Search results for: end-user trained information extraction

Authors: Yolanda Ramirez, Angel Tejada, Agustin Baidez

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In recent years, the intellectual capital reporting in higher education institutions has been acquiring progressive importance worldwide. Intellectual capital approaches becomes critical at universities, mainly due to the fact that knowledge is the main output as well as input in these institutions. Universities produce knowledge, either through scientific and technical research (the results of investigation, publications, etc.) or through teaching (students trained and productive relationships with their stakeholders). The purpose of the present paper is to identify the intangible elements about which university stakeholders demand most information. The results of a study done at Spanish universities are used to see which groups of universities have stakeholders who are more proactive to the disclosure of intellectual capital.

Keywords: intellectual capital, universities, Spain, cluster analysis

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Authors: Mina Kalantarzadeh, Claire Lockie-Williams, Caroline Howard

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DNA barcoding is increasingly used for identification of medicinal plants worldwide. In the last decade, a large number of DNA barcodes have been generated, and their application in species identification explored. The success of DNA barcoding process relies on the accuracy of the results from polymerase chain reaction (PCR) amplification step which could be negatively affected due to a presence of inhibitors or degraded DNA in herbal samples. An established DNA reference material can be used to support molecular characterisation protocols and prove system suitability, for fast and accurate identification of plant species. The present study describes the use of a novel reference material, the trnH-psbA British Pharmacopoeia Nucleic Acid Reference Material (trnH-psbA BPNARM), which was produced to aid in the identification of Ocimum tenuiflorum L., a widely used herb. During DNA barcoding of O. tenuiflorum, PCR amplifications of isolated DNA produced inconsistent results, suggesting an issue with either the method or DNA quality of the tested samples. The trnH-psbA BPNARM was produced and tested to check for the issues caused during PCR amplification. It was added to the plant material as control DNA before extraction and was co-extracted and amplified by PCR. PCR analyses revealed that the amplification was not as successful as expected which suggested that the amplification is affected by presence of inhibitors co-extracted from plant materials. Various potential issues were assessed during DNA extraction and optimisations were made accordingly. A DNA barcoding protocol for O. tenuiflorum was published in the British Pharmacopoeia 2016, which included the reference sequence. The trnH-psbA BPNARM accelerated degradation test which investigates the stability of the reference material over time demonstrated that it has been stable when stored at 56 °C for a year. Using this protocol and trnH-psbA reference material provides a fast and accurate method for identification of O. tenuiflorum. The optimisations of the DNA extraction using the trnH-psbA BPNARM provided a signposting method which can assist in overcoming common problems encountered when using molecular methods with medicinal plants.

Keywords: degradation, DNA extraction, nucleic acid reference material, trnH-psbA

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Authors: Soo Hyung Park, Seong Beom Kim, Wontae Lee, Jin Chul Joo, Jungmin Lee, Jongsoo Choi

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A newly-developed electromagnetically vibrated solid-phase microextraction (SPME) device for extracting nonpolar organic compounds from aqueous matrices was evaluated in terms of sorption equilibrium time, precision, and detection level relative to three other more conventional extraction techniques involving SPME, viz., static, magnetic stirring, and fiber insertion/retraction. Electromagnetic vibration at 300~420 cycles/s was found to be the most efficient extraction technique in terms of reducing sorption equilibrium time and enhancing both precision and linearity. The increased efficiency for electromagnetic vibration was attributed to a greater reduction in the thickness of the stagnant-water layer that facilitated more rapid mass transport from the aqueous matrix to the SPME fiber. Electromagnetic vibration less than 500 cycles/s also did not detrimentally impact the sustainability of the extracting performance of the SPME fiber. Therefore, electromagnetically vibrated SPME may be a more powerful tool for rapid sampling and solvent-free sample preparation relative to other more conventional extraction techniques used with SPME.

Keywords: electromagnetic vibration, organic compounds, precision, solid-phase microextraction (SPME), sorption equilibrium time

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

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Authors: Homayon Ahmad Panahi, Niusha Mohseni Darabi, Elham Moniri

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A new biosorbent is prepared by coupling a cibacron blue to yeast cells. The modified yeast cells with cibacron blue has been characterized by Fourier transform infrared spectroscopy (FT-IR) and elemental analysis and applied for the preconcentration and solid phase extraction of trace cadmium ion from water samples. The optimum pH value for sorption of the cadmium ions by yeast cells- cibacron blue was 5.5. The sorption capacity of modified biosorbent was 45 mg. g−1. A recovery of 98.2% was obtained for Cd(II) when eluted with 0.5 M nitric acid. The method was applied for Cd(II) preconcentration and determination in sea water sample.

