Data Collection Capabilities Research Articles

High-performance optical beam steering with nanophotonics.

The ability to control and steer optical beams is critical for emerging technologies. Among these are light detection and ranging (LiDAR), laser display, free space communication, and single pixel imaging. Improvements in these areas promise enhanced 3D data collection capabilities, orders of magnitude increase in wireless data rate, less expensive cameras, and ever more immersive virtual/augmented reality (VR/AR) consumer electronics. Bulk mechanical or liquid crystal devices are conventionally utilized platforms that achieve optical beam steering, but they are bulky and limited in speed and reliability. Instead, chip-scale photonic platforms offer faster and more elegant mechanisms to manipulate light, capable of minimizing device size, weight, and power. Additionally, a critical device metric is its far field resolution, which influences fine feature detection in imaging applications, laser display quality, and signal power and fidelity of free space communication links. Strong light matter interaction achieved with nanophotonic approaches generally makes devices smaller and more efficient, yet ultimately these effects must be scaled to suitable aperture sizes to maintain good resolution. Recent years have seen rapid development in these performance characteristics, spurred by research on active metasurfaces, slow light waveguides, and waveguide phased arrays, with different architectures encountering unique tradeoffs between device complexity, resolution, and speed, in attempting to achieve groundbreaking values for all three. We review these diverse emerging nanophotonic approaches that aspire to achieve high-performance optical beam steering.

Machine Learning in Structural Design: An Opinionated Review

The prominence gained by Artificial Intelligence (AI) over all aspects of human activity today cannot be overstated. This technology is no newcomer to structural engineering, with logic-based AI systems used to carry out design explorations as early as the 1980s. Nevertheless, the advent of low-cost data collection and processing capabilities have granted new impetus and a degree of ubiquity to AI-based engineering solutions. This review paper ends by posing the question of how long will the human engineer be needed in structural design. However, the paper does not aim to answer this question, not least because all such predictions have a history of going wrong. Instead, the paper assumes throughout as valid the claim that the need for human engineers in conventional design practice has its days numbered. In order to build the case towards the final question, the paper starts with a general description of the currently available AI frameworks and their Machine Learning (ML) sub-classes. The paper then proceeds to review a selected number of studies on the application of AI in structural engineering design. A discussion of specific challenges and future needs is presented with emphasis on the much exalted roles of “engineering intuition” and “creativity”. Finally, the conclusion section of the paper compiles the findings and outlines the challenges and future research directions.

The Past, Present, and Future of Deep-Tissue (Pressure) Injury.

The Past, Present, and Future of Deep-Tissue (Pressure) Injury.

Enriching Roadside Safety Assessments Using LiDAR Technology: Disaggregate Collision-Level Data Fusion and Analysis

Fatalities and serious injuries still represent a significant portion of run-off-the-road (ROR) collisions on highways in North America. In order to address this issue and design safer and more forgiving roadside areas, more empirical evidence is required to understand the association between roadside elements and safety. The inability to gather that evidence has been attributed in many cases to limitations in data collection and data fusion capabilities. To help overcome such issues, this paper proposes using LiDAR datasets to extract the information required to analyze factors contributing to the severity of ROR collisions on a localized collision level. Specifically, the paper proposes a new method for extracting pole-like objects and tree canopies. Information about other roadside assets, including signposts, alignment attributes, and side slopes is also extracted from the LiDAR scans in a fully automated manner. The extracted information is then attached to individual collisions to perform a localized assessment. Logistic regression is then used to explore links between the extracted features and the severity of fixed-object collisions. The analysis is conducted on 80 km of roads from 10 different highways in Alberta, Canada. The results show that roadside attributes vary significantly for the different collisions along the 80 km analyzed, indicating the importance of utilizing LiDAR to extract such features on a disaggregate collision level. The regression results show that the steepness of side slopes and the offset of roadside objects had the most significant impacts on the severity of fixed-object collisions.

A review of BIM and Facility Management Systems

The article presents the Building Information Modelling (BIM) and their data collection capabilities. We introduce what parameters can be measured. We compare the control card, remote I/O, PLC, DCS and SCADA control and process control systems to see which is worth using in a building information system. The research examines the measurement system’s ability to pre-process data that will help relieve the burden on the expert system. Currently available BIM systems can be optimized and developed using various mathematical models. The use of process control systems can perform not only measurement and data processing, but also logging and alarm management processes.

