Search Results (153)

On 24 February 2022, Russia’s invasion of Ukraine intensified a broader sense of precarity across Eastern Europe, including Romania, and longstanding anxieties related to threat, instability, and loss—rooted in the Cold War past—resurfaced with renewed urgency. This article examines how, since 1989, Romanian contemporary dance has responded to these conditions, functioning as a site for expressing and processing the physical and emotional “edginess” of the body and psyche in the post-socialist present. Drawing on archival research, audiovisual materials, and interviews, the study argues that Romanian contemporary dance operates across multiple temporalities. Looking to the past, it preserves and reactivates the memory of bodies that shaped its history, resisting their erasure amid shifting national and transnational identities. Rooted in the present, dancers and institutions navigate a fragile social terrain marked by regional conflict, migration, economic instability, and ongoing debates surrounding diversity and gender. At the same time, they confront the resurgence of nationalist, xenophobic, and exclusionary discourses within Romanian society. Through performance, pedagogy, institutional initiatives, and collaborative projects, contemporary dance in Romania emerges as a critical space of reflection and intervention, actively engaging social tensions while contributing to the reimagining of political and cultural futures. Full article

Three-dimensional point cloud understanding plays an important role in autonomous perception, robotic navigation, and LiDAR-based remote sensing. However, the irregular and unordered nature of point clouds makes it challenging to model long-range contextual dependencies while preserving geometric awareness, particularly under noise, occlusion, and non-uniform sampling. To address these challenges, we propose a Geometry-Aware Contextual Memory Network (GACM-Net) for 3D point cloud analysis. Specifically, an Adaptive Geometric Prior Encoding (AdGPE) module is introduced to dynamically coordinate multiple geometric priors, including absolute coordinates, center-relative coordinates, and distance-based cues, thereby enhancing geometry-aware contextual interaction during long-range propagation. Furthermore, a Structure-Aware Point Memory Unit (SAPM) is designed to achieve stable contextual memory learning through normalized gate interaction, peephole memory regulation, gated candidate filtering, and residual feature propagation. Based on SAPM, a Bidirectional Structure-Aware Point Memory module (BiSAPM) further captures complementary geometric dependencies from opposite propagation directions, improving contextual completeness and structural consistency for irregular point cloud representations. Extensive experiments on ModelNet40, ScanObjectNN, and ShapeNetPart demonstrate that GACM-Net achieves competitive classification and part segmentation performance with a compact model size and favorable computational efficiency. The results further show that the proposed framework provides a good balance among accuracy, efficiency, and robustness for 3D point cloud understanding. Full article

This article examines the Maritime Cultural Landscape (MCL) of the Colombian Caribbean (CC) through a participatory and interdisciplinary approach that integrates historical cartography, a legal framework, images, collective memory, and community-based knowledge. Using case studies from Cartagena de Indias, Barú Island, and the Archipelago of San Andrés and Old Providence, the research combines workshops with young participants and academic discussion sessions with local communities to explore how maritime spaces are interpreted and socially constructed. The findings show that MCL cannot be understood only as archaeological remains, but as a dynamic framework shaped by the relationship between communities and the sea. Participatory cartography proved effective in activating local memory and revealing multiple maritime landscapes, while also highlighting tensions between formal coastal representations and lived territorial experiences. The study also incorporates legal and environmental perspectives, including public goods, coastal governance, and climate change. One of its contributions is the concept of “Cultural and Legal Maritime Landscapes” (CLML), which interprets legal frameworks as part of the relationship between maritime landscapes, local communities, and the sea. This research contributes to more inclusive approaches to maritime heritage and introduces a conceptual and methodological framework for understanding the CC as a socially constructed and contested seascape. Full article

This study introduces the Painted Wolf Decision Optimizer (PWO), the first deterministic, bio-inspired decision framework for discrete multi-criteria decision making (MCDM) derived from specific observed decision behaviors of African wild dogs, including quorum sensing, dominance hierarchy, collective voting, and experience-based learning. Unlike conventional nature-inspired metaheuristics that rely on stochastic search across continuous domains, PWO defines a new class of Discrete Bio-Inspired Decision Operators. It formalizes key ethological mechanisms of Lycaon pictus: quorum sensing, hierarchical dominance, and reinforcement-based learning. Additionally, it encodes the principle of survival-through-precision, demonstrating how coordinated strategic alignment can outperform structural dominance under resource constraints, inspired by the high hunting efficiency of African wild dogs. PWO integrates three cognitive weighting components: subjective collective preferences (sneeze-based voting), objective data variability (entropy weighting), and experiential reinforcement (pack memory). These are fused via the Mathematical Compromiser, a convex operator that assigns internal trust based on signal stability rather than fixed weighting rules. Applied to European EV gigafactory location selection, PWO reconciled tensions between cost-driven executive preferences and sustainability-based performance indicators, identifying Spain as the most robust alternative. Sensitivity analysis across the dominance spectrum ($D=0\to 1$) and multiple episodes confirmed ranking stability without rank reversal. The Markovian update formalizes longitudinal learning for future multi-episode applications. Beyond discrete selection, PWO functions as a diagnostic and competitive resilience mechanism, revealing whether decisions are shaped by leadership authority, structural necessity, historical trends, or precision-based survival logic. It provides a transparent and strategically adaptive architecture for sustainable governance and high-stakes competitive decision environments. Full article

