Miracle-dz/UniFAR-FTUs huggingface dataset:
Using deep learning models for learning semantic text similarity of Arabic questions

Question-answering platforms serve millions of users seeking knowledge and solutions for their daily life problems. However, many knowledge seekers are facing the challenge to find the right answer among similar answered questions and writer’s responding to asked questions feel like they need to repeat answers many times for similar questions. This research aims at tackling the problem of learning the semantic text similarity among different asked questions by using deep learning. Three models are implemented to address the aforementioned problem: i) a supervised-machine learning model using XGBoost trained with pre-defined features, ii) an adapted Siamese-based deep learning recurrent architecture trained with pre-defined features, and iii) a Pre-trained deep bidirectional transformer based on BERT model. Proposed models were evaluated using a reference Arabic dataset from the mawdoo3.com company. Evaluation results show that the BERT-based model outperforms the other two models with an F1=92.99%, whereas the Siamese-based model comes in the second place with F1=89.048%, and finally, the XGBoost as a baseline model achieved the lowest result of F1=86.086%.
Direct and Indirect Effects of ICT Infrastructure, Skills, and Use on Entrepreneurship: A Cross-Countr y Empirical Investigation

The information and communication technology (ICT)-enabling infrastructure, requisite skills, and subsequent usage in the country could facilitate ease of doing business (EDB) and support the heterogeneous distribution of resources for new businesses to flourish and remain competitive. However, from a policy perspective, the pathways through which ICT impacts entrepreneurial activities in a country are not clearly understood. In this study, the authors empirically investigate the direct and indirect (via EDB) effect of ICT infrastructure, skills, and use (at business, government, and individual levels) in influencing entrepreneurial activity in a country. The results show that the citizens’ ICT skills and ICT use, directly and indirectly, impact entrepreneurial initiatives in a country. In contrast, ICT infrastructure and use by the government only have an indirect impact on them. The study’s findings should help guide national initiatives for promoting the creation of new businesses in a country.
ZACCARINIITE, RhNiAs, A NEW PLATINUM-GROUP MINERAL FROM LOMA PEGUERA, DOMINICAN REPUBLIC

Zaccariniite, RhNiAs, is a new platinum-group mineral discovered in the Loma Peguera ophiolitic chromitite, Dominican Republic. The mineral forms anhedral grains (1–20 μm in size) and occurs intergrown with garutiite, in association with hexaferrum, Ru-Os-Ir-Fe alloys, Ru-Os-Ir-Fe oxygenated compounds, and chromite. Zaccariniite is opaque and has a metallic luster and a grey streak. The mineral is brittle, with no cleavage. Values of VHN 5 (10 indentations on different grains of synthetic RhNiAs) fall between 166 and 286 kg/mm 2 , with a mean value of 218 kg/mm 2 , corresponding to a Mohs hardness of approximately 3.5–4. Under plane-polarized reflected light, the mineral is white with brownish to pinkish tints, has moderate to strong bireflectance, a strong white to pinkish brownish white pleochroism, and strong anisotropy, with rotation tints from orange to blue–green; it exhibits no internal reflections. Reflectance values of zaccariniite in air (R1, R2 in %) are: 49.4, 49.4 at 470 nm; 52.4, 53.2 at 546 nm; 54.2, 53.2 at 589 nm; and 56.6, 53.3 at 650 nm. Three electron microprobe analyses of natural zaccariniite gave an average composition of: Rh 41.77, Os 0.51, Ir 0.64, Ru 0.46, Pd 0.34, Ni 23.75, Fe 0.53, As 27.84, S 0.10, for a total of 96.09 wt. %, corresponding to the empirical formula (Rh 1.01 Os 0.01 Ir 0.01 Ru 0.01 Pd 0.01 ) ∑1.05 (Ni 1.00 Fe 0.02 ) ∑1.02 (As 0.92 S 0.01 ) ∑0.93 based on three atoms. The average result of twenty-eight analyses of synthetic zaccariniite is: Rh 44.57, Ni 24.50, and As 31.82 total 100.88 wt. %, corresponding to Rh 1.02 Ni 0.98 As 1.00 . The mineral is tetragonal, belongs to space group P 4/ nmm with lattice parameters a = 3.5498(1), c = 6.1573(2) A, V = 77.59(1) A 3 , Z = 2, and crystallizes with the Cu 2 Sb structure type. The strongest lines in the X-ray powder-diffraction pattern of synthetic zaccariniite [ d in A(I)( hkl )] are: 2.5092(40)(110), 2.3252(100)(111,102), 1.9453(51)(112), 1.7758(80)(103,200), 1.2555(40)(213,220), 1.1044(22)(302,311), 1.0547(23)(312), 0.9730(42)(215). The new Rh-Ni arsenide is named after Federica Zaccarini in recognition of her important contributions to the mineralogy of platinum-group elements.
Service Quality Measurement in Information Systems: An Expectation and Desire Disconfirmation Approach

Traditionally, measurements of service quality have followed the expectation-disconfirmation approach. Further, previous studies have shown that negative disconfirmation is more influential than positive disconfirmation. This research hypothesized information systems (IS) service quality scales based on the dimensionality of the expectation-disconfirmation (ED) and desire-disconfirmation (DD) approach. Using the SERVQUAL+ instrument and data collected from 321 IS users, the authors developed ED and DD-based IS service quality scales using contemporary methods, such as LISREL-based CFA. This paper proposed and empirically validated the following two new IS service quality constructs: service adequacy (difference of expected service and perceived service) and service superiority (difference of desired service and perceived service). The results indicate that both measures have shown better predictive power than earlier scales like SERVQUAL+ and the IS ZOT scales. The authors have outlined several implications of ED and DD scales to practice and research.
Arsenic Induced Changes in Growth and Physiology of Maize (Zea mays L.) Seedlingsand its Possible Amelioration Using Phosphate

The response of maize (Zea mays L.) to different levels of arsenate with or without phosphate was investigated. Arsenic exposure significantly hampered the growth of maize seedlings showing reductions in root and shoot lengths together with reductions in fresh and dry weight. The level of total chlorophyll, chlorophyll a, chlorophyll b and carotenoids decreased in arsenic treated seedlings. Arsenic toxicity was associated with an increase in the activities of the anti-oxidative scavenging enzymes catalase and peroxidase. Arsenic restricted the concentration of nucleic acids (DNA and RNA) and soluble protein content in maize seedlings. Joint application of phosphate with arsenic showed positive alterations on all parameters tested under the purview of arsenic treatment alone. Thus, phosphate application ameliorated the detrimental effects caused by arsenic.
Stable UV absorption material synthesized by intercalation of squaric acid anion into layered double hydroxides

A novel UV absorption material of squaric acid (SA) anion (O4O42−) intercalated layered double hydroxides (LDHs) was successfully synthesized by the co-precipitation method. After intercalation, the interlayer distance of MgAl-SA-LDHs increased to 1.04 nm compared to those of MgAl-CO3-LDHs and SA anions present in form of a monolayer in the interlayer of LDHs. Thermal stability of SA clearly enhanced by the intercalation and the suppression of the deintercalation ability of MgAl-SA-LDHs was superior to that of 4-hydroxy-3-methoxybenzoic acid intercalated LDHs. The results of UV-DRS indicate the potential application of MgAl-SA-LDHs as UV absorbers.
Romantic Partners Are Similar in Their Well-Being and Sociopolitical Attitudes but Change Independently Over Time

Similarity within romantic couples forms one component of the formation and maintenance of relationships, meaning that, romantic partners’ views about themselves and the world are theorized to converge over time. We advance prior research on romantic couple similarities using cross-sectional or time-lagged designs, testing convergence with dyadic trajectories of change—how changes in one person relate to changes in their partner. Dyadic growth curve models assessed initial similarities, and longitudinal convergence, for 35 measures of well-being and individual differences in 171 mixed-gender couples from a national longitudinal study (the New Zealand Attitudes and Values Survey). Results indicated consistent average-level similarities between romantic partners, a few instances of short-term convergence in sociopolitical views, and the consistent pattern that changes in people occurred independently to their partners. Findings advance theory on romantic interdependence by emphasizing the perspective that romantic partners’ convergence occurs as subjective experience rather than externally measured unification.
Gradient Bundle Analysis: A Full Topological Approach to Chemical Bonding

