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Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual–spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4–12 Hz) in visual–spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.

Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a tri-wildcard gap inserted between each pair of adjacent states enhances the flexibility of the method. Second, a given set of states is partitioned into positive (POS), negative (NEG) and boundary (BND) regions. The states in POS can only be used to construct a Tri-SASP, the states in NEG can only be matched by a tri-wildcard gap, and the states in BND can be used in both ways. Finally, horizontal and vertical algorithms are proposed to obtain frequent Tri-SASPs in a breadth-first manner. The experimental results on four real-world datasets show that (1) the discovered Tri-SASPs and temporal rules can enrich human cognition; (2) the two tri-partition strategies can bring us very meaningful and varied Tri-SASPs; and (3) the two algorithms are effective and scalable.

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

To study the effects of rare earth (RE) and Ti on the solidification microstructure of high borated stainless steels, 1.6 wt% B stainless steel doped with RE and 2.1 wt% B stainless steel doped with Ti were prepared by ingot casting, respectively. The solidification microstructure of researched steels was characterized in detail. The modification mechanism was clarified based on the heterogeneous nucleation theory and the thermodynamic calculation. The solidification microstructure of 1.6 wt% B and 2.1 wt% B stainless steels was characterized by the continuous and network-like eutectic borides around the matrix grains. It was found that the fine RE compounds could act as the heterogeneous nuclei for both borides and austenite during solidification. Thus, the eutectic borides were more dispersed in the modified steel. Moreover, lots of fine ‘eutectic cells’ were formed in the matrix regions. As a result of the preferential formation of TiB2 during solidification, the amount of the eutectic borides in the steel modified with Ti was significantly decreased. Besides, the continuity of the eutectic borides network was weakened. In a word, the present work provides a promising method to modify the solidification microstructure for high borated stainless steels.

Density and viscosity of aqueous [Bmim][BF4] were measured at the temperature range from 288.15 to 318.15 K with a step of 5 K under atmospheric pressure. All the concentrations of the aqueous solutions were controlled bellow 1.000 mol·kg−1. The values of apparent molar volumes were obtained at different temperatures with the corresponding concentrations. According to Redlich-Mayer equation, the empirical parameters (SV and BV) and the limiting apparent molar volume at infinite dilution, ϕVi0, were acquired. The volume properties were also investigated using Pitzer's equation the result showed that Redlich-Mayer's and Pitzer's equation were mutually proved. Based on the equation of ϕVi0 and T, the limiting apparent molar expansibilities, Eϕ∞, were calculated and discussed. Through the Jones-Dole empirical equation, the viscosity coefficient A and B of aqueous [Bmim][BF4] were obtained. In addition, the standard molar activation Gibbs free energy of solute, Δμ2≠0, were obtained, and the activation entropy, ΔS2≠0, as well as activation enthalpy, ΔH2≠0, can be calculated by fitting Δμ2≠0 against T. Using a semi-empirical method the viscosity of aqueous [Bmim][BF4] was predicted and the result showed a good agreement with experimental values.

Sequence pattern discovery is a key issue in multivariate time series analysis. Popular approaches first obtain the pattern of each single-variate time series and then obtain cross-variate associations. In this paper, we consider different variables at the same time during pattern construction, and propose a new type of pattern called State Transition pAttern with Periodic wildcard gaps (STAP). Compared to previous types, STAP reveals stronger cross associations among different variables and provides better interpretability for decision makers. We design an approach with two stages, namely frequent state discovery and pattern synthesis, to obtain frequent STAPs. We propose two pre-pruning and an Apriori-pruning techniques to speed up pattern discovery. We also propose two post-pruning techniques to simplify the output and a visualization way to support expert decision. Experimental results on four real-world datasets demonstrate 1) STAP captures the cross and temporal associations; 2) the five pruning and pattern synthesis techniques are quite effective; and 3) visualization technique greatly increases the readability of STAP.

