Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrogen, phosphorus and sulphur mineralization in soil treated with amended municipal solid waste compost under aerobic and anaerobic conditions

Purpose Our study had considered some organic amendments to enhance nutrient level of marketed municipal solid waste (MSW) compost for its potential use as fertilizer for growing crops in alluvial soils. Method We prepared three types of amended compost by mixing 20% mustard oil cake (MOC), and 30% poultry manure (PM) or cowdung (CD) or sugarcane press mud (SPM) with 50% MSW compost. The nitrogen (N), phosphorus (P) and sulphur (S) mineralization study was done in soil treated with three and amended and one unamended composts with three replications through an incubation experiment at a temperature of 25 ± 1°C for 82 days under aerobic (field capacity) and anaerobic (submerged) conditions. The mineralization data were fitted to the first-order kinetic model. Results The soil NO3--N content was 2-3 times higher in aerobic condition than in anaerobic condition, while the NH4+-N was higher in anaerobic soils. The kinetic model reveals that poultry manure and sugarcane press mud had higher capability to supply N for use by the crops. The P release was the highest at day 15 with three-time higher availability in anaerobic condition. The S mineralization in soil was higher in field capacity than in submerged condition. ConclusionThe compost mixture comprising MSW, MOC and SPM in a ratio of 5:2:3 demonstrated the highest cumulative N, P and S mineralization in both aerobic and anaerobic conditions. The N and S availability decreased while the P availability increased in submerged soils which result has fertilizer management implications for wet land rice crop.
A Motor-Driven and Computer Vision-Based Intelligent E-Trap for Monitoring Citrus Flies

Citrus flies are important quarantine pests in citrus plantations. Electronic traps (e-traps) based on computer vision are the most popular types of equipment for monitoring them. However, most current e-traps are inefficient and unreliable due to requiring manual operations and lack of reliable detection and identification algorithms of citrus fly images. To address these problems, this paper presents a monitoring scheme based on automatic e-traps and novel recognition algorithms. In this scheme, the prototype of an automatic motor-driven e-trap is firstly designed based on a yellow sticky trap. A motor autocontrol algorithm based on Local Binary Pattern (LBP) image analysis is proposed to automatically replace attractants in the e-trap for long-acting work. Furthermore, for efficient and reliable statistics of captured citrus flies, based on the differences between two successive sampling images of the e-trap, a simple and effective detection algorithm is presented to continuously detect the newly captured citrus flies from the collected images of the e-trap. Moreover, a Multi-Attention and Multi-Part convolutional neural Network (MAMPNet) is proposed to exploit discriminative local features of citrus fly images to recognize the citrus flies in the images. Finally, extensive simulation experiments validate the feasibility and efficiency of the designed e-trap prototype and its autocontrol algorithm, as well as the reliability and effectiveness of the proposed detection and recognition algorithms for citrus flies.
Economic Inequality, the Digital Divide, and Remote Learning During COVID-19

Wealth and education establish a cycle of intergenerational inequality. Wealthier households can provide more educational opportunities for their children, who then will have more chances to build wealth for themselves. The digital divide may have emerged as a key reinforcing mechanism of education through wealth and of future wealth through education during the pandemic. The intergenerational transmission of racial wealth inequality likely played out at rapid speed during the pandemic. We analyze the link between wealth, reliable internet and electronic device availability, remote learning time, race, and ethnicity, using the U.S. Census Bureau's Household Pulse Survey. We conclude that Black and Hispanic/Latinx households have less reliable internet and devices available. This goes along with fewer hours children spend on remote learning. The lack of internet and devices correlates with less wealth, as reflected in lower homeownership rates and greater housing instability. Black and Hispanic/Latinx households, in particular, are more likely to be renters and face housing instability.
Methane emissions from livestock in East Asia during 1961−2019

