Miracle-dz/UniFAR-FTUs huggingface dataset:
Postdrought Recovery Time Across Global Terrestrial Ecosystems

Drought, as an intermittent disturbance of the water cycle, profoundly impacts the terrestrial ecosystem. Recovery time (RT) of an ecosystem from drought is an important indicator of assessing drought impacts and ecosystem resilience, yet the spatiotemporal pattern of ecosystem postdrought RT remains controversial in existing studies. Here we investigate the spatiotemporal pattern of postdrought RT across global terrestrial ecosystems using two observation‐based gross primary productivity (GPP) data sets: direct flux‐site observations and gridded estimates by upscaling flux‐site observations using machine‐learning approach. For droughts that occur on average once every 5.2 years, the RT typically ranges between 2 and 8 months, with a global mean RT of ∼6 months. Spatially, both GPP data sets show a significant bilinear relationship between RT and moisture gradient, and that ecosystems in arid and humid regions tend to recover from drought more rapidly than semi‐arid/sub‐humid ecosystems. Additionally, forests show an overall longer RT than shrublands and grasslands. Temporally, global ecosystem RT shows a slight yet significant increasing trend (0.032 months yr−1) during 1982–2010, which is partly caused by the increasing drought for the same period. However, the observed patterns of RT across global bio‐climatic zones are not captured by the state‐of‐the‐art land surface models, which exhibit a shorter RT in semi‐arid/sub‐humid ecosystems but longer RT in arid/humid regions, and a larger increasing trend of RT over time (0.069 months yr−1). Our findings provide crucial insights into ecosystem vulnerability to sub‐decadal stress events and ecosystem recovery trajectories among diverse bio‐climatic regions and highlight potential model deficiencies that should be accounted for in future model developments.
Initial sizing of a roadable personal air vehicle using design of experiments for various engine types

Purpose The purpose of this paper is to analyze and compare the performances of novel roadable personal air vehicle (PAV) concepts that meet established operational requirements with different types of engines. Design/methodology/approach The vehicle configuration was devised considering the dimensions and operational restrictions of the roads, runways and parking lots in South Korea. A folding wing design was adopted for road operations and parking. The propulsion designs considered herein use gasoline, diesel and hybrid architectures for longer-range missions. The sizing point of the roadable PAV that minimizes the wing area was selected, and the rate of climb, ground roll distance, cruise speed and service ceiling requirements were met. For various engine types and mission profiles, the performances of differently sized PAVs were compared with respect to the MTOW, wing area, wing span, thrust-to-weight ratio, wing loading, power-to-weight ratio, brake horsepower and fuel efficiency. Findings Unlike automobiles, the weight penalty of the hybrid system because of the additional electrical components reduced the fuel efficiency considerably. When the four engine types were compared, matching the total engine system weight, the internal combustion (IC) engine PAVs had better fuel efficiency rates than the hybrid powered PAVs. Finally, a gasoline-powered PAV configuration was selected as the final design because it had the lowest MTOW, despite its slightly worse fuel efficiency compared to that of the diesel-powered engine. Research limitations/implications Although an electric aircraft powered only by batteries most capitalizes on the operating cost, noise and emissions benefits of electric propulsion, it also is most hampered by range limitations. Air traffic integration or any safety, and noise issues were not accounted in this study. Practical implications Aircraft sizing is a critical aspect of a system-level study because it is a prerequisite for most design and analysis activities, including those related to the internal layout as well as cost and system effectiveness analyses. The results of this study can be implemented to design a PAV. Social implications This study can contribute to the establishment of innovative PAV concepts that can alleviate today’s transportation problems. Originality/value This study compared the sizing results of PAVs with hybrid engines with those having IC engines.
Effects of technology‐enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking

This study investigated the effect of two technology‐enhanced learning methods (an adaptive educational computer game vs. a PowerPoint presentation with multimedia) on learners with different prior learning attitudes and knowledge regarding skills, conceptual knowledge, and overall knowledge of computational thinking (CT). Data from 70 students aged 11–12 were analysed using factorial multivariate analysis of covariance and analysis of covariance. Our findings revealed that while effective in fostering conceptual knowledge of CT (such as sequence, loop, and conditional), the conventional technology‐enhanced learning approach falls short when it comes to promoting CT skills (such as pattern recognition and debugging). The game approach, however, could simultaneously promote CT skills and conceptual knowledge, leading to improvement in the overall knowledge of CT. Additional analysis showed that learners with different prior knowledge and learning attitudes made a higher improvement using the adaptive game in terms of skills, conceptual knowledge, and overall knowledge of CT, especially for medium and lower prior knowledge learners that did not have a very high prior learning attitude. Consequently, to promote CT in primary school students, educators can employ computer game approaches that not only can foster both skills and conceptual knowledge of CT but also more effectively assist students with lower prior learning attitudes and knowledge.
Tree species consistent co-occurrence in seasonal tropical forests: an approach through association rules analysis

Aim of study: Aassessing the existence of consistent co-occurrence between tree species that characterize seasonal tropical forests, using the association rules analysis (ARA), that is a novel data mining methodology; and evaluate evaluating the taxonomic and functional similarities between associated species.Area of study: forty-four seasonal forest sites with permanent plots (40.2 ha of total sample) located in Southeast Brazil, from which we obtained species occurrences.Material and methods: we applied association rules analysis (ARA) to the dataset of species occurrence in sites considering the criteria of support equal to or greater than 0.63 and confidence equal to or greater than 0.8 to obtain the first set of associations rules between pairs of species. This set was then submitted to Fisher’s criteria exact p-value less than 0.05, lift equal to or greater than 1.1 and coverage equal to or greater than 0.63. We considered these criteria to be able to select non-random and consistent occurring associations.Main results: We obtained a final result of 238 rules for semideciduous forest and 11 rules for deciduous forests, composed of species characteristic of vegetation types. Co-occurrences are formed mainly by non-confamilial species, which have similar functional characteristics (potential size and wood density). There is a difference in the importance of co-occurrence between forest types, which tends to be less in deciduous forests.Research highlights: The results point to out the feasibility of applying ARA to ecological datasets as a tool for detecting ecological patterns of coexistence between species and the ecosystems functioning.Keywords: data mining; coexistence; semideciduous forests; deciduous forests; biotic interaction.
Identification of thresholds for accuracy comparisons of heart rate and respiratory rate in neonates

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.
Do election polls increase individual understanding of politics?

ABSTRACT Election polls have been widely used to probe and understand the public’s political attitudes and behaviour. However, they might simultaneously motivate people to seek information about the questions they do not know when they are asked in the polls. Given that past studies have ignored the role of polls in motivating individual information seeking, this study aims to examine the effect of polls on individual knowledge of the electoral system. Specifically, this study addresses whether individuals’ participation in the election poll would increase their understanding of the electoral system of the legislative election in Taiwan. Using survey data from Taiwan’s Election and Democratisation Study (TEDS) 2016 presidential and legislative elections, this study finds that people who are asked questions about the legislative election in the first survey are more likely to provide correct answers in the second survey compared to their counterparts. The findings imply that election polls are not only tools for understanding public opinion on competing parties or candidates and policy issues, but also for stimulating individuals to understand politics.
LTB4-Driven Inflammation and Increased Expression of ALOX5/ACE2 During Severe COVID-19 in Individuals With Diabetes

