Model Estimates Research Articles

Association of the Life's Essential 8 cardiovascular health score with periodontitis among US adults.

To investigate whether cardiovascular health (CVH) is associated with periodontitis. We used data from the 2009 to 2014 National Health and Nutrition Examination Survey. We quantified CVH using Life's Essential 8, which includes four health behaviours (diet, smoking, physical activity and sleep) and four health factors (body mass index, blood cholesterol, glucose and pressure). We categorized scores as low (0-49), moderate (50-79) and high (80-100). We calculated subscores of health behaviours and factors and categorized them as low, moderate and high. We used logistic regression to assess the association of CVH with periodontitis, adjusting for age, gender, race/ethnicity, education, poverty index, marital status and health insurance. We computed odds ratios (ORs) and 95% confidence intervals (CIs). This study included 9296 adults ≥30 years old. Multivariable-adjusted models showed that subjects with moderate (OR, 0.62; 95% CI: 0.52-0.74) or high (OR, 0.43; 95% CI: 0.33-0.57) CVH had significantly lower odds of periodontitis compared to those with low CVH. These results were consistent in the health behaviours model, but the estimates in the health factors model were not significant. Improving CVH may help prevent periodontitis. Longitudinal studies are needed to confirm our findings.

Toxicity of selected pharmaceuticals and their mixtures to the aquatic indicators Daphnia magna and Aliivibrio fischeri.

Despite the benefits derived from the use of pharmaceuticals, these compounds are currently considered contaminants of emerging concern because of their presence and persistence in the environment. This study aimed to determine the toxicity of 27 pharmaceuticals and the interaction effects of binary mixtures of selected compounds towards two model organisms: the microcrustacean Daphnia magna and the bacterium Aliivibrio fischeri (Microtox test). Six compounds, namely polymyxin B, polymyxin E, fluoxetine, diphenhydramine, clenbuterol and ketoprofen exhibited moderate toxicity towards D. magna. Additionally, three compounds (cefotaxime, polymyxin B, polymyxin E) also showed a moderate toxic effect on A. fischeri. The comparison of such results with model estimations showed inaccuracy in the predicted data, highlighting the relevance of experimental ecotoxicological assays. The assayed mixtures contained four selected drugs of high-hazard according to their reported concentrations in wastewater and surface water (diphenhydramine, trimethoprim, ketoprofen, and fluoxetine); data revealed interactions only in the fluoxetine-containing mixtures for D. magna, while all mixtures showed interactions (mostly synergistic) for Microtox. Chronic effects on the reproduction of D. magna were observed after exposure to fluoxetine and diphenhydramine, although higher sensitivity was determined for the latter, while the mixture of these compounds (which showed acute synergy in both models) also affected the reproduction patterns. Nonetheless, all the effects described at the acute or chronic level (for individual compounds or mixtures) were determined at concentrations higher than commonly reported at environmental levels. This work provides valuable ecotoxicological information for the risk assessment of pharmaceuticals and their mixtures in the environment.

Serological Markers of Clinical Improvement in MuSK Myasthenia Gravis.

In this retrospective longitudinal study, we aimed at exploring the role of (a) MuSK-immunoglobulin G (IgG) levels, (b) predominant MuSK-IgG subclasses, and (c) antibody affinity as candidate biomarkers of severity and outcomes in MuSK-MG, using and comparing different antibody testing techniques. Total MuSK-IgGs were quantified with radioimmunoassay (RIA), ELISA, flow cytometry, and cell-based assay (CBA) serial dilutions using HEK293 cells transfected with MuSK-eGFP. MuSK-IgG subclasses were measured by flow cytometry. SAffCon assay was used for determining MuSK-IgG affinity. Forty-three serum samples were obtained at different time points from 20 patients with MuSK-MG (median age at onset: 48 years, interquartile range = 27.5-72.5; women, 16/20), with 9 of 20 (45%) treated with rituximab. A strong correlation between MuSK-IgG levels measured by flow cytometry and RIA titers was found (rs = 0.74, 95% CI 0.41-0.89, p = 0.0003), as well as a moderate correlation between CBA end-point titers and RIA titers (rs = 0.47, 95% CI 0.01-0.77, p = 0.0414). A significant correlation was found between MuSK-IgG flow cytometry levels and disease severity (rs = 0.39, 95% CI 0.06-0.64, p = 0.0175; mixed-effects model estimate: 2.296e-06, std. error: 1.024e-06, t = 2.243, p = 0.032). In individual patients, clinical improvement was associated with decrease in MuSK-IgG levels, as measured by either flow cytometry or CBA end-point titration. In all samples, MuSK-IgG4 was the most frequent isotype (mean ± SD: 90.95% ± 13.89). A significant reduction of MuSK-IgG4 and, to a lesser extent, of MuSK-IgG2, was seen in patients with favorable clinical outcomes. A similar trend was confirmed in the subgroup of rituximab-treated patients. In a single patient, MuSK-IgG affinity increased during symptom exacerbation (KD values: 62 nM vs 0.6 nM) while total MuSK-IgG and IgG4 levels remained stable, suggesting that affinity maturation may be a driver of clinical worsening. Our data support the quantification of MuSK antibodies by flow cytometry. Through a multimodal investigational approach, we showed that total MuSK-IgG levels, MuSK-IgG4 and MuSK-IgG2 levels, and MuSK-IgG affinity may represent promising biomarkers of disease outcomes in MuSK-MG.

