Search results for: manual data inquiry

Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

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Authors: Dina L. DiSantis

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Development and implementation of a place-based watershed science course for high school students will prove to be a valuable experience for both student and teacher. By having students study and assess the watershed dynamics of a local stream, they will better understand how human activities affect this valuable resource. It is important that students gain tangible skills that will help them to have an understanding of water quality analysis and the importance of preserving our Earth's water systems. Having students participate in real world practices is the optimal learning environment and can offer students a genuine learning experience, by cultivating a knowledge of place, while promoting education for sustainability. Additionally, developing a watershed science course for high school students will give them a hands-on approach to studying science; which is both beneficial and more satisfying to students. When students conduct their own research, collect and analyze data, they will be intimately involved in addressing water quality issues and solving critical water quality problems. By providing students with activities that take place outside the confines of the indoor classroom, you give them the opportunity to gain an appreciation of the natural world. Placed-based learning provides students with problem-solving skills in everyday situations while enhancing skills of inquiry. An overview of a place-based watershed science course and its impact on student learning will be presented.

Keywords: education for sustainability, place-based learning, watershed science, water quality

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Authors: Mary T. McKinley

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As socio-economic globalization impacts education and turns knowledge into a commodity, institutions of higher education are becoming more intentional about infusing a global and intercultural perspective into education via the recruitment of international students. Coming from dissimilar cultures, many of these students are evaluated and held accountable to Euro-American values of independence, self-reliance, and autonomy. Not surprisingly, these students often experience culture shock with deleterious effects on their mental health and academic functioning. Thus, it is critical to understand the experiences of international students with the hope that such knowledge will keep the field of psychology from promulgating Eurocentric ideals and values and prevent the training of these students as good-enough White therapists. Using a critical narrative inquiry framework, this study elicits stories about the challenges encountered by international students as they navigate their clinical training in the presence of acculturative stress and potentially different worldviews. With its emphasis on story-telling as meaning making, narrative research design is hinged on the assumption that people are interpretive beings who make meaning of themselves and their world through the language of stories. Also, dominant socially-constructed narratives play a central role in creating and maintaining hegemonic structures that privilege certain individuals and ideologies at the expense of others. On this premise, narrative inquiry begins with an exploration of the experiences of participants in their lived stories. Bounded narrative segments were read, interpreted, and analyzed using a critical events approach. Throughout the process, issues of reliability and researcher bias were addressed by keeping a reflective analytic memo, as well as triangulating the data using peer-reviewers and check-ins with participants. The findings situate culture at the epicenter of international students’ acculturation challenges as well as their resiliency in psychology doctoral programs. It was not uncommon for these international students to experience ethical dilemmas inherent in learning content that conflicted with their cultural beliefs and values. Issues of cultural incongruence appear to be further exacerbated by visible markers for differences like speech accent and clothing attire. These stories also link the acculturative stress reported by international students to the experiences of perceived racial discrimination and lack of support from the faculty, administration, peers, and the society at large. Beyond the impact on the international students themselves, there are implications for internationalization in psychology with the goal of equipping doctoral programs to be better prepared to meet the needs of their international students. More than ever before, programs need to liaise with international students’ services and work in tandem to meet the unique needs of this population of students. Also, there exists a need for multiculturally competent supervisors working with international students with varying degrees of acculturation. In addition to making social justice and advocacy salient in students’ multicultural training, it may be helpful for psychology doctoral programs to be more intentional about infusing cross-cultural theories, indigenous psychotherapies, and/or when practical, the possibility for geographically cross-cultural practicum experiences in the home countries of international students while taking into consideration the ethical issues for virtual supervision.

Keywords: decolonizing pedagogies, international students, multiculturalism, psychology doctoral programs

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Authors: Halima Sadia Qureshi, Urooj Sadiq, Noshi Eram Zaman

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The goal of this study was to see how cognitive behavior hypnotherapy affected nonsuicidal self-injury. DSM 5 invites the researchers to explore the newly added condition under the chapter of conditions under further study named Nonsuicidal self-injury disorder. To date, no empirical sound intervention has been proven effective for NSSI as given in DSM 5. Nonsuicidal self-injury is defined by DSM 5 as harming one's self physically, without suicidal intention. Around 7.6% of teenagers are expected to fulfill the NSSI disorder criteria. 3 Adolescents, particularly university students, account for around 87 percent of self-harm studies. Furthermore, one of the risks associated with NSSI is an increased chance of suicide attempts, and in most cases, the cycle repeats again. 6 The emotional and psychological components of the illness might lead to suicide, either intentionally or unintentionally. 7 According to a research done at a Pakistani military hospital, over 80% of participants had no intention of committing suicide. Furthermore, it has been determined that improvements in NSSI prevention and intervention are necessary as a stand-alone strategy. The quasi-experimental study took place in Islamabad and Rawalpindi, Pakistan, from May 2019 to April 2020 and included students aged 18 to 25 years old from several institutions and colleges in the twin cities. According to the Diagnostic and Statistical Manual of Mental Disorders 5th edition, the individuals were assessed for >2 episodes without suicidal intent using the intentional self-harm questionnaire. The Clinician Administered Nonsuicidal Self-Injury Disorder Index (CANDI) was used to assess the individual for NSSI condition. Symptom checklist-90 (SCL-90) was used to screen the participants for differential diagnosis. Mclean Screening Instrument for Borderline Personality Disorder (MSI-BPD) was used to rule out the BPD cases. The selected participants, n=106 from the screening sample of 600, were selected. They were further screened to meet the inclusion and exclusion criteria, and the total of n=71 were split into two groups: intervention and control. The intervention group received cognitive behavior hypnotherapy for the next three months, whereas the control group received no treatment. After the period of three months, both the groups went through the post assessment, and after the three months’ period, follow-up assessment was conducted. The groups were evaluated, and SPSS 25 was used to analyse the data. The results showed that each of the two groups had 30 (50 percent) of the 60 participants. There were 41 males (68 percent) and 19 girls (32 percent) in all. The bulk of the participants were between the ages of 21 and 23. (48 percent). Self-harm events were reported by 48 (80 percent) of the pupils, and suicide ideation was found in 6 (ten percent). In terms of pre- and post-intervention values (d=4.90), post-intervention and follow-up assessment values (d=0.32), and pre-intervention and follow-up values (d=5.42), the study's effect size was good. The comparison of treatment and no-treatment groups revealed that treatment was more successful than no-treatment, F (1, 58) = 53.16, p.001. The results reveal that the treatment manual of CBH is effective for Nonsuicidal self-injury disorder.

Keywords: NSSI, nonsuicidal self injury disorder, self-harm, self-injury, Cognitive behaviour hypnotherapy, CBH

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Authors: Hema Vanita Kesevan

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This study investigated the impact of teacher’s language ideology on their classroom discursive practice in ESL / EFL classrooms. It examines teachers’ perceptions of the use of local variety of Malaysian English in the classroom. The investigation shows that although teachers and students are against its use in the classroom, it is widely employed. The participants of this study consist of two Malaysian non-native English teachers with different linguistic and cultural backgrounds. This study employs a comparative case study approach which focuses on the teachers and their classroom discourse practice. There are two modes of inquiry used in this study: classroom observation and semi-guided interviews. The findings are of interest to ESL / EFL teachers, policy makers and language researchers in the Malaysian and other similar ESL / EFL contexts.

Keywords: language ideology, Malaysian English, native teachers, non-native teachers

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Authors: Maria Pratt, Lynn Martin

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Context: In nursing education, clinical instructors are crucial in assessing and evaluating students' performance in clinical courses. However, instructors often struggle when assigning failing grades to students at risk of failing. Research Aim: This qualitative study aims to understand clinical instructors' experiences evaluating students with unsatisfactory performance, including how reflection and collegial support impact this evaluation process. Methodology, Data Collection, and Analysis Procedures: This study employs Gadamer's Hermeneutic Inquiry as the research methodology. A purposive maximum variation sampling technique was used to recruit eight clinical instructors from a collaborative undergraduate nursing program in Southwestern Ontario. Semi-structured, open-ended, and audio-taped interviews were conducted with the participants. The hermeneutic analysis was applied to interpret the interview data to allow for a thorough exploration and interpretation of the instructors' experiences evaluating failing students. Findings: The main findings of this qualitative research indicate that evaluating failing students was emotionally draining for the clinical instructors who experienced multiple challenges, uncertainties, and negative feelings associated with assigning failing grades. However, the analysis revealed that ongoing reflection and collegial support played a crucial role in mitigating the challenges they experienced. Conclusion: This study contributes to the theoretical understanding of nursing education by shedding light on clinical instructors' challenges in evaluating failing students. It emphasizes the emotional toll associated with this process and the role that reflection and collegial support play in alleviating those challenges. The findings underscore the need for ongoing professional development and support for instructors in nursing education. By understanding and addressing clinical instructors' experiences, nursing education programs can better equip them to effectively evaluate struggling students and provide the necessary support for their professional growth.

Keywords: clinical instructor, student evaluation, nursing, reflection, support

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Authors: Rokhmatul Insani, Hira Laksmiwati Soemitro

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Business Intelligence is a methodology that exploits the data to produce information and knowledge systematically, business intelligence can support the decision-making process. Some methods in business intelligence are data warehouse and data mining. A data warehouse can store historical data from transactional data. For data modelling in data warehouse, we apply dimensional modelling by Kimball. While data mining is used to extracting patterns from the data and get insight from the data. Data mining has many techniques, one of which is segmentation. For profiling of telecommunication customer, we use customer segmentation according to customer’s usage of services, customer invoice and customer payment. Customers can be grouped according to their characteristics and can be identified the profitable customers. We apply K-Means Clustering Algorithm for segmentation. The input variable for that algorithm we use RFM (Recency, Frequency and Monetary) model. All process in data mining, we use tools IBM SPSS modeller.

