Business process management (BPM) has been the main driver behind company optimization and operational efficiency. However, the digitization era we live in necessitates that organizations be agile and adaptable. Delivering unprecedented rates of automation-fueled agility is necessary to be a part of this digital revolution. On the other hand, BPM automation cannot be done only by concentrating on procedure space and traditional planning methodologies. With the introduction of BPM, where the deployment of BPM with cloud computing has undergone enormous development lately, cloud computing has been considered a particularly active topic of study. Cloud computing points to the provision of dependable computing environments based on improved infrastructure availability and service quality without imposing a significant cost load. This research aims to discover the relationship between technical factors, financial factors, environmental factors, security of the cloud-based information systems, and the agile development of industrial BPM (IBPM). The present study aims to fill this gap and show how partial least squares structural equation modeling (SEM) can be employed in this field. Importance–performance map analysis (IPMA) evaluated the importance and performance of factors in the SEM. IPMA enables the identification of factors with relatively low performance but relatively high importance in shaping dependent variables. The empirical findings showed that four key factors (technical, financial, environmental, and security) positively influence the agile development of IBPM.

COVID-19 is erupting globally and Wuhan successfully controlled it within a month. Infections arose from infectious persons outside hospitals. After data revision, data-based and model-based analyses are implemented and the conclusions are as follows. The incubation period of most infected people may be 6-7 days. The number of infectious persons outside hospitals in Wuhan on Jan.20 is about 10000 and reached more than 20000 on the day of Lockdown, it exceeded 72000 on Feb.4. Both data-based and model-based analyses gave out the evolution of the reproduction number, which is over 2.5 in early January, then go down to 1.62 in late January and 1.20 in early February, a sudden drop to less than 0.5 due to the strict Stay-at-home management after Feb.11. Strategies of Stay-at-home, Safe-protective measures and Ark hospitals are the main contributions to control COVID-19 in Wuhan. Two inflection points of COVID-19 in Wuhan exactly correspond to Feb.5 and Feb.15, the two days when Ark hospitals were introduced and the complete implementation of Stay-at-home. Based on the expression of the reproduction number, group immunity also is discussed. It shows that only when the group immunization rate is over 75 percent can COVID-19 be under control, group immunity actually would be full infection and the total deaths will be 220,000 for a city as big as Wuhan. Sensitivity analysis suggests that 30 percent of people staying at home in combination with better behavior changes, such as social-distancing and frequent hand-washing, can effectively contain COVID-19. But only when this proportion is over 60 percent can the control effect and efficiency like Wuhan be obtained.

The impacts of training image sizes and optimizers on deep convolutional neural networks for weed detection in alfalfa have not been well explored. In this research, AlexNet, GoogLeNet, VGGNet, and ResNet were trained with various sizes of input images, including 200 × 200, 400 × 400, 600 × 600, and 800 × 800 pixels, and deep learning optimizers including Adagrad, AdaDelta, Adaptive Moment Estimation (Adam) and Stochastic Gradient Descent (SGD). Increasing input image sizes reduced the classification accuracy of all neural networks. The neural networks trained with the input images of 200 × 200 pixels resulted in better classification accuracy than the other image sizes investigated here. The optimizers affected the performance of the neural networks for weed detection. AlexNet and GoogLeNet trained with AdaDelta and SGD outperformed Adagrad and Adam; VGGNet trained with AdaDelta outperformed Adagrad, Adam, and SGD; and ResNet trained with AdaDelta and Adagrad outperformed the Adam and SGD. When the neural networks were trained with the best-performed input image size (200 × 200 pixels) and the deep learning optimizer, VGGNet was the most effective neural network with high precision and recall values (≥0.99) in the validation and testing datasets. At the same time, ResNet was the least effective neural network for classifying images containing weeds. However, the detection accuracy did not differ between broadleaf and grass weeds for the different neural networks studied here. The developed neural networks can be used for scouting weed infestations in alfalfa and further integrated into the machine vision subsystem of smart sprayers for site-specific weed control.

In this paper, the instability of shallow-water shear flow with a sheared parallel magnetic field is studied. Waves propagating in such magnetic shear flows encounter critical levels where the phase velocity relative to the basic flow, $c-U(y)$, matches the Alfvén wave velocities $\pm B(y)/\sqrt {\mu \rho }$, based on the local magnetic field $B(y)$, the magnetic permeability $\mu$, and the mass density of the fluid $\rho$. It is shown that when the two critical levels are close to each other, the critical layer can generate an instability. The instability problem is solved, combining asymptotic solutions at large wavenumbers and numerical solutions, and the mechanism of instability explained using the conservation of momentum. For the shallow-water magnetohydrodynamic system, the paper gives the general form of the local differential equation governing such coalescing critical layers for any generic field and flow profiles, and determines precisely how the magnetic field modifies the purely hydrodynamic stability criterion based on the potential vorticity gradient in the critical layer. The curvature of the magnetic field profile, or equivalently the electric current gradient $J' = - B''/\mu$ in the critical layer, is found to play a complementary role in the instability.

