Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Strawberry Leaf Color Using RGB Mean Values Based on Soil Physicochemical Parameters Using Machine Learning Models

Intensively grown strawberries in a greenhouse require frequent and precise soil physicochemical constituents for optimal production. Strawberry leaf color analyses are the most effective way to evaluate soil status and protect against excess environmental nutrients and financial setbacks. Meanwhile, precision agriculture (PA) endorsements have been utilized to mimic solutions to these problems. This research aimed to create machine learning models such as multiple linear regression (MLR) and gradient boost regression (GBR) for simulating strawberry leaf color changes related to soil physicochemical components and plant age using RGB (red, green, and blue) mean values. The soil physicochemical properties of the largest varied colored leaves of strawberry were precisely measured by a multifunctional soil sensor from the rooting zones. Simultaneously, 400 strawberry leaflets were detached in each vegetative and reproductive stage, and individual leaves were captured using a digital imaging system. The RGB mean values of colored images were extracted using the image segmentation algorithms of image processing technique. Consequently, MLR and GBR models were developed to predict leaf RGB mean values based on soil physicochemical measurements and plant age. The GBR model vigorously fitted with RGB mean values throughout the growth stage, with R2 and RMSE values of (R = 0.77, 7.16, G = 0.72, 7.37, and B = 0.70, 5.68), respectively. Furthermore, the MLR model performed moderately with R2 and RMSE values of (R = 0.67, 8.59, G = 0.57, 9.12, and B = 0.56, 6.81) when consecutively predicting RGB mean values in strawberry leaves. Eventually, the GBR model performed more effectively than the MLR model with high-performance metrics. In addition, the leaf color model uses visualization technology to measure growth progress, and it performs well in predicting dynamic changes in strawberry leaf color.
A Novel Semi-Passive Turbulence Enhancement Method: A Flexible Vortex Generator

II Acknowledgement . IV Nomenclatures . IX List of Figures . XII List of Tables . XVII Chapter 1: Introduction. 1 1.1 Vortex Generation . 1 1.2 Proposed Method – The Flexible Vortex Generator . 3 1.3 Aim, the Research Questions and the Objectives . 5 1.4 Thesis Structure . 6 Summary of chapter . 7 Chapter 2: Backgrounds and Theories . 8 2.1 Reynolds Number . 8 2.2 Turbulence and Kinetic Budget Equations . 9 2.3 The Technical Aspects of the Downsides of Conventional Vortex Generator (VG) . 11 2.3. 1 Energy Loss on Vortex Generator . 11 2.3. 2 Pressure Loss (Penalty) on Vortex Generator . 13 2.4 Flow Separation, Vortex Formation and Vortex Shedding . 15 2.5 Vortex Generation Methods . 18 2.6 Vortex-Induced Vibration . 20 2.6. 1 Lock-in . 21 2.7 The Flexible Vortex Generator (FVG) . 24 2.7. 1 The Working Principle of the FVG . 25 Summary of Chapter . 28 Chapter 3: Literature Review . 29
Sustained engagement for employee retention in education industry

The field of education is associated with parents, students, teachers, and management. The teaching is a noble profession as it builds the future of a nation and the turnover of these teaching professionalscan adversely affect the teaching learning process and long-term health and success of the Institute.It is therefore penultimate to sustain engagementfor retainingtheseemployees.The studyexaminedthe relationship between Employee engagement and Employee retention. It also suggests measures for improvingemployee retention in higher education institutions. Primary data has been collected and analysis is done using SPSS software. It is found that when employees are valued, they get motivatedto go the extra mile.They show a sense of belonging to the institute, get engaged in productive activities and stay for a longer duration in the institute.
The analytical, the political and the personal: Swedish stakeholder narratives about alcohol policy at football stadiums

