Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

In real‐world wetland vegetation morphology (WVM) detection, large scene variations such as those due to landform, vegetation, sunlight, weather, and sky, as well as camera parameter settings such as focal length and shooting angle, require systematic and complicated artificial intelligence technology to accurately discriminate inter and intra‐class wetland objections. To deal with these challenges, we introduced a deep‐level discriminative model based on convolutional neural networks (CNN) for classifying the images of DongZhai Harbor intertidal, Lashi Lake alpine, Yancheng coastal and Zoige plateau wetlands in China. A 96‐dimensional convolution operation with kernel sizes of 11 × 11 × 3 first applied to the resized 227 × 227 WVM input pictures to acquire the effective morphologic features. The perceptron layers of the rectified linear unit and the batch normalization were used in the middle layer to achieve the better gradient propagation property during the training process. The WVM features were down‐sampled by the pooling networks to reduce the neuron dimensions. The fully connected layer linked to the output of the convolutional and pooling layers to obtain the high‐level WVM species information for the final WVM classification purpose. The deep‐level CNN‐based method was compared with the traditional shallow‐level feature‐designed algorithms of conditional maximum entropy regression, multilayer perceptron and support vector machine. The deep‐level algorithm showed the superior performance for detecting the WVM species, which provided a superior alternative routine for accurate artificial intelligence classification of WVM in the ecological engineering application.
Thin Solid Films

We have determined the complex dielectric tensor of single crystalline 3,4,9,10-perylene tetracarboxylic dianhydride ( 𝛼 -PTCDA) as a function of energy in the range between 1.4 and 5.0 eV. The results obtained reflect the monoclinic symmetry of the crystal: The principal axes of the real and the imaginary part of the tensor in general do not coincide and show chromatic dispersion. Monoclinic behavior allows rotation of the components 𝜀 𝑋 and 𝜀 𝑍 in the plane perpendicular to the unique symmetry axis 𝑌 . The experimental results indicate that the energies of the optical transitions observed in the weak 𝜀 𝑋 component coincide with energies in which a resonance effect due to coupling with the stronger 𝜀 𝑍 component occurs. These resonances appear at energies close to electronic excitations such as the optical gap, the transport gap and the highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) peak-to-peak gap and their assignments are discussed based on theoretical calculations.
Sasi in Kei Island: Transformation of Coastal Resources Managementby Community in Tanimbar Kei Island, Maluku, Indonesia

Sasi is a local Indonesian traditional resources management system, which manages responsibilities and marine tenure rights in Maluku. Dwellers have been using this system for over 400 years and it consists of spatial and temporal prohibitions on harvesting crops. Sasi was viewed as an appropriate method to achieve harmony between man and nature. It has been noted that sasi has been affected by many factors. These factors have allowed sasi to exist in some areas and become extinct in other areas, which are very essential indicators when considering how to effectively achieve sustainability. Studyingthe factors that transformed sasi , understanding villagers’ perception and reaction to sasi’s transformation and how they planned to sustainably manage their natural resources will support the community based management model for eastern Indonesia. This research was conducted in Tanimbar Kei Island, which actively follows the rules of sasi . To determine the effectiveness of sasi in terms of social and environmental sustainability, demographic, social, politic and economic factors were studied. This study used the triangulation method, which consists of literature studies, area studies, in-depth interviews and questionnaires. The data was analyzed with qualitative analysis, institutional analysis and sustainability analysis. The results show a change in perceptions and community institutions. The dominant factor affecting sasi is the political aspect, especially concerning policy autonomy and assistance from NGOs. The community trust in sasi and other community members ensures that sasi’s transformation does not cause it to lose its identity and function.
Upper Carboniferous foreland basin evolution in SW Britain

