In-situ sensing and dynamics predictions for electrothermally-actuated soft robot limbs

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor's accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.National Science Foundation; Oak Ridge Institute for Science and EducationPublished versio

A Study Among General Dental Practitioners, Orthodontists and Forensic Odontologists Regarding Forensic Identification in Orthodontic Context

The process of dental identification involves comparing the dentition of the deceased with the latest dental records of presumed dead. It may become challenging to identify an individual whose orthodontic treatment records are untraceable for any reason. In such instances the dentition of the dead who underwent orthodontic treatment may need to be compared with the records before orthodontic treatment. The goal of this study was to determine the accuracy of matching pre and post-orthodontic treatment casts among general dental practitioners (GDPs), orthodontists, and forensic odontologists. Pre and post-orthodontic treatment dental casts from ten patients were used in this study. Pre-orthodontic maxillary and mandibular dental casts were separated from post-orthodontic maxillary and mandibular dental casts. A total of fifteen assessors comprising five orthodontists, five forensic odontologists and five GDPs compared and matched the pre and post-orthodontic dental cast pairs. Results revealed higher correct matches among forensic odontologists (94%) as compared to orthodontists (88%) followed by GDPs (80%). Among the parameters, ‘specific features in the dentition’ were most successful at correct identification. Orthodontic therapy causes morphometric changes in the dentition, which can make comparative identification difficult. Unique features in the dentition present before orthodontic treatment and persistent even after orthodontic treatment such as peculiar attrition facets, etc. can prove highly accurate in the identification

“I Think it’s Something that we Should Lean in to”: The use of OpenNotes in Canadian Psychiatric Care contexts by Clinicians

Background: OpenNotes is the concept of patients having access to their health records and clinical notes in a digital form. In psychiatric settings, clinicians often feel uncomfortable with this concept, and require support during implementation. Objective: This study utilizes an implementation science lens to explore clinicians’ perceptions about using OpenNotes in Canadian psychiatric care contexts. The findings are intended to inform the co-design of implementation strategies to support the implementation of OpenNotes in Canadian contexts. Method: This qualitative descriptive study employed semi-structured interviews which were completed among health professionals of varying disciplines working in direct care psychiatric roles. Data analysis consisted of a qualitative directed content analysis using themes outlined from an international Delphi study of mental health clinicians and experts. Ethical approval was obtained from the Centre for Addiction and Mental Health and the University of Toronto. Results: In total, 23 clinicians from psychiatric settings participated in the interviews. Many of the themes outlined within the Delphi study were voiced. Benefits included enhancements to patient recall, and empowerment, improvements to care quality, strengthened relational effects and effects on professional autonomy and efficiencies. Despite the anticipated benefits of OpenNotes, identified challenges pertained to clarity surrounding exemption policies, training on patient facing notes, managing disagreements, and educating patients on reading clinical notes. Conclusion: Many benefits and challenges were identified for adopting OpenNotes in Canadian psychiatric settings. Future work should focus on applying implementation frameworks to develop interventions that address the identified challenges

Measurements of the pp → ZZ production cross section and the Z → 4ℓ branching fraction, and constraints on anomalous triple gauge couplings at √s = 13 TeV

Four-lepton production in proton-proton collisions, pp -> (Z/gamma*)(Z/gamma*) -> 4l, where l = e or mu, is studied at a center-of-mass energy of 13 TeV with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb(-1). The ZZ production cross section, sigma(pp -> ZZ) = 17.2 +/- 0.5 (stat) +/- 0.7 (syst) +/- 0.4 (theo) +/- 0.4 (lumi) pb, measured using events with two opposite-sign, same-flavor lepton pairs produced in the mass region 60 4l) = 4.83(-0.22)(+0.23) (stat)(-0.29)(+0.32) (syst) +/- 0.08 (theo) +/- 0.12(lumi) x 10(-6) for events with a four-lepton invariant mass in the range 80 4GeV for all opposite-sign, same-flavor lepton pairs. The results agree with standard model predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous ZZZ and ZZ. couplings at 95% confidence level: -0.0012 < f(4)(Z) < 0.0010, -0.0010 < f(5)(Z) < 0.0013, -0.0012 < f(4)(gamma) < 0.0013, -0.0012 < f(5)(gamma) < 0.0013

