Search results for: information seeking behaviors

Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Juliet Sorensen, Megan Schliep

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Introduction: After a multidisciplinary assessment of health and human rights issues in central Mali, a musical album was created in 2014 in Douentza, Mali to provide health information on female genital mutilation/cutting (FGM/C), malaria, HIV/AIDS, girls’ education, breastfeeding, and sanitation. The objective of this study was to assess the impact of this album. Methods: A mixed-methods assessment was conducted with 149 individuals across 10 villages in Douentza Cercle. Analyses focused on the association of radio listening habits, age, sex, ethnicity and education with a public health knowledge score. Results: Over 90% of respondents reported daily radio listening, many listening five or more hours per day. Potential risks of FGM/C cited by participants included death (59%), difficulty in childbirth (48%), sterility (34%), and fistula (33%); when asked about their level of control over FGM/C, 28% stated they would never cut their daughters. Being a listener for 1-5 hours per day was associated with a 11.5% higher score of 'public health knowledge' compared to those listening only a little or not at all (p < 0.01). Education (marginal versus no formal education) was associated with 7.6% increased score (p < 0.01). Conclusion: Radio appears to be a significant part of community members’ daily routines and may be a valuable medium for transmitting information, particularly for lower literacy individuals.

Keywords: female genital cutting, public health and social justice education, radio, Mali

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Authors: Živa Ravnikar, Alfonso Bahillo Martinez, Barbara Goličnik Marušić

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Climate change mitigation has been formally adopted and announced by countries over the globe, where cities are targeting carbon neutrality through various more or less successful, systematic, and fragmentary actions. The article is based on the fact that environmental conditions affect human comfort and the usage of space. Urban planning can, with its sustainable solutions, not only support climate mitigation in terms of a planet reduction of global warming but as well enabling natural processes that in the immediate vicinity produce environmental conditions that encourage people to walk or cycle. However, the article draws attention to the importance of integrating climate consideration into urban planning, where detailed environmental data play a key role, enabling urban planners to improve or monitor environmental conditions on cycle paths. In a practical aspect, this paper tests a particular ICT tool, a prototype used for environmental data. Data gathering was performed along the cycling lanes in Ljubljana (Slovenia), where the main objective was to assess the tool's data applicable value within the planning of comfortable cycling lanes. The results suggest that such transportable devices for in-situ measurements can help a researcher interpret detailed environmental information, characterized by fine granularity and precise data spatial and temporal resolution. Data can be interpreted within human comfort zones, where graphical representation is in the form of a map, enabling the link of the environmental conditions with a spatial context. The paper also provides preliminary results in terms of the potential of such tools for identifying the correlations between environmental conditions and different spatial settings, which can help urban planners to prioritize interventions in places. The paper contributes to multidisciplinary approaches as it demonstrates the usefulness of such fine-grained data for better consideration of microclimatology in urban planning, which is a prerequisite for creating climate-comfortable cycling lanes promoting active mobility.

Keywords: information and communication technology tools, urban planning, human comfort, microclimate, cycling lanes

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Authors: A. Rezaei, M. Huisman, W. Van Paepegem

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Atomic interactions in molecular systems are mainly studied by particle mechanics. Nevertheless, researches have also put on considerable effort to simulate them using continuum methods. In early 2000, simple equivalent finite element models have been developed to study the mechanical properties of carbon nanotubes and graphene in composite materials. Afterward, many researchers have employed similar structural simulation approaches to obtain mechanical properties of nanostructured materials, to simplify interface behavior of fiber-reinforced composites, and to simulate defects in carbon nanotubes or graphene sheets, etc. These structural approaches, however, are limited to small deformations due to complicated local rotational coordinates. This article proposes a method for the finite element simulation of molecular mechanics. For ease in addressing the approach, here it is called Structural Finite Element Molecular Modeling (SFEMM). SFEMM method improves the available structural approaches for large deformations, without using any rotational degrees of freedom. Moreover, the method simulates molecular conformation, which is a big advantage over the previous approaches. Technically, this method uses nonlinear multipoint constraints to simulate kinematics of the atomic multibody interactions. Only truss elements are employed, and the bond potentials are implemented through constitutive material models. Because the equilibrium bond- length, bond angles, and bond-torsion potential energies are intrinsic material parameters, the model is independent of initial strains or stresses. In this paper, the SFEMM method has been implemented in ABAQUS finite element software. The constraints and material behaviors are modeled through two Fortran subroutines. The method is verified for the bond-stretch, bond-angle and bond-torsion of carbon atoms. Furthermore, the capability of the method in the conformation simulation of molecular structures is demonstrated via a case study of a graphene sheet. Briefly, SFEMM builds up a framework that offers more flexible features over the conventional molecular finite element models, serving the structural relaxation modeling and large deformations without incorporating local rotational degrees of freedom. Potentially, the method is a big step towards comprehensive molecular modeling with finite element technique, and thereby concurrently coupling an atomistic domain to a solid continuum domain within a single finite element platform.

