Search Results (23,687)

Background: Tooth loss remains a major clinical challenge, affecting mastication, speech, facial aesthetics, and oral health-related quality of life. Prosthetic rehabilitation must therefore balance functional demands, biological limitations, patient preferences, maintenance capacity, and cost. Implant-assisted removable partial dentures (IARPDs) have emerged as a practical alternative to conventional removable partial dentures (RPDs) and full-arch fixed implant prostheses, particularly in distal-extension cases where conventional RPDs often demonstrate greater displacement and unfavorable stress distribution. Objectives: This narrative review summarizes current concepts in IARPDs treatment, with an emphasis on attachment-retained designs and implant-surveyed crown removable partial dentures (ISCRPDs). Methods: A narrative literature review was conducted using PubMed and Scopus. English-language articles related to IARPDs, surveyed crowns, attachment systems, material considerations, biomechanics, clinical outcomes, and complications were identified and narratively synthesized. Results: Available evidence suggests that IARPDs provide superior patient-reported outcomes compared with conventional RPDs, largely through improved retention, stability, comfort, and reduced prosthesis movement. Attachment systems offer versatile retention and aesthetic advantages but may require regular maintenance because of component wear and progressive loss of retention. In contrast, surveyed crowns provide a space-efficient and potentially more cost-effective design, with favorable implant survival and prosthetic outcomes reported in retrospective studies. However, complications such as clasp loosening, sore spots, crown dislodgement, and crown or framework fracture remain clinically relevant. Material selection for frameworks and implant crowns further influences aesthetics, durability, hygiene, and long-term maintenance. Conclusions: IARPDs treatment should be individualized according to the available prosthetic space, residual ridge anatomy, implant position, oral hygiene ability, aesthetic expectations, financial limitations, and access to follow-up care. Prospective comparative studies are needed. Full article

We study nonlinear stochastic dynamical systems that evolve in a Markov environment with separated fast and slow transition scales. These systems are also subject to impulsive perturbations under a Poisson approximation scheme. The environment is represented on a product state space, and phase aggregation is used to average the rapidly switching component while retaining the slower Markov regime explicitly. Under the stated ergodicity, regularity, integrability, tightness, state-preservation, and uniqueness assumptions, we derive an effective reduced process and establish weak convergence of both the impulsive component and the coupled nonlinear system in the Skorokhod space. The limiting jump dynamics are characterized by averaged local characteristics associated with the retained slow Markov regime. To assess the reduced model numerically, we use a nonlinear competitive Lotka–Volterra-type system and compare the deterministic dynamics, the full Markov-switching stochastic model, and the reduced approximation under several fixed parameter configurations and jump-intensity settings. The results provide a mathematical basis for the reduced modeling of nonlinear stochastic dynamical systems with separated Markov time scales and impulsive perturbations. Full article

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment polarity expressed toward a specific aspect in a sentence. Existing sequential and Transformer-based methods can effectively capture contextual semantics, but they often lack explicit modeling of aspect–opinion relations. Graph-based approaches partially address this limitation by incorporating syntactic dependency structures; however, most rely on deterministic parser-derived graphs or fixed relation weights, which may introduce noisy edges and unstable message propagation for ambiguous, informal, or domain-shifted text. To address these issues, this paper proposes a Bayesian Biaffine Variational Graph Convolutional Network (BBV-GCN) for ABSA. Specifically, a contextual encoder first generates token-level representations for each sentence–aspect pair. A biaffine relation scorer then estimates aspect-aware pairwise token interactions and constructs a soft latent relation graph. Rather than treating relation weights as deterministic values, BBV-GCN introduces latent relation variables and learns their posterior distributions through variational inference, thereby enabling uncertainty-aware graph construction. Based on the learned graph, a variational graph convolutional network performs multi-hop message passing to aggregate opinion cues, modifiers, negation patterns, and contrastive signals toward the target aspect representation. Experiments on five benchmark datasets, including Twitter, Laptop14, Restaurant14, Restaurant15, and Restaurant16, demonstrate that BBV-GCN achieves competitive and well-balanced performance relative to representative attention-based, Transformer-based, and graph-based baselines. Ablation studies further confirm the contributions of Bayesian relation modeling, KL regularization, and variational graph propagation. Visualization results illustrate how uncertainty-aware weighting can attenuate spurious relations and produce more interpretable aspect-specific latent graphs. Overall, BBV-GCN provides a robust and uncertainty-aware graph reasoning framework for fine-grained sentiment analysis. Full article

