Search Results (209)

Memristors with resistive switching capabilities are vital for information storage and brain-inspired computing, making them a key focus in current research. This study demonstrates non-volatile analog resistive switching behavior in Al/TiO_x/TiN/Si(n⁺⁺)/Al memristive devices. Analog resistive switching offers gradual, controllable conductance changes, which are essential for mimicking brain-like synaptic behavior, unlike digital/abrupt switching. The amorphous titanium oxide (TiO_x) active layer was deposited using the pulsed-DC reactive magnetron sputtering technique. The impact of increasing the oxide thickness on the electrical performance of the memristors was investigated. Electrical characterizations revealed stable, forming-free analog resistive switching, achieving endurance beyond 300 DC cycles. The charge conduction mechanisms underlying the current–voltage (I–V) characteristics are analyzed in detail, revealing the presence of ohmic behavior, Schottky emission, and space-charge-limited conduction (SCLC). Experimental results indicate that increasing the TiO_x film thickness from 31 to 44 nm leads to a notable change in the current conduction mechanism. The results confirm that the memristors have good stability (>1500 s) and are capable of exhibiting excellent long-term potentiation (LTP) and long-term depression (LTD) properties. The analog switching driven by oxygen vacancy-induced barrier modulation in the TiO_x/TiN interface is explained in detail, supported by a proposed model. The remarkable switching characteristics exhibited by the TiO_x-based memristive devices make them highly suitable for artificial synapse applications in neuromorphic computing systems. Full article

Virtual interactivity in marketing has evolved significantly in recent decades, becoming an essential tool for effectively connecting with consumers in digital environments. Through interactive digital platforms, brands can generate a two-way dialogue with their customers, allowing them to obtain instant feedback and adapt their strategies quickly and effectively. This not only improves but also contributes to strengthening brand love and fostering long-term loyalty. In this context, this research aimed to analyze the effect of virtual interactivity on customer loyalty and WOM through multiple sequential mediation paths, considering the elements of relationship marketing and examining the cognitive (trust–commitment), communicational (communication–conflict management), and experiential (brand love–WOM) mechanisms that operate in digital consumer–brand interactions. A cross-sectional and explanatory study was conducted, considering 417 frequent customers of brands who also follow brands on social media. Participants included women (60.7%) and men (39.3%), ranging in age from 19 to 52 years (M = 30.9 and SD = 6.0). Data were collected using a self-report form on virtual interactivity, relationship marketing, WOW, customer loyalty, and brand love, yielding an appropriate measurement model (α = between 0.791 and 0.908; CR = between 0.791 and 0.916; AVE = between 0.679 and 0.845) which was statistically analyzed using PLS-SEM. The hypotheses confirmed the proposed model, observing the effect of virtual interactivity on important brand factors. This research provides valid results linked to the digital world in banking contexts. Full article

The point-kernel (PK) technique has a long history in applied radiation shielding, originating from the early days of digital computers. The PK technique applied to gamma-ray attenuation is one of many successful applications, based on the linear superposition principle applied to distributed radiation sources. Mathematically speaking, the distributed source will produce a detector response equivalent to the numerical integration of the radiation received from an equivalent number of point sources. In this treatment, there is no interference between individual point sources, while inherent limitations of the PK method are its inability to simulate gamma scattering in shields and the usage of simple boundary conditions. The PK method generally works for gamma-ray shielding with corrective B-factor for scattering and only specifically for fast neutron attenuation in a hydrogenous medium with the definition of cross section removal. This paper presents theoretical and programming aspects of the PK program developed for a distributed source of photons (line, disc, plane, sphere, slab volume, etc.) and slab shields. The derived flux solutions go beyond classical textbooks as they include the analytical integration of Taylor B-factor, obtaining a closed form readily suitable for programming. The specific computational modules are unified with a graphical user interface (GUI), assisting users with input/output data and visualization, developed for the fast radiological characterization of simple shielding problems. Numerical results of the selected PK test cases are presented and verified with the CADIS hybrid shielding methodology of the MAVRIC/SCALE6.1.3 code package from the ORNL. Full article