In Situ Transmission Electron Microscopy: Signal processing challenges and examples

Transmission electron microscopy (TEM) is a powerful tool for imaging material structure and characterizing material chemistry. Recent advances in data collection technology for TEM have enabled high-volume and high-resolution data collection at a microsecond frame rate. Taking advantage of these advances in data collection rates requires the development and application of data processing tools, including image analysis, feature extraction, and streaming data processing techniques. In this article, we highlight a few areas in materials science that have benefited from combining signal processing and statistical analysis with data collection capabilities in TEM and present a future outlook on opportunities of integrating signal processing with automated TEM data analysis.

Large-Scale Assessment of Mobile Crowdsensed Data: A Case Study

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices’ ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as PPE). The present work performs statistical modelling of PPE to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on PPE. Several road properties affecting PPE as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of PPE is evaluated, incorporating previous results on the relationship between vehicle speed and PPE. These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public.

Progress in Materials Data Availability and Application: A review

Materials researchers are generating data in ever-increasing volumes, and these data are, in turn, fueling the discovery and design of new materials. These changes are enabled by advances in data capture via laboratory information management systems (LIMS), high-throughput experiment and computation, improved data collection capabilities, automated experiment selection and execution, and new analytical methods based on artificial intelligence (AI) and machine learning (ML) approaches. Data infrastructure is being developed with increasing numbers of repositories as well as data-sharing and standardization efforts.

Abstract PO-046: Building the country's first gynecologic cancer disparity SPORE: A labor of love

Abstract The NCI P20 SPORE aims to support the feasibility, development and planning activities to build cancer research programs with a dedicated translational focus. The Northwestern University Cancer Health Equity Research SPORE (NU-CHERS) is entering its second year of collaboration with the Lurie Cancer Center and John H. Stroger, Jr. Hospital of Cook County (SHCC). NU-CHERS is dedicated to conducting translational research on gynecologic cancer disparities experienced by women of color in Chicago. This presentation will report the challenges and successes of establishing the nation's first gynecologic cancer disparity SPORE, focused on understanding the clinical disparities experienced by women in Chicago, to improve health outcomes and build trusting reciprocal relationships with the community. Success of NU-CHERS hinges on local partnerships to achieve the elimination of gynecologic cancer disparities. The Lurie Cancer Center has thirty years of experience in developing and implementing cancer research programs and infrastructure. SHCC is a publicly funded hospital that provides access to healthcare to patients, including the uninsured, through a sliding scale based on income or inability to afford insurance deductibles. SHCC also serves a diverse patient population, making it an ideal community partner to facilitate a deeper understanding of gynecologic cancer disparities in the region and develop a richly diverse specimen biobank. NU-CHERS leverages these partnerships to advance research specific to underrepresented groups most impacted by gynecologic cancer disparities that will ultimately establish equitable access to precision medicine oncologic treatments. Through partnerships and infrastructure development, our team has identified lessons learned, opportunities for improvement and challenges requiring even more courage to achieve mandated milestones. For example, for the crucial step of building a robust biobank of annotated specimens, NU-CHERS supported SHCC in adapting protocols to meet local site constraints, as well as aligning data collection capabilities within clinical constraints versus research interests. Other important program achievements fall under the categories of fostering collaborations with communities through partnership with trusted healthcare organizations to facilitate education and reciprocity; best practices for establishing a multi-site research initiative with a Single Institutional Review Board (sIRB); and advancing the work of research projects during a pandemic. Sharing these findings advances the work of eliminating cancer disparities by providing a blueprint for researchers to improve upon for their collaborative cancer disparities programs. NU-CHERS is committed to establishing strong local partnerships and infrastructure in the community and thus legitimizing translational research to leverage basic science discoveries into clinical practice improvements through all the tools afforded by basic and population science researchers. Citation Format: Denisha R. Brown, Laura Tom, Magdalena Nava, Catherine A. O'Brian, Ivy Leung, Rabih Dahdouh, Edgardo Ramirez, Araceli Estrada, Sankirtana Danner, Cassandra Osei, Will Dunne, Sharon Post, Nihmot Adebayo, Ann Yau, Terri Fraterrigo, Melissa A Simon. Building the country's first gynecologic cancer disparity SPORE: A labor of love [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-046.

Detailed Analysis of the Effects of Spacer Grid and Mixing Vanes on Turbulence in a PWR Subchannel Under DFFB Conditions Based on DNS Data

Spacer grids and mixing vanes exhibit a significant role in the thermal hydraulics of pressurized water reactors (PWRs), especially in the post loss-of-coolant accident regimes. A detailed analysis of the contrasting upstream and downstream turbulent flow features is of great importance to both system codes and computational fluid dynamics (CFD)–Reynolds-averaged Navier–Stokes (RANS) modeling. Further, with the advent of supercomputing resources and machine learning research, a data-driven approach to turbulence modeling is gaining popularity. However, owing to the complexities associated with large-scale, high-fidelity data collection capabilities, the application of machine learning–based turbulence models has been limited to simple geometries. In this work, using a highly scalable CFD code PHASTA, we have collected data from direct numerical simulations of a PWR subchannel with high spatial and temporal resolution. From the collected data we extract key turbulent flow features, including mean velocities and Reynolds stresses that highlight the effects of spacer grids and mixing vanes on downstream turbulence in a typical PWR subchannel. An invariant analysis of the anisotropic stress tensor is also presented, which further elucidates their effect on the nature of turbulence in the immediate upstream and downstream vicinity. The high-resolution data from the simulations are archived and intended for the development of data-driven RANS closure models that are capable of capturing the evolution of anisotropy in PWR subchannels.

Lightweight-blockchain based privacy-preserving data aggregation for epidemic disease surveillance

As the COVID-19 pandemic is raging worldwide, epidemic surveillance helps prevent the spread of the epidemic. Protecting the privacy of patients and data providers in the monitoring system can relieve them of their concerns about the leakage of private information, thereby improving the epidemic data collection capabilities of the system. In this article, we propose a lightweight-blockchain based privacy-preserving data aggregation scheme (LBPDA) for epidemic disease surveillance, which can aggregate data without relying on a trusted third party. Especially, to protect data privacy in the data aggregation process, the case count data is aggregated based on the Paillier cryptosystem's homomorphism. Besides, to reduce the time and storage overhead, we improved the adopted Hyperledger Fabric, thus lightening the data aggregation process. Finally, we simulated the proposed scheme and analyzed its security and performance to verify its feasibility and effectiveness. The results showed that the proposed scheme could meet the government department's requirements to aggregate patient case count data for epidemic disease surveillance while protecting the privacy of patients and data providers. Moreover, we also proved that the lightweight of blockchain is effective by comparison.

A Study on the Laws Governing Facial Recognition Technology and Data Privacy in Malaysia

The advancement of technology in the past decade has led humans to achieve many great things. Among that is facial recognition technology that uses a combination of two techniques which is face detection and recognition that is capable of converting facial images of a person into readable data and connecting it with other data sets which enable it to identify, track or compare it. This study delves into the usage of facial recognition technology in Malaysia where its regulation is almost non-existent. As its usage increases, the invasive features of this technology to collect and connect its data posed a threat to the data privacy of Malaysian citizens. Due to this issue, other countries' laws and policies regarding this technology are examined and compared with Malaysia. This enables the loopholes of the current law and policies to be identified and restructured, which create a clear path on the proper regulations and changes that need to be made. Thus, this study aims to analyse the limitation of law governing data privacy and its concept in Malaysia along with changes that need to be made. This study’s finding shows the shortcoming of Malaysia’s law in governing data privacy especially when it involves complex technology that has great data collection capability like facial recognition.

Highly stretchable and sensitive strain sensor based on liquid metal composite for wearable sign language communication device

In recent years, the demand for wearable strain sensors is growing due to their motion monitoring and clinical data collection capabilities. The sensors with positive piozeconductivity, i.e. their conductivity increases when stretched, are more favored in wearable electronics because they can maintain high conductivity under stretching. However, reported composites with positive piezoconductivity mostly show low sensitivity. Furthermore, few wearable electronic systems based on strain sensors are built completely with data processing capabilities. Herein, we present a wearable sensor based on a positive piezoconductive composite composed of liquid metal microdroplets, metal microparticles, and elastic matrix. The resistance of the composite is extremely high at the relaxed state but drops drastically under any deformation. It has high stretchability (fracture strain >200%) and sensitivity (gauge factor ∼100), which shows excellent performance in detecting human motions such as joint bending. Based on this composite, we demonstrate a wearable and portable sign language communication device with a fully programmable and configurable system to help people with language disabilities communicate effectively with others from the visual and auditory perspectives. By bending the fingers, the device can type words or sentences on screen and speak relevant letters through the audio.

