Miracle-dz/UniFAR-FTUs huggingface dataset:
Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Abstract. This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, the cotton plant height (ph) and canopy cover (cc) information were retrieved from the point cloud-based digital surface models (DSMs) and orthomosaic images. Both UAS-based ph and cc follow a sigmoid growth pattern as confirmed by ground-based studies. By applying an empirical model that converts the cotton ph to cc, the estimated cc shows strong correlation (R2=0.990) with the observed cc. An attempt for modeling cotton yield was carried out using the ph and cc information obtained on June 26, 2015, the date when sigmoid growth curves for both ph and cc tended to decline in slope. In a cross-validation test, the correlation between the ground-measured yield and the estimated equivalent derived from the ph and/or cc was compared. Generally, combining ph and cc, the performance of the yield estimation is most comparable against the observed yield. On the other hand, the observed yield and cc-based estimation produce the second strongest correlation, regardless of the complexity of the models.
INCIDENCE OF MAJOR BLEEDING IN PATIENTS WITH MECHANICAL HEART VALVE AND WARFARIN; A ONE YEAR STUDY.

Received: 09 December 2017 Final Accepted: 11 January 2018 Published: February 2018 Introduction: Valve replacement surgery has dramatically improved the outcomes of the patients with valvular heart disease. In the last few years it has been estimated that about 280 000 valve substitutes are annually implanted worldwide 1 . nevertheles prosthetic valve comploications are many, including valve thrombosis and subsequently systemic embolism, these two main impacts of the prosthetic valves can be minimized by using oral anticoagulant therapy and the main antithrombotic treatment used is coumarin derivatives, commonly warfarin, which can reduce the risk of the thromboembolic events to approximately 1 in 100 patients 2 . Despite this significant effect of warfarin, it has serious side effect especially in prosthetic valve patients, as they may develop major bleeding which can be fatal if it is not managed accordingly. Methods: This study tried to focus on the incidence of the bleeding in one of Iraqi specialized cardiac centres and finding out what are the major sites of bleeding with the outcomes of each one, comparing it to the previously conducted studies. Results: 25% of mortalities was due to intracranial bleeding in spite of admission and intensive care.
Pectoralis Major in Salvage Total Laryngectomy after Irradiation: Morbidity, Mortality, Functional, and Oncological Results in a Referral Center in Egypt

Background: Nonsurgical organ preservation protocols have seen a large diffusion worldwide in the last decades. Their oncological and functional effectiveness in a real-world setting has been recently questioned because of the high morbidity of salvage procedures. The aim of this study is to review the outcomes of postirradiation salvage total laryngectomy (STL) and reconstruction with pectoralis major flap. Methods: This retrospective observational study included 37 cases of STL in the period from January 2015 to December 2021. Data for each patient were extracted from the hospital information system and reviewed. Results: The 3-year overall and disease-specific survival are, respectively, 28% and 51%. Only seven recurrences after salvage surgery were recorded and all of them died from the disease. The other 14 deaths derived from comorbidities, with diabetes being the most significant predictive parameter for overall survival. Also, lower postoperative albumin levels were associated with a higher risk of death. Conclusions: Overall survival after STL and reconstruction with PMMF is low but most deaths are due to comorbidities and not to cancer progression or recurrence.
Discrimination of Milk Freshness Based on Synchronous Two-Dimensional Visible/Near-Infrared Correlation Spectroscopy Coupled with Chemometrics

Milk is one of the preferred beverages in modern healthy diets, and its freshness is of great significance for product sales and applications. By combining the two-dimensional (2D) correlation spectroscopy technique and chemometrics, a new method based on visible/near-infrared (Vis/NIR) spectroscopy was proposed to discriminate the freshness of milk. To clarify the relationship be-tween the freshness of milk and the spectra, the changes in the physicochemical indicators of milk during storage were analyzed as well as the Vis/NIR spectra and the 2D-Vis/NIR correlation spectra. The threshold-value method, linear discriminant analysis (LDA) method, and support vector machine (SVM) method were used to construct the discriminant models of milk freshness, and the parameters of the SVM-based models were optimized by the grid search method and particle swarm optimization algorithm. The results showed that with the prolongation of storage time, the absorbance of the Vis/NIR spectra of milk gradually increased, and the intensity of autocorrelation peaks and cross peaks in synchronous 2D-Vis/NIR spectra also increased significantly. Compared with the SVM-based models using Vis/NIR spectra, the SVM-based model using 2D-Vis/NIR spectra had a >15% higher prediction accuracy. Under the same conditions, the prediction performances of the SVM-based models were better than those of the threshold-value-based or LDA-based models. In addition, the accuracy rate of the SVM-based model using the synchronous 2D-Vis/NIR autocorrelation spectra was >97%. This work indicates that the 2D-Vis/NIR correlation spectra coupled with chemometrics is a great pattern to rapidly discriminate the freshness of milk, which provides technical support for improving the evaluation system of milk quality and maintaining the safety of milk product quality.
Cohabitation inquiline-host in termite nests: does it involve distinct mechanisms?

