Florida Polytechnic University

Project-based cross-sector partnerships to address social issues (CSSPs) occur in four “arenas”: business-nonprofit, business-government, government-nonprofit, and trisector. Research on CSSPs is multidisciplinary, and different conceptual “platforms” are used: resource dependence, social issues, and societal sector platforms. This article consolidates recent literature on CSSPs to improve the potential for cross-disciplinary fertilization and especially to highlight developments in various disciplines for organizational researchers. A number of possible directions for future research on the theory, process, practice, method, and critique of CSSPs are highlighted. The societal sector platform is identified as a particularly promising framework for future research.

Digital world has produced efficiencies, new innovative products, and close customer relationships globally by the effective use of mobile, IoT (Internet of Things), social media, analytics and cloud technology to generate models for better decisions. Blockchain is recently introduced and revolutionizing the digital world bringing a new perspective to security, resiliency and efficiency of systems. While initially popularized by Bitcoin, Blockchain is much more than a foundation for crypto currency. It offers a secure way to exchange any kind of good, service, or transaction. Industrial growth increasingly depends on trusted partnerships; but increasing regulation, cybercrime and fraud are inhibiting expansion. To address these challenges, Blockchain will enable more agile value chains, faster product innovations, closer customer relationships, and quicker integration with the IoT and cloud technology. Further Blockchain provides a lower cost of trade with a trusted contract monitored without intervention from third parties who may not add direct value. It facilitates smart contracts, engagements, and agreements with inherent, robust cyber security features. This paper is an effort to break the ground for presenting and demonstrating the use of Blockchain technology in multiple industrial applications. A healthcare industry application, Healthchain, is formalized and developed on the foundation of Blockchain using IBM Blockchain initiative. The concepts are transferable to a wide range of industries as finance, government and manufacturing where security, scalability and efficiency must meet.

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent and efficient for performing tasks that usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep learning (DL), conventional neural networks, fuzzy logic, and speech recognition, with unique capabilities and functionalities that can improve the performances of modern medical sciences. Such intelligent systems simplify human intervention in clinical diagnosis, medical imaging, and decision-making ability. In the same era, the Internet of Medical Things (IoMT) emerges as a next-generation bio-analytical tool that combines network-linked biomedical devices with a software application for advancing human health. In this review, we discuss the importance of AI in improving the capabilities of IoMT and point-of-care (POC) devices used in advanced healthcare sectors such as cardiac measurement, cancer diagnosis, and diabetes management. The role of AI in supporting advanced robotic surgeries developed for advanced biomedical applications is also discussed in this article. The position and importance of AI in improving the functionality, detection accuracy, decision-making ability of IoMT devices, and evaluation of associated risks assessment is discussed carefully and critically in this review. This review also encompasses the technological and engineering challenges and prospects for AI-based cloud-integrated personalized IoMT devices for designing efficient POC biomedical systems suitable for next-generation intelligent healthcare.

Abstract It is more than a decade since Genichi Taguchi's ideas on quality improvement were inrroduced in the United States. His parameter-design approach for reducing variation in products and processes has generated a great deal of interest among both quality practitioners and statisticians. The statistical techniques used by Taguchi to implement parameter design have been the subject of much debate, however, and there has been considerable research aimed at integrating the parameter-design principles with well-established statistical techniques. On the other hand, Taguchi and his colleagues feel that these research efforts by statisticians are misguided and reflect a lack of understanding of the engineering principles underlying Taguchi's methodology. This panel discussion provides a forum for a technical discussion of these diverse views. A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it. The topics covered include the importance of variation reduction, the use of noise factors, the role of interactions, selection of quality characteristics, signal-to-noise ratios, experimental strategy, dynamic systems, and applications. The discussion also provides an up-to-date overview of recent research on alternative methods of design and analysis. KEY WORDS: Design of experimentsDispersion of effectsLocation effectsRobust designSN ratiosVariation reduction

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.

