Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-release Movement Behaviour and Survival of Kulan Reintroduced to the Steppes and Deserts of Central Kazakhstan

Asiatic wild ass, or kulan (Equus hemionus kulan) were once a key species of the Eurasian steppes and deserts. In Kazakhstan they went extinct by the 1930s. Early reintroductions have reestablished the species in two protected areas, but the species has reclaimed <1% of their former range and remained absent from central Kazakhstan. To initiate restoration in this vast region, we captured and transported a first group of nine wild kulan to a large pre-release enclosure in the Torgai region in 2017, and two more in 2019. We used direct observations and post-release movement data of four kulan equipped with GPS-Iridium collars to document their adaptation process in a vast novel habitat without conspecifics. For comparison with movements in the source populations, we additionally equipped two kulan in Altyn Emel National Park and six in Barsa Kelmes State Nature Reserve. The nine transported kulan formed a cohesive group with very high movement correlation in the enclosure. After release, the group initially stayed tightly together but started to break up by mid-May and all kulan travelled independently by mid-August. With 48,680–136,953 km2, the 95% Autocorrelated Kernel Density Estimation ranges of the reintroduced kulan were huge and about 10–100 times larger than those in the source populations. The reintroduced mares never reconnected, there was no evidence of successful reproduction, and two of the four collared mares were killed by poachers and one died of natural causes. At least one stallion survived in the wild, but the fate of the other uncollared animals remains unclear. We speculate that the fission-fusion dynamics and low movement correlation of kulan societies and the need for migratory movements harbours the risk that animals released into a novel environment loose contact with each other. This risk is likely enhanced in steppe habitats where movement constraining factors are absent. Further kulan reintroductions to the steppes and deserts of central Kazakhstan should aim to release larger groups and build up the free-ranging population quickly to reach a critical mass, increasing the chance of kulan encountering conspecifics to successfully breed and increase their chances of survival.
Predictive Analysis of Health/Physical Fitness in Health-Promoting Lifestyle of Adolescents

Objective: Explore the correlation and predictive power of health physical fitness on health-promoting lifestyle of adolescents to provide an important reference for reform in middle school physical education classroom teaching. Methods: Taking some junior and senior high school students in Nanchong City of Sichuan Province as survey objects, a stratified random sampling method was used to carry out a health/fitness test and conduct a questionnaire survey. SPSS17.0, amos 21.0, and other statistical analysis software were used to process the relevant data. Results: (1) Male adolescents had 14.4% predictive power for their overall health-promoting lifestyle through the combined effects of cardiopulmonary endurance, muscle strength, and explosive strength, while female adolescents had 16.8% predictive power for their overall health-promoting lifestyle through the combined effects of cardiopulmonary endurance, flexibility, and body mass index (BMI). (2) Ten percent of the variation in the health-promoting lifestyle of men under 14 years old was caused by the combined effects of muscle endurance and muscle strength, while 14.4% of the variation in the health-promoting lifestyle of female adolescents was caused by the combined effects of muscle endurance, muscle strength, flexibility, and cardiopulmonary endurance. (3) Some 10.9 and 17.6% of the variation in health-promoting lifestyle of male and female adolescents aged between 14 and 17 was caused by the combined effects of cardiopulmonary endurance, muscle strength, and flexibility, respectively. (4) Some 20.7% of the variation in health promoting lifestyle of male adolescents aged 17 years or above was caused by cardiopulmonary endurance, explosive force, and muscle endurance, while 16.8% of the variation in health-promoting lifestyle of female adolescents was caused by the combined effect of cardiopulmonary endurance, BMI, and muscle endurance. Conclusion: Health and physical fitness of adolescents can significantly predict their health-promoting lifestyle, and the predictive power and typical correlation between them are affected by demographic factors.
A Brain-Inspired Homeostatic Neuron Based on Phase-Change Memories for Efficient Neuromorphic Computing

One of the main goals of neuromorphic computing is the implementation and design of systems capable of dynamic evolution with respect to their own experience. In biology, synaptic scaling is the homeostatic mechanism which controls the frequency of neural spikes within stable boundaries for improved learning activity. To introduce such control mechanism in a hardware spiking neural network (SNN), we present here a novel artificial neuron based on phase change memory (PCM) devices capable of internal regulation via homeostatic and plastic phenomena. We experimentally show that this mechanism increases the robustness of the system thus optimizing the multi-pattern learning under spike-timing-dependent plasticity (STDP). It also improves the continual learning capability of hybrid supervised-unsupervised convolutional neural networks (CNNs), in terms of both resilience and accuracy. Furthermore, the use of neurons capable of self-regulating their fire responsivity as a function of the PCM internal state enables the design of dynamic networks. In this scenario, we propose to use the PCM-based neurons to design bio-inspired recurrent networks for autonomous decision making in navigation tasks. The agent relies on neuronal spike-frequency adaptation (SFA) to explore the environment via penalties and rewards. Finally, we show that the conductance drift of the PCM devices, contrarily to the applications in neural network accelerators, can improve the overall energy efficiency of neuromorphic computing by implementing bio-plausible active forgetting.
Consensus Patterns of a Set of Time Series via a Wavelet-Based Temporal Localization: Emphasizing the Utility over Point-Wise Averaging and Averaging under Dynamic Time Warping

Summarizing or averaging a sequential data set (i.e., a set of time series) can be comprehensively approached as a result of sophisticated computational tools. Averaging under Dynamic Time Warping (DTW) is one such tool that captures consensus patterns. DTW acts as a similarity measure between time series, and subsequently, an averaging method must be executed upon the behaviour of DTW. However, averaging under DTW somewhat neglects temporal aspect since it is on the search of similar appearances rather than stagnating on corresponding time-points. On the contrary, the mean series carrying point-wise averages provides only a weak consensus pattern as it may over-smooth important temporal variations. As a compromise, a pool of consensus series termed Ultimate Tamed Series (UTS) is studied here that adheres to temporal decomposition supported by the discrete Haar wavelet. We claim that UTS summarizes localized patterns, which would not be reachable via the series under DTW or the mean series. Neighbourhood of localization can be altered as a user can customize different levels of decomposition. In validation, comparisons are carried out with the series under DTW and the mean series via Euclidean distance and the distance resulted by DTW itself. Two sequential data sets are selected for this purpose from a standard repository.
App2Vec: Context-Aware Application Usage Prediction

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when, where, and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.
Legal Vulnerability and Campus Environment: Assessing Factors that Affect the Academic Engagement of Undocumented College Students

ABSTRACT Prior research has established that undocumented students experience structural marginalization which compromises their access, retention, and success in higher education. Building on this work, this study turns attention to their academic engagement, an important intermediary outcome. Using survey data collected from 1,277 undocumented undergraduate students attending California 4-year public universities, we examine the extent to which legal vulnerability, campus climate, and resource use are associated with positive and negative academic engagement. Findings reveal the sustained role of campus environment, as positive perceptions of campus climate are associated with positive engagement and negative perceptions with negative engagement. Use of various campus resources tends to be associated with increased positive engagement and decreased negative engagement. Only financial legal vulnerabilities are associated with negative engagement and are not associated with positive engagement. We contend that the campus environment plays an important role in fostering undocumented students’ academic engagement.
Spatial disparities in trade-offs: economic and environmental impacts of road infrastructure on continental level

