Exploration Tool Research Articles

Case study on a scientific oral history project using information practice analysis

Introduction. This investigation explored 21st century stewardship of a late 20th century scientific oral history project through the lens of information practice. Oral history project stewardship and reuse are understudied. Literature review. Oral histories foreground human agency in the world. Often studied singly, many reflect an oral history project’s historiographic context. Stewardship documentation by multiple stakeholders accrues over time. The author developed an information practice analysis tool for systematic exploration of oral history stewardship documentation. Method. A case study approach and qualitative content analysis techniques guided investigation of documentation for an oral history project held at Science History Institute in Philadelphia. Textual data was collected for a purposive sample. Analysis and results. Multiple stakeholders’ documentation schema for the purposive sample were analysed in Excel and Word. Results coded five information work models for stakeholder information practices. Discussion. The information work models portrayed stakeholder information practices as mechanisms for stability in the stewardship of contextual assets, and mechanisms for transformation through staff knowledge, as situational assets. Conclusion. Information practice analysis of oral history stewardship in this case study systematised stakeholders’ and user perspectives on access. Implications for theory development lie in leveraging the stability and transformative agency of stewardship and reuse.

CLOSING THE DIVIDE: ADDRESSING OBSTACLES FOR WOMEN IN ADOPTING DIGITAL FINANCE SOLUTIONS

The focal point of this research paper is to identify the challenges/obstacles to widespread adoption of digital financial services (DFS) among women. Despite DFS’s significant potential to provide accessible, acceptable, and safe banking options to underprivileged sections through cutting-edge technology such as mobile-oriented solutions, digital platforms, its adoption poses considerable challenges. The key objective is to recognize these challenges. The author investigated 14 key challenges culled from the literature. Primary data was collected from the 400 women of Haryana using multistage purposive sampling. Haryana was chosen for the study because lower sex ratio was recorded 914 females 1000 according to family health survey 2019-2020. According to census 2011, female literacy ratio approximately 65.9% lower than the national average. When it comes to workforce participation, it was 19-20% which is below the national average according to periodic labour force survey (PLFS). In order to fulfil the objectives of the study, Exploratory factor analysis tool was used. Factor analysis extracted the 3 unique factors from 14 variables such as “User Understanding and experience”, “Technical and Infrastructure concerns”, “Data Safety and confidentiality issues”. The present research is one of the first few studies which is done through Primary survey data.

Interactive data exploration websites for large-scale electrophysiology.

Methodological advances in neuroscience have enabled the collection of massive datasets which demand innovative approaches for scientific communication. Existing platforms for data storage lack intuitive tools for data exploration, limiting our ability to interact effectively with these brain-wide datasets. We introduce two public websites: (Data and Atlas) developed for the International Brain Laboratory which provide access to millions of behavioral trials and hundreds of thousands of individual neurons. These interfaces allow users to discover both the raw and processed brain-wide data released by the IBL at the scale of the whole brain, individual sessions, trials, and neurons. By hosting these data interfaces as websites they are available cross-platform with no installation. By releasing each site's code as a modular open-source framework, other researchers can easily develop their own web interfaces and explore their own data. As neuroscience datasets continue to expand, customizable web interfaces offer a glimpse into a future of streamlined data exploration and act as blueprints for future tools.

The Comparative Genome Dashboard.

The Comparative Genome Dashboard is a web-based software tool for interactive exploration of the similarities and differences in gene functions between organisms. It provides a high-level graphical survey of cellular functions, and enables the user to drill down to examine subsystems of interest in greater detail. At its highest level the Comparative Dashboard contains panels for cellular systems such as biosynthesis, energy metabolism, transport, and response to stimulus. Each panel contains a set of bar graphs that plot the numbers of compounds or gene products for each organism across a set of subsystems of that panel. Users can interactively drill down to focus on subsystems of interest and see grids of compounds produced or consumed by each organism, specific GO term assignments, pathway diagrams, and links to more detailed comparison pages. For example, the dashboard enables users to compare the cofactors that a set of organisms can synthesize, the metal ions that they are able to transport, their DNA damage repair capabilities, their biofilm-formation genes, and their viral response proteins. The dashboard enables users to quickly perform comprehensive comparisons at varying levels of detail.

Development of a Mouse Cardiac Sarcoidosis Model Using Carbon Nanotubes.

Sarcoidosis, a granulomatous disorder of unknown etiology affecting multiple organs. It is often a benign disease but can have significant morbidity and mortality when the heart is involved (often presenting with clinical manifestations such as conduction irregularities and heart failure). This study addresses a critical gap in cardiac sarcoidosis (CS) research by developing a robust animal model. The absence of a reliable animal model for cardiac sarcoidosis is a significant obstacle in advancing understanding and treatment of this condition. The proposed model utilizes carbon nanotube injection and transverse aortic constriction as stressors. Intramyocardial injection of carbon nanotubes induces histiocytes typical of sarcoid granulomas in the heart but shows limited effects on fibrosis or cardiac function. Priming the immune system with transverse aortic constriction prior to intramyocardial injection of carbon nanotubes enhances cardiac fibrosis, diminishes cardiac function, and impairs cardiac conduction. This novel, easily executable model may serve as a valuable tool for disease profiling, biomarker identification, and therapeutic exploration.

Light traps as an exploratory tool in light pollution studies: Assessment of vulnerable species and their migratory patterns

Artificial Light at Night (ALAN) is one of the most widespread stressors on coastal marine habitats. Despite their sparse use in light pollution studies, light traps are a useful methodology to explore which species are attracted by ALAN. For that purpose, we placed light traps during the day and at night in the shallow subtidal adjacent to three natural sandy shores. Additionally, to determine the origin of the catches, the sediment and the water column were also sampled in both periods by a Van Veen grab and a plankton net, respectively. Our results showed that light traps catches at night were dominated by migrant fauna, mostly amphipod crustaceans emerging from the sediment. Other species that perform diel vertical migrations, such as calanoid copepods, were also attracted. This approach may help to understand which taxa are more susceptible to ALAN in these shallow habitats.

Geo-constrained clustering of resistivity data revealing the heterogeneous lithological architectures and the distinctive geoelectrical signature of shallow deposits

For all applications, subsurface models should be consistent with all available geological and geophysical knowledge. Current practices for synergistic interpretation of geological and geophysical approaches often rely on purely qualitative comparisons, resulting sometimes in inconsistent findings. This study introduces a procedure for a statistical and geo-constrained clustering of electrical resistivity data derived from Electrical Resistivity Tomography (ERT) to address this gap, providing a quantitative parameterization for site-specific geoelectrical signatures of litho-stratigraphic architectures. Seventeen boreholes and three ERT surface profiles were employed to link geophysical inversion results to geological criteria. Core samples allowed grain size analyses, while geological-statistical clustering of electrical resistivity, driven by the observation of stratigraphic contacts in drilled boreholes, established a parametric relationship between geology and geophysics. The iterative clustering procedure, utilizing a classification algorithm, geological boundary constraints, and granulometric analyses, discriminated six distinct lithological clusters, capturing the lateral and vertical heterogeneity of shallow deposits. Subsequent spatial grouping of anthropogenic materials delineated lithological structures and facilitated the classification and identification of filling materials, silty sands, clayey sands, and clays and silts, each exhibiting distinct resistivity variations. The geo-driven geophysical clustering revealed complex lithological structures, especially paleo-channels, capturing their unique geoelectric footprints. The iterative clustering of geo-constrained resistivity data emerges as a powerful tool for subsurface exploration, contributing significantly to understanding lithological heterogeneity, quantifying statistically-based geoelectrical parametrization of shallow sediments, and evaluating the resistivity signature of different deposits. By bridging the gap between geology and geophysics, this data-driven approach establishes a benchmark for future applications. For instance, in the context of contaminated sites, it can be applied to identify pollutants versus geological heterogeneities.

Orthopaedic Surgery Residency Program Websites: Assessing Information Gaps and Opportunities for Attracting Applicants.

The quality and volume of information on orthopaedic surgery residency program websites are helpful for informed decision making of prospective applicants. The purpose of this study was to evaluate the content on orthopaedic surgery residency program websites and identify areas for improvement. In November 2023, 203 orthopaedic surgery residency programs were reviewed using the Association of American Medical Colleges Residency Explorer tool. Fourteen nonfunctional websites were excluded, leaving n = 189. Websites were assessed for 11 variables: resources for assisting resident research (eg, biomechanics laboratory, research assistant, and advisors), residents' past institutional affiliations, current resident profiles (including hometown and interests), alumni data, fellowship matches, American Board of Orthopedic Surgery I/II pass rates, Alpha Omega Alpha Society affiliation, target United States Medical Licensing Examination/ Comprehensive Osteopathic Medical Learning Exam Step 2 scores, educational activities (journal club, grand rounds, didactics), evaluation methods, and mentorship. Website variables differed markedly among identified program types, which included community programs, university programs (UPs), and affiliated programs (APs). Disparities were noted in the disclosure of residents' previous institutions ( P = 0.001), the availability of alumni names ( P = 0.001), research resources ( P = 0.001), academic activities ( P = 0.007), assigned mentorship programs ( P = 0.006), and fellowship match information ( P = 0.027). Notably, community programs shared statistically significantly less information in these areas compared with UPs and APs ( P = 0.001). While there was a proportionate sharing of information between UPs and APs, UPs exhibited a slightly higher overall percentage of websites sharing more detailed information. The quality and comprehensiveness of information on orthopaedic surgery residency program websites vary markedly. Most of the orthopaedic surgery residency programs provided information on residents' past institutions and academic activities. However, very few provided information on designated mentorship programs or objective measures such as target Step 2 examination scores and ABOS pass rates. Improving the quality of information available on program websites will enhance transparency and consistency, enabling prospective applicants to make more informed decisions about where to apply and interview.

