Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves.

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning.

Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

An adaptive spacing of root-zone hole fertilization to improve production and fertilizer utilization of rapeseed.

BACKGROUND: Root-zone hole fertilization has a positive impact on enhancing crop production and fertilization efficiency. However, a suitable spacing for hole fertilization in rapeseed cultivation is unclear. To explore an adaptive hole spacing for improving rapeseed yield and fertilization efficiency, field experiments were conducted. Four spacings of hole fertilization were designed: 10 (FD10), 20 (FD20), 30 (FD30) and 40 cm (FD40), using no fertilization (F0) and deep-banded placement of fertilizer (DBP) as controls. The burial depth was 10 cm for FD and DBP treatments. RESULTS: Compared to DBP, hole fertilization impacted soil microenvironment, crop growth and yield components, resulting in a significant increase of 28.4% in seed yield and 25.6% in oil yield. Seed yield in FD20 (4345.43 kg ha-1) increased by 4.3%, 9.4% and 15.1% compared to FD10, FD30 and FD40, respectively. Fertilizer partial factor productivity under FD20 was 4.2%, 8.6% and 13.9% greater than FD10, FD30 and FD40, respectively; whereas the increase for agronomic efficiency was 6.0%, 12.7% and 21.0%, and the increase for N recovery efficiency was 39.5%, 52.5% and 62.9%, respectively. CONCLUSION: Fertilization with a hole spacing of 17 cm is a promising practice to maintain high production and fertilization efficiency when cultivating rapeseed. These results provide a theoretical foundation and scientific basis for improving rapeseed productivity and fertilizer utilization. © 2024 Society of Chemical Industry.

Association between paraspinal muscle fat infiltration and regional kyphosis angle in thoracolumbar fracture patients: a retrospective study.

This study aims to evaluate the impact of percutaneous pedicle screw fixation (PPSF) and open pedicle screw fixation (OPSF) on the postoperative paraspinal muscle fat infiltration (FI) rate in patients with thoracolumbar fractures through magnetic resonance imaging (MRI), and explore the association between paraspinal muscle FI rate and regional kyphosis angle. We retrospectively analyzed clinical data from 35 patients who underwent either PPSF or OPSF for thoracolumbar fractures, examining data at preoperative, 1-month postoperative, and 9-months postoperative time points, which included Visual Analog Scale (VAS), Oswestry Disability Index (ODI), and regional kyphosis angle. We obtained preoperative and 9-month postoperative paraspinal muscle FI rates using T2-weighted MRI images and ImageJ software. We analyzed the correlation of FI rates with VAS, ODI, as well as the correction loss percentage of regional kyphosis angle. The analysis revealed a positive correlation between postoperative FI rate increase and correction loss percentage of regional kyphosis angle (r = 0.696, p < 0.001). The increase in paraspinal muscle FI rate was positively correlated with 9-month postoperative ODI (r = 0.763, p < 0.001). These findings indicate that an increase in postoperative paraspinal muscle FI rate may result in more significant correction loss of regional kyphosis angle and can lead to increased functional impairment in patients.

Electron Oscillation-Induced Splitting Electroluminescence from Nano-LEDs for Device-Level Encryption.

Data security is a major concern in digital age, which generally relies on algorithm-based mathematical encryption. Recently, encryption techniques based on physical principles are emerging and being developed, leading to the new generation of encryption moving from mathematics to the intersection of mathematics and physics. Here, device-level encryption with ideal security is ingeniously achieved using modulation of the electron-hole radiative recombination in a GaN-light-emitting diode (LED). When a nano-LED is driven in the non-carrier injection mode, the oscillation of confined electrons can split what should be a single light pulse into multiple pulses. The morphology (amplitude, shape, and pulse number) of those history-dependent multiple pulses that act as carriers for transmitted digital information depends highly on the parameters of the driving signals, which makes those signals mathematically uncrackable and can increase the volume and security of transmitted information. Moreover, a hardware and software platform are designed to demonstrate the encrypted data transmission based on the device-level encryption method, enabling recognition of the entire ASCII code table. The device-level encryption based on splitting electroluminescence provides an encryption method during the conversion process of digital signals to optical signals and can improve the security of LED-based communication.

Non-destructive monitoring method for leaf area of Brassica napus based on image processing and deep learning.

