Search results for: gaps in data ecosystems

Authors: Cosette Maiky

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Background: In the context of the Syrian crisis, the past few years have witnessed an exponential growth in the number of refugee mental health studies, which have essentially focused either on the affected Syrian population and/or host communities. However, the Palestinian communities in the region did not receive sufficient that much of attention. Aim: The study aimed at identifying trends and patterns of mental health and and psychosocial conditions among Palestinian refugees in the context of the Syrian crisis, including the recognition of gaps in appropriate services. Methods: The research model comprised a systematic documentary review, a mapping of available contextual analyses, a quantitative survey, focus group discussions as well as key informant interviews (with relevant stakeholders and beneficiaries). Findings: Content analysis revealed multiple effects of transgenerational transmission of trauma among Palestinian refugees in the context of the Syrian crisis, which showed to be neither linear nor one-dimensional occurrence. In addition to highlights on exposure to traumatic events and psychological sequelae, the review outlines the most prevailing coping mechanisms and essential protective factors. Conclusion: Away from a trauma-centered or symptom-focused exercise, practitioners may take account of the present study to better focus research and intervention methodologies.

Keywords: Palestine refugees, Syria crisis, psychosocial, mental health

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Authors: Roberto Machado

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We conducted a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology, analyzing 2,996 studies and synthesizing 41 of them to explore the evolution of data science and its integration into geography. By employing optimization algorithms, we accelerated the review process, significantly enhancing the efficiency and precision of literature selection. Our findings indicate that data science has developed over five decades, facing challenges such as the diversified integration of data and the need for advanced statistical and computational skills. In geography, the integration of data science underscores the importance of interdisciplinary collaboration and methodological innovation. Techniques like large-scale spatial data analysis and predictive algorithms show promise in natural disaster management and transportation route optimization, enabling faster and more effective responses. These advancements highlight the transformative potential of data science in geography, providing tools and methodologies to address complex spatial problems. The relevance of this study lies in the use of optimization algorithms in systematic reviews and the demonstrated need for deeper integration of data science into geography. Key contributions include identifying specific challenges in combining diverse spatial data and the necessity for advanced computational skills. Examples of connections between these two fields encompass significant improvements in natural disaster management and transportation efficiency, promoting more effective and sustainable environmental solutions with a positive societal impact.

Keywords: data science, geography, systematic review, optimization algorithms, supervised learning

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Authors: Hina Kausher, Sangita Srivastava

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In India, there is a lack of standard, systematic sizing approach for producing readymade garments. Garments manufacturing companies use their own created size tables by modifying international sizing charts of ready-made garments. The purpose of this study is to tabulate the anthropometric data which covers the variety of figure proportions in both height and girth. 3,000 data has been collected by an anthropometric survey undertaken over females between the ages of 16 to 80 years from some states of India to produce the sizing system suitable for clothing manufacture and retailing. This data is used for the statistical analysis of body measurements, the formulation of sizing systems and body measurements tables. Factor analysis technique is used to filter the control body dimensions from a large number of variables. Decision tree-based data mining is used to cluster the data. The standard and structured sizing system can facilitate pattern grading and garment production. Moreover, it can exceed buying ratios and upgrade size allocations to retail segments.

Keywords: anthropometric data, data mining, decision tree, garments manufacturing, sizing systems, ready-made garments

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Ying Ge

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Wetlands are one of the essential types of ecosystems in the world. They are of great value to human society thanks to their special ecosystem functions and services, such as protecting biodiversity, adjusting hydrology and climate, providing essential habitats and, products and tourism resources. However, wetlands worldwide are degrading severely due to climate change, accelerated urbanization, and rapid economic development. Both nature and human factors drive wetland change, and the influences are variable from wetland types. Thus, the objectives of this study were to (1) to compare the changes in China’s wetland area during the three decades rapid growth period (1978-2008); (2) to analyze the effects of social economy and environmental factors on wetlands change (area loss and change of wetland types) in China during the high-speed economic development. The socio-economic influencing factors include population, income, education, development of agriculture, industry, infrastructure, wastewater amount, etc. Several statistical methods (canonical correlation analysis, principal component analysis, and regression analysis) were employed to analyze the relationship between socio-economic indicators and wetland area change. This study will determine the relevant driving socio-economic factors on wetland changes, which is of great significance for wetland protection and management.

