Search results for: discriminant

Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Christian Louis Kühner, Florian Pfeffel, Valentin Nickolai

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Taking care of children, dependents, or pets can be a difficult and time-consuming task. Especially for part- and full-time employees, it can feel exhausting and overwhelming to meet these obligations besides working a job. Thus, working mostly at home and not having to drive to the company can save valuable time and stress. This study aims to show the influence that the working model has on the job satisfaction of employees with care responsibilities in comparison to employees who do not have such obligations. Using structural equation modeling (SEM), the three work models, “work from home”, “working remotely”, and a hybrid model, have been analyzed based on 13 influencing constructs on job satisfaction. These 13 factors have been further summarized into three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, an online survey was conducted with n = 684 employees from the service sector. 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. In addition, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” 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 shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that among the employees with care responsibilities, the higher the proportion of working from home in comparison to working from the office, the more satisfied the employees are with their job. Since the work models that meet the requirements of comprehensive care led to higher job satisfaction amongst employees with such obligations, adapting as a company to such private obligations by employees can be crucial to sustained success. Conversely, the satisfaction level of the working model where employees work at the office is higher for workers without caregiving responsibilities.

Keywords: care responsibilities, home office, job satisfaction, structural equation modeling

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Authors: Neşe Alkan

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Purpose: The point that applied/clinical psychology, which is the practice and research discipline of the mental health field, has reached today can be summarized as the necessity of handling the psychological well-being of people from multiple perspectives and the goal of moving it to a higher level. Clients' subjective assessment of their own condition and wellbeing is an integral part of evidence-based interventions. There is a need for tools through which clients can evaluate the effectiveness of the psychotherapy/intervention performed with them and their contribution to the wellbeing and wellbeing of this process in a valid and reliable manner. The aim of this research is to meet this need, to test the reliability and validity of the index in Turkish, and explore its usability in the practices of both researchers and psychotherapists. Method: A total of 213 adults aged between 18-54, 69.5% working and 29.5% university students, were included in the study. Along with their demographic information, the participants were administered a set of scales: wellbeing, life satisfaction, spiritual satisfaction, shopping addiction, and loneliness, namely via an online platform. The construct validity of the wellbeing scale was tested with exploratory and confirmatory factor analyses, convergent and discriminant validity were tested with two-way full and partial correlation analyses and, measurement invariance was tested with one-way analysis of variance. Results: Factor analyzes showed that the scale consisted of six dimensions as it is in its original structure. The internal consistency of the scale was found to be Cronbach α = .82. Two-way correlation analyzes revealed that the wellbeing scale total score was positively correlated with general life satisfaction (r = .62) and spiritual satisfaction (r = .29), as expected. It was negatively correlated with loneliness (r = -.51) and shopping addiction (r = -.15). While the scale score did not vary by gender, previous illness, or nicotine addiction, it was found that the total wellbeing scale scores of the participants who had used antidepressant medication during the past year were lower than those who did not use antidepressant medication (F(1,204) = 7.713, p = .005). Conclusion: It has been concluded that the 12-item wellbeing scale consisting of six dimensions can be used in research and health sciences practices as a valid and reliable measurement tool. Further research which examines the reliability and validity of the scale in different widely used languages such as Spanish and Chinese is recommended.

Keywords: wellbeing, intervention effectiveness, reliability and validity, effectiveness

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Authors: Florian Pfeffel, Valentin Nickolai, Christian Louis Kühner