Nests of the termite Constrictotermes sp. can be cohabited by obligatory inquilines Inquilinitermes sp. Recent studies have shown that inquilines establish themselves in mature nests, possibly during the nidification transition phase of the colony (e.g., from the epigeal to the arboreal habit). It is believed that cohabitation is maintained through spatial segregation of the cohabitants since the inquilines aggregate in the central nest region. Here, we described the cohabitation between Inquilinitermes microceus (Silvestri) and a Constrictotermes species in Sergipe, Northeast Brazil. We compared the cohabitation observed in distinct regions considering hypotheses related to the entry and maintenance of the inquiline in the nests. All Constrictotermes sp. nests found in this region are epigeal, unlike already reported in other studies. Approximately 45% of the nests analyzed were associated with I. microcerus, and cohabitation seemed to be dependent on nest size. The entrance of the inquiline was not related to changes in the nidification habit from soil to trees, as suggested in other studies. In addition, inquilines were not associated with and segregated into the dark walls in the central part of the nest. The cohabitation observed here could involve mechanisms which are distinct from the mechanisms reported in other regions.
Focal plane array based on HgTe nanocrystals with photovoltaic operation in the short-wave infrared

Thanks to their remarkable spectral tunability across the entire infrared range, HgTe nanocrystals present a unique platform for designing infrared optoelectronic devices. While in recent years most of the significant advances in this domain have been made on devices at the single-pixel level, there is a growing trend toward exploring the potential of this material for imaging applications. However, until recently, focal plane arrays based on HgTe colloidal nanocrystals have been limited to the photoconductive mode, which is inherently associated with a large dark current. In this work, we demonstrate a diode stack compatible with a readout integrated circuit whose back-end processing has been optimized to ensure compatibility with a complete diode stack deposition. The diode design is also optimized to generate a Fabry–Pérot cavity in which 50% of the light is effectively absorbed at the band edge. Finally, taking benefit from the full video graphics array format, high-resolution images are taken.
Evaluating Smartphone Pressure Observations for Mesoscale Analyses and Forecasts

AbstractSmartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no ob.
Automated short essay scoring system using normalized Simpson methods

The emergence of various methods in making measurement of similarity of short sentences triggered the development of the development of automatic short scoring system. The measurement of similarity of the automatic short scoring system is a brief description that compares the similarity of meanings between student answer and reference answer. The process of similarity measurement between student answer and reference answer is slightly different from measuring the similarity of meaning between two sentences. This difference lies in the process of justifying the similarity. This is because the measurement of reference answer and student answer is only based on the detection of keywords that appear in both, so that sentences that have additional information are only considered as supplementary information from keywords. This paper proposes a method that can be used to reduce words that are considered less important so that they can be used to improve the measurement of similarity between the reference answer and the student answer. The result shows that the proposed method is able to provide a smaller RMSE value of 1.2 compared with other methods used as a comparison.The emergence of various methods in making measurement of similarity of short sentences triggered the development of the development of automatic short scoring system. The measurement of similarity of the automatic short scoring system is a brief description that compares the similarity of meanings between student answer and reference answer. The process of similarity measurement between student answer and reference answer is slightly different from measuring the similarity of meaning between two sentences. This difference lies in the process of justifying the similarity. This is because the measurement of reference answer and student answer is only based on the detection of keywords that appear in both, so that sentences that have additional information are only considered as supplementary information from keywords. This paper proposes a method that can be used to reduce words that are considered less important so that they can be used to improve the measurement of similarity between the reference answer.
Investigation of the Optical Spectra of Barium-Zinc (Aluminum) Fluoroborates and Barium-Zinc Fluorocarbonate from First Principles

