Coal Flow Foreign Body Classification Based on ESCBAM and Multi-Channel Feature Fusion.

Foreign bodies often cause belt scratching and tearing, coal stacking, and plugging during the transportation of coal via belt conveyors. To overcome the problems of large parameters, heavy computational complexity, low classification accuracy, and poor processing speed in current classification networks, a novel network based on ESCBAM and multichannel feature fusion is proposed in this paper. Firstly, to improve the utilization rate of features and the network's ability to learn detailed information, a multi-channel feature fusion strategy was designed to fully integrate the independent feature information between each channel. Then, to reduce the computational amount while maintaining excellent feature extraction capability, an information fusion network was constructed, which adopted the depthwise separable convolution and improved residual network structure as the basic feature extraction unit. Finally, to enhance the understanding ability of image context and improve the feature performance of the network, a novel ESCBAM attention mechanism with strong generalization and portability was constructed by integrating space and channel features. The experimental results demonstrate that the proposed method has the advantages of fewer parameters, low computational complexity, high accuracy, and fast processing speed, which can effectively classify foreign bodies on the belt conveyor.

Complementary Hybrid Semiconducting Superlattices with Multiple Channels and Mutual Stabilization.

An organic-inorganic hybrid superlattice with near perfect synergistic integration of organic and inorganic constituents was developed to produce properties vastly superior to those of either moiety alone. The complementary hybrid superlattice is composed of multiple quantum wells of 4-mercaptophenol organic monolayers and amorphous ZnO nanolayers. Within the superlattice, multichannel formation was demonstrated at the organic-inorganic interfaces to produce an excellent-performance field effect transistor exhibiting outstanding field-effect mobility with band-like transport and steep subthreshold swing. Furthermore, mutual stabilizations between organic monolayers and ZnO effectively reduced the performance degradation notorious in exclusively organic and ZnO transistors.

Application of unilateral multiple channels approach in percutaneous vertebroplasty for osteoporotic vertebral fractures.

This study aimed to investigate the application valuable of percutaneous vertebroplasty (PVP) with unilateral multiple channels approach in osteoporotic vertebral fractures patients. A retrospective review was conducted on 685 consecutive patients with osteoporotic vertebral fracture from March 2003 to October 2012. Among them, 82 cases were given PVP procedure by unilateral multiple channels approach. The timing of surgery, bone cements injection, and complications were analyzed. By using the X-ray film, the distribution of cement was detected and scored. The visual analogue scale (VAS) score and Oswestry disability index (ODI) system were used to evaluate the pain relief and improvement of daily activity function after operation. The excellent and good rate of cement distribution was 98.8% (98/99). Before surgery, the VAS score was 2.5±0.43. After surgery, the VAS score was significantly decreased to 2.0±0.33 at 1 h. Before surgery, ODI was 40.94±2.72. ODI was significantly decreased at 1 month after surgery (9.64±2.60) and at the final follow-up (7.77±2.15).  No spine or nerve injury, bone cement leakage, pulmonary embolism, pneumothorax, bleeding or infection occurred intraoperatively. Thus, the PVP with unilateral multiple channels approach effectively relieve the pain and improve the functional activity, without occurrence of complications.

Integrated molybdenum single atom array sensors with multichannels for nitrite detection in foods.

Nitrite (NO2-) is present in a variety of foods, but the excessive intake of NO2- can indirectly lead to carcinogenic, teratogenic, mutagenicity and other risks to the human body. Therefore, the detection of NO2- is crucial for maintaining human health. In this study, an integrated array sensor for NO2- detection is developed based on molybdenum single atom material (IMSMo-SAC) using high-resolution electrohydrodynamic (EHD) printing technology. The sensor comprises three components: a printed electrode array, multichannels designed on polydimethylsiloxane (PDMS) and an electronic signal process device with bluetooth. By utilizing Mo-SAC to facilitate electron transfer during the redox reaction, rapid and efficient detection of NO2- can be achieved. The sensor has a wide linear range of 0.1 µM-107.8 mM, a low detection limit of 33 nM and a high sensitivity of 0.637 mA-1mM-1 cm-2. Furthermore, employing this portable array sensor allows simultaneously measurements of NO2- concentrations in six different foods samples with acceptable recovery rates. This array sensor holds great potential for detecting of small molecules in various fields.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties.

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Secure control scheme for CPSs under DoS attacks via multiple transmission channels and unknown input observer.

