Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of sorghum hybrids for silage production in the semiarid (BSh) region of northeastern Brazil

The objective of this study was to identify the hybrids of sorghum most suitable for the production of silage in the semiarid regions of northeastern Brazil. Twenty-three hybrids were evaluated in a randomized block design with three replicates. The dry mass yield; dry mass of leaf, stem, panicle, grains, and senescent matter; and chemical composition of the plant were evaluated. The pH, ammoniacal nitrogen (N-NH3), and chemical composition of the silage were also assessed. The hybrids 947252, FEPAGRO 18, 12F042226, and SF 11 performed best in terms of morphological characteristics, with the highest mass of leaves and grains. The hybrid FEPAGRO 19 had the highest dry matter value (443.3 ± 17.0 g kg-1), whereas the hybrid 12F042496 had the lowest value (271.3 ± 17.0 g kg-1). The dry matter (DM) content of the other hybrids was within the recommended values for silage production (280 to 400 g kg-1). High levels of crude protein were observed in the grouped hybrids 12F042496, 947254, and 12F042422. The hybrid 12F042496 had the highest neutral detergent fiber content. The hybrids 9929036, 9929030, 9929012, 947254, 947072, PROG 134 IPA, 12F042226, 12F042422, and BRS 506 had pH values ranging between 3.8 and 4.2, indicating good quality silage. Overall, the sorghum hybrids 9929030, 12F042224, 12F042150, FEPAGRO19, and FEPAGRO11 had the best growth and production characteristics, and plant and silage chemical composition, and are thus recommended for the production of silage in the semiarid of northeastern Brazil.
An Adaptive PI Controller Design for DC-Link Voltage Control of Single-Phase Grid-Connected Converters

Conventionally, standard proportional and integral (PI) controllers with constant PI gains are commonly used for the dc-link voltage control of single-phase grid-connected converters (GCCs). For such controllers, the selection of the PI gains will lead to a tradeoff between two control objectives: 1) the reduction of the dc-link voltage fluctuations caused by random swings of the active power drawn by the single-phase GCC; and 2) the reduction of the grid current harmonics mainly caused by the 2f oscillation of the active power in single-phase applications. To solve this tradeoff, this paper presents a systematic approach for the design of an adaptive PI controller for the dc-link voltage control of single-phase GCCs. The proposed design approach is simple and it provides a convenient method to properly determine the adaptive PI controller parameters. Representative simulation and experimental results are presented and discussed in order to show the effectiveness of the proposed dc-link voltage controller.
A novel iterative observer approach for real‐time harmonic estimation in power distribution networks

In power distribution networks, the realisation of online harmonic state estimation puts forward a higher demand on the algorithm accuracy, stability as well as simplicity. To meet the requirements above, a novel iterative observer based method for harmonic state estimation is proposed. This method guarantees the stability of the harmonic estimation observer when output time delays are considered in the power distribution network. Meanwhile, signals with different frequencies can be estimated iteratively. System harmonics are regarded as the unknown exogenous disturbance, and each harmonic at multiple measurements is extracted and estimated iteratively. To guarantee the overall estimator stability, multiple transmission time delay is addressed in the system modelling for the design of observer gain. The proposed method achieves high estimation accuracy from multiple measurements while maintaining a low computation commitment. Comparing with traditional harmonic state estimation methods, the iterative observer is capable of estimating the unexpected harmonics. The IEEE 13-bus power distribution system built in the real-time digital simulator demonstrates the validity of the proposed method.
Illegal Bird Hunting in Eastern Spain: A Declining Trend, But Still Worrying

The illegal killing of many threatened species is an ongoing conservation concern, especially in the Mediterranean region. This study provides quantitative data on non-game birds admitted with gunshot injuries to wildlife rehabilitation centres (WRC) of the Valencian Community (eastern Spain), over a 25-year period (1991-2015). A total of 2,076 shot non-game birds of 101 different species were admitted, including 112 individuals belonging to 17 threatened species. Raptors were the most affected group, comprising 74% of the total admissions. The number of yearly admissions was positively related to the number of issued hunting licences, and both declined over the study period. Evidently, illegal hunting occurred throughout the year, although it peaked during the official hunting season. The occurrence of birds admitted with gunshot wounds was higher in more densely populated municipalities and in those closer to WRCs. Our results show that illegal hunting of protected bird species prevails despite legislative efforts, and it could pose a major problem for some endangered species. Accordingly, it is necessary to focus current action to reduce illegal hunting in conflictive areas. —Crespo, J., Solis, I. & Barba, E. (2021). Illegal bird hunting in eastern Spain: a declining trend, but still worrying. Ardeola, 68: 181-192.
A Novel Approach to Coral Fish Detection And Classification in Underwater Footage Based on Convolutional Neural Network

