Validation of clinically related aging models based on enamel wear.

PURPOSE: Physiological and erosive wear reported in clinical studies were reviewed, and in vitro aging models were developed to simulate and compare the effect of aging on human teeth with the review data obtained from clinical studies. METHODS: A review of clinical studies and randomized clinical trials that quantify enamel wear was performed in the PubMed database. The first in vitro analysis evaluated the effect of mechanical chewing simulation only. Enamel specimens were aged in the chewing simulator (up to 1.2 million cycles) with two occlusal loads (30 and 50 N). In the second in vitro analysis, specimens were aged in two aging models. The first model (MT) simulated mechanical and thermal oral challenges: MT1- 240,000 chewing and 10,000 thermal cycles, MT2- 480,000 chewing and 20,000 thermal cycles, MT3- 1.2 million chewing and 50,000 thermal cycles. The second model (MTA) simulated mechanical, thermal, and acidic oral challenges as follows: MTA1- 240,000 chewing, 10,000 thermal and 3-h acidic cycles; MTA2: 480,000 chewing, 20,000 thermal and 6-h acidic cycles, MTA3- 1.2 million chewing, 50,000 thermal and 15-h acidic cycles. RESULTS: The review included 13 clinical studies evaluating tooth wear (eight physiological and five erosive). The results estimated the annual average physiological wear as 38.4 µm (9.37-51). In comparison, the MT1 showed wear of 60 (24) µm. Also, the average annual erosive wear in the literature was 179.5 µm (70-265) compared to MTA1-induced wear of 209 (14) µm. CONCLUSION: There was wide variation in tooth wear reported in clinical studies, suggesting a critical need for more accurate studies, possibly based on scanning technologies. Despite this, the data reported using the novel aging models are within a range to be considered consistent with and to simulate tooth wear measured in vivo.

How Well Can Quantum Embedding Method Predict the Reaction Profiles for Hydrogenation of Small Li Clusters?

Quantum computing leverages the principles of quantum mechanics in novel ways to tackle complex chemistry problems that cannot be accurately addressed using traditional quantum chemistry methods. However, the high computational cost and available number of physical qubits with high fidelity limit its application to small chemical systems. This work employed a quantum-classical framework which features a quantum active space-embedding approach to perform simulations of chemical reactions that require up to 14 qubits. This framework was applied to prototypical example metal hydrogenation reactions: the coupling between hydrogen and Li2, Li3, and Li4 clusters. Particular attention was paid to the computation of barriers and reaction energies. The predicted reaction profiles compare well with advanced classical quantum chemistry methods, demonstrating the potential of the quantum embedding algorithm to map out reaction profiles of realistic gas-phase chemical reactions to ascertain qualitative energetic trends. Additionally, the predicted potential energy curves provide a benchmark to compare against both current and future quantum embedding approaches.

Effect of noise on the performance of arthroscopic simulator.

Background: Noise is omnipresent in the operating room. The average noise in the operating room generally ranges between 60 and 65 dB and can sometimes exceed 100 dB, despite the ARS (Agence Régionale de Santé) and WHO (World Health Organization) recommending levels of 35 dB(A). This study aimed to evaluate the effect of different kinds of background auditory stimuli on the performance of surgeons during an arthroscopic simulation task. Methods: Forty-seven surgeons with varying experience in arthroscopic surgery undertook different exercises under four different conditions: quiet, classical music, hard rock, and sustained chatter. All background auditory stimuli were set at 65 dB(A). Each participant underwent double randomization for the four sound stimuli and the four exercises to be performed. A musical questionnaire was also completed by each participant. Data related to each exercise included operating time in seconds, distance from the camera or instruments in centimeters, and an overall score automatically calculated by the simulator based on safety, economy of movement, and speed (scale: 0-20 points). Results: Operative time in an environment with classical music was significantly lower than in an environment with hard rock (95.9 s vs. 128.7 s, p = 0.0003). The overall rating in an environment with chatter was significantly lower than in a silent environment (11.7 vs. 15.7, p < 0.0001). The overall rating in an environment with hard rock was significantly lower than in an environment with classical music (14.3 vs. 17.5, p = 0.0008).Surgeons who preferred listening to music in the operating room performed differently than those who did not. The mean operative time for surgeons who preferred music was 99.52 s (SD = 47.20), compared to 117.16 s (SD = 61.06) for those who did not prefer music, though this difference was not statistically significant (p = 0.082). The mean overall score for surgeons who preferred music was significantly higher at 17.46 (SD = 2.29) compared to 15.57 (SD = 3.49) for those who did not prefer music (p = 0.001). Conclusions: Our study suggests that exposure to classical music and silence may confer greater benefits to the surgeon compared to the impact of hard rock and chatter. These conclusions are grounded in significant differences observed in operative time and overall evaluations, highlighting the potential advantages of an environment characterized by acoustic tranquility for surgical professionals. Preferences for music in the operating room also play a role, with those who prefer music demonstrating better performance scores.

