Search results for: identification of emotions

Authors: Timir Baran Roy, Luis Guerreiro, Ashutosh Bagchi

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Dynamic study is important in order to design, repair and rehabilitation of structures. It has played an important role in the behavior characterization of structures; such as bridges, dams, high-rise buildings etc. There had been a substantial development in this area over the last few decades, especially in the field of dynamic identification techniques of structural systems. Frequency Domain Decomposition (FDD) and Time Domain Decomposition are most commonly used methods to identify modal parameters; such as natural frequency, modal damping, and mode shape. The focus of the present research is to study the dynamic characteristics of typical timber masonry walls commonly used in Portugal. For that purpose, a multi-storey structural prototypes of such walls have been tested on a seismic shake table at the National Laboratory for Civil Engineering, Portugal (LNEC). Signal processing has been performed of the output response, which is collected from the shaking table experiment of the prototype using accelerometers. In the present work signal processing of the output response, based on the input response has been done in two ways: FDD and Stochastic Subspace Identification (SSI). In order to estimate the values of the modal parameters, algorithms for FDD are formulated, and parametric functions for the SSI are computed. Finally, estimated values from both the methods are compared to measure the accuracy of both the techniques.

Keywords: frequency domain decomposition (fdd), modal parameters, signal processing, stochastic subspace identification (ssi), time domain decomposition

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Ivan Samokhin

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A new two-factor model of school students’ capacity levels is proposed. It considers the academic productivity and emotional condition of children taking part in the study process. Each basic level reflects the correlation of these two factors. The teacher decides whether the required result is achieved or not and write down the grade (from 'A' to 'F') in the register. During the term, the teacher can estimate the students’ progress with any intervals, but it is not desirable to exceed a two-week period (with primary school being an exception). Each boy or girl should have a special notebook to record the emotions which they feel studying a subject. The children can make their notes the way they like it – for example, using a ten-point scale or a short verbal description. It is recommended to record the emotions twice a day: after the lesson and after doing the homework. Before the students start doing this, they should be instructed by a school psychologist, who has to emphasize that an attitude to the subject – not to a person in charge of it – is relevant. At the end of the term, the notebooks are given to the teacher, who is now able to make preliminary conclusions about academic results and psychological comfort of each student. If necessary, some pedagogical measures can be taken. The data about a supposed capacity level is available for the teacher and the school administration. In certain cases, this information can be also revealed to the student’s parents, while the student learns it only after receiving a school-leaving certificate (until this moment, the results are not considered ultimate). Then a person may take these data into consideration when choosing his/her future area of higher education. We single out four main capacity levels: 'nominally low', 'inclination', 'ability' and 'gift'.

Keywords: academic productivity, capacity level, emotional condition, school students

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Authors: Younes Abed

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The successful application of in-situ testing of soils heavily depends on development of interpretation methods of tests. The pressuremeter test simulates the expansion of a cylindrical cavity and because it has well defined boundary conditions, it is more unable to rigorous theoretical analysis (i. e. cavity expansion theory) then most other in-situ tests. In this article, and in order to make the identification process more convenient, we propose a relatively simple procedure which involves the numerical identification of some mechanical parameters of a granular soil, especially, the elastic modulus and the friction angle from a pressuremeter curve. The procedure, applied here to identify the parameters of generalised prager model associated to the Drucker & Prager criterion from a pressuremeter curve, is based on an inverse analysis approach, which consists of minimizing the function representing the difference between the experimental curve and the curve obtained by integrating the model along the loading path in in-situ testing. The numerical process implemented here is based on the established finite element program. We present a validation of the proposed approach by a database of tests on expansion of cylindrical cavity. This database consists of four types of tests; thick cylinder tests carried out on the Hostun RF sand, pressuremeter tests carried out on the Hostun sand, in-situ pressuremeter tests carried out at the site of Fos with marine self-boring pressuremeter and in-situ pressuremeter tests realized on the site of Labenne with Menard pressuremeter.

Keywords: granular soils, cavity expansion, pressuremeter test, finite element method, identification procedure

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Authors: Jichan Jang

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Mycobacterium abscessus is a rapidly growing life-threatening mycobacterium with multiple drug-resistance mechanisms. In this study, we screened the library to identify active molecules targeting Mycobacterium abscessus using resazurin live/dead assays. In this screening assay, the Z-factor was 0.7, as an indication of the statistical confidence of the assay. A cut-off of 80% growth inhibition in the screening resulted in the identification of four different compounds at a single concentration (20 μM). Dose-response curves identified three different hit candidates, which generated good inhibitory curves. All hit candidates were expected to have different molecular targets. Thus, we found that compound X, identified, may be a promising candidate in the M. abscessus drug discovery pipeline.

