Workflow Process Research Articles

Smart-sight: Video-based waste characterization for RDF-3 production

A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.

A114 IMPROVING THE QUALITY OF PARACENTESIS PRACTICES IN PEOPLE WITH ADVANCED CIRRHOSIS IN AN AMBULATORY CARE SETTING

Abstract Background Spontaneous bacterial peritonitis (SBP) is a lethal complication of decompensated cirrhosis carrying a 90% mortality rate when left untreated. Despite AASLD recommendations, there is a practice gap regarding fluid cell count (FCC) collection during paracentesis. Aims We initiated prospective quality improvement (QI) project to improve FCC collection in an ambulatory care setting from a baseline of 78% to a goal of 100% compliance between September 11, 2022 and April 17, 2023. Methods We examined the effects of a quality improvement initiative in 233 adult patient encounters with cirrhosis undergoing paracentesis in an ambulatory setting located within a quaternary care centre in Toronto, Canada within the study timeframe. Using a multidisciplinary approach, the quality improvement initiatives included focused groups, education regarding AASLD guidelines, development of a paracentesis bundle, and streamlining workflow process. Descriptive statistics were collected. Statistical analysis using run charts and p-charts were conducted using QI Macros. Approval was obtained from the QI Review Committee at the institution where this QI initiative took place. Results Baseline adherence for collecting ascitic FCC per paracentesis procedure was 78%. Inconsistencies during process mapping such as variations in documentation, printing FCC labels and consistency in FCC collection were identified as root causes. Thus, bedside paracentesis bundles were introduced to standardize process and improve workflow. Post-intervention, the FCC rate increased marginally to 79.6% with no special cause variation identified (Figure 1). Subgroup analysis demonstrated FCC collection rates of 100% amongst registered nurse (RN) and nurse practitioners (NP), 97% amongst resident physicians, and 92% amongst attending physicians. Subsequently, another subgroup analysis was completed demonstrating 97% FCC collection rate amongst general hepatology group, while 40% FCC rate amongst specialty hepatology group. Overall rate of SBP was 1.62% (N=3). Conclusions This prospective QI study revealed a gap in guidance pertaining to the role of ascitic fluid cell count collection in patients with cirrhosis undergoing paracentesis in ambulatory settings. Further studies ascertaining rationale for not sending FCC may be helpful in elucidating why there is a gap in clinical practice. Additionally conducting studies in ambulatory settings to assess FCC collection's impact on morbidity and mortality will provide vital evidence and clarity for the role of FCC in this vulnerable patient group. Figure 1: P-chart demonstrating percentage FCC Collection per week from September 11, 2022 to April 17, 2023. UCL indicates upper control limit; CL, center line; LCL, lower Control Limit. Funding Agencies Toronto Centre for Liver Disease funded the MSc QI program for Mary Sedarous (writer) who completed this study as her MSc QI project.

LIVE PROCESSING AND FEEDBACK ON A COMBINED LASERSCANNING AND PHOTOGRAMMETRIC DOCUMENTATION WORKFLOW

Abstract. The use of a combined terrestrial laserscanning (TLS) and structure-from-motion (SFM) surveying approach has proved to be able to deliver advantageous benefits in terms of coverage and surface rendition. The elaboration of such approaches entails a succession of processes and are without many options for on-site feedback largely dependent on the experience of the surveyor. In support of this approach a combined automated processing workflow with augmented reality feedback is introduced, allowing operators to get a better understanding of their progress and project success during surveying. To do so native TLS and SFM software is connected to a shared network and data storage environment and controlled within an active framework. Within this, scans and images are aligned within RealityCapture and resulting registration is visualised to the operator using augmented reality (AR). To achieve complete independence of pre-scanned environments, the augmented reality application presented within this research uses a GNSS supported visual SLAM algorithm. With this the application is able to position virtual objects – such as markers representing the positions of registered cameras – within a physical space using only their geoposition as reference. The workflow presented here should be understood as a proof of concept to determine possible improvements that would enable such interactive means of surveying and would allow deeper integration of components into a combined AR supported documentation workflow.

