Amount Of Data Research Articles

Analyzing the impact of investor sentiment on S&P 500 prices using deep learning models

Purpose This study aims to investigate to what extent the predictability of the standard and poor’s 500 (S&P 500) price levels is enhanced by investors’ sentiments extracted from social media content, specifically platform X. Design/methodology/approach Two recurrent neural network (RNN) models are developed. The first RNN model is merely based on historical records and technical indicators. In addition to the variables included in the first RNN model, the second RNN model comprises the outputs of the sentiment analysis, performed using the TextBlob library. The study was conducted between December 28, 2011, and December 30, 2021, over 10 years, to obtain better results by feeding the RNN models with a significant quantity of data by extending the period and capturing an extensive timespan. Findings Comparing the performance of both models reveals that the second model, with sentiment analysis inputs, yields superior outcomes. The mean absolute error (MAE) of the second model registered 72.44, approximately 50% lower than the MAE of the technical model, its percentage value, the mean absolute percentage error, recorded 2.16%, and finally, the median absolute percentage error reached a value of 1.30%. This underscores the significant influence of digital platforms in influencing the behavior of certain assets like the S&P 500, emphasizing the relevance of sentiment analysis from social media in financial forecasting. Originality/value This study contributes to the growing body of literature by highlighting the enhanced predictive power of deep learning models that incorporate investor sentiment from social media, thereby advancing the application of behavioral finance in financial forecasting.

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics.

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

Derived Amharic alphabet sign language recognition using machine learning methods

Hearing-impaired people use sign language as a means of communication with those with no hearing disability. It is therefore difficult to communicate with hearing impaired people without the expertise of a signer or knowledge of sign language. As a result, technologies that understands sign language are required to bridge the communication gap between those that have hearing impairments and those that dont. Ethiopian Amharic alphabets sign language (EAMASL) is different from other countries sign languages because Amharic Language is spoken in Ethiopia and has a number of complex alphabets. Presently in Ethiopia, just a few studies on AMASL have been conducted. Previous works, on the other hand, only worked on basic and a few derived Amharic alphabet signs. To solve this challenge, in this paper, we propose Machine Learning techniques such as Support Vector Machine (SVM) with Convolutional Neural Network (CNN), Histogram of Oriented Gradients (HOG), and their hybrid features to recognize the remaining derived Amharic alphabet signs. Because CNN is good for rotation and translation of signs, and HOG works well for low quality data under strong illumination variation and a small quantity of training data, the two have been combined for feature extraction. CNN (Softmax) was utilized as a classifier for normalized hybrid features in addition to SVM. SVM model using CNN, HOG, normalized, and non-normalized hybrid feature vectors achieved an accuracy of 89.02%, 95.42%, 97.40%, and 93.61% using 10-fold cross validation, respectively. With the normalized hybrid features, the other classifier, CNN (sofmax), produced a 93.55% accuracy.

Leading-Edge Sentiment Analysis: A Survey of Application Context, Challenges and Advanced Techniques

Background: Data is rapidly expanding in today's digital age. The reason for the expansion of data is due i to social media sites. The Internet produces an enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, etc., concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis. Aim: This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it. Objective: The objective of the current overview is to better understand the market, gauge public opinion, and make strategic decisions. In addition, enterprises, governments, and scholars can all benefit from conducting a sentiment analysis. Methods: In this research, we review and categorize the most widely applied methods of deep learning and machine learning for analyzing sentiment. From the paper, we learn that which sentiment analysis technique is the best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume. Results: Regardless of the fact that the English language has traditionally been the focus of research in this field, other spoken languages have recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases and addressing the problems with Natural language processing that have been stated in the context of sentiment examination. Conclusion: The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays.

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models-Faster R-CNN, YOLO V2, and SSD-using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter's classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter's classification criterion. This criterion characterizes the third molar's position relative to the second molar's longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications.

