Search Results (5,142)

Aiming at the problem of the low rate of resource utilization of large amounts of grape branch pruning and the high cost of leaving the garden, we design a kind of grape branch picking and crushing collection machine that integrates the collection of strips, the picking up, crushing, and collecting operations. The crushing and collecting parts of the machine are simulated, analyzed, and tested. Using the method of numerical simulation, combined with the results of the pre-branch material properties measurement, the branch crushing process is simulated based on LS-DYNA software. Our analysis found that in the branch destruction process, not only does knife cutting exist, but the bending fracture of the opposite side of the cutting place also exists. With the increase in the knife roller speed, the cutting resistance of the tool increases, reaching 2690 N at 2500 r/min. In the cutting simulation under different tool edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N, and this edge angle is more suitable for branch crushing and cutting. In the cutting simulation under different cutting edge angles, the cutting resistance of the tool is the smallest when the edge angle is 55°, which is 1860 N, and this edge angle is more suitable for branch crushing and cutting. Using Fluent software to analyze the characteristics of the airflow field of the pulverizing device, it was found that with the increase in the knife roller speed, the inlet flow and negative pressure of the pulverizing chamber increase. When the knife roller speed is 2500 r/min, the inlet flow rate and negative pressure are 1.92 kg/s and 37.16 Pa, respectively, which will be favorable to the feeding of the branches, but the speed is too high and will also lead to the enhancement of the vortex in some areas within the pulverizing device, which will in turn affect the feeding of the branches as well as the throwing out of pulverized materials. Therefore, the speed range of the pulverizing knife roller was finally determined to be 1800~2220 r/min. Based on the ANSYS/Model module modal analysis of the crushing knife roller, the knife roller of the first six orders of the intrinsic frequency and vibration pattern, the crushing knife roller of the lowest order had a modal intrinsic frequency of 137.42 Hz, much larger than the crushing knife roller operating frequency of 37 Hz, above which the machine will not resonate during operation. The research results can provide a theoretical basis and technical support for other similar crops to be crushed and collected. Full article

The growing concern over pollution from vehicle exhausts has underscored the need for effective detection of black smoke emissions from motor vehicles. We believe that the optimal approach for the detection of black smoke is to leverage existing roadway CCTV cameras. To facilitate this, we have collected and publicly released a black smoke detection dataset sourced from roadway CCTV cameras in China. After analyzing the existing detection methods on this dataset, we found that they have subpar performance. As a result, we decided to develop a novel detection model that focuses on temporal information. This model utilizes the continuous nature of CCTV video feeds rather than treating footage as isolated images. Specifically, our model incorporates a 3D convolution module to capture short-term dynamic and semantic features in consecutive black smoke video frames. Additionally, a cross-scale feature fusion module is employed to integrate features across different scales, and a self-attention mechanism is used to enhance the detection of black smoke while minimizing the impact of noise, such as occlusions and shadows. The validation of our dataset demonstrated that our model achieves a detection accuracy of 89.42%,showing around 3% improvement over existing methods. This offers a novel and effective solution for black smoke detection in real-world applications. Full article