The "chemical bond" is a central concept in molecular sciences, but there is no consensus as to what a bond actually is. Therefore, a variety of bonding models have been developed, each defining the structure of molecules in a different manner with the goal of explaining and predicting chemical properties. This thesis describes the initial development of gradient bundle analysis (GBA), a chemical bonding model that creates a high resolution picture of chemical interactions within the charge density framework. GBA is based on concepts from the quantum theory of atoms in molecules (QTAIM), but uses a more complete picture of the topology and geometry of the electron charge density to understand and predict bonding interactions. Gradient bundles are defined as volumes bounded by zero-flux surfaces (ZFSs) in the gradient of the charge density with well-defined energies. The structure of gradient bundles provides an avenue for detecting the locations of valence electrons, which correspond to reactive regions in a molecule. The number of electrons in bonding regions, which are defined by the lowering of kinetic energy in gradient bundles, is found to correlate to bond dissociation energy in diatomic molecules. Furthermore, site reactivity can be understood and predicted by observing the motion of ZFSs bounding gradient bundles and calculating condensed gradient bundle Fukui functions. Using only the ground state charge density, I present preliminary results for a method that predicts which regions in a molecule are most likely to undergo nucleophilic and electrophilic attack, effectively locating the HOMO and LUMO.
High-performance AES-128 algorithm implementation by FPGA-based SoC for 5G communications

In this research work, a fast and lightweight AES-128 cypher based on the Xilinx ZCU102 FPGA board is presented, suitable for 5G communications. In particular, both encryption and decryption algorithms have been developed using a pipelined approach, so enabling the simultaneous processing of the rounds on multiple data packets at each clock cycle. Both the encryption and decryption systems support an operative frequency up to 220 MHz, reaching 28.16 Gbit/s maximum data throughput; besides, the encryption and decryption phases last both only ten clock periods. To guarantee the interoperability of the developed encryption/decryption system with the other sections of the 5G communication apparatus, synchronization and control signals have been integrated. The encryption system uses only 1631 CLBs, whereas the decryption one only 3464 CLBs, ascribable, mainly, to the Inverse Mix Columns step. The developed cypher shows higher efficiency (8.63 Mbps/slice) than similar solutions present in literature.
Thermal dissociation cavity-enhanced absorption spectrometer for measuring NO2, RO2NO2, and RONO2 in the atmosphere

Abstract. We developed thermal dissociation cavity-enhanced absorption spectroscopy (TD-CEAS) for the in situ measurement of NO2, total peroxy nitrates (PNs, RO2NO2), and total alkyl nitrates (ANs, RONO2) in the atmosphere. PNs and ANs were thermally converted to NO2 at the corresponding pyrolytic temperatures and detected by CEAS at 435–455 nm. The instrument sampled sequentially from three channels at ambient temperature, 453 and 653 K, with a cycle of 3 min, to measure NO2, NO2+ PNs, and NO2+ PNs + ANs. The absorptions between the three channels were used to derive the mixing ratios of PNs and ANs by spectral fitting. The detection limit (LOD, 1σ) for retrieving NO2 was 97 parts per trillion by volume (pptv) in 6 s. The measurement uncertainty of NO2 was 9 %, while the uncertainties of PN and AN detection were larger than those of NO2 due to chemical interferences that occurred in the heated channels, such as the reaction of NO (or NO2) with the peroxy radicals produced by the thermal dissociation of organic nitrates. Based on laboratory experiments and numerical simulations, we created a lookup table method to correct these interferences in PN and AN channels under various ambient organic nitrates, NO, and NO2. Finally, we present the first field deployment and compare it with other instruments during a field campaign in China. The advantages and limitations of this instrument are outlined.
Ship detection of optical remote sensing image in multiple scenes