Introduction: Leaves are important organs for photosynthesis in plants, and the restriction of leaf growth is among the earliest visible effects under abiotic stress such as nutrient deficiency. Rapidly and accurately monitoring plant leaf area is of great importance in understanding plant growth status in modern agricultural production. Method: In this paper, an image processing-based non-destructive monitoring device that includes an image acquisition device and image process deep learning net for acquiring Brassica napus (rapeseed) leaf area is proposed. A total of 1,080 rapeseed leaf image areas from five nutrient amendment treatments were continuously collected using the automatic leaf acquisition device and the commonly used area measurement methods (manual and stretching methods). Results: The average error rate of the manual method is 12.12%, the average error rate of the stretching method is 5.63%, and the average error rate of the splint method is 0.65%. The accuracy of the automatic leaf acquisition device was improved by 11.47% and 4.98% compared with the manual and stretching methods, respectively, and had the advantages of speed and automation. Experiments on the effects of the manual method, stretching method, and splinting method on the growth of rapeseed are conducted, and the growth rate of rapeseed leaves under the stretching method treatment is considerably greater than that of the normal treatment rapeseed. Discussion: The growth rate of leaves under the splinting method treatment was less than that of the normal rapeseed treatment. The mean intersection over union (mIoU) of the UNet-Attention model reached 90%, and the splint method had higher prediction accuracy with little influence on rapeseed.

Regulation of short-chain fatty acids in the immune system.

A growing body of research suggests that short-chain fatty acids (SCFAs), metabolites produced by intestinal symbiotic bacteria that ferment dietary fibers (DFs), play a crucial role in the health status of symbiotes. SCFAs act on a variety of cell types to regulate important biological processes, including host metabolism, intestinal function, and immune function. SCFAs also affect the function and fate of immune cells. This finding provides a new concept in immune metabolism and a better understanding of the regulatory role of SCFAs in the immune system, which impacts the prevention and treatment of disease. The mechanism by which SCFAs induce or regulate the immune response is becoming increasingly clear. This review summarizes the different mechanisms through which SCFAs act in cells. According to the latest research, the regulatory role of SCFAs in the innate immune system, including in NLRP3 inflammasomes, receptors of TLR family members, neutrophils, macrophages, natural killer cells, eosinophils, basophils and innate lymphocyte subsets, is emphasized. The regulatory role of SCFAs in the adaptive immune system, including in T-cell subsets, B cells, and plasma cells, is also highlighted. In addition, we discuss the role that SCFAs play in regulating allergic airway inflammation, colitis, and osteoporosis by influencing the immune system. These findings provide evidence for determining treatment options based on metabolic regulation.

Fertilization depth effect on mechanized direct-seeded winter rapeseed yield and fertilizer use efficiency.

BACKGROUND: Deep fertilization is effective for improving crop yield and fertilizer use efficiency. However, its impact on mechanized direct-seeded rapeseed and the optimal fertilization depth are poorly understood. A field experiment was conducted to evaluate the fertilization depth effect on mechanized direct-seeded rapeseed growth. Five treatments were designed: surface broadcast fertilizer, no fertilization, and fertilizer banded placement at soil depths of 5 (D5), 10 (D10), and 15 cm (D15). RESULTS: Compared with surface broadcast fertilizer, deep fertilization generally increased seed yield and partial factor productivity by 11.0%, agronomic efficiency (AE) by 22.7%, and recovery efficiency (RE) by 79.2% due to the increase of root mass density (16.8%), plant height (8.6%), height of the first branch (10.6%), stem diameter (22.4%), shoot biomass (16.1%), and shoot nitrogen (35.7%), phosphorus (29.7%), and potassium (26.2%) uptake. D10 had the highest seed yield, oil yield, fertilizer use efficiency, and economic benefits at different fertilization depth treatments. Compared with D5 and D15 respectively, D10 increased seed yield by 5.4% and 46.0%, oil yield by 7.7% and 50.5%, partial factor productivity by 5.4% and 46.0%, AE by 9.0% and 99.5%, RE of nitrogen by 48.9% and 34.9%, RE of phosphorus by 83.1% and 38.0%, and RE of potassium by 57.5% and 32.5%. The economic benefits of D10 were CNY 867.31 ha-1 and CNY 4864.23 ha-1 higher than D5 and D15 respectively. CONCLUSION: Considering rapeseed growth and its economic benefits, this study shows that 10 cm is an appropriate placement depth with regard to mechanized direct-seeded winter rapeseed production. © 2022 Society of Chemical Industry.