Keywords: finite element, large deformation, molecular mechanics, structural method

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Authors: Zixi You

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There are a number of verbal prefixes in Old Japanese. However, the selection or the compatibility of verbs and verbal prefixes is among the least investigated topics on Old Japanese language. Unlike other types of prefixes, verbal prefixes in dictionaries are more often than not listed with very brief information such as ‘unknown meaning’ or ‘rhythmic function only’. To fill in a part of this knowledge gap, this paper presents an exhaustive investigation based on the newly developed ‘Oxford Corpus of Old Japanese’ (OCOJ), which included nearly all existing resource of Old Japanese language, with detailed linguistics information in TEI-XML tags. In this paper, we propose the possibility that the following three prefixes, i-, sa-, ta- (with ta- being considered as a variation of sa-), are relevant to split intransitivity in Old Japanese, with evidence that unergative verbs favor i- and that unergative verbs favor sa-(ta-). This might be undermined by the fact that transitives are also found to follow i-. However, with several manifestations of split intransitivity in Old Japanese discussed, the behavior of transitives in verbal prefix selection is no longer as surprising as it may seem to be when one look at the selection of verbal prefix in isolation. It is possible that there are one or more features that played essential roles in determining the selection of i-, and the attested transitive verbs happen to have these features. The data suggest that this feature is a sense of ‘change’ of location or state involved in the event donated by the verb, which is a feature of typical unaccusatives. This is further discussed in the ‘affectedness’ hierarchy. The presentation of this paper, which includes a brief demonstration of the OCOJ, is expected to be of the interest of both specialists and general audiences.

Keywords: old Japanese, split intransitivity, unaccusatives, unergatives, verbal prefix selection

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Authors: Sait Ozsoy, Hacer Yasar Teke, Mustafa Dalgic, Cetin Ketenci, Ertugrul Gok, Kenan Karbeyaz, Azem Irez, Mesut Akyol

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Self-mutilating behavior or self-injury behavior (SIB) is defined as: intentional harm to one’s body without intends to commit suicide”. SIB cases are commonly seen in psychiatry and forensic medicine practices. Despite variety of SIB methods, cuts in the skin is the most common (70-97%) injury in this group of patients. Subjects with SIB have one or more other comorbidities which include depression, anxiety, depersonalization, and feeling of worthlessness, borderline personality disorder, antisocial behaviors, and histrionic personality. These individuals feel a high level of hostility towards themselves and their surroundings. Researches have also revealed a strong relationship between antisocial personality disorder, criminal behavior, and SIB. This study has retrospectively evaluated 6,599 autopsy cases performed at forensic medicine institutes of six major cities (Ankara, Izmir, Diyarbakir, Erzurum, Trabzon, Eskisehir) of Turkey in 2013. The study group consisted of all cases with SIB findings (psychopathic cuts, cigarette burns, scars, and etc.). The relationship between causes of death in the study group (SIB subjects) and the control group was investigated. The control group was created from subjects without signs of SIB. Mann-Whitney U test was used for age variables and Chi-square test for categorical variables. Multinomial logistic regression analysis was used in order to analyze group differences in respect to manner of death (natural, accident, homicide, suicide) and analysis of risk factors associated with each group was determined by the Binomial logistic regression analysis. This study used SPSS statistics 15.0 for all its statistical and calculation needs. The statistical significance was p <0.05. There was no significant difference between accidental and natural death among the groups (p=0.737). Also there was a unit increase in number of cuts in psychopathic group while number of accidental death decreased (95% CI: 0.941-0.993) by 0.967 times (p=0.015). In contrast, there was a significant difference between suicidal and natural death (p<0.001), and also between homicidal and natural death (p=0.025). SIB is often seen with borderline and antisocial personality disorder but may be associated with many psychiatric illnesses. Studies have shown a relationship between antisocial personality disorders with criminal behavior and SIB with suicidal behavior. In our study, rate of suicide, murder and intoxication was higher compared to the control group. It could be concluded that SIB can be used as a predictor of possibility of one’s harm to him/herself and other people.

Keywords: autopsy, cause of death, forensic science, self-injury behaviour

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Authors: Maryam Hazrati, Tengku Aizan Hamid, Rahimah Ibrahim, Siti Aishah Hassan, Farkhondeh Sharif, Zahra Bagheri

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Background: Emotional abuse is the most common type of spousal abuse. In a long-term marriage which lasts several decades, the couple will be faced with greater vulnerability due to illness, disability, and dependence. Emotional abuse can have a devastating impact on victims, leading to low self-esteem, depression, anxiety, and post-traumatic stress disorder. Research Aim: The aim of this study was to investigate the effects of an emotional-focused psychoeducational intervention (EFPEI) on emotional abuse and marital satisfaction among older adults couples and also to examine the dyadic effects of each partner’s emotional abuse behaviors (EAB) on his/her marital satisfaction (MS) in Shiraz-Iran. Methodology: The study was a randomized controlled trial (RCT). A total of 57 eligible couples were randomly assigned to either the experimental group or the control group. The experimental group received EFPEI, which consisted of 12 sessions, each lasting 90 minutes. The control group did not receive any intervention. Data were collected using demographic questionnaire, Multidimensional Measure of Emotional Abuse (MMEAQ), and Marital Satisfaction Questionnaire for Older People (MSQFOP). The data was analyzed using a variety of statistical methods, including repeated measures ANOVA, path analysis, and correlational analyses. Findings: The results of the study showed that the EFPEI was effective in reducing emotional abuse and increasing marital satisfaction among older adults couples. Specifically, the mean scores for emotional abuse and marital satisfaction were significantly lower in the experimental group than in the control group at the end of the intervention. These effects were maintained at a 3-month follow-up. Moreover, the dyadic analysis revealed that husbands’ EAB had no significant effects on his own marital satisfaction but a significant negative partner effect, while wives’ EAB had significant negative actor and partner effects. Conclusion: The findings of this study provide support for the use of EFPEI as an effective intervention for decreasing emotional abuse and improving marital dissatisfaction among older adults. EFPEI is a short-term, evidence-based intervention that can be delivered by trained professionals. The intervention focuses on helping couples to improve their communication skills, resolve conflict, and build a stronger emotional connection.

