Miracle-dz/UniFAR-FTUs huggingface dataset:
Observations and Modeling of Typhoon Waves in the South China Sea

Buoy-based observations of surface waves during three typhoons in the South China Sea were used to obtain the wave characteristics. With the local wind speeds kept below 35m s, the surface waves over an area with a radius 5 times that of the area inwhich themaximum sustained wind was found weremainly dominated bywindwave components, and the wave energy distribution was consistent with fetch-limited waves. Swells dominated the surfacewaves at the front of and outside the central typhoon region.Next, the dynamics of the typhoonwaves were studied numerically using a state-of-the-art third-generation wave model. Wind forcing errors made a negligible contribution to the surface wave results obtained using hindcasting. Near-realistic wind fields were constructed by correcting the idealized wind vortex using in situ observational data. If the different sets of source terms were further considered for the forcing stage of the typhoon, which was defined as the half inertial period before and after the typhoon arrival time, the best model performance had mean relative biases and root-meansquare errors of 20.7% and 0.76m, respectively, for the significant wave height, and 23.4% and 1.115 s, respectively, for the peak wave period. Different sets of source terms for wind inputs and whitecapping breaking dissipation were also used and the results compared. Finally, twin numerical experiments were performed to investigate the importance of nonlinear wave–wave interactions on the spectrum formed. There was evidence that nonlinear wave–wave interactions efficiently transfer wave energy from high frequencies to low frequencies and prevent double-peak structures occurring in the frequency-based spectrum.
ON THE USE OF A SEVIRI BASED STATISTICAL RAINFALL CLASSIFICATION TECHNIQUE CALIBRATED WITH TRMM-PR OV ER SOUTHERN MEDITERRANEAN

A SEVIRI rainfall estimation technique based on Artificial Neural Networks (ANN) has recently been developed at the University of Ferrara. The algorithm makes use of the multispectral capabilities and the increased resolution of the new sensor on board the Meteosat Second Generation satellites. The present version of the algorithm provides rain maps in five rainrate levels, at 5 km of spatial resolution and 15 minutes of time resolution. It is tested and validated over U.K. area and for summer and winter season by using the Met Office Nimrod radar precipitation maps as ground truth. Performance are evaluated by calculating the Equitable Threat Score (ETS) and BIAS for rain – no rain classification. The algorithm performances strongly depend upon the characteristics of the training dataset, such as season, climatic regime and latitude: to export the technique on a given target area is thus highly recommended to train the ANN with data from that area. In the frame of an EU Community initiative programme INTERREG III B ARCHIMED the focus of the project RiskMed are the Central and Western Mediterranean areas, where a reliable, high-resolution, ground radar based precipitation dataset, large enough to train the ANN, is not available. Nevertheless, almost the half of the domain is covered by the TRMM-PR overpasses. The TRMM product 2A25, in this first approach, is therefore used to assess the possibility to re-calibrate the U.K. trained algorithm by using a limited PR-based data set. TRMM-PR data collected over six months are used to train the ANN over the areas for two different seasons (winter and summer), and the performances are discussed.
The physical characteristics of hypersonic flows

Hypersonics is the field of study of a very particular class of flows that develop around aerodynamic bodies moving in gases at exceedingly high velocities compared to the speed of the sound waves. The description of the gas environment surrounding a hypersonic vehicle is important for the calculation of thermomechanical loads on the body. These notes provide a qualitative characterization of hypersonic flows in terms of characteristic scales encountered in engineering applications. The wealth and peculiarity of the gasdynamic phenomenology emerging around hypersonic flight systems is summarized schematically in Fig. 1 and elaborated in the remainder of these notes.
Ecohydrological changes in the Murray-Darling Basin. II. Development and tests of a water balance model.

1. The hydrological part of the FOREST-BGC model was originally developed to estimate evaporation (ET) loss from coniferous forests in the USA. Application of the model to eucalypt communities in the Murray-Darling Basin (MDB) in Australia required both modification and testing of the model. 2. A soil evaporation component was added following Ritchie (1972). Leaf water potential, originally expressed as a function of soil water content (given as a fraction of field capacity), was altered to reflect soil water potential. To model better the interaction between the evaporation of free intercepted water from the canopy and transpiration, the model was modified after McNaughton & Black (1973) to reflect the suppression of transpiration by a wet canopy
Macroplastic and Microplastic Analysis of Marine Fish and Aquatic Fish Using The Fourier Transform Infrared Spectrophotometry (FTIR) Method

Microplastic is a plastic particle that has a diameter of around 5 μm. The lower limit of the particle size included in the microplastic group has not been defined exactly, but most studies take particle objects with a minimum size of 330 μm 3 . In this review article discusses macroplastic types and characteristics of microplastic polymers that are found in marine fish and aquatic fish using the FTIR method. This study aims to examine the scientific literature related to microplastic research using the FTIR method. The method of work uses literature study by finding sources or literature in the form of primary data in the form of official books and international journals in the last 10 years (201
The Lagrangian particle-based approach is an emerging technique to model cloud microphysics and its coupling with dynamics , offering significant advantages over Eulerian approaches typically used in cloud models . MODELING OF CLOUD MICROPHYSICS Can We Do Better ?

