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The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices. There are errors and omissions in detecting foreign objects of different sizes and small objects. In order to solve the above problems, a foreign object detection method for coal mine conveyor belts based on improved YOLOv8 is proposed. The depthwise separable convolution, squeeze-and-excitation (SE) networks are used to reconstruct the Bottleneck of the C2f module in the YOLOv8 backbone network as a DSBlock, which improves the detection performance while keeping the model lightweight. To enhance the capability to obtain information from objects of different sizes, an efficient channel attention (ECA) mechanism is introduced. The input layer of ECA is subjected to adaptive average pooling and adaptive maximum pooling operations to obtain a cross channel interactive MECA module, which enhances the global visual information of the module and further improves the precision of foreign object recognition. The method modifies the 3 detection heads of YOLOv8 to 4 lightweight small object detection heads to enhance sensitivity to small objects and effectively reduce the missed and false detection rates of small object foreign objects. The experimental results show that the improved YOLOv8 achieves a precision of 91.69%, mAP@50 reached 92.27%, an increase of 3.09% and 4.07% respectively compared to YOLOv8. The detection speed of improved YOLOv8 reaches 73.92 frames/s, which can fully meet the demand for real-time detection of foreign objects on conveyor belts in coal mines. The improved YOLOv8 outperforms mainstream object detection algorithms such as SSD, Faster-RCNN, YOLOv5, and YOLOv7-tiny in terms of precision, mAP@50, number of parameters, weight size, and number of floating point operations.

ObjectiveThe content of nitrogen (N) and potassium (K) in the soil directly affects crop yield, making it a crucial indicator in agricultural production processes. Insufficient levels of the two nutrients can impede crop growth and reduce yield, while excessive levels can result in environmental pollution. Rapidly quantifying the N and K content in soil is of great importance for agricultural production and environmental protection.MethodsA rapid and quantitative method was proposed for detecting N and K nutrient ions in soil based on polydimethylsiloxane (PDMS) microfluidic chip electrophoresis and capacitively coupled contactless conductivity detection (C4D). Microfluidic chip electrophoresis enables rapid separation of multiple ions in soil. The electrophoresis microfluidic chips have a cross-shaped channel layout and were fabricated using soft lithography technology. The sample was introduced into the microfluidic chip by applying the appropriate injection voltage at both ends of the injection channel. This simple and efficient procedure ensured an accurate sample introduction. Subsequently, an electrophoretic voltage was applied at both ends of the separation channel, creating a capillary zone electrophoresis that enables the rapid separation of different ions. This process offered high separation efficiency, required a short processing time, and had a small sample volume requirement. This enabled the rapid processing and analysis of many samples. C4D enabled precise measurement of changes in conductivity. The sensing electrodes were separated from the microfluidic chips and printed onto a printed circuit board (PCB) using an immersion gold process. The ions separated under the action of an electric field and sequentially reach the sensing electrodes. The detection circuit, connected to the sensing electrodes, received and regulated the conductivity signal to reflect the variance in conductivity between the sample and the buffer solution. The sensing electrodes were isolated from the sample solution to prevent interference from the high-voltage electric field used for electrophoresis.Results and DiscussionsThe voltage used for electrophoresis, as well as the operating frequency and excitation voltage of the excitation signal in the detection system, had a significant effect on separation and detection performance. Based on the response characteristics of the system output, the optimal operating frequency of 1 000 kHz, excitation voltage of 50 V, and electrophoresis voltage of 1.5 kV were determined. A peak overshoot was observed in the electrophoresis spectrum, which was associated with the operating frequency of the system. The total noise level of the system was approximately 0.091 mV. The detection limit (S/N = 3) for soil nutrient ions was determined by analyzing a series of standard sample solutions with varying concentrations. The detection limited for potassium (K+), ammonium (NH4+), and nitrate (NO3‒) standard solutions were 0.5, 0.1 and 0.4 mg/L, respectively. For the quantitative determination of soil nutrient ion concentration, the linear relationship between peak area and corresponding concentration was investigated under optimal experimental conditions. K+, NH4+, and NO3‒ exhibit a strong linear relationship in the range of 0.5~40 mg/L, with linear correlation coefficients (R2) of 0.994, 0.997, and 0.990, respectively, indicating that this method could accurately quantify N and K ions in soil. At the same time, to evaluate the repeatability of the system, peak height, peak area, and peak time were used as evaluation indicators in repeatability experiments. The relative standard deviation (RSD) was less than 4.4%, indicating that the method shows good repeatability. In addition, to assess the ability of the C4D microfluidic system to detect actual soil samples, four collected soil samples were tested using MES/His and PVP/PTAE as running buffers. K+, NH4+,Na+, Chloride (Cl‒), NO3‒, and sulfate (SO43‒) were separated sequentially within 1 min. The detection efficiency was significantly improved. To evaluate the accuracy of this method, spiked recovery experiments were performed on four soil samples. The recovery rates ranged from 81.74% to 127.76%, indicating the good accuracy of the method.ConclusionsThis study provides a simple and effective method for the rapid detection of N and K nutrient ions in soil. The method is highly accurate and reliable, and it can quickly and efficiently detect the contents of N and K nutrient ions in soil. This contactless measurement method reduced costs and improved economic efficiency while extending the service life of the sensing electrodes and reducing the frequency of maintenance and replacement. It provided strong support for long-term, continuous conductivity monitoring.

