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In order to explore the effects of different production processes (cold pressing, leaching, and their combination) on the quality and flavor of soybean oil of third grade, the physicochemical properties and fatty acid composition of the three soybean oils were determined, and their volatile compounds were identified and analyzed by electronic nose, headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS), and headspace-gas chromatography-ion mobility spectroscopy (HS-GC-IMS). Differential analysis of the volatile compound data among the soybean oils was performed by clustering heatmap analysis, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The results showed that cold-pressed soybean oil had the lowest moisture content; the peroxide value of leached soybean oil was significantly higher and its color was the darkest. Fragrant soybean oil had the highest linoleic acid content and a higher nutritional value; and among the detected volatile components, alcohols, aldehydes and pyrazine compounds mainly contributed to the formation of soybean oil flavor, and the reasons for the formation of some flavor compounds were clarified. Finally, 45 volatile compounds with high contributions were selected by OPLS-DA, and a reliable model was constructed for identifying fragrant soybean oil. In addition, a correlation was found between the quality and flavor of soybean oil.

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.

Engineered heart tissues have been created to study cardiac biology and disease in a setting that more closely mimics in vivo heart muscle than 2D monolayer culture. Previously published studies suggest that geometrically anisotropic micro-environments are crucial for inducing “in vivo like” physiology from immature cardiomyocytes. We hypothesized that the degree of cardiomyocyte alignment and prestress within engineered tissues is regulated by tissue geometry and, subsequently, drives electrophysiological development. Thus, we studied the effects of tissue geometry on electrophysiology of micro-heart muscle arrays (μHM) engineered from human induced pluripotent stem cells (iPSCs). Elongated tissue geometries elicited cardiomyocyte shape and electrophysiology changes led to adaptations that yielded increased calcium intake during each contraction cycle. Strikingly, pharmacologic studies revealed that a threshold of prestress and/or cellular alignment is required for sodium channel function, whereas L-type calcium and rapidly rectifying potassium channels were largely insensitive to these changes. Concurrently, tissue elongation upregulated sodium channel (NaV1.5) and gap junction (Connexin 43, Cx43) protein expression. Based on these observations, we leveraged elongated μHM to study the impact of loss-of-function mutation in Plakophilin 2 (PKP2), a desmosome protein implicated in arrhythmogenic disease. Within μHM, PKP2 knockout cardiomyocytes had cellular morphology similar to what was observed in isogenic controls. However, PKP2−/− tissues exhibited lower conduction velocity and no functional sodium current. PKP2 knockout μHM exhibited geometrically linked upregulation of sodium channel but not Cx43, suggesting that post-translational mechanisms, including a lack of ion channel-gap junction communication, may underlie the lower conduction velocity observed in tissues harboring this genetic defect. Altogether, these observations demonstrate that simple, scalable micro-tissue systems can provide the physiologic stresses necessary to induce electrical remodeling of iPS-CM to enable studies on the electrophysiologic consequences of disease-associated genomic variants.

During the polishing process of complex curved PMMA parts, the polishing force is an important factor affecting the surface quality and optical performance. In this paper, a force-controlled polishing device integrated into a machining center to maintain the polishing force is investigated. In order to achieve the real-time active control of the polishing force, the linear voice coil motor and force sensors are used for motion and measurement. A compact structure was designed to couple the linear motion of the voice coil motor with the rotation for polishing. The force-controlled polishing system with a high real-time hardware architecture was developed to perform complex curved polishing path movement with precise force control. Next, the polishing force between the device and the workpiece was analyzed to obtain the mathematical model of the device. Considering the impact during the approaching phase of polishing, a fuzzy PI controller was proposed to reduce the overshoot and response time. To implement the control method, the controller model was established on Simulink and the control system was developed based on TwinCAT 3 software with real-time computing capability. Finally, a polishing experiment involving a complex curved PMMA part was conducted by a force-controlled polishing device integrated into a five-axis machining center. The results show that the device can effectively maintain the polishing force to improve surface quality and optical performance.