Keywords: spouse abuse, emotion, aged, satisfaction, dyadic effect

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Authors: Cynthia Zgheib, Yonna Sacre

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Pregnancy, this particular moment in the life of a woman, requires monitoring of eating behavior changes. However, the food choices during pregnancy should be varied and healthy, including the consumption of different food groups. Nutritional status is the process of acquisition and consumption of food. Therefore, a varied diet is associated with good nutritional status. This is why the nutrition education is a strategy commonly applied to improve maternal nutrition during pregnancy. Thus, it is crucial to assess 'The eating behavior and nutritional status of pregnant women living in Keserwan Lebanon.' In order to evaluate the association of different persona, socioeconomic and sociodemographic factors with the eating behavior and nutrition in the concerned study category, a cross-sectional descriptive study was conducted on a sample of 150 pregnant women aging between 18 and 40 years randomly selected from the hospitals and clinics located in Keserwan area and equally distributed between different cities and villages of the area according to altitude. The purpose of this study was to evaluate the eating behavior of the concerned population and to compare it to the recommendation of the food guide pyramid, their level of food awareness and finally to analyze their blood tests in order to detect any nutrients deficiency that they may face during the course of their pregnancy. Sociodemographic, lifestyle, eating behaviour, health, eating patterns, awareness, and food frequency questionnaire (FFQ) were collected through a validated questionnaire specifically adapted for the purpose of the study. Statistical analysis was carried out, and multivariate models were used in order to evaluate the association between several independent variables and the eating behaviour and nutritional status of Lebanese pregnant women The final analysis has shown that 48.7% of pregnant women were aged between 30 and 40 years old, 56% had a normal BMI between 18.5 and 24.9, thus age affects the eating behavior, so the older are the pregnant women, and the healthier is their eating behavior. In fact, 80.7% had acceptable food behavior which is based on an equilibrium between both quantity and quality of food, although the recommended foods are foods found in the food pyramid and available in the Lebanese diet. In addition, 68% had an acceptable level of awareness concerning the health importance of good eating habits, therefore, it is positively affecting their food choices. Moreover, 50 % have an acceptable nutritional status which is confirmed by their biological tests. Future governmental or national studies and programs could be settled aiming to increase the awareness about the good eating behaviors and nutritional status of Lebanese pregnant women.

Keywords: eating behavior, nutritional status, level of awareness, pregnant woman

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Authors: Yanjun Chen, Chuanping Hu, Jie Shao, Lin Mei, Chongyang Zhang

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Traditional image classification is based on encoding scheme (e.g. Fisher Vector, Vector of Locally Aggregated Descriptor) with low-level image features (e.g. SIFT, HoG). Compared to these low-level local features, deep convolutional features obtained at the mid-level layer of convolutional neural networks (CNN) have richer information but lack of geometric invariance. For scene classification, there are scattered objects with different size, category, layout, number and so on. It is crucial to find the distinctive objects in scene as well as their co-occurrence relationship. In this paper, we propose a method to take advantage of both deep convolutional features and the traditional encoding scheme while taking object-centric and scene-centric information into consideration. First, to exploit the object-centric and scene-centric information, two CNNs that trained on ImageNet and Places dataset separately are used as the pre-trained models to extract deep convolutional features at multiple scales. This produces dense local activations. By analyzing the performance of different CNNs at multiple scales, it is found that each CNN works better in different scale ranges. A scale-wise CNN adaption is reasonable since objects in scene are at its own specific scale. Second, a fisher kernel is applied to aggregate a global representation at each scale and then to merge into a single vector by using a post-processing method called scale-wise normalization. The essence of Fisher Vector lies on the accumulation of the first and second order differences. Hence, the scale-wise normalization followed by average pooling would balance the influence of each scale since different amount of features are extracted. Third, the Fisher vector representation based on the deep convolutional features is followed by a linear Supported Vector Machine, which is a simple yet efficient way to classify the scene categories. Experimental results show that the scale-specific feature extraction and normalization with CNNs trained on object-centric and scene-centric datasets can boost the results from 74.03% up to 79.43% on MIT Indoor67 when only two scales are used (compared to results at single scale). The result is comparable to state-of-art performance which proves that the representation can be applied to other visual recognition tasks.

Keywords: deep convolutional features, Fisher Vector, multiple scales, scale-specific normalization

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Authors: Sharmistha Chowdhury, Shuva Chowdhury

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Buying-selling new as well as second-hand goods like tools, furniture, household, electronics, clothing, baby stuff, vehicles, and hobbies through the Facebook marketplace has become a new paradigm for c2c sellers. This phenomenon encourages and empowers decentralised home-oriented sellers. This study adopts Elaboration Likelihood Model (ELM) to explain consumer behaviour on Facebook Marketplace (FM). ELM suggests that consumers process information through the central and peripheral routes, which eventually shape their attitudes towards posts. The central route focuses on information quality, and the peripheral route focuses on cues. Sellers’ FM posts usually include product features, prices, conditions, pictures, and pick-up location. This study uses information relevance and accuracy as central route factors. The post’s attractiveness represents cues and creates positive or negative associations with the product. A post with remarkable pictures increases the attractiveness of the post. So, post aesthetics is used as a peripheral route factor. People influenced via the central or peripheral route forms an attitude that includes multiple processes – response and purchase intention. People respond to FM posts through save, share and chat. Purchase intention reflects a positive image of the product and higher purchase intention. This study proposes trust on sellers as a moderator to test the strength of its influence on consumer attitudes and behaviour. Trust on sellers is assessed whether sellers have badges or not. A sample questionnaire will be developed and distributed among a group of random FM sellers who are selling vehicles on this platform to conduct the study. The chosen product of this study is the vehicle, a high-value purchase item. High-value purchase requires consumers to consider forming their attitude without any sign of impulsiveness seriously. Hence, vehicles are the perfect choice to test the strength of consumers attitudes and behaviour. The findings of the study add to the elaboration likelihood model and online second-hand marketplace literature.