Clouds are large ensembles of water droplets and/or ice crystals that from a distance seem suspended in the air. They typically form due to vertical air motion that leads to expansion and cooling. Latent heating associated with phase changes of the water substance (i.e., condensation, sublimation, and freezing) is a key element of cloud dynamics. Cycling of energy, water, and chemical species through Earth’s atmosphere is critically affected by the presence of clouds and accurate representation of clouds in numerical models of atmospheric flows is of paramount importance. Atmospheric flows encompass an enormous range of scales, from the subcentimeter scale of atmospheric turbulence to nearly planetary-scale Hadley and Walker circulations in the tropics and synoptic weather systems in midlatitudes. Because of this range, incorporation of clouds and cloud processes in numerical models of atmospheric dynamics involves significant challenges. Small-scale models aim at representing clouds explicitly by coupling cloud and precipitation microphysical processes to the modelpredicted cloud-scale f low. This allows the cloud and precipitation processes to directly affect buoyant circulations that are the key to cloud dynamics. Such models have been used in cloud physics research for the past 50 years (e.g., Ogura 1963; Orville 1965; Schlesinger 1975; Tapp and White 1976; Klemp and Wilhelmson 1978; Clark 1979; Lipps and Hemler 1982). Until about 20 years ago, weather and climate models were incapable of including clouds explicitly because the horizontal grid length of these models was much too coarse to resolve them. However, with steadily increasing computational power, it is now feasible to use cloud-scale models for simulating
The Superspecies Concept

The concept of "superspecies," as applied to a monophyletic group of allopatric or nearly allopatric taxa that are known or believed to have evolved to the species level, was introduced to the literature by E. Mayr and by B. Rensch. The concept is of great utility in many studies of evolution and zoogeography. The use of superspecies has been somewhat retarded by the absence of any formalized notation for them in Linnaean nomenclature. It is here proposed that brackets (= square parentheses) enclose the first named species of a superspecies: thus, superspecies Bubo [bubo], or Bubo [bubo] virginianus to indicate that the species virginianms is a member of the superspecies Bubo [bubo]. The term "allospecies" is suggested for the species comprising a superspecies. The use of parentheses ( ) should be restricted to indicating "semispecies," that is, forms believed to be subspecies, but approaching, or possibly of, species status, e.g., Accipiter (gentilis) atricapillus.
Topological M-Strings and Supergroup WZW Models

We study the boundary conditions in topologically twisted Chern-Simons matter theories with the Lie 3-algebraic structure. We find that the supersymmetric boundary conditions and the gauge invariant boundary conditions can be unified as complexified gauge invariant boundary conditions which lead to supergroup WZW models. We propose that the low-energy effective field theories on the two-dimensional intersection of multiple M2-branes on a holomorphic curve inside K3 with two non-parallel M5-branes on the K3 are supergroup WZW models from the topologically twisted BLG-model and the ABJM-model. tadashiokazaki@phys.ntu. edu.tw douglas.smith@durham.ac. uk ar X iv :1 51 2. 06 64 6v 4 [ he pth ] 1 6 Se p 20 16
Using 2D and 3D Convolutional Neural Networks to Predict Semen Quality

In this paper, we present the approach of team Jmag to solve this year’s Medico Multimedia Task as part of the MediaEval 2019 Benchmark. This year, the task focuses on automatically determining quality characteristics of human sperm through the analysis of microscopic videos of human semen and associated patient data. Our approach is based on deep convolutional neural networks (CNNs) of varying sizes and dimensions. Here, we aim to analyze both the spatial and temporal information present in the videos. The results show that the method holds promise for predicting the motility of sperm, but predicting morphology appears to be more difficult.
Near infrared spectroscopy-based method for separation of sound and insect-damaged seeds of Albizia schimperiana, a multipurpose legume