The influence of business intelligence on the effectiveness of supply chain management in the Nigerian consumer goods sector

Business performance depends on supply chain management (SCM), which can be enhanced through digital transformation to enable quick decisions to increase market share. By enabling informed decision-making, business intelligence (BI) can facilitate this improvement in SCM effectiveness and competitive edge. Real-time production and inventory level monitoring is made possible by BI capabilities, which help organisations adapt to changing market trends and meet customer demands. Particularly, BI technology can increase the SCM of enterprises in the consumer products sector by providing value-added and financial gains, enabling speedy industry adoption and rapid industry growth. The study employed a quantitative methodology for gathering data, and a simple random sampling method was employed to prevent sample bias. The study considered linear curve estimation (LCE) as a method of data analysis. The results of the model estimations showed that BI recorded positive and significant influence on SC visibility, flexibility, and resilience in the Nigerian consumer goods sector. The study concludes that BI increased the effectiveness of supply chain management (SCM) by improving visibility, anticipating demand, increasing resilience, optimising processes, and boosting productivity. SCM effectiveness depends on the efficient handling of disruptions and uncertainty. The study highlights the practical policy implications following the findings of the study.

Cost-effectiveness of respiratory syncytial virus adjuvanted vaccine in Portuguese aged ≥ 60 years

Abstract Background Respiratory syncytial virus (RSV) infections are one of the leading causes of lower respiratory tract disease among adults aged ≥60 years in Europe. This study aims to assess the potential public health impact of the RSVPreF3 OA adjuvanted vaccine, the first approved preventive intervention for the older adult population in Portugal. Methods A monthly-cycle static Markov model was developed to assess RSVPreF3 OA adjuvanted vaccine impact on adults aged 60 years and older in Portugal. A single dose of RSVPreF3 OA adjuvanted vaccine was compared to no-vaccination over a three-year time-horizon. The model captures lower respiratory tract disease (LRTD) RSV cases, hospitalizations, deaths, and healthcare resource utilization. Data inputs were based on local published literature, supplemented by the best available data when local information was lacking. Results Based on preliminary analysis considering 75% vaccine coverage, model outputs estimate that RSVPreF3 OA adjuvanted vaccine can prevent 71,433 LRTD cases requiring medical attendance, 11,067 hospitalizations, 1,793 intensive care unit admissions and 1,003 deaths. The model estimates that 32 adults need to be vaccinated to avoid one RSV LRTD case, 207 adults need to be vaccinated to avoid one hospitalization and 2,283 adults need to be vaccinated to avoid one death. The incremental cost-effectiveness ratio was estimated at 9,541€, hence the RSVPreF3 OA adjuvanted vaccine is cost-effective compared to no vaccination. Conclusions The results demonstrate a strong preventive impact of the RSVPreF3 OA adjuvanted vaccine on health outcomes and healthcare resource utilization in adults ≥60 years, leading to a significant public health benefit. This result emphasizes the importance of preventing RSV to preserve the health and well-being of Portugal’s older adults. In addition, data from ongoing studies may enhance precision and extensive sensitivity analyses validate the robustness of the model’s input assumptions. Key messages • This study shows the robust evidence of RSVPreF3 OA adjuvanted vaccine to improve public health by preventing thousands of hospitalizations and deaths among ≥60 years, in Portugal. • Vaccinating adults aged ≥ 60 years with RSVPreF3 OA adjuvanted vaccine in Portugal is a cost-effective strategy.

Determinants of health among Ukrainian refugees in Germany: comparison with other refugee groups

Abstract Background 6.5 million Ukrainian refugees have been displaced globally since 2022, with 1 million seeking asylum in Germany. Little is known about the health determinants for this group, limiting the evidence base for policy intervention. Methods We use a nationally representative survey of adult Ukrainian refugees in Germany (IAB-BiB/FReDA-BAMF-SOEP study; n = 5943), conducted in 2023, to analyse the effect of sociodemographic, pre-migration (experienced war intensity, traumatic experiences) and post-migration factors (time since arrival, accommodation, social networks) on self-rated health using multiple logistic regression. We contrast these findings with estimates of identical models obtained from a subset of the IAB-BAMF-SOEP-sample of non-Ukrainian refugees within one year of arrival (n = 1195). Results In the Ukrainian sample, post-migration factors are particularly critical for health, with those experiencing discrimination (OR 1.83; 95%CI: 1.51,2.21) and social isolation (OR: 2.70; 95%CI: 2.23, 3.28) affected by ill health, while those with high German proficiency (OR 0.34; 95%CI: 0.18, 0.64) and contact with Germans (OR: 0.66; 95%CI: 0.53,0.83) are protected. Pre-migration factors do not affect self-rated health. Among refugees from other countries of origin, post-migration factors are not associated with health within the first year of arrival, with exception of social isolation (OR: 2.03; 95%CI: 1.32,3.13). Discussion Despite the EU Temporary Protection Directive, the health of Ukrainian refugees in Germany is shaped by adverse post-migration circumstances. While other refugee groups may have had riskier escape journeys and worse arrival conditions, this is unrelated to self-rated health in the first year. This underscores the importance of considering pre-migration contexts when evaluating subjective health. Outreach services, communication campaigns and the expansion of medical interpreting services are needed to ensure access to the health system. Key messages • Despite the EU Temporary Protection Directive, the health of Ukrainian refugees in Germany is shaped by adverse post-migration circumstances. • Outreach services, communication campaigns and the expansion of medical interpreting services are needed to ensure access to the health system.

Urban Exposome and Health

Abstract Background The environment in which we live is thought to explain 70% of the non-communicable disease burden. The aim of EXPANSE project (funded from the European Union Horizon 2020 research and innovation programme under grant agreement No 874627) is to evaluate the optimal strategies for maintaining health in the context of contemporary urban environments. As a component of the project, Urban Labs allow deep phenotyping of the urban exposome and assessing its use and perception. The Urban Labs aim to improve exposure assessment, identify mediating and modifying lifestyle factors, and link the urban exposome to intermediate endpoints relevant for cardiometabolic and pulmonary health. Methods By monitoring the activities of 4,000 individuals in five distinct urban areas, Urban Labs provide a comprehensive understanding of the urban exposome, which can be compared with residential address-based model estimates. The participants complete a baseline questionnaire, repeated bi-monthly questionnaires and exposure-specific questionnaires over a period of 2 years using mobile phone application (ExpanSeeker). Two-week personalized measurements including spatial-temporal activity patterns, sensor-based environmental and behavioral measurements, and minimally invasive biological sample collection are performed covering different seasons. Results The Baseline Questionnaire (BQ) and two-weeks measurement campaigns (MC) were completed in European cities as follows: Athens BQ = 379, MC = 232; Barcelona BQ = 1040, MC = 303; Basel BQ = 731, MC = 655; Lodz BQ = 634, MC = 100; The Netherlands (multicity) BQ = 1033, MC = 326. Data collection process is ongoing. Conclusions The collection of novel data on individuals activities and perceptions within temporal and geographical contexts in Urban Labs will contribute to a more comprehensive understanding of how the urban dwellers are exposed to and interact with their environment, thereby supporting healthier urban engineering and policy making. Key messages • Urban Labs will provide a more comprehensive understanding of the impact of the urban environment on individual’s behavior, their exposome, enabling more accurate prediction of non-communicable risk. • Urban Labs will provide a base to support healthier urban engineering and policy making.