Discrimination of deposit types using magnetite geochemistry based on machine learning

The application of trace elements in magnetite for deposit genesis research is significant, highlighting its potential as a valuable mineral exploration tool. However, traditional low-dimensional analysis methods are not effective in revealing the genetic types of deposits using magnetite trace elements, as they fail to fully utilize the rich high-dimensional information provided by magnetite trace element analysis. To address this limitation, we implemented a supervised machine learning method, eXtreme Gradient Boosting (XGBoost), to correlate the multi-element composition of magnetite with deposit types. Our study encompassed 3,865 magnetite trace element datasets from six distinct deposit types (BIF, IOA, IOCG, magmatic, porphyry, and skarn deposits). It demonstrates the XGBoost classifier’s efficiency and accuracy in classifying high-dimensional magnetite trace element data based on deposit types, achieving an impressive overall accuracy of 96% with an F1 score of 95%. Interpretation of the model using the SHAPley Additive exPlanations (SHAP) tool shows that Ni, Ga, Sc, and V are the most indicative elements for classifying deposit types using magnetite trace element chemistry. Additionally, a visualization method based on XGBoost and t-SNE was proposed. Finally, Xgboost and SHAP were applied in Jinchuan magmatic sulfide Ni-Cu-PGE deposit. Based on optical characteristics and machine learning, Jinchuan magnetite can be classified into three types. Compared with type I magmatic magnetite and type III hydrothermal magnetite, type II magnetite’s paragenetic with sulfides, calcite and apatite and high Ni content may indicate that it’s origin of interaction between sulfide melts–volatile fluids. These results indicate that deep volatile fluid plays a key role for the formation of Jinchuan Cu-Ni sulfide deposit.

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

Cassiterite trace element discrimination diagrams to facilitate critical mineral exploration

Cassiterite is a weathering-resistant mineral, which can incorporate a variety of trace elements. Trace elements in cassiterite samples collected from twelve deposits in the Herberton Mineral Field, Australia, were measured with the use of laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The results were combined with published data from other tin fields, including the Andean Sn belt in South America; the Karagwe Ankole belt in Rwanda; and, from China, the Kangxiwa-Dahongliutan pegmatite field, the Youjiang basin, the Nanling belt and the Da Hinggan Range belt. Tin deposits in the dataset can be subdivided into four deposit types: 1) greisen and veins; 2) skarns; 3) Li-Cs-Ta pegmatites; and 4) polymetallic veins. The cassiterite dataset was analyzed using basic descriptive statistics, principal component analysis (PCA), and cluster analysis. Cassiterite grains from greisen and vein deposits are characterized by high concentrations of Ti (avg. 1751 ppm) and moderate concentrations of Al (avg. 97 ppm), whereas cassiterite grains from skarn deposits generally contain lower concentrations of Ti and Al. Chemical compositional boundaries in cassiterite from different deposits were recognized with cluster analysis. The relative enrichment of Al and Ti in cassiterite grains from greisen and vein deposits is likely due to greisenization reactions. The Ti vs. Al diagram can be used to differentiate between cassiterite grains derived from greisen and vein deposits, as compared to cassiterite grains derived from skarn deposits, whereas Sb vs. V diagram can be used to differentiate between cassiterite grains from polymetallic vein deposits. Zirconium and Nb concentrations are useful in identifying cassiterite grains sourced from LCT pegmatite deposits. The discrimination diagrams developed in this study through cluster analysis indicate that cassiterite grains sourced from different deposit types can be differentiated based on their trace element geochemistry and this can be a useful tool in critical mineral exploration. Therefore, these diagrams can be used effectively to understand metal association and deposit types in a region with detrital cassiterite from stream sediments, till and heavy mineral placer deposits.