Keywords: business intelligence, customer segmentation, data warehouse, data mining

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Arpine Maghakyan

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The purpose of this paper is to study the relationship between the level of industry digitalization and audit fees, especially, the relationship between Big 4 auditor fees and industry digitalization level. On the one hand, automation of business processes decreases internal control weakness and manual mistakes; increases work effectiveness and integrations. On the other hand, it may cause serious misstatements, high business risks or even bankruptcy, typically in early stages of automation. Incomplete automation can bring high audit risk especially if the auditor does not fully understand client’s business automation model. Higher audit risk consequently will cause higher audit fees. Higher audit fees for clients with high automation level are more highlighted in Big 4 auditor’s behavior. Using data of US firms from 2005-2015, we found that industry level digitalization is an interaction for the auditor quality on audit fees. Moreover, the choice of Big4 or non-Big4 is correlated with client’s industry digitalization level. Big4 client, which has higher digitalization level, pays more than one with low digitalization level. In addition, a high-digitalized firm that has Big 4 auditor pays higher audit fee than non-Big 4 client. We use audit fees and firm-specific variables from Audit Analytics and Compustat databases. We analyze collected data by using fixed effects regression methods and Wald tests for sensitivity check. We use fixed effects regression models for firms for determination of the connections between technology use in business and audit fees. We control for firm size, complexity, inherent risk, profitability and auditor quality. We chose fixed effects model as it makes possible to control for variables that have not or cannot be measured.

Keywords: audit fees, auditor quality, digitalization, Big4

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Authors: S. F. Kanaan, Fatima Al-Kadi, H. Khrais

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Background: Fibromyalgia is a chronic condition that is characterized by chronic pain that limits physical and functional activities. To our best knowledge, there is currently no key physiotherapy approach recommended to reduce pain and improve function. In addition, there are scarce studies that investigated the effect of manual therapy in the management of Fibromyalgia, and no study investigated the efficacy of Mulligan´s mobilization with movement (MWM) in particular. Methods: A 51-year-old female diagnosed with Fibromyalgia for more than a year. The patient was complaining of generalized pain including neck, lower back, shoulders, elbows, hips, and knees. In addition, the patient reported severe limitation in activities and inability to complete her work as a lawyer. The Intervention provided for the patient consisted of 4 sessions (in two weeks) of MWM for neck, lower back, shoulders, elbows, sacroiliac joint, hips, and knees. The Visual Analogue Scale of pain (VAS), Range of Motion (ROM), 10-minute walk test, Roland Morris Low Back Pain and Disability Questionnaire (RMQ), Disability of the Arm, Shoulder and Hand Score (DASH) were collected at the baseline and at the end of treatment. Results: Average improvement of ROM in the neck, lower back, shoulder, elbows, hips, and knees was 45%. VAS scale changed from pre-treatment to post-treatment as the following: neck pain (9 to 0), lower back pain (8 to 1), shoulders pain (8 to 2), elbows pain (7 to 1), and knees pain (9 to 0). The patient demonstrated improvement in all functional scale from pre-intervention to post-intervention: 10-meter walk test (9.8 to 4.5 seconds), RMQ (21 to 11/24), and DASH (88.7% to 40.5%). The patient did not report any side effect of using this approach. Conclusion: Fibromyalgia can cause joint 'faulty position' leading to pain and dysfunction, which can be reversed by using MWM. MWM showed to have clinically significant improvement in ROM, pain, and ability to walk and a clinically significant reduction in disability in only 4 sessions. This work can be expanded in a larger sample.

Keywords: mobilization, fibromyalgia, dysfunction, manual therapy

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Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

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Authors: Lutful Karim, Mohammed S. Al-kahtani

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Sensors are being used in various applications such as agriculture, health monitoring, air and water pollution monitoring, traffic monitoring and control and hence, play the vital role in the growth of big data. However, sensors collect redundant data. Thus, aggregating and filtering sensors data are significantly important to design an efficient big data framework. Current researches do not focus on aggregating and filtering data at multiple layers of sensor-based big data framework. Thus, this paper introduces (i) three layers data aggregation and framework for big data and (ii) a priority-based, dynamic data aggregation scheme (PDDA) for the lowest layer at sensors. Simulation results show that the PDDA outperforms existing tree and cluster-based data aggregation scheme in terms of overall network energy consumptions and end-to-end data transmission delay.

Keywords: big data, clustering, tree topology, data aggregation, sensor networks

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Authors: Lily Ye

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The contemporary fashion industry has witnessed the global rise of Chinese fashion designers. China plays more and more important role in this sector globally. One of the key debates in contemporary time is the conception of Chinese fashion. A close look at previous discussions on Chinese fashion reveals that most of them are explored through the lens of cultural knowledge and assumptions, using the dichotomous models of East and West. The results of these studies generate an essentialist and orientalist notion of Chinoiserie and Chinese fashion, which sees individual designers from China as undifferential collective members marked by a unique and fixed set of cultural scripts. This study challenges this essentialist conceptualization and brings fresh insights to the discussion of Chinese fashion identity against the backdrop of globalisation. Different from a culturalist approach to researching Chinese fashion, this paper presents an alternative position to address the research agenda through the mobilisation of Giddens’ (1991) theory of reflexive identity formation, privileging individuals’ agency and reflexivity. This approach to the discussion of identity formation not only challenges the traditional view seeing identity as the distinctive and essential characteristics belonging to any given individual or shared by all members of a particular social category or group but highlights fashion designers’ strategic agency and their role as fashion activist. This study draws evidence from a textual analysis of published stories of a group of established Chinese designers such as Guo Pei, Huishan Zhang, Masha Ma, Uma Wang, and Ma Ke. In line with Giddens’ concept of 'reflexive project of the self', this study uses a narrative methodology. Narratives are verbal accounts or stories relating to experiences of Chinese fashion designers. This approach offers the fashion designers a chance to 'speak' for themselves and show the depths and complexities of their experiences. It also emphasises the nuances of identity formation in fashion designers, whose experiences cannot be captured in neat typologies. Thematic analysis (Braun and Clarke, 2006) is adopted to identify and investigate common themes across the whole dataset. At the centre of the analysis is individuals’ self-articulation of their perceptions, experiences and themselves in relation to culture, fashion and identity. The finding indicates that identity is constructed around anchors such as agency, cultural hybridity, reflexivity and sustainability rather than traditional collective categories such as culture and ethnicity. Thus, the old East-West dichotomy is broken down, and essentialised social categories are challenged by the multiplicity and fragmentation of self and cultural hybridity created within designers’ 'small narratives'.

Keywords: Chinoiserie, fashion identity, fashion activism, narrative inquiry

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Authors: Gregory W. Smith, Paul J. Riccomini

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The goal of this empirical inquiry is to explore the effect of noise reducing headphones on the short-term memory recall of college students. Immediately following the presentation (via PowerPoint) of 12 unrelated and randomly selected one- and two-syllable words, students were asked to recall as many words as possible. Using a linear model with conditions marked with binary indicators, we examined the frequency and accuracy of words that were recalled. The findings indicate that for some students, a reduction of noise has a significant positive impact on their ability to recall information. As classrooms become more aurally distracting due to the implementation of cooperative learning activities, these findings highlight the need for a quiet learning environment for some learners.

Keywords: auditory distraction, education, instruction, noise, working memory

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Authors: Reem Abou Refaie, Christoph Meinel

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The paper explores the impact of the COVID-19 crisis on the internal as well as external digitalized work practices of public service providers as part of a Public-Private Partnership Model. It focuses on the effect of uncertainty on generating Open Innovation practices. Our inquiry relies on semi-structured interviews (n=14) from a case study of Rwanda’s Public Service Delivery System in the context of research cooperation with IremboGov, the country’s One-Stop-Shop Platform for public services. It presents four propositions on harnessing opportunities for OI in the context of the public sector beyond the pandemic response. Practitioners can find characterizations of OI opportunities and gain insights on fostering OI in Public Sector Organizations.

