Miracle-dz/UniFAR-FTUs huggingface dataset:
Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.
Per-clip and per-bitrate adaptation of the Lagrangian multiplier in video coding

In the past ten years there have been significant developments in optimization of transcoding parameters on a per-clip rather than per-genre basis. In our recent work we have presented per-clip optimization for the Lagrangian multiplier in Rate controlled compression, which yielded BD-Rate improvements of approximately 2% across a corpus of videos using HEVC. However, in a video streaming application, the focus is on optimizing the rate/distortion tradeoff at a particular bitrate and not on average across a range of performance. We observed in previous work that a particular multiplier might give BD rate improvements over a certain range of bitrates, but not the entire range. Using different parameters across the range would improve gains overall. Therefore here we present a framework for choosing the best Lagrangian multiplier on a per-operating point basis across a range of bitrates. In effect, we are trying to find the para-optimal gain across bitrate and distortion for a single clip. In the experiments presented we employ direct optimization techniques to estimate this Lagrangian parameter path approximately 2,000 video clips. The clips are primarily from the YouTube-UGC dataset. We optimize both for bitrate savings as well as distortion metrics (PSNR, SSIM).
Natural resources, child mortality and governance quality in African countries

This paper contributes to the literature by investigating the effect of natural resources on under-five mortality in a sample of 50 African countries over the period 1996 to 2018. We also examine the extent to which governance shapes the relationship between natural resources and under-five mortality. Our results show that natural resources have increased under-five mortality. Resource rents also have detrimental effects on child mortality by age, gender, and the three major causes of infant mortality from infectious diseases. However, an extended analysis of different types of natural resources suggests that point resources (such as oil, natural gas and mineral rents) increase under-five mortality, in contrast to the diffuse resources (such as forest rent). We also find that governance mitigates the positive effect of natural resources on child mortality. Corresponding governance policy thresholds that should be attained in order to reverse the positive effects of natural resources on child mortality are provided. We thus suggest an increase in the funds allocated to the health sector from resource rents and encourage efforts to improve governance standards in sampled countries.
The structure of South African police: Towards a single police service

Abstract The South African crime statistics released by the police minister for the first three months of 2021 reaffirmed the alarming crime problems in the country. Although, the statistics as released by the minister seemingly opined that certain crimes such as contact related crimes, property-related crimes to be declining. Other crimes such as, Truck hijacking, commercial crime, attempted murder, murder, robbery at residential premises, carjacking, robbery at non-residential premises, bank robbery and others are on the increase. This article argues that the high rate of crime in South Africa cannot be severed from the disjointed functions given to public law enforcement establishments in the country. It is argued the public policing agencies in South Africa, such as the Municipal police, Traffic police and the SAPS have the common objective of crime control against law breakers. The central argument is that the alarming rate of crime in the country is essentially because the efforts of these law enforcement agencies are not properly coordinated into a central command structure with uniform strategy and tactics. The main objective of this article is to propose a centralisation of police agencies (Municipal, Traffic and South African Police Services) which will be effective in dealing with crime in the South African communities. This article adopted qualitative research methodology. Twenty participants were purposefully selected. The article makes recommendations, emanating from the research findings.
EVALUATING PROTOTYPING SUPPORT IN EARLY TRANSFORMATIVE PSS DESIGN

Abstract Prototypes are an established tool for rapidly increasing learning, communication and decision making rationale for design projects. The proven success has spawned a litany of approaches and methods for building and planning the efficient planning and construction of prototypes. Translating these methods into simple usable tools to assist novice designers has generated broadly applicable canvases to support prototyping across the design process. Product Service System design has similarly introduced prototyping methods and tools into the process. Presently there is a lack of support for generating early phase tangible prototypes for functional PSS design aimed at more radically innovative solutions instead of currently dominant traditional products with traditional add-on services. This work explores the viability of utilizing existing prototyping support tools in the context of early PSS design through workshops with student designers and practitioners. The data from these workshops illuminates the alignments and misalignment gaps presented as guidelines to enable better support for early PSS designers.
CHEMICAL PROFILES OF ESSENTIAL OILS AND FATTY ACIDS FROM Erythrina velutina

