Information Granularity Research Articles

Maternal implications of fetal anomalies: a population-based cross-sectional study

BackgroundAlthough it is well-known that the presence of fetal anomalies is associated with maternal morbidity, granular information on these risks by type of anomaly is not available. ObjectiveTo examine adverse maternal outcomes according to the type of fetal anomaly. Study DesignThis was a repeated cross-sectional analysis of US vital statistics Live Birth/Infant Death linked data from 2011 to 2020. All pregnancies at 20 weeks or greater were included. Our primary outcome was severe maternal morbidity (SMM), defined as any maternal intensive care unit admission, transfusion, uterine rupture, or hysterectomy. Outcomes were compared between pregnancies with a specific type of fetal anomaly and pregnancies without any fetal anomaly. Fetal anomalies that were available in the dataset included anencephaly, meningomyelocele/spina bifida, cyanotic congenital heart disease, congenital diaphragmatic hernia, omphalocele, gastroschisis, cleft lip and/or palate, hypospadias, limb anomaly, and chromosomal disorders. If a fetus had more than one anomaly, it was classified as multiple anomalies. Adjusted relative risks (aRR) with 99% confidence intervals (99% CI) were calculated using modified Poisson regression. Adjusted risk differences (aRDs) were calculated using the marginal standardization form of predictive margins. ResultsOf 35,760,626 pregnancies included in the analysis, 35,655,624 pregnancies had no fetal anomaly and 105,002 had isolated or multiple fetal anomalies. Compared to pregnancies without fetal anomaly, all fetal anomalies were associated with an increased risk of SMM except for gastroschisis and limb anomaly in order of aRRs (99% CI): 1.58 (1.29–1.92) with cleft lip and/or palate; 1.75 (1.35–2.27) with multiple anomalies; 1.76 (1.18–2.63) with a chromosomal disorder; 2.19 (1.82–2.63) with hypospadias; 2.20 (1.51–3.21) with spina bifida; 2.39 (1.62–3.53) with congenital diaphragmatic hernia; 2.66 (2.27–3.13) with congenital heart disease; 3.15 (2.08–4.76) with omphalocele; and 3.27 (2.22–4.80) with anencephaly. Compared to pregnancies without fetal anomaly, all fetal anomalies were associated with an increased absolute risk of SMM except for gastroschisis and limb anomaly in order of aRDs (99% CI): 0.26 (0.12–0.40) with cleft lip and/or palate, 0.34 (0.13–0.55) with multiple anomalies, 0.34 (0.02–0.66) with a chromosomal disorder, 0.54 (0.36–0.72) with hypospadias, 0.54 (0.17–0.92) with spina bifida, 0.63 (0.21–1.05) with congenital diaphragmatic hernia, 0.75 (0.56–0.95) with congenital heart disease, 0.97 (0.38–1.56) with omphalocele, and 1.03 (0.46–1.59) with anencephaly. ConclusionThe presence of fetal anomalies is associated with adverse maternal health outcomes. The risk of SMM varies according to the type of fetal anomaly. Counseling mothers about the maternal implications of fetal anomalies is paramount to help them make informed decisions regarding their pregnancy outcome.

Study on Cultural Consumption Characteristics of Museum Short Video Platforms under the Threshold of Identifier Economics

Purpose - This study examines museums’ operations on the Shakeology platform, elucidating the characteristics of museums’ cultural consumption in relation to identity, cultural identity, value identity, and the cluster effect of certified museums. The study explores how short video platforms can serve as effective tools for cultural communication and consumption promotion. Design/Methodology/Approach - The study utilizes qualitative research to investigate how museums utilize the Shakeology platform to create a diverse content ecosystem and improve audience engagement. Data is collected through the analysis of museum activities on the platform, including content types, interaction strategies, and audience engagement. It also examines how short video platforms differ from traditional media in terms of granular information processing and community interaction. Findings - Short video platforms are more effective in communication than traditional media due to their granular information processing and community interaction. Museums that focus on Shakeology can enhance consumers’ connection to cultural heritage, optimize market positioning, and improve audience interaction. Key characteristics of cultural consumption on the platform include enhanced cultural identity, value identity, and a significant cluster effect of certified museums, leading to a richer cultural experience for audiences. Research Implications - The study holds significant implications for museums aiming to utilize modern media for audience engagement and market positioning. By understanding cultural consumption dynamics on short video platforms, museums can customize content and interaction strategies to cultivate stronger audience connections. The study suggests that museums should continue to innovate and adapt to new media trends to enhance their cultural communication efforts.

