Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications. The winter wheat diseases, in combination with nitrogen-water stress, are therefore common causes of yield loss in winter wheat in China. Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent winter wheat diseases in China. This study investigated the potential of continuous wavelet analysis to identify the powdery mildew, stripe rust and nitrogen-water stress using canopy hyperspectral data. The spectral normalization process was applied prior to the analysis. Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index. In order to reduce the number of wavelet regions, correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance. Based on the selected spectral bands, vegetation indices and wavelet features, the discriminate models were established using Fisher’s linear discrimination analysis (FLDA) and support vector machine (SVM). The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses, with overall accuracies of 0.91, 0.72, and 0.72 respectively for powdery mildew, stripe rust and nitrogen-water by using FLDA, and 0.79, 0.67 and 0.65 respectively by using SVM. FLDA was more suitable for differentiating stresses in winter wheat, with respective accuracies of 78.1%, 95.6% and 95.7% for powdery mildew, stripe rust, and nitrogen-water stress. Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification. The accuracies of high-scale and low-scale features with an overall accuracy (OA) of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88. The detection of the severity of stripe rust using this method showed an enhanced reliability (R2 =0.828). Keywords: winter wheat, crop disease, powdery mildew, stripe rust, nitrogen-water stress, continuous wavelet analysis, quantitative identification DOI: 10.25165/j.ijabe. 20181102.3467 Citation: Huang W J, Lu J J, Ye H C, Kong W P, Mortimer A H, Shi Y. Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis. Int J Agric & Biol Eng, 2018; 11(2): 145–152.
Alinhamento múltiplo de sequências com A-Star paralelo em cluster MPI

The multiple sequence alignment purpose is to highlight similarities and differences between a set of biological sequences. The multiple alignment is an NP-Hard problem and heuristic methods are used to solve it, however those do not guarantee that an optimal result is produced. Some exact techniques that can produce an optimal result are based on the A-Star graph search algorithm, being one of them the Parallel A-Star (PA-Star). The PA-Star divides the search space to multiple threads, accelerating the search for the result, but its execution is limited to a single machine. The objective of this undergraduate work is to propose, implement and evaluate the MPI-PAStar, a strategy that allows the reduction of the search time by executing the PA-Star on multiple machines, using the MPI environment to exchange messages, distributing the workload across different machines. The MPI-PAStar adds to PA-Star a pool of message processing threads and two threads responsible for sending and receiving messages. Multiple strategies are used to reduce network traffic and latency, like serialized workload blocks and compressing them before sending them, reducing negative network effects over the alignment computation. Results obtained with the MPI-PAStar showed that it can yield up to 36.8% reduction in terms of alignment time and up to 29.7% in terms of total execution time, depending on the number of sequences being aligned, the length of longest sequence and the content of the sequences.
Parámetros, correlaciones y tendencias genéticas de caracteres reproductivos en ganado Holstein de México

El objetivo fue calcular los parámetros, correlaciones y describir las tendencias genéticas de caracteres reproductivos en ganado Holstein de México en tres periodos diferentes de tiempo, en hembras nacidas entre 2006 y 2019, usando registros de la asociación Holstein de México. Los parámetros reproductivos calculados fueron: intervalo parto a primer servicio (IPPS), el número de servicios por concepción (NSC), los días abiertos (DAB), el intervalo entre partos (IEP) y la edad al primer parto (EPP). Los componentes de la varianza fueron estimados utilizando máxima verosimilitud restringida en un modelo animal, para calcular parámetros genéticos (heredabilidad en sentido estricto (h2), repetibilidad (r) y correlaciones genéticas) y correlaciones fenotípicas. También se estimaron varianzas genéticas aditivas y valores genéticos en tres periodos de tiempo (P1: 2006-2009, P2: 2010-2013 y P3: 2014-2017). Las heredabilidades calculadas fueron de 4 a 9 % y las repetibilidades del 8 al 9 %, valores cercanos a reportes previos para características de fertilidad. Las correlaciones fenotípicas fueron positivas para casi todos los parámetros reproductivos y las genéticas fueron positivas en un rango amplio (0.13-0.99). Los análisis por periodos mostraron cambios, posiblemente derivados de la influencia de la importación y uso de germoplasma de toros extranjeros (principalmente de los Estados Unido de América y Canadá) que implementan selección genómica e incluyen características fertilidad. La presente investigación ha permitido actualizar la información de parámetros reproductivos y genéticos en caracteres de fertilidad que podrán ser incorporados a las evaluaciones genéticas nacionales.
Diagnostic, Prognostic, and Therapeutic Role for Angiogenesis Markers in Head and Neck Squamous Cell Carcinoma: A Narrative Review

