Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.
Recent progress in photocatalytic degradation of chlorinated phenols and reduction of heavy metal ions in water by TiO2-based catalysts

Among the various semiconductor photocatalysts reported so far, TiO2 is still the most promising material for real applications because of its excellent chemical and thermal stability, non-toxicity, low cost and highly oxidising photogenerated holes. This review summarises the recent progress (mainly over the last five years) in photocatalytic oxidation of non-biodegradable organic pollutants (chlorophenols) and reduction of toxic heavy metal ions in aqueous solution. The review details the recently developed strategies for improving the performance of TiO2-based photocatalysts, with particular respect to the visible light activity, charge separation efficiency, stability, separability and adsorption capacity for the remediation of the aforementioned categories of water contaminants, as these factors heavily affect the practical application of this technology. Next, the underlying semiconductor photocatalytic mechanisms have been thoroughly addressed experimentally and theoretically, together with the proposed defect engineering to improve the photocatalytic performance. Finally, the prospect of TiO2 photocatalysis was discussed.
Awarua and the dragon: storytelling as a stimulus for early childhood teaching and learning in two cultural contexts

ABSTRACT In this article, we examine how teachers in two kindergartens, one in New Zealand and one in China, used the stimulus of a fictional story combining mythical creatures from Māori and Chinese culture to enhance young children’s sense of place and cultural identity. Sociocultural understandings about the role of collaboration and cultural tools in meaning-making informed the research design. Teachers’ documentation about the children’s learning throughout the project was analysed and focus group interviews were conducted with teachers and parents. Findings demonstrated that story-telling provided an effective stimulus for children’s learning using a range of multi-literacies. It was also helpful for exploring and connecting children, teachers and families with significant aspects of the cultural backgrounds of the children. The research also revealed that teachers engaged in intentional teaching practices to achieve these outcomes but experienced challenges (pedagogical knots) as they used the story. Nonetheless, we argue that the use of storytelling can be a powerful tool for fostering greater understanding for children, teachers and families of their own and others’ cultures.
Characterization of sugar from Arenga pinnata and Saccharum officinarum sugars

The study was carried out to characterize the physicochemical properties, antioxidant activities and the volatile compounds of sugars from Arenga pinnata and Saccharum officinarum. Refined cane sugar exhibited the highest L* value, whereas jaggery powder showed the lowest L* value (p<0.05). The solubility ranged from 99.18% to 99.57%. The proximate composition and chemicals properties were significantly (p<0.05) varied among different sugar samples. The highest moisture content (4.11%), crude fat (0.11%), crude fiber (0.02%) and reducing sugar (9.31%) were found in aren sugar. Highest amount of ash content (1.19%), crude protein (0.28%), titratable acidity (0.50%) and vitamin C (6.62 mg/100 g) were found in jaggery powder. As control, refined cane sugar contain significant amount of carbohydrate (99.95%), total soluble solid (90.00 °Brix) and water activity (0.55 aw). The pH values of all samples ranged from 4.14 to 6.65. The maximum DPPH radical scavenging activity and TPC were found in jaggery powder with values 4170 μg of GAE/g and 46.98% respectively. The volatile compounds detected were 5-hydroxymethylfurfural,2,3-dihydro-3,5-dihydroxy-6-methyl-4H-pyran-4-one, aminoacethydrazine, hydroxyacetic acid, hydrazine, acetic acid, [S-(R*, R*)]-2, 3-butanediol, 2, 3-dihydro-3, 5-dihydroxy-6-methyl-4H-pyran-4-one and 6-acetyl-a-d-mannose. The quality data from this characterization can be used to indicate the standard of A. pinnata and S. officinarum sugars.
An introduction of scada-based based back pressure vessel (BPV) at palm oil factory with biomass energy

Empty bunches, shells and fibers are waste biomass for renewable energy in steam power plants. The automatic control system provides many advantages for industrial use. Apart from speeding up time, work productivity is also able to reduce human error. The SCADA system includes a number of RTUs (Remote Terminal Units), Main Station / RCC (Area Control Center) and a data telecommunication network between the RTU and the Main Station. Application of scada, BPV can be controlled easily. This study introduces the application of scada to the BPV regulation in the biomass power plant at the Gunung Melayu palm oil mill
Consistent image processing based on co-saliency

In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co ‐ saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co ‐ saliency detection. Through the co ‐ saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non ‐ common saliency objects (called Region of Interest here, i
Association between Proximal Tibiofibular Joint Morphology and Knee Alignment in Osteoarthritis Patients

