Synthesis and characterization of poppy seed oil methyl esters

Response surface methodology(RSM) was used to determine the optimum conditions of the methanolysis of crude poppy seed oil using Na OCH3 as catalyst. The experiments were run according to five levels, four variable central composite rotatable design(CCRD) using RSM. The reaction variables, i.e., molar ratio of methanol/oil(3:1–9:1), catalyst concentration(0.5 wt%–1.25 wt% Na OCH3), reaction temperature(25–65 °C), and reaction time(20–90 min) were studied. We demonstrated that the molar ratio of methanol/oil, catalyst concentration,and reaction temperature were the significant parameters affecting the yield of poppy seed oil methyl esters(PSOMEs). The optimum transesterification reaction conditions, established using the RSM, which offered a89.35% PSOME yield, were found to be 7.5:1 molar ratio of methanol/oil, 0.75% catalyst concentration, 45 °C reaction temperature, and 90 min reaction time. The proposed process provided an average biodiesel yield of more than 85%. A linear correlation was constructed between the observed and predicted values of the yield.The gas chromatography(GC) analyses have shown that PSOMEs contain linoleic-, oleic-, palmitic-, and stearic-acids as main fatty acids. The FTIR spectrum of the PSOMEs was also analyzed to confirm the completion of the transesterification reaction. The fuel properties of the PSOMEs were discussed in light of biodiesel standards(ASTM D 6751 and EN 14214).     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Formation Control and Tracking for Co-operative Robots with Non-holonomic Constraints

This paper mainly addresses formation control problem of non-holonomic systems in an optimized manner. Instead of using linearization to solve this problem approximately, we designed control laws with guaranteed global convergence by leveraging nonlinear transformations. Under this nonlinear transformation, consensus of non-holonomic robots can be converted into a stabilization problem, to which optimal treatment applies. This concept is then extended to the formation control and tracking problem for a team of robots following leader-follower strategy. A trajectory generator prescribes the feasible motion of virtual reference robot, a decentralized control law is used for each robot to track the reference while maintaining the formation. The asymptotic convergence of follower robots to the position and orientation of the reference robot is ensured using the Lyapunov function which is also generated using chained form differential equations. In order to witness the efficacy of the scheme, simulations results are presented for Unicycle and Car-like robots.     

MIRRE approach: nonlinear and multimodal exploration of MIR aggregated search results

Multimedia Tools and Applications - Nowadays, web users frequently explore multimedia contents to satisfy their information needs. The exploration approaches usually provide linear interaction...     

Soil–conduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits

Neural Computing and Applications - Marston’s load theory is commonly used for understanding the soil–conduit interaction. However, there are no practical methods available which can...     

Sustaining a Community Computing Infrastructure for Online Teacher Professional Development: A Case Study of Designing Tapped In

Community computing has recently grown to become a major research area in human–computer interaction. One of the objectives of community computing is to support computer supported cooperative work among distributed collaborators working toward shared professional goals in online communities of practice. A core issue in designing and developing community computing infrastructures – the underlying socio-technical layer that supports communitarian activities – is sustainability. Many community computing initiatives fail because the underlying infrastructure does not meet end user requirements; the community is unable to maintain a critical mass of users consistently over time; it generates insufficient social capital to support significant contributions by members of the community; or, as typically happens with funded initiatives, financial and human capital resource become unavailable to further maintain the infrastructure. Based on more than nine years of design experience with Tapped In – an online community of practice for education professionals – we present a case study that discusses four design interventions that have sustained the Tapped In infrastructure and its community to date. These interventions represent broader design strategies for developing online environments for professional communities of practice.     

Autonomous mapping of HL7 RIM and relational database schema

Healthcare systems need to share information within and across the boundaries in order to provide better care to the patients. For this purpose, they take advantage of the full potential of current state of the art in healthcare standards providing interoperable solutions. HL7 V3 specification is an international message exchange and interoperability standard. HL7 V3 messages exchanged between healthcare applications are ultimately recorded into local healthcare databases, mostly in relational databases. In order to bring these relational databases in compliance with HL7, mappings between HL7 RIM (Reference Information Model) and relational database schema are required. Currently, RIM and database mapping is largely performed manually, therefore it is tedious, time consuming, error prone and expensive process. It is a challenging task to determine all correspondences between RIM and schema automatically because of extreme heterogeneity issues in healthcare databases. To reduce the amount of manual efforts as much as possible, autonomous mapping approaches are required. This paper proposes a technique that addresses the aforementioned mapping issue and aligns healthcare databases to HL7 V3 RIM specifications. Furthermore, the proposed technique has been implemented as a working application and tested on real world healthcare systems. The application loads the target healthcare schema and then identifies the most appropriate match for tables and the associated fields in the schema by using domain knowledge and the matching rules defined in the Mapping Knowledge Repository. These rules are designed to handle the complexity of semantics found in healthcare databases. The GUI allows users to view and edit/re-map the correspondences. Once all the mappings are defined, the application generates Mapping Specification, which contains all the mapping information i.e. database tables and fields with associated RIM classes and attributes. In order to enable the transactions, the application is facilitated with the autonomous code generation from the Mapping Specification. The Code Generator component focuses primarily on generating custom classes and hibernate mapping files against the runtime system to retrieve and parse the data from the data source—thus allows bi-directional HL7 to database communication, with minimum programming required. Our experimental results show 35–65% accuracy on real laboratory systems, thus demonstrating the promise of the approach. The proposed scheme is an effective step in bringing the clinical databases in compliance with RIM, providing ease and flexibility.     

A framework to explore results in multiple media information aggregated search

On web information exists in the form of text, audio, image, and video objects often referred to multiple media objects. Vertical web search provides the search of multiple media information usually via keyword-based queries. The search results in different media formats usually presented in separate panels/tabs; integration is mostly non-blended. Therefore, results exploration via vertical web search engines require the selection of a source and scrolling of a linear ranked list of results. Relationships in the results presented in separate panels/tabs are mostly not considered. Search aggregations unify results from several vertical web sources via blended integration, but exploration still requires scrolling of a linear ranked list. Multimedia search frameworks provide the exploration of results in different media formats but more focused towards the retrieval issues. We proposed a multiple media information search framework to address issues, particularly in aggregated search. Our search framework provides a mechanism to explore results via non-linear ways. The search framework realized by suggesting a framework architecture design and instantiating a search tool. The effectiveness of blended integration and browsing is measured via precision and click through rate respectively. Search task support in results exploration mechanism measured via task-based evaluation. We also validated the conformance of various search/exploration attributes discussed in the state-of-the-art in our frameworks.     

Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province,Pakistan

Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole Punjab from 2001 to 2014 by excluding the respective year from training set. Sunshine hour data were not available for all districts and therefore we tested using temperature data and average latitude-based solar radiation as surrogates. The root mean square errors (RMSEs) of the forecast results of the whole of Punjab province were 147.7 kg ha^?1 and 148.7 kg ha^?1 with a mean error of less than 5% using average and generic RFs, respectively. Forecasts for individual districts showed R² of 0.95 with RMSE of 175.6 kg ha^?1 and 5.86% mean error.     

Chaos based key expansion function for block ciphers

The Key Expansion Function is a vital constituent component of any block cipher. Many of Key Expansion Functions generate subkeys through the algorithms which are based on Feistel or Substitution Permutation Network (SPN) structures against which cryptanalytic methods have been well researched. In this very paper, an efficient method for generating subkeys based on chaotic maps has been suggested. The phenomenon behind the proposed Key Expansion Function is the mixing property of Tent Map. Using chaotic binary sequences, the proposed Key Expansion Function satisfies the specific statistical and cryptographic properties of chaotic generators. A new Bit Extraction Technique based on IEEE-754 Floating-point Standard (binary32) is used to extract the bits of subkeys from the chaotic binary sequences. The generated subkeys are then analyzed. The results show that the given Chaos-based Key Expansion Function is well protected and fully strengthened in all respects.