Search Results (2,874)

With the rapid technological advancements, persistent retention disparities, and career stability concerns among Generation Z learners, higher education in the United States needs a re-examination of student success. Student support efforts and previous student-centered frameworks require re-examination in light of the current socio-cultural context. In response, this paper proposes the CIRCLE model. This conceptual model is faculty-driven and includes evidence-based practices that predict successful outcomes by benefiting students’ socio-emotional factors. The model stems from an integrated conceptual framework that synthesizes established student success theories, contemporary research on faculty–student relationships, and digital integration in higher education. Traditional student-centered theories are merged with contemporary digital integration models and applied to the realities of Generation Z and first-generation college students. From this, the author delivers a clear, context-responsive plan for faculty supporting a diverse cohort of learners, as we all live in today’s post-pandemic, digitally immersed world. Full article

In a bid to investigate the optimum transportation method for offshore wind-produced hydrogen (H₂) and assess the feasibility of repurposing the existing oil and gas infrastructure for H₂ transmission, this paper assesses the existing H₂ transportation methods with a comprehensive review of the H₂ impact on the existing natural gas pipeline infrastructure. To establish the possibility of repurposing the existing natural gas (NG) pipelines for H₂ gas transport, this paper reviews the influential technical measures—composition, pressure, temperature, volumetric energy density, density, and pressure drop—to assess whether the characteristics of hydrogen gas are compatible with the natural gas pipeline infrastructure. Based on these reviews, it was found that the current NG pipeline pressure exacerbates the H₂ embrittlement; for the existing NG pipelines to be repurposed, the operating pressure should be reduced, and the pipeline material should be revised. It was found that higher strength steels can be re-used with major modifications, or the pipeline should be constructed from material grade X52 or below. Nevertheless, the fitness of the existing NG pipelines for H₂ transmission should be assessed on a case-by-case basis and other factors such as erosion, leakage, pressure cycling, monitoring (e.g., distributed fiber-optic sensing technology) and a rigorous assessment of welds and joints should also be considered. Full article

This article examines how algorithmic mediation reshapes social trust and solidarity in the post-secular age. Historically grounded in shared moral horizons shaped by religion, tradition, and communal practices, trust has increasingly been displaced by technocratic governance, market rationality, and algorithmic systems that mediate work, cognition, communication, and political life. Through a critical analysis of contemporary developments—including algorithmic labour management, neurotechnology, large language models, digital public spheres, technological sovereignty, and global AI governance—the article argues that algorithmic mediation intensifies the fragility of trust by instrumentalizing human agency, fragmenting public reason, and concentrating power within opaque technological infrastructures. Against technological determinism and purely procedural approaches to ethics, the article advances a normative framework rooted in solidarity and the common good. Drawing on post-secular perspectives, a retrieval of natural law normativity, and the resources of Catholic Social Teaching, it contends that trust cannot be sustained through efficiency, prediction, or regulation alone. Instead, social trust depends upon relational goods—dignity, responsibility, participation, and truth—that resist reduction to data-driven optimization. Reclaiming solidarity therefore requires re-embedding AI within moral horizons capable of guiding technological development toward integral human flourishing. In this sense, the governance of AI emerges not merely as a technical challenge but as a decisive moral and political task for post-secular societies. Full article

Comparative Life Cycle Assessments (LCAs) of bio-based materials are highly influenced by methodological choices, so the term “bio-based” does not necessarily imply a low environmental impact. This review analyzes over 50 peer-reviewed LCAs (2010–2024) to quantify how four methodological pillars—(i) attributional versus consequential modeling, (ii) timing and storage of biogenic carbon, (iii) Direct Land-Use Change (LUC) and Indirect Land-Use Change (ILUC), and (iv) allocation in multifunctional systems—drive variability across long-life construction and short-life packaging/composites; adding regionalized perspectives (e.g., water scarcity according to the AWARE initiative, and relevant inventories for the MENA region) and ex-ante LCA guidance aligned with technology readiness levels. Methods included systematic selection from Web of Science/Scopus databases, standardized functional units, system boundaries, impact methods (ReCiPe/EF/TRACI/AWARE), biogenic carbon conventions (GWP100, dynamic/GWPbio), LUC/ILUC handling, allocation rules, and end-of-life scenarios, followed by qualitative meta-synthesis. Results show ~85% of studies used attributional approaches; consequential models typically report higher climate impacts when ILUC is included. In the building applications, bio-based alternatives—particularly wood—reduced cradle-to-critical-state global warming potential (GWP) by 30–70%; a “negative GWP” only emerged when storage balances or dynamic characterization were applied. For bioplastics, climate benefits are context-dependent and can disappear once ILUC and agricultural inputs are considered; acidification and eutrophication frequently increase. We conclude that environmental performance is subject to methodological choices rather than bio-based origin; systematic trade-offs persist between reducing GWP, increasing eutrophication/acidification, and increasing pressure on water/biodiversity. Full article

Digital fabrication technologies increasingly enable designers and researchers to reinterpret historical design languages through contemporary production methods. Within this context, desktop 3D printing offers an accessible yet constrained medium for translating stylistically rich interior design objects into tangible form. This study examines how distinct twentieth-century interior design movements—Art Deco, Bauhaus, and Mid-century Modern—are mediated through desktop additive manufacturing, focusing on the preservation of formal identity rather than manufacturing performance. Representative interior objects were digitally reconstructed from archival and reference material and fabricated under standardized desktop 3D printing conditions. The investigation adopts a style-oriented evaluation framework that examines silhouette continuity, characteristic geometric features, ornamental legibility, and structural–stylistic coherence. To support comparative interpretation, a Style Preservation Index (SPI) is introduced as a structured design evaluation tool that makes stylistic assessment explicit and repeatable without reducing it to purely geometric metrics. The results demonstrate that stylistic legibility is preserved to differing degrees depending on the formal vocabulary of each design movement, with minimal and geometrically rational styles exhibiting higher compatibility with layer-based fabrication than ornamentally dense or materially expressive designs. Rather than framing these differences as technological limitations, the study interprets them as insights into how design languages interact with fabrication constraints. By positioning desktop additive manufacturing as a medium of design translation rather than replication, this work contributes a reproducible framework for design research, heritage interpretation, and education, offering a structured approach for exploring how historical styles can be re-engaged through contemporary digital fabrication. Full article

Remote photoplethysmography (rPPG) extracts physiological signals from facial videos by analyzing subtle skin color variations caused by blood flow. While this technology enables contactless health monitoring, it also raises privacy concerns because facial videos reveal both identity and sensitive biometric information. Existing privacy-preserving techniques, such as blurring or pixelation, degrade visual quality and are unsuitable for practical rPPG applications. This paper presents rePPG, a framework that inserts a desired rPPG signal into facial videos while preserving the original facial appearance. The proposed method disentangles facial appearance and physiological features, enabling replacement of the physiological signal without altering facial identity or visual quality. Skin segmentation restricts modifications to skin regions, and a cycle-consistency mechanism ensures that the injected rPPG signal can be reliably recovered from the generated video. Importantly, the extracted rPPG signals are evaluated against the injected target physiological signals rather than the subject’s original physiological state, ensuring that the evaluation measures signal rewriting accuracy. Experiments on the PURE and UBFC datasets show that rePPG successfully embeds target PPG signals, achieving 1.10 BPM MAE and 95.00% PTE6 on PURE while preserving visual quality (PSNR 24.61 dB, SSIM 0.638). Heart rate metrics are computed using a 5-second temporal window to ensure a consistent evaluation protocol. Full article

Historically, the electricity sector in Greece was based on local lignite, which provided a stable and affordable base for electricity production. However, current European policy directions, including decarbonization and climate neutrality by 2050, have accelerated the transformation of traditional energy models, resulting in a gradual phasing-out of fossil fuels and an increasing integration of Renewable Energy Sources (RES). In line with EU policy priorities and in light of the new dynamics shaped by the EU Emissions Trading System (EU ETS), lignite gradually became unprofitable for the national economy, leading the Greek government to announce an accelerated lignite phase-out plan. However, the phase-out of domestic lignite, although consistent with climate objectives, rapidly increased the country’s energy dependency on natural gas and its exposure to natural gas price volatility. At the same time, increased investment in solar and wind technologies has reshaped the electricity mix; yet market design, limited system flexibility and inadequate infrastructure and storage capacity have not allowed the full utilization of RES benefits. This structural gap, in turn, raises critical questions about resilience and affordability. The paper provides evidence on these issues and offers a critical evaluation of the decarbonization pathway that has reshaped the country’s energy dependency. Full article

