm has been used to evaluate the accuracy of correlation between traditional measures and physiological variables, with highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Taiwan’s Chi Mei Medical Center has a vision of becoming a sustainable organization. They found they violated the sustainable development goal (SDG) 13 (“Climate Action”) since its workflows consume excessive tangible resources and waste much operation time. To fulfill the vision, the Chi Mei Medical Center first introduced electronic invoicing. Implementing this electronic invoicing included adopting cloud E-invoice data and creating a software robot for auditing them. This study presents this software robot and its contributions. The software robot replaces an internal auditor to log into Taiwan’s E-invoice platform, download cloud E-invoice data, and detect the inconsistency between these cloud and on-premise data. Internal auditors of the Chi Mei Medical Center only needed to confirm the detection results. They welcome this software robot since it significantly reduces their work burdens. They approved more software robots for assisting other workflows. Besides, the Chi Mei Medical Center earned profits after eliminating paper invoices. This elimination helped release less carbon. This study concludes that chasing a sustainable organization is unnecessarily equivalent to the requirement of more costs. A software robot represents the last puzzle for constructing a paperless workflow since it can resolve the resistance of existing workers to a new workflow.

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.

Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.

Purpose The COVID-19 pandemic, triggered by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 160 million infections and 3.5 million fatalities worldwide. Evidence of SARS-CoV-2 presence in the highly vascularized penile tissue and the established correlation between COVID-19 and subsequent erectile dysfunction (ED) has emerged. Robot-assisted radical prostatectomy (RARP) is a popular procedure for prostate cancer because of its reliability and swift recovery rate. However, the ED prevalence post-surgery is believed to be comparable to open surgery, and postoperative ED significantly affects the patient's quality of life. Our study aimed to explore the effect of COVID-19 on ED recovery in patients undergoing bilateral nerve preservation during RARP. Materials and Methods We conducted a retrospective study on 85 patients who underwent bilateral nerve-sparing RARP (nsRARP) at our institution between December 2017 and May 2021. Chart reviews and the International Index of Erectile Function-5 (IIEF-5) questionnaire facilitated the analysis. Our objective was to contrast ED recovery after surgery between SARS-CoV-2–infected and non-infected patients using preoperative IIEF-5 scores and scores at 6, 12, and 24 months post-surgery. We defined successful ED recovery as an IIEF-5 score of 17 or higher and successful sexual intercourse. We also compared ED recovery rates between the two groups, regardless of the timing of COVID-19 diagnosis. IBM's SPSS version 18.0 assisted in the statistical analysis, with Fisher's exact test and chi-square test used for comparing categorical variables. Independent t-tests determined mean differences between baseline and postoperative IIEF-5 scores. A p-value less than 0.05 was considered statistically significant. Results From December 2017 to May 2021, our institution executed 213 RARP cases, with 185 of these involving nerve preservation. Among these patients, 133 had an IIEF-5 score of 17 or higher during preoperative EF assessment. We included 85 patients with consistent IIEF-5 records before surgery and at 6, 12, and 24 months post-surgery in this study. Of these, 44 and 41 patients comprised the COVID-19–positive and COVID-19–negative groups, respectively. The COVID-19–positive group had a higher proportion of diabetes mellitus (DM) patients (24 (54.55%) vs. 7 (17.07%), p<0.001). There was no significant difference in preoperative IIEF-5 scores or scores up to 6 months after surgery between the two groups. However, the IIEF-5 scores of the COVID-19–positive group were statistically lower after 12 and 24 months post-surgery. Additionally, the COVID-19–negative group showed a more considerable score increase between 6 and 12 months post-surgery, and the EF recovery rate was lower in the COVID-19–positive group starting from the 12th month after surgery. Within the COVID-19–positive group, no statistically significant difference in IIEF-5 scores was found between single infection and multiple reinfection groups. Conclusions Principal component analysis revealed the impact of COVID-19 on ED recovery in patients who underwent nsRARP. The most substantial changes in IIEF-5 scores occurred between 6 and 12 months. From 12 months onwards, the COVID-19–positive group had significantly lower scores, and fewer patients had IIEF-5 scores above 17. However, the number of COVID-19 diagnoses did not significantly affect the IIEF-5 scores.