When guided diffusion model meets zero-shot image super-resolution

Existing deep learning-based single-image super-resolution (SR) methods typically rely on vast quantities of paired data. As an essential solution, zero-shot SR methods require only a single image to handle image-specific degradation. However, these methods still struggle to recover fine-grained details due to the lack of supervised information. In this work, we propose a novel guided Diffusion model for Zero-shot image SR (ZeroDiff) to explicitly direct image quality enhancement. Specifically, we elaborate two key guidance strategies: (1) high-frequency guidance and (2) content-consistent guidance. The former concentrates on boosting fine-grained textures by embedding high-frequency information into the cross-attention mechanism of the noise estimator. The latter avoids the sampling deviating from the original image in terms of structure and low-frequency content. Specifically, the noisy images at each diffusion step are injected into the corresponding sampling step, encouraging the sampled image to be consistent with that of the corresponding diffusion step. Moreover, we design a progressive zoom-in paradigm by gradually enlarging the image size and enriching the image details to boost the sampling efficiency of diffusion models, while enabling high-quality image reconstruction. Extensive experiments reveal that our method achieves comparable results with other state-of-the-art methods in quantitative and qualitative evaluations on both face and natural images from synthetic and real-world datasets.

A Hybrid Feature Selection through Ensemble Rank Aggregation to Improve Perdition and Classification Accuracy

Today, extending credit is a difficult operation since credit databases contain a large quantity of data as well as redundant and useless information. The surplus and unrelated data can lower the categorization and prediction accuracy. In this case, feature selection is crucial for managing massive data. Different rank aggregation (RA) methods are there to ensemble individual feature selection. However not every dataset will perform at its best when using a single RA technique, the combination of multiple RA techniques are needed to increase the perdition accuracy. This study proposes a hybrid rank aggregation model that select features that are significant across different rank aggregation methods like MC4, Kendall tau distance and Borda. This study observed that the performance of ensemble rank aggregation techniques is better than the existing individual rank aggregation methods.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Main challenges on the curation of large scale datasets for pancreas segmentation using deep learning in multi-phase CT scans: Focus on cardinality, manual refinement, and annotation quality

Accurate segmentation of the pancreas in computed tomography (CT) holds paramount importance in diagnostics, surgical planning, and interventions. Recent studies have proposed supervised deep-learning models for segmentation, but their efficacy relies on the quality and quantity of the training data. Most of such works employed small-scale public datasets, without proving the efficacy of generalization to external datasets. This study explored the optimization of pancreas segmentation accuracy by pinpointing the ideal dataset size, understanding resource implications, examining manual refinement impact, and assessing the influence of anatomical subregions. We present the AIMS-1300 dataset encompassing 1,300 CT scans. Its manual annotation by medical experts required 938 h. A 2.5D UNet was implemented to assess the impact of training sample size on segmentation accuracy by partitioning the original AIMS-1300 dataset into 11 smaller subsets of progressively increasing numerosity. The findings revealed that training sets exceeding 440 CTs did not lead to better segmentation performance. In contrast, nnU-Net and UNet with Attention Gate reached a plateau for 585 CTs. Tests on generalization on the publicly available AMOS-CT dataset confirmed this outcome. As the size of the partition of the AIMS-1300 training set increases, the number of error slices decreases, reaching a minimum with 730 and 440 CTs, for AIMS-1300 and AMOS-CT datasets, respectively. Segmentation metrics on the AIMS-1300 and AMOS-CT datasets improved more on the head than the body and tail of the pancreas as the dataset size increased. By carefully considering the task and the characteristics of the available data, researchers can develop deep learning models without sacrificing performance even with limited data. This could accelerate developing and deploying artificial intelligence tools for pancreas surgery and other surgical data science applications.

Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.

"Goldmine" or "big mess"? An interview study on the challenges of designing, operating, and ensuring the durability of Clinical Data Warehouses in France and Belgium.

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research. We conducted semistructured interviews with 21 professionals working with CDWs from France and Belgium. All interviews were recorded, transcribed verbatim, and coded inductively. Prompted by the AI boom, healthcare institutions launched initiatives to repurpose data they were generating for care without a clear vision of how to generate value. Difficulties in operating CDWs arose quickly, strengthened by the multiplicity and diversity of stakeholders involved and grand discourses on the possibilities of CDWs, disjointed from their actual capabilities. Without proper management of the information flows, stakeholders struggled to build a shared vision. This was evident in our interviewees' contrasting appreciations of what mattered most to ensure data quality. Participants explained they struggled to manage knowledge inside and across institutions, generating knowledge loss, repeated mistakes, and impeding progress locally and nationally. Management issues strongly affect the deployment and operation of CDWs. This may stem from a simplistic linear vision of how this type of infrastructure operates. CDWs remain promising for research, and their design, implementation, and operation require careful management if they are to be successful. Building on innovation management, complex systems, and organizational learning knowledge will help.