Feature vectors and similarity measures are the two key issues of most existing collaborative filtering (CF) algorithms. In item-based CF algorithms, the feature vector is often defined as the ratings of all users for a given item. For a recommender system with n users, m items, and c ratings, the length of the feature vector is n; hence, the time complexity of the similarity computation is O(n). Consequently, the overall time complexity is $$O(m^2n^2)$$ , which may be computationally prohibitive for recommender systems with millions of users. In this paper, we define the multi-channel feature vector (MCFV), which is a vector of channel length c, and calculate the similarity between items using the respective MCFVs. Each element of an MCFV corresponds to the number of users with respective ratings for the item. The time complexity for the similarity computation is O(c), and the overall time complexity is $$O(m^2nc)$$ when the k-nearest neighbors and weighted average algorithms are used. Experiments were conducted on four movie recommender systems, where n ranges from a few hundred to half a million, and c is five. Results show that the recommendation algorithms using our new similarity measure are significantly faster than their counterparts without sacrificing prediction accuracy in terms of mean absolute error and root mean square error.

The time-cost-sensitive attribute reduction problem is more challenging than the classical reduct problem since the optimal solution is sparser. Ant colony optimization (ACO) is an effective approach to this problem. However, the efficiency is unsatisfactory since each ant needs to search for a complete solution. In this paper, we propose a partial-complete searching technique for ACO and design the APC algorithm. Partial searching is undertaken by pioneer ants through selecting only a few attributes to save time, while complete searching is undertaken by harvester ants for complete solutions. Experiments are undertaken on seven real-world and a set of artificial datasets with various settings of costs. Compared with two bio-inspired and two greedy algorithms, APC is more efficient while obtaining the same level of quality metrics. The APC algorithm can be also extended for other combinatorial optimization problems.

Active learning is used for classification when labeling data are costly, while the main challenge is to identify the critical instances that should be labeled. Clustering-based approaches take advantage of the structure of the data to select representative instances. In this paper, we developed the active learning through density peak clustering (ALEC) algorithm with three new features. First, a master tree was built to express the relationships among the nodes and assist the growth of the cluster tree. Second, a deterministic instance selection strategy was designed using a new importance measure. Third, tri-partitioning was employed to determine the action to be taken on each instance during iterative clustering, labeling, and classifying. Experiments were performed with 14 datasets to compare against state-of-the-art active learning algorithms. Results demonstrated that the new algorithm had higher classification accuracy using the same number of labeled data.

Active learning differs from the training–testing scenario in that class labels can be obtained upon request. It is widely employed in applications where the labeling of instances incurs a heavy manual cost. In this paper, we propose a new algorithm called tri-partition active learning through k-nearest neighbors (TALK). The optimization objective is to minimize the total teacher and misclassification costs. First, a k-nearest neighbors classifier is employed to divide unlabeled instances into three disjoint regions. Region I contains instances for which the expected misclassification cost is lower than the teacher cost, Region II contains instances to be labeled by human experts, and Region III contains the remaining instances. Various strategies are designed to determine which instances are in Region II. Second, instances in Regions I and II are labeled and added to the training set, and the tri-partition process is repeated until all instances have been labeled. Experiments are undertaken on eight University of California, Irvine, datasets using different cost settings. Compared with the state-of-the-art cost-sensitive classification and active learning algorithms, our new algorithm generally exhibits a lower total cost.

Liver fibrosis, which is the pathophysiologic process of the liver due to sustained wound healing in response to chronic liver injury, will eventually progress to cirrhosis. Puerarin, a bioactive isoflavone glucoside derived from the traditional Chinese medicine pueraria, has been reported to have many anti-inflammatory and anti-fibrosis properties. However, the detailed mechanisms are not well studied yet. This study aimed to investigate the effects of puerarin on liver function and fibrosis process in mice induced by CCl4. C57BL/6J mice were intraperitoneally injected with 10% CCl4 in olive oil(2mL/kg) with or without puerarin co-administration (100 and 200mg/kg intraperitoneally once daily) for four consecutive weeks. As indicated by the ameliorative serum hepatic enzymes and the reduced histopathologic abnormalities, the data collected showed that puerarin can protect against CCl4-induced chronic liver injury. Moreover, CCl4-induced development of fibrosis, as evidenced by increasing expression of alpha smooth muscle actin(α-SMA), collagen-1, transforming growth factor (TGF)-β and connective tissue growth factor(CTGF) in liver, were suppressed by puerarin. Possible mechanisms related to these suppressive effects were realized by inhibition on NF-κB signaling pathway, reactive oxygen species(ROS) production and mitochondrial dysfunction in vivo. In addition, these protective inhibition mentioned above were driven by down-regulation of PARP-1 due to puerarin because puerarin can attenuate the PARP-1 expression in CCl4-damaged liver and PJ34, a kind of PARP-1 inhibitor, mimicked puerarin's protection. In conclusion, puerarin played a protective role in CCl4-induced liver fibrosis probably through inhibition of PARP-1 and subsequent attenuation of NF-κB, ROS production and mitochondrial dysfunction.