The core body temperature serves as a pivotal physiological metric indicative of sow health, with rectal thermometry prevailing as a prevalent method for estimating core body temperature within sow farms. Nonetheless, employing contact thermometers for rectal temperature measurement proves to be time-intensive, labor-demanding, and hygienically suboptimal. Addressing the issues of minimal automation and temperature measurement accuracy in sow temperature monitoring, this study introduces an automatic temperature monitoring method for sows, utilizing a segmentation network amalgamating YOLOv5s and DeepLabv3+, complemented by an adaptive genetic algorithm-random forest (AGA-RF) regression algorithm. In developing the sow vulva segmenter, YOLOv5s was synergized with DeepLabv3+, and the CBAM attention mechanism and MobileNetv2 network were incorporated to ensure precise localization and expedited segmentation of the vulva region. Within the temperature prediction module, an optimized regression algorithm derived from the random forest algorithm facilitated the construction of a temperature inversion model, predicated upon environmental parameters and vulva temperature, for the rectal temperature prediction in sows. Testing revealed that vulvar segmentation IoU was 91.50%, while the predicted MSE, MAE, and R2 for rectal temperature were 0.114 °C, 0.191 °C, and 0.845, respectively. The automatic sow temperature monitoring method proposed herein demonstrates substantial reliability and practicality, facilitating an autonomous sow temperature monitoring.

Due to properties of reducing impact，improving uneven load，and lowering noise in the meshing process，the technology of tooth surface modified are very common in the wind power gear. As the last process of machining gear，form grinding determines the accuracy of gear directly. Therefore，solving geometry error of form grinding becomes vital for optimize grinding tool path and enhancing the machining precision. Thus，this paper builds a model of geometrical error. In order to solve the geometry error of form grinding， firstly， tooth surface model of gears combining the profile and axial modification （including drum and helix modification） is constructed. Then，based on the tooth surface parameter，the length of common perpendicular between axes of the gear and the wheel，the contacting curve between the gear and the wheel is solved. The form grinding tooth surface is constructed. Finally，the geometrical error of tooth surfuce form grinding is defined by using the theoretical tooth surface and form grinding tooth surface， the geometric error model of tooth surface form grinding is established， some suggestions are given to reduce the geometry error of form grinding.

A Lewis acid-catalyzed [4 ​+ ​2] cycloaddition of 3-alkyl-2-vinylindoles with β,γ-unsaturated α-ketoesters has been established in the presence of Sc(OTf)3, which afforded a series of indole-containing pyran derivatives in generally good yields (up to 90% yield) with excellent diastereoselectivities (up to ​> 95:5 dr) under mild conditions. This approach not only enriches the chemistry of 3-alkyl-2-vinylindoles, but also has provided an atom-economic method for the synthesis of indole-containing pyran derivatives with potential bioactivity.

Sow body condition scoring has been confirmed as a vital procedure in sow management. A timely and accurate assessment of the body condition of a sow is conducive to determining nutritional supply, and it takes on critical significance in enhancing sow reproductive performance. Manual sow body condition scoring methods have been extensively employed in large-scale sow farms, which are time-consuming and labor-intensive. To address the above-mentioned problem, a dual neural network-based automatic scoring method was developed in this study for sow body condition. The developed method aims to enhance the ability to capture local features and global information in sow images by combining CNN and transformer networks. Moreover, it introduces a CBAM module to help the network pay more attention to crucial feature channels while suppressing attention to irrelevant channels. To tackle the problem of imbalanced categories and mislabeling of body condition data, the original loss function was substituted with the optimized focal loss function. As indicated by the model test, the sow body condition classification achieved an average precision of 91.06%, the average recall rate was 91.58%, and the average F1 score reached 91.31%. The comprehensive comparative experimental results suggested that the proposed method yielded optimal performance on this dataset. The method developed in this study is capable of achieving automatic scoring of sow body condition, and it shows broad and promising applications.

Estrus detection is an essential operation in the breeding of sows, and accurate estrus detection is immensely important to maintain the productivity and reproductive performance of sow. However, traditional sow estrus detection relies on the manually back-pressure test, which is time-consuming and labor-intensive. This study aimed to develop an automatic method to detect estrus. In this study, a model based on the optimized yolov5s algorithm was constructed to detect the four sow postures of standing, sitting, sternum, lateral, and calculated the frequency of posture change in sows. Based on this, we studied the behavior of sows before and after estrus. The method embedded a convolutional block attention module into the backbone network to improve the feature extraction capability of the model. In addition, the object box judgment module was used to avoid interference from other sows in the detection region. Accelerate the optimized model on the TensorRT platform, ensuring that the embedded graphics card can run the model with lower latency. The result shows that the precision of estrus detection is 97.1%, and the accuracy of estrus detection is 94.1%. The processing time of a single image on the embedded graphics card is 74.4 ms, and this method could better meet the estrus detection demand in sow production.

