Service Discovery Research Articles

Research on the Inheritance Education of Yellow River Culture from the Bio-cultural Perspective

The Yellow River, as mother river, its culture has taken the lead of the overall process of Chinese civilization. The Yellow River culture originated from the interaction between local people and the Yellow River for thousands of years, so its culture is based on place. However, influenced by traditional research in the field of biology, environmental education(EE) has not included any cultural content, and EE and cultural inheritance education have always been two unrelated contents. Biocultural diversity(BCD) is the product of biological and cultural co-evolution in social-ecological systems. It advocates for the integration of culture and nature in conservation practice. Therefore, the purpose of this study is to introduce the perspective of BCD and its new experience from the latest researches into the field of education. A broad bibliographic search in the databases was carried out by EDS(EBSCO DISCOVERY SERVICE) from De La Salle University Dasmariñas on-line Library. A review of relevant literature reveals the division between nature and culture in the field of conservation and education. Therefore, adopting a BCD perspective in inheritance education of Yellow River culture recommends beyond the epistemological bias of traditional EE, which views environmental education as a single content unrelated to culture, and advocates for interdisciplinary integration and the participation of different stakeholders, in order to achieve a broader public interest through comprehensive curriculum involvement. It has both theoretical evidence and a large amount of empirical research support, indicating that the perspective introduced into education is correct and feasible for YR culture education, and it is a diverse, open, and inclusive attitude towards various disciplines and stakeholders. The curriculum development from biocultural perspective will be a beneficial educational and research attempt, which may be beneficial in improving the current problem of fine division of disciplines and weak applicability of subject knowledge.

QoIoTX: A QoE-Aware IoT Service Discovery Framework

Quality of Experience QoE is a research area that evaluates performance based on subjective and objective metrics for the use of a product or a service. Currently, the substantial majority of QoE works mainly focus on multimedia services. However, the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE evaluation. With the evolution of new IoT technologies such as machine to machine communication and artificial intelligence, utilizing additional evaluation factors alongside the traditional ones is a crucial demand. This paper proposes QoIoTX, a QoE-based service discovery framework that can be utilized to evaluate the QoE of IoT service/application on the fly considering multiple parameters about data, network, users, and services. It extends the conventional QoE and goes beyond its legacy evaluation phases. Also, provides a novel alternative approach to evaluate the IoT services subjectively rather than using the traditional approaches that do not cope with the rapidly evolving services. Although each of the identified QoE factors is measured on a different scale and may involve different units of measurement, the proposed work can effectively evaluate the QoE through providing a single value that represents the acceptability degree of each service.

Building a knowledge graph to enrich ChatGPT responses in manufacturing service discovery

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers’ weblinks, manufacturing services, certifications, and location entity types.

Web Service Implementation “AI Yoga Trainer Using Human Pose Estimation” for Web Discovery

This research work is to create a application as web service to make it available on web for web service discovery. The most significant yoga stance is well-known on a global scale and supports the health advantages advocated by the ancient sages. OpenPose estimates that computer vision technology can help yoga practitioners achieve the greatest form and alignment. The integration of the OpenCV, Python, and MediaPipe frameworks is the main emphasis of this research project in order to develop an OpenPose estimation system for yoga poses. A camera is used to capture the yoga practitioner's motions, and a deep learning model is used to predict the important body parts. The algorithm then looks at these essential aspects to determine if the practitioner is doing the pose correctly or whether any adjustments are needed. The device gives the practitioner immediate feedback, allowing them to modify their alignment and posture as necessary. The OpenPose estimate system can help with the practice of yoga by offering detailed visualizations of the essential body parts throughout each posture in addition to providing real-time feedback Practitioners may more clearly understand the right form and alignment and make the required adjustments by utilizing this visualization. Additionally, the OpenPose estimation system enables the tracking of development over time. Practitioners who want to practice alone at home or may not have access to an instructor can also benefit from it. This paper offers a MediaPipe, OpenCV, and Python-based OpenPose estimation system for yoga positions. For optimal form and alignment, the tool may offer real-time feedback and visual representations of key body parts throughout each position. It is useful tool for yoga teachers and students, enhancing yoga's safety and efficacy while supporting the practice.

