Miracle-dz/UniFAR-FTUs huggingface dataset:
Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains

The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.
Face Detection Algorithm Based on Deep Residual Network

With the rapid development of artificial intelligence, face detection technology is widely used in our daily lives, such as mobile payment, video conferencing and personal identification. However, face the scenario while the face been blocking or crowding, the face detection accuracy would be greatly reduced. Therefore, in this paper, a high-precision face detection algorithm based on deep residual network has been proposed to solve this issue. Firstly, adding neural framework branches based on the Resnet-50 to improve the detection accuracy. Then import the soft-NMS method to enhance the robustness of algorithm. Experimental test on the public data set FDDB, the results indicate that the accuracy of this algorithm can reach 94.2% with good robustness, both accuracy and speed are better than the previous algorithm.
Content of some chemical components in carrot (Daucus carota L.) roots depending on growth stimulators and stubble crops.

Agronomic factors, and mineral fertilization in particular, have a strong impact on the quality of carrot roots. In recent years, researchers have begun to design eco-friendly agricultural practices that would stimulate the quality and yield of carrot roots. Our aim has been to analyse selected quality parameters pertaining to the chemical composition of carrot roots, and relate them to the application of different growth stimulators and cover crops. For this purpose, a field experiment was conducted in 2009-2011 investigating the effect of growth stimulators and stubble crops on the accumulation of chemical components in storage roots of carrot (cv. Laguna F1). The study included three growth stimulators: Asahi SL, Bio-algeen S 90 and Tytanit. Plots without any foliar application of these growth stimulators served as control treatment. Another factor consisted of the previous crop (spring barley) and stubble crops: tansy phacelia and a mixture of spring vetch and field pea, grown after the harvest of barley and then ploughed in. All the growth stimulators significantly reduced the NO3 content in carrot roots. Asahi SL significantly increased the phenolic content. The effects of the growth stimulators on the macronutrient content in carrot roots were statistically significant in the case of Mg and Na. Stubble cropping contributed to the reduction of nitrates and stimulated an increase in the content of phenolics as well as of P, K, Ca and Mg in carrot roots.
Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for turning operations. In this work, a machining condition-based optimization has been performed to decrease the total specific energy (SEC), carbon emission (CE), and average roughness (AR) of the actively driven rotary turning (ADRT) process. The processing factors are the tool rotational speed (Tv), depth of cut (a), feed rate (fr), and workpiece speed (Wv). The turning experiments of the mold material labeled SKD11 have been conducted on a CNC lathe. The regression method is employed to develop comprehensive models of the total specific energy, carbon emissions, and average roughness. The entropy approach is then applied to drive out the weight value of each ADRT response. Finally, the non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. The findings indicated that the optimal values of the Tv, a, fr, and Wv are 77 m/min, 0.32 mm, 0.25 mm/rev., and 128 m/min, respectively. The SEC, AR, and CE are decreased by 18.07%, 10.46%, and 5.02%, respectively, as compared to the initial approach. Moreover, the developed active rotary turning operation can be considered as an effective technical solution to boost the machining efficiency of hardened steels.
Fertilizer Granulation: Comparison of Modeling Languages

Industrially produced fertilizers are of key importance to produce enough food for a growing global population. On-going work deals with models of the granulation loop in fertilizer production, based on a population balance that ﬁnds the particle size distribution of the product. The model is intended for control design in order to dampen or remove production oscillation for reduced energy consumption and improved product quality. In this paper, ef-ﬁciency of model implementation is studied in addition to the possibility to automate the computation of a linear approximation of the model for control synthesis. In the implementation study, the current tailor-made MATLAB solver for the model was cloned in computer language Julia. In addition, the implementations in both languages (MATLAB, Julia) were rewritten in a form that allows for use of the standard differential equation solvers of the respective languages. Results indicate that by changing from the tailor-made solvers to using the built-in solvers leads to a speed increase in the order of 6 times. Furthermore, results indicate that the Julia implementations are ca. 5 times faster than the MATLAB implementations. Overall, the fastest Julia implementation was 36 times faster than the current MATLAB implementation. The MATLAB execution can be sped up by using MAT-LAB Coder to convert the code to efﬁcient C-code which is then used to generate a DLL. DLLs can be executed virtually without overhead from Julia. By measuring the execution time for the C-code/DLL vs. a similar implementation in pure Julia, the pure Julia code is ca. 12% faster than the compiled C code. Next, the possibility of automatic linearization of the population balance model in Julia is studied. This is shown to be relatively straightforward. The linear approximation is very good for an input perturbation of 10%, and relatively good for an input perturbation of 50%. This indicates that it may be possible to use a linear model approximation for control design.
MODIFIED SIRD MODEL OF EPIDEMIC DISEASE DYNAMICS: A CASE STUDY OF THE COVID-19 CORONAVIRUS

