Desirable Task Research Articles

Physiological response to pain in patients with temporomandibular joint disorders during physiotherapy

Pain assessment in patients with temporomandibular joint (TMJ) disorders during physiotherapy is a challenging yet desired task. Our study addresses two issues. First, we compare the pain perception in patients with TMJ disorders and a control group. The hand dynamometer is verified as an alternate pain self-report device in the second stage. For the dynamometer validation, we employed a cuff-pressure test to induce pain of various intensities. In the second stage, physiological signals (electrodermal activity and blood volume pulse) have been acquired during the physiotherapy in a control group and patients suffering from TMJ disorder. The linear mixed-effects modeling is carried out to find differences in pain and no pain states, considering subjects’ health condition. The main result of the present pilot study is that physiological reactions to painful physiotherapy do not differ between patients with chronic pain and the control group. This finding concludes that studies on TMJ disorder procedures may also be performed on healthy subjects.

Establishing a research partnership to investigate functional loss and rehabilitation towards the end of life.

Functional loss, the inability to perform necessary or desired tasks, is a common consequence of life-limiting illnesses and associated symptoms (pain, fatigue, breathlessness, etc.) and causes suffering for patients and families. Rehabilitation, a set of interventions designed to address functional loss, is recognised as essential within palliative care, as it can improve quality of life and reduce care costs. However, not everyone has equal access to rehabilitation. Despite limited life expectancy or uncertain ability to benefit from interventions, palliative rehabilitation services are often absent. This is partly due to a lack of high-quality research around optimal models of rehabilitation. Research in this area is methodologically challenging and requires multidisciplinary and cross-speciality collaboration. We aimed to establish and grow a United Kingdom research partnership across diverse areas, commencing with partners from Edinburgh, East Anglia, Lancashire, Leeds, London and Nottingham, around the topic area of functional loss and rehabilitation in palliative and end-of-life care. The objectives were to (1) develop a multidisciplinary, cross-speciality research partnership, (2) generate high-priority unanswered research questions with stakeholders, (3) co-design and submit high-quality competitive research proposals, including (4) sharing topic and methodological expertise, and (5) to build capacity and capability to deliver nationally generalisable studies. The partnership was established with professionals from across England and Scotland with complementary areas of expertise including complex palliative and geriatric research, physiotherapy, nursing, palliative medicine and psychology. Research questions were generated through a modified version of the Child Health and Nutrition Research Initiative, which allowed for the collation and refinement of research questions relating to functional loss and rehabilitation towards the end of life. Partnership members were supported through a series of workshops to transform research ideas into proposals for submission to stage one calls by the National Institute for Health and Care Research. The partnership not only supported students, clinicians and public members with training opportunities but also supported clinicians in securing protected time from clinical duties to allow them to focus on developing local research initiatives. Through our partnership we established a network that offered researchers, clinicians, students and public members the chance to develop novel skills and explore opportunities for personal and professional development around the topic area of functional loss and rehabilitation in palliative and end-of-life care. The partnership was crucial to foster collaboration and facilitate exchange of ideas, knowledge and experiences to build joint research study proposals. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) programme as award number NIHR135171. A plain language summary of this article is available on the NIHR Journals Library website https://doi.org/10.3310/PTHC7598.

Bayesian relative composite quantile regression approach of ordinal latent regression model with L1/2 regularization

AbstractOrdinal data frequently occur in various fields such as knowledge level assessment, credit rating, clinical disease diagnosis, and psychological evaluation. The classic models including cumulative logistic regression or probit regression are often used to model such ordinal data. But these modeling approaches conditionally depict the mean characteristic of response variable on a cluster of predictive variables, which often results in non‐robust estimation results. As a considerable alternative, composite quantile regression (CQR) approach is usually employed to gain more robust and relatively efficient results. In this paper, we propose a Bayesian CQR modeling approach for ordinal latent regression model. In order to overcome the recognizability problem of the considered model and obtain more robust estimation results, we advocate to using the Bayesian relative CQR approach to estimate regression parameters. Additionally, in regression modeling, it is a highly desirable task to obtain a parsimonious model that retains only important covariates. We incorporate the Bayesian penalty into the ordinal latent CQR regression model to simultaneously conduct parameter estimation and variable selection. Finally, the proposed Bayesian relative CQR approach is illustrated by Monte Carlo simulations and a real data application. Simulation results and real data examples show that the suggested Bayesian relative CQR approach has good performance for the ordinal regression models.

Employee Motivation and Outcomes of Developing and Maintaining High-Quality Supervisor–Subordinate Guanxi in Hotel Operations

Dyadic leader–follower relationships play a significant role in sustainable development of labor-intensive and service-oriented organizations. The purpose of this study is to assess employees’ motivation and outcomes of developing and maintaining high-quality supervisor–subordinate guanxi in Chinese hotels. A survey was undertaken in four- or five-star hotels, with a total of 318 matched employees-supervisor completed responses used for data analysis, which was conducted using SPSS Statistics and SmartPLS. The study’s results show that affection and obligation significantly motivate employees to develop and maintain supervisor–subordinate guanxi, which in turn contributes to the desirable task performance delivery and supervisor autonomy support. Furthermore, the study confirms the mediating roles of supervisor–subordinate guanxi and task performance. This work extends guanxi research and is among the first to identify why hotel employees are motivated to develop and maintain high-quality supervisor–subordinate guanxi based on the contextualization of self-determination theory and Chinese cultural elements.

Designing Accessible Content Creation Support with Blind and Low Vision Creators

Today, digital creative tools are still largely designed without disabled people in mind and thus require retrofitting to achieve basic accessibility. As more creative tools begin to incorporate generative artificial intelligence, I propose a research agenda that centers the design of human-AI co-creation experiences on disabled creators and leverages such technology for accessibility from the start. Specifically, I focus on researching ways that AI-assisted creative tools could be designed to lift the expression ceiling and reduce effort for blind and low vision creators. Starting with a formative mixed-method study that uncovers the blind and low vision community's needs for visual content creation and editing support, this dissertation explores and designs accessible content creation support for three highly desired visual creative tasks: (1) private visual content obfuscation, (2) social media video editing, and (3) aesthetic visual content authoring. Together, I believe this dissertation will contribute actionable insights to lower the barriers to expressive and efficient digital content creation for blind and low vision creators.

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

Wheat lodging is a serious problem affecting grain yield, plant health, and grain quality. Addressing the lodging issue in wheat is a desirable task in breeding programs. Precise detection of lodging levels during wheat screening can aid in selecting lines with resistance to lodging. Traditional approaches to phenotype lodging rely on manual data collection from field plots, which are slow and laborious, and can introduce errors and bias. This paper presents a framework called ‘LodgeNet,’ that facilitates wheat lodging detection. Using Unmanned Aerial Vehicles (UAVs) and Deep Learning (DL), LodgeNet improves traditional methods of detecting lodging with more precision and efficiency. Using a dataset of 2000 multi-spectral images of wheat plots, we have developed a novel image registration technique that aligns the different bands of multi-spectral images. This approach allows the creation of comprehensive RGB images, enhancing the detection and classification of wheat lodging. We have employed advanced image enhancement techniques to improve image quality, highlighting the important features of wheat lodging detection. We combined three color enhancement transformations into two presets for image refinement. The first preset, ‘Haze & Gamma Adjustment,’ minimize atmospheric haze and adjusts the gamma, while the second, ‘Stretching Contrast Limits,’ extends the contrast of the RGB image by calculating and applying the upper and lower limits of each band. LodgeNet, which relies on the state-of-the-art YOLOv8 deep learning algorithm, could detect and classify wheat lodging severity levels ranging from no lodging (Class 1) to severe lodging (Class 9). The results show the mean Average Precision (mAP) of 0.952% @0.5 and 0.641% @0.50-0.95 in classifying wheat lodging severity levels. LodgeNet promises an efficient and automated high-throughput solution for real-time crop monitoring of wheat lodging severity levels in the field.

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

Self-Aligning Rotational Latching Mechanisms: Optimal Geometry for Mechanical Robustness

Abstract In concurrent work, we introduced a novel robotic package delivery system latching intelligent modular mobility system (LIMMS). Each LIMMS end effector requires a small, lightweight latching mechanism for pre-manufactured containers, such as cardboard boxes. In order to effectively process a high volume of packages, aligning the latching mechanism quickly and reliably is critical. Instead of depending on highly accurate controllers for alignment, we propose a novel self-aligning rotational mechanism to increase the system’s tolerance to misalignment. The radial latching design consists of evenly spaced blades that rotate into slots cut into the box. When misaligned, the blades contact the edges of the engagement slots, generating a self-correcting force that passively centers the blades with the slot pattern. This paper introduces a mathematical framework with closed form expressions to quantify error tolerance for these mechanisms. Through our mathematical and optimization analyses, it is shown that a two-blade design can tolerate a maximum misalignment of three times the radius to the blade tips, much larger than commonly used designs with three or more blade-like contacts. Our approach can be generalized for a class of rotational latching mechanisms with any number of blades. Utilizing this theory, a design process is laid out for developing an optimal self-aligning rotational latching mechanism given desired parameters and task constraints. With this methodology, we designed, manufactured, and verified the effectiveness of both two-blade and three-blade self-aligning in practical experiments.