This paper extends the resource-based view (RBV) of social entrepreneurship by introducing artificial intelligence (AI) as a dynamic, integrative capability that enhances the acquisition and optimization of four foundational forms of capital: human, social, political, and financial. While social ventures have long faced constraints in scaling impact due to resource limitations and institutional barriers, AI technologies—such as predictive analytics, machine learning, and natural language processing—offer new pathways for improving operational efficiency, stakeholder engagement, advocacy strategies, and financial sustainability. Through the development of a conceptual model and a series of theoretical propositions, this study positions AI as a transformative force that not only strengthens individual resource domains but also enables synergistic feedback loops across them. In doing so, the paper contributes to emerging debates on technology adoption in hybrid organizations, scalability in resource-constrained contexts, and the evolution of strategic management theory in the digital age. Practical implications are outlined for social entrepreneurs, policymakers, and funders seeking to responsibly integrate AI into social impact ecosystems, and future research directions are proposed to empirically test the framework across sectors and global settings. Full article

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) among the elderly population is a global health issue. Information and Communication Technology (ICT)-based interventions hold promises for maintaining cognition, but their viability is affected by several challenges. Objectives: This study aimed to significantly assess the effectiveness of ICT-based cognitive and memory aid technology for individuals with MCI or dementia, identify adoption drivers, and develop an implementation model to inform practice. Methods: A PRISMA-based systematic literature review, with the protocol registered in PROSPERO (CRD420251051515), was conducted using seven electronic databases published between January 2015 and January 2025 following the PECOS framework. Random effects models were used for meta-analysis, and risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklists. Results: A total of ten forms of ICT interventions that had proved effective to support older adults with MCI and dementia. Barriers to adoption included digital literacy differences, usability issues, privacy concerns, and the lack of caregiver support. Facilitators were individualized design, caregiver involvement, and culturally appropriate implementation. ICT-based interventions showed moderate improvements in cognitive outcomes (pooled Cohen’s d = 0.49, 95% CI: 0.14–1.03). A sensitivity analysis excluding high-risk studies yielded a comparable effect size (Cohen’s d = 0.50), indicating robust findings. However, trim-and-fill analysis suggested a slightly reduced corrected effect (Cohen’s d = 0.39, 95% CI: 0.28–0.49), reflecting potential small-study bias. Heterogeneity was moderate (I² = 46%) and increased to 55% after excluding high-risk studies. Subgroup analysis showed that tablet-based interventions tended to produce higher effect sizes. Conclusions: ICT-based interventions considerably enhance cognition status, autonomy, and social interaction in older adults with MCI and dementia. To ensure long-term scalability, future initiatives must prioritize user-centered design, caregiver education, equitable access to technology, accessible infrastructure and supportive policy frameworks. Full article

Outdoor experiences significantly enrich intangible cultural heritage tourism, yet limited interaction often restricts deep cultural learning. This research investigates how a mobile application, grounded in experiential learning theory, enhances tourists’ learning about Tang Sancai, a form of Chinese intangible cultural heritage, in the outdoor setting of Sancai Town, Luoyang. Employing a theory learning behavior design scenario framework, the application integrates activity-based exploration, reflective multimedia content, and immersive simulation scenarios to promote active participation, critical reflection, and context-rich outdoor learning experiences. Controlled experiments using pre- and post-tests and questionnaires revealed that participants using the application significantly outperformed those using conventional digital tools in knowledge retention, comprehension, and experiential learning outcomes. Technology acceptance analysis highlighted perceived usefulness as a critical driver of learning success. However, limited effects on behavioral progression suggest that short-term digital interactions may not sustain long-term behavioral change. This research offers a replicable mobile learning framework for outdoor cultural education, extends experiential learning theory to informal tourism contexts, and provides practical insights into using technology to enrich outdoor educational experiences, while supporting heritage preservation. Full article

The modern system of higher education and research is undergoing deep institutional transformations, accompanied by changes in funding mechanisms, increased competition, the growing importance of project forms of scientific activity organization, and more complex requirements for performance. In the conditions of digital transformation and institutional instability, higher education faces the need to form sustainable smart management systems. The modern understanding of smart education goes beyond e-learning and includes the intellectualization of all levels of organization of educational and scientific activities. This requires the creation of new models capable of integrating the behavior of teachers and researchers in the context of digital, project, and institutional logics. Thus, the task of building intelligent models capable of reflecting the complex, multi-layered structure of interactions between researchers, organizations, forms of support, and the system of evaluation of scientific work becomes relevant. This article proposes an agent-based approach to modeling the process of formation of scientific knowledge, considered as a key element of the sustainable development of scientific and educational environment. The model reflects the interaction of agents—researchers with different characteristics: age, qualification level, scientific productivity, affiliation, and trajectory of professional development. The modeling results allow us to draw conclusions about the regularities of the reproduction of scientific potential, the factors of academic environment sustainability, and the effectiveness of institutional support mechanisms. The obtained results have both theoretical and applied significance. The model can be used to forecast the effectiveness of science policy, assess the risks and prospects of scientific teams, and justify incentive systems and the long-term design of the development of scientific organizations. The presented approach allows us to form a comprehensive view of the dynamics of scientific knowledge in the context of sustainable management in higher education. Full article

In the wake of the Holocaust and the post-war reconstruction of Greece’s historic city centers, many Greek synagogues were demolished, abandoned, or appropriated, erasing centuries of Jewish architectural and communal presence. This study presents a thirty year-long research and documentation initiative aimed at preserving, recovering, and eventually digitally reconstructing these “lost” synagogues, both as individual buildings and within their urban context. Drawing on architectural surveys, archival research, oral histories, and previously unpublished materials, including the recently rediscovered Shemtov Samuel archive, the project grew through the use of technology. Beginning with in situ surveys in the early 1990s, it evolved into full-scale digitally enhanced architectural drawings that formed the basis for further digital exploration, 3D models, and virtual reality outputs. With the addition of these new tools to existing documentation, the project can restore architectural detail and cultural context with a high degree of fidelity, even in cases where only fragmentary evidence survives. These digital reconstructions have informed physical restoration efforts as well as public exhibitions, heritage education, and urban memory initiatives across Greece. By reintroducing “invisible” Jewish landmarks into contemporary consciousness, the study addresses the broader implications of post-war urban homogenization, the marginalization of minority heritage, and the ethical dimensions of digital preservation. This interdisciplinary approach, which bridges architectural history, digital humanities, urban studies, and cultural heritage, demonstrates the value of digital tools in reconstructing “lost” pasts and highlights the potential for similar projects in other regions facing comparable erasures. Full article

The emergence of generative AI has caused a major dilemma—as higher education institutions prepare students for the workforce, the development of digital skills must become a normative aim, while simultaneously preserving academic integrity and credibility. The challenge they face is not simply a matter of using AI responsibly but typically of reconciling two opposing duties: (A) preparing students for the future of work, and (B) maintaining the traditional role of developing personal academic skills, such as critical thinking, the ability to acquire knowledge, and the capacity to produce original work. Higher education institutions must typically balance these objectives while addressing financial considerations, creating value for students and employers, and meeting accreditation requirements. Against this need, this multiple-case study of fifty universities across eight countries examined institutional response to generative AI. The content analysis revealed apparent confusion and a lack of established best practices, as proposed actions varied widely, from complete bans on generated content to the development of custom AI assistants for students and faculty. Oftentimes, the onus fell on individual faculty to exercise discretion in the use of AI, suggesting an inconsistent application of academic policy. We conclude by recognizing that time and innovation will be required for the apparent confusion of higher education institutions in responding to this challenge to be resolved and suggest some possible approaches to that. Our results, however, suggest that their top concern now is the potential for irresponsible use of AI by students to cheat on assessments. We, therefore, recommend that, in the short term, and likely in the long term, the credibility of awards is urgently safeguarded and argue that this could be achieved by ensuring at least some human-proctored assessments are integrated into courses, e.g., in the form of real-location examinations and viva voces. Full article

Background/Objectives: Obliterates such as autologous bone dust (BD) or the synthetic bioactive glass S53P4 (BA) are frequently used for the obliteration of non-critical size defects (NCSDs), especially in otosurgery. Both obliterates have advantages and disadvantages, so that the combination of both for the obliteration of NCSDs is analysed. Methods: As part of a large animal project with sheep, four NCSDs were created in the calotte of thirteen animals using a drill. These were filled with BD, BD and BA, or BA, and the reference defect remained empty. After three weeks, the explanted calottes were examined with regard to their newly formed bone using digital volume tomography, bone density measurement, fluorochrome sequence labelling, and histological analysis. In addition, human cell culture analyses were carried out on the quality of the BD. Results: BD collected at 7.000 and 15.000 rpm shows a higher activity of new bone formation. In combination with BA, bone is formed centripetally and centrifugally. Defect filling with BA and BD shows a higher bone density and compactness than BD alone. Conclusions: BD should be harvested at a speed of less than 15.000 rpm. Using this BD in combination with BA to obliterate NCSDs enables the defect to be obliterated quickly and completely, with more newly formed bone, creating a bone network with incorporated BA. Further studies are needed to investigate the long-term stability of this obliteration and to determine which other parameters of the extraction can increase the amount of vital BD. Full article

Digital energy systems rely on actual data about power consumption and generation, which are not always available and, in certain situations, can be replaced with synthetic forms. This study presents a methodology for generating synthetic time-series data of electrical power consumers. It is based on the Markov chains theory, and unlike previous studies, the data are divided into hourly and hour-change monthly records, which leads to the generation of 48 transition matrices for each month. This study aimed to ensure statistical and probabilistic similarity between the original and synthetic data, which was assessed using the Frobenius distance, the coefficient of determination, variance, and standard deviation. The methodology was applied to three load profiles obtained from different types of consumers—domestic, agricultural, and industrial. In all three cases, the statistical and probabilistic characteristics of the generated data were very similar to those of the original datasets; however, the visual comparison showed that it is recommended to increase the number of states to lower the data scattering. Based on the results, recommendations are proposed on choosing the number of states for the transition matrices to optimize the statistical and probabilistic similarity. The described methodology can be used by experts involved in the design of systems with renewable energy sources and by scientists dealing with long-term studies. Full article

While digital religion and digital protest can ideally serve the common good, religious nationalist and fundamentalist movements have exploited these tools to disrupt the social fabric and create dangerous political outcomes. This paper examines how religious communicators within Tehreek-e-Labbaik Pakistan (TLP) and Alternative for Germany (AfD) perceive and enact their responsibility within digital spaces, leveraging the power of “networked communities” and the collective identity of the digital “crowd” to advance their agendas of religious fundamentalism and political conservatism. Bypassing traditional media, groups like the AfD and TLP exploit digital religion to build communities, spread propaganda that merges religion with national identity, frame political issues as religious mandates, and mobilize collective action. Campbell’s concept of the “networked community” demonstrates how digital technologies form decentralized, fluid, and global religious communities, distinct from traditional, geographically bound ones. Both the TLP and AfD have tapped into this new digital religious space, shaping and mobilizing political and religious identities across virtual borders. Gerbaudo’s idea of the “digital crowd” complements this by examining how collective action in the digital age reshapes mass mobilization, with social media transforming how political movements operate in the 21st century. Although the AfD’s platform is not overtly religious, the party strategically invokes ethno-Christian identity, framing opposition to Islam and Muslim immigration as a defense of German cultural and Christian values. Similarly, the TLP promotes religious nationalism by advocating for Pakistan’s Islamic identity against secularism and liberalism and calling for strict enforcement of blasphemy laws. Recognizing digital spaces as tools co-opted by religious nationalist movements, this paper explores how communicators in these movements understand their responsibility for the social and long term consequences of their messages. Using Luhmann’s systems theory—where communication is central to social systems—this paper analyzes how the TLP and AfD leverage individuals’ need for purpose and belonging to mobilize them digitally. By crafting emotionally charged experiences, these movements extend their influence beyond virtual spaces and into the broader public sphere. Finally, this paper will reflect on the theological implications of these dynamics both on and offline. How do religious communicators in digital spaces reconcile their theological frameworks with the social impact of their communication? Can digital religious communities be harnessed to foster social cohesion and inclusivity instead of exacerbating social divisions? Through this lens, the paper seeks to deepen our understanding of the intersection between digital religion, political mobilization, and theological responsibility in the digital age. Full article

