Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the Spatial Heterogeneity and Driving Factors of Aboveground Forest Biomass in the Urban Area of Xi'an, China

Investigating the spatial distribution of urban forest biomass and its potential influencing factors would provide useful insights for configuring urban greenspace. Although China is experiencing an unprecedented scale of urbanization, the spatial pattern of the urban forest biomass distribution as a critical component in the urban landscape has not been fully examined. Using the geographic detector method, this research examines the impacts of four geographical factors (GFs)—dominant tree species, forest categories, land types, and age groups—on the aboveground biomass distribution of urban forests in 1480 plots in Xi’an, China. The results indicate that (1) the aboveground biomass and four GFs show obvious heterogeneity regarding their spatial distribution in Xi’an; (2) the dominant tree species and age group which impacts the patterns of aboveground biomass are the primary GFs, with the independent q value (a statistic metric used to quantify the impacts of GFs in this study) reaching 0.595 and 0.202, respectively, while the forest category and land type were weakly linked to the spatial variation of aboveground biomass, with a q value of 0.087 and 0.076, respectively; and (3) the interactions among these four GFs also tend to contribute to the distribution pattern of aboveground biomass. The interactions between GFs achieved a larger impact than the sum of impacts that were independently obtained from the factors. Our results showed that the method of using a geographical detector is a useful tool in the urban area, and can reveal the driver pattern of aboveground biomass and provide a reference for city planning and management.
Covid-19: Socio-Economic Implications and Effect of Government Policy Initiatives on Developing Economies

This research investigates the socio-economic implications and effect of government policy initiative of coronavirus on developing economies from a neoliberalist theoretical perspective using a pragmatist approach and mixed method research. Quantitative and qualitative data have been employed to evaluate the effect of the virus on developing economies. The findings of this research reveal the following: that the coronavirus caused significant mortality and morbidity in developing countries and its rapid spread of across countries, and across borders led to a global economic recession. Several drastic government measures were taken to curb the spread of the virus through restriction of individual freedom and movement. These measures include social distancing, and isolation; closures of educational institutions, religious institutions, and businesses, prohibition of public events, and restriction of domestic and foreign travel. Furthermore, the prolonged lockdown measures taken by developing countries to contain the spread of the virus worsened their economic crises. It is expected that the pandemic will reduce economic growth, worsen government debt, increase inflation and worsen current account deficits. The long-term effect of the pandemic in developing countries is the worsening of inequality and poverty and wiping off the economic development strides gained in the last two decades.
Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

The development of parameterizations is a major task in the development of weather and climate models. Model improvement has been slow in the past decades, due to the difficulty of encompassing key physical processes into parameterizations, but also of calibrating or “tuning” the many free parameters involved in their formulation. Machine learning techniques have been recently used for speeding up the development process. While some studies propose to replace parameterizations by data‐driven neural networks, we rather advocate that keeping physical parameterizations is key for the reliability of climate projections. In this paper we propose to harness machine learning to improve physical parameterizations. In particular, we use Gaussian process‐based methods from uncertainty quantification to calibrate the model free parameters at a process level. To achieve this, we focus on the comparison of single‐column simulations and reference large‐eddy simulations over multiple boundary‐layer cases. Our method returns all values of the free parameters consistent with the references and any structural uncertainties, allowing a reduced domain of acceptable values to be considered when tuning the three‐dimensional (3D) global model. This tool allows to disentangle deficiencies due to poor parameter calibration from intrinsic limits rooted in the parameterization formulations. This paper describes the tool and the philosophy of tuning in single‐column mode. Part 2 shows how the results from our process‐based tuning can help in the 3D global model tuning.
Sun Exposure and Protection Practices of Spanish University Students of Education Sciences. Are Future Teachers Protected Against Skin Cancer and Other Adverse Effects?

Background: Excessive sun exposure, together with insufficient protection, is the main risk factor for the onset of Melanoma and Non Melanoma skin cancer, namely the most common cancer in fair-skinned populations across the world, and of several other skin and eye adverse health effects. Epidemiological data show the existence of scant awareness of this risk, and of high rates of sunburn among young people. The main aim of the present study is to examine sun exposure habits and protection behaviour in university students. Methods: We performed a cross-sectional questionnaire-based study to investigate sun exposure and protection practices in a group of education sciences students at a university in southern Spain. This survey provided data for a descriptive and comparative analysis, by groups and gender, of photoprotection and skin self-examination practices. The reliability and validity of the questionnaire have been analysed. Results: The questionnaire was completed by 315 students. Most of them (74.6%) have suffered at least one sunburn in the last year. The use of sunscreen or protective clothing is not frequent, and 89.5% of students do not self-examine their skin. The metric properties of the questionnaire showed high values of reliability and validity. Conclusions: The awareness of solar exposure risk in Spanish university students of Education Sciences is low, self-protection is scant, the potential exposure to dangerous levels of UV radiation is high and most students have suffered one or more sunburns in the last year. Intervention strategies should be introduced in these students to highlight the risks involved and the need of more adequate sun protection practices. Information campaigns should be conducted to address the problem so that, when these students become teachers, they have an adequate knowledge of the risk and of the benefits of a higher attention to this problem, and can translate these competences in health education.
Remote sounding of atmospheric temperature from satellites II. The selective chopper radiometer for Nimbus D

