Miracle-dz/UniFAR-FTUs huggingface dataset:
A universal stellar-mass and size relation of galaxies in GOODS-N region

We present scaling relations between stellar-mass (Mstar) and the size of galaxies at 0.3 10^11 Msun), the evolution is ~70-80 % increase in R_90 from z~3 to z~0.3, though that in R_50 is weaker. The evolution of compactness factor, R_50/R_90, which becomes smaller at lower redshift, is suggestive of minor merging effect in the outer envelope of massive galaxies.
Energy-Efficient Pairing and Power Allocation for NOMA UAV Network Under QoS Constraints

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.
On toric schemes

Studying toric varieties from a scheme-theoretical point of view leads to toric schemes, i.e. "toric varieties over arbitrary base rings". It is shown how the base ring affects the geometry of a toric scheme. Moreover, generalisations of results by Cox and Mustata allow to describe quasicoherent sheaves on toric schemes in terms of graded modules. Finally, a toric version of the Serre-Grothendieck correspondence relates cohomology of quasicoherent sheaves on toric schemes to local cohomology of graded modules.
Debris Cover Limits Subglacial Erosion and Promotes Till Accumulation

Glaciers are commonly conceptualized as bodies composed of snow and ice. Yet, many glaciers contain a substantial amount of rock, especially those abutting steep mountains. Mountain slopes erode, depositing rocks on glaciers below. This loose rock (or debris) is buried in glaciers and melts out lower down creating a debris cover. Debris cover reduces ice melt, which changes the shape and movement of glaciers. Glacier movement, specifically basal sliding, efficiently sculpts landscapes. To date, we know little about the impacts of surface debris on conditions below glaciers. To help remedy this, we run numerical model simulations which show that debris‐covered glaciers erode slower than glaciers unaffected by debris. Reduced melt under surface debris lowers sliding speeds and causes sediment to accumulate at the bed, potentially establishing conditions for surging. The influence of surface debris cover on the subglacial environment may hold substantial implications for alpine sediment storage and landscape evolution.
Lists: A Novel Experimental Method to Measure Tax Evasion

This paper presents a novel experimental method to measure the propensity to evade taxes that overcomes sensitivity bias. Drawn from political science research, the method uses list experiments to estimate the prevalence of tax evasion in different population groups. Using this method, we found that 13 percent of the Canadian population admitted to evading income taxes and 29 percent admitted to evading consumption taxes. These estimates are comparable to estimates based on audit data. They provide a new source of reliable data about evasion, which can be combined with measures of psychological drivers of evasion at the individual level.
Hybrid Machine-Learning-Based Spectrum Sensing and Allocation With Adaptive Congestion-Aware Modeling in CR-Assisted IoV Networks

Unlicensed cognitive-radio (CR)-assisted Internet of Vehicles (IoV) users can access licensed providers’ radio spectrum and concurrently utilize the dedicated channel for data transmission in vehicular communication. Optimizing channel access in cognitive IoV networks can help maximize available spectrum resources. This article proposes a novel sensing and communication integrated framework, dubbed as the CR-assisted IoV network (CRAV-Net), using a cluster-based hybrid optimization approach with adaptive congestion-aware modeling for dynamic high-mobility vehicular networks in an urban city context. In CRAV-Net, intelligent hybrid learning spectrum agents are introduced, which perform spectrum sensing (SS) using a deep learning (DL) model. It dynamically learns the multilevel spatial and temporal graphical features from input spectrograms through layer-by-layer propagation. It efficiently predicts the spectrum occupancy in the primary spectrum, without a priori knowledge of the radio environment. Then, to assign the vacant channels to the secondary vehicles, a support vector machine classifier is trained based on several learning features, including the vehicle stay time, vehicle density, and network capacity, to select the optimal resource route. The proposed framework achieves an overall accuracy of 99.74% in SS using the custom data set, outperforming state of the art by 12.60% at −25-dB signal-to-noise ratio. In addition, it brings a performance gain of 0.81% in SS accuracy when evaluated on real-world signals. Furthermore, in optimal network node allocation, the proposed framework achieves a mean accuracy of 98.45%, outperforming the existing methods by 0.63% and 18.32% in terms of accuracy and allocation time, respectively.
The Robustness of Counterfactual Explanations Over Time

