Individual Scenarios Research Articles

Mechanical properties of auxetic circular and square tubes filled with aluminum foam

The incorporation of aluminum foam in auxetic tubes can significantly enhance the overall stability, stiffness and energy absorption properties. In this work, a comparative study of the mechanical properties between auxetic circular and square tubes was conducted. The foam-filled auxetic square tube (FFAST) has the best specific energy absorption (SEA) up to 1.45 among the four auxetic tubes, and exhibits a pronounced auxetic effect throughout the minor deformation under compression, with a symmetrical and stable deformation mode. The experimental and simulated results are in excellent agreement. Hence, the finite element method is subsequently applied to the parametric analysis of FFAST, including the effects of wall thickness, height and ellipticity. Firstly, it is found that the thinner the wall thickness of FFAST is, the weaker the peak force and the greater the specific energy absorption would be. Secondly, within the same minor strain of different heights, SEA remains almost the same. Finally, with the decrease in ellipticity, the yield load of the bore tube gradually grows and SEA progressively increases. It can be concluded that FFAST with the ellipticity of 1.69 has both a high SEA and non-peak force. These findings are useful for fabricating FFAST for individual scenarios of energy absorption requirements. Such composite tubular structures will have practical applications in, e.g., protective engineering, vehicle engineering and aerospace engineering.

Interhemispheric Symmetry and Asymmetry of Absence Type Spike-Wave Discharges Caused by Systemic Administration of Pentylenetetrazole

Absence epilepsy is a specific generalized non-convulsive form occurring almost exclusively in children and adolescents. It is traditionally thought that the resulting seizure is completely generalized in the cortex of both hemispheres, as seen in humans on surface electroencephalograms and magnetoencephalograms. However, most studies were performed on animals, mainly rats – genetic (WAG/Rij and GAERS lines) and pharmacological models, since signals not only from the cortex, but also from subcortical structures such as the thalamus must be measured, what cannot be done on humans, who have no medical indications for intracranial surgery. In animals, measurements of local field potentials were almost always made from one hemisphere in order to cover the maximum number of brain structures involved. At the same time, the degree of synchrony of seizure onsets and terminations in different hemispheres in animals was practically not studied, and the markup itself in the vast majority of cases was done on the basis of one channel from motor or somato-sensory cortex. This work aims to reveal differences and similarities in the course of spike-wave discharges (the main encephalographic marker of absence seizures) in the cortex of the two hemispheres in a known pharmacological model of rats exposed to pentylentetrazole. For discharge detection, a method of automatic discharge marking is proposed and its sensitivity and specificity are evaluated using records of nine animals. We used it to mark seizures in symmetrical cortical sites from both hemispheres separately. According to the results of the analysis of seizure duration it turned out that for five out of nine animals the distribution of discharges significantly differed between the hemispheres, and for the other four was equal. Consequently, in the rat model, spike-wave activity may be generalized both symmetrically and asymmetrically, which may be due to individual peculiarities or different discharge triggering scenarios.

URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research

Abstract. The understanding of neutron transport by Monte Carlo simulations led to major advancements towards precise interpretation of measurements. URANOS (Ultra Rapid Neutron-Only Simulation) is a free software package which has been developed in the last few years in cooperation with particle physics and environmental sciences, specifically for the purposes of cosmic-ray neutron sensing (CRNS). Its versatile user interface and input/output scheme tailored for CRNS applications offers hydrologists straightforward access to model individual scenarios and to directly perform advanced neutron transport calculations. The geometry can be modeled layer-wise, whereas in each layer a voxel geometry is extruded using a two-dimensional map from pixel images representing predefined materials and allowing for the construction of objects on the basis of pixel graphics without a three-dimensional editor. It furthermore features predefined cosmic-ray neutron spectra and detector configurations and also allows for a replication of important site characteristics of study areas – from a small pond to the catchment scale. The simulation thereby gives precise answers to questions like from which location do neutrons originate? How do they propagate to the sensor? What is the neutron's response to certain environmental changes? In recent years, URANOS has been successfully employed by a number of studies, for example, to calculate the cosmic-ray neutron footprint, signals in complex geometries like mobile applications on roads, urban environments and snow patterns.