The potential of near infrared spectroscopy as a rapid method to separate sound and insect-damaged seeds of Albizia schimperiana Oliv is demonstrated. Near infrared reflectance spectra were collected on single seeds of each fraction soaked in water for one, three, six, nine and twelve hours. To separate sound and insect-damaged seeds, multivariate classification models were developed with partial least squares (PLS) regression using the digitized spectra as a regressor and a y-vector of artificial values (1 for sound and -1 for damaged seeds) as regressand. The result showed a 100% classification rate of sound and damaged seeds in test sets after 6, 9 and 12 hours of imbibition based on Hill spectrum models. A similar classification result was achieved based on selected absorption hands. PLS weights indicated that the origin of spectral differences between sound and damaged seeds is attributed to difference in relative water content. The result predicate the prospect of developing rapid filter-based sorting equipment that can easily be automated.
Institutional Factors Affecting Farmers’ Decision to Adapt to Climate Change

Adaptation to climate change has become the global focal point especially in recent years. Researchers have defined adaptation to climate change as an effective way for farmers to survive in the face of the scourge. This paper investigated how institutional factors plays a role in farmers’ decision to adapt to climate change. The population of the study was emerging farmers drawn from five districts of the Limpopo province of South Africa. The study used a two-stage cluster sampling technique to select a sample size of 206 emerging farmers. The dependent variable of the study was farmers’ decision to adapt to climate change while the explanatory variables consisted of institutional factors such as access to extension services, farmers’ organisation, membership and access to climate change information. Binary Logistic Regression model was used to determine emerging farmers’ decision to adapt to climate change in Limpopo province. The results showed that institutional factors such as access to both weather information and extension services together with special training on climate change adaptation significantly influenced farmers’ decision to adapt. Therefore, the study recommended participation of different stakeholders to provide institutional support to farmers and thus enhance their extent to adaptation.
Development of an online secondary prevention programme for stroke survivors: Prevent 2nd Stroke

Background Stroke events often result in long-term negative health outcomes. People who experience a first stroke event are 30%–40% more likely to experience a second stroke event within 5 years. An online secondary prevention programme for stroke survivors may help stroke survivors improve their health risk behaviours and lower their risk of a second stroke. Objectives This paper describes the development and early iteration testing of the usability and acceptability of an online secondary prevention programme for stroke survivors (Prevent 2nd Stroke, P2S). P2S aims to address six modifiable health risk behaviours of stroke: blood pressure, physical activity, nutrition, depression and anxiety, smoking, and alcohol consumption. Methods P2S was developed as an eight-module online secondary prevention programme for stroke survivors. Modelled on the DoTTI (Design and development, Testing early iterations, Testing for effectiveness, Integration and implementation) framework for the development of online programmes, the following stages were followed during programme development: (1) content development and design; and (2) testing early iteration. The programme was pilot-tested with 15 stroke survivors who assessed P2S on usability and acceptability. Results In stage 1, experts provided input for the content development of P2S. In stage 2, 15 stroke survivors were recruited for usability testing of P2S. They reported high ratings of usability and acceptability of P2S. P2S was generally regarded as ‘easy to use’ and ‘relevant to stroke survivors’. Participants also largely agreed that it was appropriate to offer lifestyle advice to stroke survivors through the internet. Conclusions The study found that an online secondary prevention programme was acceptable and easily usable by stroke survivors. The next step is to conduct a randomised controlled trial to assess the effectiveness of the programme regarding behaviour change and determine the cost-effectiveness of the intervention.
The Influence of Training and Development on Organizational Commitment of Academicians in Pakistan

It is recognized globally that training and development not only enhances the abilities, knowledge and skills of the staff member but it also changes their attitudes and behaviors. Such change in their attitudes and behaviors makes them committed with their respective organizations. This research work inquired association of training & development along with its dimensions with organizational commitment. We used Smart-PLS to make analysis of the data which were gathered from 320 academic staff of seven public sector universities of Khyber Pakhtunkhwa (KP), Pakistan. The results of this work found a significant influence of training & development and its dimensions on organizational commitment of academic staff. The results further suggested that training and development play crucial role in the enrichment of organizational commitment of academic staff. Committed academic staff not only stays longer with the universities but put more effort and perform better as compared to non-committed academic staff. We also concluded in this study that if the management of the universities intends to enhance the commitment of academic staff with the universities, they need to provide them maximum opportunities of training & development. We also discussed the theoretical and practical implications of the results.
Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions. Methods: In this analytic study, the database of HAM10000 (human against machine with 10000 training images) dermoscopy images, 1000 melanocytic nevi and 1000 melanoma images were employed, where in each category 900 images were selected randomly and were designated as the training set. The remaining 100 images in each category were considered as the test set. A deep learning convolutional neural network (CNN) was deployed with AlexNet (Krizhevsky et al., 2012) as a pretrained model. The network was trained with 1800 dermoscope images and subsequently was validated with 200 test images. The proposed method removes the need for cumbersome tasks of lesion segmentation and feature extraction. Instead, the CNN can automatically learn and extract useful features from the raw images. Therefore, no image preprocessing is required. Study was conducted at Shahid Beheshti University of Medical Sciences, Tehran, Iran from January to February, 2020. Results: The proposed model achieved an area under the receiver operating characteristic (ROC) curve of 0.98. Using a confidence score threshold of 0.5, a classification accuracy of 93%, sensitivity of 94%, and specificity of 92% was attained. The user can adjust the threshold to change the model performance according to preference. For example, if sensitivity is the main concern; i.e. false negative is to be avoided, then the threshold must be reduced to improve sensitivity at the cost of specificity. The ROC curve shows that to achieve sensitivity of 100%, specificity is decreased to 83%. Conclusion: The results show the strength of convolutional neural networks in melanoma detection in dermoscopy images. The proposed method can be deployed to help dermatologists in identifying melanoma. It can also be implemented for self diagnosis of photographs taken from skin lesions. This may facilitate early detection of melanoma, and hence substantially reduce the mortality chance of this dangerous malignancy.
Genetic Predisposition to Latex Allergy

