Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does Government Spending Affect Labour Force Participation and Unemployment Within the WAMZ Countries?

This study utilizes static and dynamic models in examining the short run and long run impacts of government spending on labour force participation and unemployment within the West African Monetary Zone (WAMZ) over the period 1991-2018. While the static models are estimated using the Pooled Ordinary Least Squares (POLS) technique and the Least Squares Dummy Variables (LSDV) technique, the dynamic models are estimated using the GMM-IV technique. The GMM-IV technique better addresses endogeneity issues relative to the other techniques utilized and also, the parameters obtained from the technique are confirmed to be consistent by the Arellano-Bond test for zero autocorrelation. Accordingly, this technique is given preference in this paper. The results from the technique reveal that government spending increases the labour force participation rate but has an ambiguous impact on unemployment rate. In the long run, the parameter estimates largely remain unchanged in terms of their sign and significance; however, they increase in size. Based on these findings, this paper firstly recommends that policy makers intensify efforts in increasing government spending; as a reduction may impact negatively on the labour force participation rate. Secondly, this paper recommends the formulation and implementation of fiscal policies that are robust enough to reduce the unemployment rate as they increase the labour force participation rate.
Short-Observation Measurement of Multiple Sinusoids With Multichannel Sub-Nyquist Sampling

Measurement of multiple sinusoidal signals (MSSs) is significant in instrumentation and measurement, radar, communications, and many other electric systems. Measurement methods based upon Shannon sampling theory require very high sampling rates and heavy processing to estimate the frequencies and amplitudes. In this article, we propose a multichannel time-staggered sampling system to measure MSSs with short-observation sub-Nyquist samples. The sampling scheme is composed of <inline-formula> <tex-math notation="LaTeX">$N'$ </tex-math></inline-formula> time-staggered sampling channels, where the staggered time is allowed to be greater than the Nyquist sampling interval. The estimation method is based on the frequency domain sparse common support (SCS) model and the Chinese remainder theorem (CRT). To estimate <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> frequency components in the signal without image frequency aliasing, <inline-formula> <tex-math notation="LaTeX">$N' \geq 2$ </tex-math></inline-formula> sampling channels and <inline-formula> <tex-math notation="LaTeX">$N\geq \lceil K+K/N' \rceil $ </tex-math></inline-formula> samples for each channel are enough. In the situation when image frequency aliasing exists, <inline-formula> <tex-math notation="LaTeX">$N' \geq 2K$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N\geq 2K$ </tex-math></inline-formula> samples are sufficient. We further explore the analysis of noisy signals and generalize our proposal to real-valued signals. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.
MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities

Entity Resolution (ER) aims to identify different descriptions in various Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the Variety, Volume and Veracity of entity descriptions published in the Web of Data. To address them, we propose the MinoanER framework that simultaneously fulfills full automation, support of highly heterogeneous entities, and massive parallelization of the ER process. MinoanER leverages a token-based similarity of entities to define a new metric that derives the similarity of neighboring entities from the most important relations, as they are indicated only by statistics. A composite blocking method is employed to capture different sources of matching evidence from the content, neighbors, or names of entities. The search space of candidate pairs for comparison is compactly abstracted by a novel disjunctive blocking graph and processed by a non-iterative, massively parallel matching algorithm that consists of four generic, schema-agnostic matching rules that are quite robust with respect to their internal configuration. We demonstrate that the effectiveness of MinoanER is comparable to existing ER tools over real KBs exhibiting low Variety, but it outperforms them significantly when matching KBs with high Variety.
Brief Psychotic Disorder in Relation to Coronavirus, COVID-19 Outbreaks: A Case Report

Coronaviruses are a family of virus that cause respiratory symptoms. The latest outbreaks of the virus in late December 2019, is named Covid-19. World Health Organization (WHO) declared a public health emergency on 31th of January 2020. Cases were detected across the globe. Brief psychotic disorder is a sudden onset (within two weeks) of psychosis which lasting for less than a month. The affected person will eventually return to their premorbid state. It is often in response to a stressful event in one’s life. We reported a case of 31 years old man, who presented with one week’s history of odd behaviour. He was causing a disturbance within his neighbourhood, where he was holding a knife, talking irrelevantly and acting suspiciously. This was his first presentation to hospital. He has no family history of mental illness. He did not take any illicit substances nor alcoholic. In the ward, he was given a low dose of antipsychotics, where he responded rapidly. His brother reported that the patient had been very worked up with the coronavirus outbreak news and had been constantly texting him. He was having fear and distress as he felt that there were not enough preventive measures were being put into place to curb the outbreak of the disease. Please click PDF below to download the full paper.
Long‐term monitoring reveals decreasing water beetle diversity, loss of specialists and community shifts over the past 28 years

Lentic freshwater organisms are influenced by a multitude of factors, including geomorphology, hydrology, anthropogenic impacts and climate change. Organisms that depend on patchy resources such as water beetles may also be sensitive to anthropogenic habitat degradation, like pollution, eutrophication, water level or management alteration. To assess composition and ecological trends in the water beetle communities of Central Europe, we sampled water beetles (Dytiscidae, Haliplidae, Noteridae) in 33 water bodies in Southern Germany from 1991 to 2018. We used manual, time‐standardised capture during three periods: between 1991 and 1995, 2007 and 2008, and 2017 and 2018. During the 28‐year survey period, we captured a total of 81 species. We found annual declines in both species number (ca −1%) and abundance (ca −2%). Also, community composition showed significant changes over time. The significant impact of pH on the community composition suggests that the recorded changes through time partly reflect natural succession processes. However, a pronounced decline of beetle species belonging to the moor‐related beetle associations indicated that Central European water beetles are also threatened by non‐successional factors, including desiccation, increased nitrogen input and/or mineralisation, and the loss of specific habitats. This trend to physiographical homogenisation resulted in corresponding community composition shifts. To effectively protect endangered species, conservation strategies need to be aimed at regularly creating new water bodies with mineralic bottom substratum, and maintenance of moor water bodies that represent late successional stages.
Identification and Validation a Major QTL from “Sea Rice 86” Seedlings Conferred Salt Tolerance

Saline stress severely affects rice (Oryza sativa L.) growth and development and reduces crop yield. Therefore, developing salt-tolerant and high-yielding rice using quantitative trait loci (QTLs) and linkage markers is a priority for molecular breeding. Here, the indica rice Sea Rice 86 (SR86) seedlings showed higher tolerance than ordinary rice varieties in saline soil, and a dominant effect on salinity sensitivity was demonstrated by genetic analysis. We constructed bulked segregant analysis pools using F2 populations from parents Dianjingyou 1 as the recipient and SR86 as the donor. We identified a 2.78 Mb region on chromosome 1 as the candidate region. Using simple sequence repeat markers and substitution analysis, we mapped the target region within 5.49 cM in the vicinity of markers RM8904–RM493. We speculated that this QTL, named qST1.1, might contribute significantly to the salt tolerance of SR86. The high salt tolerance of introgression lines obtained by marker assistant selection (MAS) confirmed that the qST1.1 region was associated with salinity tolerance. This newly-discovered QTL will be helpful for the analysis of the salt-tolerant mechanism of rice and breeding high-quality rice varieties using MAS.
A Utility-Preserving and Scalable Technique for Protecting Location Data with Geo-Indistinguishability

Location-based apps provide users with personalized services tailored to their geographical position. This is highly-beneficial for mobile users, who are able to find points of interest close to their location, or connect with nearby friends. However, sharing location data with service providers also introduces privacy concerns. An adversary with access to fine-grained user locations can infer private details about individuals. Geo-indistinguishability (GeoInd) adapts the popular differential privacy (DP) model to make it suitable for protecting users’ location information. However, existing techniques that implement GeoInd have major drawbacks. Some solutions, such as the planar Laplace mechanism, significantly lower data utility by adding excessive noise. Other approaches, such as the optimal mechanism, achieve good utility, but only work for small sets of candidate locations due to the use of computationally-expensive linear programming. In most cases, locations are used to answer online queries, so a quick response time is essential. In this paper, we propose a technique that achieves GeoInd and scales to large datasets while preserving data utility. Our central idea is to use the composability property of GeoInd to create a multiple-step algorithm that can be used in conjunction with a spatial index. We preserve utility by applying accurate GeoInd mechanisms and we achieve scalability by pruning the solution search space with the help of the index when seeking high-utility outcomes. Our extensive performance evaluation on top of real location datasets from social media apps shows that the proposed technique outperforms significantly the benchmark in terms of utility and/or computational overhead.
High-quality sputter-grown layered chalcogenide films for phase change memory applications and beyond

This paper summarizes recent progress on thin film growth of chalcogenides by sputtering. The materials discussed include Sb–Te, Bi–Te, Ge–Te, and their superlattices, materials that are technologically important particularly for non-volatile phase change memory. In this work, the sputter-growth behavior of high-quality layered chalcogenide films is discussed. Sputtering is one of the most commonly used thin-film growth techniques in the semiconductor industry, however, the complex interrelationship between growth parameters can lead to difficulty in fabricating high-quality films although the deposition method itself is relatively simple. Here, we successfully demonstrate the fabrication of highly-oriented layered chalcogenide materials by sputtering. The selection of the appropriate sputtering target is important. In particular, it was found that a Te-rich Sb–Te alloy target such as Sb33Te67 is necessary in order to obtain a stoichiometric Sb2Te3 film. Moreover, the growth temperature is also a key factor in obtaining a highly-oriented film, namely the ideal growth temperature for an Sb2Te3 film is between 230 °C and 250 °C after the growth of an amorphous seed layer at room temperature. Furthermore, it was found that this technique is also useful to grow films epitaxially on Al2O3 or Si(111) substrates even though there are some misoriented grains as well as twins present. Finally, we demonstrate the growth of highly-oriented Sb2Te3 films on a flexible substrate. The versatility of sputtering will become technologically more and more important for the various applications represented by phase change memory.
Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.
A Modified Nelder-Mead Algorithm for Photovoltaic Parameters Identification

Photovoltaic panels are prone to degradation after long outdoor exposure, which can be manifested in multiple forms. This phenomenon also causes an alteration of the panel's parameters. In this work, a heuristic search methodology based on the Nelder-Mead algorithm is applied in the estimation of photovoltaic panel parameters. Modifications that improve the performance of the method are also presented. The results of this modified Nelder-Mead algorithm are compared with the results of the classical method. A sensitivity analysis towards additive noise and ADC resolution is performed.
Influence Maximization Revisited: The State of the Art and the Gaps that Remain

The steady growth of graph data from social networks has resulted in wide-spread research on the influence maximization (IM) problem. This results in extension of the state-of-the-art almost every year. With the recent explosion in the application of IM in solving real-world problems, it is no longer a theoretical exercise. Today, IM is used in a plethora of real-world scenarios, with OnePlus1 series of mobile phones, Hokey Pokey2 ice-creams, and galleri5 influencer marketplace3 being the most prominent industrial use-cases. Given this scenario, navigating the maze of IM techniques to get an in-depth understanding of their utilities is of prime importance. In this tutorial, we address this paramount issue and solve the dilemma of “Which IM technique to use and under What scenarios”? “What does it really mean to claim to be the state-of-the-art”? This tutorial builds upon our benchmarking study [1], and will provide a concise and intuitive overview of the most important IM techniques, which is usually lost in the technical literature. Specifically, we will unearth a series of incorrect claims made by prominent IM papers, disseminate the inherent deficiencies of existing approaches, and surface the open challenges in IM even after a decade of research.
INTEGRATION OF VERBAL AND CONSTRUCTIONAL INFORMATION IN THE SECOND LANGUAGE PROCESSING OF ENGLISH DATIVE CONSTRUCTIONS

Abstract This study investigated the effects of construction types on Korean-L1 English-L2 learners’ verb–construction integration in online processing by presenting the ditransitive and prepositional dative constructions and manipulating the verb’s association strength within these constructions. Results of a self-paced reading experiment showed that the L2 group spent longer times in the verb–construction integration in the postverbal complement region when processing the ditransitive construction, which is less canonical and highly avoided in the learners’ L1, than when processing the prepositional dative construction, which is more canonical and shares similar structural features with the L1 counterpart. In the following spillover region, L2 learners showed faster reading times as proficiency increased when the verb was strongly associated with the prepositional dative construction. Our findings expand the scope of current models on L2 sentence processing by suggesting that construction types and L2 proficiency may affect the L2 integration of verbal and constructional information.
Relationship between petrographic parameters and physical-mechanical properties of weakly cemented sandstones

Petrographic characteristics (textural properties and modal compositions) have an intrinsic influence on the physical and mechanical (PM) properties of weakly cemented sandstones. Sixteen sandstone samples were tested to determine the PM parameters and then thin sections from those samples were used to measure the petrographic parameters. Petrographic parameters were analysed statistically to establish quantitative relationships between the petrographic characteristics and PM properties. Based on multiple linear regression (MLRA) and stepwise multiple regression analyses (SMRA), several multiple regression equations with significant petrographic variables were established for estimating the PM parameters of the sandstone. Results indicated that uniaxial compressive strength (UCS) and Young's modulus (E) were closely related to mean grain size (Mz) (φ-value), non-uniform coefficient (Cu), fractal dimension (Fd), Riley sphericity (Ψp), packing proximity (Pp), floating contacts (Fl), grain-to-grain contacts (G-G), feldspar content and organic matter content. An increased percentage of grain-to-cement contacts (G-C) and reduced percentages of grain-to-matrix contacts (G-M) and organic matter content indicated a high value of Poisson's ratio (POISS). In addition, Mz, Cu, Fd, Ψp, quartz, feldspar, and organic matter contents were found to be significant parameters for density and P-wave velocity (Vp). Furthermore, density and POISS could be predicted using the results from the MLRA and Fd was a good predictor for density, Vp and UCS using the results from the SMRA. Additionally, it was noted that the petrographic characteristics of roof sandstones had significant influence on the overburden pressure characteristics within the coal faces in the Shendong mining area.
Lagged rejuvenation of groundwater indicates internal flow structures and hydrological connectivity

Large proportions of rainwater and snowmelt infiltrate into the subsurface before contributing to stream flow and stream water quality. Subsurface flow dynamics steer the transport and transformation of contaminants, carbon, weathering products and other biogeochemistry. The distribution of groundwater ages with depth is a key feature of these flow dynamics. Predicting these ages are a strong test of hypotheses about subsurface structures and time‐varying processes. Chlorofluorocarbon (CFC)‐based groundwater ages revealed an unexpected groundwater age stratification in a 0.47 km2 forested catchment called Svartberget in northern Sweden. An overall groundwater age stratification, representative for the Svartberget site, was derived by measuring CFCs from nine different wells with depths of 2–18 m close to the stream network. Immediately below the water table, CFC‐based groundwater ages of already 30 years that increased with depth were found. Using complementary groundwater flow models, we could reproduce the observed groundwater age stratification and show that the 30 year lag in rejuvenation comes from return flow of groundwater at a subsurface discharge zone that evolves along the interface between two soil types. By comparing the observed groundwater age stratification with a simple analytical approximation, we show that the observed lag in rejuvenation can be a powerful indicator of the extent and structure of the subsurface discharge zone, while the vertical gradient of the age‐depth‐relationship can still be used as a proxy of the overall aquifer recharge even when sampled in the discharge zone. The single age stratification profile measured in the discharge zone, close to the aquifer outlet, can reveal the main structure of the groundwater flow pattern from recharge to discharge. This groundwater flow pattern provides information on the participation of groundwater in the hydrological cycle and indicates the lower boundary of hydrological connectivity.
The Role of Teachers’ Psycholinguistic awareness on Second Language Teaching and Learning

Psycholinguistics is concerned with how a person acquires a language, produces and perceives both spoken and written language. Moreover, Psycholinguistics is a branch of cognitive science and one of the major issues of cognitive science is “mind”. Knowing what interactions in the memory of the learner will happen when teaching foreign languages, what problems will happen in the process of memorization; memory capacity, short-term memory structure, working memory, long-term memory, semantic memory, and so on can be helpful for educators and designers of instructional books and facilitate and accelerate the process of teaching and learning foreign languages. Therefore, the aim of the present article is to overview psycholinguistic awareness and its contributions on English language learning (ELL) and English language teaching (ELT). This paper also attempts to critically and profoundly present the major research findings in the role of psycholinguistics and its implications and pedagogic contributions to the area of language learning and teaching, in an endeavor to shed light on the current successful and influential practices in this area of research which are incremental to second language learning development.