Sensors are considered to be an important vector for sustainable development. The demand to meet the needs of future generations is accelerating the development of intelligent sensor-systems integrated with internet of things (IoTs), fifth generation (5G) communication, artificial intelligence (AI) and machine learning (ML) strategies. The inclusion of 2D nanomaterials with the IoTs/AI/ML has revolutionized the diversified applications of sensors in healthcare, wearable electronics, safety, environment, defense, and agriculture. Owing to their unique physicochemical characteristics and surface functionalities, borophene and MXenes have emerged as advanced 2D-materials (A2M) to architect future-generation sensors. ML-AI based theoretical modeling has guided the research and development of A2M-sensors economically by reducing cost, human resources, and contamination. A2M-sensors are flexible, wearable, intelligent, biocompatible, portable, energy-efficient, self-sustained, point-of-care, and economical, which can drastically transform the conventional sensing strategies. This review provides an insight in to the state-of-the-art A2M-based physical, chemical, and biosensor to efficiently detect chemical species, gases/vapors, drugs, biomarkers/pathogens, pressure, metal ions, radiations, temperature, light, and humidity. Besides the fundamental challenges creating a gap between theoretical predictions, practical-evaluations, in-lab-technology, and commercial viability, their potential solutions, field-deployable prospects are addressed to realize commercialization, thereby ensuring ability of future generations to maintain sustainable communities.

In this paper, a forecasting algorithm is proposed to predict photovoltaic (PV) power generation using a long short term memory (LSTM) neural network (NN). A synthetic weather forecast is created for the targeted PV plant location by integrating the statistical knowledge of historical solar irradiance data with the publicly available type of sky forecast of the host city. To achieve this, a K-means algorithm is used to classify the historical irradiance data into dynamic type of sky groups that vary from hour to hour in the same season. In other words, the types of sky are defined for each hour uniquely using different levels of irradiance based on the hour of the day and the season. This can mitigate the performance limitations of using fixed type of sky categories by translating them into dynamic and numerical irradiance forecast using historical irradiance data. The proposed synthetic weather forecast is proved to embed the statistical features of the historical weather data, which results in a significant improvement in the forecasting accuracy. The performance of the proposed model is investigated using different intraday horizon lengths in different seasons. It is shown that using the synthetic irradiance forecast can achieve up to 33% improvement in accuracy in comparison to that when an hourly categorical type of sky forecast is used, and up to 44.6% in comparison to that when a daily type of sky forecast is used. This highlights the significance of utilizing the proposed synthetic forecast, and promote a more efficient utilization of the publicly available type of sky forecast to achieve a more reliable PV generation prediction. Moreover, the superiority of the LSTM NN with the proposed features is verified by investigating other machine learning engines, namely the recurrent neural network (RNN), the generalized regression neural network (GRNN) and the extreme learning machine (ELM).

Autonomous vehicles (AVs) rely on various types of sensor technologies to perceive the environment and to make logical decisions based on the gathered information similar to humans. Under ideal operating conditions, the perception systems (sensors onboard AVs) provide enough information to enable autonomous transportation and mobility. In practice, there are still several challenges that can impede the AV sensors' operability and, in turn, degrade their performance under more realistic conditions that actually occur in the physical world. This paper specifically addresses the effects of different weather conditions (precipitation, fog, lightning, etc.) on the perception systems of AVs. In this work, the most common types of AV sensors and communication modules are included, namely: RADAR, LiDAR, ultrasonic, camera, and global navigation satellite system (GNSS). A comprehensive overview of their physical fundamentals, electromagnetic spectrum, and principle of operation is used to quantify the effects of various weather conditions on the performance of the selected AV sensors. This quantification will lead to several advantages in the simulation world by creating more realistic scenarios and by properly fusing responses from AV sensors in any object identification model used in AVs in the physical world. Moreover, it will assist in selecting the appropriate fading or attenuation models to be used in any X-in-the-loop (XIL, e.g., hardware-in-the-loop, software-in-the-loop, etc.) type of experiments to test and validate the manner AVs perceive the surrounding environment under certain conditions.

The necessity of classification is highly demanded in real life. As a mathematical classification approach, the Naive Bayes classifier involves a series of probabilistic computations for the purpose of finding the best-fitted classification for a given piece of data within a problem domain. In this paper, an implementation of Naive Bayes classifier is described. This classifier can be used as a general tool kit and applicable to various domains of classifications. To ensure the correctness of all probabilistic computations involved, a sample data set is selected to test this classifier.