Africa Health Research Institute (AHRI) Household Contact study: a study protocol, statistical analysis and modelling plan

Household case-contact studies are an established method to estimate the relative infectiousness of people with pulmonary tuberculosis. Testing of child household contacts with a QuantiFERON TB Gold plus (QFT) assay provides a proxy measure of M. tuberculosis infection in exposed household members. This study aims to generate data about the effect of antiretroviral treatment (ART) and of tuberculosis symptoms on risk of intra-household M. tuberculosis transmission to children and investigate the conditions under which symptom-agnostic screening should be considered, which could provide evidence to support a paradigm shift in tuberculosis screening strategy. Index people diagnosed with bacteriologically-confirmed pulmonary tuberculosis who have been identified by routine health services and starting tuberculosis treatment at one of the 12 health facilities serving the population of the Africa Health Research Institute health and demographic surveillance area will be eligible for recruitment. For each consenting index participant, we will enroll all household members aged 2-14 years who will undergo a symptom screen and provide a blood sample for a QFT and HIV test. We will compare the proportion of child contacts who are QFT-positive in households of (i) index adults who are living with HIV and taking ART vs not taking ART and (ii) index adults engaged in ART care who report no symptoms vs those who report symptoms. We will use a novel mathematical model, incorporating new insights into TB natural history and transmission, to determine the circumstances under which screening for TB, regardless of reported symptoms, would have most impact on tuberculosis incidence and mortality, and explore comparative cost-effectiveness of interventions along the diagnostic pathway at different levels of the health system. Estimates of relative risk of intra-household M. tuberculosis transmission, combined with healthcare utilisation and cost data, will allow us to generate mathematical model estimates of the potential epidemiological impact and relative cost-effectiveness of different approaches to tuberculosis case-finding including symptom-agnostic tuberculosis screening at different levels of the health system.

Non-Conventional Data for Farming-Related Air Pollution: Contributions to Modelling and Risk Assessment in the Lombardy Region, Italy

Air pollution is one of the most critical global health concerns today. While emissions from industrial activities and combustion processes are the primary threats to air quality, intensive farming activities also contribute significantly, especially through ammonia emissions that promote the formation of secondary pollutants, such as particulate matter. Advancements in air quality research have been achieved by enhancements in emissions characterisation, modelling techniques, and sensor technology, expanding the availability of air pollution data beyond traditional ground sensor observations, which are often lacking in rural agricultural areas. Accordingly, this paper demonstrates the advantages of integrating traditional and non-conventional data to investigate farming-related air pollution through a case study in the Lombardy Region, Northern Italy. The study incorporates an array of data sources, including ground sensors and atmospheric composition model estimates. The concurrent utilisation of these diverse datasets is explored through machine learning modelling, focusing on assessing the influence of agricultural activities on particulate matter distribution patterns. Finally, the reliability of non-conventional air pollution data for health risk assessment applications is also investigated. The paper critically discusses the main findings based on empirical results, highlighting the significance of integrating multiple data sources to complement traditional air quality monitoring while outlining the main limitations in terms of the accuracy and usability of such non-conventional data.

A spatially explicit assessment of factors shaping attitudes toward African elephant conservation.

Conservation plans that explicitly account for the social landscape where people and wildlife co-occur can yield more effective and equitable conservation practices and outcomes. Yet, social data remain underutilized, often because social data are treated as aspatial or are analyzed with approaches that do not quantify uncertainty or address bias in self-reported data. We conducted a survey (questionnaires) of 177 households in a multiuse landscape in the Kenya-Tanzania borderlands. In a mixed-methods approach, we used Bayesian hierarchical models to quantify and map local attitudes toward African elephant (Loxodonta africana) conservation while accounting for response bias and then combined inference from attitude models with thematic analysis of open-ended responses and cointerpretation of results with local communities to gain deeper understanding of what explains attitudes of people living with wildlife. Model estimates showed that believing elephants have sociocultural value increased the probability of respondents holding positive attitudes toward elephant conservation in general (mean increase=0.31 [95% credible interval, CrI, 0.02-0.67]), but experiencing negative impacts from any wildlife species lowered the probability of respondents holding a positive attitude toward local elephant conservation (mean decrease=-0.20 [95% CrI -0.42 to 0.03]). Qualitative data revealed that safety and well-being concerns related to the perceived threats that elephants pose to human lives and livelihoods, and limited incentives to support conservation on community and private lands lowered positive local attitude probabilities and contributed to negative perceptions of human-elephant coexistence. Our spatially explicit modeling approach revealed fine-scale variation in drivers of conservation attitudes that can inform targeted conservation planning. Our results suggest that approaches focused on sustaining existing sociocultural values and relationships with wildlife, investing in well-being, and implementing species-agnostic approaches to wildlife impact mitigation could improve conservation outcomes in shared landscapes.