Granular transfer learning

Transfer learning is aimed at supporting the design of machine learning models in the target domain Dt, given that the knowledge (model) has already been constructed in the source domain Ds. The domains Dt and Ds (as well as the corresponding tasks Ts and Tt) are similar, yet not identical. As a result, the model transferred from Ds to Dt in this new environment exhibits its relevance (credibility) only to some limited extent. In this study, we develop an original approach, where we advocate that the knowledge transfer (model transfer) gives rise to a granular model where the level of information granularity associated with the produced results quantifies the relevance (quality or credibility) of the transferred model. In other words, we stress that the quality of knowledge transferred to Dt becomes captured through a granular generalization of the original numeric model. The overall systematic design process is elaborated on by focusing on the development process of granular neural networks carried out on a basis of the numeric neural networks coming from Ds. The key aspect of the design is to elevate the existing numeric neural network to its granular counterpart by admitting that the connections of the developed model come in the form of information granules, in particular intervals and fuzzy sets. The optimization process is guided by adjusting (optimizing) the level of information granularity being regarded as an essential design asset. The optimized performance index builds upon the descriptors of information granules commonly encountered in Granular Computing. In particular, coverage and specificity measures are treated as sound performance indicators of the quality of knowledge transfer (viz. the performance of the granular neural network expressed in the target domain). Several illustrative examples are provided to visualize the performance of the established design environment.

Systematic discrepancies in the delivery of political ads on Facebook and Instagram.

Political advertising on social media has become a central element in election campaigns. However, granular information about political advertising on social media was previously unavailable, thus raising concerns regarding fairness, accountability, and transparency in the electoral process. In this article, we analyze targeted political advertising on social media via a unique, large-scale dataset of over 80,000 political ads from Meta during the 2021 German federal election, with more than billion impressions. For each political ad, our dataset records granular information about targeting strategies, spending, and actual impressions. We then study (i) the prevalence of targeted ads across the political spectrum; (ii) the discrepancies between targeted and actual audiences due to algorithmic ad delivery; and (iii) which targeting strategies on social media attain a wide reach at low cost. We find that targeted ads are prevalent across the entire political spectrum. Moreover, there are considerable discrepancies between targeted and actual audiences, and systematic differences in the reach of political ads (in impressions-per-EUR) among parties, where the algorithm favor ads from populists over others.

Optimal solutions to granular fuzzy relation equations with fuzzy logic operations

Fuzzy relation equations are commonly utilized to describe the fuzzy relationship between the antecedent and the consequent parts of complex data environment, and play a vital role in fuzzy system modeling. An interesting topic is to determine fuzzy relation equations based on existing fuzzy logic operations, including the t-norms and t-conorms (s-norms). However, fuzzy rule-based models developed in form of numeric values still face significant challenges in accuracy and interpretability. This study aims to introduce the idea of granular computing to formulate granular fuzzy relation equations based on the fuzzy logic operations and evaluate the performance of granular fuzzy relation equations using the general principle of justifiable granularity. A two-phase development of granular fuzzy relation equations is designed: In the first phase, fuzzy relation equations are constructed based on t-norms and t-conorms; in the second phase, granular augmentation of fuzzy relation equations is realized by allocating a certain level of information granularity as the granular parameters of fuzzy relations. The capability of granular outputs is evaluated with the aid of two conflicting criteria—coverage (cov) and specificity (sp). The optimal solutions to granular fuzzy relation equations are explored using combinations of optimization algorithms. The originality of this study lies on the development of granular fuzzy relation equations with fuzzy logic operations, which provides a human-centric platform of fuzzy system modeling to enhance accuracy and interpretability. Experimental studies are carried out based on the UCI and KEEL machine learning datasets to report on the feasibility and performance of the proposed model.

FAOSTAT Food Value Chain Domain implementation: Input Output modelling and analytical applications

The recent increasing attention to the economic and policy analysis of the food systems from international fora, public institutions and academia calls for the availability of information and data capable of informing about the interrelations across economic sectors and within value chains. The international policy agenda is pushing for a more effective application of measures at country and regional level in line with the recommendations of the 2030 Agenda and its Sustainable Development Goals, for which more systematic and integrated data about economic, social and environmental impacts of policies are requested. The Food Value Chain Domain recently published in FAOSTAT responds to this call. Its data and information shed light on the distribution of final domestic food expenditures across industries (Agriculture, Food Processing, Wholesale, Retail, Accommodations and Food Services) and primary factors (e.g.: Labour, Gross Operating Surplus) on the relative food value chain. The FAOSTAT Domain offers therefore robust and granular information on both the farm and the post-farm gate component of the Food Value Chain. The applied Global Food Dollar methodology, that FAO is contributing to upscale at global level, is based on Leontief decomposition approach on the Input-Output tables. Moreover, whenever the Input-Output table are not available, it is now possible to impute them from Supply-Use tables by applying a conversion methodology, developed by FAO in compliance with European (EUROSTAT), United Nations (UNSD) and international statistical standards as the System of National Accounts. This allows to extend the analysis to several African, Asian, and Latin American countries that produce on regular basis only Supply and Use Tables, and not Industry by industry Input Output Tables. The potential time and data coverage of the methodology is therefore significantly expanded. The aim of this paper is to describe the conceptual framework of the conversion methodology of Supply-Use Tables into Input-Output Tables of the Global Food Dollar methodology, and the potential implementation scope of these methodologies. Preliminary analytical findings of the applied methodologies are presented as well. The new methods and data presented in this paper, being based on data compliant with the International Statistical Standards, as the System of National Accounts, and therefore comparable across countries, associated to larger data availability, have the potential to effectively support food policies at international, regional and national level, as well as contribute to a decision making in line with the 2030 Agenda.

Challenges in access, representativeness, and bias in smart financial data relating to income volatility and economic insecurity.

Introduction & BackgroundFinancial transaction data are highly valuable sources of digital footprints data for behavioural and economic research, but to properly create impact we must closely consider their limitations. Financial institutions hold a wealth of consumer data with untapped potential for community intelligence. These datasets combine excellent coverage with extremely granular information on consumer finances, income and spending, yet these institutions face great challenges in leveraging this data for social good. Smart Data Foundry is a university-owned, non-profit organisation that facilitates safe access to these datasets for researchers and provides insights to enable government bodies to tackle today's major challenges including the cost-of-living crisis and climate change. Objectives & ApproachWe will explore the opportunities afforded by these datasets for social and economic research. For example, using pseudonymised individual consumer banking data from NatWest Group, we have developed metrics for understanding income volatility and economic insecurity in collaboration with the Joseph Rowntree Foundation. We can also use these data to study consumer spending patterns and responses to economic changes such as interest rate rises and the net zero transition. We will assess the limitations of the data including issues of representativeness, bias, and missing data, and describe methods and mitigations to account for these challenges. We also discuss the barriers to accessing this type of data, in both relationship development with data partners, and privacy and governance concerns. Relevance to Digital FootprintsIndividual level customer transaction data provides a rich and novel form of digital footprint for behavioural and economic analyses. Every point of income or expenditure is recorded in a uniquely valuable digital footprint by financial institutions. These can provide a variety of insights, such as responses to macroeconomic shocks across demographic sets, emerging areas of financial distress, and help us better understand the drivers and risks of financial vulnerability. In both its aggregated and individual form, the data can provide an additional layer of understanding for trends we may see in other data, such as health or administrative data. Conclusions & ImplicationsHaving addressed the challenges of data access and data quality, we demonstrate that consumer banking data is an incredibly valuable form of digital footprints data, capturing key information on consumer behaviour. We conclude with a call for further research to develop use cases of this data for social good.

