Autonomous Devices Research Articles

Green showcases as a model for the conceptual re-signification of smart tourist destinations applied to the Amazon

This article presents the main functionalities of an autonomous device named S.A.N.D.R.O., powered by solar energy and featuring minimalist computational technology based on the Raspberry Pi. This device was originally conceived to enable the continuity of distance education in regions devoid of connectivity, such as many riverside localities in the Amazon. It became central in equipping schools with the purpose of remotely providing educational resources in crisis situations, as occurred in the year 2020 with the COVID-19 pandemic. These resources can be accessed through smartphones or kiosks, given that they fully replicate the modus operandi of the Internet, offering users a familiar experience despite the lack of actual internet connectivity. Beyond these adverse periods, one of the possible uses for the artifact is to enhance the ecotourism experience in the region, thereby contributing to transforming it into a Smart Tourist Destination. By adopting an innovative approach that reduces costs for consumers, the device can also be classified as a form of frugal innovation. This classification stems from its minimalist design and cost-effective technology, making advanced services accessible to communities and tourists in remote areas without requiring significant financial investment.

A Secure Auditable Remote Registry Pattern for IoT Systems

In software engineering, pattern papers serve the purpose of providing a description of a generalized, reusable solution to recurring design problems, based on practical experience and established best practices. This paper presents an architectural pattern for a Secure Auditable Registry service based on Message-Oriented Middleware to be used in large-scale IoT systems that must provide auditing capabilities to external entities. To prepare the pattern, the direct experience in applying the pattern solution in an industry-funded R&D project has been a key aspect because it has allowed us to gain a deep understanding of the problem and the solution, and it has contributed to the correctness and real-world applicability of the pattern as described. To further improve the quality of the paper, we have followed the commonly accepted practices in pattern development (including peer reviews) to ensure that the core aspects of the solution are correctly represented and that the description allows it to be applicable to similar problems in other domains, such as healthcare, autonomous devices, banking, food tracing or manufacturing to name a few. The work done in applying this pattern confirms that it solves a recurring problem for IoT systems, but also that it can be adopted in other domains, providing an effective solution in order to achieve enhancement of the auditability capabilities of the target systems. This pattern will be part of a pattern language (i.e., a family of related patterns) that we are developing for transitioning from legacy systems to IoT with an emphasis on security.

Factor analysis on the performance of elite male fencing athletes.

We analyzed how stress and heart rate variability affect the performance of male fencing athletes expected to compete in various international events representing Korea. This study included 72 fencing athletes who participated in training and competition from January 2020 to December 2023. The stress was analyzed using the Sports Competition Anxiety Test (SCAT). Heart rate variability (HRV) was measured using an autonomous neurodiagnostic device (SA-6000). Heart rate variability measurement items were time domain analysis (heart rate, standard deviation of NN interval, and root mean square of the successful differences) and frequency domain analysis (total power, low frequency, and low frequency/high-frequency ratio). All athletes were classified into two categories: victory (high performance) and defeat (low performance) groups in the recent national championships. In SCAT, there was no significant difference between high and low performance. After comparing HRV, it was found that there is a significant difference in heart rates (P=0.030) and low frequency/high-frequency ratio (P=0.006) between high and low performance. heart rates and low-frequency/high-frequency ratio during the high performance were observed to be higher than during low performance. In our study, we found that SCAT does not affect performance, while HRV has been shown to have an impact on performance. These findings can be valuable for coaches and athletes in developing strategies to improve performance results, particularly for elite male fencing athletes.

