Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of MODIS and Himawari‐8 Low Clouds Retrievals Over the Southern Ocean With In Situ Measurements From the SOCRATES Campaign

Aircraft observations collected during the Southern Ocean Cloud Radiation Aerosol Transport Experimental Study in January‐February of 2018 are used to evaluate cloud properties from three satellite‐imager datasets: (1) the Moderate Resolution Imaging Spectroradiometer level 2 (collection 6.1) cloud product, (2) the CERES‐MODIS Edition 4 cloud product, and (3) the NASA SatCORPS Himawari‐8 cloud product. Overall the satellite retrievals compare well with the in situ observations, with little bias and modest to good correlation coefficients when considering all aircraft profiles for which there are coincident MODIS observations. The Himawari‐8 product does, however, show a statistically significant mean bias of about 1.2 μm for effective radius (re) and 2.6 for optical depth (τ) when applied to a larger set of profiles with coincident Himawari‐8 observations. The low overall mean‐bias in the re retrievals is due in part to compensating errors between cases that are non‐ or lightly precipitating, with cases that have heavier precipitation. re is slightly biased high (by about 0.5–1.0 μm) for non‐ and lightly precipitating cases and biased low by about 3–4 μm for heavily precipitating cases when precipitation exits near cloud top. The bias in non‐ and lightly precipitating conditions is due to (at least in part) having assumed a drop size distribution in the retrieval that is too broad. These biases in the re ultimately propagate into the retrieved liquid water path and number concentration.
Spatially Variable Precipitation and Its Influence on Water Balance in a Headwater Alpine Basin, Nepal

The current knowledge of the spatial variability of precipitation in High Mountain Asia is based on the remotely-sensed estimates (coarse spatial and temporal resolution) or data from sparsely-distributed rain gauges. However, as precipitation is strongly affected by topography in mountainous terrain, the spatially varying precipitation and the resulting water balances are currently poorly understood. To fill this gap in knowledge, we studied the spatial variation of the precipitation and its impact on water balance in a small headwater basin located in the foothills of the Himalaya, Nepal. We deployed ten rain gauges and climate stations, spanning the whole elevation range 700–4500 m above sea level (masl) for a period of four years. Our results show a quadratic polynomial relationship between annual precipitation and station elevation, which are used to produce annual precipitation maps. The performance of the elevation-based precipitation estimates is adequate in closing the water balance while the performances of average precipitation and Thiessen polygon method are poor and inconsistent in closing the water balance. We also demonstrate that precipitation estimates from one or two gauges at the lowest basin elevation substantially underestimate the water balance. However, the precipitation from one or two rain gauges at 2000–3000 masl provide a significantly better estimate of the water balance of a small headwater basin.
The development outcomes of remittance inflows to Nigeria: the case of the Southeastern Geo-political zone

ABSTRACT Remittances to Sub-Saharan Africa (SSA) constitute a critical lifeline for millions of individual households. It is on record that these large transfers enable recipient households to raise their living standards beyond vulnerability and subsistence levels. Unfortunately, the development potentials of remittance income are seldom factored into most pro-poor targeting programmes in many SSA countries like Nigeria. This is largely due to the problems of data inconsistency as well as those related to lack of precise information on how remittances are received and spent. Using novel survey dataset involving 450 remittance recipient and non-recipient households collected in the Southeast Geopolitical Zone of Nigeria, the study uncovers significant evidence of the impact of remittances on household expenditure and povertyusingpropensityscore matching (PSM). Specifically, households that receive remittances invest between NGN186,000 (US$1,240) and NGN205,000 (US$1,366.7) more in building constructions, land acquisitions and also invest over NGN60,000 (US$400) more in household business enterprise compared to non-recipient households. Similarly, the estimated impact of remittances on poverty shows that household poverty is lower by between 30.3% and 33.6% considering the results from all the three PSM matching estimators. The differences between the recipients and non-recipients are statistically significant. The implications of the findings are discussed in terms of pro-poor targeting programmes in Nigeria.
An Evolving Understanding of Enigmatic Large Ripples on Mars

Two scales of ripples form in fine sand on Mars. The larger ripples were proposed to have an equilibrium size set by an aerodynamic process, making them larger under thinner atmospheres and distinct from smaller impact ripples. Sullivan et al. (2020, https://doi.org/10.1029/2020JE006485) show that large ripples can develop in a numerical model due to Mars’ low atmospheric pressure. Although their proposed growth‐limiting mechanism is consistent with an aerodynamic process, they argue that the ripples in their model are simply large versions of impact ripples, not a separate class of ripples. Here, we explore this debate by synthesizing recent advances in large‐ripple formation (including initiation and subsequent evolution to equilibrium). Although significant knowledge gaps remain, it is clear that large Martian ripples in well sorted sand are larger under thinner atmospheres, and thus remain a powerful paleoclimate indicator.
Towards Sustainable Residential Buildings in Saudi Arabia According to the Conceptual Framework of “Mostadam” Rating System and Vision 2030

Saudi Arabia vision 2030 adopts comprehensive development standards in all fields of Saudi society. To achieve such standards, many initiatives and programs have been launched to achieve sustainable goals while meeting the needs of the present without compromising the requirements of future generations. Sustainable buildings are highly important in achieving sustainable development goals due to their excellent ability to reduce energy consumption, increase building efficiency, and reduce harmful emissions of greenhouse gases. The Kingdom aims to be the largest investor in the world in the sustainable buildings sector. To this end, the “Mostadam” evaluation service was recently launched in order to assist existing and new buildings in achieving sustainable building standards in a way that is in keeping with the privacy and principles of Saudi society. The present paper presents an extensive investigation to assess the current state of sustainable buildings in Saudi cities. Moreover, the main categories of sustainable residential buildings according to the “Mostadam” rating systems are discussed, and the potential of such categories in Saudi Arabia is explored. Finally, suggestions are given for some practical strategies and future policies targeting the total transformation towards sustainable buildings and, consequently, towards sustainable cities. This will have a great impact on achieving comprehensive sustainable development in Saudi Arabia in line with vision 2030.
Optimization of a QuEChERS Based Analytical method for the Determination of Organophosphorus and Synthetic Pyrethroid Pesticide Residues in Betel Leaf

ABSTRACT A novel method has been developed and validated for the first time in order to determine eight pesticide residues (acephate, diazinon, malathion, fenitrothion, chlorpyrifos, quinalphos, dimethoate and cypermethrin) in betel leaf using quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction in combination to Gas Chromatography (GC) coupled with Flame Thermionic Detector (FTD) and Electron Capture Detector (ECD). In this study, the optimisation of cleanup materials was done properly and found that 600 mg anhydrous MgSO4, 150 mg activated charcoal powder, and 120 mg PSA (Primary Secondary Amine) was the best combination for proper cleanup of betel leaf matrix. Recoveries for all the selected pesticides at fortification levels of 0.02, 0.1, and 0.3 mg/kg ranged from 86% to 108% with RSDr ≤ 9% and the matrix matched calibration curve showed good linearity (r2 ≥ 0.996). The limit of detection ranged from 0.003 to 0.005 mg/kg and the limit of quantification was 0.02 mg/kg, which was lower than the EU-MRLs. The matrix effects of the selected pesticides were also evaluated in this study and found that cypermethrin had a prominent matrix effect (+124%). The proposed method was applied successfully to analyse 110 fresh betel leaf samples and found that 12 were contaminated with cypermethrin, chlorpyrifos, and dimethoate at a level above EU-MRLs. The consumers are at high health risk as the detected levels of cypermethrin and chlorpyrifos residues were up to 78 and 39 times higher than the EU-MRLs, respectively, and the estimated highest Health Risk Index (HRI) was 5.40.
Factor analysis of the Resilience Scale for Brazilian caregivers of people with Alzheimer’s disease

Abstract Introduction Resilience is a dynamic process that acts to modify the effects of an adverse life event. In this study, we aimed to test the construct validity of the Resilience Scale by employing exploratory and confirmatory procedures, and to investigate the relationship between caregiver’s resilience and clinical status of people with Alzheimer’s disease. Methods A sample of 143 dyads of people with Alzheimer’s disease and their primary caregivers were included. Results The total Resilience Scale mean score was 140.3 (standard deviation [SD] = 16.289), ranging from 25 to 175, indicating a high level of resilience. Cronbach’s alpha was high (α = 0.77), indicating excellent internal consistency. The mean of corrected item-total correlation coefﬁcients was moderate. The Resilience Scale presented a four-factor solution with a well-deﬁned structure: sense of life and self-sufficiency, perseverance, self-confidence and equanimity, and meaningfulness. Conclusion The findings indicate excellent internal consistency of the Resilience Scale when used to evaluate psychological and emotional difficulties of caregivers, even though the correlations observed between the Resilience Scale and clinical variables were not significant for functionality, mood, awareness, neuropsychiatric symptoms, or burden.
The updated ITPA global H-mode confinement database: description and analysis

The multi-machine International Tokamak Physics Activity (ITPA) Global H-mode Confinement Database has been upgraded with new data from JET with the ITER-like wall and ASDEX Upgrade with the full tungsten wall. This paper describes the new database and presents results of regression analysis to estimate the global energy confinement scaling in H-mode plasmas using a standard power law. Various subsets of the database are considered, focusing on type of wall and divertor materials, confinement regime (all H-modes, ELMy H or ELM-free) and ITER-like constraints. Apart from ordinary least squares (OLS), two other, robust regression techniques are applied, which take into account uncertainty on all variables. Regression on data from individual devices shows that, generally, the confinement dependence on density and the power degradation are weakest in the fully metallic devices. Using the multi-machine scalings, predictions are made of the confinement time in a standard ELMy H-mode scenario in ITER. The uncertainty on the scaling parameters is discussed with a view to practically useful error bars on the parameters and predictions. One of the derived scalings for ELMy H-modes on an ITER-like subset is studied in particular and compared to the IPB98(y, 2) confinement scaling in engineering and dimensionless form. Transformation of this new scaling from engineering variables to dimensionless quantities is shown to result in large error bars on the dimensionless scaling. Regression analysis in the space of dimensionless variables is therefore proposed as an alternative, yielding acceptable estimates for the dimensionless scaling. The new scaling, which is dimensionally correct within the uncertainties, suggests that some dependencies of confinement in the multi-machine database can be reconciled with parameter scans in individual devices. This includes vanishingly small dependence of confinement on line-averaged density and normalized plasma pressure (β), as well as a noticeable, positive dependence on effective atomic mass and plasma triangularity. Extrapolation of this scaling to ITER yields a somewhat lower confinement time compared to the IPB98(y, 2) prediction, possibly related to the considerably weaker dependence on major radius in the new scaling (slightly above linear). Further studies are needed to compare more flexible regression models with the power law used here. In addition, data from more devices concerning possible ‘hidden variables’ could help to determine their influence on confinement, while adding data in sparsely populated areas of the parameter space may contribute to further disentangling some of the global confinement dependencies in tokamak plasmas.
Phase stability and superconductivity of lead hydrides at high pressure

Density functional theory calculations and crystal structure predictions using the particle swarm optimization method have been combined to determine stable hydrides of lead under pressure. In contrast to other group-IVa hydrides, the stoichiometry ${\mathrm{PbH}}_{6}$ is the first hydride to become stable, at just under 1 Mbar. For two previously studied stoichiometries, ${\mathrm{PbH}}_{4}$ and ${\mathrm{PbH}}_{8}$, energetically more favorable phases were identified to become stable around 2 Mbar. In all structures, the hydrogenic sublattices comprise negatively charged ${{\mathrm{H}}_{2}}^{\ensuremath{\delta}\ensuremath{-}}$ molecules. Competitive ${\mathrm{PbH}}_{4}$ and ${\mathrm{PbH}}_{6}$ structures are layered. ${\mathrm{PbH}}_{6}$ features ${\mathrm{H}}_{2}$ molecules intercalated between hcp Pb layers, the stable phase of dense pure lead, thus offering a potentially straightforward route towards synthesis. In ${\mathrm{PbH}}_{8}$, the Pb lattice adapts a $\ensuremath{\beta}$-Sn structure, and hydrogen atoms form quasi-one-dimensional-chains. All structures were found to be metallic and to feature superconductivity in their respective stability range, with moderately high ${T}_{c}$ in the range 60--100 K for ${\mathrm{PbH}}_{4}$ and ${\mathrm{PbH}}_{6}$ and 161--178 K for ${\mathrm{PbH}}_{8}$.
Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications

Recently, different recommendation techniques in e-learning have been designed that are helpful to both the learners and the educators in a wide variety of e-learning systems. Customized learning, which requires e-learning systems designed based on educational experience that suit the interests, goals, abilities, and willingness of both the learners and the educators, is required in some situations. In this research, we develop an intelligent recommender using split and conquer strategy-based clustering that can adapt automatically to the requirements, interests, and levels of knowledge of the learners. The recommender analyzes and learns the styles and characteristics of learners automatically. The different styles of learning are processed through the split and conquer strategy-based clustering. The proposed cluster-based linear pattern mining algorithm is applied to extract the functional patterns of the learners. Then, the system provides intelligent recommendations by evaluating the ratings of frequent sequences. Experiments were conducted on different groups of learners and datasets, and the proposed model suggested essential learning activities to learners based on their style of learning, interest classification, and talent features. It was experimentally found that the proposed cluster-based recommender improves the recommendation performance by resulting in more lessons completed when compared to learners present in the no-recommender cluster category. It was found that more than 65% of the learners considered all criteria to evaluate the proposed recommender. The simulation of the proposed recommender showed that for learner size values of <1000, better metric values were produced. When the learner size exceeded 1000, significant differences were obtained in the evaluated metrics. The significant differences were analyzed in terms of a computational structure depending on L, the recommendation list size, and the attributes of learners. The learners were also satisfied with the accuracy and speed of the recommender. For the sample dataset considered, a significant difference was observed in the standard deviation σ and mean μ of parameters, in terms of the Recall (List, User) and Ranking Score (User) measures, compared to other methods. The devised method performed well concerning all the considered metrics when compared to other methods. The simulation results signify that this recommender minimized the mean absolute error metric for the different clusters in comparison with some well-known methods.
Unmanned Aerial Vehicle Pitch Control Using Deep Reinforcement Learning with Discrete Actions in Wind Tunnel Test

Deep reinforcement learning is a promising method for training a nonlinear attitude controller for fixed-wing unmanned aerial vehicles. Until now, proof-of-concept studies have demonstrated successful attitude control in simulation. However, detailed experimental investigations have not yet been conducted. This study applied deep reinforcement learning for one-degree-of-freedom pitch control in wind tunnel tests with the aim of gaining practical understandings of attitude control application. Three controllers with different discrete action choices, that is, elevator angles, were designed. The controllers with larger action rates exhibited better performance in terms of following angle-of-attack commands. The root mean square errors for tracking angle-of-attack commands decreased from 3.42° to 1.99° as the maximum action rate increased from 10°/s to 50°/s. The comparison between experimental and simulation results showed that the controller with a smaller action rate experienced the friction effect, and the controllers with larger action rates experienced fluctuating behaviors in elevator maneuvers owing to delay. The investigation of the effect of friction and delay on pitch control highlighted the importance of conducting experiments to understand actual control performances, specifically when the controllers were trained with a low-fidelity model.
CHINA’S PUBLIC DIPLOMACY: EVOLUTION, CHALLENGES, AND THE GREEK CASE

The construction of a positive international image is profoundly correlated with advancing one’s national interests abroad. Influencing foreign audiences and creating multisided links with countries of interest, is the main objective of public diplomacy. In the highly interconnected XXI century, countries have increasingly invested in that practice, assisted by major technological advancements. China is not an exception. The current paper will examine in which ways the latter conducts public diplomacy and through which actors. The study will also try to showcase the evolution and challenges that took place in this domain. Afterward, the case study of Greece will be considered, presenting the Chinese actors engaging in the country, the practices they use, and how the bilateral relationship has been affected. The essay will conclude with estimating the total effectiveness of the practices, possible challenges that exist, and several insights for future reference.
New Mineral Names

Electron microprobe analysis (average of 20) gave AlrO. 31.98, FerO, 0.60, CuO 0.54, AsrO, 66.71, sum 99.83 wt0/0, corresponding to Al, ooFefig,Cufrj,AsonuOo. ccurs as colorless aggregates with yellowish, pale green, and bluish tints, commonly containing powdery hematite or tenorite and gaseous inclusions; also as equant grains to 0.3 mm in diameter, some with poorly developed faces. Vitreous luster, white streak, brittle, no cleavage, VHNro: 449 (336-480), D-""" : 3.32(l), D*r. : 3.34 g,/cm3 for Z : 3. Stable at atmospheric conditions; soluble in dilute acids. Colorless in transmitted light, uniaxial positive, c.r : 1.596(1), r : 1.608(l). The X-ray powder pattern is identical to that of synthetic AlAsOo; by analogy, trigonal symmetry, space group P3r2l or P3221, a: 5.031(l), c : 11.226(6) A as refined from the diffractometer pattern (CuKa; 3l lines given) with strongest lines of 4.36(20,100), 4 . 06 (3 l , l 0 l ) , 3 . 442 (100 , r02 ) , 2 . 359 (15 ,104 ) , and 1 . 873 (16 ,1 l 4 ) . The mineral occurs in the fumarolic deposits of the Great Tolbachik fissure extrusion, Kamchatka Peninsula, Russia, and is associated with langbeinite, fedotovite, kluychevskite, lammerite, hematite, tenorite, Aland K-containing sulfates, and sparse nabokoite and atlasovite. The name is for the composition (aluminum arsenate). Type material is at the Mining Museum of the Mining Institute, Saint Petersburg, Russia. J.P.
A Continuous, Impedimetric Parylene Flow Sensor

A continuously recording, impedimetric thermal liquid flow sensor fabricated on a Parylene C thin film substrate is presented for the first time. The sensing concept, inspired by hot-film anemometry, includes an additional upstream unheated reference electrode pair which enabled attenuation of environmental drift in impedance by more than <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula>. This sensor design and transduction approach was motivated by the need for an alternative to traditional hot-film anemometers for <italic>in vivo</italic> applications. An analytical model was developed to describe the axial fluid temperature surrounding the impedimetric sensor and its effect on solution conductivity. The impact of heater power was modeled and matched with experimental data; similar analysis was conducted for other parameters including channel height, ambient temperature, and electrolyte concentration. The sensor achieved a <inline-formula> <tex-math notation="LaTeX">$2\sigma $ </tex-math></inline-formula> resolution of <inline-formula> <tex-math notation="LaTeX">$17~\mu \text{L}$ </tex-math></inline-formula>/min over the range 43–200 <inline-formula> <tex-math notation="LaTeX">$\mu \text{L}$ </tex-math></inline-formula>/min. The models and fabricated device significantly expand our understanding of thermal impedimetric flow sensing. [2020-0357]
GIS partial discharge defect diagnosis system and method based on Extreme Learning Machine

Partial discharge type identification is of great significance to the diagnosis of insulation faults in high-voltage power equipment. The partial discharge type recognition method based on deep learning map diagnosis has the disadvantages of large memory space and high hardware environment requirements. This paper presents a GIS discharge defect diagnosis system and method based on Extreme Learning Machine (ELM), which can be deployed on the edge layer equipment. The principal component analysis method is used to obtain the principal parameters that characterize the defects and form a sample feature data set. The sample feature data set is used to train the neural network model and continuously optimize it. Finally, the trained neural network model is used to identify the types of partial discharge defects. This method can effectively solve the problems that the partial discharge type identification method based on atlas diagnosis occupies large memory space and requires high hardware environment, which is convenient for the wide application of engineering.