Gas-coaldust hybrid explosions represent one of the most severe hazards in underground coal mining. Their reaction mechanisms are far more complex than those of pure gas explosions, making them difficult to thoroughly investigate using conventional experiments or macroscopic simulations. In this study, the suppression characteristics and underlying mechanisms of a composite inhibitor on hybrid explosions in a pipeline network were investigated through a combination of experimental analysis and machine-learning potential simulations, with a focus on the physicochemical synergistic effects. The results demonstrate that a 1:1 composite of magnesium-aluminum layered double hydroxide (LDHs) and melamine polyphosphate (MPP), designated as LM1:1, at an addition ratio of 50 wt%, exhibits optimal suppression performance. This formulation reduces the peak explosion overpressure by more than 67.6%, the explosion severity index by more than 84.9%, the flame propagation speed by more than 89.4%, and the peak flame temperature by more than 85.3%, while maintaining a flame suppression ratio above 0.92. LM1:1 significantly inhibits the pyrolysis of coal dust, lowering the weight-loss rate to 65.22%, reducing the maximum mass-loss rate to 2.51%/min, and decreasing the total heat release by 75.23%. Moreover, the composite inhibitor effectively scavenges center dot OH radicals, thereby blocking key chain-branching reaction pathways (center dot CH3 + center dot OH -> center dot CH4O -> center dot CH3O -> center dot CH2O -> center dot CHO -> CO + center dot OH -> center dot CHO2 -> CO2 and center dot CHO + center dot OH -> center dot CH2O2 -> center dot CHO2 -> CO2). Consequently, the contribution of the primary CO formation route (center dot CHO -> CO + center dot H) decreases by 4.327%, and that of the main CO2 formation route (center dot CHO2 -> CO2 + center dot H) declines by 0.677%. The reduced center dot OH concentration also suppresses center dot CH3 generation, leading to a 0.578% decrease in the contribution of the reaction center dot OH + CH4 -> center dot CH3 + H2O. In addition, metal oxides formed during the suppression process firmly coat the coal dust surface, enhancing its thermal stability and mechanical strength, which further mitigates the explosibility. This study provides valuable insights for the development of high-efficiency explosion suppressants and the enhancement of safety in coal mining.

To elucidate the formation mechanisms and conversion pathways of sulfur-containing hazardous gases (such as SOx, H2S, and COS) during coal combustion from different sulfur-containing functional groups, thereby enabling targeted control of sulfur emissions, this study employs X-ray photoelectron spectroscopy (XPS) to analyze the occurrence forms of sulfur in coals of different ranks and systematically investigates the combustion characteristics of five typical sulfur-containing functional groups (thiophene, mercaptan, sulfide, sulfone, and sulfoxide) under varying temperature conditions using ReaxFF MD simulations. The results indicate that thiophene is the predominant form of sulfur in coal. With increasing coal rank, the content of thiophenie continuously increases, while the contents of mercaptan and sulfide gradually decrease. Both temperature and molecular structure collectively regulate the energy release and oxygen consumption during the combustion of sulfur-containing systems. The core formation pathways of sulfur-containing hazardous gases, including SO2, SO3, H2S, and COS, are clearly identified. The contribution degrees and reaction rates of the key reaction pathways have been obtained, laying a theoretical foundation for sulfur emission control and the development of low-sulfur combustion technologies.

This study selected lignite, non-caking coal and meager coal as research objects. FTIR, XPS and 13C NMR were used to obtain their microstructural characteristics and construct coal molecular models. Results show that with increasing coal rank, aromaticity enhances and aliphatic side chains decrease; pyridine nitrogen and thiophene sulfur are the main occurrence forms of N and S. Based on ReaxFF-MD simulation, the effect mechanism of coal rank on combustion was explored. Simulation indicates that energy release, O2 consumption, and conversions of CO2, H2O, CO are mainly controlled by combustible elements, with insignificant coal rank impact. However, coal rank regulates free radical and hydrocarbon conversion: center dot OH and center dot O increase, while center dot O2H, center dot CHO and center dot CHO2 decrease with higher rank. This study provides theoretical basis for clean efficient utilization and combustion regulation of coal.