Keywords: consumer behaviour, elaboration likelihood model, facebook marketplace, c2c marketing

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Authors: Omer Zvi Shaked

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Intimate Partner Violence (IPV) is a widespread social problem. Since the 1970's, and due to political changes resulting from the feminist movement, western society has been changing its attitude towards the phenomenon and has been taking an active approach to reduce its magnitude. Enterprises in the form of legislation, awareness and prevention campaigns, women's shelters, and community intervention programs became more prevalent as years progressed. Although many initiatives were found to be productive, the effectiveness of one, however, remained questionable throughout the years: intervention programs for men's intimate violence. Surveys outline two main intervention models for men's intimate violence. The first is the Duluth model, which argued that men are socialized to be dominant - while women are socialized to be subordinate - and men are therefore required by social imperative to enforce, physically if necessary, their dominance. The Duluth model became the chief authorized intervention program, and some states in the US even regulated it as the standard criminal justice program for men's intimate violence. However, meta-analysis findings demonstrated that based on a partner's reports, Duluth treatment completers have 44% recidivism rate, and between 40% and 85% dropout range. The second model is the Cognitive-Behavioral Model (CBT), which is a highly accepted intervention worldwide. The model argues that cognitive misrepresentations of intimate situations precede violent behaviors frequently when anger predisposition exists. Since anger dysregulation mediates between one's cognitive schemes and violent response, anger regulation became the chief purpose of the intervention. Yet, a meta-analysis found only a 56% risk reduction for CBT interventions. It is, therefore, crucial to understand the background behind the domination of both the Duluth model and CBT interventions. This presentation will discuss the ways in which theoretical conceptualizations of men's intimate violence, as well as ideologies, had contributed to the above-mentioned interventions' wide acceptance, despite known lack of scientific and evidential support. First, the presentation will review the prominent interventions for male intimate violence, the Duluth model, and CBT. Second, the presentation will review the prominent theoretical models explaining men's intimate violence: The Patriarchal model, the Abusive Personality model, and the Post-Traumatic Stress model. Third, the presentation will discuss the interrelation between theory and practice, and the nature of affinity between research and practice regarding men's intimate violence. Finally, the presentation will set new directions for further research, aiming to improve intervention's efficiency with men's intimate violence and advance social work practice in the field.

Keywords: intimate partner violence, theory and practice relationship, Duluth, CBT, abusive personality, post-traumatic stress

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Authors: Oyenike M. Olanrewaju, Faith O. Echobu, Aderemi G. Adesoji, Emmy Danny Ajik, Joseph Nda Ndabula, Stephen Luka

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Nutrients are required for any soil with which plants thrive to improve efficient growth and productivity. Amongst these nutrients required for proper plant productivity are nitrogen, phosphorus and potassium (NPK). Due to factors like leaching, nutrient uptake by plants, soil erosion and evaporation, these elements tend to be in low quantity and the need to replenish them arises. However, this replenishment of soil nutrients cannot be done without a timely soil test to enable farmers to know the amount of each element in short quantity and evaluate the amount required to be added. Though wet soil analysis is good, it comes with a lot of challenges ranging from soil test gargets availability to the technical knowledge of how to conduct such soil tests by the common farmer. In this research, an Internet of Things test kit was developed to fill in the gaps created by wet soil analysis. The kit comprises components that were used to measure Nitrogen, Phosphorous and potassium (N, P, K) soil content, soil temperature and soil moisture at a series of intervals. In this implementation, the fieldwork was carried out within 0.2 hectares of land divided into smaller plots. Nitrogen values from the three reps range from 14.8 – 15mg/kg, Phosphorous 20.2-21.4 mg/kg, and Potassium 50.2-53 mg/kg. This information with soil moisture information obtained enabled the farmers to make informed and precise decisions on fertilizer applications, and wastage was avoided.

Keywords: internet of things, soil Nutrients, test kit, soil temperature

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Authors: A. Majeed, P. Ali

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Before designing an electrical system, the estimation of load is necessary for unit sizing and demand-generation balancing. The system could be a stand-alone system for a village or grid connected or integrated renewable energy to grid connection, especially as there are non–electrified villages in developing countries. In the classical model, the energy demand was found by estimating the household appliances multiplied with the amount of their rating and the duration of their operation, but in this paper, information exists for electrified villages could be used to predict the demand, as villages almost have the same life style. This paper describes a method used to predict the average energy consumed in each two months for every consumer living in a village by Artificial Neural Network (ANN). The input data are collected using a regional survey for samples of consumers representing typical types of different living, household appliances and energy consumption by a list of information, and the output data are collected from administration office of Piramagrun for each corresponding consumer. The result of this study shows that the average demand for different consumers from four villages in different months throughout the year is approximately 12 kWh/day, this model estimates the average demand/day for every consumer with a mean absolute percent error of 11.8%, and MathWorks software package MATLAB version 7.6.0 that contains and facilitate Neural Network Toolbox was used.