Background: Occupational exposure of healthcare workers to natural rubber latex has led to sensitization and potentially life-threatening anaphylaxis. Although environmental exposure to natural rubber latex products is necessary for sensitization, it is not sufficient. A number of genetic factors also seem to contribute to the latex sensitization; however, the multigenic nature of the allergic phenotype has made the identification of susceptibility genes difficult. The current study tests the hypothesis that known functional polymorphisms in genes encoding interleukin 4, interleukin 13, and interleukin 18 occur in a higher frequency in healthcare workers with natural rubber latex allergy. Methods: Four hundred thirty-two healthcare workers with occupational exposure to natural rubber latex were screened using a clinical history questionnaire and latex-specific immunoglobulin E serology. Genomic DNA was extracted from peripheral blood lymphocytes and analyzed for single-nucleotide polymorphisms in candidate genes of interest. Data from cases and controls were analyzed by nominal logistic regression, with P < 0.05 considered significant. Results: The latex allergy phenotype was significantly associated with promoter polymorphisms in IL13 1055 (P 0.02), IL18 607 (P 0.02), and IL18 656 (P 0.02) compared with nonatopic controls. Conclusions: The significant association of IL13 and IL18 promoter polymorphisms with latex allergy suggests a potential location for genetic control in the induction of latex allergy in individuals and extends the understanding of the genetic basis for the induction of immediate-type hypersensitivity in healthcare workers occupationally exposed to natural rubber latex.
Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico

Biogeochemical ocean models are useful tools but subject to uncertainties arising from simplifications, inaccurate parameterization of processes, and poorly known model parameters. Parameter optimization is a standard method for addressing the latter but typically cannot constrain all biogeochemical parameters because of insufficient observations. Here we assess the trade-offs between satellite observations of ocean color and biogeochemical (BGC) Argo profiles and the benefits of combining both observation types for optimizing biogeochemical parameters in a model of the Gulf of Mexico. A suite of optimization experiments is carried out using different combinations of satellite chlorophyll and profile measurements of chlorophyll, phytoplankton biomass, and particulate organic carbon (POC) from autonomous floats. As parameter optimization in 3D models is computationally expensive, we optimize the parameters in a 1D model version and then perform 3D simulations using these parameters. We show first that the use of optimal 1D parameters, with a few modifications, improves the skill of the 3D model. Parameters that are only optimized with respect to surface chlorophyll cannot reproduce subsurface distributions of biological fields. Adding profiles of chlorophyll in the parameter optimization yields significant improvements for surface and subsurface chlorophyll but does not accurately capture subsurface phytoplankton and POC distributions because the parameter for the maximum ratio of chlorophyll to phytoplankton carbon is not well constrained in that case. Using all available observations leads to significant improvements of both observed (chlorophyll, phytoplankton, and POC) and unobserved (e.g., primary production) variables. Our results highlight the significant benefits of BGCArgo measurements for biogeochemical parameter optimization and model calibration.
Supplementary Material: Deep Feedback Inverse Problem Solver

This supplementary material provides more details and thorough analysis of our deep feedback inverse problem solver. We hope the readers can gain more insights into our approach. In the following, we first showcase that our model can be combined with traditional optimizers to further improve the performance. Next, we provide more detailed ablation studies regarding the design choices that we made. Then we discuss in depth the relationships between our work and several previous approaches. To demonstrate that our method is not limited to the tasks shown in the paper, we further apply it to a dense inverse image reconstruction problem — JPEG image deblocking. Finally, we present more qualitative results.