Search Results (838)

This study develops a conceptual framework for reimagining Sustainable Development Goal 17 (SDG 17) in Africa through a reinterpretation of the Marshall Plan’s governance logic. The primary focus is to address persistent failures in development partnerships—namely, fragmentation, weak coordination, power asymmetries, and limited institutional capacity—by proposing a structured model of partnership governance. Using a theory-building methodology grounded in historical analysis and documentary evidence, the study applies a systematic adaptation logic in which core governance mechanisms from the Marshall Plan are re-specified to reflect African institutional realities. These mechanisms—coordination, mutual accountability, collective action, state capacity, and trust—are translated into eight operational pillars: co-development, institutional strengthening, structural transformation, regional integration, blended finance, digital public infrastructure, knowledge co-production, and resilience. The framework conceptualizes SDG 17 as a meta-governance system that aligns actors, institutions, and resources across sectors. By moving from historical abstraction to context-sensitive application, the study contributes a coherent, Africa-centered governance model that enhances partnership effectiveness and informs post-2030 development policy. Full article

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

The integration of generative artificial intelligence (GAI) into digital learning environments is a profound socio-technical transformation. While GAI promises enhanced accessibility and efficiency, it simultaneously obscures the human creativity and intellectual labour that underpins digital knowledge production. This opacity limits creators’ visibility into how their work is used, evaluated, and monetised. This review application work investigates how several leading large language models, including ChatGPT (GPT-4o), Gemini (1.5 Flash), and DeepSeek (V3), interact with a creative platform hosting over 300 original essays, poems, and artworks from various human creatives. Our review reveals that despite clear evidence of models engaging with original materials, standard platform analytics of the average creative record no attribution, referrals, or traceable interaction from their end, rendering creators’ labour invisible. This compels critical examination of knowledge provenance and power within AI-mediated education. To address this, we propose a socio-technical framework, Chujoyi-TraceNet, not as a technical fix, but a mechanism to re-centre ethics, justice, and recognition in digital governance. By integrating real-time tracking, blockchain-enabled licensing, and metadata watermarking, Chujoyi-TraceNet operationalises the principles of equitable attribution. This study argues for a re-imagining of digital ecosystems in education, one that links the technical act of attribution to broader debates on digital labour, platform ethics, and the pursuit of social justice, thereby contributing to more democratic and accountable learning media in the era of Industry 4.0 and 5.0. Full article

LoRaWAN’s role in global maritime logistics has allowed for efficient monitoring of ships and cargo, but it also comes with critical cybersecurity vulnerabilities. Experimental validation of three attack vectors—replay attacks, narrowband jamming and metadata inference—is conducted using a reproducible digital-twin LoRaWAN dataset reflecting Rotterdam port-like operational patterns (N = 20,000 baseline transmissions). Using controlled simulations and Kolmogorov–Smirnov statistical analysis, we show that: (1) replay attacks are feasible under Activation by Personalization (ABP) configurations lacking enforced frame-counter validation and exhibit no univariate separation from legitimate traffic under Kolmogorov–Smirnov analysis (p > 0.46 for all evaluated radio features); (2) narrowband jamming leads to significant SNR degradation (p = 2.36 × 10⁻⁵) on targeted channels without inducing broad distributional anomalies across other radio features; and (3) metadata-only analysis supports elevated metadata-based re-identification susceptibility (median $R_d=0.834$), indicating high predictability under passive observation which can reveal operationally relevant signals even when AES-128 is employed. Our proposed layered mitigation framework consists of mandatory Over-the-Air Activation (OTAA), cryptographic key rotation, channel diversity incorporating Adaptive Data Rate (ADR), gateway hardening, and protocol-level enforcement considerations, customized for maritime LPWAN scenarios. We provide experiment-backed evidence and actionable recommendations to connect academic LPWAN security research to that of industrial maritime practice. Full article