Integration of patient experience factors improves readmission prediction.

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50-0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54-0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56-0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74-0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.

Assessing the Ethical Concerns of Medical Students in the Gross Anatomy Lab.

Cadaver dissection has become the gold-standard for anatomical education in US medical schools. Ethical issues regarding cadavers may not be as obvious as in living patients, which can lead to their potential neglect in medical school curricula. In this study, we assessed the different ethical concerns (ECs) of medical students regarding cadavers in the gross anatomy lab (GAL), gathered student information, including self-reported academic performance (AP) in the GAL, and determined the best predictors for a student's EC. All second-year medical students at the University of Toledo were invited to complete an anonymous, online-survey. Participants were presented with 10 hypothetical but realistic lab scenarios and asked to rate their EC for each on a 5-point Likert scale. Gender, age, and scores received in the GAL course were also collected. A multiple linear regression model was used to find the best predictors of the total EC score. A total of 112 (63%) responses to the online-survey were recorded. The highest EC was for Q7: Taking pictures of the cadaver. The lowest EC was for Q10: The dissection of cadavers itself is an EC. Gender was the best predictor of total EC, followed by age. Female total EC was significantly higher than that for males (35.8 ± 5.5 vs 33.1 ± 7.9). Female scores for Q1 and Q2 were significantly higher than those for males. Total EC for students in the age group 25 to 34 was significantly higher than those in the age group 18 to 24 (35.9 ± 6.1 vs 33.9 ± 7.2). No significant difference was found for individual scenarios. AP was not significantly related to the total score or the scores of the individual scenarios. The significant differences in ECs of medical students found in our study indicate that not all students have the same outlook towards the GAL specifically and ECs generally.

Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation

The separation of individual pigs from the pigpen scenes is crucial for precision farming, and the technology based on convolutional neural networks can provide a low-cost, non-contact, non-invasive method of pig image segmentation. However, two factors limit the development of this field. On the one hand, the individual pigs are easy to stick together, and the occlusion of debris such as pigpens can easily make the model misjudgment. On the other hand, manual labeling of group-raised pig data is time-consuming and labor-intensive and is prone to labeling errors. Therefore, it is urgent for an individual pig image segmentation model that can perform well in individual scenarios and can be easily migrated to a group-raised environment. In order to solve the above problems, taking individual pigs as research objects, an individual pig image segmentation dataset containing 2066 images was constructed, and a series of algorithms based on fully convolutional networks were proposed to solve the pig image segmentation problem. In order to capture the long-range dependencies and weaken the background information such as pigpens while enhancing the information of individual parts of pigs, the channel and spatial attention blocks were introduced into the best-performing decoders UNet and LinkNet. Experiments show that using ResNext50 as the encoder and Unet as the decoder as the basic model, adding two attention blocks at the same time achieves 98.30% and 96.71% on the F1 and IOU metrics, respectively. Compared with the model adding channel attention block alone, the two metrics are improved by 0.13% and 0.22%, respectively. The experiment of introducing channel and spatial attention alone shows that spatial attention is more effective than channel attention. Taking VGG16-LinkNet as an example, compared with channel attention, spatial attention improves the F1 and IOU metrics by 0.16% and 0.30%, respectively. Furthermore, the heatmap of the feature of different layers of the decoder after adding different attention information proves that with the increase of layers, the boundary of pig image segmentation is clearer. In order to verify the effectiveness of the individual pig image segmentation model in group-raised scenes, the transfer performance of the model is verified in three scenarios of high separation, deep adhesion, and pigpen occlusion. The experiments show that the segmentation results of adding attention information, especially the simultaneous fusion of channel and spatial attention blocks, are more refined and complete. The attention-based individual pig image segmentation model can be effectively transferred to the field of group-raised pigs and can provide a reference for its pre-segmentation. Keywords: pig, image segmentation, Fully Convolutional Network (FCN), attention mechanism, channel and spatial attention DOI: 10.25165/j.ijabe.20231601.6528 Citation: Hu Z W, Yang H, Lou T T, Yang H W. Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation. Int J Agric & Biol Eng, 2023; 16(1): 232–242.