Tracking and identifying fish species is crucial to understanding marine ecosystem and its role in the world. In this paper, a cost-effective coral fish detection and identification method is proposed. Using the up-to-date models of Convolutional Neural Network (CNN), this paper is able to analyze an underwater footage and highlight all the detectable fish in color frames, and then identifying the species name among the detectable fish. Fish object detection was employed using Open Images Dataset and Tensorflow Object Detection. The paper further explores CNN with squeeze and excitation for fish classification. The proposed model was evaluated on fish4-knowledge dataset and achieved 100% validation accuracy after 50 epochs, better than AlexNet and RetNet, indicating that the solution is robust and practical. In addition, a dataset for coral fish classification in specific location was built using different sources. The model achieved 100% validation accuracy on the proposed dataset.
Maximum entropy modeling for the conservation of Hopea odorata in riparian forests, central Thailand

Asanok L, Kamyo T, Marod D. 2020. Maximum entropy modeling for the conservation of Hopea odorata in riparian forests, central Thailand. Biodiversitas 21: 4663-4670. Hopea odorata plays a dominant role in both ecologically and economically in Thailand. We analyzed potentially suitable areas for H. odorata in the riparian zone of the Chao Phraya River using the software MaxEnt. Modeling included 164 occurrence records along with 19 climate-related variables, slope, aspect, and elevation. Precipitation was the key climatic variable influencing the distribution of H. odorata. Riparian areas along the Chao Phraya that were predicted to be highly suitable for this species were located in the provinces of Nakhon Sawan, Ang Thong, Phra Nakhon Si Ayutthaya, Pathum Thani, Nonthaburi, and Bangkok. The ROC AUC score was 0.891, indicating that MaxEnt is an excellent tool for predicting suitable regions for the restoration or cultivation of commercially and ecologically valuable species such as H. odorata. Models such as what we have presented here can facilitate habitat conservation and sustainable resource use for rare and important plants.
Taxes for the People or for the Government? A Global Governance Perspective

The paper investigates the determinants of taxes collected net of public services for a sample of 104 countries from all over the world over 1996-2016 period. Starting from the assumption that what really matters for the taxpayer is the tax money that actually return to the society, after paying the cost of government, we introduce the concept of taxes collected net of public services as the difference between taxes to GDP ratio and general public services expenditures to the GDP ratio and investigates its determinants from governance perspective. The main determinants looked upon were worldwide governance indicators (WGI): control of corruption, government effectiveness, political stability, regulatory quality, rule of law and voice and accountability, taken from World Bank surveys. As control variable we used GDP per capita annual growth rate. We include year fixed effects in order to control for global economic crisis, and also country fixed effects to control for unobservable country-level factors that are constant over the sample period. The analysis follows the World Bank country classification on level of development: low income, lower middle-, higher middle income and high-income countries. The results show that the rule of law governance characteristic affected the taxes net of public services the most, both in terms of economic effect and of countries coverage, while the control of corruption and regulatory quality were found significant only for lower-middle income countries and low-income countries respectively. Policy recommendations were made accordingly.
Prospective elementary teachers’ perspectives on online mathematics learning during coronavirus outbreak

Coronavirus disease (COVID-19) outbreaks have changed learning models from face-to-face to online learning. This change has an impact on students' readiness in participating in learning. To deal with this situation, this study aims to investigate prospective elementary teachers’ perspective on online mathematics learning. Thirty-eight prospective elementary teachers from a public teacher institution in Pekanbaru, Riau, Indonesia, participated in this study. The informants were asked to answer an open question at the end of the course related to their views on online mathematics learning during COVID-19 pandemic. The findings showed that the online course was less effective due to poor internet connection and limited internet quota. Those problems affected their performance on understanding the mathematics contents, interacting with the teacher and other students, and doing assignments. However, few prospective teachers provided positive attitude toward online mathematics learning. An implication for this study was the government must intervene and provide support for online learning success.
The Impact of Equity Incentives on the Accuracy of Analyst’s Earning Forecasts

This study use a sample which consists of 88 observations of Egyptian listed firms, from the period of 2009 – 2019, this research examine the effect of equity incentive types on the information and knowledge environment of financial analysts, as expressed in the reports of analysts in the form of earning forecasts and stock recommendations. In addition, analysts’ forecast accuracy can be improved when companies prefer and select using restricted stock units (RSUs) as a type of reward managers, instead of stock options, in their reward program, assuming that the two forms of stock compensations (stock options or RSUs) have different and significant impacts on the accuracy of analysts’ forecasts. Finally findings revealed positive relationship between forecast accuracy and recommendations as financial analysts serve as information intermediaries by providing reports that include earnings forecasts and their stock recommendations. These recommendations help investors to make their decisions. The Impact of Equity Incentives on the Accuracy of Analyst’s Earning Forecasts Dr/ Samar Mansour Elsebaai Mohamed Moubarak ددعلا رشع يداحلا دلجملا اثلا ثل ءزجلا يناثلا 0202 769
HYALURONIC ACID-DOCETAXEL CONJUGATE LOADED NANOLIPOSOMES FOR TARGETING TUMOR CELLS