The multi-row perforated concrete wallboard with built-in sound insulation materials studied in this paper is a new type of lightweight sound insulation wall. With reference to the authorized utility model wall, this work performs sound isolation tests on walls with different internal structural parameters cast on multiple sections, using the experimental method of sound insulation room. Based on the experiment, COMSOL numerical simulation software is used to establish a sound-solid multiphysics model to achieve the numerical solution. Three kinds of changing parameters are studied with respect to the effect of the sound transmission on the wall panel. The optimal cross-section combination structure discovered is a foam concrete wall panel with a 15mm thick sound insulation layer, multiple rows of 30 mm diameter holes and a large flow resistance.

Ischemia/reperfusion (I/R) injury is considered to be a contributing factor in liver injury following major hepatic resection or liver transplantation. Bone marrow mesenchymal stem cells (BM‑MSCs) have the potential to protect against liver I/R injury; however, the precise mechanisms have not been completely elucidated. Autophagy serves an important role in protecting against various injuries, including I/R injury. The present study aimed to determine the role of autophagy and its potential regulatory mechanism in BM‑MSC‑mediated protection against liver I/R injury in rats. The results demonstrated that BM‑MSCs mitigated I/R injury and enhanced autophagy in vivo. In addition, inhibition of autophagy by 3‑methyladenine reversed the positive effects of BM‑MSCs. Furthermore, heme oxygenase‑1 (HO‑1) expression was promoted by BM‑MSCs. Using zinc protoporphyrin IX to inhibit HO‑1 demonstrated that HO‑1 was important for the promotion of autophagy. In conclusion, the present study revealed that BM‑MSCs protected against liver I/R injury via the promotion of HO‑1‑mediated autophagy.

Background Embryonic Liver Fodrin (ELF) is an adaptor protein of transforming growth factor (TGF-β) signaling cascade. Disruption of ELF results in mislocalization of Smad3 and Smad4, leading to compromised TGF-β signaling. c-Myc is an important oncogenic transcription factor, and the disruption of TGF-β signaling promotes c-Myc-induced hepatocellular carcinoma (HCC) carcinogenesis. However, the prognostic significance of c-Myc in HCC is less understood Methods The expression of c-Myc protein and mRNA were measured by immunohistochemistry (IHC) and qRT- PCR, respectively. IHC was performed to detect TGF-β1 and ELF expression in HCC tissues. Their relationship with clinicopathological factors and overall survival (OS) and disease free survival (DFS) were examined. Results The expression of c-Myc protein and mRNA in HCC tissues were significantly higher in HCC area than those in normal liver tissues. However, the expression were low compared with those adjacent to HCC area. c-Myc protein was independently predictive of DFS and OS, and it was negatively correlated with tumor size (P = 0.031), tumor number (P = 0.038), and recurrence (P = 0.001). Low c-Myc expression was associated with short-term recurrence and poor prognosis. The predictive value of c-Myc combined with TGF-β1 or/and ELF was higher than that of any other single marker. Low c-Myc, high TGF-β1 or/and low ELF expression was associated with the worst DFS and OS. Conclusions Low expression of c-Myc protein predicts poor outcomes in patients with HCC with hepatectomy. The combination of the expression of c-Myc, TGF-β1, and ELF can be used to accurately predict outcomes of patients with HCC.

Time series analysis is an important data mining task in areas such as the stock market and petroleum industry. One interesting problem in knowledge discovery is the detection of previously unknown frequent patterns. With the existing types of patterns, some similar subsequences are overlooked or dissimilar ones are matched. In this paper, we define patterns with weak-wildcard gaps to represent subsequences with noise and shift, and design efficient algorithms to obtain frequent and strong patterns. First, we convert a numeric time series into a sequence according to the data fluctuation. Second, we define the pattern mining with weak-wildcard gaps problem, where a weak-wildcard matches any character in an alphabet subset. Third, we design an Apriori-like algorithm with an efficient pruning technique to obtain frequent and strong patterns. Experimental results show that our algorithm is efficient and can discover frequent and strong patterns.

Finding an efficient approach to incrementally update and maintain frequent itemsets is an important aspect of data mining. Earlier incremental algorithms focused on reducing the number of scans of the original database while it is updated. However, they still required the database to be rescanned in some situations. Here we propose a three-way decision update pattern approach (TDUP) along with a synchronization mechanism for this issue. With two support-based measures, all possible itemsets are divided into positive, boundary, and negative regions. TDUP efficiently updates frequent itemsets online, while the synchronization mechanism is periodically triggered to recompute the itemsets offline. The operation of the mechanism based on appropriate settings of two support-based measures is examined through experiments. Results from three real-world data sets show that the proposed approach is efficient and reliable.

This paper proposes a new measure for recommendation through integrating Triangle and Jaccard similarities. The Triangle similarity considers both the length and the angle of rating vectors between them, while the Jaccard similarity considers non co-rating users. We compare the new similarity measure with eight state-of-the-art ones on four popular datasets under the leave-one-out scenario. Results show that the new measure outperforms all the counterparts in terms of the mean absolute error and the root mean square error.

To test the mixture of different thickness and different asphalt to find the problem of rutting test and evaluation index, so as to provide some reference for the improvement of index. The influence of different thickness and different asphalt mixture on dynamic stability, rut depth and percentage of Rut Depth index of asphalt mixture was studied by rutting test in this paper. The results show that there is a linear relationship between the dynamic stability index and the rut depth index of y = 4020.00x-404.48, the correlation coefficient reaches 0.9647. For AC-16 asphalt mixture 5cm and 6cm specimens, the dynamic stability and rut depth index are relatively stable with the change of thickness. The rut depth and percentage of Rut Depth index value decrease with the increase of thickness.

Background Mesenchymal stem cells (MSCs) have been studied for the treatment of acute liver failure (ALF) for several years. MSCs may exert their effect via complex paracrine mechanisms. Heme oxygenase (HO) 1, a rate-limiting enzyme in heme metabolism, exerts a wide range of anti-inflammatory, anti-apoptotic and immunoregulatory effects in a variety of diseases. However, the relationship between MSCs and HO-1 in the treatment of ALF is still unclear. We investigated the preventive and therapeutic potential of intravenously administered BMSCs. Methods Bone marrow-derived mesenchymal stem cells (BMSCs) obtained from Sprague–Dawley rats were isolated and cultured. We employed BMSCs, hemin (a HO-1 inducer) and zinc protoporphyrin (ZnPP, the HO-1 activity inhibitor) in D-galactosamine (D-Gal)/lipopolysaccharides (LPS)-induced ALF rats. Rats were sacrificed at days 1, 3, 5, and 7 post-transfusion, respectively. Blood samples and liver tissues were collected. Hepatic injury, HO-1 activity, chemokines, inflammatory cytokines, the number and oxidative activity of neutrophils, ki67, and TUNEL-positive cells were evaluated. Results HO-1 induction or BMSCs transplantation attenuated D-galactosamine/lipopolysaccharide-induced increases in alanine aminotransferase, aspartate aminotransferase, total bilirubin (TBIL), ammonia, and inflammatory cytokines. Treatment with hemin or BMSCs also inhibited neutrophil infiltration, oxidative activity, and hepatocyte apoptosis. The protective effect of BMSCs was partially neutralized by ZnPP, suggesting the key role of HO-1 in the process. Conclusions These findings may correlate with inhibition of nuclear factor-κ B activation. BMSCs ameliorated ALF by increasing the HO-1 expression, which reduced PMN infiltration and function, and played an important anti-inflammatory and anti-apoptotic role. Graphical Abstract Proposed mechanism by which BMSCs reduce inflammation, neutrophil activation, and hepatocyte apoptosis and promote hepatocyte proliferation via HO-1. BMSCs increase HO-1 expression in liver via Nrf2. HO-1 protects against LPS/D-Gal-induced ALF by inhibiting neutrophil infiltration and inflammatory burst, and hepatocyte apoptosis and necrosis. HO-1 also promotes hepatocyte proliferation. Electronic supplementary material The online version of this article (doi:10.1186/s13287-017-0524-3) contains supplementary material, which is available to authorized users.