This study focuses on the utilization of Large Language Models (LLMs) for the rapid development of applications, with a spotlight on LangChain, an open-source software library. LLMs have been rapidly adopted due to their capabilities in a range of tasks, including essay composition, code writing, explanation, and debugging, with OpenAI’s ChatGPT popularizing their usage among millions ofusers. The crux of the study centers around LangChain, designed to expedite the development of bespoke AI applications using LLMs. LangChain has been widely recognized in the AI community for its ability to seamlessly interact with various data sources and applications. The paper provides an examination of LangChain's core features, including its components and chains, acting as modular abstractions and customizable, use-case-specific pipelines, respectively. Through a series of practical examples, the study elucidates the potential of this framework in fostering the swift development of LLM-based applications.

This paper analyzes the notion of resilience in power systems from a fundamental viewpoint and thoroughly examines its practical implications. This paper aims to describe and classify different high-impact rare (HR) events, provide a more technical definition of power system resilience, and discuss linkages between resilience and other well-established concepts, such as security and reliability. Most relevant decisions of system operators in the face of HR events involve a significant level of stress and strain. In order to make informed decisions within this context, it is crucial to have an all-inclusive picture of the state of the system. This paper provides an appropriate framework that not only characterizes the various states of the system but also derives informed decisions from a resilience-oriented perspective. It also describes and analyzes diverse resilience improvement strategies. Comprehensive models and classifications are provided to clearly capture various aspects of power system resilience.

Novel physicochemistries of engineered nanomaterials (ENMs) offer considerable commercial potential for new products and processes, but also the possibility of unforeseen and negative consequences upon ENM release into the environment. Investigations of ENM ecotoxicity have revealed that the unique properties of ENMs and a lack of appropriate test methods can lead to results that are inaccurate or not reproducible. The occurrence of spurious results or misinterpretations of results from ENM toxicity tests that are unique to investigations of ENMs (as opposed to traditional toxicants) have been reported, but have not yet been systemically reviewed. Our objective in this manuscript is to highlight artifacts and misinterpretations that can occur at each step of ecotoxicity testing: procurement or synthesis of the ENMs and assessment of potential toxic impurities such as metals or endotoxins, ENM storage, dispersion of the ENMs in the test medium, direct interference with assay reagents and unacknowledged indirect effects such as nutrient depletion during the assay, and assessment of the ENM biodistribution in organisms. We recommend thorough characterization of initial ENMs including measurement of impurities, implementation of steps to minimize changes to the ENMs during storage, inclusion of a set of experimental controls (e.g., to assess impacts of nutrient depletion, ENM specific effects, impurities in ENM formulation, desorbed surface coatings, the dispersion process, and direct interference of ENM with toxicity assays), and use of orthogonal measurement methods when available to assess ENMs fate and distribution in organisms.

In this paper, we present an integrated system concept for the living assistance domain based on ambient intelligence technology and discuss the resulting challenges for the software engineering discipline. Automated living assistance systems represent a promising approach for the prolongation of an independent and self-conducted life of handicapped and elderly people thereby, enhancing their quality of life and minimizing the need for manual social/medical care. It is demonstrated that living assistance systems must realize flexibility and adaptability at the algorithmic, architectural and human interface level to an extent unknown in present systems. The construction of robust, trustworthy living assistance systems is an extremely challenging task and requires novel approaches for dependable self-adapting software architectures, resource efficiency, and self-adapting multi-modal human-computer interfaces. The resulting consequences and challenges for the discipline of software engineering are outlined in this paper.