ABSTRACT Context: East Asia is a crucial region in the global methane (CH4) budget, with significant contributions from the livestock sector. However, the long-term trend and spatial pattern of CH4 emissions from livestock in this region have not been fully assessed.Methods: Here, we estimate CH4 emissions from 10 categories of livestock in East Asia during 1961 – 2019 following the Tier 2 approaches suggested by the 2019 Refinement to the IPCC 2006 Guidelines.Results: livestock-sourced CH4 emission in 2019 was 13.22 [11.42 – 15.01] (mean [minimum%maximum of 95– confidence interval] Tg CH4 yr-1, accounting for an increase of 231% since 1961. The contribution of slaughtered populations to total emissions increased from 3% in 1961 to 24% in 2019. Spatially, the emission hotspots were mostly distributed in eastern China, South Korea, and parts of Japan, but they tend to shift northward after 2000.Conclusion: It is necessary to use dynamic emission factors and include slaughtered populations in the estimation of livestock CH4 emissions. Regions including Northern China, Mongolia, and South Korea deserve more attention in future CH4 mitigation efforts.
The Use of Supercontinuum Laser Sources in Biomedical Diffuse Optics: Unlocking the Power of Multispectral Imaging

Optical techniques based on diffuse optics have been around for decades now and are making their way into the day-to-day medical applications. Even though the physics foundations of these techniques have been known for many years, practical implementation of these technique were hindered by technological limitations, mainly from the light sources and/or detection electronics. In the past 20 years, the developments of supercontinuum laser (SCL) enabled to unlock some of these limitations, enabling the development of system and methodologies relevant for medical use, notably in terms of spectral monitoring. In this review, we focus on the use of SCL in biomedical diffuse optics, from instrumentation and methods developments to their use for medical applications. A total of 95 publications were identified, from 1993 to 2021. We discuss the advantages of the SCL to cover a large spectral bandwidth with a high spectral power and fast switching against the disadvantages of cost, bulkiness, and long warm up times. Finally, we summarize the utility of using such light sources in the development and application of diffuse optics in biomedical sciences and clinical applications.
International Trends in K-12 Computer Science Curricula Through Comparative Analysis: Implications for the Primary Curricula

The purpose of this study was to identify international trends in K–12 computer science curricula in countries that have introduced computer science education. Content analysis method was used to analyze the country-wide curricula of 10 countries which have introduced computer science education at the primary level. The K–12 Computer Science Framework was used as a theoretical frame to analyze the curricula. The results show that most countries begin their curricula with subconcepts of algorithms, program development, and under impact of computing, along with the practice of creating computational artifacts; then, countries expand upon computer science concepts and practices as learners progressed through the higher grades. Further, countries tend to introduce computer science concepts and practices in stages; once concepts and practices are introduced, they continue across multiple grades. Three approaches to implementing computer science education into the country-wide curriculum were found: introducing computer science (a) as an independent subject, (b) within multiple subjects, and/or (c) as a part of transversal competencies or an independent computer science curriculum with a cross-curricular approach. These study findings can contribute to a worldwide effort to introduce computer science education at the primary level.
Financial inclusion and green economic efficiency: evidence from China

Financial inclusion is crucial to a country’s competitiveness while green economy emerges as a policy priority along the sustainable development path, but how will they interact? This study examines the relationship between the two in the context of China, which is on the transition to a green economy based on city level data for 2011–2015. A multidimensional measurement of financial inclusion is established by considering the rapidly developing digital finance, and the measurement of green economic efficiency by applying the MinDW model. Results indicate that the development of financial inclusion can enhance green economic efficiency, which is mainly realized through the strengthening of the credit constraints on high-polluting firms. The findings provide further implications for implementing a financial development policy and maintain a balanced relationship between the government, financial institutions, and firms.
Renting-in cropland, machinery use intensity, and land productivity in rural China

ABSTRACT This study examines the impacts of renting-in cropland on machinery use intensity, utilizing an innovative endogenous-treatment Poisson regression (ETPR) model and survey data from wheat farmers in China. We also analyse how machinery use intensity affects land productivity, reflected by wheat yields and net returns, using a two-stage residual inclusion (2SRI) model. Unlike previous studies that consider general machinery use, this study considers self-owned machinery use intensity and purchased machinery service use intensity. The ETPR model results reveal that renting-in cropland significantly increases self-owned machinery use intensity. However, it has a negative and insignificant impact on purchased machinery service use intensity. The 2SRI model estimates show that increasing self-owned machinery use intensity and purchased machinery service use intensity significantly increases wheat yields and net returns. Our findings suggest that it is essential to take stakeholders’ land transfer status into account when designing policies to promote agricultural mechanization and enhance land productivity.
Austerity, political control and supplier selection in English local government: implications for autonomy in multi-level systems

