Miracle-dz/UniFAR-FTUs huggingface dataset:
Future Increase in Aridity Drives Abrupt Biodiversity Loss Among Terrestrial Vertebrate Species

The planet is predicted to become drier because of accelerating global warming, increasing great challenges to the survival of terrestrial species. However, the understanding of when and where increasing aridity in the future will lead to abrupt biodiversity loss is still limited. Here, we predicted the spatiotemporal dynamics of future vertebrate biodiversity loss driven by increasing aridity at the assemblage scale under the scenario framework of the shared socio‐economic pathways (SSPs) and representative concentration pathways (RCPs). The results show that, under the high‐emissions scenario of SSP5‐8.5, more than 24.19% of terrestrial assemblages are projected to have at least one vertebrate species exposed to unprecedented aridity conditions by 2100, leading to 55.29% of terrestrial vertebrate species experiencing local habitat loss. In addition, the mean magnitude of global exposure is expected to reach 17.47% by 2100. Within these assemblages, most species will be simultaneously exposed to unprecedented aridity conditions, with an average exposure abruptness of 78.00%, most of which will occur intensively after 2050. If we manage to reduce greenhouse gas emissions to the SSP1‐2.6 (SSP2‐4.5) scenario, the magnitude of exposure can significantly decrease to 7.35% (10.56%), and the onset of exposure can be delayed by 43 (30) years, averaging approximately 30% (14%) of vertebrate species from local habitat loss. Our findings also highlight that the concurrent extremes of increasing aridity and rising temperature in Southeast Asia and Amazon rainforests may amplify exposure risks, which can motivate decision‐makers to respond early and eﬀectively to mitigate abrupt ecological disruption.
Comparative Analysis of Machine Learning and Ensemble Learning Classifiers for Alzheimer's Disease Detection

Alzheimer's disease (AD), a psychiatric problem, availing between those who are 65 and older. Additionally, the disease's steady development of a variety of visible and invisible symptoms, such as irritability and aggression, has a substantial negative impact on a patient's overall quality of life. Although many treatments have been developed to help reduce its symptoms, AD has no known cure. As a result, the field of AD management is growing, and a comprehensive framework for the early detection of AD must be created. In this study, we created three classification models for predicting AD using machine learning and five models for predicting AD using ensemble learning. SVM, DTs, and RF are the three basis classifiers employed in the current work. Five ensemble classifiers, XGBoost, Voting Classifier, Extra Trees (ETs) Classifier, Gradient Boost, and AdaBoost, are then thoroughly compared. After thoroughly inspecting the dataset for outliers or other noise, a feature selection method known as PCA and several preprocessing techniques are used to lessen the issue of overfitting and performance enhancement. Additionally, this study utilised the longitudinal information from the OASIS website, which included 150 patients overall, 72 of whom were not demented and 78 of whom were. The RF model, which had an accuracy of 83.92% compared to the other two base classifiers, provided the best classification performance, while the ETs Classifier, an ensemble classifier, performed the best when compared to base and ensemble classifiers, with an accuracy of 86.60%.
A Machine Learning Approach to Segment the Customers of Online Sales Data for Better and Efficient Marketing Purposes

The Internet is becoming huge and is used by a more diverse audience every day. The amount of data gathered from the platform through different online lead companies are gargantuan so it needs to be maintained and segregated in order to extract meaningful data from it. A lot of companies have started to gather customer data through their own platform or through various vendors who sell it to sales companies/organizations/individuals for some profit. Sometimes these data are large and scattered enough to even confuse big sales organizations. In order for better and more effective marketing of these sales data, We propose to use four machine learning clustering algorithms(K-Means, Agglomerative, Mean-Shift and DBSCAN) in order to find customer segments based on the data provided. Based on this segmented customer group, we can be able to find a pattern and decide which customer group is better for which business.
A flat laser-generated focused ultrasound transmitter with extended focal depth

Laser generated focused ultrasound (LGFU) is potential in the field of biomedical science like ultrasound imaging with high resolution and ultrasound treatment with high spatial precision. However, the most commonly used method of realizing LGFU is to coat a photoacoustic (PA) film on to an uneven surface, which brings problem to the real application since a uniform PA film with well controlled thickness must be required to guarantee the LGFU performance. In this paper, a new strategy is employed by directly patterning the excitation region on a flat PA film. A chromium mask with a ring-shape clear window is put on a planar PA transmitter, thus forming a ring-shape PA source. The effects of ring size on the focal characteristics are explored numerically and experimentally for the first time. The focal length is proportional to the central radius of the ring due to the constant beam divergence of the acoustic wave when the ring width is fixed. And the larger the ring width, the smaller the beam divergence. Moreover, the ring-shape PA source is able to focus ultrasound with a small lateral beamwidth of several hundred micrometers within the focal depth of a few centimeters. Such a focal field would be useful in ultrasound imaging when a large imaging depth is needed.
COVID-19 vaccination and case fatality rates: a case report in a Brazilian municipality