The paper presents the conceptualization and realization of a novel platform for additive manu-facturing, an industrial robot arm-based system for additive manufacturing applications. Tradi-tional 3D printers, especially those employing fused deposition modelling (FDM) processes, are restricted to depositing material in a single toolpath plane (e.g. x-y plane). The focus of this study was to explore the feasibility of integrating commercial off the shelf (COTS) additive manufac-turing technologies with a six degree of freedom industrial robot arm to yield a 3D additive manufacturing system with the capability to perform free-form six degree of freedom fused deposition modelling. The present paper presents the development of a platform from stage 0, i.e. materials to its use, and finally a printed product with the developed extruder.

Modularity in the construction of industrial robots is becoming increasingly popular, as the modules can be assembled in different ways so as to obtain various architectures of industrial robots determined by the requirements of specific applications. Such a robot with modular structure is considered in this paper. The tracked mobile robot, with translation – rotation – translation – translation – rotation (TRTTR) modular serial structure, is the subject of national invention patent number RO132301 B1/2021, granted by the State Office for Inventions and Trademarks of Bucharest, to “Nicolae Bălcescu” Land Forces Academy of Sibiu, Romania [1]. The tracked mobile robot has been designed to perform the task of humanitarian demining operations and the mechanical structure, respectively the kinematic analysis were described in detail in papers [1,2]. A first general objective of this paper is to highlight a dynamic-organological method to achieve an architectural structure of mobile robot on tracks, able to replace the human element in areas with high risk for health and life, for detection and demining anti-personnel and anti-armor minefields in countries where military conflicts have taken place. For this scope it was performed the dynamic modeling using the Lagrange’s formalism and to achieve the constructive optimization of the TRTTR mechanical structure, in order to obtain a minimum energy consumption through an optimal arrangement of the modules in its structure. A second objective is to improve the action flexibility of the technological product in humanitarian detection and demining operations, as well as for educational purposes, by training highly educated and specialized human resources in the field of advanced military technologies. The differential equations of motion of the robot has obtained and respectively the mechanical design equations, which lead to the optimal choice of the drive motors of the modules in the robot structure. Also, it is presented the mathematical model for obtaining the driving motor of base translation modulus (MTB SIL) of the modular serial tracked robot and the constructive solution of the MTB SIL module.

This article argues that despite a citation review is a rarely used research tool, this can be very useful to assess the impact of new research topics, both from the future research direction and the bibliometric perspectives. An explorative study is presented around the research area marked as Industry 4.0 with the conference paper mentioned in the title of this citation review. Even though the given reference paper is relatively recent, there are already twenty-seven citations listed among three different scholar databases. These are Google Scholar, ResearchGate and Semantic Scholar. In light of this, the article provides a bibliometric confirmation and analysis for the progression of the line of research adopted by de Giorgio et al. in the exploration of non-traditional methods using virtual reality technology and human-robot collaboration for adaptive applications in Industry 4.0. Furthermore, it represents a model for the authors’ self-development and an example of an unconventional approach to scientific work that may help improve related bibliometric research and scholar database strategies to index new articles and topics in the future.

Anthropomorphism is a natural tendency in humans, but it is also influenced by many characteristics of the observer (the human) and the observed entity (here, the animal species). This study asked participants to complete an online questionnaire about three videos showing epimeletic behaviours in three animal species. In the videos, an individual (a sparrow, an elephant and a macaque, respectively) displayed behaviours towards an inanimate conspecific that suddenly regained consciousness at the end of the footage. A fourth video showed a robot dog being kicked by an engineer to demonstrate its stability. Each video was followed by a series of questions designed to evaluate the degree of anthropomorphism of participants, from mentaphobia (no attribution of intentions and beliefs, whatever the animal species) to full anthropomorphism (full attribution of intentions and beliefs by animals, to the same extent as in humans) and to measure how far the participants had correctly assessed each situation in terms of biological reality (current scientific knowledge of each species). There is a negative correlation (about 61%) between the mental states attributed to animals by humans to animals and the real capability of animals. The heterogeneity of responses proved that humans display different forms of anthropomorphism, from rejecting all emotional or intentional states in animals to considering animals to show the same intentions as humans. However, the scores participants attributed to animals differed according to the species shown in the video and to human sociodemographic characteristics. Understanding the potential usefulness of these factors can lead to better relationships with animals and encourage a positive view of human-robot interactions. Indeed, reflective or critical anthropomorphism can increase our humanity.