Displacement field reconstruction in landslide physical modeling by using a terrain laser scanner – Part 2: Application and large strain/displacement and water effect analysis

Deformation analysis is fundamental in geotechnical modeling. Nevertheless, there is still a lack of an effective method to obtain the deformation field under various experimental conditions. In this study, we introduce a process–based physical modeling of a pile–reinforced reservoir landslide and present an improved deformation analysis involving large strains and water effects. We collect multi–period point clouds using a terrain laser scanner and reconstruct its deformation field through a point cloud processing workflow. The results show that this method can accurately describe the landslide surface deformation at any time and area by both scalar and vector fields. The deformation fields in different profiles of the physical model and different stages of the evolutionary process provide adequate and detailed landslide information. We analyze the large strain upstream of the pile caused by the pile installation and the consequent violent deformation during the evolutionary process. Furthermore, our method effectively overcomes the challenges of identifying targets commonly encountered in geotechnical modeling where water effects are considered and targets are polluted, which facilitates the deformation analysis at the wading area in a reservoir landslide. Eventually, combining subsurface deformation as well as numerical modeling, we comprehensively analyze the kinematics and failure mechanisms of this complicated object involving landslides and pile foundations as well as water effects. This method is of great significance for any geotechnical modeling concerning large-strain analysis and water effects.

Downstream Processing of Secondary Metabolite Extraction from Medicinal Plants (Tinospora cordifolia, Ocimum tenuiflorum, Asparagus racemosus, Bacopa monnieri): Optimization and Equipment Utilization

Tinosoporacordifolia, Ocimum tenuiflorum,Asparagus racemosus and Bacopa monnieri are all important and plants with significant medicinal propertiesfrom these plants secondary metabolites extracts were derived and TLC characterization was done. This paper delves into the intricate downstream processing steps and the utilization of specialized equipment in the extraction and purification of secondary metabolites from four medicinal plants: Tinospora cordifolia, Ocimum tenuiflorum, Asparagus racemosus, and Bacopa monnieri. The study emphasizes the optimization of extraction methods, purification techniques, and the role of specific equipment in enhancing the efficiency of the downstream processing workflow.

Sizing mudsnails: Applying superpixels to scale growth detection under ocean warming

Abstract The expansion of scientific image data holds great promise to quantify individuals, size distributions and traits. Computer vision tools are especially powerful to automate data mining of images and thus have been applied widely across studies in aquatic and terrestrial ecology. Yet marine benthic communities, especially infauna, remain understudied despite their dominance of marine biomass, biodiversity and playing critical roles in ecosystem functioning. Here, we disaggregated infauna from sediment cores taken throughout the spring transition (April–June) from a near‐natural mesocosm setup under experimental warming (Ambient, +1.5°C, +3.0°C). Numerically abundant mudsnails were imaged in batches under stereomicroscopy, from which we automatically counted and sized individuals using a superpixel‐based segmentation algorithm. Our segmentation approach was based on clustering superpixels, which naturally partition images by low‐level properties (e.g., colour, shape and edges) and allow instance‐based segmentation to extract all individuals from each image. We demonstrate high accuracy and precision for counting and sizing individuals, through a procedure that is robust to the number of individuals per image (5–65) and to size ranges spanning an order of magnitude (<750 μm to 7.4 mm). The segmentation routine provided at least a fivefold increase in efficiency compared with manual measurements. Scaling this approach to a larger dataset tallied >40k individuals and revealed overall growth in response to springtime warming. We illustrate that image processing and segmentation workflows can be built upon existing open‐access R packages, underlining the potential for wider adoption of computer vision tools among ecologists. The image‐based approach also generated reproducible data products that, alongside our scripts, we have made freely available. This work reinforces the need for next‐generation monitoring of benthic communities, especially infauna, which can display differential responses to average warming.

Inflammation proteomics datasets in the ALSPAC cohort.