Objective: Docetaxel (DTX), a potent anticancer drug, is suffering from non-specificity and drug resistance as major limitations. In this investigation, we developed Hyaluronic acid (HA)-Docetaxel conjugate (HA-DTX) loaded nanoliposomes to target cancer cells via passive and active targeting approaches. Methods: HA-DTX was synthesized and characterized by UV-Visible spectrophotometry, FT-IR spectroscopy, 1H NMR spectroscopy, Differential scanning calorimetry and X-ray diffraction and then loaded into nanoliposomes (L-NLs) by thin-film hydration method. L-NLs were characterized physicochemically and evaluated for anticancer efficacy by in vitro cytotoxicity study in glioma cells (C6 glial cells); cellular uptake and apoptotic effect were investigated by fluorescence microscopy. Results: HA-DTX was successfully synthesized; L-NLs had an average size of 123.0±16.53 nm, polydispersity index of 0.246±0.01 and zeta potential of 44.4±6.79 mV. Also, L-NLs exhibited 90.54%±4.22 of drug loading efficiency and 2.68%±0.12 of drug loading, releasing about 57.72%±1.17 at pH 5.2 and only 14.14%±1.32 at pH 7.4 after 48 h. No significant change instability was observed after storage at 5 °C±3 °C as well as at 25 °C±2 °C/60% RH±5% RH for 6 mo. The cytotoxicity effect of L-NLs was higher by 10% that of marketed formulation at 10 µg/ml docetaxel concentration. Fluorescence microscopic investigation showed that more cellular uptake and apoptotic effect were observed in L-NLs treated C6 glial cells than in those treated with the marketed formulation. Conclusion: HA-DTX loaded nanoliposomes enabled docetaxel to target C6 glial cells with better efficacy and might be effective to treat glioma.
Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas

In recent years, the number of scientific publications on artificial intelligence (AI), primarily on machine learning, with respect to neurosurgery, has increased. The aim of the study was to conduct a systematic literature review and identify the main areas of AI applications in neurosurgery. Methods. Using the PubMed search engine, we found and analyzed 327 original articles published in 1996–2019. The key words specific to each topic were identified using topic modeling algorithms LDA and ARTM, which are part of the AI-based natural language processing. Results. Five main areas of neurosurgery, in which research into AI methods are underway, have been identified: neuro-oncology, functional neurosurgery, vascular neurosurgery, spinal neurosurgery, and surgery of traumatic brain injury. Specifics of these studies are characterized. Conclusion. The information presented in this review can be instrumental in planning new research projects in neurosurgery.
Study of impulsivity in bipolar affective disorder across the spectrum

Introduction: Bipolar Disorder runs across a spectrum between depression and mania. Impulsivity is known to be a core symptom of Bipolar disorder. This study aims at investigating the relationship between specific aspects of impulsivity and the affective symptoms in subjects with Bipolar Disorder. Methodology: Eligible candidates were assessed using a semi-structured proforma. The study participants were divided into four groups according to the nature of their current episode, namely: manic, depressed, mixed state or euthymic (control) group. Young’s Mania Rating Scale (YMRS) and Hamilton Depression Rating Scale (HDRS) were used to assess affective symptom severity. Barratt’s Impulsivity Scale (BIS-11) was used to assess state impulsivity in each participant and the collected data was then analysed for correlation with patient profile and clinical parameters. Results: Impulsivity was higher in mania (p
Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.
Research on Multi-sensor Data Fusion Technology

Multi-source sensor information fusion mainly collects various types of information from multiple independent decentralized sensors. Due to the different types of information, specific combinations of time and events are required to make the collected information more advanced and useful information. From the level of fusion, it will be merged from the three levels of data set, feature level and decision-making level. For different practical problems, it is necessary to use a certain level of fusion or a certain two levels of fusion according to the situation, so as to obtain the most optimal integration scheme.
Analysis of Electric Field Distribution on Composite Insulator of 500kV AC Typical Tower and Its Effect on Decay Fracture