The paper presents the possibility of using KINECT v2 module to control an industrial robot by means of gestures and voice commands. It describes elements of creating software for off-line and on-line robot control. The application for KINECT module was developed in C# language in Visual Studio environment, while the industrial robot control program was developed in RAPID language in RobotStudio environment. The development of a two-threaded application in RAPID language allowed to separate two independent tasks for the IRB120 robot. The main task of the robot is performed in thread no. 1 (responsible for movement). Simultaneously working thread no. 2 ensures continuous communication with the KINECT system and provides information about the gesture and voice commands in real time without any interference in thread no. 1. The applied solution allows the robot to work in industrial conditions without negative impact of communication task on the time of robot’s work cycles. Thanks to the development of a digital twin of the real robot station, tests of proper application functioning in off-line mode (without using a real robot) were conducted. Obtained results were verified online (on the real test station). Tests of correctness of gesture recognition were carried out, the robot recognized all programmed gestures. Another test carried out was the recognition and execution of voice commands. A difference in the time of task completion between the actual and virtual station was noticed - the average difference was 0.67 s. The last test carried out was to examine the impact of interference on the recognition of voice commands. With a 10dB difference between the command and noise, the recognition of voice commands was equal to 91.43%. The developed computer programs have a modular structure, which enables easy adaptation to process requirements.

In this study, we asked participants to answer an online questionnaire about videos showing animal epimeletic behaviours: an individual (a sparrow, an elephant and a macaque) displayed behaviours towards an inanimate conspecific who suddenly got back to conscious at the end of the footage. A fourth video showed a dog-robot kicked by an engineer to demonstrate its stability. After each video, questions were asked to score the degree of anthropomorphism of participants, from mentophobia (no attribution whatever the species) to full anthropomorphism and to measure how close participants are to biological reality (actual scientific knowledge). A first important result is that there is a negative correlation (about 61%) between the anthropomorphism score (AS) and the biological reality one (BRS) showing a wrong statement. The heterogeneity of responses proved that all levels of anthropomorphism are covered from mentaphobia to full anthropomorphism. However, the scores participants attributed to animals differ according to the species shown in the video and to human characteristics. Understanding how one can play with these factors can conduct to better relationships with animals as encourage human-robot interactions. Finally, such reflective anthropomorphism can lead to an increase of human empathy and sociality, finally increasing our humanity.

This study presents a path planning method for a mobile robot to be effectively operated through a multi-objective decision-making problem. Specifically, the proposed Fuzzy analytic hierarchy process (FAHP) determines an optimal position as a sub-goal within the multi-objective boundary. The key features of the proposed FAHP are evaluating the candidates according to the fuzzified relative importance among objectives to select an optimal solution. In order to incorporate FAHP into path planning, an AHP framework is defined, which includes the highest level (goal), middle level (objectives), and the lowest level (alternatives). The distance to the target, robot’s rotation, and safety against collision between obstacles are considered as objective functions. Comparative results obtained from the artificial potential field and AHP/FAHP simulations show that FAHP is much preferable for the mobile robot’s path planning than typical AHP.

The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, developing architectures that let them establish empathetic relationships with users. Several models have been proposed in recent years to solve this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioural models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 1007 articles found (at the end 30 articles were selected). For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analysing the aspects that can be improved so that an enjoyable interaction between robots and users can be established.

