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Probiotic intervention is a well-established approach for replacing antibiotics in the management of weaning piglet diarrhea, which involves a large number of complex systems interacting with the gut microbiota, including the endocannabinoid system; nevertheless, the specific role of the endocannabinoid system mediated by probiotics in the piglet intestine has rarely been studied. In this study, we used antibiotics (ampicillin) to perturb the intestinal microbiota of piglets. This resulted in that the gene expression of the intestinal endocannabinoid system was reprogrammed and the abundance of probiotic Lactobacillus johnsonii in the colon was lowered. Moreover, the abundance of Lactobacillus johnsonii was positively correlated with colonic endocannabinoid system components (chiefly diacylglycerol lipase beta) via correlation analysis. Subsequently, we administered another batch of piglets with Lactobacillus johnsonii. Interestingly, dietary Lactobacillus johnsonii effectively alleviated the diarrhea ratio in weaning piglets, accompanied by improvements in intestinal development and motility. Notably, Lactobacillus johnsonii administration enhanced the intestinal barrier function of piglets as evidenced by a higher expression of tight junction protein ZO-1, which might be associated with the increased level in colonic diacylglycerol lipase beta. Taken together, the dietary Lactobacillus johnsonii-mediated reprogramming of the endocannabinoid system might function as a promising target for improving the intestinal health of piglets.

Remote sensing detection of vehicle emissions is an effective supplement to the statutory periodic inspection of in-use vehicle emissions and it is a convenient technical method for real-time screening of high-emission vehicles. The principle of remote sensing detection is to inversely calculate the absolute concentrations of gaseous pollutants in vehicle exhaust according to the relative volume concentration ratio of each exhaust component to carbon dioxide (CO2) in the vehicle exhaust plume. Because the combustion mechanisms of gasoline engines and diesel engines are different, different inversion calculation methods of remote sensing data must be applied. The absolute concentrations of gasoline vehicle gaseous emissions measured by remote sensing can be calculated by the inversion calculation method based on the theoretical air–fuel ratio combustion mechanism. However, the absolute concentrations of diesel vehicle nitrogen oxide (NOx) measured by remote sensing must be calculated by the inversion calculation method based on the correction of the excess air coefficient. For the integrated remote sensing test system of gasoline and diesel vehicles, it is necessary to determine the vehicle category according to the vehicle type and license plate and adopt different inversion calculation methods to obtain the correct remote sensing results of vehicle emissions. The big data statistical analysis method for vehicle emission remote sensing results can quickly screen high-emission vehicles and dynamically determine the remote sensing emission screening threshold of high-emission vehicles as the composition of in-use vehicles changes and the overall emission of vehicles declines, so as to achieve dynamic and accurate screening of high-emission vehicles.

Automated mechanical transmission (AMT) is used as a soft starter in this paper. To improve the soft starting quality, a novel data-driven method is studied. By analyzing and comparing five common soft-starting acceleration curves, a segmented acceleration curve is put forward to be used as the soft-starting acceleration curve for the AMT. Based on the prototype model free adaptive control (MFAC) method, a modified MFAC method with jerk compensation is given to control the AMT output shaft’s angular acceleration and reduce driveline shock. Compared with the methods of prototype MFAC and traditional proportion integration differentiation (PID), the modified MFAC method with jerk compensation can better control the AMT output shaft’s angular acceleration and has excellent characteristics in terms of small tracking error and smaller shock. The research results provide a novel data-driven method for AMT as a soft starter.

Clutch control has a great effect on the starting quality and shifting quality of heavy-duty vehicles with automated mechanical transmission (AMT). The motion characteristics of a clutch actuator for heavy-duty vehicles with AMT are studied in this paper to investigate the clutch control strategy further. The modeling principle of the automatic clutch system is analyzed, and a simulation analysis is given to prove its validity and rationality. Normalized velocity and velocity modulation percentage are proposed as evaluation parameters for the clutch actuator driven by pulse width modulation (PWM) signals. Based on an AMT test bench, the actuator motion characteristics are analyzed. Experimental results show that the range of normalized velocity and velocity modulation percentage are obtained for the clutch engagement and disengagement processes. By analyzing the experimental data, the engaging velocity and disengaging velocity of the actuator are estimated using the solenoid valves in combination. The research results provide a fundamental basis for precise controlling of the clutch and improving the smoothness of heave-duty vehicles.