The present study shows that a simple epidemiological model can reproduce the real data accurately. It demonstrates indisputably that the dynamics of the COVID-19 outbreak can be explained by the modified version of the compartmental epidemiological framework Susceptible-Infected-Recovered-Dead (SIRD) model. The parameters of this model can be standardized using prior knowledge. However, out of several time-series data available on several websites, only the number of dead individuals (D(t)) can be regarded as a more reliable representation of the course of the epidemic. Therefore it is wise to convert all the equations of the SIRD Model into a single one in terms of D(t). This modified SIRD model is now able to give reliable forecasts and conveys relevant information compared to more complex models.
A new method of safety detection in high-rise building curtain walls based on natural vibration frequency

With the development of curtain wall technology, the unit curtain wall has been widely used as a decorative component of high-rise buildings, but it has brought great difficulties to the later safety inspection. Based on the theory of thin-slab vibration, the laser vibration measurement technology in this paper is used to analyze the relationship between the multi-order natural frequency and the safety status of the point-supported experimental curtain wall model. At the same time, a corresponding finite element model is established to further quantify the natural frequency and restraining force on the curtain wall. The result shows that there are different relationships between natural frequencies of different orders and the safety of the curtain wall. In actual engineering, it is more sensitive for the first-order natural frequency to changes in the safety status of the curtain wall. A new method of safety detection in high-rise building curtain walls can be achieved by the combination of remote laser vibration measurement technology.
META-ANALYSIS: MENGEVALUASI EFEKTIVITAS PROBLEM BASED LEARNING PADA KEMAMPUAN PEMAHAMAN MATEMATIS SISWA

Problem-based learning (PBL) has been widely applied as a learning method in mathematics education. This meta-analysis study was conducted to summarize the evidence on the effectiveness of PBL on students' comprehension abilities. An associated database was searched for studies that met the inclusion criteria. The estimation method used a random-effect model with a 95% confidence interval (CI). The study analyzed 35 effect sizes from 22 primary studies published between 2011 and 2020, and for a total of 1993, students were included. The Comprehensive Meta-Analysis (CMA) program was used to assist the analysis. As a result of the study, the overall effect size was 0.790 (moderate effect) with a standard error of 0.181. Analysis of the study characteristics revealed that the application of PBL was more effective in the conditions of the sample size set by 1 - 30 students; lower level of education, and combined with the use of mathematics learning software. These findings become a consideration for mathematics education practitioners for the implementation of PBL in the future.
Comparative Transcriptomics Reveals Osmotic and Ionic Stress Key Genes Contributing To Salinity Tolerance Differential in Two Pak Choi Cultivars

Background: Pak choi is an important leafy vegetable crop. Salinity is among the most harmful agents that negatively influence pak choi yield. However, the mechanism of salinity tolerance in pak choi has not been well understood. In this study, the root transcriptomics of two cultivars differing in salinity tolerant, Shanghaijimaocai (S, salinity tolerant) and Te’aiqing (T, salinity sensitive), were investigated under 0 and 100 mM NaCl treatments.Results: Using de novo assembly, 214,952 assembled unigenes were generated. Totals of 6765, 2454, 2451 and 5798 differentially expressed unigenes (DEUs) were identified in comparison of S100/S0, T100/T0, S0/T0 and S100/T100, respectively. Shanghaijimaocai is more sensitive to NaCl stress than Te’aiqing in terms of root transcriptomics. Based on GO and KEGG pathway analysis, several osmotic and ionic stress-related genes including MP3K18, PYL8, PP2C15/16/49, ARF2, bHLH112, bZIP43, COL5, CDF1/3, ERF25/60, HSFA6, MYBS3/59/92/CCA1/PHL5, POD21, GOLS7, CIPK4/7/12, NHX7, SLAH1 and ALMT13, showed higher expression in Shanghaijimaocai than in Te’aiqing. These genes, therefore, might be contributed to the difference in salinity tolerance. Moreover, the physiological shift of peroxidase activity was in accordance with dynamic transcript profile of the relevant unigenes. Conclusions: We determined digital expression profile and discovered a broad survey of unigenes associated with the difference of salinity tolerance between Shanghaijimaocai and Te’aiqing. These findings would be useful for further functional analysis as potential targets to improve resistance to salinity stress via genetic engineering.
Post-traumatic stress, personal risk and post-traumatic growth among UK journalists