Keywords: socioeconomic effects, China, wetland change, wetland type

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Chris Suma, Yingcai Xiao

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This paper presents Parallel Coordinates on a Spiral Surface (PCoSS), a parallel coordinate based interactive visualization method for high-dimensional data, and a test implementation of the method. Plots generated by the test system are compared with those generated by XDAT, a software implementing traditional parallel coordinates. Traditional parallel coordinate plots can be cluttered when the number of data points is large or when the dimensionality of the data is high. PCoSS plots display multivariate data on a 3D spiral surface and allow users to see the whole picture of high-dimensional data with less cluttering. Taking advantage of the 3D display environment in PCoSS, users can further reduce cluttering by zooming into an axis of interest for a closer view or by moving vantage points and by reorienting the viewing angle to obtain a desired view of the plots.

Keywords: human computer interaction, parallel coordinates, spiral surface, visualization

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Authors: Molly Parker

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Unintended pregnancy rates in Australia are amongst the highest in the developed world. Women with Substance Use Disorder often have riskier sexual behavior with nil contraceptive use and face disproportionately higher unintended pregnancies and Sexually Transmitted Infections, alongside Substance Use in Pregnancy (SUP) climbing at an alarming rate. In an inner-city Drug and Alcohol (D&A) service, significant barriers to sexual and reproductive health services have been identified, aligning with research. Rapid pathways were created for women seeking D&A treatment to be referred to Sexual and Reproductive Health services for the administration of Long-acting reversible contraception (LARC) and sexual health screening. For clients attending a D&A service, this is an opportunistic time to offer sexual and reproductive health services. Collaboration and multidisciplinary team input between D&A and sexual health and reproductive services are paramount, with rapid referral pathways being identified as the main strategy to improve access to sexual and reproductive health support for this population. With this evidence, a rapid referral pathway was created for women using the D&A service to access LARC, particularly in view of fertility often returning once stable on D&A treatment. A closed-ended survey was used for D&A staff to identify gaps in reproductive health knowledge and views of referral accessibility. Results demonstrated a lack of knowledge of contraception and appropriate referral processes. A closed-ended survey for clients was created to establish the need and access to services and to quantify data. A follow-up data collection will be reviewed to access uptake and satisfaction of the intervention from clients. Sexual health screening access was also identified as a deficit, particularly concerning due to the higher rates of STIs in this cohort. A rapid referral pathway will be undergoing implementation, reducing risks of untreated STIS both pre and post-conception. Similarly, pre and post-intervention structured surveys will be used to identify client satisfaction from the pathway. Although currently in progress, the research and pathway aim to be completed by December 2023. This research and implementation of sexual and reproductive health pathways from the D&A service have significant health and well-being benefits to clients and the wider community, including possible fetal/infancy outcomes. Women now have rapid access to sexual and reproductive health services, with the aim of reducing unplanned pregnancies, poor outcomes associated with SUP, client/staff trauma from termination of pregnancy, and client/staff trauma following the assumption of care of the child due to substance use, the financial cost for out of home care as required, the poor outcomes of untreated STIs to the fetus in pregnancy and the spread of STIs in the wider community. As evidence suggests, the implementation of a streamlined referral process is required between D&A and sexual and reproductive health services and has positive feedback from both clinicians and clients in improving care.

