Search results for: quadratic discriminant

Authors: Li-Wei Wu, Chung-Yu Wang

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Recently, interest has been generated in the search for a new reference framework for value creation that is centered on the co-creation process. Co-creation implies cooperative value creation between service firms and customers and requires the building of experiences as well as the resolution of problems through the combined effort of the parties in the relationship. For customers, values are always co-created through their participation in services. Customers can ultimately determine the value of the service in use. This new approach emphasizes that a customer’s participation in the service process is considered indispensable to value co-creation. An important feature of service in the context of exchange is co-production, which implies that a certain amount of participation is needed from customers to co-produce a service and hence co-create value. Co-production no doubt helps customers better understand and take charge of their own roles in the service process. Thus, this proposal is to encourage co-production, thus facilitating value co-creation of that is reflected in both customers and service firms. Four determinants of co-production are identified in this study, namely, commitment, trust, asset specificity, and decision-making uncertainty. Commitment is an essential dimension that directly results in successful cooperative behaviors. Trust helps establish a relational environment that is fundamental to cross-border cooperation. Asset specificity motivates co-production because this determinant may enhance return on asset investment. Decision-making uncertainty prompts customers to collaborate with service firms in making decisions. In other words, customers adjust their roles and are increasingly engaged in co-production when commitment, trust, asset specificity, and decision-making uncertainty are enhanced. Although studies have examined the preceding effects, to our best knowledge, none has empirically examined the simultaneous effects of all the curvilinear relationships in a single study. When these determinants are excessive, however, customers will not engage in co-production process. In brief, we suggest that the relationships of commitment, trust, asset specificity, and decision-making uncertainty with co-production are curvilinear or are inverse U-shaped. These new forms of curvilinear relationships have not been identified in existing literature on co-production; therefore, they complement extant linear approaches. Most importantly, we aim to consider both the bright and the dark sides of the determinants of co-production.

Keywords: co-production, commitment, trust, asset specificity, decision-making uncertainty

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Authors: Placide Poba-Nzaou, Anicet Tchibozo, Malatsi Galani, Ali Etkkali, Erwin Halim

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Artificial intelligence (AI) is a general-purpose technology that is transforming many industries, working life and society by stimulating economic growth and innovation. Despite the huge potential of benefits to be generated, the adoption of AI varies from one organization to another, from one region to another, and from one industry to another, due in part to obstacles that can inhibit an organization or organizations located in a specific geographic region or operating in a specific industry from adopting AI technology. In this context, these obstacles and their implications for AI adoption from the perspective of configurational theory is important for at least three reasons: (1) understanding these obstacles is the first step in enabling policymakers and providers to make an informed decision in stimulating AI adoption (2) most studies have investigating obstacles or challenges of AI adoption in isolation with linear assumptions while configurational theory offers a holistic and multifaceted way of investigating the intricate interactions between perceived obstacles and barriers helping to assess their synergetic combination while holding assumptions of non-linearity leading to insights that would otherwise be out of the scope of studies investigating these obstacles in isolation. This study aims to pursue two objectives: (1) characterize organizations by uncovering the typical profiles of combinations of 15 internal and external obstacles that may prevent organizations from adopting AI technology, (2) assess the variation in terms of intensity of AI adoption associated with each configuration. We used data from a survey of AI adoption by organizations conducted throughout the EU27, Norway, Iceland and the UK (N=7549). Cluster analysis and discriminant analysis help uncover configurations of organizations based on the 15 obstacles, including eight external and seven internal. Second, we compared the clusters according to AI adoption intensity using an analysis of variance (ANOVA) and a Tamhane T2 post hoc test. The study uncovers three strongly separated clusters of organizations based on perceived obstacles to AI adoption. The clusters are labeled according to their magnitude of perceived obstacles to AI adoption: (1) Cluster I – High Level of perceived obstacles (N = 2449, 32.4%)(2) Cluster II – Low Level of perceived obstacles (N =1879, 24.9%) (3) Cluster III – Moderate Level of perceived obstacles (N =3221, 42.7%). The proposed taxonomy goes beyond the normative understanding of perceived obstacles to AI adoption and associated implications: it provides a well-structured and parsimonious lens that is useful for policymakers, AI technology providers, and researchers. Surprisingly, the ANOVAs revealed a “high level of perceived obstacles” cluster associated with a significantly high intensity of AI adoption.

Keywords: Artificial intelligence (AI), obstacles, adoption, taxonomy.

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Authors: Mai A. Ibrahim, Mohammed El-Beltagy, Motaz Khorshid

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Emerging Markets return distributions have shown significance departure from normality were they are characterized by fatter tails relative to the normal distribution and exhibit levels of skewness and kurtosis that constitute a significant departure from normality. Therefore, the classical Markowitz Mean-Variance is not applicable for emerging markets since it assumes normally-distributed returns (with zero skewness and kurtosis) and a quadratic utility function. Moreover, the Markowitz mean-variance analysis can be used in cases of moderate non-normality and it still provides a good approximation of the expected utility, but it may be ineffective under large departure from normality. Higher moments models and median models have been suggested in the literature for asset allocation in this case. Higher moments models have been introduced to account for the insufficiency of the description of a portfolio by only its first two moments while the median model has been introduced as a robust statistic which is less affected by outliers than the mean. Tail risk measures such as Value-at Risk (VaR) and Conditional Value-at-Risk (CVaR) have been introduced instead of Variance to capture the effect of risk. In this research, higher moment models including the Mean-Variance-Skewness (MVS) and Mean-Variance-Skewness-Kurtosis (MVSK) are formulated as single-objective non-linear programming problems (NLP) and median models including the Median-Value at Risk (MedVaR) and Median-Mean Absolute Deviation (MedMAD) are formulated as a single-objective mixed-integer linear programming (MILP) problems. The higher moment models and median models are compared to some benchmark portfolios and tested on real financial data in the Egyptian main Index EGX30. The results show that all the median models outperform the higher moment models were they provide higher final wealth for the investor over the entire period of study. In addition, the results have confirmed the inapplicability of the classical Markowitz Mean-Variance to the Egyptian stock market as it resulted in very low realized profits.

Keywords: Egyptian stock exchange, emerging markets, higher moment models, median models, mixed-integer linear programming, non-linear programming

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Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng

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Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.