Autonomous Inflow Control Devices Help Maximize Life of Wells in Deepwater West Africa

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 35418, “Design Execution and Evaluation of Advanced Completions in Deepwater West Africa,” by Piyush Pankaj, SPE, Matthew S. Jackson, and Lokesh, ExxonMobil, et al. The paper has not been peer reviewed. Copyright 2024 Offshore Technology Conference. _ Deepwater wells offshore West Africa traverse a complicated faulted block with sand bodies that can feature thicknesses of tens of meters. The study described in the complete paper provides insight into designing advanced well completions under various challenging reservoir conditions with autonomous inflow control devices (AICDs) that enable maximizing the producing life of the wells. Introduction The study area for this paper has been a challenging brownfield. Wells have been drilled in difficult deepwater conditions, and the target reservoirs have been lenticular sands with interbedded shale layers. To further complicate production, the sand bodies are poorly consolidated. Openhole wells with standalone screen and gravel packs have become a common approach to address sand control. Water- and gas-injection programs have been used actively in the field during the past 25 years, which further augments production and reservoir management challenges. Water and gas breakthrough in the infill wells has been a major concern for reservoir management. To address the gas buildup in the near-wellbore region and early water production, AICDs have become a popular feature in the lower well-completion designs in high-angled wells that intersect multiple sand bodies along the lateral completion interval. Two case studies are provided in the complete paper; one is included in this synopsis. Flow-Control Technology Although technology that empowers the ability to control the flow of unwanted fluids from zones in the well has been in use since the 1990s, flow-control technology has rapidly evolved, with advancement fueled by the need for long, complex well designs. The two broad categories of these approaches include active and passive flow control. The decision to use inflow-control technologies can be driven by the following economic values: - Balance drawdown along the wellbore - Increase recovery factor and reserves - Improve well cleanup - Reduces risk of erosion or hot-spotting of sandface completion Choice of Inflow Control for West Africa Field of Study Fluidic-diode AICDs were chosen because of their applicability to encountered fluid properties and reservoir conditions and their robustness of design in providing reliable long-term solutions. The reliability is prolonged in such AICDs because they work without any moving parts. Upon changes in viscosity, density, and rate, the fluid flow path will vary to gain the required control of flow rates. This AICD design, with its special geometry, alters the flow path or pattern for preferential fluid restriction. Operational Execution Ultimately, the AICDs were deployed on an inner string. This allowed the standard wire-wrapped screens and gravel pack to function as the traditional sand-control mechanism, while the inner string, deployed inside the lower completion, provides resistance to flow from unwanted gas production.

Autonomous Inflow Control Valves Enable Better Water Control in Peru Oil Field

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218074, “Making Better Wells With Autonomous Inflow Control Valves for Water Control in the Flank of the Bretaña Norte Oil Field, Peru: A Case Study,” by Willy Garcia, SPE, J. Antonio Zegarra, SPE, and Edwar Bustamante, PetroTal Peru, et al. The paper has not been peer reviewed. _ Bretaña Norte is a heavy oil greenfield in a remote area of northeast Peru. The field development strategy was designed around horizontal wells and advanced completions with autonomous inflow control devices (AICDs). As a part of a technology evaluation program, autonomous inflow-control-valve (AICV) technology was selected for field trials because of its ability to autonomously shut off water based on fluid properties. A reservoir simulation study was conducted, and the expected performance of AICV was compared with other AICD technologies installed in the field, indicating potential benefits related to better reservoir management. Field Description Bretaña Norte is an onshore field in the Peruvian Amazon. The Vivian formation is the main reservoir of the Bretaña Norte field and consists entirely of fluvial deposits composed of massive, moderately sorted, fluvial sandstones with thicknesses ranging from 40 to 200 ft. The Vivian formation is subdivided into Vivian S1 and Vivian S2, but only the Vivian S2 formation has been exploited to date. Heavy oil is produced from the Vivian S2 formation (18–19 °API and 25 cp), which features reservoir permeability ranging from 1 to 4 darcies. An active bottom aquifer of greater than 400-ft thickness was encountered under the60-ft oil column of the reservoir, posing a difficult production challenge to the operator: how to handle high volumes of water production that must be reinjected into a different formation. The Bretaña Norte field began production in 2018 with a few initial deviated wells with electrical submersible pumps (ESPs). The first horizontal well completed with standalone screens (SAS) experienced early water breakthrough. Thus, the operator opted for advanced completions with screens and AICDs in 2019. Between 2019 and 2023, 11 wells were completed with different AICD technologies. Even though initial results were positive, such outcomes have been highly dependent on the structural position of the wells. In 2022, a trial well was completed with the AICV technology in the flank of the reservoir, significantly closer to the oil/water contact (OWC), to evaluate its performance under challenging conditions. AICV Technology The AICV is an AICD designed to modify its open-flow area according to the properties (viscosity and density) of the fluids flowing through it and to choke flow progressively as water cut or gas-volume fraction values increase. The AICV is fully reversible, meaning that, if changes in the fluids flowing through the valve occur, it will reopen or reclose accordingly. The AICV consists of two flow paths, the main flow path and the pilot flow path, with an approximate flow distribution of 97 and 3%, respectively. Fig. 1 presents a schematic of the AICV, including a heat map showing the pressure distribution across the different flow paths.