Optical Neural Network Architecture for Deep Learning with Temporal Synthetic Dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems. This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions, which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks.

동일 원작 기반 한일 영상 매체의 장애인 재현 비교 연구

The purpose of this paper is to critically examine the representation of people with autistic spectrum disability in video media from the perspective of cultural studies, and to present the desirable tasks of disability and inclusion in disability studies through a comparative study on the representation of people with autistic spectrum disability in the Korean movie Marathon and the Japanese drama Marathon based on the same original work. Therefore, the narrative of growth through communication and perception of disability and inclusion were compared between them. The one is progressive in that it emphasizes self-reliance and nothing about us without us, seeks communication and change in society, and highlights an active female image. But It shows the task of inclusion through the reality of the prejudice and conflicts of society towards people with the autistic disabled and education or vocational training that is separated from people without disabilities. The other reproduces a social foundation that is more advanced than Korea in terms of harmony with people with autistic spectrum disability and integrated workplaces, but emphasizes family love and self-denial, revealing the limitations of the individual model that disabilities must be overcome through individual efforts.

Ultrastrong bonding, on-demand debonding, and easy re-bonding of non-sticking materials enabled by reversibly interlocked macromolecular networks-based Janus-like adhesive.

Simultaneously gluing hydrophobic and hydrophilic materials is a highly desired but intractable task. Herein, we developed a facile strategy using reversibly interlocked macromolecular networks (ILNs) as an adhesive. As shown by the proof-of-concept assembly of glass/ILNs/fluoropolymer (i.e., a simplified version of a photovoltaic module), the sandwiched ILNs were stratified after hot-pressing owing to temporary decrosslinking enabled by the built-in reversible covalent bonds. The fragmented component networks were enriched near their respective thermodynamically favored substrates to form a Janus-like structure. Strong elaborate interfacial bespoke chemical bonds and mechanical interlocking were thus established accompanied by the reconstruction of ILNs after cooling, which cooperated with the robust cohesion of the core part of the ILNs resulting from topological entanglements and led to a record-high peeling strength of 64.86 N cm-1. Also, the ILN-based Janus-like adhesive possessed reversible recyclability, adhesivity and on-demand de-bondability. The molecular design detailed in this study serves as a guide for developing a high-performance smart adhesive that firmly bonds non-sticking materials. Compared with existing Janus adhesives, our ILNs-based adhesive not only shows extremely useful reversibility but also greatly simplifies the adhesion process with no surface treatment required.

Fabricating polymer/HEA-hybrid topological lattice structure for enhanced mechanical properties

Metamaterials such as architected lattice structures have aroused broad interest in being applied as mechanical supports for their lightweight and custom-shaped capabilities. Although various prior efforts have been devoted, a multiscale fabrication of micro-nano lattice structures without penalizing the mechanical properties is still a challenging but highly desirable task. Here we put forward a strategy to produce a mechanically enhanced micro-nano lattice structure by conformally depositing a CoCrFeNiTi high-entropy alloy (HEA) coating layer onto a three-dimensional (3D) printed polymer skeleton. The template for the 3D printing employs a six-membered tricapped trigonal prism (6M-TTP) structure derived from a medium-range order structure motif in amorphous alloys. The topological complexity of the 6M-TTP can substantially avoid the stress concentration by offering stress-release channels, while the HEA film incorporating with amorphous and nanocrystalline constituents can further reinforce the lattice architecture through its size hardening effect. Benefitting from the above, the fabricated polymer/HEA-hybrid lattice exhibits a high specific compressive strength (∼0.055 ​MPa ​kg-1 m3 at a density below 500 ​kg ​m-3), a superior elastic recoverability (∼70% recovery rate under >30% compression), an enhanced plasticity (40% strain) and a high specific modulus (0.135 ​MPa ​kg-1 m3). Our strategy initiates a perspective way to fabricate multiscale micro-nano lattice structures with improved mechanical properties, which could be extended to widespread metamaterial research.

VToonify

Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency. In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel VToonify framework. Specifically, VToonify leverages the mid- and high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output. Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity. This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively. Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls. Code and pretrained models are available at our project page: www.mmlab-ntu.com/project/vtoonify/.

