Development Of Database Research Articles

Artificial Intelligence-Powered Mammography: Navigating the Landscape of Deep Learning for Breast Cancer Detection.

Worldwide, breast cancer (BC) is one of the most commonly diagnosed malignancies in women. Early detection is key to improving survival rates and health outcomes. This literature review focuses on how artificial intelligence (AI), especially deep learning (DL), can enhance the ability of mammography, a key tool in BC detection, to yield more accurate results. Artificial intelligence has shown promise in reducing diagnostic errors and increasing early cancer detection chances. Nevertheless, significant challenges exist, including the requirement for large amounts of high-quality data and concerns over data privacy. Despite these hurdles, AI and DL are advancing the field of radiology, offering better ways to diagnose, detect, and treat diseases. The U.S. Food and Drug Administration (FDA) has approved several AI diagnostic tools. Yet, the full potential of these technologies, especially for more advanced screening methods like digital breast tomosynthesis (DBT), depends on further clinical studies and the development of larger databases. In summary, this review highlights the exciting potential of AI in BC screening. It calls for more research and validation to fully employ the power of AI in clinical practice, ensuring that these technologies can help save lives by improving diagnosis accuracy and efficiency.

The national distribution of lymphatic filariasis cases in Malawi using patient mapping and geostatistical modelling.

In 2020 the World Health Organization (WHO) declared that Malawi had successfully eliminated lymphatic filariasis (LF) as a public health problem. Understanding clinical case distributions at a national and sub-national level is important, so essential care packages can be provided to individuals living with LF symptoms. This study aimed to develop a national database and map of LF clinical cases across Malawi using geostatistical modelling approaches, programme-identified clinical cases, antigenaemia prevalence and climate information. LF clinical cases identified through programme house-to-house surveys across 90 sub-district administrative boundaries (Traditional Authority (TA)) and antigenaemia prevalence from 57 sampled villages in Malawi were used in a two-step geostatistical modelling process to predict LF clinical cases across all TAs of the country. First, we modelled antigenaemia prevalence in relation to climate covariates to predict nationwide antigenaemia prevalence. Second, we modelled clinical cases for unmapped TAs based on our antigenaemia prevalence spatial estimates. The models estimated 20,938 (95% CrI 18,091 to 24,071) clinical cases in unmapped TAs (70.3%) in addition to the 8,856 (29.7%), programme-identified cases in mapped TAs. In total, the overall national number of LF clinical cases was estimated to be 29,794 (95% CrI 26,957 to 32,927). The antigenaemia prevalence and clinical case mapping and modelling found the highest burden of disease in Chikwawa and Nsanje districts in the Southern Region and Karonga district in the Northern Region of the country. The models presented in this study have facilitated the development of the first national LF clinical case database and map in Malawi, the first endemic country in sub-Saharan Africa. It highlights the value of using existing LF antigenaemia prevalence and clinical case data together with modelling approaches to produce estimates that may be used for the WHO dossier requirements, to help target limited resources and implement long-term health strategies.

Development of a Reverse-Yield Factor Database Disaggregating Japanese Composite Foods into Raw Primary Commodity Ingredients Based on the Standard Tables of Food Composition in Japan.

The reverse-yield factor (RF) database was developed for qualitatively and quantitatively disaggregating Japanese composite foods into raw primary commodity (RPC) ingredients. Representative equations for four types (dried, salted, fermented and mixed foods) were developed to calculate RFs using the food content and composition data for composite foods listed in the Standard Tables of Food Composition in Japan-2020-(STFCJ), published by the Ministry of Education, Culture, Sports, Science and Technology of Japan. Out of 1150 composite foods identified in the STFCJ, RFs for 54 dried, 41 salted, 40 fermented and 818 mixed foods were obtained. RFs for 197 mixed foods could not be calculated because these foods were produced from ingredients with no specified information and/or through complex processing. The content and composition of Japanese composite foods would be interpreted representatively by RFs in the developed database.