Low-count positron emission tomography (PET) is inherently affected by Poisson-dominated noise, which degrades image contrast, structural delineation, and quantitative reliability. This study systematically evaluated residual learning-based deep neural networks to investigate the influence of residual block depth on PET image restoration performance under low-count conditions. We employed a physically controlled striatum phantom, fabricated using 3D printing technology, to ensure reproducible acquisition conditions and controlled physical variability. PET images were acquired using a clinical PET/computed tomography (CT) system with list-mode acquisition. Low-count images reconstructed from short-duration acquisition were paired with high-count reference images reconstructed from extended acquisitions. We compared conventional filtering techniques, including median, Wiener, and modified median Wiener filters, with residual network (ResNet)-based models incorporating 8, 16, and 32 residual blocks. Image quality was quantitatively assessed using contrast-to-noise ratio (CNR), coefficient of variation (COV), line profile analysis, universal quality index (UQI), and perceptual image patch similarity (LPIPS). The results demonstrated that ResNet-based restorations substantially outperformed conventional filtering techniques in contrast recovery, signal stability, and structural preservation. The ResNet-16 model achieved the most balanced performance, yielding the highest CNR (9.02) and lowest COV (0.105), while also demonstrating superior structural and perceptual similarity, as indicated by UQI (0.9224) and LPIPS (0.0174), relative to the high-count reference images. Deeper network configurations exhibited diminishing returns and reduced structural consistencies. These findings indicate that an intermediate residual block depth is optimal for low-count PET image restoration and highlight the importance of architectural optimization in deep learning-based PET image enhancement with phantom-based evaluation frameworks. Full article

Promoting irrigation efficiency is a central pillar of global water sustainability strategies but empirical evidence shows a counterintuitive outcome named the irrigation efficiency paradox or rebound effect. This occurs when on-farm water savings do not translate into basin-scale conservation and may even intensify water scarcity. This paper critically re-examines the rebound effect, moving beyond conventional hydrological and economic explanations toward an integrated socio-hydrological perspective. We argue that the paradox is not merely a technical accounting issue or a form of the Jevons Paradox, but a systemic problem arising from interactions among behavior, institutions, and political economy. The review traces the concept’s evolution and synthesizes global evidence on its main drivers and controversies. It critically evaluates dominant research paradigms, emphasizing the need for greater methodological pluralism. Significant gaps remain, particularly regarding behavioral economics, political economy, and social and environmental externalities. We conclude that overcoming the efficiency paradox requires a policy shift from technological fixes to transformative governance. Full article

The increasing number of vehicles in cities reduces the efficiency of parking infrastructure and increases traffic congestion, making it challenging to achieve sustainable transportation goals. This situation necessitates a re-evaluation of urban mobility systems in conjunction with spatial organization and digital technologies. This article examines smart parking systems as “data-oriented spaces”, analyzing their impact on urban mobility, energy efficiency and spatial organization from a multidimensional perspective. The research adopts a qualitative, multi-level approach, structured through a comprehensive literature review, a comparative analysis of five international case studies and a conceptual synthesis of the findings. The data obtained were evaluated using criteria such as technological infrastructure, spatial structure, sustainability performance and user interaction. The findings reveal that smart parking systems not only serve as vehicle storage but can also function as digital–spatial interfaces that direct urban data flows. This study presents a conceptual framework that treats smart parking systems as data-oriented architectural spaces, offering a holistic approach to the design of sustainable urban mobility infrastructures. This perspective allows for redesigning parking structures as adaptable, data-oriented architectural systems that optimize circulation patterns, reduce search-related emissions, increase spatial efficiency and support sustainable urban mobility networks. Full article

This study benchmarks renewable energy source (RES) utilization in the Visegrad Four (V4) against the EU average using Eurostat data for 2014–2022. A multi-layer framework was used to combine technology-specific per-capita indicators, sectoral RES shares, cluster analysis, and panel regression with fixed effects. The EU substantially exceeds V4 in hydropower (774.06 vs. 270.19 kWh/person), wind (972.06 vs. 161.30 kWh/person), and solar technologies. The electricity-sector gap is most pronounced (EU 41.17% vs. V4 18.69%). Paired t-tests confirmed a statistically significant persistent gap (t(8) = −20.78; p < 0.001), consistent with delayed convergence. Cluster analysis assigned all V4 countries to a single moderate-RES tier, structurally separated from Western and Nordic clusters; panel regression confirmed that the V4 coefficient was robustly negative (β = −5.783 to −9.088 pp) even after policy controls, with fossil lock-in (β = −2.404 pp) emerging as the most consistent structural determinant, whereas V4 × fossil lock-in interaction was positive (β = +2.558 pp), suggesting partial mitigation through differentiated pathways. Intra-V4 heterogeneity—Slovakia’s hydropower lock-in, Hungary’s wind prohibition, Poland’s coal dependency, and Czech Republic’s curtailed feed-in tariff—argues against homogeneous policy responses; results support technology-specific strategies (wind/solar PV in Poland/Czech Republic; solar thermal/heat pumps in Hungary/Slovakia) and grid modernisation as cross-cutting priority. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