Keywords: Mycobacterium abscessus, antibiotics, drug discovery, emerging Pathogen

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Authors: Madeleine Nicole G. Bernardo, Katrina T. Feliciano, Marcelo Nonato A. Nacionales III, Diane Frances M. Peralta, Denise Nicole V. Profeta

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This study aims to find out if heart rate variability and valence of emotion have an effect on perceived intensity of emotion. Psychology undergraduates (N = 60) from the University of the Philippines Diliman were shown 10 photographs from the Japanese Female Facial Expression (JAFFE) Database, along with a corresponding questionnaire with a Likert scale on perceived intensity of emotion. In this 3 x 2 mixed subjects factorial design, each group was either made to do a simple exercise prior to answering the questionnaire in order to increase the heart rate, listen to a heart rate of 120 bpm, or colour a drawing to keep the heart rate stable. After doing the activity, the participants then answered the questionnaire, providing a rating of the faces according to the participants’ perceived emotional intensity on the photographs. The photographs presented were either of positive or negative emotional valence. The results of the experiment showed that neither an induced fast heart rate or perceived fast heart rate had any significant effect on the participants’ perceived intensity of emotion. There was also no interaction effect of heart rate variability and valence of emotion. The insignificance of results was explained by the Philippines’ high context culture, accompanied by the prevalence of both intensely valenced positive and negative emotions in Philippine society. Insignificance in the effects were also attributed to the Cannon-Bard theory, Schachter-Singer theory and various methodological limitations.

Keywords: heart rate variability, perceived intensity of emotion, Philippines , valence of emotion

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Authors: Muhammad Ameer Nawaz Akram

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The 21st century has been revealed that many individuals around the world are living in urban settlements than in rural zones. The evolution of numerous cities in emerging and newly developed countries is accompanied by the rise of slums. The precise definition of a slum varies countries to countries, but the universal harmony is that slums are dilapidated settlements facing severe poverty and have lacked access to sanitation, water, electricity, good living styles, and land tenure. The slum settlements always vary in unique patterns within and among the countries and cities. The core objective of this study is the spatial identification and classification of slums in Gujrat city Pakistan from very high-resolution GeoEye-1 (0.41m) satellite imagery. Slums were first identified using GPS for sample site identification and ground-truthing; through this process, 425 slums were identified. Then Object-Oriented Analysis (OOA) was applied to classify slums on digital image. Spatial analysis softwares, e.g., ArcGIS 10.3, Erdas Imagine 9.3, and Envi 5.1, were used for processing data and performing the analysis. Results show that OOA provides up to 90% accuracy for the identification of slums. Jalal Cheema and Allah Ho colonies are severely affected by slum settlements. The ratio of criminal activities is also higher here than in other areas. Slums are increasing with the passage of time in urban areas, and they will be like a hazardous problem in coming future. So now, the executive bodies need to make effective policies and move towards the amelioration process of the city.

Keywords: slums, GPS, satellite imagery, object oriented analysis, zonal change detection

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Authors: Anano Tenieshvili, Teona Lodia

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Social anxiety disorder (SAD) is one of the most common mental health problems not only in adults but also in adolescents. Individuals with SAD exhibit difficulties in interpersonal relationships, understanding emotions, and regulating them as well. For social and emotional adaptation, it is crucial to identify, understand, accept and manage emotions correctly. Researchers actively learn those factors that contribute to the development and maintenance of this condition. Therefore, the main purpose of this study is to acquire knowledge about the association between social anxiety and individual characteristics, such as theory of mind (ToM), verbal irony comprehension, and personal traits. 112 adolescents aged from 12 to 18 were selected for this research. 15 of them are diagnosed with Social anxiety disorder. Statistical analysis was performed on the entire sample, and furthermore, two groups, adolescents with and without social anxiety disorder, were compared separately. Social anxiety and personal traits were assessed by questionnaires. Theory of mind and comprehension of verbal irony were measured using tests. Statistical analysis indicated a positive relationship between social anxiety and comprehension of ironic criticism. Moreover, social anxiety was significantly positively correlated with neuroticism and isolation tendency, whereas it was negatively related to extraversion and frustration tolerance. On top of that, statistical analysis revealed a positive relationship between ToM and verbal irony comprehension. However, the relationship between social anxiety and ToM was not statistically significant. In conclusion, the current research expands knowledge about social anxiety and supports the results of some previous studies.