A Orhan Pamuk’s first novel, Cevdet Bey and His Sons (Cevdet Bey ve Oğulları), published in 1982, follows three generations of an Istanbul bourgeois family from the late Ottoman period to the 1970s. Set during the transition from the Ottoman Empire to the Republic of Turkey, the novel explores the social and cultural tensions accompanying Turkish modernization. This article examines father–child relationships in the novel through the theoretical framework of multiple modernities. Rejecting the assumption that modernization follows a singular Western model, the study approaches Turkish modernization as a historically specific and layered experience shaped by local social and cultural dynamics. Within this context, the article focuses on paternal authority, masculine identity, intergenerational expectation, and memory across three generations of the family. The study further analyzes how photographs, domestic spaces, rituals, and everyday narratives preserve the symbolic presence of the father after death, allowing paternal authority to persist across generations. The article argues that the novel presents a representative portrait of an Istanbul bourgeois family negotiating the tensions between tradition and modernity during the Ottoman–Republican transition. Full article

Resource-constrained dynamic heterogeneous redundancy (DHR) systems use executor diversity and runtime reconfiguration to reduce stable attack surfaces. However, effective scheduling cannot rely only on heterogeneity or movement frequency, because repeated exposure, shared vulnerability sources, service disturbance, and switching overhead jointly shape executor-subset selection. This paper proposes RACS, a risk-aware cost-constrained scheduling method for resource-constrained DHR systems. RACS evaluates candidate subsets by jointly considering heterogeneity, historical confidence, readiness, common-vulnerability risk, exposure memory, and switching cost. We evaluate RACS using a controlled simulation protocol covering multiple scheduling principles and attacker behaviors, including common-vulnerability pressure, burst-adaptive exploitation, and adaptive target selection based on observed scheduling patterns. The results show that RACS does not optimize a single metric in isolation, but maintains a consistent security–cost trade-off. It reduces common-vulnerability risk and switching cost in common-vulnerability settings, reduces burst-triggering high-risk states under adaptive pressure, and maintains competitive risk–cost behavior when attackers adapt to historical scheduling behavior. Robustness and scalability analyses clarify the effects of vulnerability-family estimation errors, parameter choices, and executor-pool size. These findings provide controlled simulation evidence for joint risk–cost modeling in DHR executor-subset scheduling, while testbed validation remains future work. Full article

This paper explores the culinary and cultural significance of cooked leafy vegetables among Afro-descendant communities in French Guiana and Suriname, including French Guianese and Surinamese Creoles, Maroons, and Haitian migrants. While leafy greens play a major dietary role across sub-Saharan Africa, their consumption in the Americas remains understudied. This ethnobotanical study of edible leafy plants is based on surveys of local markets, gardens and residents. Drawing on 26 informal interviews conducted in four local languages (French, French Guianese Creole, Haitian Creole, and Nengee Tongo), we describe 36 species of edible leaves from 20 plant families consumed in the region. Our findings show that although the practice of eating leafy greens is widely shared, the species selected, their names, and their perceived properties vary noticeably across cultural groups. Some plants are eaten exclusively by Maroons (e.g., Cestrum latifolium, Capsicum spp.), others by Haitians (e.g., Corchorus olitorius, Rivina humilis), and some have fallen into disuse among younger generations. These differences are shaped by ecological availability, cultural memory, food-medicine beliefs, and interethnic influences. We suggest that the term callaloo (referring to both dishes and leafy vegetables), which circulates in multiple linguistic and culinary forms throughout the African diaspora, can serve as a metaphor for the interculturalization of foodways. More than ingredients, these leafy vegetables act as dynamic cultural markers—symbols of resilience, transmission, and transformation. In a context of rapid globalization, where unseen foods risk sinking further into obscurity, these plant-based traditions highlight both the adaptability and fragility of Afro-descendant culinary heritage in the Guiana Shield. Full article