The design of a six-channel radiometer for remote temperature sounding to be mounted on the Nimbus D satellite is described. Emission from carbon dioxide in the 15 μm band is monitored by the radiometer. Spectral selection in the radiometers is achieved by interference filters together with either selective absorption (to improve the vertical resolution of the instrument) or selective chopping (to enable emission from altitudes up to 50 km to be monitored).
Impact of heavy metal ions on the simultaneous saccharification and fermentation of formosan alder biomass to form lactic acid

Formosan alder (Alnus formosana) is a fast-growing, adaptable, pioneer native tree species in Taiwan, and it is particularly suitable for reforestation. In this study, steam-exploded Formosan alder biomass was employed to investigate lactic acid production by simultaneous saccharification and fermentation (SSF) in the presence of different heavy metals. Impacts of added heavy metals on saccharification processing were investigated. In the presence of 1410 mg Cr6+/L, negative impacts were observed for SSF. The same level of Cr6+ adversely affected fermentation by Lactobacillus casei and L. acidophilus compared to the blank controls. Positive impacts for SSF by Cd2+ were demonstrated with 108 mg Cd2+/L, and the same conditions favored fermentation by L. casei and L. acidophilus. No impacts for SSF by Pb2+ up to 6830 mg Pb2+/L were found for both Lactobacillus strains. This study demonstrates that SSF for production of lactic acid from Formosan alder biomass was able to tolerate a wide range of heavy metal concentration regimes. Hence, this study provides an alternative use for biomass harvested from phytoremediation sites. Such biomass can be used as sustainable regenerative biomaterial, and thereby it can further enhance the benefits of environmental remediation.
Predictive Maintenance (PdM) Structure Using Internet of Things (IoT) for Mechanical Equipment Used into Hospitals in Rwanda

The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.
How Does Aggregate Tax Policy Uncertainty Affect Default Risk?

We examine the impact of aggregate tax policy uncertainty on firm-level default risk. Due to uncertainties associated with tax policies, firms could have difficulties in determining their optimal debt level and use too much debt to increase their values. This can increase firms’ financial risk and default probabilities. At the same time, tax policy uncertainty may lead some firms to take less risk which could lower their use of debt and in turn lower the probability of default. We find that tax policy uncertainty is positively associated with firms’ expected default probabilities. In terms of economic significance, our findings show an increase of 14.83% in expected default probability, on a relative basis. Our results are robust to controlling for conditions of the economy, conditions of the stock market, financial constraints of firms, and credit quality of firms. Our evidence adds to two strands of research: research on taxation and firms’ risk profiles and the impact of policy uncertainty on firms’ decisions.
Influence of Supplementing Sesbania grandiflora Pod Meal at Two Dietary Crude Protein Levels on Feed Intake, Fermentation Characteristics, and Methane Mitigation in Thai Purebred Beef Cattle

The aim of the study was to evaluate the effect of crude protein (CP) levels in concentrate and Sesbania grandiflora pod meal (SG) supplementation on feed intake, rumen fermentation, and methane (CH4) mitigation in Thai purebred beef cattle. Four cattle with 100 ± 5.0 kg body weight were used in this study. A 2 × 2 factorial experiment in a 4 × 4 Latin square design was conducted, in which factor A was the CP levels in concentrate of 14% and 16% of dry matter (DM) and factor B was the supplement levels of SG at 0.4% and 0.6% DM intake, respectively. The results showed that the CP content in concentrate and SG supplementation had no interaction effect on intake, digestibility, ruminal ecologies, ruminal fermentation products, and nitrogen utilization. Increasing CP content to 16% significantly (p < 0.05) increased the ruminal ammonia nitrogen (NH3-N), nitrogen (N) intake, N absorption, and N retention. SG supplementation significantly (p < 0.05) decreased CP digestibility, NH3-N, blood urea nitrogen, and protozoa. In addition, SG significantly decreased acetate (C2), acetate to propionate ratio, methane, and fecal N excretion, while it significantly increased total volatile fatty acids (VFAs) and propionate (C3) concentration. In conclusion, SG could mitigate methane emission and enhance nitrogen utilization.
Über kationische Kohlenoxidkomplexe, VII. Reaktionsweisen von Rhenium (I)-Kohlenoxidkomplexen und neue Anschauungen über den Mechanismus der Basenreaktion von Metallcarbonylen