Commentary: the Past, Present, and Future of the Global Raptor Impact Network

ABSTRACT Most raptor populations are declining and nearly a fifth are threatened with extinction; thus there is a need to increase collaboration to ensure efficient and effective research, management, and conservation. Here, we introduce the Global Raptor Impact Network (GRIN; www.globalraptors.org), a tool to enhance collaboration and conservation impact of the raptor research community. We provide an overview of the history and current state of GRIN, including plans for expansion. Predecessors to GRIN include The African Raptor DataBank, which was launched in 2012 to ascertain the conservation status of raptors across Africa; and the Global Raptor Information Network, which was launched in the late 1990s as a website to provide information regarding diurnal raptors and facilitate communication among researchers. GRIN expands the data collection and storage capabilities of the African Raptor DataBank to a global scale via mobile application. We have implemented data-sharing rules to ensure the safety of sensitive species, and users of the GRIN mobile app can designate their records as confidential. GRIN staff and partners are developing analyses of species' population trends and geographic distributions to aid in conservation assessments. GRIN is also developing systematic reviews, detailed bibliographies, and online accounts that will summarize the state of knowledge for each raptor species. We hope that GRIN will benefit the entire raptor research community and aid in the collaboration necessary to help raptor populations thrive in the Anthropocene.

The Application of Imaging Flow Cytometry for Characterisation and Quantification of Bacterial Phenotypes.

Bacteria modify their morphology in response to various factors including growth stage, nutrient availability, predation, motility and long-term survival strategies. Morphological changes may also be associated with specific physiological phenotypes such as the formation of dormant or persister cells in a “viable but non-culturable” (VBNC) state which frequently display different shapes and size compared to their active counterparts. Such dormancy phenotypes can display various degrees of tolerance to antibiotics and therefore a detailed understanding of these phenotypes is crucial for combatting chronic infections and associated diseases. Cell shape and size are therefore more than simple phenotypic characteristics; they are important physiological properties for understanding bacterial life-strategies and pathologies. However, quantitative studies on the changes to cell morphologies during bacterial growth, persister cell formation and the VBNC state are few and severely constrained by current limitations in the most used investigative techniques of flow cytometry (FC) and light or electron microscopy. In this study, we applied high-throughput Imaging Flow Cytometry (IFC) to characterise and quantify, at single-cell level and over time, the phenotypic heterogeneity and morphological changes in cultured populations of four bacterial species, Bacillus subtilis, Lactiplantibacillus plantarum, Pediococcus acidilactici and Escherichia coli. Morphologies in relation to growth stage and stress responses, cell integrity and metabolic activity were analysed. Additionally, we were able to identify and morphologically classify dormant cell phenotypes such as VBNC cells and monitor the resuscitation of persister cells in Escherichia coli following antibiotic treatment. We therefore demonstrate that IFC, with its high-throughput data collection and image capture capabilities, provides a platform by which a detailed understanding of changes in bacterial phenotypes and their physiological implications may be accurately monitored and quantified, leading to a better understanding of the role of phenotypic heterogeneity in the dynamic microbiome.

Hierarchical Federated Learning for Hybrid Data Partitioning Across Multitype Sensors

Emerging hardware technology enables the utilization of a large number of multitype sensors with diverse sensing capabilities for data collection and the training of AI models. Each sensor collects partial data samples on a type-specific feature space, which results in hybrid data partitioning across the local datasets and brings challenges to developing a novel communication-efficient and scalable training algorithm. We propose a hierarchical federated learning framework for such hybrid data partitioning with a multitier-partitioned neural network architecture. Specifically, we adopt a primal-dual transform to decompose the training problem on both the sample and feature space. Then, a stochastic coordinate gradient descent ascent algorithm is implemented with intratype and intertype over-the-air aggregation for the update of the primal variables and dual variables, respectively. The incorporation of over-the-air aggregation for signal transmission naturally harnesses the channel perturbations and interference for lower communication complexity and preserved privacy. Despite the influence of transmission noise and channel distortion, convergence analysis is provided for general objective functions, which illustrates the robust training performance of the proposed algorithm with a theoretical guarantee.

A Prescriptive Machine-Learning Framework to the Price-Setting Newsvendor Problem

The approach to data-driven optimization described in this paper was developed when the authors were part of an IBM project team working with the U.S. Department of Energy, Pacific National Laboratory, and various energy utility partners on an initiative to develop a smart energy distribution infrastructure. Within this broader scope and based on the data collected in some initial controlled experiments, the paper specifically addresses the design and optimization of real-time price incentives to consumers to manage their electricity demand and determine the energy capacity to be provisioned by the utility. This latter problem fits into the well-known price-setting newsvendor problem framework, and our goal was to replace the simplistic methods in the literature by more realistic data-driven methods to take into account the data-collection capabilities and the modeling complexity of real-world applications. Our aspirations for the paper are (1) to introduce data-driven, distribution-free approaches to decision-making problems and (2) to motivate scalable conditional value-at-risk regression-based approaches for these problems.

