Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Automated segmentation of brain tumor based on improved U-Net with residual units

Multimedia Tools and Applications - The study presents a new approach to automate segmentation of clinically significant brain tumor and, to a certain extent, addresses two major issues associated...     

Synthesis,characterization and antifungal property of Ti3C2Tx MXene nanosheets

Although the antibacterial properties of MXene nanosheets containing Ti₃C₂T_x are known, their antifungal properties have not been well studied. Herein, we present for the first time a report on the antifungal properties of Ti₃C₂T_x MXene. The Ti₃C₂T_x MXene was obtained by first exfoliating MAX phase of Ti₃AlC₂ with concentrated hydrofluoric acid, then the Ti₃C₂T_x was intercalated and deliminated by ethanol treatment and ultrasonication process. The delaminated Ti₃C₂T_x MXene nanosheets (d-Ti₃C₂Tx) were characterized using field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray (EDX), X-ray diffraction spectroscopy (XRD), and Raman spectroscopy. It was found that Ti₃C₂T_x MXene was characterized by lamellar structure alternating with layers of Ti, Al and C. The EDX results revealed that the delaminated Ti₃C₂T_x MXene nanosheets were composed of Ti, C, Si, O, F, and a trace amount of Al. The XRD and Raman spectra further indicated the elimination of Al and the formation of two-dimensional Ti₃C₂T_x MXene nanosheets. The antifungal activity of the delaminated Ti₃C₂T_x MXene was determined against Trichoderma reesei using the modified agar disc method. Observation using inverted phase contrastmicroscopy revealed inhibited fungus growth with the absence of hyphae around the discs treated wtih MXene. The surrounding of the control groups without an inclusion of MXene was found with large number of hyphae and spores. In addition, the spores of the fungi treated with the samples containing d-Ti₃C₂T_x MXene nanosheets did not germinate even after 11 days of culture. The results demonstrated disruption to the hemispheric structural formation of fungi colony, inhibition of hyphae growth and cell damage for fungi grown on the d-Ti₃C₂T_x MXene nanosheets. These new findings suggest that d-Ti₃C₂T_x MXene nanosheets developed in this work could be a promising anti-fungi material.     

Transmission system restoration with co-optimization of repairs,load pickups,and generation dispatch

This paper studies the restoration of a transmission system after a significant disruption such as a natural disaster. It considers the co-optimization of repairs, load pickups, and generation dispatch to produce a sequencing of the repairs that minimizes the size of the blackout over time. The core of this process is a Restoration Ordering Problem (ROP), a non-convex mixed-integer nonlinear program that is outside the capabilities of existing solver technologies. To address this computational barrier, the paper examines two approximations of the power flow equations: The DC model and the recently proposed LPAC model. Systematic, large-scale testing indicates that the DC model is not sufficiently accurate for solving the ROP. In contrast, the LPAC power flow model, which captures line losses, reactive power, and voltage magnitudes, is sufficiently accurate to obtain restoration plans that can be converted into AC-feasible power flows. An experimental study also suggests that the LPAC model provides a robust and appealing tradeoff between accuracy and computational performance for solving the ROP.     

Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

Heterometallic sulfide cluster [Ag6Sn6S20]10 −: Solvothermal syntheses and characterizations of silver thiostannates with lanthanide complex counter cations

Novel organic hybrid silver thiostannates [Hen]₄[Ln(en)₄]₂[Ag₆Sn₆S₂₀]·3en (Ln = Er, 1; Tm, 2; Yb, 3) were prepared by the reactions of Ln₂O₃, Ag, Sn and S in ethylenediamine (en) under solvothermal conditions. Six SnS₄ tetrahedra and six AgS₃ triangles are connected into the heterometallic sulfide cluster [Ag₆Sn₆S₂₀]^10 − via edge-sharing. In the [Ag₆Sn₆S₂₀]^10 − cluster, a hexanuclear Ag₆S₆ core is enclosed by two Sn₃S₁₀ fragments. The Ag₆S₆ core is the first As–S cluster stabilized by inorganic SnS₄ ligands. In 1–3, all Ln^3 + ions are in 8-fold coordination environments that involved four bidentate en ligands, forming bicapped trigonal prisms. Compounds 1–3 show well-defined absorption edges with band gaps in the range of 2.18–2.47 eV.     

基于空气质量模拟的江苏省大气污染物排放清单比较研究

大气污染物排放清单是空气质量模拟和空气污染治理的重要依据.本研究比较分析了两套覆盖江苏省的2017年大气污染物排放清单，即分别由上海市环境科学研究院、江苏省环境科学研究院编制的"长三角清单"和"江苏省清单"，并结合区域空气质量模型CMAQ评估不同清单对长三角地区2017年1、4、7、10月的空气质量模拟的影响.清单比较结果表明，除二氧化硫（SO₂）以外，江苏省清单估算的各污染物排放量较长三角清单低.通过与观测数据比较，发现两套清单对SO₂、氮氧化物（NO_x）、臭氧（O₃）和细颗粒物（PM_2.5）的模型模拟性能均较好.江苏省清单与长三角清单两者的模拟结果空间分布接近，其中江苏省清单模拟的PM_2.5和O₃在长三角多数地区略低于长三角清单的模拟结果（1月O₃除外）.江苏省清单与长三角清单均能够用于空气质量模式模拟，可为江苏地区的细颗粒物和光化学烟雾污染的控制策略制定提供参考.     

Parallel chaos optimization algorithm with migration and merging operation

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm.