Workflow Integration Research Articles

Optimized Camera Integration in IP-Based Workflows and Infrastructures

The increasing availability of powerful IP infrastructures opens up new application possibilities for system cameras in media and entertainment applications. Additional IP interfaces on camera base stations have enabled initial applications to be covered and experience to be gained in the first step. However, the question arises as to how the integration of cameras in IP infrastructures can be optimized in such a way that an improved workflow can be achieved for almost all use cases. This includes the possibility of adapting the bandwidth requirements of the cameras to the existing IP bandwidths, but also the possibility to do this without external conversion of the video signals to IP signals. This article will highlight the various challenges and present possible solutions based on camera systems with fully integrated IP connectivity.

Integrated membranes within centrifugal microfluidic devices: a review.

Centrifugal microfluidics has evolved into a sophisticated technology capable of enabling the exploration of fundamental questions in such fields as protein analysis, environmental monitoring, and live cell handling. These microdevices also hold unique potential for translating promising academic research into many real-world scenarios, with several products already available on the market. Yet, in order to fully realize this potentially transformative technology, there remains an outstanding need to incorporate simple to operate world-to-chip interfaces alongside the integration and automation of complex workflows. This requires cost-effective and versatile materials that are, ideally, already commercially available. Membranes not only meet these exigencies, they are also capable of enhancing the inherent advantages of microdevices when thoughtfully combined. This review provides an overview of the importance of these two technologies and the manifold benefits upon their unification. The fundamental principles governing fluid flow with centrifugal actuation, as well as within porous membranes, are briefly covered in addition to a comment on their relative advantages compared to classical microdevices and porous media. The major subtypes in membrane composition, preparation, and microfluidic integration strategies are next discussed in detail, along with their relativistic capabilities and drawbacks. This is followed by recent examples in the literature displaying the enormous versatility membranes have already demonstrated within microfluidic devices, highlighting recent centrifugal microdevices wherever possible. Finally, recommendations for areas where the incorporation of these materials still face challenges, as well as possible new avenues for exploration, are also provided.

Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward

Artificial Intelligence (AI) has emerged as a transformative force in the field of diagnostic dermatology, offering unprecedented capabilities in image recognition and data analysis. Despite its promise, the integration of AI into clinical practice faces multifaceted challenges that span technical, ethical, and regulatory domains. This article provides a narrative overview of the current state of AI in dermatology, tracing its historical evolution from early diagnostic tools to contemporary hybrid supervised models. We identify and categorize six critical challenges: data quality and quantity, algorithmic development and explainability, ethical considerations, clinical workflow integration, regulatory frameworks, and stakeholder collaboration. Each challenge is dissected from the perspectives of academia, industry, and healthcare providers, offering actionable recommendations for future research and implementation. We also highlight the paradigm shift in AI research, emphasizing the potential of transformer architectures in revolutionizing diagnostic methodologies. By addressing the challenges and harnessing the latest advancements, AI has the potential to significantly impact diagnostic accuracy and patient outcomes in dermatology.

A novel data integration workflow for target delineation in cardiac radioablation

A novel data integration workflow for target delineation in cardiac radioablation

Glycomics-Assisted Glycoproteomics Enables Deep and Unbiased N-Glycoproteome Profiling of Complex Biological Specimens.

Mass spectrometry-driven glycomics and glycoproteomics, the system-wide profiling of detached glycans and intact glycopeptides from biological samples, respectively, are powerful approaches to interrogate the heterogenous glycoproteome. Efforts to develop integrated workflows employing both glycomics and glycoproteomics have been invested since the concerted application of these complementary approaches enables a deeper exploration of the glycoproteome. This protocol paper outlines, step-by-step, an integrated -omics technology, the "glycomics-assisted glycoproteomics" method, that first establishes the N-glycan fine structures and their quantitative distribution pattern of protein extracts via porous graphitized carbon-LC-MS/MS. The N-glycome information is then used to augment and guide the challenging reversed-phase LC-MS/MS-based profiling of intact N-glycopeptides from the same protein samples. Experimental details and considerations relating to the sample preparation and the N-glycomics and N-glycoproteomics data collection, analysis, and integration are discussed. Benefits of the glycomics-assisted glycoproteomics method, which can be readily applied to both simple and complex biological specimens such as protein extracts from cells, tissues, and bodily fluids (e.g., serum), include quantitative information of the protein carriers and site(s) of glycosylation, site occupancy, and the site-specific glycan structures directly from biological samples. The glycomics-assisted glycoproteomics method therefore facilitates a comprehensive view of the complexity and dynamics of the heterogenous glycoproteome.