Reflections on the use of information and communication technologies in the training of specialists in the field of culture and art during the war

The article examines the problem of self-education of culture and art experts in the context of philosophical, psychological, pedagogical, cultural studies, and art studies. The main goal of the study is to explore the role of the information field, social communication and information resources in combination with the role of the teacher and mentor in professional education and training of future specialists. Art departments of Ukrainian universities, relying on their own experience and the experience of foreign colleagues, are increasingly using innovative methods of teaching. The distance learning and the use of cloud technologies during the pandemic and the full-scale war of Russia against Ukraine became almost the only possibilities to continue the educational process. If yesterday the self-education of students of art universities was a desirable task in the implementation of educational plans of specialists in the field of culture and art, today’s challenges have proven the necessity of the proposed process in reaching the heights of personal and professional excellence. The tasks of the educational process have become more complicated, because students are required to use the existing professional training aimed at self-improvement. The ability of students to self-educate, the ability to predict the consequences of their professional activities and the search for ways to solve professional tasks through the use of existing knowledge and experience has become an urgent need of the time. Cloud technologies facilitate the distribution of various content (texts, photos, images and music files) over the Internet. The accessibility, openness, interactivity of cloud technologies enables collective use and communication both online and offline, which makes the process of self-education of future specialists in the field of culture and art meaningful, practice-oriented process and contributes to the improvement of their professional competence.

Review on Intelligent Controller Path Planning of Scorbot Robot PATH PLANNING

Robotics is spanning as a prominent component of manufacturing industries tending to affect human being at all levels, from managers of production to shop floor unskilled workers. A programmable robot with number of degrees of freedom and different configuration can perform diverse tasks with the help of associated verity of grippers. This article deals with “Intelligent Controller for Path Planning of Scorbot Robot”. Modelling and design analysis are primary requirements for manufacturing a robot. Kinematic express knowledge about performance and actions of a mechanical system and it take part in greater role for development and software management. In the first part of the article, kinematic behavior has been analyzed for scorbot-er 4u robot. On each link frame is assigned which gives spatial position by mapping and positions of gripper with respect to base is found or vice versa. Direct kinematics is the finding the position and orientations of the gripper with respect to a known reference frame for an n DOF manipulator. In the next part of the thesis an intelligent controller has been proposed using fuzzy logic technique to perform desired task of scorbot. The proposed methodology has been analyzed by simulation result and verified experimental result which is good agreement & its effectiveness of the proposed methodology.

Identifying desired qualifications, tasks, and organizational characteristics of practice managers—a cross-sectional survey among group practice physicians in Germany

BackgroundThe increase of centralization developments in primary and secondary care practices may cause the organizational needs to increase as well, as the practices grow in size. This continuous change is observed in different stages in various countries since, from the perspective of a physician, it is reinforced by the benefits it adds to flexible work configuration, professional exchange and specialization. However, in order to benefit from the joint practice system, the proper managerial skills of practice managers are required, as doctors are not naturally prepared to fulfill such tasks. This study thus aims to gain insight into physicians' views in group practices and acquire a greater understanding of expectations towards practice management and the emerging role of practice managers (PM).MethodsA cross-sectional study design was employed which utilized an anonymous online questionnaire. In total, 3,456 physicians were invited to participate in the study between February 8th and March 17th 2021 by the Association of Statutory Health Insurance Physicians of Baden-Württemberg, Germany. Bivariate and multivariate analyses were applied to characterize the expectations of physicians towards practice management.ResultsThe survey yielded 329 replies (9,5%). 50% of the participating practices already had a PM employed. In general, these practices were larger than practices without a PM. Most physicians (85%) considered a medical background to be essential for the task of a PM. While practices without a PM considered it important for PMs to have medical qualifications, practices with a PM favored qualifications in business administration. 77.2% of physicians preferred to educate and recruit PMs out of their current practice staff. Competence in organizational tasks, such as coordination of tasks and quality management, was considered to be an essential skill of a PM and had the highest agreement levels among those surveyed, followed by staff management of non-physicians, billing, bookkeeping, staff management of physicians and recruiting. Based on multivariate regression analysis, larger practices valued the role of a PM more and were more likely to employ a PM. Notably, the effect that size had on these items was more substantial for generalists than specialists.ConclusionsThe benefits and importance of PMs as well as the potential for delegation are recognized, in particular, by larger practices. The positive feelings that physicians who already employ PMs have towards their contribution to ambulatory care are even more significant. Pre-existing medical support staff has been identified to be the most desirable candidates for taking on the role of PM.

Highly economic and waste valorization strategy for multicomponent and Knoevenagel reactions using water extract of tamarind seed ash.