Chemical analysis involving gas chromatography coupled to mass spectrometry of the essential oils of red and orange flowers from E. velutina revealed a predominance of phenylpropanoids in both samples. However, qualitative and quantitative differences were found in the compositions. (E)-Asarone (16.22 ± 0.23%) and (E)-caryophyllene (12.22 ± 0.11%) were identified as the major constituents of the essential oil from the red flowers, whereas (E)-caryophyllene (41.72 ± 1.65%) and eugenol (20.99 ± 0.21%) were identified as the major constituents of the oil from the orange flowers. Seventeen fatty acids were characterized in the red and black seeds. The fatty acid composition in the two chromatic variations had a high degree of similarity. Oleic acid (41.56 ± 1.33% and 46.12 ± 1.90% in the red and black seeds, respectively), palmitic acid (15.39 ± 0.16% and 12.33 ± 0.41%) and behenic acid (9.95 ± 0.14% and 12.10 ± 0.42%) were identified as the main fatty acids. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) enabled identifying significant differences in the chemical profile of the essential oils from the red flower and orange flower varieties of E. velutina. In contrast, PCA and HCA analysis revealed no significant differences in the fatty acid profile of the red and black seeds.
Social media as a window into human-wildlife interactions and zoonotic disease risk: an examination of wild pig hunting videos on YouTube

ABSTRACT Wild pigs (Sus scrofa) damage agriculture and the environment, as well as transmit diseases to animals and people. Hunters are particularly vulnerable to zoonotic disease risks when harvesting wild pigs. Management agencies have endeavored to inform the public about disease risks associated with wild pigs and best practices for mitigating such risks. However, the extent that this guidance has reached hunters and influenced their practices is unclear. We approached this topic through an analysis of wild pig hunting videos on YouTube. We found evidence of relatively few behaviors and communications regarding disease risks and best practices for personal safety. In contrast, many videos showed behaviors that could increase the risk of disease transmission to the subjects and other animals. We emphasize the importance of understanding the influence of social media on viewers and discuss implications for management agencies, including opportunities for strategic messaging in public health campaigns.
Mapping Maize Area in Heterogeneous Agricultural Landscape with Multi-Temporal Sentinel-1 and Sentinel-2 Images Based on Random Forest

Accurate estimation of crop area is essential to adjusting the regional crop planting structure and the rational planning of water resources. However, it is quite challenging to map crops accurately by high-resolution remote sensing images because of the ecological gradient and ecological convergence between crops and non-crops. The purpose of this study is to explore the combining application of high-resolution multi-temporal Sentinel-1 (S1) radar backscatter and Sentinel-2 (S2) optical reflectance images for maize mapping in highly complex and heterogeneous landscapes in the middle reaches of Heihe River, northwest China. We proposed a new two-step method of vegetation extraction and followed by maize extraction, that is, extract the vegetation-covered areas first to reduce the inter-class variance by using a Random Forest (RF) classifier based on S2 data, and then extract the maize distribution in the vegetation area by using another RF classifier based on S1 and/or S2 data. The results demonstrate that the vegetation extraction classifier successfully identified vegetation-covered regions with an overall accuracy above 96% in the study area, and the accuracy of the maize extraction classifier constructed by the combined multi-temporal S1 and S2 images is significantly improved compared with that S1 (alone) or S2 (alone), with an overall accuracy of 87.63%, F1_Score of 0.86, and Kappa coefficient of 0.75. In addition, with the introduction of multi-temporal S1 and/or S2 images in crop growing season, the constructed RF model is more beneficial to maize mapping.
Dealing with the Understanding of the Dynamics Related to Multifactorial Temporal Interactions That Spatially Affect the Landscape of Coastal Lagoons

Models based on multifactorial interactions are needed to deal with the dynamics taking place in the eutrophication processes of coastal lagoons. However, as the number of indirect drivers stemming from anthropogenic factors increases, temporal disorders between anthropogenic activities may increase, thus hindering the understanding of their dynamics. We have built multifactorial pathways to deal with the dynamics associated with the cultural eutrophication process of a coastal lagoon. The pathways guided the identification of potential temporal disorder patterns between anthropogenic activities, which may exert influence on the disturbances associated with eutrophication process. The identification of temporal disorder patterns derived from anthropogenic activities belonging to different pathways resulted in a valuable form of support for analyzing and evaluating relationships between public policies, technological skills and environmental culture programs. All of which exert influence on the eutrophication process, which in turn cause changes on the trophic state and on the landscape of the coastal lagoon. Pathways composed of multifactorial interactions that take into account spatial and temporal aspects, contribute to improving the understanding of the inherent dynamics of the eutrophication process of coastal lagoons. Temporal disorders between anthropogenic activities may be seen to emerge, thus exerting changes on the trophic state and spatial damage on the landscapes of coastal lagoons.
Compact Broadband Rapid-Adiabatic Polarization Splitter-Rotators in a Monolithic Electronic-Photonic SOI Platform

We demonstrate a novel compact, ultra-broadband polarization splitter-rotator (PSR) based on a photonic magic-T and rapid adiabatic mode splitter (RAMS). Polarization cross-talk is <-14dB over 70 nm around 1550 nm, with IL <-0.3dB and a >5× length reduction over a conventional adiabatic PSR.
An efficient and robust algorithm for source reconstruction in the Helmholtz equation

ABSTRACT This work proposes an identification method for reconstructing the characteristic source in the Helmholtz equation from boundary measurements. We formulate the inverse source problem into a shape optimization problem by introducing a least-squares cost function. Using the shape optimization techniques, we prove the existence of an optimal solution to the considered shape optimization problem and we calculate the gradient of the cost function with respect to the shape . By using the Level set method, we present an iterative algorithm to recover numerically the shape . We develop a new technique to initialize the level set algorithm, which permits capturing different hidden shapes. To examine the validity of the proposed method, we illustrate several numerical experiments with different hidden shapes. By adding a level of noise to the measured data, we evaluate the robustness of our reconstruction algorithm.
Electrochemical formation of molybdenum phosphate on a pencil graphite electrode and its potential application for the detection of phosphate ions

The one-step electrochemical preparation of a molybdenum phosphate coated pencil graphite electrode was achieved in phosphoric acid and lithium molybdate solutions at room temperature. The electrodes were prepared by cycling the potential between −1.0 V and +1.9 V (vs. Ag/AgCl) in one-step. The surface morphology of the electrodes was investigated by scanning electron microscopic analysis. The chemical structure of the surface was determined with X-ray photoelectron spectroscopy and X-ray diffraction spectroscopy. The electrochemical behaviors of the electrodes were investigated by cyclic voltammetry and electrochemical impedance spectroscopy. The prepared electrodes were used for the detection of phosphate in soil samples. The molybdenum phosphate modified pencil graphite electrode showed excellent sensitivity for phosphate analysis with a relatively low limit of detection and a limit of quantification of 1.25 × 10−6 M and 4.15 × 10−6 M, respectively. The novel sensor described herein is economical, easy and environmentally-friendly to prepare, and can be used in many environmental analyses for the detection of phosphate with high sensitivity.
Symmetric periodic leaky‐wave antenna for open stopband suppression

In this work, a novel symmetric periodic leaky‐wave antenna is proposed for open stopband suppression. The unit cell of the proposed antenna consists of an H‐shaped ring as the leakage element. Using L–C circuit analysis, it is shown that the open stopband is effectively eliminated by optimizing the H‐shaped ring. A prototype is fabricated to verify the simulated results. The antenna radiates in the broadside direction at 7.9 GHz. The impedance bandwidth corresponding to the n = −1 space harmonic ranges between 5.7 and 11 GHz.
EFFICIENCY OF FOUR DISINFECTANTS AGAINST EIMERIA TENELLA ISOLATES FROM EGYPTIAN CHICKENS (IN VITRO ASSESSMENT)