Applications for machine learning (ML) and deep learning (DL) are constantly growing and have already been adopted in the field of particle measurement technology. Even though analytical (ultra-)centrifugation (AC/AUC) is a widely used technique for characterizing dispersed particle systems, ML and DL have not yet been applied in this area. Data evaluation and interpretation in AC/AUC can be challenging and often requires expert knowledge. DL models can help, but their development is limited by a lack of annotated training data. One solution is to generate and use synthetic data instead. In the first part of this study, a model was trained to synthesize data from experiments using a combination of Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). The results appear highly realistic. Novice users could distinguish real from synthetic samples with only 63% accuracy. Then, a classifier was trained on experimental AC data to categorize real-world examples based on their underlying separation kinetics, testing different DL architectures. After initial training, the models were further fine-tuned with synthetic AC data. ResNet34 models achieved the best performance with 94% accuracy, comparable to an AC expert (91%), while inexperienced users reached only 53%. In the second part of our study, a regression model was trained for the analysis of sedimentation coefficients. Therefore, various generative models were developed and evaluated for synthesizing AUC data based on numerically simulated sedimentation boundaries. The best results were achieved by combining VAE and GAN architectures with embedded physical constraints. However, the generative networks have so far led to additional smearing of the profiles, resulting in a broadening of the sedimentation coefficient distribution and indicating that further refinement is necessary. Full article

Background: Despite significant improvements in blood safety, the risk of transfusion-transmitted infections persists, particularly from emerging and re-emerging viruses. For red blood cell (RBC) products, this risk is exacerbated by the fact that there is no routine testing for many of these pathogens, and effective, commercially available pathogen inactivation technologies specifically for RBCs are still lacking. This gap in the safety framework means that viruses capable of establishing an asymptomatic viremia—a characteristic of many arboviruses like Zika, dengue, and West Nile virus—present a tangible threat to the blood supply, highlighting the need for broad-spectrum countermeasures. Study Design and Methods: This study aims to investigate the antiviral activity of green tea extract (GTE) and its key catechins, epigallocatechin gallate (EGCG) and epicatechin gallate (ECG), against ZIKV in both cellular models and red blood cell (RBC) products. In vitro antiviral activity was assessed using A549 cells treated with GTE (150 μg/mL) or purified EGCG/ECG (20 μM). Mechanistic studies focused on viral attachment inhibition. Additionally, ZIKV-spiked RBC products were co-incubated with GTE (300 μg/mL) for 1 h to evaluate virucidal effects. Erythrocyte integrity was confirmed via hemolysis assays. Results: Co-treatment with GTE or catechins suppressed ZIKV replication by ≥3.64 logs (p < 0.001) in A549 cells. GTE and catechins primarily inhibited viral attachment. In RBCs, GTE reduced viral infectivity by 99.99% (4-log reduction) without compromising erythrocyte membrane integrity or cellular viability. Furthermore, RBCs with added GTE demonstrated a lower hemolysis rate during storage for up to 60 days. Conclusions: GTE exhibits potent virucidal activity against ZIKV in blood matrices, highlighting its potential as a pathogen reduction agent to enhance transfusion safety. Further development of GTE-based additive solutions or technologies is warranted. Full article

Theological engagement with artificial intelligence has largely focused on applied ethics, addressing bias, governance, and labor displacement. While indispensable, this framing often presumes that algorithmic systems operate as external instruments acting upon already constituted subjects. This article argues that contemporary predictive architectures intervene at a deeper anthropological level by structuring attention, expectation, and habituation prior to deliberative judgment. It introduces the concept of ordo extractionis to designate a technologically mediated regime of formation characterized by behavioral trace extraction, probabilistic modeling, and recursive projection of statistically inferred continuity. Drawing on Augustine’s account of ordered love and temporality and Aquinas’s doctrine of habitus and the invisible mission of the Spirit, the article distinguishes algorithmic projection from sanctification as divergent pedagogies of temporal formation. Predictive systems stabilize continuity by extrapolating from measurable past behavior; sanctification reorders desire teleologically toward a final end not deducible from prior pattern and grounded in non-competitive divine causality. Algorithmic mediation is therefore interpreted pedagogically rather than metaphysically: it does not rival divine agency but participates creaturely in shaping the ecology within which habituation unfolds. Engagement with contemporary AI research on recommender systems, reinforcement learning, and generative models situates the argument within technological realism and resists determinism. The digital twin is analyzed as a probabilistic representation that acquires institutional authority when operationalized in ranking, profiling, and evaluative systems, without constituting a metaphysical competitor to the imago Dei. In response to anticipatory closure, Eucharistic anamnesis and epiclesis are developed as practices that re-situate memory and expectation within eschatological promise. The article concludes that the central theological question posed by AI is not whether machines can think, but how formative sovereignty over desire is exercised within technologically mediated modernity. Full article