Keywords: artificial neural network, load estimation, regional survey, rural electrification

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Authors: K. Khouzami, J. Brassinne, C. Branca, E. Van Ruymbeke, B. Nysten, G. D’Angelo

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The development of hybrid organic-inorganic nanocomposites has recently attracted great interest. Typically, polymer silicates represent an emerging class of polymeric nanocomposites that offer superior material properties compared to each compound alone. Among these materials, complexes based on silicate clay and polysaccharides are one of the most promising nanocomposites. The strong electrostatic interaction between chitosan and montmorillonite can induce what is called physical hydrogel, where the coordination bonds or physical crosslinks may associate and dissociate reversibly and in a short time. These mechanisms could be the main origin of the uniqueness of their rheological behavior. However, owing to their structure intrinsically heterogeneous and/or the lack of dissipated energy, they are usually brittle, possess a poor toughness and may not have sufficient mechanical strength. Consequently, the properties of these nanocomposites cannot respond to some requirements of many applications in several fields. To address the issue of weak mechanical properties, covalent chemical crosslink bonds can be introduced to the physical hydrogel. In this way, quite homogeneous dually crosslinked microstructures with high dissipated energy and enhanced mechanical strength can be engineered. In this work, we have prepared a series of chitosan-montmorillonite nanocomposites chemically crosslinked by addition of poly (ethylene glycol) diglycidyl ether. This study aims to provide a better understanding of the mechanical behavior of dually crosslinked chitosan-based nanocomposites by relating it to their microstructures. In these systems, the variety of microstructures is obtained by modifying the number of cross-links. Subsequently, a superior uniqueness of the rheological properties of chemically crosslinked chitosan-montmorillonite nanocomposites is achieved, especially at the highest percentage of clay. Their rheological behaviors depend on the clay/chitosan ratio and the crosslinking. All specimens exhibit a viscous rheological behavior over the frequency range investigated. The flow curves of the nanocomposites show a Newtonian plateau at very low shear rates accompanied by a quite complicated nonlinear decrease with increasing the shear rate. Crosslinking induces a shear thinning behavior revealing the formation of network-like structures. Fitting shear viscosity curves via Ostward-De Waele equation disclosed that crosslinking and clay addition strongly affect the pseudoplasticity of the nanocomposites for shear rates γ ̇>20.

Keywords: chitosan, crossliking, nanocomposites, rheological properties

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Authors: Liu Chyong-Ru, Wang Yao-Chin, Huang Wen-Shiung, Tang Wan-Ching

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Although gamification has been concerned and applied in the tourism industry, limited literature could be found in tourism academy. Therefore, to contribute knowledge in festival gamification, it becomes essential to start by establishing a Festival Gamification Scale (FGS). This study defines festival gamification as the extent of a festival to involve game elements and game mechanisms. Based on self-determination theory, this study developed an FGS. Through the multi-study method, in study one, five FGS dimensions were sorted through literature review, followed by twelve in-depth interviews. A total of 296 statements were extracted from interviews and were later narrowed down to 33 items under six dimensions. In study two, 226 survey responses were collected from a cycling festival for exploratory factor analysis, resulting in twenty items under five dimensions. In study three, 253 survey responses were obtained from a marathon festival for confirmatory factor analysis, resulting in the final sixteen items under five dimensions. Then, results of criterion-related validity confirmed the positive effects of these five dimensions on flow experience. In study four, for examining the model extension of the developed five-dimensional 16-item FGS, which includes dimensions of relatedness, mastery, competence, fun, and narratives, cross-validation analysis was performed using 219 survey responses from a religious festival. For the tourism academy, the FGS could further be applied in other sub-fields such as destinations, theme parks, cruise trips, or resorts. The FGS serves as a starting point for examining the mechanism of festival gamification in changing tourists’ attitudes and behaviors. Future studies could work on follow-up studies of FGS by testing outcomes of festival gamification or examining moderating effects of enhancing outcomes of festival gamification. On the other hand, although the FGS has been tested in cycling, marathon, and religious festivals, the research settings are all in Taiwan. Cultural differences of FGS is another further direction for contributing knowledge in festival gamification. This study also contributes to several valuable practical implications. First, this FGS could be utilized in tourist surveys for evaluating the extent of gamification of a festival. Based on the results of the performance assessment by FGS, festival management organizations and festival planners could learn the relative scores among dimensions of FGS, and plan for future improvement of gamifying the festival. Second, the FGS could be applied in positioning a gamified festival. Festival management organizations and festival planners could firstly consider the features and types of their festival, and then gamify their festival based on investing resources in key FGS dimensions.

Keywords: festival gamification, festival tourism, scale development, self-determination theory

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Authors: Chun-Lin Zhang, Min Wan

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With the upgrade development of the tourism industry from the simple sightseeing tour to the leisure and vacation, people's travel idea has undergone a fundamental change. More and more people begin to pursue liberal and personal tourism, so self-driving tourism has become the main form of current tourism activities. With the self-driving tourism representing the general trend, the importance of convenient tourism transportation and perfect road traffic signs have become more and more prominent. A clear urban road traffic signs can help visitors quickly identify the direction and distance to the tourism destination. The purpose of this article is analyzing the planning of urban road traffic signs which can bring positive impact on the participation in the recreation involved of self-driving tourists. The content of this article is divided into three parts. Based on the literature review and theoretical analysis, the first part constructs a structural variance model. The model is from three dimensions: the attention of the self-driving tourists to the urban traffic signs along the road, the perception of the self-driving tourists to the road traffic signs itself, the perceptions of the self-driving tourists to the tourism destination information on the traffic signs. Through this model, the paper aims to explore the influence of the urban road traffic signs to the leisure psychological involvement and leisure behavior involvement of the self-driving tourists. The second part aims to verify through the hypothesis model the questionnaire survey and come to preliminary conclusions. The preliminary conclusions are as follows: firstly, the color, shape, size, setting mode and occurrence frequency of urban road traffic sign have significant influence on the leisure psychological involvement and leisure behavior involvement of the self-driving tourists. Secondly, the influence on the leisure behavior involvement is obviously higher than the influence on the leisure psychological involvement. Thirdly, the information about the tourism destination marked on the urban road traffic signs has not obviously influence on the leisure psychological involvement, but it has distinct influence on the leisure behavior involvement of self-driving tourists. The third part puts forward that the planning of urban road traffic signs should focus on the angle of the impact of road traffic signs on people's psychology and behavior. On the basis of the above conclusions, the paper researches the color, shape, size, setting mode and information labeling of urban road traffic signs so that they can preferably satisfy the demand of the leisure involvement of self-driving tourists.

