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Three groups of composite cementitious materials (CCMs) were prepared by separate grinding, mixed grinding, and cascade grinding using iron and steel solid waste (ISSW) materials (granulated blast furnace slag (GBFS), iron ore tailings (IOTs), steel slag (SS) and desulfurization gypsum (DG)), and the influence on the strength of CCMs was investigated under different water-binder ratios (w/b). Then, the hydration hardening mechanism of the CCMs was revealed via hydration heat analysis, phase composition and structural analysis of hydration products. The results show that the properties of the CCMs prepared by cascade grinding (CGC) is better than that prepared by separate grinding and mixed grinding. When the mass percentage of IOTs and SS in the CGC was 2:3, the mix proportion of CGC is SS+IOTs: GBFS: DG=36:51.5:12.5, mixed with 1.0 % Na2SO4 and 1.2 % Na2SiO3ˑ9 H2O of the CGC, the compressive strength (CS) of mortar sample containing CGC in 28-days and 180-days reach 52.3 MPa and 70.7 MPa respectively under the conditions of standard curing and low water-binder ratios. The hydration degree of pure cement is higher than that of CGC, but types of the hydration products were similar with cement, mainly were Ca(OH)2, iolite, ettringite (AFt) and C-S-H gel. The CGC can greatly reduce the hydration heat of CCMs system in early age, and is suitable for massive concrete structures in which elevated temperature can be kept for a long time.

In this study, a composite modifier (CMCDL) containing coal gasification residue, dicyandiamide waste slag, and lake sediments was used to modify steel slag at high-temperature. Five kind of raw materials was analyzed in physicochemical properties, mineral composition and microstructure, cementitious properties and soundness, and particle size analysis, the same to steel slag after high-temperature modification (SHTM). The new mineral phases were found that tricalcium aluminate (C3A), gehlenite (C2AS), magnetite (Fe3O4), spinels (MgFe2O4), quartz (SiO2), celite (C6AF2) and diopside (CMS2) formed in the SHTM when the mass fraction increase from 10% to 30% in CMCDL. The HTM with CMCDL increases the glass phase and hydrated mineral (C3A, C3S, C2S) and in raw steel slag, promoting the disassembled of RO phase, turning the phase from FeO into MgFe2O4 and Fe3O4. MgFe2O4 and Fe3O4 have similar crystal structures, and they form mixed spinels. When the content of CMCCL (coal gasification residue: dicyandiamide waste slag: lake sediments =7:9:4) is 20%, the temperature is 1250 °C, result showed that the mass fraction of f-CaO in SHTM using CMCCL decreases from 4.81% to 1.86%, with a decrease of 61.3%, the 28-day activity index of SHTM increases up to 17.6% compared with raw steel slag, reaching 86.2%. Therefore, the CMCDL-blended SHTM can be applied harmlessly in cement and concrete, enabling low-energy fine grinding of steel slag.

The static sealing of underground gas storage (UGS), including the integrity of cap rocks and the stability of faults, is analyzed from a macro perspective using a comprehensive geological evaluation method. Changes in pore structure, permeability, and mechanical strength of cap rocks under cyclic loads may impact the rock sealing integrity during the injection and recovery phases of UGS. In this work, the mechanical deformation and failure tests of rocks, as well as rock damage tests under alternating loads, are conducted to analyze the changes in the strength and permeability of rocks under multiple-cycle intense injection and recovery of UGS. Additionally, this study proposes an evaluation method for the dynamic sealing performance of UGS cap rocks under multi-cycle alternating loads. The findings suggest that the failure strength (70%) can be used as the critical value for rock failure, thus providing theoretical support for determining the upper limit of operating pressure and the number of injection-recovery cycles for the safe operation of a UGS system.