Keywords: substance use in pregnancy, drug and alcohol, substance use disorder, sexual health, reproductive health, contraception, long-acting reversible contraception, neonatal abstinence syndrome, FASD, sexually transmitted infections, sexually transmitted infections pregnancy

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Authors: Muhammad Arsalan Khan Sherwani

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The use of Volatile Corrosion Inhibitors (VCI) to protect Aboveground Storage Tank (AST) bottom plates against soil-side corrosion is one of the emerging corrosion prevention methods, specifically for tanks constructed on oily sand pad. Oily sand pad and the presence of air gaps underneath the bottom plates lead to severe corrosion and high metal thickness loss. In such cases, the cathodic protection cannot be fully considered as effective due to Cathodic Protection (CP) current shielding. These situations sometimes result in serious failures on multiple fronts, such as; containment losses, system shutdowns, extensive repairs, environmental impact and safety concerns in case of flammable fluids. Recently, East West Pipeline Department (EWPD) of Saudi Aramco has deployed this technology to one of the crude oil storage tanks, which showed high metal thickness loss during its out of service inspection. Soil-side corrosion rustled in major repairs of bottom plates and ultimately caused enormous unplanned activities in term of time as well as cost. This paper mainly focuses on the methodology of VCI installation, corrosion monitoring system and the expected results of protection.

Keywords: Vapor Corrosion Inhibitor, Soil Side Corrosion, External Corrosion, Above Grade Storage Tank

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Authors: Felix Darko-Amoah, Daniel Acquah

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In contemporary ecosystems for developing countries like Ghana, the need to explore innovative solutions for sustainable livelihoods of farmers is more important than ever. With Ghana’s population growing steadily and the demand for food, fiber and shelter increasing, it is imperative that the construction industry and agriculture come together to address the challenges faced by farmers in the country. In order to enhance the livelihoods of cocoa farmers in Ghana, this paper provides an innovative strategy that aims to integrate the areas of civil engineering and cash crop agriculture. This study focuses on cocoa cultivation in poorer nations, where farmers confront a variety of difficulties include restricted access to financing, subpar infrastructure, and insufficient support services. We seek to improve farmers' access to financing, improve infrastructure, and provide support services that are essential to their success by combining the fields of building engineering and cocoa production. The findings of the study are beneficial to cocoa producers, community extension agents, and construction engineers. In order to accomplish our objectives, we conducted 307 of field investigations in particular cocoa growing communities in the Western Region of Ghana. Several studies have shown that there is a lack of adequate infrastructure and financing, leading to low yields, subpar beans, and low farmer profitability in developing nations like Ghana. Our goal is to give farmers access to better infrastructure, better financing, and support services that are crucial to their success through the fusion of construction engineering and cocoa production. Based on data gathered from the field investigations, the results show that the employment of appropriate technology and methods for developing structures, roads, and other infrastructure in rural regions is one of the essential components of this strategy. For instance, we find that using affordable, environmentally friendly materials like bamboo, rammed earth, and mud bricks can assist to cut expenditures while also protecting the environment. By applying simple relational techniques to the data gathered, the results also show that construction engineers are crucial in planning and building infrastructure that is appropriate for the local environment and circumstances and resilient to natural disasters like floods. Thus, the convergence of construction engineering and cash crop cultivation is another crucial component of the agriculture-construction interplay. For instance, farmers can receive financial assistance to buy essential inputs, such as seeds, fertilizer, and tools, as well as training in proper farming methods. Moreover, extension services can be offered to assist farmers in marketing their crops and enhancing their livelihoods and revenue. In conclusion, our analysis of responses from the 307 participants depicts that the combination of construction engineering and cash crop agriculture offers an innovative approach to improving farmers' livelihoods in cocoa farming communities in Ghana. In conclusion, by inculcating the findings of this study into core decision-making, policymakers can help farmers build sustainable and profitable livelihoods by addressing challenges such as limited access to financing, poor infrastructure, and inadequate support services.

Keywords: cocoa agriculture, construction engineering, farm buildings and equipment, improved livelihoods of farmers

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Authors: Amadou Wurry Jallow, Adama N. S. Bah, Karamo Bah, Shih-Ye Wang, Kuo-Chung Chu, Chien-Yeh Hsu

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Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predicting and preventing complications of chronic kidney disease (CKD). This study aimed to develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and effective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportional hazard regression analyses were performed to determine the variables with high prognostic values for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laboratory, laboratory, and metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, body mass index (BMI), and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have demonstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The machine learning models are simple to use and flexible because they work even with incomplete data and can be applied in any clinical setting, including settings where laboratory data is difficult to obtain.

