Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved quantification of cover crop biomass and ecosystem services through remote sensing-based model–data fusion

Cover crops have long been seen as an effective management practice to increase soil organic carbon (SOC) and reduce nitrogen (N) leaching. However, there are large uncertainties in quantifying these ecosystem services using either observation (e.g. field measurement, remote sensing data) or process-based modeling. In this study, we developed and implemented a model–data fusion (MDF) framework to improve the quantification of cover crop benefits in SOC accrual and N retention in central Illinois by integrating process-based modeling and remotely-sensed observations. Specifically, we first constrained and validated the process-based agroecosystem model, ecosys, using observations of cover crop aboveground biomass derived from satellite-based spectral signals, which is highly consistent with field measurements. Then, we compared the simulated cover crop benefits in SOC accrual and N leaching reduction with and without the constraints of remotely-sensed cover crop aboveground biomass. When benchmarked with remote sensing-based observations, the constrained simulations all show significant improvements in quantifying cover crop aboveground biomass C compared with the unconstrained ones, with R 2 increasing from 0.60 to 0.87, and root mean square error (RMSE) and absolute bias decreasing by 64% and 97%, respectively. On all study sites, the constrained simulations of aboveground biomass C and N at termination are 29% and 35% lower than the unconstrained ones on average. Correspondingly, the averages of simulated SOC accrual and N retention net benefits are 31% and 23% lower than the unconstrained simulations, respectively. Our results show that the MDF framework with remotely-sensed biomass constraints effectively reduced the uncertainties in cover crop biomass simulations, which further constrained the quantification of cover crop-induced ecosystem services in increasing SOC and reducing N leaching.
Growing Green Energy: A Review of Extension's Role in the Development of Advanced Biofuels

The development of advanced biofuels is expanding the possibilities for purpose-grown energy crops. Growers, producers, and other stakeholders will need a reliable source of information to assist with decision-making regarding renewable fuel supply chains. This review examines Extension's role in the innovation of advanced biofuels by documenting and summarizing Extension work in existing biomass-derived energy programs. This review highlights strategies used by Extension programs that help make renewable energy innovations successful.
Advances in Basic and Applied Sciences

Doxorubicin (DOX) is a chemotherapeutic drug with a broad spectrum of antineoplastic effects against several malignancies. Nonetheless, its clinical utility is limited due to its dose-dependent chronic cardiotoxicity, myelosuppression, and hepatotoxicity. Virgin coconut oil (VCO) consumption is associated with numerous biological and pharmaceutical impacts. The goal of the current study was to assess the protective effects of VCO against doxorubicin-induced hepatotoxicity. Four groups of rats were assigned: control, DOX (15 mg/kg
Experimental realization of an optical digital comparator using silicon microring resonators

Abstract We propose and experimentally demonstrate a silicon photonic circuit that can perform the comparison operation of two-bit digital signals based on microring resonators (MRRs). Two binary electrical signals regarded as two operands of desired comparison digital signals are applied to three MRRs to modulate their resonances through the microheaters fabricated on the top of MRRs, respectively (here, one binary electrical signal is applied to two MRRs by a 1×2 electrical power splitter, which means that the two MRRs are modulated by the same binary electrical signal). The comparison results of two binary electrical signals can be obtained at two output ports in the form of light. The proposed device is fabricated on a silicon-on-insulator substrate using the complementary metal-oxide-semiconductor fabrication process, and the dynamic characterization of the device with the operation speed of 10 kbps is demonstrated successfully.
Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

Extreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.
Are sexually transmitted infections associated with male infertility? A systematic review and in-depth evaluation of the evidence and mechanisms of action of 11 pathogens