ABSTRACT Background: Control of coccidiosis depends, in addition to the commercial preparation of herbal extracts, on good sanitation and litter management, along with the use of medication or vaccination programmes. In addition to the chemotherapeutic treatment of coccidiosis early disinfection of the poultry houses should be done using various disinfectants for controlling presence of oocysts.The objective of this research was to evaluate in vitro the action of four different coccidicidal disinfectant including (Quaternary ammonium compounds QACs, chlorocresol, glutaraldehyde and kilcox) on oocysts ofEimeriatenella. Methods: In this study the action of four coccidicidal disinfectant including (Quaternary ammonium compounds QACs, chlorocresol, glutaraldehyde and kilcox) on both sporulated and unsporulatedoocysts ofEimeriatenella was evaluated in vitro. E. tenellaOocysts were obtained from naturally infected Egyptian native breed chickens. The oocysts were exposed to the disinfectants at different concentrations and different contact times. The efficacy of the disinfection was assessed by either destruction of sporulatedoocyst or inhibition of sporulation.Results: It was observed that the most efficacious disinfectants against unsporulated and sporulatedE. tenellaoocystswaskilcox followed by chlorocresol, while QACs and, glutaraldehyde were less effective. kilcox sporulation inhibition reached 100 % on unsporulatedE. tenellaoocysts,and their destructive effect reached 99% on sporulatedoocysts. furthermore, the results showed that both inhibitory and destructive activity of the tested disinfectants was significantly increased by increasing their concentrations and/or the contact time.
Investigation of Relation between Solar Activity and Earthquakes with Deep Learning Method

Solar activity (SA) has been hypothesized to be a trigger of earthquakes, although it is not as intuitively associated as other potential triggers such as tidal stress, rainfall, and the building of artificial water reservoirs. Here, we investigate the relation between SA and global earthquake numbers (GEN) by using a deep learning method to test the hypothesis. We use the daily data of GEN and SA (1996/01/01-2019/12/31) to construct a temporal convolution network (TCN). From the computational results, we confirm that the TCN captures the relation between SA and earthquakes with magnitudes from 4.0 to 4.9. We also find that the TCN achieves better fitting and prediction performance compared with previous work.
Random Trees Are the Cornerstones of Natural Forests

Natural forests serve as the main component of the forest ecosystem. An in-depth interpretation of tree composition and structure of forest community is of great significance for natural forest conservation, monitoring, management, and near-natural silviculture of plantation forest. In this study, we explored the importance of key tree groups—random trees—in natural communities, compared the similarity between the random trees and the communities. This research studies six stem-mapped permanent plots (100 × 100 m2) of the typical natural forests in three different geographic regions of China. Several variables and their distributions were applied to study community characteristics comprehensively, including species abundance, diameter distribution, spatial pattern, mingling, crowding, and competition. The genetic absolute distance method is used to analyze the similarity between the random trees and the communities. Our results show that the features of random trees are highly consistent with the communities. The study proposes that random trees are the cornerstones of natural forests. Its quantitative advantage explains the key role that random trees play in natural forests. The study could provide a scientific insight into the protection, monitoring, and management of forests.
Recent Challenges and Methodologies in Smart Grid Demand Side Management: State-of-the-Art Literature Review

The concept of smart grid was introduced a decade ago. Demand side management (DSM) is one of the crucial aspects of smart grid that provides users with the opportunity to optimize their load usage pattern to fill the gap between energy supply and demand and reduce the peak to average ratio (PAR), thus resulting in energy and economic efficiency ultimately. The application of DSM programs is lucrative for both utility and consumers. Utilities can implement DSM programs to improve the system power quality, power reliability, system efficiency, and energy efficiency, while consumers can experience energy savings, reduction in peak demand, and improvement of system load profile, and they can also maximize usage of renewable energy resources (RERs). In this paper, some of the strategies of DSM including peak shaving and load scheduling are highlighted. Furthermore, the implementation of numerous optimization techniques on DSM is reviewed.
Machine Learning-Based Identification Strategy of Fuel Surrogates for the CFD Simulation of Stratified Operations in Low Temperature Combustion Modes

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A Bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the ‘unicity’ of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the ‘unicity’ of the surrogate composition has revealed that (i) the unicity is strongly correlated with the accuracy and that (ii) both ‘unicity’ and accuracy of the prediction are very sensitive to the high presence of aromatics.