Performance enhancement of triboelectric nanogenerator by embedding tea-leaf powder in waste polystyrene

A triboelectric nanogenerator (TENG), which harvests electrical energy from ambient mechanical vibrations, has emerged as a potential alternative to battery for future energy autonomous sensors and wireless devices. In this paper, we report a very simple and cost-effective strategy to improve the performance of a waste-material-based triboelectric nanogenerator (TENG). Tea-leaf powder is used as the filler in waste polysyrene (WPS), derived from the waste packaging material by using a very simple process. TENGs are fabricated using the composite layer comprising varying concentrations of the tea-leaf powder in WPS (Tea@WPS) as the tribopositive layer and polytetrafluoroethylene (PTFE) as the tribonegative layer. It is found that a low concentration of the tea leaf powder, such as 5%, produces the best performance with a peak voltage of ~1800 V, a surface charge density of ~180 μC/m2, and a power density of 61.25W/m2, which are approximately 4.5×, 2.8×, and 8× higher than that for a similar TENG in which no tea-leaf filler is used in WPS. Possible reasons for such huge performance enhancement are discussed. Although the performance of the device is degraded when exposed to high humidity level, it is found that the device restores it original performance when the humidity is reduced again. No degradation of performance of the device is observed for over 110 days in the same ambient condition.

Technology Focus: Offshore Drilling and Completion (October 2024)

The evolution of computational technology, coupled with the ability to digitize hardware and engage with end users, has enabled machine learning to enhance operational efficiency, safety, and cost-effectiveness in offshore oil and gas development. A key takeaway from recent literature is that, while automation tools have been present for years, their widespread adoption has been hindered by a lack of demonstrable financial benefits. To address this, the focus has shifted from mere deployment to achieving real value through significant cost savings, increased safety, and improved operational efficiencies (paper OTC 35137). Automation efforts in onshore operations prioritize efficiency, while offshore automation must focus on risk reduction and minimizing nonproductive time. For instance, a cutting-edge vessel-motion-prediction tool integrates advanced machine-learning techniques with metocean forecasts to enhance the reliability and safety of offshore drilling operations (paper OTC 35341). Paper OTC 35418 presents the importance of autonomous inflow-control devices in maximizing the production life of wells in deepwater West Africa, taking into account the various challenging reservoir conditions. Another critical insight from the research is the role of human intervention in the deployment of automation systems. Successful deployment requires iterative adjustments and feedback from operators to tailor systems to the specific needs of offshore environments. Collaboration among engineers, operators, and decision-makers is essential in refining these technologies to align them with operational goals and safety standards. Overall, these papers underscore the need for strategic adaptation of automation systems to the specific challenges of offshore environments. The success of predictive machine learning in this context depends on its ability to offer measurable financial benefits, improve safety, and drive operational efficiency. Recommended additional reading at OnePetro: www.onepetro.org. SPE 218473 Performance Optimization of Water-Injection Wells Using Autonomous Outflow Control Device Technology in Offshore Norway by G.F. Kvilaas, AkerBP, et al. SPE 218479 Use of Wired Drill Pipe, Along-String Measurements, and Advanced Logging-While-Drilling Imaging Enhances Wellbore-Condition Understanding and Improves Well Delivery Time, While Telemetry-Optimized Ultradeep Resistivity Measurements Enable Precise Geological Well Placement by Stephen Pink, NOV, et al. SPE 218360 Autonomous Restriction Navigation Through Wellbore in Wireline and Slickline Operations by S. DiPasquale, SLB, et al. OTC 35394 Intelligent Optimization of Ultradeepwater Oil-Transmission Risers Using an Enhanced Graph Neural Network by Hong Zhu, China University of Petroleum-Beijing, et al.