ABSTRACT Purpose To systematically review the evidence on the association between sexually transmitted infections (STIs) and male infertility. We sought to answer two questions: Are STIs significantly associated with detrimental changes in semen parameters? ; and, is the prevalence of STIs significantly higher in infertile than fertile men? Materials and methods PubMed, Scopus and Google Scholar databases were searched (inceptionMarch 2023) following the PRISMA guidelines. Identified original studies in English on the association between STIs and male infertility were included. Data was tabulated/described by pathogen, mechanisms of action, number of studies and their level of evidence. Results Seventy out of 903 originally retrieved articles were included in this review. For the detrimental changes in semen parameters (first question), the evidence seems equivocal based on the nearly equal number of studies and similar levels of evidence. The only exception was for Ureaplasma, where the number of studies and levels of evidence supported an association with male infertility. Pertaining to a significantly higher prevalence of STI among infertile compared to fertile men (second question), evidence was insufficient to support/deny a significant association. The two exceptions were Ureaplasma and Mycoplasma, where the number of studies and evidence levels were in favour of an association with male infertility. Conclusions Generally, the relationship between STIs and male infertility remains to be uncovered. Our appraisal of the overall state of this relationship shows that the evidence base leaves much to be desired. The exceptions are Ureaplasma and Mycoplasma, where the evidence convincingly suggests their associations with infertility in men.
A sparse dimension-reduction based person re-identification algorithm

Focused on the issue that the person re-identification across non-overlapping camera views and the high dimensional features extracted from the images, a novel person re-identification algorithm is proposed. The algorithm obtained the semantic information of each camera view by the sparse learning, and then the Canonical Correlation Analysis (CCA) is used to carry out the high-level feature projection transformation. The algorithm aims to avoid the curse of dimensionality caused by the high dimensional feature operation via improving the feature matching ability. To the end, the characteristic distance between different views can be compared. The advantages of this method is to learn the robust pedestrian image feature representation and it also builds person re-identification model with block structure feature of pedestrian dataset, and the associated optimization problem is solved by utilizing the alternating directions framework in order to improve the performance of person re-identification. At last, the experimental results show that the proposed method has higher recognition efficiency on three benchmark datasets of the PRID 2011, iLIDS-VID and VIPeR.
El conflicto Palestino–Israelí en la prensa española: la cobertura de la Operación Margen Protector en ABC y La Vanguardia

Durante el desarrollo de uno de los episodios del conflicto palestino – israeli tuvo lugar la Operacion Margen Protector, que atrajo la atencion de la prensa hacia uno de los conflictos internacionales mas largos de la historia. Esta investigacion se centra en la cobertura que dos periodicos espanoles dedicaron a este hecho: ABC y La Vanguardia. A traves un analisis de contenido cuantitativo se pudo analizar aspectos tales como la presencia y la relevancia que le otorgaron a la contienda, asi como aspectos como la presentacion de sus protagonistas y el uso de las fuentes. Tras esto, se llego a la conclusion de que, a pesar de pequenas diferencias en el estilo de ambos medios, la cobertura y la relevancia fueron similares en ambas cabeceras.
Genomics for Yield and Yield Components in Durum Wheat

In recent years, many efforts have been conducted to dissect the genetic basis of yield and yield components in durum wheat thanks to linkage mapping and genome-wide association studies. In this review, starting from the analysis of the genetic bases that regulate the expression of yield for developing new durum wheat varieties, we have highlighted how, currently, the reductionist approach, i.e., dissecting the yield into its individual components, does not seem capable of ensuring significant yield increases due to diminishing resources, land loss, and ongoing climate change. However, despite the identification of genes and/or chromosomal regions, controlling the grain yield in durum wheat is still a challenge, mainly due to the polyploidy level of this species. In the review, we underline that the next-generation sequencing (NGS) technologies coupled with improved wheat genome assembly and high-throughput genotyping platforms, as well as genome editing technology, will revolutionize plant breeding by providing a great opportunity to capture genetic variation that can be used in breeding programs. To date, genomic selection provides a valuable tool for modeling optimal allelic combinations across the whole genome that maximize the phenotypic potential of an individual under a given environment.
Seasonal changes in the PM1 chemical composition north of Mexico City