Reconstructing a 3D human skeleton from partial joint observations is an ill-posed inverse problem: when joints are missing, infinitely many anatomically distinct poses fit the observation, but most methods return a single reconstruction. We formulate single-frame reconstruction as a linear inverse problem, characterize the null space of the missing joints, and argue that the output should be a distribution over the feasible set rather than a point estimate. We model this distribution as a Bayesian posterior with a diffusion model as a learned prior. The question is not whether such a prior can reconstruct skeletons, but when it is warranted: a conjecture relates the error of linear interpolation to the curvature of the pose manifold, with a low-curvature limit in which interpolation is near-optimal. Measured on NTU RGB+D, the curvature is non-zero and intrinsic to individual motions but moderate, and the results are as follows: the prior outperforms nearest-neighbor averaging under scattered occlusion up to moderate severity, is outperformed by it under structural occlusion, and yields per-joint uncertainty that tracks the realized error ($r\approx 0.7$). The prior satisfies the kinematic constraints implicitly: explicit guidance improves bone-length fidelity but degrades accuracy, so the constraints serve the formulation and the analysis rather than the sampler. Full article

The giant honeybee, Apis dorsata, is an important pollinator native to tropical and subtropical Asia. Understanding its potential response to climate change is important for pollinator conservation, ecological risk assessment, and biosecurity planning. This study used an optimized MaxEnt ecological niche modeling framework to predict the current and future global habitat suitability of A. dorsata. The model was developed using 1060 occurrence records and seven non-collinear bioclimatic variables and projected under three global climate models (IPSL-CM6A-LR, BCC-CSM2-MR, and MPI-ESM1-2-HR) and three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) for 2041–2060, centered on 2050. The optimized model used linear, quadratic, and hinge features (LQH) with a regularization multiplier of 0.5 and demonstrated good predictive performance under 5-fold spatial cross-validation (mean AUC = 0.905 ± 0.007; TSS = 0.746 ± 0.010). The results indicate that the potential distribution of A. dorsata is primarily associated with the combined effects of seasonal temperature and moisture availability. Current projections identified high climatic suitability across South and Southeast Asia, while also revealing potentially suitable environments in parts of Africa, the Americas, and northern Australia. Future projections suggest that suitable climatic conditions will largely persist through 2050, with habitat gains generally exceeding losses and increasing under higher climate-forcing scenarios. Continued monitoring and proactive biosecurity are essential to address habitat loss within the native range and prevent establishment in newly suitable regions. This study highlights the potential redistribution of A. dorsata under future climate change. Full article

Eye-tracking studies of decision making have traditionally relied on Area-of-Interest (AOI) statistics, scan-path metrics, fixation measures, and transition analyses. Although these approaches have provided important insights, AOI-based methods require predefined semantic spatial regions and may not fully characterize the broader spatial organization of gaze behavior. This study proposes an image-based framework in which eye movement trajectories are represented as grayscale images on a regular spatial grid and analyzed using established image-analysis techniques, including texture analysis, Fourier analysis, wavelet decomposition, and singular value decomposition (SVD). Non-negative matrix factorization (NMF) was also examined as a candidate method, but was not included in the final integrated analysis because its contribution was limited in the present sample. The remaining heterogeneous image-derived features were integrated using a Tucker-1-based framework and examined using principal component analysis (PCA) and probabilistic PCA (PPCA). The framework was demonstrated using eye-tracking data from insurance decision tasks involving multiple pieces of decision-relevant information. The first principal component showed strong and consistent correlations with conventional fixation count and fixation duration across both insurance tasks, supporting its interpretation as a global fixation-intensity dimension, whereas higher-order components showed substantially weaker associations with these conventional measures. Bootstrap analyses further indicated that the exact PPCA dimensionality was sensitive to sampling variability, although relatively high-dimensional latent structures were consistently supported. Overall, the proposed framework provides a complementary representation of gaze behavior that does not require predefined semantic AOI boundaries and may capture both conventional fixation-related information and additional spatial or structural characteristics of eye movement trajectories. Full article

Background: Physical inactivity is common among people living with chronic non-communicable disease, and nurses are frequently the professionals with the most sustained patient contact. How nursing support for physical activity is experienced by patients, and how nurses understand their own role in providing it, remain poorly described. Objective: To explore how patients with chronic diseases and nursing professionals describe nursing support for physical activity and exercise adherence, and where their accounts converge or diverge. Methods: A qualitative descriptive study using individual semi-structured interviews and reflexive thematic analysis. Seventeen participants (nine patients with chronic diseases and eight nursing professionals) were purposively recruited from five healthcare institutions in the Riyadh region of Saudi Arabia. Interviews lasted 30 to 60 min (mean 45 min) and were conducted between December 2025 and February 2026. No quantitative measures of exercise frequency, intensity, duration, or session regularity were collected, so the study reports perceptions and experiences rather than measured adherence, and no causal relationships were examined. Rigor was addressed through researcher reflexivity, member checking, peer debriefing, and an audit trail; reporting follows COREQ. Results: Five themes were constructed: (1) Nursing-Facilitated Empowerment Through Education and Guidance; (2) Personalized Support and Accountability Mechanisms; (3) Barriers and Facilitators to Exercise Adherence; (4) Perceived Health Benefits and Motivation Enhancement; and (5) Gaps in Current Nursing Practice and Opportunities for Enhancement. Participants identified individualized explanation, graduated goal-setting, correction of fear-based misconceptions, scheduled exercise-specific review, and family involvement as the components of support they valued most, and lapsed follow-up, generic advice, limited interdisciplinary coordination, and scarce patient resources as the principal shortfalls. Conclusions: Patients and nurses in this setting described sustained, individualized nursing support as central to how patients engage with exercise, while both groups identified structural constraints that limit it. These accounts indicate targets that intervention studies with objective activity measurement could test; they do not establish that nursing support changes exercise behavior. Full article

Convolution is spatially fixed and channel-specific, whereas involution is location-specific and channel-agnostic, capturing spatially varying patterns efficiently. Existing involutional networks, however, generate point-estimate kernels and expose no native measure of where the operator is uncertain, while prevailing uncertainty and calibration methods act on the network output rather than on the aggregation operator itself. We propose Evidential Involution–Attention (EvIA) networks, which recast involution as a distributional operator whose per-location neighborhood aggregation is a Dirichlet distribution. This yields, in a single forward pass and at the same parameter cost as involution, a closed-form epistemic-uncertainty (vacuity) map. The vacuity drives a parameter-free, precision-weighted gate that fuses the local involution branch with a global branch, instantiated as windowed self-attention (EvIA-W) or lightweight channel attention (EvIA-C). A lemma and three propositions establish that normalized involution is the infinite-evidence limit of the operator, that convex aggregation makes it non-expansive, that the Dirichlet strength is the precision of the aggregated feature, and that the gate is the minimum-variance unbiased fusion of the two branches. An evidential head trained with a differentiable calibration objective produces reliable confidences. Over five seeds with paired tests on brain magnetic resonance imaging, chest radiography, and dermatoscopy, the windowed variant EvIA-W is significantly stronger on brain MRI, attaining 0.803 ± 0.026 accuracy against 0.714 ± 0.019 (p = 0.006) by EvIA-C, and reducing the area under the risk–coverage curve from 0.187 to 0.092 (p = 0.001). Architecture-matched controls show that the discrimination gain on brain MRI comes from the global branch, while substituting evidential for standard involution leaves accuracy, calibration, and selective risk statistically unchanged, so the operator supplies its uncertainty machinery at no measurable cost. We further report that the spatial vacuity map does not localize input corruption, because the aggregation weights are invariant to the evidence scale and no objective term supervises it, an analysis that motivates the operator-level regularizers we define for future work. Full article

This study focuses on a three-step iterative scheme, referred to as the NIP iteration, for the approximation of fixed points associated with nonexpansive mappings in uniformly convex Banach spaces. Weak convergence is established using Fejér monotonicity, asymptotic regularity and the demiclosedness principle. Strong convergence is proved under uniform convexity, compactness, and Condition (I) of Senter and Dotson. A numerical convergence and computational-cost comparison is developed numerically, showing that the NIP iteration performs better than the Ishikawa, S, Noor, Abbas–Nazir and SP schemes. Numerical experiments for nonlinear nonexpansive mappings validate the theoretical findings. An application to convex optimization via fixed point reformulation is also presented, illustrating the effectiveness of the method. Full article

Regular evaluations of larvicide resistance and operational control effectiveness are two basic components of the Integrated Mosquito Management approach. It is therefore important for mosquito control programs to assess both the efficacy and the effectiveness of larvicide treatments. Recent observations in 2023 and 2024 within areas served by the Northwest Mosquito Abatement District (NWMAD) indicated that methoprene resistance categorized as high and extreme exist in populations of Culex pipiens (L). Despite more than 20 years of exclusive use of Altosid^® XR (2.1% methoprene) larvicide in NWMAD catch basins and associated expectations of reduced effectiveness, NWMAD’s evaluations of Altosid^® XR-treated catch basins in 2022, 2023, and 2025 with found levels of control (emergence inhibition [EI]) to be similar to those reported in studies over the past 30 years of the same or similar larvicide formulation. An extreme abundance of caution is needed when interpreting findings of NWMAD’s control evaluations as these observational findings were collected over different years and areas served by NWMAD. These findings also lack inferential statistical analyses and were not collected in a scientifically rigorous and systematic manner with a true experimental design. The reported results should not be interpreted as demonstrating that methoprene resistance does not affect operational control. Full article

Recent studies on users of urban parks call for the need for more insight into the importance of green spaces as catalysts of more inclusive spaces. This paper contributes to the ongoing debate by examining urban parks as age-inclusive infrastructures in a post-socialist context. Three parks in the City of Timișoara, Romania, are selected as a comparative case study. Our analysis combines systematic field observations and GIS-based mapping of park accessibility and facilities with 42 semi-structured interviews with young, mid-aged and older visitors to the three contrasting parks: a renovated historic central park, a peripheral forest-like park and a small neighbourhood park embedded in a communist-era housing estate. The findings suggest that inclusiveness for all generational categories, as well as attachment for neighbourhood parks, are important drivers for urban parks users. Inclusiveness is driven not only by amenities, but also by park-specific attachment, as well as the quality of maintenance and lighting. These factors shape perceived equity and convenience. We advocate for a differentiated management model tailored to each park, balancing conservation-oriented quiet zones with flexible, event-capable areas. This model prioritises lighting, seating ergonomics and safety measures as core components of age-inclusive planning. Our findings support the need for different management of urban green spaces that capitalises on the social and spatial specificities of each park. The comparison further shows that the smallest and least equipped park generates the strongest attachment and the most regular use across all generations: proximity and continuity of maintenance matter more than surface area or scale of investment. Full article

Multi-view clustering has emerged as a significant research direction in the information age, as multiple feature representations become increasingly available. However, traditional multi-view clustering methods are often unsupervised and fail to exploit available label information. In practice, fully labeled data are scarce, while partially labeled data are more common and can significantly improve clustering performance. Moreover, real-world data are frequently corrupted by noise or outliers. To address these challenges, this paper proposes a Correntropy-guided Matrix Factorization and Tensor Graph Learning (CMTGL) framework for robust semi-supervised multi-view clustering. Specifically, CMTGL incorporates partial label information into a shared low-dimensional representation through constrained low-rank matrix factorization and employs the maximum correntropy criterion (MCC) to reduce the influence of noisy samples and outliers. In addition, view-specific graphs are stacked into a third-order tensor and regularized by the tensor Schatten p-norm to exploit high-order correlations across multiple views. A consensus graph is jointly learned to capture the common structural information shared among different views. The resulting optimization problem is efficiently solved by a block coordinate descent algorithm with an extrapolation accelerated block coordinate update (BCU) scheme. Extensive experiments on six public benchmark datasets demonstrate that the proposed method achieves superior clustering performance compared to state-of-the-art approaches. Full article