Additive manufacturing has emerged as a transformative technology for fabricating complex drug-eluting medical devices, offering unprecedented design freedom and functional integration capabilities. This comprehensive review systematically analyzes 3D printing technologies applied to pharmaceutical device manufacturing, focusing on drug-eluting vascular stents as a representative application. This review covers six primary additive manufacturing techniques, ranging from high-resolution vat photopolymerization (25 μm resolution) to direct energy deposition, with a focus on their capabilities for produce pharmaceutical devices with controlled drug release properties. Novel 4D/5D/6D printing technologies introduce stimuli-responsive behaviors enabling programmable drug release profiles and adaptive device functionality. Manufacturing process optimization reveals superior design flexibility compared to conventional methods, with 85–95% reduction in design iteration time and elimination of tooling costs for complex geometries. The material landscape encompasses traditional metals (316L stainless steel, cobalt–chromium), biodegradable polymers (polylactic acid, PLA; polycaprolactone, PCL; poly(lactic-co-glycolic acid), PLGA), shape-memory materials (i.e., polymers and alloys capable of recovering a pre-programmed shape upon exposure to a specific stimulus such as body temperature, moisture, or light), and advanced nanocomposites, each offering distinct drug-loading capacities (100–500 μg/cm²) and release kinetics. Critical challenges include standardization requirements (International Organization for Standardization (ISO) 5840 and American Society for Testing and Materials (ASTM) F2606), pharmaceutical-grade manufacturing protocols, and regulatory pathways for novel drug-device combinations. This review identifies key research priorities including development of biocompatible printing materials, accelerated drug release testing protocols, and scalable manufacturing processes suitable for medical device production. This analysis demonstrates that 3D printing enables integration of multiple pharmaceutical functions within single devices, controlled spatiotemporal drug delivery, and elimination of secondary manufacturing steps for drug coating processes, advancing the development of next-generation therapeutic medical devices. Full article

Sharp wave–ripples (SWRs) are transient hippocampal population events that coordinate neuronal ensemble activity and play a central role in memory consolidation and affective processing. Although SWRs exhibit marked functional specialization along the dorsoventral axis of the hippocampus, and several cellular mechanisms underlying SWRs are sex-sensitive, systematic comparisons of SWR properties between females and males are lacking. Here, we examined sex- and region-dependent differences in SWRs and associated multiunit activity (MUA) in acute hippocampal slices from adult female and male rats. Spontaneous SWRs were recorded from the CA1 stratum pyramidale of the dorsal and ventral hippocampus, and SWR occurrence rate, amplitude, ripple oscillation properties, and SWR-locked neuronal firing were quantified. Linear mixed-effects analysis revealed robust region-dependent differences across multiple SWR parameters. In contrast, sex effects were selective. SWR occurrence rate and amplitude did not differ significantly between females and males, whereas SWR-associated MUA showed a significant main effect of sex, with higher values in males. Ripple power was also influenced by sex, with higher values in females, together with a significant effect of region, suggesting differences in oscillatory structure. Baseline MUA did not differ between sexes, indicating that sex-related effects are specific to the SWR state. These findings suggest that sex does not substantially alter the generation of SWRs per se but influences neuronal recruitment and oscillatory properties during these events. Our results reveal previously underappreciated dimensions of hippocampal network organization and provide a descriptive framework for future studies investigating how sex-dependent circuit properties may shape hippocampal contributions to cognition and affective regulation. They further highlight the importance of incorporating sex as a fundamental biological variable in studies of hippocampal network dynamics. Full article

Multi-object tracking and segmentation (MOTS) aims to jointly perform pixel-level instance segmentation and temporal identity association for multiple objects in video sequences. Existing online decoupled MOTS methods face several challenges in complex scenarios, including limited front-end mask quality, corruption of memory representations under prolonged occlusion, and unstable data association and trajectory recovery. To address these limitations, we propose TrackRefine, a plug-and-play decoupled enhancement framework. TrackRefine enhances overall performance through back-end refinement without modifying the architecture of the front-end instance segmenter or relying on additional end-to-end joint training. Specifically, we introduce a lightweight Fast GrabCut-based mask refinement module to optimize mask boundaries, a multimodal long-short-term memory bank that integrates appearance, semantic, and shape cues for identity modeling, and a progressive three-stage association strategy for stable matching and long-term trajectory recovery. Experimental results on MOTS20 show that TrackRefine achieves 69.4 sMOTSA, 82.7 MOTSA, and 478 Frag. Experimental results on KITTI MOTS show that it achieves 62.4/73.7 sMOTSA and 78.0/85.4 MOTSA for pedestrians and cars, respectively. Extensive experiments with different front-end instance segmenters verify its plug-and-play flexibility and decoupled design, while ablation studies confirm the effectiveness of each core module. These results show that TrackRefine provides an efficient and practical solution for online MOTS in complex scenarios. Full article

Seismic isolation systems are widely adopted in bridge engineering to reduce earthquake-induced force transfer and improve structural resilience. Conventional lead rubber bearings (LRBs) provide effective energy dissipation and period elongation; however, their limited recentering capability may result in significant residual displacement after strong ground motions. This study investigates the seismic performance of a two-level shape memory alloy–lead rubber bearing (TL-LRB-SMA) hybrid isolation system for multi-span bridges considering structural flexibility, support compliance, and geometric irregularity. A nonlinear analytical model of the hybrid isolator was developed and validated under cyclic loading using benchmark hysteretic behavior from the literature. Subsequently, a multi-degree-of-freedom numerical model of an eleven-span benchmark bridge was established and verified through modal analysis, equivalent static analysis, and comparison with MSBridge software (MSBridge Beta 1.0.1). Nonlinear time-history analyses were performed using multiple excitation scenarios, including the 1940 El-Centro record, Kobe ground motion, oblique seismic incidence, and combined loading cases. Flexible foundation conditions were represented using equivalent translational soil springs. The results indicate that the TL-LRB-SMA system consistently improves self-centering performance and significantly reduces residual displacement relative to conventional LRBs. For the regular bridge with 48 ft piers, residual displacement decreased from 0.786 inches to 0.268 inches under El-Centro excitation, while under combined excitation it reduced from 0.264 inches to 0.087 inches. For irregular bridge configurations, substantial residual displacement reductions were also observed under both longitudinal and oblique loading. Although moderate increases in peak displacement occurred in some cases due to staged SMA activation, the overall recentering performance improved markedly. Overall, the proposed TL-LRB-SMA system demonstrates strong potential for enhancing seismic resilience and post-earthquake serviceability of bridge structures, particularly in flexible and irregular configurations. Full article

Dementia care research has been largely shaped by Western biomedical and cognitive paradigms that privilege verbal, linear, and memory-dependent methods of data collection. While these approaches have generated valuable insights, they also reproduce epistemic and ethical limitations, particularly in cross-cultural contexts. Linguistic dominance, culturally mismatched diagnostic and care frameworks, and reliance on caregivers as proxy informants can marginalize culturally and linguistically diverse communities and risk pathologizing cultural difference as cognitive deficit. In response, this conceptual paper advances a participatory arts-based framework for cross-cultural dementia care research that centers multiple ways of knowing beyond language. Drawing on principles of co-creation, shared decision-making, reflexivity, power-sharing, and relational ethics, the framework positions people living with dementia as collaborators rather than subjects. It articulates five interrelated dimensions: (1) modes of expression (visual, embodied, sensory, and performative); (2) forms of participation (co-design, co-creation, and co-analysis); (3) cultural situatedness of meaning-making; (4) relational ethics, including ongoing assent, trust, and reciprocity; and (5) intersectionality across culture, gender, migration, class, and caregiving roles. The paper illustrates how participatory arts-based methods, such as photovoice, body mapping, collaborative art-making, and sensory storytelling, can enable culturally resonant engagement across stages of dementia while addressing power asymmetries inherent in conventional research designs. By foregrounding embodied, sensory, and culturally grounded forms of expression, this framework offers a critical reorientation of dementia care research toward more inclusive, ethical, and culturally responsive knowledge production in diverse care contexts. Full article

To address the challenges of nonlinearity, strong temporal dependence, and accuracy degradation caused by sudden disturbances in power grid customer service telephone traffic forecasting, this paper proposes a novel forecasting method based on an ensemble model pairing Gated Recurrent Unit (GRU) with Correntropy loss (CL) (called EnsCL-GRU). First, to overcome the sensitivity of the traditional Mean Squared Error (MSE) loss to abnormal spikes and its difficulty in capturing the overall trend consistency of the sequence, a CL is introduced as the loss function for the GRU model. This loss function calculates the normalized Correntropy coefficient between the predicted sequence and the true sequence in the time-delay domain, guiding the model to focus on the overall shape matching of the time series data rather than point-wise error fitting. Furthermore, the gated memory mechanism of the GRU can capture long-term dependencies in the time series, while the CL constrains the consistency of the predicted dynamic trends from the sequence level. This preserves the GRU’s temporal modeling capability while enhancing the model’s response accuracy to sudden disturbances and trend changes. Second, to improve the generalization ability of a single GRU model, an ensemble strategy is employed to train multiple CL-enhanced GRU base models serially. By adaptively adjusting sample weights, the fitting capability for difficult samples (such as telephone traffic spikes) is improved, further improving the model’s robustness. Finally, Bayesian optimization is introduced to automatically search for the optimal hyperparameters of the ensemble model, efficiently approximating the global optimal configuration within a limited number of evaluations. Experimental results demonstrate that the proposed method outperforms traditional approaches. Specifically, compared with the standard GRU model, the proposed method reduces MAPE from 29.15% to 22.61%. It also consistently outperforms the ensemble baseline EnsGRU, achieving a MAPE reduction of 4.73 percentage points. The results indicate that the proposed model significantly improves forecasting accuracy and robustness, particularly under scenarios with nonlinear fluctuations and sudden disturbances, providing reliable support for optimal resource allocation in power grid customer service systems. Full article

This paper proposes the Branch-Priority Exploration (BPE) framework for autonomous coverage in confined industrial corridor environments. BPE integrates three components: (1) a symmetry-aware LiDAR branch detector; (2) a hierarchical BFS/DFS mode-switching policy; and (3) a barrier-based branch memory. Frontier-based methods often struggle in industrial corridors where branches split off from the main corridor. The symmetric layout of such environments, featuring T-shaped junctions and L-shaped turns, creates recurring geometric patterns that conventional frontier scoring fails to exploit. When the robot reaches a junction, nearby frontier candidates often receive similar scores, causing repeated target switching as the local map changes. Meanwhile, frontier cells inside a branch tend to score lower than those along the main corridor; so, the robot often bypasses the branch and continues forward, which leads to additional backtracking later. Even when the robot eventually returns, residual frontier cells near the entrance may attract the planner repeatedly, causing redundant re-entry into already-covered branches. To address these issues, a branch-priority exploration framework is developed. A symmetry-aware branch detection module uses LiDAR range measurements from multiple directions to identify T-shaped junctions and lateral openings, applying identical geometric criteria to lateral openings on either side of the robot. This allows branch entry to be triggered by explicit geometric evidence, rather than frontier score comparisons that tend to be unreliable near intersections. When a branch is detected, the robot transitions from BFS mode to DFS mode for systematic branch coverage. Entry and post-return locks prevent mode reversal before the robot commits to the new heading. Once a branch is completed, a permanent virtual barrier is placed at its entrance; so, the planner no longer routes the robot back into that branch. The framework is formalized as a constrained coverage problem on occupancy grids, and monotonic coverage progress and finite branch completion under barrier memory are established theoretically. A fully reproducible ROS implementation on a wheeled platform with differential drive is validated. Experiments span several corridor environments of increasing topological complexity. Compared to a nearest-frontier baseline, the proposed method substantially reduces both exploration time and goal cancellations while achieving complete coverage across all trials. The cancellation count matches the number of T-branches per environment, with near-zero variance across repeated runs. Full article

Unmanned Aerial Vehicle (UAV) orthophoto generation in complex environments remains challenging because weak textures, reflective surfaces, occlusions, and large scene extents can cause incomplete reconstruction, ghosting, and seam artifacts. Although 3D Gaussian Splatting (3DGS) offers an efficient explicit scene representation, its use in large-scale UAV orthophoto generation is limited by high memory consumption, unstable densification, and insufficient support for mapping-oriented orthographic rendering. This paper proposes a single-GPU 3DGS framework for UAV orthophoto generation by integrating adaptive spatial block partitioning, deterministic structure-sensitive adaptive density control, and core–buffer tiled orthographic rendering with weighted blending. The proposed framework decomposes large scenes into resource-bounded subregions, guides Gaussian densification using fixed multi-view neighborhoods and edge-enhanced dynamic consistency, and generates large-format orthophotos with reduced boundary and seam artifacts. Experiments on MatrixCity-S and multiple UAV photogrammetric datasets show that the method achieves competitive reconstruction quality and improved resource efficiency. On MatrixCity-S, it reaches 29.01 dB PSNR and 0.901 SSIM, while completing training in 1 h 49 min on a single NVIDIA RTX 3090 GPU. Compared with BlockGS, peak VRAM consumption is reduced by more than 38% across datasets. Under geo-aligned comparison conditions, line-measurement comparisons with MetaShape and Pix4DMapper yield RMSE values of 0.099 m and 0.087 m, respectively. These results demonstrate the potential of the proposed framework for memory-efficient 3DGS-based UAV orthophoto generation under constrained hardware resources, while further control-point-based validation is still needed for rigorous surveying-grade applications. Full article