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Despite their numerous excellent properties, metal sulfides are not particularly efficient at converting energy and purifying the environment, which limits their further applications. Fortunately, the energy conversion and environmental purification efficiencies of these materials have experienced notable advancements in recent years, accompanied by an improved understanding of their underlying mechanisms. Herein, progress in experimental researches in recent years on the engineering of single component metal sulfides by controlling morphology, construction of heterojunctions, and incorporating elements is reviewed. Methods to design and prepare metal sulfide‐based composites by building binary or ternary heterojunctions of metal sulfide/semiconductor/conductor are also discussed in detail. These materials are used in energy conversion and environmental purification systems, where they act as photocatalytic materials not only to split water, reduce carbon dioxide or nitrogen, but also to degrade pollutants (organic and inorganic) in water and gas. Finally, it is concluded by summarizing the research frontiers of metal sulfide nanomaterials in energy and environmental applications, as well as proposing potential challenges and future research directions. This work may contribute to a better understanding of metal sulfide nanocomposites and provide clues for the fabrication of more efficient metal sulfide‐based nanostructures for clean energy production and environmental remediation.

A new dysprosium (III) coordination polymer [Dy(Hm-dobdc) (H2O)2]·H2O (Dy-CP), was hydrothermal synthesized based on 4,6-dioxido-1,3-benzenedicarboxylate (H4m-dobdc) ligand containing carboxyl and phenolic hydroxyl groups. The Dy(III) center adopts an octa-coordinated [DyO8] geometry, which can be described as a twisted square antiprism (D4d symmetry). Neighboring Dy(III) ions are interconnected by deprotonated Hm-dobdc3− ligand to form the two-dimensional infinite layers, which are further linked to generate three-dimensional structure through abundant hydrogen bonds mediated primarily by coordinated and lattice H2O molecules. Magnetic studies demonstrates that Dy-CP shows the field-induced slow relaxation of magnetization and the energy barrier Ueff/kB and relaxation time τ0 are 35.3 K and 1.31 × 10–6 s, respectively. Following the vehicular mechanism, Dy-CP displays proton conductivity with σ equal to 7.77 × 10–8 S cm−1 at 353 K and 30%RH. Moreover, luminescence spectra reveal that H4m-dobdc can sensitize characteristic luminescence of Dy(III) ion. Herein, good magnetism, proton conduction, and luminescence are simultaneously achieved, and thus, Dy-CP is a potential multifunctional coordination polymer material.

As an important inorganic material, zirconium dioxide (ZrO2) has a wide range of applications in the fields of microelectronics, coating, catalysis and energy. Due to its high dielectric constant and thermodynamic stability, ZrO2 can be used as dielectric material to replace traditional silicon dioxide. Currently, ZrO2 dielectric films can be prepared by atomic layer deposition (ALD) using water and zirconium precursors, namely H2O-based ALD. Through density functional theory (DFT) calculations and first-principles molecular dynamics (FPMD) simulations, the adsorption and dissociation of water molecule on the ZrO2 surface and the water–solid interface reaction were investigated. The results showed that the ZrO2 (111) surface has four Lewis acid active sites with different coordination environments for the adsorption and dissociation of water. The Zr atom on the surface can interacted with the O atom of the water molecule via the p orbital of the O atom and the d orbital of the Zr atom. The water molecules could be dissociated via the water–solid interface reaction of the first or second layer of water molecules with the ZrO2 (111) surface. These insights into the adsorption and dissociation of water and the water–solid interface reaction on the ZrO2 surface could also provide a reference for the water–solid interface behavior of metal oxides, such as H2O-based ALD.