Keywords: leisure involvement, self-driving tourism, structural equation, urban road traffic signs

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Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

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Authors: Tsvetomira Braynova, Miglena Simonska

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The article presents the results of research on phonological processes in three and four-year-old children. For the purpose of the study, an author's test was developed and conducted among 120 children. The study included three areas of research - at the level of words (96 words), at the level of sentence repetition (10 sentences) and at the level of generating own speech from a picture (15 pictures). The test also gives us additional information about the articulation errors of the assessed children. The main purpose of the icing is to analyze all phonological processes that occur at this age in Bulgarian children and to identify which are typical and atypical for this age. The results show that the most common phonology errors that children make are: sound substitution, an elision of sound, metathesis of sound, elision of a syllable, and elision of consonants clustered in a syllable. All examined children were identified with the articulatory disorder from type bilabial lambdacism. Measuring the correlation between the average length of repeated speech and the average length of generated speech, the analysis proves that the more words a child can repeat in part “repeated speech,” the more words they can be expected to generate in part “generating sentence.” The results of this study show that the task of naming a word provides sufficient and representative information to assess the child's phonology.

Keywords: assessment, phonology, articulation, speech-language development

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Authors: Ray Bryant

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

Keywords: AI, technology, human trafficking, prevention

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Authors: Yusuf Bulale, Mark Prince, Geoff Tansley, Peter Brett

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Cochlear Implantation (CI) which became a routine procedure for the last decades is an electronic device that provides a sense of sound for patients who are severely and profoundly deaf. Today, cochlear implantation technology uses electrode array (EA) implanted manually into the cochlea. The optimal success of this implantation depends on the electrode technology and deep insertion techniques. However, this manual insertion procedure may cause mechanical trauma which can lead to a severe destruction of the delicate intracochlear structure. Accordingly, future improvement of the cochlear electrode implant insertion needs reduction of the excessive force application during the cochlear implantation which causes tissue damage and trauma. This study is examined tool-tissue interaction of large prototype scale digit embedded with distributive tactile sensor based upon cochlear electrode and large prototype scale cochlea phantom for simulating the human cochlear which could lead to small-scale digit requirements. The digit, distributive tactile sensors embedded with silicon-substrate was inserted into the cochlea phantom to measure any digit/phantom interaction and position of the digit in order to minimize tissue and trauma damage during the electrode cochlear insertion. The digit has provided tactile information from the digit-phantom insertion interaction such as contact status, tip penetration, obstacles, relative shape and location, contact orientation and multiple contacts. The tests demonstrated that even devices of such a relative simple design with low cost have a potential to improve cochlear implant surgery and other lumen mapping applications by providing tactile sensory feedback information and thus controlling the insertion through sensing and control of the tip of the implant during the insertion. In that approach, the surgeon could minimize the tissue damage and potential damage to the delicate structures within the cochlear caused by current manual electrode insertion of the cochlear implantation. This approach also can be applied to other minimally invasive surgery applications as well as diagnosis and path navigation procedures.

Keywords: cochlear electrode insertion, distributive tactile sensory feedback information, flexible digit, minimally invasive surgery, tool/tissue interaction

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Authors: Nikolai Brosch, Alwine Mohnen

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Academics and business leaders increasingly call for a (re)definition of a corporate purpose beyond profit-maximization to contribute to the welfare of society. This study investigates the effect of communicating such a pro-social corporate purpose on three employee-level outcomes that constitute major cost components for most organizations: workers reservation wage, work quality, and work misbehavior. To provide causal evidence, two natural field experiments were conducted with almost 2,000 workers recruited from different online labor marketplaces. Workers were randomly assigned to treatments manipulating whether or not they received information about the employer’s corporate purpose and subsequently performed a short, real-effort task for payment. The main findings in both experiments show that receiving information about an employer’s pro-social corporate purpose causes workers to accept lower wages (9% lower in the first experiment and 28% lower in the second experiment) for the same job. Workers that personally assess high importance to organizations having a pro-social purpose are most responsive. At the same time, sacrificing wage for a corporate purpose comes at no cost of quality and even decreases the likelihood of engaging in work misbehavior. In a broader context, the results provide some evidence that the (re)definition of corporate purpose in commercial organizations is not ultimately at odds with creating profits.

Keywords: corporate purpose, natural field experiment, reservation wage, work misbehavior, work quality

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Authors: Lubabatu Sada Sodangi

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The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation.