Keywords: personal traits, social anxiety, theory of mind, verbal irony comprehension

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Authors: Anano Tenieshvili, Teona Lodia

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Social anxiety disorder (SAD) is one of the most common mental health problems not only in adults but also in adolescents. Individuals with SAD exhibit difficulties in interpersonal relationships, understanding emotions and regulating them as well. For social and emotional adaptation, it is crucial to identify, understand, accept and manage emotions correctly. Researchers actively learn those factors that contribute to the development and maintenance of this condition. Therefore, the main purpose of this study is to acquire knowledge about the association between social anxiety and individual characteristics, such as the theory of mind (ToM), verbal irony comprehension and personal traits. 112 adolescents aged from 12 to 18 were selected for this research. 15 of them are diagnosed with Social anxiety disorder. Statistical analysis was performed on the entire sample and furthermore, two groups, adolescents with and without a social anxiety disorder, were compared separately. Social anxiety and personal traits were assessed by questionnaires. Theory of mind and comprehension of verbal irony was measured using tests. Statistical analysis indicated a positive relationship between social anxiety and comprehension of ironic criticism. Moreover, social anxiety was significantly positively correlated with neuroticism and isolation tendency, whereas it was negatively related to extraversion and frustration tolerance. On top of that, statistical analysis revealed a positive relationship between ToM and verbal irony comprehension. However, the relationship between social anxiety and ToM was not statistically significant. In conclusion, the current research expands knowledge about social anxiety and supports the results of some previous studies.

Keywords: personal traits, social anxiety, theory of mind, verbal irony comprehension

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Authors: Mira Trebar

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Logistics processes of perishable food in the supply chain include the distribution activities and the real time temperature monitoring to fulfil the cold chain requirements. The paper presents the use of RFID (Radio Frequency Identification) technology as an identification tool of receiving and shipping activities in the cold store. At the same time, the use of RFID data loggers with temperature sensors is presented to observe and store the temperatures for the purpose of analyzing the processes and having the history data available for traceability purposes and efficient recall management.

Keywords: logistics, warehouse, RFID device, cold chain

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Authors: Ayşe Çoban, Orhan Çoban, Murat Birekul

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In this study, the effects of radio frequency identification system on the retailing sector were theoretically analysed. The technology of Radio Frequency Identification (RFID) is a method enabling to identify the objects individually and automatically, using radio frequency. RFID generally consists of a tag and reader. RFID tags can be programmed to receive, store, and send the information of object such as Electronic Product Code (EPC). Having read the tags placed on product by the reader, the information associated with the management of supply chain can be automatically recorded and replaced. Recently, RFID technology used in many areas has particularly important effects on the businesses that are active in the retailing sector. The most important disadvantage of this technology is that the cost of installation and operation is higher compared to its alternatives. However, it provides important advantages to the business enterprises in the application process. At present, it is especially adopted by the large sized enterprises and with chain stores in the international areas. The application results point out that RFID technology provides business enterprises with the important competitive advantage.

Keywords: RFID, retailing sector, RFID technologies, electronic product code

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Authors: Michael Anton Kraus, Miriam Schuster, Geralt Siebert, Jens Schneider

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Polymers increasingly gained interest in construction materials over the last years in civil engineering applications. As polymeric materials typically show time- and temperature dependent material behavior, which is accounted for in the context of the theory of linear viscoelasticity. Within the context of this paper, the authors show, that some polymeric interlayers for laminated glass can not be considered as thermorheologically simple as they do not follow a simple TTSP, thus a methodology of identifying the thermorheologically complex constitutive bahavioir is needed. ‘Dynamical-Mechanical-Thermal-Analysis’ (DMTA) in tensile and shear mode as well as ‘Differential Scanning Caliometry’ (DSC) tests are carried out on the interlayer material ‘Ethylene-vinyl acetate’ (EVA). A navoel Bayesian framework for the Master Curving Process as well as the detection and parameter identification of the TTSPs along with their associated Prony-series is derived and applied to the EVA material data. To our best knowledge, this is the first time, an uncertainty quantification of the Prony-series in a Bayesian context is shown. Within this paper, we could successfully apply the derived Bayesian methodology to the EVA material data to gather meaningful Master Curves and TTSPs. Uncertainties occurring in this process can be well quantified. We found, that EVA needs two TTSPs with two associated Generalized Maxwell Models. As the methodology is kept general, the derived framework could be also applied to other thermorheologically complex polymers for parameter identification purposes.