Research on bidirectional human-machine interfaces will enable the smooth interaction with robotic platforms in contexts ranging from industry to tele-medicine and rescue. This paper introduces a bidirectional communication system to achieve multisensory telepresence during the gestural control of an industrial robotic arm. We complement the gesture-based control by means of a tactile-feedback strategy grounding on a spiking artificial neuron model. Force and motion from the robot are converted in neuromorphic haptic stimuli delivered on the user’s hand through a vibro-tactile glove. Untrained personnel participated in an experimental task benchmarking a pick-and-place operation. The robot end-effector was used to sequentially press six buttons, illuminated according to a random sequence, and comparing the tasks executed without and with tactile feedback. The results demonstrated the reliability of the hand tracking strategy developed for controlling the robotic arm, and the effectiveness of a neuronal spiking model for encoding hand displacement and exerted forces in order to promote a fluid embodiment of the haptic interface and control strategy. The main contribution of this paper is in presenting a robotic arm under gesture-based remote control with multisensory telepresence, demonstrating for the first time that a spiking haptic interface can be used to effectively deliver on the skin surface a sequence of stimuli emulating the neural code of the mechanoreceptors beneath.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

A Berry geometrical phase is identified in a strongly metastable system containing dynamically responsive clathrate hydrate structures within a crystal-fluid material. High energy degeneracy in the associated chemistry produces local stability and false vacuum conditions that lead to non-extensive and non-additive contributions in the fundamental thermodynamic relation. The reciprocating action of a piston expander confirms a net energy gain despite the crystal-fluid material maintaining almost constant density. Application of Ginzburg-Landau theory and the scaling laws reveals a coherence length and a penetration depth for a macro-scale dual superconductor. The coherence length determines non-extensive volume changes whilst its inverse gives the Higgs mass. The penetration depth determines the extent of QCD vacuum suppression whilst its inverse gives the vector boson mass together with indirect manifestation as non-additive hyperbolic curvature. Additionally, the dual superconductor signifies long-range entanglement within a magnetic condensate displaying relativistic effects, whilst the hyperbolic curvature is attributed to quantum interactions. Gravity-mediated entanglement within the laboratory is thereby deduced. Simultaneous emergence of the Ginzburg-Landau superconducting phase transition is consistent with gauge-invariant coupling of the scalar field to the Yang-Mills action in QCD. The discovery of an energy gap in the gradient energy term of the system Lagrangian is associated with a critical correlation length revealed in the transition from a gapped to a gapless superconducting state. Together with the emergence and absorption of the Higgs-like scalar field, a mechanism for describing the QCD mass gap arises.

Hippocampal neuronal activity generates dendritic and somatic Ca2+ signals, which depending on stimulus intensity, rapidly propagate to the nucleus and induce the expression of transcription factors and genes with crucial roles in cognitive functions. Soluble Amyloid-beta Oligomers (AβOs), the main synaptotoxins engaged in the pathogenesis of Alzheimer's disease, generate aberrant Ca2+ signals in primary hippocampal neurons, increase their oxidative tone and disrupt structural plasticity. Here, we explored the effects of sub-lethal AβOs concentrations on activity-generated nuclear Ca2+ signals and on the Ca2+-dependent expression of neuroprotective genes. To induce neuronal activity, neuron-enriched primary hippocampal cultures were treated with the GABAA receptor blocker gabazine (GBZ), and nuclear Ca2+ signals were measured in AβOs-treated or control neurons transfected with a genetically encoded nuclear Ca2+ sensor. Incubation (6 h) with AβOs significantly reduced the nuclear Ca2+ signals and the enhanced phosphorylation of cyclic AMP response element binding protein (CREB) induced by GBZ. Likewise, incubation (6 h) with AβOs significantly reduced the GBZ-induced increases in the mRNA levels of Neuronal Per Arnt Sim domain protein 4 (Npas4), Brain-derived Neurotrophic Factor (Bdnf), Ryanodine Receptor type-2 (RyR2), and the antioxidant enzyme NADPH-Quinone-Oxidoreductase (Nqo1). Based on these findings we propose that AβOs, by inhibiting the generation of activity induced nuclear Ca2+ signals, disrupt key neuroprotective gene expression pathways required for hippocampal-dependent learning and memory processes.