A general framework and decentralised algorithms for collective computational processes

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface (spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization

The exponential growth in data volumes, combined with the inherent complexity of network algorithms, has drastically affected network security. Data activities are producing voluminous network logs that often mask critical vulnerabilities. Although there are efforts to address these hidden vulnerabilities, the solutions often come at high costs or increased complexities. In contrast, the potential of open-source tools, recognized for their security analysis capabilities, remains under-researched. These tools have the potential for detailed extraction of essential network components, and they strengthen network security. Addressing this gap, our paper proposes a data analytics-driven network anomaly detection model, which is uniquely complemented with a visualization layer, making the dynamics of cyberattacks and their subsequent defenses distinctive in near real-time. Our novel approach, based on network scanning tools and network discovery services, allows us to visualize the network based on how many IP-based networking devices are live, then we implement a data analytics-based intrusion detection system that scrutinizes all network connections. We then initiate mitigation measures, visually distinguishing malicious from benign connections using red and blue hues, respectively. Our experimental evaluation shows an F1 score of 97.9% and a minimal false positive rate of 0.3% in our model, demonstrating a marked improvement over existing research in this domain.

Enhancing Online Delivery of Practical Labs for Cyber Security Course Using Virtual Laboratories

This research work is to create a application as web service to make it available on web for web service discovery. The most significant yoga stance is well-known on a global scale and supports the health advantages advocated by the ancient sages. OpenPose estimates that computer vision technology can help yoga practitioners achieve the greatest form and alignment. The integration of the OpenCV, Python, and MediaPipe frameworks is the main emphasis of this research project in order to develop an OpenPose estimation system for yoga poses. A camera is used to capture the yoga practitioner's motions, and a deep learning model is used to predict the important body parts. The algorithm then looks at these essential aspects to determine if the practitioner is doing the pose correctly or whether any adjustments are needed. The device gives the practitioner immediate feedback, allowing them to modify their alignment and posture as necessary. The OpenPose estimate system can help with the practice of yoga by offering detailed visualizations of the essential body parts throughout each posture in addition to providing real-time feedback Practitioners may more clearly understand the right form and alignment and make the required adjustments by utilizing this visualization. Additionally, the OpenPose estimation system enables the tracking of development over time. Practitioners who want to practice alone at home or may not have access to an instructor can also benefit from it. This paper offers a MediaPipe, OpenCV, and Python-based OpenPose estimation system for yoga positions. For optimal form and alignment, the tool may offer real-time feedback and visual representations of key body parts throughout each position. It is useful tool for yoga teachers and students, enhancing yoga's safety and efficacy while supporting the practice.

A Contemporary Secure Microservices Discovery Architecture with Service Tags for Smart City Infrastructures

The re-conceptualization of fundamental elements from traditional monolithic structures to the Microservices framework has emerged as a vital vision for the future of IoT systems. This transformation is pivotal in addressing the present and future challenges faced by IoT systems and enhancing their overall operational qualities. The adoption of Microservices in IoT introduces an array of opportunities for innovative research, which collectively contribute to its feasibility and success. The transition from traditional systems to Microservices in IoT brings forth various complex challenges that need to be effectively addressed. Key challenges include ensuring seamless Microservice discovery, efficient API gateway management, scalable distribution services, reliable uniform service discovery, robust containerization, and stringent access control mechanisms. These challenges encompass both technical and security-related aspects, necessitating innovative solutions to overcome them. To overcome these challenges, our research proposes a secure service discovery architecture that integrates security measures at each stage of the discovery process. This methodology employs advanced encryption techniques, authentication protocols, and authorization mechanisms to safeguard Microservices in IoT. A mathematical framework is introduced to formalize and implement these security measures, ensuring a robust and reliable approach to Microservices adoption in the IoT ecosystem. In the experimental phase of our research, we aim to validate the proposed secure service discovery architecture through a series of rigorous tests and simulations. We will assess its performance, scalability, and security efficacy under various real-world scenarios and IoT use cases. Experimental results and performance metrics will be analyzed to provide empirical evidence of the viability and effectiveness of our proposed methodology in addressing Microservices-related challenges in IoT systems.

Decentralised Global Service Discovery for the Internet of Things.

