while enjoying a relative positive revival in the digital age, Esperanto and the assessment of its language vitality is often problematic and prone to gross errors, and therefore a theoretical re-flection is required. Unlike other lesser-used languages, Esperanto is intergenerationally trans-mitted mainly outside the family, and so Fishman’s GIDS and subsequent scales such as the EGIDS cannot be applied straightforwardly for language vitality diagnosis and estimation. In particular, it is the social movement with its language activists who guarantee its vitality and developing, for more than a century. A key aspect is the digital domain, where the relatively good positioning of Esperanto does not reflect in a parallel increase in the number of activists. This paper critically assesses the digital language vitality of Esperanto on the basis of its language ide-ology and other sociolinguistic data as a starting point for a discussion to overcome the limits of Blanke’s (2006) scale of language vitality of Esperanto and its rivals. This assessment eventually leads to a more general reflection on the role of ‘coolification’, i.e., the positive effects on language attitudes and development thanks to digital visibility, its limits and the issue of placing it in the context of language vitality in general.

Droplet digital polymerase chain reaction (ddPCR) is a method used to detect and quantify nu-cleic acids even when present in exceptionally low numbers. While it has proven to be valuable for clinical studies, it has failed to be widely adopted for environmental and applied studies. Due to the complexity of the chemical and biological composition of environmental samples, protocols tailored to clinical studies are not appropriate, and results are difficult to interpret. We used en-vironmental DNA samples originating from field studies to determine a protocol for environ-mental samples. Samples included field soils which had been inoculated with the soil fungus Rhizophagus irregularis (environmental positive control), field soils that had not been inoculat-ed and the targeted fungus was not naturally present (environmental negative control), and root samples from both field categories. To control for the effect of soil inhibitors, we also in-cluded DNA samples of an organismal control extracted from pure fungal spores (organismal positive control). Finally, we included a no-template control consisting only of the PCR reaction reagents and nuclease free water instead of template DNA. Using original data, we examined which factors contribute to poor resolution in root and soil samples and propose best practises to ensure accuracy and repeatability. Furthermore, we evaluated manual and automatic threshold determination methods and we propose a novel protocol based on multiple controls that is more appropriate for environmental samples.

Digital Educational Recourses (DER) have undergone a rapid evolution and integration into the educational contexts. Teachers play a fundamental role in incorporating technology into their classrooms, so it is important to identify the value that teachers on DER and reflect on the implications for their practice. Through the qualitative methodology the necessary data is obtained with open-response inter-views with teachers. This dataset was analysed and categorised using AQUAD. The results suggest that teachers acknowledge the importance of integrating digital re-sources into their classrooms, although there was no consensus regarding the appropriate level at which to do so. Further the inter-views revealed that younger teachers tend to use more self-elaborated or selected resources. Teachers noted the benefits of using technology, especially in regard to maintaining student motivation, how-ever they also highlighted a number of extrinsic challenges and limitations. The results indicate there is a relationship between teachers’ perceptions and their practice, although this could be improved.

Being a combination of the conference call, talkback radio, audio podcast, and an online video chat, Clubhouse is a new social networking app that gained over 10 million users and over $100 in valuation in just 8 months. Unlike other social networks, it offers a real-time streaming audio chat that does not ask users to share any unnecessary information like exchanging text messages, conducting video calls, or sharing photos. Instead, Clubhouse users can listen to real-time conversations, contribute to these conversations and create their own conversations for the others to listen and to interact with. Often nicknamed a “Silicon Valley’s hottest start-up”, Clubhouse positions itself as an “exclusive” and “alternative” social network that attracts various celebrities and people who just want to talk to each other. Launched in March 2020, amidst the COVID-19 pandemic with its social distancing and lockdowns, Clubhouse offered its users a space for the digital group psychotherapy where people could solve their problems by talking them through with strangers. However, it is unclear what is going to happen to this new social network in the post-pandemic world after all of its hype eventually evaporates. This paper discusses the possible underlying motives for the Clubhouse creation and its real purposes. Moreover, it looks at the three possible scenarios of its further development.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

The management of a cultural change in higher education is becoming necessary and unavoidable: changes in teaching, changes in research and changes in governance. Digital transformation is an essential part of many countries’ modernisation agendas. The aim of this non-experimental, descriptive, survey-based study was to explore the perceptions of university students toward the digital transformation that took place in university teaching as a consequence of COVID-19. The specific objectives proposed were to: a) analyse the perceptions of students toward digital transformation in university teaching; b) determine the valuation of students about the digital transformation that occurred in university teaching as a result of COVID-19; and c) explore the resources (hardware-software), professional collaboration, digital pedagogy and student empowerment (motivation) with respect to digital education and the recent changes in university teaching due to the pandemic. The results show that a large number of items are positively correlated. In conclusion, further research should delve into motivation, collaboration, reflective experience sharing, self-learning and initiatives that promote the development of competences in future teachers.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

In this paper, a high-performance antenna array system model is presented to analyze moving-object-skin-returns and track them in the presence of stationary objects using frequency modulated continuous wave (FMCW). The main features of the paper are bonding the aspects of antenna array and electromagnetic (EM) wave multi-skin-return modeling and simulation (M&S) with the aspects of algorithm and measurement/tracking system architecture. The M&S aspect models both phase and amplitude of the signal waveform from a transmitter to the signal processing in a receiver. In the algorithm aspect, a novel scheme for FMCW signal processing is introduced by combining time- and frequency-domain methods, including a vector moving target indication filter and a vector direct current canceller in time-domain, and a constant false alarm rate detector and a mono-pulse digital beamforming angle tracker in frequency-domain. In addition, unlike previous designs of using M×N fast Fourier transform (FFT) for an M×N array, only four FFTs are used, which tremendously saves time and space in hardware. With the presented model, the detection of the moving-target-skin-return in stationary objects under a noisy environment is feasible. Therefore, to track long range and high-speed objects, the proposed technique is promising. Using a scenario having 1) a target with 17 dBm2 radar cross section (RCS) at about 40 km range with 5.93 Mach speed and 11.6 dB post processing signal to noise ratio, and 2) a strong stationary clutter with 37 dBm2 RCS located at the proximity of the target, it demonstrates that the root-mean-square errors of range, angle and Doppler measurements are about 26 meters, 0.68 degree and 1100 Hz, respectively.

The objective of this research is to measure the perception that teachers had about their own performance when they were forced to carry out an Emergency Remote Teaching due to the COVID-19 pandemic. A questionnaire was provided to teachers of every educational stage in the Basque Country (Pre-school, Primary and Secondary Education, Professional Training and Higher Education) obtaining a total of 4,586 responses. The statistical analysis of the data shows that the greatest difficulties reported by educators are shortcomings in their training in digital skills, which has made them perceive more workload during the lockdown and negative emotions. Another finding is the existing digital divide between teachers based on their gender, age and type of school. A further worrying result is the lower technological competence in lower educational levels, which are the most vulnerable in remote teaching. These results invite us to reflect on the measures to be taken to improve equity, social justice and resilience of the educational system, which align with some of the Sustainable Development Goals.

Cultivating research competencies facilitated by the use of technology in the university is crucial to boost acquisition of knowledge, educational innovation, and successful professional development. The objective of this article is to analyze the evolution and characteristics of scientific production that has had the greatest impact on the momentum of investigative competencies and their articulation within technological scenarios. Applying various selection criteria in the Scopus database, we utilized systematic, bibliometric mapping as a methodological strategy in which 250 articles related to the topic were identified, then analyzed with the semantic analysis software VOSViewer. The results indicate that there is an emerging line of research on the subject since 2016 and point to the underdevelopment of lines that associate research skills with digital literacy, information literacy, digital libraries databases, and the development of critical thinking.

A critical context is an essential aspect of practice-based research; however, a lack of structure exists to obtain and evaluate criticism from peers. This paper presents a case study of how the ‘silent student’ critique method used in Higher Education settings in the UK (Elkins, 2014) was adapted for a holographic arts research study. A ‘silent researcher’ critique session with nine experts was held in Aveiro, Portugal, June 2018 to evaluate the author’s digital holographic artwork, on display at the City Museum. The experts asked the author critical questions about the artwork while the author remained silent. The session was filmed, transcribed and processed using a general inductive approach for analysing qualitative evaluation data (Thomas, 2006). This paper outlines the benefits and drawbacks of using this new critique method for research. The benefits included; participant’s careful response to the artwork avoiding engagement of egos of critic and researcher, the drawbacks included the difficulty of evaluating against a pre-determined research question when the discussion could not be steered. This paper evaluates the artwork critiqued describing how the work contributes to the aesthetic development of the medium of holography; which used the Z-axis of holographic space to depict a chronological narrative.

3D-printing with foam concrete, which is known for its distinct physical and mechanical properties, has not yet been purposefully investigated. The article at hand presents a methodological approach for the mixture design of 3D-printable foam concretes and a systematic investigation of the potential application of this type of material in digital construction. Three different foam concrete compositions with water-to-binder ratios between 0.33 and 0.36 and having densities of 1100 to 1580 kg/m³ in the fresh state were produced with a pre-foaming technique using a protein-based foaming agent. Based on the fresh-state tests, including 3D-printing as such, an optimum composition was identified and its compressive and flexural strengths were characterised. The printable foam concrete showed compressive strength above 10 MPa and low thermal conductivity, which make it suitable for 3D-printing applications, while fulfilling both load-carrying and insulating functions.