Therapeutic ophthalmic contact lenses with prolonged drug release and improved bioavailability have been developed to circumvent tedious eye drop instillation. In this work, zwitterionic nanogels based on poly(sulfobetaine methacrylate) (PSBMA) were easily fabricated by one-step reflux-precipitation polymerization, with the advantages of being surfactant-free and morphology controlled. Then, the ophthalmic drug levofloxacin (LEV) was encapsulated into the nanogels. A set of contact lenses with varied nanogel-loading content was fabricated by the cast molding method, with the drug-loaded nanogels dispersed in pre-monomer solutions composed of 2-hydroxyethyl methacrylate (HEMA) and N-vinyl-2-pyrrolidone (NVP). The structure, surface morphology, water contact angle (WCA), equilibrium water content (EWC), transmittance, and mechanical properties of the contact lenses were subsequently investigated, and in vitro drug release and biocompatibility were further evaluated. As a result, the optimized contact lens with nanogel-loading content of 8 wt% could sustainably deliver LEV for ten days, with critical lens properties within the range of recommended values for commercial contact lenses. Moreover, cell viability assays revealed that the prepared contact lenses were cytocompatible, suggesting their significant potential as an alternative to traditional eye drops or ointment formulations for long-term oculopathy treatment.

A chiral phosphoric acid promoted enantioselective NMR analysis of atropisomeric quinazolinones was described, in which a variety of racemic arylquinazolinones such as afloqualone and IC-87114 were well recognized with up to 0. 21 ppm ΔΔδ value. With this method, the optical purities of different non-racemic substrates can be fast evaluated with high accuracy.

Core-shell (C/S) structured upconversion coated Fe3O4 nanoparticles (NPs) are of great interest due to their potential as magnetic resonance imaging (MRI) and upconversion luminescent (UCL) imaging agents, as well as near-infrared activated photodynamic therapy (PDT) platforms. When C/S structured Fe3O4@Mn2+-doped NaYF4:Yb/Er NPs were prepared previously, well-defined C/S-NPs could not be formed without the doping of Mn2+ during synthesis. Here, the role of Mn2+ doping on the synthesis of core-shell structured magnetic-upconversion nanoparticles (MUCNPs) is investigated in detail. Core-shell-shell nanoparticles (C/S/S-MUCNPs) with Fe3O4 as the core, an inert layer of Mn2+-doped NaYF4 and an outer shell consisting of Mn2+-doped NaYF4:Yb/Er were prepared. To further develop C/S/S-MUCNPs applications in the biological field, amphiphilic poly(maleic anhydride-alt-1-octadecene) (C18PMH) modified with amine functionalized methoxy poly(ethylene glycol) (C18PMH-mPEG) was used as a capping ligand to modify the surface of C/S/S-MUCNPs to improve biocompatibility. UCL imaging, T1-weighted MRI ascribed to the Mn2+ ions and T2-weighted MRI ascribed to the Fe3O4 core of C/S/S-MUCNPs were then evaluated. Finally, chlorine e6 (Ce6) was loaded on the C/S/S-MUCNPs and the PDT performance of these NPs was explored. Mn2+ doping is an effective method to control the formation of core-shell structured MUCNPs, which would be potential candidate as multifunctional nanoprobes for future T1/T2-weighted MR/UCL imaging and PDT platforms.

Polyepoxysuccinic acid (PESA) was modified by glycidyl (Gly) to obtain a phosphate-free and nitrogen-free polymer (Gly-PESA), and its scale and corrosion inhibition properties were studied.The effects of Gly-PESA concentration, calcium ion concentration and temperature to scale inhibition performance were studied by static scale inhibition method. The corrosion inhibition performance of Gly-PESA was studied by static weight-loss method. The morphology and structure of calcium scale and steel surface were characterized by spectroscopic and microscopic methods. The scale inhibition efficiency of Gly-PESA for CaCO3 and CaSO4 was 98.06% and 92.6%, respectively. Under the same experimental conditions, the scale inhibition effect of Gly-PESA was obviously better than that of PESA. Gly-PESA could effectively inhibit the growth of CaCO3 and CaSO4 crystals and cause their crystal lattice distortion or crystal dispersion. The corrosion inhibition efficiency of Gly-PESA for carbon steel was 78.17%. The adsorption of Gly-PESA on the surface of the steel test piece followed Langmuir adsorption isotherm, and formed a protective film, which increased the corrosion resistance of the steel test piece, thus effectively protecting the steel test piece. Therefore, Gly-PESA was a green scale and corrosion inhibitor, which solved the problem of environmental pollution.

