Model Estimates Research Articles

Agricultural innovation for climate change: limited but positive impacts of commercialized drought-tolerant corn

Abstract The extent to which new technologies can countervail the risks posed by climate change is a critical element for designing adaptation strategies. This study uses new experimental data spanning 17 US states from 2008 to 2023 to examine the potential impact of recently commercialized drought tolerant (DT) traits on both yield and yield resilience in US corn production. We find that there is no yield advantage for DT hybrids under average weather conditions, but they improve yield resilience, particularly with respect to precipitation. These effects are spatially heterogeneous, such that DT has a positive yield impact in the droughty, western US, but a small, or even negative, impact in the central and eastern US. In addition, the presence of DT reduces yield variance and kurtosis, and increases skewness, all of which imply a reduction in yield risk. Using the statistical model estimates, we project the impact of DT on corn yields under future climate conditions obtained from 20 general circulation models with two representative concentration pathways. The projected ensemble means of yield gains are 6.34 bu/acre and 5.39 bu/acre under moderate and extreme warming scenarios, respectively, by the mid-twenty-first century. These gains compensate for 23% and 13.5% of total yield loss due to climate change. Our results indicate that current commercial DT hybrids reduce yield risk, improve resilience with respect to precipitation, and have the potential to offer moderate benefits under climate change warming scenarios.

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.

Model Estimates of Changes in Soil Organic Matter Stocks in Forested Areas of European Russia under Different Forest Management Regimes

Model Estimates of Changes in Soil Organic Matter Stocks in Forested Areas of European Russia under Different Forest Management Regimes

Coefficient Shape Alignment in Multiple Functional Regression

In multivariate functional data analysis, different functional covariates often exhibit homogeneity. The covariates with pronounced homogeneity can be analyzed jointly within the same group, offering a parsimonious approach to modeling multivariate functional data. In this paper, a novel grouped multiple functional regression model with a new regularization approach termed “coefficient shape alignment” is developed to tackle functional covariates homogeneity. The modeling procedure includes two steps: first aggregate covariates into disjoint groups using the new regularization approach; then the grouped multiple functional regression model is established based on the detected grouping structure. In this grouped model, the coefficient functions of covariates in the same group share the same shape, invariant to scaling. The new regularization approach works by penalizing differences in the shape of the coefficients. We establish conditions under which the true grouping structure can be accurately identified and derive the asymptotic properties of the model estimates. Extensive simulation studies are conducted to assess the finite-sample performance of the proposed methods. The practical applicability of the model is demonstrated through real data analysis in the context of sugar quality evaluation. This work offers a novel framework for analyzing the homogeneity of functional covariates and constructing parsimonious models for multivariate functional data.

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.

Excess mortality associated with chikungunya epidemic in Southeast Brazil, 2023

The chikungunya virus (CHIKV) was first detected in Brazil in 2014 and has since caused major epidemics. In 2023, a major chikungunya epidemic occurred in Minas Gerais, one of the most populous states in the country. We analyzed the North and Northeast Health Macroregions of Minas Gerais, with 2.5 million inhabitants. The study used epidemiological data from the Ministry of Health’s laboratory surveillance, disease notification and mortality systems. A robust Poisson regression model was constructed to estimate expected monthly mortality. Excess deaths were calculated by comparing observed deaths with model estimates during the epidemic period. During the epidemic, there were 890 excess deaths attributed to chikungunya, translating into a mortality rate of 35.1/100,000 inhabitants. The excess mortality rate was significantly 60 times higher than the deaths reported by surveillance, with only 15 confirmed deaths. The correlation between excess deaths and laboratory-confirmed chikungunya cases was strong, while the correlation with dengue and COVID-19 was not statistically significant. The results highlighted the serious underestimation of chikungunya mortality by epidemiological surveillance. During the same year, only 420 chikungunya deaths were reported by all PAHO member countries. Routine epidemiological surveillance systems cannot capture the full impact of this disease. Excess mortality is a key measure for understanding the impact of epidemics, as demonstrated by COVID-19 and influenza pandemics. The study highlights the need for complementary tools to traditional surveillance to better assess impacts on morbidity and mortality and support priority setting in public health interventions.

Insomnia and Chronic Pain Mediate the Relationship Between Traumatic Brain Injury and Reduced Positive Airway Pressure Adherence Among Veterans.

To examine whether co-morbid insomnia, post-traumatic stress disorder (PTSD), depression, and chronic pain mediate the relationship between traumatic brain injury (TBI) and positive airway pressure (PAP) treatment adherence. One Veterans Health Administration (VHA) sleep medicine site. Veterans (n =8836) who were prescribed a modem-enabled PAP device. Secondary analysis of clinical data. We used path analysis to examine: (1) whether Veterans with a history of TBI were more likely to experience insomnia, PTSD, depression, and chronic pain; (2) in turn, whether Veterans with these co-morbid conditions exhibited lesser PAP adherence; and (3) whether Veterans with a history of TBI will exhibit lesser PAP adherence, even while accounting for such co-morbid conditions. Model estimates were adjusted for sociodemographic (eg, race/ethnicity) and clinical characteristics (eg, mask leakage). Health conditions were abstracted from the VHA medical record. PAP adherence was measured using average nightly use (hours). Among 8836 Veterans, 12% had a history of TBI. TBI history was not associated with PAP adherence when accounting for the presence of insomnia, PTSD, depression, and chronic pain. Indirect effect estimates indicated that a history of mild, moderate-severe, or unclassified TBI was associated with lesser PAP adherence, as mediated by the presence of co-morbid insomnia and chronic pain. Generally, TBI was associated with an increased likelihood of co-morbid insomnia, PTSD, depression, and chronic pain. In turn, insomnia and chronic pain, but not PTSD or depression, were associated with lesser PAP adherence. Our study offers empirical support for insomnia and chronic pain as potential explanatory mechanisms underlying the relationship between TBI history and suboptimal PAP adherence. While additional research is needed to confirm causality, findings offer preliminary evidence that can inform the development of tailored PAP adherence interventions for Veterans with TBI and obstructive sleep apnea.

Simultaneous Coefficient Clustering and Sparsity for Multivariate Mixed Models

In many applications of multivariate longitudinal mixed models, it is reasonable to assume that each response is informed by only a subset of covariates. Moreover, one or more responses may exhibit the same relationship to a particular covariate for example, if they are capturing the same underlying aspect of an individual physical, mental, and emotional health. To address the above challenges, we propose a method for simultaneous clustering and variable selection of fixed effect coefficients in multivariate mixed models. We achieve this in a computationally scalable manner via a composite likelihood approach: separate mixed models are first fitted to each response, after which the model estimates are combined into a single quadratic form resembling a multivariate Wald statistic. We then augment this with fusion- and sparsity-inducing penalties based on broken adaptive ridge regression. Simulation studies demonstrate that the proposed composite quadratic estimator is similar to or better than several existing techniques for fixed effects selection in (univariate) mixed models while being computationally much more efficient. We apply the proposed method to longitudinal panel data from Australia to quantify how an individual’s overall health, assessed via a set of eight composite scores, evolves as a function of various demographic and lifestyle variables.

Parameter estimation in vapor-liquid equilibrium modeling of compounds related to biodiesel production using opposite point-based differential evolution algorithm.

Biodiesel stands out as the most favorable alternatives to fossil-derived diesel, offering a multitude of environmental benefits. At various stages of biodiesel production, the separation processes necessitate thermodynamic models with the capability to correlate and predict phase equilibria of mixtures. In this study, application of the classical differential evolution (DE) and its new enhanced version, the OPDE algorithm, for modeling vapor-liquid equilibrium (VLE) associated with components related to biodiesel production is presented. The algorithms were analysed and contrasted in terms of their performance, in estimating parameters for Wilson, NRTL and UNIFAC models. Additionally, classical least-squares (LS) and error-in-variable (EIV) approaches were examined and compared. Also, VLE datasets for a specific system have been merged, and parameters have been estimated. The findings suggest that parameters obtained through LS approach align with those reported in literature, indicating faster convergence in all problems. In contrast, the EIV approach achieved a higher objective function value compared to the LS approach, exhibiting low deviation. OPDE outperformed DE in terms of performance. The enhancement in RMSTD value has been found within range 91%-99% for EIV approach. Further, novel findings derived from some of the studied VLE datasets are presented.

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.

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment.

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed to detect depression and assess its severity, aiming to improve diagnostic precision and provide insights into contributing health factors. Using data from the National Social Life, Health, and Aging Project (NSHAP), this framework combines classical machine learning models, static ensemble methods, and dynamic ensemble selection (DES) approaches across two stages: detection and severity prediction. The depression detection stage classifies individuals as normal or depressed, while the severity prediction stage further classifies depressed cases as mild or moderate-severe. Finally, a confirmation depression scale prediction model estimates depression severity scores to support the two stages. Explainable AI (XAI) techniques are applied to improve model interpretability, making the framework more suitable for clinical applications. The framework's FIRE-KNOP DES algorithm demonstrated high efficacy, achieving 88.33% accuracy in depression detection and 83.68% in severity prediction. XAI analysis identified mental and non-mental health indicators as significant factors in the framework's performance, emphasizing the value of these features for accurate depression assessment. This study emphasizes the potential of dynamic ensemble learning in mental health assessments, particularly in detecting and evaluating depression severity. The findings provide a strong foundation for future use of dynamic ensemble frameworks in mental health assessments, demonstrating their potential for practical clinical applications.

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.