U.S. FDA analysis of enrollment of Asian patients from India in cancer clinical trials leading to approval from 2010 to 2022.

e13779 Background: There are an estimated 4.4 million Indian Americans (Asian Indian), constituting 1.35% of the United States (US) population. Indian Americans enrolled in clinical trials are identified under the broad race category of Asian which includes Asians within the US and outside of US (Ex-US). Although cancer is the leading cause of death for Asians in US, they are under-represented in cancer clinical trials. Enrollment of Asian patients from India (API) in multiregional oncology clinical trials may provide important data regarding intrinsic and extrinsic factors impacting the etiology of cancer as well as response to anti-cancer treatment in Indian Americans. Common cancers reported amongst Asians in US are breast, prostate, colorectal, and lung, and in India are breast, head and neck, cervical and lung. Methods: We analyzed data from ~96,000 patients in 164 cancer therapeutic trials that led to an FDA approval for breast, prostate, colorectal, lung, liver, gastric, head and neck and cervical cancer indications from 2010-2022, and identified country for enrollments for all Asians. Results: Descriptive statistics of Asian patients enrolled within US, Ex-US and from India enrolled in breast, prostate, colorectal, lung, liver, gastric, head and neck, and cervical cancer trials that led to an FDA approval from 2010-2022 are summarized (Table). Enrollment of Asian patients within US was ≤1% (except liver and cervical cancer), from India was ≤2.1% of all Asians enrolled outside of US and ≤0.7% of all enrolled patients. Conclusions: It is difficult to accurately characterize the exact number of Indian Americans enrolled but are likely under-represented in cancer clinical trials leading to FDA approval.Sponsors should collect more granular information regarding enrollment of Asian patients in clinical trials. Enrollment of patients from India is extremely low relative to the population size. Efforts to increase enrollment of Indian Americans as well as patients from India in oncology clinical trials can bolster the evidence that supports FDA drug approval and its applicability to Indian Americans. [Table: see text]

The toxicity swimmer plot: A novel approach for evaluating patient-level toxicity following immune checkpoint inhibitor (ICI) therapy on the Checkmate 214 trial.

11014 Background: Treatment with ICIs creates the possibility of prolonged disease control or remission but also creates the possibility of prolonged toxicity even after treatment is discontinued. Treatment-free survival (TFS) with and without toxicity has previously been described as an integral piece of a partitioned survival analysis in advanced renal cell carcinoma (aRCC). Prior analyses have reported further partition of TFS as mean duration of TFS with and without treatment-related adverse events (TRAEs), but this metric fails to provide more granular information regarding individual patients experiencing toxicity during TFS. We present a novel extended swimmer plot that characterizes patient-level toxicity data within TFS using the example of the Checkmate 214 trial to complement analyses of the population average experience. Methods: Data were analyzed from the Checkmate 214 trial which randomized 1096 patients with aRCC to receive either first-line (1L) nivolumab plus ipilimumab (n = 550) or sunitinib (n = 546). The minimum follow-up from protocol therapy initiation was 42 months. Patients’ overall survival was partitioned into 3 survival states: time on 1L protocol therapy, TFS, and survival after subsequent therapy initiation. With a re-defined time origin relative to cessation of 1L protocol therapy, we generated swimmer plots highlighting the TFS period and documenting each patient’s experience with and without grade 2+ and grade 3+ TRAEs during TFS. The swimmer plot was sorted by duration of TFS and duration of TFS with grade 2+ TRAEs in order to illustrate patterns of individuals’ experiences. Results: The 42-month mean TFS was 7.8 months (with grade 2+ TRAEs: 3.2 months) for patients treated with nivolumab plus ipilimumab and 3.3 months (1.6 months) for patients treated with sunitinib. In the respective treatment groups, TFS with grade 2+ TRAEs was experienced by 32% and 25% of patients and comprised of 13% and 9% of patients for whom it was the entire TFS period. These percentages were consistent regardless of TFS duration. TFS with grade 3+ TRAEs was experienced by 15% and 3% of patients, respectively. For most patients having TFS with TRAEs, the TRAEs persisted from the protocol therapy period, but 7% and 1% of patients in the respective treatment groups had new-onset grade 3+ TRAE during TFS. A swimmer plot will be presented as a visual presentation of this study’s results. Conclusions: We describe a novel extension of swimmer plots for presenting patient-level information and illustrating patterns in toxicity duration relative to TFS by incorporating individual toxicity data into a swimmer plot of partitioned survival. This approach allows for a more comprehensive understanding of toxicity trends and risk of prolonged toxicity after treatment is discontinued.

