Miracle-dz/UniFAR-FTUs huggingface dataset:
Fish Assemblages in Oxbow Lakes Relative to Connectivity with the Mississippi River

Abstract The alluvial valley of the lower Mississippi River contains hundreds of fluvial lakes that are periodically connected to the river during high water, although the frequency, duration, and timing of the connections vary. To help design plans to restore and preserve fish assemblages in these alluvial lakes, this investigation tested whether predictable patterns in lake fish assemblages were linked to the level of connectivity with the river. Results suggested that connectivity played an important role in structuring fish assemblages and that it was correlated with variables such as lake size, depth, distance from the river, and age, which exhibit a continuum of predictable features as the river migrates away from abandoned channels. Annual floods homogenize the floodplain and promote connectivity to various degrees, allowing for fish exchanges between river and floodplain that directly affect fish assemblages. The major physical changes linked to reduced connectivity are loss of depth and area, whi.
A Design of 8 fJ/Conversion-Step 10-bit 8MS/s Low Power Asynchronous SAR ADC for IEEE 802.15.1 IoT Sensor Based Applications

An energy efficient, low-power 10-bit asynchronous successive approximation register (SAR) analog-to-digital (ADC) converter with the sampling frequency of 8 MS/s is presented for IEEE 802.15. 1 IoT sensor based applications. An improved common mode charge redistribution algorithm is proposed for binary weighted SAR ADC. The proposed method uses available common mode voltage (VCM) level for SAR ADC conversion, and this method reduces the switching power by more than 12% without any additional DAC driver as compared to merged capacitor switching (MCS). Mathematical analysis of the proposed switching scheme results in the lower or equal power consumption for every digital code as compared to MCS. A two stage dynamic latched comparator with adaptive power control (APC) technique is used to optimize the overall efficiency. Furthermore, to minimize the digital part power consumption, a modified asynchronous SAR logic with digitally controlled delay cells is proposed. High efficiency with low power consumption makes it suitable for low power devices especially for IEEE 802.15. 1 IoT sensor based applications. The proposed prototype is implemented using 1P6M 55 nm complementary metal-oxide-semiconductor (CMOS) technology. The measurement results that the proposed circuit achieves are 9.3 effective number of bits (ENOB) with signal-to-noise and distortion ratio (SNDR) of 58.05 dB at a sampling rate of 8 MS/s. The power consumption of SAR ADC is $45~\mu \text{W}$ when operated at 1 V power supply.
A distinct dinosaur life history?

Five factors, mobile terrestrial lifestyle, oviparity, parental care, multi-year maturation and juvenile sociality, contribute to a distinct life history for Mesozoic dinosaurs in comparison to extant archosaurs and mammals. Upright, para-sagittal gait reflects several synapomorphies of Dinosauria, and wide histological sampling suggests that multi-year maturation typified dinosaurs across a range of body sizes. Fossil support for juvenile sociality exceeds that for either oviparity or parental care. Implications of these factors include temporal segregation of adults for an extended, perhaps months-long reproductive cycle; spatial separation of adults and perhaps hatchlings to suitable nesting sites; increased likelihood for territoriality; reduced potential for long migrations; intraspecific niche segregation by age; population and community structure and macroevolutionary patterns. Fossil evidence for oviparity, parental care and juvenile sociality consists of combinations of adults, juveniles, embryos, eggs or traces and emphasises the importance of bonebeds and taphonomy in understanding dinosaur life-history strategies. Oviparity and parental care, predicted for dinosaurs by their extant phylogenetic bracket, have the least fossil support and cautions against overextending parsimonious interpretations to extinct taxa with the risk of obscuring novel or intermediate behaviours. Given the great diversity of Mesozoic dinosaurs, the proposed life history is hypothesised to represent only a general tendency.
Power Allocation in Cell-Free Massive MIMO: A Deep Learning Method

Massive multiple-input multiple-output (MIMO) is a key technology in 5G. It enables multiple users to be served in the same time-frequency block through precoding or beamforming techniques, thus increasing capacity, reliability and energy efficiency. A key issue in massive MIMO is the allocation of power to the individual antennas, in order to achieve a specific objective, e.g., the maximization of the minimum capacity guaranteed to each user. This is a nondeterministic polynomial (NP)-hard problem that needs to be solved in a timely manner since the state of the channels evolves in time and the power allocation should stay in tune with this state. Although several heuristics have been proposed to solve this problem, these entail a considerable time-complexity. As a result, with the present methods, it cannot be guaranteed that power allocation happens in time. To solve this problem, we propose a deep neural network (DNN). A DNN has a low time complexity, but requires an extensive, offline, training process before it becomes operational. The DNN we propose is the combination of two convolutional layers and four fully connected layers. It takes as input the long-term fading information and it outputs the power for each antenna element to each user. We limit ourselves to the case of time-division duplex (TDD) based sub-6GHz networks. Numerical results show that, our DNN-based method approximates very closely the results of a commonly used heuristic based on the bisection algorithm.
Automatic Building Extraction Based on High Resolution Aerial Images