A new type of composite insulator mechanical failure named decay-like fracture happens rather often in UHV AC transmission lines. However, the cause from the point view of electric field is not clear. To investigate the effect of electric field on the occurrence and development mechanism of decay-like fracture, the 3D tower-conductor-insulator models for ZB2-type and CZ34-type transmission tangent towers are established and analysed based on the finite element simulation. The conclusion can be drawn that the tower type has a significant effect on the electric field distribution along the axial direction of the insulator string, and the degradation is more likely to occur in high-voltage side of the B-phase string, which is consistent with the fracture failure investigation. Simulation results also show that the withstand electric field strength is 297kV/m. The distribution of surface potential and electric field under the actual operating voltage provide a new idea for the condition monitoring of the decay-like fracture of UHV AC composite insulators in transmission lines, as well as a reference for further research in the field of decay-like fracture failure.
Real‐time control algorithm for minimising energy consumption in parallel hybrid electric vehicles

In this work, a real-time energy management problem for a parallel hybrid electric vehicle (HEV) is proposed. The considered powertrain is built from a commercial HEV model. First, a non-linear optimal control problem under model predictive control scheme is formulated. The designed controller aims to generate the optimal power split and gear ratio schedule with respect to minimise the energy consumption of fuel and electricity. Moreover, the multiple shooting algorithm is introduced to decouple the dynamic constraints with the ability of avoiding the strong non-linearity while solving the optimisation problem. After that the optimisation problem is solved using sequential quadratic programming solver. Then, to evaluate the performance of the proposed real-time optimisation strategy on different traffic scenarios, the controller is applied to an adaptive cruise control (ACC) under connected environment. In this case, a solution of ACC with consideration of minimising energy consumption and maintaining string stability is provided. Finally, the proposed controller can be implemented in the traffic-in-theloop platform without the knowledge of the predefined driving route. Simulations reveal that the proposed real-time control scheme shows great optimisation performance under the designed scenarios.
Use of the Poisson−Boltzmann Equation To Estimate The Electrostatic Free Energy Barrier for Dielectric Models of Biological Ion Channels

We have obtained the electrostatic free energy profile of a univalent positive ion as it is moved from a bulk electrolyte solution into a biological ion channel of various model forms. The form of the results was unaffected by the method of calculation. We used a fully consistent nonlinearized Poisson−Boltzmann scheme, a fully consistent linearized Poisson−Boltzmann scheme, and a hybrid scheme. The channel models we used were designed to (crudely) mimic the shape and imbedded charge and dipolar distributions for biological ion-selective channels (such as the voltage-gated K+ “shaker” channel). The results obtained were accounted for on simple physical grounds.
Sistem Monitoring Tanaman Hidroponik Berbasis Iot (Internet of Thing) Menggunakan Nodemcu ESP8266

This Hydroponic Plant Monitoring System was created using Nodemcu ESP8226, which supports internet access. So that the monitoring process can be done through an android application. Plant data is obtained through the DHT11 sensor and Water Sensor, which is then processed using the Fuzzy method to determine the time to water the plants and add water to the hydroponic plant storage tank. From the results of sensor readings, the system can determine the time to water the plants according to weather conditions and send warnings when the water in the storage tank is less than the predetermined condition.
Low-Cost, Tiny-Sized MEMS Hydrophone Sensor for Water Pipeline Leak Detection

In this paper, we present an experimental investigation of a water pipeline leak detection system based on a low-cost, tiny-sized hydrophone sensor fabricated using the microelectromechanical system (MEMS) technologies. A 10 × 10 element arrayed MEMS hydrophone device with chip size of 3.5 × 3.5 mm<inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> was used in the experiment. The hydrophone device is packaged with a customized on-board preamplification circuit using an acoustic transparent material. The overall package size of the MEMS hydrophone is <inline-formula><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula>1.2 × 2.5 cm. The packaged MEMS hydrophone achieves an acoustic sensitivity of −180 dB (re: 1 V/<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>Pa), a bandwidth from 10 Hz to 8 kHz, and a noise resolution of around 60 dB (re: 1 <inline-formula><tex-math notation="LaTeX">$\mu \text{Pa/}\sqrt{\text{Hz}}$</tex-math></inline-formula>) at 1 kHz. A section of ductile iron water pipeline with an internal diameter of 10 cm, wall thickness of 0.73 cm, and length of 30 m is constructed as the test bed for the water leak detection. Two different leak sizes with leak flow rates of about 30 and 180 L/min are designed along the pipe, which is pressurized at 3.2 bar. Analysis of the transient signals and spectrograms shows that the MEMS hydrophone can capture the key acoustic information of the water leak, i.e., identifying the leak and locating the leak position. The measurement results demonstrate the feasibility to construct an affordable, highly efficient, real-time, and permanent in-pipe pipeline health monitoring network based on the MEMS hydrophones due to their high performance, low cost, and tiny size.