Search results for: constructivist theoretical approach

Authors: Stefano Fantin

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The present analysis outcomes from the research undertaken in the course of the European-funded project EUNITY, which targets the gaps in research and development on cybersecurity and privacy between Europe and Japan. Under these auspices, the research presents a study on the policy approach of Japan, the EU and a number of Member States of the Union with regard to the handling and discovery of software vulnerabilities, with the aim of identifying methodological differences and similarities. This research builds upon a functional comparative analysis of both public policies and legal instruments from the identified jurisdictions. The result of this analysis is based on semi-structured interviews with EUNITY partners, as well as by the participation of the researcher to a recent report from the Center for EU Policy Study on software vulnerability. The European Union presents a rather fragmented legal framework on software vulnerabilities. The presence of a number of different legislations at the EU level (including Network and Information Security Directive, Critical Infrastructure Directive, Directive on the Attacks at Information Systems and the Proposal for a Cybersecurity Act) with no clear focus on such a subject makes it difficult for both national governments and end-users (software owners, researchers and private citizens) to gain a clear understanding of the Union’s approach. Additionally, the current data protection reform package (general data protection regulation), seems to create legal uncertainty around security research. To date, at the member states level, a few efforts towards transparent practices have been made, namely by the Netherlands, France, and Latvia. This research will explain what policy approach such countries have taken. Japan has started implementing a coordinated vulnerability disclosure policy in 2004. To date, two amendments can be registered on the framework (2014 and 2017). The framework is furthermore complemented by a series of instruments allowing researchers to disclose responsibly any new discovery. However, the policy has started to lose its efficiency due to a significant increase in reports made to the authority in charge. To conclude, the research conducted reveals two asymmetric policy approaches, time-wise and content-wise. The analysis therein will, therefore, conclude with a series of policy recommendations based on the lessons learned from both regions, towards a common approach to the security of European and Japanese markets, industries and citizens.

Keywords: cybersecurity, vulnerability, European Union, Japan

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Authors: Vangelis Papadimitropoulos

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This paper is part of the research program “Techno-Social Innovation in the Collaborative Economy”, funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) for the years 2022-2024. The paper showcases the Open Food Network (OFN) as an open-sourced digital platform supporting short food supply chains in local agricultural production and consumption. The paper outlines the research hypothesis, the theoretical framework, and the methodology of research as well as the findings and conclusions. Research hypothesis: The model of open cooperativism as a vehicle for systemic change in the agricultural sector. Theoretical framework: The research reviews the OFN as an illustrative case study of the three-zoned model of open cooperativism. The OFN is considered a paradigmatic case of the model of open cooperativism inasmuch as it produces commons, it consists of multiple stakeholders including ethical market entities, and it is variously supported by local authorities across the globe, the latter prefiguring the mini role of a partner state. Methodology: Research employs Ernesto Laclau and Chantal Mouffe’s discourse analysis -elements, floating signifiers, nodal points, discourses, logics of equivalence and difference- to analyse the breadth of empirical data gathered through literature review, digital ethnography, a survey, and in-depth interviews with core OFN members. Discourse analysis classifies OFN floating signifiers, nodal points, and discourses into four themes: value proposition, governance, economic policy, and legal policy. Findings: OFN floating signifiers align around the following nodal points and discourses: “digital commons”, “short food supply chains”, “sustainability”, “local”, “the elimination of intermediaries” and “systemic change”. The current research identifies a lack of common ground of what the discourse of “systemic change” signifies on the premises of the OFN’s value proposition. The lack of a common mission may be detrimental to the formation of a common strategy that would be perhaps deemed necessary to bring about systemic change in agriculture. Conclusions: Drawing on Laclau and Mouffe’s discourse theory of hegemony, research introduces a chain of equivalence by aligning discourses such as “agro-ecology”, “commons-based peer production”, “partner state” and “ethical market entities” under the model of open cooperativism, juxtaposed against the current hegemony of neoliberalism, which articulates discourses such as “market fundamentalism”, “privatization”, “green growth” and “the capitalist state” to promote corporatism and entrepreneurship. Research makes the case that for OFN to further agroecology and challenge the current hegemony of industrial agriculture, it is vital that it opens up its supply chains into equivalent sectors of the economy, civil society, and politics to form a chain of equivalence linking together ethical market entities, the commons and a partner state around the model of open cooperativism.