To address the problems existing in traditional control methods in gas flow regulation, such as reliance on manual parameter adjustment, slow response, and insufficient control accuracy and stability, a gas flow controller based on the improved starfish optimization algorithm control is designed. The improved starfish optimization algorithm (ISFOA) is adopted to adaptively optimize the PID parameters of the flow controller, thereby enhancing the dynamic response speed, steady-state accuracy and control performance of the controller. The optimal point set initialization was introduced and a two-factor adaptive switching strategy based on the iteration progress and the change rate of the population's optimal fitness was designed to enhance the global search ability and optimization efficiency of the starfish optimization algorithm. The performance test comparison experiment of ISFOA was conducted using the benchmark function set, and the flow regulation control experiment within the range of $0-100 ~\mathrm{L} / \min$ was carried out based on the differential pressure gas flow controller. The experimental results show that ISFOA outperforms the original starfish optimization algorithm, particle swarm optimization algorithm and particle swarm optimization algorithm in terms of convergence speed, optimization accuracy and stability, verifying the feasibility of the improved strategy. The maximum regulation time for flow control is 0.5 seconds, and the maximum control accuracy is 0.65%. It can achieve rapid and smooth flow regulation, verifying feasibility and superiority in actual gas flow control, and providing a feasible technical solution for high-precision gas flow control.

This paper first considered the development of a generalized dual-frequency Walsh transform algorithm specifically for processing dual-frequency EMF signals, taking into account the measurement accuracy requirements in high-precision situations and the limited filtering performance of traditional methods. First, we build a mathematical model of the dual-frequency slurry EMF signal, which is essentially a superposition of interferences and an ideal EMF signal. Second, the match-filtering applicability of the generalized dual-frequency Walsh transform algorithm is verified by exploring the sequency-spectrum distributed features of the mathematical model. Finally, a series of theoretical measurement experiments was carried out, employing the signal-to-noise ratio (SNR) as the qualitative metric to assess the measurement performance of the proposed method. This research not only breaks the limitations of traditional excitation techniques on excitation frequency but also provides new research ideas for signal processing similar to periodic rectangular waves.

The vortex flowmeter holds a significant position in the field of flow measurement, owing to its advantages such as the absence of mechanical moving parts, adaptability to various media, and low pressure loss. The vortex signal detected by piezoelectric elements undergoes a series of amplification, filtering, and other processing steps through analog and digital circuits to achieve accurate flow measurement. To enhance the dynamic response of the vortex flowmeter, a multi-channel parallel signal processing method with a 1/f2 amplitude-frequency modulation characteristic is proposed. By employing a 1/f2 filter unit (-40 dB/dec) to lower the minimum detectable flow while keeping the maximum flow unchanged and thus increasing the turndown ratio, the measurement channel is divided into multiple parallel channels. A fast channel selection method is designed to effectively address the issue of slow dynamic response in vortex flowmeter signal processing. Results indicate that the vortex flowmeter, integrated with a four-channel algorithm, achieves a precision level of 0.5 and demonstrates superior dynamic response performance compared to Yokogawa flowmeters.

To address the challenge of density detection for small-volume unit-labeled quantitative packaged products, a computer vision-based density detection device suitable for small-volume liquids was designed. The device integrates image processing technology with an automatic injection system and micro-volume constant-volume control technology to achieve automatic weighing and volume acquisition of volumetric flasks, thereby enabling automated density measurement of samples. Additionally, a data acquisition and processing software system was developed to minimize human reading and operational errors, improving detection efficiency and measurement accuracy. Experimental results demonstrate that the device achieves a relative expanded uncertainty of $0.00069 \mathrm{g} / \text{cm}^3$ and a measurement repeatability of $0.0005 \text{g/cm}^{3}$, ensuring high detection precision.

Precise and stable flow rate control and measurement are critical for calibration accuracy and metrological traceability in microflow meter testing. Current microflow calibration systems are mostly laboratory-bound, lacking field applicability, stability, and efficiency. This paper proposes a field-deployable closed-loop control scheme for gravimetric microflow standard devices: target flow is mapped to main-pipe pressure via pressureflow calibration, with main-pipe pressure as real-time feedback and variable-frequency pump speed as the sole manipulated variable. A fractional-order PID (FO-PID) controller is adopted, whose parameters are optimized offline via metaheuristic algorithms and fine-tuned online. Fractional operators are implemented through Oustaloup approximation and discretization for real-time execution. Experiments show the optimized FO-PID reduces recovery time by 83.3% and root mean square error by 51.0% compared to traditional PID, verifying its feasibility for field deployment in quality inspection institutions.