Neuromedin U (NMU) has a critical function on the regulation of food intake in mammals, while the information is little in teleost. To investigate the function of NMU on appetite regulation of Siberian sturgeon (Acipenser baerii), this study firstly cloned nmu cDNA sequence that encoded 154 amino acids including NMU-25 peptide. Besides, the results showed that nmu mRNA was widely distributed in various tissues especially in the hypothalamus and telencephalon. The results of nutritional status (pre-feeding and post-feeding, fasting and re-feeding) experiments showed that nmu mRNA expression was significantly decreased at 1 and 3 h after feeding in different brain regions. Similarly, after feeding, the expression of nmu significantly decreased in peripheral tissues. Moreover, nmu expression in the hypothalamus was significantly increased after fasting 1 day, but decreased after fasting 17 days, which was significantly reversed after re-feeding. However, other brain regions like telencephalon and peripheral tissues like esophagus, intestinum valvula and liver have different change patterns. Further study showed that acute i.c.v. and i.p. injection of NMU and chronic i.p. injection of NMU significantly reduced the food intake in a dose dependent mode. In addition, the expressions of several critical appetite factors (nmu, aplein, cart, cck, ghrelin, npy, nucb2, pyy and ucn3) were significantly affected by acute NMU-25 administration in the hypothalamus, intestinum valvula and liver. These results indicate that NMU-25 has the anorexigenic function on food intake by affecting different appetite factors in Siberian sturgeon, which provides a foundation for further exploring the appetite regulation networks in fish.

Due to the lack of research between the inner layers in the structure of colonic mucous and the metabolism of fatty acid in the constipation model, we aim to determine the changes in the mucous phenotype of the colonic glycocalyx and the microbial community structure following treatment with Rhubarb extract in our research. The constipation and treatment models are generated using adult male C57BL/6N mice. We perform light microscopy and transmission electron microscopy (TEM) to detect a Muc2-rich inner mucus layer attached to mice colon under different conditions. In addition, 16S rDNA sequencing is performed to examine the intestinal flora. According to TEM images, we demonstrate that Rhubarb can promote mucin secretion and find direct evidence of dendritic structure-linked mucus structures with its assembly into a lamellar network in a pore size distribution in the isolated colon section. Moreover, the diversity of intestinal flora has noticeable changes in constipated mice. The present study characterizes a dendritic structure and persistent cross-links have significant changes accompanied by the alteration of intestinal flora in feces in models of constipation and pretreatment with Rhubarb extract.

A new system for preparing 14C samples was established for a compact accelerator mass spectrometer (GXNU-AMS) at Guangxi Normal University. This sample preparation system consists of three units: a vacuum maintenance unit, a CO2 purification unit, and a CO2 reduction unit, all of which were made of quartz glass. A series of radiocarbon (14C) preparation experiments were conducted to verify the reliability of the system. The recovery rate of graphite obtained was more than 80%. The carbon content in the commercial toner and wood sample was linearly fitted to the CO2 pressure in the measurement unit of the system. The results showed a good linear relationship, indicating that the reliability of the sample preparation system. AMS measurements were conducted on a batch of standard, wood, and dead graphite samples prepared using this system. The results showed that the beam current of 12C- for each sample was more than 40 μA, the carbon contamination introduced during the sample preparation process was ∼ 2 × 10–15, and that the new sample preparation system is compact, low-contamination, and efficient and meets the GXNU-AMS requirements for 14C samples.

In this paper, effects of discharge parameters and modulation frequency on the signal of laser-induced fluorescence measurements of ion velocity distribution functions are investigated in the LIF Test Source. A maximum modulation frequency is found for each given set of parameters, beyond which the signal gradually declines. Meanwhile, this maximum modulation frequency occurred consistently at ~1/10 of the theoretical frequency limit and photon counts received by a photomultiplier tube, which indicates that as modulation frequency and the associated per-pulse-excitation-event count decrease, the transition from the macroscopic statistical signal to the microscopic probabilistic signal is a gradual process.

Under global warming, many glaciers worldwide are receding. However, recent studies have suggested the extension of the Karakoram Anomaly, a region of anomalous glacier mass gain, into the western Kunlun and eastern Pamir mountains. However, the eastern limit of this anomaly in the Kunlun Mountains is unclear. This study, using changes in glacier area and surface elevation, estimates the eastern limit of the Kunlun-Pamir-Karakoram anomaly at ~85°E. Over the past 50 years, glaciers west of 85°E in the Kunlun Mountains decreased in area from 8401 to 7945 km2 at a rate of −0.12 ± 0.07% a−1, showed a reduction in the rate of retreat through time and have recently gained mass, with surface elevation changes of 0.15 ± 0.35 m a−1 over the period of 2000–2013. Glaciers east of 85°E have experienced greater rates of area change (−61 ± 12 km2 and −0.43 ± 0.13% a−1) over the past 50 years, accelerated area loss in recent years and elevation change rate of −0.51 ± 0.18 m a−1 between 2000 and 2013. These patterns of elevation and area change are consistent with regional increases in summer temperature in the eastern Kunlun Mountains and slight cooling in the western Kunlun Mountains.

This study is performed to figure out how the presence of diabetes affects the infection, progression and prognosis of 2019 novel coronavirus disease (COVID-19), and the effective therapy that can treat the diabetes-complicated patients with COVID-19. A multicentre study was performed in four hospitals. COVID-19 patients with diabetes mellitus (DM) or hyperglycaemia were compared with those without these conditions and matched by propensity score matching for their clinical progress and outcome. Totally, 2444 confirmed COVID-19 patients were recruited, from whom 336 had DM. Compared to 1344 non-DM patients with age and sex matched, DM-COVID-19 patients had significantly higher rates of intensive care unit entrance (12.43% vs. 6.58%, P = 0.014), kidney failure (9.20% vs. 4.05%, P = 0.027) and mortality (25.00% vs. 18.15%, P < 0.001). Age and sex-stratified comparison revealed increased susceptibility to COVID-19 only from females with DM. For either non-DM or DM group, hyperglycaemia was associated with adverse outcomes, featured by higher rates of severe pneumonia and mortality, in comparison with non-hyperglycaemia. This was accompanied by significantly altered laboratory indicators including lymphocyte and neutrophil percentage, C-reactive protein and urea nitrogen level, all with correlation coefficients >0.35. Both diabetes and hyperglycaemia were independently associated with adverse prognosis of COVID-19, with hazard ratios of 10.41 and 3.58, respectively.

The parasite Fasciola hepatica is an important zoonotic parasite. The development of an animal model of F. hepatica's life cycle is critical for studying the biological characteristics of the parasite in snails and mammals. Eggs of F. hepatica of bovine origin were cultured, and metacercariae were obtained after infection of Galba pervia snails. The life cycle system of F. hepatica was initiated in 2 different animals by orally infecting rabbits, SD rats and Kunming mice with the metacercariae. The animals' survival after infection, parasite migration in the animals and pathological damage to the liver were observed. We discovered that rabbits died due to acute suppurative hepatitis 60–69 days after infection, and eggs were found in the feces on day 63 of infection. The liver of SD rats showed punctate lesions on day 3 of infection, and further changes occurred as the infection progressed. However, liver repair was observed at week 9. SD rats survived for more than a year after infection and continued the F. hepatica life cycle. The liver lesions in Kunming mice after infection were similar but more severe than those in SD rats. Death was observed on the 31st post-infection day. We discovered that while rabbits, SD rats and Kunming mice can all be used as animal models of F. hepatica, SD rats are more suitable experimental animals in terms of tolerance and pathological response.

Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

It is important to know how much of the increased atmospheric CO2 is derived from fossil fuel emissions. Here, we review the progress in atmospheric fossil fuel CO2 (CO2ff) tracing over recent years by measurement of Δ14C in Chinese cities. In this paper we make progress by expanding the analysis from some locations to more regional views, by combining observations with modeling, and by making a preliminary comparison of observation-derived CO2ff with inventory-derived CO2ff. We have obtained a general picture of Chinese urban CO2ff and characteristics of its spatio-temporal variations at different scale, and identified the corresponding influencing factors. Interestingly, we found that the weekend effect of CO2ff was less evident in Chinese cities. In addition, we observed simultaneous variations in CO2ff and PM2.5 in a winter haze event in Beijing and a simultaneous decrease in annual averages of CO2ff and PM2.5 in Xi’an based on multi-year (2011–2016) Δ14CO2 monitoring. We found that local coal combustion was the main source of CO2ff in Xi’an, which is located in the Guanzhong basin, by applying a WRF-Chem model and looking at δ13C signatures. Thus, reduction of coal consumption is a crucial target for carbon emissions reduction in China.

Many waterflooding oil fields, injecting water into an oil-bearing reservoir for pressure maintenance, are in their middle to late stages of development. To explore the geological conditions and improve oilfield recovery of the most important well group of the Hu 136 block, located on the border areas of three provinces (Henan, Shandong, and Hebei), Zhongyuan Oilfield, Sinopec, central China, a 14C cross-well tracer monitoring technology was developed and applied in monitoring the development status and recognize the heterogeneity of oil reservoirs. The tracer response in the production well was tracked, and the water drive speed, swept volume of the injection fluid were obtained. Finally, the reservoir heterogeneity characteristics, such as the dilution coefficient, porosity, permeability, and average pore-throat radius, were fitted according to the mathematical model of the heterogeneous multi-layer inter-well theory. The 14C-AMS technique developed in this work is expected to be a potential analytical method for evaluating underground reservoir characteristics and providing crucial scientific guidance for the mid to late oil field recovery process.