Building extraction is a hotspot of research. Buildings in remote sensing images are numerous, and buildings have different distribution patterns in different regions, which means it is difficult to extract buildings by one traditional method for all areas. Deep learning plays more and more important part of computer version and image processing, which has made some progress in target detection and identification, especially in medical graphic recognition. In this paper, we compared three common datasets, and selected the most suitable one. Mask R- CNN is applied to this dataset. We provided a better method and this method can be used in different areas when dataset is rich enough for training. The method in this paper just need high resolution aerial images to extract buildings automatically without any other steps, because identification and extraction are achieved in one network. The research showed: the dataset we selected is suitable; Mask R-CNN can be used in high resolution remotely images; Mask R-CNN is good at building extraction, and greatly reduce the loss of classification and leakage. Therefore, this paper provided a suitable method for automatic building extraction.
PHOTOSYNTHETIC CAPACITIES AND BIOLOGICAL STRATEGIES OF GIANT‐CELLED AND SMALL‐CELLED MACRO‐ALGAE

Summary Data are presented which show that the photosynthetic and respiratory characteristics of the giant-celled alga Chaetomorpha darwinii are those of a shade plant (denned physiologically in terms of rates of photosynthesis at light and inorganic carbon source saturation, and the irradiance required to saturate photosynthesis and to compensate respiration by photosynthesis). This is in agreement with previous suggestions that giant-celled algae have the characteristics of ‘shade plants’, while small-celled macroalgae can be either ‘sun plants’ or ‘shade plants’. A comparison of giant-celled algae with small-celled macro-algae in terms of ecological strategies has been made, using previously published data on Enteromorpha intestinalis, Chaetomorpha linum (‘fugitive’small-celled), Chaetomorpha darwinii, Hydrodictyon africanum (‘stressed’ giant-celled), Cham corallina (‘canopy-dominant’/‘stressed’ giant celled) and Laminaria digitata (‘canopy-dominant’ small celled), as well as new data on net and mass spectrometric measurements of O2 exchange in L. digitata. It is suggested that the ability of giant-celled algae to compete as ‘canopy dominants’ may be related to cytoplasmic streaming in the ‘absence of many (or any) plasmodesmatal barriers to diffusion providing an efficient means of long-distance transport in a differentiated plant, as an alternative to phloem-type translocation in small-celled ‘canopy dominants’. The significance of the giant-celled habit in ‘stressed’ macroalgae is not clear, although the absence of giant-celled algae from the ‘fugitive’ category may be related to their limited capacity for rapid growth relative to small-celled algae.
Improving mosquito population predictions in the Greater Toronto Area using remote sensing imagery

West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are two of the most common mosquito-borne diseases in North America. WNV and SLE have sporadic spatial and temporal outbreaks, making their outbreaks difficult to predict. However, recent studies have found that mosquito abundance is correlated with WNV and SLE transmission, providing researchers with a starting point for the development of mosquito-borne disease forecasting systems. Mosquito populations are controlled by a variety of environmental variables, including humidity, temperature, vegetation, and available water habitat for breeding. Current mosquito population forecasting models heavily weigh precipitation and temperature inputs, as they are traditionally seen as the best estimates of available breeding space in a region of interest. Although rainfall data are easy to acquire, precipitation data may not actually be the best estimates of mosquito habitat, as water does not flow evenly across landscapes. Furthermore, precipitation data generally come at a spatial resolution of 800 m to 2,500 m, and while this resolution can help predict mosquito abundances on large spatial scales, it inhibits the estimation of mosquito populations in urban areas with granular landscape heterogeneity. To overcome these limitations, this research explores the use of multispectral imagery for predicting mosquito populations, specifically in the Greater Toronto Area. Multispectral imagery is an attractive data source for predicting mosquito abundance due to its consistent collection and comparatively high spatial resolution (e.g., 30 m for Landsat). We derive a monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2011. We then explore how spectral indices perform as a predictor for combined Cx. restuans and Cx. pipiens mosquito populations, with the ultimate aim of using multispectral imagery to forecast mosquito-borne diseases in highly urbanized areas.
Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score