As a new form of economic activity driven by data resources and digital technologies, the digital economy underscores the strategic significance of data as a core production factor. This growing importance necessitates accurate and robust valuation methods. Data valuation poses core modeling challenges due to its nonlinear nature and the instability of neural networks, including gradient vanishing, parameter sensitivity, and slow convergence. To overcome these challenges, this study proposes a genetic algorithm-optimized BP (GA-BP) model, enhancing the efficiency and accuracy of data valuation. The BP neural network employs a symmetrical architecture, with neurons organized in layers and information transmitted symmetrically during both forward and backward propagation. Similarly, the genetic algorithm maintains a symmetric evolutionary process, featuring symmetric operations in both crossover and mutation. The empirical data used in this study are sourced from the Shanghai Data Exchange, comprising 519 data samples. Based on this dataset, the model incorporates 9 primary indicators and 21 secondary indicators to comprehensively assess data value, optimizing network weights and thresholds through the genetic algorithm. Experimental results show that the GA-BP model outperforms the traditional BP network in terms of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), achieving a 47.6% improvement in prediction accuracy. Furthermore, GA-BP exhibits faster convergence and greater stability. When compared to other models such as long short-term memory (LSTM), convolutional neural networks (CNNs), and optimization-based BP variants like particle swarm optimization BP (PSO-BP) and whale optimization algorithm BP (WOA-BP), GA-BP demonstrates superior generalization and robustness. This approach provides valuable insights into the commercialization of data assets. Full article

The northern Portuguese coast has been increasingly subjected to wave-induced coastal flooding, highlighting a critical need for comprehensive overwash assessment in the region. This study systematically evaluates the total water levels (TWLs) and swash regimes over a 120 km stretch of the northern coast of Portugal. Traditional approaches to overwash assessment often rely on detailed models and location-specific data, which can be resource-intensive. The presented methodology addresses these limitations by offering a pragmatic balance between accuracy and practicality, suitable for extended coastal areas with reduced human and computational resources. A coastal digital terrain model was used to extract essential geomorphological features, including the dune toe, dune crest, and/or crown of defense structures, as well as the sub-aerial beach profile. These features help establish a critical threshold for flooding, alongside assessments of beach slope and other relevant parameters. Additionally, a wave climate derived from a SWAN regional model was integrated, providing a comprehensive time-series hindcast of sea-states from 1979 to 2023. The wave contribution to TWL was considered by using the wave runup, which was calculated using different empirical formulas based on SWAN’s outputs. Astronomical tides and meteorological surge—the latter reconstructed using a long short-term memory (LSTM) neural network—were also integrated to form the TWL. This integration of geomorphological and oceanographic data allows for a straightforward evaluation of swash regimes and consequently overwash potential. The accuracy of various empirical predictors for wave runup, a primary hydrodynamic factor in overwash processes, was assessed. Several reports from hazardous events along this stretch were used as validation for this method. This study further delineates levels of flooding hazard—ranging from swash and collision to overwash at multiple representative profiles along the coast. This regional-scale assessment contributes to a deeper understanding of coastal flooding dynamics and supports the development of targeted, effective coastal management strategies for the northern Portuguese coast. Full article

Cyberhate presents a multifaceted, context-sensitive challenge that existing detection methods often struggle to tackle effectively. Large language models (LLMs) exhibit considerable potential for improving cyberhate detection due to their advanced contextual understanding. However, detection alone is insufficient; it is crucial for software to also promote healthier user behaviors and empower individuals to actively confront the spread of cyberhate. This study investigates whether integrating large language models (LLMs) with persuasive technology (PT) can effectively detect cyberhate and encourage prosocial user behavior in digital spaces. Through an empirical study, we examine users’ perceptions of a self-monitoring persuasive strategy designed to reduce cyberhate. Specifically, the study introduces the Comment Analysis Feature to limit cyberhate spread, utilizing a prompt-based fine-tuning approach combined with LLMs. By framing users’ comments within the relevant context of cyberhate, the feature classifies input as either cyberhate or non-cyberhate and generates context-aware alternative statements when necessary to encourage more positive communication. A case study evaluated its real-world performance, examining user comments, detection accuracy, and the impact of alternative statements on user engagement and perception. The findings indicate that while most of the users (83%) found the suggestions clear and helpful, some resisted them, either because they felt the changes were irrelevant or misaligned with their intended expression (15%) or because they perceived them as a form of censorship (36%). However, a substantial number of users (40%) believed the interventions enhanced their language and overall commenting tone, with 68% suggesting they could have a positive long-term impact on reducing cyberhate. These insights highlight the potential of combining LLMs and PT to promote healthier online discourse while underscoring the need to address user concerns regarding relevance, intent, and freedom of expression. Full article