To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bioinformatics needed for efficient smart diagnostics, optimization of therapy, and investigation of therapies of higher efficacy. The urgent need for such diagnostic systems is recommended by experts in order to achieve the mass and targeted SARS-CoV-2 detection required to manage the COVID-19 pandemic through the understanding of infection progression and timely therapy decisions. To achieve these tasks, there is a scope for developing smart sensors to rapidly and selectively detect SARS-CoV-2 protein at the picomolar level. COVID-19 infection, due to human-to-human transmission, demands diagnostics at the point-of-care (POC) without the need of experienced labor and sophisticated laboratories. Keeping the above-mentioned considerations, we propose to explore the compartmentalization approach by designing and developing nanoenabled miniaturized electrochemical biosensors to detect SARS-CoV-2 virus at the site of the epidemic as the best way to manage the pandemic. Such COVID-19 diagnostics approach based on a POC sensing technology can be interfaced with the Internet of things and artificial intelligence (AI) techniques (such as machine learning and deep learning for diagnostics) for investigating useful informatics via data storage, sharing, and analytics. Keeping COVID-19 management related challenges and aspects under consideration, our work in this review presents a collective approach involving electrochemical SARS-CoV-2 biosensing supported by AI to generate the bioinformatics needed for early stage COVID-19 diagnosis, correlation of viral load with pathogenesis, understanding of pandemic progression, therapy optimization, POC diagnostics, and diseases management in a personalized manner.

Digital storytelling is a viable tool to help struggling writers resist the social position of struggling writer that is often exacerbated by state‐mandated writing assessments. While some writers may struggle with traditional literacy, tapping into new literacies may boost their motivation and scaffold their understanding of traditional literacies. Three types of struggling writers are introduced followed by descriptions of ways digital storytelling can support them as writers. Three tables include the following resources: (1) examples of digital stories, (2) tutorials and web resources for music, sound effects, graphics, and copyright information, and (3) suggested hardware and software for creating digital stories. إن الحكاية الرقمية أداة قابلة للتطبيق كي تساعد الكتّاب المواجهين صعوبة في الكتابة في مقاومة أخذ الموقف الاجتماعي المعروف للكتّاب المواجهة صعوبة في الكتابة الذي يفاقمه الاختبارات التحريرية التي تتطلبها حكومات الولايات. لكون مواجهة بعض الكتّات صعوبة في التعلم التقليدي فإن استغلال أساليب التعلم الجديدة قد يقوي حافزهم وتعزيز فهم سقالتهم نحو المعارف التقليدية. وقد تم تقديم ثلاثة أنواع من الكتّاب من هذا الوارد وتليه وصفات الطرق التي تسيتطيع الحكاية الرقمية أن تساعدهم ككتّاب. وتشمل ثلاثة جداول الموارد التالية: (1) أمثلة من حكايات رقمية و(2) موارد تعليمية على الشبكة العالمية بالنسبة إلى الموسيقا والتأثيرات الصوتية والمعلومات المتعلقة بالحفاظ على حقوق الكاتب و(3)اقتراحات لأدوات الحاسوب المادية والبرمجية من أجل خلق الحكايات الرقمية. 数码讲故事是一种可行的工具，用以帮助有写作困难的学生抵御被冠以有写作困难的写作者这种社会地位。这种不太好的社会地位，经常因美国州政府所规定的书写评估结果而加剧了其不良影响。虽然有些学生可能不擅于传统的读写文化，但利用新的读写文化却可提升他们的学习动机及给予他们认识传统读写文化的学习支架。本文介绍三种有写作困难的学生，说明数码讲故事如何支援这些学生的写作。本文列出三个表格，包含以下的教学资源：1、一些数码故事的范例；2、有关音乐、音效和图像的辅导课及网络资源，以及版权资料；3、有关创作数码故事所需的硬件和软件的建议。 Raconter une histoire numérique est un outil valable pour aider les scripteurs en difficulté à faire face à la position de lecteur en difficulté qui est souvent exacerbée par les évaluations officielles de l'écriture. Alors que certains scripteurs peuvent avoir des difficultés avec la littératie classique, avoir affaire à de nouvelles littératies peut donner une impulsion à leur motivation et étayer leur compréhension des littératies traditionnelles. On présente trois types de lecteurs en difficulté, puis une description de la façon dont raconter une histoire sur ordinateur peut les aider en tant que scripteur. Les ressources suivantes figurent dans trois tableaux: 1) des exemples d'histoires numériques, 2) des tutoriels et les ressources sur la Toile pour la musique, les effets sonores, le graphisme, et les droits d'édition, et 3) le hardware et le software pour créer des histoires numériques. Положение учеников, которым трудно дается письменная речь, и без того непростое, но проблемы зачастую усугубляются наличием обязательных проверок письменной речи, которые централизованно проводятся в каждом штате. Цифровой рассказ – хороший способ помочь таким детям, ведь среди тех, кто с трудом одолевает ступени традиционной грамотности, наверняка есть те, кому новые виды грамотности вполне по плечу. Благодаря этому подходу они окажутся более мотивированны к учебе и их шансы освоить традиционную грамотность возрастут. Авторы описывают три типа учеников, испытывающих проблемы с письмом, объясняют, как именно можно помочь им посредством работы с электронными носителями. В статье представлены следующие ресурсы: (1) примеры цифровых рассказов, (2) руководства и веб‐ресурсы для оснащения рассказов музыкой, звуковыми эффектами, графикой и корректными данными для соблюдения авторских прав, (3) техника и программное обеспечение для создания цифровых рассказов. Contar cuentos en los medios electrónicos es una manera práctica de ayudar a los escritores con dificultades a sobreponerse al estado del escritor con dificultades que es a menudo exacerbado por las evaluaciones exigidas por el estado. Aunque algunos escritores pueden tener dificultades con los métodos tradicionales de alfabetización, es posible que las nuevas competencias les motiven más y les ayuden a entender mejor las competencias tradicionales. Se presentan tres tipos de escritores con dificultades y se mencionan maneras en que el contar cuentos en los medios electrónicos les puede servir de apoyo como escritores. Tres tablas presentan los siguientes recursos: (1) ejemplos de cuentos electrónicos, (2) instrucciones guiadas y recursos en la red sobre música, efectos auditivos, dibujos, e información sobre los derechos del autor, y (3) materiales (componentes físicos) y programas para crear cuentos electrónicos.