Keywords: data mining, CHAID, multi-layer perceptron, SPSS, Adult dataset

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Authors: Linda Dusman, Linda Baker

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The Bearman Study of Audience Engagement examined the potential for real-time music education during online symphony orchestra concerts. It follows on the promising results of a preliminary study of STEAM (Science, Technology, Engineering, Arts, and Mathematics) education during live concerts, funded by the National Science Foundation with the Baltimore Symphony Orchestra. For the Bearman Study, audience groups were recruited to attend two previously recorded concerts of the National Orchestral Institute (NOI) in 2020 or the Utah Symphony in 2021. They used a smartphone app called EnCue to present real-time program notes about the music being performed. Short notes along with visual information (photos and score fragments) were designed to provide historical, cultural, biographical, and theoretical information at specific moments in the music where that information would be most pertinent, generally spaced 2-3 minutes apart to avoid distraction. The music performed included Dvorak Symphony No. 8 and Mahler Symphony No. 5 at NOI, and Mendelssohn Scottish Symphony and Richard Strauss Metamorphosen with the Utah Symphony, all standard repertoire for symphony orchestras. During each phase of the study (2020 and 2021), participants were randomly assigned to use the app to view program notes during the first concert or to use the app during the second concert. A total of 139 participants (67 in 2020 and 72 in 2021) completed three online questionnaires, one before attending the first concert, one immediately after the concert, and the third immediately after the second concert. Questionnaires assessed demographic background, expertise in music, engagement during the concert, learning of content about the composers and the symphonies, and interest in the future use of the app. In both phases of the study, participants demonstrated that they learned content presented on the app, evidenced by the fact that their multiple-choice test scores were significantly higher when they used the app than when they did not. In addition, most participants indicated that using the app enriched their experience of the concert. Overall, they were very positive about their experience using the app for real-time learning and they expressed interest in using it in the future at both live and streaming concerts. Results confirmed that informal real-time learning during concerts is possible and can generate enhanced engagement and interest in classical music.

Keywords: audience engagement, informal education, music technology, real-time learning

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Authors: Hameda Suwaed

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In today's world, knowledge and information are increasing in a way that no one can learn everything about every subject and the jobs that students will get after graduation may not yet exist. Therefore, teaching them just facts without teaching them how to use the facts in real life, how to work in a team and how to think critically is no longer enough. In Libya, a country characterized by using traditional methods of teaching, how can English language teachers train students to use such skills? And what type of activities and adaptations to classroom teaching methods will be necessary to do this? Using action research and classroom research gathered data, this study adjusts Heath's (2010) model of change that includes giving students clear directions, sufficient motivation and supportive environment. These steps were applied by encouraging students to participate actively in the classroom by using group work and variety of activities to train them how to find, interpret and use information. The findings of the study showed that following the suggested model can broaden students' perspectives on studying in their environment starting with their classroom and ending with their country. In conclusion, although this was a small scale study, the students' participation in the classroom shows that they gained self confidence in using practices such as group work, critical thinking, time management, creativity and how to present their ideas and accepting different opinions. What was remarkable is that most students were aware that is what we need in Libya nowadays.

Keywords: change, classroom practice, skills, Libya, group work

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Authors: Adriana Haulica

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Powered by Machine Learning, Precise medicine is tailored by now to use genetic and molecular profiling, with the aim of optimizing the therapeutic benefits for cohorts of patients. As the majority of Machine Language algorithms come from heuristics, the outputs have contextual validity. This is not very restrictive in the sense that medicine itself is not an exact science. Meanwhile, the progress made in Molecular Biology, Bioinformatics, Computational Biology, and Precise Medicine, correlated with the huge amount of human biology data and the increase in computational power, opens new healthcare challenges. A more accurate diagnosis is needed along with real-time treatments by processing as much as possible from the available information. The purpose of this paper is to present a deeper vision for the future of Artificial Intelligence in Precise medicine. In fact, actual Machine Learning algorithms use standard mathematical knowledge, mostly Euclidian metrics and standard computation rules. The loss of information arising from the classical methods prevents obtaining 100% evidence on the diagnosis process. To overcome these problems, we introduce MEDICOMPILLS, a new architectural concept tool of information processing in Precise medicine that delivers diagnosis and therapy advice. This tool processes poly-field digital resources: global knowledge related to biomedicine in a direct or indirect manner but also technical databases, Natural Language Processing algorithms, and strong class optimization functions. As the name suggests, the heart of this tool is a compiler. The approach is completely new, tailored for omics and clinical data. Firstly, the intrinsic biological intuition is different from the well-known “a needle in a haystack” approach usually used when Machine Learning algorithms have to process differential genomic or molecular data to find biomarkers. Also, even if the input is seized from various types of data, the working engine inside the MEDICOMPILLS does not search for patterns as an integrative tool. This approach deciphers the biological meaning of input data up to the metabolic and physiologic mechanisms, based on a compiler with grammars issued from bio-algebra-inspired mathematics. It translates input data into bio-semantic units with the help of contextual information iteratively until Bio-Logical operations can be performed on the base of the “common denominator “rule. The rigorousness of MEDICOMPILLS comes from the structure of the contextual information on functions, built to be analogous to mathematical “proofs”. The major impact of this architecture is expressed by the high accuracy of the diagnosis. Detected as a multiple conditions diagnostic, constituted by some main diseases along with unhealthy biological states, this format is highly suitable for therapy proposal and disease prevention. The use of MEDICOMPILLS architecture is highly beneficial for the healthcare industry. The expectation is to generate a strategic trend in Precise medicine, making medicine more like an exact science and reducing the considerable risk of errors in diagnostics and therapies. The tool can be used by pharmaceutical laboratories for the discovery of new cures. It will also contribute to better design of clinical trials and speed them up.

