Effective Tool Research Articles

Anaerobic Soil Disinfestation and Vermicompost to Manage Bottom Rot in Organic Lettuce.

Rhizoctonia solani Kühn (teleomorph: Thanatephorus cucumeris [Frank] Donk) is an aggressive soilborne pathogen with a wide host range that survives saprophytically between crops, presenting a challenge for organic vegetable farmers who lack effective management tools. A 2-year field experiment was conducted at two organic farms to compare anaerobic soil disinfestation (ASD) and worm-cured compost (vermicompost) to manage bottom rot caused by R. solani subspecies AG1-IB in field-grown organic lettuce (Lactuca sativa). At each farm, four replicate plots of seven treatments were arranged in a randomized complete block design. Randomization was restricted by grouping treatments to evaluate ASD, and treatments to evaluate vermicompost in starter plugs. ASD experiment treatments were three different ASD carbon sources that are commonly used and widely available to local farmers in Vermont: compost, cover crop residues, and poultry manure fertilizer, as well as a tarped control. Vermicompost experimental treatments were vermicompost compared with two types of controls: a commercial biocontrol product (RootShield PLUS + G), and unamended (untarped control). This study demonstrated that the ASD method is achievable in a field setting on Vermont farms. However, neither ASD nor vermicompost produced significant disease suppression or resulted in higher marketable yields than standard growing practices. Given the laborious nature of ASD, it is likely more appropriate in a greenhouse setting with high-value crops that could especially benefit from being grown in plastic tarped beds (e.g., tomatoes and strawberries). This study is the first known attempt of field-implemented ASD for soil pathogen control in the northeastern United States.

Designing and Implementing a Telehealth Simulation for Pain Neuroscience Education in a Doctor of Physical Therapy Curriculum: A Pilot Study

There is continuing debate over the best pedagogical approach to deliver modern pain science knowledge. Experiential learning, such as simulation, may better promote application and retention of curricular material. This study investigated changes in knowledge of pain neurophysiology, attitudes, beliefs and clinical recommendations towards chronic low back pain (CLBP) after participation in a telehealth simulation for pain neuroscience education (PNE). A convenience sample of twenty-one Doctor of Physical Therapy (DPT) students, age 23.6 (± 3.60), completed the simulation. Students completed a pre- and post-questionnaire consisting of the Revised Neurophysiology of Pain Questionnaire (rNPQ) Healthcare Providers’ Pain and Impairments Relationship Scale (HC-PAIRS) and clinical vignette. Additionally, the post simulation survey included the Simulation Effectiveness Tool Modified (SET-M). Descriptive statistics were utilized to analyze all data. Pre- and post-rNPQ mean (SD) scores improved from 5.38 ±1.24 (44.8%) to 6.48 ±1.36 (54%). HC-PAIRS mean (SD) scores lowered from 51.76 ± 9.64 to 47.14 ±5.41, indicating a positive shift in attitudes and beliefs relating to CLBP. All appropriate clinical recommendations improved after post simulation. The mean (SD) SET-M score was 54.9 ±3.66. Experiential learning via a PNE telehealth simulation improved knowledge of pain neurophysiology, positively shifted attitudes and beliefs towards CLBP, while also improving appropriate clinical recommendations. The SET-M scores present the learners perspective and provide guidance in adapting the simulation in the future. Further research should include a longitudinal design with a long-term follow up, a larger sample size across different curricula and a qualitative component to explore the student experience.

Cross-cultural adaptation and construct validity of the Chinese Version of Visual Vertigo Analogue Scale by using structural equation modeling.