Keywords: open innovation, digital transformation, public sector, Rwanda

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Authors: M. A. H. Barry

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The concept of Good faith (husn al-niyyah) and fair-dealing (Nadl) are the fundamental guiding elements in all contracts and other agreements under Islamic law. The preaching of Al-Quran and Prophet Muhammad’s (Peace Be upon Him) firmly command people to act in good faith in all dealings. There are several Quran verses and the Prophet’s saying which stressed the significance of dealing honestly and fairly in all transactions. Under the English law, the good faith is not considered a fundamental requirement for the formation of a legal contract. However, the concept of Good Faith in private contracts is recognized by the civil law system and in Article 7(1) of the Convention on International Sale of Goods (CISG-Vienna Convention-1980). It took several centuries for the international trading community to recognize the significance of the concept of good faith for the international sale of goods transactions. Nevertheless, the recognition of good faith in Civil law is only confined for the commercial contracts. Subsequently to the CISG, this concept has made inroads into the private international law. There are submissions in favour of applying the good faith concept to public international law based on tacit recognition by the international conventions and International Tribunals. However, under public international law the concept of good faith is not recognized as a source of rights or obligations. This weakens the spirit of the good faith concept, particularly when determining the international disputes. This also creates a fundamental flaw because the absence of good faith application means the breaches tainted by bad faith are tolerated. The objective of this research is to evaluate, examine and analyze the application of the concept of good faith in the modern laws and identify its limitation, in comparison with Islamic concept of good faith. This paper also identifies the problems and issues connected with the non-application of this concept to public international law. This research consists of three key components (1) the preliminary inquiry (2) subject analysis and discovery of research results, and (3) examining the challenging problems, and concluding with proposals. The preliminary inquiry is based on both the primary and secondary sources. The same sources are used for the subject analysis. This research also has both inductive and deductive features. The Islamic concept of good faith covers all situations and circumstances where the bad faith causes unfairness to the affected parties, especially the weak parties. Under the Islamic law, the concept of good faith is a source of rights and obligations as Islam prohibits any person committing wrongful or delinquent acts in any dealing whether in a private or public life. This rule is applicable not only for individuals but also for institutions, states, and international organizations. This paper explains how the unfairness is caused by non-recognition of the good faith concept as a source of rights or obligations under public international law and provides legal and non-legal reasons to show why the Islamic formulation is important.

Keywords: good faith, the civil law system, the Islamic concept, public international law

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Authors: Silvija Šiljeg, Ivan Marić, Ante Šiljeg

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The accessibility of urban green spaces (UGS) is an integral element in the quality of life. Due to rapid urbanization, UGS studies have become a key element in urban planning. The potential benefits of space for its inhabitants are frequently analysed. A functional transport network system and the optimal spatial distribution of urban green surfaces are the prerequisites for maintaining the environmental equilibrium of the urban landscape. An accessibility analysis was conducted as part of the Urban Green Belts Project (UGB). The development of a GIS database for Zadar was the first step in generating the UGS accessibility indicator. Data were collected using the supervised classification method of multispectral LANDSAT images and manual vectorization of digital orthophoto images (DOF). An analysis of UGS accessibility according to the ANGst standard was conducted in the first phase of research. The accessibility indicator was generated on the basis of seven objective measurements, which included average UGS surface per capita and accessibility according to six functional levels of green surfaces. The generated indicator was compared with subjective measurements obtained by conducting a survey (718 respondents) within statistical units. The collected data reflected individual assessments and subjective evaluations of UGS accessibility. This study highlighted the importance of using objective and subjective measures in the process of understanding the accessibility of urban green surfaces. It may be concluded that when evaluating UGS accessibility, residents emphasize the immediate residential environment, ignoring higher UGS functional levels. It was also concluded that large areas of UGS within a city do not necessarily generate similar satisfaction with accessibility. The heterogeneity of output results may serve as guidelines for the further development of a functional UGS city network.

Keywords: urban green spaces (UGS), accessibility indicator, subjective and objective measurements, Zadar

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Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Julia Huggins, Fabien Munyaneza

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Background: Partners In Health – Malawi introduced one of Operational Researches called Primary Health Care (PHC) Surveys in 2020, which seeks to assess progress of delivery of care in the district. The study consists of 5 long surveys, namely; Facility assessment, General Patient, Provider, Sick Child, Antenatal Care (ANC), primarily conducted in 4 health facilities in Neno district. These facilities include Neno district hospital, Dambe health centre, Chifunga and Matope. Usually, these annual surveys are conducted from January, and the target is to present final report by June. Once data is collected and analyzed, there are a series of reviews that take place before reaching final report. In the first place, the manual process took over 9 months to present final report. Initial findings reported about 76.9% of the data that added up when cross-checked with paper-based sources. Purpose: The aim of this approach is to run away from manually pulling the data, do fresh analysis, and reporting often associated not only with delays in reporting inconsistencies but also with poor quality of data if not done carefully. This automation approach was meant to utilize features of new technologies to create visualizations, reports, and dashboards in Power BI that are directly fished from the data source – CommCare hence only require a single click of a ‘refresh’ button to have the updated information populated in visualizations, reports, and dashboards at once. Methodology: We transformed paper-based questionnaires into electronic using CommCare mobile application. We further connected CommCare Mobile App directly to Power BI using Application Program Interface (API) connection as data pipeline. This provided chance to create visualizations, reports, and dashboards in Power BI. Contrary to the process of manually collecting data in paper-based questionnaires, entering them in ordinary spreadsheets, and conducting analysis every time when preparing for reporting, the team utilized CommCare and Microsoft Power BI technologies. We utilized validations and logics in CommCare to capture data with less errors. We utilized Power BI features to host the reports online by publishing them as cloud-computing process. We switched from sharing ordinary report files to sharing the link to potential recipients hence giving them freedom to dig deep into extra findings within Power BI dashboards and also freedom to export to any formats of their choice. Results: This data automation approach reduced research timelines from the initial 9 months’ duration to 5. It also improved the quality of the data findings from the original 76.9% to 98.9%. This brought confidence to draw conclusions from the findings that help in decision-making and gave opportunities for further researches. Conclusion: These results suggest that automating the research data process has the potential of reducing overall amount of time spent and improving the quality of the data. On this basis, the concept of data automation should be taken into serious consideration when conducting operational research for efficiency and decision-making.