Omnidirectional Triboelectric Nanogenerator for Wide-Speed-Range Wind Energy Harvesting.

The environmentally friendly harvesting of wind energy is an effective technique for achieving carbon neutrality and a green economy. In this work, a core-shell triboelectric nanogenerator (CS-TENG) for harvesting wind energy is demonstrated and the device structure parameters are optimized. The core-shell structure enables the CS-TENG to respond sensitively to wind from any direction and generate electrical output on the basis of the vertical contact-separation mode. A single device can generate a maximum power density of 0.14 W/m3 and can power 124 light-emitting diodes. In addition, wind energy can be harvested even at a wind speed as low as 2.3 m/s by paralleling CS-TENGs of different sizes. Finally, a self-powered water quality testing system that uses the CS-TENG as its power supply is built. The CS-TENG exhibits the advantages of a simple structure, environmentally friendly materials, low cost, and simple fabrication process. These features are of considerable significance for the development of green energy harvesting devices.

Distribution of Iron on FCC Catalyst and Its Effect on Catalyst Performance.

The effects of different iron contamination content on the formation of iron nodules and the performance of FCC catalysts have been studied by cyclic deactivation treatment using iron naphthenate. The catalysts were characterized by X-ray diffraction, N2 adsorption-desorption, and SEM. The catalysts' performance was evaluated by the Advanced Cracking Evaluation device. It has been found that there will be obvious nodulation on the catalyst when the iron concentration exceeds 7,400 µg/g. With the iron deposition from 53 µg/g to 11,690 µg/g, the crystal structure of zeolite will not be destroyed by iron. The surface area and pore volume of the catalyst decreased significantly; the surface area decreased from 125.3 m2/g to 91.0 m2/g, and the pore volume decreased from 0.21 cm3/g to 0.16 cm3/g. The studies also showed that the increase of iron deposition will lead to the decrease of catalytic reaction efficiency. With the iron deposition from 53 µg/g to 11,690 µg/g, the conversion decreased by 4.83%. Under the same 78 wt.% conversion, bottoms yield and coke yield increased by 2.15% and 1.31%, while gasoline yield and LCO yield decreased by 2.59% and 2.16%, respectively. The real state of the industrial iron contaminated equilibrium catalyst can be mimicked by using the cyclic deactivation method.

Response of area- and yield-scaled N2 O emissions from croplands to deep fertilization: a meta-analysis of soil, climate, and management factors.

BACKGROUND: Nitrous oxide (N2 O) is an important and persistent greenhouse gas making a significant contribution to global climate change. Deep fertilization has been demonstrated to increase crop yield and nutrient use efficiency by decreasing losses of volatilization and surface runoff. However, N2 O emissions from croplands induced by deep fertilization are variable and mitigation strategies remain uncertain. This study aimed to (i) quantify the response of area-scaled (N2 O emissions) and yield-scaled N2 O emissions (N2 O intensity) from croplands to deep fertilization, and (ii) identify the soil, climate, and management factors that mitigate N2 O emissions and N2 O intensity under deep fertilization. RESULTS: Compared with the control, deep fertilization increased N2 O emissions by 18.6% (P < 0.001) but decreased N2 O intensity by 20.1% (P = 0.018). By adopting deep fertilization, N2 O emissions could be significantly mitigated in rice-paddies soils (-48.8%), with fertilizer depth > 10 cm (-33.0%), and with fertilizer N amount > 200 kg N ha-1 (-8.2%). N2 O intensity following deep fertilization significantly decreased in soils with pH ≤6 (-22.5%), at sites with precipitation of 500-1000 mm (-25.5%), in rice-paddies soils (-53.0%), with the method of mixed fertilizer in the control (-21.2%), and with fertilizer depth > 10 cm (-33.6%). CONCLUSION: This study provides a basis for assessing the effect of deep fertilization on N2 O emissions and provides potential measures to mitigate N2 O emissions associated with deep fertilization practices.

[Qualitative and quantitative detection of minced pork quality by near infrared reflectance spectroscopy].

