Search results for: thuja label

Authors: Irene H. Maralit

Abstract:

Warning labels have been found to inform smokers about the health hazards of smoking, encourage smokers to quit, and prevent nonsmokers from starting to smoke. Warning labels on tobacco products are an ideal way of communicating with smokers. Since the intervention is delivered at the time of smoking, nearly all smokers are exposed to warning labels and pack-a-day smokers could be exposed to the warnings more than 7,000 times per year. Given the reach and frequency of exposure, the proponents want to know the effect of warning labels on smoking behavior. Its aims to identify the profile of the smokers associated with its behavioral variables that best describe the users’ perception. The behavioral variables are AVOID, THINK RISK and FORGO. This research study aims to determine if there is significant relationship between the effect of warning labels on cigarette package on Consumer behavior when grouped according to profile variable. The researcher used quota sampling to gather representative data through purposive means to determine the accurate representation of data needed in the study. Furthermore, the data was gathered through the use of a self-constructed questionnaire. The statistical method used were Frequency count, Chi square, multi regression, weighted mean and ANOVA to determine the scale and percentage of the three variables. After the analysis of data, results shows that most of the respondents belongs to age range 22–28 years old with percentage of 25.3%, majority are male with a total number of 134 with percentage of 89.3% and single with total number of 79 and percentage of 52.7%, mostly are high school graduates with total number of 59 and percentage of 39.3, with regards to occupation, skilled workers have the highest frequency of 37 with 24.7%, Majority of the income of the respondents falls under the range of Php 5,001-Php10,000 with 50.7%. And also with regards to the number of sticks consumed per day falls under 6–10 got the highest frequency with 33.3%. The respondents THINK RISK factor got the highest composite mean which is 2.79 with verbal interpretation of agree. It is followed by FORGO with 2.78 composite mean and a verbal interpretation of agree and AVOID variable with composite mean of 2.77 with agree as its verbal interpretation. In terms of significant relationship on the effects of cigarette label to consumer behavior when grouped according to profile variable, sex and occupation found to be significant.

Keywords: consumer behavior, smokers, warning labels, think risk avoid forgo

Procedia PDF Downloads 195

Authors: Sandeep Nath

Abstract:

There are good stresses and bad stresses. To tell the difference, recognize early signs of stress, and label stress conditions correctly, we need to understand stress triggers and the mechanism of stress as it arises. By understanding this in our teenage years, we can be prepared to prevent harmful stress from escalating and ruining health, physical, mental, and emotional. We can also prepare others/peers to be stress-free. The understanding of this is available in a form closest to our natural being, in ancient oriental wisdom, and is brought together as actionable habits in the movement called RENEWALism. The constructs of RENEWALism Habits are detailed in this paper, and case studies are presented of teenagers who have been equipped with both capability and capacity to handle their situations and environments independently.

Keywords: habits, renewalism, stress, teenagers

Procedia PDF Downloads 35

Authors: Arpit Rai

Abstract:

In this work, we propose PatchMix, a semi-supervised method for pre-training visual representations. PatchMix mixes patches of two images and then solves an auxiliary task of predicting the label of each patch in the mixed image. Our experiments on the CIFAR-10, 100 and the SVHN dataset show that the representations learned by this method encodes useful information for transfer to new tasks and outperform the baseline Residual Network encoders by on CIFAR 10 by 12% on ResNet 101 and 2% on ResNet-56, by 4% on CIFAR-100 on ResNet101 and by 6% on SVHN dataset on the ResNet-101 baseline model.

Keywords: self-supervised learning, representation learning, computer vision, generalization

Procedia PDF Downloads 55

Authors: Sandeep Nath

Abstract:

There are good stresses and bad stresses. To tell the difference, recognize early signs of stress, and label stress conditions correctly, we need to understand stress triggers and the mechanism of stress as it arises. By understanding this through mindfulness of body, mind, and spirit, we can be prepared to prevent harmful stress from escalating and ruining health; physical, mental, and emotional. We can also prepare others/peers to be stress-free. The understanding of this is available in a form closest to our natural being, in ancient oriental wisdom, and is brought together as actionable habits in the movement called RENEWALism. The constructs of RENEWALism Habits are detailed in this paper, and case studies presented of how mindfulness has equipped individuals with both capability and capacity to handle their situations and environments despite the odds.

