Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology and Effect of Controlled-Release Fertilizer through Physical-Biochemical Composite Approaches

For breaking the conventional simple approach to produce controlled-release fertilizer,the feasibility of physical-biochemical composite approaches used in manufacturing coated controlled-release fertilizer was studied.The controlled release characteristics,controlled release mechanism and fertilizer effect of coated urea PAU,with ammonium oxidization lignin(AOL) as coating material which could restrain both of urea and nitrification,was studied compared with coated urea PRU.The PAU's initial dissolution rate and the differential coefficient dissolution rate in water were higher than that of the PRU.The dynamic experiment on the change of the dissolved N translation in soil from the two coated urea showed that AOL dissolved from the coat obviously restrained the hydrolyzation of the urea and the translation of NH4+-N in soil.The NH4+-N and NO3——N contents in soil with PAU were lower during cultivation.The pot experiment with maize showed that compared with common urea the two kinds of coated urea could promote the growth of the plants and raised the N use efficiency under equate N condition.The total biomass of two crops of maize with PAU increased by 15.35% compared with PRU,and the N use efficiency of the first crop raised by 13.6%. Analysis of remained mineralized N in soil after reaping the maize showed that the NH4+-N in soil remained mineralized N with PAU was much higher than that with PRU,and the NO3——N was reverse.It was a new technical approach to improve controlled effect and reduce costs of operation that the physical and biochemical approaches used in producing coated controlled-release fertilizer.
Performance of rice cultivars for yield and yield attributes at twelve different dates of sowing

A field investigation was conducted to evaluate the effect of different sowing dates on yield and yield components of six rice cultivars during 2018-2019 at Indian Institute of Rice Research, Rajendranagar, Hyderabad. Experiment constituted of twelve sowing dates i.e. from 20th June 2018 to 01st Feb 2019 with an interval of 20 days between each sowing. Data on morphology, yield attributes and yield of six rice genotypes were recorded and statistically analysed by split plot design (main plot- cultivars, subplot- dates of sowing). Significant interaction studies between cultivars and dates of sowing revealed cv. JGL 3855 to perform best for grain yield over other cultivars. The yield contributing parameters productive tiller number m-2, panicle weight, grain number per panicle and spikelet fertility attributed for the high yielding feature in cv. JGL 3855. Further, interaction studies indicated that sowing date D1 (20th Jun’2018) was best suitable for all the cultivars except WGL 24071 to attain maximum grain yield. WGL 24071 performed best (7556 kg ha-1) on D8 (20th Nov’2018) sowing indicating that this cultivar is well suited for Rabi than Kharif season. D1 (20th Jun’2018) sowing also recorded maximum plant height, number of productive (panicle bearing) tillers, panicle length, panicle weight, number of grains per panicle, 1000-grain weight, filled grain per m2 and spikelet fertility %.
Loan default prediction using decision trees and random forest: A comparative study

With the improving banking sector in recent times and the increasing trend of taking loans, a large population applies for bank loans. But one of the major problem banking sectors face in this ever-changing economy is the increasing rate of loan defaults, and the banking authorities are finding it more difficult to correctly assess loan requests and tackle the risks of people defaulting on loans. The two most critical questions in the banking industry are (i) How risky is the borrower? and (ii) Given the borrower’s risk, should we lend him/her? In light of the given problems, this paper proposes two machine learning models to predict whether an individual should be given a loan by assessing certain attributes and therefore help the banking authorities by easing their process of selecting suitable people from a given list of candidates who applied for a loan. This paper does a comprehensive and comparative analysis between two algorithms (i) Random Forest, and (ii) Decision Trees. Both the algorithms have been used on the same dataset and the conclusions have been made with results showing that the Random Forest algorithm outperformed the Decision Tree algorithm with much higher accuracy.
Detailed theoretical investigation on ESIPT process of pigment yellow 101