When Will Real-World Data Fulfill Its Promise to Provide Timely Insights in Oncology?

Randomized trials provide high-quality, internally consistent data on selected clinical questions, but lack generalizability for the aging population who are most often diagnosed with cancer and have comorbid conditions that may affect the interpretation of treatment benefit. The need for high-quality, relevant, and timely data is greater than ever. Promising solutions lie in the collection and analysis of real-world data (RWD), which can potentially provide timely insights about the patient's course during and after initial treatment and the outcomes of important subgroups such as the elderly, rural populations, children, and patients with greater social health needs. However, to inform practice and policy, real-world evidence must be created from trustworthy and comprehensive sources of RWD; these may include pragmatic clinical trials, registries, prospective observational studies, electronic health records (EHRs), administrative claims, and digital technologies. There are unique challenges in oncology since key parameters (eg, cancer stage, biomarker status, genomic assays, imaging response, side effects, quality of life) are not recorded, siloed in inaccessible documents, or available only as free text or unstructured reports in the EHR. Advances in analytics, such as artificial intelligence, may greatly enhance the ability to obtain more granular information from EHRs and support integrated diagnostics; however, they will need to be validated purpose by purpose. We recommend a commitment to standardizing data across sources and building infrastructures that can produce fit-for-purpose RWD that will provide timely understanding of the effectiveness of individual interventions.

Mapping perceptions of topophilia and topophobia using a mobile app: A tale of two cities

Abstract This study evaluates positive (topophilic) and negative (topophobic) perceptions of places using participatory mapping methods. Current research on mapping perceptions of urban environments relies heavily on retrospective self-reports from citizens. These methods are often susceptible to recall bias and do not capture granular information about urban environments. Places are dynamic, and peoples’ perceptions of them vary by time and space. To address these gaps in methods, we collected data from individuals living in two cities, Olomouc, Czech Republic and Brisbane, Australia. GIS was used to analyse a combined total of 634 momentary assessments from Olomouc, and 318 assessments from Brisbane. Our findings suggest that this approach can yield accurate and reliable data about perceptions of topophobia and topophilia in the two cities as well as enable researchers to clearly define hotspots and hot times related to individual activity spaces.

Linguistic Features of Secondary School Writing: Can Natural Language Processing Shine a Light on Differences by Sex, English Language Status, or Higher Scoring Essays?

This article provides three major contributions to the literature: we provide granular information on the development of student argumentative writing across secondary school; we replicate the MacArthur et al. model of Natural Language Processing (NLP) writing features that predict quality with a younger group of students; and we are able to examine the differences for students across language status. In our study, we sought to find the average levels of text length, cohesion, connectives, syntactic complexity, and word-level complexity in this sample across Grades 7-12 by sex, by English learner status, and for essays scoring above and below the median holistic score. Mean levels of variables by grade suggest a developmental progression with respect to text length, with the text length increasing with grade level, but the other variables in the model were fairly stable. Sex did not seem to affect the model in meaningful ways beyond the increased fluency of women writers. We saw text length and word level differences between initially designated and redesignated bilingual students compared to their English-only peers. Finally, we see that the model works better with our higher scoring essays and is less effective explaining the lower scoring essays.