Keywords: habits, mindfulness, renewalism, stress

Procedia PDF Downloads 124

Authors: Nadia Miranda Lorick, Neela Badrie, Marsha Singh

Abstract:

Cassava (Manihot esculenta, Crantz) which is also known as ‘yucca’ or ‘manioc’ has been acknowledged as a millennium crop which has been utilized for food security purposes. The crop provides considerable amount of energy. The aim of the study was to assess consumer groups of both ‘fresh’ and ‘frozen’ in terms of their perceptions toward the quality and safety attributes of frozen cassava. The questionnaire included four sections: consumer demographics, consumer perceptions on quality attributes of ‘frozen’ cassava, consumer knowledge, awareness and attitudes toward food safety of ‘frozen’ cassava and consumer suggestions toward the improvement of frozen cassava. A face-to-face questionnaire was administered to 200 consumers of cassava between April and May 2016. The criteria for inclusion in the survey were that they must be 15 years and over and consumer of cassava. The sections of the questionnaire included demographics of respondents, consumer perception on quality and safety attributes of cassava and suggestions for the improvement of the value-added product. The data was analysed by descriptive and chi-square using SPSS as well as qualitative information was captured. Only 17% of respondents purchased frozen cassava and this was significantly (P<0.05) associated to income. Some (15%) of fresh cassava purchasers had never heard of frozen cassava products and 7.5% o perceived that these products were unhealthy for consumption. More than half (51.3%) of the consumers (all from the ‘fresh’ cassava group) believed that there were ‘no toxins’ within cassava. The ‘frozen’ cassava products were valued for convenience but purchasers were least satisfied with ‘value for money’ (50%), ‘product safety’ (50%) and ‘colour’ (52.9%). Cassava purchasers demonstrated highest dissatisfaction levels with the quality attribute: value for money (6.6%, 11.8%) respectively. The most predominant area outlined by respondents for frozen cassava improvement was promotion /advertising/education (23%). The ‘frozen’ cassava purchasers were ‘least satisfied’ thus most concern that clean knives and clean surface would not be used agro- processing. Fresh cassava purchasers were comparatively more knowledgeable on the potential existence of naturally occurring toxins in cassava, however with 1% respondents being able to specifically identify the toxin as ‘cyanide’. Dangerous preservatives (31%), poor hygiene (30%) and chemicals from the packaging (11%) were identified as some sources of contamination of ‘frozen’ cassava. Purchasers of frozen cassava indicated that the information on packaging label was unclear (P<0.01) when compared to ‘fresh’ cassava consumers.

Keywords: consumer satisfaction, convenience, cyanide toxin, product safety, price, label

Procedia PDF Downloads 365

Authors: Luca Lezzerini

Abstract:

Albania is suffering a continuous shrinking of its population and demographic distribution that faces all the problems connected with age increase. The paper examines the case of Gjirokastër, a city in the south of Albania that, despite having a UNESCO label as a world heritage site, is experimenting with the same shrinking phenomenon. The paper analyses in detail the current situation and propose an interdisciplinary approach based on smart technologies and sunset tourism to restart Gjirokastër’s economy and invert bad demographic trends. The proposed approach needs to review the current urban planning, reshaping and connecting some areas. It also proposes a smart city architecture to support this process.

Keywords: smart city, sunset tourism, shrinking city, Gjirokastër

Procedia PDF Downloads 55

Authors: Miraji H. Mohamed

Abstract:

The purpose of this study is to critique ‘radicalisation’ and more particularly ‘youth radicalisation’ by exploring its usage in online newspapers. ‘Radicalisation’ and ‘extremism’ have become the most common terms in terrorism studies since the 9/11 attacks. Regardless of the geographic location, when the word terrorism is used the terms ‘radicalisation’ and ‘extremism’ always follow to attempt to explore the journey of the perpetrators towards violence. These terms have come to represent a discourse of dominantly pejorative traits often used to describe spaces, groups, and processes identified as problematic. Even though ambiguously defined they feature widely in government documents, political statements, news articles, academic research, social media platforms, religious gatherings, and public discussions. Notably, ‘radicalisation’ and ‘extremism’ have been closely conflated with the term youth to form ‘youth radicalisation’ to refer to a discourse of ‘youth at risk’. The three terms largely continue to be used unquestioningly and interchangeably hence the reason why they are placed in single quotation marks to deliberately question their conventional usage. Albeit this comes timely in the Kenyan context where there has been a proliferation of academic and expert research on ‘youth radicalisation’ (used as a neutral label) without considering the political, cultural and socio-historical contexts that inform this label. This study seeks to draw these nuances by employing a genealogical approach that historicises and deconstructs ‘youth radicalisation’; and by applying a Discourse-Historical Approach (DHA) of Critical Discourse Analysis to analyse Kenyan online newspaper - The Daily Nation between 2015 and 2018. By applying the concept of representation to analyse written texts, the study reveals that the use of ‘youth radicalisation’ as a discursive strategy disproportionately affects young people especially those from cultural/ethnic/religious minority groups. Also, the ambiguous use of ‘radicalisation’ and ‘youth radicalisation’ by the media reinforces the discourse of ‘youth at risk’ which has become the major framework underpinning Countering Violent Extremism (CVE) interventions. Similarly, the findings indicate that the uncritical use of ‘youth radicalisation’ has been used to serve political interests; and has become an instrument of policing young people, thus contributing to their cultural shaping. From this, it is evident that the media could thwart rather than assist CVE efforts. By exposing the political nature of the three terms through evidence-based research, this study offers recommendations on how critical reflective reporting by the media could help to make CVE more nuanced.

Keywords: discourse, extremism, radicalisation, terrorism, youth

Procedia PDF Downloads 98

Authors: M. Ismail, E. Grifell-Tatjé, A. Paz

Abstract:

A significant discussion on the topic of blockchain as a solution to the issues of intellectual property highlights the relevance that this topic holds. Some experts label this technology as destructive since it holds immense potential to change course of traditional practices. The extent and areas to which this technology can be of use are still being researched. This paper provides an in-depth review on the intellectual property and blockchain technology. Further it explores what makes blockchain suitable for intellectual property, the practical solutions available and the support different governments are offering. This paper further studies the framework of universities in context of its outputs and how can they be streamlined using blockchain technology. The paper concludes by discussing some limitations and future research question.

Keywords: blockchain, decentralization, open innovation, intellectual property, patents, university-industry relationship

Procedia PDF Downloads 152

Authors: Ray Bryant

Abstract:

3Strands Global Foundation (3SGF), a non-profit with a mission to mobilize communities to combat human trafficking through prevention education and reintegration programs, launched a groundbreaking study that calls out the usage and benefits of artificial intelligence in the war against human trafficking. Having gathered more than 30,000 stories from counselors and school staff who have gone through its PROTECT Prevention Education program, 3SGF sought to develop a methodology to measure the effectiveness of the training, which helps educators and school staff identify physical signs and behaviors indicating a student is being victimized. The program further illustrates how to recognize and respond to trauma and teaches the steps to take to report human trafficking, as well as how to connect victims with the proper professionals. 3SGF partnered with Levity, a leader in no-code Artificial Intelligence (AI) automation, to create the research study utilizing natural language processing, a branch of artificial intelligence, to measure the effectiveness of their prevention education program. By applying the logic created for the study, the platform analyzed and categorized each story. If the story, directly from the educator, demonstrated one or more of the desired outcomes; Increased Awareness, Increased Knowledge, or Intended Behavior Change, a label was applied. The system then added a confidence level for each identified label. The study results were generated with a 99% confidence level. Preliminary results show that of the 30,000 stories gathered, it became overwhelmingly clear that a significant majority of the participants now have increased awareness of the issue, demonstrated better knowledge of how to help prevent the crime, and expressed an intention to change how they approach what they do daily. In addition, it was observed that approximately 30% of the stories involved comments by educators expressing they wish they’d had this knowledge sooner as they can think of many students they would have been able to help. Objectives Of Research: To solve the problem of needing to analyze and accurately categorize more than 30,000 data points of participant feedback in order to evaluate the success of a human trafficking prevention program by using AI and Natural Language Processing. Methodologies Used: In conjunction with our strategic partner, Levity, we have created our own NLP analysis engine specific to our problem. Contributions To Research: The intersection of AI and human rights and how to utilize technology to combat human trafficking.

