Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity of soil macroarthropods in shifting cultivation and forest ecosystem of Mizoram, Northeast India

Soil organisms are an integral part of agricultural ecosystems and are essential for the maintenance of healthy productive soils. Little is known about soil arthropods assemblages in shifting cultivation system. Therefore, we compared the diversity of soil macroarthropods in shifting cultivation (EXPTL) system and its adjacent natural forest (CTRL) ecosystem in Mizoram, northeast India and assessed the impact of shifting cultivation on the diversity. The study was conducted from 2013 to 2015, and the period was divided as pre-cultivation, cultivation and post-cultivation phases. Traditional shifting cultivation was practised in EXPTL site in the year 2014. Sampling was done by handpicking and digging from a quadrat (25×25×30 cm) located at least 10 m apart at monthly intervals. Specimens were preserved in 4% formalin and were identified up to the lowest possible taxa. A total of 97 taxa of arthropods belonging to five classes were recorded. 88 taxa and 48 taxa were recorded in CTRL and EXPTL respectively. Order-wise Shannon diversity index was significantly higher (p < .001) in CTRL as compared to EXPTL site. There were significant differences in both cultivation (p <.001) and post-cultivation (p <.001) phases between CTRL and EXPTL sites. There was a significant effect of shifting cultivation on the diversity of soil macroarthropods at the p <. 05 level for the three cultivation phases in EXPTL site. Therefore, it was concluded that shifting cultivation system negatively affected soil macroarthropod diversity at least for a short duration. This study provided the first baseline data of soil macroarthropod diversity and its interaction with land-use system from Mizoram, northeast India.
Cyanobacterial diversity from seven mangrove environments of Kerala, India

A survey on the cyanobacterial biodiversity of 7 mangrove environments viz. Vallikunnu, Kadalundi, Kallayi, Mangalavanam, Kumbalam, Kumarakom and Mekkara mangroves of Kerala was undertaken during 2014-2016. In total, 31 species of cyanobacteria coming under 10 genera and 4 families were recorded in all mangrove environments. Maximum species (13 species) of cyanobacteria were recorded from Kadalundi, whereas the minimum was recorded from Mangalavanam (2 species). The genus Oscillatoria was observed with maximum distribution (13 species), followed by Nostoc (5 species) and Lyngbya (3 species). The genus Aphanocapsa, Microcoleus and Scytonema (2 species each) and Chroococcus, Aphanothece, Merismopedia, and Phormidium (1 species each) shows equal distribution. As many as three species, namely Oscillatoria salina, Oscillatoria ornata and Oscillatoria vizagapatensis were common in all mangrove environments except Mangalavanam and Mekkara.
Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.
Cold Flow Simulation of a 30 kWth CFB Riser with CPFD

A 30 kWth Circulating fluidized bed (CFB) combustor is experimentally and numerically investigated under cold flow conditions. Barracuda software based on Computational Particle Fluid Dynamics (CPFD) method is utilized for simulations. The influences of bed inventory and drag model on flow hydrodynamics were investigated considering pressure and velocity profiles and particle concentration. Two advanced drag models, namely Energy minimization multi-scale (EMMS) and Wen-Yu/Ergun were selected for this study. The simulations were performed with initial bed material masses of 3.79, 4.55 and 5.20 kg corresponding to 2.5, 3 and 3.5 diameters height of riser, respectively. With increasing bed inventory pressure drops and solid concentration increase. The axial particle velocities slightly change with bed inventory. The comparison of simulation results with experimental measurements was resulted in good agreement (<5%) with both models. The simulation with EMMS drag model predicted the pressure profiles more accurately than Wen-Yu/Ergun drag model. The profiles of particle volume fraction and axial velocity demonstrate that core-annulus flow pattern was captured by both models. But EMMS drag model was better in revealing the meso-scale structures at instantaneous particle concentration distribution. Moreover, the influence of particle size distribution on particle volume fraction and particle velocity profiles is also investigated with two drag models.
Observations on the scaling relationship between bony labyrinth, skull size and body mass in ruminants

The bony labyrinth is the bony capsule of the inner ear, which is the center of hearing and balance in the skull of vertebrates. In mammals this osseous structure ossifies early in the development being fully formed largely before birth. This means that it does not grow after full ossification while the animal continues to grow until full adult size well after birth. At the level of an individual, the bony labyrinth thus shows a negative ontogenetic allometric relationship with the surrounding skull and with the animal’s body mass. At the evolutionary level, between species, such a negative allometry has already been evidenced for the middle ear, a component of the ear region made of three tiny bones, the ossicles, in contact with the inner ear. Herein, we test the allometric relationship between the bony labyrinth and skull length as well as body mass on a large sample in the ruminant clade (Mammalia, Ruminantia) using micro computed tomography as the imaging method of choice. We find a strong negative allometry paralleling the ontogentic allometry described earlier. This evolutionary relationship related to the timing of ossification of the bony labyrinth is probably critical in explaining the large hearing frequency range of mammals as well as their particular ecological adaptations.
Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction

Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.
Dynamic Guidance for Depth Map Restoration

The guidance of color images greatly improves the restoration accuracy of the depth map. However, due to the incomplete texture structure consistency between the color image and the depth map, and the limitations of $L^{2}$ based model, static guidance tends to cause texture copy artifacts and blurring depth discontinuities, which seriously deteriorates the quality of result. To tackle those problems, we propose a dynamic guidance model which can adaptively adjustment itself based on local smooth characteristics and continuously optimize in iteration. The proposed method can significantly alleviate the negative impact of the incomplete consistency and makes full use of the guidance information to restore fine texture at the same time. Moreover, a novel edge correction mechanism is designed to filter out incorrect depth information around boundaries, ensuring the correction of edges. In experiments, visual comparison proves that our method can alleviate texture copy artifacts and blurring depth discontinuities effectively. Quantitative results show that the proposed method restores high quality depth map with the lowest mean absolute error.
Distribution of Indian oil sardine Sardinella longiceps along south-west coast of India

Rise in temperature directly affects the poikilothermic aquatic animals such as fishes influencing their population biomass and recruitment. With this perspective, a study on spatial-temporal distribution of Indian oil sardine along south-west coast of India (SW) was conducted by classifying the study area into three strata namely: southern, central and northern region. The data was collected and segregated into three seasons, i.e. pre-monsoon, monsoon and post-monsoon. The trend for sea surface temperature was analyzed and compared among three strata and seasons. The trend of standardized catch per hour for all strata and season was also analyzed, along with observation on gear-wise catch of Indian oil sardine. This study indicates towards latitudinal extension and seasonal changes of Indian oil sardine population, besides changing its distribution into deeper water for avoiding the adverse impact of rising temperature.
Study on Influences of Water Temperature on Stress Distribution for High Arch Dam in Impounding Period

Water temperature affects not only the water quality and ecological balance of the reservoir, but the temperature and stress field distribution of the high arch dam during water storage and operation management. During low water storage and initial operation, the concrete in reservoirs over 100 m deep in particular can easily crack or expand because of low temperature reservoir water. This paper studies the water temperature distribution law of the reservoir during initial storage by establishing a three-dimensional fluid dynamics model. Based on this, this paper explores the three-dimensional temperature field and stress field of the dam body during the storage period, the causes of cracking, and stress distribution in key parts of the arch dam.
Similar Text Fragments Extraction for Identifying Common Wikipedia Communities

Similar text fragments extraction from weakly formalized data is the task of natural language processing and intelligent data analysis and is used for solving the problem of automatic identification of connected knowledge fields. In order to search such common communities in Wikipedia, we propose to use as an additional stage a logical-algebraic model for similar collocations extraction. With Stanford Part-Of-Speech tagger and Stanford Universal Dependencies parser, we identify the grammatical characteristics of collocation words. With WordNet synsets, we choose their synonyms. Our dataset includes Wikipedia articles from different portals and projects. The experimental results show the frequencies of synonymous text fragments in Wikipedia articles that form common information spaces. The number of highly frequented synonymous collocations can obtain an indication of key common up-to-date Wikipedia communities.
Ischemic Stroke Lesion Segmentation by Analyzing MRI Images Using Deep Convolutional Neural Networks

Deep Learning proves to be the best way towards image classification tasks replacing ensembles with handcrafted features. It also has applications in medical imaging such as classification of tumorous and non-tumorous part from rest of the MRI image. Lesions is one such disease occurring in brain due to lack of oxygen during stroke. Automatic segmentation of ischemic stroke lesions is the perfect problem solved by using deep learning methods especially Convolution Neural Networks (CNNs). We present a patch based approach using multi-scale convolution layers with three pathways to segment lesions using 6 modalities of MRI images. The accuracy achieved using this method is comparable to other methods.
The Development of the IES Glare Index System

The Luminance Study Panel was set up by the Technical Committee at the end of 1958 to study ways and means of introducing luminance concepts into the forthcoming revision of the IES Code. This report of the Panel describes the basis of the acceptance of the BRS glare formula, together with some amendments to the glare criterion scales. Details are given of the work of Glare Index calculation using a digital computer, the adoption of the British Zonal Classification for fittings, the evaluation of background luminance, together with the conception of conversion terms for downward flux, luminous areas and height of fittings. Full details are given of the numerous field surveys undertaken, leading to the construction of the Glare Index tables and the setting of the closed system of limiting Glare Indices.
A Soft Computing Method for Efficient Modelling of Smart Cities Noise Pollution

Noise pollution is one of the most relevant problems in urban area. The main source of noise pollution is the number and type of motor vehicles, but other parameters depending on street configuration yield to a system hardly to be exactly modelled by classical mathematical methods. Smart cities are expected to dynamically control the urban traffic to reduce not just traffic jams, but also to ensure a comfortable noise level for inhabitants. This article gives a design method for efficient genetic fuzzy modelling of traffic generated smart cities noise pollution based on fuzzy logic, multi objective genetic algorithm, gradient descent optimisation and singular value decomposition in the MATLAB environment. Genetic algorithms with objectives to minimise the maximum absolute identification error, the root mean square of the identification error, reduce model complexity and ensure maximal numerical robustness are applied to Zadeh type fuzzy partition membership function parameters preliminary identification, and then gradient descent method is used for their fine-tuning optimization, while the fuzzy rule consequence linear parameters are calculated by singular value decomposition method to find the least squares optimal training data fitting of the model. The training data set is built from measured data, combined with carefully selected simulation data to ensure the completeness of the model and its numerical robustness. Detailed analysis of the method and results by computer simulation of the identification process show the validity of the proposed method.
Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.
ShEMO - A Large-Scale Validated Database for Persian Speech Emotion Detection

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 hours and 25 minutes of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as "substantial agreement". We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female=59.4%, male=57.6%). The ShEMO is available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.
Use of Transformer Multi-Level Inverters in Parallel Operation of Marine Generating Sets with PMSG

Abstract The increase of energy efficiency in autonomous marine generating sets improves the overall efficiency of the ship’s propulsion. One of the methods to increase the efficiency of generating sets is the use of synchronous machines with permanent magnets in sets as generators (PMSG). The use of PMSG in connected with the need to install power converters in order to maintain constant parameters of the supply voltage and the possibility of reactive power’s distribution between generating sets that work in parallel. The article presents the possibility of using transformer multi-level inverters in parallel operation of marine generating sets with PMSG. On the basis of the results of simulation tests, the theoretical assumptions for the possibilities of active and passive power adjustment in the parallel operation of generating sets were confirmed.
Ensemble Kalman Filtering Based on Potential Vorticity for Atmospheric Multi-scale Data Assimilation

A multi-scale data assimilation method for the ensemble Kalman filter (EnKF) is proposed for atmospheric models in cases with insufficient observations of fast variables. This method is based on the conservation and invertibility of potential vorticity (PV). The dynamical state variables in the free atmosphere of forecast ensemble members are decomposed into balanced and unbalanced parts by applying PV inversion to the PV anomalies computed from spatially smoothed state variables. The mass variables of the two parts are adjusted to remove additional sampling errors introduced by the decomposition. The forecast error covariances between those parts are ignored in the Kalman gain to suppress spurious error correlations. This approximation makes it possible to apply different covariance localizations to each part. The Kalman gain thus obtained is used to assimilate observations. The performance of the proposed method is demonstrated with a shallow water model through twin experiments in a perfect model scenario. The results using the same localization radius for the two parts reveal that the proposed EnKF is superior in the accuracy of the analysis to a conventional EnKF unless the ensemble size is sufficiently large. It is found that the adjustment of mass variables is necessary to outperform the conventional EnKF. The benefits of the PV inversion using the Bolin–Charney balance over the quasi-geostrophic inversion are marginal in the experiments.
Evaluating Evaporation Methods for Estimating Small Reservoir Water Surface Evaporation in the Brazilian Savannah

Small reservoirs play a key role in the Brazilian savannah (Cerrado), making irrigation feasible and contributing to the economic development and social well-being of the population. A lack of information on factors, such as evaporative water loss, has an impact on the design and management of these reservoirs, as well as on regional water safety. Acquiring this information is crucial for hydrologists to develop more effective water resource management strategies and policies. This study assesses the performance of a diverse number of methods that are used to estimate evaporation and provides evaporation probability curves on a fortnightly period for small reservoirs in the Brazilian savannah region. Evaporation data were collected for a small water reservoir located in the Buriti Vermelho watershed, a typical dam of the Brazilian savannah region. Among the assessed methods, those of Kohler et al. (1955) and Linacre (1993) presented the best performances on both the daily and monthly scales for evaporation estimates. By simulating the evaporation rates for a timeseries, an increasing trend in evaporation was observed for the transition between the dry and wet seasons, jeopardizing double cropping in the region. The developed probability curves are an important tool for improving water resource planning and increasing the local water availability.