Engineering Tools Research Articles

Direct and Digital Democracy in the Ideology and Politics of the Five Star Movement

The article analyses the role of direct and e-democracy in the ideology and political practice of the Italian populist party Five Star Movement (M5S). Founded as a party in 2009, Movement stood out among European populists with its pronounced leftgreen agenda aimed at solving local problems of an urban nature. However, an even more remarkable feature of the Five Stars has become an equally clearly noticeable technoutopianism mixed with direct democracy, which argues for the need to expand democratic participation for ordinary citizens through the tools of direct democracy and digital technologies. Based on the study of primary sources (party documents, Beppe Grillo's blog and the official party blog), as well as text media materials, an attempt is made to trace how during two periods of activity of Five Star Movement in 2013–2022 the attitude of the party leaders to the role of direct and electronic democracy in the political activity of Five Stars gradually changed. The article analyses changes in Five Star Movement’s organisational structure and tools of the e-democracy in the course of the development of the party and its institutionalisation into the Italian party-political system. The article concludes that with the process of Five Stars’ incorporation into the political regime, the party has pushed technoutopianism out of its agenda and practice, strengthening party centralisation. Thus, innovative practices in the digitalisation of the political process did not lead to a real expansion of democratic participation, but were subordinated to a party discipline.

Scientometric analysis of building integrated photovoltaics research: development, themes, and main trends

ABSTRACT Building-integrated photovoltaics (BIPV), a significant technological tool to reduce carbon emissions from buildings, have attracted extensive research attention worldwide as the call for “carbon neutrality” in the building sector continues to rise. Existing studies lack a comprehensive compendium of global BIPV-related research, and articles on the latest advances in the last five years are particularly scarce. Consequently, this study conducted a scientometric review of BIPV research to systematically and visually analyse the cutting-edge BIPV literature. Through multiple rounds of searching and screening, this study collected 467 BIPV-related studies between 2019 and 2023. It utilised CiteSpace to analyse the development of BIPV literature, summarise the themes, and predict the trends. The review reveals that the cross-disciplinary nature of the discipline dynamically shapes the focus of BIPV research. Based on the findings of scientometrics, three major themes are outlined and critically analysed: science and technology, market products, and implementation impediments. By comprehensively summarising and analysing the most recent BIPV literature, this study proposes a series of research trends in the field. This study provides policymakers, architects, and researchers in the field of low-carbon buildings with a detailed compendium of the current status and trends of BIPV. It offers specific insights and sources of reference for in-depth research and the promotion of the application of BIPV technology in the building industry.

The role of technology in tourist guiding: future career, service quality, marketing and tourist experiences

Purpose This study aims to investigate the perceptions of tourist guides in Türkiye regarding the role of technology in the tourist guiding profession. Design/methodology/approach This research adopted an exploratory qualitative case study approach, conducting semi-structured interviews with 29 professional tourist guides actively working in different regions of Türkiye. Findings The findings reveal the dual roles of technology in tourist guiding. While it can facilitate service quality and enhance experiences, technologies such as robot tourist guides, augmented reality and virtual reality are perceived as disruptive and potentially damaging service quality and tourist experiences. Originality/value The research offers unique insights into the facilitative and disruptive aspects of technological tools in the tourism industry. These findings have direct implications for destination managers, travel agencies and tourist guides worldwide, equipping them with a comprehensive understanding of the role of technology in tourist guiding and guiding their future actions, strategies and policies.