Keywords: reporting, decision-making, power BI, commcare, data automation, visualizations, dashboards

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Authors: Paul Hernandez-Herrera, Fernando Montoya, Juan Manuel Rendon, Alberto Darszon, Gabriel Corkidi

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Intracellular calcium ([Ca2+]i) regulates sperm motility. The analysis of [Ca2+]i has been traditionally achieved in two dimensions while the real movement of the cell takes place in three spatial dimensions. Due to optical limitations (high speed cell movement and low light emission) important data concerning the three dimensional movement of these flagellated cells had been neglected. Visualizing [Ca2+]i in 3D is not a simple matter since it requires complex fluorescence microscopy techniques where the resulting images have very low intensity and consequently low SNR (Signal to Noise Ratio). In 4D sequences, this problem is magnified since the flagellum oscillates (for human sperm) at least at an average frequency of 15 Hz. In this paper, a novel approach to extract the flagellum’s center-line in 4D stacks is presented. For this purpose, an iterative algorithm based on the fast-marching method is proposed to extract the flagellum’s center-line. Quantitative and qualitative results are presented in a 4D stack to demonstrate the ability of the proposed algorithm to trace the flagellum’s center-line. The method reached a precision and recall of 0.96 as compared with a semi-manual method.

Keywords: flagellum, minimal path, segmentation, sperm

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Donghwan Lee, Gichun Cha, Jooyoung Park, Junkyeong Kim, Seunghee Park

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3D shape models of the existing structure are required for many purposes such as safety and operation management. The traditional 3D modeling methods are based on manual or semi-automatic reconstruction from close-range images. It occasions great expense and time consuming. The Terrestrial Laser Scanner (TLS) is a common survey technique to measure quickly and accurately a 3D shape model. This TLS is used to a construction site and cultural heritage management. However there are many limits to process a TLS point cloud, because the raw point cloud is massive volume data. So the capability of carrying out useful analyses is also limited with unstructured 3-D point. Thus, segmentation becomes an essential step whenever grouping of points with common attributes is required. In this paper, members segmentation algorithm was presented to separate a raw point cloud which includes only 3D coordinates. This paper presents a clustering approach based on a fuzzy method for this objective. The Fuzzy C-Means (FCM) is reviewed and used in combination with a similarity-driven cluster merging method. It is applied to the point cloud acquired with Lecia Scan Station C10/C5 at the test bed. The test-bed was a bridge which connects between 1st and 2nd engineering building in Sungkyunkwan University in Korea. It is about 32m long and 2m wide. This bridge was used as pedestrian between two buildings. The 3D point cloud of the test-bed was constructed by a measurement of the TLS. This data was divided by segmentation algorithm for each member. Experimental analyses of the results from the proposed unsupervised segmentation process are shown to be promising. It can be processed to manage configuration each member, because of the segmentation process of point cloud.

Keywords: fuzzy c-means (FCM), point cloud, segmentation, terrestrial laser scanner (TLS)

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: M. Bahari Mehrabani, Hua-Peng Chen

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Management and maintenance of coastal defence structures during the expected life cycle have become a real challenge for decision makers and engineers. Accurate evaluation of the current condition and future performance of flood defence structures is essential for effective practical maintenance strategies on the basis of available field inspection data. Moreover, as coastal defence structures age, it becomes more challenging to implement maintenance and management plans to avoid structural failure. Therefore, condition inspection data are essential for assessing damage and forecasting deterioration of ageing flood defence structures in order to keep the structures in an acceptable condition. The inspection data for flood defence structures are often collected using discrete visual condition rating schemes. In order to evaluate future condition of the structure, a probabilistic deterioration model needs to be utilised. However, existing deterioration models may not provide a reliable prediction of performance deterioration for a long period due to uncertainties. To tackle the limitation, a time-dependent condition-based model associated with a transition probability needs to be developed on the basis of condition grade scheme for flood defences. This paper presents a probabilistic method for predicting future performance deterioration of coastal flood defence structures based on condition grading inspection data and deterioration curves estimated by expert judgement. In condition-based deterioration modelling, the main task is to estimate transition probability matrices. The deterioration process of the structure related to the transition states is modelled according to Markov chain process, and a reliability-based approach is used to estimate the probability of structural failure. Visual inspection data according to the United Kingdom Condition Assessment Manual are used to obtain the initial condition grade curve of the coastal flood defences. The initial curves then modified in order to develop transition probabilities through non-linear regression based optimisation algorithms. The Monte Carlo simulations are then used to evaluate the future performance of the structure on the basis of the estimated transition probabilities. Finally, a case study is given to demonstrate the applicability of the proposed method under no-maintenance and medium-maintenance scenarios. Results show that the proposed method can provide an effective predictive model for various situations in terms of available condition grading data. The proposed model also provides useful information on time-dependent probability of failure in coastal flood defences.