Visual vertigo (VV) is a disease characterized by various visual signal-induced discomforts, including dizziness, unsteady balance, activity avoiding, and so forth. Distinguishing it from other kinds of dizziness is important because it needs the combination of visual training and vestibular rehabilitation together. However, there is no appropriate tool to diagnose VV in China, thus we would like to introduce an effective tool to China. The aim of this study was to establish the reliability and validity of the Chinese version of visual vertigo analogue scale (VVAS-CH) and to achieve its cross-cultural adaptation in order to promote its further usage in China. A total of 1681 patients complaining of vertigo or dizziness were enrolled and they were asked to complete the VVAS-CH. The cross-cultural adaptation, reliability and construct validity of the VVAS-CH were determined. Split-half reliability was 0.939, showing a good reliability. Factor analysis identified only one common factor for the nine items that explained 64.83% of the total variance. Most fit indices reached acceptable levels, proving the good fit of the VVAS-CH model. The VVAS-CH validated in this study can be used as an effective tool for diagnosing and evaluating VV in patients whose native language is Chinese.

Enhancing the Efficiency of the Individual Case Safety Report (ICSR) Quality and Compliance through Automation.

Over the past few years, major inspection findings have been identified in the "management of adverse reactions" that may be due to increasing workload in pharmaceutical organizations impacting the correctness of information in individual case safety reports (ICSRs). Although retrospective quality check (Retro-QC) and late submission analyses are important steps in ensuring ICSR quality, their manual application poses several challenges that can be overcome through automation. To improve the efficiency of the Retro-QC analysis and late submission analysis using a computer-operated tool called Compliance and Metrics Management (CMM) tool, and to measure the tool's effectiveness in terms of productivity, time, and cost savings by comparing against the manual process. Time savings were calculated by measuring the difference in time taken during the manual process versus the automated process. Cost savings were measured in terms of hourly remuneration for the time saved. Productivity was calculated as the difference between the number of cases handled in the manual versus automated process. Thus, the overall efficiency was measured in terms of time and cost savings along with increased productivity. Automation resulted in time savings of 49% and cost savings of 43% for Retro-QC analysis, and the productivity level increased by 67%. For late submission analysis, the CMM tool resulted in time savings of 88% and cost savings of 87%. CMM tool enhanced the efficiency of both Retro-QC and late submission analyses by increasing productivity along with time and cost savings. It also reduced the number of errors, thereby enhancing the accuracy of the process and overall compliance.

Evaluation of a Novel Battery-Operated Tumbler Device for Use in the Detection of Mouse Pathogens for Rodent Health Monitoring.

The search for alternatives to live animal sentinels in rodent health monitoring programs is fundamental to the 3Rs (Reduction, Replacement, and Refinement) of animal research. We evaluated the efficacy of a novel battery-operated tumbler device that rotates soiled bedding in direct contact with sample media against the use of exhaust sample media and soiled bedding sentinel (SBS) mice. Four rodent racks were used, each with 3 test cages: a cage with a tumbler device that rotated for 10min twice a week (TUM10), a cage with a tumbler device that rotated for 60min twice a week (TUM60), and a cage housing 2 female Crl:CD1(ICR) mice. Every 2 wk, each test cage received soiled bedding collected from all cages on each respective rack. In addition to soiled bedding, the tumbler device contained various sample collection media: a contact Reemay filter (3mo-cRF) that remained in the tumbler for the duration of the study, a contact Reemay filter (1mo-cRF) that was replaced monthly, adhesive swabs (AS) that were added at every biweekly cage change, and an exhaust Reemay filter located at the exhaust outlet of the cage. All analyses were performed by direct PCR for both sample media in the animal-free methods, and fecal pellet, body swab, and oral swabs were collected from sentinel mice. Out of 16 total pathogens detected, assessment of 1mo-Crf from both TUM10 and TUM60 cages detected 84% and 79% of pathogens, respectively, while SBS samples detected only 47% of pathogens. AS in TUM60 and TUM10 cages detected the fewest pathogens (24% and 13%, respectively). These results indicate that the novel tumbler device is an effective and reliable tool for rodent health monitoring programs and a suitable replacement for live animal sentinels. In this study, 1mo-cRF in TUM10 cages detected the highest number of pathogens.

تصميم نظام أمثل باستخدام الفاصل التقريبي لبرمجة دي نوڨو متعددة الأهداف

تُعد برمجة دي نوڤو متعددة الأهداف، أداة فعالة تتعامل مع تصميم نظام أمثل، وذلك من خلال تحديد المستوى الأمثل لتخصيص الموارد، وتحسين قيمة دوال الهدف وفقاً لسعر الموارد (هذا فيما لو كانت الظروف طبيعية ولا يوجد تقلب في الاسعار). في هذه البحث اُقتِرحَ نهجاً جديداً لحل مشكلة عدم اليقين لمشكلة برمجة دي نوفو، وذلك باستخدام أنموذج مركب يتضمن نوعين من البرمجة: الأول هو برمجة متعددة الأهداف ذات الفاصل التقريبي (RIMOP)، والثاني: برمجة دي نوڤو(DNP)، علماً بأن معاملات متغيرات القرار لدالة الهدف والقيود عبارة عن فاصل تقريبي RIC. وقد استخدامنا ثلاث طرائق لحل الانموذج المقترح وإيجاد تصميم نظام أمثل، الطريقة الاولى هي طريقة المجموع الموزون WSM والتي تُستخدم قبل اعادة صياغة النموذج المقترح RIMOP (وكانت الموارد معروفة)، وقد وجد ان نتائج طريقة WSM تعطي حلاً واحداً (حل وسط قريب من المثالية) من بين الحلول المقبولة تحت كل حد من حدود المشكلة، وبالتالي يمكن تقديم أربع حلول (بدائل) لصانع القرار (DM). اما منهجية زيلني وطريقة نسب المسار الأمثل فقد استخدمت بعد صياغة الانموذج المقترح (RIMODNP) (وكانت الموارد للطرف الأيمن للقيود ليست معروفة). وجد ان منهجية زيلني تعطي تصميم نظام أمثل واحداً لكل حد من حدود المشكلة، في حين ان طريقة المسار الأمثل طبقت بعد ان تم التأكد من حدود النموذج المقترح وفقاً النظرية شاي فقد استخدمنا ثلاثة أنواع من النسب تحت كل مشكلة فرعية، فقد وجد ان هذهِ الطريقة تعطي ثلاث تصاميم انظمة مثلى تحت كل حد للمشكلة، وقد اتضح من خلال النتائج ان طريقة نسب المسار الامثل هي أكثر كفاءة من غيرها بعد تطبيقها على النموذج المقترح، لأنها اعطت اثنا عشر بديلاً لـ(DM)، لوحظ ان الانموذج المقترح يتوافق مع شروط ونظريات RIC. يُعد الانموذج المقترح مناسباً جداً لظروف عدم اليقين، اخيراً، طبق مثال رقمي على الانموذج المقترح.

Non-IID Federated Learning With Sharper Risk Bound.

In federated learning (FL), the not independently or identically distributed (non-IID) data partitioning impairs the performance of the global model, which is a severe problem to be solved. Despite the extensive literature related to the algorithmic novelties and optimization analysis of FL, there has been relatively little theoretical research devoted to studying the generalization performance of non-IID FL. The generalization research of non-IID FL still lack effective tools and analytical approach. In this article, we propose weighted local Rademacher complexity to pertinently analyze the generalization properties of non-IID FL and derive a sharper excess risk bound based on weighted local Rademacher complexity, where the convergence rate is much faster than the existing bounds. Based on the theoretical results, we present a general framework federated averaging with local rademacher complexity (FedALRC) to lower the excess risk without additional communication costs compared to some famous methods, such as FedAvg. Through extensive experiments, we show that FedALRC outperforms FedAvg, FedProx and FedNova, and those experimental results coincide with our theoretical findings.

BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking.

Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.