Corneal alkali burns represent one of the most severe forms of ocular surface injury and frequently result in persistent inflammation, corneal neovascularization, stromal remodeling, and permanent visual impairment. Current therapeutic approaches incompletely control the inflammatory mechanisms that sustain pathological angiogenesis and tissue disorganization. In this study, we evaluated the effects of a transglutaminase-binding fusion protein (FP) in a rat model of alkali-induced corneal injury. Following standardized alkali burns, animals were treated topically with FP, secretory leukocyte protease inhibitor (SLPI), or Buffer. Corneal epithelial healing, opacity, and neovascularization were assessed clinically and by digital image-based quantification, while histological and immunofluorescence analyses were used to evaluate stromal organization and vascular invasion. Molecular mechanisms were investigated by RT-qPCR and Western blot analysis of key inflammatory, angiogenic, and signaling mediators. FP treatment significantly accelerated corneal re-epithelialization, reduced corneal opacity, and markedly attenuated corneal neovascularization compared to SLPI and Buffer controls. These effects were associated with coordinated downregulation of pro-inflammatory cytokines and angiogenic mediators, including TNF-α, IL-17, VEGF, and cPLA₂. Notably, FP suppressed transglutaminase 2 expression and induced early and sustained downregulation of NF-κB pathway components, identifying modulation of an upstream inflammatory pathway central to corneal angiogenesis and stromal remodeling. Collectively, these findings demonstrate that FP effectively limits inflammation-driven corneal neovascularization and tissue remodeling following alkali injury, supporting its potential as a disease-modifying therapeutic strategy for inflammatory ocular surface disorders. 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

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

Traumatic intracranial aneurysms are rare consequences of blunt or penetrating head injury, carrying significant morbidity and mortality. We report a 33-year-old male who sustained severe head trauma with base of skull fracture and subarachnoid hemorrhage following a motor vehicle accident. He underwent craniotomy with evacuation of an intracerebral hematoma and fixation of depressed fracture segments. During the third week, he deteriorated due to a re-bleed at the operated site. Cerebral digital subtraction angiography revealed a pseudoaneurysm from the proximal A2 segment of the left anterior cerebral artery, prompting re-exploration. This case highlights the importance of considering post-traumatic aneurysm in patients with delayed neurological decline after head injury associated with skull bone fracture and subarachnoid hemorrhage. Full article

Historic streets, as living heritage environments, preserve everyday cultural practices while facing increasing digital mediation in tourism and daily life. This study examines how a digital sense of place is constructed online in the Ming–Qing Old Street of Songyang, China. User-generated text and image data were collected primarily from Weibo, supplemented by user reviews from major travel platforms, including Dianping, Fliggy, Mafengwo, and Ctrip, and analysed through a multimodal framework. BERTopic was applied to identify thematic narratives in textual content, and ResNet-50 was used to classify visual scene elements in shared images, enabling an integrated interpretation of textual and visual representations. The results reveal four dominant dimensions of digital place perception: local food culture, living handicrafts, historic spatial fabric, and everyday atmosphere. Textual narratives emphasise emotional attachment and experiential interpretation, while visual representations highlight photogenic, performative, and shareable street scenes. The integration of these modalities forms a layered digital sense of place grounded in cultural continuity and daily life. The study demonstrates the value of multimodal social media analysis in understanding how living heritage streets are digitally represented and perceived, offering implications for sustainable heritage conservation, community-centred revitalisation, and data-informed cultural tourism management. Full article

3D digitization of museum artifacts is essential for both their virtual presentation and re-exhibition in the event of damage or loss. Given the number of artifacts that can be exhibited in a museum, the effectiveness of single-digitization practices under designed conditions is limited in terms of realism. In this study, a highly detailed sine quadrant object was digitized in a museum environment using photogrammetry and structured-light scanning (SLS) techniques. 3D models were generated from point clouds derived in photogrammetry and directly obtained from SLS. In the qualitative assessment based on the distinguishability of linear and edge details, the photogrammetric technique was found to be better; in the quantitative assessment based on the reference length values on the artifact, SLS was better, while photogrammetry was also found to be adequate. The maximum difference values for photogrammetry and SLS were 0.40 and 0.27 cm, respectively, while the average difference values were 0.24 cm and 0.10 cm. Additionally, cloud-to-cloud distance analysis revealed that two-point clouds overlapped quite well geometrically. Point clouds were also compared in terms of homogeneity using outlier detection analysis. This analysis showed that noise in the photogrammetric point cloud had a wider distribution over the artifact. In terms of data acquisition and processing time, SLS was found to be better, while the cost was comparable. After evaluating the techniques from various perspectives, photogrammetry was found to be preferable for modeling in a museum environment due to the priority need for high texture quality from the end-user’s perspective. In this respect, SLS is highly dependent on hardware capability for both data acquisition and processing. 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