ABSTRACT For public health interventions to be effective, they need to be supported or at least accepted by those affected, and social policy should therefore be understood as political and strategic. This raises questions about the relationship between the analytical, the political and the personal in policy processes. This article offers an in-depth analysis of such issues, as they were enacted during interviews with Swedish alcohol policy stakeholders. It focuses on the assumptions and a priori ‘truths’ articulated in interviews about Responsible Beverage Services (RBS) at Swedish football stadiums or ‘Football Without Bingeing’. We argue that the participants combined different narrative forms, such as seemingly objective chronological accounts and personal ethical judgments, in talking about the policy initiative. Through such narrative intersections, three key ‘truths’ were produced that reinforced the link between alcohol and violence, necessitated blanket population-level measures to reduce alcohol use and made gendered behavior an irrelevant policy target.
Comparative Analysis of Chloroplast Genomes of Dalbergia Species for Identification and Phylogenetic Analysis

Dalbergia L.f. is a pantropical genus consisting of 269 species of trees, shrubs, and woody lianas. This genus is listed in CITES Appendices because of illegal logging and trafficking driven by the high economic value of its heartwood. Some species are also used medicinally. Species identification of Dalbergia timber and herbs is challenging but essential for CITES implementation. Molecular methods had been developed for some timber species, mostly from Madagascar and Southeast Asia, but medicinal species in south China were usually not included in those studies. Here, we sequenced and assembled the chloroplast genomes of five Dalbergia species native to Hong Kong, four of which are medicinal plants. Our aim is to find potential genetic markers for the identification of medicinal Dalbergia species based on divergence hotspots detected in chloroplast genomes after comparative and phylogenetic analysis. Dalbergia chloroplast genomes displayed the typical quadripartite structure, with the 50 kb inversion found in most Papilionoideae lineages. Their sizes and gene content are well conserved. Phylogenetic tree of Dalbergia chloroplast genomes showed an overall topology similar to that of ITS sequences. Four divergence hotspots (trnL(UAA)-trnT(UGU), ndhG-ndhI, ycf1a and ycf1b) were identified and candidate markers for identification of several Dalbergia species were suggested.
Quaternion-based nonlinear trajectory tracking control of a quadrotor unmanned aerial vehicle

At present, most controllers of quadrotor unmanned aerial vehicles(UAVs) use Euler angles to express attitude. These controllers suffer a singularity problem when the pitch angle is near 90°C, which limits the maneuverability of the UAV. To overcome this problem, based on the quaternion attitude representation, a 6 degree of freedom(DOF) nonlinear controller of a quadrotor UAV is designed using the trajectory linearization control(TLC) method. The overall controller contains a position sub-controller and an attitude sub-controller. The two controllers regulate the translational and rotational motion of the UAV, respectively. The controller is improved by using the commanded value instead of the nominal value as the input of the inner control loop. The performance of controller is tested by simulation before and after the improvement, the results show that the improved controller is better. The proposed controller is also tested via numerical simulation and real flights and is compared with the traditional controller based on Euler angles. The test results confirm the feasibility and the robustness of the proposed nonlinear controller. The proposed controller can successfully solve the singularity problem that usually occurs in the current attitude control of UAV and it is easy to be realized.
Multimission Fuel-Burn Minimization in Aircraft Design: A Surrogate-Modeling Approach