Based on density functional theory (DFT) and time-dependent density functional theory (TDDFT) methods, the detailed excited state intramolecular proton transfer (ESIPT) mechanism of 2,2′-dihydroxy-1,1′-naphthalazine (P.Y. 101) has been investigated theoretically. Unlike previous theoretical investigation of P.Y. 101, our calculated results not only reproduce the absorption and fluorescence spectra reported in the previous experiment, but also were completed with considering solvent effect. It further demonstrates that the TDDFT theory we adopted is very reasonable and effective. The calculations of main bond lengths and bond angles involving in the hydrogen bondings (O1–H2⋯N3 and O4–H5⋯N6) as well as the infrared vibrational spectra and as well as the calculated hydrogen bonding energies demonstrated the intramolecular hydrogen bond was strengthened in the S1 state. In addition, qualitative and quantitative intramolecular charge transfer based on the frontier molecular orbitals provided the possibility of the ESIPT reaction. The potential energy surfaces of ground state and the first excited state have been constructed to illustrate the ESIPT mechanism. Based on our calculations, the equilibrium ESIPT process exists in the S1 state. And after the radiative transition, reversed GSIPT can also occur in the S0 state.
A Browser-based Cross Site Request Forgery Detection Model

CSRF is one of the most serious cyber attacks, and has been recognized as the main threat and one of the ten most serious vulnerabilities in web applications. The CSRF attack is when the attacker uses the implicit authentication mechanism of the HTTP protocol and the cookies cached in the browser to pass the authentication, and then executes the attack on the target website. In this article, we propose a browser-based CSRF detection model, which can detect CSRF attacks by analysing HTTP requests and web page content, thereby identifying the attacker’s CSRF attacks.
Identification of Sildenafil Analog Illegally Added in Dietary Supplement using HPLC and LC-MS/MS

OBJECTIVE To establish a method based on mass spectrometry structural assignments for identification of sildenafil analog added illegally in herbal product.METHODS A high-performance liquid chromatography-diode array detection(HPLC-DAD) method and a liquid chromatography-electrospray ionization tandem mass spectrometry(LC-ESI-MS/MS) method were applied.RESULTS Identification of hydroxyhomosildenafil,an analog of sildenafil was achieved by comparing UV spectrum and mass fragmentation results of the samples with those of sildenafil.Three batches of Suoyang Gujing Wan,a dietary supplement,were found containing hydroxyhomosildenafil.CONCLUSION Based on structural assignments of mass fragments,analog of sildenafil could be rapidly and accurately determined in herbal product.
Cambricon-G: A Polyvalent Energy-Efficient Accelerator for Dynamic Graph Neural Networks

Graph neural networks (GNNs), which extend traditional neural networks for processing graph-structured data, have been widely used in many fields. The GNN computation mainly consists of the <italic>edge processing</italic> to generate messages by combining the edge/vertex features and the <italic>vertex processing</italic> to update the vertex features with aggregated messages. In addition to nontrivial vector operations in the edge processing, huge random accesses and neural network operations in the vertex processing, the graph topology of GNNs may also vary during the computation (i.e., dynamic GNNs). The above characteristics pose significant challenges on existing architectures. In this article, we propose a novel accelerator named CAMBRICON-G for efficient processing of both dynamic and static GNNs. The key of CAMBRICON-G is to abstract the irregular computation of a broad range of GNN variants to the process of regularly tiled <italic>adjacent cuboid</italic> (which extends the traditional adjacent matrix of graph by adding the dimension of vertex features). The intuition is that the adjacent cuboid facilitates exploitation of both data locality and parallelism by offering <italic>multidimensional multilevel tiling</italic> (including spatial and temporal tiling) opportunities. To perform the <italic>multidimensional spatial tiling</italic>, the CAMBRICON-G architecture mainly consists of the cuboid engine (CE) and hybrid on-chip memory. The CE has multiple vertex processing units (VPUs) working in a coordinated manner to efficiently process the sparse data and dynamically update the graph topology with dedicated instructions. The hybrid on-chip memory contains the topology-aware cache and multiple scratchpad memory to reduce off-chip memory access. To perform the <italic>multidimensional temporal tiling</italic>, an easy-to-use programming model is provided to flexibly explore different tiling options for large graphs. Experimental results show that compared against Nvidia P100 GPU, the performance and energy efficiency can be improved by <inline-formula> <tex-math notation="LaTeX">$7.14\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$20.18\times $ </tex-math></inline-formula>, respectively, on various GNNs, which validates both the versatility and energy efficiency of CAMBRICON-G.
Simulation and optimization of mineralization of urine by electrooxidation process using artificial neural network and genetic algorithm