This study aimed to examine the hepatic and anti-steatotic protective effects of methanolic extract from Carthamus tinctorius (safflower) flowers (SFFE), using a rat model of type 2 diabetes mellitus (T2DM), and to examine the molecular mechanisms underlying these effects. Adult male Wistar rats were used for this study. First, T2DM was induced in some rats by feeding them a high-fat diet (HFD) for 4 weeks, followed by a single dose of streptozotocin (STZ) (35 mg/kg, i.p.). Experimental groups included the following five groups (n = 8 in each): control, control + SFFE, T2DM, T2DM + SFFE, and T2DM + SFFE + brusatol (an Nrf2 inhibitor, 2 mg/kg, i.p.). SFFE was administered at a concentration of 300 mg/kg, and all experiments concluded after 8 weeks. Treatments with SFFE significantly reduced fasting blood glucose levels, free fatty acids (FFAs), cholesterol, triglycerides, and low-density lipoprotein cholesterol in both the control and T2DM rats, but they failed to reduce fasting insulin levels in these groups. SFFE treatments also improved the liver structure and reduced hepatocyte vacuolization and hepatic levels of triglycerides and cholesterol in T2DM rats, in addition to increasing the hepatic mRNA levels of keap1 and the cytoplasmic levels and nuclear activities of Nrf2 in both the control and T2DM rats. SFFE also stimulated the expression levels of PPARα and CPT-1 but reduced the malondialdehyde (MDA), mRNA levels of SREBP1, fatty acid synthase, and acetyl CoA carboxylase in both the control and T2DM rats; meanwhile, it reduced hepatic mRNA and the nuclear activities of NF-κB and increased levels of glutathione, superoxide dismutase, and heme oxygenase-1 in the livers of both groups of treated rats. Furthermore, SFFE suppressed the levels of caspase-3, Bax, tumor necrosis factor-α, and interleukin-6 in the T2DM rats. Treatment with brusatol prevented all of these effects of SFFE. In conclusion, SFFE suppresses liver damage and hepatic steatosis in T2DM through Nrf2-dependent hypoglycemic, antioxidant, anti-inflammatory, and hypolipidemic effects. Full article

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal–spatial–mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases. Full article

Metal micro-droplet ejection technology has attracted attention for its potential applications in the rapid prototyping of micro-metal parts and microelectronic packaging. The current micro-droplet ejection device developed based on this technology faces challenges such as the requirement of a micro-oxygen ejection environment, a complex feeding structure, and high costs. Therefore, a drop-on-demand droplet generator for metallic pellets with impact feed ejection is designed in this paper. This device has a simple and compact structure, does not require a high-cost heat source, and can perform drop-on-demand ejection of metallic pellets in an atmospheric environment. A micro-channel feeding method based on piezoelectric ceramic actuator drives is proposed. A rigid dynamics metallic pellet flight trajectory model is established to analyze the relationships between the driving voltage and the flight trajectory of the pellets. With the help of Fluent to simulate and analyze the melting and ejection processes of the pellets inside the nozzle, the changes in the variable parameters of the flow field in the process of the melting and flight of a single molten drop are studied. The droplet generator produces stable droplets with a 500 µs pulse width and 1100 mm/s initial velocity of the projectile. The simulation results show that a single projectile has to go through three stages including feeding, melting, and ejecting, which take 39.5 ms, 7.85 ms, and 17.65 ms. The total simulation time is 65.0 ms. It is expected that the injection frequency of the metal projectile droplet-generating device will reach 15 Hz. Full article

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods. Full article

ATP-binding cassette transporter subfamily G member 2 (ABCG2) is responsible for the excretion of foreign substances, such as uric acid (UA) and indoxyl sulfate (IS), from the body. Given the importance of increased ABCG2 expression in UA excretion, we investigated the enhancement of intestinal ABCG2 expression using Lactiplantibacillus plantarum 06CC2 (LP06CC2). Mice were reared on a potassium oxonate-induced high-purine model at doses of 0.02% or 0.1% LP06CC2 for three weeks. Results showed that LP06CC2 feeding resulted in increased ABCG2 expression in the small intestine. The expression level of large intestinal ABCG2 also showed a tendency to increase, suggesting upregulation of the intestinal excretion transporter ABCG2 by LP06CC2. Overall, LP06CC2 treatment increased fecal UA excretion and showed a trend towards increased fecal excretion of IS, suggesting that LP06CC2 treatment enhanced the expression of intestinal ABCG2, thereby promoting the excretion of UA and other substances from the intestinal tract. Full article