Background: Although successful reappraisal relies on cognitive resources, how cognitive impairment affects brain processes related to cognitive reappraisal is not yet clear. Methods: Forty-four amnestic mild cognitive impairment (aMCI) subjects and 72 healthy elderly controls (HECs) were divided into the MCI-Failure (n = 23), MCI-Success (n = 21), HEC-Failure (n = 26), and HEC-Success (n = 46) groups according to changes in self-reported affect using reappraisal. All participants viewed 30 negative and 30 neutral images preceded by straightforward descriptions of these images and 30 negative images preceded by more neutral descriptions. Results: Reappraisal failure was found to be more common in people with MCI. Reappraisal failure is associated with altered neurophysiological indices of negative-reappraisal stimuli processing that are reflected in smaller theta responsivity to negative-reappraisal stimuli between 350–550 ms. The MCI-Success group showed enhanced LPP for negative-reappraisal stimuli from 1200 to 3500 ms, reflecting compensatory effort to complete the reappraisal task, while subjects in other groups showed reduced LPP for negative-reappraisal stimuli from 550 to 1200 ms. Conclusions: These findings deepen our understanding of how cognitive decline impacts reappraisal and informs early diagnosis and interventions for MCI.

The lateral stability control of tractor semi-trailer plays a vital role for enhancing its driving safety, and the distributed electric drive structure of a hub motor creates opportunities and challenges for realising the lateral stability accurately. Based on the dynamics simulation software TruckSim, a nonlinear dynamic tractor semi-trailer model is established, and a MATLAB/Simulink linear three-degree-of-freedom monorail reference model is established. The upper controller adopts fuzzy proportional–integral–derivative control to export active yaw torque values of the tractor and semi-trailer. The lower controller outputs the driving/braking torque of each wheel according to the target wheel driving/braking rules and torque distribution rules. The tractor produce an active yaw torque through conventional differential braking the hub motor is installed on both sides of the semi-trailer, and the active yaw torque is produced by the coordinated control of the driving/braking torque of the hub motor and the differential braking of the mechanical braking system. To prevent wheel locking, the slip rate of each wheel is controlled. Finally, based on the TruckSim–MATLAB/Simulink cosimulation platform, cosimulation is performed under typical working conditions. The simulation results show that the control strategy proposed in this report is superior to the conventional differential braking control (ESP). It can not only improve the lateral stability of the vehicle more effectively, but also improve the roll stability.

Wearable telemonitoring of electrocardiogram (ECG) based on wireless body Area networks (WBAN) is a promising approach in next-generation patient-centric telecardiology solutions. In order to guarantee long-term effective operation of monitoring systems, the power consumption of the sensors must be strictly limited. Compressed sensing (CS) is an effective method to alleviate this problem. However, ECG signals in WBAN are usually non-sparse, and most traditional compressed sensing recovery algorithms have difficulty recovering non-sparse signals. In this paper, we proposed a fast and robust non-sparse signal recovery algorithm for wearable ECG telemonitoring. In the proposed algorithm, the alternating direction method of multipliers (ADMM) is used to accelerate the speed of block sparse Bayesian learning (BSBL) framework. We used the famous MIT-BIH Arrhythmia Database, MIT-BIH Long-Term ECG Database and ECG datasets collected in our practical wearable ECG telemonitoring system to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm can directly recover ECG signals with a satisfactory accuracy in a time domain without a dictionary matrix. Due to acceleration by ADMM, the proposed algorithm has a fast speed, and also it is robust for different ECG datasets. These results suggest that the proposed algorithm is very promising for wearable ECG telemonitoring.