Along with the advantages associated with access to information and fast communication, screen time from increased digital media consumption has recently been associated with adverse effects on youth well-being. To get a clearer picture of its value for global youth based sustainability initiatives, this study investigates the effects of increased digital media consumption on youth's interests in ecosystem services, sustainability and science as a means for disease prevention. We achieve this, using data on 187821 adolescent students from 50 countries worldwide. Methodologically, we rely on a mixed bivariate ordered probit representation of youth's joint interest in the biosphere (ecosystem services and sustainability) and science as a means for disease prevention, which we then estimate using Markov Chain Monte Carlo (MCMC) Methods. We found that each level increase in adolescent students' reported frequency of news blogs visits and web browsing on broad science adversely affect their interests in ecosystem services, sustainability and science as a means for disease prevention. Although each level increase in youth's frequency of ecological website visits also reduces by 20% (with 95% CI [-0.36; -0.32]) their interests in the biosphere, it is found to increase however by 3% (with 95% CI [0.02; 0.05]) their interest science as a means for disease prevention. Overall, our results highlight heterogeneous effects of digital media consumption on adolescents' well-being in terms of their interests in ecosystem services, sustainability, and science as a means for disease prevention.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

As a service business models have been ranked by management consultants the management innovation of 2020, and yet are scarcely theorized. Therein, value stipulations in customer contracts lead to the shift from product to service. However, there is no available research about the nature, formula and decisional process about these value stipulations. The motive for this research is to fill in this gap. The goal is to provide first insight about value stipulations in as a service business models: their formula and decisional methodology. The article is a multiple theory building case study on leading platform as a service providers, which aims to contruct the paradigm of the value decision methodology to invest in this technology by purchasing it from dedicated vendors. The methodology works to identify the management instruments involved in the decisional activities to invest in digital technology, where each management instrument is assumed to match an activity at seller and buyer; the sequence of the activities using the Critical Path Method gives the decisional process. Dependent variables are the elements of the as a service business contracts: the customer offering (product-service solution) and the value created, captured and delivered by the solution. Independent variables are given by empirical data analysis. Findings show a relationship between dependent variable value in the as a service business model contracts and independent variables governance objective, strategy, as a service business model type, activity management techniques (roadmap, program, project, operations, lean start-up), capital allocation techniques (equity, debt, options, venture capital typology). If innovation is exploitative, then the customer offering is known and value is determinable via roadmap – project management techniques (program, project, an operations) and capital allocation techniques for precictable environment, If innovation is radical, then the customer offering is not known and value is a target used to architect the solution via lean start-up innovation techniques funded by venture capital. The contribution is a new theoretical construct and deep insight into the practice of digital transformation considering all inherent issues.

Autonomous electronic area, territory, or space protection is getting more important. One of the areas of application of the system is to monitor the quarantine zones created during epidemics. The idea to create a system of mobile sensory units for defending quarantine zones was born during one of the phases of the Covid epidemic in 2020. The paper contains a brief technical description of a mobile sensor units and their network. This network surrounds an area of interest. The work includes a mathematical and data model of the protected area. In addition, the paper presents a method for distributing mobile sensor units in areas of interest.

The use of technologies and the Internet poses problems and risks related to digital security. This article presents the results of a study on the evaluation of the digital competence of future teachers in the DigCompEdu European framework. 317 undergraduate students from Spain and Portugal answered a questionnaire with 59 items, validated by experts, in order to assess the level and predominant competence profile in initial training (including knowledge, uses and interactions and attitudinal patterns). The results show that 47% of the participants belong to the profile of teachers at medium digital risk, evidencing habitual practices that involve risks such as sharing information and digital content inappropriately, not using strong passwords, and ignoring concepts such as identity, digital “footprint” and digital reputation. The average valuations of each item in the seven categories show that future teachers have an average competence in the area of digital security. They have good attitudes toward security but less knowledge and fewer skills and practices related to the safe and responsible use of the Internet. Future lines of work are proposed, aimed at responding to the demand for a better prepared and more digitally competent citizenry. The demand for education in security, privacy and digital identity is becoming increasingly important, and these elements form an essential part of initial training.

Analytical instruments used for scientific research and education are often expensive. This limitation poses a challenge for schools and educators in low-income countries to provide practical science training to students. To address this problem, the improvisation of analytical instruments and instructional materials has emerged as a viable solution. By incorporating practical experiments into science education, students have the opportunity to engage directly with scientific instruments, conduct investigations, and collect data. This hands-on approach fosters a deeper understanding of scientific principles, enhances critical thinking skills, and cultivates scientific mindsets among students. Herein, we review and discuss, step-by-step, the approach of using a smartphone for quantitative analysis, referred to as smartphone digital image colorimetry (SDIC). SDIC, with its practicality and accessibility, utilizes smartphone digital cameras for image acquisition and the freely available image processing programs, such as ImageJ and smartphone Apps, for image quantification. Using SDIC can enable students in low-income countries to develop essential scientific skills, including critical thinking, data interpretation, and an evidence-based approach to scientific research. By leveraging the ubiquity and affordability of smartphone technology, SDIC offers an accessible and cost-effective approach to practical science education, bridging the gap between theory and practice in resource-constrained settings.

Considering the multifaceted nature of neurodegenerative diseases like dementia and the neces-sity for interprofessional knowledge, this research extends its scope to encompass professionals with diverse levels of training and experience in dementia care. A need analysis for the project “My INdividual Digital EDucation.RUHR” (MINDED.RUHR) is conducted to develop an au-tomatized recommender system for individual learning content using AI. In this sub-study, the aim was to reveal didactic specialties, knowledge gaps and structural challenges of further edu-cation in dementia care of different health professions and to derive learning preference personae. Eight focus group interviews among nine health professions and up to six participants (n = 34) each took place to survey distinct didactic experiences and learning needs. The results reflect various learning preferences, with a propensity to multimedia, practical and interactive tasks. Health professions are used to digital education but show aversions against certain e-learning formats. The derived learning preference personae constitute profound blueprints for a us-er-centred digital education design process, aiming to establish personalized and representative further education in dementia care applicable to various individual preferences and structural workplace challenges of healthcare professions.

The flourishing landscape of agritourism in Albania has witnessed a steady increase in destinations over the years. Recognizing their pivotal role in driving economic development in rural areas, considerable attention is directed towards the promotion and expansion of these agricultural tourism spots. To enhance interactivity with visitors, these agritourism destinations actively leverage social networks and various digital platforms. However, the wealth of information pertaining to these destinations is currently scattered across different platforms, posing a challenge to accessibility. To address this issue, these points are strategically placed on Google Maps, along with essential details collected from users, such as websites, contact numbers, visitor ratings, and comments. Nevertheless, other valuable data remains dispersed on various social networks. In response to this fragmentation, this study proposes the development of algorithms and scripts to systematically collect and analyze this dispersed information. By doing so, the goal is to centralize and streamline the data, enabling a comprehensive understanding of the agritourism landscape in Albania. This approach not only facilitates efficient access to information for potential visitors but also provides valuable insights for stakeholders involved in the development and management of these agritourism destinations. The study aims to contribute to the sustainable growth of agritourism in Albania by harnessing the power of digital platforms to create a unified and organized database. Through this integration, the research seeks to unveil patterns, trends, and opportunities that can further guide policy-making, marketing strategies, and overall decision-making processes in the realm of agritourism development.

This study examines Tim Rowett's YouTube channel, "Grand Illusions," which captures and maintains positive viewer engagement in the digital domain. Through a comprehensive YouTube channel analysis, this paper identifies key factors contributing to its success, including collaboration and influence, educational impact, legacy and continuity, personal style, and community engagement. Rowett, known for his extensive toy collection and in-depth knowledge of their mechanics and history, employs an educational and engaging presentation style that fosters a positive and inclusive online community. His approach entertains and educates, making him a valuable digital resource that inspires viewers of all ages. This research proposes the CECCE (Collaboration, Education, Continuity, Community Engagement) model as a framework for content creators seeking to replicate the success of "Grand Illusions." The study emphasizes the potential of tailored, passionate content to cultivate healthy digital communities and underscores the significant role of digital content creators in shaping online culture. The proposed framework will guide creators to achieve similar success.

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Especially the ports turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and are considered a reasonable fit. They are suitable for real-time data integration to support decision-making. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore operational pain points, best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal and leave space for optimisation. The experts see great potential in analysing past working shifts data to identify reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of aiding IT solutions.

Using structured light to drive colloidal motors, due to its advantages of remote manipulation, energy tunability, programmability, and controllability of spatiotemporal distribution, has been attracting much attention in the fields of targeted drug delivery, environmental control, chemical agent detection, and smart device design. Here, we focus on studying the group control of colloidal made from a photo-responsive organic polymer molecule NO-COP (N,O-Covalent organic polymer). These colloidal motors mainly respond to the light intensity patterns. We chose a digital micromirror device (DMD) to modulate the structured light field shining on the sample, by considering its merit of fast refreshing speed, good programmability, and high-power threshold. It is found that under ultraviolet or green light modulation, such colloidal motors exhibit various group behaviors including group diffusion, group patterning, and group migration. The qualitative interpretation is also provided for these observations.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

The administration and management of urban transport has become an urgent need, in the context of the increasing congestion problems and the environmental and economic damage, resulting from rapid urban expansion witnessed by the Algerian cities in recent times, in order to achieve prosperity and quality of urban life. It became necessary to use the tools of modern technology, which administrative and technical departments are seeking to use primarily to support ways of decision-making and actions required. Boosted by high capabilities in the statistical data and other information is stored, and coordination and tabulation, with the possibility of installation and analysis by operations. Especially against the inability of traditional methods, which have become less able to cope with the changes that led to the urban dynamic currently experienced by urban communities in our country. This research aims to employ geographic information systems in the administration and management of the urban transport network, facing both the population and the local administration of the city of Batna. Which was chosen as a model for major Algerian cities, which is suffering more than ever of transportation bottlenecks, as well as the unintended damage for these networks and other networks. Hence the importance of the subject of our research, which aims to use GIS MapInfo systems as a means to snipe (Data Capture) and storage (Data Storage) and address (Data Processing) and directed by spatial information (Spatial Data) and link them to the descriptive information (Attribute Data). To develop a modern digital data and charts the base of each of these elements of the urban service lines equipment and mechanisms, ... etc. As a tool available to those creating and carrying out urban spaces, as well as decision-makers. It was concluded that the possibilities offered by this system would facilitate the processes of monitoring and management in order to ensure the quality of the service.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions, and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the research question: how do practitioners researching, working or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that calls for more data to address this uncertainty obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

Infrastructure and urban network operators, city users and industrialists are faced with complex issues to ensure the sustainability of the service, maintain, operate and develop their urban sys-tems, while integrating environmental, economic and societal impacts and being resilient to un-expected geopolitical and climatic upheavals. Urban Digital Twins (UDTs), i.e., digital twins for smart cities and territories are considered as promising tools that can enable all stakeholders to collaborate across disciplinary silos and foster digital transformation in urban and territorial pro-jects to ensure sustainability, resilience and increased inventiveness. However, a widely acknowledged challenge to address in the application of UDTs is the lack of interoperability. One significant barrier is the difficulty that stakeholders have in understanding the level of complexity hidden behind this challenge, and thus the adoption of an adequate interoperability technique. This paper proposes a conceptual framework, which is unique for understanding the various lev-els of interoperability required in deploying and using the full potentials of UDTs. We show how such a high-level modeling approach supports the symbolic manipulation of UDTs interoperabil-ity notions and requirements, and thus eases the design of appropriate solutions for large-scale in-teroperable UDTs.