Urine can be considered as a special type of effluent; it has a complex composition and high organic load. In recent years the need for improvements in the treatment of human waste has increased, for this reason, electrochemical oxidation is a practical and versatile technique to estimate urine degradation and aid in traditional methods of domestic wastewater treatment. This work aims to use artificial neural networks (ANN) to model urine mineralization kinetics through the electrochemical oxidation process under the influence of the main parameters such as current density (20, 60 and 100 mA cm–2), initial concentration of the dissolved organic carbon (DOC) (1.75, 12.22 and 22.7 g L–1) and reaction time (0 to 480 min). A three-layer neural network with 9 neurons was used in the hidden layer, where a score of mean squared error = 0.0021 and mean absolute error = 0.0345 was obtained. The coupled ANN model and the genetic algorithm was used to find the best operational conditions: Percentage of normalized DOC (above 90%) at a current density of 89 mA cm–2, [DOC]0 of approximately 2.35 g L–1 and reaction time of 3.9 h.
Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review

Surface EMG-based gestures recognition systems are helping the disable to enjoy a better life. Academic institutes and commercial companies have been developing a lot of sEMG-based prosthesis, exoskeletons and rehabilitation systems. In this area, most studies are improving recognition accuracies by focusing on signals pre-processings, features and classifiers. But, in real-life circumstances, sEMG-based recognition performances are limited by many disturbances and variability from electrodes and subjects. In this paper, key advances of sEMG-based recognition systems are firs reviewed, then several disturbances or factors that limited sEMG-based performances and their solutions are summarized up in great detail. At last, prospections for improving sEMG-based robustness in the future are proposed.
DESIGN OF A CONTROLLABLE ANTENNA BASED ON EMBEDDED DIFFERENTIAL PSK MODULATION

Direct Antenna Modulation (DAM) is explored recently in many wireless communication systems. In this paper, we explore the modulation process of electromagnetic signals in the antenna circuit design directly. The proposed antenna consists of two non-concentric elliptical patches for broadband applications to suit the spread spectrum applications. To perform a Differential Phase Shift Keying (DPSK) modulation, two identical antennas are fed by a two-branch microstrip line with a phase shift. Utilizing Computer Simulation Technology of Microwave Studio (CSTMWS) based on Finite Integral Technique (FIT), an optimization based-on numerical analysis is adopted for designing the transmission line configuration at the desired frequency bands. The other significant aspect that has been achieved in this research is reducing the patch size to be suitable for wearable devices. Therefore, a cylindrical substrate is utilized for bending the proposed antenna structure. The proposed antenna design shows a gain of 4.73 dBi and 2.5 dBi for the planar and folded antenna profile, respectively. Two high-speed Positive Intrinsic Negative (PIN) diodes as switching elements of the RF signal are inserted between the identical antenna elements through a transmission line. Switch 1 (SW1) and switch 2 (SW2) are used to control the phase shift between the antenna elements by changing the switching state from (OFF-ON) and vice versa. The designed antenna is further investigated to realize the effects of radiation leakage from the antenna elements on the human body in the context of wearable applications. This study is conducted to the antenna performance when it is bent on a cylinder and compared to the flat case on four human body regions: arm, head, thigh, and chest. The proposed antenna based on PIN diodes is fabricated, measured, and tested. Using a 3D axis field strength meter, the proposed antenna system field strength is measured for different conditions at various locations of the human body. Finally, an excellent agreement is found between the obtained numerical results and measurements.
Characterization of Dissolved Organic Matter in Lake Baiyangdian Using Spectroscopic Techniques and Multivariate Statistical Analysis