Interpretable Latent Space for Meteorological Out-of-Distribution Detection via Weak Supervision

Deep neural networks (DNNs) are effective tools for learning-enabled cyber-physical systems (CPSs) that handle high-dimensional image data. However, DNNs may make incorrect decisions when presented with inputs outside the distribution of their training data. These inputs can compromise the safety of CPSs. So, it becomes crucial to detect inputs as out-of-distribution (OOD) and interpret the reasons for their classification as OOD. In this study, we propose an interpretable learning method to detect OOD caused by meteorological features like darkness, lightness, and rain. To achieve this, we employ a variational autoencoder (VAE) to map high-dimensional image data to a lower-dimensional latent space. We then focus on a specific latent dimension and encourage it to classify different intensities of a particular meteorological feature in a monotonically increasing manner. This is accomplished by incorporating two additional terms into the VAE’s loss function: a classification loss and a positional loss. During training, we optimize the utilization of label information for classification. Remarkably, our results demonstrate that using only 25% of the training data labels is sufficient to train a single pre-selected latent dimension to classify different intensities of a specific meteorological feature. We evaluate the proposed method on two distinct datasets, CARLA and Duckietown, employing two different rain-generation methods. We show that our approach outperforms existing approaches by at least 15 in the F1 score and precision when trained and tested on CARLA dataset.

Product Improvement Using Knowledge Mining and Effect Analogy

Different from new product development, design improvement aims to solve the problems of existing products. Although design knowledge and effect tools have been applied in product improvement, the existing methods for design improvement are limited in their specific application areas. A general method of product improvement is proposed in this paper using the knowledge mining and effect analogy. The length–time dimension is introduced to link the problem analysis and problem-solving for the first time. This method includes the effect knowledge base construction, length–time dimension extraction, effect retrieval, effect ranking, analogy object selection, and effect structure mapping. This method integrates a variety of algorithms and software tools in design knowledge mining to improve the efficiency of the effect analogy for product improvement. Through the comparative analysis of three effect retrieval methods and design improvement of a button battery ring device, the superiority and feasibility of the proposed method are verified.

Recent Advances in NHC-Catalyzed Chemoselective Activation of Carbonyl Compounds.

N-Heterocyclic carbenes (NHCs) catalysts have been employed as effective tools in the development of various reactions, which have made notable contributions in developing diverse reaction modes and generating significant functionalized molecules. This review provides an overview of the recent advancements in the chemo- and regioselective activation of different aldehydes using NHCs, categorized into five parts based on the different activation modes. A brief conclusion and outlook is provided to stimulate the development of novel activation modes for accessing functional molecules.

The use of Rosetta Stone as learning media to improve students' speaking

The study addressed the impact of the Rosetta Stone tool on improving English speaking skills. The research aimed to determine the contribution of the Rosetta Stone tool as a learning medium to enhance the English speaking skills of high school students at Liceo Cristiano Israel. A mixed qualitative-quantitative approach and a non-experimental design were employed. Data collection methods included surveys, document review, and checklists. Analysis focused on learning outcomes, student satisfaction, learning difficulties, and measuring the contribution of Rosetta Stone to the English curriculum. The sample consisted of 29 intentionally selected high school students: 10 from the first level, 7 from the second level, and 12 from the third level. Results showed a significant correlation between the use of Rosetta Stone and progress in English speaking, supporting the tool's effectiveness. There was a highlighted need to integrate technological tools to enhance language teaching and adapt educational approaches to promote communicative skills. The reliability of instruments was high (0.83), supporting the validity of the collected data. Rosetta Stone proved to be an effective resource for developing English speaking skills in high school students, suggesting the importance of its integration into language teaching programs to promote meaningful learning and effective communication.

Pengenalan Penggunaan Google Form Untuk Survei Kepada Warga Darma Bakti, Cengkareng, Jakarta Barat

This research aims to introduce and implement the use of google form as a survei tool to collect data from residents of Darma Bakti, Cengkareng, West Jakarta. The research method involves developing a survei form using the google form platform that can be accessed online. The research also explored the effectiveness of google form in collecting data on various aspects of Darma Bakti residents' lives and needs. The research process involved introducing the concept and functionality of google form to respondents, as well as brief training to increase participation in the survei. The data collected included demographic information, preferences, and feedback related to services and facilities obtained from Darma Bakti residents. Data analysis involved the use of automated analysis features provided by google form, as well as manual interpretation to gain deeper insights. The results of this study are expected to provide a better view of the use of google form as an effective and efficient tool for citizen surveis, as well as provide valuable input for the improvement of services and facilities in the Darma Bakti neighborhood, Cengkareng, West Jakarta. This research also contributes to the literature related to the use of online survei technology, especially in the context of local communities.