Evaluating underground gas storage (UGS) sealing capacity is essential for its safe construction and operational efficiency. This involves evaluating both the static sealing capacity of traps during hydrocarbon accumulation and the dynamic sealing capacity of UGS under intensive gas injection and withdrawal, and alternating loads. This study detailed the methodology developed by Sinopec. The approach merges disciplines like geology, geomechanics, and hydrodynamics, employing both dynamic-static and qualitative-quantitative analyses. Sinopec's evaluation methods, grounded in the in situ stress analysis, include mechanistic studies, laboratory tests, geological surveys, stress analysis, and fluid-solid interactions. Through tests on the static and dynamic sealing capacity of UGS, alongside investigations into sealing mechanisms and the geological and geomechanical properties of cap rocks and faults, A geomechanics - rock damage - seepage mechanics dynamic coupling analysis method has been developed to predict in situ stress variations relative to pore pressure changes during UGS operations and evaluate fault sealing capacity and cap rock integrity, thereby setting the maximum operational pressures. Utilizing this evaluation technique, Sinopec has defined performance metrics and criteria for evaluating the sealing capacity of depleted gas reservoirs, enabling preliminary sealing capacity evaluations at UGS sites. These evaluations have significantly informed the design of UGS construction schemes and the evaluation of fault sealing capacity and cap rock integrity during UGS operations.

Ferroptosis is a newly characterized form of iron-dependent non-apoptotic cell death, which is closely associated with cancer progression. However, the functions and mechanisms in regulation of escaping from ferroptosis during hepatocellular carcinoma (HCC) progression remain unknown. In this study, we reported that the RNA binding motif single stranded interacting protein 1 (RBMS1) participated in HCC development，and functioned as a regulator of ferroptosis. Clinically, the downregulation of RBMS1 occurred in HCC tissues, and low RBMS1 expression was associated with worse HCC patients survival. Mechanistically, RBMS1 overexpression inhibited HCC cell growth by attenuating the expression of glutathione peroxidase 4 (GPX4)and further facilitated ferroptosis in vitro and in vivo. More importantly, a novel circIDE (hsa_circ_0000251) was identified to elevate RBMS1 expression via sponging miR-19b-3p in HCC cells. Collectively, our findings established circIDE/miR-19b-3p/RBMS1 axis as a regulator of ferroptosis, which could be a promising therapeutic target and prognostic factor.

BackgroundLiver cancer, especially hepatocellular carcinoma (HCC), remains a significant global health challenge. Traditional prognostic indicators for HCC often fall short in providing comprehensive insights for individualized treatment. The integration of genomics and radiomics offers a promising avenue for enhancing the precision of HCC diagnosis and prognosis.MethodsFrom the Cancer Genome Atlas (TCGA) database, we categorized mRNA of HCC patients by Forkhead Box M1 (FOXM1) expression and performed univariate and multivariate studies to pinpoint autonomous HCC risk factors. We deployed subgroup, correlation, and interaction analyses to probe FOXM1’s link with clinicopathological elements. The connection between FOXM1 and immune cells was evaluated using the CIBERSORTx database. The functions of FOXM1 were investigated through analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). After filtering through TCGA and the Cancer Imaging Archive (TCIA) database, we employed dual-region computed tomography (CT) radiomics technology to noninvasively predict the mRNA expression of FOXM1 in HCC tissues. Radiomic features were extracted from both tumoral and peritumoral regions, and a radiomics score (RS) was derived. The performance and robustness of the constructed models were evaluated using 10-fold cross-validation. A radiomics nomogram was developed by incorporating RS and clinical variables from the TCGA database. The models’ discriminative abilities were assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic curves (ROC) and precision-recall (PR) curves.ResultsOur findings emphasized the overexpression of FOXM1 as a determinant of poor prognosis in HCC and illustrated its impact on immune cell infiltration. After selecting arterial phase CT, we chose 7 whole-tumor features and 3 features covering both the tumor and its surroundings to create WT and WP models for FOXM1 prediction. The WT model showed strong predictive capabilities for FOXM1 expression by PR curve. Conversely, the WP model did not demonstrate the good predictive ability. In our study, the radiomics score (RS) was derived from whole-tumor regions on CT images. The RS was significantly associated with FOXM1 expression, with an AUC of 0.918 in the training cohort and 0.837 in the validation cohort. Furthermore, the RS was correlated with oxidative stress genes and was integrated with clinical variables to develop a nomogram, which demonstrated good calibration and discrimination in predicting 12-, 36-, and 60-month survival probabilities. Additionally, bioinformatics analysis revealed FOXM1’s potential role in shaping the immune microenvironment, with its expression linked to immune cell infiltration.ConclusionThis study highlights the potential of integrating FOXM1 expression and radiomics in understanding HCC’s complexity. Our approach offers a new perspective in utilizing radiomics for non-invasive tumor characterization and suggests its potential in providing insights into molecular profiles. Further research is needed to validate these findings and explore their clinical implications in HCC management.

