Miracle-dz/UniFAR-FTUs huggingface dataset:
Discussing alcohol use with the GP: a qualitative study

Background Despite most GPs recognising their role in the early diagnosis of alcohol use disorder (AUD), only 23% of GPs routinely screen for alcohol use. One reason GPs report for not screening is their relationship with patients; questions regarding alcohol use are considered a disturbance of a relationship built on mutual trust. Aim To analyse the feelings and experiences of patients with AUD concerning early screening for alcohol use by GPs. Design & setting A qualitative study of patients (n = 12) with AUD in remission or treatment, recruited from various medical settings. Method Semi-structured interviews were conducted, audiorecorded, and transcribed verbatim. The authors conducted an inductive analysis based on grounded theory. The analysis was performed until theoretical data saturation was reached. Results The participants experienced AUD as a chronic, destructive, and shameful disease. The participants expected their GPs to play a primary role in addressing AUD by kind listening, and providing information and support. If the GPs expressed a non-judgmental attitude, the participants could confide in them; this moment was identified as a key milestone in their trajectory, allowing relief and a move toward treatment. The participants thought that any consultation could be an opportunity to discuss alcohol use and noted that such discussions required a psychological and benevolent approach. Conclusion The participants felt fear or denial from the GPs, even though they felt that discussing alcohol use is part of the GP’s job. The participants requested that GPs adopt non-judgmental attitudes and kindness when approaching the subject of alcohol use.
Exploring the Combined Effect of Urbanization and Climate Variability on Urban Vegetation: A Multi-Perspective Study Based on More than 3000 Cities in China

More than 3000 cities in China were used to study the effect of urbanization and local climate variability on urban vegetation across different geographical and urbanization conditions. The national scale estimation shows that China’s urban vegetation depicts a trend of degradation from 2000 to 2015, especially in developed areas such as the Yangtze River Delta. According to the panel models, the increase of precipitation (PREC), solar radiation (SRAD), air temperature (TEMP), and specific humidity (SHUM) all enhance urban vegetation, while nighttime light intensity (NLI), population density (POPDEN), and fractal dimension (FRAC) do the opposite. The effects change along the East–West gradient; the influences of PREC and SHUM become greater, while those of TEMP, SRAD, NLI, AREA, and FRAC become smaller. PREC, SHUM, and SRAD play the most important roles in Northeast, Central, and North China, respectively. The role of FRAC and NLI in East China is much greater than in other regions. POPDEN remains influential across all altitudes, while FRAC affects only low-altitude cities. NLI plays a greater role in larger cities, while FRAC and POPDEN are the opposite. In cities outside of the five major urban agglomerations, PREC has a great influence while the key factors are more diversified inside.
Open Plant Phenotype Database of Common Weeds in Denmark

For decades, significant effort has been put into the development of plant detection and classification algorithms. However, it has been difficult to compare the performance of the different algorithms, due to the lack of a common testbed, such as a public available annotated reference dataset. In this paper, we present the Open Plant Phenotype Database (OPPD), a public dataset for plant detection and plant classification. The dataset contains 7590 RGB images of 47 plant species. Each species is cultivated under three different growth conditions, to provide a high degree of diversity in terms of visual appearance. The images are collected at the semifield area at Aarhus University, Research Centre Flakkebjerg, Denmark, using a customized data acquisition platform that provides well-illuminated images with a ground resolution of ∼6.6 px mm − 1 . All images are annotated with plant species using the EPPO encoding system, bounding box annotations for detection and extraction of individual plants, applied growth conditions and time passed since seeding. Additionally, the individual plants have been tracked temporally and given unique IDs. The dataset is accompanied by two experiments for: (1) plant instance detection and (2) plant species classification. The experiments introduce evaluation metrics and methods for the two tasks and provide baselines for future work on the data.
Monitoring and Projecting Land Use/Land Cover Changes of Eleven Large Deltaic Areas in Greece from 1945 Onwards