Multimission Fuel-Burn Minimization in Aircraft Design: A Surrogate-Modeling Approach Rhea Patricia Liem Doctor of Philosophy Graduate Department of University of Toronto Institute for Aerospace Studies University of Toronto 2015 Aerodynamic shape and aerostructural design optimizations that maximize the performance at a single flight condition result in designs with unacceptable off-design performance. While considering multiple flight conditions in the optimization improves the robustness of the designs, there is a need to develop a rational strategy for choosing the flight conditions and their relative emphases such that multipoint optimizations reflect the true objective function. In addition, there is a need to consider uncertain missions and flight conditions. In this thesis, the strategies to formulate the multipoint objective functions for aerodynamic shape and aerostructural optimization are presented. To determine the flight conditions and their corresponding weights, a novel surrogate-based mission analysis is developed to efficiently analyze hundreds of actual mission data to emulate their flight condition distribution. Using accurate and reliable surrogate models to approximate the aerodynamic coefficients used in the analysis makes this procedure computationally tractable. A mixture of experts (ME) approach is developed to overcome the limitations of conventional surrogate models in modeling the complex transonic drag profile. The ME approach combines multiple surrogate models probabilistically based on the divide-andconquer strategy. Using this model in the mission analysis significantly improves the range estimation accuracy, as compared to other conventional surrogate models. As expected, the multipoint aerodynamic shape and aerostructural optimizations demonstrate a consistent drag reduction, instead of the localized improvement by the single-point optimizations. The improved robustness in the multipoint optimized designs was also observed in terms of the improved range performance and more consistent fuel-burn reduction across the different missions. The results presented in this thesis show that the surrogate-model-assisted multipoint optimization produces a robust design that is optimized for a set of flight conditions matching real-world operations, which is ensured by the use of historical flight data.
High strength TRIP/TWIP and superplastic steels : development, properties, application

Fine grained high carbon superplastic steels and high manganese TRIP steels with amounts of aluminium of about 7 wt-% exhibit high strength and fracture toughness at room temperature. The duplex microstructure of these superplastic steels causes uniform elongations up to 1000 % at medium and higher temperature (up to 860 °C). The TRIP effect (transformation induced plasticity) causes large elongations of about 80 % and high work hardening rates at low temperature (- 100 to 20 °C). The unique properties of the superplastic and of the TRIP steels are used for near net shape forming of machine parts and for deep drawing operations of sheets to tailored blanks in the car body manufacturing.
Pre- and Postnatal Vitamin D Status and Allergy Outcomes in Early Childhood

The dramatic increase in the prevalence of allergic disease in recent decades reflects environmental and behavioural changes that have altered patterns of early immune development. The very early onset of allergic diseases points to the specific vulnerability of the developing immune system to environmental changes and the development of primary intervention strategies is crucial to address this unparalleled burden. Vitamin D is known to have immunomodulatory functions. While allergic disease is multifactorial, associations with reduced sunlight exposure have led to the hypothesis that suboptimal vitamin D levels during critical early periods may be one possible explanation. Interventions to improve vitamin D status, especially in early life, may be the key to allergic disease prevention.
Crude glycerol, a biodiesel byproduct, used as a soil amendment to temporarily immobilise and then release nitrogen

Loss of nitrate‐nitrogen (NO3−–N) from Midwestern U.S. agricultural fields can impair water quality and be an economic loss to farmers. Winter cover crops have shown promise as a remedy, but low adoption illustrates the need for alternatives. Here, we tested whether adding a carbon (C)‐rich soil amendment (i.e., crude glycerol, a biodiesel byproduct) can increase soil microbial biomass (MB) and promote N immobilisation under various conditions and then determined whether and when immobilised N would be released. We conducted a laboratory incubation with a full factorial combination of four glycerol rates (0, +117, +468 and +1872 mg C kg−1 soil), three supplemental NO3−–N rates (0, +10 and +40 mg N kg−1) and two soils (Clarion clay loam and Sparta loamy sand). Soil inorganic N (NH4+–N and NO3−–N) and MB were measured at seven and three time points, respectively, across the 98 days incubation period. Across all treatments, glycerol increased MBN in both short term (7 days; 4%–1137% compared to no glycerol addition) and long term (98 days; 10%–169%) and decreased NO3−–N with increasing rate of glycerol. Adding glycerol caused net N immobilisation of 21%–61% (+117 mg C kg−1 addition) and ~100% (+468 and +1872 mg C kg−1 addition) compared to the control. Some of that immobilised inorganic N was likely released through MB turnover, but timing and rate of release depended on the soil and added N rate. Adding 40 mg N kg−1 with no glycerol showed nearly twice the net N mineralisation rate than with the low or no applied N – providing evidence for soil N priming. Overall, glycerol has the potential for use as a soil amendment to increase MB and temporarily immobilise NO3−–N and then make some of that N crop available through MB turnover.
Leonardite powder as an efficient adsorbent for cationic and anionic dyes