Virtual relational capital for business development: a case study

Purpose The study aims to investigate virtual relational capital (VRC) to evaluate to what extent virtual relations (VR), obtained by using new technologies, support the development of firms, also considering the increasing sustainability’s needs. The study addresses the literature gap on VRC dynamics through an empirical analysis. Design/methodology/approach The investigation focuses on a single case study in the lighting industry, deepening the case of a small Italian company. The case is analyzed through the lens of the CAOS framework model by Paoloni, nurtured through direct semi-structured interviews with the entrepreneur and some managers and consultants, and data collected via web scraping. Findings VRC, obtained by the use of new technological tools, contributes to developing and fostering the innovation ecosystem in which companies need to create new skills and synergic alliances with other stakeholders. Moreover, VR can improve commercial and sales performance, stakeholder engagement and sustainability, including alignment with the circular economy and waste management principles. VRC can support smaller companies with more limited resources to connect to a broader range of actors, raising their voices with policymakers and other relevant international institutions. Originality/value The study contributes to the theoretical understanding of VRC, especially in an era in which new technologies play a fundamental role for both businesses and people. It also provides practical insights into how companies, especially smaller ones, can maximize their sustainable impact by strategically adopting virtual interactions with meaningful stakeholders like customers, key executive partners, industrial associations and policymakers.

GESTÃO DE BANCO DE DADOS

It is known that nowadays, with large amounts of information, organizations have felt the need to manage and share this information quickly and efficiently. However, information is only strategic and becomes knowledge when it is managed effectively. By providing effective mechanisms for managing large groups of information, the so-called Data Management Systems fulfill the function of making the task of maintaining and retrieving large amounts of data easier. This research, based on theoretical contributions from Information Science and on frameworks of Information Technology tools, aims to analyze and optimize the process of controlling subcontractor contracts and equipment rental of a heavy construction company. This work aims to conduct a comparative study, based on general aspects of performance, between relational databases on disk and in memory. To this end, a system was used to load data into the chosen databases, as well as queries were performed to analyze the performance of read and write operations in the chosen databases.

The Use of Digital Twins to Address Smart Tourist Destinations’ Future Challenges

This research aims to conceptualise the use of the digital twins (DT) tool in tourism to address smart tourist destinations’ future challenges to enhance tourists’ experiences and residents’ quality of life through better services developed by DTs. This paper investigated the use of DT technology to tackle the new challenges facing smart tourist destinations (STDs) in terms of urban planning, sustainability, security, marketing, and tourism activities by using data on this topic from expert researchers and public and private organisations. Also, this research adopted a systematic review approach to analyse and illustrate the existing literature on the topic of DTs and their use for STDs, which helped us develop the DT concept in a STD context. The findings of this research reveal that DT technology is emerging as a disruptive technology tool that is being used to improve the management of STDs in terms of their efficiency, safety, sustainability, environmental protection, productivity, and energy consumption. Moreover, DT technology is a nascent tool in the tourism and marketing industries, but its versatility in relation to its capacity to analyse data and predict the behaviour of tourism and business ecosystems to address relevant issues at STDs gives it a key role in the tourism industry. Finally, a new definition of DT technology has been included in this study within a tourism and STDs context, filling the gap in this topic in the tourism industry according to some researchers’ suggestions. There are limited scientific publications about DT technology that jointly tackle the DT technology and STDs sphere. Accordingly, this manuscript defines DT technology and provides a new viewpoint of this technology that will help to initiate academic discussions on DT tourism within the context of STDs.

The impact of contextual culture on the implementation of blended learning in tertiary language programmes

ABSTRACT With the wide adoption of the blended approach by language practitioners, blended language learning (BLL) has developed into a new area in applied linguistics. Much research has been conducted on pedagogical innovation, technological tools, users’ perceptions and the creation of the BLL environment. Concerning the establishment of a BLL environment, BLL as a social activity among stakeholders has not raised the attention of the academic community. Grounded in Hofstede and colleagues’ cultural dimensions, the authors employed a layered approach to investigate the influence of contextual culture on the adoption of blended pedagogy in a language department in China. Through scrutinising documents and conducting interviews, they identified cultural factors rooted at the macro-, meso- and micro-levels. The study found that cultural factors at the macro-level catalysed BLL adoption, while cultural factors at meso- and micro-levels could be drivers and barriers as well.