Measurement of provider fidelity to immunization guidelines: a mixed-methods study on the feasibility of documenting patient refusals of the human papillomavirus vaccine

BackgroundAssessment and feedback is a common implementation strategy to improve healthcare provider fidelity to clinical guidelines. For immunization guidelines, fidelity is often measured with doses administered during eligible visits. Adding a patient refusal measure captures provider fidelity more completely (i.e., all instances of a provider recommending a vaccine, resulting in vaccination or refusal) and enables providers to track patient vaccine hesitancy patterns. However, many electronic health record (EHR) systems have no structured field to document multiple instances of refusals for specific vaccines, and existing billing codes for refusal are not vaccine specific. This study assessed the feasibility of a novel method for refusal documentation used in a study focused on human papillomavirus (HPV) vaccine.MethodsAn observational, descriptive-comparative, mixed-methods study design was used to conduct secondary data analysis from an implementation-effectiveness trial. The parent trial compared coach-based versus web-based practice facilitation, including assessment and feedback, to increase HPV vaccination in 21 community-based private pediatric practices. Providers were instructed to document initial HPV vaccine refusals in the EHR's immunization forms and subsequent refusals using dummy procedure codes, for use in assessment and feedback reports. This analysis examined adoption and maintenance of the refusal documentation method during eligible well visits, identified barriers and facilitators to documentation and described demographic patterns in patient refusals.ResultsSeven practices adopted the refusal documentation method. Among adopter practices, documented refusals started at 2.4% of eligible well visits at baseline, increased to 14.2% at the start of implementation, peaked at 24.0%, then declined to 18.8%. Barriers to refusal documentation included low prioritization, workflow integration and complication of the billing process. Facilitators included high motivation, documentation instructions and coach support. Among adopter practices, odds of refusing HPV vaccine were 25% higher for patients aged 15–17 years versus 11–12 years, and 18% lower for males versus females.ConclusionsWe demonstrated the value of patient refusal documentation for measuring HPV vaccination guideline fidelity and ways that it can be improved in future research. Creation of vaccine-specific refusal billing codes or EHR adaptations to enable documenting multiple instances of specific vaccine refusals would facilitate consistent refusal documentation.Trial Registration NCT03399396 Registered in ClinicalTrials.gov on 1/16/2018.

A big data platform exploiting auditable tokenization to promote good practices inside local energy communities

The Energy Community Platform (ECP) is a modular system conceived to promote a conscious use of energy by the users inside local energy communities. It is composed of two integrated subsystems: the Energy Community Data Platform (ECDP), a middleware platform designed to support the collection and the analysis of big data about the energy consumption inside local energy communities, and the Energy Community Tokenization Platform (ECTP), which focuses on tokenizing processed source data to enable incentives through smart contracts hosted on a decentralized infrastructure possibly governed by multiple authorities. We illustrate the overall design of our system, conceived considering some real-world projects (dealing with different types of local energy community, different amounts and nature of incoming data, and different types of users), analyzing in detail the key aspects of the two subsystems. In particular, the ECDP acquires data of a different nature in a heterogeneous format from multiple sources and supports a data integration workflow and a data lake workflow, designed for different uses of the data. We motivate our technological choices and present the alternatives taken into account, both in terms of software and of architectural design. On the other hand, the ECTP operates a tokenization process via smart contracts to promote good behaviors of users within the local energy community. The peculiarity of this platform is to allow external parties to audit the correct behavior of the whole tokenization process while protecting the confidentiality of the data and the performance of the platform. The main strengths of the presented system are flexibility and scalability (guaranteed by its modular architecture), which allow its applicability to any type of local energy community.

