Solid-state phase transformations toward a metal-organic framework of 7-connected Zn4O secondary building units

In the development of metal-organic frameworks (MOFs), secondary building units (SBUs) have been utilized as molecular modules for the construction of nanoporous materials with robust structures. Under solvothermal synthetic conditions, dynamic changes in the metal coordination environments and ligand coordination modes of SBUs determine the resultant product structures. Alternatively, MOF phases with new topologies can also be achieved by post-synthetic treatment of as-synthesized MOFs via the introduction of acidic or basic moieties that cause the simultaneous cleavage/reformation of coordination bonds in the solid state. In this sense, we studied the solid-state transformation of two ndc-based Zn-MOFs (ndc = 1,4-naphthalene dicarboxylate) with different SBUs but the same pcu topology to another MOF with sev topology. One of the chosen MOFs with pcu nets is [Zn₂(ndc)₂(bpy)]_n (bpy = 4,4′-bipyridine), (6C_bpy-MOF) consisting of a 6-connected pillared-paddlewheel SBU, and the other is IRMOF-7 composed of 6-connected Zn₄O(COO)₆ SBUs and ndc. Upon post-structural modification, these pcu MOFs were converted into the same MOF with sev topology constructed from the uncommon 7-connected Zn₄O(COO)₇ SBU (7C-MOF). The appropriate post-synthetic conditions for the transformation of each SBUs were systematically examined. In addition, the effect of the pillar molecules in the pillared-paddlewheel MOFs on the topology conversion was studied in terms of the linker basicity, which determines the inertness during the solid-state phase transformation. This post-synthetic modification approach is expected to expand the available methods for designing and synthesizing MOFs with controlled topologies.

     

Characterization and Cu electroless plating of laser-drilled through-wafer via-holes in GaN/Al2O3

GaN on Al₂O₃ was drilled with a high power Nd:YAG laser. Micro-Raman spectroscopy showed that the induced damage was nominal at about 15 µm from the edge of the drilled through-wafer via-holes. Cu plating was accomplished using an electroless plating technique. FIB was employed to expose the interface between electrolessly plated Cu and GaN on the sidewall of the drilled holes, followed by SEM/EDX to confirm that the sidewall of the drilled holes was successfully covered with Cu. Cu electroless plating after laser drilling has the potential to simplify device layout and improve device integration.     

A graph-theoretic approach to optimize keyword queries in relational databases

Keyword search can provide users an easy method to query large and complex databases without any knowledge of structured query languages or underlying database schema. Most of the existing studies have focused on generating candidate structured queries relevant to keywords. Due to the large size of generated queries, the execution costs may be prohibitive. However, existing studies lack the idea of a generalized method to optimize the plan of the large set of generated queries. In this paper, we introduce a graph-theoretic optimization approach. We propose a general graph model, Weighted Operator Graph, to address the costs of keyword query evaluation plans. The proposed model is flexible to integrate all of the cost-based plans in a uniform way. We define a Keyword Query Optimization Problem based on a theoretical cost model as a graph-theoretic problem and show it to be a NP-hard problem. We propose a greedy heuristic Maximum Propagation that reduces the size of the intermediate result as early as possible. The proposed algorithm allows us to achieve efficiency in terms of query evaluation costs. The experimental studies on both synthetic and real data set results show that our work outperforms the existing work.     

Finding Top‐k Answers in Node Proximity Search Using Distribution State Transition Graph

Considerable attention has been given to processing graph data in recent years. An efficient method for computing the node proximity is one of the most challenging problems for many applications such as recommendation systems and social networks. Regarding large‐scale, mutable datasets and user queries, top‐k query processing has gained significant interest. This paper presents a novel method to find top‐k answers in a node proximity search based on the well‐known measure, Personalized PageRank (PPR). First, we introduce a distribution state transition graph (DSTG) to depict iterative steps for solving the PPR equation. Second, we propose a weight distribution model of a DSTG to capture the states of intermediate PPR scores and their distribution. Using a DSTG, we can selectively follow and compare multiple random paths with different lengths to find the most promising nodes. Moreover, we prove that the results of our method are equivalent to the PPR results. Comparative performance studies using two real datasets clearly show that our method is practical and accurate.     

Keyword search in relational databases

This paper surveys research on enabling keyword search in relational databases. We present fundamental characteristics and discuss research dimensions, including data representation, ranking, efficient processing, query representation, and result presentation. Various approaches for developing the search system are described and compared within a common framework. We discuss the evolution of new research strategies to resolve the issues associated with probabilistic models, efficient top-k query processing, and schema analysis in relational databases.     

Process modeling and energy consumption of fry-drying and torrefaction of organic solid waste

A commercial fry-drying and torrefaction (FDT) plant for producing bio-solid fuel from 45?ton/d organic sludge with 80% water was proposed. The FDT plant was modeled by a process simulator with the aid of plant data. Two double-effect evaporators were used in series to vaporize water, using heat-carrying oil. Torrefied and pelletized bio-solid was produced at 413?kg/h, comprising 4.4 and 4.8% of water and oil, respectively, and 67% of its mass was used as heat source. The total energy consumption was 1,369?kW, including 93?kW electrical and 1,276?kW thermal energies. The total heat duty was 788?kcal/kg-water.     

Pragmatic correlation analysis for probabilistic ranking over relational data

It is widely recognized that effective ranking methods for relational data (e.g., tuples) enable users to overcome the limitations of the traditional Boolean retrieval model and the hardness of structured query writing. To determine the rank of a tuple, term frequency-based methods, such as tf × idf (term frequency × inverse document frequency) schemes, have been commonly adopted in the literature by simply considering a tuple as a single document. However, in many cases, we have noted that tf × idf schemes may not produce effective rankings or specific orderings for relational data with categorical attributes, which is pervasive today. To support fundamental aspects of relational data, we apply the notions of correlation analysis to estimate the extent of relationships between queries and data. This paper proposes a probabilistic ranking model to exploit statistical relationships that exist in relational data of categorical attributes. Given a set of query terms, information on correlative attribute values to the query terms is used to estimate the relevance of the tuple to the query. To quantify the information, we compute the extent of the dependency between correlative attribute values on a Bayesian network. Moreover, we avoid the prohibitive cost of computing insignificant ranking features based on a limited assumption of node independence. Our probabilistic ranking model is domain-independent and leverages only data statistics without any prior knowledge such as user query logs. Experimental results show that our work improves the effectiveness of rankings for real-world datasets and has a reasonable query processing efficiency compared to related work.