Vaccination campaigns played a crucial role in reducing the incidence of COVID-19. However, a scant number of studies evaluated the impact of vaccination on case fatality rates (CFRs), including in Brazil. Our study aimed to compare CFRs according to vaccination status among subjects living in Arapongas (Paraná State, Brazil), considering the age composition of the population. Several strategies adopted by the Arapongas City Hall to minimize the spread of the virus were also elaborated upon. We accessed the 2021 database of the Arapongas Municipal Health Department, in which a total of 16,437 confirmed cases and 425 deaths were reported. The CFR was calculated as the ratio between COVID-19 deaths and the number of confirmed cases. Differences in age composition between unvaccinated and fully vaccinated individuals were observed in our study. Considering that CFR is a crude indicator and is highly sensitive to the age composition of the population, we adopted the average age distribution of confirmed cases among the three vaccination statuses (unvaccinated, partially, and fully) as a standard age distribution. The age-standardized CFR for unvaccinated and fully vaccinated groups were 4.55% and 2.42%, respectively. Fully vaccinated individuals showed lower age-specific CFRs in all age groups above 60 years than unvaccinated populations. Our findings strengthen the role of vaccination as a critical measure for preventing deaths among infected people and is particularly important to the ongoing reassessment of public health interventions and policies.
Power System Transient Stability Assessment Based on Graph Neural Network with Interpretable Attribution Analysis

The power transient stability analysis is one of the basis for determining the control strategy of power system security and stability. Considering the influence of power grid topology on the transient stability of power system, the transient stability evaluation model is constructed based on the graph attention neural network. The electrical components and their transient operation data are mapped to the graph data with the spatial topology characteristics of power system for model training, so as to improve the topological generalization performance of the model. The marginal contribution of input characteristics to the output of transient power angle stability evaluation model is quantitatively calculated based on Shapley additive explanation (SHAP), so as to improve the interpretability of data-driven method for transient power angle stability evaluation. The effectiveness of the proposed method is verified by the IEEE 39-bus system.
Comments on “Estimating Soil Water Contents from Soil Temperature Measurements by Using an Adaptive Kalman Filter”

Abstract A scheme was proposed by Zhang et al. to estimate soil water content from soil temperature measurements by using an adaptive Kalman filter. Their scheme is based on the fact that soil heat capacity and thermal conductivity are a monotonic function of soil water content. However, thermal diffusivity, a more critical thermal parameter in such an estimation, is not a monotonic function of soil water content in most cases. This could result in multiple solutions in some cases when deriving soil water content from soil temperatures.
Super-Twisting Sliding Mode Control to Improve Performances and Robustness of a Switched Reluctance Machine for an Electric Vehicle Drivetrain Application

In electric vehicles, performances of electric vehicle drivetrains depend on the electric machine and the control. Switched Reluctance Machines (SRMs) are today an alternative to rare earth magnets machines such as Permanent Magnet Synchronous Machine (PMSM), which is used in the vehicle drivetrain. Because of its high nonlinear behavior, the classical control designed for SRMs is not sufficient to obtain good performances. The objective of this paper is to make performance and robustness comparisons of the designed robust controllers considering the high nonlinear behavior of SRMs. Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are developed and validated by simulation for the velocity control loop and the current control loops of the control strategy. However, an evaluation of their performances compared to classical control based on PI controllers is carried out. For a robustness comparison, a variation of SRM parameters is carried out by simulation using the three controllers. Finally, an experimental validation on a developed test bench using the three controllers is conducted to show that Super-Twisting Sliding Mode Control (STSMC) is the best in terms of performances and robustness for an electric vehicle application.
Measuring the cosmic star-formation rate density using deep radio surveys

There is now good agreement between the various methods of estimating the space density of the star-formation rate (SFRD) at low redshifts (z < 1), with uncertainties around 30–50%. However, the situation at higher redshifts remains much less clear, with uncertainties in the SFRD, due to e.g. poorly known dust absorption corrections, of as much as 300–500%. Radio emission from star-forming galaxies is unaffected by absorption and scales linearly with star-formation rate, thus the radio luminosity of star-forming galaxies provides an excellent independent, unbiased measure of their star-formation rate. The current deepest ‘blank field’ radio surveys (reaching <10 μJy rms at 1.4 GHz) are sensitive enough to detect starburst galaxies out to z ~ 3, and so potentially offer an excellent way to measure the SFRD. Indeed, modelling of the sub-mJy source counts requires an additional population of faint steep spectrum objects, that are very likely to be starburst galaxies.
Assessment of Arctic seasonal snow cover rates of change