Bei den Umsetzungen von Rhenium(I)-Kohlenoxidkomplexen mit verschiedensten Carbonylmetallaten konnten im wesentlichen 2 Reaktionstypen festgestellt werden. Einerseits liesen sich in geeigneten Losungsmittelsystemen weitere Carbonylsalze [Re(CO)4L2][Me(CO)nL′] (L2 = 2 CO, 2 P(C6H5)3, o-Phenanthrolin; Me V, Mn, Fe, Co; L′ CO, NO) fassen, andererseits wurden infolge einer Redoxreaktion zwischen Carbonyl-Kation und Carbonylmetallat gemas gemischte Metallcarbonyle mit einer einfachen Metall-Metall-Bindung erhalten. Dieses sehr allgemein anwendbare Reaktionsprinzip lieferte hier die Metallcarbonyle (CO)5 ReMn(CO)5, (CO)5ReCo(CO)4 und deren o-Phenanthrolinderivate. IR-Spektren und Struktur der Verbindungen werden diskutiert. – Natrium-pentacarbonylrhenat(–I) bildet mit Chlorameisensaure-athylester das fluchtige Carboathoxopentacarbonylrhenium, das von Chlorwasserstoff in Carbonyl-Kation und Athanol gespalten wird: Anhand der Reaktionsweise kationischer Kohlenoxidkomplexe von Mangan(I) und Rhenium(I) mit Alkoholaten und Hydroxyl-Ionen wird eine neue Anschauung uber die Basen-reaktion neutraler Metallcarbonyle mit Alkalilaugen entwickelt. Als einleitender Schritt wird der nuceophile Angriff eines Hydroxyl-Ions am C-Atom eines CO-Liganden postuliert.
Toward distributed pedagogical leadership for quality improvement: Evidence from a childcare centre in Singapore

This article presents findings from a representative case study of a typical childcare centre in Singapore that achieved an emerging level of quality certification despite being under-resourced as a non-profit centre. Like other centres, it was navigating a climate of early childhood care and education (ECCE) policy reforms and teacher shortage. Given that quality rating has existed for a few years, the study aimed to explore centre-based conditions that could hinder or facilitate teachers’ distributed pedagogical leadership and the development of a community of practice – both assumed to be necessary for teachers’ continued learning and improvement of centres’ practices. Qualitative data were generated across 10 months through observations, individual interviews and focus group discussions to journey with the teaching team as they negotiated relationships and built a shared vision for their practices. Results are presented here as three overarching themes that explicate: (a) the inevitable influences of the national context of the sector’s workforce; (b) organisational culture and power relations; and (c) pedagogical vision. These illustrate the intricacies involved in centre-based quality improvement work, signalling the need for more investigations into the everyday realities of ECCE reforms as experienced by teachers-as-learners and leaders.
Diversity of Iron Oxidizing and Reducing Bacteria in Flow Reactors in the Äspö Hard Rock Laboratory

Processes of iron mineralization are of great significance to the understanding of Early-Earth geochemistry. Of specific interest are processes at circumneutral pH, where chemical oxidation of Fe can outcompete biological oxidation. To better understand microbially-induced mineral formation and the composition of the involved microbial communities, we set up a series of flow-reactors in the Äspö Hard Rock Laboratory, a 3.6 km tunnel that runs under the Baltic Sea. Various aquifers of Fe2+-rich brackish to saline waters penetrate the tunnel through a series of fractions. The reactors were set up with different combinations of light and aeration conditions, and were connected to three aquifers of differing chemical composition and age. Using a combination of 454 pyrosequencing and CAtalyzed Reporter Deposition Fluorescent In Situ Hybridization we analyzed the bacterial community from these reactors in two consecutive seasons half a year apart. A general decrease in diversity was observed towards the deep part of the tunnel. Multivariate modeling of the community composition and environmental parameters shows that the overall diversity of the microbial community is controlled by salinity as well as carbon and nitrogen sources. However, the composition of iron oxidizing bacteria is driven by pH, O2 and the availability of Fe2+. The latter is mostly supplied by Fe3+ reduction in the reactors. Thus the reactors form a self-sustained ecosystem. Several genera of known aerobic and anaerobic iron oxidizing bacteria were found. Mariprofundus sp. was found to be dominant in many of the samples. This is the first detection of this marine species in groundwater. The microbial community in the reactors is unique in each site, while that in the exposed tunnel is more homogenous. Therefore we suggest that the flow reactors are a good model system to study the nonaccessible microbial communities that are likely present in cracks and crevices of the surrounding bedrock.
What motivates social entrepreneurs? A meta-analysis on predictors of the intention to found a social enterprise