Keywords: sustainability, the digital commons, open cooperativism, innovation

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Authors: Ahmed Amrani, Oussama Allali, Amira Ben Hamida, Felix Defrance, Stephanie Morland, Eva Pineau, Thomas Lacroix

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The energy planning process is a very complex task that involves several stakeholders and requires the consideration of several local and global factors and constraints. In order to optimize and simplify this process, we propose a tool-based iterative approach applied to district heating planning. We build our tool with the collaboration of a French territory using actual district data and implementing the European incentives. We set up an iterative process including data visualization and analysis, identification and extraction of information related to the area concerned by the operation, design of sustainable planning scenarios leveraging local renewable and recoverable energy sources, and finally, the evaluation of scenarios. The last step is performed by a dynamic digital twin replica of the city. Territory’s energy experts confirm that the tool provides them with valuable support towards sustainable energy planning.

Keywords: climate change, data management, decision support, digital twin, district heating, energy planning, renewables, smart city

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Francesca Radice

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Domestic and sexual violence provokes, on average in Australia, one female death per week due to intimate violence behaviours. 83% of couples meet online, and intercepting domestic and sexual violence at this level would be beneficial. It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people.

Keywords: sentiment analysis, data mining, predictive policing, virtual manipulation

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Authors: Prathmesh Vikas Patil, Yashaswini Lomte Patil

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Laminated composite materials have considerable attention in various engineering applications due to their exceptional strength-to-weight ratio and mechanical properties. The analysis of laminated composite plates in three-dimensional (3D) elasticity is a complex problem, as it requires accounting for the orthotropic anisotropic nature of the material and the interactions between multiple layers. Conventional approaches, such as the classical plate theory, provide simplified solutions but are limited in performing exact analysis of the plate. To address such a challenge, the state space method emerges as a powerful numerical technique for modeling the behavior of laminated composites in 3D. The state-space method involves transforming the governing equations of elasticity into a state-space representation, enabling the analysis of complex structural systems in a systematic manner. Here, an effort is made to perform a 3D elasticity analysis of plates with cross-ply and angle-ply laminates using the state space approach. The state space approach is used in this study as it is a mixed formulation technique that gives the displacements and stresses simultaneously with the same level of accuracy.

Keywords: cross ply laminates, angle ply laminates, state space method, three-dimensional elasticity analysis

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Authors: Sakti Mandal

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Varying distribution of land surface temperature in an urbanized environment is a globally addressed phenomenon. Usually has been noticed that criticality of surface temperature increases from the periphery to the urban centre. As the centre experiences maximum severity of heat throughout the year, it also represents most critical zone in terms of thermal condition. In this present study, an attempt has been taken to propose a quantitative approach of thermal critical zonation (TCZ) on the basis of seasonal temperature variation. Here the zonation is done by calculating thermal critical value (TCV). From the Landsat 8 thermal digital data of summer and winter seasons for the year 2014, the land surface temperature maps and thermally critical zonation has been prepared, and corresponding dataset has been computed to conduct the overall study of that particular study area. It is shown that TCZ can be clearly identified and analyzed by the help of inter-seasonal temperature range. The results of this study can be utilized effectively in future urban development and planning projects as well as a framework for implementing rules and regulations by the authorities for a sustainable urban development through an environmentally affable approach.