Diabetes is a known risk factor for severe coronavirus disease 2019 (COVID-19), the disease caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is a lack of knowledge about the mechanisms involved in the evolution of COVID-19 in individuals with diabetes. We aimed to evaluate whether the chronic low-grade inflammation of diabetes could play a role in the development of severe COVID-19. We collected clinical data and blood samples of patients with and without diabetes hospitalized for COVID-19. Plasma samples were used to measure inflammatory mediators and peripheral blood mononuclear cells, for gene expression analysis of the SARS-CoV-2 main receptor system (ACE2/TMPRSS2), and for the main molecule of the leukotriene B4 (LTB4) pathway (ALOX5). We found that diabetes activates the LTB4 pathway and that during COVID-19 it increases ACE2/TMPRSS2 as well as ALOX5 expression. Diabetes was also associated with COVID-19–related disorders, such as reduced oxygen saturation as measured by pulse oximetry/fraction of inspired oxygen (FiO2) and arterial partial pressure of oxygen/FiO2 levels, and increased disease duration. In addition, the expressions of ACE2 and ALOX5 are positively correlated, with increased expression in patients with diabetes and COVID-19 requiring intensive care assistance. We confirmed these molecular results at the protein level, where plasma LTB4 is significantly increased in individuals with diabetes. In addition, IL-6 serum levels are increased only in individuals with diabetes requiring intensive care assistance. Together, these results indicate that LTB4 and IL-6 systemic levels, as well as ACE2/ALOX5 blood expression, could be early markers of severe COVID-19 in individuals with diabetes.
Exploring the sensitivity of atmospheric nitrate concentrations to nitric acid uptake rate using the Met Office's Unified Model

Abstract. Ammonium nitrate is a major aerosol constituent over many land regions and contributes to air pollution episodes, ecosystem destruction, regional haze, and aerosol-induced climate forcing. Many climate models that represent ammonium nitrate assume that the ammonium-sulphate-nitrate chemistry reaches thermodynamic equilibrium instantaneously without considering kinetic limitations on condensation rates. The Met Office's Unified Model (UM) is employed to investigate the sensitivity of ammonium nitrate concentrations to the nitric acid uptake coefficient (γ) in a newly-developed nitrate scheme in which first order condensation theory is utilised to limit the rate at which thermodynamic equilibrium is attained. Two values of γ representing fast (γ = 0.193) and slow (γ = 0.001) uptake rates are tested in 20-year global UM integrations. The global burden of nitrate associated with ammonium in the “fast” simulation (0.11 Tg[N]) is twice as great as in the “slow” simulation (0.05 Tg[N]), while the top-of-the-atmosphere radiative impact of representing nitrate is −0.19 Wm−2 in the “fast” simulation and −0.07 Wm−2 in the “slow” simulation. In general, the “fast” simulation exhibits better spatial correlation with observed nitrate concentrations while the “slow” simulation better resolves the magnitude of concentrations. Local near-surface nitrate concentrations are found to be highly correlated with seasonal ammonia emissions suggesting that ammonia is the predominant limiting factor controlling nitrate prevalence. This study highlights the high sensitivity of ammonium nitrate concentrations to nitric acid uptake rates and provides a mechanism for reducing nitrate concentration biases in climate model simulations. The new UM nitrate scheme represents a step-change in aerosol modelling capability in the UK across weather and climate timescales.
Typology of substance use in a nationally representative sample of French adolescents

Abstract Background While typologies of substance use disorder are very informative, they are still scarcer. The aim of this study was to consider a classification system of substance use among adolescents based on types of substance used and levels of use, and to compare identified groups on sociodemographic characteristics, mental and physical health factors, and other psychological indices. Methods A total of 23,294 17-year-old French adolescents were assessed on mental health factors (depression, suicide, visits to a mental health professional), physical health factors (health self-perception, body shape perception, body mass index), family relationships, perceptions of substance use risk, and vision of the future. Results Hierarchical Cluster Analysis identified four classes of adolescents: ‘Adolescents who use Occasionally/experimentally Tobacco and Alcohol’ (n = 7328, 35.3%), ‘Adolescents who do not use drugs’ (n = 5218, 25.1%), ‘Adolescents who use Tobacco, Alcohol, Cannabis’ (n = 6804, 32.8%), and ‘Adolescents who use Multi-drugs’ (n = 1403, 6.8%). The two first were mostly high school girls, with no mental and physical health issue, accurate perception of substances uses risk, and positive vision of their future. They differed regarding parental socioeconomic status and body mass index. The two latest were mostly boys, with poor school performance, bad family relationships, mental health issues, incorrect perception of substances uses risk, and negative vision of their future. They differed regarding parental socioeconomic status, weight and weight perception. Conclusions Our results confirm the heterogeneous nature of drug use in adolescents, the relevance of typologies, and their importance for future public health policy.
Validating multidimensional entrepreneurial orientation in emerging economies