Energy Transfer by Mechanical Alloying and Electrocatalytic Performance of the As‐Sintered Self‐Supported High‐Entropy Alloy FeNiCoCuMo

Efficient, stable, and cost‐effective electrocatalysts for the oxygen evolution reaction (OER) are essential for clean energy generation through water splitting. This study presents a FeNiCoCuMo high‐entropy alloy (HEA) synthesized via mechanical alloying (MA) and sintering using hot‐pressing. The energy transfer from the milling process influences phase transformations, with the High‐Entropy Alloys Prediction Software (HEAPS) predicting the formation of an FCC phase. X‐ray diffraction (XRD) and scanning electron microscopy reveal an FCC phase after 150 h of milling, with no elemental segregation observed. The Burgio kinetic model estimates 448 kJ mol−1 is needed to achieve a 99% FCC phase. The crystallite size is 4 nm, with a lattice parameter of 0.371 nm. The as‐milled phases were preserved during hot‐pressing sintering. Electrodes (M1, M2, M3) fabricated from the HEA demonstrated high electrocatalytic efficiency, with an average overpotential of 380 mV and Tafel slope of 77 mV dec−1. At a current density of 10 mA cm−2, the electrodes maintained operation for up to 100 h. The synergy between constituent elements is key to the superior electrocatalytic performance of FeNiCoCuMo HEAs, demonstrating their potential as promising materials for OER electrocatalysis.

Wrapping input–output multipliers in confidence intervals

Input–output multipliers are typically calculated as point estimates of the Leontief quantity model. From the previous literature, they can also be estimated (and their confidence intervals) directly from establishments’/industries’ inputs and outputs data by running an appropriate econometric regression. However, previous contributions relied on analyses carried out for 1 single year, one country/region and a reduced number of degrees of freedom. This paper tests the robustness of the previous literature by using instead a time series of large-scale inter-country supply, use and input–output database with more than one country and more than 1 year, as well as panel data econometrics.

Economic Growth and Democratic Political System in Developing Countries

The relationship between a democratic political system and economic growth has been the focus of debate in recent years. This article is a contribution to the economic analysis of the relationship between democracy and economic growth. Using an econometric illustration of panel data from twenty developing countries, in a period from 2000 to 2021, Fixed-effect, random-effect and GMM model estimates show that a democratic political regime positively affects economic growth in developing countries. This result is explained by the importance of the role of democracy in implementing economic and social reforms, encouraging investment, opening up trade and protecting civil liberties and political powers.

The economic impacts of living habitat changes in the Virginia Middle Peninsula, Chesapeake Bay

Living habitats support ecologically and commercially important species but are threatened by anthropogenic stressors, including climate change. The populations of two living habitats in the Chesapeake Bay, Eastern Oyster (Crassostrea virginica) and Eelgrass (Zostera marina), are depleted compared to historical levels, but recent Oyster restoration efforts have created new reef habitat and enhanced existing Oyster reefs. Other animals (e.g., commercially important fish and invertebrates) consume organisms that associate with these living habitats, suggesting the regional economies that rely on the commercial fishing industry may be impacted by changes in living habitat coverage. Ecosystem models were developed for two rivers that have undergone Oyster restoration in the Virginia Middle Peninsula, and simulations were conducted to estimate the potential influence that changes to living habitats may have on commercial fisheries harvests. Additionally, commercial fishers were interviewed to document expenditures needed to link ecological model estimates to an economic input-output model via IMPLAN that estimated the regional economic impacts of these changes. Increases to Oyster and Eelgrass populations are predicted to enhance commercial fisheries populations and in turn, harvests, while decreases to living habitats are predicted to have negative effect on fisheries harvests. The driving factor behind landed value trends is changes with the lucrative Blue Crab (Callinectes sapidus) fishery. The primary expenses of watermen are repairs/maintenance, fuel, and bait, but greater than 37 % of the annual revenue is retained as proprietor and crew income. Oyster reef restoration alone is predicted to increase the regional economic impact of the commercial fishing industry by $1.1 M yr-1 and support 12 more full-time jobs at the end of a 20-year simulation. Oyster restoration in combination with a continued decline of Eelgrass led to a negligible increase (∼$150 K yr-1) in the regional economic impact. Eelgrass restoration to the current management goal without the influence of Oyster restoration is anticipated to elevate the regional economic impact to $16.8 M yr-1 and 152 full-time jobs, and smaller increases to the Eelgrass population, relative to current levels, will still have a positive impact on the regional economy. Combined Oyster and Eelgrass restoration are predicted to enhance economic impacts to the greatest extent, while continued Eelgrass decline is likely to minimize these benefits.