Keywords: thermal critical values (TCV), thermally critical zonation (TCZ), land surface temperature (LST), Landsat 8, Kolkata Municipal Corporation (KMC)

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Madiha Liaqat, Rehan Ahmed Khan, Shahid Kamal

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Multiple imputation with delta adjustment is a versatile and transparent technique for addressing univariate missing data in the presence of various missing mechanisms. This approach allows for the exploration of sensitivity to the missing-at-random (MAR) assumption. In this review, we outline the delta-adjustment procedure and illustrate its application for assessing the sensitivity to deviations from the MAR assumption. By examining diverse missingness scenarios and conducting sensitivity analyses, we gain valuable insights into the implications of missing data on our analyses, enhancing the reliability of our study's conclusions. In our study, we focused on assessing logPSA, a continuous biomarker in incomplete prostate cancer data, to examine the robustness of conclusions against plausible departures from the MAR assumption. We introduced several approaches for conducting sensitivity analyses, illustrating their application within the pattern mixture model (PMM) under the delta adjustment framework. This proposed approach effectively handles missing data, particularly loss to follow-up.

Keywords: loss to follow-up, incomplete response, multiple imputation, sensitivity analysis, prostate cancer

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Authors: Sergio Pérez-López, Daniel Tarrazó-Serrano, José M. Fuster, Pilar Candelas, Constanza Rubio

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Fresnel Zone Plates (FZPs) are widely used in many areas, such as optics, microwaves or acoustics. On the design of FZPs, plane wave incidence is typically considered, but that is not usually the case in ultrasounds, especially in applications where a piston emitter is placed at a certain distance from the lens. In these cases, having control of the focal distance is very important, and with the usual Fresnel equation a focal displacement from the theoretical distance is observed due to the plane wave supposition. In this work, a comparison between FZP with plane wave incidence design and FZP with point source design in the case of piston emitter is presented. Influence of the main parameters of the piston in the final focalization profile has been studied. Numerical models and experimental results are shown, and they prove that when spherical wave incidence is considered for the piston case, it is possible to have a fine control of the focal distance in comparison with the classical design method.

Keywords: focusing, Fresnel zone plates, FZP, ultrasound

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Authors: Cheng Wen, Zhang Jianhua

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This paper aims to probe into the general rules of industry specialization or diversification in a region during its process of economic growth and the specific reasons for the difference of industry specialization development in the eastern, central and western regions of China. It is found in this paper that the changes of regional industry specialization in China, like most of countries in the world, also present the U-shaped curve. Regional industrial structure is diversified in the first place. And when the per capita income exceeds a certain level, distribution of economic resources in this region will be concentrated again. From the perspective of rising total factor productivity and falling of transaction cost in the process of economic development, this paper comes up with a theoretical model to explain the U-shaped curve. Through the empirical test of China's provincial panel data, this paper explains the factors that cause the inequality of the industry specialization development in the eastern, central and western regions of China.

Keywords: u-shaped curve, regional industrial specialization, technological progress, transaction costs

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Authors: Ashley Wei-Ting Wang, Wen-Yau Hsu

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Objectives: The current study applies and expands the Tripartite Model to elaborate the link between non-suicidal self-injury (NSSI) and suicidal behavior. We propose a structural model of NSSI and suicidal risk, in which negative affect (NA) predicts both anxiety and depression, positive affect (PA) predicts depression only, anxiety is linked to NSSI, and depression is linked to suicidal risk. Method: Four hundreds and eighty seven undergraduates participated. Data were collected by administering self-report questionnaires. We performed hierarchical regression and structural equation modeling to test the proposed structural model. Results: The results largely support the proposed structural model, with one exception: anxiety was strongly associated with NSSI and to a lesser extent with suicidal risk. Conclusions: We conclude that the co-occurrence of NSSI and suicidal risk is due to NA and anxiety, and suicidal risk can be differentiated by depression. Further theoretical and practical implications are discussed.