Wetlands are areas of high biodiversity and provide many ecosystem services of high value. However, they are under constant threat from intense anthropogenic pressures, mainly agriculture intensification, urbanization, pollution, and climate change. The temporal and spatial patterns of land use/land cover (LULC) changes within eleven large wetlands in Greece were analyzed based on thematic maps generated from aerial orthophotos taken in 1945, 1975, and 2007. Socio-economic developments and the consequent need for more arable land and utilization of water resources are among the factors that mainly determine their evolution. In 2007, LULC classes related to wetland vegetation were reduced to one third as compared to 1945 and they were mainly replaced with croplands and urban infrastructures. Each of the different sub-periods that was considered (1945–1975 and 1975–2007) was distinguished by characteristic patterns of change. Agricultural land increased up to 42% from 1945 to 1975 and became the dominant LULC class in all deltaic areas but Evros. A considerable stability was observed for the period 1975–2007 for all LULC classed but it is remarkable the extent of urban areas that doubled. There is a tendency of landscape simplification and homogenization among the deltaic areas and the output of Markov chain analysis indicates that future composition of deltaic landscapes will be similar to the current one if the main driving forces remain constant. Changes in LULC composition and structure are also combined with coastal erosion in all deltaic areas. This is attributed to the modification of sedimentary deposits due to dam construction. The results summarize the change trajectories of the major deltaic areas in Greece from 1945 to 2007 thus offering a great outlook of changes that allows managers to understand how policies and socio-economic requirements affect the deltaic ecosystems and what decisions should be made to protect and enhance them.
Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.
Population genetics of the high elevation black fly Simulium (Nevermannia) feuerborni Edwards in Thailand

In this study, we examined the genetic structure and population history of the high elevation black fly Simulium feuerborni in Thailand at both cytogenetic and molecular genetic levels. Cytological examination revealed two cytoforms differentiated by fixed chromosome inversions. The distributions of the cytoforms were associated with geographic origins. Cytoform A was found in the lower north and northeast, and cytoform B was found in the upper northern region of Thailand. Molecular data based on the mitochondrial cytochrome oxidase subunit I (COI) barcoding sequence supports the separation of the cytoforms. The average sequence divergence between the two cytoforms was 3.75%, which is higher than the threshold value for the species level based on a COI barcoding sequence. Median joining network clearly differentiated the haplotypes of the cytoforms into different lineages. Population pairwise FST and amova analyses reveal significant genetic differentiation between cytoforms. This indicates that the low land areas separating these populations act as a gene flow barrier. No genetic differentiation was detected within cytoforms. This could be due to a recent sharing of population history. Mismatch distribution analysis revealed population expansion in the northern lineage of the cytoform B approximately 220 000 years ago. More recent expansion (32 000 years ago) was found in the lower north and northeast (cytoform A) lineage. The demographic history of S. feuerborni mirrored previous findings in black flies and other insect species in Thailand. This indicates the important role of Pleistocene climatic change on genetic structure and diversity of Southeast Asian mainland species.
Text Mining Business Policy Documents: Applied Data Science in Finance

In a time when the employment of natural language processing techniques in domains such as biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the authors implement a set of natural language processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for processing internal business policies. This framework relies on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision, recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was positively evaluated using a qualitative assessment.
Unexpected hardness results for Kolmogorov complexity under uniform reductions

Hardness of computing the Kolmogorov complexity of a given string is closely tied to a security proof of hitting set generators, and thus understanding hardness of Kolmogorov complexity is one of the central questions in complexity theory. In this paper, we develop new proof techniques for showing hardness of computing Kolmogorov complexity under surprisingly efficient reductions, which were previously conjectured to be impossible. It is known that the set R K of Kolmogorov-random strings is PSPACE-hard under polynomial-time Turing reductions, i.e., PSPACE ⊂ P R K , and that NEXP ⊂ NP R K , which was conjectured to be tight by Allender (CiE 2012). We prove that EXP NP ⊂ P R K , which simultaneously improves these hardness results and refutes the conjecture of Allender under the plausible assumption that EXP NP ≠ NEXP. At the core of our results is a new security proof of a pseudorandom generator via a black-box uniform reduction, which overcomes an impossibility result of Gutfreund and Vadhan (RANDOM/APPROX 2008). Our proof techniques have further consequences, including: 1. Applying our proof techniques to the case of resource-bounded Kolmogorov complexity, we obtain NP-hardness of the problem MINcKT SAT of computing conditional polynomial-time-bounded SAT-oracle Kolmogorov complexity under polynomial-time deterministic reductions. In contrast, the Minimum SAT-Oracle Circuit Size Problem, which is a version of sublinear-time-bounded Kolmogorov complexity, cannot be NP-hard under polynomial-time deterministic reductions without resolving EXP ≠ ZPP. Our hardness result is the first result that overcomes the non-NP-hardness results of MCSP. We also prove DistNP-hardness of MINKT SAT , which is a partial converse of the approach of Hirahara (FOCS 2018) for proving the equivalence between worst-case and average-case complexity of NP. 2. We prove S 2 p -hardness of Kolmogorov complexity under quasi-polynomial-time nonadaptive reductions. This is the first result that overcomes a P/poly barrier result of Allender, Buhrman, Friedman, and Loff (MFCS 2012). We also establish a firm link between non-trivial satisfiability algorithms and immunity of random strings, and obtain the following unconditional lower bounds. 1. It has been a long-standing open question whether the set of subexponential-time-bounded Kolmogorov-random strings is decidable in P. We resolve this open question, by showing that the set of super-polynomial-time-bounded Kolmogorov-random strings is P-immune, which is a much stronger lower bound than an average-case lower bound. 2. The set of Levin’s Kolmogorov-random strings is (P-uniform ACC)-immune.
Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.
A Conceptual Approach to Modeling the Geospatial Impact of Typical Urban Threats on the Habitat Quality of River Corridors

While for most of a landscape, urbanization leads to a significant habitat loss, rivers in urban areas are usually maintained or developed for their drainage function. Rivers are often the most important biophysical and ecological connection of cities with their surrounding ecosystems, although usually heavily altered due to urban impacts. For the conservation of urban rivers as ecological corridors, it is important to assess the impact of typical urban threats on habitat quality. In this study, we used the InVEST (Integrated Valuation of Environmental Services and Trade-offs) habitat quality model to assess the individual and combined impacts of built-up areas, first- and second-order road and water pollution from urban drainage, and wastewater discharge on habitat quality within a 200 m wide river corridor. The Pochote River in León, Nicaragua, was used as a case study. Our results show the spatial distribution and magnitude of the individual threat impacts, as well as the respective contribution of each threat to the overall impact of urbanization on the habitat quality within the river corridor. While close to the city center, all threats almost equally contributed to severe habitat degradation, while further downstream, an individual threat influence became more distinct with only water pollution having a consistent negative impact. We concluded that the InVEST habitat quality model can be used to assess the impact of typical urban threats on habitat quality in river corridors at a high spatial resolution. The results can help to improve urban planning and development to improve habitat conservation along urban rivers.
Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.
Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions

Potato late blight, caused by Phytophthora infestans, is a major disease worldwide that has a significant economic impact on potato crops, and remote sensing might help to detect the disease in early stages. This study aims to determine changes induced by potato late blight in two parameters of the red and red-edge spectral regions: the red-well point (RWP) and the red-edge point (REP) as a function of the number of days post-inoculation (DPI) at the leaf and canopy levels. The RWP or REP variations were modelled using linear or exponential regression models as a function of the DPI. A Support Vector Machine (SVM) algorithm was used to classify healthy and infected leaves or plants using either the RWP or REP wavelength as well as the reflectances at 668, 705, 717 and 740 nm. Higher variations in the RWP and REP wavelengths were observed for the infected leaves compared to healthy leaves. The linear and exponential models resulted in higher adjusted R2 for the infected case than for the healthy case. The SVM classifier applied to the reflectance of the red and red-edge bands of the Micasense® Dual-X camera was able to sort healthy and infected cases with both the leaf and canopy measurements, reaching an overall classification accuracy of 89.33% at 3 DPI when symptoms were visible for the first time with the leaf measurements and of 89.06% at 5 DPI, i.e., two days after the symptoms became apparent, with the canopy measurements. The study shows that RWP and REP at leaf and canopy levels allow detecting potato late blight, but these parameters are less efficient to sort healthy and infected leaves or plants than the reflectance at 668, 705, 717 and 740 nm. Future research should consider larger samples, other cultivars and the test of unmanned aerial vehicle (UAV) imagery for field-based detection.
Detection of Magnesite and Associated Gangue Minerals using Hyperspectral Remote Sensing - A Laboratory Approach

This study introduced a detection method for magnesite and associated gangue minerals, including dolomite, calcite, and talc, based on mineralogical, chemical, and hyperspectral analyses using hand samples from thirteen different source locations and Specim hyperspectral short wave infrared (SWIR) hyperspectral images. Band ratio methods and logistic regression models were developed based on the spectral bands selected by the random forest algorithm. The mineralogical analysis revealed the heterogeneity of mineral composition for naturally occurring samples, showing various carbonate and silicate minerals as accessory minerals. The Mg and Ca composition of magnesite and dolomite varied significantly, inferring the mixture of minerals. The spectral characteristics of magnesite and associated gangue minerals showed major absorption features of the target minerals mixed with the absorption features of accessory carbonate minerals and talc affected by mineral composition. The spectral characteristics of magnesite and dolomite showed a systematic shift of the Mg-OH absorption features toward a shorter wavelength with an increased Mg content. The spectral bands identified by the random forest algorithm for detecting magnesite and gangue minerals were mainly associated with spectral features manifested by Mg-OH, CO3, and OH. A two-step band ratio classification method achieved an overall accuracy of 92% and 55.2%. The classification models developed by logistic regression models showed a significantly higher accuracy of 98~99.9% for training samples and 82–99.8% for validation samples. Because the samples were collected from heterogeneous sites all over the world, we believe that the results and the approach to band selection and logistic regression developed in this study can be generalized to other case studies of magnesite exploration.