The application of solid organic waste-originated products in the preparation of synthetically and biologically significant compounds in aqueous media or pure water is a highly desired task in chemical synthesis that shows an effective solution to the circular economy and sustainable environment. In this article, we describe our research on the development of highly economic and sustainable protocols for the synthesis of biologically important oxygen-heterocycles (using a multicomponent reaction) and synthetically important olefins (via the Knoevenagel condensation reaction) using water extract of tamarind seed ash (WETS) as catalyst and aqueous reaction medium. The reactions are carried out at room temperature (RT) under toxic/problematic/volatile organic solvent-free conditions. Products of the current methods have been purified by using recrystallization technique. WETS was characterized from its FTIR, powder XRD, SEM, and EDAX data. Problematic and non-renewable solvents were avoided throughout the process from their synthesis to purification. The utilization of solid organic waste-originated catalyst and aqueous media, avoid of non-renewable substances as catalysts, media, separation solvents and promoters, and unobligating heating conditions can surely attract the attention of chemists towards exploring the waste-based products in chemical transformations.

Unsupervised Domain Transfer for Task Automation in Unmanned Underwater Vehicle Intervention Operations

During underwater intervention and monitoring operations, large amounts of imagery and sensory data are produced and stored. Moreover, these data might have the potential to help automatize future operations. In this article, we propose a method for producing segmented data that can be utilized in training of neural networks used for performing desired control tasks. By combining unlabeled images from previous operations with a paired 3-D drawing of the same monitored object, we can train a generative adversarial network to learn the domain adaption between two domains and produce synthetic images with a high resemblance to the original footage. The clear advantage is that the enhanced synthetic images contain the segmented information of the object without the expensive cost of manually segmenting the images. The enhanced segmented data are used to train an object detector that predicts bounding boxes locating the segmented object. This is used in two ways: 1) to analyze the quality of the segmentation and 2) to command a control task for a remotely operated vehicle relative to the object of interest. Controlling the yaw is the main control task to maintain the object centered in the camera frame. Additionally, we explore how overtraining of the domain adaptation network negatively impacts the accuracy of the object detector. This is done by comparing the accuracy of the object detector trained with the datasets produced by different epochs of the domain adaptation network.

Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model.

Medicinal chrysanthemum detection is one of the desirable tasks of selective chrysanthemum harvesting robots. However, it is challenging to achieve accurate detection in real time under complex unstructured field environments. In this context, we propose a novel lightweight convolutional neural network for medicinal chrysanthemum detection (MC-LCNN). First, in the backbone and neck components, we employed the proposed residual structures MC-ResNetv1 and MC-ResNetv2 as the main network and embedded the custom feature extraction module and feature fusion module to guide the gradient flow. Moreover, across the network, we used a custom loss function to improve the precision of the proposed model. The results showed that under the NVIDIA Tesla V100 GPU environment, the inference speed could reach 109.28 FPS per image (416 × 416), and the detection precision (AP50) could reach 93.06%. Not only that, we embedded the MC-LCNN model into the edge computing device NVIDIA Jetson TX2 for real-time object detection, adopting a CPU–GPU multithreaded pipeline design to improve the inference speed by 2FPS. This model could be further developed into a perception system for selective harvesting chrysanthemum robots in the future.

Semantics- and Task-Oriented Scheduling for Networked Control Systems in Practice

Networked control systems (NCSs) are feedback control loops closed over a communication network. Emerging applications, such as telerobotics, drones, and autonomous driving, are the most prominent examples of such systems. Regular and timely information sharing between the components of NCSs is essential to fulfilling the desired control tasks, as stale information can lead to performance degradation or even physical damage. In this work, we consider multiple heterogeneous NCSs that transmit their system state over a shared physical wireless channel to a gateway node. We conduct a comprehensive experimental study on selected MAC protocols using software-defined radios with state-of-the-art (SotA) solutions designed to increase information freshness and control performance. As a significant improvement over the SotA, we propose a novel contention-free algorithm that is able to outperform the existing solutions by combining their strengths in one protocol. In addition, we propose a new metric called normalized mean squared error that maps the age of information to a dimensionless quantity that captures the expected value of a control system’s next transmission. We demonstrate its adoption and effectiveness for wireless resource scheduling in a case study involving multiple inverted pendulums. From our experimental study and results, we observe that value-aware prioritization of the sub-systems contributes to minimizing the adverse effects of information staleness on control performance. In particular, as the number of devices increases, the benefit of control awareness to the quality of control stands out when compared to protocols that focus solely on maximizing information freshness.