Keywords: non-suicidal self-injury, suicidal risk, anxiety, depression, the tripartite model, hierarchical relationship

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Yalisa Tonsorn

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The objectives of this study are 1) to study the readiness of Thai tour operators in order to prepare for being ASEAN members, 2) to study opportunity and obstacles of the management of Thai tour operators, and 3) to find approach for developing value chain in order to enhance the management efficiency of Thai tour operators in order to support free trade within the framework of ASEAN cooperation. The research methodology is mixed between qualitative method and quantitative method. In-depth interview was done with key informants, including management supervisors, medium managers, and officers of the travel agencies. The questionnaire was conducted with 300 sampling. According to the study, it was found that the approach for developing value chain to enhance the management efficiency of Thai travel agencies in order to support free trade within the framework of ASEAN cooperation, the tour operators must give priority to the customer and deliver the service exceeding the customer’s expectation. There are 2 groups of customers: 1) external customers referring to tourist, and 2) internal customers referring to staff who deliver the service to the customers, including supervisors, colleagues, or subordinates. There are 2 issues which need to be developed: 1) human resource development in order to cultivate the working concept by focusing on importance of customers, and excellent service providing, and 2) working system development by building value and innovation in operational process including services to the company in order to deliver the highest impressive service to both internal and external customers. Moreover, the tour operators could support the increased number of tourists significantly. This could enhance the capacity of the business and affect the increase of competition capability in the economic dimension of the country.

Keywords: AEC (ASEAN Economic Eommunity), core activities, support activities, values chain

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Authors: Duaa Mughal, Syeda Areeba Nadeem, Shakil Ahmed, Ishtiaq Ahmed Khan

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The identification of porcine DNA in Halal food items is critical to ensuring compliance with dietary restrictions and religious beliefs. In Islam, Porcine is prohibited as clearly mentioned in Quran (Surah Al-Baqrah, Ayat 173). The purpose of this study was to compare two DNA extraction procedures for detecting 0.001% of porcine DNA in processed Halal food sample mixtures containing chicken, camel, veal, turkey and goat meat using the TaqMan Real-Time PCR technology. In this research, two different commercial kit protocols were compared. The processed sample mixtures were prepared by spiking known concentration of porcine DNA to non-porcine food matrices. Afterwards, TaqMan Real-Time PCR technique was used to target a particular porcine gene from the extracted DNA samples, which was quantified after extraction. The results of the amplification were evaluated for sensitivity, specificity, and reproducibility. The results of the study demonstrated that two DNA extraction techniques can detect 0.01% of porcine DNA in mixture of Halal food samples. However, as compared to the alternative approach, Eurofins| GeneScan GeneSpin DNA Isolation kit showed more effective sensitivity and specificity. Furthermore, the commercial kit-based approach showed great repeatability with minimal variance across repeats. Quantification of DNA was done by using fluorometric assay. In conclusion, the comparison of DNA extraction methods for detecting porcine DNA in Halal food sample mixes using the TaqMan Real-Time PCR technology reveals that the commercial kit-based approach outperforms the other methods in terms of sensitivity, specificity, and repeatability. This research helps to promote the development of reliable and standardized techniques for detecting porcine DNA in Halal food items, religious conformity and assuring nutritional.

Keywords: real time PCR (qPCR), DNA extraction, porcine DNA, halal food authentication, religious conformity

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Authors: A. A. Hassani, M. Nasri Nasrabadi

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According to population growth and increasing water needs of industrial and agricultural sections and lack of existing water sources, also increases of wastewater and new wastewater treatment plant construction’s high costs, it is inevitable to reuse wastewater with the approach of increasing wastewater treatment capacity and output sewage quality. In this regard, the first sewage reuse plan in industrial uses was designed with the approach of qualitative and quantitative improvement due to the increased organic load of the output sewage of Qom Shokohieh city’s’ in wastewater treatment plant. This research investigated qualitative factors COD, BOD, TSS, TDS, and input and output heavy metal of MBR-RO system and ability of increase wastewater acceptance capacity by existing in wastewater treatment plant. For this purpose, experimental results of seven-month navigation system have been used from 07/01/2013 to 02/01/2014. Existing data analysis showed that MBR system is able to remove 93.2% COD, 94.4% BOD, 13.8% TDS, 98% heavy metals and RO system is able to remove 98.9% TDS. This study showed that MBR-RO integration system is able to increase the capacity of refinery by 30%.