Keywords: solid phase extraction, yeast cells, Nickl, isotherm study

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Authors: Pranjali Avinash Yadav-Deshmukh

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Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results.

Keywords: disaster management, extraction technique, k-means, multi-document summarization, NLP, ontology, sentence extraction

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Authors: Alexandre Chagnes

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Lithium-ion battery is the technology of choice in the development of electric vehicles. This technology is now mature, although there are still many challenges to increase their energy density while ensuring an irreproachable safety of use. For this goal, it is necessary to develop new cathodic materials that can be cycled at higher voltages and electrolytes compatible with these materials. But the challenge does not only concern the production of efficient batteries for the electrochemical storage of energy since lithium-ion battery technology relies on the use of critical and/or strategic value resources. It is, therefore, crucial to include Lithium-ion batteries development in a circular economy approach very early. In particular, optimized recycling and reuse of battery components must both minimize their impact on the environment and limit geopolitical issues related to tensions on the mineral resources necessary for lithium-ion battery production. Although recycling will never replace mining, it reduces resource dependence by ensuring the presence of exploitable resources in the territory, which is particularly important for countries like France, where exploited or exploitable resources are limited. This conference addresses the development of a new hydrometallurgical process combining leaching of cathodic material from spent lithium-ion battery in acidic chloride media and solvent extraction process. Most of recycling processes reported in the literature rely on the sulphate route, and a few studies investigate the potentialities of the chloride route despite many advantages and the possibility to develop new chemistry, which could get easier the metal separation. The leaching mechanisms and the solvent extraction equilibria will be presented in this conference. Based on the comprehension of the physicochemistry of leaching and solvent extraction, the present study will introduce a new hydrometallurgical process for the production of cobalt, nickel, manganese and lithium from spent cathodic materials.

Keywords: lithium-ion battery, recycling, hydrometallurgy, leaching, solvent extraction

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Authors: Ajayi Oluwasola Felix

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Management information system (MIS) provides information for the managerial activities in an organization. The main purpose of this research is, MIS provides accurate and timely information necessary to facilitate the decision-making process and enable the organizations planning control and operational functions to be carried out effectively. Management information system (MIS) is basically concerned with processing data into information and is then communicated to the various departments in an organization for appropriate decision-making. MIS is a subset of the overall planning and control activities covering the application of humans technologies, and procedures of the organization. The information system is the mechanism to ensure that information is available to the managers in the form they want it and when they need it.

Keywords: Management Information Systems (MIS), information technology, decision-making, MIS in Organizations

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Gracia V. Navarro, Maria V. Gonzalez, Carlos G. Reed

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The eudaimonic perspective in philosophy and psychology suggests that a good life is closely related to developing oneself in order to contribute to the well-being and happiness of other people and of the world as a whole. Educational psychology can help to achieve this through the design and validation of educative models. Since 2004, the University of Concepcion and other Chilean universities apply an educative model to train socially responsible professionals, people that in the exercise of their profession contribute to generate equity for the development and assess the impacts of their decisions, opting for those that serve the common good. The main aim is to identify if a relationship exists between achieved learning, attitudes toward social responsibility, self-attribution of socially responsible behavior, value type, professional behavior observed and, participation in a specific model to train socially responsible (SR) professionals. The Achieved Learning and Attitudes Toward Social Responsibility Questionnaire, interview with employers and Values Questionnaire and Self-attribution of SR Behavior Questionnaire is applied to 394 students and graduates, divided into experimental and control groups (trained and not trained under the educative model), in order to identify the professional behavior of the graduates. The results show that students and graduates perceive cognitive, affective and behavioral learning, with significant differences in attitudes toward social responsibility and self-attribution of SR behavior, between experimental and control. There are also differences in employers' perceptions about the professional practice of those who were trained under the model and those who were not. It is concluded that the educative model has an impact on the learning of social responsibility and educates for a full life. It is also concluded that it is necessary to identify mediating variables of the model effect.