Automated license plate authentication framework using multi-view vehicle images

The current framework for detecting Fake License Plates (FLP) in real-time is not robust enough for patrol teams. The objective of this paper is to develop a robust license plate authentication framework, based on the Vehicle Make and Model Recognition (VMMR) and the License Plate Recognition (LPR) algorithms that is implementable at the edge devices. The contributions of this paper are (i) Development of license plate database for 547 Indian cars, (ii) Development of an image dataset with 3173 images of 547 Indian cars in 8 classes, (iii) Development of an ensemble model to recognize vehicle make and model from frontal, rear, and side images, and (iv) Development of a framework to authenticate the license plates with frontal, rear, and side images. The proposed ensemble model is compared with the state-of-the-art networks from the literature. Among the implemented networks for VMMR, the Ensembling model with a size of 303.2 MB achieves the best accuracy of 89% . Due to the limited memory size, Easy OCR is chosen to recognize license plate. The total size of the authentication framework is 308 MB. The performance of the proposed framework is compared with the literature. According to the results, the proposed framework enhances FLP recognition due to the recognition of vehicles from side images. The dataset is made public at https://www.kaggle.com/ganeshmailecture/datasets.

Design and Selection of High Entropy Alloys for Hardmetal Matrix Applications Using a Coupled Machine Learning and Calculation of Phase Diagrams Methodology

This study aims to utilize a combined machine learning (ML) and CALculation of PHAse Diagrams (CALPHAD) methodology to design hardmetal matrix phases for metal‐forming applications that can serve as the basis for carbide reinforcement. The vast compositional space that high entropy alloys (HEAs) occupy offers a promising avenue to satisfy the application design criteria of wear resistance and ductility. To efficiently explore this space, random forest ML models are constructed and trained from publicly available experimental HEA databases to make phase constitution and hardness predictions. Interrogation of the ML models constructed reveals accuracies >78.7% and a mean absolute error of 66.1 HV for phase and hardness predictions respectively. Six promising alloy compositions, extracted from the ML predictions and CALPHAD calculations, are experimentally fabricated and tested. The hardness predictions are found to be systematically under‐ and overpredicted depending on the alloy microstructure. In parallel, the phase classification models are found to lack sensitivity toward additional intermetallic phase formation. Despite the discrepancies identified between ML and experimental results, the fabricated compositions show promise for further experimental evaluation. These discrepancies are believed to be directly associated with the available databases but, importantly, have highlighted several avenues for both ML and database development.

Festivals and Youth: An Educational Cultural Route to Festivals

Festivals have emerged as a compelling subject of study within the realm of the social sciences, with disciplines like popular musicology showcasing festivals as a prominent area of investigation. These cultural events have demonstrated a profound and enduring impact on various generations, solidifying their significance in contemporary history. In the ever-evolving landscape of rapid social change, festivals have evolved into both traditions and heritage. Our research centered on the musical landscape in the city of Cáceres has culminated in the creation of a Festival Route rooted in the pertinent facets of its history and unique sociocultural context. The methodology employed in this study revolves around the development of comprehensive databases that encompass various parameters, principles, and elements. These databases are instrumental in crafting adaptable musical itineraries tailored to the individual participant’s characteristics, including age, educational background, place of origin, and explicit or intrinsic interests. Moreover, they factor into the duration of the route. These databases draw upon the findings of historiographic and ethnographic research, shedding light on the music performed and cherished within the city of Cáceres throughout its rich history. This paper primarily delves into the educational aspects derived from the socio-cultural phenomenon of festivals, specifically in the realms of music pedagogy and value-based education. Our Festival Route is designed with a particular focus on youth engagement, weaving together key locales within the city to create an enriching and educational experience.

Viscosities and Densities of Binary and Ternary Mixtures of Aliphatic and Polyaromatic Hydrocarbons: Pyrene +1-Methylnaphthalene + Dodecane at T = (293.15 to 343.15) K. Experiment and Modeling.