How Government Organizations Can Sustain Remote Work Post COVID-19

Government organizations are unprepared to sustain remote work post-COVID-19. Even though COVID-19 seems to be under control, organizations are still struggling with the aftermath of the pandemic and the need to sustain remote work. Challenges include lack of necessary information technology tools, software, technological skills, strategies for remote work, leadership skills, real-time communication, activity planning and program implementation, scheduling meetings, organizing childcare, managing caseloads, fostering team work, and effective supervision. A conceptual framework based on organizational adaptation theory guided this qualitative case study. Since the study was to ascertain how government organizations can sustain remote work post-COVID-19, semi-structured interviews were used to collect data from 12 government employees in Dallas Texas who worked remotely during COVID-19. Thematic analysis was conducted and. nine themes morphed from the study. The results highlighted the need for employee training and continuous organizational adaptation to the changing business environment.

L’excès de consommation des technologies et le dévergondage érotique dans les romans d’Anne Françoise Garréta et de Christine Ango

Abstract. The 21st century is dominated by New Information and Communication Technologies (NICT). It is also the time of indecent sexual practices. These two factors, objects of exaggerated consumption by society and supposed to actively contribute to the construction of the human species, have instead become paths to perdition and deconstruction of the nature of man par excellence. Anne Françoise Garréta, in Decomposition and Sphinx and Christine Angot in Incest exhibit this world of technological excess and trivial erotic landscape. In their works, technologies are misused by the protagonist and end up taming him. Also sexuality, leaving its ruts of delicacy, is exhibited in the public square, thus becoming a deviant practice and precipitating the individual to his own destruction. These two factors are the making of an unprecedented social mediocrity and serving ethics by the construction of a world of moral maturity. This study aims to reveal that the Garretian and Angotian texts, in both form and content, are impacted by technological tools and the sexual mediocrity of the actors in the narration. The two authors use the novel to challenge man on the excessive use of technologies and the sexual perversity that prevails in society. Mots-clés : Révolutions, Technologies, Consommation, Sexualité, Éthique. Keywords: Revolution, Technology, Consumerism, Sexuality, Ethics

Towards the development of an algorithm for automatic analysis and detection of shock waves in the schlieren visualization technique

This paper presents the development of an algorithm for identifying shock waves formed during the supersonic combustion process, detonation, in a pulsed detonation chamber. The algorithm is based on image binarization techniques, followed by the identification of regions of interest in the images, the detection and isolation of the edges of these regions, and the determination of the wave speed between two consecutive frames. The advantage of this automation process is the efficiency in analysing the repeated detonation cycles of a pulsed detonation chamber. Additionally, it can provide a more accurate evaluation of the data, making it a valuable tool for researchers and engineers in various scientific fields and shock wave applications.

Spatial Transcriptomics, Proteomics, and Epigenomics as Tools in Tissue Engineering and Regenerative Medicine

Spatial transcriptomics, proteomics, and epigenomics are innovative technologies which offer an unparalleled resolution and wealth of data in understanding and the interpretation of cellular functions and interactions. These techniques allow researchers to investigate gene and protein expressions at an individual cell level, revealing cellular heterogeneity within, for instance, bioengineered tissues and classifying novel and rare cell populations that could be essential for the function of the tissues and in disease processes. It is possible to analyze thousands of cells simultaneously, which gives thorough insights into the transcriptomic view of complex tissues. Spatial transcriptomics combines gene expressions with spatial information, conserving tissue architecture and making the mapping of gene activity across different tissue regions possible. Despite recent advancements in these technologies, they face certain limitations. Single-cell transcriptomics can suffer from technical noise and dropout events, leading to incomplete data. Its applicability has been limited by the complexity of data integration and interpretation, although better resolution and tools for the interpretation of data are developing fast. Spatial proteomics and spatial epigenomics provide data on the distribution of proteins and the gene regulatory aspects in tissues, respectively. The disadvantages of these approaches include rather costly and time-consuming analyses. Nevertheless, combining these techniques promises a more comprehensive understanding of cell function and tissue organization, which can be predicted to be useful in achieving better knowledge of cell guidance in tissue-engineered constructs and a higher quality of tissue technology products.

Exploring How Older Adults Experience semAPP, a 360° Media-Based Tool for Memory Assessment: Qualitative Study.

