Identifying product families using data mining techniques in manufacturing paradigm

Identifying product families has been considered as an effective way to accommodate the increasing product varieties across the diverse market niches. In this paper, we propose a novel framework to identifying product families by using a similarity measure for a common product design data BOM (Bill of Materials) based on data mining techniques such as frequent mining and clus-tering. For calculating the similarity between BOMs, a novel Extended Augmented Adjacency Matrix (EAAM) representation is introduced that consists of information not only of the content and topology but also of the fre-quent structural dependency among the various parts of a product design. These EAAM representations of BOMs are compared to calculate the similarity between products and used as a clustering input to group the product fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.

A novel tuning method for advanced line drop compensator and its application to response coordination of distributed generation with voltage regulating devices

Nowadays, integration of small-scale electricity generators, known as Distributed Generation (DG), into distribution networks has become increasingly popular. This tendency together with the falling price of DG units has a great potential in giving the DG a better chance to participate in voltage regulation process, in parallel with other regulating devices already available in the distribution systems. The voltage control issue turns out to be a very challenging problem for distribution engineers, since existing control coordination schemes need to be reconsidered to take into account the DG operation. In this paper, a control coordination approach is proposed, which is able to utilize the ability of the DG as a voltage regulator, and at the same time minimize the interaction of DG with another DG or other active devices, such as On-load Tap Changing Transformer (OLTC). The proposed technique has been developed based on the concepts of protection principles (magnitude grading and time grading) for response coordination of DG and other regulating devices and uses Advanced Line Drop Compensators (ALDCs) for implementation. A distribution feeder with tap changing transformer and DG units has been extracted from a practical system to test the proposed control technique. The results show that the proposed method provides an effective solution for coordination of DG with another DG or voltage regulating devices and the integration of protection principles has considerably reduced the control interaction to achieve the desired voltage correction.

Bioprocess engineering of microalgae to produce a variety of consumer products

Microalgae biotechnology has recently emerged into the lime light owing to numerous consumer products that can be harnessed from microalgae. Product portfolio stretches from straightforward biomass production for food and animal feed to valuable products extracted from microalgal biomass, including triglycerides which can be converted into biodiesel. For most of these applications, the production process is moderately economically viable and the market is developing. Considering the enormous biodiversity of microalgae and recent developments in genetic and metabolic engineering, this group of organisms represents one of the most promising sources for new products and applications. With the development of detailed culture and screening techniques, microalgal biotechnology can meet the high demands of food, energy and pharmaceutical industries. This review article discusses the technology and production platforms for development and creation of different valuable consumer products from microalgal biomass.

First-principle engineering magnetism in low-dimensional boron nitride materials

Nanotubes and nanosheets are low-dimensional nanomaterials with unique properties that can be exploited for numerous applications. This book offers a complete overview of their structure, properties, development, modeling approaches, and practical use. It focuses attention on boron nitride (BN) nanotubes, which have had major interest given their special high-temperature properties, as well as graphene nanosheets, BN nanosheets, and metal oxide nanosheets. Key topics include surface functionalization of nanotubes for composite applications, wetting property changes for biocompatible environments, and graphene for energy storage applications

Modeling and control of a robotic power line inspection vehicle

Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

Product stability and sequestration mechanisms in Solanum tuberosum engineered to biosynthesize high value ketocarotenoids

To produce commercially valuable ketocarotenoids in Solanum tuberosum, the 4, 4′ β-oxygenase (crtW) and 3, 3′ β-hydroxylase (crtZ) genes from Brevundimonas spp. have been expressed in the plant host under constitutive transcriptional control. The CRTW and CRTZ enzymes are capable of modifying endogenous plant carotenoids to form a range of hydroxylated and ketolated derivatives. The host (cv. Désirée) produced significant levels of nonendogenous carotenoid products in all tissues, but at the apparent expense of the economically critical metabolite, starch. Carotenoid levels increased in both wild-type and transgenic tubers following cold storage; however, stability during heat processing varied between compounds. Subcellular fractionation of leaf tissues revealed the presence of ketocarotenoids in thylakoid membranes, but not predominantly in the photosynthetic complexes. A dramatic increase in the carotenoid content of plastoglobuli was determined. These findings were corroborated by microscopic analysis of chloroplasts. In tuber tissues, esterified carotenoids, representing 13% of the total pigment found in wild-type extracts, were sequestered in plastoglobuli. In the transgenic tubers, this proportion increased to 45%, with esterified nonendogenous carotenoids in place of endogenous compounds. Conversely, nonesterified carotenoids in both wild-type and transgenic tuber tissues were associated with amyloplast membranes and starch granules.

Design and synthesis of a screening library using the natural product scaffold 3-chloro-4-hydroxyphenylacetic acid

The fungal metabolite 3-chloro-4-hydroxyphenylacetic acid (1) was utilized in the generation of a unique drug-like screening library using parallel solution-phase synthesis. A 20-membered amide library (3–22) was generated by first converting 1 to methyl (3-chloro-4-hydroxyphenyl)acetate (2), then reacting this scaffold with a diverse series of primary amines via a solvent-free aminolysis procedure. The structures of the synthetic analogues (3–22) were elucidated by spectroscopic data analysis. The structures of compounds 8, 12, and 22 were confirmed by single X-ray crystallographic analysis. All compounds were evaluated for cytotoxicity against a human prostate cancer cell line (LNCaP) and for antiparasitic activity toward Trypanosoma brucei brucei and Plasmodium falciparum and showed no significant activity at 10 μM. The library was also tested for effects on the lipid content of LNCaP and PC-3 prostate cancer cells, and it was demonstrated that the fluorobenzyl analogues (12–14) significantly reduced cellular phospholipid and neutral lipid levels.