The α(1,2)fucosyltransferase (Se enzyme) encoded by FUT2 is involved in the secretor status of ABH(O) blood group antigens. The sedel2 allele is one of the non-functional FUT2 (se) alleles in which 9.3 kb containing the entire coding region of FUT2 is deleted by Alu-mediated nonhomologous recombination. In addition to this allele, three SNPs of FUT2, c.375A>G, c.385A>T, and c.571C>T, appear to be prevalent in certain Oceanian populations such as Polynesians. Recently, we developed an endpoint genotyping assay to determine the sedel2 zygosity, using a FAM-labeled probe for detection of the sedel2 allele and a VIC-labeled probe for detection of FUT2. In this study, instead of the VIC probe, a HEX-labeled probe covering both c.375A>G and c.385A>T and a Cy5-labeled probe covering c.571C>T were added to the sedel2 allele assay mixture to allow simultaneous detection of these four variations by endpoint genotyping for the sedel2 zygosity and fluorescence melting curve analysis for c.375A>G, c.385A>T, and c.571C>T genotyping. The results obtained from 24 Samoan subjects with this method were identical to those obtained with previous methods. Therefore, it appears that present method can accurately determine these four variations simultaneously.

Anemia and thyroid disorders are global health issues that affect all ages but are more apparent in women. In this case, some serological components responsible for IDA and TSH disorders in women have been found actively regulated through a complex steroidal-calcitriol mediated pathway. This research has been investigated the correlation between Calcitriol and the serological components responsible for IDA and TSH disorders in childbearing and non-child-bearing women of different health conditions. Experimental sampling from 452 women suffering from both IDA and TSH disorders were taken, aged between 0 and 70 years. Serological parameters, such as iron, total iron-binding capacity and ferritin, were assessed for IDA profiling, whereas thyroid-stimulating hormone and free thyroxin were for TSH profiling based on the individual’s serum calcitriol status. The resulted serological data were interpreted using sophisticated computer programming language and algorithms for quantitative biochemical analysis. The study resulted in a significant correlation between FT4 and Calcitriol (P<0.0001) for all age groups. TSH also showed strong interactions with the fluctuation of calcitriol level (P<0.0001), except for the children aged below 10 years (P<0.063). The iron, TIBC, TSH, and FT4 showed phenomenal regulation with the steroidal-calcitriol concentration for congenital patients. Unlike the others, ferritin has a substantial connection with Calcitriol (P<0.0064) fluctuation in the serum. To ratify, the concentrations of TSH, FT4, iron, TIBC, and ferritin were found to be significantly interconnected in terms of serum calcitriol level in women suffering from IDA and TSH disorders simultaneously. To understand the accuracy and efficacy of the Calcitriol in IDA and TSH disorders, some other inflammatory markers and parathyroid hormone analysis are need in future studies, besides a large number of samples.

Virus diseases of strawberry present several complex problems. More than 25 viruses have been described in the genus Fragaria thus far. Here, we describe a novel rhabdovirus, tentatively named strawberry virus 1 (StrV-1), that infects F. ananassa and F. vesca plants. Genomic sequences of three distinct StrV-1 genotypes co-infecting a single F. ananassa host were obtained using combined Illumina and Ion Proton high-throughput sequencing. StrV-1 was transmitted to herbaceous plants via Aphis fabae and A. ruborum, further mechanically transmitted to Nicotiana occidentalis 37B and sub-transferred to N. benthamiana, N. benthamiana DCL2/4i, N. occidentalis 37B and Physalis floridana plants. Irregular chlorotic sectors on leaf blades and the multiplication of calyx leaves seem to be the diagnostic symptoms for StrV-1 on indexed F. vesca clones. StrV-1 was detected in asymptomatic grafted plants and in 49 out of 159 field strawberry samples via RT-PCR followed by Sanger sequencing. The bacilliform shape of the virions, which have a cytoplasm-limited distribution, their size, and phylogenetic relationships support the assignment of StrV-1 to a distinct species of the genus Cytorhabdovirus. Acyrthosiphon malvae, A. fabae and A. ruborum were shown to transmit StrV-1 under experimental conditions.

Penicillium digitatum is the major postharvest pathogen of citrus fruit under Mediterranean climate conditions. In the present work, we have addressed the study of the role of P. digitatum’s proteases in virulence following two complementary approaches. In a first approach, we have undertaken the functional characterization of the P. digitatum prtT gene, which codes for a transcription factor previously shown to regulate extracellular proteases in other filamentous fungi. Deletion of prtT caused a significant loss in secreted protease activity during in vitro growth assays. However, there was no effect on virulence. Gene expression of the two major secreted acid proteases was barely affected in the ΔprtT deletant during infection of citrus fruit. Hence, no conclusion could be drawn on the role of these secreted acidic proteases on the virulence of P. digitatum. In a second approach, we have studied the effect of different protease inhibitors and chelators in virulence. Co-inoculation of citrus fruit with P. digitatum conidia and a cocktail of protease inhibitors resulted in almost a complete absence of disease development. Analysis of individual inhibitors revealed that the metalloprotease inhibitor 1,10-phenanthroline was responsible for the observed effect. The application of metal ions reverted the protective effect caused by the metallopeptidase inhibitor. These results may set the basis for the development of new alternative treatments to combat this important postharvest pathogen.

Schistosoma infection in snails can be monitored by microscopy or indirectly by sentinel mice. As both these approaches sometimes miss infections, more sensitive tests are needed, particularly in low-level transmission settings. In this study, the loop-mediated isothermal amplification (LAMP) technique, designed to detect a specific 28S ribosomal S. japonicum gene with high sensitivity, was compared to microscopy using snail samples from 51 areas endemic for schistosomiasis in five Chinese provinces. The results were compared with those by polymerase chain reaction (PCR) adding DNA sequencing as a reference when needed. The testing of pooled snail samples showed that a dilution factor of 1/50, i.e., one infected snail plus 49 non-infected ones, would still result in a positive reaction after the recommended number of amplification cycles. Testing a total of 232 pooled samples, emanating from 4,006 snail specimens, with the LAMP assay showed a 6.5% rate of infection, while traditional microscopy found only 0.04% positive samples in the same materials. Parallel PCR analysis confirmed the diagnostic accuracy of the LAMP assay, with DNA sequencing even giving LAMP a slight lead. Microscopy and the LAMP test were carried out at local schistosomiasis-control stations demonstrating that the potential of the latter assay to serve as a point-of-care (POC) test with results available within 60–90 minutes, while the more complicated PCR test had to be carried out at the National Institute of Parasitic Diseases (NIPD) in Shanghai, China. In conclusion, LAMP was found to be clearly superior to microscopy and as good as, or better, than PCR. Application of LAMP testing would be useful for surveillance and risk prediction as it requires less time than other techniques and can be used under field conditions, which improves and accelerates schistosomiasis control.

Endoscopy has made a significant and noteworthy contribution to the field of medical science and technology. Nevertheless, its potential remains constrained due to the limited availability of rigid or flexible endoscopes. This paper introduces a novel hydraulic actuator based on electrorheological fluid (ERF) as a pivotal advancement in bridging the existing gap within the realm of endoscopy. Following a comprehensive introduction that briefly outlines the electrorheological effect, the subsequent section is dedicated to the elucidation of the actuator’s development process. Challenges arise, particularly in terms of miniaturization and the realization of a hydraulically sealed system with integrated valve electrodes. An internal electrorheological valve system consisting of four valves that are controlled using a pulse-width modulated high voltage was suitable for position control of the antagonistic hydraulic actuators. High-precision stereolithography (SLA) printing has proven practical for manufacturing the actuator components. For functional testing, a test bench was set up in which the actuator follows a setpoint through a PI control loop. The control deviation ranged from 0.6 to 1 degree, with a response time between 6 and 8 seconds. The experiments have demonstrated that, through the use of ERF and integrated valve electrodes, a miniaturized functional actuator can be constructed.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