Benchmark of the Physics Engine MuJoCo and Learning-based Parameter Optimization for Contact-rich Assembly Tasks

Digital simulation tools are used throughout the entire product development cycle, from designing components using finite element methods to simulating their assembly and planning their production. Especially in research, physics simulators and engines such as MuJoCo are increasingly used for rapid data generation in training machine learning models. The quality of the training data is crucial for successful training and the subsequent deployment of the models in real-world applications. The presented work reports on a systematic benchmark of the physics engine MuJoCo. Representative processes in robotic assembly applications are carried out in simulation and on a physical testbed. The results are compared, focusing on the robot arm's pose and the acting contact forces during the assembly processes using Minimal Jump Cost. Subsequently, a deep reinforcement learning agent takes over the simulation's parameterization, and a first comprehensive system for the process-specifc training of simulation parameters is developed and evaluated to increase the data quality and to partially encapsulate the setup effort involved in digital simulation, aiding offline design and validation. The approach results in overall 8% less dissimilar simulations but not in all the individual scenarios.

Development of an app-based bedside clinical decision-making tool for mechanical cardiocirculatory support in patients with cardiogenic shock: the MCS-Aid app

Abstract Background Patients presenting with cardiogenic shock remain at high risk of morbidity and mortality. Several mechanical cardiocirculatory support (MCS) devices have been developed and their use is rapidly increase in clinical practice. However, there is significant heterogeneity in patient selection, timing of implantation, and post-implantation management across centers and operators. Purpose We sought to develop and smartphone app-based clinical decision-making tool to help bedside selection and post-implantation management of MCS devices in patients presenting with cardiogenic shock or cardiac arrest. Methods The MCS-Aid app will consistent of 3 major sections: (i) initial device selection based on clinical presentation (patients with cardiogenic shock or cardiac arrest post-ROSC); (ii) guide for escalation or addition of MCS based on the individual hemodynamic scenario; (iii) guide for weaning after implantation of MCS device. The app will have an interactive interface that will allow the user to select the most appropriate next step in management based on the clinical information being entered. A calculator to derive key hemodynamic parameters (e.g. cardiac power output or pulmonary artery pulsatility index) will be incorporated in the App to inform clinical decision-making when appropriate. An example of an algorithm that will be part of the MCS-Aid app is illustrated in the figure. Conclusions The MCS-Aid app is an user-friendly bedside clinical decision tool that could help fellows-in-training, early-career interventionalist and interventional cardiologist to select the appropriate MCS device according to the individual clinical and hemodynamic scenario. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Abiomed

Milligan–Morgan hemorrhoidectomy vs. hemorrhoid artery ligation and recto-anal repair: a comparative study

BackgroundSeveral surgical techniques for the treatment of hemorrhoidal disease (HD) have been proposed. However, the selection of the most proper technique for each individual case scenario is still a matter of debate. The purpose of the present study was to compare the Milligan–Morgan (MM) hemorrhoidectomy and the hemorrhoidal artery ligation and rectoanal repair (HAL–RAR) technique.MethodsA retrospective analysis of the prospectively collected database of patients submitted to HD surgery in our department was conducted. Patients were divided into two groups, the MM group and the HAL–RAR group. Primary end points were recurrence rates and patients’ satisfaction rates. The Unpaired t test was used to compare numerical variables while the x2 test for categorical variables.ResultsA total of 124 patients were identified, submitted either to HAL–RAR or MM hemorrhoidectomy. Eight (8) patients were lost to follow up and were excluded from the analysis. Of the remaining 116 patients, 69 patients (54 males and 15 females–male / female ratio: 3.6) with a median age of 47 years old (range 18–69) were included in the HAL–RAR group while 47 patients (40 males and 7 females–male / female ratio: 5.7) with a median age of 52 years old (range 32–71) comprised the MM group. At a median follow up of 41 months (minimum 24 months–maximum 72 months), we recorded 20 recurrences (28.9%) in the HAL–RAR group and 9 recurrences in the MM group (19.1%) (p 0.229). The mean time from the procedure to the recurrence was 14.1 ± 9.74 months in the HAL–RAR group and 21 ± 13.34 months in the MM group. Patients with itching, pain or discomfort as the presenting symptoms of HD experienced statistically significantly lower recurrences (p 0.0354) and reported statistically significantly better satisfaction rates (6.72 ± 2.15 vs. 8.11 ± 1.99—p 0.0111) when submitted to MM. In the subgroup of patients with bleeding as the presenting symptom, patients satisfaction rates were significantly better (8.59 ± 1.88 vs. 6.45 ± 2.70—p 0.0013) in the HAL–RAR group.ConclusionsIn patients with pain, itching or discomfort as the presenting symptoms of HD, MM was associated with less recurrences and better patients satisfaction rates compared to HAL–RAR. In patients with bleeding as the main presenting symptom of HD, HAL–RAR was associated with better patients’ satisfaction rates and similar recurrence rates compared to MM.