ABSTRACT Analysis of 60,000 contracts awarded by English councils between 2015–19 reveals that austerity constraints are a key predictor of councils outsourcing services to for-profit suppliers, regardless of their political control. Conservative Party-controlled councils are also more likely to contract with for-profit suppliers, although we found no link between Labour-controlled councils and not-for-profit suppliers, nor evidence that political or budgetary factors influence whether councils contract with providers based in their own region. We argue that centrally imposed funding cuts, and a belief that for-profit suppliers represent a cheaper option, could be overriding Labour Party councils’ ideological preference for not-for-profit providers.
Hydrological Functioning of Maize Crops in Southwest France Using Eddy Covariance Measurements and a Land Surface Model

The primary objective of this study is to evaluate the representation of the energy budget for irrigated maize crops in soil–vegetation–atmosphere transfer (SVAT) models. To this end, a comparison between the original version of the interactions between the soil–biosphere–atmosphere (ISBA) model based on a single-surface energy balance and the new ISBA-multi-energy balance (ISBA-MEB) option was carried out. The second objective is to analyze the intra- and inter-seasonal variability of the crop water budget by implementing ISBA and ISBA-MEB over six irrigated maize seasons between 2008 and 2019 in Lamasquère, southwest France. Seasonal dynamics of the convective fluxes were properly reproduced by both models with R2 ranging between 0.66 and 0.80 (RMSE less than 59 W m−2) for the sensible heat flux and between 0.77 and 0.88 (RMSE less than 59 W m−2) for the latent heat flux. Statistical metrics also showed that over the six crop seasons, for the turbulent fluxes, ISBA-MEB was consistently in better agreement with the in situ measurements with RMSE 8–30% lower than ISBA, particularly when the canopy was heterogeneous. The ability of both models to partition the evapotranspiration (ET) term between soil evaporation and plant transpiration was also acceptable as transpiration predictions compared very well with the available sap flow measurements during the summer of 2015; (ISBA-MEB had slightly better statistics than ISBA with R2 of 0.91 and a RMSE value of 0.07 mm h−1). Finally, the results from the analysis of the inter-annual variability of the crop water budget can be summarized as follows: (1) The partitioning of the ET revealed a strong year-to-year variability with transpiration ranging between 40% and 67% of total ET, while soil evaporation was dominant in 2008 and 2010 due to the late and poor canopy development; (2) drainage losses are close to null because of an impervious layer at 60 cm depth; and (3) this very specific condition limited the inter-annual variability of irrigation scheduling as crops can always extract water that is stored in the root zone.
Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

Coronavirus disease 2019 (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Owing to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. The standard testing technique called polymerase chain reaction for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19-positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this article, we identify significant challenges regarding deep-learning-based systems and techniques that use different medical imaging modalities, including cough and breadth, chest X-ray, and computed tomography, to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges.
Financial constraints and financing decision in cross-border mergers & acquisitions: evidence from the U.S. retail sector

ABSTRACT Many global retailers in mature markets employ mergers and acquisitions (M&As) as an essential strategic tool to expand into foreign markets. Given the substantial presence of financial constraints and the importance of financing strategy in cross-border M&As, the present study attempts to answer the research question: how do different dimensions (e.g. internal, debt-focused, and equity-focused) and degrees of financial constraints affect U.S. retail multinational enterprises’ (MNEs) cross-border M&A financing decisions? Based on 91 cross-border M&As carried out by U.S. retailers during 2002–2014, our findings suggest that abundant cash reserves and large unused debt capability are associated with Cash Only financing. We also find that acquirers are more likely to adopt Debt financing than Equity financing when they face medium to high level of internal constraints but have large unused debt capabilities. Our study sheds light on retail MNEs’ best financing practices based on their financial conditions.
Debt Sustainability: Can EU Member States Use Environmental Taxes to Regain Fiscal Space?

The COVID-19 pandemic has taken its toll on the economies of the EU member states. While policymakers have been faced with rising government spending in an effort to combat the health crisis, this has led to unprecedented levels of government debt and budget deficits. Debt sustainability has been severely affected by decreasing fiscal space, and there are significant concerns that a debt crisis is looming on the horizon for the EU. The current study aims to analyze environmental taxes as a potential solution for rebuilding fiscal space and thus improving debt sustainability in the EU. The research method used to study the impact that the four types of environmental taxes may have on fiscal space is the dynamic panel regression model, estimated using the Generalized Method of Moments (GMM). The conclusions show that all four categories of environmental taxes can help the EU member states regain fiscal space and improve their debt sustainability. The research results show that the strongest positive impact on fiscal space will be achieved by energy taxes and transport taxes.
Learning Syllables Using Conv-LSTM Model for Swahili Word Representation and Part-of-speech Tagging