ABSTRACT In view of characteristics of the ship in the optical remote-sensing image, such as multiple dimensions, majority of small objects, crowded arrangement and complex background, and so on, the paper presents a ship detection framework combining the network-fusing multi-level features crossing levels, the rotation region proposal network and the bidirectional recurrent neural network fusing self-attention mechanism. Firstly, we set up a network fusing multi-level features crossing levels because of the multiple scales and diverse characteristics of the remote-sensing ships to increase the precision of feature extraction of the ship, thus improving the performance in the multiple scales, small objects, and complex background problems. Secondly, we separately design the ROI Pooling Layer and the bidirectional recurrent neural network fusing self-attention mechanism, which infuses the prior information of ship dimension and position to realize a good performance and precise ship positioning in crowded scenes. Finally, we verify the effectiveness of the proposed method through experiments, the experimental dataset includes the private dataset designed by us based on Google Earth, the ship dataset in DOTA-Ship and HRSC2016 public ship dataset. The results verify the contributions of each proposed module, and the comparison results show that our proposed method has a state-of-the-art performance.
Characterization of simulated extreme El Niño events and projected impacts on South American climate extremes by a set of Coupled Model Intercomparison Project Phase 5 global climate models

In this study the simulated extreme and moderate El Niño events (e.g., frequency of occurrence and spatial variability) are characterized and the historical teleconnections on precipitation and temperature climate extremes in South America are analysed. Future projected changes in these teleconnections under a global warming context are also addressed. For this purpose a set of 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) simulations derived from three experiments (historical, RCP4.5 and RCP8.5) are considered. The study period is 1916–2100. As El Niño is seasonally phase‐locked, peaking in late austral spring and summer, the present analysis is carried out for these two seasons, as well as annually. Results indicate that extreme El Niño events are projected to double their occurrence considering RCP4.5 and RCP8.5, while a less pronounced increase is projected for moderate El Niño events. Most of these events show a similar pattern of sea surface temperature (SST) anomalies: a positive centre in the central equatorial Pacific that extends eastward to the coast of America. In a global warming context, the resemblance between these events may decrease. Overall, the spatial structure of the impacts of El Niño are expected to be similar between moderate and extreme events, although in some cases (regions and seasons), a higher intensity is observed considering the events characterized as extreme El Niños rather than moderate ones. As extreme El Niño events are expected to be more frequent in the future, the impacts on climate extremes as well as the associated socioeconomic impacts would also take place much more frequently too.
Reviewing the potential of the Experience Sampling Method (ESM) for capturing second language exposure and use

Frequent language exposure and use are among the most important conditions for successful language learning, whether in classrooms, during study abroad, or in other informal contexts. Research probing exposure and usage often relies on one-off self-report questionnaires in which participants estimate their typical level of language exposure over extended periods of time, often long after it occurred. This may negatively affect the validity of the resulting data. This article instead explores the potential of methods used in medical and psychological research, variably known as the Experience Sampling Method (ESM), Ecological Momentary Assessment (EMA), or diary methods. These methods are often combined with electronic and mobile survey applications to elicit self-report assessments at frequent, sometimes randomized intervals. We consider the possibilities of these methods for strengthening research into language exposure and use, second language acquisition more broadly, and study abroad research specifically. The methods have the potential to drastically reduce biases associated with summative recall. Additionally, they enable researchers to collect richer data about how individuals engage with language differently over time, and the contexts in which they do so, thus ultimately contributing to our understanding of individual differences in language acquisition.
Epizotological and bacteriological investigations on Vibrio spp. isolated from some Red Sea fish larvae at Hurghada, Egypt