Guided Approach to Implant Placement, Immediate Provisionalization, and Definitive Restoration: A Case Letter.

Replacement of missing teeth is becoming more prevalent, currently with more than 700,000 dental implants being placed every year. Patients seek out dental treatment with the expectations of having procedures performed in the least number of time/visits, for as inexpensive as possible, and at the highest quality. As this continues, clinicians can adapt with changing times and adopt the newer advancements in digital dentistry. This case letter aims to provide readers with insight to integration of digital workflows to optimize patient and clinician outcomes with single tooth implant therapy.

The Johns Hopkins Activity and Mobility Promotion Program: A Framework to Increase Activity and Mobility Among Hospitalized Patients.

Greater mobility and activity among hospitalized patients has been linked to key outcomes, including decreased length of stay, increased odds of home discharge, and fewer hospital-acquired morbidities. Systematic approaches to increasing patient mobility and activity are needed to improve patient outcomes during and following hospitalization. While studies have found the Johns Hopkins Activity and Mobility Promotion (JH-AMP) program improves patient mobility and associated outcomes, program details and implementation methods are not published. JH-AMP is a systematic approach that includes 8 steps, described in this article: (1) organizational prioritization; (2) systematic measurement and daily mobility goal; (3) barrier mitigation; (4) local interdisciplinary roles; (5) sustainable education and training; (6) workflow integration; (7) data feedback; and (8) promotion and awareness. Hospitals and health care systems can use this information to guide implementation of JH-AMP at their institutions.

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.

Integration of Artificial Intelligence Into Sociotechnical Work Systems-Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review.

When introducing artificial intelligence (AI) into clinical care, one of the main objectives is to improve workflow efficiency because AI-based solutions are expected to take over or support routine tasks. This study sought to synthesize the current knowledge base on how the use of AI technologies for medical imaging affects efficiency and what facilitators or barriers moderating the impact of AI implementation have been reported. In this systematic literature review, comprehensive literature searches will be performed in relevant electronic databases, including PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, and CENTRAL. Studies in English and German published from 2000 onwards will be included. The following inclusion criteria will be applied: empirical studies targeting the workflow integration or adoption of AI-based software in medical imaging used for diagnostic purposes in a health care setting. The efficiency outcomes of interest include workflow adaptation, time to complete tasks, and workload. Two reviewers will independently screen all retrieved records, full-text articles, and extract data. The study's methodological quality will be appraised using suitable tools. The findings will be described qualitatively, and a meta-analysis will be performed, if possible. Furthermore, a narrative synthesis approach that focuses on work system factors affecting the integration of AI technologies reported in eligible studies will be adopted. This review is anticipated to begin in September 2022 and will be completed in April 2023. This systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements in medical imaging through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with a particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings. PROSPERO CRD42022303439; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=303439. PRR1-10.2196/40485.

Nurses' Experiences Implementing e PED : An iPad Application to Guide Quality Discharge Teaching.

Quality discharge teaching prepares patients and families to transition safely from hospital to home. Technology can enhance and support quality discharge teaching by promoting patient family engagement during the transition. The purpose of this mixed methods study was to explore clinical nurses' experience with using Engaging Parents in Education for Discharge, an iPad application to guide quality discharge teaching. Twelve nurses at a large Midwestern Children's Hospital participated in small focus groups after use of the Engaging Parents in Education for Discharge application and completed a questionnaire on their perception of the acceptability and feasibility of the app. Findings revealed three themes: (1) development and deployment issues focused on the importance of training and support by the study team during implementation; (2) workflow integration centered on the importance of incorporating use of the app into current workflows and to preserve effective communication strategies with parents to optimize use in the healthcare setting; and (3) nurses perceived value in the use of the Engaging Parents in Education for Discharge app for beneficial scripting, questions on discharge topics often forgotten, and guidance for complex patients. Results of this study offer insight into key components for consideration when implementing and integrating technology to aid nursing practice.