An Aerodyne Aerosol Chemical Speciation Monitor (ACSM; Aerodyne Inc.) was deployed at a site north of Mexico City from November 13, 2013 to April 30, 2014 to investigate the seasonal variability of the chemical composition of submicron particles. The ACSM provides real time information on mass concentration of the non-refractory main species (nitrate, sulfate, ammonium, chloride and organic compounds) in particulate matter less than 1 μm (NR-PM 1 ) with a 30-min time resolution. Meteorological variables (temperature, relative humidity, and wind speed and direction), as well as concentrations of black carbon, PM 1 , PM 2.5 , CO, SO 2 , NO, NO 2 , and O 3 , were also measured. The total NR-PM 1 mass concentrations plus black carbon (which must be close to the PM 1 total mass) showed a good correlation with PM 1 mass concentration measured with a Tapered Element Oscillating Micro-Balance, an indication of the soundness of the ACSM data. In average, the composition of the aerosol as well as its diurnal variability were similar to observations in previous studies using similar instruments (MCMA-2003 and MILAGRO). However, it was observed that the aerosol was persistently acidic during November and December probably due to a higher relative humidity, lower temperature, and more frequent winds from the NW, where the Tula industrial complex is located. A lower concentration of ammonia (NH 3 ) in the gas phase might affect the PM acidity too. These results suggest a seasonal variability in the aerosol chemistry in Mexico City, which should be verified with more long-term studies.
Local adjacent extrema pattern for fingerprint image classification

Fingerprint classification is as yet difficult issue due to incomplete information in ridges and low quality images. To address these problems, we propose a new and robust Local Adjacent Extrema Pattern (LAEP) is a feature descriptor for fingerprint classification. The proposed descriptor finds it values based on indexes of local adjacent extremas using first order derivatives. The intensity values of the local extremas are compared with center pixel intensity value to employ the correlation of central pixel with its neighbors. Finally, the descriptor is generated on the support of the indexes and local extremas values. Support Vector Machine (SVM) is utilized for classification of fingerprint images into five classes. To prove the effectiveness of the proposed descriptor, we have tested on Indraprastha Institute of Information Technology (IIIT) - Indian rural fingerprint database for classification. In addition, the classification results of the proposed descriptor are compared with the existing methods. The resultant LAEP descriptor proved better classification accuracy than the previous methods.
Two dimensional cavitation waterhammer model for a reservoir-pipeline-valve system

ABSTRACT A waterhammer model was proposed to simulate a transient event with cavitation in a simple pipeline system. The governing equations for a quasi-two-dimensional model were solved through a hybrid scheme of the method of characteristics and the finite difference method considering the relationship between pressure head reduction and cavitation. An alternative method to address the boundary condition for the two-dimensional model was proposed, which improved the stability of the existing approaches. The comparison of longitudinal and radial velocity profiles between the proposed and existing models exhibited significant differences, which can be explained by the sequential generation and collapse of the cavity. The time series of the pressure head obtained through experimental transient events with vapour cavitation showed good agreement with that of the proposed model. The investigation of energy under a waterhammer showed that the pattern of transition between internal energy and kinetic energy is also substantially affected by the fate of cavitation during transient events.
Search for the Higgs boson decaying to a pair of bottom quarks and produced in association with a pair of top quarks in the LHC Run 2 data with the ATLAS detector using a likelihood technique

Following the discovery of the Higgs boson by the ATLAS and CMS collaborations at the Large Hadron Collider (LHC) in 2012, the attention has been focussed on studying the properties of the newly discovered particle to test the predictions of the Standard Model (SM). An object of particular interest is the top quark Yukawa coupling - the coupling of the Higgs boson to the top quark, which is predicted to be close to unity in the SM and at the same time very sensitive to the possible effects of new physics beyond the SM. The production of the Higgs boson in association with a pair of top quarks, ttH, is the process that gives direct access to the top quark Yukawa coupling. The decay of the Higgs boson into a pair of b-quarks, H → bb, is dominant in the SM for a value of the Higgs boson mass of 125 GeV (its branching ratio is approximately 58%). This decay channel also allows measuring the b-quark Yukawa coupling, the second largest coupling of the Higgs boson to a fermion in the SM. In this dissertation the search for ttH (H → bb) in the single lepton channel, resulting from the semileptonic decay of the tt system, is presented. The analysis is based on 36.1 fb⁻¹ of pp collision data at √s = 13 TeV recorded with the ATLAS detector in 2015 and 2016. The study is performed using a likelihood-based method that exploits kinematic properties of the selected events to separate the signal from the background, which is dominated by tt produced in association with additional jets. This search relies on the high multiplicity of jets originating from b-quarks (b-jets), so identification of these jets (b-tagging) is crucial. A study on the optimisation of b-jet identification algorithms in ATLAS is also presented in this dissertation. The ratio of the measured ttH cross-section to the SM expectation is found to be μ = 0.84+0.64−0.61, assuming a Higgs boson mass of 125 GeV. This result is consistent with both the background-only hypothesis and the ttH SM prediction
Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space