the risk evaluation indicator system by enterprise resource planning (ERP) system is characterized by excessive subjectivity, lack of reasonable basis for establishing, and redundancy. The paper adopts the rough set theory, proposes attribute reduction algorithm based on information entropy, and makes attribute reduction on risk control indicators for implementation of ERP system by integrating cases from 10 enterprises that have already implemented ERP systems. The resultsshow that attribute reduction algorithm based on rough set is effective and available on reduction of risk control indicator for implementation of ERP system.

Frequent itemsets mining has been a hot area of research in recent years. In ecommerce, new data enter the system continually. Therefore people designed incremental frequent itemsets mining algorithms to deal with this situation. In this paper, we propose a three-way decision approach along with a synchronization mechanism to this issue. With this approach, all possible itemsets are divided into three regions, namely the positive, the boundary and the negative region. Itemsets in the positive region are already frequent. Itemsets in the boundary region are infrequent, however may be frequent after data increment in the near future. Itemsets in the negative region will not be frequent even after data increment. Therefore to keep the frequent itemsets up-to-date, one only need to check those in the boundary region, and the runtime is saved. Experimental results from a real-world dataset show that the proposed approach is both efficient and reliable. The synchronization mechanism with the appropriate settings of the boundary and negative region thresholds are also discussed.

The fuzzy ball drilling fluids have been developed on the basis of the circulation foam and Aphron to control lost circulation effectively. There are some difficulties in drilling U-type well, such as well-bore stability, cutting carrying problem, large torque and friction at the horizontal section, and formation damage to coal-bed. The objective of this paper was to show some applications of fuzzy ball drilling fluids on U-type wells of the Ordos Basin and prove the superiority of fuzzy ball drilling fluid in CBM drilling. To the three mentioned cases, the density of fuzzy ball drilling fluid was 0.90~1.18g/cm3, the funnel viscosity was 45~72s, the dynamic shear force was 12~19 Pa, the PV was 13~19mPa·s and the pH was ranged from 7 to 9. To use the fuzzy ball drilling fluids, the average ROP increased above 10% with no borehole complexity, such as stuck pipe, hole enlargement causing poor cleaning and etc. These cases reflected excellent properties of the fuzzy ball drilling fluids including effectively sealing, good carrying and suspension ability, formation damage control and compatible weighted by inert materials. Furthermore, the fuzzy ball drilling fluids will not affect BHA tools like motors and MWD in CBM drilling.

Fuzzy ball drilling fluids have been developed in order to effectively control lost circulation during CBM drilling. Depending upon fuzzy balls and colloids in fuzzy balls, the fuzzy ball drilling fluids changed their shapes and properties to completely plug underground heterogeneous seepage channels so as to strengthen the pressure bearing capacity of formations. This paper describes the available features of the fuzzy ball drilling fluid including efficient plugging, good carrying and suspension, formation damage control, compatible weighted by any weighted materials without auxiliary equipment. The fuzzy ball drilling fluids can finish drilling in low pressure natural gas zone, control CBM leakage; control the natural fractures, drilling in different pressures in the same open hole, combination with the air drilling mode, etc. during Ordos CBM drilling. The fuzzy ball drilling fluid will not affect down-hole motors and MWD. The fuzzy ball drilling fluid will be blend simply as conventional water based drilling fluids. The existing CBM drilling equipment can completely meet the fuzzy ball drilling mixing and it is maintained conveniently. The fuzzy ball drilling fluid is the efficient drilling fluid.