Keywords: AI, technology, human trafficking, prevention

Procedia PDF Downloads 31

Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

Abstract:

With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

Procedia PDF Downloads 58

Authors: Hassan Eshkiki, Benjamin Mora

Abstract:

The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting.

Keywords: explainable AI, EX AI, feature importance, counterfactual explanations

Procedia PDF Downloads 115

Authors: Bencherif Kada

Abstract:

In the forest management practice, landscape and Mediterranean forest are never posed as linked objects. But sustainable forestry requires the valorization of the forest landscape, and this aim involves assessing the spatial distribution of biodiversity by mapping forest landscaped units and subunits and by monitoring the environmental trends. This contribution aims to highlight, through object-oriented classifications, the landscaped biodiversity of the National Park of Tlemcen (Algeria). The methodology used is based on ground data and on the basic processing units of object-oriented classification, that are segments, so-called image-objects, representing a relatively homogenous units on the ground. The classification of Landsat Enhanced Thematic Mapper plus (ETM+) imagery is performed on image objects and not on pixels. Advantages of object-oriented classification are to make full use of meaningful statistic and texture calculation, uncorrelated shape information (e.g., length-to-width ratio, direction, and area of an object, etc.), and topological features (neighbor, super-object, etc.), and the close relation between real-world objects and image objects. The results show that per object classification using the k-nearest neighbor’s method is more efficient than per pixel one. It permits to simplify of the content of the image while preserving spectrally and spatially homogeneous types of land covers such as Aleppo pine stands, cork oak groves, mixed groves of cork oak, holm oak, and zen oak, mixed groves of holm oak and thuja, water plan, dense and open shrub-lands of oaks, vegetable crops or orchard, herbaceous plants, and bare soils. Texture attributes seem to provide no useful information, while spatial attributes of shape and compactness seem to be performant for all the dominant features, such as pure stands of Aleppo pine and/or cork oak and bare soils. Landscaped sub-units are individualized while conserving the spatial information. Continuously dominant dense stands over a large area were formed into a single class, such as dense, fragmented stands with clear stands. Low shrublands formations and high wooded shrublands are well individualized but with some confusion with enclaves for the former. Overall, a visual evaluation of the classification shows that the classification reflects the actual spatial state of the study area at the landscape level.

Keywords: forest, oaks, remote sensing, diversity, shrublands

Procedia PDF Downloads 79

Authors: Rafla Hchaichi

Abstract:

The public administration is currently experiencing in the field of quality unprecedented effervescence. However, in a globalized world more and more competitive, public services are confronted with the need to improve their performances which push public companies to implement quality approaches. Quality approaches have taken diverse forms such as service commitment, labels, certifications and the Common Assessment Framework. This paper provides an overview on the strategy for administrative development in Tunisia since the Carthaginian civilization until today. It outlines the evolution of quality management in the Tunisian public context while focusing on the National Referential of Quality of Administrative Services.

Keywords: quality approach, the common assessment framework, service commitment, label, certification, quality of public service, performance of public service, Tunisian Public Service

Procedia PDF Downloads 514

Authors: Martin Popescu D. Dana Maria

Abstract:

The aims of this research are to broaden the study on the relationship between emotional intelligence and counterproductive work behavior (CWB). The study sample consisted in 441 Romanian employees from companies all over the country. Data has been collected through web surveys and processed with SPSS. The results indicated an average correlation between the two constructs and their sub variables, employees with a high level of emotional intelligence tend to be less aggressive. In addition, labeling was considered an individual difference which has the power to influence the level of employee aggression. A regression model was used to underline the importance of emotional intelligence together with labeling as predictors of CWB. Results have shown that this regression model enforces the assumption that labeling and emotional intelligence, taken together, predict CWB. Employees, who label themselves as victims and have a low degree of emotional intelligence, have a higher level of CWB.

Keywords: aggression, CWB, emotional intelligence, labeling

Procedia PDF Downloads 437

Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

Abstract:

In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

Procedia PDF Downloads 486

Authors: Evgeny E. Tereshatov

Abstract:

Astatine-211 is considered one of the most promising radionuclides for Targeted Alpha Therapy. In order to develop reliable procedures to label biomolecules and utilize efficient delivery vehicle principles, one should understand the main chemical characteristics of astatine. The short half-life of 211At (~7.2 h) and absence of any stable isotopes of this element are limiting factors towards studying the behavior of astatine. Our team has developed a procedure for rapid and efficient isolation of astatine from irradiated bismuth material in nitric acid media based on 3-octanone and 1-octanol extraction chromatography resins. This process has been automated and it takes 20 min from the beginning of the target dissolution to the At-211 fraction elution. Our next step is to consider commercially available chromatography resins and their applicability in astatine purification in the same media. Results obtained along with the corresponding sorption mechanisms will be discussed.