Technology is already a part of our daily lives, and its influence is growing rapidly. This evolution has not spared the health care field. Nowadays, a crucial challenge is considering aspects such as design, development, and implementation, highlighting their functionality, ease of use, compatibility, performance, and safety when a new technological tool is developed. As noted in many works, the abandonment rate is usually higher when a user has a terrible experience with these instruments. It would be appropriate to incorporate the final users-whether they are patients, health care professionals, or both-in the stages of instrument design to understand their needs and preferences. Since most apps that fail did not include end users and health care professionals in the development phase, their involvement at all stages of app development may increase their commitment and improve integration, self-management, and health outcomes. This study aims (1) to develop semAPP (spatial and episodic memory assessment application), a 360° media-based tool, to assess memory in aging by simulating a real-life situation and (2) to test the usability of the app and the connected experience in an end-user population. A total of 34 older adults participated in the study: 16 (47%) healthy individuals and 18 (53%) patients with mild cognitive impairment. They used semAPP and completed qualitative and quantitative measures. The app includes 2 parts: object recognition and spatial memory tasks. During the first task, users have to navigate in an apartment freely and visit rooms, and then they must recognize the right map of the house. In the second task, users are immersed in a living room, and they have to encode and then recall some target objects, simulating a relocation. We deployed this app on an 11.2-inch iPad, and we tested its usability and the experience of users interacting with the app. We conducted descriptive analyses for both the entire sample and each subgroup; we also conducted parametric and correlation analyses to compare groups and to examine the relationship between task execution and the virtual experience, as well as the acceptance of technology. Both groups judged the app as an easy-to-use tool, and they were willing to use it. Moreover, the results match the idea that usability might be influenced by different factors depending on instrument and personal features, such as presentation, functionality, system performance, interactive behavior, attitudes, skills, and personality. The findings support the possibility of using semAPP in older patients, as well as the importance of designing and evaluating new technological tools, considering not only the general population but also the specific target ones.

Feature Engineering for the Prediction of Scoliosis in 5q-Spinal Muscular Atrophy.

5q-Spinal muscular atrophy (SMA) is now one of the 5% treatable rare diseases worldwide. As disease-modifying therapies alter disease progression and patient phenotypes, paediatricians and consulting disciplines face new unknowns in their treatment decisions. Conclusions made from historical patient data sets are now mostly limited, and new approaches are needed to ensure our continued best standard-of-care practices for this exceptional patient group. Here, we present a data-driven machine learning approach to a rare disease data set to predict spinal muscular atrophy (SMA)-associated scoliosis. We collected data from 84 genetically confirmed 5q-SMA patients who have received novel SMA therapies. We performed expert domain knowledge-directed feature engineering, correlation and predictive power score (PPS) analyses for feature selection. To test the predictive performance of the selected features, we trained a Random Forest Classifier and evaluated model performance using standard metrics. The SMA data set consisted of 1304 visits and over 360 variables. We performed feature engineering for variables related to 'interventions', 'devices', 'orthosis', 'ventilation', 'muscle contractures' and 'motor milestones'. Through correlation and PPS analysis paired with expert domain knowledge feature selection, we identified relevant features for scoliosis prediction in SMA that included disease progression markers: Hammersmith Functional Motor Scale Expanded 'HFMSE' (PPS = 0.27) and 6-Minute Walk Test '6MWT' scores (PPS = 0.44), 'age' (PPS = 0.41) and 'weight' (PPS = 0.49), 'contractures' (PPS = 0.17), the use of 'assistive devices' (PPS = 0.39, 'ventilation' (PPS = 0.16) and the presence of 'gastric tubes' (PPS = 0.35) in SMA patients. These features were validated using expert domain knowledge and used to train a Random Forest Classifier with an observed accuracy of 0.82 and an average receiver operating characteristic (ROC) area of 0.87. The introduction of disease-modifying SMA therapies, followed by the implementation of SMA in newborn screenings, has presented physicians with never-seen patients. We used feature engineering tools to overcome one of the main challenges when using data-driven approaches in rare disease data sets. Through predictive modelling of this data, we defined disease progression markers, which are easily assessed during patient visits and can help anticipate scoliosis onset. This highlights the importance of progressive features in the drug-induced revolution of this rare disease and further supports the ongoing efforts to update the SMA classification. We advocate for the consistent documentation of relevant progression markers, which will serve as a basis for data-driven models that physicians can use to update their best standard-of-care practices.

Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning.

A solar cell's internal quantum efficiency (IQE) measurement reveals critical information about the device's performance. This information can be obtained using a qualitative analysis of the shape of the curve, identifying and attributing current losses such as at the front and rear interfaces, and extracting key electrical and optical performance parameters. However, conventional methods to extract the performance parameters from IQE measurements are often time-consuming and require manual fitting approaches. While several methodologies exist to extract those parameters from silicon solar cells, there is a lack of accessible approaches for non-silicon cell technologies, like gallium arsenide cells, typically limiting the analysis to only the qualitative level. Therefore, this study proposes using a deep learning method to automatically predict multiple key parameters from IQE measurements of gallium arsenide cells. The proposed method is demonstrated to achieve a very high level of prediction accuracy across the entire range of parameter values and exhibits a high resilience for noisy measurements. By enhancing the quantitative analysis of IQE measurements, the method will unlock the full potential of quantum efficiency measurements as a powerful characterization tool for diverse solar cell technologies.

Exploring pre-service foreign language teachers’ intention to adopt technology: an investigation from the perspective of Situated Expectancy-Value Theory

Abstract The intention of pre-service foreign language teachers to adopt technology (PIAT) reflects their willingness to integrate educational technology tools into their teaching practices, which is essential for the digital transformation of language education and the professional development of teacher candidates. Drawing upon the Situated Expectancy-Value Theory, this study utilizes a structural equation model to analyze the factors influencing the intention of pre-service foreign language teachers to adopt technology. The results reveal that: (1) four motivational factors (i.e., self-efficacy, perceived enjoyment, perceived importance, and perceived usefulness) significantly predict PIAT, while perceived anxiety negatively correlates with, but does not significantly predict PIAT; (2) mindset significantly impacts perceived enjoyment, perceived importance, perceived usefulness, and perceived anxiety; (3) social influence exerts a significant influence on self-efficacy and mindset. The study concludes with implications for enhancing the digital literacy of foreign language teacher candidates.

Deep learning-based recognition of construction activities in real construction site environment

PurposeToday’s technological advancements have had a significant impact on the construction industry. Managing and controlling complex construction projects has been made significantly easier using technological tools. One such advancement is the automatic identification of workers’ activities. This study aims to classify construction worker activities by analyzing real-time motion data collected from sensors.Design/methodology/approachIn accordance with our specific goals, we utilized advanced deep-learning methodologies such as deep neural networks, convolutional neural network, long short-term memory and convolutional long short-term memory to analyze the data thoroughly. This involved experimenting with various window sizes and overlap ratios to determine the optimal combination that would result in the most accurate predictions.FindingsBased on the analysis results, the convolutional long short-term memory (ConvLSTM) deep learning model with a window size of 4.8 s and an overlap rate of 75% was found to be the most accurate prediction model. This model correctly predicted 98.64% of the basic construction worker activities in a real construction site environment.Originality/valuePrevious studies have mainly been conducted in laboratory environments and have focused on basic construction activities such as lifting, moving, sawing and hammering. However, this study collected data from real workers in a real construction site environment. Various deep learning models were employed to determine the most accurate one. Additionally, several options were tested to determine the optimal window size and overlap ratio during the data segmentation phase, aiming to select the most suitable ones for preparing the data for the model.

