An Agent Based Market Design Methodology for Combinatorial Auctions

Auction mechanisms have attracted a great deal of interest and have been used in diverse e-marketplaces. In particular, combinatorial auctions have the potential to play an important role in electronic transactions. Therefore, diverse combinatorial auction market types have been proposed to satisfy market needs. These combinatorial auction types have diverse market characteristics, which require an effective market design approach. This study proposes a comprehensive and systematic market design methodology for combinatorial auctions based on three phases: market architecture design, auction rule design, and winner determination design. A market architecture design is for designing market architecture types by Backward Chain Reasoning. Auction rules design is to design transaction rules for auctions. The specific auction process type is identified by the Backward Chain Reasoning process. Winner determination design is about determining the decision model for selecting optimal bids and auctioneers. Optimization models are identified by Forward Chain Reasoning. Also, we propose an agent based combinatorial auction market design system using Backward and Forward Chain Reasoning. Then we illustrate a design process for the general n-bilateral combinatorial auction market. This study serves as a guideline for practical implementation of combinatorial auction markets design.Combinatorial Auction, Market Design Methodology, Market Architecture Design, Auction Rule Design, Winner Determination Design, Agent-Based System

Childhood depression: a comprehensive literature review

Depression in children was found to be a serious psychological disorder that warranted clinical attention. Childhood depression was neither normal developmental phenomena nor transient disturbances that the children outgrew. The people in Korea are rarely aware of depression in children. In fact, they do not believe in the existence of depression in children. But traditional morals and family structure have rapidly changed since the 1970\u27s. Children who grow up in a nuclear family do not receive the same emotional and psychological support that used to be provided by extended family members. In addition, society has become more competitive than ever before. From early childhood, the children face a high competition with their peers and struggle with reaching their parents\u27 expectations as well as those of society. Under these circumstances the number of children who suffer from depression has increased unbelievably. Therefore, childhood depression was dealt with so that Korean parents and teachers could have a framework from which to work. The writer addressed the prevalence, etiology, nature and treatment of childhood depression, and the impact of the mother\u27s depression on the children. In addition, the implication and intervention strategies which could be used by educators were addressed. Recognizing the signs of depression, people who were working with children could help them. Korean educators have a great responsibility to help the children where there is no school counselor and/or social worker. The purpose of the paper is to acquaint the readers with childhood depression. Thus, the paper dealt with focusing on research in cognitive characteristics of depression in children, influence of the home environment of children, and their treatment. Lastly, information was suggested concerning how to work with and help the children

Consumer-perceived Value of Digital Product Innovations: Evidence from Smartphones

This research investigates the user-perceived values of digital product innovations. We evaluate how hardware-driven innovation design strategies impact the users’ appraisals of digital products. Text-mining techniques are employed to identify/classify the hardware components of a digital product and the corresponding consumers’ sentiments on the innovation designs. The classifications of product innovation strategies are incorporated into the empirical analyses in a hierarchical manner to assess the impacts of distinct digital product designs on consumers’ evaluations. The preliminary outcomes suggest that positive consumers’ evaluations are closely associated with the design strategies introducing innovative components into digital devices. We further find that hardware and software components require different innovation design approaches to enhance users’ assessments. The findings of this research are expected to create new knowledge for both researchers and practitioner in appreciating the effective digital product designs and in formulating strategic product positioning in a competitive market

Investigation of the SH3BP2 Gene Mutation in Cherubism

Cherubism is a rare developmental lesion of the jaw that is generally inherited as an autosomal dominant trait. Recent studies have revealed point mutations in the SH3BP2 gene in cherubism patients. In this study, we examined a 6-year-old Korean boy and his family. We found a Pro418Arg mutation in the SH3BP2 gene of the patient and his mother. A father and his 30-month-old younger brother had no mutations. Immunohistochemically, the multinucleated giant cells proved positive for CD68 and tartrate-resistant acid phosphatase (TRAP). Numerous spindle-shaped stromal cells expressed a ligand for receptor activator of nuclear factor kB (RANKL), but not in multinucleated giant cells. These results provide evidence that RANKL plays a critical role in the differentiation of osteoclast precursor cells to multinucleated giant cells in cherubism. Additionally, genetic analysis may be a useful method for differentiation of cherubism.</p

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework for 2D-to-3D reconstruction of microstructures called Micro3Diff using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a new concept referred to as multi-plane denoising diffusion, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic and experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs

Lazy Approaches for Interval Timing Correlation of Sensor Data Streams

We propose novel algorithms for the timing correlation of streaming sensor data. The sensor data are assumed to have interval timestamps so that they can represent temporal uncertainties. The proposed algorithms can support efficient timing correlation for various timing predicates such as deadline, delay, and within. In addition to the classical techniques, lazy evaluation and result cache are utilized to improve the algorithm performance. The proposed algorithms are implemented and compared under various workloads