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Digitalization has disrupted the traditional workplace environment by allowing many employees to work from anywhere at any time. This trend of working from home was further accelerated due to the COVID-19 crisis, which forced companies to rethink their workplace models. While in many companies, this shift happened out of pure necessity; many employees were left more satisfied with their job due to the opportunity to work from home. This study focuses on employees’ job satisfaction in the service sector in dependence on the different work models, which are defined as a “work from home” model, the traditional “work in office” model, and a hybrid model. Using structural equation modeling (SEM), these three work models have been analyzed based on 13 influencing factors on job satisfaction that have been further summarized in the three groups “classic influencing factors”, “influencing factors changed by remote working”, and “new remote working influencing factors”. Based on the influencing factors on job satisfaction, a survey has been conducted with n = 684 employees in the service sector. 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). The SEM-analysis has shown that the most significant influencing factor on job satisfaction is “identification with the work” 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 shows that the identification with the work is the most significant factor in all three work models and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees who work entirely remotely or have a hybrid work model are significantly more satisfied with their job, with a job satisfaction score of 5.0 respectively on a scale from 1 (very dissatisfied) to 7 (very satisfied), than employees do not have the option to work from home with a score of 4.6. This comes as a result of the lower identification with the work in the model without any remote working. Furthermore, the responses indicate that it is important to consider the individual preferences of each employee when it comes to the work model to achieve overall higher job satisfaction. Thus, it can be argued that companies can profit off of more motivation and higher productivity by considering the individual work model preferences, therefore, increasing the identification with the respective work.

Keywords: home-office, identification with work, job satisfaction, new work, remote work, structural equation modeling

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Authors: Rehan Ullah

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The primary purpose of the study is to observe the relationship that exists between the organizational information technology (IT) capabilities and the organizational performance in China and Pakistan. Nations like China and Pakistan utilize modern techno-how to enhance their production endeavors. Therefore, making a wide-ranging comparison of the manufacturing services between China and Pakistan was chosen due to numerous reasons. One reason for carrying out this comparison is to determine how IT of the two countries enhances organizational competency on small and medium-sized manufacturing enterprises (SMEs). The study hypothesized that organizational IT capabilities (IT infrastructure, IT competence) have a positive influence on organizational performance and the strategic benefits of IT have a mediating effect on the relationship between IT capability and organizational performance. To investigate the relationship between IT capabilities and organizational performance, surveys were sent to managers of small, medium-sized manufacturing organizations located in the southwestern region, Sichuan province of China, and Pakistani companies, which are located in Islamabad, Lahore, and Karachi. These cities were selected as typical representatives of each country. Organizational performance has been measured in terms of profitability, organizational success, growth, market share, and innovativeness. Out of 400 surveys distributed to different manufacturing organizations, 303 usable and valid responses were received that are analyzed in this research. The data were examined using SPSS and Smart PLS computer software. The results of the study, including the descriptive statistics of each variable, are used. The outer model has been measured with considerations to content validity, discriminant validity, and convergent validity. The path coefficients among the constructs were also computed when analyzing the structural model using the bootstrapping technique. The analysis of data from both China and Pakistan yields an identical but unique result. The results show that IT infrastructure, IT competence, strategic benefits of IT are all correlated to the performance of the organizations. Moreover, strategic benefits of IT have been proved to mediate the relationship between IT capabilities and organization performance. The author, concerning the role of IT on the performance of an organization, highlights the different aspects as well as its benefits in an organization. The overall study concludes several implications for both managers and academicians. It also provides the limitations of the study and offers recommendations for future studies and practice.

Keywords: organizational performance, IT capabilities, IT infrastructure, IT competence, strategic benefits of IT, China, Pakistan

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Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko

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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.

Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning

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Authors: Sheng-Yu Lee

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Aim: Opioid use disorder is often characterized by repetitive drug-seeking and drug-taking behaviors with severe public health consequences. Animal model showed that opioid-induced perturbations in the gut microbiota causally relate to neuroinflammation, deficits in reward responding, and opioid tolerance, possibly due to changes in gut microbiota. Therefore, we propose that the dysbiosis of gut microbiota can be associated with pathogenesis of opioid dependence. In this current study, we explored the differences in gut microbiota between patients and normal controls and in patients before and after initiation of methadone treatment program for 12 weeks. Methods: Patients with opioid use disorder between 20 and 65 years were recruited from the methadone maintenance outpatient clinic in 2 medical centers in the Southern Taiwan. Healthy controls without any family history of major psychiatric disorders (schizophrenia, bipolar disorder and major depressive disorder) were recruited from the community. After initial screening, 15 patients with opioid use disorder joined the study for initial evaluation (Week 0), 12 of them completed the 12-week follow-up while receiving methadone treatment and ceased heroin use (Week 12). Fecal samples were collected from the patients at baseline and the end of 12th week. A one-time fecal sample was collected from the healthy controls. The microbiota of fecal samples were investigated using 16S rRNA V3V4 amplicon sequencing, followed by bioinformatics and statistical analyses. Results: We found no significant differences in species diversity in opioid dependent patients between Week 0 and Week 12, nor compared between patients at both points and controls. For beta diversity, using principal component analysis, we found no significant differences between patients at Week 0 and Week 12, however, both patient groups showed significant differences compared to control (P=0.011). Furthermore, the linear discriminant analysis effect size (LEfSe) analysis was used to identify differentially enriched bacteria between opioid use patients and healthy controls. Compared to controls, the relative abundance of Lactobacillaceae Lactobacillus (L. Lactobacillus), Megasphaera Megasphaerahexanoica (M. Megasphaerahexanoica) and Caecibacter Caecibactermassiliensis (C Caecibactermassiliensis) were increased in patients at Week 0, while Coriobacteriales Atopobiaceae (C. Atopobiaceae), Acidaminococcus Acidaminococcusintestini (A. Acidaminococcusintestini) and Tractidigestivibacter Tractidigestivibacterscatoligenes (T. Tractidigestivibacterscatoligenes) were increased in patients at Week 12. Conclusion: In conclusion, we suggest that the gut microbiome community maybe linked to opioid use disorder, such differences may not be altered even after 12-week of cessation of opioid use.

Keywords: opioid use disorder, gut microbiota, methadone treatment, follow up study

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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: 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: 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: 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

Procedia PDF Downloads 228

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

Procedia PDF Downloads 295

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 443

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 72

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 77

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

Procedia PDF Downloads 186

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 34

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 55

Authors: Ayesha M. Facey

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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

Procedia PDF Downloads 163

Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

Procedia PDF Downloads 47

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

Procedia PDF Downloads 211

Authors: Chau-Kuang Chen, Juanita Buford, Colette Davis, Raisha Allen, John Hughes, James Tyus, Dexter Samuels

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During the post-Civil War era, the city of Nashville, Tennessee, had the highest mortality rate in the country. The elevated death and disease among ex-slaves were attributable to the unavailability of healthcare. To address the paucity of healthcare services, the College, an institution with the mission of educating minority professionals and serving the under served population, was established in 1876. This study was designed to assess if the College has accomplished its mission of serving under served communities and contributed to the elimination of health disparities in the United States. The study objective was to quantify the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities, which, in turn, was significantly associated with a health professional shortage score partly designated by the U.S. Department of Health and Human Services. Various statistical methods were used to analyze the alumni data in years 1975 – 2013. K-means cluster analysis was utilized to identify individual medical and dental graduates into the cluster groups of the practice communities (Disadvantaged or Non-disadvantaged Communities). Discriminant analysis was implemented to verify the classification accuracy of cluster analysis. The independent t test was performed to detect the significant mean differences for clustering and criterion variables between Disadvantaged and Non-disadvantaged Communities, which confirms the “content” validity of cluster analysis model. Chi-square test was used to assess if the proportion of cluster groups (Disadvantaged vs Non-disadvantaged Communities) were consistent with that of practicing specialties (primary care vs. non-primary care). Finally, the partial least squares (PLS) path model was constructed to explore the “construct” validity of analytics model by providing the magnitude effects of socioeconomic status and adverse health outcome on primary care professionals serving disadvantaged community. The social ecological theory along with statistical models mentioned was used to establish the relationship between medical and dental graduates (primary care professionals serving disadvantaged communities) and their social environments (socioeconomic status, adverse health outcome, health professional shortage score). Based on social ecological framework, it was hypothesized that the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities could be quantified. Also, primary care professionals serving disadvantaged communities related to a health professional shortage score can be measured. Adverse health outcome (adult obesity rate, age-adjusted premature mortality rate, and percent of people diagnosed with diabetes) could be affected by the latent variable, namely socioeconomic status (unemployment rate, poverty rate, percent of children who were in free lunch programs, and percent of uninsured adults). The study results indicated that approximately 83% (3,192/3,864) of the College’s medical and dental graduates from 1975 to 2013 were practicing in disadvantaged communities. In addition, the PLS path modeling demonstrated that primary care professionals serving disadvantaged community was significantly associated with socioeconomic status and adverse health outcome (p < .001). In summary, the majority of medical and dental graduates from the College provide primary care services to disadvantaged communities with low socioeconomic status and high adverse health outcomes, which demonstrate that the College has fulfilled its mission.