Enabling Seamless Connectivity: Networking Innovations in Wireless Sensor Networks for Industrial Application.

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs.

Virtual Inspection System for Pumping Stations with Multimodal Feedback.

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system's data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry.

Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment.

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers' safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app's efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.

A novel low-cost device for tool targeting training and microsurgical hand tremor assessment.

Background: The spatial accuracy of microsurgical manipulations is one of the critical factors in successful surgical interventions. The purpose of this study was to create a low-cost, high-fidelity, and easy-to-use simulator for microsurgical skills training, which can be made by residents themselves at home. Methods: In response to the COVID-19 pandemic, we created a device for spatial accuracy microsurgical skills training and implemented it in our resident's training program. We propose a design for basic and advanced models. The simulator consisted of commonly available products. Results: A low-cost, durable, and high-fidelity basic model has been developed at a total cost of <10 dollars per unit. The model allows trainees to practice the critical microsurgical skills of tool targeting in a home-based setting. Conclusion: The developed device can be assembled at an affordable price using commercially available materials. Such simulation models can provide valuable training opportunities for microsurgery residents.

Dissolution profiles of BCS class II drugs generated by the gastrointestinal simulator alpha has an edge over the compendial USP II method.

The poor water solubility of orally administered drugs leads to low dissolution in the GI tract, resulting to low oral bioavailability. Traditionally, in vitro dissolution testing using the compendial dissolution apparatuses I and II has been the gold-standard method for evaluating drug dissolution and assuring drug quality. However, these methods don't accurately represent the complex physiologies of the GI tract, making it difficult to predict in vivo behavior of these drugs. In this study, the in vivo predictive method, gastrointestinal simulator alpha (GIS-α), was used to study the dissolution profiles of commercially available BCS Class II drugs, danazol, fenofibrate, celecoxib, and ritonavir. This biorelevant transfer method utilizes multiple compartments alongside peristaltic pumps, to effectively model the transfer of material in the GI tract. In all cases, the GIS-α with biorelevant buffers gave superior dissolution profiles. In silico modeling using GastroPlusTM yielded better prediction when utilizing the results from the GIS-α as input compared to the dissolution profiles obtained from the USP II apparatus. This gives the GIS-α an edge over compendial methods in generating drug dissolution profiles and is especially useful in the early stages of drug and formulation development. This information gives insight into the dissolution behavior and potential absorption patterns of these drugs which can be crucial for formulation development, as it allows for the optimization of drug delivery systems to enhance solubility, dissolution, and ultimately, bioavailability.

GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis.

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset.

Feasibility of initiating robotic surgery during the early stages of gastrointestinal surgery education.

PURPOSE: Minimally invasive surgery for gastrointestinal cancers is rapidly advancing; therefore, surgical education must be changed. This study aimed to examine the feasibility of early initiation of robotic surgery education for surgical residents. METHODS: The ability of staff physicians and residents to handle robotic surgical instruments was assessed using the da Vinci® skills simulator (DVSS). The short-term outcomes of 32 patients with colon cancer who underwent robot-assisted colectomy (RAC) by staff physicians and residents, supervised by a dual console system, between August 2022 and March 2024 were compared. RESULTS: The performances of four basic exercises were assessed after implementation of the DVSS. Residents required less time to complete these exercises and achieved a higher overall score than staff physicians. There were no significant differences in the short-term outcomes, operative time, blood loss, incidence of postoperative complications, and length of the postoperative hospital stay of the two surgeon groups. CONCLUSION: Based on the evaluation involving the DVSS and RAC results, it appears feasible to begin robotic surgery training at an early stage of surgical education using a dual console system.

A structured curriculum for the acquisition of basic surgical endoscopic skills for surgical residents and quantification of skills improvement.