Beyond Moa's Ark and Wallace's Line: extralimital distribution of new species of Austronothrus (Acari, Oribatida, Crotoniidae) and the endemicity of the New Zealand oribatid mite fauna

The genus Austronothrus was previously known from three species recorded only from New Zealand. Austronothrus kinabalu sp. nov. is described from Sabah, Borneo and A. rostralis sp. nov. from Norfolk Island, south-west Pacific. A key to Austronothrus is included. These new species extend the distribution of Austronothrus beyond New Zealand and confirms that the subfamily Crotoniinae is not confined to former Gondwanan landmasses. The distribution pattern of Austronothrus spp., combining Oriental and Gondwanan localities, is indicative of a curved, linear track; consistent with the accretion of island arcs and volcanic terranes around the plate margins of the Pacific Ocean, with older taxa persisting on younger island though localised dispersal within island arc metapopulations. Phylogenetic analysis and an area cladogram are consistent with a broad ancestral distribution of Austronothrus in the Oriental region and on Gondwanan terranes, with subsequent divergence and distribution southward from the Sunda region to New Zealand. This pattern is more complex than might be expected if the New Zealand oribatid fauna was derived from dispersal following re-emergence of land after inundation during the Oligocene (25 mya), as well as if the fauna emanated from endemic, relictual taxa following separation of New Zealand from Gondwana during the Cretaceous (80 mya).

Applying digital product memories in industrial production

Industrial production and supply chains face increased demands for mass customization and tightening regulations on the traceability of goods, leading to higher requirements concerning flexibility, adaptability, and transparency of processes. Technologies for the ’Internet of Things' such as smart products and semantic representations pave the way for future factories and supply chains to fulfill these challenging market demands. In this chapter a backend-independent approach for information exchange in open-loop production processes based on Digital Product Memories DPMs is presented. By storing order-related data directly on the item, relevant lifecycle information is attached to the product itself. In this way, information handover between several stages of the value chain with focus on the manufacturing phase of a product has been realized. In order to report best practices regarding the application of DPM in the domain of industrial production, system prototype implementations focusing on the use case of producing and handling a smart drug case are illustrated.

A normal-distribution based rating aggregation method for generating product reputations

With the extensive use of rating systems in the web, and their significance in decision making process by users, the need for more accurate aggregation methods has emerged. The Naïve aggregation method, using the simple mean, is not adequate anymore in providing accurate reputation scores for items [6 ], hence, several researches where conducted in order to provide more accurate alternative aggregation methods. Most of the current reputation models do not consider the distribution of ratings across the different possible ratings values. In this paper, we propose a novel reputation model, which generates more accurate reputation scores for items by deploying the normal distribution over ratings. Experiments show promising results for our proposed model over state-of-the-art ones on sparse and dense datasets.

Disruptive innovations in complex product systems industries: A case study

We propose that disruptive changes pertaining to complex product systems (CoPS) will yield a different set of characteristics than those traditionally observed for commodity products, and seek evidence for this proposition in a case study of the Flash Converting technology, a disruptive CoPS innovation in the copper production industry. Our results show that unlike disruptions in commodity product industries, the incumbent CoPS technology does not overshoot mainstream market performance demand. Also, the disruptive CoPS innovation: (i) is not nurtured in low-end niche markets; (ii) initially satisfies mainstream market performance demand, and; (iii) has higher unit price than the incumbent technology.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

A candidate gene association study for growth performance in an improved Giant Freshwater Prawn (Macrobrachium rosenbergii) culture line

A candidate gene approach using type I single nucleotide polymorphism (SNP) markers can provide an effective method for detecting genes and gene regions that underlie phenotypic variation in adaptively significant traits. In the absence of available genomic data resources, transcriptomes were recently generated in Macrobrachium rosenbergii to identify candidate genes and markers potentially associated with growth. The characterisation of 47 candidate loci by ABI re-sequencing of four cultured and eight wild samples revealed 342 putative SNPs. Among these, 28 SNPs were selected in 23 growth-related candidate genes to genotype in 200 animals selected for improved growth performance in an experimental GFP culture line in Vietnam. The associations between SNP markers and individual growth performance were then examined. For additive and dominant effects, a total of three exonic SNPs in glycogen phosphorylase (additive), heat shock protein 90 (additive and dominant) and peroxidasin (additive), and a total of six intronic SNPs in ankyrin repeats-like protein (additive and dominant), rolling pebbles (dominant), transforming growth factor-β induced precursor (dominant), and UTP-glucose-1-phosphate uridylyltransferase 2 (dominant) genes showed significant associations with the estimated breeding values in the experimental animals (P =0.001−0.031). Individually, they explained 2.6−4.8 % of the genetic variance (R2=0.026−0.048). This is the first large set of SNP markers reported for M. rosenbergii and will be useful for confirmation of associations in other samples or culture lines as well as having applications in marker-assisted selection in future breeding programs.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.