This work presents new experimental viscosity and density data for aromatic and polyaromatic compounds in binary and ternary pyrene, 1-methylnaphthalene, and dodecane mixtures. The lack of experimental viscosity data for these mixtures requires the development of a new database, which is vital for understanding the behavior of mixtures in more complex systems, such as asphaltenes and fuels. The mixtures proposed in this work have been measured over a temperature range of (293.15 to 343.15) K at atmospheric pressure. Several mixture compositions have been studied at these conditions: 1.0, 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0% pyrene mass fraction. The concentration of pyrene correlates with an increase in the viscosity and density values. At the lowest temperature in binary mixtures, the corresponding values reach 4.4217 mPa·s for viscosity and 1.0447 × 103 kg·m-3 for density, respectively. In ternary mixtures, the introduction of dodecane leads to the lowest maximum values of 3.5555 mPa·s for viscosity and 1.0112 × 103 kg·m-3 for density at the same temperature. The experimental data have been employed for the specific modification of viscosity models. These modifications could facilitate the prediction of the viscosity of mixtures that are more complex than those presented in this work. Various viscosity models have been employed, such as Linear, Ratcliff and Khan, modified UNIFAC-Visco, and Krieger-Dougherty. The settings in the models used reliably reproduce the experiment reliably. However, the Ratcliff model agrees excellently with the experiment, having a low standard deviation (2.0%) compared to other models. Furthermore, a model based on the equation of state of Guo is proposed to predict the viscosity values by modifying the specific parameters and adjusting them to the mixtures proposed in this work. The results from this study are compared to previous work, where pyrene, toluene, and heptane mixtures were analyzed. In this case, we find that the decrease of aggregation grade in the present systems is predicted by the model fixed in this work.

AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks

The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system’s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions.

Chemical analysis of polymers used for 3D printing of firearms

3D-printed firearms cause challenges in criminal investigations and forensic analysis because they are difficult to trace. Indeed, in addition to being "ghost guns", they may not produce all the conventional ballistic traces normally used for firearm identification. However, 3D-printed firearms produce other very specific traces, such as polymer traces which come from the polymers used to print the firearm. To date, only a few studies have focused on the analysis of polymer traces. This study therefore aims to characterize polymer traces from 3D-printed firearms, using non-destructive spectroscopic techniques readily available in most forensic laboratories (i.e., FTIR and Raman) and evaluate the potential for association of polymer specimens or traces with their source. To do so, the study was divided into four parts. First, the population study conducted among 3D printing companies and individuals practicing 3D printing has revealed that PLA and PLA+ are the most widely used polymer types in Quebec, Canada. Second, FTIR and Raman spectroscopic analysis of polymer samples collected during the population study has allowed the development of a reference polymer spectral database. The analysis and interpretation of these spectra revealed that polymer filaments present very low intravariability, but very high intervariability, due in part to the different polymer types and the pigments used to color them. The use of chemometric tools with the spectra showed that these two spectroscopic methods were highly discriminating. Third, test firing of 3D-printed firearms has allowed for the simulation of a scene involving this type of firearm and the collection of polymer traces generated. Fourth, the comparison of chemical signatures between polymer filaments and polymer traces has allowed for the evaluation of the potential for chemical association. This study highlights the added value of chemical analysis of 3D-printed firearms polymer traces in a criminal investigation by demonstrating that polymer filaments, the polymer from which a 3D-printed firearm is made, as well as polymer traces generated during firing, can be linked chemically and provide relevant information.

Differences Between Farmers and Crop Protection Service Providers in the Use of Pesticides

EU-wide statistics on the use of pesticides in agriculture are available on the quantities placed on the market, but data on actual use in practice are limited. In this study, comparative data on pesticide use and resistance management were collected for a region with mixed agriculture (arable and animal husbandry) in Austria. Based on individual pesticide records of 30 farmers and 10 service providers who perform plant protection tasks for 30 farms, the average application rates of pesticides, the change of the active substance group as well as the shortfalls and exceedances of the maximum permissible application rate were compared. Most farmers and service providers usually make a well-considered change in the group of active substances to avoid the development of resistance. Exceedances and underdosing of authorised application rates were found for both groups of people, with farmers tending to deviate more often. The applications amounts were largely in compliance with the law, however, in 3.69% of the 2387 evaluated uses of pesticides, the authorised application rate was exceeded. This excess was found somewhat more frequently among farmers than service providers. The application rate excesses can be attributed to technical overruns due to differences between actually farmed and formally declared area, confusion caused by complex application instructions but also by intention. To avoid inadequate information on authorised application rates and to simplify information gathering, the development of databases and apps that take into account not only the indisputably essential aspect of correct registration, but especially the user and user-friendliness, would be of great benefit and value. Packaging sizes adapted to different requirements and farm sizes would also be advantageous. High priority should be given to the technical training of users and sales personnel. This can contribute to higher professionalism in the use of pesticides in terms of compliance and resistance management, but also to the reduction of pesticides.