The implementation of neural networks (NN) on edge devices enables local processing of wireless data but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNN) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources, and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as real-valued time delay neural network (RVTDNN), augmented real-valued time delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no Block RAMS compared to the DNN with similar accuracy. We experimentally validate this computationally efficient and memory-saving FPGA implementation of SSCNN for digital predistortion of RF power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

Accelerating the green transformation of the economic development mode and promoting high-quality economic development is an international trend and the general direction. This article utilizes data from manufacturing companies listed on the Shanghai and Shenzhen stock exchanges from 2011 to 2021, based on dynamic capability theory, to explore the impact of enterprise digital transformation on green technological innovation. Furthermore, dynamic capabilities are further subdivided into absorptive capacity, innovative capacity, and adaptive capacity to investigate their mediating role. The research findings demonstrate that enterprise digital transformation has a significant positive impact on green technological innovation. Moreover, enterprise digital transformation positively influences the absorptive capacity, innovative capacity, and adaptive capacity, with these three factors playing a mediating role in the impact of enterprise digital transformation on green technological innovation. The results of the bootstrap test confirm this mediating effect. The conclusions of this study are validated through a series of robustness tests. This research not only provides theoretical support for the impact mechanism of digital transformation on green technological innovation but also offers practical insights for enterprises to achieve sustainable development.

This research proposes an algorithm to sign messages using the ElGamal asymmetric cryptosystem, called: Digital signature algorithm based on ElGamal, Diffie-Hellman protocol and the number π (DSAπ). This proposal has a high level of security like the DSA standard, in addition, execution times are reduced in relation to it. The Diffie-Hellman protocol and the number π were used in the construction of the digital signature algorithm in conjunction with the Hash-Sha-512 function. Some experiments are presented to provide evidence that the bits on the right side of the decimal point of π appear randomly, such as: a color image was constructed of a string of consecutive bits chosen at random, of the number π. Subsequently, parameters such as: entropy, correlation, discrete Fourier transform, goodness-of-fit test χ2 were evaluated. It is mentioned that an S-box 8×8 is involved in the signature. The sensitivity of the signature was measured, that is, when small changes are made to the sender’s private key, or small changes to the message. Finally, an image was constructed using the signatures of random texts, and subsequently the randomness of its pixels was obtained, to verify that there is no relationship between the different signatures.

Remote Sensing and Geographic Information System are recognized as crucial instruments for conducting geographical and geospatial research. RS and GIS technology was employed to assess the morphological Characteristics of the Kasura River Watershed situated within the Parbhani and Jalna Districts of Maharashtra, India. The entire drainage area of the Kasura River basin measures 490.13 square kilometers (km²). Various morphometric characteristic were calculated and examined, encompassing linear features such as stream number, stream order, mean stream length, stream length, and stream length ratio, areal attributes including elongation ratio, circularity ratio, form factor, constant of channel maintenance, drainage density and drainage texture and relief characteristics such as relief, relief ratio, maximum relief, ruggedness number, relative relief, and length of overland flow. These findings underscore the significance of employing morphometric analysis in watershed assessment, offering a quantitative method for characterizing the surface attributes of a drainage pattern and yielding valuable insights into the topography and runoff dynamics of the region.

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of DEM ². The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

Effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level Of Detail or LOD 2. Similarly, automated solutions for 3D modeling of individual trees at different levels of details are required for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT). The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our work in this paper is based on the assumption that, similar to the canopy shape, the structure of tree trunk and branches is also an important consideration. An algorithm for automated modeling of tree trunks is proposed. Aiming to generate 3D models of tree trunks, the suggested approach starts by extracting the trunk point cloud, which is then segmented into single stems. Finally, the individual stems are modeled by dividing them into slices. The geometry of the developed tree models is represented in a matrix format. This solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands and promoting sustainable forest management. Despite the huge variation of trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models.

This research delves into the integration of green roofs with Building Information Modeling (BIM), a pivotal advancement in sustainable urban construction. Aligning with the United Nations Sustainable Development Goals, particularly Goal 11, the study explores how this integration can address global challenges like climate change and resource depletion. Using the Dynamo Visual Programming Language within Autodesk Revit, the research develops a computational modeling approach for green roofs, focusing on their thermal and structural characteristics under varied environmental conditions. Key findings demonstrate the significant influence of substrate and drainage material combinations on green roofs’ thermal performance, highlighting the need for tailored designs based on climatic conditions. The study also emphasizes the importance of considering structural performance in both dry and saturated conditions for overall building integrity. The research identifies gaps in current practices, such as limited focus on specific green roof materials and reliance on certain software tools, suggesting the need for broader material selection and software adaptability. Future research directions include expanding material selections, exploring diverse environmental conditions, and integrating green roofs with various BIM software platforms. The study's implications extend to stakeholders in the construction industry, offering a framework for architects, urban planners, and policymakers to design and implement green roofs aligned with environmental goals. This work contributes to the field by proposing a novel approach to sustainable construction, integrating ecological design with digital technology, and setting a new benchmark in the integration of green roofs with BIM. By addressing these aspects, the research paves the way for future developments in sustainable urban construction, enhancing the efficiency, sustainability, and resilience of urban communities in line with global sustainability objectives.

The emergence of the COVID-19 pandemic triggered a swift transition to online learning, remote work and an increased demand for proficiency in telehealth and digital health among health professions students as they prepare for their future careers. In responding to the question: “What are the essential constructs and the validity of a digital competence assessment scale for health professions students", this study sought to develop and validate a digital competence assessment scale (DigiCAS-HPS). The present study followed a mixed-methods approach: qualitative methods (phase 1) for generating and refining items in the scale through literature review on digital frameworks, pilot testing and expert review, and quantitative methods (phase 2) including exploratory and confirmatory factor analysis for its validation. The study revealed a two-factor structure, encompassing Digital Communication, Collaboration, and Online Security (Factor 1) and Digital Content Development and Software Mastery (Factor 2), demonstrating strong construct validity and reliability. Regarding differences in digital competence across groups, older and soon-to-graduate students exhibited higher proficiency in digital content development and software mastery. The two critical facets of DigiCAS-HPS scale could tailor training programs and ensure that health professions students are well-prepared for the evolving telehealthcare landscape. This promotes the growth and accessibility of telehealth, which is increasingly vital in the post-COVID-19 new normal era.

Aiming to investigate the impact of strategic knowledge disclosure on the continuous realization of digital innovation in enterprises, this study examines data from 168 listed companies across three high-tech industries in China from 2012 to 2021. By examining investor trust, consumer trust, worker trust, and rival trust as mediating variables within the market trust dimension, this research explores the impact mechanism of strategic knowledge disclosure on the sustainability of input and output in digital innovation. The findings reveal that strategic knowledge disclosure significantly promotes both input and output sustainability in digital innovation. Specifically, investor trust positively mediates the relationship between strategic knowledge disclosure and the sustainability of digital innovation input. Similarly, consumer trust and worker trust positively mediate the association between strategic knowledge disclosure and both input and output sustainability in digital innovation. However, it is important to note that rival trust negatively mediates the link between strategic knowledge disclosure and both input and output sustainability in digital innovation. The research findings possess significant theoretical value and provide valuable guidance for promoting the sustainable development of digital innovation in enterprises.

The Covid-19 pandemic strained global supply chains, and companies faced numerous produc-tion, logistics, or material management problems. It is claimed that the digitalization of supply chains can have the potential to make supply chains more resilient to interruptions and disrup-tions. This article focuses on describing the experiences of three large Austrian family-owned business during the supply chain disruptions caused by the pandemic and the Ukraine war. This assessment is done from the perspective of the supply chain leaders in the organization. We provide insights on what worked and what did not for these firms and what we can take away from their experiences.

Hydration and fluids detected with bioimpdance can be confounderdby ecw expansion and overload. to estimates with sufficient accuracy body fat A confrontation with imaging and dxa is confirmatory

In addition to identifying and prosecuting cyber attackers, attack attribution activities can provide valuable information guiding the defenders’ security procedures and giving them greater confidence in incident response and remediation. However, technical analysis involved in cyberattack attribution requires high skills, experience, access to up-to-date Cyber Threat Intelligence, and significant investigators’ effort. Attribution results are not always reliable, and skilful attackers often work hard to cover their traces and mislead or confuse investigators. In this article, we present a tool designed to support technical attack attribution and implemented as a machine learning model extending the OpenCTI platform. We also discuss the tool’s performance in the investigation of a recent cyberattack.

To digital grade the color fastness of fabrics after rubbing, an automatic grading method based on spectral reconstruction technology and BP neural network was proposed. Firstly, the modeling samples are prepared by rubbing the fabrics according to the ISO standard of 105-X12. Then, to comply with visual rating standards for color fastness, the modeling samples are professionally graded to obtain the visual rating result. After that, a digital camera is used to capture digital images of the modeling samples inside a closed and uniform lighting box, and the color data values of the modeling samples are obtained through spectral reconstruction technology. Finally, the color fastness prediction model for rubbing was constructed using the modeling samples data and BP neural network. The color fastness level of the testing samples was predicted using the prediction model, and the prediction results were compared with the existing color difference conversion method and curve fitting method. Experimental show that the prediction model of fabric color fastness can be better constructed using the BP neural network, where the root-mean-square error of the prediction for the training sample is 0.30, and the root-mean-square error of the prediction for the test sample is 0.25. The overall performance of the method is slightly better than the color difference conversion method, and it is significantly outperforming the curve fitting method. It can be seen that the digital rating method of fabric color fastness to rubbing based on spectral reconstruction and BP neural network has a high consistency with the visual evaluation, which will help for the automatic color fastness grading.