This study aimed to determine whether proximal tibiofibular joint (PTFJ) morphology was related to knee alignment in osteoarthritis (OA) patients. Methods: 67 OA patients were enrolled in this study. The morphology of the PTFJ including articular shape type, articular surface area, joint inclination, relative articular height, and joint declination were measured from 3D models. The knee alignment of each subject was characterized as varus, normal, or valgus according to the femorotibial angle (FTA). The FTA was measured on weight-bearing X-rays. Multinomial logistic regression analysis was used to evaluate the association between PTFJ morphology and knee alignment. Results: there were significant differences between varus, valgus, and normal FTA groups in terms of shape type (p = 0.021), inclination of the PTFJ (p = 0.025), relative articular height (p = 0.019), and PTFJ declination angle (p = 0.011). A higher relative articular height (OR: 0.608, 95% CI: 0.205–0.998, and p = 0.017) and lower declination angle (OR: 0.632, 95% CI: 0.601–0.887, and p =0.019) were found to be associated with an increased likelihood of having a valgus FTA rather than a varus FTA. Conclusion: our results indicate that PTFJ morphology is associated with knee alignment.
Expansion of direct laser writing (DLW) capabilities for usage in biomedical applications

Direct laser writing (DLW) based on the femtosecond (fs) pulse-induced light-matter interaction expanded considerably during the last decades. The key advantage of using fs lasers for DLW is the possibility to exploit various nonlinear light-matter interaction regimes as well as control the thermal aspect of the process. This work is dedicated to exploring the capabilities of expanding DLW in several possible biomedical application areas where fs lasers could yield a very attractive, high throughput solution. Namely, we will be discussing how hybrid additive-subtractive DLW can be exploited for the high-throughput fabrication of integrated microfluidic systems. Furthermore, a mechanical flexible scaffold will be presented. Finally, a possibility to produce very high precision metalized 3D structures by using pre-existing high-throughput multi-photon polymerization capabilities will be shown. In all cases, attention will be placed on the unique capabilities of fs-lasers in DLW as well as practical considerations of the processes and their up-scaling.
Characterization of Silver Nanoparticles on Porous silicon by Laser ablation in Liquid

In this article, Porous silicon was prepared using a photo-electrochemical etching method of n-type silicon at etching times (5 and 15 min.). The silver nanoparticles were coated on porous silicon by laser ablation in liquid with energy 400, 600, and 800 mJ. Atomic force microscope images showed an increase of pores size with etching time and it becomes smaller in diameter after laser ablation. Moreover, Scanning electron microscope displayed an aggregates formation when increasing laser energy. The strong peak of absorption of AgNPs found around 415-420 nm. The absorption amplitude increased when laser energy increases. Therefore, the nanostructures on the porous silicon properties play a crucial role in the formation of the homogeneity and this improves the structural stability of PSi layer.
A Review of Different Approaches for Detecting Emotion from Text

Emotion detection and analysis is one of the challenging and emerging issues in the field of natural language processing (NLP). Detecting an individual’s emotional state from textual data is an active area of study, along with identifying emotions from facial and audio records. The study of emotions can benefit from many applications in various fields, including neuroscience, data mining, psychology, human-computer interaction, e-learning, information filtering systems and cognitive science. The rich source of text available in the Social media, blogs, customer review, news articles can be a useful resource to explore various insights in text mining, including emotions. The purpose of this study is to provide a survey of existing approaches, models, datasets, lexicons, metrics and their limitations in the detection of emotions from the text useful for researchers in carrying out emotion detection activities.
Getting Students Back on Track: Persistent Effects of Flipping Accelerated Organic Chemistry on Student Achievement, Study Strategies, and Perceptions of Instruction

Converting a first-term, accelerated summer organic chemistry course to a flipped format reduced the achievement gap in the flipped course and in the second-term traditional lecture course between Non-Repeaters taking an accelerated course to “get ahead” and Repeaters taking the course to “get back on track. ” The difference in final exam performance in the second-term course was nearly halved, the GPA gap in both courses was reduced, and the gap in passing rate for the second-term course was eliminated. First-generation students who took the first-term course in the flipped format experienced a final exam score boost in the second-term course regardless of repeater status. While most students responded positively to the flipped course structure, repeating students held a stronger preference for the flipped format. These findings provide guidance on how to create courses that promote equity, access and retention of diverse students in STEM.
Impact of PRE- and POST herbicide on purple nut sedge (Cyperus rotundus L.) control and plasticulture tomato yields