Transformation as Praxis: Responding to Climate Change Uncertainties in Marginal Environments in South Asia

This paper provides some of the conceptual and methodological underpinnings being developed in the ongoing TAPESTRY project which is part of the Transformations to Sustainability (T2S) Programme. We debate how the notion of transformation may be conceptualized from ‘below’ in marginal environments that are especially marked by high levels of climate-related uncertainties. We propose the notion of transformation as praxis — where the focus is on bottom-up change, identities, wellbeing and the recovery of agency by marginalized people and explore how ‘patches’ and the ‘marginal’ offer critical conceptual templates to examine whether and how systemic transformative changes are being assembled and effected on the ground by hybrid and transformative alliances. The article concludes by discussing potential challenges of such engagements, alongside pursuing a normative and political approach to T2S

Remote Data Acquisition and Visualization on an App

In order to allow power consumers to have a better understanding of their industrial electricity, it is necessary to provide them with real-time electricity information. Now a days we need to do meter reading, analysis manually. If any meter is goes down or fluctuating we need to do manual servicing and troubleshooting. It may cause meter loss, Time loss, and financial loss. The platform enables power consumers to have a better understanding of their own electricity consumption status and reasonable arrangements for use of electricity. Its deployment is very simple. For users, they do not need to change the household electricity structure. They can easily view the home electricity information and real-time monitor and control after installing power information visualization APP on their mobile phones or other mobile terminals. User interaction needs to collect power information of all equipment, and provide users with real-time electricity price and electricity information. The basic purpose of system is M-Measure, A-Analysis, R-Record, and CControl. No human intervention for data processing analysis. This is use for Power quality analysis, Identification and prevention of downtime of assets, Alarms and triggers are used for preventive control to avoid loss. Reduce the losses by monitoring and controlling the power quality. Accurate analytics depending on the load connected to the system. It supports most of all make sensors and hardware meters to work with the system. Periodical analysis of the organization for the complete energy health check-up

Identifying Subsets of Cancer Patients with an Increased Risk of Developing Cutaneous Melanoma: A Surveillance, Epidemiology, and End Results-Based Analysis

Cancer survivors have an increased risk of developing second primary malignancies. We aimed to identify whether certain cancers lead to an increased risk of developing melanoma among cancer survivors. We evaluated the risk of developing cutaneous melanoma after the 20 most common cancers in the United States through the Surveillance, Epidemiology, and End Results database. We identified 9 primary cancers linked to increased risk of developing a subsequent cutaneous melanoma: cutaneous melanoma (standardized incidence ratio [SIR] = 9.65), leukemia (SIR = 1.76), non-Hodgkin lymphoma (SIR = 1.33), thyroid cancer (SIR = 1.32), brain and nervous system cancer (SIR = 1.31), myeloma (SIR = 1.23), breast cancer (SIR = 1.13), oral cavity/pharynx cancer (SIR= 1.12), and prostate cancer (SIR = 1.03). The risk of developing melanoma was highest 1-5 years after diagnosis of most primary cancers. Notably, individuals aged under 50 years with a prior melanoma had a 14-fold increased risk. Our findings highlight specific at-risk groups-such as those aged under 50 years with recent melanoma, individuals in their 60s diagnosed with leukemia, and those aged over 80 years with recent thyroid cancer-who may benefit from heightened clinical vigilance and tailored melanoma screening strategies

A Survey on Human-AI Teaming with Large Pre-Trained Models

In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes. The advent of Large Pre-trained Models (LPtM) has significantly transformed this landscape, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. This paper surveys the pivotal integration of LPtMs with HAI, emphasizing how these models enhance collaborative intelligence beyond traditional approaches. It examines the potential of LPtMs in augmenting human capabilities, discussing this collaboration for AI model improvements, effective teaming, ethical considerations, and their broad applied implications in various sectors. Through this exploration, the study sheds light on the transformative impact of LPtM-enhanced HAI Teaming, providing insights for future research, policy development, and strategic implementations aimed at harnessing the full potential of this collaboration for research and societal benefit