Outlier detection for heterogeneous data via fuzzy [formula omitted] covering

Outlier detection is an important research topic in data mining. The existing outlier detection method does not consider ordinal data, such as heterogeneous data. Fuzzy β covering is an information granularity representation tool, which can better fit the distribution of objects in real data and characterize the differences of objects. This paper studies outlier detection for heterogeneous data via fuzzy β covering. Firstly, some new properties of fuzzy β covering are proposed. Then, the fuzzy similarity relation between any two objects in heterogeneous data is defined, and fuzzy information granules are constructed by fuzzy β covering to obtain the new fuzzy approximation accuracy. Next, the outlier factor based on fuzzy β covering is established by integrating the outlier degree and weight function of fuzzy information granules. Moreover, the corresponding algorithm (called FBCOD algorithm) is designed. Finally, the FBCOD algorithm is compared with eight existing outlier detection algorithms on 12 UCI data sets. The experimental results of AUC value, statistical test and F1 measurement show that the FBCOD algorithm has better effectiveness and flexibility than some existing algorithms.

Influence of Antibiotic Exposure Intensity on the Risk of Clostridioides difficile Infection.

Antibiotics are a strong risk factor for Clostridioides difficile infection (CDI), and CDI incidence is often measured as an important outcome metric for antimicrobial stewardship interventions aiming to reduce antibiotic use. However, risk of CDI from antibiotics varies by agent and dependent on the intensity (i.e., spectrum and duration) of antibiotic therapy. Thus, the impact of stewardship interventions on CDI incidence is variable, and understanding this risk requires a more granular measure of intensity of therapy than traditionally used measures like days of therapy (DOT). We performed a retrospective cohort study to measure the independent association between intensity of antibiotic therapy, as measured by the antibiotic spectrum index (ASI), and hospital-associated CDI (HA-CDI) at a large academic medical center between January 2018 and March 2020. We constructed a marginal Poisson regression model to generate adjusted relative risks for a unit increase in ASI per antibiotic day. We included 35,457 inpatient encounters in our cohort. Sixty-eight percent of patients received at least one antibiotic. We identified 128 HA-CDI cases, which corresponds to an incidence rate of 4.1 cases per 10,000 patient-days. After adjusting for known confounders, each additional unit increase in ASI per antibiotic day is associated with 1.09 times the risk of HA-CDI (Relative Risk = 1.09, 95% Confidence Interval: 1.06 to 1.13). ASI was strongly associated with HA-CDI and could be a useful tool in evaluating the impact of antibiotic stewardship on HA-CDI rates, providing more granular information than the more commonly used days of therapy.

Effect of immediate initiation of invasive ventilation on mortality in acute hypoxemic respiratory failure: a target trial emulation

PurposeInvasive ventilation is a fundamental treatment in intensive care but its precise timing is difficult to determine. This study aims at assessing the effect of initiating invasive ventilation versus waiting, in patients with hypoxemic respiratory failure without immediate reason for intubation on one-year mortality.MethodsEmulation of a target trial to estimate the benefit of immediately initiating invasive ventilation in hypoxemic respiratory failure, versus waiting, among patients within the first 48-h of hypoxemia. The eligible population included non-intubated patients with SpO2/FiO2 ≤ 200 and SpO2 ≤ 97%. The target trial was emulated using a single-center database (MIMIC-IV) which contains granular information about clinical status. The hourly probability to receive mechanical ventilation was continuously estimated. The hazard ratios for the primary outcome, one-year mortality, and the secondary outcome, 30-day mortality, were estimated using weighted Cox models with stabilized inverse probability weights used to adjust for measured confounding.Results2996 Patients fulfilled the inclusion criteria of whom 792 were intubated within 48 h. Among the non-invasive support devices, the use of oxygen through facemask was the most common (75%). Compared to patients with the same probability of intubation but who were not intubated, intubation decreased the hazard of dying for the first year after ICU admission HR 0.81 (95% CI 0.68–0.96, p = 0.018). Intubation was associated with a 30-day mortality HR of 0.80 (95% CI 0.64–0.99, p = 0.046).ConclusionThe initiation of mechanical ventilation in patients with acute hypoxemic respiratory failure reduced the hazard of dying in this emulation of a target trial.