This paper explores the hazard classification method of blasting vibration on nearby buildings. The ratio of the self-vibration frequency f 0 of the protected buildings to the main vibration frequency f of blasting vibration and the ratio of the actual measured peak blasting vibration velocity to the permissible vibration velocity of blasting safety regulations are used as two indicators of the blasting vibration hazard classification method, the blasting vibration hazard index calculation formula was proposed, and the blasting vibration hazard was classified into four levels according to the blasting vibration hazard index. An open pit mine blasting project was used for monitoring, the collected data were processed by random forest method. After processing, bursting heart distance, the total explosive quantity required for one blast, the hole distance meter delay, and the row distance meter delay was selected as the input parameters; the optimal kernel function parameter gamma and c were applied to the SVR (Support Vector Regression) model by changing the search step to expand the parameter search, and improved GS-SVR (Grid Search-Support Vector Regression) model was constructed, through which peak blasting vibration velocity and vibration frequency values were predicted. The results show: improved GS-SVR is effective in predicting peak blasting vibration velocity, with the lowest relative error of 0.15% and the average error of 7.96%; the minimum relative error for the prediction of vibration frequency is 0.03% and the average error is 2.54%. The measured data in the literature of related scholars verified that the blasting vibration hazard classification method is scientific and feasible. It can provide reference and reference for blasting vibration hazard classification and prediction of similar blasting projects.

Abstract To solve the pollution problem of insulators on ultra‐high voltage DC transmission lines, superhydrophobic coatings were presented and their pollution deposit morphology and pollution flashover voltage in electric field were studied by an artificial pollution deposit system. The experimental results show that the pollution on the surface of superhydrophobic coating is distributed in clusters, which inhibits the chain distribution on the surface of silicone rubber and improves the pollution flashover voltage. Secondly, the competition mechanism of the pollution flashover on the superhydrophobic coating surface was found. This competition mechanism can lead to the failure of the insulation performance of the superhydrophobic coating in severely polluted environments. Finally, a pollution flashover model of superhydrophobic coating is proposed to explain the competition mechanism, which is affected by pollution and the discrete droplets. The conclusions of this study can provide an experimental and theoretical basis for the application of the superhydrophobic coating in the field of UHV DC external insulation.

Abstract Chronic pancreatitis (CP) is described as progressive inflammatory fibrosis of pancreas, accompanied with irreversible impaired endocrine and exocrine insufficiency. Pancreatic stellate cells (PSCs) are widely distributed in the stroma of the pancreas and PSCs activation has been shown as one of the leading causes for pancreatic fibrosis. Our previous study has revealed that autophagy is dramatically activated in CP tissues, which facilitates PSCs activation and pancreatic fibrosis. Long non-coding RNAs (LncRNAs) have been recognized as crucial regulators for fibrosis-related diseases. LncRNAs interact with RNA binding protein or construct competitive endogenous RNA (ceRNA) hypothesis which elicited the fibrotic processes. Until now, the effects of lncRNAs on PSCs activation and pancreatic fibrosis have not been clearly explored. In this study, a novel lncRNA named Lnc-PFAR was found highly expressed in mouse and human CP tissues. Our data revealed that Lnc-PFAR facilitates PSCs activation and pancreatic fibrosis via RB1CC1-induced autophagy. Lnc-PFAR reduces miR-141 expression by suppressing pre-miR-141 maturation, which eventually upregulates the RB1CC1 and fibrosis-related indicators expression. Meanwhile, Lnc-PFAR enhanced PSCs activation and pancreatic fibrosis through trigging autophagy. Our study interrogates a novel lncRNA-induced mechanism in promoting the development of pancreatic fibrosis, and Lnc-PFAR is suggested to be a prospective therapeutic target in clinical scenarios.

Mixed deposition is a sedimentary phenomenon of special significance to the tectonic movement, material supply, sea level change, and paleoclimate of the sedimentary environment. The sediments of the Late Carboniferous-Early Permian System in the North China Craton, which are composed of clastic rock and limestone, are mixed clastic-platform carbonate sediments in the epicontinental sea. Based on the geological and well logging data of nearly 100 exploratory wells and hundreds of development wells in the region, the lithologic association and cycle features of the Upper Carboniferous-Lower Permian were determined. From this data, the Benxi Formation and the Taiyuan Formation of the Daniudi Gasfield are considered to belong to a mixed clastic coastline-carbonate platform sedimentary system and coastal plain sedimentary system in the epicontinental sea. Based on the six transgressions and regressions during the Late Carboniferous-Early Permian epochs in the region, combined with the regional evolution history, this study analyzed the sedimentary evolution history of the Daniudi Gasfield from the Late Carboniferous to the Early Permian.

In this paper, a surface discharge test was conducted on a silicone rubber insulating sheet, an epoxy resin insulating sheet, a porcelain insulating sheet, and a glass insulating sheet by using an independently constructed test platform of insulating media to study the discharge characteristics along the surface of insulating media under low air pressure and strong airflow environment. Experiments were conducted to obtain the variation in the discharge characteristics along the surface of the four kinds of insulation media with respect to airflow velocity at different air pressures. The discharge voltage characteristics and discharge morphology characteristics along the surface of the insulating media in a strong airflow environment at low air pressure are elucidated. This paper provides theoretical support for the selection of insulation materials and insulation protection design for a low air pressure and strong airflow environment.

Gushiling capsule (GSLC) is an effective traditional Chinese medicine for the treatment of glucocorticoid-induced osteonecrosis of the femoral head (GIONFH). This study established the serum metabolite profiles of GSLC in rabbits and explored the metabolic mechanism and effect of GSLC on GIONFH. Seventy-five Japanese white rabbits were randomly divided into the control, model, and GSLC groups. The rabbits in the model group and the GSLC group received injection of prednisolone acetate. Meanwhile, rabbits in the GSLC group were treated by gavage at a therapeutic dose of GSLC once a day. The control group and the model group received the same volume of normal saline gavage. Three groups of serum samples were collected at different time points, and the changes in the metabolic spectrum were analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The resulting data set was analyzed using multivariate statistical analysis to identify potential biomarkers related to GSLC treatment. The metabolic pathway was analyzed by MetaboAnalyst 4.0 and a heatmap was constructed using the HEML1.0.3.7 software package. In addition, histopathological and radiography studies were carried out to verify the anti-GIONFH effects of GSLC. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) score plots revealed a significant separation trend between the control group and the model group and the GSLC group (1–3 weeks), but there were no significant differences in the GSLC group (4–6 weeks). Orthogonal PLS-DA (OPLS-DA) score plots also revealed an obvious difference between the model and the GSLC groups (4–6 weeks). Ten potential metabolite biomarkers, mainly phospholipids, were identified in rabbit serum samples and demonstrated to be associated with GIONFH. Hematoxylin and eosin staining and magnetic resonance imaging indicated that the pathological changes in femoral head necrosis in the GSLC group were less than in the model group, which was consistent with the improved serum metabolite spectrum. GSLC regulated the metabolic disorder of endogenous lipid components in GIONFH rabbits. GSLC may prevent and treat GIONFH mainly by regulating phospholipid metabolism in vivo.

Abstract Background Osteosarcoma (OS) is the most common bone malignant tumor in children, youth, and adolescents. Circular RNA hsa_circ_0005909 (circ_0005909) is involved in the progression of OS. Nevertheless, there are few reports on the role and mechanism of circ_0005909 in OS. Methods Quantitative real-time polymerase chain reaction (qRT-PCR) was executed to examine the expression of circ_0005909, miR-936, and High Mobility Group Box 1 (HMGB1) mRNA in OS tissues and cells. Cell viability, colony formation, migration, and invasion were evaluated by Cell Counting Kit-8 (CCK-8), cell colony formation, or transwell assays. Cell epithelial-mesenchymal transition (EMT) and HMGB1 protein levels were assessed through western blot analysis. The role of circ_0005909 on tumor growth in vivo was verified by xenograft assay. The relationship between circ_0005909 or HMGB1 and miR-936 was confirmed with the dual-luciferase reporter or RNA pull-down assays. Results Circ_0005909 level was upregulated in OS tissues and cells. OS patients with high circ_0005909 expression had a lower survival rate. Circ_0005909 inhibition reduced tumor growth in vivo and constrained cell viability, colony formation, migration, invasion, and EMT of OS cells in vitro. Furthermore, circ_0005909 served as a sponge for miR-936 and the repressive impacts of circ_0005909 silencing on malignant behaviors of OS cells were abolished by miR-936 inhibitors. Also, HMGB1 acted as a target for miR-936 and was modulated by circ_0005909 via miR-936. Additionally, HMGB1 overexpression restored the inhibitory influence on the malignant behaviors of OS cells mediated by circ_0005909 inhibition. Conclusions Circ_0005909 inhibition impeded the progression of OS via downregulating HMGB1 via sponging miR-936.