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

RESEARCH ON CONSTRUCTION OF NATURAL RESOURCES THREE-DIMENSIONAL SPATIO-TEMPORAL DATABASE SYSTEM

Abstract. The construction of an efficient and practical database management system based on large-scale three-dimensional spatiotemporal databases presents a novel challenge in the field of natural resource information technology. This research, with the objective of supporting the contemporary era of unified natural resource management and building upon the foundation of a national-level three-dimensional spatiotemporal natural resource database, proposes a comprehensive framework for a distributed architecture database management system. Subsequently, the research outlines the system's components, encompassing a service publishing system, online application system, professional management system, and operational supervision system, and designs core functionalities for the integrated management of natural resource elements within a three-dimensional spatiotemporal context. Building upon this framework, the research utilizes cloud-based database software and hardware environments, along with distributed technology pathways, to establish a national-level three-dimensional spatiotemporal database management system for natural resources. Furthermore, it conducts typical applications in the field of natural resources. The results demonstrate that the database management system effectively achieves the integration, representation, and analytical application of large-scale natural resource survey and monitoring data within a three-dimensional spatiotemporal context. It plays a crucial supportive role in the development of a national-level three-dimensional spatiotemporal natural resource database and holds valuable insights for the construction of provincial-level database management systems and other related systems in various domains.

Measuring and improving quality in esophageal care and swallowing disorders.

Evaluating clinical care through quality-related metrics is increasingly common. There are now numerous quality statements and indicators related to the medical management of benign and pre-malignant esophageal diseases. Expert consensus leveraging evidence-based recommendations from published society guidelines has been the most frequently used basis for developing esophageal quality statements. While surgical care of patients with esophageal malignancies, including squamous cell carcinoma, has also been developed, those related to benign esophageal disease now include domains of diagnosis, treatment, and monitoring for gastroesophageal reflux disease, eosinophilic esophagitis (EoE), achalasia, and Barrett's esophagus (BE). Several recent studies evaluating adherence to quality metrics affirm substantial variation in practice patterns with opportunities for improvement in care across esophageal diseases. In particular, patient education regarding treatment options in achalasia, frequency of esophageal biopsies among patients with dysphagia to evaluate for EoE, and endoscopic evaluation within a BE segment are areas identified to have need for improvement. As the management of esophageal diseases becomes more complex and interdisciplinary, adherence to quality metrics may be a source of standardization and improvement in delivery and ultimately patient outcomes. Indeed, the development of national quality databases has resulted in a significant growth in the use of these metrics for quality improvement activities and may form the basis for future inclusion in quality reporting and payment programs.

Development of a food composition database for the estimation of dietary S-methyl cysteine sulfoxide from vegetables

A food composition database estimating S-methyl cysteine sulfoxide (SMCSO) was created following a systematic literature search. SMCSO data (705 entries) from 19 vegetables were summarised: brassicas (n = 10) and alliums (n = 9). The highest SMCSO in brassicas was reported in Brussels sprouts (median [range]: 318 [68−420] mg/100 g fresh weight (FW)) whilst the lowest was in radish (19 [4–45] mg/100 g FW). Brussels sprouts were almost twice as concentrated in SMCSO as cauliflower, followed by cabbage, kale, broccoli, kohlrabi, swede, Chinese cabbage, and turnips. The alliums highest in SMCSO were Chinese chives (271 [185−413] mg/100 g FW) followed by rakkyo and garlic, with substantially less found in shallots, onion, and leek. Literature reporting SMCSO content in food is sparse. Further research is required to quantify SMCSO in commercially available vegetables and other foods, in order to update and expand this database for application to large populations and future intervention studies.

A Reflective Study of Computerized Database Application Technology and Maintenance

In the era of digitalization, computer databases have become an indispensable technological support across various industries. They not only house a vast amount of data but also enable efficient management and swift retrieval of this information. Concurrently, advancements in database application technology and maintenance have become crucial in ensuring the stable operation of information systems. However, contemporary computer database technology still faces numerous challenges such as data compatibility, automation levels, and flexibility in data transmission. Therefore, this study focuses on the key application techniques and maintenance mechanisms of computer databases, exploring measures to enhance data compatibility, improve production automation, strengthen the flexibility of data transmission, and bolster technical support. These efforts aim to drive the development of computer databases towards efficiency, stability, and security, thus laying a solid data foundation for the sustainable development of the entire information society.

Current opinion on large-scale prospective myomectomy databases toward evidence-based preconception and antenatal counselling utilising a standardised myomectomy operation note.

No large-scale databases exist of pregnancy outcomes and rate of uterine rupture for women after myomectomy, resulting in inconsistent antenatal counselling and decision-making regarding mode and timing of delivery. Standardising information collected at myomectomy may facilitate data collection, informing prenatal/ antenatal counselling. To determine clinician opinions regarding standardisation of myomectomy operation notes to allow comprehensive data input into a prospective database of pregnancy outcomes, toward an evidence-based approach to decision making regarding timing and mode of delivery in subsequent pregnancies. A google forms survey was emailed to all consultant (attending-level) obstetricians and gynaecologists across 25 hospitals in London, Kent, Surrey, and Sussex (UK) between March and May 2022. To enhance response rates, two further email reminders were sent alongside in-person reminders from selected local unit representatives. Senior clinician opinion for characteristics necessary to collect at time of surgery to develop a widescale database of post myomectomy pregnancy outcomes. 209/475 (44%) responses received; 95% (198/209) agreed with standardising operation notes. Criteria selected for inclusion included cavity breach (98%, 194/198), location (98%, 194/198), number of fibroids removed (93%, 185/198) and number of uterine incisions (96%, 190/198). Gynaecologists support standardising myomectomy operation notes to inform the development of prospective large-scale databases of pregnancy outcomes after myomectomy. Acquisition of clinician opinions on the development and content of a standardised myomectomy operation note to aid the development of a pregnancy-outcome database for women after myomectomy.

HIResist: a database of HIV-1 resistance to broadly neutralizing antibodies.

Changing the course of the human immunodeficiency virus type I (HIV-1) pandemic is a high public health priority with approximately 39 million people currently living with HIV-1 (PLWH) and about 1.5 million new infections annually worldwide. Broadly neutralizing antibodies (bnAbs) typically target highly conserved sites on the HIV-1 envelope glycoproteins (Envs), which mediate viral entry, and block the infection of diverse HIV-1 strains. But different mechanisms of HIV-1 resistance to bnAbs prevent robust application of bnAbs for therapeutic and preventive interventions. Here we report the development of a new database that provides data and computational tools to aid the discovery of resistant features and may assist in analysis of HIV-1 resistance to bnAbs. Bioinformatic tools allow identification of specific patterns in Env sequences of resistant strains and development of strategies to elucidate the mechanisms of HIV-1 escape; comparison of resistant and sensitive HIV-1 strains for each bnAb; identification of resistance and sensitivity signatures associated with specific bnAbs or groups of bnAbs; and visualization of antibody pairs on cross-sensitivity plots. The database has been designed with a particular focus on user-friendly and interactive interface. Our database is a valuable resource for the scientific community and provides opportunities to investigate patterns of HIV-1 resistance and to develop new approaches aimed to overcome HIV-1 resistance to bnAbs. HIResist is freely available at https://hiresist.ahc.umn.edu/.

Examining Exposure Fires from the United States National Fire Incident Reporting System between 2002 and 2020