This paper aims to investigate the uses of leonardite powder (LP) as an effective adsorbent for the removal of basic red 18 (BR18) and reactive red 180 (RR180) dyes. LP was characterized using scanning electron microscopy (SEM), energy‐dispersive X‐ray spectroscopy (EDX), Zeta potential, Brunauer–Emmett–Teller (BET) analysis, Fourier transform infrared spectroscopy (FTIR), and X‐ray fluorescence (XRF). The adsorption process was assessed based on pH, size and the amount of the adsorbent, BR18 and RR180 concentration, and the contact time. BR18 dye was completely adsorbed onto the LP (the removal efficiency equals 100%) after 45 min at the optimum condition (original pH [6.5], the particle size of 45 μm, the adsorbent dose of 0.25 g/L, and the initial concentration of 10 mg/L). For RR180, the maximum removal efficiency (74%) was obtained when 1 g/L LP with 45 um size was added to an RR180 solution of 10 mg/L concentration. Temkin isotherm was used to explain the adsorption of BR18. In contrast, RR180 adsorption was described by the Freundlich model. The adsorptions of both dyes followed the pseudo‐second‐order kinetics. The reusability of the LP was assessed. For BR18, the efficiency decreased to 96% in the second cycle and reached 42% in the fifth cycle. In RR180, LP was not able to be reused efficiently. As a result, the LP ability for BR18 removal is higher than the RR180 in terms of uptake and reusability.
Production, Manufacturing and Logistics Redesigning a warehouse network

To take advantage of economies of scale, a growing number of firms have begun to explore the possibility of integrating supply chain activities. The advent of such a possibility would necessitate the redesign of a warehouse network. Typically, a warehouse redesign problem involves the consolidation of regional warehouses into a fewer number of master stocking points and the subsequent phase-out of redundant or underutilized warehouses without deteriorating customer services. This paper develops a mixed-integer programming model to solve the warehouse redesign problem. The usefulness of the model was validated by its successful application to a real-world problem and by its sensitivity analyses when used with changing scenarios within a warehouse network configuration. � 2005 Elsevier B.V. All rights reserved.
Autosterilization Effect of Gamma Radiation in Non-sterile Radiopharmaceuticals

Gamma radiation is commonly used in sterilizing various products because of its microbial-killing property. Several radiopharmaceuticals are processed aseptically, which requires a well-managed GMP-based facility. This study aims to ensure that radiopharmaceutical can behave as an auto sterilizing agent since it contains radionuclide that emits gamma radiation. Sm-153-ethylenediaminetetramethylenephosphonate (Sm-153-EDTMP) and I-131-Hippuran were used as models in this study, in which various radioactivities of these products were added into non-sterile vials in the non-aseptic working area and tested for sterility using direct inoculation method. The result showed that samples containing 7 mCi of Sm-153-EDTMP and less than 2 mCi of I-131-orthoiodohippurate (I-131-Hippuran) changed the clarity of the media, but not for those containing higher radioactivity. The results showed that Sm-153-EDTMP and I-131-Hippuran at certain radioactivity can auto sterilize themselves, whereas the positive control sample and the products diluted with non-sterile water remained unsterile. This study showed that radiopharmaceuticals have auto sterilizing properties at relatively low radioactivity, depending on the products’ bioburden. Therefore, the usual aseptic preparation of some radiopharmaceuticals can be considered terminal sterilization.
LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing

Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvement of segmentation accuracy comes with the complexity of deep models. As a result, these models show low inference speeds and high memory usages when deploying to mobile edges. To promote the deployment of deep fundus segmentation models to mobile devices, we aim to design a lightweight fundus segmentation network. Our observation comes from the fact that high-resolution representations could boost the segmentation of tiny fundus structures, and the classification of small fundus structures depends more on local features. To this end, we propose a lightweight segmentation model called LightEyes. We first design a high-resolution backbone network to learn high-resolution representations, so that the spatial relationship between feature maps can be always retained. Meanwhile, considering high-resolution features means high memory usage; for each layer, we use at most 16 convolutional filters to reduce memory usage and decrease training difficulty. LightEyes has been verified on three kinds of fundus segmentation tasks, including the hard exudate, the microaneurysm, and the vessel, on five publicly available datasets. Experimental results show that LightEyes achieves highly competitive segmentation accuracy and segmentation speed compared with state-of-the-art fundus segmentation models, while running at 1.6 images/s Cambricon-1A speed and 51.3 images/s GPU speed with only 36k parameters.
Bottom-up synthesis of meta-atoms as building blocks in self-assembled metamaterials: recent advances and perspectives

Meta-atoms interact with light in interesting ways and offer a large range of exciting properties. They exhibit optical properties inaccessible by natural atoms but their fabrication is notoriously difficult because of the precision required. In this perspective, we present the current research landscape in making meta-atoms, with a focus on the most promising self-assembly approaches and main challenges to overcome, for the development of materials with novel properties at optical frequencies.
Advancement and New Trends in Analysis of Pesticide Residues in Food: A Comprehensive Review

Food safety is a rising challenge worldwide due to the expanding population and the need to produce food to feed the growing population. At the same time, pesticide residues found in high concentrations in fresh agriculture pose a significant threat to food safety. Presently, crop output is being increased by applying herbicides, fungicides, insecticides, pesticides, fertilizers, nematicides, and soil amendments. A combination of factors, including bioaccumulation, widespread usage, selective toxicity, and stability, make pesticides among the most toxic compounds polluting the environment. They are especially harmful in vegetables and fruits because people are exposed to them. Thus, it is critical to monitor pesticide levels in fruits and vegetables using all analytical techniques available. Any evaluation of the condition of pesticide contamination in fruits and vegetables necessitates knowledge of maximum residue levels (MRLs). We set out the problems in determining various types of pesticides in vegetables and fruits, including the complexity and the diversity of matrices in biological materials. This review examines the different analytical techniques to determine the target analytes that must be isolated before final consumption. Many processes involved determining pesticide residues in fruits and vegetables and their advantages and disadvantages have been discussed with recommendations. Furthermore, MRLs of target pesticide residues in fruit and vegetable samples are discussed in the context of data from the literature. The review also examines MRLs’ impact on the international trade of fruits and vegetables. Accurate, sensitive, and robust analytical procedures are critical to ensuring that pesticide levels in food products are effectively regulated. Despite advances in detection technology, effective sample preparation procedures for pesticide residue measurement in cereals and feedstuffs are still needed. In addition, these methods must be compatible with current analytical techniques. Multi-residue approaches that cover a wide range of pesticides are desired, even though pesticides’ diverse natures, classes, and physio-chemical characteristics make such methods challenging to assemble. This review will be valuable to food analysts and regulatory authorities to monitor the quality and safety of fresh food products.
Internal longitudinal damage detection in a steel beam using Lamb waves: Simulation and test study

Lamb waves are one of the promising tools to detect damage in aircraft structures. They can be generated in aircraft structures by piezoelectric actuators, propagate a long distance, and be picked up by several types of sensors. The waves can be easily distorted and attenuated by damage. In this study, internal longitudinal damage in a beam structure is experimentally and numerically detected. In order to create longitudinal Lamb waves, a unidirectional piezoelectric actuator is used. Plane strain models and three-dimensional solid models are developed, and dynamic simulations are performed using ABAQUS/Explicit. Lamb wave signals picked up by multiple sensors through the experiment are compared with the simulation results. Two internal notch damages are detected and the damage locations are estimated with the Lamb wave signals.