Miracle-dz/UniFAR-FTUs huggingface dataset:
GMD Perspective: the quest to improve the evaluation of groundwater representation in continental to global scale models

Abstract. Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth System above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths and limitations so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when or where large-scale models achieve their purpose or where opportunities for improvements exists so that such models better achieve their purpose. We suggest that reproducing the spatio-temporal details of regional-scale models and matching local data is not a relevant goal. Instead, it is important to decide on reasonable model expectations regarding when a large scale model is performing “well enough” in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria is quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and subsequently discuss the value of three evaluation strategies: 1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation); 2) comparing several models with each other with or without reference to actual observations (model-based evaluation); and 3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models. Thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation.
Predictors Of The Development Of Chronic Kidney Disease In Patients With Chronic Obstructive Pulmonary Disease

The aim of the study was to identify predictors of decreased glomerular filtration in patients with chronic obstructive pulmonary disease (COPD). Materials and methods. We analyzed 145 case histories of patients diagnosed with COPD. The majority (84.1%, n = 122) of them are males (the average age of men is 60.7 ± 0.9 years, the average age of women is 62.0 ± 2.7 years). A comparative analysis of the prevalence of risk factors for chronic kidney disease (CKD) in patients with COPD: age, gender, smoking, arterial hypertension, overweight, etc. The glomerular filtration rate (GFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD – EPI) formula, according to which the patients were divided into 6 groups: group 1 - hyperfiltration, group 2 - GFR CKD – EPI ≥ 90 ml / min / 1 , 73 m2, 3rd - GFR CKD – EPI 60 – 89 ml / min / 1.73 m2, 4th - GFR CKD – EPI 45 – 59 ml / min / 1.73 m2, 5th - GFR CKD – EPI 30 - 44 ml / min / 1.73 m2 and 6th-GFR CKD-EPI <30 ml / min / 1.73 m2. Results. Patients with COPD have a high incidence of CKD risk factors. A correlation was found between the prevalence of CKD risk factors and the severity of COPD. The main predictors of the development of CKD in patients with COPD: duration of COPD> 9 years, body mass index> 26.5 kg / m2, smoking index> 51.3, albumin> 44.0 g / l, total protein> 70.0 g / L, forced expiratory volume in the first second ≤ 1.6 L, right atrial size> 35.5 mm, systolic pressure in the pulmonary artery> 36.6 mm Hg. Art., the thickness of the posterior wall of the left ventricle> 10.5 mm, Tiffno's index ≤ 62%. Conclusion. It was found that patients with COPD have a high incidence of both traditional and nonspecific risk factors for a decrease in GFR. study confirms the well-known mechanisms of CKD development in high-protein diet and hypercatabolic syndrome.
A Conceptual Model Development For Persuasive E-Wom Reviews: Challenges And Future Prospect

The current study aims to extend the body of knowledge on eWOM reviews by understanding the determinants of persuasive eWOM review and their effect on tourists' attitude toward the destination abroad and travel intention. This study tries to identify critical factors that influence users' attitudes and intentions when processing a persuasive eWOM review. Past literature on eWOM, Persuasive eWOM, Travel Intention, Elaboration likelihood model (ELM)Theory of Planned Behaviour (TPB) and Trust Transfer theory was extensively reviewed to develop a conceptual model. The current study has developed several prepositions by linking the variables for the future prospect. This study contributes to understanding what factors come into play to determine potential tourists' trust in a reviewer and their acceptance, which has an important role in developing travel intention. Concerning TPB, the study further contributes by improving the theory of planned behaviour's predictive ability and explanatory power. This study extends the theory of planned behaviour by including the information acceptance construct and utilising the extended model to explain the influence of information acceptance on travel intention. To the best of the current study’s author knowledge, there is no such study has been conducted before.
The performance of ruthenium based dye sensitized solar cells in the presence of graphene