The Raman scattering, infrared absorption, and reflection spectra of hexagonal non-centrosymmetric BaZnBO3F and BaAlBO3F2 and centrosymmetric BaZn3BO3F2 and BaZnCO3F2 are calculated using the standard procedures of the CRYSTAL package with the basis of localized orbitals and the B3LYP hybrid functional within the framework of the Hartree-Fock conjugate perturbation method. It is shown that the layered structure of crystals manifests itself in the spectra of vibrational modes polarized along and perpendicular to the c axis with wavenumbers for the lattice region formed by displacements of atoms in [BaF]∞ and [MAO3]∞ (M: Zn, Al; A: B, C) layers, for molecular deformation outside and in the plane of anions BO3 and CO3. The quantitative and qualitative composition of the spectra is determined by the symmetry of the crystal lattice.
An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis

The diagnosis of complicated fault severity problems in rotating machinery systems is an important issue that affects the productivity and quality of manufacturing processes and industrial applications. However, it usually suffers from several deficiencies. (1) A considerable degree of prior knowledge and expertise is required to not only extract and select specific features from raw sensor signals, and but also choose a suitable fusion for sensor information. (2) Traditional artificial neural networks with shallow architectures are usually adopted and they have a limited ability to learn the complex and variable operating conditions. In multi-sensor-based diagnosis applications in particular, massive high-dimensional and high-volume raw sensor signals need to be processed. In this paper, an integrated multi-sensor fusion-based deep feature learning (IMSFDFL) approach is developed to identify the fault severity in rotating machinery processes. First, traditional statistics and energy spectrum features are extracted from multiple sensors with multiple channels and combined. Then, a fused feature vector is constructed from all of the acquisition channels. Further, deep feature learning with stacked auto-encoders is used to obtain the deep features. Finally, the traditional softmax model is applied to identify the fault severity. The effectiveness of the proposed IMSFDFL approach is primarily verified by a one-stage gearbox experimental platform that uses several accelerometers under different operating conditions. This approach can identify fault severity more effectively than the traditional approaches.
High-Accuracy Thermal Expansion Coefficient Measurement Based on Fiber-Optic EFPIs With Automatic Temperature Compensation

This article reports a fiber-optic instrument for the coefficient of thermal expansion (CTE) measurement with high accuracy, based on extrinsic Fabry–Perot interferometers (EFPIs) with novel automatic temperature compensation. The prototype CTE measurement instrument consists of two parallel EFPIs mounted on a quartz base. The first EFPI is formed between the endface of an optical fiber and the mirror attached to the endface of a short bar. The second EFPI is fashioned between endface of another optical fiber and the mirror attached to the endface of a long bar. The sensing elements are the short and long bars with the same material, cross Section for bars, and optical fibers. When the instrument is subjected to temperature rise, bars with two different lengths will extend, leading to relatively significant EFPIs’ cavity length variation. CTE of the bars’ material can be determined by the difference between two bars’ length changes, temperature change, difference between two bars’ lengths, and the CTE of the quartz base. The difference between two length changes eliminates temperature impact from the remaining elements, thus providing automatic temperature compensation. For experimental validation, the CTE of three types of samples is characterized in the temperature range from 20 °C to 80 °C. A resolution of $\pm 0.0032\times 10^{-{6}} /^{\circ }\text{C}$ and an accuracy of $\pm 0.0758\times 10^{-{6}} /^{\circ }\text{C}$ of CTE are achieved, respectively. Experimental results reveal that the proposed instrument is a practical and effective tool for materials’ CTE measurement, with the advantages of high resolution, simple-to-manufacture, low cost, and high accuracy, which will have the potential to be applied in the fields of material sciences.
Sturgeon Aquaculture Potentiality in Egypt in View of the Global Development of Aquaculture and Fisheries Conservation Techniques: An Overview and Outlook