Comparison between estimates of beta regression model using MSE

The multiple linear regression analysis shows the effects of explanatory variables on the dependent variable. Its function is to represent data to understand the shape and nature of the relationship between explanatory variables and the response variable. One of the main problems faced by linear regression is the nature of the data, as sometimes it does not follow a normal distribution. This has led to the emergence of different types of linear regression models such as the beta regression model, where the data of the dependent variable is less than one and confined within the period (0,1), and the data is relative and decimal. Non-traditional methods will be used to estimate beta regression parameters because some traditional methods do not provide suitable and accurate results when dealing with one of the main problems encountered in regression, which is multicollinearity. To address this issue, we used some methods include The Maximum Likelihood Estimation (MLE), ridge regression, modified Liu, and Ozkale and Kaciranlar method. Shrinking parameters are proposed for each method, and a comparison between the methods is conducted using the Mean Squared Error comparison criterion through a simulation experiment with two approaches, the simulation results showed that the ridge regression method is the best for the first and second methods of simulation. Paper type Research Paper

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.

Riverine macroplastic survey along the segments of Tullahan River in Metro Manila, Philippines

The Philippines is one of the world’s main contributors to global marine plastic pollution. However, field data remains scarce, so model estimates of riverine plastic pollution may be inaccurate. This paper aims to characterize the macrolitter observed along the Tullahan River, with focus on plastics, passing through the barangays of Quezon City and Valenzuela City in Metro Manila. The impact of solid waste management and land use activities on plastic flux were also investigated. For the floating litter, visual counting and float method were used to determine the plastic flux and river velocity, respectively. Riverbank litter was collected manually to characterize it based on plastic product and polymer type. Results show that the macroplastic flux was lowest on the site where residential houses are farthest from the stream. Waste characterization revealed that riverbank litter was primarily plastic – 30%–41% were residuals, and 5%–21% were recyclables. Both methods revealed that wrappers and thin PE plastics are the top contributors to riverine macroplastic pollution. Therefore, the entry of macroplastics into the river may be due to land use activities and weak enforcement of existing solid waste management (SWM) policies. The points discussed in the study can help in improving SWM and land use planning. The results can also increase the accuracy of model estimates.

Can Social Technologies Drive Purchases in E-Commerce Live Streaming? An Empirical Study of Broadcasters’ Cognitive and Affective Social Call-to-Actions

As an emerging social technology, live streaming has facilitated a synchronous and interactive selling setting for e-commerce sellers and consumers. Despite growing operations management (OM) in e-commerce live streaming (ELS), the prior literature has largely neglected the directive social operations of ELS broadcasters, which are crucial to a company's business operations and marketing strategies. To mitigate this gap, we investigated the effects of social call-to-actions (SCTAs), an ELS-specific directive social operation, on consumer purchase outcomes in ELS. To explicate the mechanisms driving the purchase effect of SCTAs, we draw upon the notions of cognitive and affective marketing appeals to categorize SCTAs into two types: cognitive and affective SCTAs. We measure broadcasters’ SCTAs from ELS speech-to-text data using text-mining techniques and adopt econometric model estimations via an instrumental variables identification approach to quantify the relationships between ELS broadcasters’ SCTAs and consumer purchases. Our results uncover a significant positive purchase effect of affective SCTAs but an insignificant effect of cognitive SCTAs on product-level purchase orders in ELS. Specifically, a one-unit increase in affective SCTA-related words per minute is related to increased purchase orders of a product by 0.43%, i.e., averaging a 1.10% growth in demand per minute. Additionally, we find that the effects of cognitive and affective SCTAs are contingent on product and broadcaster types. Our session-level mediation effect analysis verifies the underlying mechanisms that drive the purchase effects of broadcasters’ SCTAs. Specifically, we find that cognitive SCTAs impede social engagements and thereby purchases, whereas affective SCTAs can boost social engagements, leading to increased session traffic and ultimately product sales in ELS. Our study provides novel empirical findings and important practical implications for ELS platforms and broadcasters.

