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With the continuous increase of coal seam mining depth, the coal mine production faced with complex water inrush mechanism and changeable main controlling factors, which makes the prediction of floor water inrush more difficult. In order to accurately predict the risk of floor water inrush, the vulnerability index method was used to obtain the risk weight of floor water inrush for each sub-factor, and the analytic hierarchy process(AHP) was used to divide the risk threshold of floor water inrush, so as to establish coal seam floor water inrush prediction model. We analyzed the geological and hydro-geological data of Henan Longmen Coal Industry Changcun Coal Mine from the three aspects of water inrush sources, water inrush channels and aquifers, the influencing factors of floor water inrush are divided into eight factors: aquifer water pressure, water output, grouting volume, fault similarity dimension, fault dip, fault strike, fault drop and effective aquifer thickness. Based on eight sub-factors, the risk of floor water inrush is divided into five areas: safe area [0.25, 0.32), relatively safe area [0.32, 0.44), the more dangerous area [0.44, 0.56), the dangerous area [0.56, 0.70), and the extremely dangerous area [0.70, 0.85]. Finally, the vulnerability index method evaluation model based on AHP was established. Compared with the traditional water inrush coefficient method, the evaluation results of the vulnerability index method based on AHP have better accuracy, and the division is more refined and reasonable, which provides a scientific evaluation method and theoretical basis for the prediction of mine water hazards.

With the continuous development of YQ2 Wellblock, it is estimated that there will be about 65.42% of gas wells whose wellhead pressure cannot reach the required inlet pressure within two years, and then, the wellblock cannot be maintained at stable production. Thus, it is urgent to conduct the research on optimization of supercharged gathering scheme. Herein, 5 supercharged gathering schemes were developed based on the different locations and quantity of supercharging equipment, which were determined according to the Benedict-Webb-Rubin-Starling(BWRS) Equation and the Fourth Order Runge-Kutta Method, through the analysis on change laws of oil/casing pressure in 309 wells and the prediction of supercharging time. Then, comparison was made among the 5 supercharged gathering schemes through fuzzy evaluation using the two primary evaluation indicators of technical economy and safety, refining the seven secondary evaluation indicators, such as the wellhead abandonment pressure, pressure loss of pipeline network and total investment,and determining their weights with the Analytic Hierarchy Process(AHP) method. Finally, it was determined to adopt the scheme of centralized supercharging at the terminal station + regional supercharging at 12 stations. In general, the research results are of great significance to the efficient development of gas field, cost reducing and benefit increasing, effective reduction of wellhead abandonment pressure, and the maximization of effectiveness of supercharging equipment for stable production.

Data centers are important infrastructure in China, with constantly increasing industry size, market revenues, and electricity consumption. The major cooling systems of data centers currently in operation in China are air-cooled, with a low energy utilization rate and great potentials for energy saving. Therefore, it is imperative to develop novel cooling technologies for green and energyefficient data centers. The cooling technology needs to solve two problems. First, as equipment heat production increases, the cooling capacity of the cooling system needs to be improved to match heat production and transfer rates. Second, to address the low energy efficiency problem, green and energy-efficient cooling technologies with industrial application prospects need to be developed. This study reviews the development status of data center cooling methods and categorizes cooling technologies into air conditioner cooling, fresh air cooling, indirect cooling, direct single-phase liquid cooling, and direct two-phase liquid cooling. We analyze the development trends of cooling technologies and suggest that green and high-efficient data centers should focus on the improvement in the efficiency of cooling systems, optimizations of hot and cold fluid channels, applications of new liquid cooling materials, and promotion of heat recovery systems. Furthermore, we propose suggestions for the stable and orderly development of the data center industry from the aspects of optimizing top-level design, developing key technologies, formulating scientific standards, improving industrial layout, and focusing on energy structure.

With the advancement of micro- and nanomanufacturing technologies, electronic components and chips are increasingly being miniaturized. To automatically identify their packaging materials for ensuring the reliability of ICs, a hybrid deep learning framework termed as CNN–transformer interaction (CTI) model is designed on IC packaging images in this paper, in which several cascaded CTI blocks are designed to bidirectionally capture local and global features from the IC packaging image. Each CTI block involves a CNN branch with two designed convolutional neural networks (CNNs) for CNN local features and a transformer branch with two transformers for transformer global features and transformer local-window features. A bidirectional interaction mechanism is designed to interactively transfer the features in channel and spatial dimensions between the CNNs and transformers. Experimental results indicate that the hybrid framework can recognize three types of IC packaging materials with a good performance of 96.16% F1-score and 97.92% accuracy, which is superior to some existing deep learning methods. [ABSTRACT FROM AUTHOR]

In this study, it is shown how the photoluminescence, scintillation, and optical thermometric properties are managed via the introduction of Gd3+ ions into Pr3+:Lu2Zr2O7. Raman spectra validate that partial replacement of Lu3+ with Gd3+ can promote the phase transition of Lu2Zr2O7 host from the defective fluorite structure to the ordered pyrochlore one. Upon 289 nm excitation, all the samples emit the 483 (3P0 → 3H4), 581 (1D2 → 3H4), 611 (3P0 → 3H6), 636 (3P0 → 3F2), and 714 nm (3P0 → 3F4) emissions from Pr3+ ions, which are enhanced with the addition of Gd3+ ions due to the modification of crystal structure. Dissimilarly, the X‐ray excited luminescence spectra consist of a strong emission located at 314 nm from Gd3+ ions (6P7/2 → 8S7/2), together with the typical emissions from Pr3+ ions, which exhibit different dependences on the Gd3+ concentration. When the luminescence intensity ratio between the 3P0 → 3H6 (611 nm) and 1D2 → 3H4 (581 nm) transitions is selected for temperature sensing, Pr3+:(Lu0.75Gd0.25)2Zr2O7 shows the optimal relative sensing sensitivity of 0.78% K−1 at 303 K, which is higher than that of the Gd3+‐free sample. Therefore, the developed Pr3+:(Lu, Gd)2Zr2O7 phosphors have the applicative potential for optical thermometry, X‐ray detection, and photodynamic therapy. [ABSTRACT FROM AUTHOR]

Organic pollution is one of the main sources of environmental pollution, which poses a serious threat to the ecological environment and human health. In this study, we synthesized a composite material consisting of biochar-supported magnetite nanoparticles (Bio/MNs) and collaborated with Pseudomonas aeruginosa PAO1 (P. aeruginosa PAO1) to conduct a bio-chemical composite remediation approach for organic pollution. The results of the scanning electron microscope (SEM) and X-ray diffractometer (XRD) show that Bio/MNs composites have been prepared successfully. Under light conditions, the highest removal rate of organic pollution by Bio/MNs synergistic P. aeruginosa PAO1 reached 81.5%. Gradient experiments revealed a direct correlation between the removal rate of organic pollution and the dosage of P. aeruginosa PAO1, as well as the input of Bio/MNs, within a specific range. Moreover, due to the positively charged nature of organic pollution, its maximum removal rate reaches 98.6% at pH = 11, exhibiting a 1.76-fold increase compared to that at pH = 3. The experimental results show that the collaboration between Bio/MNs and P. aeruginosa PAO1 expedited the electron transfer rate and increased the generation of ·OH and O2−, consequently facilitating the efficient degradation of organic pollutants. These findings inspire Bio/MNs collaborative microorganisms for providing new methods for the green and sustainable removal of organic pollutants. [ABSTRACT FROM AUTHOR]