With the increasing demands for vehicle dynamic performance, economy, safety and comfort, and with ever stricter laws concerning energy conservation and emissions, vehicle power systems are becoming much more complex. To pursue high efficiency and light weight in automobile design, the power system and its vehicle integrated thermal management (VITM) system have attracted widespread attention as the major components of modern vehicle technology. Regarding the internal combustion engine vehicle (ICEV), its integrated thermal management (ITM) mainly contains internal combustion engine (ICE) cooling, turbo-charged cooling, exhaust gas recirculation (EGR) cooling, lubrication cooling and air conditioning (AC) or heat pump (HP). As for electric vehicles (EVs), the ITM mainly includes battery cooling/preheating, electric machines (EM) cooling and AC or HP. With the rational effective and comprehensive control over the mentioned dynamic devices and thermal components, the modern VITM can realize collaborative optimization of multiple thermodynamic processes from the aspect of system integration. Furthermore, the computer-aided calculation and numerical simulation have been the significant design methods, especially for complex VITM. The 1D programming can correlate multi-thermal components and the 3D simulating can develop structuralized and modularized design. Additionally, co-simulations can virtualize simulation of various thermo-hydraulic behaviors under the vehicle transient operational conditions. This article reviews relevant researching work and current advances in the ever broadening field of modern vehicle thermal management (VTM). Based on the systematic summaries of the design methods and applications of ITM, future tasks and proposals are presented. This article aims to promote innovation of ITM, strengthen the precise control and the performance predictable ability, furthermore, to enhance the level of research and development (R&D).

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With the rapid advancement of wireless communication technology, automatic modulation classification (AMC) plays a crucial role in drone communication systems, ensuring reliable and efficient communication in various non-cooperative environments. Deep learning technology has demonstrated significant advantages in the field of AMC, effectively and accurately extracting and classifying modulation signal features. However, existing deep learning models often have high computational costs, making them difficult to deploy on resource-constrained drone communication devices. To address this issue, this study proposes a lightweight Mobile Automatic Modulation Classification Transformer (MobileAmcT). This model combines the advantages of lightweight convolutional neural networks and efficient Transformer modules, incorporating the Token and Channel Conv (TCC) module and the EfficientShuffleFormer module to enhance the accuracy and efficiency of the automatic modulation classification task. The TCC module, based on the MetaFormer architecture, integrates lightweight convolution and channel attention mechanisms, significantly improving local feature extraction efficiency. Additionally, the proposed EfficientShuffleFormer innovatively improves the traditional Transformer architecture by adopting Efficient Additive Attention and a novel ShuffleConvMLP feedforward network, effectively enhancing the global feature representation and fusion capabilities of the model. Experimental results on the RadioML2016.10a dataset show that compared to MobileNet-V2 (CNN-based) and MobileViT-XS (ViT-based), MobileAmcT reduces the parameter count by 74% and 65%, respectively, and improves classification accuracy by 1.7% and 1.09% under different SNR conditions, achieving an accuracy of 62.93%. This indicates that MobileAmcT can maintain high classification accuracy while significantly reducing the parameter count and computational complexity, clearly outperforming existing state-of-the-art AMC methods and other lightweight deep learning models.

Soil phosphorus (P) can be divided into inorganic P (Pi) and organic P (Po). Microorganisms play essential roles in soil P transformation. However, there are many ways to detect P transformation, and the relationship between P forms and microorganisms under long-term fertilization is largely unclear. In this study, soil P forms were analyzed by a chemical sequential fractionation method and solution 31P nuclear magnetic resonance (31P-NMR) technique. Phospholipid fatty acid (PLFA) contents were measured by gas chromatography as the characterization of soil microbial community structures. The objective was to determine the changes of soil P forms and associated microbial community composition in mollisol with long-term fertilization. We sampled soil from a field experiment with 26-year-old continuous maize (Zea mays L.) cropping in Northeastern China. Three fertilization treatments were selected as chemical fertilization (NPK), NPK with crop straw (NPKS), and NPK with manure (NPKM). As shown in 31P-NMR spectra, orthophosphate accounted for 62.8–85.8% of total extract P. Comparison to NPK and NPKS treatments, NPKM application notably increased the concentrations of Po, Olsen-P, orthophosphate, orthophosphate monoester, and total P. Soil P fractions including resin-Pi, NaHCO3-P, NaOH-P, and HCl-P, especially Pi fractions, were enhanced by NPKM. The amounts of total PLFAs and PLFAs in bacteria, Gram-positive (G+) and Gram-negative (G−) bacteria, actinomycetes, and fungi were high in NPKM-treated soil. The percentages of PLFAs in bacteria and fungi in total soil PLFAs were 56.8% and 9.7%, respectively, which did not show any significant difference among the treatments. NPKM increased the proportions (%) of PLFAs in G+ bacteria, and NPKS increased the proportions (%) of G− bacteria in total PLFAs. The composition of soil microbial community was found to be significantly affected by soil total carbon and pH. There was a close relationship between HCl-Pi, NaHCO3-Po, orthophosphate, and pyrophosphate with anaerobe, aerobes, and G+. Manure addition directly increased soil available P concentrations, and indirectly acted through the alterations of anaerobe, aerobes, and G+. It is concluded that long-term NPKM application would lead to the accumulation of labile P and moderately labile P in mollisol through the activity of soil microbes.