Dissolved organic matter (DOM) collected from the surface and overlying waters in different functional regions in Lake Baiyangdian were analyzed by spectroscopic techniques combined with principal component analysis (PCA) and hierarchical cluster analysis (HCA), to interpret water quality, and sources of pollutants. The results showed that the deterioration of water quality has intensified at river estuary and aquaculture area. Meanwhile, these regions dominated by plants had relatively low dissolved oxygen concentrations. UV-vis spectra showed lower humification degree and simpler molecular structure in the DOM collected from the pollutant-receiving region. The analysis of PCA combined with synchronous fluorescence spectra yielded two principal components (PCs) that account for 99.44% of the variance. PC1 showed a dominant protein-like fluorophore at 279 nm and PC2 was characterized by dominated fulvic-like fluorophores (325 and 331 nm). The results of PCA indicated that DOM collected from the river estuary, the aquaculture region, and the eutrophication area exhibited a higher protein-like fluorophore, and higher PC1 and lower PC2 loadings (PC1 > 0.7, PC2 0.7). HCA identified four clusters that accorded with the PCA results. This study suggested that water quality and the sources, and spectral characteristics of DOM were influenced mainly by human activities (agriculture, industrial, and domestic wastewater from upstream, food and excrement from aquaculture) and natural processes (aquatic-plant and microorganism activities).
Assessing the effect of hook style on size selectivity and catch rate of the European eel Anguilla anguilla in Bardawil Lagoon, Eastern Mediterranean, Egypt

The European eel Anguilla anguilla (L.) is distributed and exploited in fresh, brackish and coastal waters in almost all of Europe and along the Mediterranean coasts of Africa and Asia (Moriarty and Dekker 1997). European eel is a commercially and recreationally important fishery target species. In the last decades, the eel has experienced dramatic stock declines and has been listed as critically endangered. To reduce fishing mortality, several European countries have closed the fishery or introduced stricter management measures which increase the likelihood of catch-and-release in the recreational fishery (Weltersbach et al., 2016). ARTICLE INFO ABSTRACT Article History: Received: Jan. 30, 2021 Accepted: Feb. 19, 2021 Online: Feb. 22, 2021 _______________
Can Digital Technology Bridge the Classroom Engagement Gap? Findings from a Qualitative Study of K-8 Classrooms in 10 Ontario School Boards

This study examined impacts of digital technology on a key component of the socioeconomic gap in education—gaps in student classroom engagement. Whereas print literacy has long been a source of such gaps, newer “digital divide” theories claim classrooms that use digital technology are perpetuating them further. However, these claims are not grounded in close empirical observation and may now already be dated. We aimed to advance understandings of the impact of digital technology on student engagement by examining robotics, tablets, and smart board usage across a range of classrooms, using a conceptual framework that blends theories of interaction ritual chains (IRC) and cultural capital (CC). Data came from observations and interviews with teachers and students in K-8 classrooms across 10 Ontario school boards. We report three major findings. First, almost all students across socioeconomic strata engaged easily and enthusiastically with digital technology. Second, technology spawned new classroom rituals and cultural valuations. Third, digital technology provided connections between school dictates and students’ peer-based and home lives. We argue that digital technology has the potential to narrow classroom engagement gaps that are generated by conventional print media. We end by discussing avenues for future research.
Moroccan immigrant mothers’ experiences of Italian preschool institutions. A mixed-method study