Despite refinements to diagnostic and therapeutic approaches over the last two decades, the outcome of patients with head and neck squamous cell carcinoma (HNSCC) has not shown substantial improvements, especially regarding those with advanced-stage disease. Angiogenesis is believed to be a turning point in the development of solid tumors, being a premise for mass growth and potential distant dissemination. Cancer-induced angiogenesis is a result of increased expression of angiogenic factors, decreased expression of anti-angiogenic factors, or a combination of both. The assessment of angiogenesis has also emerged as a potentially useful biological prognostic and predictive factor in HNSCC. The aim of this review is to assess the level of current knowledge on the neo-angiogenesis markers involved in the biology, behavior, and prognosis of HNSCC. A search (between 1 January 2012 and 10 October 2022) was run in PubMed, Scopus, and Web of Science electronic databases. After full-text screening and application of inclusion/exclusion criteria, 84 articles are included. The current knowledge and debate on angiogenesis in HNSCC presented in the eligible articles are stratified as follows: (i) diagnostic markers; (ii) prognostic markers; (iii) predictive markers; and (iv) markers with a potential therapeutic role. Angiogenesis is a biological and pathological indicator of malignancies progression and has negative implications in prognosis of some solid tumors; several signals capable of tripping the “angiogenic switch” have also been identified in HNSCC. Although several studies suggested that antiangiogenic agents might be a valuable adjunct to conventional chemo-radiation of HNSCC, their long-term therapeutic value remains uncertain. Further investigations are required on combinations of antiangiogenic agents with conventional chemotherapeutic ones, immunotherapeutic and molecularly targeted agents in HNSCC. Additional data are necessary to pinpoint which patients could benefit most from these treatments.
Real-time wildfire detection and tracking in Australia using geostationary satellite: Himawari-8

ABSTRACT Real-time information about the spatial extents of wildfires can assist both emergency responders and the general public to mitigate the impact of the wildfires. However, in a large spatial and temporal scale, timely and accurate information about the areas affected by ongoing wildfires is often in scarce supply. This paper investigates the feasibility of generating such real-time information in Australia using the recently launched geostationary Himawari-8 satellite. The Himawari-8 satellite offers extremely high-temporal-resolution (10 minutes) multispectral imagery, which is suitable for real-time wildfire monitoring in a large spatial and temporal scale. The potential of real-time wildfire monitoring using Himawari-8 is evaluated by a case-study of the recent 2015 Esperance, Western Australia wildfire. The results demonstrate that the detection is robust to smoke and moderate cloud obscuration and sensitive enough for early detection of wildfires. Further, fine-grained temporal changes in the rate and direction of fire spread can be monitored in real-time, which enables the potential for automated detection of abnormal fire behaviour.
Mathematical Methods for a Quantum Annealing Computer

This paper describes the logic and creativity needed in order to have high probability of solving discrete optimization problems on a quantum annealing computer. Current features of quantum computing via annealing are discussed. We illustrate the logic at the forefront of this new era of computing, describe some of the work done in this field, and indicate the distinct mindset that is used when programming this type of machine. The traveling salesman problem is formulated for solving on a quantum annealing computer, which illustrates the methods for this computer.
Environmental Disclosure and Return on Assets of Manufacturing Companies Listed in Nigeria

The return on assets indicator informs existing and potential investors how effectively management has optimally utilized its resources from investments. Studies have advanced that manufacturing companies’ level of commitment has the ability to enhance earnings and corporate return on assets. Lack of optimal utilization of corporate assets and non-compliance with environmental requirements has weakened the earnings of manufacturing companies. Evidence has revealed that not many manufacturing coming have integrated environmental disclosure into their operations. This study examined the effect of environmental disclosure on the return on assets of manufacturing companies listed in Nigeria. The study adopted an ex-post facto research design. The population consisted of the 66 listed manufacturing companies listed on the Nigerian Exchange Group as of December 31, 2021. Purposive sampling was used to select 29 manufacturing businesses. Validated data were taken from the public financial statements of the selected manufacturing enterprises for 16 years (2006-2021). A statutory audit of the financial accounts was used to ensure the data's veracity. Descriptive and inferential (multiple regression) statistics were utilized to assess the data. The study revealed that environmental disclosure affects the return on assets of manufacturing companies listed in Nigeria (Adj.R2 = 0.017, F(5, 458) = 8.333, p < 0.05). The study recommended that managers of manufacturing companies should integrate environmental disclosure as a critical duty as evidence of policies in practice in protecting the environment towards gaining legitimacy and patronage in deepening the return on assets of the companies.
Uncertainty of biomass contributions from agriculture and forestry to renewable energy resources under climate change