Upper Carboniferous foreland basins of the Rhenohercynian and Sub-Variscan parts of the Variscan orogenic belt of SW Britain (Culm and South Wales Basins) developed during the structural inversion of previously thinned crust. The transition from an extensional (Middle Devonian-Lower Carboniferous) to compressional tectonic regime (Upper Carboniferous) occurred in early Namurian times and resulted in regional D1 basin inversion. In SW England a basic correlation existed between the thickness of Middle Devonian to Lower Carboniferous syn-rift sequences and the location of subsequent Upper Carboniferous sedimentation. Areas with thick syn-rift sequences such as the Trevone Basin and the North Devon Basin/Bristol Channel region emerged as topographic highs and acted as potential thrust loads as a result of inversion. In contrast, areas of thinner syn-rift sequences (beneath the Culm Basin) became relative lows and sites of subsidence following early Namurian compression. Uplift along the Bristol Channel Fault Zone (BCFZ) a major Variscan thrust took place from late Namurian into Westphalian times and resulted in emergence of a landmass which separated and sourced the Culm and South Wales Basins. E-W structures, active during sedimentation in the South Wales Basin are thought to represent Devonian-Lower Carboniferous extensional faults, reactivated during Dl shortening.
Effect of Surface Fluid Entrapment in a Laminar Flat Plate Effect of Surface Fluid Entrapment in a Laminar Flat Plate Boundary Layer Boundary Layer

For streamlined objects in laminar flow, the drag is dominated by friction drag. To counteract this, the boundary condition of the object can be changed to introduce an apparent slip on the surface. A number of approaches have been proposed for introducing surface slip to reduce drag and viscous resistance in fluid flows. This work investigates a novel approach to providing slip via fluid entrapment in a perforated domain embedded in the surface. The fluid entrapment system uses a cavity underneath the domain to allow entrapment of fluid of the same type, or supply air to the surface perforations in the case of gas entrapment in a liquid flow. Either case provides a fluid at the surface perforations and relaxes the no-slip condition. As a model case, fluid entrapment is used for flow over a flat plate with the perforated domain at various downstream locations. Flow over the flat plate is provided by a miniature water tunnel (cross section 5cm x 7cm). Velocity profiles were measured using Particle Image Velocimetry (PIV) of the flow across the top of the two-dimensional (2-D) perforated surface at a Reynolds number (Re) in the range of 4,000-6,000. Using this method, the boundary layer profiles for a flat plate with air entrapment were analyzed and compared with the results from a solid, control flat plate and a flat plate with water entrapment. The results show slip flow at the surface for the
DESIGN AND IMPLEMENTATION OF AREA-OPTIMIZED AES BASED ON FPGA

Advanced Encryption Standard (AES), also known as Rijndael, is a block cipher algorithm that has been analyzed extensively and is now used widely. The AES algorithm hardware implementation is faster and more secure than software implementation. AES algorithm is used to encrypt and decrypt data as this can make the whole process much faster and secured communication is also established in the system. This is also extended to 176 and 192 bits in this work. Hardware implementation of Advanced Encryption Standard (AES) algorithm has been in intensive discussion since its first publication by National Institute of Standards and Technology (NIST) in 2000, for higher throughput over 1 Giga bits per second (Gbps). However, the studies of low power, low area and low cost implementations, which normally have throughput less than 1Gbps and use the data path less than 32-bit, have recently appeared in ASIC as well as in FPGA for wireless communication and embedded hardware application. In the proposed work the encryption of 128,176 and 192 bits are aimed for accurate AES implementation. This proposed work has been divided into two main phases software development and hardware development. In the development of software, it is involved with writing the code, simulation process with Xilinx 13.2 ISE tool. The hardware development covers the Xilinx Spartan 6 FPGA target board development. An AES cipher implementation that is based on the Block RAM and DSP units embedded within Xilinx’s Spartan-6 FPGAs. An iterative ―basic‖ module outputs a 32 bit column of an AES round in each clock cycle, with the throughput of 1.76 Gbit/s when processing a 128 bit inputs, one 176 bits data and 192 bits data. Finally, the ―round‖ module is replicated ten times for a fully unrolled design that yields over 55 Gbit/s of throughput. High throughput implementations are mainly used for high-end devices such as accelerator cards for e-commercial service and security trunk communications. In order to achieve higher performance in today’s utilization of hardware accelerators for cryptography algorithms and heavily loaded communication networks is more efficient.
GazPNE2: A general and annotation-free place 1 name extractor for microblogs fusing gazetteers 2 and transformer models

19 Extracting precise location information from microblogs is a crucial task in many applications. 20 Currently, there remains a lack of a robust and widely applicable place name extractor for English 21 microblogs. In this paper, we attempt to overcome the gap by presenting GazPNE2, which fuses deep 22 learning, global gazetteers (e.g., OpenStreetMap), pretrained transformer models, and rules requiring 23 no manually annotated data. GazPNE2 can extract place names at both coarse (e.g., country and 24 city) and ﬁne-grained (e.g., street and creek) levels and place names with abbreviations (e.g., ‘tx’ 25 for ‘Texas’ and ‘studemont rd’ for ‘studemont road’ ). We compare GazPNE2 with 9 competing 26 approaches on 11 public tweet data sets, containing 21,393 tweets and 16,790 place names across the 27 world. It is the ﬁrst time that diﬀerent extractors are compared on such a large public dataset. The 28 results show our proposed approach achieves SotA performance on the test data with an average F1 29 of 0.8. Code is available on the GitHub page: https://github.com/uhuohuy/GazPNE2
Real-time implementation of computer vision based farrowing prediction in pens with a possibility of temporary sow confinement

Due to growing concern over the welfare of livestock and changing legislation in the European Union pig farmers will have to adopt new management methods in farrowing pens. An important challenge related to implementation of temporary sow confinement is the optimal timing of confinement in crates, considering the sow welfare and piglet survival. In total, 71 sows and four types of farrowing pens were included in the observational study. To automatically detect the optimal timing of sow confinement we applied computer vision model You Only Look Once X (YOLOX) to detect sows’ locations, calculated activity level of sows’ based on detected locations and detected changes in sows activity trends with Kalman filtering and fixed interval smoothing algorithm. Results indicated the beginning of nest-building behaviour with a median of 12 h 51 min and ending with a median of 2 h 38 min before the beginning of farrowing based on centroids of sows extracted with YOLOX-large object detection model. It was possible to predict farrowing with good performance on validation dataset i.e. for 29 out of 44 sows, considering that the object detection model had to be trained on the farrowing pens present in the validation dataset. The developed method could be applied to warn the farmer when nest-building behaviour starts and then to confine the sow in a crate when the end of nest-building behaviour is detected. This could reduce labour costs otherwise required for regular control of sows in farrowing compartments.
Effectiveness, safety, and cost e utility of a knee brace in medial knee osteoarthritis: the ERGONOMIE randomized controlled trial

Objective: This pragmatic, multicenter, open-label, randomized controlled trial (RCT) aimed to compare the effectiveness, safety, and cost e utility of a custom-made knee brace versus usual care over 1 year in medial knee osteoarthritis (OA). Design: 120 patients with medial knee OA (VAS pain at rest > 40/100), classi ﬁ ed as Kellgren e Lawrence grade II-IV, were randomized into two groups: ODRA plus usual care (ODRA group) and usual care alone (UCA group). The primary effectiveness outcome was the change in VAS pain between M0 and M12. Secondary outcomes included changes over 1 year in KOOS (function) and OAKHQOL (quality of life) scores. Drug consumption, compliance, safety of the knee brace, and cost e utility over 1 year were also assessed. Results: The ODRA group was associated with a higher improvement in: VAS pain (adjusted mean difference of (cid:2) 11.8; 95% CI: (cid:2) 21.1 to (cid:2) 2.5); all KOOS subscales
Ecological status assessment of Bidkhun mangrove swamp from Bushehr province, Persian Gulf, using macrofauna community structure.