Current stroke risk assessment tools presume the impact of risk factors is linear and cumulative. However, both novel risk factors and their interplay influencing stroke incidence are difficult to reveal using traditional additive models. The goal of this study was to improve upon the established Revised Framingham Stroke Risk Score and design an interactive Non-Linear Stroke Risk Score. Leveraging machine learning algorithms, our work aimed at increasing the accuracy of event prediction and uncovering new relationships in an interpretable fashion. A two-phase approach was used to create our stroke risk prediction score. First, clinical examinations of the Framingham offspring cohort were utilized as the training dataset for the predictive model. Optimal Classification Trees were used to develop a tree-based model to predict 10-year risk of stroke. Unlike classical methods, this algorithm adaptively changes the splits on the independent variables, introducing non-linear interactions among them. Second, the model was validated with a multi-ethnicity cohort from the Boston Medical Center. Our stroke risk score suggests a key dichotomy between patients with history of cardiovascular disease and the rest of the population. While it agrees with known findings, it also identified 23 unique stroke risk profiles and highlighted new non-linear relationships; such as the role of T-wave abnormality on electrocardiography and hematocrit levels in a patient’s risk profile. Our results suggested that the non-linear approach significantly improves upon the baseline in the c-statistic (training 87.43% (CI 0.85–0.90) vs. 73.74% (CI 0.70–0.76); validation 75.29% (CI 0.74–0.76) vs 65.93% (CI 0.64–0.67), even in multi-ethnicity populations. The clinical implications of the new risk score include prioritization of risk factor modification and personalized care at the patient level with improved targeting of interventions for stroke prevention.
Some New Coded Caching Schemes With Smaller Subpacketization via Some Known Results

Coded caching is widely regarded as an important tool to reduce pressure on the data transmission during the peak traffic times in heterogeneous wireless networks. In this paper, we will provide two concatenating constructions of coded caching schemes based on the schemes derived by Shangguan et al. in 2018 and Yan et al. in 2017, respectively. Then we show that our new schemes have good performance in reducing packet number. Furthermore, in some case, we present that our schemes have better performances on user number, memory ratio, packet number and transmission rate than the schemes obtained by Chittoor et al. in 2019 and Krishnan in 2018.
An Inertial Data-Based Upper Body Posture Recognition Tool: A Machine Learning Study Approach

This study presents the development of an automatic and user-independent human motion recognition tool, using kinematic data from inertial measurement units. Data from 50 heterogeneous individuals were collected, and a benchmark of 4 normalization techniques, 4 dimensionality reduction algorithms and 10 machine-learning classifiers was conducted. The tool with the best performance was achieved using the z-score normalization, the mMRM as dimensionality reduction technique, and a Quadratic SVM classifier. This tool presented an overall accuracy of 94,76% in the recognition of 6 static and 10 transitional postures. This work is relevant for awkward postures identification and can be integrated with established frameworks such as RULA and LUBA for ergonomic risk assessment and workspace redesign.
Two-Stage Precoding Design in Rician Channel for HAP Massive MIMO Systems

In this paper, we propose a new two-stage precoding design for the high altitude platform (HAP) massive MultipleInput Multiple-Output (MIMO) systems. The outer precoding design is based on the lower bound of the signal-to-leakage-plusnoise-ratio (SLNR) and then the SLNR-based design problem is reduced to a trace quotient problem (TQP) in Rician channel. An iterative algorithm is proposed to achieve the optimal results for the TQP. Numerical results illustrate that the proposed method has better sum rate performance for the HAP Massive MIMO systems and lower computational complexity compared to the existing outer precoding design for the Rician channel.
Exploring medicinal use of cannabis in a time of policy change in New Zealand.

AIMS To explore patterns of medicinal cannabis use prior to implementation of the new Medicinal Cannabis Scheme (MCS) in New Zealand. METHODS An anonymous online convenience survey of 3,634 last-year medicinal users of cannabis promoted via Facebook™ from May to August 2019. RESULTS Fifty percent of the sample were female, 18% were Māori and the median age was 38 years. The medical conditions for which cannabis was most often used were pain (81%), sleep (66%) and mental health conditions (64%). Respondents perceived cannabis to be an effective therapy and reported reducing use of other pharmaceutical medicines. Fifty-two percent reported side effects from cannabis use, including increased appetite (29%), drowsiness (12%), eye irritation (11%), dependency (10%), memory impairment (10%) and lack of energy (9%). Smoking was the dominant route of administration. Nearly half (47%) had discussed their use of cannabis with a medical professional in the previous year, while 14% had requested a prescription and 5% accessed a prescribed cannabis-based product (mostly oral CBD). CONCLUSION Respondents self-medicated with cannabis to treat a wide range of health complaints. Only half discussed medicinal cannabis use with their medical professional, and a minority requested a prescription and used a prescribed cannabis-based product.
Impact of overdose prevention sites during a public health emergency in Victoria, Canada