Introduction: New strategies and methods are needed to ensure that new generations can train and acquire surgical skills in a safe environment. Materials and methods: From January 2020 to October 2020, we performed a single centre, prospective observational cohort study. 19 participants (15 students, 4 residents) enrolled and 16 participants (13 students, 3 residents) successfully completed the curriculum. We performed a quantitative data analysis to evaluate its effectiveness in gaining and improving basic surgical endoscopic skills. Results: The time for single knot tying pre-, mid-, and post-training was reduced significantly, the average time (sec) decreased by 79.5 % (p < 0.001), the total linear distance (cm) by 74.5 % (p < 0.001) and the total angular distance (rad) by 71.7 % (p < 0.001). The average acceleration (mm/s2) increased by 20 % (p = 0.041). Additionally, the average speed increased by 23.5 % (p < 0.001), while motion smoothness (m/s3) increased by 20.4 % (p = 0.02). Conclusion: The obtained performance scores showed a significant increase in participants improving their basic surgical performance skills on the endoscopic simulator. This curriculum can be easily implemented in any surgical specialty as part of the residency training curriculum before first exposure in the operation room. All 16 participants recommended the implementation of such simulator training in their surgical training curriculum.

[Development of a Head Radiography Simulator System Using Pose Estimation Techniques].

PURPOSE: This study proposes a system that can simulate head radiography by combining a technique for estimating human posture from moving images (hereafter referred to as "pose estimation technique") and use of two cameras capable of acquiring RGB images to determine body position during positioning. METHODS: The angles of the median sagittal plane (MS), axial plane (AX), and orbitomeatal baseline (OM) were obtained using the pose estimation technique from frontal and lateral images captured after positioning. The resulting radiographs were displayed according to the results. RESULTS: The head tilt during positioning could be determined based on the coordinate data of feature points acquired using the pose estimation technique. In an imaging experiment using a simulated human patient, errors increased as head tilt increased; however, the mean error values in each axis were 0.9° for MS, 0.8° for AX, and 1.5°for OM, when the patient was correctly positioned. CONCLUSION: The pose estimation technique can assist in evaluating positioning accuracy in radiography and is expected to be used as a potential simulator system.

An ovine knee simulator: description and proof of concept.

Aims: The ovine stifle is an established model for evaluation of knee treatments, such as meniscus replacement. This study introduces a novel ovine gait simulator for pre-testing of surgical treatments prior to in vivo animal trials. Furthermore, we describe a pilot study that assessed gait kinematics and contact pressures of native ovine stifle joints and those implanted with a novel fiber-matrix reinforced polyvinyl alcohol-polyethylene glycol (PVA-PEG) hydrogel meniscus to illustrate the efficacy of the simulator. Methods: The gait simulator controlled femoral flexion-extension and applied a 980N axial contact force to the distal tibia, whose movement was guided by the natural ligaments. Five right ovine stifle joints were implanted with a PVA-PEG total medial meniscus replacement, fixed to the tibia via transosseous tunnels and interference screws. Six intact and five implanted right ovine stifle joints were tested for 500 k gait cycles at 1.55 Hz. Implanted stifle joint contact pressures and kinematics in the simulator were compared to the intact group. Contact pressures were measured at 55° flexion using pressure sensitive film inserted sub-meniscally. 3D kinematics were measured optically across two 30-s captures. Results: Peak contact pressures in intact stifles were 3.6 ± 1.0 MPa and 6.0 ± 2.1 MPa in the medial and lateral condyles (p < 0.05) and did not differ significantly from previous studies (p > 0.4). Medial peak implanted pressures were 4.3 ± 2.2 MPa (p > 0.4 versus intact), while lateral peak pressures (9.4 ± 0.8 MPa) were raised post medial compartment implantation (p < 0.01). The range of motion for intact joints was flexion/extension 37° ± 1°, varus/valgus 1° ± 1°, external/internal rotation 5° ± 3°, lateral/medial translation 2 ± 1 mm, anterior/posterior translation 3 ± 1 mm and distraction/compression 1 ± 1 mm. Ovine joint kinematics in the simulator did not differ significantly from published in vivo data for the intact group, and the intact and implanted groups were comparable (p > 0.01), except for in distraction-compression (p < 0.01). Conclusion: These findings show correspondence of the ovine simulator kinematics with in vivo gait parameters. The efficacy of the simulator to evaluate novel treatments was demonstrated by implanting a PVA-PEG hydrogel medial meniscal replacement, which restored the medial peak contact pressures but not lateral. This novel simulator may enable future work on the development of surgical procedures, derisking subsequent work in live animals.

An Indigenous Suction-assisted Laryngoscopy and Airway Decontamination Simulation System.

Background: Suction-assisted laryngoscopy and airway decontamination (SALAD) is a new modality and training manikins are quite costly. Few modifications have been described with their pluses and minuses. We describe a low-cost simulator that replicates fluid contamination of the airway at various flow rates and allows the practice of SALAD in vitro. Materials and methods: We modified a standard Laerdal airway management trainer with locally available equipment to simulate varying rates of continuous vomiting or hemorrhage into the airway during intubation. The effectiveness of our SALAD simulator was tested during an advanced airway workshop of the Airway Management Foundation (AMF). The workshop had a brief common presentation on the learning objective of the SALAD technique followed by a demonstration to small groups of 5-6 participants at one time with necessary instructions. This was followed by a hands-on practical learning session on the simulator. Results: One hundred and five learners used the simulator including 15 faculties and 90 participants (48 on ICU and 42 on ENT workstations). At the end of the session, the workshop faculty and participants were asked to rate their level of confidence in managing similar situations in real practice on a four-point Likert scale. All 15 faculty members and 70 out of 90 participants felt very confident in managing similar situations in real practice. Fifteen participants felt fairly confident and 5 felt slightly confident. Conclusion: In resource-limited settings, our low-cost SALAD simulator is a good educational tool for training airway managers in the skills of managing continuously and rapidly soiling airways. How to cite this article: Kumar R, Kumar R. An Indigenous Suction-assisted Laryngoscopy and Airway Decontamination Simulation System. Indian J Crit Care Med 2024;28(7):702-705.

Driving performance of long-term users of sedating antidepressants and benzodiazepines.

OBJECTIVE: Using benzodiazepines and certain antidepressants is associated with an increased risk of motor vehicle crashes due to impaired driving skills. Hence, several countries prohibit people who use these drugs from driving. Traffic regulations for driving under the influence of these drugs are, however, largely based on single-dose studies with healthy participants. The effects of drugs on chronic users may be different because of potential development of tolerance or by adapting behavior. In this study, we test the effects of anti-depressants, hypnotics, or anxiolytics use on driving performance in patients who use these drugs for different durations and compare the effects to healthy controls' performance. METHODS: Sixty-six healthy controls and 82 medication users were recruited to perform four drives in a driving simulator. Patients were divided into groups that used anti-depressants, hypnotics, or anxiolytics, for shorter or longer than 3 years (i.e. LT3- or LT3+, respectively). The minimum term of use was 6 months. Driving behavior was measured in terms of longitudinal and lateral control (speed variability and Standard Deviation of Lateral Position: SDLP), brake reaction time, and time headway. Impaired driving performance was defined as performing similar to driving with a Blood Alcohol Concentration of 0.5‰ or higher, determined by means of non-inferiority analyses. RESULTS: Reaction time analyses revealed inconclusive findings in all groups. No significant performance differences between matched healthy controls, LT3- (n = 2), and LT3+ (n = 8) anxiolytics users were found. LT3+ antidepressants users (n = 12) did not perform inferior to their matched controls in terms of SDLP. LT3- hypnotics users (n = 6) showed more speed variability than their matched healthy controls, while this effect was not found for the LT3+ group (n = 14): the latter did not perform inferior to the healthy controls. Regarding Time Headway, no conclusions about the LT3- hypnotics group could be drawn, while the LT3+ group did not perform inferior compared to the control group. CONCLUSIONS: The small number of anxiolytics users prohibits drawing conclusions about clinical relevance. Although many outcomes were inconclusive, there is evidence that some elements of complex driving performance may not be impaired (anymore) after using antidepressants or hypnotics longer than 3 years.

Movement-based cognitive training does not significantly shorten the learning curve for acquiring arthroscopic basic skills.