Keywords: human perception, public space quality, deep learning, negative elements, street images

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Authors: Athena Daniilidou, Maria Platsidou

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Resilience is considered to greatly affect the personal and occupational wellbeing and efficacy of individuals; therefore, it has been widely studied in the social and behavioral sciences. Given its significance, several scales have been created to assess resilience of children and adults. However, most of these scales focus on examining only the internal protective or risk factors that affect the levels of resilience. The aim of the present study is to create a reliable scale that assesses both the internal and the external protective factors that affect Greek teachers’ levels of resilience. Participants were 136 secondary school teachers (89 females, 47 males) from urban areas of Greece. Connor-Davidson Resilience Scale (CD-Risc) and Resilience Scale for Adults (RSA) were used to collect the data. First, exploratory factor analysis was employed to investigate the inner structure of each scale. For both scales, the analyses revealed a differentiated factor solution compared to the ones proposed by the creators. That prompt us to create a scale that would combine the best fitting subscales of the CD-Risc and the RSA. To this end, the items of the four factors with the best fit and highest reliability were used to create the ‘Teachers' resilience scale’. Exploratory factor analysis revealed that the scale assesses the following protective/risk factors: Personal Competence and Strength (9 items, α=.83), Family Cohesion Spiritual Influences (7 items, α=.80), Social Competence and Peers Support (7 items, α=.78) and Spiritual Influence (3 items, α=.58). This four-factor model explained 49,50% of the total variance. In the next step, a confirmatory factor analysis was performed on the 26 items of the derived scale to test the above factor solution. The fit of the model to the data was good (χ2/292 = 1.245, CFI = .921, GFI = .829, SRMR = .074, CI90% = .026-,056, RMSEA = 0.43), indicating that the proposed scale can validly measure the aforementioned four aspects of teachers' resilience and thus confirmed its factorial validity. Finally, analyses of variance were performed to check for individual differences in the levels of teachers' resilience in relation to their gender, age, marital status, level of studies, and teaching specialty. Results were consistent to previous findings, thus providing an indication of discriminant validity for the instrument. This scale has the advantage of assessing both the internal and the external protective factors of resilience in a brief yet comprehensive way, since it consists 26 items instead of the total of 58 of the CD-Risc and RSA scales. Its factorial inner structure is supported by the relevant literature on resilience, as it captures the major protective factors of resilience identified in previous studies.

Keywords: protective factors, resilience, scale development, teachers

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Valentin Nickolai, Florian Pfeffel, Christian Louis Kühner

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In many industrialized nations, the demand for skilled workers rises, causing the current market for employees to be more candidate-driven than employer-driven. Therefore, losing highly skilled and experienced employees due to early or partial retirement negatively impacts firms. Therefore, finding new ways to incentivize older employees (Silver Workers) to stay longer with the company and in their job can be crucial for the success of a firm. This study analyzes how working remotely can be a valid incentive for experienced Silver Workers to stay in their job and instead work from home with more flexible working hours. An online survey with n = 684 respondents, who are employed in the service sector, has been conducted based on 13 constructs that influence job satisfaction. These have been further categorized into three groups “classic influencing factors,” “influencing factors changed by remote working,” and new remote working influencing factors,” and were analyzed using structural equation modeling (SEM). Here, Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). It was shown in the SEM-analysis that the influencing factor on job satisfaction, “identification with the work,” is the most significant with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis also shows that the identification with the work is the most significant factor in all three work models mentioned above and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees between the ages of 56 and 65 years have the highest job satisfaction when working entirely from home or remotely. Furthermore, their job satisfaction score of 5.4 on a scale from 1 (very dissatisfied) to 7 (very satisfied) is the highest amongst all age groups in any of the three work models. Due to the significantly higher job satisfaction, it can be argued that giving Silver Workers the offer to work from home or remotely can incentivize them not to opt for early retirement or partial retirement but to stay in their job full-time Furthermore, these findings can indicate that employees in the Silver Worker age are much more inclined to leave their job for early retirement if they have to entirely work in the office.

Keywords: home office, remote work instead of early or partial retirement, silver worker, structural equation modeling

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Authors: Xiaoquan Huang, Congcong Li, Tingting Wei, Changcun Bai, Na Liu, Meng Tang

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Silver is often doped on nano-titanium dioxide photocatalysts (Ag-TiO₂) by photodeposition method to improve their utilization of visible-light while increasing the toxicity of TiO₂。 However, it is not known what factors influence this toxicity and how to reduce toxicity while maintaining the maximum catalytic activity. In this study, Ag-TiO₂ photocatalysts were synthesized by the photodeposition method with different silver content (AgC) and photodeposition time (PDT). Characterization and catalytic experiments demonstrated that silver was well assembled on TiO₂ with excellent visible-light catalytic activity, and the size of silver increased with PDT. In vitro, the cell viability of lung epithelial cells A549 and BEAS-2B showed that the higher content and smaller size of silver doping caused higher toxicity. In vivo, Ag-TiO₂ catalysts with lower AgC or larger silver particle size obviously caused less pulmonary pro-inflammatory and pro-fibrosis responses. However, the visible light catalytic activity decreased with the increase in silver size. Therefore, in order to optimize the Ag-TiO₂ photocatalyst with the lowest pulmonary toxicity and highest catalytic performance, response surface methodology (RSM) was further performed to optimize the two independent variables of AgC and PDT. Visible-light catalytic activity was evaluated by the degradation rate of Rhodamine B, the antibacterial property was evaluated by killing log value for Escherichia coli, and cytotoxicity was evaluated by IC50 to BEAS-2B cells. As a result, the RSM model showed that AgC and PDT exhibited an interaction effect on catalytic activity in the quadratic model. AgC was positively correlated with antibacterial activity. Cytotoxicity was proportional to AgC while inversely proportional to PDT. Finally, the optimization values were AgC 3.08 w/w% and PDT 28 min. Under this optimal condition, the relatively high silver proportion ensured the visible-light catalytic and antibacterial activity, while the longer PDT effectively reduced the cytotoxicity. This study is of significance for the safe and efficient application of silver-doped TiO₂ photocatalysts.

Keywords: Ag-doped TiO₂, cytotoxicity, inflammtion, fibrosis, response surface methodology

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Authors: Hsiu Tan Liu, Chun Jung Liu

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It has multiple purposes to develop a sign language receptive skills test. For example, this test can be used to be an important tool for education and to understand the sign language ability of deaf children. There is no available test for these purposes in Taiwan. Through the discussion of experts and the references of standardized Taiwanese Sign Language Receptive Test for adults and adolescents, the frame of Taiwanese Sign Language Receptive Skills Test (TSL-RST) for deaf children was developed, and the items were further designed. After multiple times of pre-trials, discussions and corrections, TSL-RST is finally developed which can be conducted and scored online. There were 33 deaf children who agreed to be tested from all three deaf schools in Taiwan. Through item analysis, the items were picked out that have good discrimination index and fair difficulty index. Moreover, psychometric indexes of reliability and validity were established. Then, derived the regression formula was derived which can predict the sign language receptive skills of deaf children. The main results of this study are as follows. (1). TSL-RST includes three sub-test of vocabulary comprehension, syntax comprehension and paragraph comprehension. There are 21, 20, and 9 items in vocabulary comprehension, syntax comprehension, and paragraph comprehension, respectively. (2). TSL-RST can be conducted individually online. The sign language ability of deaf students can be calculated fast and objectively, so that they can get the feedback and results immediately. This can also contribute to both teaching and research. The most subjects can complete the test within 25 minutes. While the test procedure, they can answer the test questions without relying on their reading ability or memory capacity. (3). The sub-test of the vocabulary comprehension is the easiest one, syntax comprehension is harder than vocabulary comprehension and the paragraph comprehension is the hardest. Each of the three sub-test and the whole test are good in item discrimination index. (4). The psychometric indices are good, including the internal consistency reliability (Cronbach’s α coefficient), test-retest reliability, split-half reliability, and content validity. The sign language ability are significantly related to non-verbal IQ, the teachers’ rating to the students’ sign language ability and students’ self-rating to their own sign language ability. The results showed that the higher grade students have better performance than the lower grade students, and students with deaf parent perform better than those with hearing parent. These results made TLS-RST have great discriminant validity. (5). The predictors of sign language ability of primary deaf students are age and years of starting to learn sign language. The results of this study suggested that TSL-RST can effectively assess deaf student’s sign language ability. This study also proposed a model to develop a sign language tests.