Abstract. Arctic snow cover extent (SCE) trends and rates of change reported across recent climate assessments vary due to the time period of available data, the selection of snow products, and methodological considerations. While all reported trends are strongly negative during spring, more uncertainty exists in autumn. Motivated to increase the confidence in SCE trends reported in climate assessments, we quantify the impact of (1) year-over-year increases in time series length over the past 2 decades, (2) the choice of reference period, (3) the application of a statistical methodology to improve inter-dataset agreement, (4) the dataset ensemble size, and (5) product version changes. Results show that the rate of change during May and June has remained consistent over the past decade as time series length has increased and is largely insensitive to the choice of reference period. Although new product versions have increased spatial resolution, use more advanced reanalysis meteorology to force snow models, and include improved remote sensing retrieval algorithms, these enhancements do not result in any notable changes in the observed rate of Arctic SCE change in any month compared to a baseline set of older products. The most impactful analysis decision involves the scaling of dataset climatologies using an updated version of the NOAA snow chart climate data record as the baseline. While minor for most months, this adjustment can influence the calculated rate of change for June by a factor of 2 relative to different climatological baselines.
Virtual interferogram-generation algorithm for phase measurement using two interferograms

Phase-shifting digital holography is a technique for phase measurement with high spatial resolution and applied to many fields. This technique typically requires four phase-shifted interferograms between a signal beam and a reference beam. We focused on the two-step phase shifting algorithm, which needs only two phase-shifted interferograms and an intensity distribution of the reference beam to reduce the number of required interferograms. However, in this algorithm, the intensity of the reference beam must be much greater than that of the signal beam because this algorithm uses the quadratic formula and the inside of square root must be positive. This leads to the saturation of the dynamic range of the image sensor and the degradation of accuracy. In this paper, we propose a virtual interferogram-generation algorithm (VIGA) to improve the performance of phase-shifting digital holography only using two interferograms. This algorithm virtually generates a π phase-shifted interferogram by the intensity distribution of the signal beam and that of the reference beam with an observed interferogram. Therefore, capturing two real interferograms and generating two virtual interferograms, the four-step phase shifting algorithm can be used for this method. Comparing to the conventional algorithm, the VIGA has no limitation in terms of the magnitude of the intensity. This means that the intensity of the reference beam and that of the signal beam can be equalized and the dynamic range saturation of the image sensors can be prevented. Therefore, the VIGA makes highly accurate phase measurement possible owing to the effective utilization of the dynamic range of the image sensors.
Understanding Chinese international students’ perception of cultural conflicts in Canada: Through the case of cannabis use

The legalization of recreational cannabis consumption in Canada created a cultural conflict for international students from China, where the use of cannabis is heavily criminalized and deemed immoral. This conceptual paper theorizes this cultural conflict experienced by Chinese international students in Canada by applying three theories from macro to micro levels. Neoliberalism is first used to understand how this cultural conflict exposes collisions between the neoliberal West and the rising economic power of China as illustrated through Chinese students studying in Canada. Next, acculturation theory focuses on these students’ cultural transition and provides further insight into potential strategies for their handling of specific cultural conflicts such as cannabis use. Lastly, Cloninger’s theory of substance use is adopted to explore Chinese international students’ individual reasoning about cannabis use, particularly how they make decisions based on evaluations of various conditions. Building upon the above analyses, an integrated conceptual model is further formed to help us understand Chinese students’ potential perception of cannabis use in Canada. This conceptual framework provides an important theoretical and conceptual base for future research and practice, from which to further explore cannabis use in the context of cultural transition of different immigrant and migrant groups.
Some results on the conditioning of the near-to-Toeplitz matrix arising in linear block estimation

Crozier, Falconer and Mahmoud (1992) suggest using short-block data detection methods on fading time-dispersive communications channels. They consider linear block estimation (LBE) and decision feedback block estimation (DFBE) where it is necessary to solve near-to-Toeplitz linear systems for the data vector estimate. In the present paper they show that such linear systems have computationally efficient solutions. It is also shown that such systems may be ill-conditioned, but a method of detecting such ill-conditioning is presented.
Bronchopulmonary penetration of isavuconazole in lung transplant recipients

ABSTRACT Isavuconazole’s (ISA) pharmacokinetics was studied among lung transplant recipients to evaluate its bronchopulmonary penetration. This study included 13 patients and showed mean serum concentrations of 3.30 (standard deviation [SD] 0.45), 5.12 (SD 1.36), and 6.31 (SD 0.95) at 2 h, 4 h, and 24 h respectively. Mean concentrations in the epithelial lining fluid were 0.969 (SD 0.895), 2.141 (SD 1.265), and 2.812 (SD 0.693) at the same time points. ISA is a drug with a tolerable safety profile that achieves adequate concentrations in the lung.
Does prosocial attitude affect creativity in musical improvisation?

Disciplinary background A. Statement of background in Psychology of Creativity: Organizational psychology has demonstrated the importance of social dynamics for group creativity (Hennessey et al., 2020). Prosocial attitudes, e.g., feeling close to each other, can affect joint creativity in different ways (Oztop et al., 2018). It remains unclear whether such findings can be applied to other domains, such as music. Disciplinary background B. Statement of background in Musical Improvisation: Musical improvisation research includes descriptive analyses, as well as investigations based on quantitative methodologies. And although musical improvisation is often participatory, the social dynamics at the heart of such an activity have rarely been addressed from an interdisciplinary perspective that brings together both approaches. Abstract The present research aims to shed light on how intra-group social dynamics shape creative musical improvisation, examining the perspectives of both improvisers and external raters. To study the impact of prosocial attitude in musical improvisation, we implemented a quasi-social improvisation task (done remotely), in which novices were invited to improvise with a virtual avatar (i.e., a moving stick-figure drummer hitting a cymbal in synchrony with a backing track). Using a virtual improvisation partner ensured comparability of outcomes; and to further reduce the variance among participants we kept the baseline of expertise consistent by only recruiting novices. Furthermore, it has been shown that prosocial attitude towards a virtual partner can increase through an imagined synchronization task (Stupacher et al., 2020). Based on this finding we demonstrated in a preliminary study (n = 65) that an overt synchronization task with the virtual drummer can increase prosocial attitude, assessed through felt closeness. Building on this research, the present empirical study involved two experimental phases: in the first phase (“improvisation”), 18 novices were invited to improvise in three conditions, each starting with a priming task. In condition 1 a neutral prime was used to not alter prosocial attitude; in condition 2 participants had to synchronously move with a black dot to a metronome, which allowed to control
Antenatal Care and Health Behavior of Pregnant Women—An Evaluation of the Survey of Neonates in Pomerania

Background. The German maternity guidelines require regular medical checkup (MC) during pregnancy as a measure of prevention. Socioeconomic factors such as education, profession, income and origin, but also age and parity may influence the preventive and health behavior of pregnant women. The aim was to investigate the influence of these factors on the participation rate in MC of pregnant women. Method. The current analysis is based on the prospective population-based birth cohort study Survey of Neonates in Pomerania, which was conducted in Western Pomerania, Germany. The data of 4092 pregnant women from 2004 to 2008 were analyzed regarding the antenatal care and health behavior. Up to 12 MC were regularly offered; participation in 10 MC is defined as standard screening according to maternity guidelines. Results. Women participated in the first preventive MC on average in the 10th (±3.8 SD) week of pregnancy. 1343 (34.2%) women participated in standard screening and 2039 (51.9%) took a screening above standard. 547 (13.92%) women participated in less than the 10 standard MCs. In addition, about one-third of the pregnancies investigated in this study were unplanned. Bivariate analyses showed an association between better antenatal care behavior and higher maternal age, stabile partnerships and mother born in Germany, p < 0.05. On the contrary antenatal care below standard were more often found by women with unplanned pregnancies, less educational women and women with lower equivalent income, p < 0.001. Health behaviors also influenced antenatal care. Whereas the risk of antenatal care below standard increased by smoking during pregnancy (RRR 1.64; 95% CI 1.25, 2.14) and alcohol consumption (RRR 1.31; 95% CI 1.01, 1.69), supplementation intake was associated with decreased risk (iodine—RRR 0.66; 95% CI 0.53, 0.81; folic acid—RRR 0.56; 95% CI 0.44, 0.72). The health behavior of pregnant women also differs according to their social status. Higher maternal income was negatively correlated with smoking during pregnancy (OR 0.2; 95% CI 0.15, 0.24), but positively associated with alcohol consumption during pregnancy (OR 1.3; 95% CI 1.15, 1.48) and lower pre-pregnancy BMI (Coef. = 0.083, p < 0.001). Lower maternal education was positively correlated with smoking during pregnancy (OR 59.0; 95% CI 28.68, 121.23). Conclusions. Prenatal care according to maternity guidelines is well established with a high participation rate in MC during pregnancy of more than 85%. However, targeted preventive measures may address younger age, socioeconomic status and health-damaging behaviors (smoking, drinking) of the pregnant women because these factors were associated with antenatal care below standard.
Recurrence relations for single and product moments of generalized order statistics from Marshall–Olkin extended Pareto distributions

ABSTRACT Marshall and Olkin (1997) introduced a new method of adding parameter to expand a family of distributions. Using this concept, in this article, the Marshall–Olkin extended Pareto distribution is introduced and some recurrence relations for single and product moments of generalized order statistics are studied. Also the results are deduced for record values and order statistics.