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article

Fully homomorphic encryption (FHE) offers a promising solution for privacy-preserving machine learning by enabling arbitrary computations on encrypted data. However, the efficient evaluation of non-linear functions—such as the ReLU activation function over large integers—remains a major obstacle in practical deployments, primarily due to high bootstrapping overhead and limited precision support in existing schemes. In this paper, we propose $\mathsf{LargeIntReLU}$, a novel framework that enables efficient homomorphic ReLU evaluation over large integers (7–11 bits) via full-domain bootstrapping. Central to our approach is a signed digit decomposition algorithm, $\mathsf{SignedDecomp}$, that partitions a large integer ciphertext into signed 6-bit segments using three new low-level primitives: $\mathsf{LeftShift}$, $\mathsf{HomMod}$, and $\mathsf{CipherClean}$. This decomposition preserves arithmetic consistency, avoids cross-segment carry propagation, and allows parallelized bootstrapping. By segmenting the large integer and processing each chunk independently with optimized small-integer bootstrapping, we achieve homomorphic ReLU with full-domain bootstrapping, which significantly reduces the total number of sequential bootstrapping operations required. The security of our scheme is guaranteed by TFHE. Experimental results demonstrate that the proposed method reduces the bootstrapping cost by an average of 28.58% compared to state-of-the-art approaches while maintaining 95.2% accuracy. With execution times ranging from 1.16 s to 1.62 s across 7–11 bit integers, our work bridges a critical gap toward a scalable and efficient homomorphic ReLU function, which is useful in privacy-preserving machine learning. Furthermore, an end-to-end encrypted inference test on a CNN model with the MNIST dataset confirms its practicality, achieving 88.85% accuracy and demonstrating a complete pipeline for privacy-preserving neural network evaluation. Full article

The growing importance of integrating renewable energy sources (RESs) into mainline railway traction networks stems from the sector’s substantial electricity demand, which is traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind power to enhance energy efficiency and reduce emissions in rail transport. It details the development of digital models for simulating DC traction power systems (TPSs) coupled with RESs, specifically wind turbines. Given the complexity of TPSs, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinate algorithms, offers a universal and comprehensive framework. It enables the identification of various operational modes (normal, emergency, and special) for diverse network components, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology’s practical applicability for RES projects in DC traction networks, including advanced high-voltage systems. Full article

Benggang (Benggang), a typical landform characterized by severe erosion and a geohazard in the red-soil hilly regions of southern China, is characterized by a fragmented texture, irregular boundaries, and high similarity to background objects such as bare soil and roads, which poses a dual challenge of “multiscale variability + strong noise” for automated identification at regional scales. To address insufficient information from a single modality and the limited representation of cross-scale features, this study proposes a dual-stream feature-fusion network (DF-Net) for multisource data consisting of a digital orthophoto map (DOM) and a digital elevation model (DEM). The method adopts ResNeSt50d as the backbone of the two branches: on the DOM side, a Canny-edge channel is stacked to enhance high-frequency boundary information; on the DEM side, derived terrain factors, including slope, aspect, curvature, and hillshade, are introduced to provide morphological constraints. In the cross-modal fusion stage, a multiscale sparse attention fusion module is designed, which acquires contextual information via multiwindow average pooling and suppresses noise interference through top-K sparsification. In the decision stage, a multibranch ensemble is employed to improve classification stability. Taking Anxi County, Fujian Province, as the study area, a coregistered dataset of GF-2 (1 m) DOM and ALOS (12.5 m) DEMs is constructed, and a zonal partitioning strategy is adopted to evaluate the model’s generalization ability. The experimental results show that DF-Net achieves 97.44% accuracy, 85.71% recall, and an 82.98% F1 score in the independent test zone, outperforming multiple mainstream CNN/transformer classification models. This study indicates that the strategy of “multimodal feature enhancement + sparse attention fusion” tailored to Benggang erosional landforms can significantly improve recognition performance under complex backgrounds, providing technical support for rapid Benggang surveys and governance-effectiveness assessments. Full article