Keywords: chronic kidney disease, glomerular filtration rate, creatinine, novel metabolic indices, machine learning, risk prediction

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Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

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Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

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Authors: Peter Kress, Alexander Pflaum, Ulrich Loewen

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The number of Internet-of-Things (IoT) platforms is steadily increasing across various industries, especially for smart factories, smart homes and smart mobility. Also in the manufacturing industry, the number of Industrial-IoT platforms is growing. Both IT players, start-ups and increasingly also established industry players and small-and-medium-enterprises introduce offerings for the connection of industrial equipment on platforms, enabled by advanced information and communication technology. Beside the offered functionalities, the established ecosystem of partners around a platform is one of the key differentiators to generate a competitive advantage. The key question is how platform operators design the business model around their platform to attract a high number of customers and partners to co-create value for the entire ecosystem. The present research tries to answer this question by determining the key characteristics of business models of successful platforms in the manufacturing industry. To achieve that, the authors selected an explorative qualitative research approach and created an inductive comparative case study. The authors generated valuable descriptive insights of the business model elements (e.g., value proposition, pricing model or partnering model) of various established platforms. Furthermore, patterns across the various cases were identified to derive propositions for the successful design of business models of platforms in the manufacturing industry.

Keywords: industrial-internet-of-things, business models, platforms, ecosystems, case study

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Authors: Raffaele Danilo Esposito, Domingo Planes Meseguer, Maria Del Pilar Dorado Rodriguez

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As far as we know, it is still unavailable a commercial solution which would allow to manage, openly and configurable up to user needs, the huge amount of data generated in a modern Radiation Oncology Department. Currently, available information management systems are mainly focused on Record & Verify and clinical data, and only to a small extent on physical data. Thus, results in a partial and limited use of the actually available information. In the present work we describe the implementation at our department of a centralized information management system based on a web server. Our system manages both information generated during patient planning and treatment, and information of general interest for the whole department (i.e. treatment protocols, quality assurance protocols etc.). Our objective it to be able to analyze in a simple and efficient way all the available data and thus to obtain quantitative evaluations of our treatments. This would allow us to improve our work flow and protocols. To this end we have implemented a relational data base which would allow us to use in a practical and efficient way all the available information. As always we only use license free software.

Keywords: information management system, radiation oncology, medical physics, free software

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Authors: Komol Phaisarn, Natcha Wattanaprapa

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This research is a survey research with an objective to study the safety of data storage devices of graduate students of academic year 2013, Suan Sunandha Rajabhat University. Data were collected by questionnaire on the safety of data storage devices according to CIA principle. A sample size of 81 was drawn from population by purposive sampling method. The results show that most of the graduate students of academic year 2013 at Suan Sunandha Rajabhat University use handy drive to store their data and the safety level of the devices is at good level.

Keywords: security, safety, storage devices, graduate students

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Authors: Kaddi Mohammed, A. Benatiallah, D. Benatiallah

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Data grid is a technology that has full emergence of new challenges, such as the heterogeneity and availability of various resources and geographically distributed, fast data access, minimizing latency and fault tolerance. Researchers interested in this technology address the problems of the various systems related to the industry such as task scheduling, load balancing and replication. The latter is an effective solution to achieve good performance in terms of data access and grid resources and better availability of data cost. In a system with duplication, a coherence protocol is used to impose some degree of synchronization between the various copies and impose some order on updates. In this project, we present an approach for placing replicas to minimize the cost of response of requests to read or write, and we implement our model in a simulation environment. The placement techniques are based on a cost model which depends on several factors, such as bandwidth, data size and storage nodes.