Fish larvae were captured seasonally (2018) using light trap from coral reef lagoon of the Marine Biological Station at Hurghada on the Egyptian Red Sea coast. A total of 182 larvae belonging to seven fish families were collected and identified based on the morphological characteristics.The diseased larvae were clinically examined and macerated tissues were injected onto Trypton Soy Agar (1.5% NaCl ) and incubated at 25.5 °C for 2-5 days. The obtained bacterial isolates were identified as V. harveyi, V. parahaemolyticus, V. alginolyticus, V. vulnifacus, non virulence V. cholerae and unidentified Vibrio species by the morphological and biochemical characterization. The prevalence ratio of bacterial infection of all infected larvae was 101 larvae (55.5%). The highest number of infected larvae was recorded in summer (45 larvae, 86.5%), while the lowest was recorded in winter (10 larvae, 25%). The most frequently encountered Vibrio sp. was V. harveyi which was isolated from 28 fish larvae followed by V. parahaemolyticus which was isolated from 25 fish larvae. The delicate round herring exhibit the highest prevalence of infection with different Vibrio spp. (58.3%) during different seasons.
Phenological Differences, Genetic Diversity, and Population Structure of Genotypes Obtained from Seeds of Kaman-1 Walnut Cultivar

Walnut (Juglans regia L.) is a diploid (2n = 32), deciduous, monoecious, and generally open-pollinated tree with nuts of high nutrient content. In this study, the phenological differences, genetic diversity, and population structure of Kaman-1 and its 79 progenies obtained by open pollination were characterized by ISSR primers and some important phenological traits. As a result of the phenological observations, it was determined that the progenies differ significantly from Kaman-1. Besides, using ISSR primers, walnut genotypes were found to have genetic similarities ranging from 0.52 to 0.99. UPGMA cluster analysis showed that accessions from 2 different groups were classified, and population structure analysis confirmed this finding. Based on the results, a significant variation both phenologically and genetically was found within the walnut accessions. Also, this study confirmed that the progenies obtained from the Kaman-1 walnut cultivar have a quite wide variation and that ISSR primers and phenological traits are an important tool in determining genetic diversity.
NUMERICAL STUDY OF NATURAL-CONVECTION-DOMINATED MELTING INSIDE UNIFORMLY AND DISCRETELY HEATED RECTANGULAR CAVITIES

A numerical study has been conducted for natural-convection-dominated melting inside uniformly and discretely heated rectangular cavities. A computational methodology based on the enthalpy method for the phase change is first presented and validated with experimental data. The model is next employed to determine the effect of the source dimension β and spanη, of the aspect ratio of the cavity A, and of the wall-phase change material thermal diffusivity ratio ā on the melting process. Results show that for a uniformly heated wall, the melting time and the temperature of the wall reach a maximum for A ≈ 10. For A,≤10, convection-dominated melting is enhanced, and the melting time is reduced. For A ≥ 10, the larger heated surface area promotes conduction-dominated melting, and the resulting melting times are also reduced. For discretely heated cavities of aspect ratio A ≤, 3.0 with a low wall thermal diffusivity ratio (ā 1.5(, the source span η is the most influential parameter. For 0.625 ≤ A ≤ 1.6 and -η ≥ .
Socioeconomic Determinants of Renewable Energy Production in Emerging and Developed Countries

Energy is essential for development and economic growth, but traditional non-renewable energy sources are finite and have significant adverse environmental impacts. Therefore, there is an increasing interest in energy generation from renewable sources. However, research to date in this field does not sufficiently identify the common factors determining the uptake of renewable energy in emerging and developed countries. This paper addresses that gap by identifying the complex interrelationships between factors that determine the extent to which countries convert to renewable energy. The article's primary focus is a detailed statistical analysis of 10 developed and 16 emerging countries using annual data from 1976–2018. The objective is to examine the interrelationships and elasticities between increased production of renewable energy and three key socioeconomic variables; GDP, CO2 emissions, and oil price. This research uses panel data and time-series analyses to identify panel and country-specific elasticity of renewable energy production and dynamic causal relationships between these variables. It also applies fully modified and dynamic ordinary least square approaches. The study details the different interactions between the variables in each country. It uses an autoregressive distributed lag model to determine the long and short-run dynamics between renewable energy production and the three variables in each country. The paper shows there was a long-run elasticity between renewable energy and GDP in the developed countries and short-run dynamics between renewable energy and the other two variables. Whereas in the emerging countries category, there were long-run relationships between renewable energy and GDP, CO2 emissions, and oil price.