To enhance the safety and reliability of geological CO2 sequestration, this study proposes a finite element coupling simulation method based on representative elementary volume (REV) models reconstructed from industrial CT imaging. The objective is to simulate gas migration and stress response within the coal matrix under CO2 adsorption conditions. The authentic three-dimensional internal structure of coal was acquired via industrial CT scanning, and the optimal REV scale was determined as 60 × 60 × 60 voxels through gradient error analysis. A multi-physics coupling model incorporating seepage, adsorption, and mechanical behavior was established and implemented on the COMSOL platform to perform numerical simulations under varying adsorption durations and injection pressures of 6 MPa. The results indicate that the adsorption-induced swelling of the matrix leads to a redistribution of internal stresses, exhibiting a directional transfer from fractures toward the matrix. The stress response of the coal demonstrates a nonlinear “increase-then-decrease” trend: the fracture domain stress increases from 0.66 to 0.73 MPa in the first day and then decreases to 0.54 MPa at 7 days. These findings offer both technical support and theoretical foundations for elucidating the multi-field coupling mechanisms in coal during CO2 sequestration and for the development of robust numerical simulation methodologies.

Based on Low of Electromagnetic Induction, a uniform alternating magnetic field is provided to plant stem through excitation module. Sap flow cuts the magnetic flux lines, inducing electromotive force signal on sensor. The signal is processed by signal processing circuit, filtered, and amplified. After analog-to-digital conversion, the data is collected by microprocessor and transmitted to host computer for processing and storage. In the experiment, Radermachera Sinica was selected as the subject. The transpiration diurnal variation trends were analyzed through correlation analysis (r=0.894) between the results from the whole-plant weighing method and measurement method. The results showed a significant correlation between the two measurement methods. A linear fitting of the measurement method using the weighing method was performed (Slope: 0.0727, Intercept: 0.6611, R2=0,682) and data validation (1:1) was carried out, confirming the feasibility of the measurement method. This provides a new approach and method for studying plant transpiration.

This study presents an advanced flame-retardant system based on the synergistic effect between boric acid anhydride (B2O3) and nano-silica (SiO2) for building insulation applications. Combining molecular dynamics simulations with experimental analyses, we demonstrate that thermal dehydration of boric acid (>130 degrees C) produces B2O3, which subsequently reacts with SiO2 above 600 degrees C to form a protective borosilicate glass (Si-O-B) layer that encapsulates expanded polystyrene foam (EPS). At an optimal mass ratio of 1:2, the composite exhibits exceptional fire performance, achieving a Limiting Oxygen Index (LOI) of 35.9 % and a Underwriters Laboratories Standard 94 (UL-94) V-0 rating. The system also shows a 52.6 % reduction in peak heat release rate(PHRR) and a 69.8 % reduction in total heat release rate (THR), along with significant smoke suppression (70 % reduction in smoke density) and improved mechanical properties (95 % increase in compressive strength). The flame-retardant mechanism involves dual-phase action: quenching free radicals in the gas phase and forming a continuous glass barrier in the condensed phase. Moreover, the system maintains excellent water resistance after prolonged immersion. This innovative approach offers a robust solution for enhancing the fire safety of EPS insulation in construction.

In radar High Resolution Range Profile (HRRP) recognition field, an effective feature extraction process is the main point to improve whole recognition performance. As the targets' HRRP is the amplitude of coherent summations of complex return from scatters in each range cell, some data-driven compression methods, like PCA, LDA, etc. are widely used and achieve good results under experimental conditions, but still doesn't contribute much to solving the inherent problem of HRRP, such as small-sample problem. For these reasons, a series of SOM based feature extraction methods are introduced to explore the possibility in HRRP recognition. From machine learning perspective, these SOM based methods might more comprehensively describe the inherent structure relations within the original HRRP data distribution with its competitive learning process. In this paper, three category simulation experiments are presented to analysis the feature extraction, data storage, cluster result and classifier design. Of these experiments, SOM combined with multi-layer neural network can reduce the over fitting problem of directly apply the multi-layer neural network for HRRP recognition, which further prove the feasibility of SOM application in HRRP recognition.

Abstract With the continuous reduction of air conditioning noise, sound quality has become a new important research direction in air conditioning. This paper establishes a subjective evaluation method for sound quality based on the reference scoring method through multiple comparative studies, resulting in higher timeliness and accuracy of evaluation results. By analyzing the sensitivity of objective evaluation parameters for sound quality, including A-weighted sound level, loudness, sharpness, roughness, speech clarity, and prominence ratio, objective evaluation models for sound quality were developed using multiple linear regression analysis and neural network algorithms. The conformity of both objective evaluation models with the subjective evaluation results for sound quality reached approximately 90%, with the neural network modeling method showing slightly higher accuracy than the linear regression analysis method.

The flammability of expanded polystyrene (EPS) foams severely limits their range of applications. Although several methods have been used to improve their flame-retardant properties, there is still a lack of an efficient and cost-effective strategy to ensure their fire safety. In this work, utilizing water-based polyurethane modified epoxy resin (WEPU), layered double hydroxide (LDH) and zinc borate (ZnB) as raw materials, a highly efficient flame-retardant coating was constructed on the surface of EPS to improve its mechanical properties, flame retardancy and smoke suppression capabilities. The application of a flame-retardant coating (LDH: ZnB = 1:3) increased the compressive strength of the original EPS by 68.33 %, increased the char yield from 0.14 % to 46.43 %, increased the oxygen index to 38.8 %, enabled the vertical flame test to reach the UL-94 V-0 level, reduced the peak heat release rate by 53.02 %, reduced the peak smoke production rate by 61.45 %, and the coating exhibited excellent adhesion and water resistance. The evolution characteristics of molecular number, system potential energy, and product distribution during the combustion process of WEPU/LDH/ZnB coatings were obtained through molecular simulation. Furthermore, the synergistic flame-retardant mechanism between the condensed and gas phases of these coatings was elucidated at the microscopic level.

Pesticides are commonly used in agriculture and aquaculture. Triazophos, an organophosphate-based pesticide, is widely used in agriculture to control many insect pests. Due to its high photochemical stability and mode of action, Triazophos could persist in the aquatic ecosystem and cause toxic effects on non-target organisms. We have studied the potential toxic effects of Triazophos on L. rohita. Primarily, we determined the median lethal concentration (LC50) of Triazophos for 24 and 96 h. Next, we studied acute (96 h, LC50-96 h) toxicity. Then, we studied chronic (35 days, 1/10th LC50-24 h Treatment I: 0.609 mg/L, 1/5th LC50-96 h Treatment II: 1.044 mg/L) toxicity. We analyzed blood biomarkers such as hematology (Hb, Hct, RBC, WBC, MCV, MCH and MCHC), prolactin, cortisol, glucose and protein levels. Concurrently, we analyzed tissue biomarkers such as glycogen, GOT, GPT, LDH and histopathology. IBRv2 index assessment method was also to evaluate the Triazophos toxicity. Studied hematological, hormonal, biochemical and enzymological biomarkers were affected in Triazophos treated groups when compare to the control group. The changes in these biomarkers were statistically significant at the 0.05 alpha level. Triazophos exposed fish shown a severe degenerated primary and secondary lamellae, lamellar fusion, hypertrophy and telangiectasia in the gills. In the hepatic tissue, it caused moderate necrosis, blood congestion, distended sinusoids with minor vacuolation, prominent pyknotic nuclei, hypertrophy, cloudy swelling of cells, lipid accumulation and fibrotic lesions. In the renal tissue, Triazophos caused thickening of Bowman's capsule, hyaline droplets degeneration, irregular renal corpuscle, congestion, cellular swelling, degeneration of tubular epithelium, necrosis, shrunken glomerulus, vacuolated glomerulus, hypertrophy, exudate and edema. IBRv2 analysis suggested that tissue biomarkers are highly sensitive to Triazophos toxicity and prolonged exposure could cause serious health effects like acute toxicity in fish. Triazophos could cause multiorgan toxicity at studied concentrations.

The vortex flowmeter occupies a vital position in flow measurement with its unique advantages. It is essentially a fluid vibration instrument, and its measurement process is susceptible to interference, which seriously affects measurement accuracy. In particular, at low flow rates, it is an urgent problem to extract vortex signals from the complex noise. Among many signal processing methods, Empirical Mode Decomposition (EMD) is a time-frequency analysis method suitable for nonlinear, non-stationary signals. EMD can adaptively decompose noisy signals into noise and useful signal components arranged from high frequency to low frequency. For the above problems, an innovative, improved EMD method is proposed in this paper. The digital filter is designed according to the amplitude-frequency characteristic of vortex signals. After filtering, the vortex signal is adjusted to a fixed value, and high-frequency noise is filtered. According to the consistency of the filtered signal's amplitude, we design a decomposition stop criterion for EMD to process the output signal of the vortex sensor. This method not only maintains the characteristic of adaptive decomposition in EMD but also completes the automatic extraction of the vortex signal under complex noise. It provides a new comprehensive method for realizing high-precision and anti-interference vortex flowmeters.

This article proposes a sequency-domain match filtering method based on the generalized Walsh transform that can accurately measure the slurry electromagnetic flowmeter (EMF) flow. Referring to the construction of binary Walsh functions, we first deduce the mathematical expression of the generalized Walsh functions and provide the proof of function characteristics. Afterward, a method called the generalized Walsh transform sequency-domain-based match filtering method is proposed. This method analyzed the slurry EMF signal (generated by the three-value rectangular wave excitation) sequency spectrum distribution signatures and calculated the signal-to-noise ratio (SNR) to verify the theoretical filtering effect. As for the practical validation, experiments on calibrating the water flow and measuring the slurry flow are conducted to correct the calculation parameters and verify the practical usefulness of the method. Finally, method comparison experiments are implemented to properly evaluate and clearly understand the advantages of the proposed method in processing slurry EMF signals.

The conventional Walsh function just takes values of +1 and -1 and can only track limited signal states. However, based on the characteristics of Walsh functions that can capture the frequency of square wave signals, this article proposes a generalized Walsh transform algorithm to process multi-valued rectangular wave signals. As an extension of the traditional Walsh functions, generalized Walsh functions have advantages in signal frequency matching and sequency spectrum amplitude extraction, which makes them well suited to express the valuable signal. First, we infer the invariance displacement theory in the time and sequency domains, limiting the value of a circular time shift to ensure the concentrated distribution of the signal sequency energy. This limitation lays a good foundation for constructing generalized Walsh functions. Then, two types of generalized Walsh functions are built by combining the characteristics of different periodic rectangular waves. We deduce two properties of orthogonality and completeness to prove the ability of the constructed functions to match the frequency and the extracted energy. Finally, we compare multiple filtering methods to verify the reliability of the proposed method.

The signal processing techniques are crucial in applying the electromagnetic flowmeter (EMF), and its filtering mechanism directly influences the measurement accuracy. Considering heavy noise frequently contaminates EMF signals, a sequency match filtering method based on the Walsh transform for processing EMF signals is proposed for the first time. The proposed method can accurately match the signal’s sequency and realize excellent harmonic extraction by analyzing features of the sequency spectrum. For method effectiveness validation, it not only has a high signal-to-noise ratio (SNR > 75 dB) in theoretical filtering but also has a low relative indication error (e<2%) in experiments on the water flow calibration and the slurry flow measurement and a low En number (En<0.5) on performance evaluation, which indicates that the proposed method enables the efficient and accurate extraction of EMF signals.

Vortex flowmeters measure the fluid flow rate using the principle of fluid oscillation, making the measurement process susceptible to external vibration. Therefore, the research on anti-vibration signal processing of vortex flowmeters is of great significance. Based on a large number of experiments, an iterative search method based on the Kalman filter for vortex flowmeter (hereinafter referred to as ISKF) is proposed innovatively. First, the mathematical models of vortex flow signal and interference are constructed. According to the mathematical model, the interference is characterized as white Gaussian noise, periodic vibration interference, and transient impact interference. Then a Kalman filter-based vortex flow system model is created to eliminate the white Gaussian noise by linearizing the vortex flow signal model. For the periodic vibration, an iterative search criterion is designed based on the amplitude-frequency relationship of the vortex flow signal. Finally, offline and online experiments are conducted on the gas experimental setup. The experimental results show that the ISKF method can better overcome periodic vibration and transient impact interference. In general, this article lays a solid theoretical foundation for the commercialization of a new type of anti-vibration flowmeter.