A density map-based method for counting wheat ears.

Field wheat ear counting is an important step in wheat yield estimation, and how to solve the problem of rapid and effective wheat ear counting in a field environment to ensure the stability of food supply and provide more reliable data support for agricultural management and policy making is a key concern in the current agricultural field. There are still some bottlenecks and challenges in solving the dense wheat counting problem with the currently available methods. To address these issues, we propose a new method based on the YOLACT framework that aims to improve the accuracy and efficiency of dense wheat counting. Replacing the pooling layer in the CBAM module with a GeM pooling layer, and then introducing the density map into the FPN, these improvements together make our method better able to cope with the challenges in dense scenarios. Experiments show our model improves wheat ear counting performance in complex backgrounds. The improved attention mechanism reduces the RMSE from 1.75 to 1.57. Based on the improved CBAM, the R2 increases from 0.9615 to 0.9798 through pixel-level density estimation, the density map mechanism accurately discerns overlapping count targets, which can provide more granular information. The findings demonstrate the practical potential of our framework for intelligent agriculture applications.

Effectiveness of Using AI-Driven Hotspot Mapping for Active Case Finding of Tuberculosis in Southwestern Nigeria.

Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.

A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology

Background: This paper examines scientific papers in the field of digital twins to explore the different areas of application in supply chains. Methods: Using a machine learning-based topic modeling approach, this study aims to provide insights into the key areas of supply chain management that benefit from digital twin capabilities. Results: The research findings highlight key priorities in the areas of infrastructure, construction, business, technology, manufacturing, blockchain, and agriculture, providing a comprehensive perspective. Conclusions: Our research findings confirm several recommendations. First, the machine learning-based model identifies new areas that are not addressed in the human review results. Second, while the human review results put more emphasis on practicality, such as management activities, processes, and methods, the machine learning results pay more attention to macro perspectives, such as infrastructure, technology, and business. Third, the machine learning-based model is able to extract more granular information; for example, it identifies core technologies beyond digital twins, including AI/reinforcement learning, picking robots, cybersecurity, 5G networks, the physical internet, additive manufacturing, and cloud manufacturing.

Household debt and economic growth in Europe

Abstract We investigate the role and impact of household debt on the economic performance of European economies during the double-dip recession of 2008–2013. We use a loan-level data set of millions of residential mortgages originated between 2000 and 2013 to calculate regional indicators of household debt. The granular information allows us to construct a measure of interest rate mispricing during the housing boom that we use to identify the effect of a credit shock (CS) on household debt. Our analysis provides three main conclusions. First, in the period 2004–2006, the measure of CS was negative in most European regions which indicates that credit conditions were significantly relaxed relative to earlier years. Second, we find that regions in which household leverage increased more rapidly during the 2002–2007 period experienced a more severe decline in output and employment after 2008. Third, we find that the CS had the largest effect on increasing leverage for the low-income and the middle-income households, although the leverage of the high-income households represents a more powerful predictor of the decline in economic activity.

Detection and metrology of micro-nano particles through the integration of image and laser modes

Micro-nano particles are an indispensable component in various industrial and scientific applications. Accurate granular information is critical for manufacturing products involving these particles. This study presents a novel detection method that merges image and laser modes to achieve precise size and shape determination of micro-nano particles. The proposed method integrates adaptive image analysis with laser particle size fitting to ensure accurate feature detection. Experimental results using ternary precursor samples demonstrate that the fusion mode outperforms both the laser and image modes in terms of size and shape accuracy. The errors of D10, D50, and D90 of the fusion mode are 4.89%, 1.88%, and 2.94%, respectively, which are significantly lower than those obtained using the laser or image mode alone. Additionally, the fusion mode retains the particle shape information from the image mode. The fusion model offers a promising approach for dual-mode detection of micro-nano particles, demonstrating enhanced robustness compared to single-mode methods. Furthermore, the study introduces an image processing method based on a BP neural network classification model that accurately classifies and detects agglomerated and non-agglomerated particles. The classification accuracy reaches 94.98%, and the particle size information remains within the acceptable range.