The structural assignment of isomers at hydrogen-bonded aggregates in solution has been determined by computational and 1H NMR spectroscopy. The results showed that the aggregates incorporating C3 symmetric hub(M3) groups were more stable than those that incorporate assymmetric hub(M3) groups. The number and symmetries of these aggregates were minimally influenced by temperature and solvent.

The temperature dependence and magnetic susceptibilities of the complexes alpha-Mn3(SALADHP)2(acetate)4(CH3OH)2, alpha-Mn3(SALADHP)2(acetate)4(H2O2)2 and alpha-Mn3(SALATHM)2(acetate)(CH3OH)2, where H2SALADHP = 1,3-dihydroxy-2-methyl-2-(salicylideneamino)propane and H2SALATHM = tris(hydroxymethyl)(salicylideneamino)methane are investigated. The influence of axial ligation, angular orientation and metal separation, phenolate ring substitution and altered bridging ligands on the spin exchange of these ligands is determined.

The Mn(IV) oxide dimer of 1,3-bis(salicylideneamino)propane and the Mn(III) chloride dimer of methanolic 1-(salicylideneamino)-3-hydroxypropane complement 20 other oxide- and alkoxide-bridged manganese complex dimers in a correlation study between Mn-Mn separation and the Mn-O-Mn bridge angle. The study shows that mu-oxo bridges follow the law of cosine correlation while single-atom mu-alkoxo bridges do not. Despite deviations, the study manages to set an empirical correlation slope of 0.03 angstroms/degree within the 2.86-3.81 angstrom range. The implications of these findings in the behavior of multinuclear manganese enzymes are discussed.

Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating to reduce the springback of 45 steel sheets and investigated the effects of bending parameters on springback behavior. Initially, a thermodynamic coupling model and a static model were created utilizing the VOCE hardening model and the von Mises yield criterion in the ANSYS workbench 2022 R2 software. The springback behavior and stress distribution of the sheets were then investigated under different temperatures (room temperature and 800 °C) and bending angles (15°, 30°, 45°, 60°, 75°, and 90°). Simultaneously, the experiments were performed to investigate springback behavior and guarantee the accuracy of the model. The results indicate that the springback reached a minimum value at the 45° bending angle at room temperature while increasing with the increasing bending angle under 800 °C local heating. The springback under local heating can be decreased by 75.2% and the error of the bending angle can improve by 2–7° compared with samples processed in room temperature.

The use of environmentally friendly corrosion inhibitors (CIs) is an effective way to overcome the environmental problems caused by existing CIs that contain nitrogen or phosphorus components. Plant extracts, as a type of environmentally friendly CI, have attracted considerable attention. In this work, the corrosion inhibition properties of spinach extract (AESPE) on Q235 steel were studied in a hydrochloric acid medium. First, AESPE was extracted from spinach by an ethanol-acetone reflux method and its main structural and stability parameters in hydrochloric acid medium were determined by infrared spectroscopy (FTIR) and ultraviolet spectroscopy (UV–vis). Second, the corrosion inhibition properties of AESPE on Q235 steel in a 0.5 M hydrochloric acid solution were studied by weight loss (WTL), dynamic potential polarisation (PDP), linear polarisation (LPR), electrochemical impedance spectroscopy (EIS) and quantum chemical calculation methods. The results showed that AESPE could significantly slow the corrosion rate (Vcorr) of Q235 steel in 0.5 M hydrochloric acid medium, which was via a mixed corrosion inhibition mechanism at the cathode and anode. As the AESPE concentration increased, the cathode and anode currents decreased and the active corrosion sites were blocked; thus, the Vcorr decreased and the corrosion inhibition efficiency (E (%)) increased. Under the conditions of 303 K and 300 mg⋅L–1 AESPE, the E (%) of AESPE for Q235 steel was>90%. As the system temperature increased, the E% of AESPE for Q235 steel decreased but the extent of this decrease was small. This work shows that AESPE is an environmentally friendly CI with a good corrosion inhibition effect.