Keywords: industrial wastewater, wastewater reuse, MBR, RO

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Authors: Omid Allan

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The advent of ultra-narrow diameter implants initially offered an alternative to wider conventional implants. However, their design limitations have restricted their applicability primarily to overdentures and cement-retained fixed prostheses, often with unpredictable long-term outcomes. The introduction of the new Miniature Implants has revolutionized the field of implant dentistry, leading to a more streamlined approach. The utilization of Miniature Implants has emerged as a promising alternative to the traditional approach that entails the traumatic sequential bone drilling procedures and the use of conventional implants for full and partial arch restorations. The innovative "BioMiniatures Implant System serves as a groundbreaking bridge connecting mini implants with standard implant systems. This system allows practitioners to harness the advantages of ultra-small implants, enabling minimally invasive insertion and facilitating the application of fixed screw-retained prostheses, which were only available to conventional wider implant systems. This approach streamlines full and partial arch rehabilitation with minimal or even no bone drilling, significantly reducing surgical risks and complications for clinicians while minimizing patient morbidity. The ultra-narrow diameter and self-advancing features of these implants eliminate the need for invasive and technically complex procedures such as bone augmentation and guided bone regeneration (GBR), particularly in cases involving thin alveolar ridges. Furthermore, the absence of a microcap between the implant and abutment eliminates the potential for micro-leakage and micro-pumping effects, effectively mitigating the risk of marginal bone loss and future peri-implantitis. The cumulative experience of restoring over 50 full and partial arch edentulous cases with this system has yielded an outstanding success rate exceeding 97%. The long-term success with a stable marginal bone level in the study firmly establishes these implants as a dependable alternative to conventional implants, especially for full arch rehabilitation cases. Full arch rehabilitation with these implants holds the promise of providing a simplified solution for edentulous patients who typically present with atrophic narrow alveolar ridges, eliminating the need for extensive GBR and bone augmentation to restore their dentition with fixed prostheses.

Keywords: mini-implant, biominiatures, miniature implants, minimally invasive dentistry, full arch rehabilitation

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Authors: Meng Wu, Atif Rahman, Eng Gee, Lim, Jeong Jin Yu, Rong Yan

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Context: The COVID-19 pandemic has had a profound impact on mental health, with increased rates of anxiety and depression observed. Social support, a critical factor in mental well-being, has also undergone significant changes during the pandemic. This study aims to explore the relationship between social support, anxiety, and depression during COVID-19, taking into account various demographic and contextual factors. Research Aim: The main objective of this study is to conduct a comprehensive systematic review and meta-analysis to examine the impact of social support on anxiety and depression during the COVID-19 pandemic. The study aims to determine the consistency of these relationships across different age groups, occupations, regions, and research paradigms. Methodology: A scoping review and meta-analytic approach were employed in this study. A search was conducted across six databases from 2020 to 2022 to identify relevant studies. The selected studies were then subjected to random effects models, with pooled correlations (r and ρ) estimated. Homogeneity was assessed using Q and I² tests. Subgroup analyses were conducted to explore variations across different demographic and contextual factors. Findings: The meta-analysis of both cross-sectional and longitudinal studies revealed significant correlations between social support, anxiety, and depression during COVID-19. The pooled correlations (ρ) indicated a negative relationship between social support and anxiety (ρ = -0.30, 95% CI = [-0.333, -0.255]) as well as depression (ρ = -0.27, 95% CI = [-0.370, -0.281]). However, further investigation is required to validate these results across different age groups, occupations, and regions. Theoretical Importance: This study emphasizes the multifaceted role of social support in mental health during the COVID-19 pandemic. It highlights the need to reevaluate and expand our understanding of social support's impact on anxiety and depression. The findings contribute to the existing literature by shedding light on the associations and complexities involved in these relationships. Data Collection and Analysis Procedures: The data collection involved an extensive search across six databases to identify relevant studies. The selected studies were then subjected to rigorous analysis using random effects models and subgroup analyses. Pooled correlations were estimated, and homogeneity was assessed using Q and I² tests. Question Addressed: This study aimed to address the question of the impact of social support on anxiety and depression during the COVID-19 pandemic. It sought to determine the consistency of these relationships across different demographic and contextual factors. Conclusion: The findings of this study highlight the significant association between social support, anxiety, and depression during the COVID-19 pandemic. However, further research is needed to validate these findings across different age groups, occupations, and regions. The study emphasizes the need for a comprehensive understanding of social support's multifaceted role in mental health and the importance of considering various contextual and demographic factors in future investigations.