The exhaust gas recirculation (EGR) valve plays an important role in improving engine fuel economy and reducing emissions. In order to improve the positioning accuracy and robustness of the EGR valve under uncertain dynamics and external disturbances, this paper proposes a positioning servo system design for an electromotive (EM) EGR valve based on the Kalman filter. Taking a novel valve driven by a central winding limited angle torque motor (LATM) as the object, we have fully considered the influence of the motor rotor position and load current, as well as the magnetic field saturation and cogging effect, improved the existing LTAM model, and derived accurate torque expression. The parameter uncertainty of the above internal model and the external stochastic disturbance were unified as “total disturbance”, and a Kalman filter-based observer was designed for disturbance estimations and real-time feed-forward compensation. Furthermore, using non-contact magnetic angle measurements to obtain accurate valve position information, a position control model with real-time response and high accuracy was established. Numerous simulated and experimental data show that in the presence of ± 25% plant model parameter fluctuations and random shock-type disturbances, the servo system scheme proposed in this paper achieves a maximum position deviation of 0.3 mm, a repeatability of positioning accuracy after disturbances of 0.01 mm, and a disturbance recovery time of not more than 250 ms. In addition, the above performance is insensitive to the duration of the disturbance, which demonstrates the strong robustness, high accuracy, and excellent dynamic response capability of the proposed design. Full article

The topography measurement accuracy of coherence scanning interferometry (CSI) suffers from the local characteristic of micro-structured surfaces, such as local surface slopes. A cylindrical reference artefact made of single-mode fiber with high roundness and low roughness has been proposed in this manuscript to traceably investigate the surface tilting induced measurement deviations using coherence scanning interferometry with high NA objectives. A feed-forward neural network (FF-NN) is designed and trained to model and thereafter compensate the systematic measurement deviations due to local surface tilting. Experimental results have verified that the FF-NN approach can well enhance the accuracy of the CSI for radius measurement of cylindrical samples up to 0.3%. Further development of the FF-NN for modelling of the measurement errors in CSI due to the optical properties of surfaces including areal roughness is outlined. Full article

Cone crushers have a central role in the processing of quarry rocks, besides coarser ore preparation in several mineral processing plants. This is particularly true in the case of Itabirite iron ore preparation plants in Brazil, so optimizing their performance is of central importance for reaching maximum productivity of the circuit. The work presents results of modeling the HP500 cone crusher in operation in an industrial plant in Brazil (Minas Rio), from surveys carried out over a few years with different feeds and crushing conditions. A version of the Andersen–Whiten cone crusher model was implemented in the Integrated Extraction Simulator featuring a non-normalizable breakage response and a fit-for-purpose throughput model. The results demonstrate the good ability of the model to predict crusher performance when dealing with different closed-side settings and feed size distributions. Full article

Glycation Stress (GS), induced by advanced glycation end-products (AGEs), significantly impacts aging processes. This study introduces a new model of GS of Caenorhabditis elegans by feeding them Escherichia coli OP50 cultured in a glucose-enriched medium, which better simulates human dietary glycation compared to previous single protein–glucose cross-linking methods. Utilizing WormCNN, a deep learning model, we assessed the health status and calculated the Healthy Aging Index (HAI) of worms with or without GS. Our results demonstrated accelerated aging in the GS group, evidenced by increased autofluorescence and altered gene expression of key aging regulators, daf-2 and daf-16. Additionally, we observed elevated pharyngeal pumping rates in AGEs-fed worms, suggesting an addictive response similar to human dietary patterns. This study highlights the profound effects of GS on worm aging and underscores the critical role of computer vision in accurately assessing health status and aiding in the establishment of disease models. The findings provide insights into glycation-induced aging and offer a comprehensive approach to studying the effects of dietary glycation on aging processes. Full article

The present study aimed to test over a six-month period different synthetic flavors in zebrafish (Danio rerio) as an experimental model. Specifically, two attractive and one repulsive synthetic flavors were added (1% w/w) to a specific zebrafish diet, which was administered to the fish during the whole life cycle (from larvae to adults), to evaluate their physiological responses, emphasizing fish welfare, feed intake, growth, reward mechanisms, and reproductive performances. Fish welfare was not affected by all tested flavors, while both attractive flavors promoted fish feed ingestion and growth. The results were supported by both molecular and immunohistochemical analyses on appetite-regulating neurohormonal signals, along with the influence of the feed hedonic properties induced by the brain reward sensation, as demonstrated by the dopamine receptor gene expression. Finally, the present study demonstrated that a higher feed intake also had positive implications on fish reproductive performances, suggesting a promising role of synthetic flavors for the aquaculture industry. In conclusion, the results highlighted the potential of synthetic flavors to improve fish feeding strategies by providing a consistent and effective alternative to traditional stimulants, thereby reducing dependence on natural sources. Full article