Bearing fault detection system based on a deep diffusion model

Bearings are crucial components of modern high-precision machinery and rotating machines. Excellent bearing failure detection systems are vital for ensuring that machines operate precisely. Advances in artificial neural networks (ANNs) and increases in computer processing speed have led to the application of many ANN models in various fields, including bearing failure detection, with excellent outcomes being achieved. However, to construct an ANN model that can precisely detect bearing failures, large quantities of data must be collected on various types of bearing failures. Thus, considerable time must be spent in data collection before rotating machines are operated on the production line, which increases costs for manufacturers. To overcome this problem, the present study used a diffusion model for data augmentation to improve the accuracy of an ANN model trained on a small quantity of bearing sound data. This study performed time-delay mapping to preprocess the data and convert them into a two-dimensional time-delay mapping diagram to reduce the dimensionality of the data features, a novel approach in the field of bearing failure detection. Finally, this study used a convolutional neural network model, which exhibited the optimal classification performance for time-delay mapping diagrams, for bearing failure detection. By comparing the results obtained from augmented and raw data, this study confirmed that using a diffusion model to augment data can improve the generalization ability of bearing failure detection models trained on a small quantity of data.

E-waste Management Using Blockchain Technology

Introduction; Bridging the digital divide requires the provision of affordable, fair and quality ICT. With nearly two-thirds of the world’s population still offline, there is a need to provide affordable web access for everyone. For developing countries, increasing the popularity of information and communication technology has become the most important factor in reducing poverty. The danger of electrical and electronic waste disposal contains hazardous substances, but most of the electrical and electronic equipment is still disposed of in an unhealthy environment in the field development area, affecting the level of contamination in Water, Air and Soil ultimately affecting people’s health. Eliminating E-waste responsibility and protecting the environment is a challenge for countries. Smart cities can solve environmental problems through proper waste management for improving human health, protecting water resources, and reducing pollution. Objective; In this paper, we explore how blockchain technology can help smart cities to manage E-waste by providing consistency, immutability, transparency, and accountability control in a distributed, reliable, and secure manner. We discussed the advantages of blockchain technology in various aspects of E-waste management, such as instant tracking and monitoring, E-waste disposal and E-waste management regulation compliance, proper disposal management, E-waste management, and material handling, etc. All examples of disposal services, but in our study we have found that there is no fool proof system to check the disposal of E-waste whether it has been disposed off Fully or Partially. We mainly focused on the tracking of E-waste management system for 100% safe and eco-friendly disposal from the originating point of E-waste to end disposal point of total disposal. Methods; For this, we have used machine-learning model to find the existing percentage of disposal of E-waste at the end-point which reveals that it is never 100%. And partial disposal of E-waste means we have still partial E-waste around us in different forms, which will be a threat for the society to be indulged in hazardous after effects of randomly dumping E-waste.Results; After this we have modelled a Disposal Tracking System(DTS) using blockchain technology to create an E-waste data storage as Decentralized Shareable Ledger (DSL) which records the quantity and state of E-waste data from its originating point to a different level of disposal unit and finally reflect the balance of E-waste data as NIL at the end of last disposal point.Conclusion; This system will helpful for safe and ecofriendly E-waste management and it provides complete transparency and traceability of E-waste during the life cycle of complete disposal. After implementation of this system any district or Block or Village authority can ensure to its citizens for E-waste hazards free environment and safety of natural resources

Globus service enhancements for exascale applications and facilities

Many extreme-scale applications require the movement of large quantities of data to, from, and among leadership computing facilities, as well as other scientific facilities and the home institutions of facility users. These applications, particularly when leadership computing facilities are involved, can touch upon edge cases (e.g., terabyte files) that had not been a focus of previous Globus optimization work, which had emphasized rather the movement of many smaller (megabyte to gigabyte) files. We report here on how automated client-driven chunking can be used to accelerate both the movement of large files and the integrity checking operations that have proven to be essential for large data transfers. We present detailed performance studies that provide insights into the benefits of these modifications in a range of file transfer scenarios.

Reducing Training Data Using Pre-Trained Foundation Models: A Case Study on Traffic Sign Segmentation Using the Segment Anything Model.