With advances in information technology, big data, mobile communications, and robotics, digital technologies are increasingly being used in factories around the world. This digital transformation is named industry 4.0. Today, industrial companies are looking at how to adopt this era and implement these technologies 4.0 while improving their performance and generating more profits. The objective of this paper is to help companies to better choose the appropriate digital technologies according to their activities using a multi-experts-multi-criteria decision-making approach under hesitant fuzzy information. The proposed model is a generic model based on Multi-Agent Systems allowing to have an idea of the parameters necessary to apply the adopted approach. The adopted approach allows a better representation of uncertainty and subjectivity of experts’ judgments. It would be of great interest, especially, when exact quantitative data are not available. A real case company example is exposed (automotive company) towards putting into practice the proposed approach.

Loess is a special soil with high water sensitivity, which covers a large area in northwest China. Cracks are prone to generate in loess under the arid and semi-arid climate, which will provide the preferential channel for water and reduce the mechanical properties of soils. It is of great significance to understand the evolution characteristics and mechanisms of the cracks in loess areas. At present, the researches on cracks in soils are mainly concentrated in the characteristics of different cracking patterns. However, the spacing cracks are mostly discussed in materials like rock and concrete, rather than soils. The cracking characteristics and mechanisms of spacing cracks in loess is still inadequate. In this research, the drying tests of loess are carried out with different sizes of specimens. The parameters of spacing cracks and local strain distributions of the loess samples are obtained via PCAS and DIC methods during desiccation, respectively. The cracking modes, space cracking laws like insertion and saturation of spacing cracks are revealed. Finally, the size effect on the characteristics of spacing cracks is verified with a discrete element software MatDEM.

The contemporary discourse focuses on the fourth industrial revolution and its impact on compa-nies. An explicative, quantitative, questionnaire survey was conducted with managers of 101 companies in Hungary and 54 in Austria. Our study focused on assessing their preparedness for Industry 4.0 and the key factors they take into account when implementing Industry 4.0 initiatives. We also examined whether there is a correlation between the areas in which companies have made improvements to support Industry 4.0. To complement our quantitative analysis, we conducted a series of four semi-structured interviews with two Hungarian and two Austrian companies. The aim of the interviews was to further investigate the digital readiness of companies and to formulate recommendations for companies venturing into the field of digitalisation. A particular focus was put on comparing the digital maturity of these two neighbouring Central European countries, given the significant differences in their digital maturity as shown by the macro indicators of the Digital Economy and Society Index (DESI). Therefore, we conducted a micro-level analysis to investigate the extent to which DESI scores correspond to the digital performance of companies at the orga-nisational level.

The device and method in accordance with the constant pressure regulation of micro droplet PCR in microfluidic chips are developed to explore the optimization way for the micro droplet movement, fragmentation, and bubble generation in microfluidic chips in existing digital PCR technology. In the developed device, an air source device is adopted to regulate the pressure in the chip, such that micro droplet generation and PCR amplification without bubbles can be achieved. In 3 min, the sample in 20ul will be distributed into nearly 50,000 water-in-oil droplets exhibiting a diameter of about 87μm, and the micro droplet will be subjected to a close arrangement in the chip without air bubbles. The device and chip are adopted to quantitatively detect the human gene . As indicated by the experimental results, a good linear relationship exists between detection signal and DNA concentration ranging from 101~ 105 copies /μL (R2=0.999). The micro droplet PCR devices based on constant pressure regulation chips exhibit a wide variety of advantages (e.g., achieving high pollution resistance, micro droplet fragmentation and integration avoidance, reducing human interference, and standardizing results). Thus, micro droplet PCR devices based on constant pressure regulation chips have promising applications in nucleic acid quantification.

Clinical trials are research studies performed on people that are aimed at evaluating a medical, surgical, or behavioural intervention. It is a primary way that researchers find out if a new treatment, like a new drug or diet, or medical device is safe and effective in people. In this paper blockchain technology is embedded in a digital twin aiming to improve the quality of clinical trials through the boost of data integrity among the quality-related activities, and to promote participants' safety. First, a blockchain-embedded quality-enabled digital twin is proposed and the interactions among the enablers and peers are highlighted. Then, a prototype of the proposed system is developed, and the data of a pilot trial is used to justify the applicability of the system. The results showed that the proposed system is efficient, and hence it is feasible to adopt into a clinical trial. With the successful development of the proposed system, it is promising to provide effective data integration and knowledge management for improving participant safety.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

The digital twin (DT) research field is experiencing rapid expansion, yet the research in industrial practices in this area remains poorly understood. This paper aims to address this knowledge gap by sharing feedback and future requirements from the manufacturing industry. The methodology employed in this study involves an examination of a survey that received 99 responses and interviews with 14 experts from 10 prominent UK organisations, most of which are in the defence industry in the UK. The survey and interviews explored topics such as DT design, return on investment, drivers, inhibitors, and future directions for DT development in manufacturing. The study findings indicate that DTs should possess characteristics such as adaptability, scalability, interoperability, and the ability to support assets throughout their entire life cycle. On average, completed DT projects reach the break-even point in less than two years. The primary motivators behind DT development were identified to be autonomy, customer satisfaction, safety, awareness, optimisation, and sustainability. Meanwhile, the main obstacles include a lack of expertise, funding, and interoperability. The study concludes that the federation of twins and a paradigm shift in industrial thinking are essential components for the future of DT development.

We provide quantitative results from GIS-based modelling of urban emission functions for a range of representative low- and mid-rise locations, ranging from individual streets to residential communities within cities as well as entire towns and city regions with the aim of whether lantern photometry or built environment has the dominant effect on light pollution. We demonstrate the scalability of our work by providing results for the largest urban area modelled to date, comprising the central 117 km2 area of Dublin City and containing nearly 42,000 public lights. Our results show a general similarity in the shape of the azimuthally-averaged emission function for all areas examined, with differences in total light output distribution depending primarily on the nature of the lighting and, to a smaller extent, on the obscuring environment including seasonal foliage effects. A comparison with global satellite observations shows that they are consistent with the deduced angular emission function for other low-rise areas worldwide. We further validate our approach by comparing results for a range of urban locations by the close agreement observed in a detailed comparison of with calibrated imagery from the International Space Station. To our knowledge, this is the first such detailed quantitative verification of light loss calculations.

This study aims to determine the role of Industry 4.0 sensors in digital transformation. Using a systematic literature review with a bibliometric analysis, we examine a sample of 52 studies from the SCOPUS database to identify research activity on this topic until December 2022. We find that 2022 was the year with the highest number of peer-reviewed documents on the subject, with 18 publications. We also examine current themes and find that sensors can connect multiple systems and devices, thus enabling machine-to-machine communication and facilitating great precision during product designing, development, and production, thus enabling customization and improving quality. However, despite these benefits, the current sensor technologies face multiple challenges that undermine their maximum adoption.

Design for Manufacture and Assembly (DfMA) in architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current needs for further research and development. The DfMA's conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concept of DfMA and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures associated with Dfab and DfAM, as well as its AM assembly process, were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. Large research gaps in the DfMA for Dfab and DfAM can be filled to increase operational efficiency and sustainable practices.

Digital wellbeing concerns the balance and health we may experience in digital use, and the existing studies have focused on adolescents and adults. However, young children are more vulnerable to digital overuse and addiction than adults; thus, their digital wellbeing deserves empirical exploration. This scoping review synthesized and evaluated 35 collected studies on young children’s digital use and their wellbeing that were published until October of 2022 to understand the definitions, measurements, contributors, and interventions. The synthesis of evidence revealed that: (1) there was no consensus about its definition; (2) there were no effective measurements of young children’s digital wellbeing; (3) both child factors (duration and place of digital use, child demographic characteristics) and parent factors (digital use, parental perception, and mediation) contribute to young children’s wellbeing; and (4) there were some effective applications and interventions. This review contributes to the theoretical development by mapping the existing work on young children's digital wellbeing, proposing a model, and identifying the research gaps for future studies.

Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestible, and implantable are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Time series classification (TSC) is very commonly used in modeling digital clinical measures. While deep learning models for TSC are very common and powerful, there exist some fundamental challenges. This review presents non-deep learning models commonly used for time series classification in biomedical applications that achieve high performance. Objective: We performed a systematic review to characterize the techniques used in time series classification of digital clinical measures throughout all stages of data processing and model building. Methods: We conducted a literature search on PubMed, and the Institute of Electrical and Electronics Engineers (IEEE), Web of Science, and SCOPUS databases using a range of search terms to retrieve peer-reviewed articles reporting academic research on digital clinical measures in the five year period between June 2016 and June 2021. We identified and categorized research studies based on the types of classification algorithms and sensor input types. Results: We found 452 papers in total from four different databases: PubMed, IEEE, Web of Science Database, and SCOPUS. After removing duplicates and irrelevant papers, 135 articles remained for detailed review and data extraction. Among these, engineered features using time series methods that were subsequently fed into widely-used machine learning classifiers was the most commonly used technique and also most frequently achieved the best performance metrics (77 out of 135 articles). Statistical modeling (24 out of 135 articles) algorithms were the second most common and also second best classification technique. Wavelet-based classification models (8 out of 135 articles) were also common. Electroencephalogram (29 out of 135 articles) was the most common data type used as an input. Accuracy was the most commonly reported performance metric, with 67.65% of articles reporting on accuracy. In this review paper, we provide summaries of signal pre-processing methods, feature engineering and selection methods, time series models, as well as model interpretations. Importantly, we found that about 50% of the papers only report one performance metric, which may result in a skewed view of overall performance. Conclusion: While high time series classification performance has been achieved in digital clinical, physiological, or biomedical measures, no standard benchmark datasets, modeling methods, or reporting methodology exist. There is no single widely used method for time series model development or feature interpretation– many different methods have proven successful.