In this work, the development of the eco-friendly comprehensive scale and corrosion inhibitor based on green polyaspartic acid (PASP) was presented. In this view, PASPG was prepared by a ring-opening graft modification reaction of polysuccinimide (PSI) with glycidyl. In addition, the molecular structure and the thermal stability of PASPG were characterized by using three different methods (FTIR, 1H NMR, and TGA). PASPG’s scale inhibition efficiency and corrosion inhibition efficiency were also evaluated, respectively. More concretely, the scale inhibition efficiency of PASPG achieved 94.6 % and 95.1 % for CaCO3 and CaSO4, respectively. With the aid of the FTIR and SEM measurement techniques, it was found that PASPG could induce the irregular growth of the CaCO3 and CaSO4 morphology and destroy the formation of crystals. On the other hand, the higher corrosion efficiency of 85.17 % was achieved by PASPG in comparison with PASP (72.53 %). PASPG is a mixed inhibitor and the adsorption of PASPG on the Q235 steel surface followed the Langmuir mono-layer adsorption isotherm. The formation of a protective film on the surface of carbon steel was proved by PASPG’s adsorption, which increased the resistance to be eroded. Thus, the surface of carbon steel can be effectively protected. The present work provides a simple and effective pathway for the synthesis of high-efficiency green scale and corrosion inhibitor, by introducing a functional group into the PASP chains. The implementation of such type of chemical modification method may also be an effective strategy for improving the efficiency of other polymers green scale and corrosion inhibitors.

Extraction of the laser fringe center line is a key step in the 3D reconstruction of linear structured light, the accuracy of which is directly related to the quality of the 3D model. A laser center line extraction method based on an improved gray center of gravity method is proposed to solve the problem of low extraction accuracy. Firstly, a smoothing method is used to eliminate the flat top of the laser line, and the Gaussian curve is adopted to fit the peak position of the curve. Then, the gray threshold is set to automatically extract the laser linewidth, and based on the window opening, the grayscale center of gravity method is improved to extract the coordinates of the center pixel for the second time. Finally, experiments show that the average absolute error of the improved laser line extraction method is 0.026 pixels, which is 2.3 times lower than the gray center of gravity method, 1.9 times lower than the curve fitting method, and the standard error can reach 0.005 pixels. Compared with the gray center of gravity method and the curve fitting method, the influence of gray value change on the center line extraction is more fully considered, and the center of the light strip can be extracted more accurately, achieving sub-pixel accuracy.

Metal-ion-based self-assembly supramolecular theranostics exhibit excellent performance in biomedical applications owing to their potential superiorities for simultaneous precise diagnosis, targeted drug delivery, and monitoring the response to therapy in real-time. Specially, the rational designed systems could achieve specific in vivo self-assembly through complexation or ionic interaction to improve tissue-specific accumulation, penetration, and cell internalization, thereby reducing toxicities of drugs in diagnostics and therapy. Furthermore, such imaging traceable nanosystems could provide real-timely information of drug accumulation and therapeutic effects in a non-invasive and safe manner. Herein, the article highlights the recent prominent applications based on the metal ions self-assembly in cancer treatment. This strategy may open up new research directions to develop novel drug delivery systems for cancer theranostics.

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.

In order to improve the response characteristics of magnetorheological fluid brake (MRB) and reduce the braking fluctuation rate, an improved grey wolf optimization algorithm was proposed to adjust the parameters of the proportion integration differentiation (PID) controller. Firstly, an MRB system was designed and constructed, and its transfer function was determined. Moreover, by adopting the iterative method of logistic curve, an enhanced grey wolf optimization algorithm (EGWOA) was presented. Using the EGWOA, the parameters of the PID controller were optimized to improve the control performance of the system. Finally, the simulation and experiment were carried out. The results showed that EGWOA has a faster response output and overall better performance without overshoot compared with the conventional PID and grey wolf optimization algorithm (GWOA) PID controller.