Keywords: comprehension test, elementary school, sign language, Taiwan sign language

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Authors: Sara Gallo, Margherita Pasini, Margherita Brondino, Daniela Raccanello, Roberto Burro, Elisa Menardo

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Most of the studies on the ability of environments to recover people’s cognitive resources have been conducted in laboratory using simulated environments (e.g., photographs, videos, or virtual reality), based on the implicit assumption that exposure to simulated environments has the same effects of exposure to real environments. However, the technical characteristics of simulated environments, such as the dynamic or static characteristics of the stimulus, critically affect their perception. Measuring perceived restorativeness in situ rather than in laboratory could increase the validity of the obtained measurements. Personal mobile devices could be useful because they allow accessing immediately online surveys when people are directly exposed to an environment. At the same time, it becomes important to develop short and reliable measuring instruments that allow a quick assessment of the restorative qualities of the environments. One of the frequently used self-report measures to assess perceived restorativeness is the “Perceived Restorativeness Scale” (PRS) based on Attention Restoration Theory. A lot of different versions have been proposed and used according to different research purposes and needs, without studying their validity. This longitudinal study reported some preliminary validation analyses on a short version of original scale, the PRS-6, developed to be quick and mobile-friendly. It is composed of 6 items assessing fascination and being-away. 102 Italian university students participated to the study, 84% female with age ranging from 18 to 47 (M = 20.7; SD = 2.9). Data were obtained through a survey online that asked them to report their perceived restorativeness of the environment they were in (and the kind of environment) and their positive emotion (Positive and Negative Affective Schedule, PANAS) once a day for seven days. Cronbach alpha and item-total correlations were used to assess reliability and internal consistency. Confirmatory Factor Analyses (CFA) models were run to study the factorial structure (construct validity). Correlation analyses between PRS and PANAS scores were used to check discriminant validity. In the end, multigroup CFA models were used to study measurement invariance (configural, metric, scalar, strict) between different mobile devices and between day of assessment. On the whole, the PRS-6 showed good psychometric proprieties, similar to those of the original scale, and invariance across devices and days. These results suggested that the PRS-6 could be a valid alternative to assess perceived restorativeness when researchers need a brief and immediate evaluation of the recovery quality of an environment.

Keywords: restorativeness, validation, short scale development, psychometrics proprieties

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Authors: Mohd Sabri Minhat, Nurul Adilla Mohd Subha

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The PUSPATI TRIGA Reactor (RTP), Malaysia reached its first criticality on June 28, 1982, with power capacity 1MW thermal. The Feedback Control Algorithm (FCA) which is conventional Proportional-Integral (PI) controller, was used for present power control method to control fission process in RTP. It is important to ensure the core power always stable and follows load tracking within acceptable steady-state error and minimum settling time to reach steady-state power. At this time, the system could be considered not well-posed with power tracking performance. However, there is still potential to improve current performance by developing next generation of a novel design nuclear core power control. In this paper, the dual-mode predictions which are proposed in modelling Optimal Model Predictive Control (OMPC), is presented in a state-space model to control the core power. The model for core power control was based on mathematical models of the reactor core, OMPC, and control rods selection algorithm. The mathematical models of the reactor core were based on neutronic models, thermal hydraulic models, and reactivity models. The dual-mode prediction in OMPC for transient and terminal modes was based on the implementation of a Linear Quadratic Regulator (LQR) in designing the core power control. The combination of dual-mode prediction and Lyapunov which deal with summations in cost function over an infinite horizon is intended to eliminate some of the fundamental weaknesses related to MPC. This paper shows the behaviour of OMPC to deal with tracking, regulation problem, disturbance rejection and caters for parameter uncertainty. The comparison of both tracking and regulating performance is analysed between the conventional controller and OMPC by numerical simulations. In conclusion, the proposed OMPC has shown significant performance in load tracking and regulating core power for nuclear reactor with guarantee stabilising in the closed-loop.

Keywords: core power control, dual-mode prediction, load tracking, optimal model predictive control

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Authors: Noelene Rose

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The DSM has been plagued with conceptual issues since its inception, not least discriminant validity and comorbidity issues. An attempt to remain a-theoretical in the divide between the psycho-dynamicists and the behaviourists contributed to much of this, in particular relating to the Personality Disorders. With the DSM-5, although the criterion have remained unchanged, major conceptual and structural directions have been flagged and proposed in section III. The biggest changes concern the Personality Disorders. While Narcissistic Personality Disorder (NPD) was initially tagged for removal, instead the addition of section III proposes a move away from a categorical approach to a more dimensional approach, with a measure of Global Function of Personality. This global measure is an assessment of impairment of self-other relations; a measure of trait narcissism. In the same way mainstream psychology has struggled in its diagnosis of narcissism, so too in its treatment. Kohut’s self psychology represents the most significant inroad in theory and treatment for the narcissistic disorders. Kohut had moved away from a categorical system, towards disorders of the self. According to this theory, disorders of the self are the result of childhood trauma (impaired attunement) resulting in a developmental arrest. Self-psychological, Psychodynamic treatment of narcissism, however, is expensive, in time and money and outside the awareness or access of most people. There is more than a suggestion that narcissism is on the increase, created in trauma and worsened by a fearful world climate. A dimensional model of narcissism, from mild to severe, requires cut off points for diagnosis. But where do we draw the line? Mainstream psychology is inclined to set it high when there is some degree of impairment in functioning in daily life. Transpersonal Psychology is inclined to set it low, with the concept that we all have some degree of narcissism and that it is the point and the path of our life journey to transcend our focus on our selves. Mainstream psychology stops its focus on trait narcissism with a healthy level of self esteem, but it is at this point that Transpersonal Psychology can complement the discussion. From a Transpersonal point of view, failure to begin the process of self-transcendence will also create emotional symptoms of meaning or purpose, often later in our lives, and is also conceived of as a developmental arrest. The maps for this transcendence are hidden in plain sight; in the chakras of kundalini yoga, in the sacraments of the Catholic Church, in the Kabbalah tree of life of Judaism, in Maslow’s hierarchy of needs, to name a few. This paper outlines some proposed research exploring the use of daily practices that can be incorporated into the therapy room; practices that utilise meditation, visualisation and imagination: that are informed by spiritual technology and guided by the psychodynamic theory of Self Psychology.