Development and research of an autonomous mobile platform with reduced computing power requirements

Today, the market is saturated with a variety of autonomous robotic devices that offer a variety of solutions to opti-mize workflows in various industries. One of the leading areas of their application is the automation of warehouse operations, where store robots move and optimize warehouse processes, reducing labor costs and speeding up task completion. These technologies reveal some limitations and shortcomings. For example, categories of robots such as transport carts, forklifts, and automated towing vehicles are limited in their functionality, often resulting in the need to use multiple types of devices for different tasks. This leads to equipment redundancy and complicates integration into existing warehouse systems. Another significant aspect is the need to improve the routing and nav-igation algorithms for these devices. Modern autonomous robot systems often face route optimization challenges, especially in dynamic and complex warehouse environments. This can lead to inefficiencies and increased time delays [1]. The goal of this work is to develop an autonomous mobile platform that would solve possible problems with navigation, have well-developed routing algorithms combined with low cost for affordable use in small busi-nesses. The research methods involve conducting various experiments on a real mobile platform to collect data and evaluate performance, including measuring the speed, accuracy, and power consumption of transportation tasks. Development and optimization of new algorithms for managing a mobile platform, planning routes, analyz-ing signals from sensors, processing images and video from a camera. The result of the work is that the possibility of compatibility of the on-board computer (single board computer) with the flight controller for movement on the ground surface has been investigated. On this basis, a mobile platform was created, which is presented in this arti-cle. This configuration is suitable for moving the platform either autonomously or using a control panel. The role of the technical vision system in providing navigation has been studied. The results of the work highlight innovation and the use of model functionality to create effective autonomous land navigation systems, including coordinates. The mobile platform can be used for transporting goods in warehouses, for working in extreme situations, for environmental monitoring or for educational purposes.

INVESTIGATION OF A DYNAMICALLY CHANGING SIGNAL USING WAVELET TRANSFORMATIONS

In the presented work, a wavelet analysis of the patient’s electroencephalogram was performed, followed by the construction of a scalogram. This approach made it possible to identify the frequency components of the electroencephalogram and conduct a comprehensive analysis of them. The obtained results can be used to monitor the state of the patient’s brain activity. The main purpose of the study is to analyze and filter the signal to determine the main composite frequencies of the electroencephalographic signal, on the basis of which it is possible to determine the state of brain activity at certain points in time, which may reflect various cognitive processes, emotional states and concentration levels of the patient. Methodology. An electroencephalogram of the patient is taken, and using wavelet transformations, a set of frequencies at each moment of time with their amplitude is obtained. Then the signal is filtered from noise, and the wavelet transform is re-applied to obtain a set of frequencies. After that, the frequencies are analyzed at each point in time, and based on the data, the state of the patient’s brain activity is determined. The results of the study. As a result of the study of the electroencephalographic signal analysis process, it was possible to filter the original signal from noise and identify the main frequencies that make up the electroencephalographic signal. After that, on the basis of frequencies, different states of consciousness of the patient at each moment of time are determined. The scope of application. The introduction of the principles of wavelet analysis into the architecture of programmable logic integrated circuits (FPGAs) for analyzing captured signals using ultrasonic sensors on FPGAs, thus implementing an autonomous device.

Thermal Energy Resource Potentials of the Kara-Bogaz-Gol Gulf as a “Solar Pond”

Introduction. The use of environmentally-friendly engineering systems including solar energy technologies makes it possible to reduce energy costs and therefore to lower production costs and anthropogenic stress on the environment.Aim of the Study. The authors used innovative techniques to assess the thermal resource potential of solar radiation, to analyze the salt deposits of the Kara-Bogaz-Gol Gulf as thermal accumulators for the development, introduction and use of solar thermal technologies and to justify the technical and economic feasibility of their use in engineering systems in the Kara-Bogaz-Gol Gulf (Caspian region).Meterials and Methods. The study design is based on systematic theoretical calculations of the gross, technical, economic and ecological potentials of solar radiation taking into account environmental conditions. For calculating there were used the methods of mathematical modeling of heat and mass transfer processes in active solar energy systems when converting solar energy into thermal energy in the salty reservoir of the Kara-Bogaz-Gol Gulf as a “solar pond”.Results. There have been assessed the solar energy characteristics for the introduction of various engineering storage systems and technologies. There have been determined the results of energy storage on the reservoir salt surface during the day: in winter – 1 009.0 W/m2 per day, in summer – 1 574.7 W/m2 per day. It has been proven that the potential of solar energy conversion into thermal energy varies from 40 to 70% depending on the season. Aaccording to theoretical calculations, the solar pond efficiency in winter is 11.4% and in summer – 14.6%. In summer, there was measured the average temperature on the salt surface of the reservoir bottom, it ranges from 55.04 to 79.8 ºC, in winter from 20.0 to 25.6 ºC.Discussion and Conclusion. The results obtained can be used for strengthening energy security, developing energy systems and producing autonomous thermal power devices based on solar energy that will reduce the energy consumption of fossil fuels and improve the environmental situation in the region. The materials of the article can be used in preparing design estimates and feasibility study for developing various solar energy systems and technologies in the Caspian region.

Instrumented bio-inspired cable-driven compliant continuum robot: static modeling and experimental evaluation

Abstract Energy efficiency is inherent for autonomous robotic device. Snakes are well known for their ability to low energy consumption when swimming. However, the swimming know-how is poorly understood. Designing a snake robot inspired by snakes as a tool to find out the swimming energy efficiency crucial point will lead to the development of hyper efficient undulating locomotors. In this article, we introduce a four tendons driven continuum robot made of bio-inspired compliant vertebrae to assess the energy consumption of a planar and a spatial snake motion. The tendon-driven continuum robot constitutes the head–neck part of a locomotor snake robot. A static modeling coupled with an optimization method was implemented to generate bio-inspired motions recorded on snake swimming head. A friction model describing the friction between cables and the disks is investigated and compared to a frictionless model. The proposed prototype is equipped with exteroceptive sensors to record motion and proprioceptive sensors to measure cable forces applied at the tip of the robot. Hence, the work of the forces, thus the energy required to execute a trajectory are computed and analyzed. The energy is introduced as a key criterion to assess the swimming motion of a locomotor snake robot.

Assessment of the effectiveness of an autonomous device for pre-sowing scarification and sorting of acorns

Assessment of the effectiveness of an autonomous device for pre-sowing scarification and sorting of acorns

Emerging Capacitive Materials for On-Chip Electronics Energy Storage Technologies

Miniaturized energy storage devices, such as electrostatic nanocapacitors and electrochemical micro-supercapacitors (MSCs), are important components in on-chip energy supply systems, facilitating the development of autonomous microelectronic devices with enhanced performance and efficiency. The performance of the on-chip energy storage devices heavily relies on the electrode materials, necessitating continuous advancements in material design and synthesis. This review provides an overview of recent developments in electrode materials for on-chip MSCs and electrostatic (micro-/nano-) capacitors, focusing on enhancing energy density, power density, and device stability. The review begins by discussing the fundamental requirements for electrode materials in MSCs, including high specific surface area, good conductivity, and excellent electrochemical stability. Subsequently, various categories of electrode materials are evaluated in terms of their charge storage mechanisms, electrochemical performance, and compatibility with on-chip fabrication processes. Furthermore, recent strategies to enhance the performance of electrode materials are discussed, including nanostructuring, doping, heteroatom incorporation, hybridization with other capacitive materials, and electrode configurations.