Determination of milk concentrations and pharmacokinetics of salicylic acid following acetylsalicylic acid (aspirin) administration in postpartum dairy cows.

The objectives of this descriptive study were to (1) describe the pharmacokinetics of salicylic acid (SA) in the milk and plasma of postpartum dairy cattle following oral administration of acetylsalicylic acid (ASA; aspirin), (2) to estimate a recommended milk withdrawal period for dairy cattle treated with ASA, and (3) to determine the effect of ASA administration on plasma prostaglandin E2 metabolite (PGEM) concentrations. Primiparous (n = 3) and multiparous (n = 7) postpartum Holstein dairy cows received 2 oral treatments with ASA at 200 mg/kg of body weight, 24 h apart. Concentrations of SA in plasma and milk from 0 h through 120 h after ASA administration were analyzed using ultra performance liquid chromatography triple quadrupole mass spectrometry and a milk withdrawal period was estimated using the United States Food and Drug Administration Milk Discard App in R. Two withdrawal periods were estimated: (1) a whole-herd treatment scenario with no dilution factor and (2) an individual animal treatment scenario with a bulk tank factor included in analysis. Plasma PGEM concentrations in samples from 0 h to 24 h after ASA administration were determined using a commercially available competitive ELISA. Milk SA concentrations were undetected in all cows by 48 h after the last ASA treatment. Secondary peaks were observed in plasma at 58 and 82 h after the last treatment and in milk at 87 h after the last treatment. In the absence of a tolerance for SA in milk, the estimated milk withdrawal periods were (1) 156 h for the whole-herd treatment scenario and (2) 120 h for the individual animal treatment scenario. Plasma PGEM concentrations were reduced compared with baseline for up to 12 h after ASA administration, with the greatest reduction observed at 2 h. Results from this study suggest that the current milk withhold recommendation for dairy cattle administered ASA may need revision to 120 h (5 d) and that ASA administration may mitigate postpartum inflammation through reduction in prostaglandin production for up to 12 h after treatment. Pharmacokinetic and milk withdrawal data from this study will inform future recommendations for extra-label use of aspirin in postpartum dairy cows. Further research is required to determine the basis for the secondary SA peaks and to elucidate the long-term effects of ASA administration on dairy cow health.

Holistic Resource Scheduling for Data Center In-Network Computing

The recent trend towards more programmable switching hardware in data centers opens up new possibilities for distributed applications to leverage in-network computing (INC). Literature so far has largely focused on individual application scenarios of INC, leaving aside the problem of coordinating usage of potentially scarce and heterogeneous switch resources among multiple INC scenarios, applications, and users. Alas, the traditional model of resource pools of isolated compute containers does not fit an INC-enabled data center. This paper describes HIRE, a holistic INC-aware resource manager which allows for server-local and INC resources to be coordinated in unison. HIRE introduces a novel flexible resource (meta-)model to address heterogeneity and resource interchangeability, and includes two approaches for INC scheduling: (a) retrofitting existing schedulers; (b) designing a new one. For (a), HIRE presents a retrofitting API and demonstrates it with four state-of-the-art schedulers. For (b), HIRE proposes a flow-based scheduler, cast as a min-cost max-flow problem, where a unified cost model is used to integrate the different costs. Experiments with a workload trace of a 4000 machine cluster show that HIRE makes better use of INC resources by serving 8–30% more INC requests, while simultaneously reducing network detours by 20% and reducing tail placement latency by 50%.