In this paper, we evaluated graphene effect on the performance of ruthenium based dye‐sensitized solar cells. Two groups of photo‐electrodes were investigated in this study. Various weight percentages of graphene were added only into blocking layer for the first group and into blocking layer, TiO2 paste, and ruthenium‐based dye for the second group samples. The graphene loading clearly influenced on some basic parameters such as conversion and charge collection efficiencies, diffusion coefficient, relaxation time, etc. We showed that loading graphene up to 0.33 % wt increases the efficiency of our samples. Furthermore, we also studied the variation of Hall carrier concentration and mobility for the graphene loaded electrodes. We demonstrated that graphene sheets increases carrier concentration, mobility and the performance of dye sensitized solar cell.
Quantile function regression and variable selection for sparse models

This article considers linear quantile regression and variable selection for high‐dimensional data. In general, an ordinary quantile regression estimator is obtained for a single, fixed quantile level. Therefore, the estimated coefficient does not have continuity with respect to the quantile level, and hence, the behaviour of the estimator and estimated active variable set could change rapidly for different but sufficiently close quantile levels. To obtain a stable estimator for a given quantile level, this study proposes a new quantile regression method to estimate the coefficient as a function of the quantile level of interest in a given region Δ⊂(0,1) , which is denoted quantile function regression. In quantile function regression, we approximate the coefficient function of the quantile level using a B‐spline model, and hence, the estimated conditional quantile is continuous as it is a B‐spline curve. To employ variable selection, a group lasso‐type sparse penalty is used to estimate a non‐zero coefficient function of the quantile level, which indicates the estimated active set that remains unchanged in Δ . Therefore, quantile function regression can achieve global variable selection. The proposed estimator exhibits an asymptotic rate of convergence and consistency in variable selection. Simulation studies and applications to real data further reveal that the proposed method yields good performance.
Statistical learning and its consolidation in persons with aphasia

Learning is the process of acquiring knowledge about skills and behaviors through exposure to environmental stimuli. Learning can take place in two ways: explicitly and implicitly [1]. To learn a behavior, explicit learning necessitates awareness and attention. On the other hand, implicit learning occurs without the use of attention, consciousness, or knowledge of the behavior’s properties [2,3]. After multiple exposures to a stimulus, implicit learning occurs. Implicit learning is assumed to happen without placing any demands on attention and working memory mechanisms. Implicit learning has various forms of learning, namely priming, category learning, artificial grammar learning, and statistical learning. Statistical learning is the paradigmatic measure of implicit learning. Implicit learning is interchangeably termed and used as statistical Purpose: The present study aimed to investigate statistical learning abilities and its consolidation in Persons with Aphasia (PWA).
Evaluating analytics DSS for the COVID-19 pandemic through WHO-INTEGRATE EtD for health policy

ABSTRACT Global pandemics cause strong negative impacts on global health, economies, education, socialisation, and recreation. The COVID-19 global pandemic has challenged government policymakers and business decision-makers to make efficient, effective, and ethical decisions to mitigate its damages. In this research, we evaluate how ‘Analytics Decision Support Systems (DSS)’ are supporting decision making in public health with descriptive, predictive, and prescriptive analytics. We use the WHO-INTEGRATE Evidence-to-Decision (EtD) Framework and selectively evaluate recent research in Analytics DSS published in the Analytics and Public Health domains. Our findings reveal concentration in the utilization of predictive analytics and the focusing only on two of the six core criteria: balance of health benefits/harms and societal implications. We suggest that future research address the other four areas: human rights and socio-cultural acceptability; health equity, equality, and non-discrimination; financial and economic considerations; feasibility and health system considerations, and the integration of big data advanced technologies.
Understanding students’ environmental perceptions and some of their determinants in Gauteng province: a case study at the University of Johannesburg, South Africa

ABSTRACT The purpose of this study was to explore students’ environmental perceptions and some of the determinants of these perceptions at the University of Johannesburg in the Gauteng province, South Africa. Students were asked if they perceive a positive future for the environment in Gauteng. Study results revealed that a large majority of students were neutral on whether they perceived a positive future for the environment in Gauteng, which may indicate their indifference or ambivalence to environmental issues in the province. The findings also alluded to the significance of gender (females), hometown origin (urban), and age as determinants of students’ perceptions of the environment. Variables like faculty and campus were not strong determinants of students’ perceptions of the environment. These results may be considered in the design of environmental policies and education meant to improve environmental behaviour in the province.
Using deep learning for trajectory classification in imbalanced dataset

Deep learning has gained much popularity in the past years due to GPU advancements, cloud computing improvements, and its supremacy, considering the accuracy results when trained on massive datasets. As with machine learning, deep learning models may experience low performance when handled with imbalanced datasets. In this paper, we focus on the trajectory classification problem, and we examine deep learning techniques for coping with imbalanced class data. We extend a deep learning model, called DeepeST (Deep Learning for Sub-Trajectory classification), to predict the class or label for sub-trajectories from imbalanced datasets. DeepeST is the first deep learning model for trajectory classification that provides approaches for coping with imbalanced dataset problems from the authors' knowledge. In this paper, we perform the experiments with three real datasets from LBSN (Location-Based Social Network) trajectories to identify who is the user of a sub-trajectory (similar to the Trajectory-User Linking problem). We show that DeepeST outperforms other deep learning approaches from state-of-the-art concerning the accuracy, precision, recall, and F1-score.
Recent Advances in Reconfigurable Metasurfaces and Their Applications

Recently, reconfigurable metasurfaces have attracted intense attention in the field of electromagnetic metasurfaces. Compared with other metasurfaces, reconfigurable metasurfaces that uses steerable devices or materials to control the electromagnetic wave in real time are more versatile and show great promise in engineering applications. Our team has continuously explored advances of reconfigurable metasurfaces and also studied the microwave region from the perspectives of theory, technique and applications. This study reviews the research history of reconfigurable metasurfaces and summarizes some of our previous works, including a study on the amplitude, phase and polarization modulation of electromagnetic waves and their applications. Finally, the study discusses future challenges and possibilities for reconfigurable metasurfaces.
Global stability of HIV-1 and HIV-2 model with drug resistance compartment

In this paper we propose a new mathematical model describing the dynamics of the human immunodeficiency virus 1 and 2 with drug resistant compartment. We proved the positivity and boundedness of solutions with non-negative initial conditions. The dynamical system admits four equilibrium states: free equilibrium disease, one endemic equilibrium of each strain and one of the two strains. Two basic reproduction numbers are calculated. The global stability of the four equilibrium points is proved by using suitable Lyapunov functions. Numerical simulations were carried out to illustrate our results and a parameter sensitivity analysis completed this work.
Prediction Model for Severe Mycoplasma pneumoniae Pneumonia in Pediatric Patients by Admission Laboratory Indicators.

OBJECTIVE The purpose of this study was to develop a model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in pediatric patients with Mycoplasma pneumoniae pneumonia (MPP) on admission by laboratory indicators. METHODS Pediatric patients with MPP from January 2019 to December 2020 in our hospital were enrolled in this study. SMPP was diagnosed according to guideline for diagnosis and treatment of community-acquired pneumonia in children (2019 version). Prediction model was developed according to the admission laboratory indicators. Receiver operating characteristic curve and Goodness-of-fit test were analyzed for the predictive value. RESULTS A total of 233 MPP patients were included in the study, with 121 males and 112 females, aged 4.541 (1-14) years. Among them, 84 (36.1%, 95% CI 29.9-42.6%) pediatric patients were diagnosed as SMPP. Some admission laboratory indicators (immunoglobulins M (IgM), eosinophil proportion, eosinophil count, hemoglobin, erythrocyte sedimentation rate (ESR), total protein, albumin and prealbumin) were found statistically different (p < 0.05) between non-SMPP group and SMPP group. Logistic regress analysis showed IgM, eosinophil proportion, eosinophil count, ESR and prealbumin were independent risk factors for SMPP. According to these five admission laboratory indicators, the prediction model for SMPP in pediatric patients was developed. The area under curve of the prediction model was 0.777, and the goodness-of-fit test showed that the predicted SMPP incidence by the model was consistent with the actual incidence (χ2 = 244.51, p = 0.203). CONCLUSION We developed a model for predicting SMPP in pediatric patients by admission laboratory indicators. This model has good discrimination and calibration, which provides a basis for the early identification SMPP on admission. However, this model should be validated by multicenter studies with large sample.
Supporting Spanish 11th grade students to make scientific writing when learning chemistry in English: the case of logical connectives

ABSTRACT Logical connectives are grammatical linkers that appear as essential components in the production of logical argumentation of inquiry and critical thinking in school science activities. However, these cohesive terms represent a language barrier for science student learning. Therefore, students’ lack in their understanding of academic connectives as well as their inability to properly use them in science school activities are fundamental issues concerning science teachers’ awareness of the particular difficulties students usually experience with this type of academic words. Thus, this study has aimed to help 11th grade science students to improve their ability to perform scientific writing in English and then to exemplify for science teachers how they can incorporate specific supporting language activities into their science lessons. Particularly, student reasoning enhancement has focused on giving them multiple exposures and opportunities to practice with logical connectives in an engaging context. This teaching support has successfully scaffolded student ability to gain knowledge on those terms, which has prompted their capacity to express themselves scientifically.
Roles of culture media and oxygen transfer in the scale-up from shake flasks to pneumatic bioreactor of the plant growth-promoting bacterium Rhizobium phaseoli

The success of a Rhizobium sp. inoculant depends on a product with a high number of live bacteria that can nodulate a leguminous plant. Culture media and oxygen transfer were studied to improve the production of R. phaseoli in chemically defined and undefined media for scale-up from shake flasks to a pneumatic bioreactor. The effects of carbon and phosphate on R. phaseoli growth were evaluated, and the specific volumetric oxygen transfer coefficient (KLa) was determined in shake flasks and scale-up to bioreactors. Six-fold more viable biomass was obtained by increasing the phosphate concentration by 7.5-fold when using glucose. However, this increase was not observed when succinate was used. In the undefined medium, glucose induced a higher production of R. phaseoli in comparison with other carbon sources. The KLa of 6–11 h-1 produced a five-fold growth of biomass in shake-flask cultures. This KLa was used to produce a R. phaseoli-based inoculant in a bioreactor. The formation of nitrogen-fixing nodules was confirmed in bean plants (Phaseolus vulgaris). The improvement of culture media with a suitable oxygen transfer demonstrates that R. phaseoli can be used as an inoculant that exerts beneficial effects on the initial growth of bean plants.
Art therapists’, supervisors’ and school counselors’ perceptions of the substitute art therapist's role in the education system during maternity leave

ABSTRACT Background One of the key pillars underlying a successful outcome in psychotherapy is the personal relationship between the therapist and the client. However, when the therapist is absent for a long period of time, clients are sometimes referred to substitute therapist to maintain the continuity of the therapeutic process. Aim To study the participants’ perceptions related to the replacement of art therapists in the educational system in Israel, in which when art therapists go on maternity leave, their clients are eligible by law to receive therapy from a replacement. Method Six substitute art therapists, four art therapists who had previously been on maternity leave, five supervisors, and three school counselors were interviewed on the issues related to art therapist's substitution. The interviews were analysed according to the principles of Consensual Qualitative Research. Results Disparities between the education system's view of the therapist as an employee, and the art therapists themselves with respect to their therapeutic and educational role emerged as key challenges. Implications for policy The lack of standardised procedures and budgets makes it difficult to cope with these challenges. The discussion suggests ways to streamline this process. Plain-language summary Many art therapists are obliged to be absent for an extended period of time from their work. In some cases, referral of clients to a substitute therapist is important. However, replacement is complex because the essence and success of all therapeutic relationships are founded on the establishment of trust, presence and the consistency of the therapist. The issues become more challenging in the education system, where the encounter between the two languages – the educational and the therapeutic, is in any case complex. This study examined the role of art therapist substitute in the education system by focusing on the perceptions of six substitute art therapists, four art therapists who had been on maternity leave, five field supervisors who had accompanied the transition process for a substitute, and three educational counselors who also accompanied these processes. The study applied the principles of qualitative research to better characterise and understand the phenomenon of the art therapy replacement and lay an initial foundation for the creation of an effective working model for the substitution period. The findings deal with five key areas: systemic and organisational aspects of the process of replacing art therapists in educational frameworks, the actual process of replacement, professional challenges and dilemmas facing the substitute during the replacement period, the interviewees’ experiences and feelings during replacement, and contributory factors and recommendations for the substitution process. The importance of appropriate procedures for the replacement of art therapists when dealing with the complexity and management of an optimal substitution process is discussed.