Keywords: narcissism, self-psychology, self-practice, self-transcendence

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Authors: Ishaq Kashif, Muhammad Younas, Muhammad Riaz, Mubarak Ali

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Poor feeding management during pre-weaning period is one of the factors resulting in compromised growth of Beetal kids fattened for meat purpose. The main reason for this anomaly may be less milk offered to kids and non-serious efforts for its management. This study was planned to find the most appropriate protein level suiting the age of the weaning while shifting animals to high input feeding system. Total of 42 Beetal male kids having 30 (±10), 60 (±10) and 90 (±10) days of age were selected with 16 in each age group. They were designated as G30, G60 and G90, respectively. The weights of animals were; 8±2 kg (G30), 12±2 kg (G60) and 16±2 kg (G90), respectively. All animals were weaned by introducing the total mix feed gradually and withdrawing the milk during the adjustment period of two weeks. The pelleted starter ration (total mix feed) with three various dietary protein levels designated as R1 (16% CP), R2 (20% CP) and R3 (26% CP) were introduced. The control group was reared on the fodder (Maize). The starter rations were iso-caloric and were offered for six-week duration. All animals were exposed to treatment using two-factor factorial (3×3) plus control treatment arrangement under completely randomized design. The data were collected on average daily feed intake (ADFI), average daily gain (ADG), gain to intake ratio, Klieber ratio (KR), body measurements and blood metabolites of kids. The data was analyzed using aov function of R-software. The statistical analysis showed that starter feed protein levels and age of weaning had significant interaction for ADG (P < 0.001), KR (P < 0.001), ADFI (P < 0.05) and blood urea nitrogen (P < 0.05) while serum creatinine and feed conversion had non-significant interaction. The trend analysis revealed that ADG had significant quadratic interaction (P < 0.05) within protein levels and age of weaning. It was found that animals weaned at 30 or 60 days, on R2 diet had better ADG (46.8 gm/day and 87.06 gm/day, respectively) weaned at 60 days of age. The animals weaned at 90 days had best ADG (127 gm/day) with R1. It is concluded that animal weaned at 30 or 40 days required 20% CP for better growth performance while animal at 90 days showed better performance with 16% CP.

Keywords: average daily gain, starter protein levels, weaning age, gain to intake ratio

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Authors: Silvia Portarena, Chiara Anselmi, Chiara Baldacchini, Enrico Brugnoli

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Extra virgin olive oil (EVOO) is a complex matrix mainly composed by fatty acid and other minor compounds, among which carotenoids are well known for their antioxidative function that is a key mechanism of protection against cancer, cardiovascular diseases, and macular degeneration in humans. EVOO composition in terms of such constituents is generally the result of a complex combination of genetic, agronomical and environmental factors. To selectively improve the quality of EVOOs, the role of each factor on its biochemical composition need to be investigated. By selecting fruits from four different cultivars similarly grown and harvested, it was demonstrated that Raman spectroscopy, combined with chemometric analysis, is able to discriminate the different cultivars, also as a function of the harvest date, based on the relative content and composition of fatty acid and carotenoids. In particular, a correct classification up to 94.4% of samples, according to the cultivar and the maturation stage, was obtained. Moreover, by using gas chromatography and high-performance liquid chromatography as reference techniques, the Raman spectral features further allowed to build models, based on partial least squares regression, that were able to predict the relative amount of the main fatty acids and the main carotenoids in EVOO, with high coefficients of determination. Besides genetic factors, climatic parameters, such as light exposition, distance from the sea, temperature, and amount of precipitations could have a strong influence on EVOO composition of both major and minor compounds. This suggests that the Raman spectra could act as a specific fingerprint for the geographical discrimination and authentication of EVOO. To understand the influence of environment on EVOO Raman spectra, samples from seven regions along the Italian coasts were selected and analyzed. In particular, it was used a dual approach combining Raman spectroscopy and isotope ratio mass spectrometry (IRMS) with principal component and linear discriminant analysis. A correct classification of 82% EVOO based on their regional geographical origin was obtained. Raman spectra were obtained by Super Labram spectrometer equipped with an Argon laser (514.5 nm wavelenght). Analyses of stable isotope content ratio were performed using an isotope ratio mass spectrometer connected to an elemental analyzer and to a pyrolysis system. These studies demonstrate that RR spectroscopy is a valuable and useful technique for the analysis of EVOO. In combination with statistical analysis, it makes possible the assessment of specific samples’ content and allows for classifying oils according to their geographical and varietal origin.

Keywords: authentication, chemometrics, olive oil, raman spectroscopy

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Authors: Christopher Baiyegunhi, Kuiwu Liu, Oswald Gwavava

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Grain size analysis is a vital sedimentological tool used to unravel the hydrodynamic conditions, mode of transportation and deposition of detrital sediments. In this study, detailed grain-size analysis was carried out on thirty-five sandstone samples from the Ecca Group in the Eastern Cape Province of South Africa. Grain-size statistical parameters, bivariate analysis, linear discriminate functions, Passega diagrams and log-probability curves were used to reveal the depositional processes, sedimentation mechanisms, hydrodynamic energy conditions and to discriminate different depositional environments. The grain-size parameters show that most of the sandstones are very fine to fine grained, moderately well sorted, mostly near-symmetrical and mesokurtic in nature. The abundance of very fine to fine grained sandstones indicates the dominance of low energy environment. The bivariate plots that the samples are mostly grouped, except for the Prince Albert samples that show scattered trend, which is due to the either mixture of two modes in equal proportion in bimodal sediments or good sorting in unimodal sediments. The linear discriminant function (LDF) analysis is dominantly indicative of turbidity current deposits under shallow marine environments for samples from the Prince Albert, Collingham and Ripon Formations, while those samples from the Fort Brown Formation are fluvial (deltaic) deposits. The graphic mean value shows the dominance of fine sand-size particles, which point to relatively low energy conditions of deposition. In addition, the LDF results point to low energy conditions during the deposition of the Prince Albert, Collingham and part of the Ripon Formation (Pluto Vale and Wonderfontein Shale Members), whereas the Trumpeters Member of the Ripon Formation and the overlying Fort Brown Formation accumulated under high energy conditions. The CM pattern shows a clustered distribution of sediments in the PQ and QR segments, indicating that the sediments were deposited mostly by suspension and rolling/saltation, and graded suspension. Furthermore, the plots also show that the sediments are mainly deposited by turbidity currents. Visher diagrams show the variability of hydraulic depositional conditions for the Permian Ecca Group sandstones. Saltation is the major process of transportation, although suspension and traction also played some role during deposition of the sediments. The sediments were mainly in saltation and suspension before being deposited.