ABSTRACT Social entrepreneurship (SE) is acknowledged as a valuable tool for tackling social problems. Whereas SE intention (SEI) is considered an important prerequisite for founding a social enterprise, empirical research on SEI-antecedents lacks structure and quantitative integration. We use a newly developed framework featuring individual-, social-, and economic-level antecedents of SEI to summarize prior research on SEI in a meta-analysis (k = 21; N = 8697). Results show that our framework is empirically feasible, as significant effects of individual, social, and economic variables on SEI emerge. Furthermore, we find that the national economic stage, sample composition, and SEI-measurement act as moderators.
Using Primary Sources to Improve Classroom Climate and Promote Shared Responsibility

ABSTRACT To address a deteriorating classroom climate at the midpoint of a two-semester course sequence, we employed a novel intervention: reading a research manuscript together with students as an invitation to legitimate peripheral participation in scholarly reflection on teaching and learning. This intervention resolved many student complaints about the structure and level of the course, and the manuscript’s discussion of the didactical contract helped students claim their share of responsibility for their own learning. We propose that reading research literature with students can be an effective tool for addressing classroom challenges, and recommend the didactical contract for promoting shared responsibility.
The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka

ABSTRACT Formulation of effective policies to enhance the resilience of tourism following the COVID-19 pandemic essentially requires comprehensive empirical information on changes in tourism demand and associated economic costs. The paper makes a novel contribution to tourism literature by employing regionally and temporally disaggregated tourism data and Google search data in improving the accuracy of tourism forecasts. Further, the paper adopts two timeseries variables namely tourist arrivals and guest nights in order to understand the changes due to COVID-19 in tourism demand more comprehensively. Monthly data on international tourist arrivals, guest nights and Google trends from 2004 to 2019 are used to produce regionally disaggregated (Europe, Asia, the Pacific, America, Other) monthly tourism forecasts for Sri Lanka. We find that SARMAX models outperform the other models (ARIMA, ARIMAX, SARIMA) in forecasting tourism demand following COVID-19. Interestingly, the paper makes a further step in utilizing forecasts in estimating foregone economic benefits due to COVID-19 pandemic. We find a notable difference in estimated direct economic loss depending on the variable used in estimates. The percentage loss is 40% when arrival forecasts are used in estimates and 29% when guest night forecasts are used in estimates. This provides important policy implications for improving post-COVID tourism.
Páramo Ecosystems in Ecuador’s Southern Region: Conservation State and Restoration

The páramo is home to a significant proportion of global biodiversity and provides essential services for the development of life for millions of people in Ecuador. However, land use/land cover (LULC) changes threaten biodiversity and modify its functioning. The objectives of this study were: (1) to evaluate the conservation status of the herbaceous páramo (HP) ecosystem by analyzing its LULC in Ecuador’s southern region. (2) to identify possible regions where the native páramo ecosystem is being restored. We analyzed Landsat 8 images using Object-Based Image Analysis (OBIA) and a Classifier Decision Tree (CDT) to achieve these objectives. The results show that the native herbaceous páramo (NHP) ecosystem is being transformed into an anthropogenic HP (AHP). The area covered by the NHP ecosystem (296,964 ha) has been reduced by 50% (149,834 ha). Nevertheless, we identified five regions where the NHP is upgrading. These regions are relevant for studying NHP regeneration in Ecuador’s southern region, where soils are mostly andosols. The LU of the páramo, with cycles of exploitation, abandonment, and regeneration in a secondary páramo, is transforming the NHP ecosystem. These exploitation practices, global climate change, and lack of knowledge about the NHP ecosystem’s regeneration and its soils’ recovery threaten to substantially reduce the NHP area, its functionality, and its ecosystem services.
A Survey on IoT Big Data

Driven by the core technologies, i.e., sensor-based autonomous data acquisition and the cloud-based big data analysis, IoT automates the actuation of data-driven intelligent actions on the connected objects. This automation enables numerous useful real-life use-cases, such as smart transport, smart living, smart cities, and so on. However, recent industry surveys reflect that data-related challenges are responsible for slower growth of IoT in recent years. For this reason, this article presents a systematic and comprehensive survey on IoT Big Data (IoTBD) with the aim to identify the uncharted challenges for IoTBD. This article analyzes the state-of-the-art academic works in IoT and big data management across various domains and proposes a taxonomy for IoTBD management. Then, the survey explores the IoT portfolio of major cloud vendors and provides a classification of vendor services for the integration of IoT and IoTBD on their cloud platforms. After that, the survey identifies the IoTBD challenges in terms of 13 V’s challenges and envisions IoTBD as “Big Data 2.0. ” Then the survey provides comprehensive analysis of recent works that address IoTBD challenges by highlighting their strengths and weaknesses to assess the recent trends and future research directions. Finally, the survey concludes with discussion on open research issues for IoTBD.