Search results for: uncertain lead times and processing times

Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio

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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.

Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction

Procedia PDF Downloads 310

Authors: O. Ekrami, P. Claes, S. Van Dongen

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Fluctuating asymmetry (FA) has been studied for many years as an indicator of developmental stability or ‘genetic quality’ based on the assumption that perfect symmetry is ideally the expected outcome for a bilateral organism. Further studies have also investigated the possible link between FA and attractiveness or levels of masculinity or femininity. These hypotheses have been mostly examined using 2D images, and the structure of interest is usually presented using a limited number of landmarks. Such methods have the downside of simplifying and reducing the dimensionality of the structure, which will in return increase the error of the analysis. In an attempt to reach more conclusive and accurate results, in this study we have used high-resolution 3D scans of human faces and have developed an algorithm to measure and localize FA, taking a spatially-dense approach. A symmetric spatially dense anthropometric mask with paired vertices is non-rigidly mapped on target faces using an Iterative Closest Point (ICP) registration algorithm. A set of 19 manually indicated landmarks were used to examine the precision of our mapping step. The protocol’s accuracy in measurement and localizing FA is assessed using simulated faces with known amounts of asymmetry added to them. The results of validation of our approach show that the algorithm is perfectly capable of locating and measuring FA in 3D simulated faces. With the use of such algorithm, the additional captured information on asymmetry can be used to improve the studies of FA as an indicator of fitness or attractiveness. This algorithm can especially be of great benefit in studies of high number of subjects due to its automated and time-efficient nature. Additionally, taking a spatially dense approach provides us with information about the locality of FA, which is impossible to obtain using conventional methods. It also enables us to analyze the asymmetry of a morphological structures in a multivariate manner; This can be achieved by using methods such as Principal Components Analysis (PCA) or Factor Analysis, which can be a step towards understanding the underlying processes of asymmetry. This method can also be used in combination with genome wide association studies to help unravel the genetic bases of FA. To conclude, we introduced an algorithm to study and analyze asymmetry in human faces, with the possibility of extending the application to other morphological structures, in an automated, accurate and multi-variate framework.

Keywords: developmental stability, fluctuating asymmetry, morphometrics, 3D image processing

Procedia PDF Downloads 141

Authors: Pothula Madhusudhan Reddy

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Introduction: Yoga is a way of life. of uniting the mind, body and soul. It is also an art of living the right way. The techniques of Yoga are very practical, so they can always be applied. This is the reason why Yoga has been practiced for thousands of years and is still valid today. Importance of Yoga: Yoga that helps to inculcate healthy habits and adopt a healthy lifestyle to achieve good health Research Aim: The aim of this study is to explore the potential benefits of yoga for holistic health and wellbeing, both at an individual and societal level The ultimate goal of human being is to attain the state of perfect freedom from the shackles of ignorance, which is the generator of all the pangs and miseries of life. Methodology: This research follows a thematic and practical experience approach. Yoga includes body postures and movements (stretching), breathing practices, imagery, meditation, and progressive relaxation techniques. Data Collection: The data for this research is collected through a combination of literature review, expert interviews, and practical yoga sessions. The literature review provides a comprehensive understanding of the principles and practices of yoga, while expert interviews offer insights from experienced practitioners. Practical yoga sessions allow for first hand experiences and observations, facilitating a deeper understanding of the subject matter. Analysis Procedures: The collected data is analyzed thematically, where key themes and patterns related to the benefits and effects of yoga on holistic health and wellbeing are identified. The findings are then interpreted and synthesized to draw meaningful conclusions. Questions Addressed: This research addresses the following questions: What are the potential benefits of yoga for holistic health and wellbeing? How does yoga promote rejuvenate the body, mind, and senses? What are the implications of a society embracing yoga for overall societal wellbeing and happiness? Findings: The research highlights that practicing yoga can lead to increased awareness of the body, mind, and senses. It promotes overall physical and mental health, helping individuals achieve a state of happiness and contentment. Moreover, the study emphasizes that a society embracing yoga can contribute to the development of a healthy and happy community. Theoretical Importance: The study of yoga for holistic health and wellbeing holds theoretical importance as it provides insights into the science of yoga and its impact on individuals and society. It contributes to the existing body of knowledge on the subject and further establishes yoga as a potential tool for enhancing overall wellness. Conclusion: The study concludes that yoga is a powerful practice for achieving holistic health and wellbeing. This research provides valuable insights into the science of yoga and its potential as a tool for promoting overall wellness.

Keywords: yoga, asana, pranayama, meditation

Procedia PDF Downloads 82

Authors: Shahrukh Ahmad, Purnendu Bose

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Algal-Bacterial photobioreactors (ABPBRs) have emerged as a promising technology for sustainable biomass production and wastewater treatment. Nutrient removal is seldom done in sewage treatment plants and large volumes of wastewater which still have nutrients are being discharged and that can lead to eutrophication. That is why ABPBR plays a vital role in wastewater treatment. However, improving the efficiency of ABPBR remains a significant challenge. This study aims to enhance ABPBR efficiency by focusing on two key aspects: nutrient removal and cost-effective optimization of the light source. By integrating nutrient removal and cost analysis for light source optimization, this study proposes practical strategies for improving ABPBR efficiency. To reduce organic carbon and convert ammonia to nitrates, domestic wastewater from a 130 MLD sewage treatment plant (STP) was aerated with a hydraulic retention time (HRT) of 2 days. The treated supernatant had an approximate nitrate and phosphate values of 16 ppm as N and 6 ppm as P, respectively. This supernatant was then fed into the ABPBR, and the removal of nutrients (nitrate as N and phosphate as P) was observed using different colored LED bulbs, namely white, blue, red, yellow, and green. The ABPBR operated with a 9-hour light and 3-hour dark cycle, using only one color of bulbs per cycle. The study found that the white LED bulb, with a photosynthetic photon flux density (PPFD) value of 82.61 µmol.m-2 .sec-1 , exhibited the highest removal efficiency. It achieved a removal rate of 91.56% for nitrate and 86.44% for phosphate, surpassing the other colored bulbs. Conversely, the green LED bulbs showed the lowest removal efficiencies, with 58.08% for nitrate and 47.48% for phosphate at an HRT of 5 days. The quantum PAR (Photosynthetic Active Radiation) meter measured the photosynthetic photon flux density for each colored bulb setting inside the photo chamber, confirming that white LED bulbs operated at a wider wavelength band than the others. Furthermore, a cost comparison was conducted for each colored bulb setting. The study revealed that the white LED bulb had the lowest average cost (Indian Rupee)/light intensity (µmol.m-2 .sec-1 ) value at 19.40, while the green LED bulbs had the highest average cost (INR)/light intensity (µmol.m-2 .sec-1 ) value at 115.11. Based on these comparative tests, it was concluded that the white LED bulbs were the most efficient and costeffective light source for an algal photobioreactor. They can be effectively utilized for nutrient removal from secondary treated wastewater which helps in improving the overall wastewater quality before it is discharged back into the environment.

Keywords: algal bacterial photobioreactor, domestic wastewater, nutrient removal, led bulbs

Procedia PDF Downloads 79

Authors: Disha Bhanot, Vinish Kathuria

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This study focusses on the issue of distress sale in horticulture sector in India, which faces unique challenges, given the perishable nature of horticulture crops, seasonal production and paucity of post-harvest produce management links. Distress sale, from a farmer’s perspective may be defined as urgent sale of normal or distressed goods, at deeply discounted prices (way below the cost of production) and it is usually characterized by unfavorable conditions for the seller (farmer). The small and marginal farmers, often involved in subsistence farming, stand to lose substantially if they receive lower prices than expected prices (typically framed in relation to cost of production). Distress sale maximizes price uncertainty of produce leading to substantial income loss; and with increase in input costs of farming, the high variability in harvest price severely affects profit margin of farmers, thereby affecting their survival. The objective of this study is to model the occurrence of distress sale by tomato cultivators in the Indian state of Maharashtra, against the background of differential access to set of factors such as - capital, irrigation facilities, warehousing, storage and processing facilities, and institutional arrangements for procurement etc. Data is being collected using primary survey of over 200 farmers in key tomato growing areas of Maharashtra, asking information on the above factors in addition to seeking information on cost of cultivation, selling price, time gap between harvesting and selling, role of middleman in selling, besides other socio-economic variables. Farmers selling their produce far below the cost of production would indicate an occurrence of distress sale. Occurrence of distress sale would then be modelled as a function of farm, household and institutional characteristics. Heckman-two-stage model would be applied to find the probability/likelihood of a famer falling into distress sale as well as to ascertain how the extent of distress sale varies in presence/absence of various factors. Findings of the study would recommend suitable interventions and promotion of strategies that would help farmers better manage price uncertainties, avoid distress sale and increase profit margins, having direct implications on poverty.

Keywords: distress sale, horticulture, income loss, India, price uncertainity

Procedia PDF Downloads 243

Authors: Ismaila Haruna

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The global downfall in fossil energy prices and dwindling oil reserves in Nigeria has ignited interest in the search for alternative sources of foreign income for the country. Solid minerals, particularly Uranium and other base metals like Lead and Zinc have been considered as potentially good options. Several occurrences of this mineral have been discovered in both the sedimentary and granitic rocks of the Hawal and Adamawa Massifs as well as in the adjoining Benue Trough in northeastern Nigeria. However, the paucity of geochemical data and consequent poor petrogenetic knowledge of the granitoids in this region has made exploration works difficult. Song, a small area within the Hawal Massif, was mapped and the collected samples chemically determined in Activation Laboratory, Canada through fusion dissolution technique of Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Field mapping results show that the area is underlain by Granites, diorites with pockets of gneisses and pegmatites and that these rocks consists of microcline, quartz, plagioclase, biotite, hornblende, pyroxene and accessory apatite, zircon, sphene, magnetite and opaques in various proportions. Geochemical data show continous compositional variation from diorite to granites within silica range of 52.69 to 76.04 wt %. Plot of the data on various Harker variation diagrams show distinct evolutionary trends from diorites to granites indicated by decreasing CaO, Fe2O3, MnO, MgO, Ti2O, and increasing K2O with increasing silica. This pattern is reflected in trace elements data which, in general, decrease from diorite to the granites with rising Rb and K. Tectonic, triangular and other diagrams, indicate high-K calc-alkaline trends, syn-collisional granite signatures, I-type characteristics, with CNK/A of less than 1.1 (minimum of 0.58 and maximum of 0.94) and strong potassic character (K2O/Na2O˃1). However, only the granites are slightly peraluminous containing high silica percentage (68.46 to 76.04), K2O (2.71 to 6.16 wt %) with low CaO (1.88 on the average). Chondrite normalised rare earth elements trends indicate strongly fractionated REEs and enriched LREEs with slightly increasing negative Eu anomaly from the diorite to the granite. On the basis of field and geochemical data, the granitoids are interpreted to be high-K calc-alkaline, I-type, formed as a result of hybridization between mantle-derived magma and continental source materials (probably older meta-sediments) in a syn-collisional tectonic setting.

Keywords: geochemistry, granite, Hawal Massif, Nigeria, petrogenesis, song

Procedia PDF Downloads 236

Authors: Svetlana Kostrubina, Anastasia Prokopeva

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This research presents a comprehensive study of the word formation processes in computer terminology within English and Russian languages and provides listeners with a system of exercises for training these skills. The originality is that this study focuses on a comparative approach, which shows both general patterns and specific features of English and Russian computer terms word formation. The key point is the system of exercises development for training computer terminology based on Bloom’s taxonomy. Data contain 486 units (228 English terms from the Glossary of Computer Terms and 258 Russian terms from the Terminological Dictionary-Reference Book). The objective is to identify the main affixation models in the English and Russian computer terms formation and to develop exercises. To achieve this goal, the authors employed Bloom’s Taxonomy as a methodological framework to create a systematic exercise program aimed at enhancing students’ cognitive skills in analyzing, applying, and evaluating computer terms. The exercises are appropriate for various levels of learning, from basic recall of definitions to higher-order thinking skills, such as synthesizing new terms and critically assessing their usage in different contexts. Methodology also includes: a method of scientific and theoretical analysis for systematization of linguistic concepts and clarification of the conceptual and terminological apparatus; a method of nominative and derivative analysis for identifying word-formation types; a method of word-formation analysis for organizing linguistic units; a classification method for determining structural types of abbreviations applicable to the field of computer communication; a quantitative analysis technique for determining the productivity of methods for forming abbreviations of computer vocabulary based on the English and Russian computer terms, as well as a technique of tabular data processing for a visual presentation of the results obtained. a technique of interlingua comparison for identifying common and different features of abbreviations of computer terms in the Russian and English languages. The research shows that affixation retains its productivity in the English and Russian computer terms formation. Bloom’s taxonomy allows us to plan a training program and predict the effectiveness of the compiled program based on the assessment of the teaching methods used.

Keywords: word formation, affixation, computer terms, Bloom's taxonomy

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Authors: Moayyad Shawaqfah, Ibtehal Alqdah, Amjad Adaileh

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Coridoor area (400 km2) lies to the north – east of Amman (60 km). It lies between 285-305 E longitude and 165-185 N latitude (according to Palestine Grid). It been subjected to exploitation of groundwater from new eleven wells since the 1999 with a total discharge of 11 MCM in addition to the previous discharge rate from the well field 14.7 MCM. Consequently, the aquifer balance is disturbed and a major decline in water level. Therefore, suitable groundwater resources management is required to overcome the problems of over pumping and its effect on groundwater quality. Three–dimensional groundwater flow model Processing Modeflow for Windows Pro (PMWIN PRO, 2003) has been used in order to calculate the groundwater budget, aquifer characteristics, and to predict the aquifer response under different stresses for the next 20 years (2035). The model was calibrated for steady state conditions by trial and error calibration. The calibration was performed by matching observed and calculated initial heads for year 2001. Drawdown data for period 2001-2010 were used to calibrate transient model by matching calculated with observed one, after that, the transient model was validated by using the drawdown data for the period 2011-2014. The hydraulic conductivities of the Basalt- A7/B2 aquifer System are ranging between 1.0 and 8.0 m/day. The low conductivity value was found at the north-west and south-western parts of the study area, the high conductivity value was found at north-western corner of the study area and the average storage coefficient is about 0.025. The water balance for the Basalt and B2/A7 formation at steady state condition with a discrepancy of 0.003%. The major inflows come from Jebal Al Arab through the basalt and through the limestone aquifer (B2/A7 12.28 MCMY aquifer and from excess rainfall is about 0.68 MCM/a. While the major outflows from the Basalt-B2/A7 aquifer system are toward Azraq basin with about 5.03 MCMY and leakage to A1/6 aquitard with 7.89 MCMY. Four scenarios have been performed to predict aquifer system responses under different conditions. Scenario no.2 was found to be the best one which indicates that the reduction the abstraction rates by 50% of current withdrawal rate (25.08 MCMY) to 12.54 MCMY. The maximum drawdowns were decreased to reach about, 7.67 and 8.38m in the years 2025 and 2035 respectively.

Keywords: Amman/Zarqa Basin, Jordan, groundwater management, groundwater modeling, modflow

Procedia PDF Downloads 216

Authors: Pedro Augusto Da Silva E Souza Miranda, Niloofar Jalali, Shweta Mistry

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BACKGROUND: Software developers utilize automated solutions to scrape users’ reviews to extract meaningful knowledge to identify problems (e.g., bugs, compatibility issues) and possible enhancements (e.g., users’ requests) to their solutions. However, most of these solutions do not consider the health risk aspects to users. Recent works have shed light on the importance of including health risk considerations in the development cycle of mHealth apps to prevent harm to its users. PROBLEM: The COVID-19 Pandemic in Canada (and World) is currently forcing physical distancing upon the general population. This new lifestyle made the usage of mHealth applications more essential than ever, with a projected market forecast of 332 billion dollars by 2025. However, this new insurgency in mHealth usage comes with possible risks to users’ health due to mHealth apps problems (e.g., wrong insulin dosage indication due to a UI error). OBJECTIVE: These works aim to raise awareness amongst mHealth developers of the importance of considering risks to users’ health within their development lifecycle. Moreover, this work also aims to help mHealth developers with a Proof-of-Concept (POC) solution to understand, process, and identify possible health risks to users of mHealth apps based on users’ reviews. METHODS: We conducted a mixed-method study design. We developed a crawler to mine the negative reviews from two samples of mHealth apps (my fitness, medisafe) from the Google Play store users. For each mHealth app, we performed the following steps: • The reviews are divided into two groups, before starting the COVID-19 (reviews’ submission date before 15 Feb 2019) and during the COVID-19 (reviews’ submission date starts from 16 Feb 2019 till Dec 2020). For each period, the Latent Dirichlet Allocation (LDA) topic model was used to identify the different clusters of reviews based on similar topics of review The topics before and during COVID-19 are compared, and the significant difference in frequency and severity of similar topics are identified. RESULTS: We successfully scraped, filtered, processed, and identified health-related topics in both qualitative and quantitative approaches. The results demonstrated the similarity between topics before and during the COVID-19.

Keywords: natural language processing (NLP), topic modeling, mHealth, COVID-19, software engineering, telemedicine, health risks

Procedia PDF Downloads 130

Authors: Rommila Chandra, Harshika Choudhary

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The value of integrated, participatory, and community based sustainable development strategies is eminent, but in practice, it still remains fragmentary and often leads to short-lived results. Despite the global presence of climate change, its impacts are felt differently by different communities based on their vulnerability. The developing countries have the low adaptive capacity and high dependence on environmental variables, making them highly susceptible to outmigration and poverty. We need to understand how to enable these approaches, taking into account the various governmental and non-governmental stakeholders functioning at different levels, to deliver long-term socio-economic and environmental well-being of local communities. The research assessed the financial and natural vulnerability of Himalayan networks, focusing on their potential to adapt to various changes, through accessing their perceived reactions and local knowledge. The evaluation was conducted by testing indices for vulnerability, with a major focus on indicators for adaptive capacity. Data for the analysis were collected from the villages around Govind National Park and Wildlife Sanctuary, located in the Indian Himalayan Region. The villages were stratified on the basis of connectivity via road, thus giving two kinds of human settlements connected and isolated. The study focused on understanding the complex relationship between outmigration and the socio-cultural sentiments of local people to not abandon their land, assessing their adaptive capacity for livelihood opportunities, and exploring their contribution that integrated participatory methodologies can play in delivering sustainable development. The result showed that the villages having better road connectivity, access to market, and basic amenities like health and education have a better understanding about the climatic shift, natural hazards, and a higher adaptive capacity for income generation in comparison to the isolated settlements in the hills. The participatory approach towards environmental conservation and sustainable use of natural resources were seen more towards the far-flung villages. The study helped to reduce the gap between local understanding and government policies by highlighting the ongoing adaptive practices and suggesting precautionary strategies for the community studied based on their local conditions, which differ on the basis of connectivity and state of development. Adaptive capacity in this study has been taken as the externally driven potential of different parameters, leading to a decrease in outmigration and upliftment of the human environment that could lead to sustainable livelihood development in the rural areas of Himalayas.

Keywords: adaptive capacity, Indian Himalayan region, participatory, sustainable livelihood development

Procedia PDF Downloads 118

Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

Procedia PDF Downloads 418

Authors: Jasmal A. Syamsu, Muhammad Yusuf, Hikmah M. Ali, Mawardi A. Asja, Zulkharnaim

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This study was conducted and aimed in identifying and mapping the pasture as an ecological base of beef cattle. A survey was carried out during a period of April to June 2016, in Suppa, Mattirobulu, the district of Pinrang, South Sulawesi province. The mapping process of grazing area was conducted in several stages; inputting and tracking of data points into Google Earth Pro (version 7.1.4.1529), affirmation and confirmation of tracking line visualized by satellite with a variety of records at the point, a certain point and tracking input data into ArcMap Application (ArcGIS version 10.1), data processing DEM/SRTM (S04E119) with respect to the location of the grazing areas, creation of a contour map (a distance of 5 m) and mapping tilt (slope) of land and land cover map-making. Analysis of land cover, particularly the state of the vegetation was done through the identification procedure NDVI (Normalized Differences Vegetation Index). This procedure was performed by making use of the Landsat-8. The results showed that the topography of the grazing areas of hills and some sloping surfaces and flat with elevation vary from 74 to 145 above sea level (asl), while the requirements for growing superior grass and legume is an altitude of up to 143-159 asl. Slope varied between 0 - > 40% and was dominated by a slope of 0-15%, according to the slope/topography pasture maximum of 15%. The range of NDVI values for pasture image analysis results was between 0.1 and 0.27. Characteristics of vegetation cover of pasture land in the category of vegetation density were low, 70% of the land was the land for cattle grazing, while the remaining approximately 30% was a grove and forest included plant water where the place for shelter of the cattle during the heat and drinking water supply. There are seven types of graminae and 5 types of legume that was dominant in the region. Proportionally, graminae class dominated up 75.6% and legume crops up to 22.1% and the remaining 2.3% was another plant trees that grow in the region. The dominant weed species in the region were Cromolaenaodorata and Lantana camara, besides that there were 6 types of floor plant that did not include as forage fodder.

Keywords: pastoral, ecology, mapping, beef cattle

Procedia PDF Downloads 354

Authors: Natalie Tyldesley-Marshall, Janette Parr, Anna Brown, Yen-Fu Chen, Amy Grove

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It is widely recognised in policy and research that the provision of services for children and young people (CYP) with Special Educational Needs and Disabilities (SEND) is enhanced when health and social care, and education services collaborate and interact effectively. In the UK, there have been significant changes to policy and provisions which support and improve collaboration. However, professionals responsible for implementing these changes face multiple challenges, including a lack of specific implementation guidance or framework to illustrate how effective multi-agency working could or should work. This systematic review will identify the key components of effective multi-agency working in services for CYP with SEND; and the most effective forms of partnership working in this setting. The review highlights interventions that lead to service improvements; and the conditions in the local area that support and encourage success. A protocol was written and registered with PROSPERO registration: CRD42022352194. Searches were conducted on several health, care, education, and applied social science databases from the year 2012 onwards. Citation chaining has been undertaken, as well as broader grey literature searching to enrich the findings. Qualitative, quantitative, mixed methods studies and systematic reviews were included, assessed independently, and critically appraised or assessed for risk of bias using appropriate tools based on study design. Data were extracted in NVivo software and checked by a more experienced researcher. A convergent segregated approach to synthesis and integration was used in which the quantitative and qualitative data were synthesised independently and then integrated using a joint display integration matrix. Findings demonstrate the key ingredients for effective partnership working for services delivering SEND. Interventions deemed effective are described, and lessons learned across interventions are summarised. Results will be of interest to educators and health and social care professionals that provide services to those with SEND. These will also be used to develop policy recommendations for how UK healthcare, social care, and education services for CYP with SEND aged 0-25 can most effectively collaborate and achieve service improvement. The review will also identify any gaps in the literature to recommend areas for future research. Funding for this review was provided by the Department for Education.

Keywords: collaboration, joint commissioning, service delivery, service improvement

Procedia PDF Downloads 107

Authors: Babarina Maria, Andropova Margarita

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Introduction: Hypoparathyroidism (HypoPT) is a rare endocrine disorder with an estimated prevalence of 0.25 per 1000 individuals. The most common cause of HypoPT is the loss of active parathyroid tissue following thyroid or parathyroid surgery. Sometimes permanent postoperative HypoPT occures, manifested by hypocalcemia in combination with low levels of PTH during 6 months or more after surgery. Cognitive impairments in patients with hypocalcemia due to chronic HypoPT are observed, and this can lead to problems and challenges in everyday living: memory loss and impaired concentration, that may be the cause of poor compliance. Clinical case: Patient K., 66 years old, underwent thyroidectomy in 2013 (at the age of 55) because of papillary thyroid cancer T1NxMx, histopathology findings confirmed the diagnosis. 5 years after the surgery, she was followed up on an outpatient basis, TSH levelsonly were monitored, and the dose of levothyroxine was adjusted. In 2018 due to, increasing complaints include tingling and cramps in the arms and legs, memory loss, sleep disorder, fatigue, anxiety, hair loss, muscle pain, tachycardia, positive Chvostek, and Trousseau signs were diagnosed during examination, also in blood analyses: total Ca 1.86 mmol/l (2.15-2.55), Ca++ 0.96 mmol/l (1.12-1.3), P 1.55 mmol/l (0.74-1.52), Mg 0.79 mmol/l (0.66-1.07) - chronic postoperative HypoPT was diagnosed. Therapy was initiated: alfacalcidol 0.5 mcg per day, calcium carbonate 2000 mg per day, cholecalciferol 1000 IU per day, magnesium orotate 3000 mg per day. During the case follow-up, hypocalcemia, hyperphosphatemia persisted, hypercalciuria15.7 mmol/day (2.5-6.5) was diagnosed. Dietary recommendations were given because of the high content of phosphorus rich foods, and therapy was adjusted: the dose of alfacalcidol was increased to 2.5 mcg per day, and the dose of calcium carbonate was reduced to 1500 mg per day. As part of the screening for complications of hypoPT, data for cataracts, Fahr syndrome, nephrocalcinosis, and kidney stone disease were not obtained. However, HypoPT compensation was not achieved, and therefore hydrochlorothiazide 25 mg was initiated, the dose of alfacalcidol was increased to 3 mcg per day, calcium carbonate to 3000 mg per day, magnesium orotate and cholecalciferol were continued at the same doses. Therapeutic goals were achieved: calcium phosphate product <4.4 mmol2/l2, there were no episodes of hypercalcemia, twenty-four-hour urinary calcium excretion was significantly reduced. Conclusion: Timely prescription, careful explanation of drugs usage rules, and monitoring and maintaining blood and urine parameters within the target contribute to the prevention of HypoPT complications development and life-threatening events.

Keywords: hypoparathyroidism, hypocalcemia, hyperphosphatemia, hypercalciuria

Procedia PDF Downloads 108

Authors: Agnetha Schuchardt, Jennifer Hartmann, Laura Schulte, Roman Peperhove, Lars Gerhold

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If critical infrastructures fail, even for a short period of time, it can have significant negative consequences for the affected population. This is especially true for the food sector that is strongly interlinked with other sectors like the power supply. A blackout could lead to several cities being without food supply for numerous days, simply because cash register systems do no longer work properly. Following the public opinion, securing the food supply in emergencies is considered a task of the state, however, in the German context, the key players are private enterprises and private households. Both are not aware of their responsibility and both cannot be forced to take any preventive measures prior to an emergency. This problem became evident to officials and politicians so that the law covering food security was revised in order to include private stakeholders into mitigation processes. The paper will present a scientific review of governmental and regulatory literature. The focus is the inclusion of the food industry through a law reform and the challenges that still exist. Together with legal experts, an analysis of regulations will be presented that explains the development of the law reform concerning food security and emergency storage in Germany. The main findings are that the existing public food emergency storage is out-dated, insufficient and too expensive. The state is required to protect food as a critical infrastructure but does not have the capacities to live up to this role. Through a law reform in 2017, new structures should to established. The innovation was to include the private sector into the civil defense concept since it has the required knowledge and experience. But the food industry is still reluctant. Preventive measures do not serve economic purposes – on the contrary, they cost money. The paper will discuss respective examples like equipping supermarkets with emergency power supply or self-sufficient cash register systems and why the state is not willing to cover the costs of these measures, but neither is the economy. The biggest problem with the new law is that private enterprises can only be forced to support food security if the state of emergency has occurred already and not one minute earlier. The paper will cover two main results: the literature review and an expert workshop that will be conducted in summer 2018 with stakeholders from different parts of the food supply chain as well as officials of the public food emergency concept. The results from this participative process will be presented and recommendations will be offered that show how the private economy could be better included into a modern food emergency concept (e. g. tax reductions for stockpiling).

Keywords: critical infrastructure, disaster control, emergency food storage, food security, private economy, resilience

Procedia PDF Downloads 187

Authors: Sanjay Rathee, Arti Kashyap

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Extraction of useful information from large datasets is one of the most important research problems. Association rule mining is one of the best methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent patterns) is most important part of the association rule mining. There exist many algorithms to find frequent patterns but Apriori algorithm always remains a preferred choice due to its ease of implementation and natural tendency to be parallelized. Many single-machine based Apriori variants exist but massive amount of data available these days is above capacity of a single machine. Therefore, to meet the demands of this ever-growing huge data, there is a need of multiple machines based Apriori algorithm. For these types of distributed applications, MapReduce is a popular fault-tolerant framework. Hadoop is one of the best open-source software frameworks with MapReduce approach for distributed storage and distributed processing of huge datasets using clusters built from commodity hardware. However, heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce-based platforms are being developed for parallel computing in recent years. Among them, two platforms, namely, Spark and Flink have attracted a lot of attention because of their inbuilt support to distributed computations. Earlier we proposed a reduced- Apriori algorithm on Spark platform which outperforms parallel Apriori, one because of use of Spark and secondly because of the improvement we proposed in standard Apriori. Therefore, this work is a natural sequel of our work and targets on implementing, testing and benchmarking Apriori and Reduced-Apriori and our new algorithm ReducedAll-Apriori on Apache Flink and compares it with Spark implementation. Flink, a streaming dataflow engine, overcomes disk I/O bottlenecks in MapReduce, providing an ideal platform for distributed Apriori. Flink's pipelining based structure allows starting a next iteration as soon as partial results of earlier iteration are available. Therefore, there is no need to wait for all reducers result to start a next iteration. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of the Apriori and RA-Apriori algorithm on Flink.

Keywords: apriori, apache flink, Mapreduce, spark, Hadoop, R-Apriori, frequent itemset mining

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Authors: Desmond F. Bellamy

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Discourses about animal rights usually assume an ontological abyss between human and animal. This supposition of non-animality allows us to utilise and exploit non-humans, particularly those with commercial value, with little regard for their rights or interests. We can and do confine them, inflict painful treatments such as castration and branding, and slaughter them at an age determined only by financial considerations. This paper explores the way images and texts depicting human cannibalism reflect this deprivation of rights back onto our species and examines how this offers new perspectives on our granting or withholding of rights to farmed animals. The animals we eat – sheep, pigs, cows, chickens and a small handful of other species – are during processing de-animalised, turned into commodities, and made unrecognisable as formerly living beings. To do the same to a human requires the cannibal to enact another step – humans must first be considered as animals before they can be commodified or de-animalised. Different iterations of cannibalism in a selection of fiction and non-fiction texts will be considered: survivalism (necessitated by catastrophe or dystopian social collapse), the primitive savage of colonial discourses, and the inhuman psychopath. Each type of cannibalism shows alternative ways humans can be animalised and thereby dispossessed of both their human and animal rights. Human rights, summarised in the UN Universal Declaration of Human Rights as ‘life, liberty, and security of person’ are stubbornly denied to many humans, and are refused to virtually all farmed non-humans. How might this paradigm be transformed by seeing the animal victim replaced by an animalised human? People are fascinated as well as repulsed by cannibalism, as demonstrated by the upsurge of films on the subject in the last few decades. Cannibalism is, at its most basic, about envisaging and treating humans as objects: meat. It is on the dinner plate that the abyss between human and ‘animal’ is most challenged. We grasp at a conscious level that we are a species of animal and may become, if in the wrong place (e.g., shark-infested water), ‘just food’. Culturally, however, strong traditions insist that humans are much more than ‘just meat’ and deserve a better fate than torment and death. The billions of animals on death row awaiting human consumption would ask the same if they could. Depictions of cannibalism demonstrate in graphic ways that humans are animals, made of meat and that we can also be butchered and eaten. These depictions of us as having the same fleshiness as non-human animals reminds us that they have the same capacities for pain and pleasure as we do. Depictions of cannibalism, therefore, unconsciously aid in deconstructing the human/animal binary and give a unique glimpse into the often unnoticed repudiation of animal rights.

Keywords: animal rights, cannibalism, human/animal binary, objectification

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Authors: Tin Yee Ling, Kavita Maravar, Ruzica Ardalic

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Background: Uterine torsion (UT) in pregnancy of more than 45-degree along the longitudinal axis is a rare occurrence, and the aetiology remains unclear. Case: A 34-year-old G2P1 woman with a history of one previous caesarean section presented at 36+2 weeks with sudden onset lower abdominal pain, syncopal episode, and tender abdomen on examination. She was otherwise haemodynamically stable. Cardiotocography showed a pathological trace with initial prolonged bradycardia followed by a subsequent tachycardia with reduced variability. An initial diagnosis of uterine dehiscence was made, given the history and clinical presentation. She underwent an emergency caesarean section which revealed a 180-degree UT along the longitudinal axis, with oedematous left round ligament lying transverse anterior to the uterus and a segment of large bowel inferior to the round ligament. Detorsion of uterus was performed prior to delivery of the foetus, and anterior uterine wall was intact with no signs of rupture. There were no anatomical uterine abnormalities found other than stretched left ovarian and round ligaments, which were repaired. Delivery was otherwise uneventful, and she was discharged on day 2 postpartum. Discussion: UT is rare as the number of reported cases is within the few hundreds worldwide. Generally, the uterus is supported in place by uterine ligaments, which limit the mobility of the structure. The causes of UT are unknown, but risk factors such as uterine abnormalities, increased uterine ligaments’ flexibility in pregnancy, and foetal malposition has been identified. UT causes occlusion of uterine vessels, which can lead to ischaemic injury of the placenta causing premature separation of the placenta, preterm labour, and foetal morbidity and mortality if delivery is delayed. Diagnosing UT clinically is difficult as most women present with symptoms similar to placenta abruption or uterine rupture (abdominal pain, vaginal bleeding, shock), and one-third are asymptomatic. The management of UT involves surgical detorsion of the uterus and delivery of foetus via caesarean section. Extra vigilance should be taken to identify the anatomy of the uterus experiencing torsion prior to hysterotomy. There have been a few cases reported with hysterotomy on posterior uterine wall for delivery of foetus as it may be difficult to identify and reverse a gravid UT when foetal well-being is at stake. Conclusion: UT should be considered a differential diagnosis of acute abdominal pain in pregnancy. It is crucial that the torsion is addressed immediately as it is associated with maternal and foetal morbidity and mortality.

Keywords: uterine torsion, pregnancy complication, abdominal pain, torted uterus

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Authors: Neha Batra Bali, Monika Tomar, Vinay Gupta

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A reagentless biosensor for detection of urea based on ZnO-CuO composite thin film is presented in following work. Biosensors have immense potential for varied applications ranging from environmental to clinical testing, health care, and cell analysis. Immense growth in the field of biosensors is due to the huge requirement in today’s world to develop techniques which are both cost effective and accurate for prevention of disease manifestation. The human body comprises of numerous biomolecules which in their optimum levels are essential for functioning. However mismanaged levels of these biomolecules result in major health issues. Urea is one of the key biomolecules of interest. Its estimation is of paramount significance not only for healthcare sector but also from environmental perspectives. If level of urea in human blood/serum is abnormal, i.e., above or below physiological range (15-40mg/dl)), it may lead to diseases like renal failure, hepatic failure, nephritic syndrome, cachexia, urinary tract obstruction, dehydration, shock, burns and gastrointestinal, etc. Various metal nanoparticles, conducting polymer, metal oxide thin films, etc. have been exploited to act as matrix to immobilize urease to fabricate urea biosensor. Amongst them, Zinc Oxide (ZnO), a semiconductor metal oxide with a wide band gap is of immense interest as an efficient matrix in biosensors by virtue of its natural abundance, biocompatibility, good electron communication feature and high isoelectric point (9.5). In spite of being such an attractive candidate, ZnO does not possess a redox couple of its own which necessitates the use of electroactive mediators for electron transfer between the enzyme and the electrode, thereby causing hindrance in realization of integrated and implantable biosensor. In the present work, an effort has been made to fabricate a matrix based on ZnO-CuO composite prepared by pulsed laser deposition (PLD) technique in order to incorporate redox properties in ZnO matrix and to utilize the same for reagentless biosensing applications. The prepared bioelectrode Urs/(ZnO-CuO)/ITO/glass exhibits high sensitivity (70µAmM⁻¹cm⁻²) for detection of urea (5-200 mg/dl) with high stability (shelf life ˃ 10 weeks) and good selectivity (interference ˂ 4%). The enhanced sensing response obtained for composite matrix is attributed to the efficient electron exchange between ZnO-CuO matrix and immobilized enzymes, and subsequently fast transfer of generated electrons to the electrode via matrix. The response is encouraging for fabricating reagentless urea biosensor based on ZnO-CuO matrix.

Keywords: biosensor, reagentless, urea, ZnO-CuO composite

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Authors: Maria Inês Pinho

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Recent literature on issues of Cultural Management (also called Strategic Management for cultural organizations) systematically seeks for models that allow such equipment to adapt to the constant change that occurs in contemporary societies. In the last decade, the world, and in particular Europe has experienced a serious financial problem that has triggered defensive mechanisms, both in the direction of promoting the balance of public accounts and in the sense of the anonymous loss of the democratic and cultural values of each nation. If in the first case emerged the Troika that led to strong cuts in funding for Culture, deeply affecting those organizations; in the second case, the commonplace citizen is seen fighting for the non-closure of cultural equipment. Despite this, the cultural manager argues that there is no single formula capable of solving the need to adapt to change. In another way, it is up to this agent to know the existing scientific models and to adapt them in the best way to the reality of the institution he coordinates. These actions, as a rule, are concerned with the best performance vis-à-vis external audiences or with the financial sustainability of cultural organizations. They forget, therefore, that all this mechanics cannot function without its internal public, without its Human Resources. The employees of the cultural organization must then have an entrepreneurial posture - must be intrapreneurial. This paper intends to break this form of action and lead the cultural manager to understand that his role should be in the sense of creating value for society, through a good organizational performance. This is only possible with a posture of strategic entrepreneurship. In other words, with a link between: Cultural Management, Cultural Entrepreneurship and Cultural Intrapreneurship. In order to prove this assumption, the case study methodology was used with the symbol of the European Capital of Culture (Casa da Música) as well as qualitative and quantitative techniques. The qualitative techniques included the procedure of in-depth interviews to managers, founders and patrons and focus groups to public with and without experience in managing cultural facilities. The quantitative techniques involved the application of a questionnaire to middle management and employees of Casa da Música. After the triangulation of the data, it was proved that contemporary management of cultural organizations must implement among its practices, the concept of Strategic Entrepreneurship and its variables. Also, the topics which characterize the Cultural Intrapreneurship notion (job satisfaction, the quality in organizational performance, the leadership and the employee engagement and autonomy) emerged. The findings show then that to be sustainable, a cultural organization should meet the concerns of both external and internal forum. In other words, it should have an attitude of citizenship to the communities, visible on a social responsibility and a participatory management, only possible with the implementation of the concept of Strategic Entrepreneurship and its variable of Cultural Intrapreneurship.

Keywords: cultural entrepreneurship, cultural intrapreneurship, cultural organizations, strategic management

Procedia PDF Downloads 182

Authors: Jayati Bhattacharjee

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Teaching is a very dynamic profession. Teaching Science is as much challenging as Learning the subject if not more. For instance teaching of Chemistry. From the introductory concepts of subatomic particles to atoms of elements and their symbols and further presenting the chemical equation and so forth is a challenge on both side of the equation Teaching Learning. This paper combines the Neuroscience of Learning and memory with the knowledge of Learning style (VAK) and presents an effective tool for the teacher to authenticate Learning. The model of ‘Working Memory’, the Visio-spatial sketchpad, the central executive and the phonological loop that transforms short-term memory to long term memory actually supports the psychological theory of Learning style i.e. Visual –Auditory-Kinesthetic. A closer examination of David Kolbe’s learning model suggests that learning requires abilities that are polar opposites, and that the learner must continually choose which set of learning abilities he or she will use in a specific learning situation. In grasping experience some of us perceive new information through experiencing the concrete, tangible, felt qualities of the world, relying on our senses and immersing ourselves in concrete reality. Others tend to perceive, grasp, or take hold of new information through symbolic representation or abstract conceptualization – thinking about, analyzing, or systematically planning, rather than using sensation as a guide. Similarly, in transforming or processing experience some of us tend to carefully watch others who are involved in the experience and reflect on what happens, while others choose to jump right in and start doing things. The watchers favor reflective observation, while the doers favor active experimentation. Any lesson plan based on the model of Prescriptive design: C+O=M (C: Instructional condition; O: Instructional Outcome; M: Instructional method). The desired outcome and conditions are independent variables whereas the instructional method is dependent hence can be planned and suited to maximize the learning outcome. The assessment for learning rather than of learning can encourage, build confidence and hope amongst the learners and go a long way to replace the anxiety and hopelessness that a student experiences while learning Science with a human touch in it. Application of this model has been tried in teaching chemistry to high school students as well as in workshops with teachers. The response received has proven the desirable results.

Keywords: working memory model, learning style, prescriptive design, assessment for learning

Procedia PDF Downloads 351

Authors: Mihai Palade, Gina Cecilia Pistol, Mariana Stancu, Veronica Chedea, Ionelia Taranu

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Nutrition is an important determinant of general health status, with especially focus on prevention and/or attenuation of the inflammatory-associated pathologies. People with chronic kidney disease can experience chronic inflammation that can lead to cardiovascular disease and even an increased rate of death. There are important links between chronic kidney diseases, inflammation and nutritional strategies that may prevent or protect against undesirable inflammation and oxidative stress. The grape by-products either seeds or pomace are rich in polyphenols which may be beneficial in prevention of inflammatory, antioxidant and antimicrobial processes. As a model for studying the impact of grape seeds on renal inflammation and oxidative stress, we used in this study weaned piglets. After a feeding trial of 30 days with a control diet and an experimental diet containing 5% grape seed (GS), kidney samples were collected. In renal tissues were determined the expression and activity of important markers of immune respose and oxidative stress: pro-inflammatory cytokines (TNF-alpha, IL-1 beta, IL-6, IL-8, IFN-gamma), anti-inflammatory cytokines (IL-4, IL-10), anti-oxidant enzymes (catalase CAT, superoxide dismutase SOD, glutathione peroxidise GPx) and important mediators belonging to nuclear receptors (NF-kB1, Nrf-2 and PPAR-gamma). Gene expression was evaluated by qPCR, whereas protein concentration was determined using proteomic techniques (ELISA). The activity of anti-oxidant enzymes was determined using specific kits. Our results showed that GS enriched in polyphenols does not have effect on TNF-alpha, IL-6 and IL-1 beta gene expression and protein concentration in kidney. By contrast, the gene expression and protein level of IL-8 and IFN-gamma were decreased in GS kidney. Anti-inflammatory cytokines IL-4 and IL-10 gene levels were increased in kidneys collected from GS piglets in comparison with controls, with no modification of protein levels between the two groups. The activities of anti-oxidant enzymes CAT and GPx were increased in kidney by GS, whereas SOD activity was unmodified in comparison with control samples. Also, the GS diet was associated with no modulation of mRNAs for nuclear receptors NF-kB1, Nrf-2 and PPAR-gamma gene expressions in kidneys. In conclusion, our results demonstrated that GS enriched in bioactive compounds such polyphenols could modulate inflammation and oxidative stress markers in kidney tissues. Further studies are necessary to elucidate the mechanism of action of GS compounds in case kidney inflammation associated with oxidative stress, and signalling molecules involved in these mechanisms.

Keywords: animal model, kidney inflammation, oxidative stress, grape seed

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Authors: Buruk Kitachew Wossenyeleh

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Groundwater is the largest freshwater reservoir in the world. Like the other reservoirs of the hydrologic cycle, it is a finite resource. This study focused on the groundwater modeling of the Ter Kamerenbos well field to understand the groundwater flow system and the impact of different scenarios. The study area covers 68.9Km2 in the Brussels Capital Region and is situated in two river catchments, i.e., Zenne River and Woluwe Stream. The aquifer system has three layers, but in the modeling, they are considered as one layer due to their hydrogeological properties. The catchment aquifer system is replenished by direct recharge from rainfall. The groundwater recharge of the catchment is determined using the spatially distributed water balance model called WetSpass, and it varies annually from zero to 340mm. This groundwater recharge is used as the top boundary condition for the groundwater modeling of the study area. During the groundwater modeling using Processing MODFLOW, constant head boundary conditions are used in the north and south boundaries of the study area. For the east and west boundaries of the study area, head-dependent flow boundary conditions are used. The groundwater model is calibrated manually and automatically using observed hydraulic heads in 12 observation wells. The model performance evaluation showed that the root means the square error is 1.89m and that the NSE is 0.98. The head contour map of the simulated hydraulic heads indicates the flow direction in the catchment, mainly from the Woluwe to Zenne catchment. The simulated head in the study area varies from 13m to 78m. The higher hydraulic heads are found in the southwest of the study area, which has the forest as a land-use type. This calibrated model was run for the climate change scenario and well operation scenario. Climate change may cause the groundwater recharge to increase by 43% and decrease by 30% in 2100 from current conditions for the high and low climate change scenario, respectively. The groundwater head varies for a high climate change scenario from 13m to 82m, whereas for a low climate change scenario, it varies from 13m to 76m. If doubling of the pumping discharge assumed, the groundwater head varies from 13m to 76.5m. However, if the shutdown of the pumps is assumed, the head varies in the range of 13m to 79m. It is concluded that the groundwater model is done in a satisfactory way with some limitations, and the model output can be used to understand the aquifer system under steady-state conditions. Finally, some recommendations are made for the future use and improvement of the model.

Keywords: Ter Kamerenbos, groundwater modelling, WetSpass, climate change, well operation

Procedia PDF Downloads 152

Authors: Yang Cao

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Analyzing the oral production of Chinese-speaking learners of English as a second language (L2), we can find a large variety of verb inflections – Why does it seem so hard for them to use consistent correct past morphologies in obligatory past contexts? Failed Functional Features Hypothesis (FFFH) attributes the rather non-target-like performance to the absence of [±past] feature in their L1 Chinese, arguing that for post puberty learners, new features in L2 are no more accessible. By contrast, Missing Surface Inflection Hypothesis (MSIH) tends to believe that all features are actually acquirable for late L2 learners, while due to the mapping difficulties from features to forms, it is hard for them to realize the consistent past morphologies on the surface. However, most of the studies are limited to the verb morphologies in finite clauses and few studies have ever attempted to figure out these learners’ performance in non-finite clauses. Additionally, it has been discussed that Chinese learners may be able to tell the finite/infinite distinction (i.e. the [±finite] feature might be selected in Chinese, even though the existence of [±past] is denied). Therefore, adopting a self-paced reading task (SPR), the current study aims to analyze the processing patterns of Chinese-speaking learners of L2 Russian, in order to find out if they are sensitive to misuse of tense morphologies in both finite and non-finite clauses and whether they are sensitive to the finite/infinite distinction presented in Russian. The study targets L2 Russian due to its systematic morphologies in both present and past tenses. A native Russian group, as well as a group of English-speaking learners of Russian, whose L1 has definitely selected both [±finite] and [±past] features, will also be involved. By comparing and contrasting performance of the three language groups, the study is going to further examine and discuss the two theories, FFFH and MSIH. Preliminary hypotheses are: a) Russian native speakers are expected to spend longer time reading the verb forms which violate the grammar; b) it is expected that Chinese participants are, at least, sensitive to the misuse of inflected verbs in non-finite clauses, although no sensitivity to the misuse of infinitives in finite clauses might be found. Therefore, an interaction of finite and grammaticality is expected to be found, which indicate that these learners are able to tell the finite/infinite distinction; and c) having selected [±finite] and [±past], English-speaking learners of Russian are expected to behave target-likely, supporting L1 transfer.

Keywords: features, finite clauses, morphosyntax, non-finite clauses, past morphologies, present morphologies, Second Language Acquisition, self-paced reading task, verb inflections

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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Authors: Johannes Gantner, Michael Held, Rafael Horn, Matthias Fischer

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At the United Nations Climate Change Conference 2015 a global agreement on the reduction of climate change was achieved stating CO₂ reduction targets for all countries. For instance, the EU targets a reduction of 40 percent in emissions by 2030 compared to 1990. In order to achieve this ambitious goal, the environmental performance of the different European electricity grid mixes is crucial. First, the electricity directly needed for everyone’s daily life (e.g. heating, plug load, mobility) and therefore a reduction of the environmental impacts of the electricity grid mix reduces the overall environmental impacts of a country. Secondly, the manufacturing of every product depends on electricity. Thereby a reduction of the environmental impacts of the electricity mix results in a further decrease of environmental impacts of every product. As a result, the implementation of the two-degree goal highly depends on the decarbonization of the European electricity mixes. Currently the production of electricity in the EU27 is based on fossil fuels and therefore bears a high GWP impact per kWh. Due to the importance of the environmental impacts of the electricity mix, not only today but also in future, within the European research projects, CommONEnergy and Senskin, time-dynamic Life Cycle Assessment models for all EU27 countries were set up. As a methodology, a combination of scenario modeling and life cycle assessment according to ISO14040 and ISO14044 was conducted. Based on EU27 trends regarding energy, transport, and buildings, the different national electricity mixes were investigated taking into account future changes such as amount of electricity generated in the country, change in electricity carriers, COP of the power plants and distribution losses, imports and exports. As results, time-dynamic environmental profiles for the electricity mixes of each country and for Europe overall were set up. Thereby for each European country, the decarbonization strategies of the electricity mix are critically investigated in order to identify decisions, that can lead to negative environmental effects, for instance on the reduction of the global warming of the electricity mix. For example, the withdrawal of the nuclear energy program in Germany and at the same time compensation of the missing energy by non-renewable energy carriers like lignite and natural gas is resulting in an increase in global warming potential of electricity grid mix. Just after two years this increase countervailed by the higher share of renewable energy carriers such as wind power and photovoltaic. Finally, as an outlook a first qualitative picture is provided, illustrating from environmental perspective, which country has the highest potential for low-carbon electricity production and therefore how investments in a connected European electricity grid could decrease the environmental impacts of the electricity mix in Europe.

Keywords: electricity grid mixes, EU27 countries, environmental impacts, future trends, life cycle assessment, scenario analysis

Procedia PDF Downloads 186

Authors: Jose R. Garcia, Lexi Frankel, Amalia Ardeljan, Sergio Medina, Ali Yasback, Omar Rashid

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Background: Helicobacter pylori (H. pylori) is a gram-negative, spiral-shaped bacterium that affects nearly half of the population worldwide and humans serve as the principal reservoir. Infection rates usually follow an inverse relationship with hygiene practices and are higher in developing countries than developed countries. Incidence varies significantly by geographic area, race, ethnicity, age, and socioeconomic status. H. pylori is primarily associated with conditions of the gastrointestinal tract such as atrophic gastritis and duodenal peptic ulcers. Infection is also associated with an increased risk of carcinogenesis as there is evidence to show that H. pylori infection may lead to gastric adenocarcinoma and mucosa-associated lymphoid tissue (MALT) lymphoma. It is suggested that H. pylori infection may be considered as a systemic condition, leading to various novel associations with several different neoplasms such as colorectal cancer, pancreatic cancer, and lung cancer, although further research is needed. Emerging evidence suggests that H. pylori infection may offer protective effects against Mycobacterium tuberculosis as a result of non-specific induction of interferon- γ (IFN- γ). Similar methods of enhanced immunity may affect the development of bronchogenic carcinoma due to the antiproliferative, pro-apoptotic and cytostatic functions of IFN- γ. The purpose of this study was to evaluate the correlation between Helicobacter pylori infection and the incidence of bronchogenic carcinoma. Methods: The data was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate the patients infected versus patients not infected with H. pylori using ICD-10 and ICD-9 codes. Access to the database was granted by the Holy Cross Health, Fort Lauderdale for the purpose of academic research. Standard statistical methods were used. Results:-Between January 2010 and December 2019, the query was analyzed and resulted in 163,224 in both the infected and control group, respectively. The two groups were matched by age range and CCI score. The incidence of bronchogenic carcinoma was 1.853% with 3,024 patients in the H. pylori group compared to 4.785% with 7,810 patients in the control group. The difference was statistically significant (p < 2.22x10-16) with an odds ratio of 0.367 (0.353 - 0.383) with a confidence interval of 95%. The two groups were matched by treatment and incidence of cancer, which resulted in a total of 101,739 patients analyzed after this match. The incidence of bronchogenic carcinoma was 1.929% with 1,962 patients in the H. pylori and treatment group compared to 4.618% with 4,698 patients in the control group with treatment. The difference was statistically significant (p < 2.22x10-16) with an odds ratio of 0.403 (0.383 - 0.425) with a confidence interval of 95%.

Keywords: bronchogenic carcinoma, helicobacter pylori, lung cancer, pathogen-associated molecular patterns

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Vandenbroucke Karel, Georges Annabel, Schuurman Dimitri

Abstract:

In the past years, bundling of transport flows, whether or not implemented in an intermodal process, has popped up as a promising concept in the logistics sector. Bundling of transport flows is a process where two or more shippers decide to synergize their shipped goods over a common transport lane. Promoted by the European Commission, several programs have been set up and have shown their benefits. Bundling promises both shippers and logistics service providers economic, societal and ecological benefits. By bundling transport flows and thus reducing truck (or other carrier) capacity, the problems of driver shortage, increased fuel prices, mileage charges and restricted hours of service on the road are solved. In theory, the advantages of bundled transport exceed the drawbacks, however, in practice adoption among shippers remains low. In fact, bundling is mentioned as a disruptive process in the rather traditional logistics sector. In this context, a Belgian company asked iMinds Living Labs to set up a Living Lab research project with the goal to investigate how the uptake of bundling transport flows can be accelerated and to check whether an online data sharing platform can overcome the adoption barriers. The Living Lab research was conducted in 2016 and combined quantitative and qualitative end-user and market research. Concretely, extensive desk research was conducted and combined with insights from expert interviews with four consultants active in the Belgian logistics sector and in-depth interviews with logistics professionals working for shippers (N=10) and LSP’s (N=3). In the article, we present findings which show that there are several factors slowing down the uptake of bundling transport flows. Shippers are hesitant to change how they currently work and they are hesitant to work together with other shippers. Moreover, several practical challenges impede shippers to work together. We also present some opportunities that can accelerate the adoption of bundling of transport flows. First, it seems that there is not enough support coming from governmental and commercial organizations. Secondly, there is the chicken and the egg problem: too few interested parties will lead to no or very few matching lanes. Shippers are therefore reluctant to partake in these projects because the benefits have not yet been proven. Thirdly, the incentive is not big enough for shippers. Road transport organized by the shipper individually is still seen as the easiest and cheapest solution. A solution for the abovementioned challenges might be found in the online data sharing platform of the Belgian company. The added value of this platform is showing shippers possible matching lanes, without the shippers having to invest time in negotiating and networking with other shippers and running the risk of not finding a match. The interviewed shippers and experts indicated that the online data sharing platform is a very promising concept which could accelerate the uptake of bundling of transport flows.

Keywords: adoption barriers, bundling of transport, shippers, transport optimization

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Authors: Sudha T., Naveen C.

Abstract:

Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

Procedia PDF Downloads 65