The ageing transformation of digital health services faces issues of how to distinguish influencing factors, redesign services, and effectively promote measures and policies. In this study, in-depth interviews are conducted and grounded theory applied to open coding, main axis coding, and selective coding to form concepts and categories. Trajectory equifinality modeling clarified the evolution logic of digital transformation. Based on the theory of service ecology, a digital health service ageing model is constructed from the "macro-medium-micro" stages and includes governance, service, and technology transformation paths. The macro stage relies on organizational elements to promote the institutionalization of management and guide the transformation of governance for value realization, including the construction of three categories: mechanism, indemnification, and decision-making. The meso stage relies on service elements to promote service design and realize service transformation suitable for aging design, including the construction of three categories: organization, resources, and processes. The micro stage relies on technical elements to practice experiencing humanization, including the construction of three categories: target, methods, and evaluation. These results deepen the understanding of the main behaviors and roles of macro-organizational, meso-service, and micro-technical elements in digital transformation practice and have positive significance for health administrative agencies to implement action strategies.

As a stimulus signal, coded electrical signals can control the motion behavior of animals, which has been widely used in the field of animal robots. In current research, most of the stimulus signals used by researchers are traditional waveform, such as square waves. To enrich the stimulus waveform, a wireless animal robot stimulation system based on neuronal electrical signal characteristics is presented in this paper. The stimulator uses the CC1101 wireless module to control animal behavior through brain stimulation. The LabVIEW-based graphical user interface(GUI) can manipulate brain stimulation remotely while the stimulator powered by battery. Additionally, the spikes of animals have been simulated by this system through Direct Digital Synthesizer(DDS) algorithm. The GUI enable users to customize the combination of these analog spike signals. The recombined signals are sent to the stimulator through CC1101 as stimulus signals. In vivo experiments conducted on five pigeons verified the efficacy of the stimulation mechanism. The analog spike signal with an amplitude of 3-5V successfully caused the pigeon’s turning behavior. The feasibility of the analog spike signals as stimulus signals was successfully verified. Increased the diversity of stimulus waveform in the field of animal robots.

Welding processes offer a unique capability with a wide range of applications in industries. In recent times, welding has established itself as a tool for large scale additive manufacturing. In general, the quality and repeatability assurance for welding and specifically for additive manufacturing necessitates integrating process monitoring techniques with existing welding and additive manufacturing processes. The process-specific signals such as welding current fluctuations, temperature, and acoustic, generated during the welding operations, make them a suitable candidate for digitization. This chapter comprehensively describes the process monitoring techniques relevant to welding and additive manufacturing. Firstly, various sensors used during welding are described for their construction and working. Subsequently, specific applications of the sensors in digitizing the welding processes are presented.

This article analyzes the assessment of the four development levels of the Digital Teaching Competence (DTC) to recognize the needs and formulation of challenges in training and educational innovation required in the pedagogical practices of university professors under the current context of the Covid-19 pandemic. The study used an empirical-analytical methodology with a non-experimental, transactional, descriptive design. The sample design was probabilistic, estimated with 95% confidence and 5% error among 252 teachers from various faculties of the University of La Guajira. The selected instrument corresponded to the rubric's application to evaluate the university professor's digital teaching competence in Latin America. Among the study's significant results, we highlight that the rubric presented a high Cronbach's alpha reliability (α: 0.947). In the general assessment of DTC development, it was estimated that 78.2% of teachers are in the first two levels of DTC development assessment (Beginning and Middle). The evaluation rubric allows identifying challenges and opportunities that teacher training must address to advance the professional development of professors.

A progressive FEA fatigue degradation model for composites was developed and implemented using a UMAT user material subroutine in Abaqus. Numerical results were compared to experimental strain field data from high frequency Digital Image Correlation (DIC) of split disk fatigue testing of pressure vessel cut outs with holes. The model correctly predicted the onset and evolution of damage in the matrix as well as the onset of fiber failure. The model use progressive failure analysis based on the maximum strain failure criterion, the cycle jump method and Miner sum damage accumulation rule. A parameter study on matrix properties was needed to capture the scatter in strain fields observed experimentally by DIC. S-N curve for the matrix material had to be lowered by 0% to 60% to capture the experimental scatter. The onset of local fiber failure had to be described by local S-N curves measured by DIC having 2.5 times greater strain than that of S-N curves found from standard coupon testing.

The education system has become even more complex following the global pandemic, which saw face-to-face teaching transition to virtual teaching. To cope with this abrupt transition, it is essential that teachers had a sufficient level of digital teaching competence. This article aims to increase awareness of teachers' self-perception of their digital teaching competences in the educational field. Specifically, this study explores Spanish secondary school teachers’ knowledge and use of different ICT tools by evaluating their competence based on different areas proposed by The National Institute of Technology and Professional Development (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado, INTEF), Madrid, Spain. From the results of the questionnaires, we have determined that teachers consider themselves to have an upper intermediate level of digital teaching competence, although there are still shortcomings that need to be addressed in order to improve this level of competence, and its true integration in the teaching–learning process.

Digital microfluidics (DMF) devices enable precise manipulation of small liquid volumes in point-of-care testing. Printed circuit board (PCB) substrate is commonly utilized to build DMF devices. However, inkjet printing can be used to fabricate DMF circuits, providing a less expensive alternative to PCB-based DMF designs while enabling more rapid design iteration cycles. We demonstrate the fabrication process of the inkjet-printed DMF circuit. We compare Kapton and polymethyl methacrylate (PMMA) as dielectric coatings by measuring the minimal droplet actuation voltage for a range of actuation frequencies. The minimum actuation voltage of 5.6 V was required for droplet movement with the PMMA layer thickness of 0.2 μm and a hydrophobic layer of 0.17 μm. Significant issues with PMMA dielectric breakdown were observed at actuation voltages above 10 V. In comparison, devices that utilized Kapton were found to be more robust even at the actuation voltage up to 100 V.

Supply chain agility and resilience are key factors for the success of manufacturing companies in their attempt to respond to dynamic changes. Circular economy, the need for optimized material flows, ad-hoc responses and personalization are some of the trends that require supply chains to become “cognitive”, i.e. able to predict trends and flexible enough in dynamic environments, ensuring optimized operational performance. Digital Twins (DTs) is a promising technology, and a lot of work is done on the factory level. In this paper, the concept of Cognitive Digital Twins (CDTs) and how they can be deployed in connected and agile supply chains is elaborated. The need for CDTs in the supply chain as well as the main CDT enablers and how they can be deployed under an operational model in agile networks is described. Emphasis is given on the modelling, cognition and governance aspects as well as on how a supply chain can be configured as a network of connected CDTs. Finally, a deployment methodology of the developed model into an example of a circular supply chain is proposed.

Beer fermentation is typically monitored by periodic sampling and off-line analysis. In-line sensors would remove the need for time-consuming manual operation and provide real-time evaluation of the fermenting media. This work uses a low-cost ultrasonic sensor combined with machine learning to predict the alcohol concentration during beer fermentation. The highest accuracy model (R2=0.952, MAE=0.265, MSE=0.136) used a transmission-based ultrasonic sensing technique along with the measured temperature. However, the second most accurate model (R2=0.948, MAE=0.283, MSE=0.146) used a reflection-based technique without the temperature. Both the reflection-based technique and the omission of the temperature data are novel to this research and demonstrate the potential for a non-invasive sensor to monitor beer fermentation.

During the last 20 years, with the development of Information and Communication Technologies (ICTs), an emerging interest has appeared in Digital Community Engagement (DCE) in the process of cultural heritage management. Due to a growing need to involve a broader community in the Historic Urban Landscape approach, social media are considered one of the most important platforms to promote the public participation process of urban heritage conservation in the context of rapid urbanization. Despite the growing literature on DCE, which has delivered a general overview of different digital technologies and platforms to enhance heritage conservation, little research has been done on taking stock of the utilization of social media in this process. This study aims to fill the research gap by providing a more comprehensive picture of the functionalities of social media platforms and impacts on sustainable urban development through a systematic literature review. As a result, 19 out of 248 DCE relevant articles are selected as objects to illustrate the contribution of social media. The study identified the characteristics of these applied social media tools, explores their roles and influences in cases. The article concludes with directions for further research.

Both types of surgical guides – with and without metal sleeves are founded beneficial in clinical studies. The aim of this in vitro study was to compare time of surgical procedure in dental implantology depending of used type of the surgical guide. Ten three dimensional (3D) printed models of lower jaws were prepared based on complete virtual model of patient clinical conditions with missing teeth 37, 46 and 47. Five of this models were used for implant sites preparation performed with use of surgical guide without metal sleeves and dedicated surgical kit, and next five were used for the same procedure performed with surgical guide with metal sleeves and dedicated surgical kit. Time of implant site preparation were measured and noted. Statistical analysis was performed using Student’s t test for independent samples. Difference of time in both groups were founded as statistically significant (t=-9.94; df =28; p=0.0000). Type of surgical guide is important factor which can impact on time of implant site preparation and all surgical procedure.

Defensive structure is important in transportation field for kinds of intentional or unintentional explosion. Structures subjected to unconfined and confined explosion will bear different blast loads and their dynamic responses are different. The present work focus on the dynamic response mechanism of steel plate under unconfined and confined blast loads through both numerical and experimental studies. In the experiment, Digital Image Correlation (DIC) technique was applied to record and analyze the dynamic response process of large-scale field blast test. The DIC measured curve and the numerical calculated curves agrees well in both the trends and the peak values. Then, the dynamic response mechanism of steel plate under unconfined blast (UB) load and confined blast (CB) load were compared and discussed. Results show that the dynamic response of plate can be divided into three phases under both UB and CB loads, while with different mechanism. In phase I, plastic hinge starts from the center and moves to the boundary in UB condition, while in case of CB, plastic hinge occurs close to the boundary and moves in the opposite direction. In phase II, two plastic hinge lines propagates towards each other, and a platform exists between the boundary and the central area remains undeformed in UB condition, while in CB condition, larger deformation in peripheral region rather than central area produces.

Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing exercise program (Exergame), which can be executed during hemodialysis treatment. The Exergame is virtually supervised based on its interactive feedback via wearable sensors attached on lower extremities. We examined the effectiveness of this program to reduce depression symptom compared to supervised exercise in 73 hemodialysis patients (age=64.5±8.7years, BMI=31.6±7.6kg/m2). Participants were randomized into an Exergame group (EG) or a Supervised-exercise group (SG). Both groups received similar exercise tasks for 4-week, 3-session per week, 30-min per session, during hemodialysis treatment. Depression symptom was assessed at baseline and 4-week using Center for Epidemiologic Studies Depression (CES-D). Both groups showed significant reduction in depression score (37%, p<0.001, Cohen’s effect size d=0.69 in EG vs. 41%, p<0.001, d=0.65 in SG) with no between-group difference for the observed effect (p>0.050). The EG expressed a positive exercise experience including fun, safety, and helpfulness of sensor-feedback. Together, results suggested that the virtually-supervised low-intensity Exergame is feasible during routine hemodialysis treatment. It is as effective as supervised-exercise to reduce depression symptom, while reducing burden of administrating exercise in dialysis clinics.

Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, and muscle kinetics and kinematics, from wearable sensor data. The use of physical models for estimation of these quantities often requires many wearable devices making practical implementation more difficult. However, regression techniques may provide a viable alternative by allowing the use of a reduced number of sensors for estimating biomechanical time-series. Herein, we review 46 articles that used regression algorithms to estimate joint, segment, and muscle kinematics and kinetics. We present a high-level comparison of the many different techniques identified and discuss the implications of our findings concerning practical implementation and further improving estimation accuracy. In particular, we found that several studies report the incorporation of domain knowledge often yielded superior performance. Further, most models were trained on small datasets in which case nonparametric regression often performed best. No models were open-sourced, and most were subject-specific and not validated on impaired populations. Future research should focus on developing open-source algorithms using complementary physics-based and machine learning techniques that are validated in clinically impaired populations. This approach may further improve estimation performance and reduce barriers to clinical adoption.

The art work of this present paper is to show properly conditions and aspects to reach stability in the process control, as also to avoid instability, by using digital PID controllers, with the intention of help the industrial community. The discussed control strategy used to reach stability is to manipulate variables that are directly proportional to their controlled variables. To validate and to put the exposed principles into practice, it was done two simulations of a continuous fermentation tank process start-up with the yeast strain S. Cerevisiae NRRL-Y-132, in which the substrate feed stream contains different types of sugar derived from cane bagasse hydrolysate. These tests were carried out through the resolution of conservation laws, more specifically mass and energy balances, in which the fluid temperature and level inside the tank were the controlled variables. The results showed the facility in stabilize the system by following the exposed proceedings.

This study examines the activities conducted at the archaeological site of Aptera in Crete, Greece. The research was part of the DIACHRONIC LANDSCAPES International Design Workshop, organized by the CAM (Center for Mediterranean Architecture), TUC (Technological University of Crete School of Architecture), and UNIFE (University of Ferrara Department of Architecture). This article outlines the methods used for data acquisition and processing on a territorial scale, which generated digital outputs necessary for the analysis and design phases of the workshop, as well as for further examination of the results. The collected data, obtained through low-cost aerial photogrammetric surveying and GNSS terrestrial coordinate detection, were integrated in a Structure from Motion workflow that led to the creation and exportation of various digital outputs, such as point clouds, DTM, DSM, orthophotos, and contour lines. An accuracy analysis was performed to evaluate the effectiveness and efficiency of the digital models compared to the implemented surveying strategies, including the Ground Control Point and Quality Check Point marker positioning strategy. The resulting digital models proved to be valuable assets for analysis and design within the workshop and provided insightful prospects for future research and territorial-scale projects.

The Internet of Things (IoT) is a technological paradigm that has gained significant momentum that last decade and, among others, enables the development of intelligent and interoperable device networks. In this respect, it has triggered the creation and evolution of VANETs (Vehicular Ad Hoc Networks), which are initially implemented in order to guarantee the safety of the drivers and the avoidance of traffic accidents. The drawback is that this fast evolution flags serious concerns as far as the privacy of users is concerned, while the population of attackers or entities that try to eavesdrop and intercept information has significantly increased. This imposes a serious risk for drivers moving across a Smart City. The research presented in this paper aims to evaluate privacy protection mechanisms in VANET environments, based on their efficiency and security level ensured, considering the fact that VANETs provide limited resources to users/drivers. Moreover, the usage of the elliptic curve cryptography in reduced resources environments is discussed. Finally, this paper compares the performance of three cryptographic algorithms, i.e., Elliptic Curve Cryptography (ECC), Hyperelliptic Curve Cryptography genus 2 (HECC-2) and HECC genus 3 (HECC-3), employed for an efficient authentication and safe message transmission mechanism in VANETs, targeting to extract conclusions related to the implementation of each cryptographic scheme in this specific application area.

Artificial intelligence (AI) and 3D printing (3DP) play critical roles in what is known as the 4th Industrial Revolution by developing data- and machine-intelligence-based integrated production technologies. This shift has led to increasingly complex design requirements and fabrication methods, posing several challenges for implementing design, fabrication process, and architectural quality standardization. This paper systematically reviews AI techniques applied in diverse stages of 3DP production in architecture. The research goals are to (1) offer a comprehensive critical analysis of the body of literature; (2) identify and categorize approaches to integrating AI in the production of 3D-printed structures; (3) identify and discuss challenges and opportunities of AI integration in architectural production of 3DP structures; and (4) identify gap and provide recommendations for future research. The findings indicate that topics related to AI applications are current and trending, receiving increasing attention from researchers in general and particularly those working in machine and deep learning, artificial neural networks, and pattern recognition. However, the potential of the application of AI in architectural production using additive manufacturing still needs to be explored. Considering this, the paper emphasizes the necessity of redefining traditional field boundaries and opening new opportunities in the sector.

Thoracic aortopathy encompasses a spectrum of conditions affecting the aorta, ranging from aneurysms to dissections, posing significant challenges in management and care. In recent years, remote care, facilitated by telemedicine and digital health technologies, has emerged as a promising approach to overcome barriers in delivering timely and efficient healthcare services. This paper explores the practical considerations and challenges associated with implementing remote care strategies in the management of thoracic aortopathy in India with the largest population in the world. By evaluating existing literature and drawing upon clinical experiences, we discuss the potential benefits, limitations, and best practices for incorporating remote care into the management of patients with thoracic aortopathy. Additionally, we address important factors such as patient engagement, technology integration, privacy concerns, and regulatory requirements to optimize the delivery of remote care in this specialized field.

Inclusive and sustainable development has become an important part of China's sustainable development agenda, the self-recovery and stability of China's economy cannot be achieved without the macro-governance of the government. Figuring out the internal logic of digital finance and economic resilience is an important part of effective government governance. Based on the panel data of 193 counties in five provinces in northwest China from 2014 to 2021, the paper tested digital finance and county-level economic resilience by using two-way fixed-effect model, intermediary-effect model and threshold-effect model, to test whether financial development promotes government governance in underdeveloped regions. The results prove that digital inclusive finance significantly enhances the resilience of county economies by improving capital allocation efficiency, stimulating entrepreneurship and employment, and reducing pollution emissions. In addition, there are dual threshold effects between capital allocation efficiency and employment vitality, in which capital allocation efficiency has a gradually increasing threshold effect, and entrepreneurship and employment vitality have a fluctuating increasing threshold effect. Therefore, "Digital inclusive finance" in underdeveloped regions is an indispensable part of financial globalization；promoting the healthy development of digital finance is the basis and premise for providing a good financial environment for government management; deepening the reform in the financial field and promoting the green and sustainable development of finance are the future trend and direction of efforts.

Despite their relatively low incidence globally, CNS tumors remain amongst the most lethal cancers, with only a few other malignancies surpassing them in 5-year mortality rates. Treatment decisions for brain tumors heavily rely on histopathological analysis, particularly intraoperatively, to guide surgical interventions and optimize patient outcomes. Frozen sectioning has emerged as a vital intraoperative technique, allowing for highly accurate, rapid analysis of tissue samples, although it poses challenges regarding interpretive errors and tissue distortion. Raman histology, based on Raman spectroscopy, has shown great promise in providing label-free, molecular information for accurate intraoperative diagnosis, aiding in tumor resection and identification of neurodegenerative disease. Techniques including Stimulated Raman Scattering (SRS), Coherent Anti-Stokes Raman Scattering (CARS), Surface-Enhanced Raman Scattering (SERS), and Tip-Enhanced Raman Scattering (TERS) have profoundly enhanced the speed and resolution of Raman imaging. Similarly, Confocal Laser Endomicroscopy (CLE) allows for real-time imaging and rapid intraoperative histologic evaluation of specimens. While CLE is primarily utilized in gastrointestinal procedures, its application in neurosurgery is promising, particularly in the context of gliomas and meningiomas. This review focuses on discussing the immense progress in intraoperative histology within neurosurgery and provides insight into the impact of these advancements on enhancing patient outcomes..

Diabetes management requires constant monitoring and individualized adjustments. This study proposes a novel approach that leverages digital twins and Personal Health Knowledge Graphs (PHKGs) to revolutionize diabetes care. Our key contribution lies in developing a real-time, patient-centric digital twin framework built on PHKGs. This framework integrates data from diverse sources, adhering to HL7 standards and enabling seamless information access and exchange. PHKGs offer a flexible and efficient format that supports various applications. As new knowledge about the patient becomes available, the PHKG can be easily extended to incorporate it. This dynamic approach fosters continuous improvement and facilitates the development of new applications. As proof of concept, we have demonstrated the versatility of our digital twin by applying it to different use cases in diabetes management. These include predicting glucose levels, optimizing insulin dosage, providing personalized lifestyle recommendations, and visualizing health data. By enabling real-time, patient-specific care, this research paves the way for more precise and personalized healthcare interventions, potentially improving long-term diabetes management outcomes.