The Biomechanics Research and Innovation Challenge: Development, Implementation, Uptake, and Reflections on the Inaugural Program.

Biomechanics as a discipline is ideally placed to increase awareness and participation of girls and women in science, technology, engineering, and mathematics. A nationwide Biomechanics and Research Innovation Challenge (BRInC) centered on mentoring and role modeling was developed to engage high school girls (mentees) and early-mid-career women (mentors) in the field of biomechanics through the completion of a 100-day research and/or innovation project. This manuscript describes the development, implementation, and uptake of the inaugural BRInC program and synthesizes the research and innovation projects undertaken, providing a framework for adoption of this program within the global biomechanics community. Eighty-seven high school girls in years 9 and 10 (age range: 14-16y) were mentored in teams (n = 17) by women in biomechanics (n = 24). Using a design thinking approach, teams generated solutions to biomechanics-based problem(s)/research question(s). Eight key reflections on program strengths, as well as areas for improvement and planned changes for future iterations of the BRInC program, are outlined. These key reflections highlight the innovation, impact, and scalability of the program; the importance of a program framework and effective communication tools; and implementation of strategies to sustain the program as well as the importance of diversity and building a sense of community.

Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations

Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning. However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework for representing the visual semantics in BNNs such as human functional brain networks (FBNs). To answer this question, we propose a novel computational framework, Synchronized Activations (Sync-ACT), to couple the visual representation spaces and semantics between ANNs and BNNs in human brain based on naturalistic functional magnetic resonance imaging (nfMRI) data. With this approach, we are able to semantically annotate the neurons in ANNs with biologically meaningful descriptions derived from human brain imaging for the first time. We evaluated the Sync-ACT framework on two publicly available movie-watching nfMRI datasets. The experiments demonstrate a) the significant correlation and similarity of the semantics between the visual representations in FBNs and those in a variety of convolutional neural networks (CNNs) models; b) the close relationship between CNN’s visual representation similarity to BNNs and its performance in image classification tasks. Overall, our study introduces a general and effective paradigm to couple the ANNs and BNNs and provides novel insights for future studies such as brain-inspired artificial intelligence.

The accuracy of reliability coefficients: A reanalysis of existing simulations.

Controversy over which reliability estimators should be used persists due to a lack of knowledge about their accuracy. Simulation is an effective tool to obtain an answer, but existing simulation studies yield contradictory results regarding which reliability estimators are the best. The causes of these inconsistent conclusions have yet to be discussed. This study reanalyzes existing studies to understand these contradictions. The most important reason is that previous studies consider only a few reliability estimators. This study examines approximately 30 reliability estimators and finds that there is no single, most accurate reliability estimator across all data types. Instead, several reliability estimators are accurate to comparable levels for unidimensional data (congeneric reliability, Guttman's lambda2, and ten Berge-Zegers's mu). Likewise, multiple reliability estimators perform similarly for multidimensional data (multidimensional parallel reliability, correlated factors reliability, and second-order factor reliability). Whereas many recent studies support factor analysis (FA) reliability estimators, this study shows that not all FA reliability estimators are accurate and that some cause severe overestimation. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Squirrel search method for deep learning-based anomaly identification in videos

The monitoring of human behavior and traffic surveillance in various locations has become increasingly important in recent years. However, identifying abnormal activity in real-world settings is a challenging task due to the many different types of worrisome and abnormal actions, including theft, violence, and accidents. To address this issue, this paper proposes a new framework for deep learning-based anomaly identification in videos using the squirrel search algorithm and bidirectional long short-term memory (BiLSTM). The proposed method combines the squirrel search algorithm, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The framework uses the knowledge gained from a sequence of frames to categorize the video as either typical or abnormal. The proposed method was exhaustively tested in several benchmark datasets for anomaly detection to confirm its functionality in challenging surveillance circumstances. The results show that the proposed framework outperforms existing methods in terms of area under curve (AUC) values, with a test set AUC score of 93.1%. The paper also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional long short-term memory (LSTM) models for anomaly detection in videos. Overall, the proposed framework provides a highly precise computerization of the system, making it an effective tool for identifying abnormal human behavior in surveillance footage.