ABSTRACT Remote sensing and geospatial techniques are being used to provide large-scale and regional solutions for achieving the sustainable development goals (SDGs) of the United Nations, including sustainable infrastructure development. Road transportation infrastructure has a significant contribution to the economy, but it also increases environmental pressure. However, little knowledge is available about spatial characteristics in the relationship between road impacts on the economy and impacts on the roadside environment. This research explores the spatial disparities in the relationship of road impacts on a continental level in Australia from 2011 to 2016. The performance of road transportation infrastructure is characterized from the perspectives of road density, connectivity, traffic volumes, and service to communities, other transportations (e.g. ports and airports), and industries, using remote sensing data and spatial heterogeneity models. Local economy and roadside environment are respectively presented using resident income and the change of roadside Enhanced Vegetation Index (EVI) and Aerosol Optical Depth (AOD) derived from the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite generated from Google Earth Engine. The road impacts of variables and their interaction on the economy and environment were calculated using an optimal parameters-based geographical detectors model (OPGD). Results reveal that the interaction of road density and traffic volumes can explain 47.4% of the resident income. In addition, results demonstrate the significant spatial disparities in the relationship between road impacts on the economy and impacts on the local environment. In major cities, such as Sydney and Melbourne, the pressure of roadside environment is increased with the economic growth, but the roadside environment has been improved in suburban and rural areas. Areas with the service to industries range from 64.4 km to 128 km have the most significant roadside EVI increase (2.5%). To the best of our knowledge, this is the first research to explore spatially differentiated trade-offs between the economic and roadside environmental impacts of roads using remotely sensed data, geospatial data, and spatial heterogeneity model at the continental level. Findings from this study provide an in-depth understanding of the interactions and trade-offs of road impacts on the local economy and the environment. Geospatial trade-offs and impact analysis methods in the study can be applied in wider fields to achieve global and regional SDGs.
Evaluating the Effect of Legal Vulnerabilities and Social Support on the Mental Health of Undocumented College Students

ABSTRACT Undocumented students face considerable stressors due to their precarious legal status, economic disadvantages, and social exclusion. Building on a growing body of the literature that has examined the mental health and psychological wellbeing of undocumented students, we disentangle the effects of multiple dimensions of legal vulnerability. Specifically, we examine whether multiple dimensions of legal vulnerability are associated with increased emotional distress and whether social support moderates this relationship. Using a survey of 1,277 undocumented students in California, we conducted hierarchical regression analyses to determine the unique and combined effects of legal vulnerability and social support on anxiety and depression. We find that legal vulnerabilities, including discrimination, social exclusion, the threat of deportation, and economic insecurity, and social support have direct effects on depression and anxiety symptomatology. The moderating effects of social support are only partially supported. Our results demonstrate the move beyond the study of immigration status to examine the effects of legal vulnerabilities on mental health.
3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3-Dimensional Graph Convolution Network (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.
Smart supply chain and firm performance: the role of digital technologies

PurposeThis study aims to explore the impact of digital transformation on firm performance with the help of smart technologies.Design/methodology/approachThis study used a valid and well-structured survey to collect data from 460 respondents from 150 Small and medium-sized enterprises (SMEs) in Saudi Arabia. In order to analyze this data, SmartPLS software was used.FindingsThe empirical results of this study found that the digital transformation has no significant direct impact on firm performance. Therefore, in this situation, smart technologies play an important role in developing the link between digital transformation and firm performance.Originality/valueThis study contributes to the digital supply chain from SMEs' perspective by investigating the mediating role of smart technologies between digital transformation and firm performance.
Unambiguous Direction-of-Arrival Estimation for Improved Scanning Efficiency in Subarray-Based Hybrid Array

Hybrid antenna array consisting of analog subarrays provides a low-cost solution to achieving high beamforming gain in millimeter-wave/terahertz communication systems. For beam adjustment, beam scanning can be readily applied to find the line-of-sight (LoS) path. Conventionally, the beam scanning process is inefficient because the number of grids, together with the scanning period, has to be large to achieve high resolution. To improve the beam scanning efficiency, in this paper we propose an off-gird direction-of-arrival (DoA) estimation method utilizing unequal-sized subarrays. Specifically, we first provide a new solution to overcome the ambiguity problem, i.e., the hybrid antenna array may render the DoA estimate non-unique. That new solution is formed by designing the antenna numbers in the subarrays and can be easily combined with the beam scanning process. We then propose a three-stage beam scanning strategy. At the first stage, coarse scanning is applied to roughly steer the subarrays along the LoS path. At the second stage, a simple DoA estimator is employed to obtain the candidate DoAs. Finally, beam scanning is performed again on the candidate DoAs to obtain the final DoA estimate. Simulation results demonstrate that the proposed method can achieve higher DoA estimation accuracy in shorter scanning period than the conventional beam scanning method; and compared with existing methods developed for equal-sized subarrays, the proposed method is more robust against multi-path interference.
Partitioning Behavioral Retrieval: An Efficient Computational Approach With Transitive Rules