In the future, Germany’s land-use policies and the impacts of climate change on yields will affect the amount of biomass available for energy production. We used recent published data on biomass potentials in the federal states of Germany to assess the uncertainty caused by climate change effects in the potential supply of biomass available for energy production. In this study we selected three climate scenarios representing the maximum, mean and minimum temperature increase for Germany out of 21 CMIP5 -projections driven by the Representative Concentration Pathways ( RCP ) 8.5 scenario. Each of the three selected projections was downscaled using the regional statistical climate model STARS . We analysed the yield changes of four biomass feedstock crops (forest, short-rotation coppices (SRC), cereal straw (winter wheat) and energy maize) for the period 2031–2060 in comparison to 1981–2010. The mean annual yield changes of energy wood from forest and short-rotation coppices were modelled using the process-based forest growth model 4C . The yield changes of winter wheat and energy maize from agricultural production were simulated with the statistical yield model IRMA . Germany’s annual biomass potential of 1500 PJ varies between minus 5 % and plus 8 % depending on the climate scenario realisation. Assuming that 1500 PJ of biomass utilisation can be achieved, climate change effects of minus 75 (5 %) PJ or plus 120 (8 %) PJ do not impede overall bioenergy targets of 1287 PJ in 2020 and 1534 PJ in 2050. In ﬁve federal states the climate scenarios lead to decreasing yields of energy maize and winter wheat. Impacts of climate scenarios on forest yields are mainly positive and show both positive and negative effects on yields of SRC.
Characterization and antibiotic susceptibility of Escherichia coli isolated from selected internal organs of donkeys slaughtered in abattoirs in Kaduna State, Nigeria

Escherichia coli (E. coli) is a natural inhabitant of the gastrointestinal tract of both humans and animals. Several strains exist some are harmless while some are pathogenic causing various fatal conditions in both humans and animals. To characterize and determine the antibiotic susceptibility patterns of E. coli isolated from liver, spleen and intestine of donkeys slaughtered in Maraban Idah, Kaduna State, Nigeria, a total of 384 samples were collected from 128 donkeys from April to August 2018. The samples were processed using standard methods of cultural enrichment, growth on selective media, biochemical and antibiotics susceptibility test. The overall isolation rate was 5.2% with organ distribution of 4.7%. 4.7% and 6.3% for intestine, liver and spleen, respectively. Escherichia coli was slightly higher in the spleen than in the liver and intestine though there was no significant association (p = 0.810) between the presence of E. coli and the organs sampled. Most of the isolates were susceptible to ciprofloxacin (90%), enrofloxacin (80%) and colistin (70%) while some were highly resistant to common antibiotics such as penicillin (95%), oxytetracycline (90%) and amoxycilline (75%). Most of the isolates (75%) displayed resistance to two (2) different classes of antimicrobials with a total of 5 resistance patterns. Therefore, there is a need for education and public awareness regarding hygienic processing and consumption of offals of slaughtered donkeys. Also, further studies should be done to ascertain the genes responsible for resistance toantimicrobials observed in the E. coli isolates from the slaughtered donkeys.
Virtual DC Motor Control Based on Virtual Capacitor for DC Microgrid

With the access of high-density distributed power supply, the inertia level of DC micro-grid continues to decline, especially under the condition of independent power supply of new energy, its inertia is far lower than the minimum inertia demand. In order to effectively improve the system inertia and enhance the dynamic stability of the power grid, a new virtual DC motor control based on virtual capacitance is proposed for the energy storage port converter. Firstly, according to the definition of inertia of DC microgrid, the main sources of inertia of DC microgrid are defined. Secondly, according to the small-signal model of the virtual capacitor, a first-order additional inertial loop is constructed, and the virtual DC motor control method based on the additional inertial loop is obtained, which realizes the improvement of the virtual DC motor control and further enhances the inertial support capability of the system. Then, the small-signal model of the new virtual DC motor control is established to explore the influence of virtual angular velocity on system stability, and the advantages of voltage dynamic performance and stability under this control method are analyzed. Finally, the correctness and effectiveness of the proposed control strategy are verified by simulation analysis.
Ensuring Long-Term Trustworthy Collaboration in IoT Networks Using Contract Theory and Reputation Mechanism on Blockchain