Purpose This study aims to examine the Lumpkin and Dess (1996) conceptualization of entrepreneurial orientation (EO) in an emerging economy, focusing on India. It ascertains the dimensions of EO that are valuable in securing superior firm performance in emerging economies, which might differ from a developed market setting. It further examines the difference in the significance of dimensions between the manufacturing and services industry. Design/methodology/approach A novel primary data set consisting of responses from 228 new ventures incorporated in India was created through an online questionnaire survey following the tailored design method principles. A disaggregated approach was adopted and the data were analyzed using multiple regression in SPSS 21. Findings Among the dimensions of EO, competitive aggressiveness positively affected new venture performance, whereas proactiveness did not show any effect. Autonomy also exhibited a positive impact, except for new venture growth in manufacturing firms. Innovativeness exhibited partial support, only for the effectiveness of service firms. Risking-taking exhibited a negative effect on performance, particularly for manufacturing firms. Practical implications The findings guide entrepreneurs and managers operating their new ventures in emerging economies by suggesting the dimensions that are most likely to benefit firm performance and those that might be detrimental. Originality/value This study empirically validates the multidimensional conceptualization of EO in India and extends previous studies, which have typically focused on an aggregated EO scale. This study’s findings attest that the manifestation of EO in emerging economies might be different compared to mature economies. The contrast between the manufacturing and service sectors is also shown.
A New Model Averaging Approach in Predicting Credit Risk Default

The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.
Study on the Effect Fractional Ablative Erbium: YAG Laser in Treatment of Melasma

Introduction: Melasma is a kind of acquired chronic hyperpigmentation that primarily have an effect on women on sun-exposed regions of the body, particularly on the face in Fitzpatrick skin types III-V. The investigation background is at the Dermatology Clinic at NILES, Cairo University, Egypt. The aim: This investigation aims to evaluate the clinical value and protection of fractional ablative erbium: YAG Laser therapy in handling the melasma. Patients, materials and methods: This study included 30 patients with melasma on their face aged 20-50 years. The patients were treated by four sessions of fractional erbium YAG laser at a period of four weeks. Results: The percentage of improvement in patients treated by fractional erbium YAG laser was 20 – 80% with a mean 54.33 ± 18.01. Conclusion: It was monitored that fractional erbium YAG is successful in treatment of patients
Qualitative Risk Analysis of the Transmission of Highly Pathogenic Avian Influenza (HPAI) H5N1 through Manure Trade in Côte d'Ivoire

The contamination with Highly Pathogenic Avian Influenza (HPAI) H5N1 viruses occurs via the digestive tract following the ingestion of water or food contaminated with droppings of asymptomatic carriers or sick birds. Regarding the local practice of the use of poultry manure as an agricultural fertilizer, this study focuses on the risk of spread of the HPAI through the manure trade pathway in Côte d'Ivoire. For this purpose, epidemiological data and 96 poultry droppings samples were collected from 18 farms. The droppings samples were tested using the real time polymerase chain reaction (RT-PCR). The qualitative risk assessment (QRA) took into account event patterns Original Research Article Cyril et al.; AFSJ, 20(7): 38-46, 2021; Article no.AFSJ. 68866 39 by integrating all the pathways involved in the spread of HPAI. From the diagnostic test, all the 96 samples tested negative. Further investigations revealed that 74% of the Agnibilékrou farms experienced HPAI outbreaks in the past two years. The main risk factors identified were the movement of people, animals and fomites from one infected area to another. Additionally, the duration of storage of droppings and the distance between stockpiles and farms were potential risk factors. The QRA identified two levels of risk: moderate to high (60%) and low to negligible (40%). The estimated high risk occurs when the dropping is fresh and is low after an optimal period of storage. It is therefore necessary to make storage systematic and mandatory as a measure of treatment before the adoption of other complex measures such as composting and industrial processing.