Sturgeon conservation is a global issue, with wild sturgeon amounts dropping rapidly and continuously. This article explores the essential role that aquaculture plays in the conservation of these critically endangered fish, the replenishing of the natural population, fulfilling the expanding demand and possibilities for caviar markets, and reducing pressure on fisheries’ catch. It also reviews the history of controlled breeding programs designed to supplement wild Caspian Sea populations, the possibilities for sturgeon aquaculture production, and the worldwide caviar trade industry in the coming years. Globally, the successful and profitable expansion of captive sturgeon farming over the last three decades has fulfilled the consumer market's demand for caviar and meat, resulting in a considerable decline in the global caviar price. Given the presence of successful sturgeon farming in the Arabian Gulf region (Saudi Arabia and the United Arab Emirates), the prospect of introducing the sturgeon farming industry in Egypt is underlined. In comparison to other nations in the region, Egypt has excellent prospects for establishing such an aquaculture business, as Egypt's aquaculture sector is by far the largest in Africa and the sixth largest internationally. Furthermore, the availability of qualified workers, the diversity of water sources, and Egypt's moderate climate and environment increase the likelihood of successful sturgeon farming.
Comparative analysis of deep learning and classical time series methods to forecast natural gas demand during COVID-19 pandemic

ABSTRACT The lockdown measures implemented to contain the COVID-19 pandemic have had a considerable effect on the consumption of natural gas, which is closely linked to the economic growth of countries. Accurately forecasting natural gas demand is critical for making informed decisions in unprecedented and unexpected situations. This study aims to compare artificial learning-based algorithms and classical statistical time series models in predicting natural gas demand during the pandemic, using Turkey as a case study. Common time series prediction methods, including Autoregressive Integrated Moving Average (ARIMA), Nonlinear Autoregression Neural Network (NARNN), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM), were utilized for this purpose. The impact of the pandemic on natural gas demand was analyzed by including 2-year natural gas consumption data since its onset. Root mean square error (RMSE), correlation coefficient (R), and mean absolute error (MAE) criteria were used as performance evaluation metrics to select the best model. The results confirmed that the deep-learning-based LSTM model provided better prediction accuracy than time-series benchmark models, with the lowest RMSE (9.442) and the highest R (0.997) values in the test dataset. Furthermore, the results were validated by statistical analysis using the Diebold-Mariano and Nemenyi tests.
Fusion of Appearance and Motion Features for Daily Activity Recognition from Egocentric Perspective

Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person’s activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP–TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
The Role of Virgin Coconut Oil in Corn Starch/NCC-Based Nanocomposite Film Matrix: Physical, Mechanical, and Water Vapor Transmission Characteristics

Corn starch-based nanocomposite films usually have low moisture barrier properties. Adding virgin coconut oil (VCO) as a hydrophobic component can improve the nanocomposite film’s characteristics, especially the film’s permeability and elongation properties. This study aimed to determine the role of VCO with various concentrations (0, 3, 5 wt%) on the physical, mechanical, and water vapor transmission characteristics of corn starch/NCC-based nanocomposite films. Adding 3% VCO to the film showed the lowest WVTR value by 4.721 g/m2.h. At the same time, the value of tensile strength was 4.243 MPa, elongation 69.28%, modulus of elasticity 0.062 MPa, thickness 0.219 mm, lightness 98.77, and water solubility 40.51%. However, adding 5 wt% VCO to the film increased the film’s elongation properties by 83.87%. The SEM test showed that adding VCO formed a finer structure with pores in several areas. The FTIR films showed that adding VCO caused a slightly higher absorption peak shift at the O–H groups and new absorption peaks at wave numbers 1741 cm−1 and 1742 cm−1. The results of this study may provide opportunities for the development of nanocomposite films as biodegradable packaging in the future.
An Adaptive Two-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems

Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective optimization problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population evolutionary algorithm, named ATEA, which dynamically exploits promising information under infeasible solutions to facilitate objective optimization and constraint satisfaction. Specifically, a two-population collaboration mechanism is designed to balance the unconstrained Pareto front search and constrained Pareto front search. Moreover, an adaptive constraint handling strategy is presented to reasonably deploy search resources. Furthermore, a promising infeasibility-based environmental selection and an elitist feasibility-based environmental selection are developed for the two populations to break through complex infeasible barriers and enhance selection pressure, respectively. Comparison experimental results of ATEA with five state-of-the-art algorithms on 33 benchmark test problems and 4 real-word CMOPs demonstrate that ATEA performs competitively with the chosen designs.