Mangrove ecosystems are known as productive ecosystems with high ecological and economic importance. Anthropogenic effects have destroyed these habitats in many cases. Evaluating ecological and environmental status help to find stress sources in order to conserve them against more destruction. Present study was designed to evaluate ecological health status of Bidkhun mangrove swamp, Bushehr province, Persian Gulf. This habitat is surrounded by industrial establishments and is affected by discharge of urban sewage. Investigating macrofauna structure and calculating ecological indices including Simpson, Shannon-Wiener, AMBI and M-AMBI, were performed to assess its ecological integrity. Sampling was done in six stations during four seasons. Macrofauna and environmental parameter were investigated. Totally 35 macrobenthic species where identified. Polychaeta was the main taxa. There were special and temporal changes in macrofauna composition. Generally, station four, which was located close to sewage canal opening, had different macrobenthic and environmental features. High concentration of nutrients as a result of sewage discharge, have led to algal bloom and decrease of macrofauna density, consequently. Ecological status of present habitat was good or high in most stations but it was poor and very bad in station four. Ecological indices showed that this station is extremely polluted. Besides, invasive freshwater reeds have surrounded the mangrove trees in mentioned station. It is concluded that discharge of sewage is main source of habitat degradation for Bidkhun mangrove swamp. Hence, it is necessary to decrease its impacts by sewage treatment or stopping sewage discharge.
: Molybdenum and Tungsten Disulﬁde as Novel Two-dimensional Nanomaterials in Separation Science

Beyond graphene-based membranes for water desalination a vast horizon of new materials has been discovered for solutes separation from water. In this realm, the transition-metal dichalcogenides (TMDs) molybdenum and tungsten disulﬁde (MoS 2 and WS 2 , respectively) stand as promising two-dimensional (2D) materials. Their tailoring for nanoﬂuidics as well as the emerging synthesis and production methods unfold the possibility of applying MoS 2 and WS 2 in modern desalination processes based on 2D membranes. We present here an overview from their theoretical conception to their state-of-the-art applications, highlighting the challenges and opportunities associated with measuring water ﬂow and ionic rejection rates at nanoscale. In a world full of environmental concerns, both the theoretical gaps and experimental perspectives point toward a promising use of MoS 2 and WS 2 as green components in separation technologies, contributing to increase the availability of clean, potable water.
Application of Semantic Web Technologies for Learning Pattern of Storm Damages

It seems that Semantic Web technologies have not had strong connections with problems of natural disaster prediction, such as storm forecast, which remains a big challenge for science. Aiming to estimate potential damages of storms to societies and economic systems in the view of Machine Learning and Data Mining, we suggest an investigation of storm data in the SRBench dataset using Statistical Unit Node Set approach, to learn five hurricanes in The USA. Our study considers some locations affected by the hurricanes to construct data matrices, in which the rows indicates time instances of storm-situation, while the columns represent weather observations at locations, attributes of storms and known and unknown damage indices of each location. Two matrix completion algorithms are used to perform stream-reasoning for filling the unknown entries, which can be considered as predicted values of potential losses. One of the biggest obstacles is the availability and quality of data, which are incomplete and carry noises. However, results show that our model can deal with this issue and reflect the pattern of storm damages. Therefore, we recommend this achievement as a meaningful alterna-tive to, or support to, meteorological models for governmental and business organizations, especially in developing countries where infrastructure and resources of weather forecasting are limited. Keywords— Matrix completion; statistical unit node set; storm
The Essence of Generalized Algebraic Data Types

This paper considers direct encodings of generalized algebraic data types (GADTs) in a minimal suitable lambda-calculus. To this end, we develop an extension of System Fω with recursive types and internalized type equalities with injective constant type constructors. We show how GADTs and associated pattern-matching constructs can be directly expressed in the calculus, thus showing that it may be treated as a highly idealized modern functional programming language. We prove that the internalized type equalities in conjunction with injectivity rules increase the expressive power of the calculus by establishing a non-macro-expressibility result in Fω, and prove the system type-sound via a syntactic argument. Finally, we build two relational models of our calculus: a simple, unary model that illustrates a novel, two-stage interpretation technique, necessary to account for the equational constraints; and a more sophisticated, binary model that relaxes the construction to allow, for the first time, formal reasoning about data-abstraction in a calculus equipped with GADTs.
Estimating body weight of individual beef heifers using point-cloud reconstruction and machine learning