Abstract Background Ubiquitin-like protein 4A (UBL4A) plays a significant role in protein metabolism and the maintenance of cellular homeostasis. In cancer, UBL4A represses tumorigenesis and is involved in various signaling pathways. Pancreatic ductal adenocarcinoma (PDAC) is still a major cause of cancer-related death and the underlying molecular mechanism of UBL4A and PDAC remains unknown. Methods First, the prognostic role of UBL4A and its expression in human PDAC patients and in pancreatic cancer cell lines were detected by survival analysis and qRT-PCR, western blotting, and immunohistochemistry. Next, the effects of UBL4A on proliferation and metastasis in pancreatic cancer were evaluated by functional assays in vitro and in vivo. In addition, chloroquine was introduced to determine the role of autophagy in UBL4A-related tumor proliferation and metastasis. Ultimately, coimmunoprecipitation was used to confirm the interaction between UBL4A and lysosome associated membrane protein-1 (LAMP1), and western blotting was performed to explore the UBL4A mechanism. Results We found that UBL4A was decreased in PDAC and that high levels of UBL4A correlated with a favorable prognosis. We observed that UBL4A inhibited tumor proliferation and metastasis through suppression of autophagy, a critical intracellular catabolic process that reportedly protects cells from nutrient starvation and other stress conditions. UBL4A caused impaired autophagic degradation in vitro, a crucial process in autophagy, by disturbing the function of lysosomes and contributing to autophagosome accumulation. We found a positive correlation between UBL4A and LAMP1. Furthermore, UBL4A caused lysosomal dysfunction by directly interacting with LAMP1, and LAMP1 overexpression reversed the antitumor effects of UBL4A in pancreatic cancer. In addition, we demonstrated that UBL4A suppressed tumor growth and metastasis in a pancreatic orthotopic tumor model. Conclusions These findings suggest that UBL4A exerts an antitumor effect on autophagy-related proliferation and metastasis in PDAC by directly targeting LAMP1. Herein, we describe a novel mechanism of UBL4A that suppresses the progression of pancreatic cancer. UBL4A might be a promising target for the treatment and prognostication of PDAC.

In order to accurately describe the reservoir distribution of the oil and gas field, the precision of the portrayal of the channel microfacies is getting higher and higher. Aiming at the existing problems and difficulties in sedimentary microfacies, a method of determining the location of river channels and describing the river channels with high accuracy is proposed by calculating the tooth rate of logging curves. The tooth rate is characterized by the amplitude of the tooth, the density of the tooth and the frequency of the tooth distribution. The general idea is to calculate the three parameters of logging first, and then give each parameter a certain weight coefficient, and get the tooth rate of the curve section of the log. According to the size of the tooth rate, the specific location of the log in the channel is analyzed, and the channel is finely depicted. Compared with the traditional method of sedimentary facies research, the method has improved the accuracy of boundary characterization and achieved semi-quantitative or quantitative accuracy. In the application of sedimentary research of the Hangjinqi area in the Ordos Basin of China, the position of the river is determined by the tooth rate, and the accuracy of the boundary is obviously improved. The research results can be applied directly to the production and guide the optimization of the favorable area and the deployment of the well position. Keywords: Sedimentary microfacies, Logging curve parameters, Tooth rate, Favorable area optimization