ABSTRACT Background: Journalists covering traumatic news events can develop symptoms of post-traumatic stress disorder (PTSD). However, they may also experience perceived post-traumatic growth (PTG). The outcome may be affected by whether work-related traumatic stress has a degree of personal risk. Objective: To investigate the relationship between PTSD symptoms and PTG among journalists who experienced work-related trauma and to examine whether positive associations would exist between exposure to personal risk and PTG. Method: A web-based survey measuring post-traumatic stress symptoms and post-traumatic growth was completed by print and broadcast journalists (N = 69) working for UK-based media organizations. An open-ended question asked participants how media organizations can help to promote growth after work-related trauma. Results: The findings show a significant relationship between PTSD symptoms and PTG (p = 0.04). Journalists working in war-zones had significantly more PTSD symptoms (p < .001) and PTG scores (p < .001) than those who did not. Journalists who described their worst, work-related trauma as having a degree of personal, life-threatening risk, also reported higher levels of PTG than those who did not (p < .001). This was consistent across all PTG subscales. Conclusions: This study, the first to examine PTSD symptoms, personal risk and post-traumatic growth within journalists, suggests those working in conflict areas experience significantly higher levels of post-traumatic stress and post-traumatic growth, than those who do not. Those who experience personal risk also had high PTG levels. Media companies can help develop PTG by recognizing when personal risk plays a role in covering demanding assignments. Participants suggested organizations also needed to allow sufficient time for reflection and meaning-making for all those working in hostile environments. HIGHLIGHTS • This is the first study examining PTSD and post-traumatic growth (PTG) in UK journalists experiencing work-related trauma with personal risk. • Those in war zones showed more PTSD symptoms and higher PTG. • Journalists called for time and support to reflect after traumatic events.
Diversity of lactic acid bacteria associated with raw yak
(Bos grunniens) milk produced in Pakistan

Pir Mehr Ali Shah Arid Agriculture University, 1 Institute of Food and Nutritional Sciences, 2 Institute of Soil Sciences Shamsabad, Muree Road, Rawalpindi 46000, Punjab, Pakistan 3 National Agricultural Research Centre (NARC), Food Sciences Research Institute Centre Park Road, Islamabad 44000, Pakistan 4 Pir Mehr Ali Shah Arid Agriculture University, Institute of Biochemistry and Biotechnology Shamsabad, Muree Road, Rawalpindi 46000, Punjab, Pakistan
Simulating the thickness effect of the graphene oxide layer in CsPbBr3- based solar cells

Perovskite-based inverted structure of solar cells with TiO2, graphene oxide (GO), and TiO2-GO photo-anodes were analyzed by SCAPS profile. The photovoltaic performance showed that GO produces the maximum power conversion efficiency of 10.34% under illumination. Quantum efficiency, carrier generation, recombination and concentrations through layers of devices were investigated. Then, the impact of GO thickness on photovoltaic characteristics was investigated to optimize the GO thickness. By optimizing the thickness of the GO layer, the efficiency of the device improved from 10.34% to 10.91% in the thickness of 40 nm. These simulation results could provide guidelines to fabricate higher efficiency solar cells.
Amyloid-like amelogenin nanoribbons template mineralization via a low-energy interface of ion binding sites

Significance Understanding how protein scaffolds direct mineral morphogenesis is crucial for engineering bone and tooth and would open new vistas in materials design. In the case of tooth enamel, which is the hardest tissue in the body and consists of organized bundles of coaligned apatite crystals, amyloid-like amelogenin nanoribbons are hypothesized to provide the scaffold. We show that these nanoribbons are far more potent calcium phosphate nucleators than other amelogenin motifs or collagen, which provides the scaffold for bone. This potency stems from a periodic array of charged sites that provide a template for calcium phosphate ion binding on a low-energy interface. The ubiquity of β-sheet protein structures suggests that this mechanism can be adopted for the design of synthetic mineralization-directing scaffolds.
Accelerating ray tracing simulation using tensor completion for 3D radio map