Counterfactual explanations are a prominent example of post-hoc interpretability methods in the explainable Artificial Intelligence (AI) research domain. Differently from other explanation methods, they offer the possibility to have recourse against unfavourable outcomes computed by machine learning models. However, in this paper we show that retraining machine learning models over time may invalidate the counterfactual explanations of their outcomes. We provide a formal definition of this phenomenon and we introduce a method, namely counterfactual data augmentation, to help improving the robustness of counterfactual explanations over time. We test our method in an empirical study where we simulate different model retraining scenarios. Our results show that counterfactual data augmentation improves the robustness of counterfactual explanations over time, therefore contributing to their use in real-world machine learning applications.
Reflections on One of the World’s Harshest COVID-19 Lockdowns, and on the Possibility of Eliminating COVID-19 in Australia

The first case of COVID-19 in Australia was diagnosed on January 25, 2020 (Australian Government Department of Health, 2020), at about the same time as the first case in the US. Within a fortnight, the virus was present in at least three of Australia’s eight major states/territories, and community transmission was occurring (Liebig et al., 2020). By March 18, Australia had recorded a cumulative total of 567 COVID-19 cases and 5 deaths, and the federal government declared the virus to be a biosecurity emergency (COVID-19 Data, 2020; Our World in Data, 2020; Parliament of Australia, 2020a). All of Australia’s state/territory government subsequently implemented lockdowns, asking people to work and study from home, closing non-essential businesses, banning unnecessary travel, and prohibiting mass gatherings (Parliament of Australia, 2020b). Australia also implemented a variety of other responses – for example: vigorously tracing the contacts of known cases, forcibly isolating those contacts, limiting and quarantining international travelers, undertaking extensive testing (including of asymptomatic people), and limiting access to high-risk groups (including those in residential care settings). By the first week of April, we had effectively ‘flattened the curve’ (COVID-19 Data, 2020). Our COVID-19 response was celebrated as “among the most successful in the world” (Duckett & Stobart, 2020).
Improved Bread and Durum Wheat Cultivars Showed Contrasting Performances in N-Efficiency and N-Responsiveness

Wheat productivity can be increased by applying nitrogen (N) in the form of chemical fertilizers. However, owning to the high prices, chemical fertilizers are unaffordable to resource-poor farmers in Ethiopia. The use of N-efficient cultivars rather makes an alternative option for sustainable wheat production. Six bread and six durum wheat cultivars were thus evaluated under low N (1 g·pot−1) and optimum N (5 g·pot−1) in six replications. The pot-based treatments were arranged in randomized complete block design in the lathe house at Kulumsa Agricultural Research Center. Results showed that the number of effective tillers (NET), spike length (SL), total dry biomass yield (TBY), grain yield (GY), NDVI values, total N uptake (TNUP), N utilization efficiency (NUtE), N uptake efficiency (NUpE), N use efficiency (NUE), grain, and straw N uptakes were significantly influenced by wheat cultivars and N levels. Under low N, Hidase and Kingbird gave significantly higher GY, whereas Danda’a and Hidase gave significantly higher GY under optimum N. Under low N, Hidase, Kingbird, and Lemu were identified as the most N-efficient, while Ude, Landrace, and Utuba identified as N-inefficient based on multicriterion performance (GY, TBY, SL, NET, TNUP, NHI, NUpE, NUtE, and NUE). Under optimum N, Danda’a, Shorima, Hidase, and Lemu were identified as the most responsive, while Ude, Landrace, and Kingbird identified as nonresponsive to N application. In conclusion, Kingbird is recommended for low N input, while Danda’a and Shorima are recommended for N input intensive, and Hidase and Lemu are recommended for both low and high N input intensive wheat production.
Solving the multicommodity flow problem with the analytic center cutting plane method

La these porte sur le developpement de methodes numeriques performantes pour la resolution de problemes de multiflots ou plusieurs entites (messages en telecommunications, marchandises ou usagers en transport) entrent en competition pour l'utilisation d'un reseau a capacite limitee. Pour chaque arc du reseau, le flot qu'il supporte engendre un cout d'utlisation et l'objectif est de minimiser la somme des couts. La contribution de la these porte principalement sur le developpement et la specialisation d'une methode de resolution efficace, ACCPM, et sur la mise en oeuvre d'une strategie d'ensemble actif permettant de reduire considerablement la taille des problemes traites. Les travaux realises dans le cadre de la these ameliorent les resultats publies dans la litterature de maniere systematique et significative. Ils permettent egalement de traiter des problemes dont la taille depasse de beaucoup ceux rencontres dans la litterature.
Do tourists seek the same information at destinations? Analysis of digital tourist information searches according to different types of tourists

The study analyses the searches for digital tourist information that different types of tourists, with different characteristics and connectivity preferences, make at destinations on the main topics they may require, allowing to know which digital sources they use, but also at what moment and in what situation. The study was based on an online survey that asked users about searches for tourist information they made at the destination during their last trip. The results show that tourists increasingly seek more tourist information at the destination, and search differently depending on the topic and their connectivity preferences in the different digital channels and locations. The results will allow Destination Management Organizations (DMOs) to provide tourists with suitable information, at the time and place they so require.
Design of Electric Vehicle Battery Charger With Reduced Switching Frequency Variation