Purple nut sedge ( Cyperus rotundus L.) is a problematic weed in plasticulture tomato ( Solanum lycopersicum L.) production in Iran as it is difficult to control due to its ability to penetrate plastic mulch. Field trials were carried out at the experimental area of the state farm Safadasht, Shahriar region, Iran, over the fall of 2015 and spring of 2016 to investigate PRE- and POST herbicide programs (PRE transplanting, POST transplanting, and combination of PRE- and POST transplanting) to control purple nut sedge in plasticulture tomato production. PRE herbicide treatment metribuzin and ( S )-metolachlor were not effective when applied alone, and did not reduce purple nut sedge plants compared to the weedy control. POST transplanting application of halosulfuron did not significantly affect purple nut sedge plants at 12 wk after application (WAA) in the fall 2015, but treatment favorably reduced density of purple nut sedge at 16 WAA in both seasons. PRE transplanting application of metribuzin or ( S )-metolachlor plus POST halosulfuron exerted the greatest control, and purple nut sedge numbers were eventually diminished in both seasons. Experimental treatments did not negatively affect tomato height and yields. Based on our findings, multiple PRE and POST herbicide programs were effective in greater purple nut sedge suppression compared to the lone application of PRE and POST herbicides. The herbicides were selected due to routine use by tomato producers in the area. Using active ingredients from the various herbicide families with different modes of action could facilitate effective management of herbicide-resistant purple nut sedge in tomato fields.
Influence of Buoyant Forces on Magnetohydrodynamics (MHD) Blood Flow with an Interaction of Thermal Radiation

Received: 25 October 2019 Accepted: 2 December 2020 In this study, the heat and mass transfer of the blood flow, particularly in a capillary tube having a porous lumen and permeable wall in the presence of external magnetic field are considered. The velocity, temperature and concentration of blood flow become unsteady due to the time dependence of the stretching velocity, surface temperature and surface concentration. The thermal and mass buoyancy effect on blood flow, heat transfer and mass transfer are taken into account in the presence of thermal radiation. This analysis is very much useful in the treatment of cardiovascular disorders. The equations governing the flow under some assumptions are complex in nature, but capable of presenting the realistic model of blood flow using the theory of boundary layer approximation and similarity transformation. First, the system of coupled partial differential equations (PDEs) is converted into a system of coupled ordinary differential equations (ODEs). Then the solutions are obtained by Runge-Kutta method of 4thorder with shooting technique. The effects of various parameters such as Hartman number, radiation parameter, unsteadiness parameter, permeable parameter, thermal buoyancy parameter, Prandtl number, mass buoyancy parameter, velocity slip parameter, thermal slip parameter, Schmidt number on velocity, temperature, concentration, skin friction, Nusselt number and Sherwood number are depicted through graphs. Local Sherwood number enhances because of increase in Schmidt number. Moreover, some of the important results, which are discussed in the present study and have an impact on diseases like hyperthermia, stoke and moyamoya in human body.
An iterative algorithm for artifacts removal in Fourier-domain optical coherence tomography

FD-OCT is a widely used technology which could provide high-resolution 3D reconstruction images. Conventional OCT uses IDFT reconstruction method, which could obtain an FFT-limited axial resolution. Recently, several optimization-based methods have reportedly achieved a resolution improvement, while undesired noise artifacts might appear in their result, which could degrade the image quality. In this work, we proposed an iterative error reduction method in order to remove the artifacts as well as improve the resolution. A numerical simulation is designed to validate our algorithm. Two reconstruction methods including IDFT reconstruction, and l1 minimization are selected for a comparative study. Specifically, we conduct the simulation at four different noise levels. The result shows that our proposed method could greatly suppress the artifact and obtain a great reconstruction even when the SNR is reduced.
Inherent Size Effects on XANES of Nanometer Metal Clusters: Size-Selected Platinum Clusters on Silica

X-ray absorption near-edge structure (XANES) is commonly used to probe the oxidation state of metal-containing nanomaterials; however, as the particle size in the material drops below a few nanometers, it becomes important to consider inherent size effects on the electronic structure of the materials. In this paper, we analyze a series of size-selected Ptn/SiO2 samples, using X-ray photoelectron spectroscopy (XPS), low energy ion scattering, grazing-incidence small-angle X-ray scattering, and XANES. The oxidation state and morphology are characterized both as-deposited in UHV, and after air/O2 exposure and annealing in H2. The clusters are found to be stable during deposition and upon air exposure, but sinter if heated above ∼150 °C. XANES shows shifts in the Pt L3 edge, relative to bulk Pt, that increase with decreasing cluster size, and the cluster samples show high white line intensity. Reference to bulk standards would suggest that the clusters are oxidized; however, XPS shows that they are not. Inste.
Assessment of the Upstream Slope Stability of Darbandikhan Rockfill Dam during Drawdown