Keywords: bayesian parameter identification, generalized Maxwell model, linear viscoelasticity, thermorheological complex

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Authors: Olga Hernán-Vega, Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio, Ignacio Bravo

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Non-Cooperative Target Identification has become a key research domain in the Defense industry since it provides the ability to recognize targets at long distance and under any weather condition. High Resolution Range Profiles, one-dimensional radar images where the reflectivity of a target is projected onto the radar line of sight, are widely used for identification of flying targets. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition to a matrix of range profiles is presented. Target Identification based on one-dimensional radar images compares a collection of profiles of a given target, namely test set, with the profiles included in a pre-loaded database, namely training set. The classification is improved by using Singular Value Decomposition since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information such as noise. Singular Value Decomposition permits to define a signal subspace which contain the highest percentage of the energy, and a noise subspace which will be discarded. This way, only the valuable information of each target is used in the recognition process. The identification algorithm is based on finding the target that minimizes the angle between subspaces and takes place in a transformed domain. Two metrics, F1 and F2, based on Singular Value Decomposition are accomplished in the identification process. In the case of F2, the angle is weighted, since the top vectors set the importance in the contribution to the formation of a target signal, on the contrary F1 simply shows the evolution of the unweighted angle. In order to have a wide database or radar signatures and evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, since measured profiles suffer from noise, clutter and other unwanted information and simulated profiles don't. In this case, the test and training samples have similar nature and usually a similar high signal-to-noise ratio, so as to assess the feasibility of the approach, the addition of noise has been considered before the creation of the test set. The identification results applying the unweighted and weighted metrics are analysed for demonstrating which algorithm provides the best robustness against noise in an actual possible scenario. So as to confirm the validity of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results. Considering the dissimilarities between the test and training sets when noise is added, the recognition performance has been improved when weighting is applied. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.

Keywords: HRRP, NCTI, simulated/synthetic database, SVD

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Authors: Predrag Simović, Katarina Stojanović, Milena Radenković, Dimitrija Savić Zdravković, Aleksandar Milosavljević, Bratislav Predić, Milenka Božanić, Ana Petrović, Djuradj Milošević

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Mayflies (Ephemeroptera), stoneflies (Plecoptera), and caddisflies (Trichoptera) (collectively referred to as EPT) are key participants in most freshwater habitats and often exhibit high diversity. Moreover, their presence and relative abundance are used in freshwater ecological and biomonitoring studies. Current methods for freshwater ecosystem biomonitoring follow a traditional approach of taxa monitoring based on morphological characters, which is time-consuming, and often generates data sets with low taxonomic resolution and unverifiable identification precision. To assist in solving identification problems and contribute to the knowledge of the distribution of many species, there was a need to develop alternative approaches in macroinvertebrate sample identification. Here, we establish an automatic machine-based identification approach for EPT taxa (Insect) using deep Convolutional Neural Networks (CNNs) and computer vision to increase the efficiency and taxonomic resolution in biomonitoring. The 5 550 specimens were collected from freshwater ecosystems of Serbia, and the deep model was built upon 90 EPT taxa. The protocol for obtaining images included the following stages: taxonomic identification by human experts and DNA barcoding validation, mounting the larvae, and photographing the dorsal side using a stereomicroscope and camera (16 650 individuals). The most informative image regions (the dorsal segments of individuals) for the decision-making process in the deep learning model were visualized using Gradient Weighted Class Activation Mapping (Grad-CAM). After training the artificial neural network, a CNN model was then built that was able to classify the 90 EPT taxa into their respective taxonomic categories automatically with 98.7%. Our approach offers a straightforward and efficient solution for routine monitoring programs, focusing on key biotic descriptors, such as EPT taxa. In addition, this application provides a streamlined solution that not only saves time, reduces equipment and expert requirements but also significantly enhances reliability and information content. The identification of the EPT larvae is difficult because of the variation of morphological features even within a single genus or the close resemblance of several species, and therefore, future research should focus on increasing the number of entities (species) in the model.

Keywords: convolutional neural networks, DNA barcoding, EPT taxa, biomonitoring

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Authors: Richard Abel

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In the United States, increased incarceration rates have caused many adolescents to feel the strain of parental absence. This absence is then manifest through adolescent feelings of parental rejection. Additionally, upon reentry maternal incarceration may be related to adolescents experienced perceived excessive disciple. It is possible parents engage in this manner of discipline attempting to prevent the child from taking the same path to incarceration as the parent. According to General Strain Theory, adolescents encountering strain are likely to experience negative emotions. The emotion that is most likely to lead to delinquency is anger through reduced inhibitions and motivation to act. Additionally, males are more likely to engage in delinquent behavior, regardless of experiencing strain. This is not the case for every male who experiences maternal incarceration, parental rejection, excessive discipline, or anger. There are protective factors that enable agency within individuals. One such protective factor is mastery, or the perception that one is in control of his or her own future. The model proposed in this research suggests maternal incarceration is associated with increased parental rejection and excessive discipline in males. Males experiencing parental rejection and excessive discipline are likely to experience increased anger, which is then associated with increases in delinquent behavior. This model explores whether agency, in the form of mastery, mediates the relationship between strains and negative emotions, or between negative emotions and delinquent behavior. The Kaplan Longitudinal and Multigenerational Study (KLAMS) dataset is uniquely situated to analyze this model providing longitudinal data collected from both parents and their offspring. Maternal incarceration is constructed using parental responses such that the mother was incarcerated after the child’s birth, and any incarceration that happened prior to birth is excluded. The remaining variables of the study are all constructed from varying waves of the adolescent survey. Parental rejection, along with control variables for age, race, parental socioeconomic status, neighborhood effects, delinquent peers, and prior delinquent behavior are all constructed using Wave I data. To increase causal inference, the negative emotion of anger and the mediating variable of mastery are measured during Wave II. Lastly, delinquent behavior is measured at Wave III. Results of the analysis show expected relationships such that adolescent males encountering maternal incarceration show increased perception of parental rejection and excessive discipline. Additionally, there is a positive relationship between parental rejection and excessive discipline at Wave I and feelings of anger at Wave II for males. For males experiencing either of these strains in Wave I, feelings of anger in Wave II are found to be associated with increased delinquent behavior in Wave III. Mastery was found to mediate the relationship between both parental rejection and excessive discipline and anger, but no such mediation occurs in the relationship between anger and delinquency, regardless of the strain being experienced. These findings suggest adolescent males who feel they are in control of their own lives are less likely to experience negative emotions produced by the occurrence of strain, thereby decreasing male engagement in delinquent behavior later in life.

Keywords: delinquency, mastery, maternal incarceration, strain

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Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Doyoung Kim, Hyo Seon Park

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Studies of System Identification(SI) based on Structural Health Monitoring(SHM) have actively conducted for structural safety. Recently SI techniques have been rapidly developed with output-only SI paradigm for estimating modal parameters. The features of these output-only SI methods consist of Frequency Domain Decomposition(FDD) and Stochastic Subspace Identification(SSI) are using the algorithms based on orthogonal decomposition such as singular value decomposition(SVD). But the SVD leads to high level of computational complexity to estimate modal parameters. This paper proposes the technique to estimate mode shape with lower computational cost. This technique shows pseudo modal Operating Deflections Shape(ODS) through bandpass filter and suggests time history Modal Assurance Criterion(MAC). Finally, mode shape could be estimated from pseudo modal ODS and time history MAC. Analytical simulations of vibration measurement were performed and the results with mode shape and computation time between representative SI method and proposed method were compared.

Keywords: modal assurance criterion, mode shape, operating deflection shape, system identification

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

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The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

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Authors: Angelika Magiera, Weronika Penar, Czesław Klocek

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Intellectual disability (ID) affects approximately 1.55% of children and adults in the society of developed countries. Depending on the ID degree, the patient is burdened with additional disease entities. Intellectual disability does not only limits a person’s opportunities to participate in social life but also affects whole families. People with ID belong to the group of risk of mental illnesses, they are less emotionally stable, while families are predisposed to depression. The study was held in a day care center for people with intellectual disabilities (of various degrees of disability) on 26 people. Nurses and carers also took part. The age range of study groups ranged from 22 to 67 years. Therapeutic classes were held for four independent mixed groups (sex and intellectual disability degree) from 6 to 7 people each, lasting no more than 30 minutes. They were created by the facility's staff to make sure that a group is stable. The animal assisted activity took place with a 2.5-year-old Ragdoll cat. The animal has passed the exam (certificate entitling her to take part in felinotherapy) and had 1.5 years of work experience. Due to the different degrees of ID, an individual emotional state survey was conducted among the caregivers of those who were involved in the activity, to assess the impact of animal assisted activity with a cat on patients. A positive effect on the emotional state of people with different types of intellectual disability was observed. Caregivers and nurses of those participating in the study express willingness to continue these types of classes and consider them necessary for this group of people.