The gas–solid interface of high-voltage insulating equipment is a weaker part of insulating equipment insulation, and preventing the occurrence of discharge along the surface of insulating equipment is a critical problem for high-voltage insulation. This article investigates the discharge characteristics and charge movement characteristics of insulating media under an airflow environment. The surface discharge characteristics of the insulating medium in the airflow environment were obtained by using a high-velocity airflow test platform, and the surface discharge voltage characteristics, discharge path characteristics, and force conditions of the discharge process were analyzed. The surface charge motion characteristics of the insulating medium in the high-velocity airflow environment were also tested, and the distribution characteristics, dissipation characteristics and conduction mechanism of the surface charge of the insulating medium in the high-velocity airflow environment were revealed. The research results showed that: the discharge voltage along the insulating medium surface gradually increases with the increasing velocity of airflow; the discharge path along the surface of the insulating medium gradually shifts backward under the action of airflow; under the action of airflow, the charge on the insulating medium surface is blown away, thus reducing the charge concentration on the insulating medium surface; the trap level center of the insulating medium gradually decreases under the action of airflow, which provides the conditions for the charge blowing effect on the insulating medium surface. This investigation supplies the theory support for the protection of insulation equipment in an airflow environment and technical guidance for the insulation design of insulating equipment in an airflow environment to ensure the secure and steady running of insulating equipment in high-speed trains and high-voltage transmission lines.

Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.

Introduction. This study aimed to compare and analyze the effect of preoperative zoledronic acid (ZOL) administration on pain intensity after percutaneous vertebroplasty (PVP) for osteoporotic vertebral compression fracture (OVCF). Methods. The study included 242 patients with OVCFs who underwent PVP in our hospital between January 2015 and June 2018. The patients were randomly assigned to either a ZOL group (n = 121) or a control group (n = 121). The patients in the ZOL group were treated preoperatively with intravenous infusion of 5 mg ZOL. Those in the control group were treated without ZOL. All the patients were followed up for 1 year. Results. No statistically significant differences in age, sex, weight, and body mass index (BMI) were found between the two groups. During the follow-up period, the visual analog scale score and Oswestry dysfunction index score in the ZOL group were lower than those in the control group. The bone mineral density at 6 or 12 months after treatment was significantly higher and the levels of the bone metabolism markers were significantly lower in the ZOL group than in the control group (P

In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms.

Collapse or large deformation of fractured surrounding rock mass occurs frequently in underground tunnelling and results in many casualties and extensive property damage. This paper proposed a new type of remote telemetry system for monitoring the mechanical responses of underground tunnels during unloading. This system adopted both wired and wireless networking schemes, including a signal collection and transmission subsystem, a management analysis subsystem, and a remote receiving subsystem, in the tunnels. The application of this new approach in a subway tunnel indicated that the complete unloading performance of a surrounding rock mass can be captured in real time and high frequency using this method, recording the deformation of the surrounding rock, the stress in the bolts, and the stress in the shotcrete between the surrounding rock and steel arch. The in situ experimental study also found that deformation of the fractured surrounding rock mass in the Dashizi Tunnel showed a step-like fluctuating growth pattern. Additionally, the mechanical response of the surrounding rock mass during unloading tended to stabilize when the opening face was approximately 35 m away from the monitoring section, providing new ways to optimize the excavation process and support measured.

Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems could benefit from RL, e.g., industrial robotics, medical treatment, and trade execution. As a representative model-free RL algorithm, deep Q-network (DQN) has recently achieved great success on RL problems and even exceed the human performance through introducing deep neural networks. However, such classical deep neural network-based models cannot well handle the uncertainty in sequential decision-making and then limit their learning performance. In this paper, we propose a new model-free RL algorithm based on a Bayesian deep model. To be specific, deep kernel learning (i.e., a Gaussian process with deep kernel) is adopted to learn the hidden complex action-value function instead of classical deep learning models, which could encode more uncertainty and fully take advantage of the replay memory. The comparative experiments on standard RL testing platform, i.e., OpenAI-Gym, show that the proposed algorithm outweighs the DQN. Further investigations will be directed to applying RL for supporting dynamic decision-making in complex environments.