Keywords: educative model, good life, professional social responsibility, values

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Authors: Payel Ghosh, Rama Chandra Pradhan, Sabyasachi Mishra

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Jamun (Syzygium cuminii L.) is one of the important indigenous minor fruit with high medicinal value. The jamun cultivation is unorganized and there is huge loss of this fruit every year. The perishable nature of the fruit makes its postharvest management further difficult. Due to the strong cell wall structure of pectin-protein bonds and hard seeds, extraction of juice becomes difficult. Enzymatic treatment has been commercially used for improvement of juice quality with high yield. The objective of the study was to optimize the best treatment method for juice extraction. Enzymes (Pectinase and Tannase) from different stains had been used and for each enzyme, best result obtained by using response surface methodology. Optimization had been done on the basis of physicochemical property, nutritional property, sensory quality and cost estimation. According to quality aspect, cost analysis and sensory evaluation, the optimizing enzymatic treatment was obtained by Pectinase from Aspergillus aculeatus strain. The optimum condition for the treatment was 44 ^oC with 80 minute with a concentration of 0.05% (w/w). At these conditions, 75% of yield with turbidity of 32.21NTU, clarity of 74.39%T, polyphenol content of 115.31 mg GAE/g, protein content of 102.43 mg/g have been obtained with a significant difference in overall acceptability.

Keywords: enzymatic treatment, Jamun, optimization, physicochemical property, sensory analysis

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Authors: Nkem Angela Udeani

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Despite the widespread use of synthetic dyes, natural dyes are still exploited and used to enhance its inherent aesthetic qualities as a major material for the beautification of the body. Centuries before the discovery of synthetic dye, natural dyes were the only source of dye open to mankind. Dyes are extracted from plant - leaves, roots, and barks, insect secretions, and minerals. However, research findings have made it clear that of all, plant- leaves, roots, barks or flowers are the most explored and exploited. Henna (Lawsonia innermis) is one of those plants. The experiment has also shown that henna is used in body painting in conjunction with an alkaline (Ammonium Sulphate) as a fixing agent. This of course gives a clue that if colour derived from henna is properly investigated, it may not only be used as body decoration but possibly, may have affinity to fibre substrate. This paper investigates the dyeing potentials - dyeing ability and fastness qualities of henna dye extract on cotton and linen fibres using mordants like ammonium sulphate and other alkalies (hydrosulphate and caustic soda, potash, common salt and alum). Hot and cold water and ethanol solvent were used in the extraction of the dye to investigate the most effective method of extraction, dyeing ability and fastness qualities of these extracts under room temperature. The results of the experiment show that cotton have a high rate of dye intake than linen fibre. On a similar note, the colours obtained depend most on the solvent and or the mordant used. In conclusion, hot water extraction appear more effective. While the colours obtained from ethanol and both cold and hot method of extraction range from light to dark yellow, light green to army green, there are to some extent shades of brown hues.

Keywords: dye, fabrics, henna leaves, potential

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Totik Sri Mariani, Sukrasno Isna, Tet Fatt Chia

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Purwoceng (Pimpinella pruatjan Molk) is a legend plant used for increasing stamina by Kings in Java Island, Indonesia. Purpose of this study was to perform meristem culture and detected its stigmasterol by thin layer chromatography (TLC). Our result show that meristem culture could be propagated and grew into plantlet. After extracting intact acclimatized plant derived from meristem culture by hexane, we could detected stigmasterol by TLC. For suggestion, our extraction and TLC method could be used for detecting stigmasterol in others plant.

Keywords: purwoceng (pimpinella pruatjan), meristem culture, extraction, thin layer chromatography

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Authors: Selvavinayagan Babiharan

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This research aims to develop a deep learning platform to enhance mathematics education for secondary students in Sri Lanka. The platform will be designed to incorporate interactive and user-friendly features to engage students in active learning and promote their mathematical skills. The proposed platform will be developed using TensorFlow and Keras, two widely used deep learning frameworks. The system will be trained on a large dataset of math problems, which will be collected from Sri Lankan school curricula. The results of this research will contribute to the improvement of mathematics education in Sri Lanka and provide a valuable tool for teachers to enhance the learning experience of their students.

Keywords: information technology, education, machine learning, mathematics

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Authors: Bao Trang Thi Nguyen, Stephen Moore

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A large number of Vietnamese lecturers leave their home institutions every year to pursue an education in Australia and in other countries and most of whom return home to careers back in the Vietnamese work context. However, to the authors’ best knowledge, there is little empirical knowledge about these Vietnamese returnees. Much less is about how these overseas-trained returnees continue doing research while taking a lecturing role, though research has recently received growing heightened attention in Vietnamese Higher Education institutions and returnees are an important source of human resources. The research is mixed-methods in nature with questionnaires and interviews as the main instruments of data collection. Seven-six Vietnamese returnees working from a broad range of disciplines from different higher education institutions in central Vietnam completed a questionnaire on their perceived constraints and affordances in teaching and continuing doing research upon return from their overseas education. Twenty-five of these returnees took part in a subsequent in-depth interview which lasted from 30 minutes to an hour, which further seeks understanding of their lived individual experiences and stories. The overall results show that time constraint, heavy teaching loads, and varied administrative and familial roles are among inhibiting factors. However, these factors were more constraining for some returnees more than others. Their motivations to do research varied, from passion to work pressure and self-perceived responsibilities. Above all, these were mediated by personal, institutional and disciplinary contexts. The paper argues for a nuanced understanding of returnee academics’ life as complex and layered with the multiple identities they associated themselves with and the differing trajectories they embarked on as to what they perceived important as a university lecturer. Implications for Higher Education management and administration and professional development are addressed.