Abstract A hybrid chaotic mapping algorithm(HCM) based on 1‐DCM (one‐dimensional chaotic map) and Lorenz is proposed to address the security issues of low‐dimensional chaotic systems and the complexity of high‐dimensional chaotic systems. Firstly, the color images are divided into three high 4‐bit planes (BP) and low 4‐bit planes. By optimizing the parameters and structure of trigonometric function, the new arcsine‐sine (AS) function is designedto generate the 1‐DCM and permutation matrix, which is constructed to change the plane position and pixel position of the high 12BP to obtain the scrambling plane, which is encrypted by Lorenz chaotic system to produce the selfencryption matrix, with which the low 12BP is encrypted to generate the low bit encryption planes. Then, the ciphertext image is obtained by combining the high bit scrambling plane and low bit encryption plane. The SHA function is also used as the chaotic system's parameter generator, which improves the correlation between plaintext images and the key and resists selective plaintext attacks. The simulation results show that by modularizing the image's high and low bit planes, the encryption process is simplified and improved. The security and reliability of color image transmission are also improved.

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices (FDCM) with discrete cosine transform (DCT) operation. After that, a two-dimensional (2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually, under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc. [ABSTRACT FROM AUTHOR]

The rapid quantification of nitrate nitrogen concentration plays a pivotal role in monitoring soil nutrient content. Nevertheless, the low detection efficiency limits the application of traditional methods in rapid testing. For this investigation, we utilized a digital microfluidic platform and 3D-printed microfluidics to accomplish automated detection of soil nitrate nitrogen with high sensitivity across numerous samples. The system combines digital microfluidics (DMF), 3D-printed microfluidics, a peristaltic pump, and a spectrometer. The soil solution, obtained after extraction, was dispensed onto the digital microfluidic platform using a micropipette. The digital microfluidic platform regulated the movement of droplets until they reached the injection area, where they were then aspirated into the 3D-printed microfluidic device for absorbance detection. Implementing this approach allows for the convenient sequential testing of multi-samples, thereby enhancing the efficiency of nitrate nitrogen detection. The results demonstrate that the device exhibits rapid detection (200 s for three samples), low reagent consumption (40 µL per sample), and low detection limit (95 µg/L). In addition, the relative error between the detected concentration and the concentration measured by ultraviolet spectrophotometry is kept within 20%, and the relative standard deviation (RSD) of the measured soil samples is between 0.9% and 4.7%. In the foreseeable future, this device will play a significant role in improving the efficiency of soil nutrient detection and guiding fertilization practices. [ABSTRACT FROM AUTHOR]

Traditional methods for coal gangue sorting exhibit low efficiency, significant safety hazards, and limited applicability. Existing machine vision-based coal gangue image recognition methods struggle to balance model recognition speed and accuracy. In response to these challenges, this paper first utilizes the improved MSRCR algorithm to process images, enhancing the dark areas of coal gangue images while ensuring uniform brightness enhancement and image clarity. Furthermore, a novel lightweight coal gangue recognition method is proposed based on YOLOv8n, aiming to reduce data redundancy and improve recognition accuracy. Experimental results demonstrate that the improved lightweight model has a computational load of 7.1 GFLOPs, representing only 86.6% of the original model. The model detection rate is 73 fps, a 17 fps improvement over the original model. The accuracy, recall rate, and average precision reach 98.1%, 97.6%, and 98.9%, respectively, with improvements of 1.1, 0.9, and 0.5% points over the original model. The missing detection phenomenon is avoided effectively, and the accuracy and portability of the model are improved. [ABSTRACT FROM AUTHOR]