Managing Critical Patient-Reported Outcome Measures in Oncology Settings: System Development and Retrospective Study.

Remote monitoring programs based on the collection of patient-reported outcome (PRO) data are being increasingly adopted in oncology practices. Although PROs are a great source of patient data, the management of critical PRO data is not discussed in detail in the literature. This first-of-its-kind study aimed to design, describe, and evaluate a closed-loop alerting and communication system focused on managing PRO-related alerts in cancer care. We designed and developed a novel solution using an agile software development methodology by incrementally building new capabilities. We evaluated these new features using participatory design and the Fit between Individuals, Task, and Technology framework. A total of 8 questionnaires were implemented using alerting features, resulting in an alert rate of 7.82% (36,838/470,841) with 13.28% (10,965/82,544) of the patients triggering at least one alert. Alerts were reviewed by 501 staff members spanning across 191 care teams. All the alerts were reviewed with a median response time of 1 hour (SD 185 hours) during standard business hours. The most severe (red) alerts were documented 56.83% (2592/4561) of the time, whereas unlabeled alerts were documented 27.68% (1298/4689) of the time, signaling clinician concordance with the alert thresholds. A PRO-based alert and communication system has some initial benefits in reviewing clinically meaningful PRO data in a reasonable amount of time. We have discussed key system design considerations, workflow integration, and the mitigation of potential impact on the burden of care teams. The introduction of a PRO-based alert and communication system provides a reliable mechanism for care teams to review and respond to patient symptoms quickly. The system was standardized across many different oncology settings, demonstrating system flexibility. Future studies should focus on formally evaluating system usability through qualitative methods.

Use of an open-source electronic health record to establish a “virtual hospital”: A tale of two curricula

BackgroundThe electronic health record (EHR) is central to medical informatics. Its use is also recognized as an important skill for future clinicians. Typically, medical students' first exposure to an EHR is when they start their clinical internships, and medical informatics students may or may not get experience with an EHR before graduation. We describe the process of implementing an open-source EHR in two curricula: Medicine and Medical informatics. For medical students, the primary goals were to allow students to practice analyzing information from the EHR, creating therapeutic plans, and communicating with their colleagues via the EHR before they start their first clinical rotations. For medical informatics students, the primary goal was to give students hands-on experience with creating decision support in an EHR. ApproachWe used the OpenMRS electronic health record with a custom decision support module based on Arden Syntax. Medical students needed a secure, stable environment to practice medical reasoning. Medical informatics students needed a more isolated system to experiment with the EHR's internal configuration. Both student groups needed synthetic patient cases that were realistic, but in different aspects. For medical students, it is essential that these cases are clinically consistent, and events unfold in a logical order. By contrast, synthetic data for medical informatics students should mimic the data quality problems found in real patient data. OutcomesMedical informatics students show more mature reasoning about data quality issues and workflow integration than prior to using the EHR. Comments on both course evaluations have been positive, including comments on how working with a real-world EHR provides a realistic experience. ConclusionThe open-source EHR OpenMRS has proven to be a valuable addition to both the medicine and medical informatics curriculum. Both sets of students experience use of the EHR as giving them valuable, realistic learning experiences.

Multisource spatial data integration for use cases applications

AbstractThe reuse and integration of data give big opportunities, supported by the FAIR data principles. Seamless data integration from heterogenous sources has been an interest of the geospatial community for a long time. However, 3D city models, building information models, and information supporting smart cities present higher semantic and geometrical complexity, which pose new challenges never tackled in a comprehensive methodology. Building on previous theories and studies, this article proposes an overarching workflow and framework for multisource (geo)spatial data integration. It starts from the definition of use case‐based requirements for the integrated data, guides the analysis of integrability of the involved datasets, suggesting actions to harmonize them, until data merging and validation. It is finally tested and exemplified in a case study. This approach allows the development of consistent, well‐documented, and inclusive data integration workflows, for the sake of use case automation in various geospatial domains and the production of interoperable and reusable data.