Keywords: disadvantaged community, K-means cluster analysis, PLS path modeling, primary care

Procedia PDF Downloads 520

Authors: Wonil Nam, Xiang Ren, Inyoung Kim, Masoud Agah, Wei Zhou

Abstract:

Despite its ultrasensitive detection capability, surface-enhanced Raman spectroscopy (SERS) faces challenges as a quantitative biochemical analysis tool due to the significant dependence of local field intensity in hotspots on nanoscale geometric variations of plasmonic nanostructures. Therefore, despite enormous progress in plasmonic nanoengineering of high-performance SERS devices, it is still challenging to quantitatively correlate the measured SERS signals with the actual molecule concentrations at hotspots. A significant effort has been devoted to developing SERS calibration methods by introducing internal standards. It has been achieved by placing Raman tags at plasmonic hotspots. Raman tags undergo similar SERS enhancement at the same hotspots, and ratiometric SERS signals for analytes of interest can be generated with reduced dependence on geometrical variations. However, using Raman tags still faces challenges for real-world applications, including spatial competition between the analyte and tags in hotspots, spectral interference, laser-induced degradation/desorption due to plasmon-enhanced photochemical/photothermal effects. We show that electronic Raman scattering (ERS) signals from metallic nanostructures at hotspots can serve as the internal calibration standard to enable quantitative SERS analysis and improve biostatistical analysis. We perform SERS with Au-SiO₂ multilayered metal-insulator-metal nano laminated plasmonic nanostructures. Since the ERS signal is proportional to the volume density of electron-hole occupation in hotspots, the ERS signals exponentially increase when the wavenumber is approaching the zero value. By a long-pass filter, generally used in backscattered SERS configurations, to chop the ERS background continuum, we can observe an ERS pseudo-peak, IERS. Both ERS and SERS processes experience the |E|⁴ local enhancements during the excitation and inelastic scattering transitions. We calibrated IMRS of 10 μM Rhodamine 6G in solution by IERS. The results show that ERS calibration generates a new analytical value, ISERS/IERS, insensitive to variations from different hotspots and thus can quantitatively reflect the molecular concentration information. Given the calibration capability of ERS signals, we performed label-free SERS analysis of living biological systems using four different breast normal and cancer cell lines cultured on nano-laminated SERS devices. 2D Raman mapping over 100 μm × 100 μm, containing several cells, was conducted. The SERS spectra were subsequently analyzed by multivariate analysis using partial least square discriminant analysis. Remarkably, after ERS calibration, MCF-10A and MCF-7 cells are further separated while the two triple-negative breast cancer cells (MDA-MB-231 and HCC-1806) are more overlapped, in good agreement with the well-known cancer categorization regarding the degree of malignancy. To assess the strength of ERS calibration, we further carried out a drug efficacy study using MDA-MB-231 and different concentrations of anti-cancer drug paclitaxel (PTX). After ERS calibration, we can more clearly segregate the control/low-dosage groups (0 and 1.5 nM), the middle-dosage group (5 nM), and the group treated with half-maximal inhibitory concentration (IC50, 15 nM). Therefore, we envision that ERS calibrated SERS can find crucial opportunities in label-free molecular profiling of complicated biological systems.

Keywords: cancer cell drug efficacy, plasmonics, surface-enhanced Raman spectroscopy (SERS), SERS calibration

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