Miracle-dz/UniFAR-FTUs huggingface dataset:
Whistle variation in Mediterranean common bottlenose dolphin: The role of geographical, anthropogenic, social, and behavioral factors

Abstract The studies on the variation of acoustic communication in different species have provided insight that genetics, geographic isolation, and adaptation to ecological and social conditions play important roles in the variability of acoustic signals. The dolphin whistles are communication signals that can vary significantly among and within populations. Although it is known that they are influenced by different environmental and social variables, the factors influencing the variation between populations have received scant attention. In the present study, we investigated the factors associated with the acoustic variability in the whistles of common bottlenose dolphin (Tursiops truncatus), inhabiting two Mediterranean areas (Sardinia and Croatia). We explored which factors, among (a) geographical isolation of populations, (b) different environments in terms of noise and boat presence, and (c) social factors (including group size, behavior, and presence of calves), were associated with whistle characteristics. We first applied a principal component analysis to reduce the number of collinear whistle frequency and temporal characteristics and then generalized linear mixed models on the first two principal components. The study revealed that both geographic distance/isolation and local environment are associated with whistle variations between localities. The prominent differences in the acoustic environments between the two areas, which contributed to the acoustic variability in the first principal component (PC1), were found. The calf's presence and foraging and social behavior were also found to be associated with dolphin whistle variation. The second principal component (PC2) was associated only with locality and group size, showing that longer and more complex tonal sound may facilitate individual recognition and cohesion in social groups. Thus, both social and behavioral context influenced significantly the structure of whistles, and they should be considered when investigating acoustic variability among distant dolphin populations to avoid confounding factors.
A visual analog scale for patient‐reported voice outcomes: The VAS voice

Although patient‐reported outcome measures (PROMs) can be useful for assessing quality of life, they can be complex and cognitively burdensome. In this study, we prospectively evaluated a simple patient‐reported voice assessment measure on a visual analog scale (VAS voice) and compared it with the Voice Handicap Index (VHI‐10).
Excitons in 2D perovskites for ultrafast terahertz photonic devices

The quantum well in 2D perovskite provides an additional channel for photoexcited electrons to relax back at ultrafast time scale. In recent years, two-dimensional (2D) Ruddlesden-Popper perovskites have emerged as promising candidates for environmentally stable solar cells, highly efficient light-emitting diodes, and resistive memory devices. The remarkable existence of self-assembled quantum well (QW) structures in solution-processed 2D perovskites offers a diverse range of optoelectronic properties, which remain largely unexplored. Here, we experimentally observe ultrafast relaxation of free carriers in 20 ps due to the quantum confinement of free carriers in a self-assembled QW structures that form excitons. Furthermore, hybridizing the 2D perovskites with metamaterials on a rigid and a flexible substrate enables modulation of terahertz fields at 50-GHz modulating speed, which is the fastest for a solution-processed semiconductor-based photonic device. Hence, an exciton-based ultrafast response of 2D perovskites opens up large avenues for a wide range of scalable dynamic photonic devices with potential applications in flexible photonics, ultrafast wavefront control, and short-range wireless terahertz communications.
Affluence and adolescent obesity in a city in south-east Nigerian: A cross-sectional survey

Background: Pediatric obesity is becoming a public health challenge in both developed and developing countries. While poverty is a known risk factor for pediatric obesity in developed countries, the role of socioeconomic class in developing countries remains unclear. This study aims to determine the prevalence of adolescent obesity in Enugu, south-east Nigeria, and relate same to socioeconomic class. Materials and Methods: A cross-sectional study of adolescents aged 10-19 years in Enugu metropolis. The participants were enrolled by multistage sampling method. Their weights and heights were measured and body mass index (BMI) was calculated. The participants were classified as obese, overweight, and normal using age- and sex-specific BMI percentiles. Socioeconomic class was determined using parental income and educational level. Data were analyzed with the Statistical Package for Social Sciences (SPSS) version 20.0. Results: A total of 2,419 participants were included in the study (1,242 males and 1,177 females). Their mean age was 14.80 ± 2.07 years. The prevalence rates of obesity and overweight were 5.7% and 7.2%, respectively. Females were more likely to be overweight and obese [odds ratio (OR) = 2.55, P < 0.001, OR = 2.66, P < 0.001, respectively]. Adolescents from the upper socioeconomic class were more likely to be obese [OR = 2.57, P < 0.001, 95% confidence interval (CI): 1.80–3.67]. Conclusion: Adolescent obesity in Enugu, south-east Nigeria, is more common among children from the upper socioeconomic class.
Outcome studies in schizophrenia

Abstract Objectives: To outline the limitations of traditional studies of outcome in schizophrenia and to review the findings arising from ‘first episode’ psychosis studies. Method: An extensive literature search was performed and relevant papers were examined and analysed. Results: Current knowledge regarding outcome predictors in schizophrenia has primarily been derived from a series of ‘consecutive admission’ and ‘long-term follow-back’ studies. However, methodological considerations may limit the generalisability of these studies' findings. The prospective evaluation of first episode cohorts has advanced our knowledge regarding the relative importance of premorbid and intercurrent factors in determining outcome in schizophrenia. Conclusions: To date, the ‘first episode’ strategy has highlighted some potentially clinically modifiable outcome predictors. These findings may open the way for targeted introduction of measures aimed at preventing poor outcomes in schizophrenia.
The Role of the Internet in Palliative Care

Introduction Initially, the Internet was developed as an organisational tool in the academie and research setting. Today, it enjoys exponential growth as new uses are discovered, particularly in the business sector, making it possible to conduct financial services, make travel plans, buy stocks, sell real estate, or even order groceries online. A Medline® search using only "Internet" as a keyword revealed over 2500 matches when conducted in April of this year, whereas the same search in November of last year revealed only 1500 matches. Topics ranged from patient confidentiality to watching surgery online. Although there are several articles about using the Internet as a forum for cancer survivor support groups ( 1-4 ), a search of "Internet" with "palliative care" revealed no articles from 1997-99 and only four articles be fore 1997. The number of websites addressing "palliative care" or related topics has soared in the past few years, including several developed by palliative care organisations (Table 1). However, little has been published in peer-reviewed journals about the Internet's role in palliative care. Pereira and Bruera, who described the process of creating a palliative medicine website in Edmonton, Canada, at the Regional Palliative Care Program (5,6) searched the World Wide Web using the popular search engine Yahoo with sorne terms common in palliative care: "hospice", "palliative care", "pain", "cancer pain", "cancer", "terminally ill", and "bereavement" (6). A search repeated this year using the same terms showed significant increases in the number of sites (Table 2). Although its role has been primarily in dissemination of information, the potential of the Internet for research, clinicat care, and education is limitless. Here, we review a clinician's perspective of the vast potential it holds for the future practice of palliative care, and sorne of the problems of
Comparing Intergenerational Educational Mobility for Minorities Around the World

Current measures of IEM are inconsistent and difficult to compare with each other. Absolute upward mobility (E(y|x=25), where x is child education rank and y is parent education rank) cannot be calculated with education data because the parent at the 25th education percentile is in a coarsely measured education bin; this measure is undefined. Thus, even in the United States, there is no study that produces an estimate of intergenerational education mobility for a racial subgroup that can be measured over time. For example, in a recent working paper, Card, Domnisoru and Taylor (2018) compare educational mobility over time by comparing the average education rank of a child born to parents in the middle of the educational distribution (e.g. percentiles ~30-70) in the 1920s, to the average income rank of a child born to parents at the 25th percentile of the income distribution in the 1980s — this is the best they can do with the conventional methods, but it is clearly not the ideal comparison. The same problem is faced by Alesina et al. (2019), who define upward mobility in Africa as the likelihood that a child born to a parent who has not completed primary school manages to do so—thus conditioning on substantially different parts of the education distribution in different times and places. None of these estimates can be meaningfully compared, because all these papers are constrained by the available bin boundaries in the education data.
A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.
Urban Land-Cover Classification Using Side-View Information from Oblique Images

Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features.
Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).
Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.
Landscape Representation by a Permanent Forest Plot and Alternative Plot Designs in a Typhoon Hotspot, Fushan, Taiwan

Permanent forest dynamics plots have provided valuable insights into many aspects of forest ecology. The evaluation of their representativeness within the landscape is necessary to understanding the limitations of findings from permanent plots at larger spatial scales. Studies on the representativeness of forest plots with respect to landscape heterogeneity and disturbance effect have already been carried out, but knowledge of how multiple disturbances affect plot representativeness is lacking—particularly in sites where several disturbances can occur between forest plot censuses. This study explores the effects of five typhoon disturbances on the Fushan Forest Dynamics Plot (FFDP) and its surrounding landscape, the Fushan Experimental Forest (FEF), in Taiwan where typhoons occur annually. The representativeness of the FFDP for the FEF was studied using four topographical variables derived from a digital elevation model and two vegetation indices (VIs), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII), calculated from Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI data. Representativeness of four alternative plot designs were tested by dividing the FFDP into subplots over wider elevational ranges. Results showed that the FFDP neither represents landscape elevational range (<10%) nor vegetation cover (<7% of the interquartile range, IQR). Although disturbance effects (i.e., ΔVIs) were also different between the FFDP and the FEF, comparisons showed no under- or over-exposure to typhoon damage frequency or intensity within the FFDP. In addition, the ΔVIs were of the same magnitudes in the plots and the reserve, and the plot covered 30% to 75.9% of IQRs of the reserve ΔVIs. Unexpectedly, the alternative plot designs did not lead to increased representation of damage for 3 out of the 4 tested typhoons and they did not suggest higher representativeness of rectangular vs. square plots. Based on the comparison of mean Euclidian distances, two rectangular plots had smaller distances than four square or four rectangular plots of the same area. Therefore, this study suggests that the current FFDP provides a better representation of its landscape disturbances than alternatives, which contained wider topographical variation and would be more difficult to conduct ground surveys. However, upscaling needs to be done with caution as, in the case of the FEF, plot representativeness varied among typhoons.
Ideal Angular Orientation of Selected 64-Channel Multi Beam Lidars for Mobile Mapping Systems

Lidar technology is thriving nowadays for different applications mainly for autonomous navigation, mapping, and smart city technology. Lidars vary in different aspects and can be: multi beam, single beam, spinning, solid state, full 360 field of view FOV, single or multi pulse returns, and many other geometric and radiometric aspects. Users and developers in the mapping industry are continuously looking for new released Lidars having high properties of output density, coverage, and accuracy while keeping a lower cost. Accordingly, every Lidar type should be well evaluated for the final intended mapping aim. This evaluation is not easy to implement in practice because of the need to have all the investigated Lidars available in hand and integrated into a ready to use mapping system. Furthermore, to have a fair comparison; it is necessary to ensure the test applied in the same environment at the same travelling path among other conditions. In this paper, we are evaluating two state-of-the-art multi beam Lidar types: Ouster OS-1-64 and Hesai Pandar64 for mapping applications. The evaluation of the Lidar types is applied in a simulation environment which approximates reality. The paper shows the determination of the ideal orientation angle for the two Lidars by assessing the density, coverage, and accuracy and presenting clear performance quantifications and conclusions.
PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.