Perceived safety determines whether urban greenways are used in everyday life, yet it is rarely measurable at the boundary scale where design decisions are made. Existing street-view models split into black-box networks whose predictions cannot be traced to design elements and pixel-ratio regressions whose interpretability rests on weak, unstructured representations, while greenspace studies lean on GIS proximity variables that confound design with context. We present the CPTED-Guided Perception Network (CGPN), which fuses a visual branch with a masked, learnable projection of segmentation ratios onto five CPTED dimensions. Because the mask confines learning to a theory-defined support, the prior regularizes the representation while every coordinate of the model remains tied to a named CPTED dimension, whose directional effect on the prediction we verify by perturbation. On 110,633 street-view images, CGPN is statistically equivalent to the strongest black-box baseline in pairwise ranking accuracy (0.649 vs. 0.652; equivalence test within a 1.5-point margin, $p=0.006$, attains the best $R^2$ (0.192), and improves on its unconstrained variant in goodness of fit across three seeds ($\mathrm{\Delta }{R}^2=+0.031$, $p=0.042$). Applied to 218 greenway-adjacent residential boundaries in Boston and New York, it uncovers a threshold-like negative association for barrier-dominated access control and an inverted-U distance profile whose weakest segment lies within 100 m of the greenway edge ($p=0.007$). Full article

Multimodal Sequential recommendation alleviates the semantic insufficiency and data sparsity of item-ID-based models by incorporating side information such as text and images. However, multimodal systems face the dual challenges of feature-space heterogeneity and modality-specific noise, in addition to the dynamic evolution of user interests over time. Existing methods still struggle to jointly handle cross-modal alignment and time-aware preference modeling. To address these challenges, we propose a multimodal sequential recommendation framework with cross-modal alignment and temporal gating, which leverages item ID, text, and image modalities to capture users’ dynamic interests. The proposed model contains three core components. First, a cross-modal alignment mixture-of-experts module preserves modality-specific features with dedicated experts and captures shared semantics with common experts, thereby mitigating the semantic mismatch inherent in direct fusion. Second, a hierarchical time-aware mixture-of-experts module uses short-term intervals, long-term spans, and periodic time encodings for expert routing, and applies a time-aware modality gate to adaptively adjust the importance of ID, text, and image modalities under different temporal contexts. Third, a sequential interest contrastive learning objective enhances the discriminability of ID-based sequential interest representations by leveraging dynamic temperature scaling, multi-scale positive samples, hard negative mining, and diversity regularization. Experiments on games, beauty, and toys demonstrate that the proposed method consistently outperforms representative sequential and multimodal recommendation baselines on Normalized Discounted Cumulative Gain (NDCG)@5, NDCG@10, Mean Reciprocal Rank (MRR)@5, and MRR@10. Furthermore, ablation results validate the effectiveness of each proposed component. Full article

Architecture-dependent systems pose a local-to-global problem in which locally admissible and locally evaluated changes need not admit a globally coherent scalar representation. We develop a systems-theoretic framework combining system state and architecture, admissible directions of change, local evaluation, and induced dynamics within a differential-geometric structure. We establish that, under connectedness and bracket generation, a global scalar representation exists if and only if the horizontal evaluation form has zero circulation along every closed horizontal curve, with an equivalent result if horizontal evaluation is path-independent. When it exists, the representation is unique up to an additive constant and reproduces the primitive canonical rational correspondence. Along canonical rational trajectories, its rate of change equals the maximal local evaluative rate; with additional regularity, feasible inaction, and precompactness, this yields strict ascent outside rational equilibrium, excludes non-equilibrium recurrence, and confines limiting behaviour to rational equilibria. An endogenous-opportunity application shows when locally rational economic adjustments admit a global utility or welfare representation. Representability is equivalent to the cross-channel compatibility condition ($r\in Im\left(C^{\mathsf{T}}\right)$), while distance from this subspace quantifies failure of global welfare coherence. The framework therefore distinguishes local optimisation from the stronger systems property of global evaluative coherence. Full article