Application & Challenges Of Big Data Technologies In Albanian Health Care System

In today's world, technology is one of the most significant and debated areas about its application within healthcare systems. Technology holds the potential to revolutionize healthcare by enabling more advanced research on diseases, their causes, and cures. It can make treatment faster, more efficient, cost-effective, and simpler for healthcare professionals to administer. However, the challenges associated with its implementation should not be overlooked. Key concerns include the need for staff training, the potential for technological errors, and issues related to patient data privacy and hacking. This research focuses on the readiness and capacity of the Albanian healthcare system to adopt Big Data technologies. Big Data analytics refers to the process of analyzing large datasets to uncover patterns, trends, and preferences, leading to informed decision-making (Pouyanfar, Yang, Chen, Shyu, & Iyengar, 2018). Although technological tools have been gradually introduced in healthcare, numerous barriers to their full implementation still exist. This study includes a questionnaire distributed to various healthcare system employees in Albania, focusing on the technological innovations implemented, the advantages gained, and the challenges they have encountered. The data collected were analyzed and displayed graphically, resulting in conclusions and recommendations for future research. Received: 18 August 2024 / Accepted: 14 November 2024 / Published: 3 December 2024

Streamlining Entrepreneurship Education: Lessons from SK A&G’s Approaches and Pedagogical Innovations

This report investigates the optimization of entrepreneurship education through a comprehensive analysis of SK A&G’s educational strategies and their impact. As a prominent consultancy in the start-up ecosystem, SK A&G has implemented various initiatives, including conferences, bootcamps, and mentorship programs, aimed at enhancing entrepreneurial skills. This study evaluates the effectiveness of these strategies, focusing on practical learning, mentorship, and technology integration. The findings indicate that while SK A&G has made significant strides, there is room for improvement in areas such as technology integration, group activities, and the inclusion of industry experts. Recommendations include enhancing technological tools, strengthening group projects, updating the curriculum, and increasing female mentor representation. The research highlights the alignment of educational approaches with industry trends and student needs as crucial for effective entrepreneurial training. By addressing identified gaps, SK A&G aims to refine its programs, support student development more effectively, and contribute to the broader entrepreneurial ecosystem. The insights provided are intended to guide SK A&G in its mission to empower 600 million youths by 2035 and ensure its programs remain at the cutting edge of entrepreneurship education. Keywords: Entrepreneurship Education, Training, Consultancy, Startup Ecosystem

Exploring Blended Learning Models Enhanced by Mobile Interactive Technology in Higher Education

With the continuous advancement of information technology, mobile interactive technology has been increasingly applied in higher education, providing robust support for blended learning models. These models integrate the advantages of traditional classroom teaching and online learning, enhancing the flexibility and interactivity of the educational process. In recent years, research on mobile interactive technology and blended learning has expanded. However, most studies have primarily focused on the application of technological tools and platform construction, with limited exploration of specific interaction models and learning experiences. Furthermore, research on student learning objective identification and group stratification have been constrained, failing to effectively meet the needs of personalized learning. In response, this study explores the integration of interaction models and learning experiences within a mobile environment for university students in blended learning settings. Additionally, methods for identifying learning objectives and stratifying student groups based on mobile technology were investigated, aiming to provide theoretical and practical guidance for the implementation of blended learning in higher education.

Examination of elementary school students digital literacy levels in terms of different variables

The aim of this study is to examine the digital literacy levels of primary school students according to different variables. The survey model was used to investigate how technology, which has become one of the most important needs of today, affects the digital literacy of primary school students who have experienced distance education. The participants of the study consisted of 100 students studying in a city center located in the Black Sea region of Turkey. The data were obtained by using the personal information form developed by the researchers and the "Digital Literacy" scale developed by Pala and Başıbüyük (2010). In the analysis of the data, percentage, arithmetic mean, gender, independent sample t-test for the variables of availability of technological tools, one-way analysis of variance (ANOVA) was used for the variables of parental education level, place of residence, time spent with technology and family income level. As a result of the research, it was seen that the digital literacy level of the students was at an average level and that the digital literacy of the students showed significance according to the family income level and the availability of internet at home, while there was no significant difference according to variables such as gender, parental education level, and the availability of technological devices. Based on the data of the study, it is recommended to include related courses in the curriculum for the positive development of students' digital literacy.