Keywords: grain size analysis, hydrodynamic condition, depositional environment, Ecca Group, South Africa

Procedia PDF Downloads 481

Authors: H. Minor-Pérez, A. M. Mota-Silva, S. Ortiz-Barrios

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Substances of vegetable origin with antioxidant capacity have a high potential for application on the conservation of some foods, can prevent or reduce for example oxidation of lipids. However a food is a complex system whose wide variety of components wich can reduce or eliminate this antioxidant capacity. The antioxidant activity can be determined with the ABTS technique. The radical ABTS+ is generated from the acid 2, 2´ - Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). This radical is a composite color bluish-green, stable and with a spectrum of absorption into the UV-visible. The addition of antioxidants causes discoloration, value that can be reported as a percentage of inhibition of the cation radical ABTS+. The objective of this study was evaluated the effect of the combination of the pH and the essential oil of cinnamon (EOC) on inhibition of the radical ABTS+, using statistical design of central point (Design Expert) to obtain mathematical models that describe this phenomenon. Were evaluated 17 treatments with combinations of pH 5, 6 and 7 (citrate-phosphate buffer) and the concentration of essential oil of cinnamon (C): 0 µg/mL, 100 µg/mL and 200 µg/mL. The samples were analyzed using the ABTS technique. The reagent was dissolved in methanol 80% to standardized the absorbance to 0.7 +/- 0.1 at 754 nm. Then samples were mixed with reagent standardized ABTS and after 1 min and 7 min absorbance was read for each treatment at 754 nm. Was used a curve pattern with vitamin C and reported the values as inhibition (%) of radical ABTS+. The statistical analysis shows the experimental results were adjusted to a quadratic model, to the times of 1 min and 7 min. This model describes the influence of the factors investigated independently: pH and cinnamon essential oil (µg/mL) and the effect of the interaction between pH*C, as well as the square of the pH2 and C2. The model obtained was Y = 10.33684 - 3.98118*pH + 1.17031*C + 0.62745*pH2 - 3.26675*10-3*C2 - 0.013112*pH*C, where Y is the response variable. The coefficient of determination was 0.9949 for 1 min. The equation was obtained at 7 min and = - 10.89710 + 1.52341*pH + 1.32892*C + 0.47953*pH2 - 3.56605*10- *C2 - 0.034687*pH*C. The coefficient of determination was 0.9970. This means that only 1% of the total variation is not explained by the developed models. At 100 µg/mL of EOC was obtained an inhibition percentage of 80%, 84% and 97% for the pH values of 5,6 and 7 respectively, while a value of 200 µg/mL the inhibition (%) was very similar for the treatments. In these values of pH was obtained an inhibition close 97%. In conclusion the pH does not have a significant effect on the antioxidant capacity, while the concentration of EOC was decisive for the antioxidant capacity. The authors acknowledge the funding provided by the CONACYT for the project 131998.

Keywords: antioxidant activity, ABTS technique, essential oil of cinnamon, mathematical models

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Authors: Lakshmidevi Sreeramareddy, Komalpreet Kaur, Nane Pothier

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Graphical-based passwords have existed for decades. Their major advantage is that they are easier to remember than an alphanumeric password. However, their disadvantage (especially recognition-based passwords) is the smaller password space, making them more vulnerable to brute force attacks. Graphical passwords are also highly susceptible to the shoulder-surfing effect. The gesture-based password method that we developed is a grid-free, template-free method. In this study, we evaluated the gesture-based passwords for usability and vulnerability. The results of the study are significant. We developed a gesture-based password application for data collection. Two modes of data collection were used: Creation mode and Replication mode. In creation mode (Session 1), users were asked to create six different passwords and reenter each password five times. In replication mode, users saw a password image created by some other user for a fixed duration of time. Three different duration timers, such as 5 seconds (Session 2), 10 seconds (Session 3), and 15 seconds (Session 4), were used to mimic the shoulder-surfing attack. After the timer expired, the password image was removed, and users were asked to replicate the password. There were 74, 57, 50, and 44 users participated in Session 1, Session 2, Session 3, and Session 4 respectfully. In this study, the machine learning algorithms have been applied to determine whether the person is a genuine user or an imposter based on the password entered. Five different machine learning algorithms were deployed to compare the performance in user authentication: namely, Decision Trees, Linear Discriminant Analysis, Naive Bayes Classifier, Support Vector Machines (SVMs) with Gaussian Radial Basis Kernel function, and K-Nearest Neighbor. Gesture-based password features vary from one entry to the next. It is difficult to distinguish between a creator and an intruder for authentication. For each password entered by the user, four features were extracted: password score, password length, password speed, and password size. All four features were normalized before being fed to a classifier. Three different classifiers were trained using data from all four sessions. Classifiers A, B, and C were trained and tested using data from the password creation session and the password replication with a timer of 5 seconds, 10 seconds, and 15 seconds, respectively. The classification accuracies for Classifier A using five ML algorithms are 72.5%, 71.3%, 71.9%, 74.4%, and 72.9%, respectively. The classification accuracies for Classifier B using five ML algorithms are 69.7%, 67.9%, 70.2%, 73.8%, and 71.2%, respectively. The classification accuracies for Classifier C using five ML algorithms are 68.1%, 64.9%, 68.4%, 71.5%, and 69.8%, respectively. SVMs with Gaussian Radial Basis Kernel outperform other ML algorithms for gesture-based password authentication. Results confirm that the shorter the duration of the shoulder-surfing attack, the higher the authentication accuracy. In conclusion, behavioral features extracted from the gesture-based passwords lead to less vulnerable user authentication.

Keywords: authentication, gesture-based passwords, machine learning algorithms, shoulder-surfing attacks, usability

