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The objective of this study was to evaluate the technical-economic process efficiency of obtaining simultaneous lipo-soluble (LSF) and water-soluble (WSF) fractions from annatto seeds.

The batches of annatto seeds were submitted to the refrigerated solid-liquid extraction process in four stages: pre-extraction, aqueous extraction, separation by decantation and filtration. After that, LSF and WSF from annatto seeds were obtained. The process efficiency and the quality of LSF and WSF were analyzed in terms of average yield and bioactive compounds (bixin, norbixin, phenolics and flavonoids) and their antioxidant and antimicrobial activities. Furthermore, they were economically evaluated in terms of costs of manufacturing and profitability parameters.

The process was efficient in terms of overall average yield (LSF = 8.68% and WSF = 2.76%) (w/w) and in terms of quality, mainly with higher average yields of bixin (82.34% in LSF) and norbixin (29.59% in WSF) (w/w). The concentration of bioactive compounds in the fractions promoted an increase in inhibiting free radicals (DPPH* and ABTS*+) and in the ferric-reducing power (FRAP). LSF showed a minimum inhibitory concentration of 0.06 mg mL-1 for S. aureus and 0.13 mg mL-1 for S. Typhimurium and S. Enteritidis. The lowest manufacturing costs were obtained for the LSF due to its higher extraction yield compared to the WSF. Plants on an industrial scale of 100 and 1000 L were considered economically viable, with a return on investment of 5 and 2 years.

Thus, fractions (WSF and LSF) can be applied as natural additives, as sources of bioactive compounds for nutraceutical and/or pharmaceutical, and in the development of other innovative processes. These results have practical applicability for pharmaceutical and food industry.

1. Green processing of annatto seeds obtains fractions rich in antioxidant compounds.

2. Efficiently presents a high yield of bixin and other bioactive compounds.

3. Effective in concentrating compounds that inhibit microbial growth.

4. Fractions are more accessible sources of bioactive compounds for isolation.

5. Cost of manufacturing (COM) and profitability are studied.

Green processing of annatto seeds obtains fractions rich in antioxidant compounds.

Efficiently presents a high yield of bixin and other bioactive compounds.

Effective in concentrating compounds that inhibit microbial growth.

Fractions are more accessible sources of bioactive compounds for isolation.

Cost of manufacturing (COM) and profitability are studied.

The purpose of this paper is to obtain circumference sizes from 2D feature sizes in the parts of three vital measurements of young female, the dimensions of chest width, chest depth, waist width, waist depth, hip width, hip depth, chest girth, waist girth and hip girth of 400 young female samples are collected.

Inside which, 300 samples are used as learning samples, and the remaining 100 samples are used as training samples, the sample data are entered to the network constructed by support vector machine regression (SVR) and the predictive value of circumference sizes are gained.

Finally, the regression model is established between 2D feature size and the corresponding circumference size. Through the trained prediction model based on SVR, the circumference sizes in three vital measurement parts of a new sample are predicted for convenient mass measurement.

The research of measurement regression relationship in parts of three vital measurements of young female is the basis for conveniently obtaining dimensions in garment mass measurement. It can provide the accurate data to feminine dress industry, and has high precision.

Palmprint recognition is a very interesting and promising area of research. Much work has already been done in this area, but much more needs to be done to make the systems more efficient. In this paper, a multimodal biometrics system based on fusion of left and right palmprints of a person is proposed to overcome limitations of unimodal systems.

Features are extracted using some proposed multi-block local descriptors in addition to MBLBP. Fusion of extracted features is done at feature level by a simple concatenation of feature vectors. Then, feature selection is performed on the resulting global feature vector using evolutionary algorithms such as genetic algorithms and backtracking search algorithm for a comparison purpose. The benefits of such step selecting the relevant features are known in the literature, such as increasing the recognition accuracy and reducing the feature set size, which results in runtime saving. In matching step, Chi-square similarity measure is used.

The resulting feature vector length representing a person is compact and the runtime is reduced.

Intensive experiments were done on the publicly available IITD database. Experimental results show a recognition accuracy of 99.17 which prove the effectiveness and robustness of the proposed multimodal biometrics system than other unimodal and multimodal biometrics systems.