Keywords: astatine-211, chromatography, automation, mechanism, radiopharmaceuticals

Procedia PDF Downloads 60

Authors: Genevieve Pugh, Johnathan Burns, Stefan Howorka

Abstract:

Single-molecule sensing via protein nanopores has achieved a step-change in portable and label-free DNA sequencing. However, protein pores of both natural or engineered origin are not able to produce the tunable diameters needed for effective protein sensing. Here, we describe a generic strategy to build synthetic DNA nanopores that are wide enough to accommodate folded protein. The pores are composed of interlinked DNA duplexes and carry lipid anchors to achieve the required membrane insertion. Our demonstrator pore has a contiguous cross-sectional channel area of 50 nm2 which is 6-times larger than the largest protein pore. Consequently, transport of folded protein across bilayers is possible. The modular design is amenable for different pore dimensions and can be adapted for protein sensing or to create molecular gates in synthetic biology.

Keywords: biosensing, DNA nanotechnology, DNA origami, nanopore sensing

Procedia PDF Downloads 289

Authors: Bilal Tebboub, AmelLabbani

Abstract:

This article introduces a compact sensor based on high-transmission, high-sensitivity two-dimensional photonic crystals. The photonic crystal consists of a square network of silicon rods in the air. The sensor is composed of two waveguide couplers and a microcavity designed for monitoring the percentage of hydrogen in the air and identifying gas types. Through the Finite-Difference Time-Domain (FDTD) method, we demonstrate that the sensor's resonance wavelength is contingent upon changes in the gas refractive index. We analyze transmission spectra, quality factors, and sensor sensitivity. The sensor exhibits a notable quality factor and a sensitivity value of 1374 nm/RIU. Notably, the sensor's compact structure occupies an area of 74.5 μm2, rendering it suitable for integrated optical circuits.

Keywords: 2-D photonic crystal, sensitivity, F.D.T.D method, label-free biosensing

Procedia PDF Downloads 45

Authors: Belaynesh Chekol, Numan Çelebi

Abstract:

The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

Procedia PDF Downloads 250

Authors: Haley Gershon

Abstract:

The transition from using fossil fuel-based building material to formulating eco-friendly and biobased building materials plays a key role in sustainable building. The growing demand on a global level for biobased materials in the building and construction industries heightens the importance of carbon-14 testing, an analytical method used to determine the percentage of biobased content that comprises a material’s ingredients. This presentation will focus on the use of carbon-14 analysis within the building materials sector. Carbon-14, also known as radiocarbon, is a weakly radioactive isotope present in all living organisms. Any fossil material older than 50,000 years will not contain any carbon-14 content. The radiocarbon method is thus used to determine the amount of carbon-14 content present in a given sample. Carbon-14 testing is performed according to ASTM D6866, a standard test method developed specifically for biobased content determination of material in solid, liquid, or gaseous form, which requires radiocarbon dating. Samples are combusted and converted into a solid graphite form and then pressed onto a metal disc and mounted onto a wheel of an accelerator mass spectrometer (AMS) machine for the analysis. The AMS instrument is used in order to count the amount of carbon-14 present. By submitting samples for carbon-14 analysis, manufacturers of building materials can confirm the biobased content of ingredients used. Biobased testing through carbon-14 analysis reports results as percent biobased content, indicating the percentage of ingredients coming from biomass sourced carbon versus fossil carbon. The analysis is performed according to standardized methods such as ASTM D6866, ISO 16620, and EN 16640. Products 100% sourced from plants, animals, or microbiological material are therefore 100% biobased, while products sourced only from fossil fuel material are 0% biobased. Any result in between 0% and 100% biobased indicates that there is a mixture of both biomass-derived and fossil fuel-derived sources. Furthermore, biobased testing for building materials allows manufacturers to submit eligible material for certification and eco-label programs such as the United States Department of Agriculture (USDA) BioPreferred Program. This program includes a voluntary labeling initiative for biobased products, in which companies may apply to receive and display the USDA Certified Biobased Product label, stating third-party verification and displaying a product’s percentage of biobased content. The USDA program includes a specific category for Building Materials. In order to qualify for the biobased certification under this product category, examples of product criteria that must be met include minimum 62% biobased content for wall coverings, minimum 25% biobased content for lumber, and a minimum 91% biobased content for floor coverings (non-carpet). As a result, consumers can easily identify plant-based products in the marketplace.