Procedia PDF Downloads 107

Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

Procedia PDF Downloads 105

Authors: Mariam Mtunguja, Henry Laswai, Yasinta Muzanilla, Joseph Ndunguru

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Starchy tuberous roots of cassava provide food for people but also find application in various industries. Recently there has been the focus of concentrated research efforts to fully exploit its potential as a sustainable multipurpose crop. High starch yield is the important trait for commercial cassava production for the starch industries. Furthermore, cyanide present in cassava root poses a health challenge in the use of cassava for food. Farming communities where cassava is a staple food, prefer bitter (high cyanogenic) varieties as protection from predators and thieves. As a result, food insecure farmers prefer growing bitter cassava. This has led to cyanide intoxication to this farming communities. Cassava farmers can benefit from marketing cassava to starch producers thereby improving their income and food security. This will decrease dependency on cassava as staple food as a result of increased income and be able to afford other food sources. To achieve this, adequate information is required on the right cassava cultivars and appropriate harvesting period so as to maximize cassava production and profitability. This study aimed at identifying suitable cassava cultivars and optimum time of harvest to maximize starch production. Six commonly grown cultivars were identified and planted in a complete random block design and further analysis was done to assess variation in physicochemical characteristics, starch yield and cyanogenic potentials across three environments. The analysis showed that there is a difference in physicochemical characteristics between landraces (p ≤ 0.05), and can be targeted to different industrial applications. Among landraces, dry matter (30-39%), amylose (11-19%), starch (74-80%) and reducing sugars content (1-3%) varied when expressed on a dry weight basis (p ≤ 0.05); however, only one of the six genotypes differed in crystallinity and mean starch granule particle size, while glucan chain distribution and granule morphology were the same. In contrast, the starch functionality features measured: swelling power, solubility, syneresis, and digestibility differed (p ≤ 0.05). This was supported by Partial least square discriminant analysis (PLS-DA), which highlighted the divergence among the cassavas based on starch functionality, permitting suggestions for the targeted uses of these starches in diverse industries. The study also illustrated genotypic difference in starch yield and cyanogenic potential. Among landraces, Kiroba showed potential for maximum starch yield (12.8 t ha-1) followed by Msenene (12.3 t ha-1) and third was Kilusungu (10.2 t ha-1). The cyanide content of cassava landraces was between 15 and 800 ppm across all trial sites. GGE biplot analysis further confirmed that Kiroba was a superior cultivar in terms of starch yield. Kilusungu had the highest cyanide content and average starch yield, therefore it can also be suitable for use in starch production.

Keywords: cyanogen, cassava starch, food security, starch yield

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Authors: Gaelle Candel, David Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning

Procedia PDF Downloads 144

Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

Procedia PDF Downloads 78

Authors: I. Valdez, J. Perdomo, M. Colindres, N. Castro

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Today, digital servo systems are extensively used in industrial manufacturing processes, robotic applications, vehicles and other areas. In such control systems, control action is provided by digital controllers with different compensation algorithms, which are designed to meet specific requirements for a given application. Due to the constant search for optimization in industrial processes, it is of interest to design digital controllers that offer ease of realization, improved computational efficiency, affordable return rates, and ease of tuning that ultimately improve the performance of the controlled actuators. There is a vast range of options of compensation algorithms that could be used, although in the industry, most controllers used are based on a PID structure. This research article compares different types of digital compensators implemented in a servo system for DC motor position control. PID compensation is evaluated on its two most common architectures: PID position form (1 DOF), and PID speed form (2 DOF). State feedback algorithms are also evaluated, testing two modern control theory techniques: discrete state observer for non-measurable variables tracking, and a linear quadratic method which allows a compromise between the theoretical optimal control and the realization that most closely matches it. The compared control systems’ performance is evaluated through simulations in the Simulink platform, in which it is attempted to model accurately each of the system’s hardware components. The criteria by which the control systems are compared are reference tracking and disturbance rejection. In this investigation, it is considered that the accurate tracking of the reference signal for a position control system is particularly important because of the frequency and the suddenness in which the control signal could change in position control applications, while disturbance rejection is considered essential because the torque applied to the motor shaft due to sudden load changes can be modeled as a disturbance that must be rejected, ensuring reference tracking. Results show that 2 DOF PID controllers exhibit high performance in terms of the benchmarks mentioned, as long as they are properly tuned. As for controllers based on state feedback, due to the nature and the advantage which state space provides for modelling MIMO, it is expected that such controllers evince ease of tuning for disturbance rejection, assuming that the designer of such controllers is experienced. An in-depth multi-dimensional analysis of preliminary research results indicate that state feedback control method is more satisfactory, but PID control method exhibits easier implementation in most control applications.

Keywords: control, DC motor, discrete PID, discrete state feedback

Procedia PDF Downloads 266

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 79

Authors: Andrey Davydov, Konstantin Sveshnikov, Yulia Voronina

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There is now a lot of interest to the non-perturbative QED-effects, caused by diving of discrete levels into the negative continuum in the supercritical static or adiabatically slowly varying Coulomb fields, that are created by the localized extended sources with Z > Z_cr. Such effects have attracted a considerable amount of theoretical and experimental activity, since in 3+1 QED for Z > Z_cr,1 ≈ 170 a non-perturbative reconstruction of the vacuum state is predicted, which should be accompanied by a number of nontrivial effects, including the vacuum positron emission. Similar in essence effects should be expected also in both 2+1 D (planar graphene-based hetero-structures) and 1+1 D (one-dimensional ‘hydrogen ion’). This report is devoted to the study of such essentially non-perturbative vacuum effects for the supercritical Dirac-Coulomb systems in 1+1D and 2+1D, with the main attention drawn to the vacuum polarization energy. Although the most of works considers the vacuum charge density as the main polarization observable, vacuum energy turns out to be not less informative and in many respects complementary to the vacuum density. Moreover, the main non-perturbative effects, which appear in vacuum polarization for supercritical fields due to the levels diving into the lower continuum, show up in the behavior of vacuum energy even more clear, demonstrating explicitly their possible role in the supercritical region. Both in 1+1D and 2+1D, we explore firstly the renormalized vacuum density in the supercritical region using the Wichmann-Kroll method. Thereafter, taking into account the results for the vacuum density, we formulate the renormalization procedure for the vacuum energy. To evaluate the latter explicitly, an original technique, based on a special combination of analytical methods, computer algebra tools and numerical calculations, is applied. It is shown that, for a wide range of the external source parameters (the charge Z and size R), in the supercritical region the renormalized vacuum energy could significantly deviate from the perturbative quadratic growth up to pronouncedly decreasing behavior with jumps by (-2 x mc^2), which occur each time, when the next discrete level dives into the negative continuum. In the considered range of variation of Z and R, the vacuum energy behaves like ~ -Z^2/R in 1+1D and ~ -Z^3/R in 2+1D, exceeding deeply negative values. Such behavior confirms the assumption of the neutral vacuum transmutation into the charged one, and thereby of the spontaneous positron emission, accompanying the emergence of the next vacuum shell due to the total charge conservation. To the end, we also note that the methods, developed for the vacuum energy evaluation in 2+1 D, with minimal complements could be carried over to the three-dimensional case, where the vacuum energy is expected to be ~ -Z^4/R and so could be competitive with the classical electrostatic energy of the Coulomb source.