Fuzzy ball material which imitating the structure of bacteria have been tested and developed as a new lost circulation material. The fuzzy plugging theory include three pressure distribution methods: the pressure decomposition filling mechanism, the pressure reducing filling mechanism and the pressure support filling mechanism. Fuzzy ball material relies on its own particular structure, achieving full-size sealing and self-adjustment to match the porous media. Fuzzy ball working fluids can be prepared without auxiliary equipment and its non-weighted density is 0.85~1.0g/cm3. Its density can be up to 1.2g/cm3 when it is weighted by inert materials to meet the need of near/under balanced drilling. Indoor test showed that novel working fluids have significant inhibitive capacity and sealing characteristic. There are three properties of fuzzy three ball working fluids: inhibitive capacity, anti-temperature capacity and sealing characteristic. Fuzzy ball working fluid has been successful applied in hundreds of wells to solve the problems such as collapse, severe losses and etc.

The molar heat capacities of the room temperature ionic liquid 1-butyl-3-methylimidazolium hexafluoroborate (BMIPF6) were measured by an adiabatic calorimeter in temperature range from 80 to 390 K. The dependence of the molar heat capacity on temperature is given as a function of the reduced temperature (X) by polynomial equations, C P,m (J K−1 mol−1) = 204.75 + 81.421X − 23.828 X 2 + 12.044X 3 + 2.5442X 4 [X = (T − 132.5)/52.5] for the solid phase (80–185 K), C P,m (J K−1 mol−1) = 368.99 + 2.4199X + 1.0027X 2 + 0.43395X 3 [X = (T − 230)/35] for the glass state (195 − 265 K), and C P,m (J K−1 mol−1) = 415.01 + 21.992X − 0.24656X 2 + 0.57770X 3 [X = (T − 337.5)/52.5] for the liquid phase (285–390 K), respectively. According to the polynomial equations and thermodynamic relationship, the values of thermodynamic function of the BMIPF6 relative to 298.15 K were calculated in temperature range from 80 to 390 K with an interval of 5 K. The glass transition of BMIPF6 was measured to be 190.41 K, the enthalpy and entropy of the glass transition were determined to be ΔH g = 2.853 kJ mol−1 and ΔS g = 14.98 J K−1 mol−1, respectively. The results showed that the milting point of the BMIPF6 is 281.83 K, the enthalpy and entropy of phase transition were calculated to be ΔH m = 20.67 kJ mol−1 and ΔS m = 73.34 J K−1 mol−1.

A novel metal-organic frameworks [Cu-2(OH)(2,2'-bpy)(2)(BTC) center dot A 2H(2)O](n) (CuMOF, BTC = benzene-1,3,5-tricarboxylic acid, 2,2'-bpy = 2,2'-bipyridine) has been synthesized hydrothermally and characterized by single crystal XRD, FT-IR spectra. The low-temperature molar heat capacities were measured by temperature modulated differential scanning calorimetry (TMDSC) for the first time. The thermodynamic parameters such as entropy and enthalpy relative to reference temperature 298.15 K were derived based on the above molar heat capacity data. Moreover, the thermal stability and the decomposition mechanism of CuMOF were investigated by TG-MS (thermogravimetry-mass spectrometer). A four-stage mass loss was observed in the TG curve. MS curve indicated that the gas products for oxidative degradation of CuMOF were H2O, CO2, NO and NO2.

The thermodynamic properties of 2-pyridinealdoxime were investigated through the thermogravimetric (TG) analysis and differential scanning calorimetry (DSC). Low-temperature heat capacity Cp,m of 2-pyridinealdoxime (C6H6N2O; CAS 873-69-8) was measured in the temperature range from (80 to 373) K with a high precision automated adiabatic calorimeter. No phase transition or thermal anomaly was observed in this range. The thermodynamic functions [HT − H298.15] and [ST − S298.15] were calculated in the range from (80 to 375) K. The constant-volume energy and standard molar enthalpy of combustion have been determined, Δ c U ( C 6 H 6 N 2 O,cr ) = Δ c H m ∘ (C6H6N2O, cr) = − (3297.11 ± 1.53) kJ · mol−1 (based on Δn being zero in reaction of the combustion), by means of a precision oxygen-bomb combustion calorimeter at T = (298.15 ± 0.001) K. The standard molar enthalpy of formation has been derived, Δ f H m ∘ (C6H6N2O, cr) = (78.56 ± 2.43) kJ · mol−1, from the standard molar enthalpy of combustion in combination with other auxiliary thermodynamic quantities through a Hess thermochemical cycle.