Nanosystems have shown encouraging outcomes and substantial progress in the areas of drug delivery and biomedical applications.

Smart power grids are being enhanced by adding a communication infrastructure to improve their reliability, sustainability, and efficiency. Despite all of these significant advantages, their open communication architecture and connectivity renders the power systems' vulnerability to a range of cyberattacks. This article proposes a novel resilient control system for load frequency control (LFC) system under false data injection (FDI) attacks. It is common to use encryption in data transfer links as the first layer of defending mechanism; here, we propose a second defense layer that can jointly detect and mitigate FDI attacks on power systems. In this article, we propose a new anomaly detection technique that consists of a Luenberger observer and an artificial neural network (ANN). Since FDI attacks can happen rapidly, the observer structure is enhanced by the extended Kalman filter to improve the ANN ability for online detection and estimation. The resilient controller is designed based on the attack estimation, which can eliminate the need for control reconfiguration. The resiliency of the proposed design against FDI attacks is tested on the LFC system. The simulation results clearly show that the proposed control system can successfully detect anomalies and compensate for their adverse effects.

The continuous deterioration of the environment due to extensive industrialization and urbanization has raised the requirement to devise high-performance environmental remediation technologies. Membrane technologies, primarily based on conventional polymers, are the most commercialized air, water, solid, and radiation-based environmental remediation strategies. Low stability at high temperatures, swelling in organic contaminants, and poor selectivity are the fundamental issues associated with polymeric membranes restricting their scalable viability. Polymer-metal-carbides and nitrides (MXenes) hybrid membranes possess remarkable physicochemical attributes, including strong mechanical endurance, high mechanical flexibility, superior adsorptive behavior, and selective permeability, due to multi-interactions between polymers and MXene's surface functionalities. This review articulates the state-of-the-art MXene-polymer hybrid membranes, emphasizing its fabrication routes, enhanced physicochemical properties, and improved adsorptive behavior. It comprehensively summarizes the utilization of MXene-polymer hybrid membranes for environmental remediation applications, including water purification, desalination, ion-separation, gas separation and detection, containment adsorption, and electromagnetic and nuclear radiation shielding. Furthermore, the review highlights the associated bottlenecks of MXene-Polymer hybrid-membranes and its possible alternate solutions to meet industrial requirements. Discussed are opportunities and prospects related to MXene-polymer membrane to devise intelligent and next-generation environmental remediation strategies with the integration of modern age technologies of internet-of-things, artificial intelligence, machine-learning, 5G-communication and cloud-computing are elucidated.