Keywords: non-perturbative QED-effects, one- and two-dimensional Dirac-Coulomb systems, supercritical fields, vacuum polarization

Procedia PDF Downloads 202

Authors: Natcha Lueangapapong, Chariya Chuthapisith, Lunliya Thampratankul

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Background: There is a need to find an appropriate tool to help healthcare providers determine sleep problems in children for early diagnosis and management. The Japanese Sleep Questionnaire for Preschoolers (JSQ-P) is a parent-reported sleep questionnaire that has good psychometric properties and can be used in the context of Asian culture, which is likely suitable for Thai children. Objectives: This study aimed to translate and validate the Japanese Sleep Questionnaire for Preschoolers (JSQ-P) into a Thai version and to evaluate factors associated with sleep disorders in preschoolers. Methods: After approval by the original developer, the cross-cultural adaptation process of JSQ-P was performed, including forward translation, reconciliation, backward translation, and final approval of the Thai version of JSQ-P (TH-JSQ-P) by the original creator. This study was conducted between March 2021 and February 2022. The TH-JSQ-P was completed by 2,613 guardians whose children were aged 2-6 years twice in 10-14 days to assess its reliability and validity. Content validity was measured by an index of item-objective congruence (IOC) and a content validity index (CVI). Face validity, content validity, structural validity, construct validity (discriminant validity), criterion validity and predictive validity were assessed. The sensitivity and specificity of the TH-JSQ-P were also measured by using a total JSQ-P score cutoff point 84, recommended by the original JSQ-P and each subscale score among the clinical samples of obstructive sleep apnea syndrome. Results: Internal consistency reliability, evaluated by Cronbach’s α coefficient, showed acceptable reliability in all subscales of JSQ-P. It also had good test-retest reliability, as the intraclass correlation coefficient (ICC) for all items ranged between 0.42-0.84. The content validity was acceptable. For structural validity, our results indicated that the final factor solution for the Th-JSQ-P was comparable to the original JSQ-P. For construct validity, age group was one of the clinical parameters associated with some sleep problems. In detail, parasomnias, insomnia, daytime excessive sleepiness and sleep habits significantly decreased when the children got older; on the other hand, insufficient sleep was significantly increased with age. For criterion validity, all subscales showed a correlation with the Epworth Sleepiness Scale (r = -0.049-0.349). In predictive validity, the Epworth Sleepiness Scale was significantly a strong factor that correlated to sleep problems in all subscales of JSQ-P except in the subscale of sleep habit. The sensitivity and specificity of the total JSQ-P score were 0.72 and 0.66, respectively. Conclusion: The Thai version of JSQ-P has good internal consistency reliability and test-retest reliability. It passed 6 validity tests, and this can be used to evaluate sleep problems in preschool children in Thailand. Furthermore, it has satisfactory general psychometric properties and good reliability and validity. The data collected in examining the sensitivity of the Thai version revealed that the JSQ-P could detect differences in sleep problems among children with obstructive sleep apnea syndrome. This confirmed that the measure is sensitive and can be used to discriminate sleep problems among different children.

Keywords: preschooler, questionnaire, validation, Thai version

Procedia PDF Downloads 104

Authors: Mai Abdul Latif, Yuntian Feng

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Applied Element Method (AEM) is a method that was developed to aid in the analysis of the collapse of structures. Current available methods cannot deal with structural collapse accurately; however, AEM can simulate the behavior of a structure from an initial state of no loading until collapse of the structure. The elements in AEM are connected with sets of normal and shear springs along the edges of the elements, that represent the stresses and strains of the element in that region. The elements are rigid, and the material properties are introduced through the spring stiffness. Nonlinear dynamic analysis has been widely modelled using the finite element method for analysis of progressive collapse of structures; however, difficulties in the analysis were found at the presence of excessively deformed elements with cracking or crushing, as well as having a high computational cost, and difficulties on choosing the appropriate material models for analysis. The Applied Element method is developed and coded to significantly improve the accuracy and also reduce the computational costs of the method. The scheme works for both linear elastic, and nonlinear cases, including elasto-plastic materials. This paper will focus on elastic and elasto-plastic material behaviour, where the number of springs required for an accurate analysis is tested. A steel cantilever beam is used as the structural element for the analysis. The first modification of the method is based on the Gaussian Quadrature to distribute the springs. Usually, the springs are equally distributed along the face of the element, but it was found that using Gaussian springs, only up to 2 springs were required for perfectly elastic cases, while with equal springs at least 5 springs were required. The method runs on a Newton-Raphson iteration scheme, and quadratic convergence was obtained. The second modification is based on adapting the number of springs required depending on the elasticity of the material. After the first Newton Raphson iteration, Von Mises stress conditions were used to calculate the stresses in the springs, and the springs are classified as elastic or plastic. Then transition springs, springs located exactly between the elastic and plastic region, are interpolated between regions to strictly identify the elastic and plastic regions in the cross section. Since a rectangular cross-section was analyzed, there were two plastic regions (top and bottom), and one elastic region (middle). The results of the present study show that elasto-plastic cases require only 2 springs for the elastic region, and 2 springs for the plastic region. This showed to improve the computational cost, reducing the minimum number of springs in elasto-plastic cases to only 6 springs. All the work is done using MATLAB and the results will be compared to models of structural elements using the finite element method in ANSYS.

Keywords: applied element method, elasto-plastic, Gaussian springs, nonlinear

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Authors: Manon Ghislain, Timothée Bonnet, Olivier Gimenez, Olivier Dehorter, Pierre-Yves Henry

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Climatic fluctuations affect the demography of animal populations, generating changes in population size, phenology, distribution and community assemblages. However, very few studies have identified the underlying demographic processes. For short-lived species, like common passerine birds, are these changes generated by changes in adult survival or in fecundity and recruitment? This study tests for an effect of annual climatic conditions (spring and winter) on annual, local adult survival at very large spatial (a country, 252 sites), temporal (25 years) and biological (25 species) scales. The Constant Effort Site ringing has allowed the collection of capture - mark - recapture data for 100 000 adult individuals since 1989, over metropolitan France, thus documenting annual, local survival rates of the most common passerine birds. We specifically developed a set of multi-year, multi-species, multi-site Bayesian models describing variations in local survival and recapture probabilities. This method allows for a statistically powerful hierarchical assessment (global versus species-specific) of the effects of climate variables on survival. A major part of between-year variations in survival rate was common to all species (74% of between-year variance), whereas only 26% of temporal variation was species-specific. Although changing spring climate is commonly invoked as a cause of population size fluctuations, spring climatic anomalies (mean precipitation or temperature for March-August) do not impact adult survival: only 1% of between-year variation of species survival is explained by spring climatic anomalies. However, for sedentary birds, winter climatic anomalies (North Atlantic Oscillation) had a significant, quadratic effect on adult survival, birds surviving less during intermediate years than during more extreme years. For migratory birds, we do not detect an effect of winter climatic anomalies (Sahel Rainfall). We will analyze the life history traits (migration, habitat, thermal range) that could explain a different sensitivity of species to winter climate anomalies. Overall, we conclude that changes in population sizes for passerine birds are unlikely to be the consequences of climate-driven mortality (or emigration) in spring but could be induced by other demographic parameters, like fecundity.

Keywords: Bayesian approach, capture-recapture, climate anomaly, constant effort sites scheme, passerine, seasons, survival

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Authors: Ayesha M. Facey

Abstract:

The evaluation sought to determine the factors associated with the high failure rate among students taking a first-year introductory statistics course. By utilizing Tyler’s Objective Based Model, the main objectives were: to assess the effectiveness of the lecturer’s teaching strategies; to determine the proportion of students who attends lectures and tutorials frequently and to determine the impact of infrequent attendance on performance; to determine how the assigned activities assisted in students understanding of the course content; to ascertain the possible issues being faced by students in understanding the course material and obtain possible solutions to the challenges and to determine whether the learning outcomes have been achieved based on an assessment of the second in-course examination. A quantitative survey research strategy was employed and the study population was students enrolled in semester one of the academic year 2015/2016. A convenience sampling approach was employed resulting in a sample of 98 students. Primary data was collected using self-administered questionnaires over a one-week period. Secondary data was obtained from the results of the second in-course examination. Data were entered and analyzed in SPSS version 22 and both univariate and bivariate analyses were conducted on the information obtained from the questionnaires. Univariate analyses provided description of the sample through means, standard deviations and percentages while bivariate analyses were done using Spearman’s Rho correlation coefficient and Chi-square analyses. For secondary data, an item analysis was performed to obtain the reliability of the examination questions, difficulty index and discriminant index. The examination results also provided information on the weak areas of the students and highlighted the learning outcomes that were not achieved. Findings revealed that students were more likely to participate in lectures than tutorials and that attendance was high for both lectures and tutorials. There was a significant relationship between participation in lectures and performance on examination. However, a high proportion of students has been absent from three or more tutorials as well as lectures. A higher proportion of students indicated that they completed the assignments obtained from the lectures sometimes while they rarely completed tutorial worksheets. Students who were more likely to complete their assignments were significantly more likely to perform well on their examination. Additionally, students faced a number of challenges in understanding the course content and the topics of probability, binomial distribution and normal distribution were the most challenging. The item analysis also highlighted these topics as problem areas. Problems doing mathematics and application and analyses were their major challenges faced by students and most students indicated that some of the challenges could be alleviated if additional examples were worked in lectures and they were given more time to solve questions. Analysis of the examination results showed that a number of learning outcomes were not achieved for a number of topics. Based on the findings recommendations were made that suggested adjustments to grade allocations, delivery of lectures and methods of assessment.

Keywords: evaluation, item analysis, Tyler’s objective based model, university statistics

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Authors: Jesus Salmeron, Carmen De Vega, Maria Soledad Vicente, Mireia Olabarria, Olaia Martinez

Abstract:

Dysphagia or difficulty in swallowing affects mostly elder people: 56-78% of the institutionalized and 44% of the hospitalized. Liquid food thickening is a necessary measure in this situation because it reduces the risk of penetration-aspiration. Until now, and as proposed by the American Dietetic Association in 2002, possible consistencies have been categorized in three groups attending to their viscosity: nectar (50-350 mPa•s), honey (350-1750 mPa•s) and pudding (>1750 mPa•s). The adequate viscosity level should be identified for every patient, according to her/his impairment. Nevertheless, a systematic review on dysphagia diet performed recently indicated that there is no evidence to suggest that there is any transition of clinical relevance between the three levels proposed. It was also stated that other physical properties of the bolus (slipperiness, density or cohesiveness, among others) could influence swallowing in affected patients and could contribute to the amount of remaining residue. Texture parameters need to be evaluated as possible alternative to viscosity. The aim of this study was to evaluate the instrumental extrusion-compression test as a possible tool to characterize changes along time in water thickened with various products and in the three theoretical consistencies. Six commercial thickeners were used: NM® (NM), Multi-thick® (M), Nutilis Powder® (Nut), Resource® (R), Thick&Easy® (TE) and Vegenat® (V). All of them with a modified starch base. Only one of them, Nut, also had a 6,4% of gum (guar, tara and xanthan). They were prepared as indicated in the instructions of each product and dispensing the correspondent amount for nectar, honey and pudding consistencies in 300 mL of tap water at 18ºC-20ºC. The mixture was stirred for about 30 s. Once it was homogeneously spread, it was dispensed in 30 mL plastic glasses; always to the same height. Each of these glasses was used as a measuring point. Viscosity was measured using a rotational viscometer (ST-2001, Selecta, Barcelona). Extrusion-compression test was performed using a TA.XT2i texture analyzer (Stable Micro Systems, UK) with a 25 mm diameter cylindrical probe (SMSP/25). Penetration distance was set at 10 mm and a speed of 3 mm/s. Measurements were made at 1, 5, 10, 20, 30, 40, 50 and 60 minutes from the moment samples were mixed. From the force (g)–time (s) curves obtained in the instrumental assays, maximum force peak (F) was chosen a reference parameter. Viscosity (mPa•s) and F (g) showed to be highly correlated and had similar development along time, following time-dependent quadratic models. It was possible to predict viscosity using F as an independent variable, as they were linearly correlated. In conclusion, compression-extrusion test could be an alternative and a useful tool to assess physical characteristics of thickened liquids.

Keywords: compression-extrusion test, dysphagia, texture analyzer, thickener

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Authors: Lubna Naz, Munir Ahmad, G. M. Arif

Abstract:

This study analyzes static and dynamic welfare impacts of food price changes for various landholding groups in Pakistan. The study uses three classifications of land ownership, landless, small landowners and large landowners, for analysis. The study uses Panel Survey, Pakistan Rural Household Survey (PRHS) of Pakistan Institute of Development Economics Islamabad, of rural households from two largest provinces (Sindh and Punjab) of Pakistan. The study uses all three waves (2001, 2004 and 2010) of PRHS. This research work makes three important contributions in literature. First, this study uses Quadratic Almost Ideal Demand System (QUAIDS) to estimate demand functions for eight food groups-cereals, meat, milk and milk products, vegetables, cooking oil, pulses and other food. The study estimates food demand functions with Nonlinear Seemingly Unrelated (NLSUR), and employs Lagrange Multiplier and test on the coefficient of squared expenditure term to determine inclusion of squared expenditure term. Test results support the inclusion of squared expenditure term in the food demand model for each of landholding groups (landless, small landowners and large landowners). This study tests for endogeneity and uses control function for its correction. The problem of observed zero expenditure is dealt with a two-step procedure. Second, it creates low price and high price periods, based on literature review. It uses elasticity coefficients from QUAIDS to analyze static and dynamic welfare effects (first and second order Tylor approximation of expenditure function is used) of food price changes across periods. The study estimates compensation variation (CV), money metric loss from food price changes, for landless, small and large landowners. Third, this study compares the findings on welfare implications of food price changes based on QUAIDS with the earlier research in Pakistan, which used other specification of the demand system. The findings indicate that dynamic welfare impacts of food price changes are lower as compared to static welfare impacts for all landholding groups. The static and dynamic welfare impacts of food price changes are highest for landless. The study suggests that government should extend social security nets to landless poor and categorically to vulnerable landless (without livestock) to redress the short-term impact of food price increase. In addition, the government should stabilize food prices and particularly cereal prices in the long- run.

Keywords: QUAIDS, Lagrange multiplier, NLSUR, and Tylor approximation

Procedia PDF Downloads 364