Keywords: carbon-14 testing, biobased, biobased content, radiocarbon dating, accelerator mass spectrometry, AMS, materials

Procedia PDF Downloads 129

Authors: Rajesh Bera, Durbadal Mandal, Rajib Kar, Sakti P. Ghoshal

Abstract:

This paper describes optimal thinning of an Elliptical Cylindrical Array (ECA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Particle Swarm Optimization (PSO) method, which represents a new approach for optimization problems in electromagnetic, is used in the optimization process. The PSO is used to determine the optimal set of ‘ON-OFF’ elements that provides a radiation pattern with maximum SLL reduction. Optimization is done without prefixing the value of First Null Beam Width (FNBW). The variation of SLL with element spacing of thinned array is also reported. Simulation results show that the number of array elements can be reduced by more than 50% of the total number of elements in the array with a simultaneous reduction in SLL to less than -27dB.

Keywords: thinned array, Particle Swarm Optimization, Elliptical Cylindrical Array, Side Lobe Label.

Procedia PDF Downloads 412

Authors: Roengchai Tansuchat, Wassanai Wattanutchariya, Aree Wiboonpongse

Abstract:

Parboiled rice is one of the most important food grains and classified in cereal and cereal product. In 2015, parboiled rice was traded more than 14.34 % of total rice trade. The major parboiled rice export countries are Thailand and India, while many countries in Africa and the Middle East such as Nigeria, South Africa, United Arab Emirates, and Saudi Arabia, are parboiled rice import countries. In the global rice market, parboiled rice pricing differs from white rice pricing because parboiled rice is semi-processing product, (soaking, steaming and drying) which affects to their color and texture. Therefore, parboiled rice export pricing does not depend only on the trade volume, length of grain, and percentage of broken rice or purity but also depend on their rice seed attributes such as color, whiteness, consistency of color and whiteness, and their texture. In addition, the parboiled rice price may depend on the country of origin, and other attributes, such as certification mark, label, packaging, and sales locations. The objectives of this paper are to study the attributes of parboiled rice sold in different countries and to evaluate the relationship between parboiled rice price in different countries and their attributes by using hedonic pricing model. These results are useful for product development, and marketing strategies development. The 141 samples of parboiled rice were collected from 5 major parboiled rice consumption countries, namely Nigeria, South Africa, Saudi Arabia, United Arab Emirates and Spain. The physicochemical properties and optical properties, namely size and shape of seed, colour (L*, a*, and b*), parboiled rice texture (hardness, adhesiveness, cohesiveness, springiness, gumminess, and chewiness), nutrition (moisture, protein, carbohydrate, fat, and ash), amylose, package, country of origin, label are considered as explanatory variables. The results from parboiled rice analysis revealed that most of samples are classified as long grain and slender. The highest average whiteness value is the parboiled rice sold in South Africa. The amylose value analysis shows that most of parboiled rice is non-glutinous rice, classified in intermediate amylose content range, and the maximum value was found in United Arab Emirates. The hedonic pricing model showed that size and shape are the key factors to determine parboiled rice price statistically significant. In parts of colour, brightness value (L*) and red-green value (a*) are statistically significant, but the yellow-blue value (b*) is insignificant. In addition, the texture attributes that significantly affect to the parboiled rice price are hardness, adhesiveness, cohesiveness, and gumminess. The findings could help both parboiled rice miller, exporter and retailers formulate better production and marketing strategies by focusing on these attributes.