Proteomics is the identification, detection and quantification of proteins within a biological sample. The complete set of proteins expressed by an organism is known as the proteome. The availability of new high-throughput proteomic technologies, such as Olink Proteomic Proximity Extension Assay (PEA) technology has enabled detailed investigation of the circulating proteome in large-scale epidemiological studies. In particular, the Olink® Target 96 inflammatory panel allows the measurement of 92 circulating inflammatory proteins. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based cohort study which recruited pregnant women in 1991-1992 and has followed these women, their partners, and their offspring ever since. In this data note, we describe the newly-released proteomic data available in ALSPAC. Ninety-two proteins were analysed in 9000 blood plasma samples using the Olink® Target 96 inflammatory panel. Samples were derived from 2968 fasted mothers (mean age 47.5; Focus on Mothers 1 (FOM1)), 3005 non-fasted offspring at age 9 (Focus@9) and 3027 fasted offspring at age 24 (Focus@24). Post sample filtering, 1834 offspring have data at both timepoints and 1119 of those have data from their mother available. We performed quality control analyses using a standardised data processing workflow ( metaboprep) to produce a filtered dataset of 8983 samples for researchers to use in future analyses. Initial validation analyses indicate that IL-6 measured using the Olink® Target 96 inflammatory panel is highly correlated with IL-6 previously measured by clinical chemistry (Pearson's correlation = 0.77) and we are able to reproduce the reported positive correlation between body mass index (BMI) and IL-6. The pre-processing and validation analyses indicate a rich proteomic dataset to further characterise the role of inflammation in health and disease.

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

Drug Traceability - Divine or Challenge for Pharma Sector

Blockchain technology is driving the digital transformation of various industries, and one sector benefiting from this innovation is the pharmaceutical industry. This industry grapples with challenges like a lack of transparency, difficulties in tracking products, a deficit of trust, and issues related to shipping expired products. To address these concerns, blockchain technology has been harnessed as a solution. Notably, counterfeit drug prevention emerged as the most prevalent category, aligning with the pharmaceutical industry's primary objective. Blockchain technology is an emerging innovation that is finding applications in various industries, including healthcare. In the healthcare sector, Blockchain networks are being utilized to securely store and exchange patient data across hospitals, diagnostic laboratories, pharmacies, and medical practitioners. These applications of Blockchain can effectively identify and mitigate critical errors, including potentially hazardous ones within the realm of healthcare. Consequently, this technology holds the promise of enhancing the efficiency, security, and transparency of medical data sharing within the healthcare system. Moreover, it offers valuable tools for medical institutions to gain insights and improve the analysis of medical records. It visually represents the diverse capabilities, enablers, and the unified workflow process of Blockchain technology in supporting healthcare on a global scale. Additionally, the paper presents a thorough discussion of fourteen significant applications of Blockchain in healthcare, underscoring its pivotal role in addressing issues like deception in clinical trials. Blockchain's potential in healthcare lies in improving data efficiency, assuaging concerns about data manipulation, and establishing a robust data storage framework with the highest level of security. It delivers features such as versatility, interconnectivity, accountability, and authentication for data access. The protection and confidentiality of health records are of paramount importance, and Blockchain facilitates decentralized data security in healthcare while mitigating specific threats.

Daily Hospital Emerging Infectious Disease(EID) Workflow & Data Processing Automation.

Following the onset of the covid pandemic two years ago, the Ministry of Health(MOH)'s required all health care cluster groups to provide daily reporting of the emergency department as well as the inpatient situation in the respective healthcare institutions for oversight of the covid situation. In view of the improvements in the data availability and relief of the tedious manual collation, DAO was entrusted with the task to help enable and setup the standardized report and generation process moving forward.

Observation of spikelets in steady-state surface nuclear magnetic resonance data

Spikelets in nuclear magnetic resonance (NMR) data occur at predictable frequencies dependent on the repetition time of the excitation sequence. Although spikelets are well documented in other NMR fields, we report their presence in steady-state surface NMR data for the first time. These observations are accompanied by analytical developments to understand and predict their behavior, which follow directly from existing steady-state surface NMR modeling. We show that spikelets represent copies of the surface NMR signal occurring at multiple locations in the frequency domain, including locations that are distinct from the Larmor frequency. These features are shown to be detectable without requiring additional effort in the field and are shown to be readily processed and modeled with only minor modifications to the processing and modeling workflows. Finally, field spikelet data are also inverted to demonstrate that these data can be fit using subsurface models consistent with a reference surface NMR inversion.

Re-Engineering Data Processing Workflow to Automate Abbreviations Audit of Clinical Documentation.

In KK Women's and Children's Hospital (KKH), clinical case notes audits are conducted quarterly for compliance of approved acronym usage. Existing process involves the retrieval of mixed hardcopy and electronic case notes for referencing manually to the list of approved abbreviations by clinical coder. Through the use of process re-engineering and excel application, audit coverage can thus be expanded with reduction in human dependency and errors with significant resultant savings in time spent.