Obesity represents a worldwide epidemic disorder, increasing the overall morbidity and mortality rate. In this study we investigated the impact of obesity on peri-operative and long-term functional outcomes of Robotic assisted simple prostatectomy (RASP). Baseline flowmetry parameters and validated questionnaires’ scores were prospectively recorded. Follow-up assessments included Flowmetry and validated questionnaires. Composite outcomes (Trifecta) was defined as combination of: post-operative Q-max>15 ml/sec, IPSS score<8 and absence of complications. Pentafecta included also post-operative ejaculation persistence (MSHQ score>0) and the Erectile function maintenance (∆IEEF<6). Data were stratified by BMI (<30 or ≥30). 81 patients underwent RASP in our Institution. Baseline demographics and clinical features, questionnaires score and baseline flowmetry results were comparable between obese and non-obese cohorts. At follow-up, obese patients reported lower subjective improvement in IPSS (p=0.02) and OABQ scores (p<0.001) and higher incidence of stress incontinence requiring Duloxetin (p<0.001). Flowmetry outcomes, were also worse in this cohort (p=0.02 and p=0.03, respectively). At comprehensive outcomes assessment, obese patients had comparable Trifecta (67% vs 54%, p=0.39) and pentafecta achievement rate (p=0.76). Our prelimirary results proved that obesity is associated with worse functional outcomes (storage LUTS and incontinence rate) after RASP, but doesn’t affect trifecta and pentafecta achievement rate.

By constructing a static map, the library robot can navigate and access documents autonomously, which greatly improves efficiency; however, for libraries where shelves can be moved, map changes do not allow for direct navigation, and also sensors such as radar and camera are relatively expensive, so we propose a low-cost navigation algorithm based on real-time ranging data and map matrices, and designed a mobile archive access robot. In order to obtain real-time distance data, nine near/long range sensors were mounted on the chassis and a composite digital filtering algorithm was designed based on different moving area characteristics; then the access task matrix and map matrix were designed based on the archive access task and the location characteristics of the archive shelf placement, the robot can rely on the range data to update the map matrix during the moving process, complete its own positioning, and use the task matrix The robot can use the task matrix to complete autonomous navigation and access to multiple files. Experiments show that the filtered positioning accuracy can reach 1cm, while the robot can move to the target shelf autonomously, which is more practical and less costly.

The upper extremities limitation represents one of the essential functional impairments in patients with cervical spinal cord injury. Electromechanics assisted devices and robots are increasingly used in neurorehabilitation to help functional improvement in patients with neurological diseases. This review aimed to systematically report the evidence-based, state-of-art on clinical applications and robotic-assisted arm training (RAT) in motor and functional recovery in subjects affected by cervical spinal cord injury. The present study has been carried out within the framework of the Italian Consensus Conference on "Rehabilitation assisted by robotic and electromechanical devices for persons with disability of neurological origin" (CICERONE). PubMed/MEDLINE, Cochrane Library, and Physiotherapy Evidence Database (PEDro) databases were systematically searched from inception to September 2021. The 10-item PEDro scale assessed the study quality for the RCT and the AMSTAR-2 for the systematic review. Two different authors rated the studies included in this review. If consensus was not achieved after discussion, a third reviewer was interrogated. The 5-item Oxford CEBM scale was used to rate the level of evidence. A total of 11 studies were included. The selected studies were: two systematic reviews, two RCTs, one parallel-group controlled trial, one longitudinal intervention study and five case series. One RCT was scored as a high-quality study, while the systematic review was of low quality. RAT was reported as feasible and safe. Initial positive effects of RAT were found for arm function and quality of movement in addition to conventional therapy. The high clinical heterogeneity of treatment programs and the variety of robot devices could severely affect the generalizability of the study results; therefore, future studies are warranted to standardize the type of intervention and evaluate the role of robotic-assisted training in subjects affected by cervical spinal cord injury.