The need to capture intra-word information in natural language processing (NLP) tasks has inspired research in learning various word representations at word, character, or morpheme levels, but little attention has been given to syllables from a syllabic alphabet. Motivated by the success of compositional models in morphological languages, we present a Convolutional-long short term memory (Conv-LSTM) model for constructing Swahili word representation vectors from syllables. The unified architecture addresses the word agglutination and polysemous nature of Swahili by extracting high-level syllable features using a convolutional neural network (CNN) and then composes quality word embeddings with a long short term memory (LSTM). The word embeddings are then validated using a syllable-aware language model (31.267) and a part-of-speech (POS) tagging task (98.78), both yielding very competitive results to the state-of-art models in their respective domains. We further validate the language model using Xhosa and Shona, which are syllabic-based languages. The novelty of the study is in its capability to construct quality word embeddings from syllables using a hybrid model that does not use max-over-pool common in CNN and then the exploitation of these embeddings in POS tagging. Therefore, the study plays a crucial role in the processing of agglutinative and syllabic-based languages by contributing quality word embeddings from syllable embeddings, a robust Conv–LSTM model that learns syllables for not only language modeling and POS tagging, but also for other downstream NLP tasks.
The effectiveness of orientation and mobility training on the quality of life for students who are blind in Iran

Orientation and mobility (O&M) training is a practical and helpful method to prepare people who are blind for independent living. Hence, this study aimed to survey the effect of O&M training on the quality of life in students who are blind in Iran. This research was a quasi-randomized controlled trial design. Participants were 24 third-grade to sixth-grade students from two schools for blind in Isfahan, Iran. Using cluster sampling method, one school was assigned to the experimental group (n = 12) and the other to the control group (n = 12). The experimental group participated in the 8-session O&M training program. Both groups completed a World Health Organization Quality of Life (WHOQOL-BREF) at pre-intervention and post-intervention. The obtained data were analyzed using analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) test. The results showed significant increases in quality of life and all subscales (physical health, mental health, social relationships, and environmental health) in the experimental group than in the control group. The findings suggest that O&M training can promote quality of life among students who are blind in Iran. Moreover, these findings increase the interest of children and adolescents who are blind in O&M training.
Possible design of PBR for passive decay heat removal

Conditions for design parameters of above-ground and underground, prismatic high-temperature gas-cooled reactor (HTGR)s for passive decay heat removal based on fundamental heat transfer mechanisms were obtained in the previous works. In the present study, analogous conditions were obtained for pebble bed reactors by performing the same procedure using the model for heat transfer in porous media of COMSOL 4.3a software, and the results were compared. For the power density profile, several approximated distributions together with original one throughout the 10-MWt high-temperature gas-cooled reactor-test module (HTR-10) were used, and it was found that an HTR-10 with a uniform power density profile has the higher safety margin than those with other profiles. In other words, the safety features of a PBR can be enhanced by flattening the power density profile. We also found that a prismatic HTGR with a uniform power density profile throughout the core has a greater safety margin than a PBR with the same design characteristics. However, when the power density profile is not flattened during the operation, the PBR with the linear power density profile has more safety margin than the prismatic HTGR with the same design parameters and with the power density profile by cosine and Bessel functions.
The variations of characteristic scale length of friction evolution affect earthquake dynamics

Within a fault governing model the characteristic scale length is one of the most relevant physical parameters because it accounts for the so–called fracture energy (density) of the system, its dynamics, the time during which the accumulated stress is released and the seismic waves are excited, the amount of slip developed during an instability event. Friction laboratory experiments reveal that it is not a material property, but that it changes with the sliding velocity. We propose two rather different analytical models to fit laboratory evidence and we incorporate them into a fault model able to simulate repeated earthquakes in the framework of various formulations of rate and state friction. We demonstrate that temporal variations of the scale length do not prevent the system to reach its limit cycle, but they systematically reduce the magnitude of the expected event (both in term of developed slip, and thus seismic moment, and released stress) and also reduce the inter–event time (recurrence interval). Depending on the friction model, the system can penetrate into the stable regime and can either continue the accelerating phase toward to failure or decelerate and abort instability.