Keywords: Vietnamese overseas-trained returnees, higher education, teaching, doing research, constraints, affordances

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Authors: Albert Kwabena Osei Konadu

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Pterocarpus erinaceus, also known as Rosewood, is tropical wood, endemic in forest savannah transition zones within the middle and northern portion of Ghana. Its economic viability has made it increasingly popular and in high demand, leading to widespread conservation concerns. Ghana’s forest resource management regime for these ecozones is mainly on conservation and very little on resource utilization. Consequently, commercial logging management standards are at teething stage and not fully developed, leading to a deficiency in the monitoring of logging operations and quantification of harvested trees volumes. Tree information form (TIF); a volume estimation and tracking regime, has proven to be an effective, sustainable management tool for regulating timber resource extraction in the high forest zones of the country. This work aims to generate TIF that can track and capture requisite parameters to accurately estimate the volume of harvested rosewood within forest savannah transition zones. Tree information forms were created on three scenarios of individual billets, stacked billets and conveying vessel basis. These TIFs were field-tested to deduce the most viable option for the tracking and estimation of harvested volumes of rosewood using the smallian and cubic volume estimation formula. Overall, four districts were covered with individual billets, stacked billets and conveying vessel scenarios registering mean volumes of 25.83m3,45.08m3 and 32.6m3, respectively. These adduced volumes were validated by benchmarking to assigned volumes of the Forestry Commission of Ghana and known standard volumes of conveying vessels. The results did indicate an underestimation of extracted volumes under the quotas regime, a situation that could lead to unintended overexploitation of the species. The research revealed conveying vessels route is the most viable volume estimation and tracking regime for the sustainable management of the Pterocarpous erinaceus species as it provided a more practical volume estimate and data extraction protocol.

Keywords: convention on international trade in endangered species, cubic volume formula, forest transition savannah zones, pterocarpus erinaceus, smallian’s volume formula, tree information form

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Authors: Gideon C. Joseph

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The ability to localize mobile devices is quite important, as some applications may require location information of these devices to operate or deliver better services to the users. Although there are several ways of acquiring location data of mobile devices, the WLAN fingerprinting approach has been considered in this work. This approach uses the Received Signal Strength Indicator (RSSI) measurement as a function of the position of the mobile device. RSSI is a quantitative technique of describing the radio frequency power carried by a signal. RSSI may be used to determine RF link quality and is very useful in dense traffic scenarios where interference is of major concern, for example, indoor environments. This research aims to design a system that can predict the location of a mobile device, when supplied with the mobile’s RSSIs. The developed system takes as input the RSSIs relating to the mobile device, and outputs parameters that describe the location of the device such as the longitude, latitude, floor, and building. The relationship between the Received Signal Strengths (RSSs) of mobile devices and their corresponding locations is meant to be modelled; hence, subsequent locations of mobile devices can be predicted using the developed model. It is obvious that describing mathematical relationships between the RSSIs measurements and localization parameters is one option to modelling the problem, but the complexity of such an approach is a serious turn-off. In contrast, we propose an intelligent system that can learn the mapping of such RSSIs measurements to the localization parameters to be predicted. The system is capable of upgrading its performance as more experiential knowledge is acquired. The most appealing consideration to using such a system for this task is that complicated mathematical analysis and theoretical frameworks are excluded or not needed; the intelligent system on its own learns the underlying relationship in the supplied data (RSSI levels) that corresponds to the localization parameters. These localization parameters to be predicted are of two different tasks: Longitude and latitude of mobile devices are real values (regression problem), while the floor and building of the mobile devices are of integer values or categorical (classification problem). This research work presents artificial neural network based intelligent systems to model the relationship between the RSSIs predictors and the mobile device localization parameters. The designed systems were trained and validated on the collected WLAN fingerprint database. The trained networks were then tested with another supplied database to obtain the performance of trained systems on achieved Mean Absolute Error (MAE) and error rates for the regression and classification tasks involved therein.