The Internet of Things (IoT) consists of millions of devices deployed over hundreds of thousands of different networks, providing an ever-expanding resource to improve our understanding of and interactions with the physical world. Global service discovery is key to realizing the opportunities of the IoT, spanning disparate networks and technologies to enable the sharing, discovery, and utilisation of services and data outside of the context in which they are deployed. In this paper, we present Decentralised Service Registries (DSRs), a novel trustworthy decentralised approach to global IoT service discovery and interaction, building on DSF-IoT to allow users to simply create and share public and private service registries, to register and query for relevant services, and to access both current and historical data published by the services they discover. In DSR, services are registered and discovered using signed objects that are cryptographically associated with the registry service, linked into a signature chain, and stored and queried for using a novel verifiable DHT overlay. In contrast to existing centralised and decentralised approaches, DSRs decouple registries from supporting infrastructure, provide privacy and multi-tenancy, and support the verification of registry entries and history, service information, and published data to mitigate risks of service impersonation or the alteration of data. This decentralised approach is demonstrated through the creation and use of a DSR to register and search for real-world IoT devices and their data as well as qualified using a scalable cluster-based testbench for the high-fidelity emulation of peer-to-peer applications. DSRs are evaluated against existing approaches, demonstrating the novelty and utility of DSR to address key IoT challenges and enable the sharing, discovery, and use of IoT services.

DateLife: Leveraging Databases and Analytical Tools to Reveal the Dated Tree of Life.

Chronograms-phylogenies with branch lengths proportional to time-represent key data on timing of evolutionary events, allowing us to study natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www.datelife.org, that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life's phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life's synthetic phylogeny or one provided by the user. Congruified node ages are used as secondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8, and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife's node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results.

The role of animal-assisted programs in physical health improvement of children and adolescents with special education needs - a systematic review

Programs involving animals in therapeutic programs are becoming increasingly prevalent. These programs can vary greatly in their approach, scope, and objectives, and they can significantly impact the development of healthy children and those with various disorders. In this systematic review, we sought to investigate the psychological ramifications of animal-assisted activities (AAA), therapies (AAT), and interventions (AAI). We searched for relevant studies using the EBSCO Discovery Service search engine across 85 databases, utilising appropriate keywords. Our search generated 262 results, of which 21 were selected for inclusion after title and abstract screening, as well as full-text analysis. Our findings indicate that dogs and horses are animal-assisted programs’ most commonly used animals. Additionally, autism, cerebral palsy, and ADHD were found to be overrepresented in these programs. Furthermore, the length of sessions and overall program duration exhibited considerable variation, regardless of patient age or disease type. The principal measures centred on the physiological variables related to the nervous system and motorium-related indicators. The studies were generally of exceptional methodological soundness. Frequently, the studies narrowed their scope to a single segment or just the child or adolescent, but the outcomes lacked contextual interpretation. Expanding the range of studies by comparing psychological and physiological indicators and conducting follow-up analysis with a longitudinal design would be beneficial.

DNP-Authored Articles in Peer-Reviewed Journals 2011-2021.

With an increasing number of doctor of nursing practice (DNP) graduates, the volume of peer-reviewed journal publications among DNP-prepared nurses is rising. The primary aim of this study was to quantify, analyze, and categorize DNP-authored peer-reviewed journal publications. A descriptive research design was used to analyze DNP-authored peer-reviewed journal publications in the WorldCat, EBSCO Discovery Service, and PubMed databases from 2011 through 2021. The 3839 journal publications included at least one DNP-prepared nurse author. There were 2495 (65%) publications with a DNP first author, 921 (24%) with a DNP solo author, and 2918 (76%) included publication collaboration. The majority of publications were practice- (40%), nursing- (17%), and education-focused (15%). Science translation in evidence-based practice and quality improvement publications accounted for 24%. This study demonstrates the proliferation of DNP-authored peer-reviewed journal publications, which may indicate an increase in the number of nursing care quality initiatives occurring in the clinical setting.

Augmenting context with power information for green context-awareness in smart environments

The increase in the use of smart devices has led to the realization of the Internet of Everything (IoE). The heart of an IoE environment is a Context-Aware System that facilitates service discovery, delivery, and adaptation based on context classification. The context has been defined in a domain-dependent way, traditionally. The classical models of context have been focused on rich context and lack Cost of Context (CoC) that can be used for decision support. The authors present a philosophy-inspired mathematical model of context that includes confidence in activity classification of context, the actions performed, and the power information. Since a single recurring activity can lead to distinct actions performed at different times, it is better to record the actions. The power information includes the power consumed in the complete context processing and is a quality attribute of the context. Power consumption is a useful metric as CoC and is suitable for power-constrained context awareness. To demonstrate the effectiveness of the proposed work, example contexts are described, and the context model is presented mathematically in this study. The context is aggregated with power information, and actions and confidence on the classification outcome lead to the concept of situational context. The results show that the context gathered through sensor data and deduced through remote services can be made more rich with CoC parameters.

A data model for enabling deep learning practices on discovery services of cyber‐physical systems

AbstractThe W3C Web of Things (WoT) is a leading technology that facilitates dynamic information management in the Internet of Things (IoT). In most IoT scenarios, devices and their associated information change continuously, generating a large amount of data. Hence, to correctly use the information and the data generated by different devices, a new perspective of managing and ensuring data quality is recommended. Applying Data Science techniques to create the data model can help to manage and ensure data quality by creating a common schema that can be reused in future projects, as well as producing recommendations to facilitate Service Discovery. In addition, due to the dynamic devices that change over time or under specific circumstances, the data model created must be sufficiently abstract to add new instances and to support new requirements that devices should incorporate. The use of models helps to raise the abstraction level, adapting it to the continuous changes of devices by defining instances associated with the data model. This paper proposes two data models: one for Cyber‐Physical Systems (CPS) to define device information fetched by a Discovery Service, and another for applying Deep Learning in natural language problems through a Transformer approach. The latter matches user queries in natural language sentences with WoT devices or services. These data models expand the Thing Description model to help find similar CPSs by giving a confidence level to each CPS based on features such as security and the number of times the device was accessed. The results show how the proposed models support the search process of CPSs in syntactic and natural language searches. Furthermore, the four levels of the FAIR principles are validated for the proposed data models, thus ensuring the data's transparency, reproducibility, and reusability.

ENHANCING SERVICE DISCOVERY IN MOBILE AD HOC NETWORKS USING SEMANTIC CLUSTERING AND HYBRID TRUST MANAGEMENT

Mobile Ad Hoc Networks (MANETs) facilitate communication among mobile devices without relying on fixed infrastructure. However, service discovery in MANETs faces challenges due to the dynamic topology and limited resources of nodes. Existing solutions often lack efficient resource utilization and fail to address trust management adequately. The conventional approaches for service discovery in MANETs are inefficient due to their inability to incorporate semantic clustering and robust trust management. Semantic clustering enhances the accuracy of service discovery by grouping nodes based on similar interests or functionalities. Meanwhile, traditional trust management mechanisms are inadequate in dynamic environments, leading to unreliable service discovery results. The proposed methodology involves developing a semantic clustering algorithm to organize nodes based on their semantic similarities. Additionally, a hybrid trust management system is implemented to assess the reliability of discovered services. The system combines both reputation-based and recommendation-based trust models to enhance the accuracy of trust evaluations. Through extensive simulations and experiments, the effectiveness of the proposed approach is demonstrated. The results indicate that the integration of semantic clustering and hybrid trust management significantly improves service discovery efficiency and reliability in MANETs. The approach achieves higher precision and recall rates compared to conventional methods, even under dynamic network conditions.

DTSSN: A Distributed Trustworthy Sensor Service Network Architecture for Smart City

The smart city is an increasingly popular concept when it comes to urban development. In a smart city, numerous sensor services are generated by IoT sensors in a distributed manner, requiring proper management and effective interaction to guarantee the connectivity of different regions. However, the sensitive nature of sensor data raises concerns over joining public cloud centers or edge servers, despite assurances of their reliability from providers. Local deployment and maintenance of sensor services may cause these service providers to become ”data isolated islands”, hindering the construction process of smart city. This paper proposes a distributed trustworthy sensor service network architecture named DTSSN to support the building of a fully distributed sensor service network. The proposed network architecture operates through the collaboration of two core devices, the sensor service switch and router, to effectively enable the registration, discovery, invocation, transaction, and monitoring of cross-region sensor services. Then, a lightweight trustworthy transaction mechanism based on blockchain is proposed to realize SLA-based automatic service transaction while reducing potential risks in the service network. Comparative analysis and simulation experiments validate the effectiveness of the DTSSN architecture in terms of scalability, availability, and trustworthiness, underscoring its potential in advancing smart city development and governance.