PURPOSE: Skilful arthroscopy requires an aboveaverage level of manual dexterity. It is evident that particular motor skills can be learned and trained before arthroscopic training. The aim of this prospective cohort study was to investigate the impact of movement-related cognitive training on the learning curve during arthroscopic basic training. METHODS: Fifty right-handed participants without arthroscopic experience were matched to an intervention group (n = 25) and a control group (n = 25). Prior to basic arthroscopic skill training with a simulator, the intervention group underwent 12 weeks of movement-related cognitive training. Cognitive and motor skills were assessed in both groups by using standardised tests (CogniFit test, angle reproduction test, two-arm coordination test) as a pretest and, for the intervention group, again before arthroscopic training as a posttest. For arthroscopic simulator training, three tasks ('Telescoping', 'Periscoping', 'Triangulation') from the Fundamentals of Arthroscopic Surgery Training module were selected and practiced 10 times with the camera in the right and left hands. The learning progress was quantified by exercise time, camera path length and hook path length. RESULTS: No significant differences in sex distribution, age distribution or the results of the pretests between the intervention group (n = 21) and the control group (n = 25) were found (n.s.). The intervention group improved significantly from the pretest to the posttest in the CogniFit (p = 0.003) and two-arm coordination test in terms of time (p < 0.001) and errors (p = 0.002) but not in the angle reproduction test. No significant differences were found between the groups for the three arthroscopic tasks. CONCLUSION: The hypothesis that movement-related cognitive training shortens the learning curve for acquiring arthroscopic basic skills cannot be confirmed. Other factors influencing the learning curve such as talent, teaching method and motivation have a greater impact on the acquisition of complex motor skills. LEVEL OF EVIDENCE: Level II.

Corrigendum: Tracheal intubation in patients with Pierre Robin sequence: development, application, and clinical value based on a 3-dimensional printed simulator.

[This corrects the article DOI: 10.3389/fphys.2023.1292523.].

Robotic hepaticojejunostomy training in novices using robotic simulation and dry-lab suturing (ROSIM): randomized controlled crossover trial.

BACKGROUND: Robotic suturing training is in increasing demand and can be done using suture-pads or robotic simulation training. Robotic simulation is less cumbersome, whereas a robotic suture-pad approach could be more effective but is more costly. A training curriculum with crossover between both approaches may be a practical solution. However, studies assessing the impact of starting with robotic simulation or suture-pads in robotic suturing training are lacking. METHODS: This was a randomized controlled crossover trial conducted with 20 robotic novices from 3 countries who underwent robotic suturing training using an Intuitive Surgical® X and Xi system with the SimNow (robotic simulation) and suture-pads (dry-lab). Participants were randomized to start with robotic simulation (intervention group, n = 10) or suture-pads (control group, n = 10). After the first and second training, all participants completed a robotic hepaticojejunostomy (HJ) in biotissue. Primary endpoint was the objective structured assessment of technical skill (OSATS) score during HJ, scored by two blinded raters. Secondary endpoints were force measurements and a qualitative analysis. After training, participants were surveyed regarding their preferences. RESULTS: Overall, 20 robotic novices completed both training sessions and performed 40 robotic HJs. After both trainings, OSATS was scored higher in the robotic simulation-first group (3.3 ± 0.9 vs 2.5 ± 0.8; p = 0.049), whereas the median maximum force (N) (5.0 [3.2-8.0] vs 3.8 [2.3-12.8]; p = 0.739) did not differ significantly between the groups. In the survey, 17/20 (85%) participants recommended to include robotic simulation training, 14/20 (70%) participants preferred to start with robotic simulation, and 20/20 (100%) to include suture-pad training. CONCLUSION: Surgical performance during robotic HJ in robotic novices was significantly better after robotic simulation-first training followed by suture-pad training. A robotic suturing curriculum including both robotic simulation and dry-lab suturing should ideally start with robotic simulation.

NoC simulation steered by NEST: McAERsim and a Noxim patch.

Introduction: Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Methods: Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. Results: By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. Discussion: The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

UOS_IOTSH_2024: A Comprehensive network traffic dataset for sinkhole attacks in diverse RPL IoT networks.

The proliferation of Internet of Things (IoT) implementations has enabled significant advancements across various applications, from smart homes to industrial automation. IoT networks typically consist of wirelessly interconnected, resource-constrained heterogeneous nodes. They are usually built using the energy-efficient Low Power and Lossy Network (LLN) standard, and employ the Routing Protocol for Low-Power and Lossy Networks (RPL) due to its efficiency in accommodating the constraints of IoT devices. However, RPL-based networks are susceptible to various security attacks that target the organization of the network. Chief among these is the sinkhole attack, which disrupts the network topology to attract traffic towards the malicious node by advertising false routing information. This work addresses the challenge of detecting sinkhole attacks on RPL-based IoT networks by introducing the extensive UOS_IOTSH_2024 dataset. This dataset is comprised mainly of raw network traffic collected through simulations of realistic IoT networks using the COOJA simulator. The dataset contains samples representing single and dual attackers in small and medium-sized IoT networks. It also covers both single-DODAG and dual-DODAG network architectures, as well as attackers at various locations across different topological positions for each scenario. In total, the dataset comprises 1,771,880 samples covering 60 different scenarios.