Keywords: indoor localization, WLAN fingerprinting, neural networks, classification, regression

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Authors: Anton V. Dolzhenko, Tahir Mehmood Khan

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An ability to make an evidence-based decision is a critically important skill required for practicing pharmacists. The development of this skill is incorporated into the pharmacy curriculum. We aimed in our study to estimate perception of pharmacy students regarding herbal medicines and their ability to assess information on herbal medicines professionally. The current Monash University curriculum in Pharmacy does not provide comprehensive study material on herbal medicines and students should find their way to find information, assess its quality and make a professional decision. In the Pharmacy course, students are trained how to apply this process to conventional medicines. In our survey of 93 undergraduate students from year 1-4 of Pharmacy course at Monash University Malaysia, we found that students’ view on herbal medicines is sometimes associated with common beliefs, which affect students’ ability to make evidence-based conclusions regarding the therapeutic potential of herbal medicines. The use of herbal medicines is widespread and 95.7% of the participated students have prior experience of using them. In the scale 1 to 10, students rated the importance of acquiring herbal medicine knowledge for them as 8.1±1.6. More than half (54.9%) agreed that herbal medicines have the same clinical significance as conventional medicines in treating diseases. Even more, students agreed that healthcare settings should give equal importance to both conventional and herbal medicine use (80.6%) and that herbal medicines should comply with strict quality control procedures as conventional medicines (84.9%). The latter statement also indicates that students consider safety issues associated with the use of herbal medicines seriously. It was further confirmed by 94.6% of students saying that the safety and toxicity information on herbs and spices are important to pharmacists and 95.7% of students admitting that drug-herb interactions may affect therapeutic outcome. Only 36.5% of students consider herbal medicines as s safer alternative to conventional medicines. The students use information on herbal medicines from various sources and media. Most of the students (81.7%) obtain information on herbal medicines from the Internet and only 20.4% mentioned lectures/workshop/seminars as a source of such information. Therefore, we can conclude that students attained the skills on the critical assessment of therapeutic properties of conventional medicines have a potential to use their skills for evidence-based decisions regarding herbal medicines.

Keywords: evidence-based decision, pharmacy education, student perception, traditional medicines

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Authors: Renukswamy Chikkamath, Vishvapalsinhji Ramsinh Parmar, Christoph Hewel, Markus Endres

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Given a patent document, identifying distinct semantic annotations is an interesting research aspect. Text annotation helps the patent practitioners such as examiners and patent attorneys to quickly identify the key arguments of any invention, successively providing a timely marking of a patent text. In the process of manual patent analysis, to attain better readability, recognising the semantic information by marking paragraphs is in practice. This semantic annotation process is laborious and time-consuming. To alleviate such a problem, we proposed a dataset to train machine learning algorithms to automate the highlighting process. The contributions of this work are: i) we developed a multi-class dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, and iv) future path to extend this work using Deep Learning and domain-specific pre-trained language models to develop a tool to highlight is provided. This work assists patent practitioners in highlighting semantic information automatically and aids in creating a sustainable and efficient patent analysis using the aptitude of machine learning.

Keywords: machine learning, patents, patent sentiment analysis, patent information retrieval

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Authors: Olga Kononova, Sergey Lyapin