The concept drift problem is a pervasive phenomenon in real-world data stream applications. It makes well-trained static learning models lose accuracy and become outdated as time goes by. The existence of different types of concept drift makes it more difficult for learning algorithms to track. This paper proposes a novel adaptive ensemble algorithm, the Active Fuzzy Weighting Ensemble, to handle data streams involving concept drift. During the processing of data instances in the data streams, our algorithm first identifies whether or not a drift occurs. Once a drift is confirmed, it uses data instances accumulated by the drift detection method to create a new base classifier. Then, it applies fuzzy instance weighting and a dynamic voting strategy to organize all the existing base classifiers to construct an ensemble learning model. Experimental evaluations on seven datasets show that our proposed algorithm can shorten the recovery time of accuracy drop when concept drift occurs, adapt to different types of concept drift, and obtain better performance with less computation costs than the other adaptive ensembles.

Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques.

The evaluation of solutions is an important phase in power distribution system planning (PDSP) which allows issues such as quality of supply, cost, social service and environmental implications to be considered and usually involves the judgments of a group of experts. The planning problem is thus suitable for the multi-criteria group decision-making (MCGDM) method. The evaluation process and evaluation criteria often involve uncertainties incorporated in quantitative analysis with crisp values and qualitative judgments with linguistic terms; therefore, fuzzy sets techniques are applied in this study. This paper proposes a fuzzy multi-criteria group decision-making (FMCGDM) method for PDSP evaluation and applies a fuzzy multi-criteria group decision support system (FMCGDSS) to support the evaluation task. We introduce a PDSP evaluation model, which has evaluation criteria within three levels, based on the characteristics of a power distribution system. A case-based example is performed on a test distribution network and demonstrates how all the problems in a PDSP evaluation are addressed using FMCGDSS. The results are acceptable to expert evaluators.

Bilevel decision techniques have been mainly developed for solving decentralized management problems with decision makers in a hierarchical organization. When multiple followers are involved in a bilevel decision problem, called a bilevel multi-follower (BLMF) decision problem, the leader’s decision will be affected, not only by the reactions of these followers, but also by the relationships among these followers. The referential-uncooperative situation is one of the popular cases of BLMF decision problems where these multiple followers don’t share decision variables with each other but may take others’ decisions as references to their decisions. This paper presents a model for the referential-uncooperative BLMF decision problem. As the kth-best approach is one of the most successful approaches in dealing with normal bilevel decision problems, this paper then proposes an extended kth-best approach to solve the referential-uncooperative BLMF problem. Finally an example of logistics planning illustrates the application of the proposed extended kth-best approach.

Situation awareness (SA) is an important element to support responses and decision making to crisis problems. Decision making for a complex situation often needs a team to work cooperatively to get consensus awareness for the situation. Team SA is characterized including information sharing, opinion integration and consensus SA generation. In the meantime, various uncertainties are involved in team SA during information collection and awareness generation. Also, the collaboration between team members may be across distances and need web-based technology to facilitate. This paper presents a web-based fuzzy group decision support system (WFGDSS) and demonstrates how this system can provide a means of support for generating team SA in a distributed team work context with the ability of handling uncertain information.

Recommender systems are one of the most successful applications of using AI for providing personalized e-services to customers. However, data sparsity is presenting enormous challenges that are hindering the further development of advanced recommender systems. Although cross-domain recommendation partly overcomes data sparsity by transferring knowledge from a source domain with relatively dense data to augment data in the target domain, the current methods do not handle heterogeneous data very well. For example, using today's cross-domain transfer learning schemes with data comprising clicks, ratings, user reviews, item metadata, and knowledge graphs will likely result in a poorly performing model. User preferences will not be comprehensively profiled, and accurate recommendations will not be generated. To solve these three challenges—handling heterogeneous data, avoiding negative transfer, and dealing with data sparsity—we designed a new end-to-end deep Adversarial Multi-channel Transfer network for Cross-Domain Recommendation named AMT-CDR. Heterogeneous data is handled by constructing a cross-domain graph based on real-world knowledge graphs—we used Freebase and YAGO. Negative transfer is prevented through an adversarial learning strategy that maintains consistency across the different data channels. Data sparsity is addressed with an end-to-end neural network that considers data across multiple channels and generates accurate recommendations by leveraging knowledge from both the source and target domains. Extensive experiments on three dual-target cross-domain recommendation tasks demonstrate the superiority of AMT-CDR compared to eight state-of-the-art methods. All source code is available at https://github.com/bjtu-lucas-nlp/AMT-CDR. [ABSTRACT FROM AUTHOR]