Provider Perspectives on Artificial Intelligence–Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study

Background The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. Objective This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. Methods A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. Results Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool’s promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. Conclusions The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. Trial Registration ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087

REVOLUTIONIZING DENTAL DIAGNOSTICS: ADVANCEMENTS AND CHALLENGES IN AI-POWERED IMAGING SYSTEMS

This study explores AI-powered dental imaging systems in detail, emphasizing how they are revolutionizing diagnoses. These systems analyze radiographs, cone-beam computed tomography (CBCT) scans, and magnetic resonance imaging (MRI) scans with efficiency thanks to machine learning algorithms. We give a summary of their potential for automated interpretation, treatment suggestions, and dental disease prognosis. Reviews from academic institutions highlight how much better they are at identifying dental decay and creating individualized treatment plans than older techniques. Furthermore, the effectiveness of computer vision methods—in particular, convolutional neural networks (CNNs)—in identifying dental caries is emphasized. Issues with interpretability, ethical issues, and workflow integration still exist despite their promise. In order to overcome these obstacles and optimize the advantages of AI in dentistry, we stress the significance of interdisciplinary cooperation, which will eventually improve patient care and results.

Evaluating the efficiency of popular species identification analytical methods, and integrative workflow using morphometry and barcoding bioinformatics for taxonomy and origin of traded cryptic brown frogs

The unregulated wildlife trade increases the risk of global biological invasions and, therefore, accurate taxonomic assignments and origin-tracing methods are critical. First, using a meta-analysis comparing studies from the last 10 years, we quantified the efficiency of popular analytical methods used for species assignments and confirmed the higher sensitivity of coalescent-based methods in isolating cryptic operational taxonomic units (OTUs). Second, we proposed a workflow for species identification of unidentified animals from the trade, in this case cryptic Brown frogs ( Rana ) imported into the Republic of Korea. This integrated workflow relies on the use of a single-locus 16S rRNA gene barcoding along with morphometry, phylogenetic trait, species delimitation modelling and phylogeography. Out of 171 samples, we identified three erroneously imported non-native species: R. chensinensis, R. amurensis and R. kukunoris. Bayes factor delimitation modelling most supported the presence of 12 OTUs from the trade, highlighting a hidden genetic diversity. Both molecular and morphological analyses converged towards a high phenotypic crypticity and similarity in genetic sequences between Korean R. huanrenensis and Chinese R. chensinensis. The combined model-based OTUs and 16S rRNA gene phylogeny of traded and control specimens ( n = 230) recovered the trade pathways, and revealed the widespread and likely wild-harvested origins of traded Rana individuals. Our results also highlight the independent evolution of toe webbings in Rana for the last 12.0 Mya, a potential key trait for species identification of northeastern Asian Rana . With the workflow for large-scale species identification developed herein, we urge the development of trade monitoring and legislation on Rana species in northeast Asia. Schematic diagram explaining the unifying framework used in this study to quantify hidden crypticity in trades species. This highlights the major components of crypticity in biological invasion, derived from Jarić et al. (2019) . The integration consisted of three aspects: phylogeography, morphometry and taxonomy along with conventional genetic barcoding. It provides an effective solutions to trace and measure the crypticity of introduced Rana through the wildlife trade. The blue coloured arrows represent the flow for species assignment with physical morphometry and the red coloured arrows represent the flow for species assignment with DNA sequences. • Cross-study comparison on efficiency of analytical methods commonly used for species delimitations in the recent 10 years. • An integrative workflow using single-locus barcoding, morphometry and bioinformatics to infer cryptic amphibians from trade. • Incorporation of model-based Operational Taxonomic Units into phylogeny to trace trade pathways for brown frogs in northeast Asia. • Toe webbing character as a key morphometric trait to differentiate phenotypically cryptic brown frogs across northeast Asia.