Role of Novel Motorized Enteroscopy in the Evaluation of Small Bowel Diseases: A Systematic Review and Meta-analysis.

Motorized spiral enteroscopy (MSE) has recently been introduced for small bowel evaluation. In this systematic review and meta-analysis, we aim to evaluate the safety and efficacy of MSE for evaluation of small bowel diseases. A literature search was performed in Embase, PubMed, Medline databases for studies evaluating MSE between January -2010 and October-2022. The primary outcome of the study was diagnostic yield with MSE. Secondary outcomes included technical success, procedure duration, depth of maximum insertion (DMI), rate of pan-enteroscopy and adverse events. 10 studies with 961 patients [581 (60.5%) males] were included in the analysis. 1068 MSE procedures were performed by antegrade route in 698, retrograde route in 215 and bidirectional in 155 patients. Technical success was achieved in 94.9% (95% CI 92.9% to 96.4%) procedures. The pooled diagnostic yield of MSE was 73.7% (95% CI 70.7% to 76.4%). Pooled rate of pan-enteroscopy by antegrade route was 21.9% (95% CI 18.1% to 26.1%), retrograde route was 6.9% (95% CI 2.4% to 18.3%) and combined route was 61.2% (95% CI 52.4% to 69.3%). Pooled rate of major adverse events was 1.9% (95% CI 1.2% to 3.2%). MSE is a safe and effective tool for evaluating small bowel disorders. High diagnostic yield and low rate of adverse events make it a potential alternative to balloon enteroscopy. However, comparative trials are required in the future.

Is Single Enough? A Joint Spatiotemporal Feature Learning Framework for Multivariate Time Series Prediction.

A fuzzy cognitive map (FCM) is a simple but effective tool for modeling and predicting time series. This article focuses on the problem of multivariate time series prediction (TSP), which is essential and challenging in data mining. Although several FCM-based approaches have been designed to solve this problem, their feature extraction module designed for single mode falls short in capturing the nonlinear spatiotemporal dependencies among variates, thereby resulting in low prediction accuracy in forecasting multivariate time series, which shows that the single mode learning is not enough. Therefore, in this article, we propose a joint spatiotemporal feature learning framework for multivariate TSP, where a mix-resolution spatial module consisting of multiple sparse autoencoders (SAEs) is designed to extract the feature series with different spatial resolutions, and a mix-order spatiotemporal module concluding multiple high-order FCMs (HFCMs) is designed to model the spatiotemporal dynamics of these feature series. Finally, the outputs of the two modules are concatenated to predict future values. We refer to this framework as the spatiotemporal FCM (STFCM). Especially, an efficient learning algorithm is designed to update the integral weights of STFCM based on the batch gradient descent algorithm when it deems necessary. We validate the performance of the STFCM on four real-world datasets. Compared with the existing state-of-the-art (SOTA) methods, the experimental results not only show the advantages of the two designed modules in the STFCM but also show the excellent performance of the STFCM.

Journey to 1 Million Steps: A Retrospective Case Series Analyzing the Implementation of Robotic-Assisted Gait Training Into an Outpatient Pediatric Clinic.

To describe the implementation of an exoskeleton program in a rehabilitation setting using a Design Thinking framework. This is a retrospective case series of 3 randomly selected children who participated in skilled physical therapy using a pediatric exoskeleton that occurred on our journey to walking 1 000 000 steps in the exoskeleton devices. Participants ranged in age from 3 to 5 years, and all had neurologic disorders. All participants improved toward achieving their therapy goals, tolerated the exoskeleton well, and had an increased number of steps taken over time. The implementation of new technology into pediatric care and an established outpatient therapy clinic is described. The Design Thinking process applies to health care professionals and improves clinical care. Exoskeletons are effective tools for use in pediatric physical therapy.