Co-creating and evaluating a citizen science program for monitoring submerged aquatic vegetation in Chesapeake Bay

Research on submerged aquatic vegetation (SAV) directly informs policy and management decisions in Chesapeake Bay; however, limitations to data collection capabilities of professional scientists have left knowledge gaps that hinder scientific research and management efforts. Experts have called upon volunteers to help collect the local-scale data that are necessary for understanding SAV growth and species distribution within one of the world’s largest estuaries. We collaborated with various environmental stakeholders, including SAV experts, volunteer monitoring coordinators, and citizen scientists, to design a multi-tiered monitoring protocol and training program for citizen scientists monitoring SAV in Chesapeake Bay. We employed a sequence of research approaches, which included online surveys, literature review, a pilot study, participant observation with volunteers in the field, and a protocol-testing workshop. From these approaches, we were able to better understand the stakeholders’ various needs and requirements for the Chesapeake Bay SAV Watchers program. Evaluative interviews helped us collect feedback on the process of developing the program, as well as on the program itself. We learned that the collaborative process of co-creating the monitoring program led to positive outcomes for the stakeholders involved, as well as for the perceived scientific rigor of the program. Furthermore, our use of science communication and user-centered design increased the program’s functionality and credibility. Finally, our interview results suggested that continuing to incorporate participants’ motivations into the program’s management will improve volunteer retention and therefore increase the program’s impact and longevity. This research will inform the creation of other collaboratively-developed participatory research programs, as well as citizen science programs that simultaneously prioritize engagement, education, and collection of high-quality data.

Statistical Estimation Framework for State Awareness in Microgrids Based on IoT Data Streams

This paper presents an event-triggered statistical estimation strategy and a data collection architecture for situational awareness (SA) in microgrids. An estimation agent structure based on the event-triggered Kalman filter is proposed and implemented for state estimation layer of the SA using long range wide area network (LoRAWAN) protocol. A setup has been developed which provides enormous data collection capabilities from smart meters in order to realize an adequate level of SA in microgrids. Thingsboard Internet of things (IoT) platform is used for the SA visualization with a customized dashboard. It is shown that by using the developed estimation strategy, an adequate level of SA can be achieved with a minimum installation and communication cost to have an accurate average state estimation of the microgrid.

Analysis of postoperative strokes in patients after resection or suturing of the left atrial appendage

Abstract Funding Acknowledgements Type of funding sources: None. Introduction In 30%, the cause of strokes is AF. 90% of cases, thromboembolism occurs in the cerebral arteries, condemning the patient not only to disability, but also to death. Considering that the formation of blood clots occurs in hemodynamically inactive areas of the LA, which is the apendege of the LA, it is advisable to combine surgical treatment of AF with its exclusion from systemic blood flow. Purpose Analyze the frequency of strokes in the follow up period in patients who have undergone surgical treatment of AF in combination with LAA resection. Materials and Methods 364 patients were divided into 3 groups depending on the volume of operative treatment and the rhythm of the controlling strategy, 105 dropped out of the study due to the lack of data collection capability. • Group I included 80 patients who underwent surgical AF treatment with LAA resection combined with valvular pathology correction or myocardial revascularization. Lethal outcome in this group was recorded in 6 (7.5%) patients caused by non-cardiac pathology. Surgical treatment of AF was performed by MAZE IV and left side MAZE. • Group II - 140 people who performed valve pathology correction, without surgical treatment of AF and without LAA resection. • Group III included 39 patients with a persistent form of AF who performed only LAA resection in combination with correction with another cardiac pathology. Group II and III mortality were 23 patients. All patients in group I and III underwent LAA resection to exclude a possible source of blood clots. The stump was sutured with a continuous two-row suture on the base up to 2 mm from the LA wall. After 36 months, patients from group I underwent echocardiography, electrocardiography, Holter monitoring, and a cardiologist examination. Group II and III analyses were performed by patient telemetry. Results • Long-term three-year results showed complete 100% freedom from acute cerebral circulation disorder in group I, where surgical treatment of AF was performed in combination with LAA resection; • In group I with a persistent form of AF without LAA resection - acute cerebral circulation disorder occurred in 36 patients, which was 28.8%; • In group III with permanent fibrillation with LAA resection, of the 31 patients, thromboembolic complication occurred in 4 patients (12.9%). It is worth noting that after surgery in the distant period, patients of group I increased the average value of the ejection fraction by 4%, decreased volumes of the LA, and the functional class on NYHA changed from III to II, I. Conclusion The study performed the highest degree of absence of stroke in group I in early and follow up period. First of all, this is due to a comprehensive approach to the treatment of cardiac pathology, the preservation of sinus rhythm and LAA resection. Which, in addition to absence of stroke, leads to an improvement in the quality of life and a decrease in the functional class of heart failure.