Understanding big consumer opinion data for market-driven product design

Big consumer data provide new opportunities for business administrators to explore the value to fulfil customer requirements (CRs). Generally, they are presented as purchase records, online behaviour, etc. However, distinctive characteristics of big data, Volume, Variety, Velocity and Value or ‘4Vs’, lead to many conventional methods for customer understanding potentially fail to handle such data. A visible research gap with practical significance is to develop a framework to deal with big consumer data for CRs understanding. Accordingly, a research study is conducted to exploit the value of these data in the perspective of product designers. It starts with the identification of product features and sentiment polarities from big consumer opinion data. A Kalman filter method is then employed to forecast the trends of CRs and a Bayesian method is proposed to compare products. The objective is to help designers to understand the changes of CRs and their competitive advantages. Finally, using opinion data in Amazon.com, a case study is presented to illustrate how the proposed techniques are applied. This research is argued to incorporate an interdisciplinary collaboration between computer science and engineering design. It aims to facilitate designers by exploiting valuable information from big consumer data for market-driven product design

A SMART WIDE-AREA MONITORING SYSTEM BASED ON COGNITIVE RADIO

ABSTRAC

Prioritising engineering characteristics based on customer online reviews for quality function deployment

In market-driven product design, customer requirements (CRs) are usually obtained from consumer surveys. However, valuable CRs can also be found in a large number of online reviews. Largely due to their free text nature and the quantity, these reviews are often neglected and are seldom utilised directly by designers. In this work, one important question in quality function deployment on how to prioritise engineering characteristics (ECs) is investigated. Customer opinions concerning ECs are extracted from online reviews. By taking advantage of such opinion information, an ordinal classification approach is proposed to prioritise ECs. In a pairwise manner, in which customer opinions are deemed as features and the overall customer satisfactions are regarded as the target values, the weights of ECs are derived. Furthermore, an integer linear programming model is implemented to convert the pairwise results into the original customer satisfaction ratings. Finally, an exploratory case study is presented using reviews of four branded printers collected from Amazon and their analysis was conducted by two experienced design engineers. The experimental study reveals the merits of the proposed approach

Low-energy Scattering of (D∗Dˉ∗)±(D^{*}\bar{D}^{*})^\pm System and the Resonance-like Structure Zc(4025)Z_c(4025)

In this paper, low-energy scattering of the $(D^{*}\bar{D}^{*})^\pm$ meson system is studied within L\"uscher's finite-size formalism using $N_{f}=2$ twisted mass gauge field configurations. With three different pion mass values, the $s$-wave threshold scattering parameters, namely the scattering length $a_0$ and the effective range $r_0$, are extracted in $J^P=1^+$ channel. Our results indicate that, in this particular channel, the interaction between the two vector charmed mesons is weakly repulsive in nature hence do not support the possibility of a shallow bound state for the two mesons, at least for the pion mass values being studied. This study provides some useful information on the nature of the newly discovered resonance-like structure $Z_c(4025)$ observed in various experiments.Comment: 11 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1403.131

Coesite in Eclogite from the North Qaidam Mountains and Its Implications

Coesite provides direct evidence for ultrahigh pressure metamorphism. Although coesite has been found as inclusions in zircon in paragneiss of the north Qaidam Mountains, it has never been identified in eclogite. In this contribution, based on petrographic observations and in situ Raman microprobe spectroscopy, coesite was identified as inclusions in garnet of eclogite from the Aercituoshan, Dulan UHP metamorphic unit, north Qaidam Mountains. Coesite is partly replaced by quartz, showing a palisade texture. This is the first report on coesite in eclogite from the north Qaidam Mountains, and is also supported by garnet-omphacite-phengite geothermobarometry (2.7–3.25 GPa, 670–730°C). Coesite and its pseudomorphs have not been found in eclogites and associated rocks of other units of the north Qaidam Mountains. Further studies are required to confirm if all metamorphic units in the north Qaidam Mountains underwent the ultrahigh-pressure metamorphism

Molecular mechanism of ethylene stimulation of latex yield in rubber tree (Hevea brasiliensis) revealed by de novo sequencing and transcriptome analysis

Differential expression of unigenes involved in hormone signaling in E8 and E24 compared to C samples of Hevea brasiliensis. Ethylene signalling pathway: ETR1: ETHYLENE RESPONSE 1; CTR1: CONSTITUTIVE TRIPLE RESPONSE 1; EIN2: ETHYLENE INSENSITIVE 2; EIN3: ETHYLENE INSENSITIVE 3; ERF1/2: ETHYLENE RESPONSE FACTOR 1/2; EBF1/2: EIN3 binding F-Box protein 1/2; BR signaling pathway: BRI1: Brassinosteroid-Insensitive 1; BAK1: BRI1-associated kinase 1; BKI1: BRI1 KINASE INHIBITOR 1; BSK: BR SIGNALING KINASE; BSU1: bri1 SUPPRESSOR 1; BIN2: BRASSINOSTEROID-INSENSITIVE 2; BZR1/2: BRASSINAZOLE RESISTANT 1/2; TCH: TOUCH genes; CYCD3: CYCLIN D3; GA signaling pathway: GID1: GIBBERELLIN INSENSITIVE DWARF 1; GID2: GIBBERELLIN INSENSITIVE DWARF 2; DELLAs: DELLA growth inhibitors; TF: transcriptional factor; Auxin signaling pathway: AUX1: AUXIN1; TIR1: TRANSPORT INHIBITOR RESPONSE 1; IAA: INDOLE ACETIC ACID; ARF: AUXIN RESPONSE FACTOR; SAUR: Small Auxin-Up RNA; G10H: geraniol 10-hydroxylase gene; Cytokinin signaling pathway: CRE1: CYTOKININ RESPONSE 1; AHP: histidine phosphotransfer protein; B-ARR: type-B response regulator (ARR); A-ARR: type-A response regulator (ARR); SA signalling pathway: NPR1: Non-expressor of pathogenesis-related genes 1; TGA: the bZIP transcription factors; PR1: pathogenesis related protein 1; JA signaling pathway: JAR1: JASMONATES RESISTANT 1; JA-Ile: jasmonoyl isoleucine; JAZ: Jasmonate ZIM-domain-containing protein; MYC2: a basic helix-loop-helix (bHLH) transcription factor; ORCA3: Octadecanoid-derivative Responsive Catharanthus AP2-domain gene; ABA signalling pathway: PYR1/PYLs: Pyrabactin Resistance Protein1/PYR-Like proteins; PP2Cs: protein phosphatases which fall under the category of type 2C; SnRK2: SNF1 (Sucrose-Nonfermenting Kinase1)-related protein kinase 2: ABF: ABA responsive element (ABRE) binding factors. Cells with gray border lines in the upper rows represent differentially expressed unigenes in E8 compared to C and cells with green border lines in the lower rows represent differentially expressed unigenes in E24 compared to C. Relative levels of expression are showed by a color gradient from low (blue) to high (red). (JPG 249 kb