Are We Dosing Correctly? Population Pharmacokinetics of Cefepime in Critically Ill Pediatric Patients

Abstract Background Dosing guidelines of cefepime are well defined in healthy pediatric and adult populations but have not been extensively studied in critically ill pediatric patients. Patients who are critically ill may have significant alterations in antibiotic pharmacokinetics (PK) and pharmacodynamics (PD) due to many factors. Receipt of ECMO or CRRT may further alter PK/PD due to changes in clearance and volume of distribution and the depletion of plasma proteins. Since critically ill pediatric patients are likely to receive multiple courses of antimicrobials, optimization of drug dosing and delivery is critically important for individual patient outcomes. This study will test the hypothesis that the PK of cefepime is substantially altered in critically ill pediatric patients. Methods We prospectively enrolled patients in VUMC pediatric ICUs receiving cefepime, including those receiving ECMO or CRRT. Plasma samples were collected at opportune timepoints enriched for specific PK/PD metrics. Cefepime plasma concentrations were measured by LC-MS/MS. Weight, dose, interval, and administration time were extracted from the medical record. Monolix was used to fit population PK models using one and two-compartment models with a proportional error model. Covariate modeling was performed with a priori selected covariates: ECMO, CRRT, sex, race, ethnicity, gestational age, and creatinine clearance (CrCl). The final covariate model was used to perform probability of target attainment (PTA) analysis using Monte Carlo simulation. 6,000 subjects were simulated for each dosing regimen, with 3 weight categories (≤9 kg, 9-25 kg, and >25 kg) and 2 CrCl categories (≤90 mL/min and >90 mL/min). PTA was calculated over a grid of possible MIC values based on three targets, 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC. Results Preliminary data from 84 pediatric participants receiving cefepime were analyzed. Of these, 9 received CRRT and 10 received ECMO. An average of 3 samples were collected per participant. A two compartment with proportional error model was selected as the base model. Population PK parameter estimates for the base model, base model with weight, and final covariate model were calculated. Inclusion of weight and CrCl significantly improved model fit. CRRT, ethnicity, and gestational age did significantly improve model fit, but their inclusion in the model did not provide evidence of being significantly better than the model with only weight and CrCl. The results from the PTA analysis are seen in Figure 1. We observed an overall ordering of PTA by dosing regimen across all three targets, with q8h 3-hour infusion having the highest PTA and q12h 30-minute infusion having the lowest PTA. Increased CrCl led to reduced PTA and as weight increased, PTA also increased. Conclusion This study will be among the first and largest to characterize cefepime PK in the critically ill pediatric population. Clinicians should incorporate local antibiograms with these population PK models to determine optimal dosing in critically ill pediatric patients. The use of extended infusions may be beneficial for PK/PD target achievement, particularly for resistant pathogens. Since clinical covariates affect PK parameters, this population may also benefit from therapeutic drug monitoring. Figure 1. Comparison of PTA among four dosing regimens of cefepime at targets of 65% fT>MIC, 100% fT>MIC, and 100% fT>4×MIC.

Multiple objectives optimization of injection-moulding process for dashboard using soft computing and particle swarm optimization

This research focuses on utilizing injection moulding to assess defects in plastic products, including sink marks, shrinkage, and warpages. Process parameters, such as pure cooling time, mould temperature, melt temperature, and pressure holding time, are carefully selected for investigation. A full factorial design of experiments is employed to identify optimal settings. These parameters significantly affect the physical and mechanical properties of the final product. Soft computing methods, such as finite element (FE), help mitigate behaviour by considering different input parameters. A CAD model of a dashboard component integrates into an FE simulation to quantify shrinkage, warpage, and sink marks. Four chosen parameters of the injection moulding machine undergo comprehensive experimental design. Decision tree, multilayer perceptron, long short-term memory, and gated recurrent units models are explored for injection moulding process modelling. The best model estimates defects. Multiple objectives particle swarm optimisation extracts optimal process parameters. The proposed method is implemented in MATLAB, providing 18 optimal solutions based on the extracted Pareto-Front.