Abstract To accurately identify ideal operating conditions for scientific cloud seeding and to collect evidence of the effects of seeding, a set of three flights, with figure‐8‐like paths, was designed for supercooled liquid water detection. These flights were conducted near the cloud top before seeding, during seeding, and upon re‐entry. Using both in situ aircraft and satellite remote sensing observations, a comprehensive analysis was conducted to understand the response of cloud properties to seeding within supercooled cloud tops. Satellite observations showed clear icing cloud tracks after seeding with abundant supercooled liquid water near the cloud top. Radar observations confirmed enhanced radar reflectivity echoes in cloud‐seeding regions. For regions cloud seeded with supercooled liquid water, the liquid droplets quickly froze into ice crystals, which further grew into large ice crystals via vapor deposition, riming, and collision‐coalescence processes. With these processes, large ice crystals formed and rapidly fell downward, causing the sinking of cloud tops and precipitation to fall to the ground. By contrast, there were no such phenomena in the regions without supercooled liquid water or in those regions with supercooled liquid, but otherwise without seeding. In addition, radar reflectivity echoes quickly decreased in all regions without seeding. The evolutionary characteristics of radar echoes and in situ observed cloud and precipitation properties suggest that the response of cloud microphysical characteristics to seeding varies with time and location due to various seeding potentials, leading to different formation conditions of both cloud‐top icing and cloud tracks.

Due to the temperature-sensitive characteristic of plexiglass materials, it is necessary to maintain a constant small contact force to avoid surface burn damage when polishing complex curved plexiglass parts. To handle the issue, in this paper a pneumatic force-controlled actuator was developed to keep the normal contact force between the polishing tool and the workpiece constant during the robotic polishing process. The force-controlled actuator is configured with a double-acting cylinder as the driving element, and two electrical proportional valves are used to control the output force by adjusting the pressure difference between the two air chambers of the cylinder. In this case, a small contact force can be exactly achieved, and the cylinder can always work within the optimal pressure range. In order to judge the stability of the system and reduce the commissioning time of the force-controlled actuator, a mathematical model of the force-controlled actuator is established. Meanwhile, for eliminating the influence of the gravity of the polishing tool on the contact force control, a gravity compensation algorithm is also given according to the roll-pitch-yaw (RPY) angle calculation method. Since there are some nonlinear factors in the operation of the force-controlled actuator, a fuzzy proportion-integral-derivative (PID) control strategy is adopted without steady-state errors. Finally, the polishing experiment of a complex curved plexiglass part was carried out by using the robot automatic polishing system. The experimental results show that the contact force control effect of the force-controlled actuator meets the processing requirements, and the curved plexiglass part has good surface quality and optical performance after polishing.

Feedrate has a great influence on contour error in five-axis machining. Accordingly, it is of great significance to plan the time-optimal feedrate curve considering the contour error constraint to achieve high-accuracy and high-efficiency machining. Aiming at improving the error control accuracy of model linearization loss and optimizing the machining time, the PSO-based feedrate optimization algorithm for five-axis machining with constraint of contour error is proposed in this paper. Firstly, the relationship between parametric feedrate and contour error constraint is clarified that provides a model basis for accurately controlling contour error by optimizing the feedrate curve. Then, the feedrate optimization model, which takes the control vertices of the feedrate curve expressed by B-spline as the decision variables and minimizes the machining time as the optimization objective, is established. Subsequently, to overcome the shortcomings of low accuracy and low efficiency caused by single optimization of global control vertices, the group search particle swarm optimization (GSPSO) algorithm based on window movement is adopted to optimize the feedrate curve in segments. Finally, the effectiveness of the proposed feedrate optimization algorithm is validated by three typical test toolpaths on an open double-turntable five-axis machine tool. In light of the experiment, the proposed algorithm is able to fully release the potential of the machine tools while accurately controlling the contour error of the cutter tip and cutter orientation.

Thin−walled parts are widely used in many important fields because of performance and structural lightweight requirements. They are critical parts because they usually carry the core functions of high−end equipment. However, their high−performance machining has been facing severe challenges, among which the dynamics problem is one of the most important obstacles. The machining system is easily subjected to chatter due to the weak rigidity of the thin−walled structure and slender cutting tool, which significantly deteriorates the surface quality and reduces the machining efficiency. Extensive studies aiming at eliminating machining chatter have been carried out in the recent decades. This paper systematically reviews previous studies on the identification of system dynamic characteristics, modeling and prediction of chatter stability, and chatter elimination/suppression methods and devices. Finally, existing problems are summarized, and future research is concluded.

The fiber-optic distributed acoustic sensor (DAS), which utilizes existing communication cables as its sensing media, plays an important role in urban infrastructure monitoring and natural disaster prediction. In the face of a wide, dynamic environment in urban areas, a fast, accurate DAS signal recognition method is proposed with an end-to-end attention-enhanced ResNet model. In preprocessing, an objective evaluation method is used to compare the distinguishability of different input features with the Euclidean distance between the posterior probabilities classified correctly and incorrectly; then, an end-to-end ResNet is optimized with the chosen time-frequency feature as input, and a convolutional block attention module (CBAM) is added, which can quickly focus on key information from different channels and specific signal structures and improves the system recognition performance further. The results show that the proposed ResNet+CBAM model has the best performance in recognition accuracy, convergence rate, generalization capability, and computational efficiency compared with 1-D CNN, 2-D CNN, ResNet, and 2-D CNN+CBAM. An average accuracy of above 99.014% can be achieved in field testing; while dealing with multi-scenario scenes and inconsistent laying or burying environments, it can still be kept above 91.08%. The time cost is only 3.3 ms for each signal sample, which is quite applicable in online long-distance distributed monitoring applications.

A SEIARN compartment model with the asymptomatic infection and secondary infection is proposed to predict the trend of COVID-19 more accurately. The model is extended according to the propagation characteristics of the novel coronavirus, the concepts of the asymptomatic infected compartment and secondary infection are introduced, and the contact rate parameters of the improved model are updated in real time by using the LSTM trajectory, in order to make accurate predictions. This SEIARN model first builds on the traditional SEIR compartment model, taking into account the asymptomatic infection compartment and secondary infection. Secondly, it considers the disorder of the trajectory and uses the improved LSTM model to predict the future trajectory of the current patients and cross-track with the susceptible patients to obtain the contact rate. Then, we conduct real-time updating of exposure rates in the SEIARN model and simulation of epidemic trends in Tianjin, Xi’an, and Shijiazhuang. Finally, the comparison experiments show that the SEIARN model performs better in prediction accuracy, MSE, and RMSE.

It has widely been accepted that food restriction (FR) without malnutrition has multiple health benefits. Various calorie restriction (CR) and intermittent fasting (IF) regimens have recently been reported to exert neuroprotective effects in traumatic brain injury (TBI) through variable mechanisms. However, the evidence connecting CR or IF to neuroprotection in TBI as well as current issues remaining in this research field have yet to be reviewed in literature. The objective of our review was therefore to weigh the evidence that suggests the connection between CR/IF with recovery promotion following TBI. Medline, Google Scholar and Web of Science were searched from inception to 25 February 2022. An overwhelming number of results generated suggest that several types of CR/IF play a promising role in promoting post-TBI recovery. This recovery is believed to be achieved by alleviating mitochondrial dysfunction, promoting hippocampal neurogenesis, inhibiting glial cell responses, shaping neural cell plasticity, as well as targeting apoptosis and autophagy. Further, we represent our views on the current issues and provide thoughts on the future direction of this research field.

Optogenetics is emerging as an ideal method for controlling cellular activity. It overcomes some notable shortcomings of conventional methods in the elucidation of neural circuits, promotion of neuroregeneration, prevention of cell death and treatment of neurological disorders, although it is not without its own limitations. In this review, we narratively review the latest research on the improvement and existing challenges of optogenetics, with a particular focus on the field of brain injury, aiming at advancing optogenetics in the study of brain injury and collating the issues that remain. Finally, we review the most current examples of research, applying photostimulation in clinical treatment, and we explore the future prospects of these technologies.