The primary objective of this study was to examine the impacts associated with implementation of overdose preventions sites (OPSs) in Victoria, Canada during a declared provincial public health overdose emergency. A rapid case study design was employed with three OPSs constituting the cases. Data were collected through semi-structured interviews with 15 staff, including experiential staff, and 12 service users. Theoretically, we were informed by the Consolidated Framework for Implementation Research. This framework, combined with a case study design, is well suited for generating insight into the impacts of an intervention. Zero deaths were identified as a key impact and indicator of success. The implementation of OPSs increased opportunities for early intervention in the event of an overdose, reducing trauma for staff and service users, and facilitated organizational transitions from provision of safer supplies to safer spaces. Providing a safer space meant drug use no longer needed to be concealed, with the effect of mitigating drug related stigma and facilitating a shift from shame and blame to increasing trust and development of relationships with increased opportunities to provide connections to other services. These impacts were achieved with few new resources highlighting the commitment of agencies and harm reduction workers, particularly those with lived experience, in achieving beneficial impacts. Although mitigating harms of overdose, OPSs do not address the root problem of an unsafe drug supply. OPSs are important life-saving interventions, but more is needed to address the current contamination of the illicit drug supply including provision of a safer supply.
CPGAN: Curve Clustering Architecture Based on Projected Latent Vector of Generative Adversarial Network

Although Generative Adversarial Network(GAN) has obtained remarkable achievements in the image analysis and generation, its exploration in GAN-based curve clustering is still limited. The latent space of curve data is often used for clustering. However, the distance geometry in the latent space does not reflect the inherent clusters. In this paper, we propose CPGAN(Curve Clustering Architecture based on Projected Latent Vector of Generative Adversarial Network) for the clustering of curve dataset. Firstly, a novel GAN network structure, which utilizes a projector ${P}$ (composed of the transposed convolutional network) to reconstruct the latent space of curve data, is proposed. CPGAN utilizes the concatenation of discrete code and Gaussian noise as a latent vector to preserve the implicit signal and structure of the cluster. Secondly, the loss function with two regularizations for CPGAN is proposed to guarantee the robustness and effectiveness of the model. Based on these, the jointly trained projector ${P}$ is used to participate in the clustering process, while the generator can be used in the generating process. Finally, the spectral dataset from the LAMOST survey, the UCI dataset, and the UCR dataset are used as experimental data to evaluate clustering performance, the robustness of CPGAN, and further application on anomalous detection. CPGAN presents higher results than other methods.
Antibiotic Resistance in Salmonella Isolated from Tegus (Tupinambis spp.)

Abstract In recent years, an increase in human clinical cases of reptile-associated salmonellosis has been identified, and it has been attributed to the increased popularity of these animals as pets. Limited information is available regarding the distribution of Salmonella spp. serotypes in different reptile species and the antimicrobial resistance patterns of Salmonella spp. isolated from pet reptiles. This article describes the prevalence of Salmonella spp. , distribution of serotypes, and antibiotic susceptibility patterns from isolates cultured from cloacal swabs obtained from 14 tegu lizards ( Tupinambis spp.). Eighteen strains of Salmonella belonging to different serotypes were obtained from the 14 tegu lizards. Of the 18 Salmonella spp. isolates, 8 (44.4%) were from Salmonella subspecies I, with a majority of isolates belonging to the Eastbourne serotype (3 strains), Nottingham serotype (2 strains), and Brancaster serotype (2 strains), and only 1 belonging to the Apapa serotype. Less common serotypes were detected in 5 isolates, including 2 each belonging to Salmonella subspecies II and IIIb, respectively, and 1 to Salmonella subspecies IIIa. The serotype of 5 other Salmonella isolates could not be determined. All 18 isolates were resistant to at least 6 of the antimicrobial drugs tested. These results confirm the potential zoonotic risk from handling reptiles, suggesting that measures to educate the reptile-owning public are necessary.
Correlação entre absorção de água e tempo de cozimento de cultivares de feijão

The purpose of this work was to evaluate the behavior of bean cultivars regarding water soaking test and cooking time as well as the possible correlation of these variables to identify bean lines with high cooking capacity. A factorial of two cultivars (TPS Nobre and Perola) and different times (2 to 18 hours of soaking) in a completely randomized design, with three replications, was conducted. Soaking capacity was increased over time up to 13h07min for TPS Nobre and 13h12min for Perola cultivar. Cooking time was reduced after 12h49min of soaking for both cultivars. There was a high and negative correlation between water soaking and cooking time.
Integrated Low Pass Filter for M-Sequence UWB Radars

This paper deals with the design of an active low pass filter for M-sequence UWB radars. The proposed filter realized in the form of an application-specific integrated circuit uses a modified Sallen-Key topology, exploiting a Cherry-Hooper amplifier in place of an operational amplifier and employs varactors enabling the tuning of the filter cutoff frequency. The feasibility of the concept is proven by simulation and on-die measurement results. According to the measurements, the low pass filter with nominal cutoff frequency of 1 GHz and slope of 10 dB per octave was obtained in the cost-efficient 0.35 µm SiGe BiCMOS technology which is suitable for the integration of the whole M-sequence radar system on a single chip.