Keywords: intellectual disabilities, animal-assisted activity, cat, feline, emotions

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Authors: Natalia Alonso-Alberca, Ana I. Vergara

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From the very first years of their life, children are confronted with situations in which they need to deal with emotions. The family provides the first emotional experiences, and it is in the family context that children usually take their first steps towards acquiring emotion knowledge. Parents play a key role in this important task, helping their children develop emotional skills that they will need in challenging situations throughout their lives. Specifically, mothers are models imitated by their children. They create specific spatial and temporal contexts in which children learn about emotions, their causes, consequences, and complexity. This occurs not only through what mothers say or do directly to the child. Rather, it occurs, to a large extent, through the example that they set using their own emotional skills. The aim of the current study was to analyze how maternal abilities to perceive and to manage emotions influence children’s emotion knowledge, specifically, their emotion situation knowledge, taking into account the role played by the mother’s personality, the time spent together, and controlling the effect of age, sex and the child’s verbal abilities. Participants were 153 children from 4 schools in Spain, and their mothers. Children (41.8% girls)age range was 35 - 72 months. Mothers (N = 140) age (M = 38.7; R = 27-49). Twelve mothers had more than one child participating in the study. Main variables were the child´s emotion situation knowledge (ESK), measured by the Emotion Matching Task (EMT), and receptive language, using the Picture Vocabulary Test. Also, their mothers´ Emotional Intelligence (EI), through the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT) and personality, with The Big Five Inventory were analyzed. The results showed that the predictive power of maternal emotional skills on ESK was moderated by the mother’s personality, affecting both the direction and size of the relationships detected: low neuroticism and low openness to experience lead to a positive influence of maternal EI on children’s ESK, while high levels in these personality dimensions resulted in a negative influence on child´s ESK. The time that the mother and the child spend together was revealed as a positive predictor of this EK, while it did not moderate the influence of the mother's EI on child’s ESK. In light of the results, we can infer that maternal EI is linked to children’s emotional skills, though high level of maternal EI does not necessarily predict a greater degree of emotionknowledge in children, which seems rather to depend on specific personality profiles. The results of the current study indicate that a good level of maternal EI does not guarantee that children will learn the emotional skills that foster prosocial adaptation. Rather, EI must be accompanied by certain psychological characteristics (personality traits in this case).

Keywords: emotional intelligence, emotion situation knowledge, mothers, personality, young children

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Authors: Lo Kai Chung

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Fathers in the family of disabled children play a crucial role in fostering child development. Previous studies addressing emotions of father involvement in rearing children with special needs have been rare. With reference to the cultural orientation and masculine idea of Chinese fathers, shame and guilt are probable causal emotions that affect fathers’ psycho-behavioral reactions and, thus, father involvement. Based on the findings of our earlier qualitative studies, the current study aims to develop and validate a multi-item scale of guilt or shame and explore their relations with and fatherhood in families with children with special needs. A model is proposed to understand the roles that shame and guilt play in affecting fathers’ involvement in their family system. The severity and type of the child’s special needs are regarded as independent variables affecting the father’s emotional responses – shame and guilt. It is hypothesized that shame and guilt, under the influence of masculinity, lead to avoidance and compensation, respectively, which subsequently decrease and increase father involvement with children with special needs. A cross-sectional online questionnaire survey of fathers with children with special needs recruited by convenience sampling was conducted. Potential participants were reached by bulk emails, related groups on the Internet and education/social services providers. Totally 537 valid sets of online questionnaires were collected from fathers of children with special needs. EFA on the items pool of shame and guilt was performed, resulting in an x-item single-factor solution and y-item single-factor solution, respectively. Further path model analysis revealed that shame and guilt, under the influence of masculinity, showed differential avoidance and compensation responses and resulted in a decrease and increase in father involvement with special needs children. Demographic and key confounding variables were controlled in the analysis. The shame and guilt scales developed show good psychometric properties. Furthermore, they showed significant differential impacts, under the influence of masculinity, on avoidance and compensation behaviours, consequently resulting in a decrease/increase in father involvement in the expected directions. The findings have important theoretical and practical implications. At the community and policy level, the findings inform the design of strategies for strengthening the role of men in families with special needs children.