Internet of Things (IoT) devices operate in an untrusted environment, and blockchain technology can provide a secure distributed collaboration method. However, the widespread distribution of heterogeneous devices in the IoT creates complex information asymmetry issues, leading to inefficient resource allocation. This is especially true when information asymmetry occurs in long-term cooperative relationships, where contract design often faces more complex incentive problems. This article focuses on studying long-term cooperation between IoT devices based on the blockchain technology and proposes a solution to information asymmetry in long-term cooperation based on contract theory and reputation mechanism: 1) we design more flexible pricing strategies based on contract theory to attract more devices to become validators and provide different long-term job contracts to overcome information asymmetry issues in the cooperative process and 2) we propose a subjective reliability-linked reputation evaluation mechanism to construct the career of validators, which can constrain unethical behavior and incentivize validators to comply with rules and protect data security in long-term cooperation. Experimental results show that our scheme can improve device willingness to cooperate, overcome information asymmetry in long-term cooperation, improve the efficiency of computing resource allocation, reduce network security risks, achieve long-term trusted cooperation between devices, and provide effective security for the development of the IoT.
Electromagnetic properties of massive neutrinos in low-energy elastic neutrino-electron scattering

A thorough account of electromagnetic interactions of massive neutrinos in the theoretical formulation of low-energy elastic neutrino-electron scattering is given. The formalism of neutrino charge, magnetic, electric, and anapole form factors defined as matrices in the mass basis is employed under the assumption of three-neutrino mixing. The flavor change of neutrinos traveling from the source to the detector is taken into account and the role of the source-detector distance is inspected. The effects of neutrino flavor-transition millicharges and charge radii in the scattering experiments are pointed out.
A Novel Kernel Algorithm for Finite Impulse Response Channel Identiﬁcation

Over the last few years, kernel adaptive ﬁlters have gained in importance as the kernel trick started to be used in classic linear adaptive ﬁlters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying ﬁnite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive deﬁnite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the ﬁrst of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.
A Multiplicity Result for a Non-local Critical Problem

In this paper, we are interested in the multiple solutions of the following fractional critical problem (−∆)su = |u| ∗ s−2u+ λu in Ω, u = 0 on R \ Ω, where s ∈ (0, 1), N > 4s, 2s = 2N/(N−2s), Ω is a smooth bounded domain in R and (−∆) is the fractional Laplace operator. Because the nonlocal property of fractional Laplacian makes the variational functional of the fractional critical problem different from the one of local operator −∆. To the best of our knowledge, it is still unknown whether multiple solutions of the fractional critical problem exist for all λ > 0. In this paper, we give a partial answer. Precisely, by introducing some new ideas and careful estimates, we prove that for any s ∈ (0, 1), the fractional critical problem has at least [(N + 1)/2] pairs of nontrivial solutions if 0 < λ 6= λn, and has [(N + 1− l)/2] pairs if λ = λn with multiplicity number 0 < l < min{n,N + 2}, via constraint method and Krasnoselskii genus. Here λn denotes the n-th eigenvalue of (−∆) with zero Dirichlet boundary data in Ω and [a] denotes the least positive integer k such that k ≥ a.
A Unified and Accurate Subpixel Detector of Control Points for Camera Calibration

Camera calibration is a necessary step in 3-D computer vision, and the accuracy of calibration is largely dependent on the localization precision of the control points of the calibration patterns. However, the sub-pixel localization methods for the commonly used two types of control points, corners of the squares and centers of the circles, are markedly different from each other. In this work, we propose a unified approach to sub-pixel localization for the two types of control points. The approach is derived from theoretical analysis and consists of a closed-form solution, followed by algorithms for practical implementation. Both synthetic datasets and real calibration experiments have been used to test the proposed approach and good results have been obtained. Compared with state-of-the-art techniques, the proposed approach provides a unified and high-precision sub-pixel localization method of the control points for the commonly used calibration patterns.
An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts

The heart rate (HR) is a widely used clinical variable that provides important information on a physical user’s state. One of the most commonly used methods for ambulatory HR monitoring is photoplethysmography (PPG). The PPG signal retrieved from wearable devices positioned on the user’s wrist can be corrupted when the user is performing tasks involving the motion of the arms, wrist, and fingers. In these cases, the obtained HR is altered as well. This problem increases when trying to monitor people with autism spectrum disorder (ASD), who are very reluctant to use foreign bodies, notably hindering the adequate attachment of the device to the user. This work presents a machine learning approach to reconstruct the user’s HR signal using an own monitoring wristband especially developed for people with ASD. An experiment is carried out, with users performing different daily life activities in order to build a dataset with the measured signals from the monitoring wristband. From these data, an algorithm is applied to obtain a reliable HR value when these people are performing skill improvement activities where intensive wrist movement may corrupt the PPG.