Keywords: condition grading, flood defense, performance assessment, stochastic deterioration modelling

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Authors: Auntika Thipjumnong

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This research study aimed to investigate the production process and the market target of Kraseasin’s woven cloth including the customers’ behaviors towards the local woven products. The suggestions of a better process of production were recommended in this study. This survey research was conducted by using a questionnaire and interview, which were considered as the practical instruments to collect the data. The 200 Kraseasin’s woven makers and consumers were subjects by using a purposive sampling. Percentages, means and standard deviation were used to analyze data. The findings revealed that only 22 local woven members owned their 18 manual weavers in producing the raw materials like cotton or fiber. The main products were flowery woven cloth e.g. pikul, puangchompoo, pakakrong and ban mai roo roiy, and the others were rainy, glass wall, dice glass ball and yok dok etc. At the present, all local woven products were applied to be modernized but the strong point of those products were keeping the quality standard and firming textures, not thickness. The main objective of producing these local woven products was to earn and increase their extra incomes. Moreover, there were two dominant sales: Firstly, the makers sold their own products by themselves in their community and malls; and secondly, they would weave their products by customers’ orders. The prices’ allocation was on the difficulties in producing process. The government officials and non-government officials in local were normally customers. However the drawback of producing this local product was lack of raw material and this brought about the higher investment. The community’s customers were now lacking of interest in wearing these local products, even though they maintained their quality standard. The factors in customers’ purchasing decision were product (M = 3.93), price (M = 3.74), distribution (M = 3.73) and promotion (M = 3.97) for marketing mix well-known. Suggestion was a designing pattern of products had to be matched to the customers’ needs.

Keywords: marketing, consumer behavior, cloth products weaves, Songkhla Thailand

Procedia PDF Downloads 277

Authors: Ofoegbu Ositadinma Edward

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This research paper discusses a web based monitoring system that enables effective monitoring of fuel pump output and sales volume from distributed fuel stations under the domain of a single company/organization. The traditional method of operation by these organizations in Nigeria is non-automated and accounting for dispensed product is usually approximated and manual as there is little or no technology implemented to presently provide information relating to the state of affairs in the station both to on-ground staff and to supervisory staff that are not physically present in the station. This results in unaccountable losses in product and revenue as well as slow decision making. Remote monitoring technology as a vast research field with numerous application areas incorporating various data collation techniques and sensor networks can be applied to provide information relating to fuel pump status in distributed fuel stations reliably. Thus, the proposed system relies upon a microcontroller, keypad and pump to demonstrate the traditional fuel dispenser. A web-enabled PC with an accompanying graphic user interface (GUI) was designed using virtual basic which is connected to the microcontroller via the serial port which is to provide the web implementation.

Keywords: fuel pump, microcontroller, GUI, web

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Authors: Teerapong Lorchitamnuay, Thirarut Worapishet

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In the present digital age, today's customers explore various channels before finalizing a purchase, with abundant options and information at their disposal. Despite this, there is a strong digital interconnectedness. With just a few mouse clicks, customers can gather comprehensive information about a product, free from the influence of a salesperson. Salespeople must embrace cutting-edge technology to truly redefine the essence of selling if they are to thrive in this digital era. The significance of customer-salesperson communication in companies is becoming increasingly evident. It prompts the inquiry of how companies can modify or reshape their sales teams' approaches to effectively respond to evolving customer preferences and effectively manage external shifts, all in pursuit of sustaining and expanding their enterprises. Research highlights that digital and intercultural skills are the latest competencies sought by customers from salespeople in today's fast-paced world prior to making purchases of products and services. This study seeks to examine the pivotal influences of these salesperson skills in achieving customer satisfaction. The research design encompasses the analysis of descriptive statistics and quantitative data through a regression model. Data were gathered from an online convenience survey involving 260 respondents who are customers of an air express service provider in Thailand and who engage with salespeople in a traditional manner. The findings underscore that intercultural skills have a substantial impact on customer satisfaction in the digital era, particularly concerning adaptability, foreign language proficiency, active listening, and empathy skills. Organizations should focus on nurturing beneficial habits among their salespeople; since it signifies this effort, it should extend beyond just the frontline but should extend to encompass backline units and high-level management, ensuring that everyone possesses the same customer-oriented skills. The conclusions drawn from this research provide valuable insights, affirming that digital and intercultural skills can empower organizations to optimize their workforce's competencies, thereby achieving customer satisfaction in the digital age.