Earth and rockfill dams face a variety of loading conditions during lifetime. One of the most critical loading conditions is the rapid drawdown of water level after steady state conditions. Rapid drawdown may cause instability of upstream slope of the dam. The present work examines the stability of a rockfill dam under different drawdown rates in terms of factor of safety for the upstream slope of the dam. For this purpose, a computer software named GeoStudio 2012 SEEP/W and SLOPE/W has been used for the numerical analysis. The results showed that the drawdown rate has a significant effect on stability of rockfill dam in which increasing the drawdown rate from 1 m/day to 10 m/day decreases the stability of the dam by 33%. Based on the outcomes, for the studied case the drawdown rate (1 m/day) can be recommended.
Electric-Field-Induced Feshbach Resonances in Ultracold Alkali Metal Mixtures

We present a detailed analysis of Feshbach resonances in ultracold collisions of Li and Cs atoms in the presence of superimposed electric and magnetic fields. We show that electric fields induce resonances through couplings between the $s$- and $p$-wave scattering channels and modify the scattering length to a great extent. Electric-field-induced resonances lead to the anisotropy of ultracold scattering and provide the diagnostics for magnetic $p$-wave resonances in ultracold gases. We show that the electric field couplings may shift the positions of $s$-wave magnetic resonances, thereby making the electric field control of ultracold atoms possible even far away from $p$-wave resonances. Finally, we demonstrate that electric fields may rotate and spin up the collision complex of ultracold atoms at substantial rates.
Analyzing Purchase Decisions Using Dynamic Location Data

Retailers’ efforts to monetize consumer location data remain dominated by inefficient protocols (e.g., geofencing) that customize marketing interactions based solely on app users’ current location. Although extant trajectory mining techniques can remedy these shortcomings, they require high-frequency location data, which poses severe risks to consumers’ privacy. The authors present a novel method to extract marketing value from low-granularity urban mobility data and demonstrate its use in analyzing gas station choice to value customers. The data, also used to infer gas station visits, contain 1.06 million location records on nearly 27,000 devices observed near selected retailers including gas stations during a six-month period in Staten Island, New York. The authors pool consumers’ mobility trajectories from several days to dynamically calculate the distance of stores from consumers’ anticipated trajectories. They then supplement the data with station-level daily fuel prices and estimate a conditional logit model to assess how consumers trade off gas prices versus store distance. In addition to a generally high station loyalty, the authors find that consumers strongly prefer not to deviate far from their common trajectories for fueling trips. Applying their methods in a predictive context, the authors infer the value of newly acquired customers to the studied gas stations to be between $3.00 and $7.59.
A ML framework to predict permeability of highly porous media based on PSD

Using machine learning (ML) method to predict permeability of porous media has shown great potential in recent years. A current problem is the lack of effective models to account for highly porous media with dilated pores. This study includes (1) generation of media (porosity = 0.8) via a Boolean process, (2) the pore size distribution (PSD) control by using different groups of homogeneous packed spherical particles (3) PSD data obtainment using the spherical contact distribution model (4) computation of the permeability via LBM simulations, (4) training of artificial neuron network (ANN) and (5) analysis of the model. It is found that the PSD could outperform the previous geometry descriptors as an input of ML framework to deal with highly porous structures with different fractions of dilated pores, however there is still room for precision enhancement.
Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed

Forecasting wind speed is one of the most important and challenging problems in the wind power prediction for electricity generation. Long short-term memory was used as a solution to short-term memory to address the problem of the disappearance or explosion of gradient information during the training process experienced by the recurrent neural network (RNN) when used to study time series. In this study, this problem is addressed by proposing a prediction model based on long short-term memory and a deep neural network developed to forecast the wind speed values of multiple time steps in the future. The weather database in Halifax, Canada was used as a source for two series of wind speeds per hour. Two different seasons spring (March 2015) and summer (July 2015) were used for training and testing the forecasting model. The results showed that the use of the proposed model can effectively improve the accuracy of wind speed prediction.