Four-Dimension Seismic Analysis in Carbonate: A Closed Loop Study

Four-dimensional seismic analysis is an effective reservoir surveillance tool to track the changes of fluid and pressure phases in the oil and gas reservoirs over time of the baseline and monitoring seismic acquisition. In practice, the 4D seismic signal associated with such changes may be negligible, especially in heterogeneous carbonate reservoirs. Therefore, 4D seismic analysis is a model for integrating various disciplines in the oil and gas industry, such as seismic, petrophysics, reservoir engineering, and production engineering. In this study, we started the 4D seismic workflow with a 1D well-based 4D feasibility study to detect the likelihood of 4D signals before performing 4D seismic co-processing of the baseline and monitoring surveys starting from the seismic field data of both datasets. As part of a full 4D seismic co-processing of the baseline and monitor surveys, 4D seismic metric attributes were analyzed over the survey area to measure the improvement in seismic acquisition repeatability for the scarce 1994 baseline seismic and the 2014 monitor seismic survey. For the monitor survey, a 4D time-trace shift was performed using the baseline survey as a reference to measure the time shifts between the baseline and monitor surveys at 20-year intervals. The 4DFour-dimensional dynamic trace warping was followed by a 4D seismic inversion to compare the 4D difference in the seismic inverted data with the difference in seismic amplitude. The seismic inversion helped overcome noise, multiple contaminations, and differences in dynamic amplitude range between the baseline and monitor seismic surveys. We then examined the relationship between well logs and seismic volumes by predicting a volume of log properties at the well locations of the seismic volume. In this method, we computed a possibly nonlinear operator that can predict well logs based on the properties of adjacent seismic data. We then tested a Deep Feed Forward Neural Network (DFNN) on six wells to adequately train and validate the machine learning approach using the baseline and monitoring seismic inverted data. The objective of trying such a deep machine learning approach was to predict the density and porosity of both the baseline and the monitoring seismic data to validate the accuracy of the 4D seismic inversion and evaluate the changes in reservoir properties over a time-lapse of 20 years of production from 1994 to 2014. Finally, we matched the 4D seismic signal with changes in reservoir production properties, investigating the mechanism underlying the observed 4D signal. It was found that the detectability of 4D signals is primarily related to changes in fluid saturation and pressure changes in the reservoir, which increased from 1994 to 2014. This innovative closed-loop research proved that the low repeatability of seismic acquisition can be compensated by optimal 4D seismic co-processing with a complete integration workflow to assess the reliability of the 4D seismic observed signal.

Comparing observed and unobserved fishing characteristics in the drift gillnet fishery for swordfish

Central to the success of partial coverage observer programs is the assumption that observed fishing behaviors are representative of unobserved fishing behaviors. Traditionally, this was a difficult assumption to test beyond comparing catch compositions for observed and unobserved trips. With the proliferation of vessel monitoring systems (VMS) however, a suite of trip- and set-level fishing characteristics can be engineered from vessel locations that facilitate quantitative comparisons between observed and unobserved behaviors. The U.S. West Coast drift gillnet (DGN) fishery for swordfish (Xiphias gladius) is a small fishery with a long history of bycatch concerns and an observer coverage rate around 20–30 %. We developed an integrated data set composed of VMS, logbook, landings, and observer data for the DGN fishery from 2013 to 2019. We used a suite of machine learning models to classify unobserved fishing behaviors and we characterized these fishing behaviors at the trip- and set-level. Analyses of the trip- and set-level metrics (e.g., trip duration, fishing depth, sea surface temperatures, distance from port, and catch per unit effort) revealed that operational characteristics of the fishery were largely similar for observer and unobserved trips and vessels, but also highly variable. In the fall season, observed trip distances were an average of 53 % (std. error 20 %) longer than unobserved trips for the same vessels. These longer trips occurred in water that was, on average, about 39 % (std. error 16 %) deeper. However, characteristics like amounts of fishing effort, revenue, and water temperatures were generally similar between observed and unobserved trips. We present a methodical workflow for data integration and feature engineering and a simple approach for assessing differences across fleet behaviors in space and time that could be applied to many fisheries with limited observer coverage. This approach could also be used to target specific strata within fisheries that may benefit from increases or shifts in observer coverage.