Keywords: customer behavior, customer satisfaction, digital era, digital skill, intercultural skill

Procedia PDF Downloads 77

Authors: Shizra Waris, Saleem Akhtar

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Big data is a research area receiving attention from academia and IT communities. In the digital world, the amounts of data produced and stored have within a short period of time. Consequently this fast increasing rate of data has created many challenges. In this paper, we use functionalism and structuralism paradigms to analyze the genesis of big data applications and its current trends. This paper presents a complete discussion on state-of-the-art big data technologies based on group and stream data processing. Moreover, strengths and weaknesses of these technologies are analyzed. This study also covers big data analytics techniques, processing methods, some reported case studies from different vendor, several open research challenges and the chances brought about by big data. The similarities and differences of these techniques and technologies based on important limitations are also investigated. Emerging technologies are suggested as a solution for big data problems.

Keywords: computer, it community, industry, big data

Procedia PDF Downloads 185

Authors: Dalicia Bouallouche, Jean-Baptiste Vioix, Stéphane Millot, Eric Busvelle

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When researchers or logistics software developers deal with vehicle routing optimization, they mainly focus on minimizing the total travelled distance or the total time spent in the tours by the trucks, and maximizing the number of visited customers. They assume that the upstream real data given to carry the optimization of a transporter tours is free from errors, like customers’ real constraints, customers’ addresses and their GPS-coordinates. However, in real transporter situations, upstream data is often of bad quality because of address geocoding errors and the irrelevance of received addresses from the EDI (Electronic Data Interchange). In fact, geocoders are not exempt from errors and could give impertinent GPS-coordinates. Also, even with a good geocoding, an inaccurate address can lead to a bad geocoding. For instance, when the geocoder has trouble with geocoding an address, it returns those of the center of the city. As well, an obvious geocoding issue is that the mappings used by the geocoders are not regularly updated. Thus, new buildings could not exist on maps until the next update. Even so, trying to optimize tours with impertinent customers GPS-coordinates, which are the most important and basic input data to take into account for solving a vehicle routing problem, is not really useful and will lead to a bad and incoherent solution tours because the locations of the customers used for the optimization are very different from their real positions. Our work is supported by a logistics software editor Tedies and a transport company Upsilon. We work with Upsilon's truck routes data to carry our experiments. In fact, these trucks are equipped with TOMTOM GPSs that continuously save their tours data (positions, speeds, tachograph-information, etc.). We, then, retrieve these data to extract the real truck routes to work with. The aim of this work is to use the experience of the driver and the feedback of the real truck tours to validate GPS-coordinates of well geocoded addresses, and bring a correction to the badly geocoded addresses. Thereby, when a vehicle makes its tour, for each visited customer, the vehicle might have trouble with finding this customer’s address at most once. In other words, the vehicle would be wrong at most once for each customer’s address. Our method significantly improves the quality of the geocoding. Hence, we achieve to automatically correct an average of 70% of GPS-coordinates of a tour addresses. The rest of the GPS-coordinates are corrected in a manual way by giving the user indications to help him to correct them. This study shows the importance of taking into account the feedback of the trucks to gradually correct address geocoding errors. Indeed, the accuracy of customer’s address and its GPS-coordinates play a major role in tours optimization. Unfortunately, address writing errors are very frequent. This feedback is naturally and usually taken into account by transporters (by asking drivers, calling customers…), to learn about their tours and bring corrections to the upcoming tours. Hence, we develop a method to do a big part of that automatically.

Keywords: driver experience feedback, geocoding correction, real truck tours

Procedia PDF Downloads 669

Authors: Dela Cruz Abraham, Bachoco Janine

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Spirituality plays a central role among patients dealing with HIV mostly on the LGBT community in the world today particularly in the Philippines. This study seeks to contribute to the growing body of knowledge in LGBT psychology particularly on gay men living with HIV and their spiritual aspect. In line with this, the researchers aim to describe (1) how young Filipino gay men relate their experiences as an HIV-positive in relations to their self and significant others (partners, family, friends and community); (2) how young Filipino gay men make sense of their experiences as an HIV-positive, in connection to God, this also includes their meaning making and purpose of their life experiences. To recruit participants, the researchers will employ purposive sampling using snowball technique, and conduct a semi-structured interview. Verbatim transcriptions of the participant will be analyzed using interpretative phenomenological analysis.

Keywords: interpretative phenomenological analysis, living with HIV, spirituality, young Filipino gay men

Procedia PDF Downloads 306

Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

Procedia PDF Downloads 512