Drilling Technical Difficulties and Solutions in Development of Hot Dry Rock Geothermal Energy

The exploration and development of hot dry rock resources, first of all, needs to address the drilling issues in deep, hot, hard and unstable formations. By studying geological features and storage conditions of hot dry rocks, the key technical difficulties of hot dry rock drilling are presented. The high-temperature resistance performance index of oil and gas drilling technologies at home and abroad are investigated. The applicability of high effective rock breaking tools, MWD instruments, drilling fluid systems, well cementing and completion technologies are analyzed, and feasibility analyses have been conducted on gas drilling, dry wellbore cementing and foam pressurized drilling techniques. On the basis of the above analyses, the developing directions and issues urgently to be addressed about domestic hot dry rock drilling technology are discussed so as to provide references for drilling program optimization and drilling technology research in the development of hot dry rock geothermal energy

Holonomy Lie algebra of a fiber-type arrangement

We prove that the holonomy Lie algebra of a fiber-type arrangement is an iterated almost-direct product of a series of free Lie algebras with ranks the exponents of the arrangement. This is a Lie algebra version analogue of the well-known result of Falk-Randell that the fundamental group of the complement of a fiber-type arrangement is an iterated almost-direct product of a series of free groups with ranks the exponents of the arrangements. By using Jambu-Papadima's deformation method, we generalize the result to hypersolvable arrangements. As byproducts, we reprove the LCS formula for those arrangements.Comment: 10 pages, No figure

Multi-task learning for intelligent data processing in granular computing context

Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction,feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e.,multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection

Mitochondrial nutrients improve immune dysfunction in the type 2 diabetic Goto-Kakizaki rats.

The development of type 2 diabetes is accompanied by decreased immune function and the mechanisms are unclear. We hypothesize that oxidative damage and mitochondrial dysfunction may play an important role in the immune dysfunction in diabetes. In the present study, we investigated this hypothesis in diabetic Goto-Kakizaki rats by treatment with a combination of four mitochondrial-targeting nutrients, namely, R-alpha-lipoic acid, acetyl-L-carnitine, nicotinamide and biotin. We first studied the effects of the combination of these four nutrients on immune function by examining cell proliferation in immune organs (spleen and thymus) and immunomodulating factors in the plasma. We then examined, in the plasma and thymus, oxidative damage biomarkers, including lipid peroxidation, protein oxidation, reactive oxygen species, calcium and antioxidant defence systems, mitochondrial potential and apoptosis-inducing factors (caspase 3, p53 and p21). We found that immune dysfunction in these animals is associated with increased oxidative damage and mitochondrial dysfunction and that the nutrient treatment effectively elevated immune function, decreased oxidative damage, enhanced mitochondrial function and inhibited the elevation of apoptosis factors. These effects are comparable to, or greater than, those of the anti-diabetic drug pioglitazone. These data suggest that a rational combination of mitochondrial-targeting nutrients may be effective in improving immune function in type 2 diabetes through enhancement of mitochondrial function, decreased oxidative damage, and delayed cell death in the immune organs and blood

Applying Minimum-Risk Criterion to Stochastic Hub Location Problems

AbstractThis paper presents a new class of two-stage stochastic hub location (HL) programming problems with minimum-risk criterion, in which uncertain demands are characterized by random vector. Meanwhile we demonstrate that the twostage programming problem is equivalent to a single-stage stochastic P-model. Under mild assumptions, we develop a deterministic binary programming problem by using standardization, which is equivalent to a binary fractional programming problem. Moreover, we show that the relaxation problem of the binary fractional programming problem is a convex programming problem. Taking advantage of branch-and-bound method, we provide a number of experiments to illustrate the efficiency of the proposed modeling idea

The adoption of sustainable practices: A supplier’s perspective

Suppliers' adoption of sustainable practices plays a critical role for global companies to improve environmental performance. Based on the absorptive capacity theory, this study empirically explores how suppliers' knowledge bases and power relationships influence their adoption of sustainability practices. A longitudinal case study with eight suppliers has been conducted. We find that the adoption of leading sustainable practices requires a supplier's good knowledge base whereas a supplier can adopt imitating and toddling sustainable practices even if it has a limited knowledge base. Both the power of internal sustainability teams and external buyers enhance the effects of suppliers' knowledge bases on the adoption of sustainable practices. Suppliers choose the strategy used in adopting sustainable practices according to the configuration of internal and external power. The results enhance the current understandings of the mechanisms through which knowledge bases and power relationships affect the adoption of sustainable practices. The findings can also help global companies improve the effectiveness of their supplier development efforts and enhance the environmental performance of supply chains

A Targeted Quantitative Proteomic Method Revealed a Substantial Reprogramming of Kinome during Melanoma Metastasis.

Kinases are involved in numerous critical cell signaling processes, and dysregulation in kinase signaling is implicated in many types of human cancers. In this study, we applied a parallel-reaction monitoring (PRM)-based targeted proteomic method to assess kinome reprogramming during melanoma metastasis in three pairs of matched primary/metastatic human melanoma cell lines. Around 300 kinases were detected in each pair of cell lines, and the results showed that Janus kinase 3 (JAK3) was with reduced expression in the metastatic lines of all three pairs of melanoma cells. Interrogation of The Cancer Genome Atlas (TCGA) data showed that reduced expression of JAK3 is correlated with poorer prognosis in melanoma patients. Additionally, metastatic human melanoma cells/tissues exhibited diminished levels of JAK3 mRNA relative to primary melanoma cells/tissues. Moreover, JAK3 suppresses the migration and invasion of cultured melanoma cells by modulating the activities of matrix metalloproteinases 2 and 9 (MMP-2 and MMP-9). In summary, our targeted kinome profiling method provided by far the most comprehensive dataset for kinome reprogramming associated with melanoma progression, which builds a solid foundation for examining the functions of other kinases in melanoma metastasis. Moreover, our results reveal a role of JAK3 as a potential suppressor for melanoma metastasis