The molar heat capacities of the aqueous Li2B4O7 solution at the concentration of 0.0187 mol.kg(-1) have been measured using a precision automated adiabatic calorimeter in the temperature range from 80 K to 355 K. The dependence of the molar heat capacity on temperature was given as a function of the reduced temperature X by polynomial equations C-p,C-m (J.K-1.mol(-1)) = 25.44 + 11.99X + 0.454X(2) + 1.671X(3) for the solid phase [80 K similar to 258 K, X = (T - 169)/89] and C-p,C-m (J.K-1.mol(-1)) = 77.33 for the liquid phase [274 K similar to 355 K], respectively. The phase transition of the solution was determined on the basis of the curve of the heat capacity with temperature. The temperature of the phase transition was observed at 273.04 K, and the enthalpy and entropy of the phase transition were calculated to be Delta H-m = 4.650 kJ.mol(-1) and Delta S-m = 17.03 J.K-1.mol(-1), respectively. According to the polynomial equations and thermodynamic relationship, the values of the thermodynamic function of the aqueous Li2B4O7 solution relative to 298.15 K were calculated in the temperature range from 80 K to 355 K with an interval of 5 K. The relative apparent molar heat capacities of the aqueous Li2B4O7 solution, C-p,C-phi, were calculated every 5 K in the temperature range from 80 K to 355 K from the experimental heat capacities of the aqueous Li2B4O7 solution and the heat capacities of pure water. The molar heat capacities of the aqueous Li2B4O7 solution at the concentration of 0.0187 mol.kg(-1) have been measured using a precision automated adiabatic calorimeter in the temperature range from 80 K to 355 K. The dependence of the molar heat capacity on temperature was given as a function of the reduced temperature X by polynomial equations C-p,C-m (J.K-1.mol(-1)) = 25.44 + 11.99X + 0.454X(2) + 1.671X(3) for the solid phase [80 K similar to 258 K, X = (T - 169)/89] and C-p,C-m (J.K-1.mol(-1)) = 77.33 for the liquid phase [274 K similar to 355 K], respectively. The phase transition of the solution was determined on the basis of the curve of the heat capacity with temperature. The temperature of the phase transition was observed at 273.04 K, and the enthalpy and entropy of the phase transition were calculated to be Delta H-m = 4.650 kJ.mol(-1) and Delta S-m = 17.03 J.K-1.mol(-1), respectively. According to the polynomial equations and thermodynamic relationship, the values of the thermodynamic function of the aqueous Li2B4O7 solution relative to 298.15 K were calculated in the temperature range from 80 K to 355 K with an interval of 5 K. The relative apparent molar heat capacities of the aqueous Li2B4O7 solution, C-p,C-phi, were calculated every 5 K in the temperature range from 80 K to 355 K from the experimental heat capacities of the aqueous Li2B4O7 solution and the heat capacities of pure water.

The constant-volume energy of combustion of crystalline anhydrous caffeine (C8H10N4O2) in α (lower temperature steady) crystal form was measured by a bomb combustion calorimeter, the standard molar enthalpy of combustion of caffeine at T = 298.15 K was determined to be −(4255.08 ± 4.30) kJ · mol−1, and the standard molar enthalpy of formation was derived as −(322.15 ± 4.80) kJ · mol−1. The heat capacity of caffeine in the same crystal form was measured in the temperature range from (80 to 387) K by an adiabatic calorimeter. No phase transition or thermal anomaly was observed in the above temperature range. The thermal behavior of the compound was further examined by thermogravimetry (TG), differential thermal analysis (DTA) over the range from (300 to 700) K and by differential scanning calorimetry (DSC) over the range from (300 to 540) K, respectively. From the above thermal analysis a (solid–solid) and a (solid–liquid) phase transition of the compound were found at T = (413.39 and 509.00) K, respectively; and the corresponding molar enthalpies of these transitions were determined to be (3.43 ± 0.02) kJ · mol−1for the (solid–solid) transition, and (19.86 ± 0.03) kJ · mol−1 for the (solid–liquid) transition, respectively.