Miracle-dz/UniFAR-FTUs huggingface dataset:
Comment on esurf-2021-35

The manuscript presents the development of a landscape evolution model (LEM) based on analytic solutions to the stream power incision model (SPIM), a simplified representation of vertical fluvial incision. Despite its simplicity, the SPIM is widely used in landscape evolution models for its demonstrated capabilities in reconstructing fluvial relief in response to changing boundary conditions at a relatively large spatial scale (i.e., Venditti et al. 2020). Model simplicity lent itself to explicit analytic solutions. Recently, there has been a growing interest in the behavior of the analytical solutions as part of studies that explored the expected dynamics of river long profiles as a function of changing geomorphic parameters and boundary conditions (i.e., Royden and Perron, 2013) and inversion applications (i.e., Goren et al. 2014a, Fox et al. 2014, Rudge et al. 2015, and many more).
Mechanics of a Dual Cross-Link Gel with Dynamic Bonds: Steady State Kinetics and Large Deformation Effects

We develop a three-dimensional continuum theory for a dual cross-link gel with permanent and transient bonds. This theory connects the breaking and re-forming kinetics of the transient bonds to the large deformation behavior of the gel. Although based on similar principles as our previous theory for the same gel, the new theory addresses a limitation of the previous theory and agrees equally well with experimental data. Specifically, in the new theory we assume that breaking and re-forming kinetics of transient bonds reaches a steady state. This assumption leads to fewer material parameters and enables the new theory to capture the healing process of the dual cross-link gel. In addition, the steady state assumption also allows the new theory to reduce to classical linear viscoelastic theory in the limit of infinitesimal strains, where the kinetics of bond breaking and re-formation can be linked directly to the relaxation function. We also extend the theory to account for strain stiffening under very large.
Nonlinear Nonsingular Fast Terminal Sliding Mode Control Using Deep Deterministic Policy Gradient

Background: As a control strategy of industrial robots, sliding mode control has the advantages of fast response and simple physical implementation, but it still has the problems of chattering and low tracking accuracy caused by chattering. This paper proposes a new sliding mode control strategy for the application of industrial robot control, which effectively solves these problems. Methods: In this paper, a deep deterministic policy gradient–nonlinear nonsingular fast terminal sliding mode control (DDPG–NNFTSMC) strategy is proposed for industrial robot control. In order to improve the tracking control accuracy and anti-interference ability, DDPG is used to approach the uncertainties of the system in real time, which ensures the robustness of the system in various uncertain environments. Lyapunov function is used to prove the stability and finite time convergence of the system. Compared with the nonsingular terminal sliding mode control (NTSMC), the time to reach the equilibrium point is shorter. With the help of MATLAB/Simulink, the tracking accuracy and control effects are compared with traditional terminal sliding mode control (TSMC), NTSMC and radial basis function–sliding mode control (RBF–SMC), the results showed that it had the advantages of nonsingularity, finite time convergence, small tracking error. The motion accuracy and anti-interference ability of the uncertain manipulator system was further improved, and the chattering problem of the system in the motion process is effectively eliminated.
Audience Engagement with COVID-19 News: The Impact of Lockdown and Live Coverage, and the Role of Polarization

ABSTRACT Existing research on media and the COVID-19 pandemic is largely based on quantitative data, focused on digital media, limited to single-country studies, and often West-centred. As such, it has limited capacity to provide a holistic account of the causes and consequences of audience engagement with COVID-19 news, or to consider the impact of systemic political and media factors. To compensate for that, we examine a large set of qualitative interviews and media diaries collected in four eastern European countries during the first wave of the pandemic. We show that changes in news consumption—including the resurgence of television and decline of print consumption—were not driven solely by audience demand for up-to-date information, but also by practical constrains of home-bound life in lockdown, and the introduction of live briefings. Our findings underscore disruption and uncertainty as key elements of audience experiences and highlight the markedly privatized and depoliticized nature of public debate in the early phase of the pandemic. We argue that the pandemic was an unpredictable, open-ended, and exhausting media event with high potential for divisiveness and polarization, especially in contexts marked by low levels of media freedom, declining democratic standards, and elite-led politicization of the crisis.
Evaluation of p-wave Dispersion and Left Atrium Ejection Fraction in Patients with Cardioembolic Ischemic Stroke