Advancements in printed wiring technology have led to an increased demand for technicians with the knowledge and manual skills necessary to perform modifications, rework or repairs to these assemblies. Five key requirements are necessary to control the process for repair. These elements include technical expertise, analytical assessment, structured procedures, proper tooling and performance inspection criteria. In this paper all five elements are analysed and selected guidelines are given for common rework and repair situations.

The purpose of this paper is to propose an automatic metadata extraction and retrieval system to extract bibliographical information from digital academic documents in portable document formats (PDFs).

The authors use PDFBox to extract text and font size information, a rule-based method to identify titles, and an Hidden Markov Model (HMM) to extract the titles and authors. Finally, the extracted titles and authors (possibly incorrect or incomplete) are sent as query strings to digital libraries (e.g. ACM, IEEE, CiteSeerX, SDOS, and Google Scholar) to retrieve the rest of metadata.

Four experiments are conducted to examine the feasibility of the proposed system. The first experiment compares two different HMM models: multi-state model and one state model (the proposed model). The result shows that one state model can have a comparable performance with multi-state model, but is more suitable to deal with real-world unknown states. The second experiment shows that our proposed model (without the aid of online query) can achieve as good performance as other researcher's model on Cora paper header dataset. In the third experiment the paper examines the performance of our system on a small dataset of 43 real PDF research papers. The result shows that our proposed system (with online query) can perform pretty well on bibliographical data extraction and even outperform the free citation management tool Zotero 3.0. Finally, the paper conducts the fourth experiment with a larger dataset of 103 papers to compare our system with Zotero 4.0. The result shows that our system significantly outperforms Zotero 4.0. The feasibility of the proposed model is thus justified.

For academic implication, the system is unique in two folds: first, the system only uses Cora header set for HMM training, without using other tagged datasets or gazetteers resources, which means the system is light and scalable. Second, the system is workable and can be applied to extracting metadata of real-world PDF files. The extracted bibliographical data can then be imported into citation software such as endnote or refworks to increase researchers’ productivity.

For practical implication, the system can outperform the existing tool, Zotero v4.0. This provides practitioners good chances to develop similar products in real applications; though it might require some knowledge about HMM implementation.

The HMM implementation is not novel. What is innovative is that it actually combines two HMM models. The main model is adapted from Freitag and Mccallum (1999) and the authors add word features of the Nymble HMM (Bikel et al, 1997) to it. The system is workable even without manually tagging the datasets before training the model (the authors just use cora dataset to train and test on real-world PDF papers), as this is significantly different from what other works have done so far. The experimental results have shown sufficient evidence about the feasibility of our proposed method in this aspect.

The purpose of this paper is to empirically examine whether corporate insiders extract information from activity of outsiders, specifically the short sellers.

Using portfolio approach and Fama-MacBeth regressions, this study examines the relation between short interest and subsequent insider trading activities.

The following results are reported. First, there is a strong inverse relation between short selling and subsequent insider trading, which is partially due to common private information and same target firm characteristics. Second, insiders extract information from shorts. This information extraction effect is more pronounced for firms whose insiders have stronger incentives to extract shorts information (insider purchases, higher short sale constraints, and better information environments). Third, during the September 2008 shorting ban, the information extraction affect disappeared among the large banned firms, whose shorting activities were distorted.

The findings contradict the of-cited accusations corporate executives hold against short sellers. Instead, corporate insiders appear to trade in the same direction as suggested by shorting activities.

Among the vocal critics of short sellers are corporate insiders, who allege that short sellers beat down their stock prices. Many corporations even engage in stock repurchases to show confidence that the stock will perform well going forward despite the short sellers’ actions. This paper’s analysis on their personal portfolios suggests the other way around.

By focusing on how corporate insider trading is related to shorts information, this paper sheds new light on whether corporate decisions convey the true information the corporate insiders possess.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

The aim of this study is to understand a family firm's choice of related-party transaction (RPT) types and analyze their value impacts to separate the abusive from benign RPTs.

It uses a 10-year panel of BSE-listed 378 family (and 200 non-family) firms. The fixed effects, logit and difference-in-difference (DID) models help examine value effects, propensity and persistence of harmful RPTs.