ABSTRACT Being an immigrant mother demands both the redefinition of one’s identity as a woman and as a mother and a ‘double cultural mediation’ in children’s upbringing, between the culture of origin and that of the host country. Early Childhood Education and Care (ECEC) are key settings to supporting the wellbeing and the integration of immigrants. This contribution presents research conducted in Italy within the international ISOTIS project (www.isotis.org). Drawing on 114 structured interviews and 12 narrative-biographical interviews, this paper analyses how Moroccan mothers described their relationship with the ECEC system, teachers, and other parents. While survey data indicated that mothers who perceived discrimination were likely to participate less, the qualitative interviews showed that the ECEC settings were generally supportive and non-discriminatory. The ECEC services’ support resulted to be a turning point in the educational and social path of children and mothers themselves. The relationship with the teacher was depicted mostly as positive and meaningful, though the Italian parents’ network seemed harder to join, unveiling a segmented experience of social inclusion and exclusion between in and out of the school context. The findings may contribute to identifying factors facilitating or hindering immigrant parents’ full inclusion and participation in the community life.
Hawksbill sea turtle life stage durations, somatic growth patterns, and age at maturation

: Sea turtles exhibit complex life histories, encompassing intermittent use of multiple spatially separated habitats throughout long lifespans. This broad scope presents challenges for collecting comprehensive biological and ecological data, yet absence of such information compli-cates evaluation of management strategies for populations at risk of extinction. Hawksbill sea turtles Eretmochelys imbricata are endangered worldwide, primarily due to long-term, directed harvest. However, available information regarding life stage durations, somatic growth patterns, and maturation attributes to enhance understanding of anthropogenic impacts and recovery potential remains constrained. To address these data gaps in the western North Atlantic, we conducted skeletochronological analysis for hawksbills stranded along US coastlines to generate straight-line carapace length (SCL)-at-age and somatic growth data. Generalized additive mixed models and bootstrapped von Bertalanffy growth curves were used to characterize age at maturation and covariate influence on somatic growth. For a subset of turtles, annual bone growth increment-specific stable isotope and trace element analyses were incorporated to evaluate habitat use relative to age. Integration of these data sources indicated that juveniles transitioned from oceanic to neritic habitat at 1−3 yr old and mean SCLs of 23−24 cm (range 15.7−35.0 cm). Initial ages at maturation for this population at minimum nesting female SCLs were estimated at 15−25 yr. Somatic growth varied significantly relative to size, age, and stranding location, while no association with sex or calendar year was observed. Our results demonstrate the utility of these complementary analytical approaches for generating baseline data fundamental to characterizing hawksbill sea turtle population attributes.
Chromium(VI) removal from aqueous medium by maize cane and agave bagasse biomasses

ABSTRACT Chromium(VI) is a major water pollutant from industrial effluents whose concentration is to be reduced within the permissible limits. In this paper, the use of maize cane and agave bagasse as inexpensive adsorbent for the removal of Cr(VI) from aqueous solutions is considered. In the first place, they were conditioned and characterized to determine their physicochemical properties with scanning electron microscopy; the elemental composition with energy-dispersive x-ray spectroscopy (EDS); the principal functional groups with Fourier transform infrared spectroscopy; thermal stability with thermogravimetric analysis; and surface properties by specific surface hydration kinetics, point of zero charge determination by mass titration, and active site density are described. In the second place, their adsorption properties for Cr(VI) from aqueous solution were evaluated in a batch system. The kinetics of Cr(VI) sorption with both natural adsorbents were described with a pseudo-second-order model. Equilibrium data were found to be best represented for both adsorbents by the Freundlich isotherm model. Maize cane and agave bagasse can be effectively used as an adsorbents for the removal of Cr(VI) ions from the aqueous solution.
Automatic Deep Neural Network Hyper-Parameter Optimization for Maize Disease Detection

Deep Convolutional Neural Networks (DCNNs) have proved to be very useful for image classification. These need to be optimized for the dataset in hand, for its optimal use. DCNNs have many attached hyper parameters. These hyper-parameters are fine-tuned for optimizing the DCNN model. The present research uses the Bayesian Optimization technique for fine-tuning the hyper-parameters for AlexNet DCNN model. The model is trained and tested on Maize (corn) disease sub-dataset of the Plant Village dataset. The trained DCNN model with optimized parameters achieved the accuracy of 96.05%.