Reconfigurable MIMO-based self-powered battery-less light communication system

Simultaneous lightwave information and power transfer (SLIPT), co-existing with optical wireless communication, holds an enormous potential to provide continuous charging to remote Internet of Things (IoT) devices while ensuring connectivity. Combining SLIPT with an omnidirectional receiver, we can leverage a higher power budget while maintaining a stable connection, a major challenge for optical wireless communication systems. Here, we design a multiplexed SLIPT-based system comprising an array of photodetectors (PDs) arranged in a 3 × 3 configuration. The system enables decoding information from multiple light beams while simultaneously harvesting energy. The PDs can swiftly switch between photoconductive and photovoltaic modes to maximize information transfer rates and provide on-demand energy harvesting. Additionally, we investigated the ability to decode information and harvest energy with a particular quadrant set of PDs from the array, allowing beam tracking and spatial diversity. The design was explored in a smaller version for higher data rates and a bigger one for higher power harvesting. We report a self-powering device that can achieve a gross data rate of 25.7 Mbps from a single-input single-output (SISO) and an 85.2 Mbps net data rate in a multiple-input multiple-output (MIMO) configuration. Under a standard AMT1.5 illumination, the device can harvest up to 87.33 mW, around twice the power needed to maintain the entire system. Our work paves the way for deploying autonomous IoT devices in harsh environments and their potential use in space applications.

Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3-5% better accuracy results.

Optimized intelligent 3D localization in wireless sensor networks for better data sharing

WSNs are networks of small, autonomous devices called sensors or nodes that are equipped with sensors, processing capabilities, and wireless communication capabilities. These networks are designed to monitor and collect data from their surrounding environments and transmit that data wirelessly to a central location for analysis. However, challenges such as packet loss and limited throughput have impeded their efficiency. To address these issues, this research presents an innovative approach known as Squirrel-based Elman Neural Localization (SbENL) to optimize 3D localization in WSNs. Squirrel fitness tracks the node's location in the sensor network environment. This study initially configures WSN nodes with constrained energy resources, and data rates are continually monitored and predicted. The outcome is improved data sharing with higher throughput rates. The research assesses the performance of SbENL against existing localization techniques, demonstrating its effectiveness in minimizing data loss, reducing energy consumption, and maximizing data transfer rates. This optimized intelligent 3D localization approach holds promise for enhancing data sharing and communication efficiency in WSNs, benefiting a wide range of applications. Finally, the communication metrics were measured and valued with recent existing approaches. A SbENL has minimized the data loss energy consumption and maximized the data transferring rate.

Platform Design for Optical Screening and Conditioning for Injury Resilience.