Keywords: emotions, father involvement, guilt, shame, special needs

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Authors: David Martinez-Pernia, Alvaro Rivera-Rei, Alejandro Troncoso, Gonzalo Forno, Andrea Slachevsky, David Huepe, Victoria Silva-Mack, Jorge Calderon, Mayte Vergara, Valentina Carrera

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Background: Recent theories in the field of emotions have taken the relevance of motor control beyond a system related to personal autonomy (walking, running, grooming), and integrate it into the emotional dimension. However, to our best knowledge, there are no studies that specifically investigate how emotional stimuli related to motor control modify emotional states in terms of postural control and phenomenological experience. Objective: The main aim of this work is to investigate the emotions produced by stimuli of bodily imbalance (neutral, pleasant and unpleasant) in the postural control and the phenomenological experience of young, healthy adults. Methodology: 46 healthy young people are shown emotional videos (neutral, pleasant, motor unpleasant, and non-motor unpleasant) related to the body imbalance. During the period of stimulation of each of the videos (60 seconds) the participant is standing on a force platform to collect temporal and spatial data of postural control. In addition, the electrophysiological activity of the heart and electrodermal activity is recorded. In relation to the two unpleasant conditions (motor versus non-motor), a phenomenological interview is carried out to collect the subjective experience of emotion and body perception. Results: Pleasant and unpleasant emotional videos have significant changes with respect to the neutral condition in terms of greater area, higher mean velocity, and greater mean frequency power on the anterior-posterior axis. The results obtained with respect to the electrodermal response was that the pleasurable and unpleasant conditions produced a significant increase in the phasic component with respect to the neutral condition. Regarding the electrophysiology of the heart, no significant change was found in any condition. Phenomenological experiences in the two unpleasant conditions differ in body perception and the emotional meaning of the experience. Conclusion: Emotional stimuli related to bodily imbalance produce changes in postural control, electrodermal activity, and phenomenological experience. This experimental setting could be relevant to be implemented in people with motor disorders (Parkinson, Stroke, TBI) to know how emotions affect motor control.

Keywords: body imbalance stimuli, emotion, phenomenological experience, postural control

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Authors: Merve Denizci Nazligul

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Dancers seem to have much higher risk levels for the development of eating disorders, compared to non-dancing counterparts. In a remarkably competitive nature of dance environment, perfectionism and emotion regulation difficulties become inevitable risk factors. Moreover, early maladaptive schemas are associated with various eating disorders. In the current study, it was aimed to investigate the mediating role of difficulties with emotion regulation on the relationship between perfectionism and disordered eating behaviors, as well as on the relationship between early maladaptive schemas and disordered eating behaviors. A total of 70 volunteer dancers (n = 47 women, n = 23 men) were recruited in the study (M age = 25.91, SD = 8.9, range 19–63) from the university teams or private clubs in Turkey. The sample included various types of dancers (n = 26 ballets or ballerinas, n =32 Latin, n = 10 tango, n = 2 hiphop). The mean dancing hour per week was 11.09 (SD = 7.09) within a range of 1-30 hours. The participants filled a questionnaire set including demographic information form, Dutch Eating Behavior Questionnaire, Multidimensional Perfectionism Scale, three subscales (Emotional Inhibition, Unrelenting Standards-Hypercriticalness, Approval Seeking-Recognition Seeking) from Young Schema Questionnaire-Short Form-3 and Difficulties in Emotion Regulation Scale. The mediation hypotheses were tested using the PROCESS macro in SPSS. The findings revealed that emotion regulation difficulties significantly mediated the relationship between three distinct subtypes of perfectionism and emotional eating. The results of the Sobel test suggested that there were significant indirect effects of self-oriented perfectionism (b = .06, 95% CI = .0084, .1739), other-oriented perfectionism (b = .15, 95% CI = .0136, .4185), and socially prescribed perfectionism (b = .09, 95% CI = .0104, .2344) on emotional eating through difficulties with emotion regulation. Moreover, emotion regulation difficulties significantly mediated the relationship between emotional inhibition and emotional eating (F(1,68) = 4.67, R2 = .06, p < .05). These results seem to provide some evidence that perfectionism might become a risk factor for disordered eating behaviors when dancers are not able to regulate their emotions. Further, gaining an understanding of how inhibition of emotions leads to inverse effects on eating behavior may be important to develop intervention strategies to manage their disordered eating patterns in risk groups. The present study may also support the importance of using unified protocols for transdiagnostic approaches which focus on identifying, accepting, prompting to express maladaptive emotions and appraisals.