Self-healing anti-corrosion materials are widely regarded as a promising long-term corrosion protection strategy, and this is even more significant if the damage can be monitored in real-time and consequently repaired. Inspired by the hierarchical structure of human skin, self-healing, solvent-free polyurethane/carbon nanotubes composites (SFPUHE-HTF-CNTs) with a skin-like bilayer structure were constructed. The SFPUHE-HTF-CNTs were composed of two layers, namely, a hydrophobic solvent-free polyurethane (SFPUHE-HTF) containing disulfide bonds and fluorinated polysiloxane chain segments consisting of a self-healing layer and CNTs with good electrical conductivity consisting of a corrosion protection layer, which also allowed for the real-time monitoring of damage. The results of corrosion protection experiments indicated that the SFPUHE-HTF-CNTs had a low corrosion current density (8.94 × 10−9 A·cm−2), a positive corrosion potential (−0.38 V), and a high impedance modulus (|Z| = 4.79 × 105 Ω·cm2). The impedance modulus could still reach 4.54 × 104 Ω·cm2 after self-healing, showing excellent self-healing properties for anti-corrosion protection. Synchronously, the SFPUHE-HTF-CNTs exhibited a satisfactory damage sensing performance, enabling the real-time monitoring of fractures at different sizes. This work realized the effective combination of self-healing with corrosion protection and damage detection functions through a bionic design, and revealed the green, and low-cost preparation of advanced composites, which have the advantage of scale production.

To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.

Sulfonyl drugs are widely used to treat various types of diseases, in which sultones have several desirable properties that have led to their increased use, including improving its simple and effective synthetic methods and/or potentially enhancing potency of drugs. This review focuses on the recent progress in typical synthesis of sultones and the latest developments in various therapeutic applications, including carbonic anhydrase inhibitory activity, HIV-I and HIV-II inhibitory activity, human cytomegalovirus inhibitory activity, and cholinesterase inhibitory activity and their structure–activity relationship. We hope that this review will encourage medicinal chemists to consider the potential benefits to incorporating sultone scaffold into future drug discovery.

Cathode electrolyte interphase (CEI) has obtained increasing attention due to the importance in sustaining full cell life. However, it has rarely been overviewed. Therefore, it is timely and necessary to write this mini-review on CEI. This mini-review will involve the formation mechanism and modification methods of CEI. The perspective on CEI is also given out. We expect that this mini-review will provide rational guidelines for the CEI design of highly-performance lithium-ion batteries (LIBs).

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence’s current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round’s loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.

Relation extraction (RE) is the core link of downstream tasks, such as information retrieval, question answering systems, and knowledge graphs. Most of the current mainstream RE technologies focus on the sentence-level corpus, which has great limitations in practical applications. Moreover, the previously proposed models based on graph neural networks or transformers try to obtain context features from the global text, ignoring the importance of local features. In practice, the relation between entity pairs can usually be inferred just through a few keywords. This paper proposes a keyword detection and context filtering method based on the Self-Attention mechanism for document-level RE. In addition, a Self-Attention Memory (SAM) module in ConvLSTM is introduced to process the document context and capture keyword features. By searching for word embeddings with high cross-attention of entity pairs, we update and record critical local features to enhance the performance of the final classification model. The experimental results on three benchmark datasets (DocRED, CDR, and GBA) show that our model achieves advanced performance within open and specialized domain relationship extraction tasks, with up to 0.87% F1 value improvement compared to the state-of-the-art methods. We have also designed experiments to demonstrate that our model can achieve superior results by its stronger contextual filtering capability compared to other methods.

Dynamic context selector, a kind of mask idea, will divide the matrix into some regions, selecting the information of region as the input of model dynamically. There is a novel thought that improvement is made on the entity relation extraction (ERE) by applying the dynamic context to the training. In reality, most existing models of joint extraction of entity and relation are based on static context, which always suffers from the feature missing issue, resulting in poor performance. To address the problem, we propose a span-based joint extraction method based on dynamic context and multi-feature fusion (SPERT-DC). The context area is picked dynamically with the help of threshold in feature selecting layer of the model. It is noted that we also use Bi-LSTM_ATT to improve compatibility of longer text in feature extracting layer and enhance context information by combining with the tags of entity in feature fusion layer. Furthermore, the model in this paper outperforms prior work by up to 1% F1 score on the public dataset, which has verified the efficiency of dynamic context on ERE model.