Body trait measurements (e.g., body weight) are routinely performed and provide critical information for producers to assess growth, reproductive performance, and marketing window for individual beef cattle in extensive ranches. However, most of the body trait information is obtained via visual assessment conducted by experienced workers, which has a high rate of inaccuracy. This study aimed to estimate the live body weight of individual beef heifers via three-dimensional (3D) sensing, point-cloud reconstruction, and machine learning regression. 67 beef yearling heifers weighing between 282.13 and 439.98 kg were used for data collection. Top-view RGB-depth videos were recorded when individual heifer walked through by using a 3D sensing camera. Selected RGB-depth images were used to reconstruct point clouds, in which backgrounds were removed. Cattle point clouds were preprocessed to extract 2D and 3D features. Eight machine learning regression models were developed and assessed to determine the optimal model for estimating body weights. Results show that middle slices along x - and y - axis provide good cattle’s back features and support estimation purposes. Using the Adaptive Bosting model, the best R 2 and mean absolute errors for liveweight regression were 0.934 and 5.632 kg, respectively. The developed pipeline, including preprocessing of point clouds, point clouds feature extraction, and machine learning model prediction, took 3.670±0.013 seconds per heifer point cloud for weight regression. The developed techniques provide supportive tools for precision beef cattle management.
Ion ﬂ occulation in water: From bulk to nanoporous membrane desalination

In this work, we employed molecular dynamics simulations of a mixture of 1:1 and 3:1 salts in order to explore the ﬂ occulation as aprominentfactor indesalinationthroughnanoporous membranes.TheNaClrejectionbythe nanomembranes isenhanceddue to theaddition ofthe multivalent cation from ~70% to100%. Our ﬁ ndingsindi-catethat themechanismbehindthiseffectisthe ionicclustering duetothepresence of themultivalent ion combined with the dielectric discontinuity at the nanopore. We show that the con ﬁ nement increases the sodium ‑ sodium aggregation and that the clusters are able to support the high pressures applied in the non-equilibrium simulations. Our results indicate that a simple ﬂ occulant can enhance ion rejection rate of the nanomembrane, which is consistent with recent experimental ﬁ ndings that the rejection sharply increases with the particle size.
The Role of Akkermansia muciniphila and Faecalibacterium prausnitzii in the Pathogenesis of Ulcerative Colitis and Crohn's Disease.

BACKGROUND Crohn's disease (CD) and ulcerative colitis (UC) are inflammatory bowel diseases with uncertain etiology. We aimed to determine the amounts of Akkermansia muciniphila and Faecalibacterium prausnitzii in the intestinal microbiota of these patients and to correlate their amounts with blood IL-8, IL-10, and IL-12 cytokine levels. METHODS Thirty UC, 30 CDs, and 46 healthy controls were included. IL-8, IL-10, and IL-12 levels of blood samples were analyzed by ELISA. The amounts of Akkermansia muciniphila and Faecalibacterium prausnitzii were determined by the LightCycler 480 qPCR system. RESULTS F. prausnitzii, A. muciniphila, IL-10, and IL-12 decreased in patient groups, while IL-8 decreased in UC but increased in CD. A significant difference was detected between the patient and control groups in terms of F. prausnitzii, A. muciniphila, and IL-8, but not for others. The amount of F. prausnitzii was correlated with IL-8 and IL-10 in UC and with IL-10 in CD patients. CONCLUSIONS The decrease in the amount of F. prausnitzii was associated with the increase in UC disease severity. A. muciniphila and F. prausnitzii were detected in lower amounts in both diseases. F. prausnitzii decreased more with the severity of UC, suggesting that these bacteria may have complex roles in their etiopathogenesis.