The utilization of robust, pre-trained foundation models enables simple adaptation to specific ongoing tasks. In particular, the recently developed Segment Anything Model (SAM) has demonstrated impressive results in the context of semantic segmentation. Recognizing that data collection is generally time-consuming and costly, this research aims to determine whether the use of these foundation models can reduce the need for training data. To assess the models' behavior under conditions of reduced training data, five test datasets for semantic segmentation will be utilized. This study will concentrate on traffic sign segmentation to analyze the results in comparison to Mask R-CNN: the field's leading model. The findings indicate that SAM does not surpass the leading model for this specific task, regardless of the quantity of training data. Nevertheless, a knowledge-distilled student architecture derived from SAM exhibits no reduction in accuracy when trained on data that have been reduced by 95%.

A Combined CNN Architecture for Speech Emotion Recognition.

Emotion recognition through speech is a technique employed in various scenarios of Human-Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%.

A generative adversarial learning strategy for spatial inspection of compaction quality

Reliable prediction methods play a crucial role in enhancing the compaction quality and implementing the intelligent compaction (IC). The predictive performance of contemporary data-driven models is significantly influenced by the quantity and quality of training data. The scarcity and high dimensionality of field compaction data may lead to overfitting issues in prediction models. In this study, an innovative methodology, spatial trajectory condition generative adversarial networks (STC-GAN), is proposed to synthesize IC datasets by considering the construction procedures. The dataset expansion is driven both by the non-attentional algorithm and the engineering practice. The augmented dataset is used to train the extreme gradient boosting (XGBoost) for estimating the compaction quality. The model accuracy is consistently enhanced by adopting the STC-GAN augmented datasets. The optimal model is achieved after a 2.5-fold expansion by performing the sensitivity study. The proposed STC-GAN-PSO-XGBoost is a physical-based intelligent model, enhancing the reliability of data augmentation and the compaction evaluation model. This method is beneficial to minimize the amount of field tests, enabling the continuous evaluation of compaction quality for IC.

A spectroscopic analysis code for spatially resolved x-ray absorption data from the COAX platform.

Sophisticated tools such as computer vision techniques in combination with 1D lineout type analyses have been used in automating the analysis of spectral data for high energy density (HED) plasmas. Standardized automation can solve the problems posed by the complexity of HED spectra and the quantity of data. We present a spectroscopic code written for automated and streamlined analysis of spatially resolved x-ray absorption data from the COAX platform on Omega-60. COAX uses radiographs and spectroscopic diagnostics to provide shock position and density information. We also obtain the more novel spectral-derived spatial profile of the supersonic radiation flow into a low-density foam. Considerable effort has been spent modernizing our previous spectroscopic analysis method, including the development of new tools characterized by a faster runtime and minimal user input to reduce bias and a testing suite for verifying the accuracy of the various functions within the code. The new code analyzes our spectroscopic images in 1-2min, with added uncertainty and confidence.

Delineating aquitard characteristics within a Silurian dolostone aquifer using high-density hydraulic head and fracture datasets

Fractured aquifers are heterogeneous due to the variable frequency, orientation, and intersections of rock discontinuities. A ~100-m-thick Silurian dolostone sequence provides a bedrock aquifer supplying the city of Guelph, Canada. Here, fracture network characteristics and associated influences on hydraulic head were examined using several data types obtained from 24 cored holes in a study that is novel for the quantity and quality of data. High (50–90°) angle joint orientations, heights, and terminations relative to bedding features were determined from acoustic televiewer logs and outcrop scanlines. These data were compared to high-resolution hydraulic head profiles showing head loss over depth-discrete intervals identifying zones with lower vertical hydraulic conductivity. This study reveals that the marl-rich Vinemount Member, traditionally considered the principal aquitard, corresponds to head loss in only 62% of the 24 boreholes. The vertical position of head loss varies across the 90-km2 study area and occurs in any of the lithostratigraphic units of the Lockport Group. Within this sedimentary sequence, aquitards are laterally discontinuous or “patchy” at variable depths and relate to: (1) the frequency of the high-angle joints; (2) shorter joint height; and (3) the type of joint terminations. The head loss occurs in thin (2–2.5 m) intervals where the frequency of the high-angle joints is low. Where a large proportion of small joints cross-cut marl bedding planes, head loss is negligible, suggesting that the vertical hydraulic conductivity is not reduced. Overall, these findings are potentially applicable to assessing aquitard and cap rock integrity in carbonate sedimentary sequences worldwide.