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Global information society requirements are transparency and reliability of data, as well as ability to manage information resources independently; particularly to search, to analyze, to evaluate information, thereby obtaining new expertise. Moreover, it is satisfying the society information needs that increases the efficiency of the enterprise management and public administration. The study of structurally organized thematic and semantic contexts of different types, automatically extracted from unstructured data, is one of the important tasks for the application of information technologies in education, science, culture, governance and business. The objectives of this study are the contextual knowledge typologization, selection or creation of effective tools for extracting and analyzing contextual knowledge. Explication of various kinds and forms of the contextual knowledge involves the development and use full-text search information systems. For the implementation purposes, the authors use an e-library 'Humanitariana' services such as the contextual search, different types of queries (paragraph-oriented query, frequency-ranked query), automatic extraction of knowledge from the scientific texts. The multifunctional e-library «Humanitariana» is realized in the Internet-architecture in WWS-configuration (Web-browser / Web-server / SQL-server). Advantage of use 'Humanitariana' is in the possibility of combining the resources of several organizations. Scholars and research groups may work in a local network mode and in distributed IT environments with ability to appeal to resources of any participating organizations servers. Paper discusses some specific cases of the contextual knowledge explication with the use of the e-library services and focuses on possibilities of new types of the contextual knowledge. Experimental research base are science texts about 'e-government' and 'computer games'. An analysis of the subject-themed texts trends allowed to propose the content analysis methodology, that combines a full-text search with automatic construction of 'terminogramma' and expert analysis of the selected contexts. 'Terminogramma' is made out as a table that contains a column with a frequency-ranked list of words (nouns), as well as columns with an indication of the absolute frequency (number) and the relative frequency of occurrence of the word (in %% ppm). The analysis of 'e-government' materials showed, that the state takes a dominant position in the processes of the electronic interaction between the authorities and society in modern Russia. The media credited the main role in these processes to the government, which provided public services through specialized portals. Factor analysis revealed two factors statistically describing the used terms: human interaction (the user) and the state (government, processes organizer); interaction management (public officer, processes performer) and technology (infrastructure). Isolation of these factors will lead to changes in the model of electronic interaction between government and society. In this study, the dominant social problems and the prevalence of different categories of subjects of computer gaming in science papers from 2005 to 2015 were identified. Therefore, there is an evident identification of several types of contextual knowledge: micro context; macro context; dynamic context; thematic collection of queries (interactive contextual knowledge expanding a composition of e-library information resources); multimodal context (functional integration of iconographic and full-text resources through hybrid quasi-semantic algorithm of search). Further studies can be pursued both in terms of expanding the resource base on which they are held, and in terms of the development of appropriate tools.

Keywords: contextual knowledge, contextual search, e-library services, frequency-ranked query, paragraph-oriented query, technologies of the contextual knowledge extraction

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Authors: Arun Das

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One of the basic objectives of Independent India five decades ago was to meet the increasing demand for food to its growing population. Self-sufficiency was accomplished towards food production and it was attained through launching green revolution program. The green revolution repercussions were not realized at that moment. Many projects were undertaken. Especially, major and minor irrigation projects were executed to harness the river water in the dry land regions of Karnataka. In the elevated topographical lands, extraction of underground water was a solace given by the government to protect the interest of the dry land farmers whose land did not come under the command area. Free borewell digging, pump sets, and electricity were provided. Thus, the self-sufficiency was achieved. Contrary to this, the Continuous long-term extraction of water for agriculture from bore well and in the irrigated tracks has lead to two-way effect such as soil leeching (Alkalinity and Salinity), secondly, depleted underground water to incredible deeps has pushed the natural process to an un-reparable damage which in turn the nature lost to support even a tiny plants like grass to grow, discouraging human and animal habitation, Both the process is silently turning southwestern, central, northeastern and north western regions of Karnataka into desert. The grave situation of Karnataka green revolution is addressed in this paper to alert reckless use of water and also some of the suggestions are recommended based on the ground information.

Keywords: alkalinity, desertification, green revolution, salinity, water

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Authors: Eru Ihie Joel

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This study was carried out to investigate the influence of information technology on financial management practice in secondary schools for transformation. In Zone C Senatorial District of Benue state. The study answered four research questions and tested four hypotheses. Related literature was reviewed to show the gap to be filled in the study. The population was 196 respondents made up of principals and finance clerks of secondary schools. The descriptive survey was adopted for the study. A structured 20 item questionnaire (IITFMPSQ) was constructed and used to collect date for the study. Data obtained were analyzed using descriptive and inferential statistic. Mean and standard deviation were used to analyze the research question while the chi- square (x2) test of goodness of fit was used to test the hypothesis. The major findings revealed that the use of computer system significantly influences budgeting in secondary schools in zone senatorial district of Benue State for transformation. It was also established that the use of internet facilities influences the funding of secondary schools for transformation in the zone. Based on the findings of the study, it was recommended among other things that administrators and teachers in schools should be trained to make effective use of the computer in budgeting so as to facilitate delegations, control, evaluation, accountability for transformation. It was further suggested that the study be replicated on the effective use of information communication teaching (ITC) in teaching and learning in secondary school for transformation.