Abstract Air contamination is one of the foremost concerns of environmentalists worldwide, which has elevated global public health concerns for monitoring air contaminants and implementing appropriate safety policies. These facts have generated nascent global demand for exploring sustainable and translational strategies required to engineer affordable, intelligent, and miniaturized sensors because commercially available sensors lack lower detection limits, room temperature operation, and poor selectivity. The state‐of‐the‐art sensors are concerned with architecting advanced nanomaterials to achieve desired sensing performance. Recent studies demonstrate that neither pristine metal carbides/nitrides (MXenes) nor polymers (P) can address these practical challenges. However, synergistic combinations of various precursors as hybrid‐nanocomposites (MXP‐HNCs) have emerged as superior sensing materials to develop next‐generation intelligent environmental, industrial, and biomedical sensors. The expected outcomes could be manipulative due to optimizing physicochemical and morphological attributes like tunable interlayer‐distance, optimum porosity, enlarged effective surface area, rich surface functionalities, mechanical flexibility, and tunable conductivity. This review intends to detail a comprehensive summary of the advancements in state‐of‐the‐art MXP‐HNCs chemiresistors. Moreover, the underlying sensing phenomenon, chemiresistor architecture, and their monitoring performance are highlighted. Besides, an overview of challenges, potential solutions, and prospects of MXP‐HNCs as next‐generation intelligent field‐deployable sensors with the integration of IoT and AI are outlined.

This third Guideline for Reasonable and Appropriate Care in the Emergency Department (GRACE-3) from the Society for Academic Emergency Medicine is on the topic adult patients with acute dizziness and vertigo in the emergency department (ED). A multidisciplinary guideline panel applied the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of evidence and strength of recommendations regarding five questions for adult ED patients with acute dizziness of less than 2 weeks' duration. The intended population is adults presenting to the ED with acute dizziness or vertigo. The panel derived 15 evidence-based recommendations based on the timing and triggers of the dizziness but recognizes that alternative diagnostic approaches exist, such as the STANDING protocol and nystagmus examination in combination with gait unsteadiness or the presence of vascular risk factors. As an overarching recommendation, (1) emergency clinicians should receive training in bedside physical examination techniques for patients with the acute vestibular syndrome (AVS; HINTS) and the diagnostic and therapeutic maneuvers for benign paroxysmal positional vertigo (BPPV; Dix-Hallpike test and Epley maneuver). To help distinguish central from peripheral causes in patients with the AVS, we recommend: (2) use HINTS (for clinicians trained in its use) in patients with nystagmus, (3) use finger rub to further aid in excluding stroke in patients with nystagmus, (4) use severity of gait unsteadiness in patients without nystagmus, (5) do not use brain computed tomography (CT), (6) do not use routine magnetic resonance imaging (MRI) as a first-line test if a clinician trained in HINTS is available, and (7) use MRI as a confirmatory test in patients with central or equivocal HINTS examinations. In patients with the spontaneous episodic vestibular syndrome: (8) search for symptoms or signs of cerebral ischemia, (9) do not use CT, and (10) use CT angiography or MRI angiography if there is concern for transient ischemic attack. In patients with the triggered (positional) episodic vestibular syndrome, (11) use the Dix-Hallpike test to diagnose posterior canal BPPV (pc-BPPV), (12) do not use CT, and (13) do not use MRI routinely, unless atypical clinical features are present. In patients diagnosed with vestibular neuritis, (14) consider short-term steroids as a treatment option. In patients diagnosed with pc-BPPV, (15) treat with the Epley maneuver. It is clear that as of 2023, when applied in routine practice by emergency clinicians without special training, HINTS testing is inaccurate, partly due to use in the wrong patients and partly due to issues with its interpretation. Most emergency physicians have not received training in use of HINTS. As such, it is not standard of care, either in the legal sense of that term ("what the average physician would do in similar circumstances") or in the common parlance sense ("the standard action typically used by physicians in routine practice").