Secondary organic aerosol formation from mixed volatile organic compounds: Effect of RO2 chemistry and precursor concentration

Secondary organic aerosol (SOA) plays a significant role in contributing to atmospheric fine particles, as well as in global air quality and climate. However, the current understanding of the atmospheric formation of SOA and its simulation is still highly uncertain due to the complexity of its precursor VOCs. In our study, SOA formation in different mixed VOC scenarios was investigated using a 30 m3 indoor smog chamber. By comparing SOA formation in individual VOC scenarios, it was found that SOA yield from anthropogenic VOCs (AVOCs) can be positively (+83.9%) affected by coexisting AVOCs, while inhibited (−51.4%) by the presence of isoprene, via the OH scavenging effect. The cross-reactions of peroxyl radical (RO2) generated from different AVOCs were proved to be the main contributor (up to 39.0%) to SOA formation, highlighting the importance of RO2 + RʹO2 reactions in mixed VOC scenarios. Meanwhile, the formation of gas-phase organic intermediates of different volatility categories from the RO2 reactions was also affected by the precursor concentration, and a higher SOA yield was found at lower precursor concentrations due to the larger contribution of intermediates with lower volatility. Our study provides new insights into SOA formation by considering the interactions between intermediate products from mixed VOCs.

Pharmacological treatment of systemic sclerosis-associated interstitial lung disease: an updated review and current approach to patient care.

Interstitial lung disease (ILD) has a high prevalence among patients with systemic sclerosis (SSc), carrying high mortality and morbidity. During the last decade, the emergence of new pharmacological therapies for SSc-associated ILD (SSc-ILD) and improved tools for its diagnosis and monitoring have changed the prevailing clinical approach, highlighting the need for early recognition and prompt treatment for SSc-ILD. Furthermore, the recent approval of multiple therapies for SSc-ILD poses challenges for the rheumatologist and pulmonologist in choosing the appropriate therapy for individual clinical scenarios. We review the pathophysiology of SSc-ILD, and the mechanisms of action and rationale behind current therapies. We also review the evidence of the efficacy and safety of immunosuppressive drugs, antifibrotic agents, and immunomodulators from cyclophosphamide and mycophenolate to novel agents such as nintedanib and tocilizumab. We also emphasise the importance of early diagnosis and monitoring and describe our approach to pharmacological therapy for SSc-ILD patients.

Development of an app-based bedside clinical decision-making tool for mechanical cardiocirculatory support in patients with cardiogenic shock: the MCS-Aid app

Abstract Background Patients presenting with cardiogenic shock remain at high risk of morbidity and mortality. Several mechanical cardiocirculatory support (MCS) devices have been developed and their use is rapidly increase in clinical practice. However, there is significant heterogeneity in patient selection, timing of implantation, and post-implantation management across centers and operators. Purpose We sought to develop and smartphone app-based clinical decision-making tool to help bedside selection and post-implantation management of MCS devices in patients presenting with cardiogenic shock or cardiac arrest. Methods The MCS-Aid app will consistent of 3 major sections: (i) initial device selection based on clinical presentation (patients with cardiogenic shock or cardiac arrest post-ROSC); (ii) guide for escalation or addition of MCS based on the individual hemodynamic scenario; (iii) guide for weaning after implantation of MCS device. The app will have an interactive interface that will allow the user to select the most appropriate next step in management based on the clinical information being entered. A calculator to derive key hemodynamic parameters (e.g. cardiac power output or pulmonary artery pulsatility index) will be incorporated in the App to inform clinical decision-making when appropriate. An example of an algorithm that will be part of the MCS-Aid app is illustrated in the figure. Conclusions The MCS-Aid app is an user-friendly bedside clinical decision tool that could help fellows-in-training, early-career interventionalist and interventional cardiologist to select the appropriate MCS device according to the individual clinical and hemodynamic scenario. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Abiomed

Self-Supervised Speech Representation Learning: A Review

Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels required for many downstream scenarios. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. Other approaches rely on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years. This review presents approaches for self-supervised speech representation learning and their connection to other research areas. Since many current methods focus solely on automatic speech recognition as a downstream task, we review recent efforts on benchmarking learned representations to extend the application beyond speech recognition.