The number of process models in process repositories has grown in recent years. For this reason, it is becoming increasingly important to efficiently retrieve process models from the repositories for management purposes. In fact, the method of retrieval is also essential to increase the potential of additional analysis such as similarity and behavioral analysis and compliance checking. Most retrieval methods can be classified as structural or behavioral retrieval. Behavioral retrieval considers the relationships between activities during process execution. In this paper, we introduce such a new behavior-based retrieval method called Partitioning Behavioral Retrieval. It is the first retrieval method that allows process models to contain inclusive (OR) gateways. The method is based on the dominance relation and a node-based process structure tree known from compiler theory. Although it uses instance subgraph checking as a computational basis, it derives new behavioral relations by using transitive rules. Currently, the method is limited to acyclic process models for ease of introduction. Experiments show the time advantage offered by our new method.
Power Flow Modeling of Islanded Microgrids Based on Holomorphic Embedding

Power flow calculation is a key problem in the operation and planning of the islanded microgrid. In this paper, a holomorphic embedding (HE)-based model is proposed for the islanded microgrid power flow calculation with droop-regulated distributed generations (DGs). Through proper embedding, this model preserves the advantage of HE that can yield reliable solutions. Also, the VF (voltage and frequency)-dependent bus model compatible with the HE is developed step by step and is exemplified by an illustrative case. Numerical tests are performed to verify the effectiveness of the proposed model.
Modeling and Simulation of Respiratory System for Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19

The respiratory system is one of the complicated systems in the human body. In this paper, the mathematical model for the respiratory system was proposed based on the electro-acoustic bio-impedance analysis with huge number of equations. Further, the behavior of each generation from 24 generations of the respiratory system can be analyzed separately. Furthermore, the pressure with ventilated frequency from 1–1200 min−1 at any generation can be predicted and calculated. The proposed mathematical model was validated by comparison the results with the published results. The simulated results for ARDS disease are presented. Also, the comparison between normal case (healthy) and ARDS diseased case is presented. The proposed mathematical model with huge number of equations is implemented by Matlab program package. This proposed mathematical model can be used as a reversed problem solution to detect and diagnose the medical situation of the respiratory system for patients especially patients associated with COVID-19.
Detection of Surface Enrichment Driven by Molecular Weight Disparity in Virtually Monodisperse Polymers.

The preference for a shorter chain component at a polymer blend surface impacts surface properties key to application-specific performance. While such segregation is known for blends containing low molecular weight additives or systems with large polydispersity, it has not been reported for anionically polymerized polymers that are viewed, in practice, as monodisperse. Observations with surface layer matrix-assisted laser desorption ionization time-of-flight mass spectrometry (SL-MALDI-ToF-MS), which distinguishes surface species without labeling and provides the entire molecular weight distribution, demonstrate that entropically driven surface enrichment of shorter chains occurs even in low polydispersity materials. For 6 kDa polystyrene the number-average molecular weight (Mn) at the surface is ca. 300 Da (5%) lower than that in the bulk, and for 7 kDa poly(methyl methacryalate) the shift is ca. 500 Da. These observations are in qualitative agreement with results from a mean-field theory that considers a homopolymer melt with a molecular-weight distribution matched to the experiments.
Channel Estimation-Free Deep Direct Beamforming With Low Complexity in mmWave Massive MIMO

Hybrid precoding is used in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems instead of full digital precoding to reduce the cost and power consumption. Conventional hybrid precoding however requires complex channel estimation before beamforming. In this paper, we propose a low complexity low-cost channel estimation-free deep direct beamforming method composed of two basic steps-beam impulse and beamforming for time division duplex (TDD). This method is built upon randomized singular value decomposition (RSVD) and can directly complete the process of beamforming. Furthermore, a realistic dataset of mmWave channel-DeepMIMO is adopted to evaluate and determine the practicability of this method. The simulation results demonstrate that the proposed deep direct beamforming achieves high performance while its complexity and power consumption are very low. Compared with other methods without perfect channel status information (CSI), the proposed method costs less and requires lower computational resources while achieves higher spectral efficiency. Furthermore, the RSVD can be easily combined with conventional beamforming methods hence the proposed method is easily extendable to other applications.