Lessons Learned From Implementation of Mpox Surveillance During an Outbreak Response in Tennessee, 2022.

Mpox surveillance was integral during the 2022 outbreak response. We evaluated implementation of mpox surveillance in Tennessee during an outbreak response and made recommendations for surveillance during emerging infectious disease outbreaks. To understand surveillance implementation, system processes, and areas for improvement, we conducted 8 semistructured focus groups and 7 interviews with 36 health care, laboratory, and health department representatives during September 9-20, 2022. We categorized and analyzed session transcription and notes. We analyzed completeness and timeliness of surveillance data, including 349 orthopoxvirus-positive laboratory reports from commercial, public health, and health system laboratories during July 1-August 31, 2022. Participants described an evolving system and noted that existing informatics platforms inefficiently supported iterations of reporting requirements. Clear communication, standardization of terminology, and shared, adaptable, and user-friendly informatics platforms were prioritized for future emerging infectious disease surveillance systems. Laboratory-reported epidemiologic information was often incomplete; only 55% (191 of 349) of reports included patient address and telephone number. The median time from symptom onset to specimen collection was 5 days (IQR, 3-6 d), from specimen collection to laboratory reporting was 3 days (IQR, 1-4 d), from laboratory reporting to patient interview was 1 day (IQR, 1-3 d), and from symptom onset to patient interview was 9 days (IQR, 7-12 d). Future emerging infectious disease responses would benefit from standardized surveillance approaches that facilitate rapid implementation. Closer collaboration among informatics, laboratory, and clinical partners across jurisdictions and agencies in determining system priorities and designing workflow processes could improve flexibility of the surveillance platform and completeness and timeliness of laboratory reporting. Improved timeliness will facilitate public health response and intervention, thereby mitigating morbidity.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Clinical Needs Assessment of a Machine Learning-Based Asthma Management Tool: User-Centered Design Approach.

Personalized asthma management depends on a clinician's ability to efficiently review patient's data and make timely clinical decisions. Unfortunately, efficient and effective review of these data is impeded by the varied format, location, and workflow of data acquisition, storage, and processing in the electronic health record. While machine learning (ML) and clinical decision support tools are well-positioned as potential solutions, the translation of such frameworks requires that barriers to implementation be addressed in the formative research stages. We aimed to use a structured user-centered design approach (double-diamond design framework) to (1) qualitatively explore clinicians' experience with the current asthma management system, (2) identify user requirements to improve algorithm explainability and Asthma Guidance and Prediction System prototype, and (3) identify potential barriers to ML-based clinical decision support system use. At the "discovery" phase, we first shadowed to understand the practice context. Then, semistructured interviews were conducted digitally with 14 clinicians who encountered pediatric asthma patients at 2 outpatient facilities. Participants were asked about their current difficulties in gathering information for patients with pediatric asthma, their expectations of ideal workflows and tools, and suggestions on user-centered interfaces and features. At the "define" phase, a synthesis analysis was conducted to converge key results from interviewees' insights into themes, eventually forming critical "how might we" research questions to guide model development and implementation. We identified user requirements and potential barriers associated with three overarching themes: (1) usability and workflow aspects of the ML system, (2) user expectations and algorithm explainability, and (3) barriers to implementation in context. Even though the responsibilities and workflows vary among different roles, the core asthma-related information and functions they requested were highly cohesive, which allows for a shared information view of the tool. Clinicians hope to perceive the usability of the model with the ability to note patients' high risks and take proactive actions to manage asthma efficiently and effectively. For optimal ML algorithm explainability, requirements included documentation to support the validity of algorithm development and output logic, and a request for increased transparency to build trust and validate how the algorithm arrived at the decision. Acceptability, adoption, and sustainability of the asthma management tool are implementation outcomes that are reliant on the proper design and training as suggested by participants. As part of our comprehensive informatics-based process centered on clinical usability, we approach the problem using a theoretical framework grounded in user experience research leveraging semistructured interviews. Our focus on meeting the needs of the practice with ML technology is emphasized by a user-centered approach to clinician engagement through upstream technology design.