A Distributed Cloud Architecture Based on General De Bruijn Overlay Network

Distributed cloud systems enable the distribution of computing resources across various geographical locations. While offering benefits like accelerated content delivery, the scalability and coherence maintenance of these systems pose significant challenges. Recent studies reveal shortcomings in existing distributed system schemes to meet modern cloud application demands and maintain coherence among heterogeneous system elements. This paper proposes a service-oriented network architecture for distributed cloud computing networks. Using a De Bruijn network as a software-defined overlay network, the architecture ensures scalability and coherence. Through service-based addressing, requests are issued to designated service address bands, streamlining service discovery. The architecture's evaluation through extensive simulations showcases sustainable scalability and inherent load-balancing properties. The paper concludes with insights into future research directions, emphasizing the extension of the proposed architecture to emerging distributed cloud use cases and decentralized security.

Exploring the Role of Wi-Fi Direct's Service Discovery Protocol in Forming Wireless Collaboration Networks: A Scoping Review

The increasing ubiquity of mobile devices and the Internet has become a hallmark of modern society, yet challenges persist in ensuring reliable communication networks during unforeseen disruptions, such as pandemics, war, or natural disasters. Despite the advancement of Wi-Fi technology, the development of a comprehensive model for temporary communication networks based on Wi-Fi interfaces during these disruptions remains inconclusive. The temporary and infrastructure-less wireless network created by mobile devices is referred to as a Wireless Collaboration Network (WCN). To assess the extent to which wireless protocols have been leveraged in forming Wireless WCN, a scoping review was conducted. This review aimed to provide a comprehensive overview of the existing literature, specifically focusing on the Service Discovery (SD) protocol of Wi-Fi Direct within the Mobile Ad Hoc Network (MANET) domain. Our investigation delved into solutions developed by researchers in this domain and the various metrics employed to evaluate the performance of these solutions. The scoping review protocol encompassed nine distinct steps, ranging from formulating research questions to synthesizing results. The review process involved examining three major databases: IEEE Xplore, ACM Digital Library, and Scopus, with the assistance of Mendeley for data organization and filtering. In total, our search identified 186 records across these databases. After rigorous screening, which included removing duplicates, irrelevant articles, and inaccessible content, we selected 35 articles for comprehensive review. These reviewed articles were then summarized and presented in a table, offering valuable insights into article titles, objectives, measurement parameters, techniques employed, and the noteworthy contributions of each work. The table serves as an invaluable resource for researchers in the field, facilitating a deeper understanding of the research landscape and enabling easier access to the collective knowledge within this domain.

LCPCWSC: a Web service classification approach based on label confusion and priori correction

Purpose With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. Design/methodology/approach This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Findings Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. Originality/value This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

The Use of Brief Mindfulness Interventions in the Context of Perioperative Care.

The purpose of this review was to synthesize and categorize the literature on the use of brief mindfulness interventions for both patients and physicians across the spectrum of perioperative care. Web-based discovery services and discipline-specific databases were queried. Brief mindfulness interventions were defined as sessions lasting 30min or less on any single occasion, with a total practice accumulation not exceeding 100min per week, and a duration of up to 4weeks. Study screening and data extraction were facilitated through the Covidence software platform. After screening 1047 potential studies, 201 articles were identified based on initial abstract and title screening; 10 studies ultimately met inclusion criteria. All ten studies were published between 2019 and 2023; most (n = 9) reports focused on patients (total joint arthroplasty, n = 3; stereotactic breast biopsy, n = 2; minimally invasive foregut surgery, n = 1; septorhinoplasty, n = 1; cardiac surgery, n = 1; and other/multiple procedures, n = 1); one studied investigated mindfulness interventions among surgeons. The duration of the interventions varied (3min to 29min). The most common issue that the mindfulness intervention aimed to address was pain (n = 6), followed by narcotic use (n = 3), anxiety (n = 2), delirium (n = 1), or patient satisfaction (n = 1). While most studies included a small sample size and had inconclusive results, brief mindfulness interventions were noted to impact various health-related outcomes, including mental health outcomes, anxiety, and pain perception. Mindfulness interventions may be a scalable, low-cost, time-limited intervention that has the potential to optimize well-being and surgical outcomes broadly construed.