Keywords: bio-semantic units, multiple conditions diagnosis, NLP, omics

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Authors: Hanlin Liu, Hua Li, Yintan AI

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Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: Awatif Habeeb Al-Shamare

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The number of social media users amongst special education teachers and parents of children with Down Syndrome (DS) is increasing annually. This is also the case in the Kingdom of Saudi Arabia (KSA). However, according to the best of the author’s knowledge, there are no qualitative studies which testify to the true nature of the interaction between teachers and parents when using social media, nor the role of social media in supporting and assisting parents and teachers with regards to the children’s educational needs in KSA. Therefore, this ongoing study aims to identify the role of social media in supporting parents and teachers of DS students, with a special emphasis on early intervention services in KSA. By bridging the knowledge gap on social media and special education in KSA and presenting socially relevant and applied information on the topic, this research provides a theoretical and practical base for the establishment of appropriate and effective programmes between the ministries of Information and Special Education in particular. A qualitative approach was selected because it was the most suitable approach for exploring the participants’ experiences, which could not be determined through scientific tests. Interviewing, chosen as the research instrument, allowed the researcher to obtain a detailed understanding of the topic linked to the study objectives. Initially, a pilot study was conducted at the Daycare Center in May 2016. Its aim was to examine and refine the methodology and assess whether the questions were understood with the potential for re-drafting them, if necessary. The main study consists of five teachers and five mothers with experience of using social media and with links to the Daycare Center. Thematic Analysis has been chosen for analysing the findings because it is a flexible method that allows themes to emerge from the data. Results of the current study are still in the initial stages, but the preliminary findings are as follows: (1) social media is an important tool in encouraging parents and teachers to access the necessary information and knowledge about, and experience in, early intervention services; (2) it acts as a support network for the parents; (3) it helps raise awareness about DS and the need for early intervention; (4) it can be used to put pressure on the government for an expansion in early intervention services, and finally (5) its use can be problematic in that parents and teachers face some difficulties and challenges when using the different platforms. It can be concluded that social media plays a significant role in the lives of teachers and parents with special needs children in KSA.

Keywords: down syndrome, early intervention services, social media, support parents and teachers

Procedia PDF Downloads 132

Authors: Kenneth O. Otasowie, Matthew Ikuabe, Clinton Aigbavboa, Ayodeji Oke

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The quantity surveying (QS) profession is one of the professions in the construction industry, and it is saddled with the responsibility of measuring the number of materials as well as the workmanship required to get work done in the industry. This responsibility is vital to the success of a construction project as it determines if a project will be completed on time, within budget, and up to the required standard. However, the practice has been criticised severally for failure to accurately execute her responsibility. The need to reduce errors, inaccuracies and omissions has made the adoption of modern technologies such as building information modeling (BIM) inevitable in its practice. Nevertheless, there are barriers to the adoption of BIM in QS practice in South Africa (SA). Thus, this study aims to investigate these barriers. A survey design was adopted. A total number of one hundred and fifteen (115) questionnaires were administered to quantity surveyors in Guateng Province, SA, and ninety (90) were returned and found suitable for analysis. Collected data were analysed using percentage, mean item score, standard deviation, one-sample t-test, and Kruskal-Wallis. The findings show that lack of BIM expertise, lack of government enforcement, resistance to change, and no client demand for BIM are the most significant barriers to the adoption of BIM in QS practice. As a result, this study recommends that trainings on BIM technology be prioritised, and government must take the lead in BIM adoption in the country, particularly in public projects.

Keywords: barriers, BIM, quantity surveying practice, South Africa

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Authors: Ana Carolina Kogawa, Selma Gutierrez Antonio, Hérida Regina Nunes Salgado

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Rifaximin is an oral antimicrobial, gut - selective and not systemic with adverse effects compared to placebo. It is used for the treatment of hepatic encephalopathy, travelers diarrhea, irritable bowel syndrome, Clostridium difficile, ulcerative colitis and acute diarrhea. The crystalline form present in the rifaximin with minimal systemic absorption is α, being the amorphous form significantly different. Regulators are increasingly attention to polymorphisms. Polymorphs can change the form by altering the drug characteristics compromising the effectiveness and safety of the finished product. International Conference on Harmonization issued the ICH Guidance Q6A, which aim to improve the control of polymorphism in new and existing pharmaceuticals. The objective of this study was to obtain polymorphic forms of rifaximin employing recrystallization processes and characterize them by thermal analysis (thermogravimetry - TG and differential scanning calorimetry - DSC), X-ray diffraction, scanning electron microscopy and solubility test. Six polymorphic forms of rifaximin, designated I to VI were obtained by the crystallization process by evaporation of the solvent. The profiles of the TG curves obtained from polymorphic forms of rifaximin are similar to rifaximin and each other, however, the DTG are different, indicating different thermal behaviors. Melting temperature values of all the polymorphic forms were greater to that shown by the rifaximin, indicating the higher thermal stability of the obtained forms. The comparison of the diffractograms of the polymorphic forms of rifaximin with rifaximin α, β and γ constant in patent indicate that forms III, V and VI are formed by mixing polymorph β and α and form III is formed by polymorph β. The polymorphic form I is formed by polymorph β, but with a significant amount of amorphous material. Already, the polymorphic form II consists of polymorph γ, amorphous. In scanning electron microscope is possible to observe the heterogeneity of morphological characteristics of crystals of polymorphic forms among themselves and with rifaximin. The solubility of forms I and II was greater than the solubility of rifaximin, already, forms III, IV and V presented lower solubility than of rifaximin. Similarly, the bioavailability of the amorphous form of rifaximin is considered significantly higher than the form α, the polymorphic forms obtained in this work can not guarantee the excellent tolerability of the reference medicine. Therefore, studies like these are extremely important and they point to the need for greater requirements by the regulatory agencies competent about polymorphs analysis of the raw materials used in the manufacture of medicines marketed globally. These analyzes are not required in the majority of official compendia. Partnerships between industries, research centers and universities would be a viable way to consolidate researches in this area and contribute to improving the quality of solid drugs.

Keywords: electronic microscopy, polymorphism, rifaximin, solubility, X-ray diffraction

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