Keywords: influence, finance, management, technology

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Authors: Omasan Urhie Urhie, Adenipekun C. O, Eke W., Ogwu K., Erinle K. O

Abstract:

Polychlorinated Biphenyls (PCBs) are Persistent organic pollutants (POPs) that are very toxic; they possess ability to accumulate in soil and in human tissues hence resulting in health issues like birth defect, reproductive disorder and cancer. The air is polluted by PCBs through volatilization and dispersion; they also contaminate soil and sediments and are not easily degraded. Soil samples were collected from a depth of 0-15 cm from three substations (Warri, Ughelli and Ibadan) of Power Holding Company of Nigeria (PHCN) where old transformers were dumped in Nigeria. Extraction and cleanup of soil samples were conducted using Accelerated Solvent Extraction (ASE) with Pressurized Liquid extraction (PLE). The concentration of PCBs was determined using gsas chromatography/mass spectrometry (GC/MS). Mean total PCB concentrations in the soil samples increased in the order Ughelli ˂ Ibadan˂ Warri, 2.457757ppm Ughelli substation 4.198926ppm, for Ibadan substation and 14.05065ppm at Warri substation. In the Warri samples, PCB-167 was the most abundant at about 30% (4.28086ppm) followed by PCB-157 at about 20% (2.77871), of the total PCB concentrations (14.05065ppm). Of the total PCBs in the Ughelli and Ibadan samples, PCB-156 was the most abundant at about 44% and 40%, respectively. This study provides a baseline report on the presence of PCBs in the vicinity of abandoned electrical power facilities in different cities in Nigeria.

Keywords: polychlorintated biphenyls, persistent organic pollutants, soil, transformer

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Authors: S. G. Vasantharaju, Viswanath Guptha, Raghavendra Shetty

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Introduction: Aminophylline is a methylxanthine derivative belonging to the class bronchodilator. From the literature survey, reported methods reveals the solid phase extraction and liquid liquid extraction which is highly variable, time consuming, costly and laborious analysis. Present work aims to develop a simple, highly sensitive, precise and accurate high-performance liquid chromatography method for the quantification of Aminophylline in rat plasma samples which can be utilized for preclinical studies. Method: Reverse Phase high-performance liquid chromatography method. Results: Selectivity: Aminophylline and the internal standard were well separated from the co-eluted components and there was no interference from the endogenous material at the retention time of analyte and the internal standard. The LLOQ measurable with acceptable accuracy and precision for the analyte was 0.5 µg/mL. Linearity: The developed and validated method is linear over the range of 0.5-40.0 µg/mL. The coefficient of determination was found to be greater than 0.9967, indicating the linearity of this method. Accuracy and precision: The accuracy and precision values for intra and inter day studies at low, medium and high quality control samples concentrations of aminophylline in the plasma were within the acceptable limits Extraction recovery: The method produced consistent extraction recovery at all 3 QC levels. The mean extraction recovery of aminophylline was 93.57 ± 1.28% while that of internal standard was 90.70 ± 1.30%. Stability: The results show that aminophylline is stable in rat plasma under the studied stability conditions and that it is also stable for about 30 days when stored at -80˚C. Pharmacokinetic studies: The method was successfully applied to the quantitative estimation of aminophylline rat plasma following its oral administration to rats. Discussion: Preclinical studies require a rapid and sensitive method for estimating the drug concentration in the rat plasma. The method described in our article includes a simple protein precipitation extraction technique with ultraviolet detection for quantification. The present method is simple and robust for fast high-throughput sample analysis with less analysis cost for analyzing aminophylline in biological samples. In this proposed method, no interfering peaks were observed at the elution times of aminophylline and the internal standard. The method also had sufficient selectivity, specificity, precision and accuracy over the concentration range of 0.5 - 40.0 µg/mL. An isocratic separation technique was used underlining the simplicity of the presented method.

Keywords: Aminophyllin, preclinical pharmacokinetics, rat plasma, RPHPLC

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Authors: Yi Anson Lam, Siu Ming Yiu, Kam Pui Chow

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Digital evidence has been tendering to courts at an exponential rate in recent years. As an industrial practice, most digital evidence is extracted and preserved using specialized and well-accepted forensics tools. On the other hand, the advancement in technologies enables the creation of quite a few emerging platforms such as Telegram, Signal etc. Existing (well-accepted) forensics tools were not designed to extract evidence from these emerging platforms. While new forensics tools require a significant amount of time and effort to be developed and verified, this paper tries to address how to fill this gap using quick-fix alternative methods for digital evidence collection (e.g., based on APIs provided by Apps) and discuss issues related to the admissibility of this evidence to courts with support from international courts’ stance and the circumstances of accepting digital evidence using these proposed alternatives.