Generally, the sound signal produced by transmission unit or cutting unit contains abundant information about the working state of a machine. The acoustic-based diagnosis system presents some distinct advantages in some severe conditions particularly due to its unique non-contact measurement and unlimited use at the installation site. However, the original acoustic signal collected from manufacture process is always polluted by various background noises. In order to eliminate noise components from machinery sound effectively, an empirical mode decomposition (EMD) threshold denoising method optimized by an improved fruit fly optimization algorithm (IFOA) is launched in this paper. The acoustic signal was first decomposed by the adaptive EMD to obtain a series of intrinsic mode functions (IMFs). Then, the soft threshold function was applied to shrink the IMF coefficients. While the threshold of each IMF was determined by statistical estimation and empirical value for traditional EMD denoising, the denoising effect was often not desired and time-consuming. To solve these disadvantages, fruit fly optimization algorithm (FOA) was introduced to search global optimal threshold of each IMF. Moreover, to enhance the group diversity during production of the next generation of fruit flies and balance the local and global searching ability, a variation coefficient and a disturbance coefficient was introduced to the basic FOA. Then, a piece of simulated acoustic signal produced by the train was applied to validate the proposed EMD and IFOA threshold denoising (EMD-IFOA). The simulation results, which decreased 35.40% and 18.92% in mean squared error (MSE) and percent root mean square difference (PRD) respectively, and increased 40.36% in signal-to-noise ratio improvement (SNRimp) compared with basic EMD denoising scheme at SNR = 5 dB, illustrated the effectiveness and superiority of the proposed approach. Finally, the proposed EMD-IFOA was conducted on an actual acoustic-based diagnosis system for cutting state recognition of the coal mining shearer to demonstrate the practical effect.

In order to accurately acquire shearer cutting status, this paper proposed a pattern recognition method, based on the local mean decomposition (LMD), time-frequency statistical analysis, improved Laplacian score (LS), and fuzzy C-means (FCM) clustering algorithm. The LMD was employed to preprocess the vibration signals of shear cutting coal seam, and several product functions (PFs) were obtained. Following this, 14 time-frequency statistical parameters of the original signal and optimal PF were extracted. Additionally, the improved LS algorithm was designed to ensure the accurate estimation of features, and a new feature vector could be selected. Subsequently, the obtained eigenvector matrix was fed into a FCM to be clustered, for optimal clustering performance. The experimental examples were provided to verify the effectiveness of the methodology and the results indicated that the proposed algorithm could be applied to recognize the different categories of shearer cutting status.

To measure the pushing distance of a hydraulic-powered roof support, and reduce the cost from a non-reusable displacement sensor embedded in pushing a hydraulic cylinder, an inertial sensor is used to measure the pushing distance, and a Kalman filter is applied to process the inertial data. To obtain better estimation performance, an improved fruit fly optimization algorithm (IFOA) is proposed to tune the parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R. The key procedures of the proposed method, including state-space model, fitness function, and Kalman filter implementation, are presented. Finally, an artificial signal is utilized to verify the feasibility of the proposed method, and the tuning results of other algorithms, particle swarm optimization (PSO), genetic algorithm (GA), basic FOA, and 3D-FOA are compared. The proposed method is also applied in the pushing distance estimation scenario. The simulation and application results prove the effectiveness and superiority of the proposed method.

As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means (FCM) and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm (FGOA). Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform (WPT), and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm (FOA), is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect.

As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

In order to accurately evaluate the health condition of a shearer, a hybrid prediction method was proposed based on the integration of a fuzzy neural network (FNN) and improved particle swarm optimization (IPSO). The parameters of FNN were optimized by the use of PSO, which was coupled with a premature judgment and mutation mechanism to increase the convergence speed and enhance the generalization ability. The key technologies are elaborated and the flowchart of the proposed approach was designed. Furthermore, an experiment example was carried out and the comparison results indicated that the proposed approach was feasible and outperforms others. Finally, a field application example in coal mining face was demonstrated to specify the effect of the proposed system.