Impact of an E-Highway on the Required Battery Capacities and Charging Infrastructure for Cargo Transport with E-Trucks on the Basis of a Real Use Case

With the goal of reducing greenhouse gas emissions, the logistics sector is increasingly coming into focus. While increasing electrification is taking place in the road transport sector, the numbers in heavy-goods transport have so far been vanishingly small. Payload limitations, high investment costs, and charging times make it difficult for logistics companies to think about a conversion. An e-highway on Austria’s highways could provide an approach to counter these problems. Based on route data of an entire truck fleet in the construction logistics sector and by creating a model with Openrouteservice and MATLAB, calculations are carried out to show the savings potential of required battery capacities and charging infrastructure. The results show a high potential for reducing battery capacities and the required charging infrastructure at the locations approached. The results show high reduction potential, keeping the average required capacities in all scenarios below 350 kWh. Having a higher-powered e-highway of 150 kW nets slightly better results, but a major effect can still be achieved with a power of 60 kW. The cost reduction potential related to batteries and charging stations is up to 65% for individual scenarios. Thus, the result of this work primarily aims at presenting the advantages of a potential e-highway for logistics companies operating on Austria’s roads but can also be considered from the regulatory side when it comes to incentivizing sustainable logistics solutions from the political side.

Assessment of environmental and farm business impacts of phosphorus policies in two European regions

This paper presents analyses of the economic and environmental impact of relevant phosphorus management policy scenarios, using representative pig and poultry farms in the Emilia-Romagna region (Po River basin, Italy) and Niedersachsen region (Ems River basin, Germany) as case studies. The analysis was done by using a farm-level linear programming bioeconomic model developed for different farm and animal types—sows, fattening pigs, laying hens and broilers. The baseline for the assessed scenarios involved farms situated within the Nitrate Vulnerable Zones (NVZs) and therefore, were subject to indirect phosphorus fertilization limitations via the Nitrates Directive (ND). The analyzed phosphorus management policy scenarios included the implementation of two different balances of 8.7 kg and 4.35 kg of P per ha per year, inspired by the German Fertilizer Ordinance for phosphorus fertilization implemented in 2007 and later updated from 2017 up to 2023, respectively. We also included a more rigorous zero P balance scenario. The results of the scenario simulations, based on model assumptions, reveal that the introduction of direct phosphorus management policies for pig and poultry farms situated in high livestock dense regions can be done without causing any significant impact on farm gross margin (around 2% of reduction) for both regions and all four animal types, except for sows and broiler production in the Ems River Basin (up to 12% of reduction). Selected technologies and methods, inspired by current practices, have been analyzed for their cost efficiency to achieve the target P balance of the individual scenarios, including export of slurry out of the farm or export of separation solids. Results also highlight that the Nitrates Directive alone is not enough to handle the P issue in monogastric livestock farms.

A Meta-Analysis on Remote HRI and In-Person HRI: What Is a Socially Assistive Robot to Do?

Recently, due to the COVID-19 pandemic and the related social distancing measures, in-person activities have been significantly reduced to limit the spread of the virus, especially in healthcare settings. This has led to loneliness and social isolation for our most vulnerable populations. Socially assistive robots can play a crucial role in minimizing these negative affects. Namely, socially assistive robots can provide assistance with activities of daily living, and through cognitive and physical stimulation. The ongoing pandemic has also accelerated the exploration of remote presence ranging from workplaces to home and healthcare environments. Human–robot interaction (HRI) researchers have also explored the use of remote HRI to provide cognitive assistance in healthcare settings. Existing in-person and remote comparison studies have investigated the feasibility of these types of HRI on individual scenarios and tasks. However, no consensus on the specific differences between in-person HRI and remote HRI has been determined. Furthermore, to date, the exact outcomes for in-person HRI versus remote HRI both with a physical socially assistive robot have not been extensively compared and their influence on physical embodiment in remote conditions has not been addressed. In this paper, we investigate and compare in-person HRI versus remote HRI for robots that assist people with activities of daily living and cognitive interventions. We present the first comprehensive investigation and meta-analysis of these two types of robotic presence to determine how they influence HRI outcomes and impact user tasks. In particular, we address research questions regarding experience, perceptions and attitudes, and the efficacy of both humanoid and non-humanoid socially assistive robots with different populations and interaction modes. The use of remote HRI to provide assistance with daily activities and interventions is a promising emerging field for healthcare applications.

Prior Extracorporeal Membrane Oxygenation (ECMO) Experience and Performance in High-Fidelity Simulation Scenarios.

BackgroundExtracorporeal Membrane Oxygenation (ECMO) provides a heart-lung bypass for patients with life-threatening cardiorespiratory failure. It is a classic low-volume, a high-risk procedure that requires specialized training to develop and maintain competence. Therefore, our ability to train efficiently and effectively is essential. The purpose of this study is to determine if specific participant training or experience leads to better performance in emergency ECMO scenarios during high-fidelity simulation training.MethodsFifty-one physicians, nurses, and respiratory/medical technicians participated in a study comparing an animal model vs. simulation-based ECMO education. All completed a multiple-choice questionnaire about prior ECMO experience and other demographics, as well as a four-hour pre-lab didactic session. They completed individual ECMO scenarios with both modalities during two sessions, and task completion times (minutes) and scores (percentage) were measured using a validated ECMO skills assessment tool. The scores of the 19 participants who completed the simulation-based scenarios during their first session were further analyzed in the context of their self-reported ECMO experience, and participants were divided into a novice group and an experienced group. Statistical testing included the Mann-Whitney U test (times) and Fisher’s exact test (scores).ResultsData from the 19 participants who completed the simulation-based ECMO training on the first session showed no statistically significant differences in the task completion time or scores among those in the novice group vs. the experienced group in the years of ECMO experience category (28 vs. 34 minutes; p=0.66 and 61% vs. 62%; p=0.54), a number of cannulations category (30 vs. 25 minutes; p=0.11 and 59% vs. 62%; p=0.82) or the number of ECMO patients cared for category (28 vs. 34 minutes; p=0.30 and 57% vs. 62%; p=0.54). Findings were similar for both the lecture-based training and simulation-based training categories, respectively (33 vs. 28 minutes; p=0.71 and 62% vs. 60%; p=0.91 and 34 vs. 28 minutes; p=0.74 and 63% vs. 58%; p=0.12).ConclusionAmong this small subset of participants, we observed no statistically significant differences in performance based on participant experience during simulation-based ECMO scenarios. The didactic/review sessions preceding the training may have contributed to an effective form of training for participants with no prior ECMO experience. Due to the small sample size of this study, further studies are needed to better elucidate what factors lead to better performance in emergency ECMO scenarios.

Autonomous surgical robotic systems and the liability dilemma.

BackgroundAdvances in machine learning and robotics have allowed the development of increasingly autonomous robotic systems which are able to make decisions and learn from experience. This distribution of decision-making away from human supervision poses a legal challenge for determining liability.MethodsThe iRobotSurgeon survey aimed to explore public opinion towards the issue of liability with robotic surgical systems. The survey included five hypothetical scenarios where a patient comes to harm and the respondent needs to determine who they believe is most responsible: the surgeon, the robot manufacturer, the hospital, or another party.ResultsA total of 2,191 completed surveys were gathered evaluating 10,955 individual scenario responses from 78 countries spanning 6 continents. The survey demonstrated a pattern in which participants were sensitive to shifts from fully surgeon-controlled scenarios to scenarios in which robotic systems played a larger role in decision-making such that surgeons were blamed less. However, there was a limit to this shift with human surgeons still being ascribed blame in scenarios of autonomous robotic systems where humans had no role in decision-making. Importantly, there was no clear consensus among respondents where to allocate blame in the case of harm occurring from a fully autonomous system.ConclusionsThe iRobotSurgeon Survey demonstrated a dilemma among respondents on who to blame when harm is caused by a fully autonomous surgical robotic system. Importantly, it also showed that the surgeon is ascribed blame even when they have had no role in decision-making which adds weight to concerns that human operators could act as “moral crumple zones” and bear the brunt of legal responsibility when a complex autonomous system causes harm.