Loans/guarantees (irrespective of counterparties) destroy firm value. Capital asset RPTs decrease the firm value but enhance value when undertaken with holding parties. Operating RPTs increase firm value and profitability. They improve asset utilization and reduce discretionary expenses (especially when made with controlled entities). Family firms have larger loans/guarantees and capital asset volumes but have smaller operating RPTs than non-family firms. They are less likely to undertake loans/guarantees (and even operating RPTs) and more capital RPTs vis-à-vis non-family firms. Family firms persist with dubious loans/guarantees but hold back beneficial operating RPTs, despite RPTs being in investor cross-hairs amid the Satyam scam.

Rent extractability and counterparty incentives supplement each other. (1) The higher extractability of related-party loans and guarantees (RPLGs) dominates the lower extraction incentives of controlled parties. (2) Holding parties' bringing assets, providing a growth engine and adding value dominate their higher extraction incentives (3) The big gains to the operational efficiency come from operating RPTs with controlled parties, generally operating companies in the family house. (4) Dubious RPTs seem more integral to family firms' choices than non-family firms. (5) Counterparty incentives behind the divergent use of RPTs deserve more research attention. Future studies can give more attention to how family characteristics affect divergent motives behind RPTs.

First, the study does not single out family firms for dubious use of all RPTs. Second, investors, auditors or creditors must pay close attention to RPLGs as a special expropriation mechanism. Third, operating RPTs (and capital RPTs with holding parties) benefit family firms. However, solid procedural safeguards are necessary. Overall, results may help clarify the dilemma Indian regulators face in balancing the abusive and business sides of RPTs.

The study fills the gap by arguing why some RPTs may be dubious or benign and then shows how RPTs' misuse depends on counterparty types. It shows operating RPTs enhance operating efficiencies on several dimensions and that benefits may vary with counterparty types. It also presents the first evidence that family firms favor dubious RPTs more and efficient RPTs less than non-family firms.

This study focused on how to realize automatic recognition of young women's neck-shoulder shape based on the front and side images.

The reverse engineering software was used to measure the body sizes of the neck-shoulder part based on the young women's three-dimensional (3D) point cloud data, and the important parameters closely related to the neck-shoulder shape were determined. The neck-shoulder shape of the subjects was classified to establish the classification rules. Then, based on the front and side images, the human body contour was extracted by Matlab, and the data required for neck-shoulder shape classification were obtained by identifying the feature points.

Through the cluster analysis based on the shoulder angle, back angle, shoulder depth/width ratio and armpit depth/width ratio, young women's neck-shoulder shape was divided into four categories, namely round wide shoulder, flat narrow shoulder, round drop shoulder and hunchback flat shoulder. The neck-shoulder shape could be automatically recognized based on the established classification rules and two-dimensional (2D) body measurement method, with an accuracy rate of 90%. The neck-shoulder shape automatic recognition system constructed based on this method is effective.

This study proposed a simple neck-shoulder automatic recognition method based on the 2D body images. This approach can be extended to other group of human body or other parts of the body.

The purpose of this study was to evaluate the extract of Moringa stenopetala seed oil, by organic solvents (methanol and hexane), for its efficacy against microbial activity on cotton fabrics. The selected microbes for the study were two types of bacteria which are Gram-positive (S. aureus) and Gram-negative (E. coli).

Two types of bacteria, Gram-positive (S. aureus) and Gram-negative (E. coli) were used. The extract was applied on fabrics at a concentration of 5, 10 and 15 g/L using the pad-dry-cure method and antibacterial activities verified by the bacterial-growth reduction method. The treated fabrics were evaluated for antimicrobial activity against the bacteria before and after 15 washing cycles. The extract was examined for molecular structural change using fourier transform infrared spectroscopy (FTIR) and physical properties of the fabric; tensile strength, elongation, air permeability, stiffness and wettability were evaluated.

Results showed treated fabrics reduces the growth of Gram-positive (S. aureus) and Gram-negative (E. coli) bacteria from 77.6%–100% before wash and 45.8%–85.2% after wash for both extract concentrations. Comparing extracts, hexane extract reduces all bacteria growth than methanol extract for both extract concentrations while S. aureus was more susceptible to antimicrobial agents than E. coli at a lower concentration. As result, the tensile strength and air permeability were relatively lower than untreated ones without affecting the comfort properties of the fabric.

This study indicates that the Moringa stenopetala seed oil extract has a strong antimicrobial activity.