Fires resulting from antecedent fires, known as exposure fires, can manifest across diverse environments, including suburban, urban, and rural areas. Notably, exposure fires represented by structure-destroying fires within the wildland–urban interface (WUI) can extend into non-WUI suburban and urban regions, presenting significant challenges. Leveraging data from the United States National Fire Incident Reporting System (NFIRS) spanning 2002 to 2020, this study investigates 131,739 exposure fire incidents impacting 348,089 features (incidents). We analyze reported economic costs, affected feature types, and property utilization patterns for these exposure fires. We also compare these exposure fires to information documented in other databases. Finally, we examine structure separation distance at residential dwellings and describe ignition pathways for selected fires. Reported property losses for some fire incidents amounted to USD 5,647,121,172, with content losses totaling USD 1,777,345,793. Prominent fire incident categories include buildings, vehicles, and natural vegetation fires, predominantly occurring in residential, outdoor, and storage areas. While the NFIRS lacked information on most major structure-destroying WUI fires, highlighting this analysis’s lack of statistical representation, it did provide insights into less extensive exposure fires, both WUI and non-WUI, unrecorded elsewhere. Our study reveals significant distinctions in the distribution of separation distances between damaged-to-damaged structures (average separation of 6.5 m) and damaged-to-not-damaged structures (average separation of 18.1 m). Notably, 84% of the incidents in exposure fires involved fire suppression defensive actions. These defensive actions contributed to the differences in structure separation distance distributions, highlighting the often-neglected role of these measures in assessing structure responses during WUI fires. We examined ignition pathways at select exposure fires, highlighting some common features involved in fire spread and challenges in documenting these pathways. Finally, we propose a set of idealized attributes for documenting exposure fires, accentuating the inherent difficulties in collecting such data across expansive geographical areas, particularly when striving for statistical representation. Our findings yield valuable insights into the multifaceted nature of exposure fires, informing future research and database development to aid in mitigating their impact on vulnerable communities.

How to Identify e-Cigarette Brands Available in the United States During 2020-2022: Development and Usability Study.

Prior studies have demonstrated that the e-cigarette market contains a large number of brands. Identifying these existing e-cigarette brands is a key element of market surveillance, which will further assist in policy making and compliance checks. To facilitate the surveillance of the diverse product landscape in the e-cigarette market, we constructed a semantic database of e-cigarette brands that have appeared in the US market as of 2020-2022. In order to build the brand database, we searched and compiled e-cigarette brands from a comprehensive list of retail channels and sources, including (1) e-liquid and disposable brands sold in web-based stores, (2) e-cigarette brands sold in brick-and-mortar stores and collected by the Nielsen Retail Scanner Data, (3) e-cigarette brands compiled by Wikipedia, (4) self-reported e-cigarette brands from the 2020 International Tobacco Control Four-Country Smoking and Vaping (ITC 4CV) US survey, and (5) e-cigarette brands on Twitter. We also estimated the top 5 e-cigarette brands by sales volume in brick-and-mortar stores, by the frequency and variety of offerings in web-based shops, and by the frequency of self-reported brands from the 2020 ITC 4CV US survey. As of 2020-2022, a total of 912 e-cigarette brands have been sold by various retail channels. During 2020-2022, the top 5 brands are JUUL, vuse, njoy, blu, and logic in brick-and-mortar stores; blu, king, monster, twist, and air factory for e-liquids in web-based stores; hyde, pod mesh, suorin, vaporlax, and xtra for disposables sold in web-based stores; and smok, aspire, vaporesso, innokin, and eleaf based on self-reported survey data. As the US Food and Drug Administration enforces the premarket tobacco market authorization, many e-cigarette brands may become illegal in the US market. In this context, how e-cigarette brands evolve and consolidate in different retail channels will be critical for understanding the regulatory impacts on product availability. Our semantic database of e-cigarette brands can serve as a useful tool to monitor product and marketplace development, conduct compliance checks, assess manufacturers' marketing behaviors, and identify regulatory impacts.

Instruments for racial health equity: a scoping review of structural racism measurement, 2019-2021.

Progress toward racial health equity cannot be made if we cannot measure its fundamental driver: structural racism. As in other epidemiologic studies, the first step is to measure the exposure. But how to measure structural racism is an ongoing debate. To characterize the approaches epidemiologists and other health researchers use to quantitatively measure structural racism, highlight methodological innovations, and identify gaps in the literature, we conducted a scoping review of the peer-reviewed and gray literature published during 2019-2021 to accompany the 2018 published work of Groos etal., in which they surveyed the scope of structural racism measurement up to 2017. We identified several themes from the recent literature: the current predominant focus on measuring anti-Black racism; using residential segregation as well as other segregation-driven measures as proxies of structural racism; measuring structural racism as spatial exposures; increasing calls by epidemiologists and other health researchers to measure structural racism as a multidimensional, multilevel determinant of health and related innovations; the development of policy databases; the utility of simulated counterfactual approaches in the understanding of how structural racism drives racial health inequities; and the lack of measures of antiracism and limited work on later life effects. Our findings sketch out several steps to improve the science related to structural racism measurements, which is key to advancing antiracism policies.