As new-energy electricity increasingly enters the post-subsidy era, traditional fixed feed-in tariffs and guaranteed purchase policies are not conducive to the optimal allocation of large-scale, high-proportion new-energy power due to the high pressure of subsidy funds and the fairness issues of power-generation grid connection. Encouraging new energy to participate in electricity market transactions is considered an effective solution. However, existing studies have presupposed the adverse effects of new energy in proposing market mechanism optimization designs for new-energy participation without quantitative results to support this, which is not conducive to a true assessment of the comprehensive impact of individual instances of new-energy participation in the market. To this end, this study, based on the actual experience and data cases of China’s electricity spot market pilot provinces, considers both unit commitment and economic dispatch in the electricity distribution process, and constructs a two-stage optimization model for electricity spot market clearing. According to the differences in grid connection time and the construction costs of new-energy power, differentiated proportions of new-energy participation in the market and bidding strategies are set. By analyzing the quantitative results of new energy participating in spot market transactions under multiple scenarios, using both typical daily data for normal loads and peak loads, the study provides theoretical support and a data basis for the optimized design of market mechanisms. The research results show that there is a non-linear relationship between the scale of new energy entering the market and its bidding strategies and market-clearing electricity prices. In the transition phase of the low-carbon transformation of the power sector, the impacts of thermal power technology with a certain generation capacity and changes in the relationship between power supply and demand on electricity prices are significant. From the perspective of the individual interests of new-energy providers, the analysis of their bidding strategies in the market is important. Full article

Mixed plastic/biomass co-gasification stands out as a promising and environmentally friendly technology, since it reduces wide solid wastes and produces green hydrogen. High-quality syngas can be obtained by virtue of the process design and optimization of a downdraft fixed-bed co-gasifier. The design is based on the actual reaction zones within a real gasifier to ensure accurate results. The methodology shows that (i) the co-gasifier modeling is validated using the adiabatic RGibbs model in Aspen Plus, (ii) the performance of the co-gasifier is evaluated using cold-gas efficiency (CGE) and carbon conversion efficiency (CCE) as indicators, and (iii) the multi-objective optimization (MOO) is employed to optimize these indicators simultaneously, utilizing a standard genetic algorithm (GA) combined with response surface methodology (RSM) to identify the Pareto frontier. The optimal conditions, resulting in a CGE of 91.78% and a CCE of 83.77% at a gasifier temperature of 967.89 °C, a steam-to-feed ratio of 1.40, and a plastic-to-biomass ratio of 74.23%, were identified using the technique for order of preference by similarity to ideal solution (TOPSIS). The inclusion of plastics enhances gasifier performance and syngas quality, leading to significant improvements in CGE and CCE values. Full article

We present a mathematical model for agri-food industry residual streams flow management, which serves as a decision support tool for optimizing their valorization. The aim is to determine, under a cost-benefit analysis approach, the best strategy at a global level. The proposed mathematical model provides the optimal valorization scenario, namely the set of routes followed by agri-food industry residual streams that maximizes the total profit obtained. The model takes into account the complete stoichiometry of the residual stream at each step of the valorization route. Furthermore, the model allows for the calculations of different scenarios to support decision-making. The proposed approach is illustrated through a case study using a real-case network of a region. The case study bears evidence that the use of the model can lead to significant profit increases compared to those obtained with current practices. Moreover, notable profit improvements are obtained in the case study if the selling price of all the value-added products considered increases or if the processing cost of the animal feed producer decreases. Therefore, our model enables the detection of key factors that influence the optimal strategy, making it a powerful decision-support tool for optimizing the valorization of agri-food industry residual streams. Full article