Keywords: extraction, digital evidence, laws, investigation

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Authors: Nikita Rajendra Sharma, Jai Prakash Kushvah, Gerhard Rinkenauer

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Driving a car is a discrete aiming movement in which drivers aim at successful extraction of relevant information and elimination of potentially distracting one. It is the motor preparation which enables one to react to certain stimuli onsite by allowing perceptual process for optimal adjustment. Drivers prepare their responses according to the available resources of advanced and ongoing information to drive efficiently. It requires constant programming and reprogramming of the motor system. The reaction time (RT) is shorter when a response signal is preceded by a warning signal. The reason behind this reduced time in responding to targets is that the warning signal causes the participant to prepare for the upcoming response by updating the motor program before the execution. While performing the primary task of changing lanes while driving, the simultaneous occurrence of additional information during the presentation of cues (congruent or incongruent with respect to target cue) might impact the motor preparation and execution. The presence of additional information (other than warning or response signal) between warning signal and imperative stimulus influences human motor preparation to a reasonable extent. The present study was aimed to assess the impact of congruent and incongruent additional information (with respect to imperative stimulus) on driving performance (reaction time, steering wheel amplitude, and steering wheel duration) during a lane change task. implementing movement pre-cueing paradigm. 22 young valid car-drivers (Mage = 24.1+/- 3.21 years, M = 10, F = 12, age-range 21-33 years) participated in the study. The study revealed that additional information influenced the overall driving performance as potential distractors and relevant information. Findings suggest that the events of additional information relatively influenced the reaction time and steering wheel angle as potential distractor or irrelevant information. Participants took longer to respond, and higher steering wheel angles were reported for targets coupled with additional information in comparison with warning signs preceded by potential distractors and the participants' response time was more for a higher number of lanes (2 Lanes > 1 Lane). The same additional information appearing interchangeably at warning signals and targets worked as relevant information facilitating the motor programming in the trails where they were congruent with the direction of lane change direction.

Keywords: additional information, lane change task, motor preparation, movement pre-cueing, reaction time, steering wheel amplitude

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Authors: Marivane Turim Koschevic, Maycon dos Santos, Marcello Lima Bertuci, Farayde Matta Fakhouri, Silvia Maria Martelli

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Natural fibers are rich raw materials in cellulose and abundant in the world, its use for the cellulose nanocrystals extraction is promising as an example cited is the cattail, macrophyte native weed in South America. This study deals with the pre-treatment cattail of crushed fibers, at six different methods of mercerization, followed by the use of bleaching. As a result, have found The positive effects of treating fibers by means of optical microscopy and spectroscopy, Fourier transform (FTIR). The sample selected for future testing of cellulose nanocrystals extraction was treated in 2.5% NaOH for 2 h, 60 °C in the first stage and 30vol H2O2, NaOH 5% in the proportion 30/70% (v/v) for 1 hour 60 °C, followed by treatment at 50/50% (v/v) 15 minutes, 50°C, with the same constituents of the solution.

Keywords: cellulose nanocrystal, chemical treatment, mercerization, natural fibers

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: S. Lekhavat, T. Kajsongkram, S. Sang-han

Abstract:

Dried ginger was extracted and investigated the effect of ethanol concentration and enzyme pre-treatment on its bioactive compounds in solvent extraction process. Sliced fresh gingers were dried by oven dryer at 70 °C for 24 hours and ground to powder using grinder which their size were controlled by passing through a 20-mesh sieve. In enzyme pre-treatment process, ginger powder was sprayed with 1 % (w/w) cellulase and then was incubated at 45 °C for 2 hours following by extraction process using ethanol at concentration of 0, 20, 40, 60 and 80 % (v/v), respectively. The ratio of ginger powder and ethanol are 1:9 and extracting conditions were controlled at 80 °C for 2 hours. Bioactive compounds extracted from ginger, either enzyme-treated or non enzyme-treated samples, such as total phenolic content (TPC), 6-Gingerol (6 G), 6-Shogaols (6 S) and antioxidant activity (IC50 using DPPH assay), were examined. Regardless of enzyme treatment, the results showed that 60 % ethanol provided the highest TPC (20.36 GAE mg /g. dried ginger), 6G (0.77%), 6S (0.036 %) and the lowest IC50 (625 μg/ml) compared to other ratios of ethanol. Considering the effect of enzyme on bioactive compounds and antioxidant activity, it was found that enzyme-treated sample has more 6G (0.17-0.77 %) and 6S (0.020-0.036 %) than non enzyme-treated samples (0.13-0.77 % 6G, 0.015-0.036 % 6S). However, the results showed that non enzyme-treated extracts provided higher TPC (6.76-20.36 GAE mg /g. dried ginger) and Lowest IC50 (625-1494 μg/ml ) than enzyme-treated extracts (TPC 5.36-17.50 GAE mg /g. dried ginger, IC50 793-2146 μg/ml).

Keywords: antioxidant activity, enzyme, extraction, ginger

Procedia PDF Downloads 256