Keywords: social support, anxiety, depression, COVID-19, meta-analysis

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Shilpa Balan

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Radio Frequency Identification, also popularly known as RFID is used to automatically identify and track tags attached to items. This study focuses on the application of RFID in healthcare. The adoption of RFID in healthcare is a crucial technology to patient safety and inventory management. Data from RFID tags are used to identify the locations of patients and inventory in real time. Medical errors are thought to be a prominent cause of loss of life and injury. The major advantage of RFID application in healthcare industry is the reduction of medical errors. The healthcare industry has generated huge amounts of data. By discovering patterns and trends within the data, big data analytics can help improve patient care and lower healthcare costs. The number of increasing research publications leading to innovations in RFID applications shows the importance of this technology. This study explores the current state of research of RFID in healthcare using a text mining approach. No study has been performed yet on examining the current state of RFID research in healthcare using a data mining approach. In this study, related articles were collected on RFID from healthcare journal and news articles. Articles collected were from the year 2000 to 2015. Significant keywords on the topic of focus are identified and analyzed using open source data analytics software such as Rapid Miner. These analytical tools help extract pertinent information from massive volumes of data. It is seen that the main benefits of adopting RFID technology in healthcare include tracking medicines and equipment, upholding patient safety, and security improvement. The real-time tracking features of RFID allows for enhanced supply chain management. By productively using big data, healthcare organizations can gain significant benefits. Big data analytics in healthcare enables improved decisions by extracting insights from large volumes of data.

Keywords: RFID, data mining, data analysis, healthcare

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Authors: Dorota Rucińska

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This theoretical paper combines the academic discourse regarding a specific part of dark tourism. Based on the literature analysis, distinction of natural disasters in thanatourism was investigated, which is connected with dynamic geographical conditions. Natural disasters used to play an important role in social life by their appearance in myths and religions. Nowadays, tourists pursuing natural hazards can be divided into three groups: Those interested in natural hazards themselves; those interested in landscape deformation and experiencing emotions shortly after extreme events - natural disasters - occur; and finally those interested in historic places log after an extreme event takes place. An important element of the natural disaster tourism is quick access to information on the location of a disaster and the destination of a potential excursion. Natural disaster tourism suits alternative tourism, yet it is opposed culture tourism, and sustainable tourism. The paper compares types and groups of tourists. It also considers the contradictions that describe dualism, which exists in dark tourism.

Keywords: dark tourism, dualism, natural disasters, natural hazards, thanatoursim

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Authors: N. Chetty, K. Singh

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In this work, the relationship between the melanin content in a tissue and subsequent absorption of light through that tissue was determined using a digital camera. This technique proved to be simple, cost effective, efficient and reliable. Tissue phantom samples were created using milk and soy sauce to simulate the optical properties of melanin content in human tissue. Increasing the concentration of soy sauce in the milk correlated to an increase in melanin content of an individual. Two methods were employed to measure the light transmitted through the sample. The first was direct measurement of the transmitted intensity using a conventional lux meter. The second method involved correctly calibrating an ordinary digital camera and using image analysis software to calculate the transmitted intensity through the phantom. The results from these methods were then graphically compared to the theoretical relationship between the intensity of transmitted light and the concentration of absorbers in the sample. Conclusions were then drawn about the effectiveness and efficiency of these low cost methods.

Keywords: tissue phantoms, scattering coefficient, albedo, low-cost method

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Authors: Yi-De Liu

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The aim of this paper is to discuss how the construct of networking and social capital can be used to understand the effect events can have on the cultural sector. Based on case study, this research sought the views of those working in the cultural sector on Liverpool’s year as the European Capital of Culture (ECOC). Methodologically, this study involves literature review to prompt theoretical sensitivity, the collection of primary data via online survey (n= 42) and follow-up telephone interviews (n= 8) to explore the emerging findings in more detail. The findings point to a number of ways in which the ECOC constitutes a boost for networking and its effects on city’s cultural sector, including organisational learning, aspiration and leadership. The contributions of this study are two-fold: (1) Evaluating the long-term effects on network formation in the cultural sector following major event; (2) conceptualising the impact assessment of organisational social capital for future ECOC or similar events.

Keywords: network, social capital, cultural impact, european capital of culture

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Authors: Md. Mahamud Hassan, Shantanu Biswas Linkon, Nur Mohammad Khan

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Over the last three decades, the primary education system in Bangladesh has experienced significant improvement, but it has failed to cope with different social and cultural aspects, which present many challenges for children, families, and the public school system. Neglecting our own contextual learning environment, it is a matter of sorrow that much attention has been paid to the more physical outcome-focused model, which is nothing but mere infrastructural development, and less subtle to the environment that suits the child's psychology and improves their social, emotional, physical, and moral competency. In South Asia, the symbol of education was never the little red house of colonial architecture but “A Guru sitting under a tree", whereas a responsive and inclusive design approach could help to create more innovative learning environments. Such an approach incorporates how the built, natural, and cultural environment shapes the learner; in turn, learners shape the learning. This research will be conducted to, i) identify the major issues and drawbacks of government policy for primary education development programs; ii) explore and evaluate the morphology of the conventional model of school, and iii) propose an alternative model in a collaborative design process with the stakeholders for maximizing the relationship between the physical learning environments and learners by treating “the built environment” as “the third teacher.” Based on observation, this research will try to find out to what extent built, and natural environments can be utilized as a teaching tool for a more optimal learning environment. It should also be evident that there is a significant gap in the state policy, predetermined educational specifications, and implementation process in response to stakeholders’ involvement. The outcome of this research will contribute to a people-place sensitive design approach through a more thoughtful and responsive architectural process.

Keywords: built environment, conventional planning, indigenous learning space, responsive design

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Authors: Shane-Anthony Smith

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Teachers across grade levels and content areas face a myriad of challenges in the classroom. From inconsistent attendance to disruptive behaviors, these challenges can have a dire impact on the educational space, untimely leading to a loss of instructional time and student disenfranchisement from learning. While these challenges are not new to the educational landscape, the post-COVID classroom has, in many instances, been more severely impacted by behaviors that are not conducive to learning. Despite the mounting challenges, the role of the teacher remains unchanged - that is, to create and maintain a safe environment that is conducive to learning and promotes successful learning outcomes. Accomplishing this feat is no easy task. Yet, there are steps teachers can - indeed, must - take to better set themselves and their students up for success. The key to achieving this success is effective classroom transitions. This paper presents a four-step approach for teachers to engage in successful classroom transitions to promote meaningful student engagement and active positive learning outcomes. The transition strategy I will explore is called PREP (Pause, Reset, Establish Expectations, and Proceed). I developed this strategy in my work as a Residency Director for my university’s teacher residency program. In this role, I am tasked with coaching emerging teachers and their in-service teaching mentors in the field, as well as providing mentorship to special education resident teachers pursuing teaching degrees in the program. As a teacher educator, being in Middle and High school classrooms provides an intricate and critical understanding of the challenges, opportunities, and possibilities in the classroom. For this paper, I will explore how teachers can optimize the opportunities PREP provides to keep students engaged and, thus, improve student achievement. I will describe the approach, explain its use, and provide case-study examples of its classroom application.

Keywords: classroom management, teaching strategies, student engagement, classroom transition

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Munir Kassa, Mengistu Hailemariam, Meghan Obermeyer, Kefelegn Baruda, Yonas Getachew, Asnakech Dessie

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Improving the quality of sexual and reproductive health services that women receive is expected to have an impact on women’s satisfaction with the services, on their continued use and, ultimately, on their ability to achieve their fertility goals or reproductive intentions. Surprisingly, however, there is little empirical evidence of either whether this expectation is correct, or how best to improve service quality within sexual and reproductive health programs so that these impacts can be achieved. The Recent focus on quality has prompted more physicians to do quality improvement work, but often without the needed skill sets, which results in poorly conceived and ultimately unsuccessful improvement initiatives. As this renders the work unpublishable, it further impedes progress in the field of health care improvement and widens the quality chasm. Moreover, since 2014, the Center for International Reproductive Health Training (CIRHT) has worked diligently with 11 teaching hospitals across Ethiopia to increase access to contraception and abortion care services. This work has included improving pre-service training through education and curriculum development, expanding hands-on training to better learn critical techniques and counseling skills, and fostering a “team science” approach to research by encouraging scientific exploration. This is the first time this systematic approach has been applied and documented to improve access to high-quality services in Ethiopia. The purpose of this article is to report initiatives undertaken, and findings concluded by the clinical service team at CIRHT in an effort to provide a pragmatic approach to quality improvement projects. An audit containing nearly 300 questions about several aspects of patient care, including structure, process, and outcome indicators was completed by each teaching hospital’s quality improvement team. This baseline audit assisted in identifying major gaps and barriers, and each team was responsible for determining specific quality improvement aims and tasks to support change interventions using Shewart’s Cycle for Learning and Improvement (the Plan-Do-Study-Act model). To measure progress over time, quality improvement teams met biweekly and compiled monthly data for review. Also, site visits to each hospital were completed by the clinical service team to ensure monitoring and support. The results indicate that applying an evidence-based, participatory approach to quality improvement has the potential to increase the accessibility and quality of services in a short amount of time. In addition, continued ownership and on-site support are vital in promoting sustainability. This approach could be adapted and applied in similar contexts, particularly in other African countries.

Keywords: abortion, contraception, quality improvement, service provision

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Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: Sami Baraketi, Jean Marie Garcia, Olivier Brun

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Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods.

Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic

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Authors: Clinton Adas

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Industrial animal farming contributes to numerous problems that humans face, and among these, antimicrobial resistance (AMR) has been identified by the World Health Organisation as a real possibility for the 21st Century. While numerous factors contribute to AMR, one of them is industrial animal farming and its effect on the food chain and environment. In 2017, livestock were given around 73% of all antibiotics worldwide to make them grow faster for profit purposes, to prevent illness caused by unhealthy living conditions, and to treat disease when it breaks out. Many of the antibiotics used provide little benefit to animals, and most are the same as those used by humans - including many deemed critical to human health that should be used sparingly. AMR contributes to millions of illnesses, and in 2019 was responsible for around 4.95 million deaths worldwide. It costs Europe around nine billion euros per year, while it costs the United States (US) around 20 billion dollars per year. While not a simple or quick solution, one way to begin to address the challenge of AMR and other harms from this type of farming is to focus on animal welfare as part of a One Health and Welfare approach, as better welfare requires less antibiotics usage, which may begin to break the cycle.

Keywords: animal and human welfare, industrial animal farming, antimicrobial resistance, one health and welfare, sustainable development goals

Procedia PDF Downloads 93