Keywords: hedonic pricing model, optical properties, parboiled rice, physicochemical properties

Procedia PDF Downloads 302

Authors: Afaf Alharbi, Qianni Zhang

Abstract:

The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper introduces a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple-instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation of an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: attention multiple instance learning, MIL and transfer learning, histopathological slides, cancer tissue classification

Procedia PDF Downloads 58

Authors: Matodzi Rapalalani

Abstract:

In context with Spencer's (2018) classic labelling theory, labels influence the perceptions of both the individual and other members of society. That is, once labelled, the individual act in ways that confirm the stereotypes attached to the label. This study, therefore, investigates the understanding of gendered labelling and its effects on Vhavenda women. Gender socialization and patriarchy have been viewed as the core causes of the problem. The literature presented the development of gendered labelling, forms of it, and other aspects. A qualitative method of data collection was used in this study, and semi-structural interviews were conducted. A total of 6 participants were used as it is easy to deal with a small sample. Thematic analysis was used as the data was interpreted and analyzed. Ethical issues such as confidentiality, informed consent, and voluntary participation were considered. Through the analysis and data interpretation, causes such as lack of Christian values, insecurities, and lust were mentioned as well as some of the effects such as frustrations, increased divorce, and low self-esteem.

Keywords: gender, naming, Venda, women, African culture

Procedia PDF Downloads 55

Authors: Sorin Valcan, Mihail Gaianu

Abstract:

Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need for labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to an algorithm used for the generation of ground truth data for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher, which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual label adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: labeling automation, infrared camera, driver monitoring, eye detection, convolutional neural networks

Procedia PDF Downloads 76

Authors: Jovana Dj. Jovanovic, Svetlana M. Stevovic

Abstract:

The highest priority of so called, projected passive houses is to meet the appropriate energy demand. Every single material and layer which is injected into a dwelling has a certain energy quantity stored. The passive houses include optimized insulation levels with minimal thermal bridges, minimum of air leakage through the building, utilization of passive solar and internal gains, and good circulation of air which leans on mechanical ventilation system. The focus of this paper is on passive house features, benefits and targets, their feasibility and energy demands which are set up during each project. Numerous passive house-standards outline the very significant role of zero-energy dwellings towards the modern label of sustainable development. It is clear that the performance of both built and existing housing stock must be addressed if the population across the world sets out the energy objectives. This scientific article examines passive house features since the many passive house cases are launched.

Keywords: benefits, energy demands, passive houses, sustainable development

Procedia PDF Downloads 303

Authors: Paiboon Jeamponk, Tikamporn Thipsaeng

Abstract:

This paper is aimed to investigate farmers’ level of awareness and behavior of chemical pesticide uses, by using a case study of Suan Luang Sub- District Municipality, Ampawa, Samut Songkram Province. Questionnaire was employed in this study with the farmers from 46 households to explore their level of awareness in chemical pesticide uses, while interview and observation were adopted in exploring their behavior of chemical pesticide uses. The findings reflected the farmers’ high level of awareness in chemical pesticide uses in the hazardous effects of the chemical to human and environmental health, while their behavior of chemical pesticide uses explained their awareness paid to the right way of using pesticides, for instance reading the direction on the label, keeping children and animals away from the area of pesticide mixing, covering body with clothes and wearing hat and mask, no smoking, eating or drinking during pesticide spray or standing in windward direction.

Keywords: awareness, behavior, pesticide, farmers

Procedia PDF Downloads 395

Authors: Avatharam Ganivada, Krishna Shah

Abstract:

The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

Procedia PDF Downloads 41

Authors: Abdullah Al Mamun, Tarek R. Sheltami

Abstract:

Video conferencing is a highly demanding facility now a days in order to its real time characteristics, but faster communication is the prior requirement of this technology. Multi Protocol Label Switching (MPLS) IP Virtual Private Network (VPN) address this problem and it is able to make a communication faster than others techniques. However, this paper studies the performance comparison of video traffic between two routing protocols namely the Enhanced Interior Gateway Protocol(EIGRP) and Open Shortest Path First (OSPF). The combination of traditional routing and MPLS improve the forwarding mechanism, scalability and overall network performance. We will use GNS3 and OPNET Modeler 14.5 to simulate many different scenarios and metrics such as delay, jitter and mean opinion score (MOS) value are measured. The simulation result will show that OSPF and BGP-MPLS VPN offers best performance for video conferencing application.

Keywords: OSPF, BGP, EIGRP, MPLS, Video conference, Provider router, edge router, layer3 VPN

Procedia PDF Downloads 309

Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

Abstract:

Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

Procedia PDF Downloads 43