P-glycoprotein activity in vivo in older adults: physiological, pathophysiological and pharmacokinetic interplay at the blood-brain barrier

p-glycoprotein (P-gp) is an efflux transporter of xenobiotic and endogenous compounds across the blood-brain barrier (BBB). P-gp plays an essential role by limiting passage of these compounds into the brain tissue. It is susceptible to drug-drug interactions when interactors drugs are co-administrated. The efficiency of P-gp may be affected by the aging process and the development of neurodegenerative diseases. Studying this protein in older adults is therefore highly relevant for all these reasons. Understanding P-gp activity in vivo is essential when considering the physiological, pathophysiological, and pharmacokinetic perspectives, as these aspects seem to be interconnected to some extent. In vivo exploration in humans is based on neuroimaging techniques, which have been improving over the last years. The advancement of exploration and diagnostic tools is opening up new prospects for understanding P-gp activity at the BBB.

P-glycoprotein activity in vivo in older adults: physiological, -pathophysiological and pharmacokinetic interplay at the blood-brain barrier

p-glycoprotein (P-gp) is an efflux transporter of xenobiotic and endogenous compounds across the blood-brain barrier (BBB). P-gp plays an essential role by limiting passage of these compounds into the brain tissue. It is susceptible to drug-drug interactions when interactors drugs are co-administrated. The efficiency of P-gp may be affected by the aging process and the development of neurodegenerative diseases. Studying this protein in older adults is therefore highly relevant for all these reasons. Understanding P-gp activity in vivo is essential when considering the physiological, pathophysiological, and pharmacokinetic perspectives, as these aspects seem to be interconnected to some extent. In vivo exploration in humans is based on neuroimaging techniques, which have been improving over the last years. The advancement of exploration and diagnostic tools is opening up new prospects for understanding P-gp activity at the BBB.

Fluid identification with Graph Transformer using well logging data

The prediction of fluid through well logging is a cornerstone in guiding exploratory efforts in the energy sector. Comprehending the fluid composition beneath the surface empowers exploration teams to effectively gauge the extent, reserves, and caliber of oil and gas resources. This leads to enhanced strategies in exploration and the judicious use of resources. We introduce an innovative machine learning framework named “Graph Transformer” for predicting fluid. This model melds graph convolutional layers with a Transformer module. It excels in decoding spatial and temporal patterns within well logging data, thus unraveling complex geological dependencies by factoring in the interconnectedness of various data points. Additionally, it features a Positional Encoding module to enhance understanding of sequential data points in terms of depth, thereby overcoming the limitations of sequence independence. The Transformer's Multi-Head Self-Attention mechanism is pivotal in discerning and integrating spatial and temporal interconnections across various depths, elevating its capability to represent geological structures. Initially, the model harnesses key well log data like Density, Acoustic, Gamma-ray, and Compensated Neutron Logs for extracting geological features. These insights are then processed through the Graph Transformer to establish relationship between fluid characteristics and logging parameters. Furthermore, we compare this model with other leading models using precision, recall, and accuracy metrics. Experimental findings affirm the model's high accuracy in predicting fluid within intricate geological settings. Its exceptional adaptability makes it apt for various geological conditions and logging tools. Thus, our Graph Transformer model stands out as a sophisticated, efficient machine learning solution in the realm of well logging fluid prediction, offering geologists and engineers precise tools for exploration and development.

Prescriptive procedure for manual code smell annotation

– Code smells are structures in code that present potential software maintainability issues. Manually constructing high-quality datasets to train ML models for code smell detection is challenging. Inconsistent annotations, small size, non-realistic smell-to-non-smell ratio, and poor smell coverage hinder the dataset quality. These issues arise mainly due to the time-consuming nature of manual annotation and annotators’ disagreements caused by ambiguous and vague smell definitions.To address challenges related to building high-quality datasets suitable for training ML models for smell detection, we designed a prescriptive procedure for manual code smell annotation. The proposed procedure represents an extension of our previous work, aiming to support the annotation of any smell defined by Fowler. We validated the procedure by employing three annotators to annotate smells following the proposed annotation procedure.The main contribution of this paper is a prescriptive annotation procedure that benefits the following stakeholders: annotators building high-quality smell datasets that can be used to train ML models, ML researchers building ML models for smell detection, and software engineers employing ML models to enhance the software maintainability. Secondary contributions are the code smell dataset containing Data Class, Feature Envy, and Refused Bequest, and DataSet Explorer tool which supports annotators during the annotation procedure.

From Data to Decisions: Optimizing Supply Chain Management with Machine Learning-Infused Dashboards

This paper examines how business users can leverage machine learning and data analytics through dashboards to optimize their decision making in demand-side supply chain management. We present a case study of an Austrian B2B hygiene product retailer that needed to provide its top management, sales representatives, and marketing managers with more relevant information to improve business intelligence and to enhance customer acquisition and retention. To generate this information, we utilized various data analysis and machine learning methods, including RFM analysis, market basket analysis, TURF analysis, and demand forecasting, using real-life transaction data. To provide business users with easy access to this information, we developed dashboards that integrate these methods providing an interactive and visual tool for data exploration and understanding. We conclude that dashboards can enable, business users to make better informed and effective decisions on the demand side of supply chains leading to improved sales performance and increased customer satisfaction.

Gendered bodies: A graphic medicine commentary

This final commentary, in comic format, frames this special issue using Graphic Medicine methodologies to explore broader themes and meanings related to the scientific study of gender and health. Comics can be seen as a way to introduce complex human narratives and as an exploratory tool to ask broader social-contextual and ethical questions about health and medicine. This piece is also constructed through the lens of queer scholarship, which, together with the comics format, provides opportunities to build more embodied, complicated narratives about gender, sexuality and health. Most importantly, comics are used as a modality to tell compelling narratives about how individuals, rather than populations, may be impacted by biomedical conceptualizations of gender and health. The commentary includes a series of graphic narratives containing hypothetical stories and cases: stories of how individuals may be harmed within healthcare systems by rigid framings of gender, sex and sexuality, and stories about how gender socialization may impact health in subtle ways. These narratives furthermore examine the inextricable link between gender and power, illustrating how overt and covert manifestations of power may shape a person's health over the life course. Finally, the piece explores how expansive views of gender may contribute to positive health care experiences. The intention of this piece is to nudge scientific researchers and clinicians alike to approach the topic of gender, sexuality and health with nuance and curiosity.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement

Vision-guided Autonomous Underwater Vehicles (AUVs) have gradually become significant tools for human exploration of the ocean. However, distorted images severely limit the visual ability, making it difficult to meet the needs of complex underwater environment perception. Fortunately, recent advancements in deep learning have led to rapid developments in underwater image enhancement. The emergence of the Transformer architecture has further enhanced the capabilities of deep learning. However, the direct application of Transformer to underwater image enhancement presents challenges in computing pixel-level global information and extracting local features. In this paper, we present a novel approach that merges dimension decomposition Transformer with semi-supervised learning for underwater image enhancement. To begin, dimension decomposition attention is proposed, which enables Transformer to compute global dependencies directly at the original scale and correct color distortions effectively. Concurrently, we employ convolutional neural networks to compensate for Transformer's limitations in extracting local features, thereby enriching details and textures. Subsequently, a multi-stage Transformer strategy is introduced to divide the network into high- and low-resolution stages for multi-scale global information extraction. It helps correct color distortions while enhancing the network's focus on regions with severe degradation. Moreover, we design a semi-supervised learning framework to reduce the reliance on paired datasets and construct a corresponding multi-scale fusion discriminator to enhance the sensitivity to input data. Experimental results demonstrate that our method outperforms state-of-the-art approaches, showcasing excellent learning and generalization capabilities on subjective perception and overall evaluation metrics. Furthermore, outstanding results highlight the significant improvements it brings to downstream visual engineering applications. The code of the proposed DDformer is available at https://github.com/ZhiGao-hfuu/DDformer.

The role of geophysics in geologic hydrogen resources

Abstract Transition to cleaner energy sources is crucial for reducing carbon emissions to zero. Among these new clean energy types, there is a growing awareness of the potential for naturally occurring geologic hydrogen (H2) as a primary energy resource that can be readily introduced into the existing energy supply. It is anticipated that geophysics will play a critical role in such endeavors. There are two major different types of geologic H2. One is natural H2 (referred to as gold H2), which is primarily accumulating naturally in reservoirs in certain geological setting; and the other is stimulated H2 (referred to as orange H2), which is produced artificially from source rocks through chemical and physical stimulations. We will first introduce geophysics in geologic H2 in comparison and contrast to the scenarios of blue and green H2. We will then discuss the significance of geophysics in both natural H2 and stimulated H2 in term of both exploration and monitoring tools. Comparing and contrasting the current geophysical tools in hydrocarbon exploration and production, we envision the innovative geophysical technologies and strategies for geologic H2 resources based on our current understanding of both natural and stimulated geologic hydrogen systems. The strategies for H2 exploration will involve a shift from reservoir- to source rock-centered approaches. Last, we believe that the geophysical methods including integration of multi-geophysics, efficient data acquisition, and machine learning in geologic H2 could be potentially provide sufficient new directions and significant opportunities to pursue research for the next one or two decades.