This research aims to explore the complex interplay between supply chain resilience (SCR), digital supply chain (DSC), and sustainability, focusing on the moderating influence of supply chain dynamism. The goal is to understand how these elements interact within the framework of contemporary supply chain management and how they collectively contribute to enhancing sustainability outcomes. The sample size is 300 CEOs and managers. The study approach integrates quantitative research methods. Structural equation modeling (SEM) is utilized to quantitatively analyze the direct and indirect effects of SCR and DSC on sustainability. The numerous surveys we conduct among supply chain ecosystem stakeholders provide a rich picture of practical implications and contextual nuances. In sum, our early findings generally support a positive relationship between SCR and sustainability in and of itself decrying the need for more resilient supply networks for sustainability. We further find the beneficial impact of digital technologies in promoting sustainability via enhancing environmental control and controlling for efficiency in supply chains. We also offer evidence to show that supply chain dynamism compounds the positive logic between SCR and DSC and sustainability. As a final word, it must be noted that our work speaks to the burgeoning Supply Chain Dynamism as moderator literature by examining the direct and contingent effects of SCR and DSC on not only performance but sustainability. By shedding light on the moderating role of dynamism, the study provides fresh insights into the multifaceted nature of supply chain management and sustainability practices. The study's findings enhance theoretical understanding by elucidating the synergistic effects of SCR, DSC, and sustainability in dynamic supply chain settings. It augments existing theoretical frameworks by integrating concepts of resilience, digitalization, and sustainability into a comprehensive model. Practical and economical, the research offers actionable guidance for organizations aiming to improve sustainability performance through resilient and digitally advanced supply chains. By acknowledging the role of supply chain dynamism, managers can tailor strategies to manage disruptions effectively and leverage digital innovations. Economically, adopting sustainable practices can result in cost savings and competitive advantages. The research emphasizes the importance of aligning supply chain strategies with sustainability goals to drive long-term value and societal impact.

In the digital era, enhancing the heritage resources of a tourist route means describing and networking them to promote their visibility and attractiveness. Prior studies mainly assess mountainous region’s natural resources through multi-indicator descriptive evaluation. This study aims to develop a comprehensive measurement of indicator’s value while assessing the tourism development potential of mountainous rural area. It concerns the main tourist route of the UNESCO M'Goun Geopark, located in the central High Atlas of Morocco. It also involves analyzing the criteria for choosing this destination by segmenting tourists' consumption experiences. Statistical analysis of the collected data reveals five dimensions of destination choice and four visitor groups. This article proposes to discuss findings and their applicability in terms of tourism destination management and decision-making, which can help local governments to rationally allocate tourism resources and land use.

Current VAT rules for cross-border e-commerce companies in the EU are not sustainable, because they are extremely complex and often opaque, due to the lack of tax harmonization. The aim of the research is to explore, using a qualitative method, what kind of harmonization would make the current legislation more uniform, simpler, more transparent, and easier to understand, without conflicting with the rules of any Member State. To this end, the aspects of the new platform regulation introduced on 1 July 2021 and its potential for expansion are assessed through information from possible sales models. The research suggests that the rules on platforms do not cover all possible sales models, which in our view imposes an unnecessary extra administrative burden on some businesses and makes their tax activities less transparent. In the light of the results of the research, we propose that the current EU platform regulation should be extended to cover B2B, B2C transactions and services. As a result of this suggested harmonization, a simpler, more transparent, uniform and sustainable tax system would contribute to the whitening of the economy, generating additional tax revenue for Member States.

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tubes signals would serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method are presented.

Over the last decades, an important number of regional and global Digital Elevation Models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs require to be expressed in the same Coordinate Reference System (CRS), sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention), and with heights relative to the same Vertical Reference System (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements, or disparities, may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect or curvature. Therefore, the control of DEM planimetric accuracy is certainly the most important task to perform at first before any comparison. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy, by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have been already computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been performed in a 11x11 grid with a 0.1-pixel step in both directions. The Copernicus DEM GLO-30 has been interpolated by a “classical” bicubic to achieve these 11x11 displacements. This validation method has been applied in four 10 km x 10 km DEMIX tiles showing different height distributions. Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with with typical errors (e_b) ranging from 12 to 20 % of the pixel size (worst case in Croatia).These errors in displacement retrievals are not constant in the 11x11 grid and the overall error E_b depends on the roughness (altimetry variations) encountered in the different tiles. The second idea of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d=1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles making the bicubic parameter vary between -1.5 and 0.0 by step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around -0.95) and higher in mountainous areas (around -0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r=0.717). The logarithmic regression formula estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC.

To accurately and non-destructively monitor the growth of spring wheat in the Hetao Irrigation Area, UAV remote sensing was employed during various fertility stages. Digital image indices from diverse fertilization treatments were calculated and compared with physiological indices to identify the most sensitive digital image indices corresponding to these indices. The study underscored the critical importance of the flowering stage in the growth of spring wheat, thus highlighting the necessity of focusing on this stage. This finding reiterated that the flowering stage was pivotal for spring wheat development in the Hetao Irrigation Area. Several digital image indices, such as GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI, exhibited a high frequency of significant correlations with physiological indices during the four primary reproductive stages of wheat. Consequently, these sensitive digital image indices during the flowering stage can more effectively characterize the physiological indices of spring wheat.

In the age of social media, images come from unreliable sources that may change their content to fit a narrative. Automated detection of forged images is a complex task, especially considering recent technological advances in image manipulation software. However, in the existing literature, it is possible to identify two general approaches adopted by detection methods, active and passive. Active techniques preemptively act on an image by inserting structures before any manipulation is made that can verify its authenticity. In contrast, passive methods analyze the content of an image in search of traces of manipulation. In this way, this research proposes a novel approach to image manipulation detection, combining two passive methods through neural networks, creating a generalist approach capable of detecting with greater accuracy than the methods that compose it. Furthermore, this work used a combination of four datasets available in the literature for training and evaluation. After training, the merged approach obtained an accuracy of 89.59% in the set of validation images, significantly higher than the model trained with only unaltered images, which obtained 78.64%, and the two other models trained using images with a feature selection method applied to enhance inconsistencies that obtained 68.02% for Error-Level Analysis images and 50.70% for the method using Discrete Wavelet Transform. In addition to the performance improvement, the proposed approach’s accuracy variation was lower than the other models.

UV-digital printing belongs to the commonly used method for custom large-area substrate decoration. Despite low surface energy and adhesion, transparent polymer materials, such as polymethylmethacrylate (PMMA) and polycarbonate (PC), represent an ideal substrate for such purposes. The diffuse coplanar surface barrier discharge (DCSBD) in a novel compact configuration was used for substrate activation to improve ink adhesion to the polymer surface. This industrially applicable version of DCSBD was prepared, tested, and successfully implemented for the UV-digital printing process. Furthermore, wettability and surface free energy measurement, X-ray photoelectron spectroscopy, atomic force and scanning electron microscopy evaluated the surface chemistry and morphology changes. The changes in adhesion of the surface and of ink were analyzed by a peel-force and a crosscut test, respectively. A short plasma treatment (1-5 s) enhanced the substrate's properties of PMMA and PC while providing the pre-treatment suitable for further in-line UV-digital printing. Furthermore, we did not observe damage or significant change of roughness affecting the substrate's initial transparency.

Digital transformation has become a strategic priority for the enterprises, the public organizations, and the cities lately since it addresses the adoption of cutting-edge information and communication technologies (ICT) for optimizing their business processes and for generating new types of value to their end users. Although the Covid-19 pandemic is claimed to be over, it appears that organizations and cities changed their digital transformation strategies that had defined before the outbreak. The aim of this paper is to investigate the type of changes that were caused both to enterprises and cities due to the pandemic. It investigates whether and in which directions it caused business changes and how the smart city strategic objectives were affected due to pandemic, especially in cases where digital transformation strategies were defined before the outbreak. This study uses bibliometric analysis to determine the effects of Covid-19 on businesses and cities. Findings specify strategic changes and important trends, like the migration of the production supply chains to nearby territories. Moreover, this article utilizes the European Intelligent Cities Challenge initiative, in which cities had defined their priorities for their digital transformation before the outbreak. From the analysis of the strategic plans of the involved cities, it is attempted to determine whether, which cities and in which directions they changed their strategies due to the pandemic.

Numerous researchers acknowledge that occupational pension protects employees. However, the total occupational pension is shared by employees and employers in China, which is a cost. This study aimed to explore the occupational pension on corporate social responsibility (CSR) and organizational resilience. We drew upon insights from cost-stickiness and resource-based theories to develop a model that illuminates how occupational pension affected firms’ stance toward CSR in the context of the COVID-19 pandemic and how this, in turn, has result organizational resilience. This study categorized CSR into strategic and responsive activities based on cost stickiness. We selected a total of 34,145 observations of Chinese listed companies over the period 2010-2023 as a sample to investigate whether occupational pension changes CSR strategies. The results showed that the cost pressure to contribute to occupational pension drove firms to reduce their responsive CSR and boost their strategic CSR. Moreover, strategic CSR improved organizational resilience, but responsive CSR did not. This relationship between occupational pension and CSR was significantly and negatively moderated by the minimum wage and population aging. Whereas, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities. This study has implications for decision-making regarding high-quality firm development.

The emergence of digital twins (DTs) represents a recent advancement in technological processes that support the planning, construction, and oversight of constructed assets. A DT is essentially a digital model that facilitates simulations and two-way information exchange with its real-world counterpart (a physical twin), creating opportunities for data-driven decision-making. Despite the promising prospects of DTs, both the technology and the methodologies for developing them, as well as their potential applications, remain not fully understood in both academic and industrial contexts. This research explores the applications of DTs for the management of existing assets within the AEC/FM industry. The study presents the results of a comprehensive literature review that explores existing knowledge in this domain. The findings reveal that procedures for creating DTs are still in their early stages, emphasizing potential applications in asset management throughout their lifespan, facilitating sustainability upgrades, and supporting the maintenance and restoration of heritage assets. The review concludes by providing recommendations for future research in this evolving field.

Digital twin, an innovative technology propelled by data and models, plays a seminal role in the digital transformation and intelligent upgrade of ships. This study introduces a digital twin methodology for real-time monitoring of ship structure deformation fields, based on finite discrete strain data, and a visualization tool framework is developed using virtual reality technology. First, the inverse Finite Element Method (iFEM) is employed to derive the deformation field of the ship structure in real time via sensor strain data. Secondly, the deformation field data obtained based on the iFEM algorithm is converted into general visualization data conducive to interpretation within VR applications. Lastly, a digital twin software tool is built to enable synchronous response and interaction between the virtual scene and the physical scene, directly superposing particular virtual objects (data acquired by sensors, CAD virtual models, and deformation field cloud images) onto the physical scene in real-time. The digital twin tool embodies a virtual reality visualization system framework integrating physical data measurement, reconstruction, analysis, expression, storage, rendering and interaction of deformation field data. Through practical application, the flexibility, effectiveness and compatibility of the developed prototype tool are verified. According to the results, the system can enhance the efficiency of scientific communication, model validation, and interdisciplinary sharing during the analysis and evaluation of mechanical properties of ship structures.

This study investigates effective strategies for second language education in primary schools, analyzing the efficacy of immersive and interactive methods, digital literacy tools, and mixed-method approaches. The findings reveal significant improvements in language proficiency and student engagement through these methodologies, with immersive and interactive strategies showing substantial benefits in oral proficiency and engagement, digital tools enhancing accessibility and engagement, and mixed-method approaches yielding the highest overall effectiveness. The study underscores the importance of a balanced, flexible approach to language education, integrating traditional and modern methods to cater to diverse learner needs. These strategies not only advance linguistic skills but also contribute to the broader cognitive and emotional development of young learners, emphasizing the critical role of effective language education in preparing students for a globalized world.

Media is obtaining a leading role in contemporary strategic communication, the development especially visible in the context of social-political and geopolitical conflicts. This study introduces an interdisciplinary theory of Social Laser and applies it in the analysis of social mobilization around the current militarized situation in Eastern Europe. We analyze quantitatively the content of three Swedish newspapers and qualitatively the related Facebook discussions. As demonstrated, social lasing has been employed as an instrument in information warfare. An “information tsunami” was generated by the Swedish national mass media at the outbreak of military events in March-May 2022. It shaped social dynamics on the related social media platform. The intensity of dissident actions increased when the information pressure subsided.

In an era driven by technology and connectivity, where the sphere of learning goes beyond conventional classrooms, e-Service-Learning (e-SL) merges the possibilities of technology with the principles of experiential education in a powerful blend [1]. Service-learning (SL) is an educational proposal that has been cited by UNESCO [2] as an innovative educational tool through which to address the complex challenges of our society. The Sustainable Development Goals [3] provide a framework around which to structure the SL projects and thus develop the skills needed to act on the full range of goals. Embedding digital means open a debate on the intricate relationship between technology, education, and societal impact [4]. By analyzing two focus groups involving faculty, students and community partners, this contribution aims to explore the potential of combining SL and digital empowerment (DE). The focus group participants discussed the opportunities for innovation, the expected impact on them and the society at large, their needs and the pre-conditions needed to make SL and DE successful, and limitations. The results are presented according to the six principles of sustainability education in the university environment, highlighting the transformative potential of combining these principles within SL and DE.

Purpose: This study investigates the implementation and impact of maker culture—viewed as a tool for developing green digital skills—in higher education institutions in Hong Kong. Maker culture, a collaborative educational approach, embraces students’ capacity for self-paced, autonomous learning and applies this knowledge to creative problem-solving and innovation, key aspects of sustainability education. Methods: An empirical study was conducted, focusing on the experiences of teachers in the higher education sector in Hong Kong. Eight individuals were interviewed to gain insights into their perceptions and experiences with maker education within sustainability contexts. The sample was limited to ensure cross-sectional comparability and direct weighting of teachers’ experiences within a singular, complementary educational setting. Results: The findings provide valuable insights into the benefits and challenges of integrating maker education into traditional educational systems to foster green digital skills. It became evident that adequate resources, effective teachers, and improved administrative systems play significant roles in the successful implementation of this approach. Conclusion: Maker education, as a tool for developing green digital skills, offers a promising alternative to traditional performance-based studies. It has the potential to lead to a future of education that is creative, innovative, and student-directed, fostering sustainability competences. Therefore, despite the challenges, with the right support and resources, the integration of maker culture into educational systems could significantly transform teaching and learning processes, advancing sustainability education.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics and Logit regression. Majority of the farmers relied on perception and other non-conventional approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil test and digital application respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizer to improve soil fertility. Although, 4.8% of the farmers indicated that they had digital application on their mobile phones, only 2.9% reported to have used such. More than half (56.4%) of the famers stated that lack of awareness of the existence of digital application and internet enabled telephones were reasons they have not been able to use digital applications. Majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital application which can provide more information especially on soil fertility if introduced. Logit model results revealed that literacy level, membership of farmers’ association and extension contact significantly increased the likelihood of willing to use soil digital application, but older farmers are less likely to use digital application.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

Clarifying the impact mechanism of digital transformation of enterprises on the M&A efficiency of listed companies can provide a factual basis for improving capital market regulatory policies. Taking the mergers and acquisitions of listed companies from 2007 to 2021 as a research sample, the influence mechanism of the digital transformation degree of companies on their M&A efficiency was studied. The research results show that the digital transformation of listed companies will improve their M&A efficiency. Digital transformation will reduce the degree of mispricing of stocks of M&A companies, curb conflicts between managers and agents of M&A companies, and improve their M&A efficiency. Further research finds that the promotion effect of digital transformation on M&A efficiency is more significant in non-state-owned companies, with a higher degree of financing constraint and high analyst attention. In the future, regulatory authorities should actively promote the digital transformation of listed companies, curb mispricing and management agency problems in the capital market with digital governance, and improve the efficiency of mergers and acquisitions in the capital market.

Purpose: The rapid development of technological innovations is causing major transformations in organisations. These changes are facilitating the transition of organisations from traditional to digital cultures and are influencing the positioning and even the survival of many of them. Higher Education Institutions are no exception. This article aims to understand the main factors that facilitate and drive this transformation from the point of view of their external stakeholders. Design/methodology/approach - Due to the complexity of the study, the case study method has been used as an appropriate methodology. Findings - As the results of the study show, the use of new agile methodologies, the development of continuous improvement processes, the automation of tasks with the use of new technologies, data-driven decision making, and the management of new digital talent are enablers. Research limitations/implications - Further research could conduct similar analyses with other stakeholders, to achieve a complete management model. Practical implications - The case study allows researchers to learn from practitioners and business leaders, while providing a theoretical model that can help other managers in the process of transforming their organisations. Originality/value - The main contribution of this study has been to apply the study to a specific Higher Education Institution.

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran's I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China's urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon-emission efficiency in the early stages of development, but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems and increase the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30% to 100%, and methods can be expanded to other fields.

The paper deals with the reverse engineering, namely the as built documentation of the actual construction. The input is data measured in field using geodetic methods. In addition to modern methods of surveying 3D objects, such as laser scanning, it is still necessary to use classic surveying using a total station. The bottle neck of the process is the creation of documentation of the construction, which is still created manually in the appropriate CAD/BIM software. The goal of the research was to find a method that would reduce the amount of manual work when drawing documentation in CAD/BIM to a minimum. The core of the solution is the use of a topology that interconnects the points measured in field. The entire procedure has 2 parts: 1. Creating a topo-logical drawing template in a suitable CAD/GIS software (digital sketch), 2. Adding geometry to this sketch and creating a drawing using topological codes. The method was verified in practice by applying it to several specific buildings in the Czech Republic. The practical application of the method demonstrated a 30% time saving, a reduction in work and error rate in the entire process.

Many parameters and complex boundaries are involved in the spatial arrangement of an under-ground powerhouse for a hydropower station, which requires reference to many relevant cases and specifications. However, in practical applications, retrieving relevant cases or specifications is difficult, and there is a lack of knowledge of cascading logic among design parameters. For this question, a targeted knowledge graph based on knowledge graph management technology is established to support subsequent applications. This paper proposes a new concept of con-structing a knowledge graph for building information modeling (BIM) underground power-houses of hydropower stations. Firstly, the ontology skeleton of hydropower station spatial ar-rangement design, which represents the knowledge organization structure of the knowledge graph, is reconstructed by carefully analyzing the requirements for intelligent modeling of un-derground powerhouses. A large amount of unstructured data is identified based on optical character recognition (OCR) technology and is divided into words to extract correlation knowledge based on THULAC. In the next step, the knowledge triad of the spatial arrangement of the underground powerhouse is extracted based on ChatGPT and stored in a Neo4j knowledge base to build a knowledge graph. Finally, the knowledge graph is serviced to realize the query of knowledge and parameter recommendation to assist the digital intelligent design of spatial arrangement of an underground powerhouse of pumped storage hydropower stations.

eHealth applications are an essential component in achieving Universal Health Coverage. (1) Background: The lack of skilled healthcare personnel has hindered successful integration and use, especially in developing and low and middle-income countries. (2) Methods: A descriptive study was conducted using an online survey to collect quantitative data from 37 organizations to address this issue. (3) Results: The findings showed that there are limited training opportunities available for policymakers and eHealth specialists. Additionally, obstacles such as insufficient funds, inadequate infrastructure, poor leadership, and governance were identified and found to be context specific. To mitigate these challenges, the study proposed continuous in-service training, the implementation of computer-based systems, and the integration of academic modules. The importance of strong eHealth policies and committed leaders was also emphasized. (4) Conclusions: The results of this study support previous research and emphasize the importance of implementing practical and uncomplicated solutions to overcome eHealth barriers to capacity building in low- and middle-income countries. It is essential to recognize that the data collected for this study only pertains to BETTEReHEALTH.