The present study is concerning qualitative and quantitative detection of minced pork quality based on FT-near infrared (FT-NIR) spectroscopy and achieving the rapid approach to detecting the minced pork quality. Firstly, FT-NIR spectroscopy combined with partial least squares (PLS) and least squares-support vector machine (LS-SVM) was used for minced pork quality prediction including discrimination of the different muscle type of pig and quantitative detection of the fat, protein and moisture content of pork. The result indicated that 100% recognition ratio for calibration and 96% recognition ratio for validation were achieved by PLSDA for 4 different muscle types of pig. These two methods for chemical composition detection both have good performances in predicting fat and moisture content, the correlation coefficient for calibration and validation was all more than 0.9, but the models for protein content prediction were of less well performances, the correlation coefficients for calibration and validation, RMSEC, RMSEP and RMSECV respectively were 0.722, 0.593, 1.595, 1.550 and 1.888, respectively. The LS-SVM method is more accurate in predicting each quality index than the PLSR method. The result shows that the prediction models for fat and moisture content based on LS-SVM have a better performance with high precision, good stability and adaptability and can be used to predict the fat and moisture content of minced pork rapidly, and provide a fast approach to discrimination of the different muscle type of pig.

[Prediction of minced pork quality attributes using visible and near infrared reflectance spectroscopy].

The objective of the present study was to estimate minced pork meat quality using visible and near infrared (Vis-NIR) spectroscopy. Two hundred twenty five carcasses samples from longissimus dorsi muscle were scanned over the Vis-NIR spectral range from 350 to 1 015 nm and analysed for intramuscular fat (IMF), protein and moisture according to the official methods. Wavelet transform was employed to eliminate the spectra noise. Partial least square regression (PLSR) and support vector machine (SVM) were used to develop Vis-NIR spectroscopy models for chemical composition detection. According to calibration statistics, the best model to predict intramuscular fat content was developed by SVM with the denoised spectra, the correlation coefficient was 0.889 for calibration and 0.888 for validation. For protein and moisture, the best model was achieved with the PLS method with the correlation coefficient of 0.869 and 0.881 for protein calibration and validation sets and 0.877 and 0.848 for moisture calibration and validation sets, respectively. And all the ratios of standard deviation of validation set to root mean square error of prediction (RPD) were not more than 3.0. Results indicated that it was possible to predict chemical composition in minced pork meat. As a fast predictor of meat quality using Vis-NIR spectroscopy, it is necessary to improve the precision and the robustness of the model for practice.

On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy.

Visible/near-infrared (Vis/NIR) spectroscopy was tested to predict the quality attributes of fresh pork (content of intramuscular fat, protein and water, pH and shear force value) on-line. Vis/NIR spectra (350-1100 nm) were obtained from 211 samples using a prototype. Partial least-squares regression (PLSR) models were developed by external validation with wavelet de-noising and several pre-processing methods. The 6th order Daubechies wavelet with 6 decomposition levels (db6-6) showed high de-noising ability with good information preservation. The first derivative of db6-6 de-noised spectra combined with multiplicative scatter correction yielded the prediction models with the highest coefficient of determination (R(2)) for all traits in both calibration and validation periods, which were all above 0.757 except for the prediction of shear force value. The results indicate that Vis/NIR spectroscopy is a promising technique to roughly predict the quality attributes of intact fresh pork on-line.

[Online determination of pH in fresh pork by visible/near-infrared spectroscopy].

The present research was focused on determination of the pH value online by visible and near-infrared spectroscopy. In the part of data gathering, fresh pork longissimus dorsi was moving at the constant velocity of 0.25 m x s(-1) on the conveyor belt, and the visible and near-infrared diffuse reflectance spectrum (350-1 000 nm) was captured. In the part of data processing, band of 510-980 nm of the spectra was chosen to calibrate reflex distance, then to set up online detection model of pH value in fresh pork by partial least squares regression (PLSR). Kennard-stone algorithm was applied to divide the samples to the calibration set and validation set. The performances of several PLSR models employing various preprocessing methods including multiple scatter correction, derivative and both of them combined were compared. Further, the best performance model was optimized by interval PLSR to decrease the modeling variables of wavelength. The results indicated that the PLSR model based on preprocessing of multiple scatter correction (MSC) combined with first derivative gave the best performance with 0.905 of the correlation coefficient for validation set and 0.051 of the root of mean square errors for validation set. For the best PLSR model performance, the correlation coefficient of validation set increased to 0.926 and the root of mean square errors for validation set to 0.045 in the optimization interval PLSR model. However, only half of variables were used. The research demonstrates that using visible and near-infrared spectroscopy to determine fresh pork pH online is feasible.