Musculoskeletal injuries (MSKIs) among active duty soldiers result in more than 10 million limited duty days each year and account for more than 70% of the medically nondeployable population. Overuse injuries in lower limbs from running, foot marching long distances with heavy loads, and lifting heavy objects are the most common types of injuries in the military. Physical training and rehabilitation exercises for greater resiliency through aerobic, muscle strength, endurance, and agility conditioning programs can prevent or reduce the effects of MSKIs if Soldiers adhere to proper biomechanics and training techniques. We are introducing a three-dimensional (3D) camera-based platform for Optical Screening and Conditioning for Injury Resilience (OSCIR) that is designed to identify and correct high-risk movement patterns based on quantifiable biomechanical measurements in clinical or field settings. Our goal is to improve resilience to MSKI by offering greater access to quality of movement skills in warfighters through an autonomous device that can be used in Sports Medicine and Reconditioning Team (SMART) clinics and High-Intensity Tactical Training (HITT) sites. OSCIR fuses four pairs of Kinect Azure cameras into a concise footprint to achieve suitable sampling rates and an unobstructed field of view for accurate dynamic movement tracking using a custom point cloud solution. We designed a unique multistage 3D joint tracking algorithm architecture to methodically isolate the human body point cloud from the background, identify individual limb segments, and perform iterative joint optimization at the global and local joint levels. We evaluated the feasibility of our prototype system among N = 12 control participants (6 M/6 F; 21-37 years) in compliance with the Western Institutional Review Board (Tracking #20225920, approved on November 4, 2022). Five task-specific MSKI outcome metrics identified by end-user physical therapists and athletic trainers as indicators for movement quality were assessed across 7 lower-extremity exercises derived from standardized MSK assessment/conditioning batteries used in the military. Data were recorded concurrently by OSCIR and a reference standard Vicon motion capture system for validating system accuracy. Task-specific MSKI indicators for knee flexion and hip flexion range of motion achieved an average error of 4.05 ± 2.34°, while 3D position-based postural outcomes of left-right foot distance, left-right hand distance, and step length obtained mean absolute errors of 2.58 ± 2.30 cm. Results support the feasibility of our system in achieving outcomes that are comparable to currently accepted laboratory standards. Our study describes the integration process for a 3D camera-based clinical system for MSKI conditioning and rehabilitation. The impact of our system will enable key stakeholders in the military to manage MSKIs in warfighters by automating key assessment and rehabilitation test batteries; making tests more readily accessible, and interpretations more accurate by providing objective biomechanical measures. OSCIR is undergoing turn-key design features to serve as a screening tool for warfighters to readily assess susceptibility to MSKI or as a training platform to help guide exercise techniques to achieve resiliency against future injuries.

What Makes a Photobattery Light-Rechargeable?

The demand for autonomous off-grid devices has led to the development of "photobatteries", which integrate light-energy harvesting and electrochemical energy storage in the same architecture. Despite several photobattery chemistries and designs being reported recently, there have been few insights into the physical conditions necessary for charge transfer between the photoelectrode and counter electrode. Here, we use a three-electrode photobattery with a dye-sensitized TiO2 photoelectrode, triiodide (I-/I3 -) catholyte, and anodes with varying intercalation potentials to confirm that photocharging is only feasible when the conduction band quasi-Fermi level (EFc) is positioned above the anode intercalation/plating potential. We also show that parasitic reactions after the battery is fully charged can be accelerated if the voltage of the battery and solar cell are not matched. The integration of multiple anodes in the same photobattery ensures well-controlled measurement conditions, allowing us to demonstrate the physical conditions necessary for charge transfer in photobatteries, which has been a topic of controversy in the field.

Autonomous Devices with an Evaporation–Condensation Cycle for Thermal Control of Heat-Loaded Equipment

Autonomous Devices with an Evaporation–Condensation Cycle for Thermal Control of Heat-Loaded Equipment

Television shows ideation, and testing with smart digital twins to advance ratings

AbstractGenerating ideas for immersive television shows is fundamental to the television industry. TV channel managers are looking to stay ahead of their competitors and are turning to many advanced technologies like artificial intelligence (AI), the Internet of Things, virtual reality, cloud and fog computing. These technologies with other autonomous devices, technologies, surveys, models, and software are creating extensive, complex, and diverse television data sets. These data diversity and heterogeneity may hinder television research. Thus, there is a clear need to synthesize, synchronize, and integrate the large-scale data sets according to predefined decision rules and research objectives. Against this backdrop, this paper introduces a new platform of data integration and modeling—television digital twins. Digital twins (DTs) are virtual copies of products, services, processes, or humans encompassing all the relevant entities’ qualities. Although numerous research studies have been published on DTs, none hitherto have been conducted in media and television. This research aims to bridge two perspectives: on one side, the authors acknowledge the value of TVDT as a data fusion platform. On the other side, the authors build on previous scholarship to suggest a conceptual framework for implementing this platform in future TV studies.