The paper investigates the secure control problems for cyber-physical systems (CPSs) when the transmission channels suffer from Denial-of-Service (DoS) attacks based on switching observer and unknown input reconstruction (UIR). Firstly, an augmented system whose system state consists of the original system state and the measurement noises is set up, and the preconditions for the original system and augmented system are discussed in detail. Secondly, a full-order observer is constructed to generate the estimations of the augmented system state. Besides, based on the state estimation, an algebraic UIR method is developed and the UIR decouples the control input signal successfully. Thirdly, under the situation that some transmission channels suffer from DoS attacks, an observer-based secure controller is designed based on state estimation feedback and UIR feedback in view of a switching system. The stability of the switching system is analyzed as well. Finally, to verify the effectiveness of the proposed protocols, two simulation examples and the comparison with existing methods are given.

Integrated Biochip-Electronic System with Single-Atom Nanozyme for in Vivo Analysis of Nitric Oxide.

Nitric oxide (NO) exhibits a crucial role in various versatile and distinct physiological functions. Hence, its real-time sensing is highly important. Herein, we developed an integrated nanoelectronic system comprising a cobalt single-atom nanozyme (Co-SAE) chip array sensor and an electronic signal processing module (INDCo-SAE) for both in vitro and in vivo multichannel qualifying of NO in normal and tumor-bearing mice. The high atomic utilization and catalytic activity of Co-SAE endowed an ultrawide linear range for NO varying from 36 to 4.1 × 105 nM with a low detection limit of 12 nM. Combining in situ attenuated total reflectance surface enhanced infrared spectroscopy (ATR-SEIRAS) measurements and density function calculation revealed the activating mechanism of Co-SAE toward NO. The NO adsorption on an active Co atom forms *NO, followed by the reaction between *NO and OH-, which could help design relevant nanozymes. Further, we investigated the NO-producing behaviors of various organs of both normal and tumor-bearing mice using the proposed device. We also evaluated the NO yield produced by the wounded mouse using the designed device and found it to be approximately 15 times that of the normal mouse. This study bridges the technical gap between a biosensor and an integrated system for molecular analysis in vitro and in vivo. The as-fabricated integrated wireless nanoelectronic system with multiple test channels significantly improved the detection efficiency, which can be widely used in designing other portable sensing devices with multiplexed analysis capability.

Multichannel Superposition of Grafted Perfect Vortex Beams.

Inspired by plant grafting, grafted vortex beams can be formed through grafting two or more helical phase profiles of optical vortex beams. Recently, grafted perfect vortex beams (GPVBs) have attracted much attention due to their unique optical properties and potential applications. However, the current method to generate and manipulate GPVBs requires a complex and bulky optical system, hindering further investigation and limiting its practical applications. Here, a compact metasurface approach for generating and manipulating GPVBs in multiple channels is proposed and demonstrated, which eliminates the need for such a complex optical setup. A single metasurface is utilized to realize various superpositions of GPVBs with different combinations of topological charges in four channels, leading to asymmetric singularity distributions. The positions of singularities in the superimposed beam can be further modulated by introducing an initial phase difference in the metasurface design. The work demonstrates a compact metasurface platform that performs a sophisticated optical task that is very challenging with conventional optics, opening opportunities for the investigation and applications of GPVBs in a wide range of emerging application areas, such as singular optics and quantum science.

Spiking neural P systems with multiple channels and polarizations.

In this paper, we investigate a new variant of spiking neural P systems (SNP systems, in short), called spiking neural P systems with multiple channels and polarizations (SNP-MCP systems, in short). The variant integrates two interesting features: multiple channel and polarization. Each neuron can use its multiple channels to connect one or more subsequent sets of neurons. Moreover, both polarizations and regular expressions are used in rules to control the spiking of neurons. The computational power of the variant is discussed. The Turing universality of the variant as number generating/accepting devices is proven, and then a small universal system with 150 neurons is constructed to compute any Turing computable function.

Spiking neural P systems with multiple channels and anti-spikes.

Spiking neural P systems (SN P systems) with multiple channels are a variant of SN P systems presented recently. By introducing anti-spikes in neurons, SN P systems with multiple channels and anti-spikes are constructed in this work, where both spikes and anti-spikes are used in rules with channel labels. The Turing universality as number generating and accepting devices is proved at first, and then a universal SN P systems with multiple channels and anti-spikes for computing functions is investigated. At last, a small universal system using 65 neurons for computing any Turing computable function is given.

Spiking neural P systems with multiple channels.

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices.