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The equipment for coal mine fully mechanized working faces comes from different manufacturers, with inconsistent interfaces, different data systems and business logic, resulting in data barriers and slow data exchange between systems. Based on the industrial Internet architecture, an intelligent fully mechanized mining technology architecture including equipment layer, access layer, edge layer, PaaS layer and application layer is proposed. Based on this architecture, coal mine fully mechanized mining equipment is treated as the overall data object, and a method for constructing an information model of coal mine fully mechanized mining equipment is designed. Four key elements and modeling rules, including attributes, methods, events, and alarms are defined to achieve seamless communication between various physical entities and heterogeneous systems. This means defining, describing, and associating information resources of fully mechanized mining equipment, providing a complete and unified data object expression, description, and operation model. A modeling element optimization mechanism based on importance and semantic similarity is proposed to address the problem of excessive attribute elements in the information model of fully mechanized mining equipment. A fully mechanized mining equipment information model for the 81004 working face of No.1 Mine of Huayang New Materials Technology Group Co., Ltd. is established using the above method. The operation status of the electric motors of the fully mechanized mining equipment is evaluated. The results show that based on this model, all electric motors in the working face could be monitored for starting times in a short period of time, multiple motor equipment starting power balance, and the operating efficiency. Analysis results are generated to provide data support for decision-making work.

Abstract This paper aims to evaluate the culture and arts of Kyoto Prefecture through the calculation of its cultural GDP in 2015. The method for calculating the cultural GDP in Japan is mainly based on the criterion of the UNESCO Culture Satellite Account (CSA). Results reveal that the ratio of the cultural GDP to the total GDP in Kyoto Prefecture (about 2.0%) is slightly higher than that of the whole country in Japan (about 1.8%) and each domain shows different characteristics from the national situation. Considering the growth strategies of cultural industries formulated by Japanese government, Kyoto Prefecture should focus on developing its competitive tertiary industry and vigorously promote its cultural industries as well as activate the development of the traditional culture.

Abstract Conventional crop height measurements performed using aerial drone images require 3D reconstruction results of several aerial images obtained through structure from motion. Therefore, they require extensive computation time and their measurement accuracy is not high; if the 3D reconstruction result fails, several aerial photos must be captured again. To overcome these challenges, this study proposes a high-precision measurement method that uses a drone equipped with a monocular camera and real-time kinematic global navigation satellite system (RTK-GNSS) for real-time processing. This method performs high-precision stereo matching based on long-baseline lengths (approximately 1 m) during the flight by linking the RTK-GNSS and aerial image capture points. As the baseline length of a typical stereo camera is fixed, once the camera is calibrated on the ground, it does not need to be calibrated again during the flight. However, the proposed system requires quick calibration in flight because the baseline length is not fixed. A new calibration method that is based on zero-mean normalized cross-correlation and two stages least square method, is proposed to further improve the accuracy and stereo matching speed. The proposed method was compared with two conventional methods in natural world environments. It was observed that error rates reduced by 62.2% and 69.4%, for flight altitudes between 10 and 20 m respectively. Moreover, a depth resolution of 1.6 mm and reduction of 44.4% and 63.0% in the error rates were achieved at an altitude of 4.1 m, and the execution time was 88 ms for images with a size of 5472 × 3468 pixels, which is sufficiently fast for real-time measurement.

This study investigated the effects of reaction temperature (300–360 °C), reaction time (0–60 min), and the ratio of raw materials to solvent (1:5–1:15; g/mL) on the HydroThermal Liquefaction (HTL) of Chinese Herb Residues (CHR) for Bio-Oil (BO) production. Optimal HTL conditions for CHR were determined. To enhance both the yield and quality of BO, metal-modified catalysts including Fe/MCM-41, Co/MCM-41, and Ni/MCM-41 were prepared. These catalysts, after hydrogen reduction, loaded metal elements in their elemental state onto the carrier. Subsequently, CHR catalytic HTL experiments were conducted at 330 °C for 15 min with a ratio of 1:10 (g/mL). BO analysis was performed using EA, GC-MS, and FT-IR. Under the optimal HTL conditions (330 °C/15 min/1:10), the BO yield reached 24.57 wt.%, with a Higher Heating Value (HHV) of 25.96 MJ/kg. The major components of the BO included phenols, ketones, acids, and esters. In the catalytic HTL, Fe/MCM-41 (26.15 wt.%), Ni/MCM-41 (26.2 wt.%), and Co/MCM-41 (27.05 wt.%) catalysts each achieved higher BO yields. When using Ni/MCM-41 catalyst, the highest HHV of BO reached 32.01 MJ/kg, representing an 81% improvement over CHR HHV of 17.66 MJ/kg. Additionally, with Fe/MCM-41 catalyst, the lowest oxygen content in BO was 9.68%, and the selectivity for phenols and ketones was the highest, showing increases of 15.22% and 33.15%, respectively, reaching 44.13% and 24.5%. The results indicate that HTL can effectively convert high-moisture CHR into valuable products. Hydrogen-reduced metal-modified catalysts contribute significantly to increasing BO yield, promoting deoxygenation and hydrogenation reactions, and markedly improving HHV and the selectivity of chemical components, thereby effectively enhancing both the yield and quality of BO.

A highly selective fluorescent "turn on" probe for Al3+ based on the conjugate of aminourea and pyrazolone was devised and synthesized. The fluorescence intensity of the probe solution was significantly enhanced by the addition of Al3+ to the probe solution. Other cations such as Cu2+, Ni2+, Fe3+, Co2+, Ag+, Mn2+, Sn2+, Sn4+, Mg2+, Li+, Zn2+, Zr4+, Ce3+, Ca2+, and Na+ did not interfere the detection of Al3+. The 1 : 1 complexation between Al3+ and the probe was confirmed by Job's plot curve. In addition, the solution of the complex formed by Al3+ and the probe can be used for the specific detection of Hg2+, appearing as fluorescence quenching. [ABSTRACT FROM AUTHOR]

Complex signaling pathways are believed to be responsible for drug resistance. Drug combinations perturbing multiple signaling targets have the potential to reduce drug resistance. The large-scale multi-omic datasets and experimental drug combination synergistic score data are valuable resources to study mechanisms of synergy (MoS) to guide the development of precision drug combinations. However, signaling patterns of MoS are complex and remain unclear, and thus it is challenging to identify synergistic drug combinations in clinical. Herein, we proposed a novel integrative and interpretable graph AI model, DeepSignalingFlow, to uncover the MoS by integrating and mining multi-omic data. The major innovation is that we uncover MoS by modeling the signaling flow from multi-omic features of essential disease proteins to the drug targets, which has not been introduced by the existing models. The model performance was assessed utilizing four distinct drug combination synergy evaluation datasets, i.e., NCI ALMANAC, O'Neil, DrugComb, and DrugCombDB. The comparison results showed that the proposed model outperformed existing graph AI models in terms of synergy score prediction, and can interpret MoS using the core signaling flows. The code is publicly accessible via Github: https://github.com/FuhaiLiAiLab/DeepSignalingFlow [ABSTRACT FROM AUTHOR]

This paper investigates the correlation between a pilot's brain electroencephalographic (EEG) activity and his driving posture, addressing the intricate relationship between cognition and behavior in aviation. We designed and implemented a simulated experiment that recorded the fly pilot's attitude and collected EEG information as experimental data. The experiment is only based on EEG data to predict flight posture (pull, down, left, right). We propose a flight-driving attitude prediction model (CA-FAP) based on CEBRA and self-attention mechanism, with a prediction accuracy of 0.83. This model is better than common spatial pattern (CSP) and uniform manifold approximation and projection (UMAP) dimension reduction methods in the experiment. Moreover, a better effect can be obtained in the larger attitude radian dataset (accuracy is 0.85), and the effect is not obvious in the dataset of closing the six-axis motion platform, indicating that the model prediction of flight attitude is closely related to the pilot position transformation. By comparing the prediction effect of each category, the pull-up and drop are better than the steering prediction result. The study can help pilots adjust their posture and decisions, and serve as a basis for studying flight pilot cognitive load, mental load, mood changes, and flight performance. [ABSTRACT FROM AUTHOR]

Background: Individuals with low socioeconomic status (SES) are at a higher risk of developing depression. However, evidence on the role of cardiovascular health (CVH) in this chain is sparse and limited. The purpose of this research was to assess the mediating role of Life's Essential 8 (LE8), a recently updated measurement of CVH, in the association between SES and depression according to a nationally representative sample of adults. Methods: Data was drawn from the National Health and Nutrition Examination Survey (NHANES) in 2013–2018. Multivariate logistic regression analysis was applied to analyze the association of SES (measured via the ratio of family income to poverty (FIPR), occupation, educational level, and health insurance) and LE8 with clinically relevant depression (CRD) (evaluated using the Patient Health Questionnaire (PHQ-9)). Multiple linear regression analysis was performed to analyze the correlation between SES and LE8. Mediation analysis was carried out to explore the mediating effect of LE8 on the association between SES and CRD. Moreover, these associations were still analyzed by sex, age, and race. Results: A total of 4745 participants with complete PHQ-9 surveys and values to calculated LE8 and SES were included. In the fully adjusted model, individuals with high SES had a significantly higher risk of CRD (odds ratio = 0.21; 95% confidence interval: 0.136 to 0.325, P < 0.01) compared with those with low SES. Moreover, LE8 was estimated to mediate 22.13% of the total association between SES and CRD, and the mediating effect of LE8 varied in different sex and age groups. However, the mediating effect of LE8 in this chain was significant in different sex, age, and racial subgroups except for Mexican American (MA) individuals. Conclusion: The results of our study suggest that LE8 could mediate the association between SES and CRD. Additionally, the mediating effect of LE8 in this chain could be influenced by the race of participants. [ABSTRACT FROM AUTHOR]

In the cycloaddition reaction of carbon dioxide (CO2) with epoxides, halogen catalysts lead to halogen loss and environmental pollution. It is necessary to design efficient and environmentally friendly catalysts. A series of halogen‐free phosphonium ionic liquids (ILs) regulated by steric hindrance were designed and synthesized. It is further used as effective catalysts for the catalytic conversion of CO2 into cyclic carbonates. The effect of different catalyst structures on catalytic activity has been investigated. Amino group has been shown to contribute to increased activity, and its location determines the degree of increased activity. Among them, [Bu4P]2[5‐AMP] with meta‐substitution showed the best catalytic activity, the conversion of propylene oxide (PO) was 96 % at the optimum conditions of 120 °C, 2 MPa and 3 h. Based on characterization analysis and DFT calculations, the interaction of ILs with CO2 and PO were revealed. Finally, a plausible synergistic catalytic mechanism was proposed. The halogen‐free, metal‐free phosphonium ILs proposed in this paper provide a new insight on catalyst design and CO2 utilization. [ABSTRACT FROM AUTHOR]