This article presents the design of an electric vehicle (EV) battery charger isolation stage for minimizing switching frequency variation with the proposed duplex control. Traditionally, the realization of an EV charger with a resonant power conversion stage involves extreme frequency variation in the constant voltage (CV) mode resulting in switching losses, imposes severe constraints on the optimal design of magnetics, and presents control complexities due to secondary resonance and degraded pulsewidth modulation resolution. In this context, reported hybrid control strategies are complex and entail the simultaneous variation of control variables. In order to simplify control complexity and design constraints, the EV charger isolation stage with the quadruple rectifier and employing the proposed duplex control is presented with notable frequency reduction. Furthermore, the effect of high-frequency transformer nonidealities on the soft-switching range is delineated. The proposed duplex control contributes to the efficiency enhancement of the EV charger over a comprehensive range during CV charging, ensures soft switching, and prevents secondary resonance. It also offers continuous resonant current to overcome the underutilization of the transformer arising from high-frequency distortions. To validate the presented analysis, a 1.25-kW hardware prototype is designed and tested in the laboratory, and experimental results are presented.
Solving constrained portfolio optimization model using stochastic fractal search approach

PurposeOptimum utilization of investments has always been considered one of the most crucial aspects of capital markets. Investment into various securities is the subject of portfolio optimization intent to maximize return at minimum risk. In this series, a population-based evolutionary approach, stochastic fractal search (SFS), is derived from the natural growth phenomenon. This study aims to develop portfolio selection model using SFS approach to construct an efficient portfolio by optimizing the Sharpe ratio with risk budgeting constraints.Design/methodology/approachThis paper proposes a constrained portfolio optimization model using the SFS approach with risk-budgeting constraints. SFS is an evolutionary method inspired by the natural growth process which has been modeled using the fractal theory. Experimental analysis has been conducted to determine the effectiveness of the proposed model by making comparisons with state-of-the-art from domain such as genetic algorithm, particle swarm optimization, simulated annealing and differential evolution. The real datasets of the Indian stock exchanges and datasets of global stock exchanges such as Nikkei 225, DAX 100, FTSE 100, Hang Seng31 and S&P 100 have been taken in the study.FindingsThe study confirms the better performance of the SFS model among its peers. Also, statistical analysis has been done using SPSS 20 to confirm the hypothesis developed in the experimental analysis.Originality/valueIn the recent past, researchers have already proposed a significant number of models to solve portfolio selection problems using the meta-heuristic approach. However, this is the first attempt to apply the SFS optimization approach to the problem.
Stable Turnkey Laser System for a Yb/Ba Trapped-Ion Quantum Computer

This work presents a stable and reliable turnkey continuous-wave laser system for a Yb/Ba multispecies trapped-ion quantum computer. The compact and rack-mountable optics system exhibits high robustness, operating over a year without realignment, regardless of temperature changes in the laboratory. The overall optical system is divided into a few isolated modules interconnected by optical fibers for easy maintenance. The light sources are frequency-stabilized by comparing their frequencies with two complementary references: 1) a commercial Fizeau wavelength meter and 2) a high-finesse optical cavity. This scheme enables automatic frequency stabilization for days with a sub-MHz precision.
Approaches to the classification of wine aroma ageing potential. Applications to the case of terpenoids in Valpolicella red wines

Valpolicella is an Italian wine-producing region famous for premium red wines. Valpolicella wines often undergo a minimum ageing period between 1 and 4 years, according to the wine type. Therefore, identifying relevant types of aroma precursors and the related chemical mechanisms behind aroma development during ageing is of great importance for Valpolicella wines. This study assessed free and glycosidically-bound terpene compounds of Valpolicella wines as potential precursors of different ageing-related terpenes using a model ageing experimental approach. Results showed that terpene profiles changed substantially with ageing. The occurrence of cyclic terpenes (p-menthane-1,8-diol, 1,8-cineole and 1,4-cineole) was mostly associated with the content of free monoterpenols, linalool in particular, in young wines, whereas glycosylated monoterpenols appeared to play a minor role. Accumulation of cineoles was strongly modulated with pH; in fact, low pH values promoted higher cineole yields.
Factors leading to child labor exploitation and the involvement of non-governmental organizations (NGOs) in Addis Abeba, Ethiopia

Abstract This study examined factors leading to child labor exploitation and non-governmental organizations (NGOs) involvement in Addis Abeba, Ethiopia. It involved two NGOs in Addis Ababa: Sheger Child and Family Development Society (SCFDS) and Future Hopes International Development Organization (FHIDO). A cross-sectional descriptive research design was used. The findings of this study revealed that sex, age, household size, child relationship with the family, family employment status, marital status, and educational level of the household were significant predictors of child labor abuse in the research areas. The results also showed that both FHIDO and SCFDS provided various services to end child labor abuses in the study areas. Also, the chosen non-governmental organizations provide school materials and supplies and educational and vocational training on child labor exploitation and how to handle it for the children and their parents/guardians. Despite political reasons, the NGOs’ training for households and children was not effective in altering the problem of child labor abuse. Therefore, the researcher recommends that there should be collaborative and strong vocational and educational training that raises awareness in society as a whole of the potential impact of premature child work and that these NGOs should improve their ways of providing their wakefulness creation training for the community.
Earth System Model Overestimation of Cropland Temperatures Scales With Agricultural Intensity

Intensive crop growth can modify regional climate by partitioning energy to latent heating through transpiration, cooling growing season temperatures. Recent work shows that cooling associated with agriculture can dampen anthropogenic warming over breadbasket regions. However, it is unknown whether climate models reproduce crop influences on regional climate, and thus the future risk of extreme climate events over global breadbasket regions. We show that models overestimate growing season temperatures and underestimate evapotranspiration (ET) over global croplands, and that these differences increase with cropped area. We trace this warm and dry difference through each model's representation of the surface energy budget, showing that model differences in transpiration, leaf area index, and the ratio of transpiration to total ET drive the overall effect. While the implications of these model deficiencies for future projections are uncertain, they point to the importance of improving representations of crop‐climate processes to better assess breadbasket vulnerability to climate change.
Depression and Alcohol Use Behaviours among Primary and Secondary School Teachers in Mwanza, Tanzania; A Cross-Sectional Study

Background: Depressive disorders and alcohol use disorders are highly prevalent in the workplace and have an enormously negative impact on work performance, productivity, absenteeism, and disability costs. Little is known about the burden among primary and secondary school teachers in Tanzania. Method: A cross-sectional study was conducted at Mwanza City in the Lake and Western zones of the United Republic of Tanzania aimed at determining the prevalence and factors associated with depression and alcohol use behaviours among teachers where primary and secondary school teachers from both private and public schools were involved. A total of 300 teachers were recruited and interviewed using Patient Health Questionnaire-9 (PHQ-9) and Alcohol Use Disorder Identification Test (AUDIT). A systematic sampling approach was used to select both schools and participants. Results: The mean age of the participants was 36.1 (SD = 8.465), the majority of the participants were from primary and public schools, 60.67% and 55.67%, respectively. Using cut-off points of four and eight for PHQ-9 and AUDIT, respectively, 51% of the participants had symptoms of depression, and 16% had harmful alcohol use. While the male gender, being married, having a low number of children, and degree level of education was statistically less likely to be associated with depression, on the other hand, male gender, age group of 41-50, being married and having a high number of children were statistically more likely to be associated with harmful alcohol use. Limitations: A cross-sectional study was used, which relies on self-report of symptoms which could lead to recall bias. Despite that, the study was done among both public and private primary and secondary school teachers from the largest city in the lake and western zone of Tanzania, which serves a diverse population still, and regional differences could be there. Conclusion: Depression and alcohol use disorders are high among a sample of our teachers in Mwanza Tanzania; further studies are needed to explore and measure the incidence, causal inference and the association between outcomes and risk factors.
Pro-and Anti-inflammatory Biomarkers as Potential Prognosis Markers in Oral Squamous Cell Carcinoma

: The most widely used method to classify prognostic factors in cancers today is TNM. However, Oral Squamous Cell Carcinoma (OSCC) often demonstrates different behaviors in relation to aggressiveness and therapeutic response at the same TNM stage. So, in such cases biomarkers can be used to identify the biological diversity of these tumors more reliably, leading to better therapeutic strategies and disease management. The presence of inflammatory immune cells in the tumor microenvironment can have pro or antitumor effects and the investigation of the expression of inflammatory markers in OSSC can be usefulto design immunotherapeutic interventions. The Transforming Growth Factor alpha is a potent stimulator of cell migration that acts on cell proliferation, invasion and metastasis of cancer, as well as immune suppression and angiogenesis. Inflammatory cytokines, such as Interferon-gamma, mediate macrophage differentiation. Macrophages are an important component of the OSCC microenvironment. The greater amount of tu-mor-associated macrophages, especially the M2 phenotype, may be associated with a more aggressive biological behavior of the OSCC and, consequently, with reduced survival.