Abstract The use of modern data science has recently emerged as a promising new path to tackling the complex challenges involved in the creation of next-generation chemistry and materials. However, despite the appeal of this potentially transformative development, the chemistry community has yet to incorporate it as a central tool in every-day work. Our research program is designed to enable and advance this emerging research approach. It is centred around the creation of a software ecosystem that brings together physics-based modelling, high-throughput in silico screening and data analytics (i.e. the use of machine learning and informatics for the validation, mining and modelling of chemical data). This cyberinfrastructure is devised to offer a comprehensive set of data science techniques and tools as well as a general-purpose scope to make it as versatile and widely applicable as possible. It also emphasises user-friendliness to make it accessible to the community at large. It thus provides the means for the large-scale exploration of chemical space and for a better understanding of the hidden mechanisms that determine the properties of complex chemical systems. Such insights can dramatically accelerate, streamline and ultimately transform the way chemical research is conducted. Aside from serving as a production-level tool, our cyberinfrastructure is also designed to facilitate and assess methodological innovation. Both the software and method development work are driven by concrete molecular design problems, which also allow us to assess the efficacy of the overall cyberinfrastructure.
Computational optimization and implementation of control system for mechatronic treadmill with body weight support system

The purpose of this paper is to present a novel mechatronic system for gait re-education which consists of a body weight support system (BWS system) and a treadmill. This publication covers mainly issues related to the design and optimization process of a control algorithm dedicated for the unloading system. The proposed control system is based on a fuzzy logic controller coupled with a PID regulator. The optimization of parameters for regulators has been conducted based on numerical simulations in which a hybrid optimization method combining a genetic algorithm with a gradient algorithm has been used. The developed control system has been tested experimentally.
A multi-layer modelling approach for mining versatile ports of a global maritime transportation network

ABSTRACT International ports play critical roles in maintaining transportation flows and sustaining the effectiveness of global maritime logistics. Although the concept of versatile ports has been introduced to represent the specific patterns that these ports play at the regional and global levels, there is still a need for the design and development of computational approaches that support these notions. This paper introduces a flexible multi-layer network approach for an international maritime network whose interest is that it offers a flexible model that favours the identification of the different transportation flows and versatile ports. The model is complemented by a series of structural indices complemented by a new overlap measure that evaluates the specific role of a given port across various transportation trades. The whole approach is implemented and experimented with using massive AIS maritime data that supports the automatic generation of the multi-layer network, and derivation of the structural measures while maintaining a flexible view of the maritime network. The experiments applied to the global maritime transportation network identify key versatile ports and highlight significant differences at the regional and trade flow levels.
WCCI 2018 TrackML Particle Tracking Challenge

Can Machine Learning assist High Energy Physics (HEP) in discovering and characterizing of new particules? With event rates already reaching hundred of millions of collisions per second, physicists must sift through ten of petabytes of data per year. Ever better software is needed for real-time pre-processing and filtering of the most promising events, as the resolution of detectors improve, leading to an ever larger quantity of data. To mobilise the scientific community around this problem, we are organizing the TrackML challenge, whose objective is to use machine learning to quickly reconstruct particle tracks from dotted line traces left in the silicon detectors. The challenge refers to recognizing trajectories in the 3D images of proton collisions at the Large Hadron Collider (LHC) at CERN. Think of this as the picture of a fireworks: the time information is lost, but all particle trajectories have roughly the same origin and therefore there is a correspondence between arc length and time ordering. Given the coordinates of the impact of particles on detectors (3D points), the problem is to ``connect the dots'' or rather the points, i.e. return all sets of points belonging to alleged particle trajectories. The challenge will be conducted in 2 phases, the first one favoring innovation over efficiency and the second one aiming at real-time reconstruction.