This paper presents a system developed for the assistance with a collaborative robot in hand-assisted laparoscopic surgery (HALS). The system includes a sensing glove with piezoresistive sensors which capture continuously the flexion degree of the surgeon's fingers. These data are analyzed using an algorithm that detects and recognize the selected movements. This information is sent as commands to the collaborative robot throughout the surgical operation. The bending patterns, speed and execution times of the movements are modelled in a pre-phase in which it will extract all the necessary information for later detection during the motion execution. The results obtained with 10 different volunteers show a high degree of accuracy and a low false discovery rate.

Angiotensin-converting enzyme (ACE) inhibitors are used primarily in the treatment of hypertension, heart failure, and in the acute phase of myocardial infarction. Lisinopril [N2-[(1S)-1-car-boxy-3-phenylpropyl]-L-lysyl-L-proline], enalapril [(S)-1-[N-[1-(ethoxycarbonyl)-3-phenylpropyl]-L-alanyl]-L-proline] and ramipril [2-aza-bicyclo-[3.3.0]-octane-3-carboxylic acid] are all five-membered heterocycles and three of the most prevalent ACE inhibitors in clinical use worldwide. ACEi-induced angioedema (ACEi-AE) is clinically characterized by self-limited edema of the dermis and subcutaneous lipid tissue, localized on face skin, oral mucosa and tongue in most cases. However, severe episodes of intestinal AE misdiagnosed as acute appendicitis and laryngeal AE requiring incubation have been reported. The pathophysiology of ACEi induced-AE is attributed to the accumulation of bradykinin, which is a potent vasodilator with proinflammatory activity that is normally degraded by angiotensin-converting enzyme (ACE) and aminopeptidase P; however, a small proportion of treated patients is affected. Given that patients do not respond to anti-H1 antihistamines and steroids, the early clinical recognition and the ACEi discontinuation are the treatment of choice for the long term management of ACEi-AE.

There is increasing interest in identifying biological and imaging markers for the early detection of neurocognitive decline. In addition, non-pharmacological strategies including physical exercise and cognitive interventions may be beneficial for those developing cognitive impairment. The Feuerstein Instrumental Enrichment (FIE) Program is a cognitive intervention based on Structural Cognitive Modifiability and the Mediated Learning Experience (MLE) and aims to promote problem-solving strategies and metacognitive abilities. The FIE program uses a variety of instruments to enhance the cognitive capacity of the individual as a result of mediation. A specific version of the FIE program was developed for the cognitive enhancement of older adults, focusing on strengthening orientation skills, categorization skills, deductive reasoning and memory. We performed a prospective interventional pilot observational study on older subjects with MCI who participated in 30 mediated FIE sessions (two sessions weekly for 15 weeks). Of the 21 subjects who completed the study, there was a significant improvement in memory on the Neurotrax battery comparing pre- and post-intervention scores (pre: M=95.3, SD=12.2, post: M=101.2, SD=7.9, p<.05). Complete sets of anatomical MRI data for voxel-based morphometry, taken at the beginning and the end of the study, were obtained from 16 participants (mean age 83.5 years). Voxel-based morphometry showed an unexpected increase in grey matter (GM) in the anterolateral occipital border and the middle cingulate cortex. These initial findings of our pilot study support the design of randomized trials to evaluate the effect of cognitive training using the FIE Program on brain volumes and cognitive function.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Being hospitalized is a threatening and stressful experience for many children. From a psychological point of view, children may experience increased feelings of anxiety and fear that can negatively interfere with behavioral, cognitive, and emotional outcomes. To limit these impacts on children's well-being and mental health, interventions that could contribute to protecting the emotional domain of hospitalized children are welcomed. The present research reported a single-setting case study intervention to evaluate the impact of educational play-based activity with a CoderBot robot in a pediatric short-term recovery ward (N=61). The methodology included multiple sources of data (i.e., children, parents, nurses), observations on the field, and a sequential (quantitative-qualitative) mixed-method approach to data analysis. Results supported the idea that robot-based activities were associated with increased participant well-being (particularly positive emotions). The conclusions of this pilot study discuss the strengths, limitations, and further developments of using robots with hospitalized children.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.