Introduction: Studies show that p-wave dispersion and left atrial ejection fraction are predictors of cardiovascular events. Purpose: To verify the association of p-wave dispersion and left atrial ejection fraction with cardioembolic ischemic stroke. Methods: This is an observational, cross-sectional, case-control study with 61 patients, mean age of 65.6, with sinus rhythm ischemic stroke who underwent clinical evaluation with CHA2DS2-VASc score, electrocardiogram, echocardiogram and ultrasonography with Doppler of both carotid and vertebral arteries. The groups were divided into cardioembolic (cases) and non-cardioembolic (controls). The p-wave dispersion was obtained with a 12-lead electrocardiographic tracing at 50 mm/s by subtracting the largest p-wave by the smallest one. Left atrial ejection fraction was obtained using the modified biplane Simpson method. This study was approved by the UERN Research Ethics Committee (# 2,536,483). Results: Mean values for p-wave dispersion were 62.5 ms for cases and 49 ms for controls (p = 0.025). For left atrial ejection fraction, the cases presented a mean of 42.9% and the controls a mean of 55.8% (p = 0.003). For the CHA2DS2-VASc score, the mean values were 3.6 and 3.1 for cases and controls, respectively (p = 0.35). Cardiovascular risk factors showed similar distribution in both groups. Conclusion: p-wave dispersion and left atrial ejection fraction were associated with the cardioembolic patients with sinus rhythm that have gone through ischemic stroke.
Using geometric mean to compute robust mixture designs

Mixture experiments involve developing a dedicated formulation for specific applications. We propose the weighted optimality criterion using the geometric mean as the objective function for the genetic algorithms. We generate a robust mixture design using genetic algorithms (GAs) of which the region of interest is an irregularly shaped polyhedral region formed by constraints on proportions of the mixture component. When specific terms in the initial model display unimportant effects, it is assumed that they are removed. The design generation objective requires model robustness across the set of the reduced models of the design. Proposing an alternative way to tackle the problem, we find that the proposed GA designs based on G‐ or/and IV‐efficiency are robust to model misspecification.
The relationship between sponsorship objectives and sport team reputation among sponsor companies: An exploratory study focusing on sponsors of a Japanese professional women’s volleyball club

ABSTRACT This study explored the relationship between sponsorship objectives and sport team reputation from the perspective of companies. Our data were based on a questionnaire sent to sponsors of Victorina Himeji, which is a professional women’s volleyball club in Japan. One hundred ninety-four companies completed a survey about sponsorship objectives, sport team reputation, and sponsorship satisfaction. We conducted factor analysis, cluster analysis, t-test, and a two-way analysis of variance to examine the research questions. The results demonstrated that sponsorship objectives were composed of three factors (marketing objectives, corporate objectives, and team involvement). They were classified into a “highly valued” cluster and a “lowly valued” cluster. The t-test showed that the “highly valued” cluster was significantly higher than the “lowly valued” cluster in all variables except for spectator-orientation. Furthermore, comparing sport team reputation of each cluster with sponsorship satisfaction showed that the more satisfied the sponsorship of both clusters, the higher the sport team reputation. This study concludes that sponsorship objectives are important for both sponsors and sport teams.
May Measurement Month 2019: an analysis of blood pressure screening results from Armenia

Abstract Elevated blood pressure (BP) is an enormous public health problem and a growing burden worldwide and the biggest single risk factor for cardiovascular death. May Measurement Month (MMM) is a global initiative aimed to raise awareness of high BP and to act as a temporary solution to the lack of screening programmes worldwide. An opportunistic cross-sectional survey of volunteers aged ≥18 years was carried out in May to July 2019. Blood pressure measurement, the definition of hypertension and statistical analysis followed the standard MMM protocol. The study was conducted in public areas (23 sites in the capital city Yerevan and 13 in other regions), both indoor and outdoor, as well as in primary and secondary healthcare centres. In total, 9818 individuals (11.3% participated in either MMM2017/2018 or both) were screened of which 9786 had three BP measurements available, 1.6% of them reported never having had their BP measured. The mean age of screened participants was 47.6 (SD ± 16.5) years, 61.9% were female. After multiple imputation, 41.6% of participants had hypertension and 72.8% of them were aware of their high BP, 65.4% were on treatment, and of those treated, 46.5% had controlled BP (<140/90 mmHg). Of 4088 participants with hypertension, 30.4% had controlled BP. Of all risk factors analysed, reported use of anti-hypertensive medication and a previous diagnosis of hypertension were the strongest predictors of higher levels of BP. We found that the prevalence of hypertension, untreated and treated, but uncontrolled hypertension is still substantial in Armenia, which maybe a vital contributor to the growing burden of non-communicable diseases.
Attention Network for Rail Surface Defect Detection via Consistency of Intersection-over-Union(IoU)-Guided Center-Point Estimation

Rail surface defect inspection based on machine vision faces challenges against the complex background with interference and severe data imbalance. To meet these challenges, in this article, we regard defect detection as a key-point estimation problem and present the proposed attention neural network for rail surface defect detection via consistency of Intersection-over-Union(IoU)-guided center-point estimation (CCEANN). The CCEANN contains two crucial components. The two components are the stacked attention Hourglass backbone via cross-stage fusion of multiscale features (CSFA-Hourglass) and the CASIoU-guided center-point estimation head module (CASIoU-CEHM). Furthermore, the CASIoU-guided center-point estimation head module integrating the delicate coordinate compensation mechanism regresses detection boxes flexibly to adapt to defects’ large-scale variation, in which the proposed CASIoU loss, a loss regressing the consistency of intersection-over-union (IoU), central-point distance, area ratio, and scale ratio between the targeted defect and the predicted defect, achieves higher regression accuracy than state-of-the-art IoU-based losses. The experiments demonstrate that the CCEANN outperforms competitive deep learning-based methods in four surface defect datasets.
The Ontogeny of Kinship Categorization

Human kinship systems play a central role in social organization, as anthropologists have long demonstrated. Much less is known about how cultural schemas of relatedness are transmitted across generations. How do children learn kinship concepts? To what extent is learning affected by known cross-cultural variation in how humans classify kin? This review draws on research in developmental psychology, linguistics, and anthropology to present our current understanding of the social and cognitive foundations of kinship categorization. Amid growing interest in kinship in the cognitive sciences, the paper aims to stimulate new research on the ontogeny of kinship categorization, a rich domain for studying the nexus of language, culture, and cognition. We introduce an interdisciplinary research toolkit to help streamline future research in this area.
Diet of the introduced red fox
 Vulpes vulpes
 in Australia: analysis of temporal and spatial patterns

The red fox Vulpes vulpes is one of the world’s most widespread carnivores. A key to its success has been its broad, opportunistic diet. The fox was introduced to Australia about 150 years ago, and within 30 years of its introduction was already recognised as a threat to livestock and native wildlife. We reviewed 85 fox diet studies (totalling 31693 samples) from throughout the species’ geographic range within Australia. Mammals were a major component of fox diet, being present in 70 ± 19% of samples across n = 160 locations. Invertebrates (38 ± 26% n = 130) and plant material (26 ± 25% n = 123) were also both staple foods and often the dominant food category recorded. Birds (13 ± 11% n = 137) and reptiles (10 ± 15% n = 132) were also commonly reported, while frogs were scarcely represented (1.6 ± 3.6% n = 111) in fox diet studies. Biogeographical differences reveal factors that likely determine prey availability. Diet composition varied with ecosystem, level of vegetation clearing and condition, and climate zone. Sample type (i.e. stomach versus scat samples) also significantly influenced reporting of diet composition. Livestock and frogs were underrepresented in records based on analysis of scats, whereas small mammals (native rodents, dasyurid marsupials, and bats) were more likely to be recorded in studies of scats than in studies of stomach contents. Diet varied seasonally, reflecting activity patterns of prey species and food availability. This synthesis also captures temporal shifts in fox diet over 70 years (1951–2020), as foxes have switched to consuming more native species in the wake of successful broadscale biological control of the invasive European rabbit Oryctolagus cuniculus. Diet analyses, such as those summarised in this review, capture the evidence required to motivate for greater control of foxes in Australia. This synthesis also highlights the importance of integrated pest species management to meet biodiversity conservation outcomes.
Cracks to Craters: College Advising During COVID-19

The COVID-19 (coronavirus disease–2019) pandemic disrupted the education of students across the globe in the spring of 2020. Students who were previously at most risk for falling behind their peers and through the cracks because of academic, financial, racial, and/or generational disadvantage faced a wide range of additional obstacles in the pursuit of their college goals. This qualitative study sought to uncover postsecondary advising implications for students through the perspectives of near-peer college advisers (n = 23) serving in high-need schools in two different states as intensive, in-person advising was forced to adapt to virtual formats. Two key thematic findings reveal that advisers faced new communication challenges and existing systemic barriers for marginalized students became even larger. For seniors who had not yet made final postsecondary decisions or who had remaining to-dos, the impact of school closures and distanced advising may have fatally widened existing cracks in the path to college.
Role of Governance in Debt-Growth Relationship: Evidence from Panel Data Estimations

This paper provides an empirical analysis of the relationship between debt and national output mediated by a measure of the quality of state governance. Using WGIs dataset of 106 countries for the period 1996–2015, the paper analyzes the mediated effect of governance on debt-growth relationship. For this purpose, we use the fixed effect (LSDV) and system GMM estimation technique in order to overcome the possible problem of endogeneity. Results show the non-linear pattern between public debt and economic growth via governance. Although, public debt has negative impact on economic growth, but the results are statistically positive and significant when public debt is interacted with governance, which confirms that governance is a channel by which public debt influences economic growth. Moreover, we calculate the threshold of governance which shows that the public debt has positive impact on economic growth when the governance level is higher than the threshold and adversely affects the economic growth in the case of low level of governance than threshold. Evidence from this study reveals the fact that governance plays a mediating role in debt-growth relationship as there is a pattern of complementarity between public debt and governance: the higher the level of governance, the lesser the adverse effect of public debt on economic growth.
Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition

The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital‐twin for industrial practice. It has low‐power wireless‐enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital‐twin‐assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool‐holder that integrates a k‐type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool‐holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.
Chemical Cracking Effect of Aqua Regia on the Purification of Metallurgical-Grade Silicon

The chemical cracking effect on metallurgical-grade silicon (MG-Si) during leaching by aqua regia was investigated. The results showed that the granularity of the particles was reduce by acid leaching, and the phenomenon was more noticeable with the increase in the silicon particle size. The cracking was essentially ascribed to the dissolution of the major impurities that depended on the composition and distribution of the impurity phases in MG-Si. In addition, the removal efficiency of the main metallic impurities with the occurrence of the chemical cracking effect during the leaching process was studied. The remarkable efficiency was obtained from the coarse particle size,where 7.9% Fe, 77% Al, 45% Ca, and 63% were removed. The reason could be attributed to the strong oxidation of aqua regia that promoted the dissolution Of the Si Al-Fe soluble phase, which resulted in the enhanced removal effect of acid on the impurities.
Changes in growth, yield and nutrient status of nine vegetables grown onto a soil substrate with two-phase olive mill waste sludge addition

Abstract The present experimental work was carried out in order to evaluate changes in growth, yield and nutrient status of nine vegetables grown on soil substrates with four doses (0%, 2.5%, 5% and 10% v/v) of a two-phase olive mill waste (OMW) sludge as an alternative strategy of their handling. Growth results showed that under 2.5% OMW, zucchini and cucumber mini plants tended to increase their total plant fresh weight (FW) whereas cucumber long and large-sized tomato presented the opposite trend. Under 5% OMW, watermelon, cucumber mini and zucchini presented the lowest total plant FW change whereas pepper and large-sized tomato the greatest decrease. Under 10% OMW addition, large-sized tomato and pepper, followed by watermelon, showed the greatest total plant FW decrease whereas all plum tomato plants under 10% OMW died at the end of the experiment. Yield results showed that among the vegetables tested, melon followed by large-sized tomato, plum tomato and cucumber long presented higher yield increase, eggplant the lowest one whereas watermelon produced no fruits. Based on the fact that yield gathers the greatest commercial interest for vegetable cultivation, we could assume that the addition of either 2.5% OMW for the majority of vegetable tested (except eggplant and watermelon) or 5% for melon, plum tomato, large-sized tomato and cucumber long could provide a sustainable alternative methodology for OMW management. Regarding plant nutrient status, the majority of the nutrient element concentrations determined revealed their non-significant change.
Homomorphic SVM Inference for Fraud Detection

—Nowadays, cloud computing has become a very promising solution for almost all companies, as it offers the possibility of saving costs by outsourcing computation on-demand. However, some companies deal with private information, which must be protected before outsourcing. Banks, whose ﬁnancial information is highly sensitive, are one remarkable example of this problem. Their typical processes must be run on their systems for security and regulation reasons, which impedes to take advantage of the scalability and ﬂexibility beneﬁts introduced by the cloud. A relevant example on which we focus in this work is the case of fraud detection systems, for which we propose the use of modern lattice-based homomorphic encryption for its secure execution. To this end, we implement and validate the performance of a homomorphic SVM (Support Vector Machine) classiﬁer for secure fraud detection, showing the feasibility of securely outsourcing fraud detection inference.
Motivated to express: Salience of oppression toward other women encourages women’s self-expression

Women’s oppression undermines and inhibits women but may also prompt an enterprising reaction. In this paper, three studies explored the extent to which women respond to awareness of the oppression of other women with an increased desire for self-expression, a reactive but constructive response. Study 1 explored reactions to two forms of other women’s oppression: restricted self-expression and restricted economic opportunities. Women reported an increased desire to self-express after exposure to either form of oppression, as compared to a control group. Study 2 compared British women’s reactions to stories of a woman versus a man being oppressed, finding the former group wrote more words about an unrelated, but timely and consequential topic (Brexit). Finally, Study 3 replicated the effect of greater self-expression after being exposed to women’s oppression, and furthermore identified an indirect effect through reactance. Findings are discussed in relation to identity, constructive forms of reactance, and implications for current women’s rights movements.