Keywords: dancers, disordered eating, emotion regulation difficulties, perfectionism

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Rebecca Angeles

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This case study features the Kuehne + Nagel PharmaChain solution for ‘cold chain’ pharmaceutical and biologic product shipments with IOT-enabled features for shipment temperature and location tracking. Using the case study method and content analysis, this research project investigates the application of the structurational model of technology theory introduced by Orlikowski in order to interpret the firm’s entry and participation in the IOT-impelled marketplace.

Keywords: Internet of Things (IOT), radio frequency identification (RFID), structurational model of technology (Orlikowski), supply chain management

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Authors: Hadri Hussain, M.Nasir Ibrahim, Chee-Ming Ting, Mariani Idroas, Fuad Numan, Alias Mohd Noor

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A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted.

Keywords: electrocardiogram, phonocardiogram, hidden markov model, mel frequency cepstral coeffiecients, client identification

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Authors: Henrik Prinzhorn, Peter Nyhuis, Johannes Wagner, Peter Burggräf, Torben Schmitz, Christina Reuter

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Assembling large-scale products, such as airplanes, locomotives, or wind turbines, involves frequent process interruptions induced by e.g. delayed material deliveries or missing availability of resources. This leads to a negative impact on the logistical performance of a producer of xxl-products. In industrial practice, in case of interruptions, the identification, evaluation and eventually the selection of an alternative order of assembly activities (‘assembly alternative’) leads to an enormous challenge, especially if an optimized logistical decision should be reached. Therefore, in this paper, an innovative, optimization model for the identification of assembly alternatives that addresses the given problem is presented. It describes make-to-order, large-scale product assembly processes as a resource constrained project scheduling (RCPS) problem which follows given restrictions in practice. For the evaluation of the assembly alternative, a cost-based definition of the logistical objectives (delivery reliability, inventory, make-span and workload) is presented.

Keywords: assembly scheduling, large-scale products, make-to-order, optimization, rescheduling

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Authors: Erick C. Valverde, Wansu Lim

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Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER system.

Keywords: deep learning, face detection, facial emotion recognition, network optimization methods

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Authors: José Dinis-Carvalho, Levi Guimarães, Celina Leão, Rui Sousa, Rosa Eliza Vieira, Larissa Thomaz, Kelliane Guerreiro

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This research paper investigates the efficacy of waste identification diagrams (WIDs) as a tool for waste reduction and management within the Manaus Industrial Pole. The study focuses on assessing the practical application and effectiveness of WIDs in identifying, categorizing, and mitigating various forms of waste generated across industrial processes. Employing a mixed-methods approach, including a qualitative questionnaire applied to 5 companies and quantitative data analysis with SPSS statistical software, the research evaluates the implementation and impact of WIDs on waste reduction practices in select industries within the Manaus Industrial Pole. The findings contribute to understanding the utility of WIDs as a proactive strategy for waste management, offering insights into their potential for fostering sustainable practices and promoting environmental stewardship in industrial settings. The study also discusses challenges, best practices, and recommendations for optimizing the utilization of WIDs in industrial waste management, thereby addressing the broader implications for sustainable industrial development.

Keywords: waste identification diagram, value stream mapping, overall equipment effectiveness, lean manufacturing

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Authors: Leili Esmaeilani, Jafar Ghaisari, Mohsen Ahmadian

Abstract:

Hammerstein–Wiener model is a block-oriented model where a linear dynamic system is surrounded by two static nonlinearities at its input and output and could be used to model various processes. This paper contains an optimization approach method for analysing the problem of Hammerstein–Wiener systems identification. The method relies on reformulate the identification problem; solve it as constraint quadratic problem and analysing its solutions. During the formulation of the problem, effects of adding noise to both input and output signals of nonlinear blocks and disturbance to linear block, in the emerged equations are discussed. Additionally, the possible parametric form of matrix operations to reduce the equation size is presented. To analyse the possible solutions to the mentioned system of equations, a method to reduce the difference between the number of equations and number of unknown variables by formulate and importing existing knowledge about nonlinear functions is presented. Obtained equations are applied to an instance H–W system to validate the results and illustrate the proposed method.

Keywords: identification, Hammerstein-Wiener, optimization, quantization

Procedia PDF Downloads 238