Benzyl isothiocyanate (BITC) is widely utilized in multiple biomedical fields, due to its significant antibacterial properties and low toxicity. However, poor water solubility and pungent odor has limited its application in the food industry. In this study, we first prepared inclusion complexes of BITC in GLU-β-CD and HP-β-CD using ultrasound, which is able to overcome the hindrance of poor water solubility and high volatility. Then, the BITC-β-CD inclusion complexes were characterized by using high-performance liquid chromatography (HPLC), nuclear magnetic resonance hydrogen spectra (1H-NMR), infrared absorption spectra (IR), and differential scanning calorimetry (DSC) to confirm their stability. Further, the evaluation of antibacterial and antitumor effects of the BITC-β-CD inclusion complexes showed that they had great bactericidal activity against both Escherichia coli and Staphylococcus aureus cells, and also inhibited the growth of HepG2 cells in vitro. In addition, our results indicated that BITC-β-CD complexes were able to inhibit the growth of S. aureus in broccoli juice and extend the shelf life of broccoli juice, demonstrating the potential of β-cyclodextrin to improve the stability and controlled release of BITC. Taken together, our results show that BITC-β-CD complexes have good potential for application in the food industry.

The ocean connects all continents and is an important space for human activities. Ship detection with electro-optical images has shown great potential due to the abundant imaging spectrum and, hence, strongly supports human activities in the ocean. A suitable imaging spectrum can obtain effective images in complex marine environments, which is the premise of ship detection. This paper provides an overview of ship detection methods with electro-optical images in marine environments. Ship detection methods with sea–sky backgrounds include traditional and deep learning methods. Traditional ship detection methods comprise the following steps: preprocessing, sea–sky line (SSL) detection, region of interest (ROI) extraction, and identification. The use of deep learning is promising in ship detection; however, it requires a large amount of labeled data to build a robust model, and its targeted optimization for ship detection in marine environments is not sufficient.

Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

Buthus martensii Karsch (BmK), is a kind of traditional Chinese medicine, which has been used for a long history for the treatment of many diseases, such as inflammation, pain and cancer. In this study, DKK-SP1/2/3 genes were screened and extracted from the cDNA library of BmK. The DKK-SP1/2/3 were expressed by using plasmid pSYPU-1b in E. coli BL21, and recombinant proteins were obtained by column chromatography. In the xylene-induced mouse ear swelling and carrageenan-induced rat paw swelling model, DKK-SP1 exerted a significant anti-inflammatory effect by inhibiting the expression of Nav1.8 channel. Meanwhile, the release of pro-inflammatory cytokines (COX-2, IL-6) was decreased significantly and the release of anti-inflammatory cytokines (IL-10) were elevated significantly. Moreover, DKK-SP1 could significantly decrease the Nav1.8 current in acutely isolated rat DRG neurons. In the acetic acid-writhing and ION-CCI model, DKK-SP2 displayed significant analgesic activity by inhibiting the expression of the Nav1.7 channel. Moreover, DKK-SP2 could significantly inhibit the Nav1.7 current in the hNav1.7-CHO cells.

A series of novel spirooxindole-O-naphthoquinone-tetrazolo[1,5-a]pyrimidine hybrids were designed, synthesized and evaluated as potent antitumor agents. These hybrids exhibited relatively high cytotoxic activity against cancer cell line HepG2 (IC50 = 2.86–36.34 μM), while normal cell line LO2 was less sensitive to these hybrids (IC50 = 36.37–248.39 μM). On the whole, among all the compounds tested, compound 4e, with a mean IC50 value of 2.86 μM, was the most active. The novel hybrids may find their pharmaceutical applications after further investigations.