Imaging CO2$_2$ reinjection into basalts at the CarbFix2 reinjection reservoir (Hellisheiði, Iceland) with body‐wave seismic interferometry

AbstractAs part of the Synergetic Utilisation of CO storage Coupled with geothermal EnErgy Deployment project, investigating CO reinjection with different seismic methods, both passive and active seismic surveys have been conducted at the geothermal power plant at Hellisheiði, Iceland. During the 2021 survey, two geophone lines recorded noise for a week. We process the passive‐source data with seismic interferometry to image the subsurface structure around the CarbFix2 reinjection reservoir. To improve image quality, we perform an illumination analysis to select only noise panels dominated by body‐wave energy. The results show that most noise panels are dominated by air‐wave energy arriving from the direction of the power plant. We use panels with a near‐vertical incidence to create a zero‐offset image and a larger selection of body‐wave‐dominated panels to create virtual common‐shot gathers. We process the gathers with a simple reflection seismology processing workflow to obtain stacked images. The zero‐offset images show a relatively lower signal‐to‐noise ratio and only horizontal reflectors. The stacked images show slightly dipping reflectors and possibly lateral amplitude variations around the expected injection region. This could indicate a region of interest for future research into the reinjection reservoir.

Secretome processing for proteomics: A methods comparison.

The cancer cell secretome comprises a treasure-trove for biomarkers since it reflects cross-talk between tumor cells and their surrounding environment with high detectability in biofluids. In this study, we evaluated six secretome sample processing workflows coupled to single-shot mass spectrometry: (1) Protein concentration by ultrafiltration with a molecular weight cut-off (MWCO) filter and sample preparation through in-gel digestion (IGD); (2) Acetone protein precipitation coupled to IGD; (3) MWCO filter-based protein concentration followed by to in-solution digestion (ISD); (4) Acetone protein precipitation coupled to ISD; (5) Direct ISD; (6) Secretome lyophilization and ISD. To this end, we assessed workflow triplicates in terms of total number of protein identifications, unique identifications, reproducibility of protein identification and quantification and detectability of small proteins with important functions in cancer biology such as cytokines, chemokines, and growth factors. Our findings revealed that acetone protein precipitation coupled to ISD outperformed the other methods in terms of the number of identified proteins (2246) and method reproducibility (correlation coefficient between replicates (r=0.94, CV=19%). Overall, especially small proteins such as those from the classes mentioned above were better identified using ISD workflows. Concluding, herein we report that secretome protein precipitation coupled to ISD is the method of choice for high-throughput secretome proteomics via single shot nanoLC-MS/MS.

High-resolution repeat topography of drifting ice floes in the Arctic Ocean from terrestrial laser scanning

Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km2 drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.

EcoPrintAnalyzer: Assessing Sustainability in Material Extrusion Additive Manufacturing for Informed Decision-Making

Sustainability is fundamental in the field of additive manufacturing (AM) for improving eco-consciousness and driving evolution toward environmentally responsible production methods. Compared to traditional manufacturing processes, AM technologies can be more resource-efficient and offer innovative solutions for creating eco-friendly processes and products. Nevertheless, there is significant potential for improvement in additive manufacturing sustainability. The key factors driving this improvement include design optimization and increased awareness. Designers and engineers can create designs that optimize material efficiency and reduce support structures. Raising awareness and educating stakeholders about the environmental benefits of AM can promote responsible choices throughout the industrial process. The development of a tool to assess the environmental impact of AM processes could be a significant contribution to advancing sustainability in the AM field. The EcoPrintAnalyzer, introduced as a complementary plugin for UltiMaker Cura, offers data on the equivalent carbon dioxide footprint and energy consumption in material extrusion additive manufacturing. This tool facilitates informed decision-making regarding materials, designs, and settings, enabling users to optimize their AM processes for reduced waste and enhanced energy efficiency. Beyond aiding decision-making, the EcoPrintAnalyzer fosters environmental consciousness and encourages the adoption of sustainable practices within the AM ecosystem. The efficacy of the tool is demonstrated through the 3DBenchy model case study, showcasing its intuitive interface and seamless integration within the AM process workflow for immediate and comparative environmental impact assessments across different process configurations.

A protocol and training guidelines for mosquito sampling in remote areas with limited power supply

Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.