Keywords: response time, query, consistency, bandwidth, storage capacity, CERN

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Authors: Lu Song Ma Li Zhi

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With the rapid advancement of artificial intelligence technology, large language models (LLMs) have become a milestone in the field of natural language processing, demonstrating remarkable capabilities in semantic understanding, intelligent question answering, and text generation. These models are gradually penetrating various industries, particularly showcasing significant application potential in the data analysis domain. However, retraining or fine-tuning these models requires substantial computational resources and ample downstream task datasets, which poses a significant challenge for many enterprises and research institutions. Without modifying the internal parameters of the large models, prompt engineering techniques can rapidly adapt these models to new domains. This paper proposes a prompt design strategy aimed at leveraging the capabilities of large language models to automate the generation of data analysis code. By carefully designing prompts, data analysis requirements can be described in natural language, which the large language model can then understand and convert into executable data analysis code, thereby greatly enhancing the efficiency and convenience of data analysis. This strategy not only lowers the threshold for using large models but also significantly improves the accuracy and efficiency of data analysis. Our approach includes requirements for the precision of natural language descriptions, coverage of diverse data analysis needs, and mechanisms for immediate feedback and adjustment. Experimental results show that with this prompt design strategy, large language models perform exceptionally well in multiple data analysis tasks, generating high-quality code and significantly shortening the data analysis cycle. This method provides an efficient and convenient tool for the data analysis field and demonstrates the enormous potential of large language models in practical applications.

Keywords: large language models, prompt design, data analysis, code generation

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Authors: N. Samarinas, C. Evangelides, C. Vrekos

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The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.

Keywords: classification, fuzzy logic, tolerance relations, rainfall data

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Authors: S. Anastasiou, C. Nathanailides

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The purpose of this study is to examine the possible link between employee and customer satisfaction. The service provided by employees, help to build a good relationship with customers and can help at increasing their loyalty. Published data for job satisfaction and indicators of customer services were gathered from relevant published works which included data from five different countries. The reviewed data indicate a significant correlation between indicators of customer and employee satisfaction in the Banking sector. There was a significant correlation between the two parameters (Pearson correlation R2=0.52 P<0.05) The reviewed data provide evidence that there is some practical evidence which links these two parameters.

Keywords: job satisfaction, job performance, customer’ service, banks, human resources management

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Authors: Reihaneh Mahdavishahri, Dumayi Gutierrez

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Emotionally focused couple therapy (EFCT) is one of the most effective and evidence-based approaches to couple therapy. Yet, literature is scarce of its effective application with trans and non-binary couples. It is estimated that 1.4 million trans adults live in the United Stated, with about 40% of these individuals experiencing serious psychological distress within the past month. Trans and nonbinary adults are significantly likely to experience discrimination, harassment, family rejection, and relationship challenges throughout the course of their lives. As systemic therapists, applying an informed lens when working with trans and nonbinary couples can contribute to providing effective mental health care to these individuals. This paper aims to provide a comprehensive, intersectional, and culturally informed application of EFCT with trans and nonbinary couples. We will address the current literature on applications of EFCT with diverse couples, EFCT’s strengths and limitations on cultural humility, and the gaps within current systems of care for trans and nonbinary couples. We will then provide an adaptive application of EFCT to help trans, and nonbinary couples recover from potential attachment injuries in their relationships, intersecting gender minority stressors, and achieve healing and restoration in their interpersonal dynamics.

Keywords: attachment, culturally informed care, emotionally focused couple therapy, trans and nonbinary couples

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Authors: Suman Podder

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As Australian ‘Open Banking’ allows customers to share their financial data with accredited Third-Party Providers (‘TPPs’), it is necessary to evaluate whether the regulators have achieved the balance between protecting customer data privacy and promoting data-related innovation. Recognising the need to increase customers’ influence on their own data, and the benefits of data-related innovation, the Australian Government introduced ‘Consumer Data Right’ (‘CDR’) to the banking sector through Open Banking regulation. Under Open Banking, TPPs can access customers’ banking data that allows the TPPs to tailor their products and services to meet customer needs at a more competitive price. This facilitated access and use of customer data will promote innovation by providing opportunities for new products and business models to emerge and grow. However, the success of Open Banking depends on the willingness of the customers to share their data, so the regulators have augmented the protection of data by introducing new privacy safeguards to instill confidence and trust in the system. The dilemma in policymaking is that, on the one hand, lenient data privacy laws will help the flow of information, but at the risk of individuals’ loss of privacy, on the other hand, stringent laws that adequately protect privacy may dissuade innovation. Using theoretical and doctrinal methods, this paper examines whether the privacy safeguards under Open Banking will add to the compliance burden of the participating financial institutions, resulting in the undesirable effect of stifling other policy objectives such as innovation. The contribution of this research is three-fold. In the emerging field of customer data sharing, this research is one of the few academic studies on the objectives and impact of Open Banking in the Australian context. Additionally, Open Banking is still in the early stages of implementation, so this research traces the evolution of Open Banking through policy debates regarding the desirability of customer data-sharing. Finally, the research focuses not only on the customers’ data privacy and juxtaposes it with another important objective of promoting innovation, but it also highlights the critical issues facing the data-sharing regime. This paper argues that while it is challenging to develop a regulatory framework for protecting data privacy without impeding innovation and jeopardising yet unknown opportunities, data privacy and innovation promote different aspects of customer welfare. This paper concludes that if a regulation is appropriately designed and implemented, the benefits of data-sharing will outweigh the cost of compliance with the CDR.

Keywords: consumer data right, innovation, open banking, privacy safeguards

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Authors: Sadhana Shukla, Pushplata Singh, Nidhi Didwania

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Arbuscular mycorrhizal (AM) fungi are a highly advantageous and versatile group of fungi that significantly contribute to the formation of soil organic matter by creating a demand for plant carbon (C) and distributing it through below-ground hyphal biomass, regardless of their substantial contribution in enhancing net primary productivity and accumulating additional photosynthetic fixed C in the soil. The genetic role of AM fungi in carbon cycling is largely unexplored. In our study, we propose that AM fungi significantly interact with the soil, particularly: the provision of photosynthates by plants. We have studied the expression of AM fungi genes involved in CO₂ sequestration during host-plant interaction was investigated by qPCR studies. We selected Rhizophagus proliferus (AM fungi) and Oryza sativa (Rice) (inoculated with or without 200ppg AMF inoculums per plant) and investigated the effect of AM fungi on soil organic carbon (SOC) and rice growth under field conditions. Results thus provided faster SOC turnover, 35% increased nutrient uptake in plants and pronounced hyphal biomass of AM fungi which enhanced soil carbon storage by 15% in comparison to uninoculated plants. This study will offer a foundation for delving into various carbon-soil studies while also advancing our comprehension of the relationship between AM fungi and the sustainability of agricultural ecosystems.

Keywords: arbuscular mycorrhizal (AM) fungi, carbon sequestration, gene expression, soil health, plant development.

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Authors: Kuhorfah Emmanuel Mawuli

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Generally, for firms to enhance their operational efficiency of logistics, it is imperative to assess the logistics function. The cost of logistics conventionally represents a key consideration in the pricing decisions of firms, which suggests that cost efficiency in logistics can go a long way to improve margins. Warehousing, which is a key part of logistics operations, has the prospect of influencing operational efficiency in logistics management as well as customer value, but this potential has often not been recognized. It has been found that there is a paucity of research that evaluates the efficiency of warehouses. Indeed, limited research has been conducted to examine potential barriers to effective warehousing management. Due to this paucity of research, there is limited knowledge on how to address the obstacles associated with warehousing management. In order for warehousing management to become profitable, there is the need to integrate, balance, and manage the economic inputs and outputs of the entire warehouse operations, something that many firms tend to ignore. Management of warehousing is not solely related to storage functions. Instead, effective warehousing management requires such practices as maximum possible mechanization and automation of operations, optimal use of space and capacity of storage facilities, organization through "continuous flow" of goods, a planned system of storage operations, and safety of goods. For example, there is an important need for space utilization of the warehouse surface as it is a good way to evaluate the storing operation and pick items per hour. In the setting of Mantrac Ghana, not much knowledge regarding the management of the warehouses exists. The researcher has personally observed many gaps in the management of the warehouse facilities in the case organization Mantrac Ghana. It is important, therefore, to assess the warehouse facility management of the case company with the objective of identifying weaknesses for improvement. The study employs an in-depth qualitative research approach using interviews as a mode of data collection. Respondents in the study mainly comprised warehouse facility managers in the studied company. A total of 10 participants were selected for the study using a purposive sampling strategy. Results emanating from the study demonstrate limited warehousing effectiveness in the case company. Findings further reveal that the major barriers to effective warehousing facility management comprise poor layout, poor picking optimization, labour costs, and inaccurate orders; policy implications of the study findings are finally outlined.

Keywords: assessing, warehousing, facility, management

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Imme Petersen, Wiebke Sick, Regine Kollek

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In systems medicine, high-throughput technologies produce large amounts of data on different biological and pathological processes, including (disturbed) gene expressions, metabolic pathways and signaling. The large volume of data of different types, stored in separate databases and often located at different geographical sites have posed new challenges regarding data handling and processing. Tools based on bioinformatics have been developed to resolve the upcoming problems of systematizing, standardizing and integrating the various data. However, the heterogeneity of data gathered at different levels of biological complexity is still a major challenge in data analysis. To build multilayer disease modules, large and heterogeneous data of disease-related information (e.g., genotype, phenotype, environmental factors) are correlated. Therefore, a great deal of attention in systems medicine has been put on data standardization, primarily to retrieve and combine large, heterogeneous datasets into standardized and incorporated forms and structures. However, this data-centred concept of standardization in systems medicine is contrary to the debate in science and technology studies (STS) on standardization that rather emphasizes the dynamics, contexts and negotiations of standard operating procedures. Based on empirical work on research consortia that explore the molecular profile of diseases to establish systems medical approaches in the clinic in Germany, we trace how standardized data are processed and shaped by bioinformatics tools, how scientists using such data in research perceive such standard operating procedures and which consequences for knowledge production (e.g. modeling) arise from it. Hence, different concepts and meanings of standardization are explored to get a deeper insight into standard operating procedures not only in systems medicine, but also beyond.

Keywords: data, science and technology studies (STS), standardization, systems medicine

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Authors: Kavian Khosravinia, Mohd Khair Hassan, Ribhan Zafira Abdul Rahman, Syed Abdul Rahman Al-Haddad

Abstract:

The CAN (controller area network) bus is introduced as a multi-master, message broadcast system. The messages sent on the CAN are used to communicate state information, referred as a signal between different ECUs, which provides data consistency in every node of the system. OBD-II Dongles that are based on request and response method is the wide-spread solution for extracting sensor data from cars among researchers. Unfortunately, most of the past researches do not consider resolution and quantity of their input data extracted through OBD-II technology. The maximum feasible scan rate is only 9 queries per second which provide 8 data points per second with using ELM327 as well-known OBD-II dongle. This study aims to develop and design a programmable, and latency-sensitive vehicle data acquisition system that improves the modularity and flexibility to extract exact, trustworthy, and fresh car sensor data with higher frequency rates. Furthermore, the researcher must break apart, thoroughly inspect, and observe the internal network of the vehicle, which may cause severe damages to the expensive ECUs of the vehicle due to intrinsic vulnerabilities of the CAN bus during initial research. Desired sensors data were collected from various vehicles utilizing Raspberry Pi3 as computing and processing unit with using OBD (request-response) and direct CAN method at the same time. Two types of data were collected for this study. The first, CAN bus frame data that illustrates data collected for each line of hex data sent from an ECU and the second type is the OBD data that represents some limited data that is requested from ECU under standard condition. The proposed system is reconfigurable, human-readable and multi-task telematics device that can be fitted into any vehicle with minimum effort and minimum time lag in the data extraction process. The standard operational procedure experimental vehicle network test bench is developed and can be used for future vehicle network testing experiment.

Keywords: CAN bus, OBD-II, vehicle data acquisition, connected cars, telemetry, Raspberry Pi3

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Authors: Sergio Zabala-Vargas, Miguel Jiménez-Barrera, Luz VArgas-Sánchez

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In recent years, information related to project management in organizations has been increasing exponentially. Performance data, management statistics, indicator results have forced the collection, analysis, traceability, and dissemination of project managers to be essential. In this sense, there are current trends to facilitate efficient decision-making in emerging technology projects, such as: Machine Learning, Data Analytics, Data Mining, and Big Data. The latter is the most interesting in this project. This research is part of the thematic line Construction methods and project management. Many authors present the relevance that the use of emerging technologies, such as Big Data, has taken in recent years in project management in the construction sector. The main focus is the optimization of time, scope, budget, and in general mitigating risks. This research was developed in the northeastern region of Colombia-South America. The first phase was aimed at diagnosing the use of emerging technologies (Big-Data) in the construction sector. In Colombia, the construction sector represents more than 50% of the productive system, and more than 2 million people participate in this economic segment. The quantitative approach was used. A survey was applied to a sample of 91 companies in the construction sector. Preliminary results indicate that the use of Big Data and other emerging technologies is very low and also that there is interest in modernizing project management. There is evidence of a correlation between the interest in using new data management technologies and the incorporation of Building Information Modeling BIM. The next phase of the research will allow the generation of guidelines and strategies for the incorporation of technological tools in the construction sector in Colombia.

Keywords: big data, building information modeling, tecnology, project manamegent

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Authors: Y. Sarı Nayim, B. N. Nayim

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This research proposal includes the defining and evaluation of the semi-natural and cultural ecosystems at Bartın University main campus in Turkey in terms of landscape mosaics. The ecosystem mosaic of the main campus was divided into zones based on ecological classification technique. Based on the results from the study, it was found that 6 different ecosystem mosaics should be used as a base in the planning and design of the existing and future landscape planning of Kutlubeyyazıcılar campus. The first landscape zone involves the 'social areas'. These areas include yards, dining areas, recreational areas and lawn areas. The second landscape zone is 'main vehicle and pedestrian areas'. These areas include vehicle access to the campus landscape, moving in the campus with vehicles, parking and pedestrian walk ways. The third zone is 'landscape areas with high visual landscape quality'. These areas will be the places where attractive structural and plant landscape elements will be used. Fourth zone will be 'landscapes of building borders and their surroundings.' The fifth and important zone that should be survived in the future is 'Actual semi-natural forest and bush areas'. And the last zone is 'water landscape' which brings ecological value to landscape areas. While determining the most convenient areas in the planning and design of the campus, these landscape mosaics should be taken into consideration. This zoning will ensure that the campus landscape is protected and living spaces in the campus apart from the areas where human activities are carried out will be used properly.

Keywords: campus landscape planning and design, landscape ecology, landscape mosaics, Bartın

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Authors: Nazaket Gazieva

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Voice biometric data associated with physiological, psychological and other factors are widely used in forensic phonoscopy. There are various methods for identifying and verifying a person by voice. This article explores the minimum speech signal data as individual parameters of a speech signal. Monozygotic twins are believed to be genetically identical. Using the minimum data of the speech signal, we came to the conclusion that the voice imprint of monozygotic twins is individual. According to the conclusion of the experiment, we can conclude that the minimum indicators of the speech signal are more stable and reliable for phonoscopic examinations.

Keywords: phonogram, speech signal, temporal characteristics, fundamental frequency, biometric fingerprints

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Authors: Farnaz Farzin, Julie C. Thomson, Rob Dekkers, Geoff Whittam

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Research has shown that techno-entrepreneurship is economically significant. Therefore, it is suggested that teaching techno-entrepreneurship may be important because such programmes would prepare current and future generations of learners to recognize and act on high-technology opportunities. Education in techno-entrepreneurship may increase the knowledge of how to start one’s own enterprise and recognize the technological opportunities for commercialisation to improve decision-making about starting a new venture; also it influence decisions about capturing the business opportunities and turning them into successful ventures. Universities can play a main role in connecting and networking techno-entrepreneurship students towards a cooperative attitude with real business practice and industry knowledge. To investigate and answer whether education for techno-entrepreneurs really helps, this paper chooses a comparison of literature reviews as its method of research. Then, 6 different studies were selected. These particular papers were selected based on a keywords search and as their aim, objectives, and gaps were close to the current research. In addition, they were all based on the influence of techno-entrepreneurship education in self-employment and intention of students to start new ventures. The findings showed that teaching techno-entrepreneurship education may have an influence on students’ intention and their future self-employment, but which courses should be covered and the duration of programmes needs further investigation.

Keywords: techno entrepreneurship education, training, higher education, intention, self-employment

Procedia PDF Downloads 337