: We proposed a ray-tracing (RT) acceleration method using the tensor completion technique for a three-dimensional (3D) radio map. Internet of things (IoT) has become widespread recently, and analysis of indoor radio wave propagation has become important in optimizing the installation location of the IoT devices. Generally, RT is used for radio wave propagation analysis. However, RT is time-consuming to simulate. The simulation time required for RT is even larger when calculating the receiving points in 3D space with many objects. Hence, in the proposed method, we reduced the number of receiving points at which the RT calculation is performed, and we interpolated by tensor completion the received power not calculated by RT.
Monte Carlo Methods in Quantitative Photoacoustic Tomography

Quantitative photoacoustic tomography (QPAT) is a hybrid biomedical imaging technique that derives its specificity from the wavelength-dependent absorption of near-infrared/visible laser light, and its sensitivity from ultrasonic waves. This promising technique has the potential to reveal more than just structural information, it can also probe tissue function. Specifically, QPAT has the capability to estimate concentrations of endogenous chromophores, such as the concentrations of oxygenated and deoxygenated haemoglobin (from which blood oxygenation can be calculated), as well as the concentrations of exogenous chromophore, e.g. near-infrared dyes or metallic nanoparticles. This process is complicated by the fact that a photoacoustic image is not directly related to the tissue properties via the absorption coefficient, but is proportional to the wavelength-dependent absorption coefficient times the internal light fluence, which is also wavelength-dependent and is in general unknown. This thesis tackles this issue from two angles; firstly, the question of whether certain experimental conditions allow the impact of the fluence to be neglected by assuming it is constant with wavelength, a `linear inversion', is addressed. It is demonstrated that a linear inversion is appropriate only for certain bands of illumination wavelengths and for limited depth. Where this assumption is not accurate, an alternative approach is proposed, whereby the fluence inside the tissue is modelled using a novel Monte Carlo model of light transport. This model calculates the angle-dependent radiance distribution by storing the field in Fourier harmonics, in 2D, or spherical harmonics, in 3D. This thesis demonstrates that a key advantage of computing the radiance in this way is that it simplifies the computation of functional gradients when the estimation of the absorption and scattering coefficients is cast as a nonlinear least-squares problem. Using this approach, it is demonstrated in 2D that the estimation of the absorption coefficient can be performed to a useful level of accuracy, despite the limited accuracy in reconstruction of the scattering coefficient.
Comparative Studies on the Effects of High Sound Levels on the Haematological Parameters and Antioxidant Levels of Wistar Albino Rats

This is a detailed experimental and comparative study evaluating the harmful effects of graded loud noise on hematological and antioxidant in Wistar albino rats. Wistar albino rats were divided into a control group (those kept away from the noise due to a generator set), groups 1 (exposed to varied, 85 - 105 db levels of loud noise from a generator) and groups 2 (exposed to less, 40 - 55 db levels of loud noise from generator set) and exposed to way off fumes from electrical generator sounds at different sound level for 8 hours each day for 28 days. The result indicated that activities and concentrations of the antioxidant enzymes: glutathione peroxidase, catalase and glutathione increased significantly (p < 0.05) when compared with the control group in group 1. There was also an increase in the concentrations of malondialdehyde and hematological parameters (RBC, WBC, PCV, hemoglobin, platelets) among the rats exposed to loud noise from a generator set in group 2, when compared to the control group.
Ormosia (Fabaceae: Faboideae) from the Miocene of southeastern China support historical expansion of the tropical genus in East Asia

ABSTRACT The development of the Asian monsoonal climate sparked a latitudinal shift in vegetation zones from the Oligocene to the Miocene. However, little is known about the history of the megathermal floristic response to the geologic events in key regions like subtropical China. Here, a species of Fabaceae, Ormosia cyclocarpa sp. nov., is recognised based on well-preserved fruits from the Miocene of Zhejiang in middle-subtropical China. This species features dehiscent, compressed suborbicular single-seeded pods with an intramarginal vein. Extensive comparisons with extant/fossil fabaceous taxa confidently support the present taxonomic consideration. Considering the extant distribution, diversity centre and regional climate history, the known fossil occurrences in southern China imply that Ormosia extended northwards during the Miocene from probable tropics of origin into subtropical China, where a once broad latitudinal arid zone prevailed throughout much of the Palaeogene. Such a changing phytogeographic pattern across tropical-subtropical China probably resulted from a drastic East Asian climate change around the Oligo-Miocene boundary, and closely coincided with the early-middle Miocene warming. The spreading of Ormosia and other tropical genera (e.g. Dipterocarpus, Shorea, Bauhinia, Dacrycarpus) sharing similar floristic history in subtropical China indicates a palaeotropical and Miocene origin for some clades of the extant East Asian flora.